id stringlengths 9 10 | submitter stringlengths 1 64 ⌀ | authors stringlengths 4 20.7k | title stringlengths 4 246 | comments stringlengths 1 523 ⌀ | journal-ref stringlengths 4 404 ⌀ | doi stringlengths 11 153 ⌀ | report-no stringlengths 2 254 ⌀ | categories stringlengths 5 98 | license stringclasses 9 values | orig_abstract stringlengths 14 3.35k | versions listlengths 1 60 | update_date stringlengths 10 10 | authors_parsed listlengths 1 1.35k | abstract stringlengths 11 3.34k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1702.02471 | Adrien Bizeray | Adrien M. Bizeray, Jin-Ho Kim, Stephen R. Duncan and David A. Howey | Identifiability and parameter estimation of the single particle
lithium-ion battery model | 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on
Control Systems Technology | null | 10.1109/TCST.2018.2838097 | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the identifiability and estimation of the parameters
of the single particle model (SPM) for lithium-ion battery simulation.
Identifiability is addressed both in principle and in practice. The approach
begins by grouping parameters and partially non-dimensionalising the SPM to
determine the maximum expected degrees of freedom in the problem. We discover
that, excluding open circuit voltage, there are only six independent
parameters. We then examine the structural identifiability by considering
whether the transfer function of the linearised SPM is unique. It is found that
the model is unique provided that the electrode open circuit voltage functions
have a known non-zero gradient, the parameters are ordered, and the electrode
kinetics are lumped into a single charge transfer resistance parameter. We then
demonstrate the practical estimation of model parameters from measured
frequency-domain experimental electrochemical impedance spectroscopy (EIS)
data, and show additionally that the parametrised model provides good
predictive capabilities in the time domain, exhibiting a maximum voltage error
of 20 mV between model and experiment over a 10 minute dynamic discharge.
| [
{
"created": "Wed, 8 Feb 2017 15:28:15 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Jan 2018 17:33:05 GMT",
"version": "v2"
}
] | 2018-10-03 | [
[
"Bizeray",
"Adrien M.",
""
],
[
"Kim",
"Jin-Ho",
""
],
[
"Duncan",
"Stephen R.",
""
],
[
"Howey",
"David A.",
""
]
] | This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially non-dimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that, excluding open circuit voltage, there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearised SPM is unique. It is found that the model is unique provided that the electrode open circuit voltage functions have a known non-zero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy (EIS) data, and show additionally that the parametrised model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between model and experiment over a 10 minute dynamic discharge. |
2403.12976 | Vincenzo Barbuto | Vincenzo Barbuto, Claudio Savaglio, Roberto Minerva, Noel Crespi,
Giancarlo Fortino | Towards an Edge Intelligence-Based Traffic Monitoring System | null | null | 10.1109/SMC53992.2023.10393907 | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | Cities have undergone significant changes due to the rapid increase in urban
population, heightened demand for resources, and growing concerns over climate
change. To address these challenges, digital transformation has become a
necessity. Recent advancements in Artificial Intelligence (AI) and sensing
techniques, such as synthetic sensing, can elevate Digital Twins (DTs) from
digital copies of physical objects to effective and efficient platforms for
data collection and in-situ processing. In such a scenario, this paper presents
a compre-hensive approach for developing a Traffic Monitoring System (TMS)
based on Edge Intelligence (EI), specifically designed for smart cities. Our
approach prioritizes the placement of intelligence as close as possible to data
sources, and leverages an "opportunistic" interpretation of DT (ODT), resulting
in a novel and interdisciplinary strategy to re-engineering large-scale
distributed smart systems. The preliminary results of the proposed system have
shown that moving computation to the edge of the network provides several
benefits, including (i) enhanced inference performance, (ii) reduced bandwidth
and power consumption, (iii) and decreased latencies with respect to the
classic cloud -centric approach.
| [
{
"created": "Mon, 5 Feb 2024 17:08:18 GMT",
"version": "v1"
}
] | 2024-03-21 | [
[
"Barbuto",
"Vincenzo",
""
],
[
"Savaglio",
"Claudio",
""
],
[
"Minerva",
"Roberto",
""
],
[
"Crespi",
"Noel",
""
],
[
"Fortino",
"Giancarlo",
""
]
] | Cities have undergone significant changes due to the rapid increase in urban population, heightened demand for resources, and growing concerns over climate change. To address these challenges, digital transformation has become a necessity. Recent advancements in Artificial Intelligence (AI) and sensing techniques, such as synthetic sensing, can elevate Digital Twins (DTs) from digital copies of physical objects to effective and efficient platforms for data collection and in-situ processing. In such a scenario, this paper presents a compre-hensive approach for developing a Traffic Monitoring System (TMS) based on Edge Intelligence (EI), specifically designed for smart cities. Our approach prioritizes the placement of intelligence as close as possible to data sources, and leverages an "opportunistic" interpretation of DT (ODT), resulting in a novel and interdisciplinary strategy to re-engineering large-scale distributed smart systems. The preliminary results of the proposed system have shown that moving computation to the edge of the network provides several benefits, including (i) enhanced inference performance, (ii) reduced bandwidth and power consumption, (iii) and decreased latencies with respect to the classic cloud -centric approach. |
2404.14248 | Zongwei Wu | Xiaoning Liu, Zongwei Wu, Ao Li, Florin-Alexandru Vasluianu, Yulun
Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, Zhi Jin, Hongjun Wu,
Chenxi Wang, Haitao Ling, Yuanhao Cai, Hao Bian, Yuxin Zheng, Jing Lin, Alan
Yuille, Ben Shao, Jin Guo, Tianli Liu, Mohao Wu, Yixu Feng, Shuo Hou, Haotian
Lin, Yu Zhu, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang, Qingsen Yan,
Wenbin Zou, Weipeng Yang, Yunxiang Li, Qiaomu Wei, Tian Ye, Sixiang Chen,
Zhao Zhang, Suiyi Zhao, Bo Wang, Yan Luo, Zhichao Zuo, Mingshen Wang, Junhu
Wang, Yanyan Wei, Xiaopeng Sun, Yu Gao, Jiancheng Huang, Hongming Chen, Xiang
Chen, Hui Tang, Yuanbin Chen, Yuanbo Zhou, Xinwei Dai, Xintao Qiu, Wei Deng,
Qinquan Gao, Tong Tong, Mingjia Li, Jin Hu, Xinyu He, Xiaojie Guo,
Sabarinathan, K Uma, A Sasithradevi, B Sathya Bama, S. Mohamed Mansoor Roomi,
V.Srivatsav, Jinjuan Wang, Long Sun, Qiuying Chen, Jiahong Shao, Yizhi Zhang,
Marcos V. Conde, Daniel Feijoo, Juan C. Benito, Alvaro Garc\'ia, Jaeho Lee,
Seongwan Kim, Sharif S M A, Nodirkhuja Khujaev, Roman Tsoy, Ali Murtaza,
Uswah Khairuddin, Ahmad 'Athif Mohd Faudzi, Sampada Malagi, Amogh Joshi,
Nikhil Akalwadi, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudenagudi, Wenyi
Lian, Wenjing Lian, Jagadeesh Kalyanshetti, Vijayalaxmi Ashok Aralikatti,
Palani Yashaswini, Nitish Upasi, Dikshit Hegde, Ujwala Patil, Sujata C,
Xingzhuo Yan, Wei Hao, Minghan Fu, Pooja choksy, Anjali Sarvaiya, Kishor
Upla, Kiran Raja, Hailong Yan, Yunkai Zhang, Baiang Li, Jingyi Zhang, Huan
Zheng | NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results | NTIRE 2024 Challenge Report | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper reviews the NTIRE 2024 low light image enhancement challenge,
highlighting the proposed solutions and results. The aim of this challenge is
to discover an effective network design or solution capable of generating
brighter, clearer, and visually appealing results when dealing with a variety
of conditions, including ultra-high resolution (4K and beyond), non-uniform
illumination, backlighting, extreme darkness, and night scenes. A notable total
of 428 participants registered for the challenge, with 22 teams ultimately
making valid submissions. This paper meticulously evaluates the
state-of-the-art advancements in enhancing low-light images, reflecting the
significant progress and creativity in this field.
