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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2205.01159
|
Reza Hojjaty Saeedy
|
Reza Hojjaty Saeedy, Richard A. Messner
|
Saliency map using features derived from spiking neural networks of
primate visual cortex
|
19 pages, 8 figures, 1 table
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose a framework inspired by biological vision systems to produce
saliency maps of digital images. Well-known computational models for receptive
fields of areas in the visual cortex that are specialized for color and
orientation perception are used. To model the connectivity between these areas
we use the CARLsim library which is a spiking neural network(SNN) simulator.
The spikes generated by CARLsim, then serve as extracted features and input to
our saliency detection algorithm. This new method of saliency detection is
described and applied to benchmark images.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 18:52:39 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Saeedy",
"Reza Hojjaty",
""
],
[
"Messner",
"Richard A.",
""
]
] |
new_dataset
| 0.995267 |
2205.01167
|
Marcus Schwarting
|
Jim James, Nathan Pruyne, Tiberiu Stan, Marcus Schwarting, Jiwon Yeom,
Seungbum Hong, Peter Voorhees, Ben Blaiszik, Ian Foster
|
3D Convolutional Neural Networks for Dendrite Segmentation Using
Fine-Tuning and Hyperparameter Optimization
| null | null | null | null |
cs.CV cond-mat.mtrl-sci eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Dendritic microstructures are ubiquitous in nature and are the primary
solidification morphologies in metallic materials. Techniques such as x-ray
computed tomography (XCT) have provided new insights into dendritic phase
transformation phenomena. However, manual identification of dendritic
morphologies in microscopy data can be both labor intensive and potentially
ambiguous. The analysis of 3D datasets is particularly challenging due to their
large sizes (terabytes) and the presence of artifacts scattered within the
imaged volumes. In this study, we trained 3D convolutional neural networks
(CNNs) to segment 3D datasets. Three CNN architectures were investigated,
including a new 3D version of FCDense. We show that using hyperparameter
optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures
can be trained to outperform the previous state of the art. The 3D U-Net
architecture trained in this study produced the best segmentations according to
quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement
error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and
best segmentations according to visual inspection. The trained 3D CNNs are able
to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus
hastening the progress towards a deeper understanding of phase transformation
phenomena such as dendritic solidification.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 19:20:05 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"James",
"Jim",
""
],
[
"Pruyne",
"Nathan",
""
],
[
"Stan",
"Tiberiu",
""
],
[
"Schwarting",
"Marcus",
""
],
[
"Yeom",
"Jiwon",
""
],
[
"Hong",
"Seungbum",
""
],
[
"Voorhees",
"Peter",
""
],
[
"Blaiszik",
"Ben",
""
],
[
"Foster",
"Ian",
""
]
] |
new_dataset
| 0.999007 |
2205.01183
|
Ben Titzer
|
Ben L. Titzer
|
A fast in-place interpreter for WebAssembly
| null | null | null | null |
cs.PL cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
WebAssembly (Wasm) is a compact, well-specified bytecode format that offers a
portable compilation target with near-native execution speed. The bytecode
format was specifically designed to be fast to parse, validate, and compile,
positioning itself as a portable alternative to native code. It was pointedly
not designed to be interpreted directly. Instead, design considerations at the
time focused on competing with native code, utilizing optimizing compilers as
the primary execution tier. Yet, in JIT scenarios, compilation time and memory
consumption critically impact application startup, leading many Wasm engines to
later deploy baseline (single-pass) compilers. Though faster, baseline
compilers still take time and waste code space for infrequently executed code.
A typical interpreter being infeasible, some engines resort to compiling Wasm
not to machine code, but to a more compact, but easy to interpret format. This
still takes time and wastes memory. Instead, we introduce in this article a
fast in-place interpreter for WebAssembly, where no rewrite and no separate
format is necessary. Our evaluation shows that in-place interpretation of Wasm
code is space-efficient and fast, achieving performance on-par with
interpreting a custom-designed internal format. This fills a hole in the
execution tier space for Wasm, allowing for even faster startup and lower
memory footprint than previous engine configurations.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 20:01:32 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Titzer",
"Ben L.",
""
]
] |
new_dataset
| 0.997459 |
2205.01198
|
Zhening Huang
|
Zhening Huang, Weiwei Chen, Abir Al-Tabbaa, Ioannis Brilakis
|
NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack
Detection Algorithms
|
Accepted at EC3 2022
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Crack detection plays a key role in automated pavement inspection. Although a
large number of algorithms have been developed in recent years to further boost
performance, there are still remaining challenges in practice, due to the
complexity of pavement images. To further accelerate the development and
identify the remaining challenges, this paper conducts a comparison study to
evaluate the performance of the state of the art crack detection algorithms
quantitatively and objectively. A more comprehensive annotated pavement crack
dataset (NHA12D) that contains images with different viewpoints and pavements
types is proposed. In the comparison study, crack detection algorithms were
trained equally on the largest public crack dataset collected and evaluated on
the proposed dataset (NHA12D). Overall, the U-Net model with VGG-16 as backbone
has the best all-around performance, but models generally fail to distinguish
cracks from concrete joints, leading to a high false-positive rate. It also
found that detecting cracks from concrete pavement images still has huge room
for improvement. Dataset for concrete pavement images is also missing in the
literature. Future directions in this area include filling the gap for concrete
pavement images and using domain adaptation techniques to enhance the detection
results on unseen datasets.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 20:22:50 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Huang",
"Zhening",
""
],
[
"Chen",
"Weiwei",
""
],
[
"Al-Tabbaa",
"Abir",
""
],
[
"Brilakis",
"Ioannis",
""
]
] |
new_dataset
| 0.999781 |
2205.01213
|
Andrea Pizzo
|
Andrea Pizzo, Angel Lozano, Sundeep Rangan, Thomas Marzetta
|
Line-of-Sight MIMO via Reflection From a Smooth Surface
|
5 pages, 5 figures, accepted for presentation at the 2022 IEEE Veh.
Techn. Conf. (VTC)
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We provide a deterministic channel model for a scenario where wireless
connectivity is established through a reflection from a planar smooth surface
of an infinite extent. The developed model is rigorously built upon the physics
of wave propagation, and is as precise as tight are the unboundedness and
smoothness assumptions on the surface. This model allows establishing that
line-of-sight spatial multiplexing can take place via reflection off an
electrically large surface, a situation of high interest for mmWave and
terahertz frequencies.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 21:09:28 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Pizzo",
"Andrea",
""
],
[
"Lozano",
"Angel",
""
],
[
"Rangan",
"Sundeep",
""
],
[
"Marzetta",
"Thomas",
""
]
] |
new_dataset
| 0.996079 |
2205.01235
|
Edward Staley
|
Edward W. Staley and Jared Markowitz
|
Triangular Dropout: Variable Network Width without Retraining
| null | null | null | null |
cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
One of the most fundamental design choices in neural networks is layer width:
it affects the capacity of what a network can learn and determines the
complexity of the solution. This latter property is often exploited when
introducing information bottlenecks, forcing a network to learn compressed
representations. However, such an architecture decision is typically immutable
once training begins; switching to a more compressed architecture requires
retraining. In this paper we present a new layer design, called Triangular
Dropout, which does not have this limitation. After training, the layer can be
arbitrarily reduced in width to exchange performance for narrowness. We
demonstrate the construction and potential use cases of such a mechanism in
three areas. Firstly, we describe the formulation of Triangular Dropout in
autoencoders, creating models with selectable compression after training.
Secondly, we add Triangular Dropout to VGG19 on ImageNet, creating a powerful
network which, without retraining, can be significantly reduced in parameters.
Lastly, we explore the application of Triangular Dropout to reinforcement
learning (RL) policies on selected control problems.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 21:58:16 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Staley",
"Edward W.",
""
],
[
"Markowitz",
"Jared",
""
]
] |
new_dataset
| 0.987784 |
2205.01253
|
Takahiro Miura Mr.
|
Takahiro Miura, Ichiro Sakata
|
Storyteller: The papers co-citing Sleeping Beauty and Prince before
awakening
|
preprint, submitted to ASIS&T SIG-MET Workshop, extended abstract
| null | null | null |
cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the Cumulative Advantage(CA) model, which is one of the most fundamental
approaches to understand the mechanism of citation dynamics, papers receive
citations depending on how much they have been already cited. On the other
hand, a substantial effect not included in CA is that some surprising
discoveries suddenly acquire citations after a long time from publishing. This
phenomenon is known as Sleeping Beauty(SB). Since disrupting discoveries need
long-time discussion by the research community to accept, SBs can capture
innovative findings and reveal the nature of disruptive scientific knowledge
production. To research SBs citation burst mechanism, bibliometricians consider
the existence of the Prince(PR) for each SBs, which can be the trigger of SBs
awakeness. For example, the discovery of Green Fluorescent Protein(GFP), which
got Nobel prize in chemistry, had been overlooked for 30 years until Chalfie
and Tsien, who also received the prize, developed a method to use GFP as a
marker protein in genetic engineering. However, how does Chalfies and Tsiens
research relight the hidden knowledge in the research community? If we can
clarify such a mechanism rediscovering from nearly nothing, it can be helpful
in science support and policy decision-making. This study proposes a
Storyteller that focuses on the connection between SB and PR before SB gets
citation burst by co-citation. PR is found to be the paper awakening SB in
retrospect, but it is not easy to detect it as the trigger of SBs awakeness at
the time of PR submission. We named the papers which co-cites SB and PR before
the citation burst of SB as Storyteller(ST) and analyze (1) how ST contributes
to broadening the novelty of SB&PR connections and (2) how much ST leads the
citation burst after awakening.
|
[
{
"version": "v1",
"created": "Tue, 3 May 2022 00:35:33 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Miura",
"Takahiro",
""
],
[
"Sakata",
"Ichiro",
""
]
] |
new_dataset
| 0.990516 |
2205.01290
|
Jayetri Bardhan
|
Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang
|
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured
Electronic Health Records For Medicine Related Queries
|
15 pages (including Appendix section), 7 figures
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper develops the first question answering dataset (DrugEHRQA)
containing question-answer pairs from both structured tables and unstructured
notes from a publicly available Electronic Health Record (EHR). EHRs contain
patient records, stored in structured tables and unstructured clinical notes.
The information in structured and unstructured EHRs is not strictly disjoint:
information may be duplicated, contradictory, or provide additional context
between these sources. Our dataset has medication-related queries, containing
over 70,000 question-answer pairs. To provide a baseline model and help analyze
the dataset, we have used a simple model (MultimodalEHRQA) which uses the
predictions of a modality selection network to choose between EHR tables and
clinical notes to answer the questions. This is used to direct the questions to
the table-based or text-based state-of-the-art QA model. In order to address
the problem arising from complex, nested queries, this is the first time
Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL)
has been used to test the structure of query templates in EHR data. Our goal is
to provide a benchmark dataset for multi-modal QA systems, and to open up new
avenues of research in improving question answering over EHR structured data by
using context from unstructured clinical data.
|
[
{
"version": "v1",
"created": "Tue, 3 May 2022 03:50:50 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Bardhan",
"Jayetri",
""
],
[
"Colas",
"Anthony",
""
],
[
"Roberts",
"Kirk",
""
],
[
"Wang",
"Daisy Zhe",
""
]
] |
new_dataset
| 0.998066 |
2205.01381
|
Mike Zhang
|
Mike Zhang, Kristian N{\o}rgaard Jensen, Barbara Plank
|
Kompetencer: Fine-grained Skill Classification in Danish Job Postings
via Distant Supervision and Transfer Learning
|
7 pages, accepted to LREC 2022. arXiv admin note: text overlap with
arXiv:2204.12811
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Skill Classification (SC) is the task of classifying job competences from job
postings. This work is the first in SC applied to Danish job vacancy data. We
release the first Danish job posting dataset: Kompetencer (en: competences),
annotated for nested spans of competences. To improve upon coarse-grained
annotations, we make use of The European Skills, Competences, Qualifications
and Occupations (ESCO; le Vrang et al., 2014) taxonomy API to obtain
fine-grained labels via distant supervision. We study two setups: The zero-shot
and few-shot classification setting. We fine-tune English-based models and
RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our
results show RemBERT significantly outperforms all other models in both the
zero-shot and the few-shot setting.
|
[
{
"version": "v1",
"created": "Tue, 3 May 2022 09:13:55 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Zhang",
"Mike",
""
],
[
"Jensen",
"Kristian Nørgaard",
""
],
[
"Plank",
"Barbara",
""
]
] |
new_dataset
| 0.999024 |
2205.01506
|
Nayla Escribano
|
Nayla Escribano, Jon Ander Gonz\'alez, Julen Orbegozo-Terradillos,
Ainara Larrondo-Ureta, Sim\'on Pe\~na-Fern\'andez, Olatz Perez-de-Vi\~naspre
and Rodrigo Agerri
|
BasqueParl: A Bilingual Corpus of Basque Parliamentary Transcriptions
|
9 pages, 14 figures, 4 tables. To be published in LREC 2022
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Parliamentary transcripts provide a valuable resource to understand the
reality and know about the most important facts that occur over time in our
societies. Furthermore, the political debates captured in these transcripts
facilitate research on political discourse from a computational social science
perspective. In this paper we release the first version of a newly compiled
corpus from Basque parliamentary transcripts. The corpus is characterized by
heavy Basque-Spanish code-switching, and represents an interesting resource to
study political discourse in contrasting languages such as Basque and Spanish.
We enrich the corpus with metadata related to relevant attributes of the
speakers and speeches (language, gender, party...) and process the text to
obtain named entities and lemmas. The obtained metadata is then used to perform
a detailed corpus analysis which provides interesting insights about the
language use of the Basque political representatives across time, parties and
gender.
|
[
{
"version": "v1",
"created": "Tue, 3 May 2022 14:02:24 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Escribano",
"Nayla",
""
],
[
"González",
"Jon Ander",
""
],
[
"Orbegozo-Terradillos",
"Julen",
""
],
[
"Larrondo-Ureta",
"Ainara",
""
],
[
"Peña-Fernández",
"Simón",
""
],
[
"Perez-de-Viñaspre",
"Olatz",
""
],
[
"Agerri",
"Rodrigo",
""
]
] |
new_dataset
| 0.998181 |
2205.01515
|
Nikolas Ebert
|
Nikolas Ebert, Patrick Mangat, Oliver Wasenm\"uller
|
Multitask Network for Joint Object Detection, Semantic Segmentation and
Human Pose Estimation in Vehicle Occupancy Monitoring
|
This paper has been accepted at IEEE Intelligent Vehicles Symposium
(IV), 2022 (ORAL)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to ensure safe autonomous driving, precise information about the
conditions in and around the vehicle must be available. Accordingly, the
monitoring of occupants and objects inside the vehicle is crucial. In the
state-of-the-art, single or multiple deep neural networks are used for either
object recognition, semantic segmentation, or human pose estimation. In
contrast, we propose our Multitask Detection, Segmentation and Pose Estimation
Network (MDSP) -- the first multitask network solving all these three tasks
jointly in the area of occupancy monitoring. Due to the shared architecture,
memory and computing costs can be saved while achieving higher accuracy.
Furthermore, our architecture allows a flexible combination of the three
mentioned tasks during a simple end-to-end training. We perform comprehensive
evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the
superior performance.
|
[
{
"version": "v1",
"created": "Tue, 3 May 2022 14:11:18 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Ebert",
"Nikolas",
""
],
[
"Mangat",
"Patrick",
""
],
[
"Wasenmüller",
"Oliver",
""
]
] |
new_dataset
| 0.999574 |
2205.01569
|
TianSheuan Chang
|
Shu-Hung Kuo, and Tian-Sheuan Chang
|
PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory
Processor for Keyword Spotting
|
5 pages, 7 figures, published in IEEE ISCAS 2022
| null | null | null |
cs.AR cs.LG eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Computing-in-memory (CIM) has attracted significant attentions in recent
years due to its massive parallelism and low power consumption. However,
current CIM designs suffer from large area overhead of small CIM macros and bad
programmablity for model execution. This paper proposes a programmable CIM
processor with a single large sized CIM macro instead of multiple smaller ones
for power efficient computation and a flexible instruction set to support
various binary 1-D convolution Neural Network (CNN) models in an easy way.
Furthermore, the proposed architecture adopts the pooling write-back method to
support fused or independent convolution/pooling operations to reduce 35.9\% of
latency, and the flexible ping-pong feature SRAM to fit different feature map
sizes during layer-by-layer execution.The design fabricated in TSMC 28nm
technology achieves 150.8 GOPS throughput and 885.86 TOPS/W power efficiency at
10 MHz when executing our binary keyword spotting model, which has higher power
efficiency and flexibility than previous designs.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 09:58:18 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Kuo",
"Shu-Hung",
""
],
[
"Chang",
"Tian-Sheuan",
""
]
] |
new_dataset
| 0.99672 |
2205.01571
|
TianSheuan Chang
|
Kuo-Wei Chang, Hsu-Tung Shih, Tian-Sheuan Chang, Shang-Hong Tsai,
Chih-Chyau Yang, Chien-Ming Wu, Chun-Ming Huang
|
A Real Time 1280x720 Object Detection Chip With 585MB/s Memory Traffic
|
11 pages, 14 figures, to be published IEEE Transactions on Very Large
Scale Integration (VLSI) Systems
| null |
10.1109/TVLSI.2022.3149768
| null |
cs.AR cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Memory bandwidth has become the real-time bottleneck of current deep learning
accelerators (DLA), particularly for high definition (HD) object detection.
