id
stringlengths 9
10
| submitter
stringlengths 2
52
⌀ | authors
stringlengths 4
6.51k
| title
stringlengths 4
246
| comments
stringlengths 1
523
⌀ | journal-ref
stringlengths 4
345
⌀ | doi
stringlengths 11
120
⌀ | report-no
stringlengths 2
243
⌀ | categories
stringlengths 5
98
| license
stringclasses 9
values | abstract
stringlengths 33
3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.09488
|
Matteo Risso
|
Daniele Jahier Pagliari, Matteo Risso, Beatrice Alessandra Motetti,
Alessio Burrello
|
PLiNIO: A User-Friendly Library of Gradient-based Methods for
Complexity-aware DNN Optimization
|
Accepted at the 2023 Forum on Specification & Design Languages (FDL)
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate yet efficient Deep Neural Networks (DNNs) are in high demand,
especially for applications that require their execution on constrained edge
devices. Finding such DNNs in a reasonable time for new applications requires
automated optimization pipelines since the huge space of hyper-parameter
combinations is impossible to explore extensively by hand. In this work, we
propose PLiNIO, an open-source library implementing a comprehensive set of
state-of-the-art DNN design automation techniques, all based on lightweight
gradient-based optimization, under a unified and user-friendly interface. With
experiments on several edge-relevant tasks, we show that combining the various
optimizations available in PLiNIO leads to rich sets of solutions that
Pareto-dominate the considered baselines in terms of accuracy vs model size.
Noteworthy, PLiNIO achieves up to 94.34% memory reduction for a <1% accuracy
drop compared to a baseline architecture.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 07:11:14 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Pagliari",
"Daniele Jahier",
""
],
[
"Risso",
"Matteo",
""
],
[
"Motetti",
"Beatrice Alessandra",
""
],
[
"Burrello",
"Alessio",
""
]
] |
new_dataset
| 0.996531 |
2307.09533
|
Aditya Potukuchi
|
Charlie Carlson, Ewan Davies, Alexandra Kolla, and Aditya Potukuchi
|
Approximately counting independent sets in dense bipartite graphs via
subspace enumeration
|
15 pages
| null | null | null |
cs.DS math.CO
|
http://creativecommons.org/licenses/by/4.0/
|
We give a randomized algorithm that approximates the number of independent
sets in a dense, regular bipartite graph -- in the language of approximate
counting, we give an FPRAS for #BIS on the class of dense, regular bipartite
graphs. Efficient counting algorithms typically apply to ``high-temperature''
problems on bounded-degree graphs, and our contribution is a notable exception
as it applies to dense graphs in a low-temperature setting. Our methods give a
counting-focused complement to the long line of work in combinatorial
optimization showing that CSPs such as Max-Cut and Unique Games are easy on
dense graphs via spectral arguments. The proof exploits the fact that dense,
regular graphs exhibit a kind of small-set expansion (i.e. bounded threshold
rank), which via subspace enumeration lets us enumerate small cuts efficiently.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 18:23:24 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Carlson",
"Charlie",
""
],
[
"Davies",
"Ewan",
""
],
[
"Kolla",
"Alexandra",
""
],
[
"Potukuchi",
"Aditya",
""
]
] |
new_dataset
| 0.957902 |
2307.09549
|
Richard Derbyshire
|
Richard Derbyshire, Benjamin Green, Charl van der Walt, David
Hutchison
|
Dead Man's PLC: Towards Viable Cyber Extortion for Operational
Technology
|
13 pages, 19 figures
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
For decades, operational technology (OT) has enjoyed the luxury of being
suitably inaccessible so as to experience directly targeted cyber attacks from
only the most advanced and well-resourced adversaries. However, security via
obscurity cannot last forever, and indeed a shift is happening whereby less
advanced adversaries are showing an appetite for targeting OT. With this shift
in adversary demographics, there will likely also be a shift in attack goals,
from clandestine process degradation and espionage to overt cyber extortion
(Cy-X). The consensus from OT cyber security practitioners suggests that, even
if encryption-based Cy-X techniques were launched against OT assets, typical
recovery practices designed for engineering processes would provide adequate
resilience. In response, this paper introduces Dead Man's PLC (DM-PLC), a
pragmatic step towards viable OT Cy-X that acknowledges and weaponises the
resilience processes typically encountered. Using only existing functionality,
DM-PLC considers an entire environment as the entity under ransom, whereby all
assets constantly poll one another to ensure the attack remains untampered,
treating any deviations as a detonation trigger akin to a Dead Man's switch. A
proof of concept of DM-PLC is implemented and evaluated on an academically peer
reviewed and industry validated OT testbed to demonstrate its malicious
efficacy.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 18:48:47 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Derbyshire",
"Richard",
""
],
[
"Green",
"Benjamin",
""
],
[
"van der Walt",
"Charl",
""
],
[
"Hutchison",
"David",
""
]
] |
new_dataset
| 0.987443 |
2307.09553
|
Joseph W. Cutler
|
Joseph W. Cutler, Christopher Watson, Phillip Hilliard, Harrison
Goldstein, Caleb Stanford, Benjamin C. Pierce
|
Stream Types
|
Submitted to POPL'24
| null | null | null |
cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a rich foundational theory of typed data streams and stream
transformers, motivated by two high-level goals: (1) the type of a stream
should be able to express complex sequential patterns of events over time, and
(2) it should describe the parallel structure of the stream to enable
deterministic stream processing on parallel and distributed systems. To this
end, we introduce stream types, with operators capturing sequential
composition, parallel composition, and iteration, plus a core calculus of
transformers over typed streams which naturally supports a number of common
streaming idioms, including punctuation, windowing, and parallel partitioning,
as first-class constructions. The calculus exploits a Curry-Howard-like
correspondence with an ordered variant of the logic of Bunched Implication to
program with streams compositionally and uses Brzozowski-style derivatives to
enable an incremental, event-based operational semantics. To validate our
design, we provide a reference interpreter and machine-checked proofs of the
main results.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 19:05:22 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Cutler",
"Joseph W.",
""
],
[
"Watson",
"Christopher",
""
],
[
"Hilliard",
"Phillip",
""
],
[
"Goldstein",
"Harrison",
""
],
[
"Stanford",
"Caleb",
""
],
[
"Pierce",
"Benjamin C.",
""
]
] |
new_dataset
| 0.959341 |
2307.09621
|
Ka Chun Shum
|
Ka Chun Shum, Hong-Wing Pang, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit
Yeung
|
Conditional 360-degree Image Synthesis for Immersive Indoor Scene
Decoration
|
ICCV2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we address the problem of conditional scene decoration for
360-degree images. Our method takes a 360-degree background photograph of an
indoor scene and generates decorated images of the same scene in the panorama
view. To do this, we develop a 360-aware object layout generator that learns
latent object vectors in the 360-degree view to enable a variety of furniture
arrangements for an input 360-degree background image. We use this object
layout to condition a generative adversarial network to synthesize images of an
input scene. To further reinforce the generation capability of our model, we
develop a simple yet effective scene emptier that removes the generated
furniture and produces an emptied scene for our model to learn a cyclic
constraint. We train the model on the Structure3D dataset and show that our
model can generate diverse decorations with controllable object layout. Our
method achieves state-of-the-art performance on the Structure3D dataset and
generalizes well to the Zillow indoor scene dataset. Our user study confirms
the immersive experiences provided by the realistic image quality and furniture
layout in our generation results. Our implementation will be made available.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 20:28:31 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Shum",
"Ka Chun",
""
],
[
"Pang",
"Hong-Wing",
""
],
[
"Hua",
"Binh-Son",
""
],
[
"Nguyen",
"Duc Thanh",
""
],
[
"Yeung",
"Sai-Kit",
""
]
] |
new_dataset
| 0.982002 |
2307.09630
|
Behrouz Minaei-Bidgoli
|
Huda AlShuhayeb, Behrouz Minaei-Bidgoli, Mohammad E. Shenassa,
Sayyed-Ali Hossayni
|
Noor-Ghateh: A Benchmark Dataset for Evaluating Arabic Word Segmenters
in Hadith Domain
|
15 pages, 2 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There are many complex and rich morphological subtleties in the Arabic
language, which are very useful when analyzing traditional Arabic texts,
especially in the historical and religious contexts, and help in understanding
the meaning of the texts. Vocabulary separation means separating the word into
different parts such as root and affix. In the morphological datasets, the
variety of labels and the number of data samples helps to evaluate the
morphological methods. In this paper, we present a benchmark data set for
evaluating the methods of separating Arabic words which include about 223,690
words from the book of Sharia alIslam, which have been labeled by experts. In
terms of the volume and variety of words, this dataset is superior to other
existing data sets, and as far as we know, there are no Arabic Hadith Domain
texts. To evaluate the dataset, we applied different methods such as Farasa,
Camel, Madamira, and ALP to the dataset and we reported the annotation quality
through four evaluation methods.
|
[
{
"version": "v1",
"created": "Thu, 22 Jun 2023 16:50:40 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"AlShuhayeb",
"Huda",
""
],
[
"Minaei-Bidgoli",
"Behrouz",
""
],
[
"Shenassa",
"Mohammad E.",
""
],
[
"Hossayni",
"Sayyed-Ali",
""
]
] |
new_dataset
| 0.999836 |
2307.09652
|
Jeremy McMahan
|
Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu, Qiaomin Xie
|
VISER: A Tractable Solution Concept for Games with Information Asymmetry
|
17 pages, 6 figures
| null | null | null |
cs.GT cs.AI cs.CR cs.MA cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Many real-world games suffer from information asymmetry: one player is only
aware of their own payoffs while the other player has the full game
information. Examples include the critical domain of security games and
adversarial multi-agent reinforcement learning. Information asymmetry renders
traditional solution concepts such as Strong Stackelberg Equilibrium (SSE) and
Robust-Optimization Equilibrium (ROE) inoperative. We propose a novel solution
concept called VISER (Victim Is Secure, Exploiter best-Responds). VISER enables
an external observer to predict the outcome of such games. In particular, for
security applications, VISER allows the victim to better defend itself while
characterizing the most damaging attacks available to the attacker. We show
that each player's VISER strategy can be computed independently in polynomial
time using linear programming (LP). We also extend VISER to its Markov-perfect
counterpart for Markov games, which can be solved efficiently using a series of
LPs.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 21:51:47 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"McMahan",
"Jeremy",
""
],
[
"Wu",
"Young",
""
],
[
"Chen",
"Yudong",
""
],
[
"Zhu",
"Xiaojin",
""
],
[
"Xie",
"Qiaomin",
""
]
] |
new_dataset
| 0.978935 |
2307.09653
|
Xiaotian Duan
|
Xiaotian Duan
|
HAT-CL: A Hard-Attention-to-the-Task PyTorch Library for Continual
Learning
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Catastrophic forgetting, the phenomenon in which a neural network loses
previously obtained knowledge during the learning of new tasks, poses a
significant challenge in continual learning. The Hard-Attention-to-the-Task
(HAT) mechanism has shown potential in mitigating this problem, but its
practical implementation has been complicated by issues of usability and
compatibility, and a lack of support for existing network reuse. In this paper,
we introduce HAT-CL, a user-friendly, PyTorch-compatible redesign of the HAT
mechanism. HAT-CL not only automates gradient manipulation but also streamlines
the transformation of PyTorch modules into HAT modules. It achieves this by
providing a comprehensive suite of modules that can be seamlessly integrated
into existing architectures. Additionally, HAT-CL offers ready-to-use HAT
networks that are smoothly integrated with the TIMM library. Beyond the
redesign and reimplementation of HAT, we also introduce novel mask manipulation
techniques for HAT, which have consistently shown improvements across various
experiments. Our work paves the way for a broader application of the HAT
mechanism, opening up new possibilities in continual learning across diverse
models and applications.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 21:53:40 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Duan",
"Xiaotian",
""
]
] |
new_dataset
| 0.996694 |
2307.09670
|
Eleanor Row
|
Eleanor Row, Jingjing Tang and George Fazekas
|
JAZZVAR: A Dataset of Variations found within Solo Piano Performances of
Jazz Standards for Music Overpainting
|
Pre-print accepted for publication at CMMR2023, 12 pages, 4 figures
| null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Jazz pianists often uniquely interpret jazz standards. Passages from these
interpretations can be viewed as sections of variation. We manually extracted
such variations from solo jazz piano performances. The JAZZVAR dataset is a
collection of 502 pairs of Variation and Original MIDI segments. Each Variation
in the dataset is accompanied by a corresponding Original segment containing
the melody and chords from the original jazz standard. Our approach differs
from many existing jazz datasets in the music information retrieval (MIR)
community, which often focus on improvisation sections within jazz
performances. In this paper, we outline the curation process for obtaining and
sorting the repertoire, the pipeline for creating the Original and Variation
pairs, and our analysis of the dataset. We also introduce a new generative
music task, Music Overpainting, and present a baseline Transformer model
trained on the JAZZVAR dataset for this task. Other potential applications of
our dataset include expressive performance analysis and performer
identification.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 22:48:54 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Row",
"Eleanor",
""
],
[
"Tang",
"Jingjing",
""
],
[
"Fazekas",
"George",
""
]
] |
new_dataset
| 0.999827 |
2307.09679
|
Santiago Figueira
|
Santiago Figueira, Gabriel Goren Roig
|
A Modal Logic with n-ary Relations Over Paths: Comonadic Semantics and
Expressivity
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Game comonads give a categorical semantics for comparison games in Finite
Model Theory, thus providing an abstract characterisation of logical
equivalence for a wide range of logics, each one captured through a specific
choice of comonad. However, data-aware logics such as CoreDataXPath present
sophisticated notions of bisimulation which defy a straightforward comonadic
encoding. In this work we begin the comonadic treatment of data-aware logics by
introducing a generalisation of Modal Logic that allows relation symbols of
arbitrary arity as atoms of the syntax, which we call Path Predicate Modal
Logic or PPML. We motivate this logic as arising from a shift in perspective on
a already studied restricted version of CoreDataXPath, called DataGL, and prove
that PPML recovers DataGL for a specific choice of signature. We argue that
this shift in perspective allows the capturing and designing of new data-aware
logics. On the other hand, PPML enjoys an intrinsic motivation in that it
extends Modal Logic to predicate over more general models. Having defined the
simulation and bisimulation games for PPML and having proven a
Hennessy-Milner-type theorem, we define the PPML comonad and prove that it
captures these games, following analogous results in the literature. Our
treatment is novel in that we explicitly prove that our comonad satisfies the
axioms of arboreal categories and arboreal covers. Using the comonadic
machinery, we immediately obtain a tree-model property for PPML. Finally, we
define a translation functor from relational structures into Kripke structures
and use its properties to prove a series of polynomial-time reductions from
PPML problems to their Basic Modal Logic counterparts. Our results explain
precisely in what sense PPML lets us view general relational structures through
the modal lens.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 23:17:28 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Figueira",
"Santiago",
""
],
[
"Roig",
"Gabriel Goren",
""
]
] |
new_dataset
| 0.971602 |
2307.09688
|
Wei Jin
|
Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen,
Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, Zhen Li, Monica Xiao Cheng,
Rahul Goutam, Haiyang Zhang, Karthik Subbian, Suhang Wang, Yizhou Sun,
Jiliang Tang, Bing Yin, Xianfeng Tang
|
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for
Recommendation and Text Generation
|
Dataset for KDD Cup 2023, https://kddcup23.github.io/
| null | null | null |
cs.IR cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modeling customer shopping intentions is a crucial task for e-commerce, as it
directly impacts user experience and engagement. Thus, accurately understanding
customer preferences is essential for providing personalized recommendations.
Session-based recommendation, which utilizes customer session data to predict
their next interaction, has become increasingly popular. However, existing
session datasets have limitations in terms of item attributes, user diversity,
and dataset scale. As a result, they cannot comprehensively capture the
spectrum of user behaviors and preferences. To bridge this gap, we present the
Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It
is the first multilingual dataset consisting of millions of user sessions from
six different locales, where the major languages of products are English,
German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can
help us enhance personalization and understanding of user preferences, which
can benefit various existing tasks as well as enable new tasks. To test the
potential of the dataset, we introduce three tasks in this work: (1)
next-product recommendation, (2) next-product recommendation with domain
shifts, and (3) next-product title generation. With the above tasks, we
benchmark a range of algorithms on our proposed dataset, drawing new insights
for further research and practice. In addition, based on the proposed dataset
and tasks, we hosted a competition in the KDD CUP 2023 and have attracted
thousands of users and submissions. The winning solutions and the associated
workshop can be accessed at our website https://kddcup23.github.io/.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 00:08:49 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Jin",
"Wei",
""
],
[
"Mao",
"Haitao",
""
],
[
"Li",
"Zheng",
""
],
[
"Jiang",
"Haoming",
""
],
[
"Luo",
"Chen",
""
],
[
"Wen",
"Hongzhi",
""
],
[
"Han",
"Haoyu",
""
],
[
"Lu",
"Hanqing",
""
],
[
"Wang",
"Zhengyang",
""
],
[
"Li",
"Ruirui",
""
],
[
"Li",
"Zhen",
""
],
[
"Cheng",
"Monica Xiao",
""
],
[
"Goutam",
"Rahul",
""
],
[
"Zhang",
"Haiyang",
""
],
[
"Subbian",
"Karthik",
""
],
[
"Wang",
"Suhang",
""
],
[
"Sun",
"Yizhou",
""
],
[
"Tang",
"Jiliang",
""
],
[
"Yin",
"Bing",
""
],
[
"Tang",
"Xianfeng",
""
]
] |
new_dataset
| 0.999787 |
2307.09693
|
Liu He
|
Liu He, Daniel Aliaga
|
GlobalMapper: Arbitrary-Shaped Urban Layout Generation
|
Accepted by ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modeling and designing urban building layouts is of significant interest in
computer vision, computer graphics, and urban applications. A building layout
consists of a set of buildings in city blocks defined by a network of roads. We
observe that building layouts are discrete structures, consisting of multiple
rows of buildings of various shapes, and are amenable to skeletonization for
mapping arbitrary city block shapes to a canonical form. Hence, we propose a
fully automatic approach to building layout generation using graph attention
networks. Our method generates realistic urban layouts given arbitrary road
networks, and enables conditional generation based on learned priors. Our
results, including user study, demonstrate superior performance as compared to
prior layout generation networks, support arbitrary city block and varying
building shapes as demonstrated by generating layouts for 28 large cities.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 00:36:05 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"He",
"Liu",
""
],
[
"Aliaga",
"Daniel",
""
]
] |
new_dataset
| 0.975316 |
2307.09699
|
Zhihua Jin
|
Zhihua Jin, Gaoping Huang, Zixin Chen, Shiyi Liu, Yang Chao, Zhenchuan
Yang, Quan Li, Huamin Qu
|
ActorLens: Visual Analytics for High-level Actor Identification in MOBA
Games
|
15 pages, 9 figures
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multiplayer Online Battle Arenas (MOBAs) have garnered a substantial player
base worldwide. Nevertheless, the presence of noxious players, commonly
referred to as "actors", can significantly compromise game fairness by
exhibiting negative behaviors that diminish their team's competitive edge.
Furthermore, high-level actors tend to engage in more egregious conduct to
evade detection, thereby causing harm to the game community and necessitating
their identification. To tackle this urgent concern, a partnership was formed
with a team of game specialists from a prominent company to facilitate the
identification and labeling of high-level actors in MOBA games. We first
characterize the problem and abstract data and events from the game scene to
formulate design requirements. Subsequently, ActorLens, a visual analytics
system, was developed to exclude low-level actors, detect potential high-level
actors, and assist users in labeling players. ActorLens furnishes an overview
of players' status, summarizes behavioral patterns across three player cohorts
(namely, focused players, historical matches of focused players, and matches of
other players who played the same hero), and synthesizes key match events. By
incorporating multiple views of information, users can proficiently recognize
and label high-level actors in MOBA games. We conducted case studies and user
studies to demonstrate the efficacy of the system.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 01:04:01 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Jin",
"Zhihua",
""
],
[
"Huang",
"Gaoping",
""
],
[
"Chen",
"Zixin",
""
],
[
"Liu",
"Shiyi",
""
],
[
"Chao",
"Yang",
""
],
[
"Yang",
"Zhenchuan",
""
],
[
"Li",
"Quan",
""
],
[
"Qu",
"Huamin",
""
]
] |
new_dataset
| 0.996887 |
2307.09727
|
Zi Li
|
Zi Li and Lin Tian and Tony C. W. Mok and Xiaoyu Bai and Puyang Wang
and Jia Ge and Jingren Zhou and Le Lu and Xianghua Ye and Ke Yan and Dakai
Jin
|
SAMConvex: Fast Discrete Optimization for CT Registration using
Self-supervised Anatomical Embedding and Correlation Pyramid
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating displacement vector field via a cost volume computed in the
feature space has shown great success in image registration, but it suffers
excessive computation burdens. Moreover, existing feature descriptors only
extract local features incapable of representing the global semantic
information, which is especially important for solving large transformations.