| [
{
"created": "Mon, 22 Apr 2024 15:01:12 GMT",
"version": "v1"
}
] | 2024-04-23 | [
[
"Liu",
"Xiaoning",
""
],
[
"Wu",
"Zongwei",
""
],
[
"Li",
"Ao",
""
],
[
"Vasluianu",
"Florin-Alexandru",
""
],
[
"Zhang",
"Yulun",
""
],
[
"Gu",
"Shuhang",
""
],
[
"Zhang",
"Le",
""
],
[
"Zhu",
"Ce",
""
],
[
"Timofte",
"Radu",
""
],
[
"Jin",
"Zhi",
""
],
[
"Wu",
"Hongjun",
""
],
[
"Wang",
"Chenxi",
""
],
[
"Ling",
"Haitao",
""
],
[
"Cai",
"Yuanhao",
""
],
[
"Bian",
"Hao",
""
],
[
"Zheng",
"Yuxin",
""
],
[
"Lin",
"Jing",
""
],
[
"Yuille",
"Alan",
""
],
[
"Shao",
"Ben",
""
],
[
"Guo",
"Jin",
""
],
[
"Liu",
"Tianli",
""
],
[
"Wu",
"Mohao",
""
],
[
"Feng",
"Yixu",
""
],
[
"Hou",
"Shuo",
""
],
[
"Lin",
"Haotian",
""
],
[
"Zhu",
"Yu",
""
],
[
"Wu",
"Peng",
""
],
[
"Dong",
"Wei",
""
],
[
"Sun",
"Jinqiu",
""
],
[
"Zhang",
"Yanning",
""
],
[
"Yan",
"Qingsen",
""
],
[
"Zou",
"Wenbin",
""
],
[
"Yang",
"Weipeng",
""
],
[
"Li",
"Yunxiang",
""
],
[
"Wei",
"Qiaomu",
""
],
[
"Ye",
"Tian",
""
],
[
"Chen",
"Sixiang",
""
],
[
"Zhang",
"Zhao",
""
],
[
"Zhao",
"Suiyi",
""
],
[
"Wang",
"Bo",
""
],
[
"Luo",
"Yan",
""
],
[
"Zuo",
"Zhichao",
""
],
[
"Wang",
"Mingshen",
""
],
[
"Wang",
"Junhu",
""
],
[
"Wei",
"Yanyan",
""
],
[
"Sun",
"Xiaopeng",
""
],
[
"Gao",
"Yu",
""
],
[
"Huang",
"Jiancheng",
""
],
[
"Chen",
"Hongming",
""
],
[
"Chen",
"Xiang",
""
],
[
"Tang",
"Hui",
""
],
[
"Chen",
"Yuanbin",
""
],
[
"Zhou",
"Yuanbo",
""
],
[
"Dai",
"Xinwei",
""
],
[
"Qiu",
"Xintao",
""
],
[
"Deng",
"Wei",
""
],
[
"Gao",
"Qinquan",
""
],
[
"Tong",
"Tong",
""
],
[
"Li",
"Mingjia",
""
],
[
"Hu",
"Jin",
""
],
[
"He",
"Xinyu",
""
],
[
"Guo",
"Xiaojie",
""
],
[
"Sabarinathan",
"",
""
],
[
"Uma",
"K",
""
],
[
"Sasithradevi",
"A",
""
],
[
"Bama",
"B Sathya",
""
],
[
"Roomi",
"S. Mohamed Mansoor",
""
],
[
"Srivatsav",
"V.",
""
],
[
"Wang",
"Jinjuan",
""
],
[
"Sun",
"Long",
""
],
[
"Chen",
"Qiuying",
""
],
[
"Shao",
"Jiahong",
""
],
[
"Zhang",
"Yizhi",
""
],
[
"Conde",
"Marcos V.",
""
],
[
"Feijoo",
"Daniel",
""
],
[
"Benito",
"Juan C.",
""
],
[
"García",
"Alvaro",
""
],
[
"Lee",
"Jaeho",
""
],
[
"Kim",
"Seongwan",
""
],
[
"A",
"Sharif S M",
""
],
[
"Khujaev",
"Nodirkhuja",
""
],
[
"Tsoy",
"Roman",
""
],
[
"Murtaza",
"Ali",
""
],
[
"Khairuddin",
"Uswah",
""
],
[
"Faudzi",
"Ahmad 'Athif Mohd",
""
],
[
"Malagi",
"Sampada",
""
],
[
"Joshi",
"Amogh",
""
],
[
"Akalwadi",
"Nikhil",
""
],
[
"Desai",
"Chaitra",
""
],
[
"Tabib",
"Ramesh Ashok",
""
],
[
"Mudenagudi",
"Uma",
""
],
[
"Lian",
"Wenyi",
""
],
[
"Lian",
"Wenjing",
""
],
[
"Kalyanshetti",
"Jagadeesh",
""
],
[
"Aralikatti",
"Vijayalaxmi Ashok",
""
],
[
"Yashaswini",
"Palani",
""
],
[
"Upasi",
"Nitish",
""
],
[
"Hegde",
"Dikshit",
""
],
[
"Patil",
"Ujwala",
""
],
[
"C",
"Sujata",
""
],
[
"Yan",
"Xingzhuo",
""
],
[
"Hao",
"Wei",
""
],
[
"Fu",
"Minghan",
""
],
[
"choksy",
"Pooja",
""
],
[
"Sarvaiya",
"Anjali",
""
],
[
"Upla",
"Kishor",
""
],
[
"Raja",
"Kiran",
""
],
[
"Yan",
"Hailong",
""
],
[
"Zhang",
"Yunkai",
""
],
[
"Li",
"Baiang",
""
],
[
"Zhang",
"Jingyi",
""
],
[
"Zheng",
"Huan",
""
]
] | This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. |
1702.01394 | Jeffrey Shallit | J\"org Endrullis, Jeffrey Shallit, Tim Smith | Undecidability and Finite Automata | null | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using a novel rewriting problem, we show that several natural decision
problems about finite automata are undecidable (i.e., recursively unsolvable).
In contrast, we also prove three related problems are decidable. We apply one
result to prove the undecidability of a related problem about k-automatic sets
of rational numbers.
| [
{
"created": "Sun, 5 Feb 2017 12:37:36 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Feb 2017 20:47:33 GMT",
"version": "v2"
}
] | 2017-03-01 | [
[
"Endrullis",
"Jörg",
""
],
[
"Shallit",
"Jeffrey",
""
],
[
"Smith",
"Tim",
""
]
] | Using a novel rewriting problem, we show that several natural decision problems about finite automata are undecidable (i.e., recursively unsolvable). In contrast, we also prove three related problems are decidable. We apply one result to prove the undecidability of a related problem about k-automatic sets of rational numbers. |
1009.0072 | Wei Yang | Wei Yang, Lihua Li, Gang Wu, and Haifeng Wang | Joint Relay Selection and Link Adaptation for Distributed Beamforming in
Regenerative Cooperative Networks | Accepted by 2010 International Symposium on Information Theory and
its Applications (ISITA) | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relay selection enhances the performance of the cooperative networks by
selecting the links with higher capacity. Meanwhile link adaptation improves
the spectral efficiency of wireless data-centric networks through adapting the
modulation and coding schemes (MCS) to the current link condition. In this
paper, relay selection is combined with link adaptation for distributed
beamforming in a two-hop regenerative cooperative system. A novel signaling
mechanism and related optimal algorithms are proposed for joint relay selection
and link adaptation. In the proposed scheme, there is no need to feedback the
relay selection results to each relay. Instead, by broadcasting the link
adaptation results from the destination, each relay will automatically
understand whether it is selected or not. The lower and upper bounds of the
throughput of the proposed scheme are derived. The analysis and simulation
results indicate that the proposed scheme provides synergistic gains compared
to the pure relay selection and link adaptation schemes.