Under resource constraints, this paper proposes a low memory traffic DLA chip
with joint hardware and software optimization. To maximize hardware utilization
under memory bandwidth, we morph and fuse the object detection model into a
group fusion-ready model to reduce intermediate data access. This reduces the
YOLOv2's feature memory traffic from 2.9 GB/s to 0.15 GB/s. To support group
fusion, our previous DLA based hardware employes a unified buffer with
write-masking for simple layer-by-layer processing in a fusion group. When
compared to our previous DLA with the same PE numbers, the chip implemented in
a TSMC 40nm process supports 1280x720@30FPS object detection and consumes 7.9X
less external DRAM access energy, from 2607 mJ to 327.6 mJ.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 09:58:39 GMT"
}
] | 2022-05-04T00:00:00 |
[
[
"Chang",
"Kuo-Wei",
""
],
[
"Shih",
"Hsu-Tung",
""
],
[
"Chang",
"Tian-Sheuan",
""
],
[
"Tsai",
"Shang-Hong",
""
],
[
"Yang",
"Chih-Chyau",
""
],
[
"Wu",
"Chien-Ming",
""
],
[
"Huang",
"Chun-Ming",
""
]
] |
new_dataset
| 0.996534 |
1901.10736
|
Christian Schilling
|
Sergiy Bogomolov, Marcelo Forets, Goran Frehse, Kostiantyn Potomkin,
Christian Schilling
|
JuliaReach: a Toolbox for Set-Based Reachability
|
Accepted in Proceedings of HSCC'19: 22nd ACM International Conference
on Hybrid Systems: Computation and Control (HSCC'19)
|
HSCC 2019
|
10.1145/3302504.3311804
| null |
cs.SY math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present JuliaReach, a toolbox for set-based reachability analysis of
dynamical systems. JuliaReach consists of two main packages: Reachability,
containing implementations of reachability algorithms for continuous and hybrid
systems, and LazySets, a standalone library that implements state-of-the-art
algorithms for calculus with convex sets. The library offers both concrete and
lazy set representations, where the latter stands for the ability to delay set
computations until they are needed. The choice of the programming language
Julia and the accompanying documentation of our toolbox allow researchers to
easily translate set-based algorithms from mathematics to software in a
platform-independent way, while achieving runtime performance that is
comparable to statically compiled languages. Combining lazy operations in high
dimensions and explicit computations in low dimensions, JuliaReach can be
applied to solve complex, large-scale problems.
|
[
{
"version": "v1",
"created": "Wed, 30 Jan 2019 10:02:35 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2019 09:17:47 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Bogomolov",
"Sergiy",
""
],
[
"Forets",
"Marcelo",
""
],
[
"Frehse",
"Goran",
""
],
[
"Potomkin",
"Kostiantyn",
""
],
[
"Schilling",
"Christian",
""
]
] |
new_dataset
| 0.995809 |
1902.00815
|
Bj{\o}rn Kjos-Hanssen
|
Bj{\o}rn Kjos-Hanssen and Lei Liu
|
The number of languages with maximum state complexity
|
Algebra Universalis, accepted for publication. Preliminary version
in: Theory and Applications of Models of Computation (TAMC) 2019. Lecture
Notes in Computer Science 11436 (2019)
| null | null | null |
cs.FL math.CO math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
C\^{a}mpeanu and Ho (2004) determined the maximum finite state complexity of
finite languages, building on work of Champarnaud and Pin (1989). They stated
that it is very difficult to determine the number of maximum-complexity
languages. Here we give a formula for this number. We also generalize their
work from languages to functions on finite sets.
|
[
{
"version": "v1",
"created": "Sat, 2 Feb 2019 23:44:04 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 23:25:28 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Kjos-Hanssen",
"Bjørn",
""
],
[
"Liu",
"Lei",
""
]
] |
new_dataset
| 0.996006 |
1911.00889
|
Dimitrios Stathis
|
Dimitrios Stathis, Chirag Sudarshan, Yu Yang, Matthias Jung, Syed Asad
Mohamad Hasan Jafri, Christian Weis, Ahmed Hemani, Anders Lansner, Norbert
Wehn
|
eBrainII: A 3 kW Realtime Custom 3D DRAM integrated ASIC implementation
of a Biologically Plausible Model of a Human Scale Cortex
| null | null |
10.1007/s11265-020-01562-x
| null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Artificial Neural Networks (ANNs) like CNN/DNN and LSTM are not
biologically plausible and in spite of their initial success, they cannot
attain the cognitive capabilities enabled by the dynamic hierarchical
associative memory systems of biological brains. The biologically plausible
spiking brain models, for e.g. cortex, basal ganglia and amygdala have a
greater potential to achieve biological brain like cognitive capabilities.
Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically
plausible spiking model of cortex. A human scale model of BCPNN in real time
requires 162 TFlops/s, 50 TBs of synaptic weight storage to be accessed with a
bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s.
A hand optimized implementation of rodent scale BCPNN has been implemented on
Tesla K80 GPUs require 3 kW, we extrapolate from that a human scale network
will require 3 MW. These power numbers rule out such implementations for field
deployment as advanced cognition engines in embedded systems. The key
innovation that this paper reports is that it is feasible and affordable to
implement real time BCPNN as a custom tiled ASIC in 28 nm technology with
custom 3D DRAM - eBrain II - that consumes 3 kWs for human scale and 12 W for
rodent scale cortex model. Such implementations eminently fulfill the demands
for field deployment.
|
[
{
"version": "v1",
"created": "Sun, 3 Nov 2019 14:02:58 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Stathis",
"Dimitrios",
""
],
[
"Sudarshan",
"Chirag",
""
],
[
"Yang",
"Yu",
""
],
[
"Jung",
"Matthias",
""
],
[
"Jafri",
"Syed Asad Mohamad Hasan",
""
],
[
"Weis",
"Christian",
""
],
[
"Hemani",
"Ahmed",
""
],
[
"Lansner",
"Anders",
""
],
[
"Wehn",
"Norbert",
""
]
] |
new_dataset
| 0.998567 |
2007.03180
|
Chuang Yang
|
Chuang Yang (1), Zhiwen Zhang (1), Zipei Fan (1 and 2), Renhe Jiang (1
and 2), Quanjun Chen (1 and 2), Xuan Song (1 and 2), Ryosuke Shibasaki (1 and
2) ((1) Center for Spatial Information Science, The University of Tokyo, (2)
SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University
of Science and Technology)
|
EpiMob: Interactive Visual Analytics of Citywide Human Mobility
Restrictions for Epidemic Control
| null |
IEEE Transactions on Visualization and Computer Graphics, 2022
|
10.1109/TVCG.2022.3165385
| null |
cs.HC cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The outbreak of coronavirus disease (COVID-19) has swept across more than 180
countries and territories since late January 2020. As a worldwide emergency
response, governments have implemented various measures and policies, such as
self-quarantine, travel restrictions, work from home, and regional lockdown, to
control the spread of the epidemic. These countermeasures seek to restrict
human mobility because COVID-19 is a highly contagious disease that is spread
by human-to-human transmission. Medical experts and policymakers have expressed
the urgency to effectively evaluate the outcome of human restriction policies
with the aid of big data and information technology. Thus, based on big human
mobility data and city POI data, an interactive visual analytics system called
Epidemic Mobility (EpiMob) was designed in this study. The system interactively
simulates the changes in human mobility and infection status in response to the
implementation of a certain restriction policy or a combination of policies
(e.g., regional lockdown, telecommuting, screening). Users can conveniently
designate the spatial and temporal ranges for different mobility restriction
policies. Then, the results reflecting the infection situation under different
policies are dynamically displayed and can be flexibly compared and analyzed in
depth. Multiple case studies consisting of interviews with domain experts were
conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area)
to demonstrate that the system can provide insight into the effects of
different human mobility restriction policies for epidemic control, through
measurements and comparisons.
|
[
{
"version": "v1",
"created": "Tue, 7 Jul 2020 03:01:59 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jan 2021 08:02:21 GMT"
},
{
"version": "v3",
"created": "Mon, 29 Nov 2021 16:39:24 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Yang",
"Chuang",
"",
"1 and 2"
],
[
"Zhang",
"Zhiwen",
"",
"1 and 2"
],
[
"Fan",
"Zipei",
"",
"1 and 2"
],
[
"Jiang",
"Renhe",
"",
"1\n and 2"
],
[
"Chen",
"Quanjun",
"",
"1 and 2"
],
[
"Song",
"Xuan",
"",
"1 and 2"
],
[
"Shibasaki",
"Ryosuke",
"",
"1 and\n 2"
]
] |
new_dataset
| 0.998216 |
2012.02087
|
Mohamed Sayed
|
Mohamed Sayed, Robert Cinca, Enrico Costanza, Gabriel Brostow
|
LookOut! Interactive Camera Gimbal Controller for Filming Long Takes
|
V3: - ToG version with final edits
|
ACM Trans. Graph. 41, 3, Article 30 (March 2022), 22 pages
|
10.1145/3506693
| null |
cs.GR cs.HC cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The job of a camera operator is challenging, and potentially dangerous, when
filming long moving camera shots. Broadly, the operator must keep the actors
in-frame while safely navigating around obstacles, and while fulfilling an
artistic vision. We propose a unified hardware and software system that
distributes some of the camera operator's burden, freeing them up to focus on
safety and aesthetics during a take. Our real-time system provides a solo
operator with end-to-end control, so they can balance on-set responsiveness to
action vs planned storyboards and framing, while looking where they're going.
By default, we film without a field monitor.
Our LookOut system is built around a lightweight commodity camera gimbal
mechanism, with heavy modifications to the controller, which would normally
just provide active stabilization. Our control algorithm reacts to speech
commands, video, and a pre-made script. Specifically, our automatic monitoring
of the live video feed saves the operator from distractions. In pre-production,
an artist uses our GUI to design a sequence of high-level camera "behaviors."
Those can be specific, based on a storyboard, or looser objectives, such as
"frame both actors." Then during filming, a machine-readable script, exported
from the GUI, ties together with the sensor readings to drive the gimbal. To
validate our algorithm, we compared tracking strategies, interfaces, and
hardware protocols, and collected impressions from a) film-makers who used all
aspects of our system, and b) film-makers who watched footage filmed using
LookOut.
|
[
{
"version": "v1",
"created": "Thu, 3 Dec 2020 17:20:45 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Dec 2020 22:04:49 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Apr 2022 21:38:45 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Sayed",
"Mohamed",
""
],
[
"Cinca",
"Robert",
""
],
[
"Costanza",
"Enrico",
""
],
[
"Brostow",
"Gabriel",
""
]
] |
new_dataset
| 0.996168 |
2103.12827
|
Cat Le
|
Cat P. Le, Mohammadreza Soltani, Juncheng Dong, Vahid Tarokh
|
Fisher Task Distance and Its Application in Neural Architecture Search
|
Published in IEEE Access, Volume 10, 2022
| null |
10.1109/ACCESS.2022.3171741
| null |
cs.LG eess.IV stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
We formulate an asymmetric (or non-commutative) distance between tasks based
on Fisher Information Matrices, called Fisher task distance. This distance
represents the complexity of transferring the knowledge from one task to
another. We provide a proof of consistency for our distance through theorems
and experiments on various classification tasks from MNIST, CIFAR-10,
CIFAR-100, ImageNet, and Taskonomy datasets. Next, we construct an online
neural architecture search framework using the Fisher task distance, in which
we have access to the past learned tasks. By using the Fisher task distance, we
can identify the closest learned tasks to the target task, and utilize the
knowledge learned from these related tasks for the target task. Here, we show
how the proposed distance between a target task and a set of learned tasks can
be used to reduce the neural architecture search space for the target task. The
complexity reduction in search space for task-specific architectures is
achieved by building on the optimized architectures for similar tasks instead
of doing a full search and without using this side information. Experimental
results for tasks in MNIST, CIFAR-10, CIFAR-100, ImageNet datasets demonstrate
the efficacy of the proposed approach and its improvements, in terms of the
performance and the number of parameters, over other gradient-based search
methods, such as ENAS, DARTS, PC-DARTS.
|
[
{
"version": "v1",
"created": "Tue, 23 Mar 2021 20:43:31 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Mar 2021 14:13:56 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Sep 2021 19:48:59 GMT"
},
{
"version": "v4",
"created": "Fri, 21 Jan 2022 22:23:26 GMT"
},
{
"version": "v5",
"created": "Sat, 30 Apr 2022 04:40:37 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Le",
"Cat P.",
""
],
[
"Soltani",
"Mohammadreza",
""
],
[
"Dong",
"Juncheng",
""
],
[
"Tarokh",
"Vahid",
""
]
] |
new_dataset
| 0.999519 |
2107.03761
|
Akhila Sri Manasa Venigalla
|
Akhila Sri Manasa Venigalla, Kowndinya Boyalakunta and Sridhar
Chimalakonda
|
GitQ- Towards Using Badges as Visual Cues for GitHub Projects
|
5 pages, 3 figures
| null |
10.1145/3524610.3527876
| null |
cs.SE cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
GitHub hosts millions of software repositories, facilitating developers to
contribute to many projects in multiple ways. Most of the information about the
repositories is text-based in the form of stars, forks, commits, and so on.
However, developers willing to contribute to projects on GitHub often find it
challenging to select appropriate projects to contribute to or reuse due to the
large number of repositories present on GitHub. Further, obtaining this
required information often becomes a tedious process, as one has to carefully
mine information hidden inside the repository. To alleviate the effort
intensive mining procedures, researchers have proposed npm-badges to outline
information relating to build status of a project. However, these badges are
static and limit their usage to package dependency and build details. Adding
visual cues such as badges to the repositories might reduce the search space
for developers. Hence, we present GitQ, to automatically augment GitHub
repositories with badges representing information about source code and project
maintenance. Presenting GitQ as a browser plugin to GitHub could make it easily
accessible to developers using GitHub. GitQ is evaluated with 15 developers
based on the UTAUT model to understand developer perception towards its
usefulness. We observed that 11 out of 15 developers perceived GitQ to be
useful in identifying the right set of repositories using visual cues such as
generated by GitQ. The source code and tool are available for download on
GitHub at https://github.com/gitq-for-github/plugin, and the demo can be found
at https://youtu.be/c0yohmIat3A.
|
[
{
"version": "v1",
"created": "Thu, 8 Jul 2021 11:11:48 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 17:26:47 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Venigalla",
"Akhila Sri Manasa",
""
],
[
"Boyalakunta",
"Kowndinya",
""
],
[
"Chimalakonda",
"Sridhar",
""
]
] |
new_dataset
| 0.978282 |
2107.11041
|
Yoonsik Kim
|
Junyeop Lee, Yoonsik Kim, Seonghyeon Kim, Moonbin Yim, Seung Shin,
Gayoung Lee, Sungrae Park
|
RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of
Text Contents and Styles
|
CVPRW 2022 - AI for Content Creation Workshop
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Scene text editing (STE), which converts a text in a scene image into the
desired text while preserving an original style, is a challenging task due to a
complex intervention between text and style. In this paper, we propose a novel
STE model, referred to as RewriteNet, that decomposes text images into content
and style features and re-writes a text in the original image. Specifically,
RewriteNet implicitly distinguishes the content from the style by introducing
scene text recognition. Additionally, independent of the exact supervisions
with synthetic examples, we propose a self-supervised training scheme for
unlabeled real-world images, which bridges the domain gap between synthetic and
real data. Our experiments present that RewriteNet achieves better generation
performances than other comparisons. Further analysis proves the feature
decomposition of RewriteNet and demonstrates the reliability and robustness
through diverse experiments. Our implementation is publicly available at
\url{https://github.com/clovaai/rewritenet}
|
[
{
"version": "v1",
"created": "Fri, 23 Jul 2021 06:32:58 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 11:30:26 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Lee",
"Junyeop",
""
],
[
"Kim",
"Yoonsik",
""
],
[
"Kim",
"Seonghyeon",
""
],
[
"Yim",
"Moonbin",
""
],
[
"Shin",
"Seung",
""
],
[
"Lee",
"Gayoung",
""
],
[
"Park",
"Sungrae",
""
]
] |
new_dataset
| 0.986409 |
2108.07154
|
Yinhe Zheng Dr.
|
Yinhe Zheng, Guanyi Chen, Xin Liu, Jian Sun
|
MMChat: Multi-Modal Chat Dataset on Social Media
|
Accepted by LREC2022. Dataset available in
https://github.com/silverriver/MMChat
| null | null | null |
cs.CL cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Incorporating multi-modal contexts in conversation is important for
developing more engaging dialogue systems. In this work, we explore this
direction by introducing MMChat: a large-scale Chinese multi-modal dialogue
corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous
corpora that are crowd-sourced or collected from fictitious movies, MMChat
contains image-grounded dialogues collected from real conversations on social
media, in which the sparsity issue is observed. Specifically, image-initiated
dialogues in common communications may deviate to some non-image-grounded
topics as the conversation proceeds. To better investigate this issue, we
manually annotate 100K dialogues from MMChat and further filter the corpus
accordingly, which yields MMChat-hf. We develop a benchmark model to address
the sparsity issue in dialogue generation tasks by adapting the attention
routing mechanism on image features. Experiments demonstrate the usefulness of
incorporating image features and the effectiveness of handling the sparsity of
image features.
|
[
{
"version": "v1",
"created": "Mon, 16 Aug 2021 15:27:49 GMT"
},
{
"version": "v2",
"created": "Sat, 9 Apr 2022 02:04:48 GMT"
},
{
"version": "v3",
"created": "Sun, 1 May 2022 09:51:17 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Zheng",
"Yinhe",
""
],
[
"Chen",
"Guanyi",
""
],
[
"Liu",
"Xin",
""
],
[
"Sun",
"Jian",
""
]
] |
new_dataset
| 0.999839 |
2109.04919
|
Shutong Feng
|
Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin,
Michael Heck, Carel van Niekerk and Milica Ga\v{s}i\'c
|
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion
Recognition in Task-Oriented Dialogue Systems
|
Accepted for publication at LREC 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to recognise emotions lends a conversational artificial
intelligence a human touch. While emotions in chit-chat dialogues have received
substantial attention, emotions in task-oriented dialogues remain largely
unaddressed. This is despite emotions and dialogue success having equally
important roles in a natural system. Existing emotion-annotated task-oriented
corpora are limited in size, label richness, and public availability, creating
a bottleneck for downstream tasks. To lay a foundation for studies on emotions
in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually
emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on
MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than
11K dialogues with more than 83K emotion annotations of user utterances. In
addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine
dialogues within the same set of domains to sufficiently cover the space of
various emotions that can happen during the lifetime of a data-driven dialogue
system. To the best of our knowledge, this is the first large-scale open-source
corpus of its kind. We propose a novel emotion labelling scheme, which is
tailored to task-oriented dialogues. We report a set of experimental results to
show the usability of this corpus for emotion recognition and state tracking in
task-oriented dialogues.
|
[
{
"version": "v1",
"created": "Fri, 10 Sep 2021 15:00:01 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 08:34:11 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Feng",
"Shutong",
""
],
[
"Lubis",
"Nurul",
""
],
[
"Geishauser",
"Christian",
""
],
[
"Lin",
"Hsien-chin",
""
],
[
"Heck",
"Michael",
""
],
[
"van Niekerk",
"Carel",
""
],
[
"Gašić",
"Milica",
""
]
] |
new_dataset
| 0.999782 |
2110.07731
|
Patrick Huber
|
Patrick Huber, Armen Aghajanyan, Barlas O\u{g}uz, Dmytro Okhonko,
Wen-tau Yih, Sonal Gupta, Xilun Chen
|
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
|
9 pages, Findings of NAACL 2022
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rise of large-scale pre-trained language models, open-domain
question-answering (ODQA) has become an important research topic in NLP. Based
on the popular pre-training fine-tuning approach, we posit that an additional
in-domain pre-training stage using a large-scale, natural, and diverse
question-answering (QA) dataset can be beneficial for ODQA. Consequently, we
propose a novel QA dataset based on the Common Crawl project in this paper.