To address the discussed issues, we propose SAMConvex, a fast coarse-to-fine
discrete optimization method for CT registration that includes a decoupled
convex optimization procedure to obtain deformation fields based on a
self-supervised anatomical embedding (SAM) feature extractor that captures both
local and global information. To be specific, SAMConvex extracts per-voxel
features and builds 6D correlation volumes based on SAM features, and
iteratively updates a flow field by performing lookups on the correlation
volumes with a coarse-to-fine scheme. SAMConvex outperforms the
state-of-the-art learning-based methods and optimization-based methods over two
inter-patient registration datasets (Abdomen CT and HeadNeck CT) and one
intra-patient registration dataset (Lung CT). Moreover, as an
optimization-based method, SAMConvex only takes $\sim2$s ($\sim5s$ with
instance optimization) for one paired images.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 02:28:41 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Li",
"Zi",
""
],
[
"Tian",
"Lin",
""
],
[
"Mok",
"Tony C. W.",
""
],
[
"Bai",
"Xiaoyu",
""
],
[
"Wang",
"Puyang",
""
],
[
"Ge",
"Jia",
""
],
[
"Zhou",
"Jingren",
""
],
[
"Lu",
"Le",
""
],
[
"Ye",
"Xianghua",
""
],
[
"Yan",
"Ke",
""
],
[
"Jin",
"Dakai",
""
]
] |
new_dataset
| 0.993098 |
2307.09729
|
Yixuan Gao
|
Xiaohong Liu, Xiongkuo Min, Wei Sun, Yulun Zhang, Kai Zhang, Radu
Timofte, Guangtao Zhai, Yixuan Gao, Yuqin Cao, Tengchuan Kou, Yunlong Dong,
Ziheng Jia, Yilin Li, Wei Wu, Shuming Hu, Sibin Deng, Pengxiang Xiao, Ying
Chen, Kai Li, Kai Zhao, Kun Yuan, Ming Sun, Heng Cong, Hao Wang, Lingzhi Fu,
Yusheng Zhang, Rongyu Zhang, Hang Shi, Qihang Xu, Longan Xiao, Zhiliang Ma,
Mirko Agarla, Luigi Celona, Claudio Rota, Raimondo Schettini, Zhiwei Huang,
Yanan Li, Xiaotao Wang, Lei Lei, Hongye Liu, Wei Hong, Ironhead Chuang, Allen
Lin, Drake Guan, Iris Chen, Kae Lou, Willy Huang, Yachun Tasi, Yvonne Kao,
Haotian Fan, Fangyuan Kong, Shiqi Zhou, Hao Liu, Yu Lai, Shanshan Chen, Wenqi
Wang, Haoning Wu, Chaofeng Chen, Chunzheng Zhu, Zekun Guo, Shiling Zhao,
Haibing Yin, Hongkui Wang, Hanene Brachemi Meftah, Sid Ahmed Fezza, Wassim
Hamidouche, Olivier D\'eforges, Tengfei Shi, Azadeh Mansouri, Hossein
Motamednia, Amir Hossein Bakhtiari, Ahmad Mahmoudi Aznaveh
|
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
| null | null | null | null |
cs.CV cs.MM eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement
Challenge, which will be held in conjunction with the New Trends in Image
Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to
address a major challenge in the field of video processing, namely, video
quality assessment (VQA) for enhanced videos. The challenge uses the VQA
Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211
enhanced videos, including 600 videos with color, brightness, and contrast
enhancements, 310 videos with deblurring, and 301 deshaked videos. The
challenge has a total of 167 registered participants. 61 participating teams
submitted their prediction results during the development phase, with a total
of 3168 submissions. A total of 176 submissions were submitted by 37
participating teams during the final testing phase. Finally, 19 participating
teams submitted their models and fact sheets, and detailed the methods they
used. Some methods have achieved better results than baseline methods, and the
winning methods have demonstrated superior prediction performance.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 02:33:42 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Liu",
"Xiaohong",
""
],
[
"Min",
"Xiongkuo",
""
],
[
"Sun",
"Wei",
""
],
[
"Zhang",
"Yulun",
""
],
[
"Zhang",
"Kai",
""
],
[
"Timofte",
"Radu",
""
],
[
"Zhai",
"Guangtao",
""
],
[
"Gao",
"Yixuan",
""
],
[
"Cao",
"Yuqin",
""
],
[
"Kou",
"Tengchuan",
""
],
[
"Dong",
"Yunlong",
""
],
[
"Jia",
"Ziheng",
""
],
[
"Li",
"Yilin",
""
],
[
"Wu",
"Wei",
""
],
[
"Hu",
"Shuming",
""
],
[
"Deng",
"Sibin",
""
],
[
"Xiao",
"Pengxiang",
""
],
[
"Chen",
"Ying",
""
],
[
"Li",
"Kai",
""
],
[
"Zhao",
"Kai",
""
],
[
"Yuan",
"Kun",
""
],
[
"Sun",
"Ming",
""
],
[
"Cong",
"Heng",
""
],
[
"Wang",
"Hao",
""
],
[
"Fu",
"Lingzhi",
""
],
[
"Zhang",
"Yusheng",
""
],
[
"Zhang",
"Rongyu",
""
],
[
"Shi",
"Hang",
""
],
[
"Xu",
"Qihang",
""
],
[
"Xiao",
"Longan",
""
],
[
"Ma",
"Zhiliang",
""
],
[
"Agarla",
"Mirko",
""
],
[
"Celona",
"Luigi",
""
],
[
"Rota",
"Claudio",
""
],
[
"Schettini",
"Raimondo",
""
],
[
"Huang",
"Zhiwei",
""
],
[
"Li",
"Yanan",
""
],
[
"Wang",
"Xiaotao",
""
],
[
"Lei",
"Lei",
""
],
[
"Liu",
"Hongye",
""
],
[
"Hong",
"Wei",
""
],
[
"Chuang",
"Ironhead",
""
],
[
"Lin",
"Allen",
""
],
[
"Guan",
"Drake",
""
],
[
"Chen",
"Iris",
""
],
[
"Lou",
"Kae",
""
],
[
"Huang",
"Willy",
""
],
[
"Tasi",
"Yachun",
""
],
[
"Kao",
"Yvonne",
""
],
[
"Fan",
"Haotian",
""
],
[
"Kong",
"Fangyuan",
""
],
[
"Zhou",
"Shiqi",
""
],
[
"Liu",
"Hao",
""
],
[
"Lai",
"Yu",
""
],
[
"Chen",
"Shanshan",
""
],
[
"Wang",
"Wenqi",
""
],
[
"Wu",
"Haoning",
""
],
[
"Chen",
"Chaofeng",
""
],
[
"Zhu",
"Chunzheng",
""
],
[
"Guo",
"Zekun",
""
],
[
"Zhao",
"Shiling",
""
],
[
"Yin",
"Haibing",
""
],
[
"Wang",
"Hongkui",
""
],
[
"Meftah",
"Hanene Brachemi",
""
],
[
"Fezza",
"Sid Ahmed",
""
],
[
"Hamidouche",
"Wassim",
""
],
[
"Déforges",
"Olivier",
""
],
[
"Shi",
"Tengfei",
""
],
[
"Mansouri",
"Azadeh",
""
],
[
"Motamednia",
"Hossein",
""
],
[
"Bakhtiari",
"Amir Hossein",
""
],
[
"Aznaveh",
"Ahmad Mahmoudi",
""
]
] |
new_dataset
| 0.99967 |
2307.09730
|
Monica Li
|
Monica S. Li and Hannah S. Stuart
|
AcousTac: Tactile sensing with acoustic resonance for electronics-free
soft skin
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Sound is a rich information medium that transmits through air; people
communicate through speech and can even discern material through tapping and
listening. To capture frequencies in the human hearing range, commercial
microphones typically have a sampling rate of over 40kHz. These accessible
acoustic technologies are not yet widely adopted for the explicit purpose of
giving robots a sense of touch. Some researchers have used sound to sense
tactile information, both monitoring ambient soundscape and with embedded
speakers and microphones to measure sounds within structures. However, these
options commonly do not provide a direct measure of steady state force, or
require electronics integrated somewhere near the contact location. In this
work, we present AcousTac, an acoustic tactile sensor for electronics-free
force sensitive soft skin. Compliant silicone caps and plastic tubes compose
the resonant chambers that emit pneumatic-driven sound measurable with a
conventional off-board microphone. The resulting frequency changes depend on
the external loads on the compliant end caps. We can tune each AcousTac taxel
to specific force and frequency ranges, based on geometric parameters,
including tube length and end-cap geometry and thus uniquely sense each taxel
simultaneously in an array. We demonstrate AcousTac's functionality on two
robotic systems: a 4-taxel array and a 3-taxel astrictive gripper. AcousTac is
a promising concept for force sensing on soft robotic surfaces, especially in
situations where electronics near the contact are not suitable. Equipping
robots with tactile sensing and soft skin provides them with a sense of touch
and the ability to safely interact with their surroundings.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 02:47:58 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Li",
"Monica S.",
""
],
[
"Stuart",
"Hannah S.",
""
]
] |
new_dataset
| 0.999462 |
2307.09776
|
Shaun Azzopardi
|
Shaun Azzopardi, Nir Piterman, Gerardo Schneider, Luca di Stefano
|
LTL Synthesis on Infinite-State Arenas defined by Programs
| null | null | null | null |
cs.LO cs.FL cs.PL cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper deals with the problem of automatically and correctly controlling
infinite-state reactive programs to achieve LTL goals. Applications include
adapting a program to new requirements, or to repair bugs discovered in the
original specification or program code. Existing approaches are able to solve
this problem for safety and some reachability properties, but require an a
priori template of the solution for more general properties. Fully automated
approaches for full LTL exist, reducing the problem into successive finite LTL
reactive synthesis problems in an abstraction-refinement loop. However, they do
not terminate when the number of steps to be completed depends on unbounded
variables. Our main insight is that safety abstractions of the program are not
enough -- fairness properties are also essential to be able to decide many
interesting problems, something missed by existing automated approaches. We
thus go beyond the state-of-the-art to allow for automated reactive program
control for full LTL, with automated discovery of the knowledge, including
fairness, of the program needed to determine realisability. We further
implement the approach in a tool, with an associated DSL for reactive programs,
and illustrate the approach through several case studies.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 06:33:51 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Azzopardi",
"Shaun",
""
],
[
"Piterman",
"Nir",
""
],
[
"Schneider",
"Gerardo",
""
],
[
"di Stefano",
"Luca",
""
]
] |
new_dataset
| 0.994364 |
2307.09777
|
Shuo Huang
|
Shuo Huang, Chengpeng Hu, Julian Togelius, Jialin Liu
|
Generating Redstone Style Cities in Minecraft
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Procedurally generating cities in Minecraft provides players more diverse
scenarios and could help understand and improve the design of cities in other
digital worlds and the real world. This paper presents a city generator that
was submitted as an entry to the 2023 Edition of Minecraft Settlement
Generation Competition for Minecraft. The generation procedure is composed of
six main steps, namely vegetation clearing, terrain reshaping, building layout
generation, route planning, streetlight placement, and wall construction. Three
algorithms, including a heuristic-based algorithm, an evolving layout
algorithm, and a random one are applied to generate the building layout, thus
determining where to place different redstone style buildings, and tested by
generating cities on random maps in limited time. Experimental results show
that the heuristic-based algorithm is capable of finding an acceptable building
layout faster for flat maps, while the evolving layout algorithm performs
better in evolving layout for rugged maps. A user study is conducted to compare
our generator with outstanding entries of the competition's 2022 edition using
the competition's evaluation criteria and shows that our generator performs
well in the adaptation and functionality criteria
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 06:36:01 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Huang",
"Shuo",
""
],
[
"Hu",
"Chengpeng",
""
],
[
"Togelius",
"Julian",
""
],
[
"Liu",
"Jialin",
""
]
] |
new_dataset
| 0.999006 |
2307.09834
|
Keeley Erhardt
|
Keeley Erhardt and Saurabh Khanna
|
Who Provides the Largest Megaphone? The Role of Google News in Promoting
Russian State-Affiliated News Sources
| null |
The 9th International Conference on Computational Social Science
(IC2S2). 2023
| null | null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The Internet has not only digitized but also democratized information access
across the globe. This gradual but path-breaking move to online information
propagation has resulted in search engines playing an increasingly prominent
role in shaping access to human knowledge. When an Internet user enters a
query, the search engine sorts through the hundreds of billions of possible
webpages to determine what to show. Google dominates the search engine market,
with Google Search surpassing 80% market share globally every year of the last
decade. Only in Russia and China do Google competitors claim more market share,
with approximately 60% of Internet users in Russia preferring Yandex (compared
to 40% in favor of Google) and more than 80% of China's Internet users
accessing Baidu as of 2022. Notwithstanding this long-standing regional
variation in Internet search providers, there is limited research showing how
these providers compare in terms of propagating state-sponsored information.
Our study fills this research gap by focusing on Russian cyberspace and
examining how Google and Yandex's search algorithms rank content from Russian
state-controlled media (hereon, RSM) outlets. This question is timely and of
practical interest given widespread reports indicating that RSM outlets have
actively engaged in promoting Kremlin propaganda in the lead-up to, and in the
aftermath of, the Russian invasion of Ukraine in February 2022.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 08:44:11 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Erhardt",
"Keeley",
""
],
[
"Khanna",
"Saurabh",
""
]
] |
new_dataset
| 0.964704 |
2307.09860
|
Ke Li
|
Ke Li, Susanne Schmidt, Tim Rolff, Reinhard Bacher, Wim Leemans, Frank
Steinicke
|
Magic NeRF Lens: Interactive Fusion of Neural Radiance Fields for
Virtual Facility Inspection
|
This work has been submitted to the IEEE TVCG for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessible
| null | null | null |
cs.GR cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Large industrial facilities such as particle accelerators and nuclear power
plants are critical infrastructures for scientific research and industrial
processes. These facilities are complex systems that not only require regular
maintenance and upgrades but are often inaccessible to humans due to various
safety hazards. Therefore, a virtual reality (VR) system that can quickly
replicate real-world remote environments to provide users with a high level of
spatial and situational awareness is crucial for facility maintenance planning.
However, the exact 3D shapes of these facilities are often too complex to be
accurately modeled with geometric primitives through the traditional
rasterization pipeline.
In this work, we develop Magic NeRF Lens, an interactive framework to support
facility inspection in immersive VR using neural radiance fields (NeRF) and
volumetric rendering. We introduce a novel data fusion approach that combines
the complementary strengths of volumetric rendering and geometric
rasterization, allowing a NeRF model to be merged with other conventional 3D
data, such as a computer-aided design model. We develop two novel 3D magic lens
effects to optimize NeRF rendering by exploiting the properties of human vision
and context-aware visualization. We demonstrate the high usability of our
framework and methods through a technical benchmark, a visual search user
study, and expert reviews. In addition, the source code of our VR NeRF
framework is made publicly available for future research and development.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 09:43:47 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Li",
"Ke",
""
],
[
"Schmidt",
"Susanne",
""
],
[
"Rolff",
"Tim",
""
],
[
"Bacher",
"Reinhard",
""
],
[
"Leemans",
"Wim",
""
],
[
"Steinicke",
"Frank",
""
]
] |
new_dataset
| 0.99566 |
2307.09885
|
Dawen Zhang
|
Dawen Zhang, Thong Hoang, Shidong Pan, Yongquan Hu, Zhenchang Xing,
Mark Staples, Xiwei Xu, Qinghua Lu, Aaron Quigley
|
Test-takers have a say: understanding the implications of the use of AI
in language tests
| null | null | null | null |
cs.CY cs.AI cs.CL cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Language tests measure a person's ability to use a language in terms of
listening, speaking, reading, or writing. Such tests play an integral role in
academic, professional, and immigration domains, with entities such as
educational institutions, professional accreditation bodies, and governments
using them to assess candidate language proficiency. Recent advances in
Artificial Intelligence (AI) and the discipline of Natural Language Processing
have prompted language test providers to explore AI's potential applicability
within language testing, leading to transformative activity patterns
surrounding language instruction and learning. However, with concerns over AI's
trustworthiness, it is imperative to understand the implications of integrating
AI into language testing. This knowledge will enable stakeholders to make
well-informed decisions, thus safeguarding community well-being and testing
integrity. To understand the concerns and effects of AI usage in language
tests, we conducted interviews and surveys with English test-takers. To the
best of our knowledge, this is the first empirical study aimed at identifying
the implications of AI adoption in language tests from a test-taker
perspective. Our study reveals test-taker perceptions and behavioral patterns.
Specifically, we identify that AI integration may enhance perceptions of
fairness, consistency, and availability. Conversely, it might incite mistrust
regarding reliability and interactivity aspects, subsequently influencing the
behaviors and well-being of test-takers. These insights provide a better
understanding of potential societal implications and assist stakeholders in
making informed decisions concerning AI usage in language testing.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 10:28:59 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Zhang",
"Dawen",
""
],
[
"Hoang",
"Thong",
""
],
[
"Pan",
"Shidong",
""
],
[
"Hu",
"Yongquan",
""
],
[
"Xing",
"Zhenchang",
""
],
[
"Staples",
"Mark",
""
],
[
"Xu",
"Xiwei",
""
],
[
"Lu",
"Qinghua",
""
],
[
"Quigley",
"Aaron",
""
]
] |
new_dataset
| 0.982314 |
2307.09905
|
Martin Balla
|
Martin Balla, George E.M. Long, Dominik Jeurissen, James Goodman,
Raluca D. Gaina, Diego Perez-Liebana
|
PyTAG: Challenges and Opportunities for Reinforcement Learning in
Tabletop Games
|
Accepted for Publication in: IEEE Conference on Games (2023)
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, Game AI research has made important breakthroughs using
Reinforcement Learning (RL). Despite this, RL for modern tabletop games has
gained little to no attention, even when they offer a range of unique
challenges compared to video games. To bridge this gap, we introduce PyTAG, a
Python API for interacting with the Tabletop Games framework (TAG). TAG
contains a growing set of more than 20 modern tabletop games, with a common API
for AI agents. We present techniques for training RL agents in these games and
introduce baseline results after training Proximal Policy Optimisation
algorithms on a subset of games. Finally, we discuss the unique challenges
complex modern tabletop games provide, now open to RL research through PyTAG.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 11:08:59 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Balla",
"Martin",
""
],
[
"Long",
"George E. M.",
""
],
[
"Jeurissen",
"Dominik",
""
],
[
"Goodman",
"James",
""
],
[
"Gaina",
"Raluca D.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
new_dataset
| 0.999183 |
2307.09972
|
Zuozhuo Dai
|
Zuozhuo Dai, Fangtao Shao, Qingkun Su, Zilong Dong, Siyu Zhu
|
Fine-grained Text-Video Retrieval with Frozen Image Encoders
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
State-of-the-art text-video retrieval (TVR) methods typically utilize CLIP
and cosine similarity for efficient retrieval. Meanwhile, cross attention
methods, which employ a transformer decoder to compute attention between each
text query and all frames in a video, offer a more comprehensive interaction
between text and videos. However, these methods lack important fine-grained
spatial information as they directly compute attention between text and
video-level tokens. To address this issue, we propose CrossTVR, a two-stage
text-video retrieval architecture. In the first stage, we leverage existing TVR
methods with cosine similarity network for efficient text/video candidate
selection. In the second stage, we propose a novel decoupled video text cross
attention module to capture fine-grained multimodal information in spatial and
temporal dimensions. Additionally, we employ the frozen CLIP model strategy in
fine-grained retrieval, enabling scalability to larger pre-trained vision
models like ViT-G, resulting in improved retrieval performance. Experiments on
text video retrieval datasets demonstrate the effectiveness and scalability of
our proposed CrossTVR compared to state-of-the-art approaches.
|
[
{
"version": "v1",
"created": "Fri, 14 Jul 2023 02:57:00 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Dai",
"Zuozhuo",
""
],
[
"Shao",
"Fangtao",
""
],
[
"Su",
"Qingkun",
""
],
[
"Dong",
"Zilong",
""
],
[
"Zhu",
"Siyu",
""
]
] |
new_dataset
| 0.980426 |
2307.10008
|
Yunfei Liu
|
Yunfei Liu, Lijian Lin, Fei Yu, Changyin Zhou, Yu Li
|
MODA: Mapping-Once Audio-driven Portrait Animation with Dual Attentions
|
Accepted by ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Audio-driven portrait animation aims to synthesize portrait videos that are
conditioned by given audio. Animating high-fidelity and multimodal video
portraits has a variety of applications. Previous methods have attempted to
capture different motion modes and generate high-fidelity portrait videos by
training different models or sampling signals from given videos. However,
lacking correlation learning between lip-sync and other movements (e.g., head
pose/eye blinking) usually leads to unnatural results. In this paper, we
propose a unified system for multi-person, diverse, and high-fidelity talking
portrait generation. Our method contains three stages, i.e., 1) Mapping-Once
network with Dual Attentions (MODA) generates talking representation from given
audio. In MODA, we design a dual-attention module to encode accurate mouth
movements and diverse modalities. 2) Facial composer network generates dense
and detailed face landmarks, and 3) temporal-guided renderer syntheses stable
videos. Extensive evaluations demonstrate that the proposed system produces
more natural and realistic video portraits compared to previous methods.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 14:45:11 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Liu",
"Yunfei",
""
],
[
"Lin",
"Lijian",
""
],
[
"Yu",
"Fei",
""
],
[
"Zhou",
"Changyin",
""
],
[
"Li",
"Yu",
""
]
] |
new_dataset
| 0.997548 |
2307.10018
|
Felipe B. Martins
|
Aline Lima de Oliveira, Cau\^e Addae da Silva Gomes, Cec\'ilia
Virginia Santos da Silva, Charles Matheus de Sousa Alves, Danilo Andrade
Martins de Souza, Driele Pires Ferreira Ara\'ujo Xavier, Edgleyson Pereira da
Silva, Felipe Bezerra Martins, Lucas Henrique Cavalcanti Santos, Lucas Dias
Maciel, Matheus Paix\~ao Gumercindo dos Santos, Matheus Lafayette
Vasconcelos, Matheus Vin\'icius Teotonio do Nascimento Andrade, Jo\~ao
Guilherme Oliveira Carvalho de Melo, Jo\~ao Pedro Souza Pereira de Moura,
Jos\'e Ronald da Silva, Jos\'e Victor Silva Cruz, Pedro Henrique Santana de
Morais, Pedro Paulo Salman de Oliveira, Riei Joaquim Matos Rodrigues, Roberto
Costa Fernandes, Ryan Vinicius Santos Morais, Tamara Mayara Ramos Teobaldo,
Washington Igor dos Santos Silva, Edna Natividade Silva Barros
|
Rob\^oCIn Small Size League Extended Team Description Paper for RoboCup
2023
| null | null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Rob\^oCIn has participated in RoboCup Small Size League since 2019, won its
first world title in 2022 (Division B), and is currently a three-times
Latin-American champion. This paper presents our improvements to defend the
Small Size League (SSL) division B title in RoboCup 2023 in Bordeaux, France.