| [
{
"created": "Wed, 1 Sep 2010 02:02:32 GMT",
"version": "v1"
}
] | 2010-09-02 | [
[
"Yang",
"Wei",
""
],
[
"Li",
"Lihua",
""
],
[
"Wu",
"Gang",
""
],
[
"Wang",
"Haifeng",
""
]
] | Relay selection enhances the performance of the cooperative networks by selecting the links with higher capacity. Meanwhile link adaptation improves the spectral efficiency of wireless data-centric networks through adapting the modulation and coding schemes (MCS) to the current link condition. In this paper, relay selection is combined with link adaptation for distributed beamforming in a two-hop regenerative cooperative system. A novel signaling mechanism and related optimal algorithms are proposed for joint relay selection and link adaptation. In the proposed scheme, there is no need to feedback the relay selection results to each relay. Instead, by broadcasting the link adaptation results from the destination, each relay will automatically understand whether it is selected or not. The lower and upper bounds of the throughput of the proposed scheme are derived. The analysis and simulation results indicate that the proposed scheme provides synergistic gains compared to the pure relay selection and link adaptation schemes. |
2310.07393 | Matteo El Hariry | Matteo El-Hariry, Antoine Richard, Miguel Olivares-Mendez | RANS: Highly-Parallelised Simulator for Reinforcement Learning based
Autonomous Navigating Spacecrafts | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, realistic simulation environments are essential to validate and
build reliable robotic solutions. This is particularly true when using
Reinforcement Learning (RL) based control policies. To this end, both robotics
and RL developers need tools and workflows to create physically accurate
simulations and synthetic datasets. Gazebo, MuJoCo, Webots, Pybullets or Isaac
Sym are some of the many tools available to simulate robotic systems.
Developing learning-based methods for space navigation is, due to the highly
complex nature of the problem, an intensive data-driven process that requires
highly parallelized simulations. When it comes to the control of spacecrafts,
there is no easy to use simulation library designed for RL. We address this gap
by harnessing the capabilities of NVIDIA Isaac Gym, where both physics
simulation and the policy training reside on GPU. Building on this tool, we
provide an open-source library enabling users to simulate thousands of parallel
spacecrafts, that learn a set of maneuvering tasks, such as position, attitude,
and velocity control. These tasks enable to validate complex space scenarios,
such as trajectory optimization for landing, docking, rendezvous and more.
| [
{
"created": "Wed, 11 Oct 2023 11:21:45 GMT",
"version": "v1"
}
] | 2023-10-12 | [
[
"El-Hariry",
"Matteo",
""
],
[
"Richard",
"Antoine",
""
],
[
"Olivares-Mendez",
"Miguel",
""
]
] | Nowadays, realistic simulation environments are essential to validate and build reliable robotic solutions. This is particularly true when using Reinforcement Learning (RL) based control policies. To this end, both robotics and RL developers need tools and workflows to create physically accurate simulations and synthetic datasets. Gazebo, MuJoCo, Webots, Pybullets or Isaac Sym are some of the many tools available to simulate robotic systems. Developing learning-based methods for space navigation is, due to the highly complex nature of the problem, an intensive data-driven process that requires highly parallelized simulations. When it comes to the control of spacecrafts, there is no easy to use simulation library designed for RL. We address this gap by harnessing the capabilities of NVIDIA Isaac Gym, where both physics simulation and the policy training reside on GPU. Building on this tool, we provide an open-source library enabling users to simulate thousands of parallel spacecrafts, that learn a set of maneuvering tasks, such as position, attitude, and velocity control. These tasks enable to validate complex space scenarios, such as trajectory optimization for landing, docking, rendezvous and more. |
2303.15012 | Senmao Li | Senmao Li, Joost van de Weijer, Yaxing Wang, Fahad Shahbaz Khan,
Meiqin Liu, Jian Yang | 3D-Aware Multi-Class Image-to-Image Translation with NeRFs | Accepted by CVPR2023 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in 3D-aware generative models (3D-aware GANs) combined with
Neural Radiance Fields (NeRF) have achieved impressive results. However no
prior works investigate 3D-aware GANs for 3D consistent multi-class
image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation
methods suffers from unrealistic shape/identity change. To perform 3D-aware
multi-class I2I translation, we decouple this learning process into a
multi-class 3D-aware GAN step and a 3D-aware I2I translation step. In the first
step, we propose two novel techniques: a new conditional architecture and an
effective training strategy. In the second step, based on the well-trained
multi-class 3D-aware GAN architecture, that preserves view-consistency, we
construct a 3D-aware I2I translation system. To further reduce the
view-consistency problems, we propose several new techniques, including a
U-net-like adaptor network design, a hierarchical representation constrain and
a relative regularization loss. In extensive experiments on two datasets,
quantitative and qualitative results demonstrate that we successfully perform
3D-aware I2I translation with multi-view consistency.
| [
{
"created": "Mon, 27 Mar 2023 08:54:51 GMT",
"version": "v1"
}
] | 2023-03-28 | [
[
"Li",
"Senmao",
""
],
[
"van de Weijer",
"Joost",
""
],
[
"Wang",
"Yaxing",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Liu",
"Meiqin",
""
],
[
"Yang",
"Jian",
""
]
] | Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multi-class I2I translation, we decouple this learning process into a multi-class 3D-aware GAN step and a 3D-aware I2I translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In extensive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware I2I translation with multi-view consistency. |
1102.1265 | Joakim Jalden | Joakim Jalden and Petros Elia | Sphere decoding complexity exponent for decoding full rate codes over
the quasi-static MIMO channel | 19 Pages, 4 figures. Submitted to the IEEE Transactions on
Information Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the setting of quasi-static multiple-input multiple-output (MIMO)
channels, we consider the high signal-to-noise ratio (SNR) asymptotic
complexity required by the sphere decoding (SD) algorithm for decoding a large
class of full rate linear space-time codes. With SD complexity having random
fluctuations induced by the random channel, noise and codeword realizations,
the introduced SD complexity exponent manages to concisely describe the
computational reserves required by the SD algorithm to achieve arbitrarily
close to optimal decoding performance. Bounds and exact expressions for the SD
complexity exponent are obtained for the decoding of large families of codes
with arbitrary performance characteristics. For the particular example of
decoding the recently introduced threaded cyclic division algebra (CDA) based
codes -- the only currently known explicit designs that are uniformly optimal
with respect to the diversity multiplexing tradeoff (DMT) -- the SD complexity
exponent is shown to take a particularly concise form as a non-monotonic
function of the multiplexing gain. To date, the SD complexity exponent also
describes the minimum known complexity of any decoder that can provably achieve
a gap to maximum likelihood (ML) performance which vanishes in the high SNR
limit.