Using the readily available schema.org annotation, we extract around 130
million multilingual question-answer pairs, including about 60 million English
data-points. With this previously unseen number of natural QA pairs, we
pre-train popular language models to show the potential of large-scale
in-domain pre-training for the task of question-answering. In our experiments,
we find that pre-training question-answering models on our Common Crawl
Question Answering dataset (CCQA) achieves promising results in zero-shot, low
resource and fine-tuned settings across multiple tasks, models and benchmarks.
|
[
{
"version": "v1",
"created": "Thu, 14 Oct 2021 21:23:01 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 17:43:22 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Huber",
"Patrick",
""
],
[
"Aghajanyan",
"Armen",
""
],
[
"Oğuz",
"Barlas",
""
],
[
"Okhonko",
"Dmytro",
""
],
[
"Yih",
"Wen-tau",
""
],
[
"Gupta",
"Sonal",
""
],
[
"Chen",
"Xilun",
""
]
] |
new_dataset
| 0.997999 |
2111.12358
|
Binhui Xie
|
Binhui Xie, Mingjia Li and Shuang Li
|
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via
Semantic Prototype-based Contrastive Learning
|
23 pages, 9 figures; The code is publicly available at
https://github.com/BinhuiXie/SPCL
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although there is significant progress in supervised semantic segmentation,
it remains challenging to deploy the segmentation models to unseen domains due
to domain biases. Domain adaptation can help in this regard by transferring
knowledge from a labeled source domain to an unlabeled target domain. Previous
methods typically attempt to perform the adaptation on global features,
however, the local semantic affiliations accounting for each pixel in the
feature space are often ignored, resulting in less discriminability. To solve
this issue, we propose a novel semantic prototype-based contrastive learning
framework for fine-grained class alignment. Specifically, the semantic
prototypes provide supervisory signals for per-pixel discriminative
representation learning and each pixel of source and target domains in the
feature space is required to reflect the content of the corresponding semantic
prototype. In this way, our framework is able to explicitly make intra-class
pixel representations closer and inter-class pixel representations further
apart to improve the robustness of the segmentation model as well as alleviate
the domain shift problem. Our method is easy to implement and attains superior
results compared to state-of-the-art approaches, as is demonstrated with a
number of experiments. The code is publicly available at
https://github.com/BinhuiXie/SPCL.
|
[
{
"version": "v1",
"created": "Wed, 24 Nov 2021 09:26:07 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Apr 2022 08:02:22 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Xie",
"Binhui",
""
],
[
"Li",
"Mingjia",
""
],
[
"Li",
"Shuang",
""
]
] |
new_dataset
| 0.980052 |
2112.02732
|
Yueqing Sun
|
Yueqing Sun, Qi Shi, Le Qi, Yu Zhang
|
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for
Commonsense Question Answering
|
Accepted by NAACL 2022 main conference (Long paper)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing KG-augmented models for commonsense question answering primarily
focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge
graphs (KGs). However, they ignore (i) the effectively fusing and reasoning
over question context representations and the KG representations, and (ii)
automatically selecting relevant nodes from the noisy KGs during reasoning. In
this paper, we propose a novel model, JointLK, which solves the above
limitations through the joint reasoning of LM and GNN and the dynamic KGs
pruning mechanism. Specifically, JointLK performs joint reasoning between LM
and GNN through a novel dense bidirectional attention module, in which each
question token attends on KG nodes and each KG node attends on question tokens,
and the two modal representations fuse and update mutually by multi-step
interactions. Then, the dynamic pruning module uses the attention weights
generated by joint reasoning to prune irrelevant KG nodes recursively. We
evaluate JointLK on the CommonsenseQA and OpenBookQA datasets, and demonstrate
its improvements to the existing LM and LM+KG models, as well as its capability
to perform interpretable reasoning.
|
[
{
"version": "v1",
"created": "Mon, 6 Dec 2021 01:46:46 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 08:28:35 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Sun",
"Yueqing",
""
],
[
"Shi",
"Qi",
""
],
[
"Qi",
"Le",
""
],
[
"Zhang",
"Yu",
""
]
] |
new_dataset
| 0.985124 |
2112.04532
|
Jiang Liu
|
Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil
Feizi
|
Segment and Complete: Defending Object Detectors against Adversarial
Patch Attacks with Robust Patch Detection
|
CVPR 2022 camera ready
| null | null | null |
cs.CV cs.CR eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object detection plays a key role in many security-critical systems.
Adversarial patch attacks, which are easy to implement in the physical world,
pose a serious threat to state-of-the-art object detectors. Developing reliable
defenses for object detectors against patch attacks is critical but severely
understudied. In this paper, we propose Segment and Complete defense (SAC), a
general framework for defending object detectors against patch attacks through
detection and removal of adversarial patches. We first train a patch segmenter
that outputs patch masks which provide pixel-level localization of adversarial
patches. We then propose a self adversarial training algorithm to robustify the
patch segmenter. In addition, we design a robust shape completion algorithm,
which is guaranteed to remove the entire patch from the images if the outputs
of the patch segmenter are within a certain Hamming distance of the
ground-truth patch masks. Our experiments on COCO and xView datasets
demonstrate that SAC achieves superior robustness even under strong adaptive
attacks with no reduction in performance on clean images, and generalizes well
to unseen patch shapes, attack budgets, and unseen attack methods. Furthermore,
we present the APRICOT-Mask dataset, which augments the APRICOT dataset with
pixel-level annotations of adversarial patches. We show SAC can significantly
reduce the targeted attack success rate of physical patch attacks. Our code is
available at https://github.com/joellliu/SegmentAndComplete.
|
[
{
"version": "v1",
"created": "Wed, 8 Dec 2021 19:18:48 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 14:59:39 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Liu",
"Jiang",
""
],
[
"Levine",
"Alexander",
""
],
[
"Lau",
"Chun Pong",
""
],
[
"Chellappa",
"Rama",
""
],
[
"Feizi",
"Soheil",
""
]
] |
new_dataset
| 0.999641 |
2112.04960
|
Siddhartha Srivastava
|
X. Zhang, G.H. Teichert, Z. Wang, M. Duschenes, S. Srivastava, E.
Livingston, J. Holber, M. Faghih Shojaei, A. Sundararajan and K. Garikipati
|
mechanoChemML: A software library for machine learning in computational
materials physics
| null | null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present mechanoChemML, a machine learning software library for
computational materials physics. mechanoChemML is designed to function as an
interface between platforms that are widely used for machine learning on one
hand, and others for solution of partial differential equations-based models of
physics. Of special interest here, and the focus of mechanoChemML, are
applications to computational materials physics. These typically feature the
coupled solution of material transport, reaction, phase transformation,
mechanics, heat transport and electrochemistry. Central to the organization of
mechanoChemML are machine learning workflows that arise in the context of
data-driven computational materials physics. The mechanoChemML code structure
is described, the machine learning workflows are laid out and their application
to the solution of several problems in materials physics is outlined.
|
[
{
"version": "v1",
"created": "Thu, 9 Dec 2021 14:42:04 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Apr 2022 19:45:07 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Zhang",
"X.",
""
],
[
"Teichert",
"G. H.",
""
],
[
"Wang",
"Z.",
""
],
[
"Duschenes",
"M.",
""
],
[
"Srivastava",
"S.",
""
],
[
"Livingston",
"E.",
""
],
[
"Holber",
"J.",
""
],
[
"Shojaei",
"M. Faghih",
""
],
[
"Sundararajan",
"A.",
""
],
[
"Garikipati",
"K.",
""
]
] |
new_dataset
| 0.999432 |
2112.05637
|
Yang Hong
|
Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, Juyong Zhang
|
HeadNeRF: A Real-time NeRF-based Parametric Head Model
|
Accepted by CVPR2022. Project page:
https://crishy1995.github.io/HeadNeRF-Project/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model
that integrates the neural radiance field to the parametric representation of
the human head. It can render high fidelity head images in real-time on modern
GPUs, and supports directly controlling the generated images' rendering pose
and various semantic attributes. Different from existing related parametric
models, we use the neural radiance fields as a novel 3D proxy instead of the
traditional 3D textured mesh, which makes that HeadNeRF is able to generate
high fidelity images. However, the computationally expensive rendering process
of the original NeRF hinders the construction of the parametric NeRF model. To
address this issue, we adopt the strategy of integrating 2D neural rendering to
the rendering process of NeRF and design novel loss terms. As a result, the
rendering speed of HeadNeRF can be significantly accelerated, and the rendering
time of one frame is reduced from 5s to 25ms. The well designed loss terms also
improve the rendering accuracy, and the fine-level details of the human head,
such as the gaps between teeth, wrinkles, and beards, can be represented and
synthesized by HeadNeRF. Extensive experimental results and several
applications demonstrate its effectiveness. The trained parametric model is
available at https://github.com/CrisHY1995/headnerf.
|
[
{
"version": "v1",
"created": "Fri, 10 Dec 2021 16:10:13 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Dec 2021 03:05:45 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Apr 2022 13:57:53 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Hong",
"Yang",
""
],
[
"Peng",
"Bo",
""
],
[
"Xiao",
"Haiyao",
""
],
[
"Liu",
"Ligang",
""
],
[
"Zhang",
"Juyong",
""
]
] |
new_dataset
| 0.991708 |
2112.07522
|
Mengjie Zhao
|
Mengjie Zhao, Fei Mi, Yasheng Wang, Minglei Li, Xin Jiang, Qun Liu,
Hinrich Sch\"utze
|
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a
Language-Model-as-a-Service Framework
|
Findings of ACL: NAACL 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Vast efforts have been devoted to creating high-performance few-shot
learners, i.e., large-scale pretrained language models (PLMs) that perform well
with little downstream task training data. Training PLMs has incurred
significant cost, but utilizing the few-shot learners is still challenging due
to their enormous size. This work focuses on a crucial question: How to make
effective use of these few-shot learners? We propose LMTurk, a novel approach
that treats few-shot learners as crowdsourcing workers. The rationale is that
crowdsourcing workers are in fact few-shot learners: They are shown a few
illustrative examples to learn about a task and then start annotating. LMTurk
employs few-shot learners built upon PLMs as workers. We show that the
resulting annotations can be utilized to train models that solve the task well
and are small enough to be deployable in practical scenarios. Active learning
is integrated into LMTurk to reduce the amount of queries made to PLMs,
minimizing the computational cost of running PLM inference passes. Altogether,
LMTurk is an important step towards making effective use of current PLMs.
|
[
{
"version": "v1",
"created": "Tue, 14 Dec 2021 16:34:22 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 09:20:46 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Zhao",
"Mengjie",
""
],
[
"Mi",
"Fei",
""
],
[
"Wang",
"Yasheng",
""
],
[
"Li",
"Minglei",
""
],
[
"Jiang",
"Xin",
""
],
[
"Liu",
"Qun",
""
],
[
"Schütze",
"Hinrich",
""
]
] |
new_dataset
| 0.997075 |
2201.05281
|
Yaxiong Xie
|
Yaxiong Xie, Kyle Jamieson
|
NG-Scope: Fine-Grained Telemetry for NextG Cellular Networks
| null | null |
10.1145/3508032
| null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Accurate and highly-granular channel capacity telemetry of the cellular last
hop is crucial for the effective operation of transport layer protocols and
cutting-edge applications, such as video on demand and videotelephony. This
paper presents the design, implementation, and experimental performance
evaluation of NG-Scope, the first such telemetry tool able to fuse
physical-layer channel occupancy readings from the cellular control channel
with higher-layer packet arrival statistics and make accurate capacity
estimates. NG-Scope handles the latest cellular innovations, such as when
multiple base stations aggregate their signals together to serve mobile users.
End-to-end experiments in a commercial cellular network demonstrate that
wireless capacity varies significantly with channel quality, mobility,
competing traffic within each cell, and the number of aggregated cells. Our
experiments demonstrate significantly improved cell load estimation accuracy,
missing the detection of less than 1% of data capacity overall, a reduction of
82% compared to OWL, the state-of-the-art in cellular monitoring. Further
experiments show that MobileInsight-based CLAW has a root-mean-squared capacity
error of 30.5 Mbit/s, which is 3.3x larger than NG-Scope (9.2 Mbit/s)
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 02:47:59 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jan 2022 21:50:45 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Xie",
"Yaxiong",
""
],
[
"Jamieson",
"Kyle",
""
]
] |
new_dataset
| 0.999558 |
2201.06223
|
Jooyoung Choi
|
Changwook Jun, Jooyoung Choi, Myoseop Sim, Hyun Kim, Hansol Jang,
Kyungkoo Min
|
Korean-Specific Dataset for Table Question Answering
|
7 pages including references and 4 figures
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Existing question answering systems mainly focus on dealing with text data.
However, much of the data produced daily is stored in the form of tables that
can be found in documents and relational databases, or on the web. To solve the
task of question answering over tables, there exist many datasets for table
question answering written in English, but few Korean datasets. In this paper,
we demonstrate how we construct Korean-specific datasets for table question
answering: Korean tabular dataset is a collection of 1.4M tables with
corresponding descriptions for unsupervised pre-training language models.
Korean table question answering corpus consists of 70k pairs of questions and
answers created by crowd-sourced workers. Subsequently, we then build a
pre-trained language model based on Transformer and fine-tune the model for
table question answering with these datasets. We then report the evaluation
results of our model. We make our datasets publicly available via our GitHub
repository and hope that those datasets will help further studies for question
answering over tables, and for the transformation of table formats.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 05:47:44 GMT"
},
{
"version": "v2",
"created": "Sun, 1 May 2022 12:35:19 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Jun",
"Changwook",
""
],
[
"Choi",
"Jooyoung",
""
],
[
"Sim",
"Myoseop",
""
],
[
"Kim",
"Hyun",
""
],
[
"Jang",
"Hansol",
""
],
[
"Min",
"Kyungkoo",
""
]
] |
new_dataset
| 0.999664 |
2201.10248
|
Prabhat Kumar
|
Prabhat Kumar
|
HoneyTop90: A 90-line MATLAB code for topology optimization using
honeycomb tessellation
| null |
Optimization and Engineering, 2022
|
10.1007/s11081-022-09715-6
| null |
cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper provides a simple, compact and efficient 90-line pedagogical
MATLAB code for topology optimization using hexagonal elements (honeycomb
tessellation). Hexagonal elements provide nonsingular connectivity between two
juxtaposed elements and, thus, subdue checkerboard patterns and point
connections inherently from the optimized designs. A novel approach to generate
honeycomb tessellation is proposed. The element connectivity matrix and
corresponding nodal coordinates array are determined in 5 (7) and 4 (6) lines,
respectively. Two additional lines for the meshgrid generation are required for
an even number of elements in the vertical direction. The code takes a fraction
of a second to generate meshgrid information for the millions of hexagonal
elements. Wachspress shape functions are employed for the finite element
analysis, and compliance minimization is performed using the optimality
criteria method. The provided Matlab code and its extensions are explained in
detail. Options to run the optimization with and without filtering techniques
are provided. Steps to include different boundary conditions, multiple load
cases, active and passive regions, and a Heaviside projection filter are also
discussed. The code is provided in Appendix~A, and it can also be downloaded
along with supplementary materials from
\url{https://github.com/PrabhatIn/HoneyTop90}.
|
[
{
"version": "v1",
"created": "Tue, 25 Jan 2022 11:41:43 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Mar 2022 05:42:52 GMT"
},
{
"version": "v3",
"created": "Wed, 16 Mar 2022 01:06:30 GMT"
},
{
"version": "v4",
"created": "Sun, 24 Apr 2022 17:09:19 GMT"
},
{
"version": "v5",
"created": "Sat, 30 Apr 2022 23:25:09 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Kumar",
"Prabhat",
""
]
] |
new_dataset
| 0.999305 |
2202.07265
|
Massimo Battaglioni Dr.
|
Massimo Battaglioni and Paolo Santini and Giulia Rafaiani and Franco
Chiaraluce and Marco Baldi
|
Analysis of a blockchain protocol based on LDPC codes
| null | null | null | null |
cs.CR cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a blockchain Data Availability Attack (DAA), a malicious node publishes a
block header but withholds part of the block, which contains invalid
transactions. Honest full nodes, which can download and store the full
blockchain, are aware that some data are not available but they have no formal
way to prove it to light nodes, i.e., nodes that have limited resources and are
not able to access the whole blockchain data. A common solution to counter
these attacks exploits linear error correcting codes to encode the block
content. A recent protocol, called SPAR, employs coded Merkle trees and
low-density parity-check codes to counter DAAs. In this paper, we show that the
protocol is less secure than claimed, owing to a redefinition of the
adversarial success probability. As a consequence we show that, for some
realistic choices of the parameters, the total amount of data downloaded by
light nodes is larger than that obtainable with competitor solutions.
|
[
{
"version": "v1",
"created": "Tue, 15 Feb 2022 09:20:56 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Apr 2022 15:33:37 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Apr 2022 18:16:41 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Battaglioni",
"Massimo",
""
],
[
"Santini",
"Paolo",
""
],
[
"Rafaiani",
"Giulia",
""
],
[
"Chiaraluce",
"Franco",
""
],
[
"Baldi",
"Marco",
""
]
] |
new_dataset
| 0.996411 |
2203.00101
|
Hossein Keshavarz
|
Hossein Keshavarz and Meiyappan Nagappan
|
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
| null | null | null | null |
cs.SE cs.AI cs.LG cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect
prediction. ApacheJIT consists of clean and bug-inducing software changes in
popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239
bug-inducing and 78,435 clean commits). Having a large number of commits makes
ApacheJIT a suitable dataset for machine learning models, especially deep
learning models that require large training sets to effectively generalize the
patterns present in the historical data to future data.
|
[
{
"version": "v1",
"created": "Mon, 28 Feb 2022 21:26:14 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Apr 2022 01:42:25 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Keshavarz",
"Hossein",
""
],
[
"Nagappan",
"Meiyappan",
""
]
] |
new_dataset
| 0.999846 |
2203.00757
|
Ben Greenspan PhD
|
Ben Greenspan, Eric M. Gallo, Andreea Danielescu
|
FlexKeys: Rapidly Customizable 3D Printed Tactile Input Devices with No
Assembly Required
|
Abstract accepted, paper in review for 13th International Conference
on Applied Human Factors and Ergonomics (AHFE 2022). July 24-28, 2022
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Physical input devices serve as a tactile interface between users and
computing systems. These devices are often complex assemblies that consist of
both electrical and mechanical components making customization difficult and
out of reach for non-engineers. While these components can now be 3D printed on
demand, they must still be independently designed and assembled. We present
FlexKeys, an approach in which devices that include both electrical and
deformable components can be created in a single print on a multi-material 3D
printer, requiring no assembly. Designers can customize devices including the
input type, travel distance and layout of keys, textures of surfaces, and route
all electrical signals directly to a microcontroller socket. In many instances,
these devices require no support material, producing a functional device the
moment a print finishes. We demonstrate this approach by creating a customized
keyboard and report on validation measurements of individual input keys as well
as highlighting additional designs. This work provides the first step towards
lowering the barrier to entry for non-engineers to design custom tactile
inputs, enabling occupational and physical therapists, clinicians, and
educators to design and create devices directly based on their assessments of
individual user needs.
|
[
{
"version": "v1",
"created": "Tue, 1 Mar 2022 21:51:53 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 18:57:47 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Greenspan",
"Ben",
""
],
[
"Gallo",
"Eric M.",
""
],
[
"Danielescu",
"Andreea",
""
]
] |
new_dataset
| 0.998635 |
2203.11480
|
Shuai Zhao
|
Sha Yuan, Shuai Zhao, Jiahong Leng, Zhao Xue, Hanyu Zhao, Peiyu Liu,
Zheng Gong, Wayne Xin Zhao, Junyi Li and Jie Tang
|
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models
|
Some data problems cannot be obtained
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Compared with the domain-specific model, the vision-language pre-training
models (VLPMs) have shown superior performance on downstream tasks with fast
fine-tuning process. For example, ERNIE-ViL, Oscar and UNIMO trained VLPMs with
a uniform transformers stack architecture and large amounts of image-text
paired data, achieving remarkable results on downstream tasks such as
image-text reference(IR and TR), vision question answering (VQA) and image
captioning (IC) etc. During the training phase, VLPMs are always fed with a
combination of multiple public datasets to meet the demand of large-scare
training data. However, due to the unevenness of data distribution including
size, task type and quality, using the mixture of multiple datasets for model
training can be problematic. In this work, we introduce a large-scale
multi-modal corpora named WuDaoMM, totally containing more than 650M image-text
pairs. Specifically, about 600 million pairs of data are collected from
multiple webpages in which image and caption present weak correlation, and the
other 50 million strong-related image-text pairs are collected from some
high-quality graphic websites. We also release a base version of WuDaoMM with 5
million strong-correlated image-text pairs, which is sufficient to support the
common cross-modal model pre-training. Besides, we trained both an
understanding and a generation vision-language (VL) model to test the dataset
effectiveness. The results show that WuDaoMM can be applied as an efficient
dataset for VLPMs, especially for the model in text-to-image generation task.