This paper aims to share some of the academic research that our team developed
over the past year. Our team has successfully published 2 articles related to
SSL at two high-impact conferences: the 25th RoboCup International Symposium
and the 19th IEEE Latin American Robotics Symposium (LARS 2022). Over the last
year, we have been continuously migrating from our past codebase to
Unification. We will describe the new architecture implemented and some points
of software and AI refactoring. In addition, we discuss the process of
integrating machined components into the mechanical system, our development for
participating in the vision blackout challenge last year and what we are
preparing for this year.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 14:58:30 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"de Oliveira",
"Aline Lima",
""
],
[
"Gomes",
"Cauê Addae da Silva",
""
],
[
"da Silva",
"Cecília Virginia Santos",
""
],
[
"Alves",
"Charles Matheus de Sousa",
""
],
[
"de Souza",
"Danilo Andrade Martins",
""
],
[
"Xavier",
"Driele Pires Ferreira Araújo",
""
],
[
"da Silva",
"Edgleyson Pereira",
""
],
[
"Martins",
"Felipe Bezerra",
""
],
[
"Santos",
"Lucas Henrique Cavalcanti",
""
],
[
"Maciel",
"Lucas Dias",
""
],
[
"Santos",
"Matheus Paixão Gumercindo dos",
""
],
[
"Vasconcelos",
"Matheus Lafayette",
""
],
[
"Andrade",
"Matheus Vinícius Teotonio do Nascimento",
""
],
[
"de Melo",
"João Guilherme Oliveira Carvalho",
""
],
[
"de Moura",
"João Pedro Souza Pereira",
""
],
[
"da Silva",
"José Ronald",
""
],
[
"Cruz",
"José Victor Silva",
""
],
[
"de Morais",
"Pedro Henrique Santana",
""
],
[
"de Oliveira",
"Pedro Paulo Salman",
""
],
[
"Rodrigues",
"Riei Joaquim Matos",
""
],
[
"Fernandes",
"Roberto Costa",
""
],
[
"Morais",
"Ryan Vinicius Santos",
""
],
[
"Teobaldo",
"Tamara Mayara Ramos",
""
],
[
"Silva",
"Washington Igor dos Santos",
""
],
[
"Barros",
"Edna Natividade Silva",
""
]
] |
new_dataset
| 0.980907 |
2307.10022
|
Konstantinos Pitas
|
Konstantinos Pitas
|
Europepolls: A Dataset of Country-Level Opinion Polling Data for the
European Union and the UK
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
I propose an open dataset of country-level historical opinion polling data
for the European Union and the UK. The dataset aims to fill a gap in available
opinion polling data for the European Union. Some existing datasets are
restricted to the past five years, limiting research opportunities. At the same
time, some larger proprietary datasets exist but are available only in a visual
preprocessed time series format. Finally, while other large datasets for
individual countries might exist, these could be inaccessible due to language
barriers. The data was gathered from Wikipedia, and preprocessed using the
pandas library. Both the raw and the preprocessed data are in the .csv format.
I hope that given the recent advances in LLMs and deep learning in general,
this large dataset will enable researchers to uncover complex interactions
between multimodal data (news articles, economic indicators, social media) and
voting behavior. The raw data, the preprocessed data, and the preprocessing
scripts are available on GitHub.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:05:55 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Pitas",
"Konstantinos",
""
]
] |
new_dataset
| 0.999783 |
2307.10034
|
Carlo Sartiani
|
Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli,
Carlo Sartiani, Stefanie Scherzinger
|
Validation of Modern JSON Schema: Formalization and Complexity
| null | null | null | null |
cs.DB cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
JSON Schema is the de-facto standard schema language for JSON data. The
language went through many minor revisions, but the most recent versions of the
language added two novel features, dynamic references and annotation-dependent
validation, that change the evaluation model. Modern JSON Schema is the name
used to indicate all versions from Draft 2019-09, which are characterized by
these new features, while Classical JSON Schema is used to indicate the
previous versions.
These new "modern" features make the schema language quite difficult to
understand, and have generated many discussions about the correct
interpretation of their official specifications; for this reason we undertook
the task of their formalization. During this process, we also analyzed the
complexity of data validation in Modern JSON Schema, with the idea of
confirming the PTIME complexity of Classical JSON Schema validation, and we
were surprised to discover a completely different truth: data validation, that
is expected to be an extremely efficient process, acquires, with Modern JSON
Schema features, a PSPACE complexity.
In this paper, we give the first formal description of Modern JSON Schema,
which we consider a central contribution of the work that we present here. We
then prove that its data validation problem is PSPACE-complete. We prove that
the origin of the problem lies in dynamic references, and not in
annotation-dependent validation. We study the schema and data complexities,
showing that the problem is PSPACE-complete with respect to the schema size
even with a fixed instance, but is in PTIME when the schema is fixed and only
the instance size is allowed to vary. Finally, we run experiments that show
that there are families of schemas where the difference in asymptotic
complexity between dynamic and static references is extremely visible, even
with small schemas.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:18:27 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Attouche",
"Lyes",
""
],
[
"Baazizi",
"Mohamed-Amine",
""
],
[
"Colazzo",
"Dario",
""
],
[
"Ghelli",
"Giorgio",
""
],
[
"Sartiani",
"Carlo",
""
],
[
"Scherzinger",
"Stefanie",
""
]
] |
new_dataset
| 0.9915 |
2307.10039
|
Bryan Tjandra
|
Bryan Tjandra, Made S. N. Negara, Nyoo S. C. Handoko
|
Deteksi Sampah di Permukaan dan Dalam Perairan pada Objek Video dengan
Metode Robust and Efficient Post-Processing dan Tubelet-Level Bounding Box
Linking
|
14 pages, in Indonesian language, 14 figures
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Indonesia, as a maritime country, has a significant portion of its territory
covered by water. Ineffective waste management has resulted in a considerable
amount of trash in Indonesian waters, leading to various issues. The
development of an automated trash-collecting robot can be a solution to address
this problem. The robot requires a system capable of detecting objects in
motion, such as in videos. However, using naive object detection methods in
videos has limitations, particularly when image focus is reduced and the target
object is obstructed by other objects. This paper's contribution provides an
explanation of the methods that can be applied to perform video object
detection in an automated trash-collecting robot. The study utilizes the YOLOv5
model and the Robust & Efficient Post Processing (REPP) method, along with
tubelet-level bounding box linking on the FloW and Roboflow datasets. The
combination of these methods enhances the performance of naive object detection
from YOLOv5 by considering the detection results in adjacent frames. The
results show that the post-processing stage and tubelet-level bounding box
linking can improve the quality of detection, achieving approximately 3% better
performance compared to YOLOv5 alone. The use of these methods has the
potential to detect surface and underwater trash and can be applied to a
real-time image-based trash-collecting robot. Implementing this system is
expected to mitigate the damage caused by trash in the past and improve
Indonesia's waste management system in the future.
|
[
{
"version": "v1",
"created": "Fri, 14 Jul 2023 04:04:15 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Tjandra",
"Bryan",
""
],
[
"Negara",
"Made S. N.",
""
],
[
"Handoko",
"Nyoo S. C.",
""
]
] |
new_dataset
| 0.999131 |
2307.10054
|
Soheil Abbasloo
|
Soheil Abbasloo
|
Internet Congestion Control Benchmarking
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
How do we assess a new Internet congestion control (CC) design? How do we
compare it with other existing schemes? Under what scenarios and using what
network parameters? These are just a handful of simple questions coming up
every time a new CC design is going to be evaluated. Interestingly, the number
of specific answers to these questions can be as large as the number of CC
designers. In this work, we aim to highlight that the network congestion
control, as a hot and active research topic, requires a crystal clear set(s) of
\textit{CC Benchmarks} to form a common ground for quantitatively comparing and
unambiguously assessing the strengths and weaknesses of a design with respect
to the existing ones. As a first step toward that goal, we introduce general
benchmarks that can capture the different performance of the existing Internet
CC schemes. Using these benchmarks, we rank the Internet CC algorithms and
illustrate that there is still lots of room for more innovations and
improvements in this topic.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:26:56 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Abbasloo",
"Soheil",
""
]
] |
new_dataset
| 0.997808 |
2307.10080
|
Nir Weinberger
|
Nir Weinberger, Ilan Shomorony
|
Fundamental Limits of Reference-Based Sequence Reordering
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The problem of reconstructing a sequence of independent and identically
distributed symbols from a set of equal size, consecutive, fragments, as well
as a dependent reference sequence, is considered. First, in the regime in which
the fragments are relatively long, and typically no fragment appears more than
once, the scaling of the failure probability of maximum likelihood
reconstruction algorithm is exactly determined for perfect reconstruction and
bounded for partial reconstruction. Second, the regime in which the fragments
are relatively short and repeating fragments abound is characterized. A
trade-off is stated between the fraction of fragments that cannot be adequately
reconstructed vs. the distortion level allowed for the reconstruction of each
fragment, while still allowing vanishing failure probability
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:53:54 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Weinberger",
"Nir",
""
],
[
"Shomorony",
"Ilan",
""
]
] |
new_dataset
| 0.968373 |
2307.10088
|
Christopher Rawles
|
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy
Lillicrap
|
Android in the Wild: A Large-Scale Dataset for Android Device Control
| null | null | null | null |
cs.LG cs.CL cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
There is a growing interest in device-control systems that can interpret
human natural language instructions and execute them on a digital device by
directly controlling its user interface. We present a dataset for
device-control research, Android in the Wild (AITW), which is orders of
magnitude larger than current datasets. The dataset contains human
demonstrations of device interactions, including the screens and actions, and
corresponding natural language instructions. It consists of 715k episodes
spanning 30k unique instructions, four versions of Android (v10-13),and eight
device types (Pixel 2 XL to Pixel 6) with varying screen resolutions. It
contains multi-step tasks that require semantic understanding of language and
visual context. This dataset poses a new challenge: actions available through
the user interface must be inferred from their visual appearance. And, instead
of simple UI element-based actions, the action space consists of precise
gestures (e.g., horizontal scrolls to operate carousel widgets). We organize
our dataset to encourage robustness analysis of device-control systems, i.e.,
how well a system performs in the presence of new task descriptions, new
applications, or new platform versions. We develop two agents and report
performance across the dataset. The dataset is available at
https://github.com/google-research/google-research/tree/master/android_in_the_wild.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:57:24 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Rawles",
"Christopher",
""
],
[
"Li",
"Alice",
""
],
[
"Rodriguez",
"Daniel",
""
],
[
"Riva",
"Oriana",
""
],
[
"Lillicrap",
"Timothy",
""
]
] |
new_dataset
| 0.999889 |
2307.10097
|
Junhao Dong
|
Junhao Dong, Zhu Meng, Delong Liu, Zhicheng Zhao and Fei Su
|
Boundary-Refined Prototype Generation: A General End-to-End Paradigm for
Semi-Supervised Semantic Segmentation
|
53 pages, 7 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Prototype-based classification is a classical method in machine learning, and
recently it has achieved remarkable success in semi-supervised semantic
segmentation. However, the current approach isolates the prototype
initialization process from the main training framework, which appears to be
unnecessary. Furthermore, while the direct use of K-Means algorithm for
prototype generation has considered rich intra-class variance, it may not be
the optimal solution for the classification task. To tackle these problems, we
propose a novel boundary-refined prototype generation (BRPG) method, which is
incorporated into the whole training framework. Specifically, our approach
samples and clusters high- and low-confidence features separately based on a
confidence threshold, aiming to generate prototypes closer to the class
boundaries. Moreover, an adaptive prototype optimization strategy is introduced
to make prototype augmentation for categories with scattered feature
distributions. Extensive experiments on the PASCAL VOC 2012 and Cityscapes
datasets demonstrate the superiority and scalability of the proposed method,
outperforming the current state-of-the-art approaches. The code is available at
xxxxxxxxxxxxxx.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 16:12:37 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Dong",
"Junhao",
""
],
[
"Meng",
"Zhu",
""
],
[
"Liu",
"Delong",
""
],
[
"Zhao",
"Zhicheng",
""
],
[
"Su",
"Fei",
""
]
] |
new_dataset
| 0.996539 |
2307.10166
|
Jiajie Fan
|
Jiajie Fan, Laure Vuaille, Hao Wang, Thomas B\"ack
|
Adversarial Latent Autoencoder with Self-Attention for Structural Image
Synthesis
|
18 pages, 8 figures
| null | null | null |
cs.CV cs.CE eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Generative Engineering Design approaches driven by Deep Generative Models
(DGM) have been proposed to facilitate industrial engineering processes. In
such processes, designs often come in the form of images, such as blueprints,
engineering drawings, and CAD models depending on the level of detail. DGMs
have been successfully employed for synthesis of natural images, e.g.,
displaying animals, human faces and landscapes. However, industrial design
images are fundamentally different from natural scenes in that they contain
rich structural patterns and long-range dependencies, which are challenging for
convolution-based DGMs to generate. Moreover, DGM-driven generation process is
typically triggered based on random noisy inputs, which outputs unpredictable
samples and thus cannot perform an efficient industrial design exploration. We
tackle these challenges by proposing a novel model Self-Attention Adversarial
Latent Autoencoder (SA-ALAE), which allows generating feasible design images of
complex engineering parts. With SA-ALAE, users can not only explore novel
variants of an existing design, but also control the generation process by
operating in latent space. The potential of SA-ALAE is shown by generating
engineering blueprints in a real automotive design task.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 17:50:03 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Fan",
"Jiajie",
""
],
[
"Vuaille",
"Laure",
""
],
[
"Wang",
"Hao",
""
],
[
"Bäck",
"Thomas",
""
]
] |
new_dataset
| 0.99982 |
2307.10171
|
Sean Bin Yang
|
Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang and Christian S.
Jensen
|
LightPath: Lightweight and Scalable Path Representation Learning
|
This paper has been accepted by ACM SIGKDD-23
| null | null | null |
cs.LG cs.AI cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
Movement paths are used widely in intelligent transportation and smart city
applications. To serve such applications, path representation learning aims to
provide compact representations of paths that enable efficient and accurate
operations when used for different downstream tasks such as path ranking and
travel cost estimation. In many cases, it is attractive that the path
representation learning is lightweight and scalable; in resource-limited
environments and under green computing limitations, it is essential. Yet,
existing path representation learning studies focus on accuracy and pay at most
secondary attention to resource consumption and scalability.
We propose a lightweight and scalable path representation learning framework,
termed LightPath, that aims to reduce resource consumption and achieve
scalability without affecting accuracy, thus enabling broader applicability.
More specifically, we first propose a sparse auto-encoder that ensures that the
framework achieves good scalability with respect to path length. Next, we
propose a relational reasoning framework to enable faster training of more
robust sparse path encoders. We also propose global-local knowledge
distillation to further reduce the size and improve the performance of sparse
path encoders. Finally, we report extensive experiments on two real-world
datasets to offer insight into the efficiency, scalability, and effectiveness
of the proposed framework.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 17:57:27 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Yang",
"Sean Bin",
""
],
[
"Hu",
"Jilin",
""
],
[
"Guo",
"Chenjuan",
""
],
[
"Yang",
"Bin",
""
],
[
"Jensen",
"Christian S.",
""
]
] |
new_dataset
| 0.994726 |
1302.3820
|
Neal Patwari
|
Neal Patwari, Lara Brewer, Quinn Tate, Ossi Kaltiokallio, and Maurizio
Bocca
|
Breathfinding: A Wireless Network that Monitors and Locates Breathing in
a Home
| null | null |
10.1109/JSTSP.2013.2287473
| null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
This paper explores using RSS measurements on many links in a wireless
network to estimate the breathing rate of a person, and the location where the
breathing is occurring, in a home, while the person is sitting, laying down,
standing, or sleeping. The main challenge in breathing rate estimation is that
"motion interference", i.e., movements other than a person's breathing,
generally cause larger changes in RSS than inhalation and exhalation. We
develop a method to estimate breathing rate despite motion interference, and
demonstrate its performance during multiple short (3-7 minute) tests and during
a longer 66 minute test. Further, for the same experiments, we show the
location of the breathing person can be estimated, to within about 2 m average
error in a 56 square meter apartment. Being able to locate a breathing person
who is not otherwise moving, without calibration, is important for applications
in search and rescue, health care, and security.
|
[
{
"version": "v1",
"created": "Fri, 15 Feb 2013 17:46:50 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Patwari",
"Neal",
""
],
[
"Brewer",
"Lara",
""
],
[
"Tate",
"Quinn",
""
],
[
"Kaltiokallio",
"Ossi",
""
],
[
"Bocca",
"Maurizio",
""
]
] |
new_dataset
| 0.966386 |
1309.0750
|
Mohammad Noshad
|
Mohammad Noshad and Maite Brandt-Pearce
|
Application of Expurgated PPM to Indoor Visible Light Communications -
Part I: Single-User Systems
|
Journal of Lightwave Technology
| null |
10.1109/JLT.2013.2293341
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visible light communications (VLC) in indoor environments suffer from the
limited bandwidth of LEDs as well as from the inter-symbol interference (ISI)
imposed by multipath. In this work, transmission schemes to improve the
performance of indoor optical wireless communication (OWC) systems are
introduced. Expurgated pulse-position modulation (EPPM) is proposed for this
application since it can provide a wide range of peak to average power ratios
(PAPR) needed for dimming of the indoor illumination. A correlation decoder
used at the receiver is shown to be optimal for indoor VLC systems, which are
shot noise and background-light limited. Interleaving applied on EPPM in order
to decrease the ISI effect in dispersive VLC channels can significantly
decrease the error probability. The proposed interleaving technique makes EPPM
a better modulation option compared to PPM for VLC systems or any other
dispersive OWC system. An overlapped EPPM pulse technique is proposed to
increase the transmission rate when bandwidth-limited white LEDs are used as
sources.
|
[
{
"version": "v1",
"created": "Tue, 3 Sep 2013 17:20:27 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Noshad",
"Mohammad",
""
],
[
"Brandt-Pearce",
"Maite",
""
]
] |
new_dataset
| 0.965798 |
1408.2192
|
Feng Jiang
|
Feng Jiang and Jie Chen and A. Lee Swindlehurst and Jose A.
Lopez-Salcedo
|
Massive MIMO for Wireless Sensing with a Coherent Multiple Access
Channel
|
32 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Feb. 2015
| null |
10.1109/TSP.2015.2417508
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the detection and estimation of a zero-mean Gaussian signal in a
wireless sensor network with a coherent multiple access channel, when the
fusion center (FC) is configured with a large number of antennas and the
wireless channels between the sensor nodes and FC experience Rayleigh fading.