| [
{
"created": "Mon, 7 Feb 2011 10:24:09 GMT",
"version": "v1"
}
] | 2011-02-08 | [
[
"Jalden",
"Joakim",
""
],
[
"Elia",
"Petros",
""
]
] | In the setting of quasi-static multiple-input multiple-output (MIMO) channels, we consider the high signal-to-noise ratio (SNR) asymptotic complexity required by the sphere decoding (SD) algorithm for decoding a large class of full rate linear space-time codes. With SD complexity having random fluctuations induced by the random channel, noise and codeword realizations, the introduced SD complexity exponent manages to concisely describe the computational reserves required by the SD algorithm to achieve arbitrarily close to optimal decoding performance. Bounds and exact expressions for the SD complexity exponent are obtained for the decoding of large families of codes with arbitrary performance characteristics. For the particular example of decoding the recently introduced threaded cyclic division algebra (CDA) based codes -- the only currently known explicit designs that are uniformly optimal with respect to the diversity multiplexing tradeoff (DMT) -- the SD complexity exponent is shown to take a particularly concise form as a non-monotonic function of the multiplexing gain. To date, the SD complexity exponent also describes the minimum known complexity of any decoder that can provably achieve a gap to maximum likelihood (ML) performance which vanishes in the high SNR limit. |
1806.04346 | Ruidan He | Ruidan He and Wee Sun Lee and Hwee Tou Ng and Daniel Dahlmeier | Exploiting Document Knowledge for Aspect-level Sentiment Classification | Accepted to ACL 2018 (short paper) | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attention-based long short-term memory (LSTM) networks have proven to be
useful in aspect-level sentiment classification. However, due to the
difficulties in annotating aspect-level data, existing public datasets for this
task are all relatively small, which largely limits the effectiveness of those
neural models. In this paper, we explore two approaches that transfer knowledge
from document- level data, which is much less expensive to obtain, to improve
the performance of aspect-level sentiment classification. We demonstrate the
effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015,
and 2016, and we show that attention-based LSTM benefits from document-level
knowledge in multiple ways.
| [
{
"created": "Tue, 12 Jun 2018 06:04:11 GMT",
"version": "v1"
}
] | 2018-06-13 | [
[
"He",
"Ruidan",
""
],
[
"Lee",
"Wee Sun",
""
],
[
"Ng",
"Hwee Tou",
""
],
[
"Dahlmeier",
"Daniel",
""
]
] | Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document- level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways. |
2006.02163 | Xuan Phi Nguyen | Xuan-Phi Nguyen, Shafiq Joty, Thanh-Tung Nguyen, Wu Kui, Ai Ti Aw | Cross-model Back-translated Distillation for Unsupervised Machine
Translation | Accepted to a conference paper at ICML 2021 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent unsupervised machine translation (UMT) systems usually employ three
main principles: initialization, language modeling and iterative
back-translation, though they may apply them differently. Crucially, iterative
back-translation and denoising auto-encoding for language modeling provide data
diversity to train the UMT systems. However, the gains from these
diversification processes has seemed to plateau. We introduce a novel component
to the standard UMT framework called Cross-model Back-translated Distillation
(CBD), that is aimed to induce another level of data diversification that
existing principles lack. CBD is applicable to all previous UMT approaches. In
our experiments, CBD achieves the state of the art in the WMT'14
English-French, WMT'16 English-German and English-Romanian bilingual
unsupervised translation tasks, with 38.2, 30.1, and 36.3 BLEU respectively. It
also yields 1.5-3.3 BLEU improvements in IWSLT English-French and
English-German tasks. Through extensive experimental analyses, we show that CBD
is effective because it embraces data diversity while other similar variants do
not.
| [
{
"created": "Wed, 3 Jun 2020 10:57:21 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Oct 2020 14:07:14 GMT",
"version": "v2"
},
{
"created": "Sat, 6 Feb 2021 18:02:41 GMT",
"version": "v3"
},
{
"created": "Mon, 24 May 2021 16:07:26 GMT",
"version": "v4"
}
] | 2021-05-25 | [
[
"Nguyen",
"Xuan-Phi",
""
],
[
"Joty",
"Shafiq",
""
],
[
"Nguyen",
"Thanh-Tung",
""
],
[
"Kui",
"Wu",
""
],
[
"Aw",
"Ai Ti",
""
]
] | Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently. Crucially, iterative back-translation and denoising auto-encoding for language modeling provide data diversity to train the UMT systems. However, the gains from these diversification processes has seemed to plateau. We introduce a novel component to the standard UMT framework called Cross-model Back-translated Distillation (CBD), that is aimed to induce another level of data diversification that existing principles lack. CBD is applicable to all previous UMT approaches. In our experiments, CBD achieves the state of the art in the WMT'14 English-French, WMT'16 English-German and English-Romanian bilingual unsupervised translation tasks, with 38.2, 30.1, and 36.3 BLEU respectively. It also yields 1.5-3.3 BLEU improvements in IWSLT English-French and English-German tasks. Through extensive experimental analyses, we show that CBD is effective because it embraces data diversity while other similar variants do not. |
1912.11194 | Qi Qi | Qi Qi, Yan Yan, Xiaoyu Wang, Tianbao Yang | A Simple and Effective Framework for Pairwise Deep Metric Learning | 16 pages, 5 figures | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep metric learning (DML) has received much attention in deep learning due
to its wide applications in computer vision. Previous studies have focused on
designing complicated losses and hard example mining methods, which are mostly
heuristic and lack of theoretical understanding. In this paper, we cast DML as
a simple pairwise binary classification problem that classifies a pair of
examples as similar or dissimilar. It identifies the most critical issue in
this problem--imbalanced data pairs. To tackle this issue, we propose a simple
and effective framework to sample pairs in a batch of data for updating the
model. The key to this framework is to define a robust loss for all pairs over
a mini-batch of data, which is formulated by distributionally robust
optimization. The flexibility in constructing the uncertainty decision set of
the dual variable allows us to recover state-of-the-art complicated losses and
also to induce novel variants. Empirical studies on several benchmark data sets
demonstrate that our simple and effective method outperforms the
state-of-the-art results. Codes are available at:
https://github.com/qiqi-helloworld/A-Simple-and-Effective-Framework-for-Pairewise-Distance-Metric-Learning
| [
{
"created": "Tue, 24 Dec 2019 03:47:25 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Jan 2020 15:58:11 GMT",
"version": "v2"
},
{
"created": "Thu, 18 Jun 2020 15:44:21 GMT",
"version": "v3"
}
] | 2020-06-19 | [
[
"Qi",
"Qi",
""
],
[
"Yan",
"Yan",
""
],
[
"Wang",
"Xiaoyu",
""
],
[
"Yang",
"Tianbao",
""
]
] | Deep metric learning (DML) has received much attention in deep learning due to its wide applications in computer vision. Previous studies have focused on designing complicated losses and hard example mining methods, which are mostly heuristic and lack of theoretical understanding. In this paper, we cast DML as a simple pairwise binary classification problem that classifies a pair of examples as similar or dissimilar. It identifies the most critical issue in this problem--imbalanced data pairs. To tackle this issue, we propose a simple and effective framework to sample pairs in a batch of data for updating the model. The key to this framework is to define a robust loss for all pairs over a mini-batch of data, which is formulated by distributionally robust optimization. The flexibility in constructing the uncertainty decision set of the dual variable allows us to recover state-of-the-art complicated losses and also to induce novel variants. Empirical studies on several benchmark data sets demonstrate that our simple and effective method outperforms the state-of-the-art results. Codes are available at: https://github.com/qiqi-helloworld/A-Simple-and-Effective-Framework-for-Pairewise-Distance-Metric-Learning |
2003.08951 | Yuya Obinata | Yuya Obinata and Takuma Yamamoto | Temporal Extension Module for Skeleton-Based Action Recognition | Accepted on ICPR2020, 7 pages, 4 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a module that extends the temporal graph of a graph convolutional
network (GCN) for action recognition with a sequence of skeletons. Existing
methods attempt to represent a more appropriate spatial graph on an
intra-frame, but disregard optimization of the temporal graph on the
interframe. Concretely, these methods connect between vertices corresponding
only to the same joint on the inter-frame. In this work, we focus on adding
connections to neighboring multiple vertices on the inter-frame and extracting
additional features based on the extended temporal graph. Our module is a
simple yet effective method to extract correlated features of multiple joints
in human movement. Moreover, our module aids in further performance
improvements, along with other GCN methods that optimize only the spatial
graph. We conduct extensive experiments on two large datasets, NTU RGB+D and
Kinetics-Skeleton, and demonstrate that our module is effective for several
existing models and our final model achieves state-of-the-art performance.