The data is released at https://data.wudaoai.cn
|
[
{
"version": "v1",
"created": "Tue, 22 Mar 2022 06:12:20 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Mar 2022 12:44:43 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Mar 2022 00:35:53 GMT"
},
{
"version": "v4",
"created": "Tue, 19 Apr 2022 00:49:22 GMT"
},
{
"version": "v5",
"created": "Sun, 1 May 2022 02:34:42 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Yuan",
"Sha",
""
],
[
"Zhao",
"Shuai",
""
],
[
"Leng",
"Jiahong",
""
],
[
"Xue",
"Zhao",
""
],
[
"Zhao",
"Hanyu",
""
],
[
"Liu",
"Peiyu",
""
],
[
"Gong",
"Zheng",
""
],
[
"Zhao",
"Wayne Xin",
""
],
[
"Li",
"Junyi",
""
],
[
"Tang",
"Jie",
""
]
] |
new_dataset
| 0.99973 |
2203.14712
|
Fadime Sener
|
Fadime Sener and Dibyadip Chatterjee and Daniel Shelepov and Kun He
and Dipika Singhania and Robert Wang and Angela Yao
|
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding
Procedural Activities
|
CVPR 2022, https://assembly-101.github.io/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Assembly101 is a new procedural activity dataset featuring 4321 videos of
people assembling and disassembling 101 "take-apart" toy vehicles. Participants
work without fixed instructions, and the sequences feature rich and natural
variations in action ordering, mistakes, and corrections. Assembly101 is the
first multi-view action dataset, with simultaneous static (8) and egocentric
(4) recordings. Sequences are annotated with more than 100K coarse and 1M
fine-grained action segments, and 18M 3D hand poses. We benchmark on three
action understanding tasks: recognition, anticipation and temporal
segmentation. Additionally, we propose a novel task of detecting mistakes. The
unique recording format and rich set of annotations allow us to investigate
generalization to new toys, cross-view transfer, long-tailed distributions, and
pose vs. appearance. We envision that Assembly101 will serve as a new challenge
to investigate various activity understanding problems.
|
[
{
"version": "v1",
"created": "Mon, 28 Mar 2022 12:59:50 GMT"
},
{
"version": "v2",
"created": "Sun, 1 May 2022 14:49:02 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Sener",
"Fadime",
""
],
[
"Chatterjee",
"Dibyadip",
""
],
[
"Shelepov",
"Daniel",
""
],
[
"He",
"Kun",
""
],
[
"Singhania",
"Dipika",
""
],
[
"Wang",
"Robert",
""
],
[
"Yao",
"Angela",
""
]
] |
new_dataset
| 0.999911 |
2204.09033
|
Oded Padon
|
Mingkuan Xu and Zikun Li and Oded Padon and Sina Lin and Jessica
Pointing and Auguste Hirth and Henry Ma and Jens Palsberg and Alex Aiken and
Umut A. Acar and Zhihao Jia
|
Quartz: Superoptimization of Quantum Circuits (Extended Version)
|
28 pages. Extended version of the paper presented in PLDI 2022. Typos
corrected and artifact reference updated
| null | null | null |
cs.PL quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Existing quantum compilers optimize quantum circuits by applying circuit
transformations designed by experts. This approach requires significant manual
effort to design and implement circuit transformations for different quantum
devices, which use different gate sets, and can miss optimizations that are
hard to find manually. We propose Quartz, a quantum circuit superoptimizer that
automatically generates and verifies circuit transformations for arbitrary
quantum gate sets. For a given gate set, Quartz generates candidate circuit
transformations by systematically exploring small circuits and verifies the
discovered transformations using an automated theorem prover. To optimize a
quantum circuit, Quartz uses a cost-based backtracking search that applies the
verified transformations to the circuit. Our evaluation on three popular gate
sets shows that Quartz can effectively generate and verify transformations for
different gate sets. The generated transformations cover manually designed
transformations used by existing optimizers and also include new
transformations. Quartz is therefore able to optimize a broad range of circuits
for diverse gate sets, outperforming or matching the performance of hand-tuned
circuit optimizers.
|
[
{
"version": "v1",
"created": "Tue, 19 Apr 2022 17:52:59 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 07:13:21 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Xu",
"Mingkuan",
""
],
[
"Li",
"Zikun",
""
],
[
"Padon",
"Oded",
""
],
[
"Lin",
"Sina",
""
],
[
"Pointing",
"Jessica",
""
],
[
"Hirth",
"Auguste",
""
],
[
"Ma",
"Henry",
""
],
[
"Palsberg",
"Jens",
""
],
[
"Aiken",
"Alex",
""
],
[
"Acar",
"Umut A.",
""
],
[
"Jia",
"Zhihao",
""
]
] |
new_dataset
| 0.999385 |
2204.10685
|
Harikumar Kandath
|
Tanuja Joshi, Hariprasad Kodamana, Harikumar Kandath, and Niket
Kaisare
|
TASAC: a twin-actor reinforcement learning framework with stochastic
policy for batch process control
|
11 pages
| null | null | null |
cs.LG cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Due to their complex nonlinear dynamics and batch-to-batch variability, batch
processes pose a challenge for process control. Due to the absence of accurate
models and resulting plant-model mismatch, these problems become harder to
address for advanced model-based control strategies. Reinforcement Learning
(RL), wherein an agent learns the policy by directly interacting with the
environment, offers a potential alternative in this context. RL frameworks with
actor-critic architecture have recently become popular for controlling systems
where state and action spaces are continuous. It has been shown that an
ensemble of actor and critic networks further helps the agent learn better
policies due to the enhanced exploration due to simultaneous policy learning.
To this end, the current study proposes a stochastic actor-critic RL algorithm,
termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of
actors for learning, in a maximum entropy framework, for batch process control.
|
[
{
"version": "v1",
"created": "Fri, 22 Apr 2022 13:00:51 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 09:31:58 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Joshi",
"Tanuja",
""
],
[
"Kodamana",
"Hariprasad",
""
],
[
"Kandath",
"Harikumar",
""
],
[
"Kaisare",
"Niket",
""
]
] |
new_dataset
| 0.998474 |
2204.12261
|
Djordje Jevdjic
|
Dehui Lin, Yasamin Tabatabaee, Yash Pote, and Djordje Jevdjic
|
Managing Reliability Skew in DNA Storage
|
In Proceedings of the International Symposium on Computer
Architecture (ISCA 2022)
| null |
10.1145/3470496.3527441
| null |
cs.ET cs.AR cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
DNA is emerging as an increasingly attractive medium for data storage due to
a number of important and unique advantages it offers, most notably the
unprecedented durability and density. While the technology is evolving rapidly,
the prohibitive cost of reads and writes, the high frequency and the peculiar
nature of errors occurring in DNA storage pose a significant challenge to its
adoption. In this work we make a novel observation that the probability of
successful recovery of a given bit from any type of a DNA-based storage system
highly depends on its physical location within the DNA molecule. In other
words, when used as a storage medium, some parts of DNA molecules appear
significantly more reliable than others. We show that large differences in
reliability between different parts of DNA molecules lead to highly inefficient
use of error-correction resources, and that commonly used techniques such as
unequal error-correction cannot be used to bridge the reliability gap between
different locations in the context of DNA storage. We then propose two
approaches to address the problem. The first approach is general and applies to
any types of data; it stripes the data and ECC codewords across DNA molecules
in a particular fashion such that the effects of errors are spread out evenly
across different codewords and molecules, effectively de-biasing the underlying
storage medium. The second approach is application-specific, and seeks to
leverage the underlying reliability bias by using application-aware mapping of
data onto DNA molecules such that data that requires higher reliability is
stored in more reliable locations, whereas data that needs lower reliability is
stored in less reliable parts of DNA molecules. We show that the proposed data
mapping can be used to achieve graceful degradation in the presence of high
error rates, or to implement the concept of approximate storage in DNA.
|
[
{
"version": "v1",
"created": "Tue, 26 Apr 2022 12:34:46 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 22:09:56 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Lin",
"Dehui",
""
],
[
"Tabatabaee",
"Yasamin",
""
],
[
"Pote",
"Yash",
""
],
[
"Jevdjic",
"Djordje",
""
]
] |
new_dataset
| 0.967348 |
2204.12433
|
Minjia Shi
|
Minjia Shi, Haodong Lu, Shuang Zhou, Jiarui Xu, Yuhang Zhu
|
Equivalence and Duality of Polycyclic Codes Associated with Trinomials
over Finite Fields
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
In this paper, several conjectures proposed in [2] are studied, involving the
equivalence and duality of polycyclic codes associated with trinomials.
According to the results, we give methods to construct isodual and self-dual
polycyclic codes, and study the self-orthogonal and dual-containing polycyclic
codes over F2.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 10:02:49 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Apr 2022 01:14:00 GMT"
},
{
"version": "v3",
"created": "Sun, 1 May 2022 02:29:42 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Shi",
"Minjia",
""
],
[
"Lu",
"Haodong",
""
],
[
"Zhou",
"Shuang",
""
],
[
"Xu",
"Jiarui",
""
],
[
"Zhu",
"Yuhang",
""
]
] |
new_dataset
| 0.995854 |
2205.00211
|
Hong-Shuo Chen
|
Hong-Shuo Chen, Shuowen Hu, Suya You and C.-C. Jay Kuo
|
DefakeHop++: An Enhanced Lightweight Deepfake Detector
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
On the basis of DefakeHop, an enhanced lightweight Deepfake detector called
DefakeHop++ is proposed in this work. The improvements lie in two areas. First,
DefakeHop examines three facial regions (i.e., two eyes and mouth) while
DefakeHop++ includes eight more landmarks for broader coverage. Second, for
discriminant features selection, DefakeHop uses an unsupervised approach while
DefakeHop++ adopts a more effective approach with supervision, called the
Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral
features are first derived from facial regions and landmarks automatically.
Then, DFT is used to select a subset of discriminant features for classifier
training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M
parameters targeting at mobile applications), DefakeHop++ has a model of 238K
parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms
MobileNet v3 in Deepfake image detection performance in a weakly-supervised
setting.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 08:50:25 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Chen",
"Hong-Shuo",
""
],
[
"Hu",
"Shuowen",
""
],
[
"You",
"Suya",
""
],
[
"Kuo",
"C. -C. Jay",
""
]
] |
new_dataset
| 0.98182 |
2205.00220
|
Yi Chen
|
Yi Chen, Chong Han, Ziming Yu, Guangjian Wang
|
Channel Measurement, Characterization and Modeling for Terahertz Indoor
Communications Above 200 GHz
|
30 pages
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Terahertz (THz) communications are envisioned as a promising technology for
sixth-generation (6G) and beyond systems, owing to its unprecedented
multi-gigahertz (GHz) bandwidth. In this paper, channel measurement campaigns
in indoor scenarios at 201-209~GHz are reported. Four different communication
scenarios including 90 transmitter-receiver pairs are measured in two channel
measurement campaigns of a meeting room and an office room, respectively. The
two measurement campaigns contains four scenarios, namely, a meeting room,
cubicle area, hallway and non-line-of-sight (NLoS) case. The propagation of
multi-path components (MPCs) in the four scenarios is characterized by the
power-delay-angular profiles. Based on them, the temporal and spatial
consistency for varying receiver locations in the complex hallway and NLoS
scenarios are verified. To characterize, the large-scale best-direction and
omni-directional path losses in indoor scenarios are separately analyzed and
modeled by the close-in (CI) model. Furthermore, the small-scale channel
parameters, e.g., the number of clusters, delay spread, angular spread, and
cluster time-of-arrival are analyzed and modeled by proper distributions. As a
general framework, a ray-tracing-statistical hybrid model is proposed for
wireless propagation at 201-209~GHz, although, admittedly, the measurement
results and analysis reveal that the channel characteristics in various indoor
scenarios exhibit noticeable differences that need tailored parameter settings.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 09:51:33 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Chen",
"Yi",
""
],
[
"Han",
"Chong",
""
],
[
"Yu",
"Ziming",
""
],
[
"Wang",
"Guangjian",
""
]
] |
new_dataset
| 0.994382 |
2205.00254
|
Yifan Gao
|
Yifan Gao
|
PGD: A Large-scale Professional Go Dataset for Data-driven Analytics
|
IEEE Conference on Games 2022. Dataset is available at
https://github.com/Gifanan/Professional-Go-Dataset
| null | null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lee Sedol is on a winning streak--does this legend rise again after the
competition with AlphaGo? Ke Jie is invincible in the world championship--can
he still win the title this time? Go is one of the most popular board games in
East Asia, with a stable professional sports system that has lasted for decades
in China, Japan, and Korea. There are mature data-driven analysis technologies
for many sports, such as soccer, basketball, and esports. However, developing
such technology for Go remains nontrivial and challenging due to the lack of
datasets, meta-information, and in-game statistics. This paper creates the
Professional Go Dataset (PGD), containing 98,043 games played by 2,148
professional players from 1950 to 2021. After manual cleaning and labeling, we
provide detailed meta-information for each player, game, and tournament.
Moreover, the dataset includes analysis results for each move in the match
evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we
further analyze the data and extract meaningful in-game features based on prior
knowledge related to Go that can indicate the game status. With the help of
complete meta-information and constructed in-game features, our results
prediction system achieves an accuracy of 75.30%, much higher than several
state-of-the-art approaches (64%-65%). As far as we know, PGD is the first
dataset for data-driven analytics in Go and even in board games. Beyond this
promising result, we provide more examples of tasks that benefit from our
dataset. The ultimate goal of this paper is to bridge this ancient game and the
modern data science community. It will advance research on Go-related analytics
to enhance the fan experience, help players improve their ability, and
facilitate other promising aspects. The dataset will be made publicly
available.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 12:53:04 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Gao",
"Yifan",
""
]
] |
new_dataset
| 0.999753 |
2205.00257
|
Hui Kong
|
Yubin Guo, Haobo Jiang, Xinlei Qi, Jin Xie, Cheng-Zhong Xu and Hui
Kong
|
Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation
from Dual-Modality Cameras
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cross-spectrum depth estimation aims to provide a depth map in all
illumination conditions with a pair of dual-spectrum images. It is valuable for
autonomous vehicle applications when the vehicle is equipped with two cameras
of different modalities. However, images captured by different-modality cameras
can be photometrically quite different. Therefore, cross-spectrum depth
estimation is a very challenging problem. Moreover, the shortage of large-scale
open-source datasets also retards further research in this field. In this
paper, we propose an unsupervised visible-light image guided cross-spectrum
(i.e., thermal and visible-light, TIR-VIS in short) depth estimation framework
given a pair of RGB and thermal images captured from a visible-light camera and
a thermal one. We first adopt a base depth estimation network using RGB-image
pairs. Then we propose a multi-scale feature transfer network to transfer
features from the TIR-VIS domain to the VIS domain at the feature level to fit
the trained depth estimation network. At last, we propose a cross-spectrum
depth cycle consistency to improve the depth result of dual-spectrum image
pairs. Meanwhile, we release a large dual-spectrum depth estimation dataset
with visible-light and far-infrared stereo images captured in different scenes
to the society. The experiment result shows that our method achieves better
performance than the compared existing methods. Our datasets is available at
https://github.com/whitecrow1027/VIS-TIR-Datasets.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 12:58:35 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Guo",
"Yubin",
""
],
[
"Jiang",
"Haobo",
""
],
[
"Qi",
"Xinlei",
""
],
[
"Xie",
"Jin",
""
],
[
"Xu",
"Cheng-Zhong",
""
],
[
"Kong",
"Hui",
""
]
] |
new_dataset
| 0.995456 |
2205.00323
|
Nadya Peek
|
Blair Subbaraman, Nadya Peek
|
p5.fab: Direct Control of Digital Fabrication Machines from a Creative
Coding Environment
|
Submitted to DIS 2022, 12 pages plus references
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Machine settings and tuning are critical for digital fabrication outcomes.
However, exploring these parameters is non-trivial. We seek to enable
exploration of the full design space of digital fabrication. To identify where
we might intervene, we studied how practitioners approach 3D printing. We found
that beyond using CAD/CAM, they create bespoke routines and workflows to
explore interdependent material and machine settings. We seek to provide a
system that supports this workflow development. We identified design goals
around material exploration, fine-tuned control, and iteration. Based on these,
we present p5.fab, a system for controlling digital fabrication machines from
the creative coding environment p5.js. We demonstrate p5.fab with examples of
3D prints that cannot be made with traditional 3D printing software. We
evaluate p5.fab in workshops and find that it encourages novel printing
workflows and artifacts. Finally, we discuss implications for future digital
fabrication systems.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 18:52:55 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Subbaraman",
"Blair",
""
],
[
"Peek",
"Nadya",
""
]
] |
new_dataset
| 0.999373 |
2205.00331
|
Wei Jiang
|
Wei Jiang and Hans Dieter Schotten
|
Dual-Beam Intelligent Reflecting Surface for Millimeter and THz
Communications
|
2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Intelligent reflecting surface (IRS) is a cost-efficient technique to improve
power efficiency and spectral efficiency. However, IRS-aided multi-antenna
transmission needs to jointly optimize the passive and active beamforming,
imposing a high computational burden and high latency due to its iterative
optimization process. Making use of hybrid analog-digital beamforming in
high-frequency transmission systems, a novel technique, coined dual-beam IRS,
is proposed in this paper. The key idea is to form a pair of beams towards the
IRS and user, respectively. Then, the optimization of passive and active
beamforming can be decoupled, resulting in a simplified system design.
Simulation results corroborate that it achieves a good balance between the
cell-edge and cell-center performance. Compared with the performance bound, the
gap is moderate, but it remarkably outperforms other sub-optimal schemes.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 19:39:23 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Jiang",
"Wei",
""
],
[
"Schotten",
"Hans Dieter",
""
]
] |
new_dataset
| 0.984209 |
2205.00347
|
Kerem Turgutlu
|
Kerem Turgutlu, Sanat Sharma and Jayant Kumar
|
LayoutBERT: Masked Language Layout Model for Object Insertion
|
8 pages main paper, 6 pages supplemental material. Accepted to AI4CC
Workshop @CVPR 2022
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Image compositing is one of the most fundamental steps in creative workflows.
It involves taking objects/parts of several images to create a new image,
called a composite. Currently, this process is done manually by creating
accurate masks of objects to be inserted and carefully blending them with the
target scene or images, usually with the help of tools such as Photoshop or
GIMP. While there have been several works on automatic selection of objects for
creating masks, the problem of object placement within an image with the
correct position, scale, and harmony remains a difficult problem with limited
exploration. Automatic object insertion in images or designs is a difficult
problem as it requires understanding of the scene geometry and the color
harmony between objects. We propose LayoutBERT for the object insertion task.