For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy
Detector (ED), and find optimal values for the sensor transmission gains. For
the NP detector which requires channel state information (CSI), we show that
detection performance remains asymptotically constant with the number of FC
antennas if the sensor transmit power decreases proportionally with the
increase in the number of antennas. Performance bounds show that the benefit of
multiple antennas at the FC disappears as the transmit power grows. The results
of the NP detector are also generalized to the linear minimum mean squared
error estimator. For the ED which does not require CSI, we derive optimal gains
that maximize the deflection coefficient of the detector, and we show that a
constant deflection can be asymptotically achieved if the sensor transmit power
scales as the inverse square root of the number of FC antennas. Unlike the NP
detector, for high sensor power the multi-antenna ED is observed to empirically
have significantly better performance than the single-antenna implementation. A
number of simulation results are included to validate the analysis.
|
[
{
"version": "v1",
"created": "Sun, 10 Aug 2014 06:58:50 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Mar 2015 00:07:59 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Jiang",
"Feng",
""
],
[
"Chen",
"Jie",
""
],
[
"Swindlehurst",
"A. Lee",
""
],
[
"Lopez-Salcedo",
"Jose A.",
""
]
] |
new_dataset
| 0.998009 |
1505.07192
|
Hongyang Li
|
Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price
|
Inner and Inter Label Propagation: Salient Object Detection in the Wild
|
The full version of the TIP 2015 publication
| null |
10.1109/TIP.2015.2440174
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.
|
[
{
"version": "v1",
"created": "Wed, 27 May 2015 05:24:03 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Li",
"Hongyang",
""
],
[
"Lu",
"Huchuan",
""
],
[
"Lin",
"Zhe",
""
],
[
"Shen",
"Xiaohui",
""
],
[
"Price",
"Brian",
""
]
] |
new_dataset
| 0.993453 |
1509.01226
|
Sajjad AbdollahRamezani
|
Sajjad AbdollahRamezani, Kamalodin Arik, Amin Khavasi, Zahra Kavehvash
|
Analog Computing Using Graphene-based Metalines
| null | null |
10.1364/OL.40.005239
| null |
cs.ET cond-mat.mes-hall physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce the new concept of "metalines" for manipulating the amplitude
and phase profile of an incident wave locally and independently. Thanks to the
highly confined graphene plasmons, a transmit-array of graphene-based metalines
is used to realize analog computing on an ultra-compact, integrable and planar
platform. By employing the general concepts of spatial Fourier transformation,
a well-designed structure of such meta-transmit-array combined with graded
index lenses can perform two mathematical operations; i.e. differentiation and
integration, with high efficiency. The presented configuration is about 60
times shorter than the recent structure proposed by Silva et al.(Science, 2014,
343, 160-163); moreover, our simulated output responses are in more agreement
with the desired analytic results. These findings may lead to remarkable
achievements in light-based plasmonic signal processors at nanoscale instead of
their bulky conventional dielectric lens-based counterparts.
|
[
{
"version": "v1",
"created": "Wed, 2 Sep 2015 19:54:03 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"AbdollahRamezani",
"Sajjad",
""
],
[
"Arik",
"Kamalodin",
""
],
[
"Khavasi",
"Amin",
""
],
[
"Kavehvash",
"Zahra",
""
]
] |
new_dataset
| 0.995961 |
1910.00964
|
Venet Osmani
|
Seyedmostafa Sheikhalishahi, Vevake Balaraman, Venet Osmani
|
Benchmarking machine learning models on multi-centre eICU critical care
dataset
|
Source code to replicate the results
https://github.com/mostafaalishahi/eICU_Benchmark
| null |
10.1371/journal.pone.0235424
| null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Progress of machine learning in critical care has been difficult to track, in
part due to absence of public benchmarks. Other fields of research (such as
computer vision and natural language processing) have established various
competitions and public benchmarks. Recent availability of large clinical
datasets has enabled the possibility of establishing public benchmarks. Taking
advantage of this opportunity, we propose a public benchmark suite to address
four areas of critical care, namely mortality prediction, estimation of length
of stay, patient phenotyping and risk of decompensation. We define each task
and compare the performance of both clinical models as well as baseline and
deep learning models using eICU critical care dataset of around 73,000
patients. This is the first public benchmark on a multi-centre critical care
dataset, comparing the performance of clinical gold standard with our
predictive model. We also investigate the impact of numerical variables as well
as handling of categorical variables on each of the defined tasks. The source
code, detailing our methods and experiments is publicly available such that
anyone can replicate our results and build upon our work.
|
[
{
"version": "v1",
"created": "Wed, 2 Oct 2019 14:04:24 GMT"
},
{
"version": "v2",
"created": "Wed, 6 May 2020 17:44:40 GMT"
},
{
"version": "v3",
"created": "Thu, 5 Aug 2021 08:36:29 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Sheikhalishahi",
"Seyedmostafa",
""
],
[
"Balaraman",
"Vevake",
""
],
[
"Osmani",
"Venet",
""
]
] |
new_dataset
| 0.997697 |
2002.04077
|
Joshua Lawrence Benjamin
|
Joshua L. Benjamin, Thomas Gerard, Domani\c{c} Lavery, Polina Bayvel
and Georgios Zervas
|
PULSE: Optical circuit switched Data Center architecture operating at
nanosecond timescales
|
16 pages, 12 figures
| null |
10.1109/JLT.2020.2997664
| null |
cs.NI cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce PULSE, a sub-microsecond optical circuit-switched data centre
network architecture controlled by distributed hardware schedulers. PULSE is a
flat architecture that uses parallel passive coupler-based broadcast and select
networks. We employ a novel transceiver architecture, for dynamic
wavelength-timeslot selection, to achieve a reconfiguration time down to
O(100ps), establishing timeslots of O(10ns). A novel scheduling algorithm that
has a clock period of 2.3ns performs multiple iterations to maximize
throughput, wavelength usage and reduce latency, enhancing the overall
performance. In order to scale, the single-hop PULSE architecture uses
sub-networks that are disjoint by using multiple transceivers for each node in
64 node racks. At the reconfiguration circuit duration (epoch = 120 ns), the
scheduling algorithm is shown to achieve up to 93% throughput and 100%
wavelength usage of 64 wavelengths, incurring an average latency that ranges
from 0.7-1.2 microseconds with best-case 0.4 microsecond median and 5
microsecond tail latency, limited by the timeslot (20 ns) and epoch size (120
ns). We show how the 4096-node PULSE architecture allows up to 260k optical
channels to be re-used across sub-networks achieving a capacity of 25.6 Pbps
with an energy consumption of 85 pJ/bit.
|
[
{
"version": "v1",
"created": "Mon, 10 Feb 2020 20:34:20 GMT"
},
{
"version": "v2",
"created": "Mon, 25 May 2020 09:32:08 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Benjamin",
"Joshua L.",
""
],
[
"Gerard",
"Thomas",
""
],
[
"Lavery",
"Domaniç",
""
],
[
"Bayvel",
"Polina",
""
],
[
"Zervas",
"Georgios",
""
]
] |
new_dataset
| 0.999546 |
2011.04408
|
Hanjiang Hu
|
Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Jiacheng Zhu,
Zuxin Liu, Wenhao Ding, Ding Zhao, Hesheng Wang
|
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and
Benchmark under Multiple Environments
|
Accepted by IROS 2023, 23 pages, 13 figures, 10 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Different environments pose a great challenge to the outdoor robust visual
perception for long-term autonomous driving, and the generalization of
learning-based algorithms on different environments is still an open problem.
Although monocular depth prediction has been well studied recently, few works
focus on the robustness of learning-based depth prediction across different
environments, e.g. changing illumination and seasons, owing to the lack of such
a multi-environment real-world dataset and benchmark. To this end, the first
cross-season monocular depth prediction dataset and benchmark, SeasonDepth, is
introduced to benchmark the depth estimation performance under different
environments. We investigate several state-of-the-art representative
open-source supervised and self-supervised depth prediction methods using
newly-formulated metrics. Through extensive experimental evaluation on the
proposed dataset and cross-dataset evaluation with current autonomous driving
datasets, the performance and robustness against the influence of multiple
environments are analyzed qualitatively and quantitatively. We show that
long-term monocular depth prediction is still challenging and believe our work
can boost further research on the long-term robustness and generalization for
outdoor visual perception. The dataset is available on
https://seasondepth.github.io, and the benchmark toolkit is available on
https://github.com/ SeasonDepth/SeasonDepth.
|
[
{
"version": "v1",
"created": "Mon, 9 Nov 2020 13:24:45 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Jun 2021 14:35:07 GMT"
},
{
"version": "v3",
"created": "Wed, 14 Jul 2021 09:31:15 GMT"
},
{
"version": "v4",
"created": "Sat, 28 Aug 2021 17:07:45 GMT"
},
{
"version": "v5",
"created": "Fri, 17 Dec 2021 02:38:04 GMT"
},
{
"version": "v6",
"created": "Mon, 21 Nov 2022 05:43:10 GMT"
},
{
"version": "v7",
"created": "Mon, 17 Jul 2023 23:30:43 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Hu",
"Hanjiang",
""
],
[
"Yang",
"Baoquan",
""
],
[
"Qiao",
"Zhijian",
""
],
[
"Liu",
"Shiqi",
""
],
[
"Zhu",
"Jiacheng",
""
],
[
"Liu",
"Zuxin",
""
],
[
"Ding",
"Wenhao",
""
],
[
"Zhao",
"Ding",
""
],
[
"Wang",
"Hesheng",
""
]
] |
new_dataset
| 0.999637 |
2112.03587
|
Yang Liu
|
Yang Liu, Keze Wang, Lingbo Liu, Haoyuan Lan, Liang Lin
|
TCGL: Temporal Contrastive Graph for Self-supervised Video
Representation Learning
|
This work has been published in IEEE Transactions on Image
Processing. The code is publicly available at
https://github.com/YangLiu9208/TCGL. arXiv admin note: substantial text
overlap with arXiv:2101.00820
| null |
10.1109/TIP.2022.3147032
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video self-supervised learning is a challenging task, which requires
significant expressive power from the model to leverage rich spatial-temporal
knowledge and generate effective supervisory signals from large amounts of
unlabeled videos. However, existing methods fail to increase the temporal
diversity of unlabeled videos and ignore elaborately modeling multi-scale
temporal dependencies in an explicit way. To overcome these limitations, we
take advantage of the multi-scale temporal dependencies within videos and
proposes a novel video self-supervised learning framework named Temporal
Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and
intra-snippet temporal dependencies for temporal representation learning with a
hybrid graph contrastive learning strategy. Specifically, a Spatial-Temporal
Knowledge Discovering (STKD) module is first introduced to extract
motion-enhanced spatial-temporal representations from videos based on the
frequency domain analysis of discrete cosine transform. To explicitly model
multi-scale temporal dependencies of unlabeled videos, our TCGL integrates the
prior knowledge about the frame and snippet orders into graph structures, i.e.,
the intra-/inter- snippet Temporal Contrastive Graphs (TCG). Then, specific
contrastive learning modules are designed to maximize the agreement between
nodes in different graph views. To generate supervisory signals for unlabeled
videos, we introduce an Adaptive Snippet Order Prediction (ASOP) module which
leverages the relational knowledge among video snippets to learn the global
context representation and recalibrate the channel-wise features adaptively.
Experimental results demonstrate the superiority of our TCGL over the
state-of-the-art methods on large-scale action recognition and video retrieval
benchmarks.The code is publicly available at
https://github.com/YangLiu9208/TCGL.
|
[
{
"version": "v1",
"created": "Tue, 7 Dec 2021 09:27:56 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jan 2022 03:44:26 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Mar 2022 07:23:39 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Liu",
"Yang",
""
],
[
"Wang",
"Keze",
""
],
[
"Liu",
"Lingbo",
""
],
[
"Lan",
"Haoyuan",
""
],
[
"Lin",
"Liang",
""
]
] |
new_dataset
| 0.998875 |
2202.12364
|
Rashmi Boragolla
|
Rashmi Boragolla and Pradeepa Yahampath
|
Orthonormal Matrix Codebook Design for Adaptive Transform Coding
|
Accepted as a poster on the DCC 2022
| null |
10.1109/TIP.2023.3289064
| null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A novel algorithm for designing optimized orthonormal transform-matrix
codebooks for adaptive transform coding of a non-stationary vector process is
proposed. This algorithm relies on a block-wise stationary model of a
non-stationary process and finds a codebook of transform-matrices by minimizing
the end-to-end mean square error of transform coding averaged over the
distribution of stationary blocks of vectors. The algorithm, which belongs to
the class of block-coordinate descent algorithms, solves an intermediate
minimization problem involving matrix-orthonormality constraints in a
computationally efficient manner by mapping the problem from the Euclidean
space to the Stiefel manifold. As such, the algorithm can be broadly applied to
any adaptive transform coding problem. Preliminary results obtained with
inter-prediction residuals in an H265 video codec are presented to demonstrate
the advantage of optimized adaptive transform codes over non-adaptive codes
based on the standard DCT.
|
[
{
"version": "v1",
"created": "Thu, 24 Feb 2022 21:04:36 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Boragolla",
"Rashmi",
""
],
[
"Yahampath",
"Pradeepa",
""
]
] |
new_dataset
| 0.977353 |
2207.12308
|
Haoxin Ma
|
Haoxin Ma, Jiangyan Yi, Chenglong Wang, Xinrui Yan, Jianhua Tao, Tao
Wang, Shiming Wang, Ruibo Fu
|
CFAD: A Chinese Dataset for Fake Audio Detection
|
FAD renamed as CFAD
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fake audio detection is a growing concern and some relevant datasets have
been designed for research. However, there is no standard public Chinese
dataset under complex conditions.In this paper, we aim to fill in the gap and
design a Chinese fake audio detection dataset (CFAD) for studying more
generalized detection methods. Twelve mainstream speech-generation techniques
are used to generate fake audio. To simulate the real-life scenarios, three
noise datasets are selected for noise adding at five different signal-to-noise
ratios, and six codecs are considered for audio transcoding (format
conversion). CFAD dataset can be used not only for fake audio detection but
also for detecting the algorithms of fake utterances for audio forensics.
Baseline results are presented with analysis. The results that show fake audio
detection methods with generalization remain challenging. The CFAD dataset is
publicly available at: https://zenodo.org/record/8122764.
|
[
{
"version": "v1",
"created": "Tue, 12 Jul 2022 13:27:21 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Feb 2023 07:13:54 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Jul 2023 04:21:40 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Ma",
"Haoxin",
""
],
[
"Yi",
"Jiangyan",
""
],
[
"Wang",
"Chenglong",
""
],
[
"Yan",
"Xinrui",
""
],
[
"Tao",
"Jianhua",
""
],
[
"Wang",
"Tao",
""
],
[
"Wang",
"Shiming",
""
],
[
"Fu",
"Ruibo",
""
]
] |
new_dataset
| 0.999775 |
2209.14971
|
Puhong Duan
|
Puhong Duan and Xudong Kang and Pedram Ghamisi
|
Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection
with an Isolation Forest-Guided Unsupervised Detector
| null | null |
10.1109/TGRS.2023.3268944
| null |
cs.CV cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Oil spill detection has attracted increasing attention in recent years since
marine oil spill accidents severely affect environments, natural resources, and
the lives of coastal inhabitants. Hyperspectral remote sensing images provide
rich spectral information which is beneficial for the monitoring of oil spills
in complex ocean scenarios. However, most of the existing approaches are based
on supervised and semi-supervised frameworks to detect oil spills from
hyperspectral images (HSIs), which require a huge amount of effort to annotate
a certain number of high-quality training sets. In this study, we make the
first attempt to develop an unsupervised oil spill detection method based on
isolation forest for HSIs. First, considering that the noise level varies among
different bands, a noise variance estimation method is exploited to evaluate
the noise level of different bands, and the bands corrupted by severe noise are
removed. Second, kernel principal component analysis (KPCA) is employed to
reduce the high dimensionality of the HSIs. Then, the probability of each pixel
belonging to one of the classes of seawater and oil spills is estimated with
the isolation forest, and a set of pseudo-labeled training samples is
automatically produced using the clustering algorithm on the detected
probability. Finally, an initial detection map can be obtained by performing
the support vector machine (SVM) on the dimension-reduced data, and then, the
initial detection result is further optimized with the extended random walker
(ERW) model so as to improve the detection accuracy of oil spills. Experiments
on airborne hyperspectral oil spill data (HOSD) created by ourselves
demonstrate that the proposed method obtains superior detection performance
with respect to other state-of-the-art detection approaches.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 02:26:42 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Duan",
"Puhong",
""
],
[
"Kang",
"Xudong",
""
],
[
"Ghamisi",
"Pedram",
""
]
] |
new_dataset
| 0.999244 |
2212.02405
|
Oleg Zaikin
|
Oleg Zaikin
|
Inverting Cryptographic Hash Functions via Cube-and-Conquer
|
40 pages, 11 figures. A revised submission to JAIR
| null | null | null |
cs.CR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
MD4 and MD5 are seminal cryptographic hash functions proposed in early 1990s.
MD4 consists of 48 steps and produces a 128-bit hash given a message of
arbitrary finite size. MD5 is a more secure 64-step extension of MD4. Both MD4
and MD5 are vulnerable to practical collision attacks, yet it is still not
realistic to invert them, i.e. to find a message given a hash. In 2007, the
39-step version of MD4 was inverted via reducing to SAT and applying a CDCL
solver along with the so-called Dobbertin's constraints. As for MD5, in 2012
its 28-step version was inverted via a CDCL solver for one specified hash
without adding any additional constraints. In this study, Cube-and-Conquer (a
combination of CDCL and lookahead) is applied to invert step-reduced versions
of MD4 and MD5. For this purpose, two algorithms are proposed. The first one
generates inversion problems for MD4 by gradually modifying the Dobbertin's
constraints. The second algorithm tries the cubing phase of Cube-and-Conquer
with different cutoff thresholds to find the one with minimal runtime
estimation of the conquer phase. This algorithm operates in two modes: (i)
estimating the hardness of a given propositional Boolean formula; (ii)
incomplete SAT-solving of a given satisfiable propositional Boolean formula.
While the first algorithm is focused on inverting step-reduced MD4, the second
one is not area-specific and so is applicable to a variety of classes of hard
SAT instances. In this study, 40-, 41-, 42-, and 43-step MD4 are inverted for
the first time via the first algorithm and the estimating mode of the second
algorithm. 28-step MD5 is inverted for four hashes via the incomplete
SAT-solving mode of the second algorithm. For three hashes out of them this is
done for the first time.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 16:44:47 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 08:53:42 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Zaikin",
"Oleg",
""
]
] |
new_dataset
| 0.984619 |
2302.06060
|
Xuxiang Sun
|
Xuxiang Sun, Gong Cheng, Lei Pei, Hongda Li, and Junwei Han
|
Threatening Patch Attacks on Object Detection in Optical Remote Sensing
Images
| null | null |
10.1109/TGRS.2023.3273287
| null |
cs.CV cs.AI cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Advanced Patch Attacks (PAs) on object detection in natural images have
pointed out the great safety vulnerability in methods based on deep neural
networks. However, little attention has been paid to this topic in Optical
Remote Sensing Images (O-RSIs). To this end, we focus on this research, i.e.,
PAs on object detection in O-RSIs, and propose a more Threatening PA without
the scarification of the visual quality, dubbed TPA. Specifically, to address
the problem of inconsistency between local and global landscapes in existing
patch selection schemes, we propose leveraging the First-Order Difference (FOD)
of the objective function before and after masking to select the sub-patches to
be attacked. Further, considering the problem of gradient inundation when
applying existing coordinate-based loss to PAs directly, we design an IoU-based
objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL),
which pushes the detected bounding boxes far from the initial ones until there
are no intersections between them. Finally, on two widely used benchmarks,
i.e., DIOR and DOTA, comprehensive evaluations of our TPA with four typical
detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable
effectiveness. To the best of our knowledge, this is the first attempt to study
the PAs on object detection in O-RSIs, and we hope this work can get our
readers interested in studying this topic.
|
[
{
"version": "v1",
"created": "Mon, 13 Feb 2023 02:35:49 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Sun",
"Xuxiang",
""
],
[
"Cheng",
"Gong",
""
],
[
"Pei",
"Lei",
""
],
[
"Li",
"Hongda",
""
],
[
"Han",
"Junwei",
""
]
] |
new_dataset
| 0.999089 |
2304.12125
|
Iacopo Catalano
|
Iacopo Catalano, Ha Sier, Xianjia Yu, Tomi Westerlund, Jorge Pena
Queralta
|
UAV Tracking with Solid-State Lidars:Dynamic Multi-Frequency Scan
Integration
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing use of drones across various industries, the navigation
and tracking of these unmanned aerial vehicles (UAVs) in challenging
environments (such as GNSS-denied environments) have become critical issues. In
this paper, we propose a novel method for a ground-based UAV tracking system
using a solid-state LiDAR, which dynamically adjusts the LiDAR frame
integration time based on the distance to the UAV and its speed. Our method
fuses two simultaneous scan integration frequencies for high accuracy and
persistent tracking, enabling reliable estimates of the UAV state even in
challenging scenarios. The use of the Inverse Covariance Intersection method
and Kalman filters allow for better tracking accuracy and can handle
challenging tracking scenarios.
We have performed a number of experiments for evaluating the performance of
the proposed tracking system and identifying its limitations. Our experimental
results demonstrate that the proposed method achieves comparable tracking
performance to the established baseline method, while also providing more
reliable and accurate tracking when only one of the frequencies is available or
unreliable.
|
[
{
"version": "v1",
"created": "Mon, 24 Apr 2023 14:30:20 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 12:05:55 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Catalano",
"Iacopo",
""
],
[
"Sier",
"Ha",
""
],
[
"Yu",
"Xianjia",
""
],
[
"Westerlund",
"Tomi",
""
],
[
"Queralta",
"Jorge Pena",
""
]
] |
new_dataset
| 0.990889 |
2305.02763
|
Vageesh Saxena
|
Vageesh Saxena, Nils Rethmeier, Gijs Van Dijck, Gerasimos Spanakis
|
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants &
Potential Aliases on Darknet Markets
| null | null | null | null |
cs.CY cs.CL cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The anonymity on the Darknet allows vendors to stay undetected by using
multiple vendor aliases or frequently migrating between markets. Consequently,
illegal markets and their connections are challenging to uncover on the
Darknet. To identify relationships between illegal markets and their vendors,
we propose VendorLink, an NLP-based approach that examines writing patterns to
verify, identify, and link unique vendor accounts across text advertisements
(ads) on seven public Darknet markets. In contrast to existing literature,
VendorLink utilizes the strength of supervised pre-training to perform
closed-set vendor verification, open-set vendor identification, and
low-resource market adaption tasks. Through VendorLink, we uncover (i) 15
migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17
migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii)
75 migrants and 10 potential aliases in the Traderoute-Agora dataset.