| [
{
"created": "Thu, 19 Mar 2020 18:00:04 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Oct 2020 02:39:04 GMT",
"version": "v2"
}
] | 2020-10-20 | [
[
"Obinata",
"Yuya",
""
],
[
"Yamamoto",
"Takuma",
""
]
] | We present a module that extends the temporal graph of a graph convolutional network (GCN) for action recognition with a sequence of skeletons. Existing methods attempt to represent a more appropriate spatial graph on an intra-frame, but disregard optimization of the temporal graph on the interframe. Concretely, these methods connect between vertices corresponding only to the same joint on the inter-frame. In this work, we focus on adding connections to neighboring multiple vertices on the inter-frame and extracting additional features based on the extended temporal graph. Our module is a simple yet effective method to extract correlated features of multiple joints in human movement. Moreover, our module aids in further performance improvements, along with other GCN methods that optimize only the spatial graph. We conduct extensive experiments on two large datasets, NTU RGB+D and Kinetics-Skeleton, and demonstrate that our module is effective for several existing models and our final model achieves state-of-the-art performance. |
2402.18419 | Shubham Vatsal | Shubham Vatsal, Ayush Singh and Shabnam Tafreshi | Can GPT Improve the State of Prior Authorization via Guideline Based
Automated Question Answering? | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Health insurance companies have a defined process called prior authorization
(PA) which is a health plan cost-control process that requires doctors and
other healthcare professionals to get clearance in advance from a health plan
before performing a particular procedure on a patient in order to be eligible
for payment coverage. For health insurance companies, approving PA requests for
patients in the medical domain is a time-consuming and challenging task. One of
those key challenges is validating if a request matches up to certain criteria
such as age, gender, etc. In this work, we evaluate whether GPT can validate
numerous key factors, in turn helping health plans reach a decision drastically
faster. We frame it as a question answering task, prompting GPT to answer a
question from patient electronic health record. We experiment with different
conventional prompting techniques as well as introduce our own novel prompting
technique. Moreover, we report qualitative assessment by humans on the natural
language generation outputs from our approach. Results show that our method
achieves superior performance with the mean weighted F1 score of 0.61 as
compared to its standard counterparts.
| [
{
"created": "Wed, 28 Feb 2024 15:39:53 GMT",
"version": "v1"
}
] | 2024-02-29 | [
[
"Vatsal",
"Shubham",
""
],
[
"Singh",
"Ayush",
""
],
[
"Tafreshi",
"Shabnam",
""
]
] | Health insurance companies have a defined process called prior authorization (PA) which is a health plan cost-control process that requires doctors and other healthcare professionals to get clearance in advance from a health plan before performing a particular procedure on a patient in order to be eligible for payment coverage. For health insurance companies, approving PA requests for patients in the medical domain is a time-consuming and challenging task. One of those key challenges is validating if a request matches up to certain criteria such as age, gender, etc. In this work, we evaluate whether GPT can validate numerous key factors, in turn helping health plans reach a decision drastically faster. We frame it as a question answering task, prompting GPT to answer a question from patient electronic health record. We experiment with different conventional prompting techniques as well as introduce our own novel prompting technique. Moreover, we report qualitative assessment by humans on the natural language generation outputs from our approach. Results show that our method achieves superior performance with the mean weighted F1 score of 0.61 as compared to its standard counterparts. |
1803.02101 | Frank Meyer | Wissam Siblini and Frank Meyer and Pascale Kuntz | VIPE: A new interactive classification framework for large sets of short
texts - application to opinion mining | 8 pages | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new interactive opinion mining tool that helps users to
classify large sets of short texts originated from Web opinion polls, technical
forums or Twitter. From a manual multi-label pre-classification of a very
limited text subset, a learning algorithm predicts the labels of the remaining
texts of the corpus and the texts most likely associated to a selected label.
Using a fast matrix factorization, the algorithm is able to handle large
corpora and is well-adapted to interactivity by integrating the corrections
proposed by the users on the fly. Experimental results on classical datasets of
various sizes and feedbacks of users from marketing services of the
telecommunication company Orange confirm the quality of the obtained results.
| [
{
"created": "Tue, 6 Mar 2018 10:45:27 GMT",
"version": "v1"
}
] | 2018-03-07 | [
[
"Siblini",
"Wissam",
""
],
[
"Meyer",
"Frank",
""
],
[
"Kuntz",
"Pascale",
""
]
] | This paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results. |
2108.08987 | Cl\'ement Canonne | Cl\'ement L. Canonne and Hongyi Lyu | Uniformity Testing in the Shuffle Model: Simpler, Better, Faster | Accepted to the SIAM Symposium on Simplicity in Algorithms (SOSA
2022). Added some details and discussions | null | null | null | cs.DS cs.CR cs.DM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uniformity testing, or testing whether independent observations are uniformly
distributed, is the prototypical question in distribution testing. Over the
past years, a line of work has been focusing on uniformity testing under
privacy constraints on the data, and obtained private and data-efficient
algorithms under various privacy models such as central differential privacy
(DP), local privacy (LDP), pan-privacy, and, very recently, the shuffle model
of differential privacy.
In this work, we considerably simplify the analysis of the known uniformity
testing algorithm in the shuffle model, and, using a recent result on "privacy
amplification via shuffling," provide an alternative algorithm attaining the
same guarantees with an elementary and streamlined argument.
| [
{
"created": "Fri, 20 Aug 2021 03:43:12 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Oct 2021 08:20:42 GMT",
"version": "v2"
}
] | 2021-10-19 | [
[
"Canonne",
"Clément L.",
""
],
[
"Lyu",
"Hongyi",
""
]
] | Uniformity testing, or testing whether independent observations are uniformly distributed, is the prototypical question in distribution testing. Over the past years, a line of work has been focusing on uniformity testing under privacy constraints on the data, and obtained private and data-efficient algorithms under various privacy models such as central differential privacy (DP), local privacy (LDP), pan-privacy, and, very recently, the shuffle model of differential privacy. In this work, we considerably simplify the analysis of the known uniformity testing algorithm in the shuffle model, and, using a recent result on "privacy amplification via shuffling," provide an alternative algorithm attaining the same guarantees with an elementary and streamlined argument. |
2406.05631 | Sana Ayromlou | Sana Ayromlou, Teresa Tsang, Purang Abolmaesumi, Xiaoxiao Li | CCSI: Continual Class-Specific Impression for Data-free Class
Incremental Learning | null | null | null | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | In real-world clinical settings, traditional deep learning-based
classification methods struggle with diagnosing newly introduced disease types
because they require samples from all disease classes for offline training.
Class incremental learning offers a promising solution by adapting a deep
network trained on specific disease classes to handle new diseases. However,
catastrophic forgetting occurs, decreasing the performance of earlier classes
when adapting the model to new data. Prior proposed methodologies to overcome
this require perpetual storage of previous samples, posing potential practical
concerns regarding privacy and storage regulations in healthcare. To this end,
we propose a novel data-free class incremental learning framework that utilizes
data synthesis on learned classes instead of data storage from previous
classes. Our key contributions include acquiring synthetic data known as
Continual Class-Specific Impression (CCSI) for previously inaccessible trained
classes and presenting a methodology to effectively utilize this data for
updating networks when introducing new classes. We obtain CCSI by employing
data inversion over gradients of the trained classification model on previous
classes starting from the mean image of each class inspired by common landmarks
shared among medical images and utilizing continual normalization layers
statistics as a regularizer in this pixel-wise optimization process.