It uses a novel self-supervised masked language model objective and
bidirectional multi-head self-attention. It outperforms previous layout-based
likelihood models and shows favorable properties in terms of model capacity. We
demonstrate the effectiveness of our approach for object insertion in the image
compositing setting and other settings like documents and design templates. We
further demonstrate the usefulness of the learned representations for
layout-based retrieval tasks. We provide both qualitative and quantitative
evaluations on datasets from diverse domains like COCO, PublayNet, and two new
datasets which we call Image Layouts and Template Layouts. Image Layouts which
consists of 5.8 million images with layout annotations is the largest image
layout dataset to our knowledge. We also share ablation study results on the
effect of dataset size, model size and class sample size for this task.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 21:35:38 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Turgutlu",
"Kerem",
""
],
[
"Sharma",
"Sanat",
""
],
[
"Kumar",
"Jayant",
""
]
] |
new_dataset
| 0.999359 |
2205.00377
|
Haoming Guo
|
Haoming Guo, Tianyi Huang, Huixuan Huang, Mingyue Fan, Gerald
Friedland
|
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The sharing of fake news and conspiracy theories on social media has
wide-spread negative effects. By designing and applying different machine
learning models, researchers have made progress in detecting fake news from
text. However, existing research places a heavy emphasis on general,
common-sense fake news, while in reality fake news often involves rapidly
changing topics and domain-specific vocabulary. In this paper, we present our
methods and results for three fake news detection tasks at MediaEval benchmark
2021 that specifically involve COVID-19 related topics. We experiment with a
group of text-based models including Support Vector Machines, Random Forest,
BERT, and RoBERTa. We find that a pre-trained transformer yields the best
validation results, but a randomly initialized transformer with smart design
can also be trained to reach accuracies close to that of the pre-trained
transformer.
|
[
{
"version": "v1",
"created": "Sun, 1 May 2022 01:48:48 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Guo",
"Haoming",
""
],
[
"Huang",
"Tianyi",
""
],
[
"Huang",
"Huixuan",
""
],
[
"Fan",
"Mingyue",
""
],
[
"Friedland",
"Gerald",
""
]
] |
new_dataset
| 0.998556 |
2205.00440
|
Rajdeep Mukherjee
|
Raghav R, Adarsh Vemali, Rajdeep Mukherjee
|
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using
A Generative Approach
|
9 pages, accepted at SemEval 2022 (collocated with NAACL 2022)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a
text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a
sentiment polarity p towards a target t through a sentiment expression e. While
prior works explore graph-based or sequence labeling-based approaches for the
task, we in this paper present a novel unified generative method to solve SSA,
a SemEval2022 shared task. We leverage a BART-based encoder-decoder
architecture and suitably modify it to generate, given a sentence, a sequence
of opinion tuples. Each generated tuple consists of seven integers respectively
representing the indices corresponding to the start and end positions of the
holder, target, and expression spans, followed by the sentiment polarity class
associated between the target and the sentiment expression. We perform rigorous
experiments for both Monolingual and Cross-lingual subtasks, and achieve
competitive Sentiment F1 scores on the leaderboard in both settings.
|
[
{
"version": "v1",
"created": "Sun, 1 May 2022 10:39:53 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"R",
"Raghav",
""
],
[
"Vemali",
"Adarsh",
""
],
[
"Mukherjee",
"Rajdeep",
""
]
] |
new_dataset
| 0.997566 |
2205.00445
|
Ehud Karpas Dr.
|
Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber,
Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor
Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon
Shashua, Moshe Tenenholtz
|
MRKL Systems: A modular, neuro-symbolic architecture that combines large
language models, external knowledge sources and discrete reasoning
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Huge language models (LMs) have ushered in a new era for AI, serving as a
gateway to natural-language-based knowledge tasks. Although an essential
element of modern AI, LMs are also inherently limited in a number of ways. We
discuss these limitations and how they can be avoided by adopting a systems
approach. Conceptualizing the challenge as one that involves knowledge and
reasoning in addition to linguistic processing, we define a flexible
architecture with multiple neural models, complemented by discrete knowledge
and reasoning modules. We describe this neuro-symbolic architecture, dubbed the
Modular Reasoning, Knowledge and Language (MRKL, pronounced "miracle") system,
some of the technical challenges in implementing it, and Jurassic-X, AI21 Labs'
MRKL system implementation.
|
[
{
"version": "v1",
"created": "Sun, 1 May 2022 11:01:28 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Karpas",
"Ehud",
""
],
[
"Abend",
"Omri",
""
],
[
"Belinkov",
"Yonatan",
""
],
[
"Lenz",
"Barak",
""
],
[
"Lieber",
"Opher",
""
],
[
"Ratner",
"Nir",
""
],
[
"Shoham",
"Yoav",
""
],
[
"Bata",
"Hofit",
""
],
[
"Levine",
"Yoav",
""
],
[
"Leyton-Brown",
"Kevin",
""
],
[
"Muhlgay",
"Dor",
""
],
[
"Rozen",
"Noam",
""
],
[
"Schwartz",
"Erez",
""
],
[
"Shachaf",
"Gal",
""
],
[
"Shalev-Shwartz",
"Shai",
""
],
[
"Shashua",
"Amnon",
""
],
[
"Tenenholtz",
"Moshe",
""
]
] |
new_dataset
| 0.99913 |
2205.00467
|
Federico Pigozzi Mr
|
Federico Pigozzi
|
Shape Change and Control of Pressure-based Soft Agents
|
Accepted at ALife'22 conference as full paper
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Biological agents possess bodies that are mostly of soft tissues. Researchers
have resorted to soft bodies to investigate Artificial Life (ALife)-related
questions; similarly, a new era of soft-bodied robots has just begun.
Nevertheless, because of their infinite degrees of freedom, soft bodies pose
unique challenges in terms of simulation, control, and optimization. Here we
propose a novel soft-bodied agents formalism, namely Pressure-based Soft Agents
(PSAs): they are bodies of gas enveloped by a chain of springs and masses, with
pressure pushing on the masses from inside the body. Pressure endows the agents
with structure, while springs and masses simulate softness and allow the agents
to assume a large gamut of shapes. Actuation takes place by changing the length
of springs or modulating global pressure. We optimize the controller of PSAs
for a locomotion task on hilly terrain and an escape task from a cage; the
latter is particularly suitable for soft-bodied agents, as it requires the
agent to contort itself to squeeze through a small aperture. Our results
suggest that PSAs are indeed effective at those tasks and that controlling
pressure is fundamental for shape-changing. Looking forward, we envision PSAs
to play a role in the modeling of soft-bodied agents, including soft robots and
biological cells. Videos of evolved agents are available at
https://pressuresoftagents.github.io.
|
[
{
"version": "v1",
"created": "Sun, 1 May 2022 13:36:27 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Pigozzi",
"Federico",
""
]
] |
new_dataset
| 0.999292 |
2205.00485
|
Liuhui Deng
|
Liuhui Deng, Roger Hsiao, Arnab Ghoshal
|
Bilingual End-to-End ASR with Byte-Level Subwords
|
5 pages, to be published in IEEE ICASSP 2022
| null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we investigate how the output representation of an end-to-end
neural network affects multilingual automatic speech recognition (ASR). We
study different representations including character-level, byte-level, byte
pair encoding (BPE), and byte-level byte pair encoding (BBPE) representations,
and analyze their strengths and weaknesses. We focus on developing a single
end-to-end model to support utterance-based bilingual ASR, where speakers do
not alternate between two languages in a single utterance but may change
languages across utterances. We conduct our experiments on English and Mandarin
dictation tasks, and we find that BBPE with penalty schemes can improve
utterance-based bilingual ASR performance by 2% to 5% relative even with
smaller number of outputs and fewer parameters. We conclude with analysis that
indicates directions for further improving multilingual ASR.
|
[
{
"version": "v1",
"created": "Sun, 1 May 2022 15:01:01 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Deng",
"Liuhui",
""
],
[
"Hsiao",
"Roger",
""
],
[
"Ghoshal",
"Arnab",
""
]
] |
new_dataset
| 0.983512 |
2205.00613
|
Tianyuan Zhang
|
Tianyuan Zhang, Xuanyao Chen, Yue Wang, Yilun Wang, Hang Zhao
|
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
|
Appear on CVPR 2022 Workshop on Autonomous Driving
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate and consistent 3D tracking from multiple cameras is a key component
in a vision-based autonomous driving system. It involves modeling 3D dynamic
objects in complex scenes across multiple cameras. This problem is inherently
challenging due to depth estimation, visual occlusions, appearance ambiguity,
etc. Moreover, objects are not consistently associated across time and cameras.
To address that, we propose an end-to-end \textbf{MU}lti-camera
\textbf{TR}acking framework called MUTR3D. In contrast to prior works, MUTR3D
does not explicitly rely on the spatial and appearance similarity of objects.
Instead, our method introduces \textit{3D track query} to model spatial and
appearance coherent track for each object that appears in multiple cameras and
multiple frames. We use camera transformations to link 3D trackers with their
observations in 2D images. Each tracker is further refined according to the
features that are obtained from camera images. MUTR3D uses a set-to-set loss to
measure the difference between the predicted tracking results and the ground
truths. Therefore, it does not require any post-processing such as non-maximum
suppression and/or bounding box association. MUTR3D outperforms
state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code is
available at: \url{https://github.com/a1600012888/MUTR3D}.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 01:45:41 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Zhang",
"Tianyuan",
""
],
[
"Chen",
"Xuanyao",
""
],
[
"Wang",
"Yue",
""
],
[
"Wang",
"Yilun",
""
],
[
"Zhao",
"Hang",
""
]
] |
new_dataset
| 0.995177 |
2205.00618
|
Aleksandar Zlateski
|
Bram Wasti, Jos\'e Pablo Cambronero, Benoit Steiner, Hugh Leather and
Aleksandar Zlateski
|
LoopStack: a Lightweight Tensor Algebra Compiler Stack
| null | null | null | null |
cs.LG cs.PF cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present LoopStack, a domain specific compiler stack for tensor operations,
composed of a frontend, LoopTool, and an efficient optimizing code generator,
LoopNest. This stack enables us to compile entire neural networks and generate
code targeting the AVX2, AVX512, NEON, and NEONfp16 instruction sets while
incorporating optimizations often missing from other machine learning compiler
backends. We evaluate our stack on a collection of full neural networks and
commonly used network blocks as well as individual operators, and show that
LoopStack generates machine code that matches and frequently exceeds the
performance of in state-of-the-art machine learning frameworks in both cases.
We also show that for a large collection of schedules LoopNest's compilation is
orders of magnitude faster than LLVM, while resulting in equal or improved run
time performance. Additionally, LoopStack has a very small memory footprint - a
binary size of 245KB, and under 30K lines of effective code makes it ideal for
use on mobile and embedded devices.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 01:57:58 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Wasti",
"Bram",
""
],
[
"Cambronero",
"José Pablo",
""
],
[
"Steiner",
"Benoit",
""
],
[
"Leather",
"Hugh",
""
],
[
"Zlateski",
"Aleksandar",
""
]
] |
new_dataset
| 0.99878 |
2205.00661
|
Runzhou Tao
|
Runzhou Tao, Yunong Shi, Jianan Yao, Xupeng Li, Ali Javadi-Abhari,
Andrew W. Cross, Frederic T. Chong, Ronghui Gu
|
Giallar: Push-Button Verification for the Qiskit Quantum Compiler
|
PLDI 2022; Improves arXiv:1908.08963
| null | null | null |
cs.PL quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents Giallar, a fully-automated verification toolkit for
quantum compilers. Giallar requires no manual specifications, invariants, or
proofs, and can automatically verify that a compiler pass preserves the
semantics of quantum circuits. To deal with unbounded loops in quantum
compilers, Giallar abstracts three loop templates, whose loop invariants can be
automatically inferred. To efficiently check the equivalence of arbitrary input
and output circuits that have complicated matrix semantics representation,
Giallar introduces a symbolic representation for quantum circuits and a set of
rewrite rules for showing the equivalence of symbolic quantum circuits. With
Giallar, we implemented and verified 44 (out of 56) compiler passes in 13
versions of the Qiskit compiler, the open-source quantum compiler standard,
during which three bugs were detected in and confirmed by Qiskit. Our
evaluation shows that most of Qiskit compiler passes can be automatically
verified in seconds and verification imposes only a modest overhead to
compilation performance.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 05:37:18 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Tao",
"Runzhou",
""
],
[
"Shi",
"Yunong",
""
],
[
"Yao",
"Jianan",
""
],
[
"Li",
"Xupeng",
""
],
[
"Javadi-Abhari",
"Ali",
""
],
[
"Cross",
"Andrew W.",
""
],
[
"Chong",
"Frederic T.",
""
],
[
"Gu",
"Ronghui",
""
]
] |
new_dataset
| 0.986824 |
2205.00777
|
TianSheuan Chang
|
Dun-Hao Yang, and Tian-Sheuan Chang
|
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient
Pixel Attention
|
5 pages, 5 figures, published in IEEE ISCAS 2022
| null | null | null |
cs.AR cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Increasingly, convolution neural network (CNN) based super resolution models
have been proposed for better reconstruction results, but their large model
size and complicated structure inhibit their real-time hardware implementation.
Current hardware designs are limited to a plain network and suffer from lower
quality and high memory bandwidth requirements. This paper proposes a super
resolution hardware accelerator with hardware efficient pixel attention that
just needs 25.9K parameters and simple structure but achieves 0.38dB better
reconstruction images than the widely used FSRCNN. The accelerator adopts full
model block wise convolution for full model layer fusion to reduce external
memory access to model input and output only. In addition, CNN and pixel
attention are well supported by PE arrays with distributed weights. The final
implementation can support full HD image reconstruction at 30 frames per second
with TSMC 40nm CMOS process.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 09:56:29 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Yang",
"Dun-Hao",
""
],
[
"Chang",
"Tian-Sheuan",
""
]
] |
new_dataset
| 0.999467 |
2205.00806
|
Tharindu Ranasinghe Dr
|
Alistair Plum, Tharindu Ranasinghe, Spencer Jones, Constantin Orasan,
Ruslan Mitkov
|
Biographical: A Semi-Supervised Relation Extraction Dataset
|
Accepted to ACM SIGIR 2022
| null | null | null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Extracting biographical information from online documents is a popular
research topic among the information extraction (IE) community. Various natural
language processing (NLP) techniques such as text classification, text
summarisation and relation extraction are commonly used to achieve this. Among
these techniques, RE is the most common since it can be directly used to build
biographical knowledge graphs. RE is usually framed as a supervised machine
learning (ML) problem, where ML models are trained on annotated datasets.
However, there are few annotated datasets for RE since the annotation process
can be costly and time-consuming. To address this, we developed Biographical,
the first semi-supervised dataset for RE. The dataset, which is aimed towards
digital humanities (DH) and historical research, is automatically compiled by
aligning sentences from Wikipedia articles with matching structured data from
sources including Pantheon and Wikidata. By exploiting the structure of
Wikipedia articles and robust named entity recognition (NER), we match
information with relatively high precision in order to compile annotated
relation pairs for ten different relations that are important in the DH domain.
Furthermore, we demonstrate the effectiveness of the dataset by training a
state-of-the-art neural model to classify relation pairs, and evaluate it on a
manually annotated gold standard set. Biographical is primarily aimed at
training neural models for RE within the domain of digital humanities and
history, but as we discuss at the end of this paper, it can be useful for other
purposes as well.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 10:48:23 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Plum",
"Alistair",
""
],
[
"Ranasinghe",
"Tharindu",
""
],
[
"Jones",
"Spencer",
""
],
[
"Orasan",
"Constantin",
""
],
[
"Mitkov",
"Ruslan",
""
]
] |
new_dataset
| 0.999822 |
2205.00868
|
Jeanine Treffers-Daller Professor
|
Jeanine Treffers-Daller and, Ozlem \c{C}etino\u{g}lu
|
TuGeBiC: A Turkish German Bilingual Code-Switching Corpus
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we describe the process of collection, transcription, and
annotation of recordings of spontaneous speech samples from Turkish-German
bilinguals, and the compilation of a corpus called TuGeBiC. Participants in the
study were adult Turkish-German bilinguals living in Germany or Turkey at the
time of recording in the first half of the 1990s. The data were manually
tokenised and normalised, and all proper names (names of participants and
places mentioned in the conversations) were replaced with pseudonyms.
Token-level automatic language identification was performed, which made it
possible to establish the proportions of words from each language. The corpus
is roughly balanced between both languages. We also present quantitative
information about the number of code-switches, and give examples of different
types of code-switching found in the data. The resulting corpus has been made
freely available to the research community.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 12:53:05 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"and",
"Jeanine Treffers-Daller",
""
],
[
"Çetinoğlu",
"Ozlem",
""
]
] |
new_dataset
| 0.996744 |
2205.00871
|
Alexandra Buchmann
|
Alexandra Buchmann, Bernadett Kiss, Alexander Badri-Sprowitz and
Daniel Renjewski
|
Power to the springs: Passive elements are sufficient to drive push-off
in human walking
|
12 pages, 4 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For the impulsive ankle push-off (APO) observed in human walking two
muscle-tendon-units (MTUs) spanning the ankle joint play an important role:
Gastrocnemius (GAS) and Soleus (SOL). GAS and SOL load the Achilles tendon to
store elastic energy during stance followed by a rapid energy release during
APO. We use a neuromuscular simulation (NMS) and a bipedal robot to investigate
the role of GAS and SOL on the APO. We optimize the simulation for a robust
gait and then sequentially replace the MTUs of (1) GAS, (2) SOL and (3) GAS and
SOL by linear springs. To validate the simulation, we implement NMS-3 on a
bipedal robot. Simulation and robot walk steady for all trials showing an
impulsive APO. Our results imply that the elastic MTU properties shape the
impulsive APO. For prosthesis or robot design that is, no complex ankle
actuation is needed to obtain an impulsive APO, if more mechanical intelligence
is incorporated in the design.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 15:05:38 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Buchmann",
"Alexandra",
""
],
[
"Kiss",
"Bernadett",
""
],
[
"Badri-Sprowitz",
"Alexander",
""
],
[
"Renjewski",
"Daniel",
""
]
] |
new_dataset
| 0.999349 |
2205.00889
|
Vera Traub
|
Jannis Blauth, Stephan Held, Dirk M\"uller, Niklas Schlomberg, Vera
Traub, Thorben Tr\"obst, Jens Vygen
|
Vehicle Routing with Time-Dependent Travel Times: Theory, Practice, and
Benchmarks
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop theoretical foundations and practical algorithms for vehicle
routing with time-dependent travel times. We also provide new benchmark
instances and experimental results. First, we study basic operations on
piecewise linear arrival time functions. In particular, we devise a faster
algorithm to compute the pointwise minimum of a set of piecewise linear
functions and a monotonicity-preserving variant of the Imai-Iri algorithm to
approximate an arrival time function with fewer breakpoints.
Next, we show how to evaluate insertion and deletion operations in tours
efficiently and update the underlying data structure faster than previously
known when a tour changes. Evaluating a tour also requires a scheduling step
which is non-trivial in the presence of time windows and time-dependent travel
times. We show how to perform this in linear time.