Altogether, our approach can help Law Enforcement Agencies (LEA) make more
informed decisions by verifying and identifying migrating vendors and their
potential aliases on existing and Low-Resource (LR) emerging Darknet markets.
|
[
{
"version": "v1",
"created": "Thu, 4 May 2023 12:04:33 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Saxena",
"Vageesh",
""
],
[
"Rethmeier",
"Nils",
""
],
[
"Van Dijck",
"Gijs",
""
],
[
"Spanakis",
"Gerasimos",
""
]
] |
new_dataset
| 0.98967 |
2306.01987
|
Sidong Feng
|
Sidong Feng, Chunyang Chen
|
Prompting Is All You Need: Automated Android Bug Replay with Large
Language Models
|
Accepted to 46th International Conference on Software Engineering
(ICSE 2024)
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Bug reports are vital for software maintenance that allow users to inform
developers of the problems encountered while using the software. As such,
researchers have committed considerable resources toward automating bug replay
to expedite the process of software maintenance. Nonetheless, the success of
current automated approaches is largely dictated by the characteristics and
quality of bug reports, as they are constrained by the limitations of
manually-crafted patterns and pre-defined vocabulary lists. Inspired by the
success of Large Language Models (LLMs) in natural language understanding, we
propose AdbGPT, a new lightweight approach to automatically reproduce the bugs
from bug reports through prompt engineering, without any training and
hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought
reasoning to elicit human knowledge and logical reasoning from LLMs to
accomplish the bug replay in a manner similar to a developer. Our evaluations
demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3%
of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines
and ablation studies. We also conduct a small-scale user study to confirm the
usefulness of AdbGPT in enhancing developers' bug replay capabilities.
|
[
{
"version": "v1",
"created": "Sat, 3 Jun 2023 03:03:52 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 06:20:51 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Feng",
"Sidong",
""
],
[
"Chen",
"Chunyang",
""
]
] |
new_dataset
| 0.98545 |
2306.07229
|
Giuseppe Silano
|
Daniel Hert and Tomas Baca and Pavel Petracek and Vit Kratky and
Robert Penicka and Vojtech Spurny and Matej Petrlik and Matous Vrba and David
Zaitlik and Pavel Stoudek and Viktor Walter and Petr Stepan and Jiri Horyna
and Vaclav Pritzl and Martin Sramek and Afzal Ahmad and Giuseppe Silano and
Daniel Bonilla Licea and Petr Stibinger and Tiago Nascimento and Martin Saska
|
MRS Drone: A Modular Platform for Real-World Deployment of Aerial
Multi-Robot Systems
|
49 pages, 39 figures, accepted for publication to the Journal of
Intelligent & Robotic Systems
|
Journal of Intelligent & Robotic Systems, 2023, vol. 108, issue 64
|
10.1007/s10846-023-01879-2
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV)
platform called the Multi-robot Systems (MRS) Drone that can be used in a large
range of indoor and outdoor applications. The MRS Drone features unique
modularity with respect to changes in actuators, frames, and sensory
configuration. As the name suggests, the platform is specially tailored for
deployment within a MRS group. The MRS Drone contributes to the
state-of-the-art of UAV platforms by allowing smooth real-world deployment of
multiple aerial robots, as well as by outperforming other platforms with its
modularity. For real-world multi-robot deployment in various applications, the
platform is easy to both assemble and modify. Moreover, it is accompanied by a
realistic simulator to enable safe pre-flight testing and a smooth transition
to complex real-world experiments. In this manuscript, we present mechanical
and electrical designs, software architecture, and technical specifications to
build a fully autonomous multi UAV system. Finally, we demonstrate the full
capabilities and the unique modularity of the MRS Drone in various real-world
applications that required a diverse range of platform configurations.
|
[
{
"version": "v1",
"created": "Mon, 12 Jun 2023 16:41:59 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Hert",
"Daniel",
""
],
[
"Baca",
"Tomas",
""
],
[
"Petracek",
"Pavel",
""
],
[
"Kratky",
"Vit",
""
],
[
"Penicka",
"Robert",
""
],
[
"Spurny",
"Vojtech",
""
],
[
"Petrlik",
"Matej",
""
],
[
"Vrba",
"Matous",
""
],
[
"Zaitlik",
"David",
""
],
[
"Stoudek",
"Pavel",
""
],
[
"Walter",
"Viktor",
""
],
[
"Stepan",
"Petr",
""
],
[
"Horyna",
"Jiri",
""
],
[
"Pritzl",
"Vaclav",
""
],
[
"Sramek",
"Martin",
""
],
[
"Ahmad",
"Afzal",
""
],
[
"Silano",
"Giuseppe",
""
],
[
"Licea",
"Daniel Bonilla",
""
],
[
"Stibinger",
"Petr",
""
],
[
"Nascimento",
"Tiago",
""
],
[
"Saska",
"Martin",
""
]
] |
new_dataset
| 0.99928 |
2306.15788
|
F\'abio Vin\'icius Moreira Perez
|
Maria Carolina Penteado, F\'abio Perez
|
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for
Brazilian Portuguese
|
Download the full source to access the dataset. Accepted to LatinX in
AI (LXAI) Research at ICML 2023
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We investigate the effectiveness of GPT-3.5 and GPT-4, two large language
models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese
and compare their performance against Microsoft Word and Google Docs. We
introduce a GEC dataset for Brazilian Portuguese with four categories: Grammar,
Spelling, Internet, and Fast typing. Our results show that while GPT-4 has
higher recall than other methods, LLMs tend to have lower precision, leading to
overcorrection. This study demonstrates the potential of LLMs as practical GEC
tools for Brazilian Portuguese and encourages further exploration of LLMs for
non-English languages and other educational settings.
|
[
{
"version": "v1",
"created": "Tue, 27 Jun 2023 20:37:54 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 13:31:56 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Penteado",
"Maria Carolina",
""
],
[
"Perez",
"Fábio",
""
]
] |
new_dataset
| 0.992695 |
2307.04964
|
Songyang Gao
|
Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang,
Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi,
Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng,
Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang,
Xipeng Qiu, Xuanjing Huang
|
Secrets of RLHF in Large Language Models Part I: PPO
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs.
|
[
{
"version": "v1",
"created": "Tue, 11 Jul 2023 01:55:24 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 08:44:47 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Zheng",
"Rui",
""
],
[
"Dou",
"Shihan",
""
],
[
"Gao",
"Songyang",
""
],
[
"Hua",
"Yuan",
""
],
[
"Shen",
"Wei",
""
],
[
"Wang",
"Binghai",
""
],
[
"Liu",
"Yan",
""
],
[
"Jin",
"Senjie",
""
],
[
"Liu",
"Qin",
""
],
[
"Zhou",
"Yuhao",
""
],
[
"Xiong",
"Limao",
""
],
[
"Chen",
"Lu",
""
],
[
"Xi",
"Zhiheng",
""
],
[
"Xu",
"Nuo",
""
],
[
"Lai",
"Wenbin",
""
],
[
"Zhu",
"Minghao",
""
],
[
"Chang",
"Cheng",
""
],
[
"Yin",
"Zhangyue",
""
],
[
"Weng",
"Rongxiang",
""
],
[
"Cheng",
"Wensen",
""
],
[
"Huang",
"Haoran",
""
],
[
"Sun",
"Tianxiang",
""
],
[
"Yan",
"Hang",
""
],
[
"Gui",
"Tao",
""
],
[
"Zhang",
"Qi",
""
],
[
"Qiu",
"Xipeng",
""
],
[
"Huang",
"Xuanjing",
""
]
] |
new_dataset
| 0.956846 |
2307.05935
|
Zeqing Zhang
|
Zeqing Zhang, Ruixing Jia, Youcan Yan, Ruihua Han, Shijie Lin, Qian
Jiang, Liangjun Zhang, Jia Pan
|
GRAINS: Proximity Sensing of Objects in Granular Materials
|
35 pages, 5 figures,2 tables. Videos available at
https://sites.google.com/view/grains2/home
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Proximity sensing detects an object's presence without contact. However,
research has rarely explored proximity sensing in granular materials (GM) due
to GM's lack of visual and complex properties. In this paper, we propose a
granular-material-embedded autonomous proximity sensing system (GRAINS) based
on three granular phenomena (fluidization, jamming, and failure wedge zone).
GRAINS can automatically sense buried objects beneath GM in real-time manner
(at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different
granules without the use of vision or touch. We introduce a new spiral
trajectory for the probe raking in GM, combining linear and circular motions,
inspired by a common granular fluidization technique. Based on the observation
of force-raising when granular jamming occurs in the failure wedge zone in
front of the probe during its raking, we employ Gaussian process regression to
constantly learn and predict the force patterns and detect the force anomaly
resulting from granular jamming to identify the proximity sensing of buried
objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided
exploration strategy to successfully localize underground objects and outline
their distribution using proximity sensing without contact or digging. This
work offers a simple yet reliable method with potential for safe operation in
building habitation infrastructure on an alien planet without human
intervention.
|
[
{
"version": "v1",
"created": "Wed, 12 Jul 2023 06:00:14 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 04:51:23 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Zhang",
"Zeqing",
""
],
[
"Jia",
"Ruixing",
""
],
[
"Yan",
"Youcan",
""
],
[
"Han",
"Ruihua",
""
],
[
"Lin",
"Shijie",
""
],
[
"Jiang",
"Qian",
""
],
[
"Zhang",
"Liangjun",
""
],
[
"Pan",
"Jia",
""
]
] |
new_dataset
| 0.995524 |
2307.06932
|
Vasileios Mavroeidis Dr.
|
Konstantinos Fysarakis, Alexios Lekidis, Vasileios Mavroeidis,
Konstantinos Lampropoulos, George Lyberopoulos, Ignasi Garcia-Mil\`a Vidal,
Jos\'e Carles Ter\'es i Casals, Eva Rodriguez Luna, Alejandro Antonio Moreno
Sancho, Antonios Mavrelos, Marinos Tsantekidis, Sebastian Pape, Argyro
Chatzopoulou, Christina Nanou, George Drivas, Vangelis Photiou, George
Spanoudakis, Odysseas Koufopavlou
|
PHOENI2X -- A European Cyber Resilience Framework With
Artificial-Intelligence-Assisted Orchestration, Automation and Response
Capabilities for Business Continuity and Recovery, Incident Response, and
Information Exchange
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As digital technologies become more pervasive in society and the economy,
cybersecurity incidents become more frequent and impactful. According to the
NIS and NIS2 Directives, EU Member States and their Operators of Essential
Services must establish a minimum baseline set of cybersecurity capabilities
and engage in cross-border coordination and cooperation. However, this is only
a small step towards European cyber resilience. In this landscape,
preparedness, shared situational awareness, and coordinated incident response
are essential for effective cyber crisis management and resilience. Motivated
by the above, this paper presents PHOENI2X, an EU-funded project aiming to
design, develop, and deliver a Cyber Resilience Framework providing
Artificial-Intelligence-assisted orchestration, automation and response
capabilities for business continuity and recovery, incident response, and
information exchange, tailored to the needs of Operators of Essential Services
and the EU Member State authorities entrusted with cybersecurity.
|
[
{
"version": "v1",
"created": "Thu, 13 Jul 2023 17:53:25 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 10:05:40 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Fysarakis",
"Konstantinos",
""
],
[
"Lekidis",
"Alexios",
""
],
[
"Mavroeidis",
"Vasileios",
""
],
[
"Lampropoulos",
"Konstantinos",
""
],
[
"Lyberopoulos",
"George",
""
],
[
"Vidal",
"Ignasi Garcia-Milà",
""
],
[
"Casals",
"José Carles Terés i",
""
],
[
"Luna",
"Eva Rodriguez",
""
],
[
"Sancho",
"Alejandro Antonio Moreno",
""
],
[
"Mavrelos",
"Antonios",
""
],
[
"Tsantekidis",
"Marinos",
""
],
[
"Pape",
"Sebastian",
""
],
[
"Chatzopoulou",
"Argyro",
""
],
[
"Nanou",
"Christina",
""
],
[
"Drivas",
"George",
""
],
[
"Photiou",
"Vangelis",
""
],
[
"Spanoudakis",
"George",
""
],
[
"Koufopavlou",
"Odysseas",
""
]
] |
new_dataset
| 0.998785 |
2307.08763
|
Kumar Ashutosh
|
Kumar Ashutosh, Santhosh Kumar Ramakrishnan, Triantafyllos Afouras,
Kristen Grauman
|
Video-Mined Task Graphs for Keystep Recognition in Instructional Videos
|
Technical Report
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Procedural activity understanding requires perceiving human actions in terms
of a broader task, where multiple keysteps are performed in sequence across a
long video to reach a final goal state -- such as the steps of a recipe or a
DIY fix-it task. Prior work largely treats keystep recognition in isolation of
this broader structure, or else rigidly confines keysteps to align with a
predefined sequential script. We propose discovering a task graph automatically
from how-to videos to represent probabilistically how people tend to execute
keysteps, and then leverage this graph to regularize keystep recognition in
novel videos. On multiple datasets of real-world instructional videos, we show
the impact: more reliable zero-shot keystep localization and improved video
representation learning, exceeding the state of the art.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 18:19:36 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Ashutosh",
"Kumar",
""
],
[
"Ramakrishnan",
"Santhosh Kumar",
""
],
[
"Afouras",
"Triantafyllos",
""
],
[
"Grauman",
"Kristen",
""
]
] |
new_dataset
| 0.997927 |
2307.08781
|
Eric Orenstein
|
Eric Orenstein, Kevin Barnard, Lonny Lundsten, Genevi\`eve Patterson,
Benjamin Woodward, and Kakani Katija
|
The FathomNet2023 Competition Dataset
|
Competition was presented as part of the 10th Fine Grained Visual
Categorization workshop at the 2023 Computer Vision and Pattern Recognition
conference. 4 pages, 4 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Ocean scientists have been collecting visual data to study marine organisms
for decades. These images and videos are extremely valuable both for basic
science and environmental monitoring tasks. There are tools for automatically
processing these data, but none that are capable of handling the extreme
variability in sample populations, image quality, and habitat characteristics
that are common in visual sampling of the ocean. Such distribution shifts can
occur over very short physical distances and in narrow time windows. Creating
models that are able to recognize when an image or video sequence contains a
new organism, an unusual collection of animals, or is otherwise out-of-sample
is critical to fully leverage visual data in the ocean. The FathomNet2023
competition dataset presents a realistic scenario where the set of animals in
the target data differs from the training data. The challenge is both to
identify the organisms in a target image and assess whether it is
out-of-sample.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 18:50:53 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Orenstein",
"Eric",
""
],
[
"Barnard",
"Kevin",
""
],
[
"Lundsten",
"Lonny",
""
],
[
"Patterson",
"Geneviève",
""
],
[
"Woodward",
"Benjamin",
""
],
[
"Katija",
"Kakani",
""
]
] |
new_dataset
| 0.999857 |
2307.08850
|
Senthil Yogamani
|
Sambit Mohapatra, Senthil Yogamani, Varun Ravi Kumar, Stefan Milz,
Heinrich Gotzig and Patrick M\"ader
|
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception
Network for Autonomous Driving
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR
perception has the largest body of literature after camera perception. However,
multi-task learning across tasks like detection, segmentation, and motion
estimation using LiDAR remains relatively unexplored, especially on
automotive-grade embedded platforms. We present a real-time multi-task
convolutional neural network for LiDAR-based object detection, semantics, and
motion segmentation. The unified architecture comprises a shared encoder and
task-specific decoders, enabling joint representation learning. We propose a
novel Semantic Weighting and Guidance (SWAG) module to transfer semantic
features for improved object detection selectively. Our heterogeneous training
scheme combines diverse datasets and exploits complementary cues between tasks.
The work provides the first embedded implementation unifying these key
perception tasks from LiDAR point clouds achieving 3ms latency on the embedded
NVIDIA Xavier platform. We achieve state-of-the-art results for two tasks,
semantic and motion segmentation, and close to state-of-the-art performance for
3D object detection. By maximizing hardware efficiency and leveraging
multi-task synergies, our method delivers an accurate and efficient solution
tailored for real-world automated driving deployment. Qualitative results can
be seen at https://youtu.be/H-hWRzv2lIY.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 21:22:17 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Mohapatra",
"Sambit",
""
],
[
"Yogamani",
"Senthil",
""
],
[
"Kumar",
"Varun Ravi",
""
],
[
"Milz",
"Stefan",
""
],
[
"Gotzig",
"Heinrich",
""
],
[
"Mäder",
"Patrick",
""
]
] |
new_dataset
| 0.9996 |
2307.08946
|
Ning Gao
|
Ning Gao, Qiying Huang, Cen Li, Shi Jin, Michail Matthaiou
|
EsaNet: Environment Semantics Enabled Physical Layer Authentication
| null | null | null | null |
cs.CR eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wireless networks are vulnerable to physical layer spoofing attacks due to
the wireless broadcast nature, thus, integrating communications and security
(ICAS) is urgently needed for 6G endogenous security. In this letter, we
propose an environment semantics enabled physical layer authentication network
based on deep learning, namely EsaNet, to authenticate the spoofing from the
underlying wireless protocol. Specifically, the frequency independent wireless
channel fingerprint (FiFP) is extracted from the channel state information
(CSI) of a massive multi-input multi-output (MIMO) system based on environment
semantics knowledge. Then, we transform the received signal into a
two-dimensional red green blue (RGB) image and apply the you only look once
(YOLO), a single-stage object detection network, to quickly capture the FiFP.
Next, a lightweight classification network is designed to distinguish the
legitimate from the illegitimate users. Finally, the experimental results show
that the proposed EsaNet can effectively detect physical layer spoofing attacks
and is robust in time-varying wireless environments.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 03:28:26 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Gao",
"Ning",
""
],
[
"Huang",
"Qiying",
""
],
[
"Li",
"Cen",
""
],
[
"Jin",
"Shi",
""
],
[
"Matthaiou",
"Michail",
""
]
] |
new_dataset
| 0.998728 |
2307.08985
|
Seungho Baek
|
Seungho Baek, Hyerin Im, Jiseung Ryu, Juhyeong Park, Takyeon Lee
|
PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative
Dialogue with LLM
|
5 pages, AI & HCI Workshop at the 40 International Conference on
Machine Learning (ICML) 2023
| null | null | null |
cs.HC cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Text-to-image generation model is able to generate images across a diverse
range of subjects and styles based on a single prompt. Recent works have
proposed a variety of interaction methods that help users understand the
capabilities of models and utilize them. However, how to support users to
efficiently explore the model's capability and to create effective prompts are
still open-ended research questions. In this paper, we present PromptCrafter, a
novel mixed-initiative system that allows step-by-step crafting of
text-to-image prompt. Through the iterative process, users can efficiently
explore the model's capability, and clarify their intent. PromptCrafter also
supports users to refine prompts by answering various responses to clarifying
questions generated by a Large Language Model. Lastly, users can revert to a
desired step by reviewing the work history. In this workshop paper, we discuss
the design process of PromptCrafter and our plans for follow-up studies.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 05:51:00 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Baek",
"Seungho",
""
],
[
"Im",
"Hyerin",
""
],
[
"Ryu",
"Jiseung",
""
],
[
"Park",
"Juhyeong",
""
],
[
"Lee",
"Takyeon",
""
]
] |
new_dataset
| 0.970902 |
2307.09000
|
Tengfei Xue
|
Tengfei Xue, Yuqian Chen, Chaoyi Zhang, Alexandra J. Golby, Nikos
Makris, Yogesh Rathi, Weidong Cai, Fan Zhang, Lauren J. O'Donnell
|
TractCloud: Registration-free tractography parcellation with a novel
local-global streamline point cloud representation
|
MICCAI 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Diffusion MRI tractography parcellation classifies streamlines into
anatomical fiber tracts to enable quantification and visualization for clinical
and scientific applications. Current tractography parcellation methods rely
heavily on registration, but registration inaccuracies can affect parcellation
and the computational cost of registration is high for large-scale datasets.
Recently, deep-learning-based methods have been proposed for tractography
parcellation using various types of representations for streamlines. However,
these methods only focus on the information from a single streamline, ignoring
geometric relationships between the streamlines in the brain. We propose
TractCloud, a registration-free framework that performs whole-brain
tractography parcellation directly in individual subject space. We propose a
novel, learnable, local-global streamline representation that leverages
information from neighboring and whole-brain streamlines to describe the local
anatomy and global pose of the brain. We train our framework on a large-scale
labeled tractography dataset, which we augment by applying synthetic transforms
including rotation, scaling, and translations. We test our framework on five
independently acquired datasets across populations and health conditions.
TractCloud significantly outperforms several state-of-the-art methods on all
testing datasets. TractCloud achieves efficient and consistent whole-brain
white matter parcellation across the lifespan (from neonates to elderly
subjects, including brain tumor patients) without the need for registration.
The robustness and high inference speed of TractCloud make it suitable for
large-scale tractography data analysis. Our project page is available at
https://tractcloud.github.io/.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 06:35:12 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Xue",
"Tengfei",
""
],
[
"Chen",
"Yuqian",
""
],
[
"Zhang",
"Chaoyi",
""
],
[
"Golby",
"Alexandra J.",
""
],
[
"Makris",
"Nikos",
""
],
[
"Rathi",
"Yogesh",
""
],
[
"Cai",
"Weidong",
""
],
[
"Zhang",
"Fan",
""
],
[
"O'Donnell",
"Lauren J.",
""
]
] |
new_dataset
| 0.999691 |
2307.09002
|
Susu Cui
|
Susu Cui, Cong Dong, Meng Shen, Yuling Liu, Bo Jiang, Zhigang Lu
|
CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic
Detection
|
Submitted to IEEE TIFS
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning and neural networks have become increasingly popular
solutions for encrypted malware traffic detection. They mine and learn complex
traffic patterns, enabling detection by fitting boundaries between malware
traffic and benign traffic. Compared with signature-based methods, they have
higher scalability and flexibility. However, affected by the frequent variants
and updates of malware, current methods suffer from a high false positive rate
and do not work well for unknown malware traffic detection. It remains a
critical task to achieve effective malware traffic detection. In this paper, we
introduce CBSeq to address the above problems. CBSeq is a method that
constructs a stable traffic representation, behavior sequence, to characterize
attacking intent and achieve malware traffic detection. We novelly propose the
channels with similar behavior as the detection object and extract side-channel
content to construct behavior sequence. Unlike benign activities, the behavior
sequences of malware and its variant's traffic exhibit solid internal
correlations. Moreover, we design the MSFormer, a powerful Transformer-based
multi-sequence fusion classifier. It captures the internal similarity of
behavior sequence, thereby distinguishing malware traffic from benign traffic.