Subsequently, we update the network by combining the synthesized data with new
class data and incorporate several losses, including an intra-domain
contrastive loss to generalize the deep network trained on the synthesized data
to real data, a margin loss to increase separation among previous classes and
new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse
effects of imbalanced distributions in training data.
| [
{
"created": "Sun, 9 Jun 2024 03:52:21 GMT",
"version": "v1"
}
] | 2024-06-11 | [
[
"Ayromlou",
"Sana",
""
],
[
"Tsang",
"Teresa",
""
],
[
"Abolmaesumi",
"Purang",
""
],
[
"Li",
"Xiaoxiao",
""
]
] | In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental learning offers a promising solution by adapting a deep network trained on specific disease classes to handle new diseases. However, catastrophic forgetting occurs, decreasing the performance of earlier classes when adapting the model to new data. Prior proposed methodologies to overcome this require perpetual storage of previous samples, posing potential practical concerns regarding privacy and storage regulations in healthcare. To this end, we propose a novel data-free class incremental learning framework that utilizes data synthesis on learned classes instead of data storage from previous classes. Our key contributions include acquiring synthetic data known as Continual Class-Specific Impression (CCSI) for previously inaccessible trained classes and presenting a methodology to effectively utilize this data for updating networks when introducing new classes. We obtain CCSI by employing data inversion over gradients of the trained classification model on previous classes starting from the mean image of each class inspired by common landmarks shared among medical images and utilizing continual normalization layers statistics as a regularizer in this pixel-wise optimization process. Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data. |
2012.00267 | Hongyang Du | Hongyang Du, Jiayi Zhang, Ke Guan, Dusit Niyato, Huiying Jiao, Zhiqin
Wang, and Thomas K\"urner | Performance and Optimization of Reconfigurable Intelligent Surface Aided
THz Communications | null | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | TeraHertz (THz) communications can satisfy the high data rate demand with
massive bandwidth. However, severe path attenuation and hardware imperfection
greatly alleviate its performance. Therefore, we utilize the reconfigurable
intelligent surface (RIS) technology and investigate the RIS-aided THz
communications. We first prove that the small-scale amplitude fading of THz
signals can be accurately modeled by the fluctuating two-ray distribution based
on two THz signal measurement experiments conducted in a variety of different
scenarios. To optimize the phase-shifts at the RIS elements, we propose a novel
swarm intelligence-based method that does not require full channel estimation.
We then derive exact statistical characterizations of end-to-end
signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR).
Moreover, we present asymptotic analysis to obtain more insights when the SNDR
or the number of RIS's elements is high. Finally, we derive analytical
expressions for the outage probability and ergodic capacity. The tight upper
bounds of ergodic capacity for both ideal and nonideal radio frequency chains
are obtained. It is interesting to find that increasing the number of RIS's
elements can significantly improve the THz communications system performance.
For example, the ergodic capacity can increase up to 25% when the number of
elements increases from 40 to 80, which incurs only insignificant costs to the
system.
| [
{
"created": "Tue, 1 Dec 2020 05:00:38 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Mar 2022 08:43:14 GMT",
"version": "v2"
}
] | 2022-03-22 | [
[
"Du",
"Hongyang",
""
],
[
"Zhang",
"Jiayi",
""
],
[
"Guan",
"Ke",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Jiao",
"Huiying",
""
],
[
"Wang",
"Zhiqin",
""
],
[
"Kürner",
"Thomas",
""
]
] | TeraHertz (THz) communications can satisfy the high data rate demand with massive bandwidth. However, severe path attenuation and hardware imperfection greatly alleviate its performance. Therefore, we utilize the reconfigurable intelligent surface (RIS) technology and investigate the RIS-aided THz communications. We first prove that the small-scale amplitude fading of THz signals can be accurately modeled by the fluctuating two-ray distribution based on two THz signal measurement experiments conducted in a variety of different scenarios. To optimize the phase-shifts at the RIS elements, we propose a novel swarm intelligence-based method that does not require full channel estimation. We then derive exact statistical characterizations of end-to-end signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR). Moreover, we present asymptotic analysis to obtain more insights when the SNDR or the number of RIS's elements is high. Finally, we derive analytical expressions for the outage probability and ergodic capacity. The tight upper bounds of ergodic capacity for both ideal and nonideal radio frequency chains are obtained. It is interesting to find that increasing the number of RIS's elements can significantly improve the THz communications system performance. For example, the ergodic capacity can increase up to 25% when the number of elements increases from 40 to 80, which incurs only insignificant costs to the system. |
2405.08200 | Rolando Garcia | Rolando Garcia | Interactive Lab Notebooks for Robotics Researchers | null | null | null | null | cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interactive notebooks, such as Jupyter, have revolutionized the field of data
science by providing an integrated environment for data, code, and
documentation. However, their adoption by robotics researchers and model
developers has been limited. This study investigates the logging and
record-keeping practices of robotics researchers, drawing parallels to the
pre-interactive notebook era of data science. Through interviews with robotics
researchers, we identified the reliance on diverse and often incompatible tools
for managing experimental data, leading to challenges in reproducibility and
data traceability. Our findings reveal that robotics researchers can benefit
from a specialized version of interactive notebooks that supports comprehensive
data entry, continuous context capture, and agile data staging. We propose
extending interactive notebooks to better serve the needs of robotics
researchers by integrating features akin to traditional lab notebooks. This
adaptation aims to enhance the organization, analysis, and reproducibility of
experimental data in robotics, fostering a more streamlined and efficient
research workflow.
| [
{
"created": "Mon, 13 May 2024 21:33:58 GMT",
"version": "v1"
}
] | 2024-05-15 | [
[
"Garcia",
"Rolando",
""
]
] | Interactive notebooks, such as Jupyter, have revolutionized the field of data science by providing an integrated environment for data, code, and documentation. However, their adoption by robotics researchers and model developers has been limited. This study investigates the logging and record-keeping practices of robotics researchers, drawing parallels to the pre-interactive notebook era of data science. Through interviews with robotics researchers, we identified the reliance on diverse and often incompatible tools for managing experimental data, leading to challenges in reproducibility and data traceability. Our findings reveal that robotics researchers can benefit from a specialized version of interactive notebooks that supports comprehensive data entry, continuous context capture, and agile data staging. We propose extending interactive notebooks to better serve the needs of robotics researchers by integrating features akin to traditional lab notebooks. This adaptation aims to enhance the organization, analysis, and reproducibility of experimental data in robotics, fostering a more streamlined and efficient research workflow. |
2310.18615 | Xiangchen Song | Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen,
Juan Carlos Niebles, Eric Xing, Kun Zhang | Temporally Disentangled Representation Learning under Unknown
Nonstationarity | NeurIPS 2023. arXiv admin note: text overlap with arXiv:2210.13647 | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | In unsupervised causal representation learning for sequential data with
time-delayed latent causal influences, strong identifiability results for the
disentanglement of causally-related latent variables have been established in
stationary settings by leveraging temporal structure. However, in nonstationary
setting, existing work only partially addressed the problem by either utilizing
observed auxiliary variables (e.g., class labels and/or domain indexes) as side
information or assuming simplified latent causal dynamics. Both constrain the
method to a limited range of scenarios. In this study, we further explored the
Markov Assumption under time-delayed causally related process in nonstationary
setting and showed that under mild conditions, the independent latent
components can be recovered from their nonlinear mixture up to a permutation
and a component-wise transformation, without the observation of auxiliary
variables. We then introduce NCTRL, a principled estimation framework, to
reconstruct time-delayed latent causal variables and identify their relations
from measured sequential data only. Empirical evaluations demonstrated the
reliable identification of time-delayed latent causal influences, with our
methodology substantially outperforming existing baselines that fail to exploit
the nonstationarity adequately and then, consequently, cannot distinguish
distribution shifts.