Based on these results, we develop a local search heuristic to solve
real-world vehicle routing problems with various constraints efficiently and
report experimental results on classical benchmarks. Since most of these do not
have time-dependent travel times, we generate and publish new benchmark
instances that are based on real-world data. This data also demonstrates the
importance of considering time-dependent travel times in instances with tight
time windows.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 13:01:55 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Blauth",
"Jannis",
""
],
[
"Held",
"Stephan",
""
],
[
"Müller",
"Dirk",
""
],
[
"Schlomberg",
"Niklas",
""
],
[
"Traub",
"Vera",
""
],
[
"Tröbst",
"Thorben",
""
],
[
"Vygen",
"Jens",
""
]
] |
new_dataset
| 0.994945 |
2205.00911
|
Emil H\"aglund
|
Emil H\"aglund and Johanna Bj\"orklund
|
AI-Driven Contextual Advertising: A Technology Report and Implication
Analysis
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Programmatic advertising consists in automated auctioning of digital ad
space. Every time a user requests a web page, placeholders on the page are
populated with ads from the highest-bidding advertisers. The bids are typically
based on information about the user, and to an increasing extent, on
information about the surrounding media context. The growing interest in
contextual advertising is in part a counterreaction to the current dependency
on personal data, which is problematic from legal and ethical standpoints. The
transition is further accelerated by developments in Artificial Intelligence
(AI), which allow for a deeper semantic understanding of context and, by
extension, more effective ad placement. In this article, we begin by
identifying context factors that have been shown in previous research to
positively influence how ads are received. We then continue to discuss
applications of AI in contextual advertising, where it adds value by, e.g.,
extracting high-level information about media context and optimising bidding
strategies. However, left unchecked, these new practices can lead to unfair ad
delivery and manipulative use of context. We summarize these and other concerns
for consumers, publishers and advertisers in an implication analysis.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 13:44:58 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Häglund",
"Emil",
""
],
[
"Björklund",
"Johanna",
""
]
] |
new_dataset
| 0.990936 |
2205.00916
|
Kai Wang
|
Xiaohong Li, Xiang Wang, Kai Wang, Shiguo Lian
|
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM
|
This paper has been published on CISP-BMEI 2021. See
https://ieeexplore.ieee.org/document/9624360
| null |
10.1109/CISP-BMEI53629.2021.9624360
| null |
cs.SD cs.AI cs.CV cs.GR eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Generating synchronized and natural lip movement with speech is one of the
most important tasks in creating realistic virtual characters. In this paper,
we present a combined deep neural network of one-dimensional convolutions and
LSTM to generate vertex displacement of a 3D template face model from
variable-length speech input. The motion of the lower part of the face, which
is represented by the vertex movement of 3D lip shapes, is consistent with the
input speech. In order to enhance the robustness of the network to different
sound signals, we adapt a trained speech recognition model to extract speech
feature, and a velocity loss term is adopted to reduce the jitter of generated
facial animation. We recorded a series of videos of a Chinese adult speaking
Mandarin and created a new speech-animation dataset to compensate the lack of
such public data. Qualitative and quantitative evaluations indicate that our
model is able to generate smooth and natural lip movements synchronized with
speech.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 13:57:50 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Li",
"Xiaohong",
""
],
[
"Wang",
"Xiang",
""
],
[
"Wang",
"Kai",
""
],
[
"Lian",
"Shiguo",
""
]
] |
new_dataset
| 0.965512 |
2205.00952
|
Sriram Baireddy
|
Sriram Baireddy and Da-Young Lee and Carlos Gongora-Canul and
Christian D. Cruz and Edward J. Delp
|
Leaf Tar Spot Detection Using RGB Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tar spot disease is a fungal disease that appears as a series of black
circular spots containing spores on corn leaves. Tar spot has proven to be an
impactful disease in terms of reducing crop yield. To quantify disease
progression, experts usually have to visually phenotype leaves from the plant.
This process is very time-consuming and is difficult to incorporate in any
high-throughput phenotyping system. Deep neural networks could provide quick,
automated tar spot detection with sufficient ground truth. However, manually
labeling tar spots in images to serve as ground truth is also tedious and
time-consuming. In this paper we first describe an approach that uses automated
image analysis tools to generate ground truth images that are then used for
training a Mask R-CNN. We show that a Mask R-CNN can be used effectively to
detect tar spots in close-up images of leaf surfaces. We additionally show that
the Mask R-CNN can also be used for in-field images of whole leaves to capture
the number of tar spots and area of the leaf infected by the disease.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 14:56:06 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Baireddy",
"Sriram",
""
],
[
"Lee",
"Da-Young",
""
],
[
"Gongora-Canul",
"Carlos",
""
],
[
"Cruz",
"Christian D.",
""
],
[
"Delp",
"Edward J.",
""
]
] |
new_dataset
| 0.997603 |
2205.00973
|
Stefano Savazzi
|
Marco Santoboni, Riccardo Bersan, Stefano Savazzi, Alberto Zecchin,
Vittorio Rampa Daniele Piazza
|
Wireless LAN sensing with smart antennas
|
Accepted for publication in EuCAP 2022, https://www.eucap2022.org/
| null | null | null |
cs.NI cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper targets the problem of human motion detection using Wireless Local
Area Network devices (WiFi) equipped with pattern reconfigurable antennas.
Motion sensing is obtained by monitoring the body-induced alterations of the
ambient WiFi signals originated from smart antennas supporting the
beam-steering technology, thus allowing to channelize the antenna radiation
pattern to pre-defined spots of interest. We first discuss signal and Channel
State Information (CSI) processing and sanitization. Next, we describe the
motion detection algorithm based on Angle-of-Arrival (AoA) monitoring. Proposed
algorithms are validated experimentally inside a large size smart home
environment.
|
[
{
"version": "v1",
"created": "Wed, 27 Apr 2022 17:29:24 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Santoboni",
"Marco",
""
],
[
"Bersan",
"Riccardo",
""
],
[
"Savazzi",
"Stefano",
""
],
[
"Zecchin",
"Alberto",
""
],
[
"Piazza",
"Vittorio Rampa Daniele",
""
]
] |
new_dataset
| 0.996927 |
2205.01044
|
Adrianus Vinck
|
A.J. Han Vinck
|
Coding Concepts and Reed-Solomon Codes
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The material in this book is presented to graduate students in Information
and Communication theory. The idea is that we give an introduction to
particular applications of information theory and coding in digital
communications. The goal is to bring understanding of the underlying concepts,
both in theory as well as in practice. We mainly concentrate on our own
research results. After showing obtainable performance, we give a specific
implementation using Reed-Solomon (RS) codes. The reason for using RS codes is
that they can be seen as optimal codes with maximum obtainable minimum
distance. Furthermore, the structure of RS codes enables specific applications
that fit perfectly into the developed concepts. We do not intend to develop the
theory of error correcting codes.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 17:26:43 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Vinck",
"A. J. Han",
""
]
] |
new_dataset
| 0.992334 |
2205.01048
|
Xiaobao Wei
|
Shang Liu, Xiaobao Wei, Lulu Wang, Jing Zhang, Boyu Li and Haosong Yue
|
Center-of-Mass-based Robust Grasp Pose Adaptation Using RGBD Camera and
Force/Torque Sensing
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Object dropping may occur when the robotic arm grasps objects with uneven
mass distribution due to additional moments generated by objects' gravity. To
solve this problem, we present a novel work that does not require extra wrist
and tactile sensors and large amounts of experiments for learning. First, we
obtain the center-of-mass position of the rod object using the widely fixed
joint torque sensors on the robot arm and RGBD camera. Further, we give the
strategy of grasping to improve grasp stability. Simulation experiments are
performed in "Mujoco". Results demonstrate that our work is effective in
enhancing grasping robustness.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 17:32:17 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Liu",
"Shang",
""
],
[
"Wei",
"Xiaobao",
""
],
[
"Wang",
"Lulu",
""
],
[
"Zhang",
"Jing",
""
],
[
"Li",
"Boyu",
""
],
[
"Yue",
"Haosong",
""
]
] |
new_dataset
| 0.974453 |
2205.01086
|
Felix Wu
|
Felix Wu, Kwangyoun Kim, Shinji Watanabe, Kyu Han, Ryan McDonald,
Kilian Q. Weinberger, Yoav Artzi
|
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo
Languages
|
Code available at https://github.com/asappresearch/wav2seq
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce Wav2Seq, the first self-supervised approach to pre-train both
parts of encoder-decoder models for speech data. We induce a pseudo language as
a compact discrete representation, and formulate a self-supervised pseudo
speech recognition task -- transcribing audio inputs into pseudo subword
sequences. This process stands on its own, or can be applied as low-cost
second-stage pre-training. We experiment with automatic speech recognition
(ASR), spoken named entity recognition, and speech-to-text translation. We set
new state-of-the-art results for end-to-end spoken named entity recognition,
and show consistent improvements on 20 language pairs for speech-to-text
translation, even when competing methods use additional text data for training.
Finally, on ASR, our approach enables encoder-decoder methods to benefit from
pre-training for all parts of the network, and shows comparable performance to
highly optimized recent methods.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 17:59:02 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Wu",
"Felix",
""
],
[
"Kim",
"Kwangyoun",
""
],
[
"Watanabe",
"Shinji",
""
],
[
"Han",
"Kyu",
""
],
[
"McDonald",
"Ryan",
""
],
[
"Weinberger",
"Kilian Q.",
""
],
[
"Artzi",
"Yoav",
""
]
] |
new_dataset
| 0.994491 |
2205.01089
|
Chuang Gan
|
Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba,
Joshua B. Tenenbaum, Chuang Gan
|
ComPhy: Compositional Physical Reasoning of Objects and Events from
Videos
|
ICLR 2022. Project page: https://comphyreasoning.github.io/
| null | null | null |
cs.CV cs.AI cs.LG cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Objects' motions in nature are governed by complex interactions and their
properties. While some properties, such as shape and material, can be
identified via the object's visual appearances, others like mass and electric
charge are not directly visible. The compositionality between the visible and
hidden properties poses unique challenges for AI models to reason from the
physical world, whereas humans can effortlessly infer them with limited
observations. Existing studies on video reasoning mainly focus on visually
observable elements such as object appearance, movement, and contact
interaction. In this paper, we take an initial step to highlight the importance
of inferring the hidden physical properties not directly observable from visual
appearances, by introducing the Compositional Physical Reasoning (ComPhy)
dataset. For a given set of objects, ComPhy includes few videos of them moving
and interacting under different initial conditions. The model is evaluated
based on its capability to unravel the compositional hidden properties, such as
mass and charge, and use this knowledge to answer a set of questions posted on
one of the videos. Evaluation results of several state-of-the-art video
reasoning models on ComPhy show unsatisfactory performance as they fail to
capture these hidden properties. We further propose an oracle neural-symbolic
framework named Compositional Physics Learner (CPL), combining visual
perception, physical property learning, dynamic prediction, and symbolic
execution into a unified framework. CPL can effectively identify objects'
physical properties from their interactions and predict their dynamics to
answer questions.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 17:59:13 GMT"
}
] | 2022-05-03T00:00:00 |
[
[
"Chen",
"Zhenfang",
""
],
[
"Yi",
"Kexin",
""
],
[
"Li",
"Yunzhu",
""
],
[
"Ding",
"Mingyu",
""
],
[
"Torralba",
"Antonio",
""
],
[
"Tenenbaum",
"Joshua B.",
""
],
[
"Gan",
"Chuang",
""
]
] |
new_dataset
| 0.999662 |
1508.07593
|
Remi Ronfard
|
Remi Ronfard and Vineet Gandhi and Laurent Boiron and Vaishnavi Ameya
Murukutla
|
The Prose Storyboard Language: A Tool for Annotating and Directing
Movies
|
20 pages, extended version includes new figures and references
| null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The prose storyboard language is a formal language for describing movies shot
by shot, where each shot is described with a unique sentence. The language uses
a simple syntax and limited vocabulary borrowed from working practices in
traditional movie-making, and is intended to be readable both by machines and
humans. The language is designed to serve as a high-level user interface for
intelligent cinematography and editing systems.
|
[
{
"version": "v1",
"created": "Sun, 30 Aug 2015 16:12:59 GMT"
},
{
"version": "v2",
"created": "Fri, 6 Dec 2019 07:56:06 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Dec 2019 22:48:24 GMT"
},
{
"version": "v4",
"created": "Fri, 30 Oct 2020 11:55:13 GMT"
},
{
"version": "v5",
"created": "Fri, 29 Apr 2022 07:02:49 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Ronfard",
"Remi",
""
],
[
"Gandhi",
"Vineet",
""
],
[
"Boiron",
"Laurent",
""
],
[
"Murukutla",
"Vaishnavi Ameya",
""
]
] |
new_dataset
| 0.985775 |
2101.06838
|
Jaime Arias
|
Jaime Arias, {\L}ukasz Ma\'sko, Wojciech Penczek, Laure Petrucci and
Teofil Sidoruk
|
Minimal Schedule with Minimal Number of Agents in Attack-Defence Trees
| null | null | null | null |
cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Expressing attack-defence trees in a multi-agent setting allows for studying
a new aspect of security scenarios, namely how the number of agents and their
task assignment impact the performance, e.g. attack time, of strategies
executed by opposing coalitions. Optimal scheduling of agents' actions, a
non-trivial problem, is thus vital. We discuss associated caveats and propose
an algorithm that synthesises such an assignment, targeting minimal attack time
and using minimal number of agents for a given attack-defence tree.
|
[
{
"version": "v1",
"created": "Mon, 18 Jan 2021 02:08:53 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Feb 2021 18:38:37 GMT"
},
{
"version": "v3",
"created": "Sun, 14 Feb 2021 09:49:51 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Apr 2021 07:35:59 GMT"
},
{
"version": "v5",
"created": "Fri, 29 Apr 2022 13:19:09 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Arias",
"Jaime",
""
],
[
"Maśko",
"Łukasz",
""
],
[
"Penczek",
"Wojciech",
""
],
[
"Petrucci",
"Laure",
""
],
[
"Sidoruk",
"Teofil",
""
]
] |
new_dataset
| 0.996518 |
2101.08750
|
Emiliano De Cristofaro
|
Antonis Papasavva, Max Aliapoulios, Cameron Ballard, Emiliano De
Cristofaro, Gianluca Stringhini, Savvas Zannettou, and Jeremy Blackburn
|
The Gospel According to Q: Understanding the QAnon Conspiracy from the
Perspective of Canonical Information
| null |
Published in the Proceedings of the 16th International AAAI
Conference on Web and Social Media (ICWSM 2022). Please cite accordingly
| null | null |
cs.CY cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty
politicians and media personalities are engaged in a war to destroy society. By
interpreting cryptic "drops" of information from an anonymous insider calling
themself Q, adherents of the conspiracy theory believe that Donald Trump is
leading them in an active fight against this cabal. QAnon has been covered
extensively by the media, as its adherents have been involved in multiple
violent acts, including the January 6th, 2021 seditious storming of the US
Capitol building. Nevertheless, we still have relatively little understanding
of how the theory evolved and spread on the Web, and the role played in that by
multiple platforms.
To address this gap, we study QAnon from the perspective of "Q" themself. We
build a dataset of 4,949 canonical Q drops collected from six "aggregation
sites," which curate and archive them from their original posting to anonymous
and ephemeral image boards. We expose that these sites have a relatively low
(overall) agreement, and thus at least some Q drops should probably be
considered apocryphal. We then analyze the Q drops' contents to identify topics
of discussion and find statistically significant indications that drops were
not authored by a single individual. Finally, we look at how posts on Reddit
are used to disseminate Q drops to wider audiences. We find that dissemination
was (initially) limited to a few sub-communities and that, while heavy-handed
moderation decisions have reduced the overall issue, the "gospel" of Q persists
on the Web.
|
[
{
"version": "v1",
"created": "Thu, 21 Jan 2021 18:03:24 GMT"
},
{
"version": "v2",
"created": "Thu, 20 May 2021 17:57:49 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Apr 2022 10:32:00 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Papasavva",
"Antonis",
""
],
[
"Aliapoulios",
"Max",
""
],
[
"Ballard",
"Cameron",
""
],
[
"De Cristofaro",
"Emiliano",
""
],
[
"Stringhini",
"Gianluca",
""
],
[
"Zannettou",
"Savvas",
""
],
[
"Blackburn",
"Jeremy",
""
]
] |
new_dataset
| 0.999474 |
2106.07560
|
Marios Papachristou
|
Marios Papachristou, Jon Kleinberg
|
Allocating Stimulus Checks in Times of Crisis
|
Accepted at WWW 2022 (Proceedings of the Web Conference)
| null |
10.1145/3485447.3512047
| null |
cs.SI cs.GT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We study the problem of allocating bailouts (stimulus, subsidy allocations)
to people participating in a financial network subject to income shocks. We
build on the financial clearing framework of Eisenberg and Noe that allows the
incorporation of a bailout policy that is based on discrete bailouts motivated
by the types of stimulus checks people receive around the world as part of
COVID-19 economical relief plans. We show that optimally allocating such
bailouts on a financial network in order to maximize a variety of social
welfare objectives of this form is a computationally intractable problem. We
develop approximation algorithms to optimize these objectives and establish
guarantees for their approximation rations. Then, we incorporate multiple
fairness constraints in the optimization problems and establish relative bounds
on the solutions with versus without these constraints. Finally, we apply our
methodology to a variety of data, both in the context of a system of large
financial institutions with real-world data, as well as in a realistic societal
context with financial interactions between people and businesses for which we
use semi-artificial data derived from mobility patterns. Our results suggest
that the algorithms we develop and study have reasonable results in practice
and outperform other network-based heuristics. We argue that the presented
problem through the societal-level lens could assist policymakers in making
informed decisions on issuing subsidies.
|
[
{
"version": "v1",
"created": "Mon, 14 Jun 2021 16:17:50 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Aug 2021 15:03:31 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Sep 2021 19:54:09 GMT"
},
{
"version": "v4",
"created": "Fri, 29 Apr 2022 13:50:06 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Papachristou",
"Marios",
""
],
[
"Kleinberg",
"Jon",
""
]
] |
new_dataset
| 0.994723 |
2109.07703
|
Guanxiong Chen
|
Guanxiong Chen, Haoyu Yang and Ian M. Mitchell
|
ROS-X-Habitat: Bridging the ROS Ecosystem with Embodied AI
|
Camera-ready version submitted to Canadian Conference on Computer and
Robot Vision (CRV) 2022
| null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce ROS-X-Habitat, a software interface that bridges the AI Habitat
platform for embodied learning-based agents with other robotics resources via
ROS. This interface not only offers standardized communication protocols
between embodied agents and simulators, but also enables physically and
photorealistic simulation that benefits the training and/or testing of
vision-based embodied agents. With this interface, roboticists can evaluate
their own Habitat RL agents in another ROS-based simulator or use Habitat Sim
v2 as the test bed for their own robotic algorithms. Through in silico
experiments, we demonstrate that ROS-X-Habitat has minimal impact on the
navigation performance and simulation speed of a Habitat RGBD agent; that a
standard set of ROS mapping, planning and navigation tools can run in Habitat
Sim v2; and that a Habitat agent can run in the standard ROS simulator Gazebo.
|
[
{
"version": "v1",
"created": "Thu, 16 Sep 2021 03:53:52 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Sep 2021 04:27:25 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Apr 2022 06:11:42 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Chen",
"Guanxiong",
""
],
[
"Yang",
"Haoyu",
""
],
[
"Mitchell",
"Ian M.",
""
]
] |
new_dataset
| 0.973579 |
2111.04576
|
Malintha Fernando
|
Malintha Fernando, Ransalu Senanayake, Martin Swany
|
CoCo Games: Graphical Game-Theoretic Swarm Control for
Communication-Aware Coverage
|
8 pages, 7 figures
|
2022 - IEEE Robotics and Automation Letters
|
10.1109/LRA.2022.3160968
| null |
cs.RO cs.AI cs.SY eess.SY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We propose a novel framework for real-time communication-aware coverage
control in networked robot swarms. Our framework unifies the robot dynamics
with network-level message-routing to reach consensus on swarm formations in
the presence of communication uncertainties by leveraging local information.