Our evaluations demonstrate that CBSeq performs effectively in various known
malware traffic detection and exhibits superior performance in unknown malware
traffic detection, outperforming state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 06:38:20 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Cui",
"Susu",
""
],
[
"Dong",
"Cong",
""
],
[
"Shen",
"Meng",
""
],
[
"Liu",
"Yuling",
""
],
[
"Jiang",
"Bo",
""
],
[
"Lu",
"Zhigang",
""
]
] |
new_dataset
| 0.997206 |
2307.09044
|
Qipeng Li
|
Qipeng Li, Yuan Zhuang, Yiwen Chen, Jianzhu Huai, Miao Li, Tianbing
Ma, Yufei Tang, Xinlian Liang
|
3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in
Autonomous Driving
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For the SLAM system in robotics and autonomous driving, the accuracy of
front-end odometry and back-end loop-closure detection determine the whole
intelligent system performance. But the LiDAR-SLAM could be disturbed by
current scene moving objects, resulting in drift errors and even loop-closure
failure. Thus, the ability to detect and segment moving objects is essential
for high-precision positioning and building a consistent map. In this paper, we
address the problem of moving object segmentation from 3D LiDAR scans to
improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D
Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately
segment the scene into moving and static objects, such as moving and static
cars. Different from the existing projected-image method, we process the raw 3D
point cloud and build a 3D convolution neural network for MOS task. In
addition, to make full use of the spatio-temporal information of point cloud,
we propose a point cloud residual mechanism using the spatial features of
current scan and the temporal features of previous residual scans. Besides, we
build a complete SLAM framework to verify the effectiveness and accuracy of
3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed
3D-SeqMOS method can effectively detect moving objects and improve the accuracy
of LiDAR odometry and loop-closure detection. The test results show our
3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the
proposed method to the SemanticKITTI: Moving Object Segmentation competition
and achieve the 2nd in the leaderboard, showing its effectiveness.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 07:55:17 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Li",
"Qipeng",
""
],
[
"Zhuang",
"Yuan",
""
],
[
"Chen",
"Yiwen",
""
],
[
"Huai",
"Jianzhu",
""
],
[
"Li",
"Miao",
""
],
[
"Ma",
"Tianbing",
""
],
[
"Tang",
"Yufei",
""
],
[
"Liang",
"Xinlian",
""
]
] |
new_dataset
| 0.998788 |
2307.09070
|
GyuMin Shim
|
Gyumin Shim, Jaeseong Lee, Junha Hyung, Jaegul Choo
|
PixelHuman: Animatable Neural Radiance Fields from Few Images
|
8 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we propose PixelHuman, a novel human rendering model that
generates animatable human scenes from a few images of a person with unseen
identity, views, and poses. Previous work have demonstrated reasonable
performance in novel view and pose synthesis, but they rely on a large number
of images to train and are trained per scene from videos, which requires
significant amount of time to produce animatable scenes from unseen human
images. Our method differs from existing methods in that it can generalize to
any input image for animatable human synthesis. Given a random pose sequence,
our method synthesizes each target scene using a neural radiance field that is
conditioned on a canonical representation and pose-aware pixel-aligned
features, both of which can be obtained through deformation fields learned in a
data-driven manner. Our experiments show that our method achieves
state-of-the-art performance in multiview and novel pose synthesis from
few-shot images.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 08:41:17 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Shim",
"Gyumin",
""
],
[
"Lee",
"Jaeseong",
""
],
[
"Hyung",
"Junha",
""
],
[
"Choo",
"Jaegul",
""
]
] |
new_dataset
| 0.991995 |
2307.09090
|
Georgios Karachalias
|
Marco Perone and Georgios Karachalias
|
Cr\`eme de la Crem: Composable Representable Executable Machines
(Architectural Pearl)
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we describe how to build software architectures as a
composition of state machines, using ideas and principles from the field of
Domain-Driven Design. By definition, our approach is modular, allowing one to
compose independent subcomponents to create bigger systems, and representable,
allowing the implementation of a system to be kept in sync with its graphical
representation.
In addition to the design itself we introduce the Crem library, which
provides a concrete state machine implementation that is both compositional and
representable, Crem uses Haskell's advanced type-level features to allow users
to specify allowed and forbidden state transitions, and to encode complex state
machine -- and therefore domain-specific -- properties. Moreover, since Crem's
state machines are representable, Crem can automatically generate graphical
representations of systems from their domain implementations.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 09:17:13 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Perone",
"Marco",
""
],
[
"Karachalias",
"Georgios",
""
]
] |
new_dataset
| 0.999196 |
2307.09112
|
Stefan Lionar
|
Stefan Lionar, Xiangyu Xu, Min Lin, Gim Hee Lee
|
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and
Repulsive UDF
|
Project page: https://numcc.github.io/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Remarkable progress has been made in 3D reconstruction from single-view RGB-D
inputs. MCC is the current state-of-the-art method in this field, which
achieves unprecedented success by combining vision Transformers with
large-scale training. However, we identified two key limitations of MCC: 1) The
Transformer decoder is inefficient in handling large number of query points; 2)
The 3D representation struggles to recover high-fidelity details. In this
paper, we propose a new approach called NU-MCC that addresses these
limitations. NU-MCC includes two key innovations: a Neighborhood decoder and a
Repulsive Unsigned Distance Function (Repulsive UDF). First, our Neighborhood
decoder introduces center points as an efficient proxy of input visual
features, allowing each query point to only attend to a small neighborhood.
This design not only results in much faster inference speed but also enables
the exploitation of finer-scale visual features for improved recovery of 3D
textures. Second, our Repulsive UDF is a novel alternative to the occupancy
field used in MCC, significantly improving the quality of 3D object
reconstruction. Compared to standard UDFs that suffer from holes in results,
our proposed Repulsive UDF can achieve more complete surface reconstruction.
Experimental results demonstrate that NU-MCC is able to learn a strong 3D
representation, significantly advancing the state of the art in single-view 3D
reconstruction. Particularly, it outperforms MCC by 9.7% in terms of the
F1-score on the CO3D-v2 dataset with more than 5x faster running speed.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 10:02:09 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Lionar",
"Stefan",
""
],
[
"Xu",
"Xiangyu",
""
],
[
"Lin",
"Min",
""
],
[
"Lee",
"Gim Hee",
""
]
] |
new_dataset
| 0.955881 |
2307.09132
|
Vladimir Vlassov
|
Gibson Chikafa, Sina Sheikholeslami, Salman Niazi, Jim Dowling,
Vladimir Vlassov
|
Cloud-native RStudio on Kubernetes for Hopsworks
|
8 pages, 4 figures
| null | null | null |
cs.DC cs.AI cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In order to fully benefit from cloud computing, services are designed
following the "multi-tenant" architectural model, which is aimed at maximizing
resource sharing among users. However, multi-tenancy introduces challenges of
security, performance isolation, scaling, and customization. RStudio server is
an open-source Integrated Development Environment (IDE) accessible over a web
browser for the R programming language. We present the design and
implementation of a multi-user distributed system on Hopsworks, a
data-intensive AI platform, following the multi-tenant model that provides
RStudio as Software as a Service (SaaS). We use the most popular cloud-native
technologies: Docker and Kubernetes, to solve the problems of performance
isolation, security, and scaling that are present in a multi-tenant
environment. We further enable secure data sharing in RStudio server instances
to provide data privacy and allow collaboration among RStudio users. We
integrate our system with Apache Spark, which can scale and handle Big Data
processing workloads. Also, we provide a UI where users can provide custom
configurations and have full control of their own RStudio server instances. Our
system was tested on a Google Cloud Platform cluster with four worker nodes,
each with 30GB of RAM allocated to them. The tests on this cluster showed that
44 RStudio servers, each with 2GB of RAM, can be run concurrently. Our system
can scale out to potentially support hundreds of concurrently running RStudio
servers by adding more resources (CPUs and RAM) to the cluster or system.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 10:28:55 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Chikafa",
"Gibson",
""
],
[
"Sheikholeslami",
"Sina",
""
],
[
"Niazi",
"Salman",
""
],
[
"Dowling",
"Jim",
""
],
[
"Vlassov",
"Vladimir",
""
]
] |
new_dataset
| 0.990835 |
2307.09168
|
Joohyung Lee
|
Martin Gebser, Joohyung Lee, Yuliya Lierler
|
Elementary Sets for Logic Programs
|
6 pages. AAAI 2006, 244-249. arXiv admin note: substantial text
overlap with arXiv:1012.5847
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
By introducing the concepts of a loop and a loop formula, Lin and Zhao showed
that the answer sets of a nondisjunctive logic program are exactly the models
of its Clark's completion that satisfy the loop formulas of all loops.
Recently, Gebser and Schaub showed that the Lin-Zhao theorem remains correct
even if we restrict loop formulas to a special class of loops called
``elementary loops.'' In this paper, we simplify and generalize the notion of
an elementary loop, and clarify its role. We propose the notion of an
elementary set, which is almost equivalent to the notion of an elementary loop
for nondisjunctive programs, but is simpler, and, unlike elementary loops, can
be extended to disjunctive programs without producing unintuitive results. We
show that the maximal unfounded elementary sets for the ``relevant'' part of a
program are exactly the minimal sets among the nonempty unfounded sets. We also
present a graph-theoretic characterization of elementary sets for
nondisjunctive programs, which is simpler than the one proposed in (Gebser &
Schaub 2005). Unlike the case of nondisjunctive programs, we show that the
problem of deciding an elementary set is coNP-complete for disjunctive
programs.
|
[
{
"version": "v1",
"created": "Sat, 15 Jul 2023 08:00:46 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Gebser",
"Martin",
""
],
[
"Lee",
"Joohyung",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
new_dataset
| 0.967959 |
2307.09243
|
Daniel De Almeida Braga
|
Daniel De Almeida Braga, Natalia Kulatova, Mohamed Sabt, Pierre-Alain
Fouque, Karthikeyan Bhargavan
|
From Dragondoom to Dragonstar: Side-channel Attacks and Formally
Verified Implementation of WPA3 Dragonfly Handshake
|
Accepted at 2023 IEEE 8th European Symposium on Security and Privacy
(EuroS&P)
| null |
10.1109/EuroSP57164.2023.00048
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
It is universally acknowledged that Wi-Fi communications are important to
secure. Thus, the Wi-Fi Alliance published WPA3 in 2018 with a distinctive
security feature: it leverages a Password-Authenticated Key Exchange (PAKE)
protocol to protect users' passwords from offline dictionary attacks.
Unfortunately, soon after its release, several attacks were reported against
its implementations, in response to which the protocol was updated in a
best-effort manner.
In this paper, we show that the proposed mitigations are not enough,
especially for a complex protocol to implement even for savvy developers.
Indeed, we present **Dragondoom**, a collection of side-channel vulnerabilities
of varying strength allowing attackers to recover users' passwords in widely
deployed Wi-Fi daemons, such as hostap in its default settings. Our findings
target both password conversion methods, namely the default probabilistic
hunting-and-pecking and its newly standardized deterministic alternative based
on SSWU. We successfully exploit our leakage in practice through
microarchitectural mechanisms, and overcome the limited spatial resolution of
Flush+Reload. Our attacks outperform previous works in terms of required
measurements.
Then, driven by the need to end the spiral of patch-and-hack in Dragonfly
implementations, we propose **Dragonstar**, an implementation of Dragonfly
leveraging a formally verified implementation of the underlying mathematical
operations, thereby removing all the related leakage vector. Our implementation
relies on HACL*, a formally verified crypto library guaranteeing
secret-independence. We design Dragonstar, so that its integration within
hostap requires minimal modifications to the existing project. Our experiments
show that the performance of HACL*-based hostap is comparable to OpenSSL-based,
implying that Dragonstar is both efficient and proved to be leakage-free.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 13:22:21 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Braga",
"Daniel De Almeida",
""
],
[
"Kulatova",
"Natalia",
""
],
[
"Sabt",
"Mohamed",
""
],
[
"Fouque",
"Pierre-Alain",
""
],
[
"Bhargavan",
"Karthikeyan",
""
]
] |
new_dataset
| 0.999556 |
2307.09298
|
Rodrigo San-Jos\'e
|
Philippe Gimenez, Diego Ruano, Rodrigo San-Jos\'e
|
Subfield subcodes of projective Reed-Muller codes
| null | null | null | null |
cs.IT math.AC math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Explicit bases for the subfield subcodes of projective Reed-Muller codes over
the projective plane and their duals are obtained. In particular, we provide a
formula for the dimension of these codes. For the general case over the
projective space, we are able to generalize the necessary tools to deal with
this case as well: we obtain a universal Gr\"obner basis for the vanishing
ideal of the set of standard representatives of the projective space and we are
able to reduce any monomial with respect to this Gr\"obner basis. With respect
to the parameters of these codes, by considering subfield subcodes of
projective Reed-Muller codes we are able to obtain long linear codes with good
parameters over a small finite field.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 14:38:26 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Gimenez",
"Philippe",
""
],
[
"Ruano",
"Diego",
""
],
[
"San-José",
"Rodrigo",
""
]
] |
new_dataset
| 0.998425 |
2307.09316
|
Jiahui Liu
|
Jiahui Liu, Chirui Chang, Jianhui Liu, Xiaoyang Wu, Lan Ma, Xiaojuan
Qi
|
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on
Multi-Scan 3D Point Clouds
| null |
The IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D semantic segmentation on multi-scan large-scale point clouds plays an
important role in autonomous systems. Unlike the single-scan-based semantic
segmentation task, this task requires distinguishing the motion states of
points in addition to their semantic categories. However, methods designed for
single-scan-based segmentation tasks perform poorly on the multi-scan task due
to the lacking of an effective way to integrate temporal information. We
propose MarS3D, a plug-and-play motion-aware module for semantic segmentation
on multi-scan 3D point clouds. This module can be flexibly combined with
single-scan models to allow them to have multi-scan perception abilities. The
model encompasses two key designs: the Cross-Frame Feature Embedding module for
enriching representation learning and the Motion-Aware Feature Learning module
for enhancing motion awareness. Extensive experiments show that MarS3D can
improve the performance of the baseline model by a large margin. The code is
available at https://github.com/CVMI-Lab/MarS3D.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 14:59:19 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Liu",
"Jiahui",
""
],
[
"Chang",
"Chirui",
""
],
[
"Liu",
"Jianhui",
""
],
[
"Wu",
"Xiaoyang",
""
],
[
"Ma",
"Lan",
""
],
[
"Qi",
"Xiaojuan",
""
]
] |
new_dataset
| 0.997725 |
2307.09320
|
Ettore Randazzo
|
Ettore Randazzo and Alexander Mordvintsev
|
Biomaker CA: a Biome Maker project using Cellular Automata
|
20 pages, 23 figures. For code base, see https://tinyurl.com/2x8yu34s
| null | null | null |
cs.AI cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA).
In Biomaker CA, morphogenesis is a first class citizen and small seeds need to
grow into plant-like organisms to survive in a nutrient starved environment and
eventually reproduce with variation so that a biome survives for long
timelines. We simulate complex biomes by means of CA rules in 2D grids and
parallelize all of its computation on GPUs through the Python JAX framework. We
show how this project allows for several different kinds of environments and
laws of 'physics', alongside different model architectures and mutation
strategies. We further analyze some configurations to show how plant agents can
grow, survive, reproduce, and evolve, forming stable and unstable biomes. We
then demonstrate how one can meta-evolve models to survive in a harsh
environment either through end-to-end meta-evolution or by a more surgical and
efficient approach, called Petri dish meta-evolution. Finally, we show how to
perform interactive evolution, where the user decides how to evolve a plant
model interactively and then deploys it in a larger environment. We open source
Biomaker CA at: https://tinyurl.com/2x8yu34s .
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:03:40 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Randazzo",
"Ettore",
""
],
[
"Mordvintsev",
"Alexander",
""
]
] |
new_dataset
| 0.99955 |
2307.09322
|
Sara Benatmane
|
Sara Benatmane, Nuh Aydin, Behloul Djilali, and Prokash Barman
|
A New Hybrid Cryptosystem Involving DNA,Rabin, One Time Pad and Fiestel
|
11 pages
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Information security is a crucial need in the modern world. Data security is
a real concern, and many customers and organizations need to protect their
sensitive information from unauthorized parties and attackers. In previous
years, numerous cryptographic schemes have been proposed. DNA cryptography is a
new and developing field that combines the computational and biological worlds.
DNA cryptography is intriguing due to its high storage capacity, secure data
transport, and massive parallel computing. In this paper, a new combination is
proposed that offers good security by combining DNA, the Rabin algorithm, one
time pad, and a structure inspired by Fiestel. This algorithm employs two keys.
The first key is a DNA OTP key which is used for only one secure communication
session. The second key, which combines the public and private keys, is a Rabin
key. Additionally, by using a Feistel inspired scheme and randomness provided
by DNA, the ciphertext is made harder to obtain without the private key.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:06:35 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Benatmane",
"Sara",
""
],
[
"Aydin",
"Nuh",
""
],
[
"Djilali",
"Behloul",
""
],
[
"Barman",
"Prokash",
""
]
] |
new_dataset
| 0.993246 |
2307.09349
|
Thomas Place
|
Thomas Place, Marc Zeitoun
|
A generic characterization of generalized unary temporal logic and
two-variable first-order logic
| null | null | null | null |
cs.FL cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We investigate an operator on classes of languages. For each class $C$, it
outputs a new class $FO^2(I_C)$ associated with a variant of two-variable
first-order logic equipped with a signature$I_C$ built from $C$. For $C =
\{\emptyset, A^*\}$, we get the variant $FO^2(<)$ equipped with the linear
order. For $C = \{\emptyset, \{\varepsilon\},A^+, A^*\}$, we get the variant
$FO^2(<,+1)$, which also includes the successor. If $C$ consists of all Boolean
combinations of languages $A^*aA^*$ where $a$ is a letter, we get the variant
$FO^2(<,Bet)$, which also includes ``between relations''. We prove a generic
algebraic characterization of the classes $FO^2(I_C)$. It smoothly and
elegantly generalizes the known ones for all aforementioned cases. Moreover, it
implies that if $C$ has decidable separation (plus mild properties), then
$FO^2(I_C)$ has a decidable membership problem.
We actually work with an equivalent definition of \fodc in terms of unary
temporal logic. For each class $C$, we consider a variant $TL(C)$ of unary
temporal logic whose future/past modalities depend on $C$ and such that $TL(C)
= FO^2(I_C)$. Finally, we also characterize $FL(C)$ and $PL(C)$, the
pure-future and pure-past restrictions of $TL(C)$. These characterizations as
well imply that if \Cs is a class with decidable separation, then $FL(C)$ and
$PL(C)$ have decidable membership.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:36:07 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Place",
"Thomas",
""
],
[
"Zeitoun",
"Marc",
""
]
] |
new_dataset
| 0.997056 |
2307.09351
|
Guiyu Zhao
|
Guiyu Zhao and Zhentao Guo and Xin Wang and Hongbin Ma
|
SphereNet: Learning a Noise-Robust and General Descriptor for Point
Cloud Registration
|
15 pages, under review for IEEE Transactions on Circuits and Systems
for Video Technology
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Point cloud registration is to estimate a transformation to align point
clouds collected in different perspectives. In learning-based point cloud
registration, a robust descriptor is vital for high-accuracy registration.
However, most methods are susceptible to noise and have poor generalization
ability on unseen datasets. Motivated by this, we introduce SphereNet to learn
a noise-robust and unseen-general descriptor for point cloud registration. In
our method, first, the spheroid generator builds a geometric domain based on
spherical voxelization to encode initial features. Then, the spherical
interpolation of the sphere is introduced to realize robustness against noise.
Finally, a new spherical convolutional neural network with spherical integrity
padding completes the extraction of descriptors, which reduces the loss of
features and fully captures the geometric features. To evaluate our methods, a
new benchmark 3DMatch-noise with strong noise is introduced. Extensive
experiments are carried out on both indoor and outdoor datasets. Under
high-intensity noise, SphereNet increases the feature matching recall by more
than 25 percentage points on 3DMatch-noise. In addition, it sets a new
state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with
93.5\% and 75.6\% registration recall and also has the best generalization
ability on unseen datasets.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:37:35 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Zhao",
"Guiyu",
""
],
[
"Guo",
"Zhentao",
""
],
[
"Wang",
"Xin",
""
],
[
"Ma",
"Hongbin",
""
]
] |
new_dataset
| 0.993035 |
2307.09356
|
Dongming Wu
|
Dongming Wu, Tiancai Wang, Yuang Zhang, Xiangyu Zhang, Jianbing Shen
|
OnlineRefer: A Simple Online Baseline for Referring Video Object
Segmentation
|
Accepted by ICCV2023. The code is at
https://github.com/wudongming97/OnlineRefer
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Referring video object segmentation (RVOS) aims at segmenting an object in a
video following human instruction. Current state-of-the-art methods fall into
an offline pattern, in which each clip independently interacts with text
embedding for cross-modal understanding. They usually present that the offline
pattern is necessary for RVOS, yet model limited temporal association within
each clip. In this work, we break up the previous offline belief and propose a
simple yet effective online model using explicit query propagation, named
OnlineRefer. Specifically, our approach leverages target cues that gather
semantic information and position prior to improve the accuracy and ease of
referring predictions for the current frame. Furthermore, we generalize our
online model into a semi-online framework to be compatible with video-based
backbones. To show the effectiveness of our method, we evaluate it on four
benchmarks, \ie, Refer-Youtube-VOS, Refer-DAVIS17, A2D-Sentences, and
JHMDB-Sentences. Without bells and whistles, our OnlineRefer with a Swin-L
backbone achieves 63.5 J&F and 64.8 J&F on Refer-Youtube-VOS and Refer-DAVIS17,
outperforming all other offline methods.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:43:35 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Wu",
"Dongming",
""
],
[
"Wang",
"Tiancai",
""
],
[
"Zhang",
"Yuang",
""
],
[
"Zhang",
"Xiangyu",
""
],
[
"Shen",
"Jianbing",
""
]
] |
new_dataset
| 0.993367 |
2307.09364
|
Roger Moore
|
Roger K. Moore
|
Local Minima Drive Communications in Cooperative Interaction
|
6 page conference paper
| null | null | null |
cs.AI cs.MA cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
An important open question in human-robot interaction (HRI) is precisely when
an agent should decide to communicate, particularly in a cooperative task.