| [
{
"created": "Sat, 28 Oct 2023 06:46:03 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2024 09:43:57 GMT",
"version": "v2"
}
] | 2024-08-02 | [
[
"Song",
"Xiangchen",
""
],
[
"Yao",
"Weiran",
""
],
[
"Fan",
"Yewen",
""
],
[
"Dong",
"Xinshuai",
""
],
[
"Chen",
"Guangyi",
""
],
[
"Niebles",
"Juan Carlos",
""
],
[
"Xing",
"Eric",
""
],
[
"Zhang",
"Kun",
""
]
] | In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure. However, in nonstationary setting, existing work only partially addressed the problem by either utilizing observed auxiliary variables (e.g., class labels and/or domain indexes) as side information or assuming simplified latent causal dynamics. Both constrain the method to a limited range of scenarios. In this study, we further explored the Markov Assumption under time-delayed causally related process in nonstationary setting and showed that under mild conditions, the independent latent components can be recovered from their nonlinear mixture up to a permutation and a component-wise transformation, without the observation of auxiliary variables. We then introduce NCTRL, a principled estimation framework, to reconstruct time-delayed latent causal variables and identify their relations from measured sequential data only. Empirical evaluations demonstrated the reliable identification of time-delayed latent causal influences, with our methodology substantially outperforming existing baselines that fail to exploit the nonstationarity adequately and then, consequently, cannot distinguish distribution shifts. |
1312.7793 | Zhao Tan | Zhao Tan, Yonina C. Eldar, Arye Nehorai | Direction of Arrival Estimation Using Co-prime Arrays: A Super
Resolution Viewpoint | Submitted on December 17th, 2013 | null | 10.1109/TSP.2014.2354316 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of direction of arrival (DOA) estimation using a
newly proposed structure of non-uniform linear arrays, referred to as co-prime
arrays, in this paper. By exploiting the second order statistical information
of the received signals, co-prime arrays exhibit O(MN) degrees of freedom with
only M + N sensors. A sparsity based recovery method is proposed to fully
utilize these degrees of freedom. Unlike traditional sparse recovery methods,
the proposed method is based on the developing theory of super resolution,
which considers a continuous range of possible sources instead of discretizing
this range into a discrete grid. With this approach, off-grid effects inherited
in traditional sparse recovery can be neglected, thus improving the accuracy of
DOA estimation. In this paper we show that in the noiseless case one can
theoretically detect up to M N sources with only 2M + N sensors. The noise 2
statistics of co-prime arrays are also analyzed to demonstrate the robustness
of the proposed optimization scheme. A source number detection method is
presented based on the spectrum reconstructed from the sparse method. By
extensive numerical examples, we show the superiority of the proposed method in
terms of DOA estimation accuracy, degrees of freedom, and resolution ability
compared with previous methods, such as MUSIC with spatial smoothing and the
discrete sparse recovery method.
| [
{
"created": "Mon, 30 Dec 2013 17:42:58 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Tan",
"Zhao",
""
],
[
"Eldar",
"Yonina C.",
""
],
[
"Nehorai",
"Arye",
""
]
] | We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of non-uniform linear arrays, referred to as co-prime arrays, in this paper. By exploiting the second order statistical information of the received signals, co-prime arrays exhibit O(MN) degrees of freedom with only M + N sensors. A sparsity based recovery method is proposed to fully utilize these degrees of freedom. Unlike traditional sparse recovery methods, the proposed method is based on the developing theory of super resolution, which considers a continuous range of possible sources instead of discretizing this range into a discrete grid. With this approach, off-grid effects inherited in traditional sparse recovery can be neglected, thus improving the accuracy of DOA estimation. In this paper we show that in the noiseless case one can theoretically detect up to M N sources with only 2M + N sensors. The noise 2 statistics of co-prime arrays are also analyzed to demonstrate the robustness of the proposed optimization scheme. A source number detection method is presented based on the spectrum reconstructed from the sparse method. By extensive numerical examples, we show the superiority of the proposed method in terms of DOA estimation accuracy, degrees of freedom, and resolution ability compared with previous methods, such as MUSIC with spatial smoothing and the discrete sparse recovery method. |
2305.06732 | Eleonore Bach | Eleonore Bach, Friedrich Eisenbrand, Rom Pinchasi | Integer points in the degree-sequence polytope | 14 pages | null | null | null | cs.DM | http://creativecommons.org/licenses/by-sa/4.0/ | An integer vector $b \in \mathbb{Z}^d$ is a degree sequence if there exists a
hypergraph with vertices $\{1,\dots,d\}$ such that each $b_i$ is the number of
hyperedges containing $i$. The degree-sequence polytope $\mathscr{Z}^d$ is the
convex hull of all degree sequences. We show that all but a $2^{-\Omega(d)}$
fraction of integer vectors in the degree sequence polytope are degree
sequences. Furthermore, the corresponding hypergraph of these points can be
computed in time $2^{O(d)}$ via linear programming techniques. This is
substantially faster than the $2^{O(d^2)}$ running time of the current-best
algorithm for the degree-sequence problem. We also show that for $d\geq 98$,
the degree-sequence polytope $\mathscr{Z}^d$ contains integer points that are
not degree sequences. Furthermore, we prove that the linear optimization
problem over $\mathscr{Z}^d$ is $\mathrm{NP}$-hard. This complements a recent
result of Deza et al. (2018) who provide an algorithm that is polynomial in $d$
and the number of hyperedges.
| [
{
"created": "Thu, 11 May 2023 11:20:40 GMT",
"version": "v1"
}
] | 2023-05-12 | [
[
"Bach",
"Eleonore",
""
],
[
"Eisenbrand",
"Friedrich",
""
],
[
"Pinchasi",
"Rom",
""
]
] | An integer vector $b \in \mathbb{Z}^d$ is a degree sequence if there exists a hypergraph with vertices $\{1,\dots,d\}$ such that each $b_i$ is the number of hyperedges containing $i$. The degree-sequence polytope $\mathscr{Z}^d$ is the convex hull of all degree sequences. We show that all but a $2^{-\Omega(d)}$ fraction of integer vectors in the degree sequence polytope are degree sequences. Furthermore, the corresponding hypergraph of these points can be computed in time $2^{O(d)}$ via linear programming techniques. This is substantially faster than the $2^{O(d^2)}$ running time of the current-best algorithm for the degree-sequence problem. We also show that for $d\geq 98$, the degree-sequence polytope $\mathscr{Z}^d$ contains integer points that are not degree sequences. Furthermore, we prove that the linear optimization problem over $\mathscr{Z}^d$ is $\mathrm{NP}$-hard. This complements a recent result of Deza et al. (2018) who provide an algorithm that is polynomial in $d$ and the number of hyperedges. |
1602.07720 | Renato Paes Leme | Renato Paes Leme, Martin Pal, Sergei Vassilvitskii | A Field Guide to Personalized Reserve Prices | Accepted to WWW'16 | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the question of setting and testing reserve prices in single item
auctions when the bidders are not identical. At a high level, there are two
generalizations of the standard second price auction: in the lazy version we
first determine the winner, and then apply reserve prices; in the eager version
we first discard the bidders not meeting their reserves, and then determine the
winner among the rest. We show that the two versions have dramatically
different properties: lazy reserves are easy to optimize, and A/B test in
production, whereas eager reserves always lead to higher welfare, but their
optimization is NP-complete, and naive A/B testing will lead to incorrect
conclusions. Despite their different characteristics, we show that the overall
revenue for the two scenarios is always within a factor of 2 of each other,
even in the presence of correlated bids. Moreover, we prove that the eager
auction dominates the lazy auction on revenue whenever the bidders are
independent or symmetric. We complement our theoretical results with
simulations on real world data that show that even suboptimally set eager
reserve prices are preferred from a revenue standpoint.