Specifically, we formulate the communication-aware coverage as a cooperative
graphical game, and use variational inference to reach mixed strategy Nash
equilibria of the stage games. We experimentally validate the proposed approach
in a mobile ad-hoc wireless network scenario using teams of aerial vehicles and
terrestrial user equipment (UE) operating over a large geographic region of
interest. We show that our approach can provide wireless coverage to stationary
and mobile UEs under realistic network conditions.
|
[
{
"version": "v1",
"created": "Mon, 8 Nov 2021 15:37:15 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Apr 2022 15:08:38 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Apr 2022 15:17:25 GMT"
},
{
"version": "v4",
"created": "Mon, 25 Apr 2022 16:10:58 GMT"
},
{
"version": "v5",
"created": "Thu, 28 Apr 2022 20:29:40 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Fernando",
"Malintha",
""
],
[
"Senanayake",
"Ransalu",
""
],
[
"Swany",
"Martin",
""
]
] |
new_dataset
| 0.997076 |
2111.11535
|
Kanav Vats
|
Kanav Vats, William McNally, Pascale Walters, David A. Clausi, John S.
Zelek
|
Ice hockey player identification via transformers and weakly supervised
learning
|
CVSports 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Identifying players in video is a foundational step in computer vision-based
sports analytics. Obtaining player identities is essential for analyzing the
game and is used in downstream tasks such as game event recognition.
Transformers are the existing standard in Natural Language Processing (NLP) and
are swiftly gaining traction in computer vision. Motivated by the increasing
success of transformers in computer vision, in this paper, we introduce a
transformer network for recognizing players through their jersey numbers in
broadcast National Hockey League (NHL) videos. The transformer takes temporal
sequences of player frames (also called player tracklets) as input and outputs
the probabilities of jersey numbers present in the frames. The proposed network
performs better than the previous benchmark on the dataset used. We implement a
weakly-supervised training approach by generating approximate frame-level
labels for jersey number presence and use the frame-level labels for faster
training. We also utilize player shifts available in the NHL play-by-play data
by reading the game time using optical character recognition (OCR) to get the
players on the ice rink at a certain game time. Using player shifts improved
the player identification accuracy by 6%.
|
[
{
"version": "v1",
"created": "Mon, 22 Nov 2021 21:10:26 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Apr 2022 18:35:01 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Vats",
"Kanav",
""
],
[
"McNally",
"William",
""
],
[
"Walters",
"Pascale",
""
],
[
"Clausi",
"David A.",
""
],
[
"Zelek",
"John S.",
""
]
] |
new_dataset
| 0.998557 |
2201.05510
|
Youde Liu
|
Youde Liu, Jian Guan, Qiaoxi Zhu and Wenwu Wang
|
Anomalous Sound Detection using Spectral-Temporal Information Fusion
|
To appear at ICASSP 2022
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised anomalous sound detection aims to detect unknown abnormal sounds
of machines from normal sounds. However, the state-of-the-art approaches are
not always stable and perform dramatically differently even for machines of the
same type, making it impractical for general applications. This paper proposes
a spectral-temporal fusion based self-supervised method to model the feature of
the normal sound, which improves the stability and performance consistency in
detection of anomalous sounds from individual machines, even of the same type.
Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed
method achieved 81.39\%, 83.48\%, 98.22\% and 98.83\% in terms of the minimum
AUC (worst-case detection performance amongst individuals) in four types of
real machines (fan, pump, slider and valve), respectively, giving 31.79\%,
17.78\%, 10.42\% and 21.13\% improvement compared to the state-of-the-art
method, i.e., Glow\_Aff. Moreover, the proposed method has improved AUC
(average performance of individuals) for all the types of machines in the
dataset. The source codes are available at
https://github.com/liuyoude/STgram_MFN
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 15:29:47 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Apr 2022 08:40:47 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Apr 2022 02:30:24 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Liu",
"Youde",
""
],
[
"Guan",
"Jian",
""
],
[
"Zhu",
"Qiaoxi",
""
],
[
"Wang",
"Wenwu",
""
]
] |
new_dataset
| 0.976236 |
2202.01284
|
Wenzel Jakob
|
Wenzel Jakob, S\'ebastien Speierer, Nicolas Roussel, Delio Vicini
|
Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering
|
To appear at SIGGRAPH 2022
|
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2022)
|
10.1145/3528223.3530099
| null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dr.Jit is a new just-in-time compiler for physically based rendering and its
derivative. Dr.Jit expedites research on these topics in two ways: first, it
traces high-level simulation code (e.g., written in Python) and aggressively
simplifies and specializes the resulting program representation, producing
data-parallel kernels with state-of-the-art performance on CPUs and GPUs.
Second, it simplifies the development of differentiable rendering algorithms.
Efficient methods in this area turn the derivative of a simulation into a
simulation of the derivative. Dr.Jit provides fine-grained control over the
process of automatic differentiation to help with this transformation.
Specialization is particularly helpful in the context of differentiation,
since large parts of the simulation ultimately do not influence the computed
gradients. Dr.Jit tracks data dependencies globally to find and remove
redundant computation.
|
[
{
"version": "v1",
"created": "Wed, 2 Feb 2022 21:13:42 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Apr 2022 18:39:27 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Jakob",
"Wenzel",
""
],
[
"Speierer",
"Sébastien",
""
],
[
"Roussel",
"Nicolas",
""
],
[
"Vicini",
"Delio",
""
]
] |
new_dataset
| 0.994198 |
2202.02013
|
Vijini Pilana Liyanage
|
Vijini Liyanage, Davide Buscaldi, Adeline Nazarenko
|
A Benchmark Corpus for the Detection of Automatically Generated Text in
Academic Publications
|
9 pages including references, submitted to LREC 2022. arXiv admin
note: text overlap with arXiv:2110.10577 by other authors
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Automatic text generation based on neural language models has achieved
performance levels that make the generated text almost indistinguishable from
those written by humans. Despite the value that text generation can have in
various applications, it can also be employed for malicious tasks. The
diffusion of such practices represent a threat to the quality of academic
publishing. To address these problems, we propose in this paper two datasets
comprised of artificially generated research content: a completely synthetic
dataset and a partial text substitution dataset. In the first case, the content
is completely generated by the GPT-2 model after a short prompt extracted from
original papers. The partial or hybrid dataset is created by replacing several
sentences of abstracts with sentences that are generated by the Arxiv-NLP
model. We evaluate the quality of the datasets comparing the generated texts to
aligned original texts using fluency metrics such as BLEU and ROUGE. The more
natural the artificial texts seem, the more difficult they are to detect and
the better is the benchmark. We also evaluate the difficulty of the task of
distinguishing original from generated text by using state-of-the-art
classification models.
|
[
{
"version": "v1",
"created": "Fri, 4 Feb 2022 08:16:56 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 12:04:33 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Liyanage",
"Vijini",
""
],
[
"Buscaldi",
"Davide",
""
],
[
"Nazarenko",
"Adeline",
""
]
] |
new_dataset
| 0.996373 |
2202.05735
|
Kevin Kotzen
|
Kevin Kotzen, Peter H. Charlton, Sharon Salabi, Lea Amar, Amir
Landesberg and Joachim A. Behar
|
SleepPPG-Net: a deep learning algorithm for robust sleep staging from
continuous photoplethysmography
|
11 pages, 10 figures
| null | null | null |
cs.LG eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Introduction: Sleep staging is an essential component in the diagnosis of
sleep disorders and management of sleep health. It is traditionally measured in
a clinical setting and requires a labor-intensive labeling process. We
hypothesize that it is possible to perform robust 4-class sleep staging using
the raw photoplethysmography (PPG) time series and modern advances in deep
learning (DL). Methods: We used two publicly available sleep databases that
included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We
developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG
time series. SleepPPG-Net was trained end-to-end and consists of a residual
convolutional network for automatic feature extraction and a temporal
convolutional network to capture long-range contextual information. We
benchmarked the performance of SleepPPG-Net against models based on the
best-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked on
a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ($\kappa$)
score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good
generalization performance to an external database, obtaining a $\kappa$ score
of 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net provides
new SOTA performance. In addition, performance is high enough to open the path
to the development of wearables that meet the requirements for usage in
clinical applications such as the diagnosis and monitoring of obstructive sleep
apnea.
|
[
{
"version": "v1",
"created": "Fri, 11 Feb 2022 16:17:42 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Feb 2022 19:13:21 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Mar 2022 16:55:41 GMT"
},
{
"version": "v4",
"created": "Fri, 29 Apr 2022 15:00:18 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Kotzen",
"Kevin",
""
],
[
"Charlton",
"Peter H.",
""
],
[
"Salabi",
"Sharon",
""
],
[
"Amar",
"Lea",
""
],
[
"Landesberg",
"Amir",
""
],
[
"Behar",
"Joachim A.",
""
]
] |
new_dataset
| 0.997813 |
2203.06229
|
Eric Koskinen
|
Adam Chen, Parisa Fathololumi, Eric Koskinen, Jared Pincus
|
Veracity: Declarative Multicore Programming with Commutativity
| null | null | null | null |
cs.PL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
There is an ongoing effort to provide programming abstractions that ease the
burden of exploiting multicore hardware. Many programming abstractions (e.g.,
concurrent objects, transactional memory, etc.) simplify matters, but still
involve intricate engineering. We argue that some difficulty of multicore
programming can be meliorated through a declarative programming style in which
programmers directly express the independence of fragments of sequential
programs.
In our proposed paradigm, programmers write programs in a familiar,
sequential manner, with the added ability to explicitly express the conditions
under which code fragments sequentially commute. Putting such commutativity
conditions into source code offers a new entry point for a compiler to exploit
the known connection between commutativity and parallelism. We give a semantics
for the programmer's sequential perspective and, under a correctness condition,
find that a compiler-transformed parallel execution is equivalent to the
sequential semantics. Serializability/linearizability are not the right fit for
this condition, so we introduce scoped serializability and show how it can be
enforced with lock synthesis techniques.
We next describe a technique for automatically verifying and synthesizing
commute conditions via a new reduction from our commute blocks to logical
specifications, upon which symbolic commutativity reasoning can be performed.
We implemented our work in a new language called Veracity, implemented in
Multicore OCaml. We show that commutativity conditions can be automatically
generated across a variety of new benchmark programs, confirm the expectation
that concurrency speedups can be seen as the computation increases, and apply
our work to a small in-memory filesystem and an adaptation of a crowdfund
blockchain smart contract.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 20:13:32 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 15:04:14 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Chen",
"Adam",
""
],
[
"Fathololumi",
"Parisa",
""
],
[
"Koskinen",
"Eric",
""
],
[
"Pincus",
"Jared",
""
]
] |
new_dataset
| 0.99551 |
2204.08746
|
Maneet Singh
|
Maneet Singh, S.R.S. Iyengar, Akrati Saxena and Rishemjit Kaur
|
A Bi-level assessment of Twitter in predicting the results of an
election: Delhi Assembly Elections 2020
|
15 pages, 11 figures and 2 tables
| null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Elections are the backbone of any democratic country, where voters elect the
candidates as their representatives. The emergence of social networking sites
has provided a platform for political parties and their candidates to connect
with voters in order to spread their political ideas. Our study aims to use
Twitter in assessing the outcome of Delhi Assembly elections held in 2020,
using a bi-level approach, i.e., concerning political parties and their
candidates. We analyze the correlation of election results with the activities
of different candidates and parties on Twitter, and the response of voters on
them, especially the mentions and sentiment of voters towards a party. The
Twitter profiles of the candidates are compared both at the party level as well
as the candidate level to evaluate their association with the outcome of the
election. We observe that the number of followers and the replies to the tweets
of candidates are good indicators for predicting actual election outcome.
However, we observe that the number of tweets mentioning a party and the
sentiment of voters towards the party shown in tweets are not aligned with the
election result. We also use machine learning models on various features such
as linguistic, word embeddings and moral dimensions for predicting the election
result (win or lose). The random forest model using tweet features provides
promising results for predicting if the tweet belongs to a winning or losing
candidate.
|
[
{
"version": "v1",
"created": "Tue, 19 Apr 2022 08:40:18 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 06:10:55 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Singh",
"Maneet",
""
],
[
"Iyengar",
"S. R. S.",
""
],
[
"Saxena",
"Akrati",
""
],
[
"Kaur",
"Rishemjit",
""
]
] |
new_dataset
| 0.980492 |
2204.10687
|
Alfio Di Mauro
|
Alfio Di Mauro, Arpan Suravi Prasad, Zhikai Huang, Matteo Spallanzani,
Francesco Conti, Luca Benini
|
SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based
Convolutions
|
Accepted at DATE22
| null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Event-based sensors are drawing increasing attention due to their high
temporal resolution, low power consumption, and low bandwidth. To efficiently
extract semantically meaningful information from sparse data streams produced
by such sensors, we present a 4.5TOP/s/W digital accelerator capable of
performing 4-bits-quantized event-based convolutional neural networks (eCNN).
Compared to standard convolutional engines, our accelerator performs a number
of operations proportional to the number of events contained into the input
data stream, ultimately achieving a high energy-to-information processing
proportionality. On the IBM-DVS-Gesture dataset, we report 80uJ/inf to
261uJ/inf, respectively, when the input activity is 1.2% and 4.9%. Our
accelerator consumes 0.221pJ/SOP, to the best of our knowledge it is the lowest
energy/OP reported on a digital neuromorphic engine.
|
[
{
"version": "v1",
"created": "Fri, 22 Apr 2022 13:05:02 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 16:54:45 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Di Mauro",
"Alfio",
""
],
[
"Prasad",
"Arpan Suravi",
""
],
[
"Huang",
"Zhikai",
""
],
[
"Spallanzani",
"Matteo",
""
],
[
"Conti",
"Francesco",
""
],
[
"Benini",
"Luca",
""
]
] |
new_dataset
| 0.971212 |
2204.11333
|
Antonio Casares
|
Antonio Casares, Thomas Colcombet, Karoliina Lehtinen
|
On the size of good-for-games Rabin automata and its link with the
memory in Muller games
| null | null | null | null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we look at good-for-games Rabin automata that recognise a
Muller language (a language that is entirely characterised by the set of
letters that appear infinitely often in each word). We establish that minimal
such automata are exactly of the same size as the minimal memory required for
winning Muller games that have this language as their winning condition. We
show how to effectively construct such minimal automata. Finally, we establish
that these automata can be exponentially more succinct than equivalent
deterministic ones, thus proving as a consequence that chromatic memory for
winning a Muller game can be exponentially larger than unconstrained memory.
|
[
{
"version": "v1",
"created": "Sun, 24 Apr 2022 18:40:45 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Apr 2022 15:07:13 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Casares",
"Antonio",
""
],
[
"Colcombet",
"Thomas",
""
],
[
"Lehtinen",
"Karoliina",
""
]
] |
new_dataset
| 0.999438 |
2204.11736
|
Yang An
|
Yang An, Bo Jin, Xiaopeng Wei
|
KnowAugNet: Multi-Source Medical Knowledge Augmented Medication
Prediction Network with Multi-Level Graph Contrastive Learning
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Predicting medications is a crucial task in many intelligent healthcare
systems. It can assist doctors in making informed medication decisions for
patients according to electronic medical records (EMRs). However, medication
prediction is a challenging data mining task due to the complex relations
between medical codes. Most existing studies focus on utilizing inherent
relations between homogeneous codes of medical ontology graph to enhance their
representations using supervised methods, and few studies pay attention to the
valuable relations between heterogeneous or homogeneous medical codes from
history EMRs, which further limits the prediction performance and application
scenarios. Therefore, to address these limitations, this paper proposes
KnowAugNet, a multi-sourced medical knowledge augmented medication prediction
network which can fully capture the diverse relations between medical codes via
multi-level graph contrastive learning framework. Specifically, KnowAugNet
first leverages the graph contrastive learning using graph attention network as
the encoder to capture the implicit relations between homogeneous medical codes
from the medical ontology graph and obtains the knowledge augmented medical
codes embedding vectors. Then, it utilizes the graph contrastive learning using
a weighted graph convolutional network as the encoder to capture the
correlative relations between homogeneous or heterogeneous medical codes from
the constructed medical prior relation graph and obtains the relation augmented
medical codes embedding vectors. Finally, the augmented medical codes embedding
vectors and the supervised medical codes embedding vectors are retrieved and
input to the sequential learning network to capture the temporal relations of
medical codes and predict medications for patients.
|
[
{
"version": "v1",
"created": "Mon, 25 Apr 2022 15:47:41 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Apr 2022 18:03:43 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"An",
"Yang",
""
],
[
"Jin",
"Bo",
""
],
[
"Wei",
"Xiaopeng",
""
]
] |
new_dataset
| 0.997176 |
2204.12061
|
Diptesh Kanojia
|
Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia,
Constantin Or\u{a}san
|
PLOD: An Abbreviation Detection Dataset for Scientific Documents
|
Accepted at LREC 2022, 8 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The detection and extraction of abbreviations from unstructured texts can
help to improve the performance of Natural Language Processing tasks, such as
machine translation and information retrieval. However, in terms of publicly
available datasets, there is not enough data for training
deep-neural-networks-based models to the point of generalising well over data.