Perceptual Control Theory (PCT) tells us that agents are able to cooperate on a
joint task simply by sharing the same 'intention', thereby distributing the
effort required to complete the task among the agents. This is even true for
agents that do not possess the same abilities, so long as the goal is
observable, the combined actions are sufficient to complete the task, and there
is no local minimum in the search space. If these conditions hold, then a
cooperative task can be accomplished without any communication between the
contributing agents. However, for tasks that do contain local minima, the
global solution can only be reached if at least one of the agents adapts its
intention at the appropriate moments, and this can only be achieved by
appropriately timed communication. In other words, it is hypothesised that in
cooperative tasks, the function of communication is to coordinate actions in a
complex search space that contains local minima. These principles have been
verified in a computer-based simulation environment in which two independent
one-dimensional agents are obliged to cooperate in order to solve a
two-dimensional path-finding task.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:48:37 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Moore",
"Roger K.",
""
]
] |
new_dataset
| 0.978272 |
2307.09371
|
Manolis Ploumidis
|
Manolis Ploumidis, Fabien Chaix, Nikolaos Chrysos, Marios Assiminakis,
Vassilis Flouris, Nikolaos Kallimanis, Nikolaos Kossifidis, Michael
Nikoloudakis, Polydoros Petrakis, Nikolaos Dimou, Michael Gianioudis, George
Ieronymakis, Aggelos Ioannou, George Kalokerinos, Pantelis Xirouchakis,
George Ailamakis, Astrinos Damianakis, Michael Ligerakis, Ioannis Makris,
Theocharis Vavouris, Manolis Katevenis, Vassilis Papaefstathiou, Manolis
Marazakis, Iakovos Mavroidis
|
The ExaNeSt Prototype: Evaluation of Efficient HPC Communication
Hardware in an ARM-based Multi-FPGA Rack
|
45 pages, 23 figures
| null | null |
Report-no:TR-488
|
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present and evaluate the ExaNeSt Prototype, a liquid-cooled rack prototype
consisting of 256 Xilinx ZU9EG MPSoCs, 4 TBytes of DRAM, 16 TBytes of SSD, and
configurable interconnection 10-Gbps hardware. We developed this testbed in
2016-2019 to validate the flexibility of FPGAs for experimenting with efficient
hardware support for HPC communication among tens of thousands of processors
and accelerators in the quest towards Exascale systems and beyond. We present
our key design choices reagrding overall system architecture, PCBs and runtime
software, and summarize insights resulting from measurement and analysis. Of
particular note, our custom interconnect includes a low-cost low-latency
network interface, offering user-level zero-copy RDMA, which we have tightly
coupled with the ARMv8 processors in the MPSoCs. We have developed a system
software runtime on top of these features, and have been able to run MPI. We
have evaluated our testbed through MPI microbenchmarks, mini, and full MPI
applications. Single hop, one way latency is $1.3$~$\mu$s; approximately
$0.47$~$\mu$s out of these are attributed to network interface and the
user-space library that exposes its functionality to the runtime. Latency over
longer paths increases as expected, reaching $2.55$~$\mu$s for a five-hop path.
Bandwidth tests show that, for a single hop, link utilization reaches $82\%$ of
the theoretical capacity. Microbenchmarks based on MPI collectives reveal that
broadcast latency scales as expected when the number of participating ranks
increases. We also implemented a custom Allreduce accelerator in the network
interface, which reduces the latency of such collectives by up to $88\%$. We
assess performance scaling through weak and strong scaling tests for HPCG,
LAMMPS, and the miniFE mini application; for all these tests, parallelization
efficiency is at least $69\%$, or better.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:51:43 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Ploumidis",
"Manolis",
""
],
[
"Chaix",
"Fabien",
""
],
[
"Chrysos",
"Nikolaos",
""
],
[
"Assiminakis",
"Marios",
""
],
[
"Flouris",
"Vassilis",
""
],
[
"Kallimanis",
"Nikolaos",
""
],
[
"Kossifidis",
"Nikolaos",
""
],
[
"Nikoloudakis",
"Michael",
""
],
[
"Petrakis",
"Polydoros",
""
],
[
"Dimou",
"Nikolaos",
""
],
[
"Gianioudis",
"Michael",
""
],
[
"Ieronymakis",
"George",
""
],
[
"Ioannou",
"Aggelos",
""
],
[
"Kalokerinos",
"George",
""
],
[
"Xirouchakis",
"Pantelis",
""
],
[
"Ailamakis",
"George",
""
],
[
"Damianakis",
"Astrinos",
""
],
[
"Ligerakis",
"Michael",
""
],
[
"Makris",
"Ioannis",
""
],
[
"Vavouris",
"Theocharis",
""
],
[
"Katevenis",
"Manolis",
""
],
[
"Papaefstathiou",
"Vassilis",
""
],
[
"Marazakis",
"Manolis",
""
],
[
"Mavroidis",
"Iakovos",
""
]
] |
new_dataset
| 0.962589 |
2307.09473
|
Anish Mukherjee
|
Samir Datta, Asif Khan, Anish Mukherjee
|
Dynamic Planar Embedding is in DynFO
|
To appear at MFCS 2023
| null | null | null |
cs.DS cs.CC cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Planar Embedding is a drawing of a graph on the plane such that the edges do
not intersect each other except at the vertices. We know that testing the
planarity of a graph and computing its embedding (if it exists), can
efficiently be computed, both sequentially [HT] and in parallel [RR94], when
the entire graph is presented as input.
In the dynamic setting, the input graph changes one edge at a time through
insertion and deletions and planarity testing/embedding has to be updated after
every change. By storing auxilliary information we can improve the complexity
of dynamic planarity testing/embedding over the obvious recomputation from
scratch. In the sequential dynamic setting, there has been a series of works
[EGIS, IPR, HIKLR, HR1], culminating in the breakthrough result of polylog(n)
sequential time (amortized) planarity testing algorithm of Holm and Rotenberg
[HR2].
In this paper, we study planar embedding through the lens of DynFO, a
parallel dynamic complexity class introduced by Patnaik et al. [PI] (also
[DST95]). We show that it is possible to dynamically maintain whether an edge
can be inserted to a planar graph without causing non-planarity in DynFO. We
extend this to show how to maintain an embedding of a planar graph under both
edge insertions and deletions, while rejecting edge insertions that violate
planarity.
Our main idea is to maintain embeddings of only the triconnected components
and a special two-colouring of separating pairs that enables us to side-step
cascading flips when embedding of a biconnected planar graph changes, a major
issue for sequential dynamic algorithms [HR1, HR2].
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 15:50:46 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Datta",
"Samir",
""
],
[
"Khan",
"Asif",
""
],
[
"Mukherjee",
"Anish",
""
]
] |
new_dataset
| 0.999102 |
2307.09474
|
En Yu
|
Liang Zhao, En Yu, Zheng Ge, Jinrong Yang, Haoran Wei, Hongyu Zhou,
Jianjian Sun, Yuang Peng, Runpei Dong, Chunrui Han, Xiangyu Zhang
|
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring
Instruction Tuning
|
15 pages, 8 figures
| null | null | null |
cs.CL cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human-AI interactivity is a critical aspect that reflects the usability of
multimodal large language models (MLLMs). However, existing end-to-end MLLMs
only allow users to interact with them through language instructions, leading
to the limitation of the interactive accuracy and efficiency. In this study, we
present precise referring instructions that utilize diverse reference
representations such as points and boxes as referring prompts to refer to the
special region. This enables MLLMs to focus on the region of interest and
achieve finer-grained interaction. Based on precise referring instruction, we
propose ChatSpot, a unified end-to-end multimodal large language model that
supports diverse forms of interactivity including mouse clicks, drag-and-drop,
and drawing boxes, which provides a more flexible and seamless interactive
experience. We also construct a multi-grained vision-language
instruction-following dataset based on existing datasets and GPT-4 generating.
Furthermore, we design a series of evaluation tasks to assess the effectiveness
of region recognition and interaction. Experimental results showcase ChatSpot's
promising performance.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 17:56:06 GMT"
}
] | 2023-07-19T00:00:00 |
[
[
"Zhao",
"Liang",
""
],
[
"Yu",
"En",
""
],
[
"Ge",
"Zheng",
""
],
[
"Yang",
"Jinrong",
""
],
[
"Wei",
"Haoran",
""
],
[
"Zhou",
"Hongyu",
""
],
[
"Sun",
"Jianjian",
""
],
[
"Peng",
"Yuang",
""
],
[
"Dong",
"Runpei",
""
],
[
"Han",
"Chunrui",
""
],
[
"Zhang",
"Xiangyu",
""
]
] |
new_dataset
| 0.991261 |
1909.07750
|
Raghu Rajan
|
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio
Ferreira, Andr\'e Biedenkapp, Jan Ole von Hartz and Frank Hutter
|
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
|
Same version as the one published in JAIR Vol. 77 (2023)
|
Journal of Artificial Intelligence Research 77 (2023) 821-890
|
10.1613/jair.1.14314
| null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present MDP Playground, a testbed for Reinforcement Learning (RL) agents
with dimensions of hardness that can be controlled independently to challenge
agents in different ways and obtain varying degrees of hardness in toy and
complex RL environments. We consider and allow control over a wide variety of
dimensions, including delayed rewards, sequence lengths, reward density,
stochasticity, image representations, irrelevant features, time unit, action
range and more. We define a parameterised collection of fast-to-run toy
environments in OpenAI Gym by varying these dimensions and propose to use these
to understand agents better. We then show how to design experiments using MDP
Playground to gain insights on the toy environments. We also provide wrappers
that can inject many of these dimensions into any Gym environment. We
experiment with these wrappers on Atari and Mujoco to allow for understanding
the effects of these dimensions on environments that are more complex than the
toy environments. We also compare the effect of the dimensions on the toy and
complex environments. Finally, we show how to use MDP Playground to debug
agents, to study the interaction of multiple dimensions and describe further
use-cases.
|
[
{
"version": "v1",
"created": "Tue, 17 Sep 2019 12:41:20 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Dec 2019 12:46:17 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Oct 2020 13:06:35 GMT"
},
{
"version": "v4",
"created": "Fri, 25 Jun 2021 12:38:37 GMT"
},
{
"version": "v5",
"created": "Fri, 14 Jul 2023 11:59:40 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Rajan",
"Raghu",
""
],
[
"Diaz",
"Jessica Lizeth Borja",
""
],
[
"Guttikonda",
"Suresh",
""
],
[
"Ferreira",
"Fabio",
""
],
[
"Biedenkapp",
"André",
""
],
[
"von Hartz",
"Jan Ole",
""
],
[
"Hutter",
"Frank",
""
]
] |
new_dataset
| 0.999709 |
1911.10519
|
Priyansh Saxena
|
Priyansh Saxena, Ram Kishan Dewangan
|
Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles
using Salp Swarm Algorithm
|
This work has been previously published in the 'Journal of
Experimental & Theoretical Artificial Intelligence' and can be accessed at
https://www.tandfonline.com/doi/abs/10.1080/0952813X.2022.2059107
| null |
10.1080/0952813X.2022.2059107
| null |
cs.RO cs.MA cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of
translation and rotational steps from a given start location to the destination
goal location. The goal of the route planning problem is to determine the most
optimal route avoiding any collisions with the obstacles present in the
environment. Route planning is an NP-hard optimization problem. In this paper,
a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is
compared with deterministic and other Nature-Inspired Algorithms (NIAs). The
results illustrate that SSA outperforms all the other meta-heuristic algorithms
in route planning for multiple UAVs in a 3D environment. The proposed approach
improves the average cost and overall time by 1.25% and 6.035% respectively
when compared to recently reported data. Route planning is involved in many
real-life applications like robot navigation, self-driving car, autonomous UAV
for search and rescue operations in dangerous ground-zero situations, civilian
surveillance, military combat and even commercial services like package
delivery by drones.
|
[
{
"version": "v1",
"created": "Sun, 24 Nov 2019 12:36:18 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Dec 2019 11:31:55 GMT"
},
{
"version": "v3",
"created": "Wed, 19 May 2021 15:14:31 GMT"
},
{
"version": "v4",
"created": "Sun, 16 Jul 2023 12:35:26 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Saxena",
"Priyansh",
""
],
[
"Dewangan",
"Ram Kishan",
""
]
] |
new_dataset
| 0.99379 |
2004.11862
|
Aleksandr Popov
|
Kevin Buchin, Chenglin Fan, Maarten L\"offler, Aleksandr Popov,
Benjamin Raichel, Marcel Roeloffzen
|
Fr\'echet Distance for Uncertain Curves
|
48 pages, 11 figures. This is the full version of the paper to be
published in ICALP 2020
|
ACM Transactions on Algorithms 19.3 (2023), article no. 29
|
10.1145/3597640
| null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we study a wide range of variants for computing the (discrete
and continuous) Fr\'echet distance between uncertain curves. We define an
uncertain curve as a sequence of uncertainty regions, where each region is a
disk, a line segment, or a set of points. A realisation of a curve is a
polyline connecting one point from each region. Given an uncertain curve and a
second (certain or uncertain) curve, we seek to compute the lower and upper
bound Fr\'echet distance, which are the minimum and maximum Fr\'echet distance
for any realisations of the curves.
We prove that both the upper and lower bound problems are NP-hard for the
continuous Fr\'echet distance in several uncertainty models, and that the upper
bound problem remains hard for the discrete Fr\'echet distance. In contrast,
the lower bound (discrete and continuous) Fr\'echet distance can be computed in
polynomial time. Furthermore, we show that computing the expected discrete
Fr\'echet distance is #P-hard when the uncertainty regions are modelled as
point sets or line segments. The construction also extends to show #P-hardness
for computing the continuous Fr\'echet distance when regions are modelled as
point sets.
On the positive side, we argue that in any constant dimension there is a
FPTAS for the lower bound problem when $\Delta / \delta$ is polynomially
bounded, where $\delta$ is the Fr\'echet distance and $\Delta$ bounds the
diameter of the regions. We then argue there is a near-linear-time
3-approximation for the decision problem when the regions are convex and
roughly $\delta$-separated. Finally, we also study the setting with
Sakoe--Chiba time bands, where we restrict the alignment between the two
curves, and give polynomial-time algorithms for upper bound and expected
discrete and continuous Fr\'echet distance for uncertainty regions modelled as
point sets.
|
[
{
"version": "v1",
"created": "Fri, 24 Apr 2020 17:12:42 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Buchin",
"Kevin",
""
],
[
"Fan",
"Chenglin",
""
],
[
"Löffler",
"Maarten",
""
],
[
"Popov",
"Aleksandr",
""
],
[
"Raichel",
"Benjamin",
""
],
[
"Roeloffzen",
"Marcel",
""
]
] |
new_dataset
| 0.990738 |
2103.13389
|
Vadim Sushko
|
Vadim Sushko, Dan Zhang, Juergen Gall, Anna Khoreva
|
Generating Novel Scene Compositions from Single Images and Videos
|
The paper is under consideration at Computer Vision and Image
Understanding. Code repository:
https://github.com/boschresearch/one-shot-synthesis
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given a large dataset for training, generative adversarial networks (GANs)
can achieve remarkable performance for the image synthesis task. However,
training GANs in extremely low data regimes remains a challenge, as overfitting
often occurs, leading to memorization or training divergence. In this work, we
introduce SIV-GAN, an unconditional generative model that can generate new
scene compositions from a single training image or a single video clip. We
propose a two-branch discriminator architecture, with content and layout
branches designed to judge internal content and scene layout realism separately
from each other. This discriminator design enables synthesis of visually
plausible, novel compositions of a scene, with varying content and layout,
while preserving the context of the original sample. Compared to previous
single image GANs, our model generates more diverse, higher quality images,
while not being restricted to a single image setting. We further introduce a
new challenging task of learning from a few frames of a single video. In this
training setup the training images are highly similar to each other, which
makes it difficult for prior GAN models to achieve a synthesis of both high
quality and diversity.
|
[
{
"version": "v1",
"created": "Wed, 24 Mar 2021 17:59:07 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Oct 2021 10:55:52 GMT"
},
{
"version": "v3",
"created": "Thu, 17 Mar 2022 16:03:00 GMT"
},
{
"version": "v4",
"created": "Sun, 16 Jul 2023 04:42:07 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Sushko",
"Vadim",
""
],
[
"Zhang",
"Dan",
""
],
[
"Gall",
"Juergen",
""
],
[
"Khoreva",
"Anna",
""
]
] |
new_dataset
| 0.997651 |
2201.06845
|
Jiale Xu
|
Yuting Xiao, Jiale Xu, Shenghua Gao
|
Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor
Series
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Benefiting from the continuous representation ability, deep implicit
functions can represent a shape at infinite resolution. However, extracting
high-resolution iso-surface from an implicit function requires
forward-propagating a network with a large number of parameters for numerous
query points, thus preventing the generation speed. Inspired by the Taylor
series, we propose Taylo3DNet to accelerate the inference of implicit shape
representations. Taylor3DNet exploits a set of discrete landmark points and
their corresponding Taylor series coefficients to represent the implicit field
of a 3D shape, and the number of landmark points is independent of the
resolution of the iso-surface extraction. Once the coefficients corresponding
to the landmark points are predicted, the network evaluation for each query
point can be simplified as a low-order Taylor series calculation with several
nearest landmark points. Based on this efficient representation, our
Taylor3DNet achieves a significantly faster inference speed than classical
network-based implicit functions. We evaluate our approach on reconstruction
tasks with various input types, and the results demonstrate that our approach
can improve the inference speed by a large margin without sacrificing the
performance compared with state-of-the-art baselines.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 09:47:40 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Jul 2023 09:28:11 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Xiao",
"Yuting",
""
],
[
"Xu",
"Jiale",
""
],
[
"Gao",
"Shenghua",
""
]
] |
new_dataset
| 0.998404 |
2203.14184
|
Guodong Li
|
Guodong Li, Min Ye, Sihuang Hu
|
All the codeword symbols in polar codes have the same SER under the SC
decoder
|
We extend the results in the previous version to polar codes over
finite fields. Previously, we only proved the results for binary polar codes
|
IEEE Transactions on Communications ( Volume: 71, Issue: 7, July
2023)
|
10.1109/TCOMM.2023.3265687
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider polar codes constructed from the $2\times 2$ kernel
$\begin{bmatrix}
1 & 0 \\
\alpha & 1 \end{bmatrix}$ over a finite field $\mathbb{F}_{q}$, where $q=p^s$
is a power of a prime number $p$, and $\alpha$ satisfies that
$\mathbb{F}_{p}(\alpha) = \mathbb{F}_{q}$. We prove that for any
$\mathbb{F}_{q}$-symmetric memoryless channel, any code length, and any code
dimension, all the codeword symbols in such polar codes have the same symbol
error rate (SER) under the successive cancellation (SC) decoder.
|
[
{
"version": "v1",
"created": "Sun, 27 Mar 2022 02:13:44 GMT"
},
{
"version": "v2",
"created": "Sat, 7 May 2022 03:17:29 GMT"
},
{
"version": "v3",
"created": "Thu, 3 Nov 2022 08:37:07 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Li",
"Guodong",
""
],
[
"Ye",
"Min",
""
],
[
"Hu",
"Sihuang",
""
]
] |
new_dataset
| 0.992574 |
2209.05379
|
Asha Rani
|
Asha Rani, Pankaj Yadav, Yashaswi Verma
|
Action-based Early Autism Diagnosis Using Contrastive Feature Learning
|
This preprint has not undergone peer review (when applicable) or any
postsubmission improvements or corrections. The Version of Record of this
article is published in Multimedia Systems (2023), and is available online at
https://doi.org/10.1007/s00530-023-01132-8
| null |
10.1007/s00530-023-01132-8
| null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autism, also known as Autism Spectrum Disorder (or ASD), is a neurological
disorder. Its main symptoms include difficulty in (verbal and/or non-verbal)
communication, and rigid/repetitive behavior. These symptoms are often
indistinguishable from a normal (control) individual, due to which this
disorder remains undiagnosed in early childhood leading to delayed treatment.