This paper presents PLOD, a large-scale dataset for abbreviation detection and
extraction that contains 160k+ segments automatically annotated with
abbreviations and their long forms. We performed manual validation over a set
of instances and a complete automatic validation for this dataset. We then used
it to generate several baseline models for detecting abbreviations and long
forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89
for detecting their corresponding long forms. We release this dataset along
with our code and all the models publicly in
https://github.com/surrey-nlp/PLOD-AbbreviationDetection
|
[
{
"version": "v1",
"created": "Tue, 26 Apr 2022 03:52:21 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Apr 2022 19:08:19 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Zilio",
"Leonardo",
""
],
[
"Saadany",
"Hadeel",
""
],
[
"Sharma",
"Prashant",
""
],
[
"Kanojia",
"Diptesh",
""
],
[
"Orăsan",
"Constantin",
""
]
] |
new_dataset
| 0.999846 |
2204.13743
|
Diptesh Kanojia
|
Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri,
Diptesh Kanojia, Pushpak Bhattacharyya
|
HiNER: A Large Hindi Named Entity Recognition Dataset
|
Accepted at LREC 2022, 8 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Named Entity Recognition (NER) is a foundational NLP task that aims to
provide class labels like Person, Location, Organisation, Time, and Number to
words in free text. Named Entities can also be multi-word expressions where the
additional I-O-B annotation information helps label them during the NER
annotation process. While English and European languages have considerable
annotated data for the NER task, Indian languages lack on that front -- both in
terms of quantity and following annotation standards. This paper releases a
significantly sized standard-abiding Hindi NER dataset containing 109,146
sentences and 2,220,856 tokens, annotated with 11 tags. We discuss the dataset
statistics in all their essential detail and provide an in-depth analysis of
the NER tag-set used with our data. The statistics of tag-set in our dataset
show a healthy per-tag distribution, especially for prominent classes like
Person, Location and Organisation. Since the proof of resource-effectiveness is
in building models with the resource and testing the model on benchmark data
and against the leader-board entries in shared tasks, we do the same with the
aforesaid data. We use different language models to perform the sequence
labelling task for NER and show the efficacy of our data by performing a
comparative evaluation with models trained on another dataset available for the
Hindi NER task. Our dataset helps achieve a weighted F1 score of 88.78 with all
the tags and 92.22 when we collapse the tag-set, as discussed in the paper. To
the best of our knowledge, no available dataset meets the standards of volume
(amount) and variability (diversity), as far as Hindi NER is concerned. We fill
this gap through this work, which we hope will significantly help NLP for
Hindi. We release this dataset with our code and models at
https://github.com/cfiltnlp/HiNER
|
[
{
"version": "v1",
"created": "Thu, 28 Apr 2022 19:14:21 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Murthy",
"Rudra",
""
],
[
"Bhattacharjee",
"Pallab",
""
],
[
"Sharnagat",
"Rahul",
""
],
[
"Khatri",
"Jyotsana",
""
],
[
"Kanojia",
"Diptesh",
""
],
[
"Bhattacharyya",
"Pushpak",
""
]
] |
new_dataset
| 0.999655 |
2204.13848
|
Daniel Deutsch
|
Daniel Deutsch and Dan Roth
|
Repro: An Open-Source Library for Improving the Reproducibility and
Usability of Publicly Available Research Code
| null | null | null | null |
cs.CL cs.AI cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Repro, an open-source library which aims at improving the
reproducibility and usability of research code. The library provides a
lightweight Python API for running software released by researchers within
Docker containers which contain the exact required runtime configuration and
dependencies for the code. Because the environment setup for each package is
handled by Docker, users do not have to do any configuration themselves. Once
Repro is installed, users can run the code for the 30+ papers currently
supported by the library. We hope researchers see the value provided to others
by including their research code in Repro and consider adding support for their
own research code.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 01:54:54 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Deutsch",
"Daniel",
""
],
[
"Roth",
"Dan",
""
]
] |
new_dataset
| 0.999677 |
2204.13915
|
Pavel P\v{r}ib\'a\v{n}
|
Pavel P\v{r}ib\'a\v{n}, Josef Steinberger
|
Czech Dataset for Cross-lingual Subjectivity Classification
|
Accepted to LREC2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we introduce a new Czech subjectivity dataset of 10k manually
annotated subjective and objective sentences from movie reviews and
descriptions. Our prime motivation is to provide a reliable dataset that can be
used with the existing English dataset as a benchmark to test the ability of
pre-trained multilingual models to transfer knowledge between Czech and English
and vice versa. Two annotators annotated the dataset reaching 0.83 of the
Cohen's \k{appa} inter-annotator agreement. To the best of our knowledge, this
is the first subjectivity dataset for the Czech language. We also created an
additional dataset that consists of 200k automatically labeled sentences. Both
datasets are freely available for research purposes. Furthermore, we fine-tune
five pre-trained BERT-like models to set a monolingual baseline for the new
dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing
English dataset for which we obtained results that are on par with the current
state-of-the-art results. Finally, we perform zero-shot cross-lingual
subjectivity classification between Czech and English to verify the usability
of our dataset as the cross-lingual benchmark. We compare and discuss the
cross-lingual and monolingual results and the ability of multilingual models to
transfer knowledge between languages.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 07:31:46 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Přibáň",
"Pavel",
""
],
[
"Steinberger",
"Josef",
""
]
] |
new_dataset
| 0.999761 |
2204.13973
|
Zhongyuan Hau
|
Zhongyuan Hau, Soteris Demetriou, Emil C. Lupu
|
Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle
Perception
|
To appear in the Proceedings of the 2022 IEEE Security and Privacy
Workshop on the Internet of Safe Things (SafeThings 2022)
| null | null | null |
cs.CV cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable
spatial perception of their surroundings and help make driving decisions.
Recent works demonstrated attacks that aim to hide objects from AV perception,
which can result in severe consequences. 3D shadows, are regions void of
measurements in 3D point clouds which arise from occlusions of objects in a
scene. 3D shadows were proposed as a physical invariant valuable for detecting
spoofed or fake objects. In this work, we leverage 3D shadows to locate
obstacles that are hidden from object detectors. We achieve this by searching
for void regions and locating the obstacles that cause these shadows. Our
proposed methodology can be used to detect an object that has been hidden by an
adversary as these objects, while hidden from 3D object detectors, still induce
shadow artifacts in 3D point clouds, which we use for obstacle detection. We
show that using 3D shadows for obstacle detection can achieve high accuracy in
matching shadows to their object and provide precise prediction of an
obstacle's distance from the ego-vehicle.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 09:49:29 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Hau",
"Zhongyuan",
""
],
[
"Demetriou",
"Soteris",
""
],
[
"Lupu",
"Emil C.",
""
]
] |
new_dataset
| 0.99086 |
2204.13979
|
Mohammad Mehdi Jaziriyan
|
Mohammad Mehdi Jaziriyan, Ahmad Akbari, Hamed Karbasi
|
ExaASC: A General Target-Based Stance Detection Corpus in Arabic
Language
|
6 pages, 1 figure, 4 tables. Accepted at ICCKE 2021
|
2021 11th International Conference on Computer Engineering and
Knowledge (ICCKE)
|
10.1109/ICCKE54056.2021.9721486
| null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Target-based Stance Detection is the task of finding a stance toward a
target. Twitter is one of the primary sources of political discussions in
social media and one of the best resources to analyze Stance toward entities.
This work proposes a new method toward Target-based Stance detection by using
the stance of replies toward a most important and arguing target in source
tweet. This target is detected with respect to the source tweet itself and not
limited to a set of pre-defined targets which is the usual approach of the
current state-of-the-art methods. Our proposed new attitude resulted in a new
corpus called ExaASC for the Arabic Language, one of the low resource languages
in this field. In the end, we used BERT to evaluate our corpus and reached a
70.69 Macro F-score. This shows that our data and model can work in a general
Target-base Stance Detection system. The corpus is publicly available1.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 10:03:51 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Jaziriyan",
"Mohammad Mehdi",
""
],
[
"Akbari",
"Ahmad",
""
],
[
"Karbasi",
"Hamed",
""
]
] |
new_dataset
| 0.995802 |
2204.14034
|
Haotang Li
|
Haotang Li, Shengtao Guo, Kailin Lyu, Xiao Yang, Tianchen Chen,
Jianqing Zhu, Huanqiang Zeng
|
A Challenging Benchmark of Anime Style Recognition
|
accepted by CVPRW 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given two images of different anime roles, anime style recognition (ASR) aims
to learn abstract painting style to determine whether the two images are from
the same work, which is an interesting but challenging problem. Unlike
biometric recognition, such as face recognition, iris recognition, and person
re-identification, ASR suffers from a much larger semantic gap but receives
less attention. In this paper, we propose a challenging ASR benchmark. Firstly,
we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of
190 anime works and each work at least has ten different roles. In addition to
the large-scale, LSASRD contains a list of challenging factors, such as complex
illuminations, various poses, theatrical colors and exaggerated compositions.
Secondly, we design a cross-role protocol to evaluate ASR performance, in which
query and gallery images must come from different roles to validate an ASR
model is to learn abstract painting style rather than learn discriminative
features of roles. Finally, we apply two powerful person re-identification
methods, namely, AGW and TransReID, to construct the baseline performance on
LSASRD. Surprisingly, the recent transformer model (i.e., TransReID) only
acquires a 42.24% mAP on LSASRD. Therefore, we believe that the ASR task of a
huge semantic gap deserves deep and long-term research. We will open our
dataset and code at https://github.com/nkjcqvcpi/ASR.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 12:09:42 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Li",
"Haotang",
""
],
[
"Guo",
"Shengtao",
""
],
[
"Lyu",
"Kailin",
""
],
[
"Yang",
"Xiao",
""
],
[
"Chen",
"Tianchen",
""
],
[
"Zhu",
"Jianqing",
""
],
[
"Zeng",
"Huanqiang",
""
]
] |
new_dataset
| 0.999859 |
2204.14044
|
Minyi Zhao
|
Minyi Zhao, Miao Wang, Fan Bai, Bingjia Li, Jie Wang, Shuigeng Zhou
|
C3-STISR: Scene Text Image Super-resolution with Triple Clues
|
Accepted by IJCAI 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Scene text image super-resolution (STISR) has been regarded as an important
pre-processing task for text recognition from low-resolution scene text images.
Most recent approaches use the recognizer's feedback as clues to guide
super-resolution. However, directly using recognition clue has two problems: 1)
Compatibility. It is in the form of probability distribution, has an obvious
modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains
wrong information, thus will mislead the main task and degrade super-resolution
performance. In this paper, we present a novel method C3-STISR that jointly
exploits the recognizer's feedback, visual and linguistical information as
clues to guide super-resolution. Here, visual clue is from the images of texts
predicted by the recognizer, which is informative and more compatible with the
STISR task; while linguistical clue is generated by a pre-trained
character-level language model, which is able to correct the predicted texts.
We design effective extraction and fusion mechanisms for the triple cross-modal
clues to generate a comprehensive and unified guidance for super-resolution.
Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA
methods in fidelity and recognition performance. Code is available in
https://github.com/zhaominyiz/C3-STISR.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 12:39:51 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Zhao",
"Minyi",
""
],
[
"Wang",
"Miao",
""
],
[
"Bai",
"Fan",
""
],
[
"Li",
"Bingjia",
""
],
[
"Wang",
"Jie",
""
],
[
"Zhou",
"Shuigeng",
""
]
] |
new_dataset
| 0.999587 |
2204.14116
|
Benjamin Provan-Bessell
|
Benjamin Provan-Bessell, Marco Dalla, Andrea Visentin, Barry
O'Sullivan
|
SATfeatPy -- A Python-based Feature Extraction System for Satisfiability
|
8 pages, 2 figures, code available at
https://github.com/bprovanbessell/SATfeatPy
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Feature extraction is a fundamental task in the application of machine
learning methods to SAT solving. It is used in algorithm selection and
configuration for solver portfolios and satisfiability classification. Many
approaches have been proposed to extract meaningful attributes from CNF
instances. Most of them lack a working/updated implementation, and the limited
descriptions lack clarity affecting the reproducibility. Furthermore, the
literature misses a comparison among the features. This paper introduces
SATfeatPy, a library that offers feature extraction techniques for SAT problems
in the CNF form. This package offers the implementation of all the structural
and statistical features from there major papers in the field. The library is
provided in an up-to-date, easy-to-use Python package alongside a detailed
feature description. We show the high accuracy of SAT/UNSAT and problem
category classification, using five sets of features generated using our
library from a dataset of 3000 SAT and UNSAT instances, over ten different
classes of problems. Finally, we compare the usefulness of the features and
importance for predicting a SAT instance's original structure in an ablation
study.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 14:10:01 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Provan-Bessell",
"Benjamin",
""
],
[
"Dalla",
"Marco",
""
],
[
"Visentin",
"Andrea",
""
],
[
"O'Sullivan",
"Barry",
""
]
] |
new_dataset
| 0.956773 |
2204.14204
|
Sunanda Thunder
|
Thomas Egler, Hans Dittmann, Sunanda Thunder and Artur Useinov
|
3T-1R Analog Write and Digital Read of MRAM for RNG and Low Power Memory
Application
| null | null | null | null |
cs.ET
|
http://creativecommons.org/licenses/by/4.0/
|
This work represents integration of MTJ with 30nm FinFET for low voltage
analog write operations and readout optimization for the p-bit or true random
number generator (TRNG), where the induced p-bit, the probabilistic state of
the magnetic tunnel junction (MTJ), is detected within only a single
computational period. The period contains two sub-cycles: write and joined read
& reset cycles. The operation with MTJ becomes stochastic, independent after
calibrating at the desired working point against the factors, which can induce
the signal deviations, e.g. temperature, material degradation or external
magnetic field.
|
[
{
"version": "v1",
"created": "Wed, 27 Apr 2022 08:19:18 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Egler",
"Thomas",
""
],
[
"Dittmann",
"Hans",
""
],
[
"Thunder",
"Sunanda",
""
],
[
"Useinov",
"Artur",
""
]
] |
new_dataset
| 0.996798 |
2204.14240
|
Pablo Azagra Millan
|
Pablo Azagra, Carlos Sostres, \'Angel Ferrandez, Luis Riazuelo, Clara
Tomasini, Oscar Le\'on Barbed, Javier Morlana, David Recasens, Victor M.
Batlle, Juan J. G\'omez-Rodr\'iguez, Richard Elvira, Julia L\'opez, Cristina
Oriol, Javier Civera, Juan D. Tard\'os, Ana Cristina Murillo, Angel Lanas and
Jos\'e M.M. Montiel
|
EndoMapper dataset of complete calibrated endoscopy procedures
|
11 pages, 7 figures, 4 tables
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computer-assisted systems are becoming broadly used in medicine. In
endoscopy, most research focuses on automatic detection of polyps or other
pathologies, but localization and navigation of the endoscope is completely
performed manually by physicians. To broaden this research and bring spatial
Artificial Intelligence to endoscopies, data from complete procedures are
needed. This data will be used to build a 3D mapping and localization systems
that can perform special task like, for example, detect blind zones during
exploration, provide automatic polyp measurements, guide doctors to a polyp
found in a previous exploration and retrieve previous images of the same area
aligning them for easy comparison. These systems will provide an improvement in
the quality and precision of the procedures while lowering the burden on the
physicians. This paper introduces the Endomapper dataset, the first collection
of complete endoscopy sequences acquired during regular medical practice,
including slow and careful screening explorations, making secondary use of
medical data. Its original purpose is to facilitate the development and
evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in
real endoscopy data. The first release of the dataset is composed of 59
sequences with more than 15 hours of video. It is also the first endoscopic
dataset that includes both the computed geometric and photometric endoscope
calibration with the original calibration videos. Meta-data and annotations
associated to the dataset varies from anatomical landmark and description of
the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions,
simulated sequences with groundtruth and meta-data related to special cases,
such as sequences from the same patient. This information will improve the
research in endoscopic VSLAM, as well as other research lines, and create new
research lines.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 17:10:01 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Azagra",
"Pablo",
""
],
[
"Sostres",
"Carlos",
""
],
[
"Ferrandez",
"Ángel",
""
],
[
"Riazuelo",
"Luis",
""
],
[
"Tomasini",
"Clara",
""
],
[
"Barbed",
"Oscar León",
""
],
[
"Morlana",
"Javier",
""
],
[
"Recasens",
"David",
""
],
[
"Batlle",
"Victor M.",
""
],
[
"Gómez-Rodríguez",
"Juan J.",
""
],
[
"Elvira",
"Richard",
""
],
[
"López",
"Julia",
""
],
[
"Oriol",
"Cristina",
""
],
[
"Civera",
"Javier",
""
],
[
"Tardós",
"Juan D.",
""
],
[
"Murillo",
"Ana Cristina",
""
],
[
"Lanas",
"Angel",
""
],
[
"Montiel",
"José M. M.",
""
]
] |
new_dataset
| 0.999609 |
2204.14244
|
Marcos V. Conde
|
Marcos V. Conde, Kerem Turgutlu
|
CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
|
CVPR CVFAD Workshop 2021
|
Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR) Workshops, 2021, pp. 3956-3960
|
10.1109/CVPRW53098.2021.00444
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Existing computer vision research in artwork struggles with artwork's
fine-grained attributes recognition and lack of curated annotated datasets due
to their costly creation. To the best of our knowledge, we are one of the first
methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural
network on a variety of artwork images and text descriptions pairs. CLIP is
able to learn directly from free-form art descriptions, or, if available,
curated fine-grained labels. Model's zero-shot capability allows predicting
accurate natural language description for a given image, without directly
optimizing for the task. Our approach aims to solve 2 challenges: instance
retrieval and fine-grained artwork attribute recognition. We use the iMet
Dataset, which we consider the largest annotated artwork dataset. In this
benchmark we achieved competitive results using only self-supervision.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 17:17:24 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Conde",
"Marcos V.",
""
],
[
"Turgutlu",
"Kerem",
""
]
] |
new_dataset
| 0.991143 |
2204.14249
|
Kai Katsumata
|
Kai Katsumata and Duc Minh Vo and Hideki Nakayama
|
OSSGAN: Open-Set Semi-Supervised Image Generation
|
Accepted at CVPR 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a challenging training scheme of conditional GANs, called
open-set semi-supervised image generation, where the training dataset consists
of two parts: (i) labeled data and (ii) unlabeled data with samples belonging
to one of the labeled data classes, namely, a closed-set, and samples not
belonging to any of the labeled data classes, namely, an open-set. Unlike the
existing semi-supervised image generation task, where unlabeled data only
contain closed-set samples, our task is more general and lowers the data
collection cost in practice by allowing open-set samples to appear. Thanks to
entropy regularization, the classifier that is trained on labeled data is able
to quantify sample-wise importance to the training of cGAN as confidence,
allowing us to use all samples in unlabeled data. We design OSSGAN, which
provides decision clues to the discriminator on the basis of whether an
unlabeled image belongs to one or none of the classes of interest, smoothly
integrating labeled and unlabeled data during training. The results of
experiments on Tiny ImageNet and ImageNet show notable improvements over
supervised BigGAN and semi-supervised methods. Our code is available at
https://github.com/raven38/OSSGAN.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 17:26:09 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"Katsumata",
"Kai",
""
],
[
"Vo",
"Duc Minh",
""
],
[
"Nakayama",
"Hideki",
""
]
] |
new_dataset
| 0.999464 |
2204.14272
|
Chenyu You
|
Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou
|
End-to-end Spoken Conversational Question Answering: Task, Dataset and
Model
|
In Findings of NAACL 2022. arXiv admin note: substantial text overlap
with arXiv:2010.08923
| null | null | null |
cs.CL cs.AI cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
In spoken question answering, the systems are designed to answer questions
from contiguous text spans within the related speech transcripts. However, the
most natural way that human seek or test their knowledge is via human
conversations. Therefore, we propose a new Spoken Conversational Question
Answering task (SCQA), aiming at enabling the systems to model complex dialogue
flows given the speech documents. In this task, our main objective is to build
the system to deal with conversational questions based on the audio recordings,
and to explore the plausibility of providing more cues from different
modalities with systems in information gathering. To this end, instead of
directly adopting automatically generated speech transcripts with highly noisy
data, we propose a novel unified data distillation approach, DDNet, which
effectively ingests cross-modal information to achieve fine-grained
representations of the speech and language modalities. Moreover, we propose a
simple and novel mechanism, termed Dual Attention, by encouraging better
alignments between audio and text to ease the process of knowledge transfer. To
evaluate the capacity of SCQA systems in a dialogue-style interaction, we
assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with
more than 40k question-answer pairs from 4k conversations. The performance of
the existing state-of-the-art methods significantly degrade on our dataset,
hence demonstrating the necessity of cross-modal information integration. Our
experimental results demonstrate that our proposed method achieves superior
performance in spoken conversational question answering tasks.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 17:56:59 GMT"
}
] | 2022-05-02T00:00:00 |
[
[
"You",
"Chenyu",
""
],
[
"Chen",
"Nuo",
""
],
[
"Liu",
"Fenglin",
""
],
[
"Ge",
"Shen",
""
],
[
"Wu",
"Xian",
""
],
[
"Zou",
"Yuexian",
""
]
] |
new_dataset
| 0.994814 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.