Since the learning curve is steep during the initial age, an early diagnosis of
autism could allow to take adequate interventions at the right time, which
might positively affect the growth of an autistic child. Further, the
traditional methods of autism diagnosis require multiple visits to a
specialized psychiatrist, however this process can be time-consuming. In this
paper, we present a learning based approach to automate autism diagnosis using
simple and small action video clips of subjects. This task is particularly
challenging because the amount of annotated data available is small, and the
variations among samples from the two categories (ASD and control) are
generally indistinguishable. This is also evident from poor performance of a
binary classifier learned using the cross-entropy loss on top of a baseline
encoder. To address this, we adopt contrastive feature learning in both self
supervised and supervised learning frameworks, and show that these can lead to
a significant increase in the prediction accuracy of a binary classifier on
this task. We further validate this by conducting thorough experimental
analyses under different set-ups on two publicly available datasets.
|
[
{
"version": "v1",
"created": "Mon, 12 Sep 2022 16:31:34 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Mar 2023 19:05:56 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Jul 2023 05:59:18 GMT"
},
{
"version": "v4",
"created": "Mon, 17 Jul 2023 10:53:03 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Rani",
"Asha",
""
],
[
"Yadav",
"Pankaj",
""
],
[
"Verma",
"Yashaswi",
""
]
] |
new_dataset
| 0.962676 |
2210.03600
|
Tam\'as Kar\'acsony
|
Jo\~ao Carmona and Tam\'as Kar\'acsony and Jo\~ao Paulo Silva Cunha
|
BlanketSet -- A clinical real-world in-bed action recognition and
qualitative semi-synchronised MoCap dataset
|
4 pages, Dataset available at:
https://rdm.inesctec.pt/dataset/nis-2022-004
|
2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG),
Porto, Portugal, 2023, pp. 116-119
|
10.1109/ENBENG58165.2023.10175335
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Clinical in-bed video-based human motion analysis is a very relevant computer
vision topic for several relevant biomedical applications. Nevertheless, the
main public large datasets (e.g. ImageNet or 3DPW) used for deep learning
approaches lack annotated examples for these clinical scenarios. To address
this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of
sequences performed in a hospital bed. This dataset has the potential to help
bridge the improvements attained in more general large datasets to these
clinical scenarios. Information on how to access the dataset is available at
https://rdm.inesctec.pt/dataset/nis-2022-004.
|
[
{
"version": "v1",
"created": "Fri, 7 Oct 2022 14:58:27 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Oct 2022 16:17:29 GMT"
},
{
"version": "v3",
"created": "Sun, 19 Mar 2023 19:13:23 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Carmona",
"João",
""
],
[
"Karácsony",
"Tamás",
""
],
[
"Cunha",
"João Paulo Silva",
""
]
] |
new_dataset
| 0.999833 |
2210.12035
|
Tam\'as Kar\'acsony
|
Jo\~ao Carmona, Tam\'as Kar\'acsony, Jo\~ao Paulo Silva Cunha
|
BlanketGen - A synthetic blanket occlusion augmentation pipeline for
MoCap datasets
|
4 pages, Code and further information to generate the dataset is
available at:
https://gitlab.inesctec.pt/brain-lab/brain-lab-public/blanket-gen-releases
|
2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG),
Porto, Portugal, 2023, pp. 112-115
|
10.1109/ENBENG58165.2023.10175320
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Human motion analysis has seen drastic improvements recently, however, due to
the lack of representative datasets, for clinical in-bed scenarios it is still
lagging behind. To address this issue, we implemented BlanketGen, a pipeline
that augments videos with synthetic blanket occlusions. With this pipeline, we
generated an augmented version of the pose estimation dataset 3DPW called
BlanketGen-3DPW. We then used this new dataset to fine-tune a Deep Learning
model to improve its performance in these scenarios with promising results.
Code and further information are available at
https://gitlab.inesctec.pt/brain-lab/brain-lab-public/blanket-gen-releases.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 15:27:58 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Mar 2023 19:14:01 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Carmona",
"João",
""
],
[
"Karácsony",
"Tamás",
""
],
[
"Cunha",
"João Paulo Silva",
""
]
] |
new_dataset
| 0.997848 |
2210.13815
|
Yulin Zhu
|
Yulin Zhu, Liang Tong, Gaolei Li, Xiapu Luo, Kai Zhou
|
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node
Classification
| null | null | null | null |
cs.LG cs.CR cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which
will generate a poisoned graph as the input to the GNN models. We present
FocusedCleaner as a poisoned graph sanitizer to effectively identify the poison
injected by attackers. Specifically, FocusedCleaner provides a sanitation
framework consisting of two modules: bi-level structural learning and victim
node detection. In particular, the structural learning module will reverse the
attack process to steadily sanitize the graph while the detection module
provides ``the focus" -- a narrowed and more accurate search region -- to
structural learning. These two modules will operate in iterations and reinforce
each other to sanitize a poisoned graph step by step. As an important
application, we show that the adversarial robustness of GNNs trained over the
sanitized graph for the node classification task is significantly improved.
Extensive experiments demonstrate that FocusedCleaner outperforms the
state-of-the-art baselines both on poisoned graph sanitation and improving
robustness.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 07:41:57 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 12:33:11 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Zhu",
"Yulin",
""
],
[
"Tong",
"Liang",
""
],
[
"Li",
"Gaolei",
""
],
[
"Luo",
"Xiapu",
""
],
[
"Zhou",
"Kai",
""
]
] |
new_dataset
| 0.996396 |
2211.11629
|
Bowen Li
|
Bowen Li, Ziyuan Huang, Junjie Ye, Yiming Li, Sebastian Scherer, Hang
Zhao, Changhong Fu
|
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
|
18 pages, 10 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Visual object tracking is essential to intelligent robots. Most existing
approaches have ignored the online latency that can cause severe performance
degradation during real-world processing. Especially for unmanned aerial
vehicles (UAVs), where robust tracking is more challenging and onboard
computation is limited, the latency issue can be fatal. In this work, we
present a simple framework for end-to-end latency-aware tracking, i.e.,
end-to-end predictive visual tracking (PVT++). Unlike existing solutions that
naively append Kalman Filters after trackers, PVT++ can be jointly optimized,
so that it takes not only motion information but can also leverage the rich
visual knowledge in most pre-trained tracker models for robust prediction.
Besides, to bridge the training-evaluation domain gap, we propose a relative
motion factor, empowering PVT++ to generalize to the challenging and complex
UAV tracking scenes. These careful designs have made the small-capacity
lightweight PVT++ a widely effective solution. Additionally, this work presents
an extended latency-aware evaluation benchmark for assessing an any-speed
tracker in the online setting. Empirical results on a robotic platform from the
aerial perspective show that PVT++ can achieve significant performance gain on
various trackers and exhibit higher accuracy than prior solutions, largely
mitigating the degradation brought by latency.
|
[
{
"version": "v1",
"created": "Mon, 21 Nov 2022 16:43:33 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Mar 2023 03:28:46 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Jul 2023 03:33:14 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Li",
"Bowen",
""
],
[
"Huang",
"Ziyuan",
""
],
[
"Ye",
"Junjie",
""
],
[
"Li",
"Yiming",
""
],
[
"Scherer",
"Sebastian",
""
],
[
"Zhao",
"Hang",
""
],
[
"Fu",
"Changhong",
""
]
] |
new_dataset
| 0.972251 |
2211.13435
|
GuanLin Li
|
Guanlin Li, Guowen Xu, Tianwei Zhang
|
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we consider the instance segmentation task on a long-tailed
dataset, which contains label noise, i.e., some of the annotations are
incorrect. There are two main reasons making this case realistic. First,
datasets collected from real world usually obey a long-tailed distribution.
Second, for instance segmentation datasets, as there are many instances in one
image and some of them are tiny, it is easier to introduce noise into the
annotations. Specifically, we propose a new dataset, which is a large
vocabulary long-tailed dataset containing label noise for instance
segmentation. Furthermore, we evaluate previous proposed instance segmentation
algorithms on this dataset. The results indicate that the noise in the training
dataset will hamper the model in learning rare categories and decrease the
overall performance, and inspire us to explore more effective approaches to
address this practical challenge. The code and dataset are available in
https://github.com/GuanlinLee/Noisy-LVIS.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 06:34:29 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jul 2023 08:42:40 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Li",
"Guanlin",
""
],
[
"Xu",
"Guowen",
""
],
[
"Zhang",
"Tianwei",
""
]
] |
new_dataset
| 0.997387 |
2212.05468
|
Maxwell Pirtle
|
Maxwell Pirtle (Northeastern University, USA), Luka Jovanovic
(Northeastern University, USA), Gene Cooperman (Northeastern University, USA)
|
McMini: A Programmable DPOR-Based Model Checker for Multithreaded
Programs
| null |
The Art, Science, and Engineering of Programming, 2024, Vol. 8,
Issue 1, Article 1
|
10.22152/programming-journal.org/2024/8/1
| null |
cs.LO cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Context: Model checking has become a key tool for gaining confidence in
correctness of multi-threaded programs. Unit tests and functional tests do not
suffice because of race conditions that are not discovered by those tests. This
problem is addressed by model checking tools. A simple model checker is useful
for detecting race conditions prior to production.
Inquiry: Current model checkers hardwire the behavior of common thread
operations, and do not recognize application-dependent thread paradigms or
functions using simpler primitive operations. This introduces additional
operations, causing current model checkers to be excessively slow. In addition,
there is no mechanism to model the semantics of the actual thread wakeup
policies implemented in the underlying thread library or operating system.
Eliminating these constraints can make model checkers faster.
Approach: McMini is an **extensible** model checker based on DPOR (Dynamic
Partial Order Reduction). A mechanism was invented to declare to McMini new,
primitive thread operations, typically in 100~lines or less of C~code. The
mechanism was extended to also allow a user of McMini to declare alternative
thread wakeup policies, including spurious wakeups from condition variables.
Knowledge: In McMini, the user defines new thread operations. The user
optimizes these operations by declaring to the DPOR algorithm information that
reduces the number of thread schedules to be searched. One declares: (i) under
what conditions an operation is enabled; (ii) which thread operations are
independent of each other; and (iii) when two operations can be considered as
co-enabled. An optional wakeup policy is implemented by defining when a wait
operation (on a semaphore, condition variable, etc.) is enabled. A new enqueue
thread operation is described, allowing a user to declare alternative wakeup
policies.
Grounding: McMini was first confirmed to operate correctly and efficiently as
a traditional, but extensible model checker for mutex, semaphore, condition
variable, and reader-writer lock. McMini's extensibility was then tested on
novel primitive operations, representing other useful paradigms for
multithreaded operations. An example is readers-and-two-writers. The speed of
model checking was found to be five times faster and more, as compared to
traditional implementations on top of condition variables. Alternative wakeup
policies (e.g., FIFO, LIFO, arbitrary, etc.) were then tested using an enqueue
operation. Finally, spurious wakeups were tested with a program that exposes a
bug **only** in the presence of a spurious wakeup.
Importance: Many applications employ functions for multithreaded paradigms
that go beyond the traditional mutex, semaphore, and condition variables. They
are defined on top of basic operations. The ability to directly define new
primitives for these paradigms makes model checkers run faster by searching
fewer thread schedules. The ability to model particular thread wakeup policies,
including spurious wakeup for condition variables, is also important. Note that
POSIX leaves undefined the wakeup policies of `pthread_mutex_lock`, `sem_wait`,
and `pthread_cond_wait`. The POSIX thread implementation then chooses a
particular policy (e.g., FIFO, arbitrary), which can be directly modeled by
McMini.
|
[
{
"version": "v1",
"created": "Sun, 11 Dec 2022 10:52:47 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jul 2023 18:30:14 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Pirtle",
"Maxwell",
"",
"Northeastern University, USA"
],
[
"Jovanovic",
"Luka",
"",
"Northeastern University, USA"
],
[
"Cooperman",
"Gene",
"",
"Northeastern University, USA"
]
] |
new_dataset
| 0.993082 |
2212.08071
|
Po-Yao Huang
|
Po-Yao Huang, Vasu Sharma, Hu Xu, Chaitanya Ryali, Haoqi Fan, Yanghao
Li, Shang-Wen Li, Gargi Ghosh, Jitendra Malik, Christoph Feichtenhofer
|
MAViL: Masked Audio-Video Learners
|
Technical report
| null | null | null |
cs.CV cs.MM cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
We present Masked Audio-Video Learners (MAViL) to train audio-visual
representations. Our approach learns with three complementary forms of
self-supervision: (1) reconstruction of masked audio and video input data, (2)
intra- and inter-modal contrastive learning with masking, and (3) self-training
by reconstructing joint audio-video contextualized features learned from the
first two objectives. Pre-training with MAViL not only enables the model to
perform well in audio-visual classification and retrieval tasks but also
improves representations of each modality in isolation, without using
information from the other modality for fine-tuning or inference. Empirically,
MAViL sets a new state-of-the-art on AudioSet (53.1 mAP) and VGGSound (67.1%
accuracy). For the first time, a self-supervised audio-visual model outperforms
ones that use external supervision on these benchmarks.
|
[
{
"version": "v1",
"created": "Thu, 15 Dec 2022 18:59:59 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 05:44:35 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Huang",
"Po-Yao",
""
],
[
"Sharma",
"Vasu",
""
],
[
"Xu",
"Hu",
""
],
[
"Ryali",
"Chaitanya",
""
],
[
"Fan",
"Haoqi",
""
],
[
"Li",
"Yanghao",
""
],
[
"Li",
"Shang-Wen",
""
],
[
"Ghosh",
"Gargi",
""
],
[
"Malik",
"Jitendra",
""
],
[
"Feichtenhofer",
"Christoph",
""
]
] |
new_dataset
| 0.999598 |
2212.12912
|
Israel Leyva-Mayorga
|
Israel Leyva-Mayorga, Marc M. Gost, Marco Moretti, Ana P\'erez-Neira,
Miguel \'Angel V\'azquez, Petar Popovski, and Beatriz Soret
|
Satellite edge computing for real-time and very-high resolution Earth
observation
|
To be published in IEEE Transactions on Communications
| null |
10.1109/TCOMM.2023.3296584
| null |
cs.NI astro-ph.IM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In real-time and high-resolution Earth observation imagery, Low Earth Orbit
(LEO) satellites capture images that are subsequently transmitted to ground to
create an updated map of an area of interest. Such maps provide valuable
information for meteorology or environmental monitoring, but can also be
employed in near-real time operation for disaster detection, identification,
and management. However, the amount of data generated by these applications can
easily exceed the communication capabilities of LEO satellites, leading to
congestion and packet dropping. To avoid these problems, the Inter-Satellite
Links (ISLs) can be used to distribute the data among the satellites for
processing. In this paper, we address an energy minimization problem based on a
general satellite mobile edge computing (SMEC) framework for real-time and
very-high resolution Earth observation. Our results illustrate that the optimal
allocation of data and selection of the compression parameters increase the
amount of images that the system can support by a factor of 12 when compared to
directly downloading the data. Further, energy savings greater than 11% were
observed in a real-life scenario of imaging a volcanic island, while a
sensitivity analysis of the image acquisition process demonstrates that
potential energy savings can be as high as 92%.
|
[
{
"version": "v1",
"created": "Sun, 25 Dec 2022 14:36:37 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Jul 2023 16:27:55 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Leyva-Mayorga",
"Israel",
""
],
[
"Gost",
"Marc M.",
""
],
[
"Moretti",
"Marco",
""
],
[
"Pérez-Neira",
"Ana",
""
],
[
"Vázquez",
"Miguel Ángel",
""
],
[
"Popovski",
"Petar",
""
],
[
"Soret",
"Beatriz",
""
]
] |
new_dataset
| 0.992708 |
2301.01319
|
Bryce Doerr
|
Bryce Doerr, Keenan Albee, Monica Ekal, Rodrigo Ventura, Richard
Linares
|
The ReSWARM Microgravity Flight Experiments: Planning, Control, and
Model Estimation for On-Orbit Close Proximity Operations
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
On-orbit close proximity operations involve robotic spacecraft maneuvering
and making decisions for a growing number of mission scenarios demanding
autonomy, including on-orbit assembly, repair, and astronaut assistance. Of
these scenarios, on-orbit assembly is an enabling technology that will allow
large space structures to be built in-situ, using smaller building block
modules. However, robotic on-orbit assembly involves a number of technical
hurdles such as changing system models. For instance, grappled modules moved by
a free-flying "assembler" robot can cause significant shifts in system inertial
properties, which has cascading impacts on motion planning and control portions
of the autonomy stack. Further, on-orbit assembly and other scenarios require
collision-avoiding motion planning, particularly when operating in a
"construction site" scenario of multiple assembler robots and structures. These
complicating factors, relevant to many autonomous microgravity robotics use
cases, are tackled in the ReSWARM flight experiments as a set of tests on the
International Space Station using NASA's Astrobee robots. RElative Satellite
sWarming and Robotic Maneuvering, or ReSWARM, demonstrates multiple key
technologies for close proximity operations and on-orbit assembly: (1) global
long-horizon planning, accomplished using offline and online sampling-based
planner options that consider the system dynamics; (2) on-orbit reconfiguration
model learning, using the recently-proposed RATTLE information-aware planning
framework; and (3) robust control tools to provide low-level control robustness
using current system knowledge. These approaches are detailed individually and
in an "on-orbit assembly scenario" of multi-waypoint tracking on-orbit.
Additionally, detail is provided discussing the practicalities of hardware
implementation and unique aspects of working with Astrobee in microgravity.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 19:18:04 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Jul 2023 18:22:21 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Doerr",
"Bryce",
""
],
[
"Albee",
"Keenan",
""
],
[
"Ekal",
"Monica",
""
],
[
"Ventura",
"Rodrigo",
""
],
[
"Linares",
"Richard",
""
]
] |
new_dataset
| 0.99978 |
2302.06494
|
Yanjun Liu
|
Yanjun Liu and Wenming Yang
|
Explicit3D: Graph Network with Spatial Inference for Single Image 3D
Object Detection
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Indoor 3D object detection is an essential task in single image scene
understanding, impacting spatial cognition fundamentally in visual reasoning.
Existing works on 3D object detection from a single image either pursue this
goal through independent predictions of each object or implicitly reason over
all possible objects, failing to harness relational geometric information
between objects. To address this problem, we propose a dynamic sparse graph
pipeline named Explicit3D based on object geometry and semantics features.
Taking the efficiency into consideration, we further define a relatedness score
and design a novel dynamic pruning algorithm followed by a cluster sampling
method for sparse scene graph generation and updating. Furthermore, our
Explicit3D introduces homogeneous matrices and defines new relative loss and
corner loss to model the spatial difference between target pairs explicitly.
Instead of using ground-truth labels as direct supervision, our relative and
corner loss are derived from the homogeneous transformation, which renders the
model to learn the geometric consistency between objects. The experimental
results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves
better performance balance than the-state-of-the-art.
|
[
{
"version": "v1",
"created": "Mon, 13 Feb 2023 16:19:54 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jul 2023 10:25:29 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Liu",
"Yanjun",
""
],
[
"Yang",
"Wenming",
""
]
] |
new_dataset
| 0.957856 |
2302.13509
|
Jing Liang
|
Jing Liang, Sanghyun Son, Ming Lin, Dinesh Manocha
|
GeoLCR: Attention-based Geometric Loop Closure and Registration
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel algorithm specially designed for loop detection and
registration that utilizes Lidar-based perception. Our approach to loop
detection involves voxelizing point clouds, followed by an overlap calculation
to confirm whether a vehicle has completed a loop. We further enhance the
current pose's accuracy via an innovative point-level registration model. The
efficacy of our algorithm has been assessed across a range of well-known
datasets, including KITTI, KITTI-360, Nuscenes, Complex Urban, NCLT, and
MulRan. In comparative terms, our method exhibits up to a twofold increase in
the precision of both translation and rotation estimations. Particularly
noteworthy is our method's performance on challenging sequences where it
outperforms others, being the first to achieve a perfect 100% success rate in
loop detection.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 04:16:16 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 01:56:31 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 18:54:09 GMT"
},
{
"version": "v4",
"created": "Thu, 2 Mar 2023 15:14:05 GMT"
},
{
"version": "v5",
"created": "Sat, 4 Mar 2023 03:08:17 GMT"
},
{
"version": "v6",
"created": "Mon, 17 Jul 2023 02:33:00 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Liang",
"Jing",
""
],
[
"Son",
"Sanghyun",
""
],
[
"Lin",
"Ming",
""
],
[
"Manocha",
"Dinesh",
""
]
] |
new_dataset
| 0.999423 |
2303.03323
|
Hritik Bansal
|
Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei
Chang
|
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive
Learning
|
22 pages. Accepted at ICCV 2023
| null | null | null |
cs.CV cs.AI cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal contrastive pretraining has been used to train multimodal
representation models, such as CLIP, on large amounts of paired image-text
data. However, previous studies have revealed that such models are vulnerable
to backdoor attacks. Specifically, when trained on backdoored examples, CLIP
learns spurious correlations between the embedded backdoor trigger and the
target label, aligning their representations in the joint embedding space.
Injecting even a small number of poisoned examples, such as 75 examples in 3
million pretraining data, can significantly manipulate the model's behavior,
making it difficult to detect or unlearn such correlations. To address this
issue, we propose CleanCLIP, a finetuning framework that weakens the learned
spurious associations introduced by backdoor attacks by independently
re-aligning the representations for individual modalities. We demonstrate that
unsupervised finetuning using a combination of multimodal contrastive and
unimodal self-supervised objectives for individual modalities can significantly
reduce the impact of the backdoor attack. Additionally, we show that supervised
finetuning on task-specific labeled image data removes the backdoor trigger
from the CLIP vision encoder. We show empirically that CleanCLIP maintains
model performance on benign examples while erasing a range of backdoor attacks
on multimodal contrastive learning. The code and checkpoints are available at
https://github.com/nishadsinghi/CleanCLIP.
|
[
{
"version": "v1",
"created": "Mon, 6 Mar 2023 17:48:32 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Mar 2023 07:04:14 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Jul 2023 06:03:16 GMT"
}
] | 2023-07-18T00:00:00 |
[
[
"Bansal",
"Hritik",
""
],
[
"Singhi",
"Nishad",
""
],
[
"Yang",
"Yu",
""
],
[
"Yin",
"Fan",
""
],
[
"Grover",
"Aditya",
""
],
[
"Chang",
"Kai-Wei",
""
]
] |
new_dataset
| 0.977964 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.