| [
{
"created": "Wed, 24 Feb 2016 21:39:17 GMT",
"version": "v1"
}
] | 2016-02-26 | [
[
"Leme",
"Renato Paes",
""
],
[
"Pal",
"Martin",
""
],
[
"Vassilvitskii",
"Sergei",
""
]
] | We study the question of setting and testing reserve prices in single item auctions when the bidders are not identical. At a high level, there are two generalizations of the standard second price auction: in the lazy version we first determine the winner, and then apply reserve prices; in the eager version we first discard the bidders not meeting their reserves, and then determine the winner among the rest. We show that the two versions have dramatically different properties: lazy reserves are easy to optimize, and A/B test in production, whereas eager reserves always lead to higher welfare, but their optimization is NP-complete, and naive A/B testing will lead to incorrect conclusions. Despite their different characteristics, we show that the overall revenue for the two scenarios is always within a factor of 2 of each other, even in the presence of correlated bids. Moreover, we prove that the eager auction dominates the lazy auction on revenue whenever the bidders are independent or symmetric. We complement our theoretical results with simulations on real world data that show that even suboptimally set eager reserve prices are preferred from a revenue standpoint. |
1906.09029 | Vincenzo Matta | Augusto Santos, Vincenzo Matta, and Ali H. Sayed | Topology Inference over Networks with Nonlinear Coupling | Submitted for publication | null | null | null | cs.MA cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work examines the problem of topology inference over discrete-time
nonlinear stochastic networked dynamical systems. The goal is to recover the
underlying digraph linking the network agents, from observations of their
state-evolution. The dynamical law governing the state-evolution of the
interacting agents might be nonlinear, i.e., the next state of an agent can
depend nonlinearly on its current state and on the states of its immediate
neighbors. We establish sufficient conditions that allow consistent graph
learning over a special class of networked systems, namely, logistic-type
dynamical systems.
| [
{
"created": "Fri, 21 Jun 2019 09:44:29 GMT",
"version": "v1"
}
] | 2019-06-24 | [
[
"Santos",
"Augusto",
""
],
[
"Matta",
"Vincenzo",
""
],
[
"Sayed",
"Ali H.",
""
]
] | This work examines the problem of topology inference over discrete-time nonlinear stochastic networked dynamical systems. The goal is to recover the underlying digraph linking the network agents, from observations of their state-evolution. The dynamical law governing the state-evolution of the interacting agents might be nonlinear, i.e., the next state of an agent can depend nonlinearly on its current state and on the states of its immediate neighbors. We establish sufficient conditions that allow consistent graph learning over a special class of networked systems, namely, logistic-type dynamical systems. |
2201.06539 | Keuntaek Lee | Keuntaek Lee, David Isele, Evangelos A. Theodorou, Sangjae Bae | Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement
Learning | IEEE Robotics and Automation Letters (RA-L) | null | null | null | cs.RO cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | It can be difficult to autonomously produce driver behavior so that it
appears natural to other traffic participants. Through Inverse Reinforcement
Learning (IRL), we can automate this process by learning the underlying reward
function from human demonstrations. We propose a new IRL algorithm that learns
a goal-conditioned spatiotemporal reward function. The resulting costmap is
used by Model Predictive Controllers (MPCs) to perform a task without any
hand-designing or hand-tuning of the cost function. We evaluate our proposed
Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL
framework together with MPC in the CARLA simulator for autonomous driving, lane
keeping, and lane changing tasks in a challenging dense traffic highway
scenario. Our proposed methods show higher success rates compared to other
baseline methods including behavior cloning, state-of-the-art RL policies, and
MPC with a learning-based behavior prediction model.
| [
{
"created": "Mon, 17 Jan 2022 17:36:29 GMT",
"version": "v1"
}
] | 2022-01-19 | [
[
"Lee",
"Keuntaek",
""
],
[
"Isele",
"David",
""
],
[
"Theodorou",
"Evangelos A.",
""
],
[
"Bae",
"Sangjae",
""
]
] | It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from human demonstrations. We propose a new IRL algorithm that learns a goal-conditioned spatiotemporal reward function. The resulting costmap is used by Model Predictive Controllers (MPCs) to perform a task without any hand-designing or hand-tuning of the cost function. We evaluate our proposed Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL framework together with MPC in the CARLA simulator for autonomous driving, lane keeping, and lane changing tasks in a challenging dense traffic highway scenario. Our proposed methods show higher success rates compared to other baseline methods including behavior cloning, state-of-the-art RL policies, and MPC with a learning-based behavior prediction model. |
1903.06473 | Zerong Zheng | Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, Yebin Liu | DeepHuman: 3D Human Reconstruction from a Single Image | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated from SMPL model as an additional input. One key
feature of our network is that it fuses different scales of image features into
the 3D space through volumetric feature transformation, which helps to recover
accurate surface geometry. The visible surface details are further refined
through a normal refinement network, which can be concatenated with the volume
generation network using our proposed volumetric normal projection layer. We
also contribute THuman, a 3D real-world human model dataset containing about
7000 models. The network is trained using training data generated from the
dataset. Overall, due to the specific design of our network and the diversity
in our dataset, our method enables 3D human model estimation given only a
single image and outperforms state-of-the-art approaches.
| [
{
"created": "Fri, 15 Mar 2019 11:38:15 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2019 05:44:21 GMT",
"version": "v2"
}
] | 2019-03-29 | [
[
"Zheng",
"Zerong",
""
],
[
"Yu",
"Tao",
""
],
[
"Wei",
"Yixuan",
""
],
[
"Dai",
"Qionghai",
""
],
[
"Liu",
"Yebin",
""
]
] | We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image. To reduce the ambiguities associated with the surface geometry reconstruction, even for the reconstruction of invisible areas, we propose and leverage a dense semantic representation generated from SMPL model as an additional input. One key feature of our network is that it fuses different scales of image features into the 3D space through volumetric feature transformation, which helps to recover accurate surface geometry. The visible surface details are further refined through a normal refinement network, which can be concatenated with the volume generation network using our proposed volumetric normal projection layer. We also contribute THuman, a 3D real-world human model dataset containing about 7000 models. The network is trained using training data generated from the dataset. Overall, due to the specific design of our network and the diversity in our dataset, our method enables 3D human model estimation given only a single image and outperforms state-of-the-art approaches. |
2404.18721 | Shreya Santra | Shreya Santra, Kentaro Uno, Gen Kudo, and Kazuya Yoshida | Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging
Global and Local Environmental Data | 6 pages, 11 figures. Manuscript accepted at the IEEE International
Conference on Space Robotics 2024 | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper presents a novel 3D myopic coverage path planning algorithm for
lunar micro-rovers that can explore unknown environments with limited sensing
and computational capabilities. The algorithm expands upon traditional
non-graph path planning methods to accommodate the complexities of lunar
terrain, utilizing global data with local topographic features into motion cost
calculations. The algorithm also integrates localization and mapping to update
the rover's pose and map the environment. The resulting environment map's
accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were
conducted to validate the algorithm's efficacy in sim-to-real scenarios. The
results showed that the algorithm could achieve high coverage with low energy
consumption and computational cost, while incrementally exploring the terrain
and avoiding obstacles. This study contributes to the advancement of path
planning methodologies for space exploration, paving the way for efficient,
scalable and autonomous exploration of lunar environments by small rovers.
| [
{
"created": "Mon, 29 Apr 2024 14:10:13 GMT",
"version": "v1"
}
] | 2024-04-30 | [
[
"Santra",
"Shreya",
""
],
[
"Uno",
"Kentaro",
""
],
[
"Kudo",
"Gen",
""
],
[
"Yoshida",
"Kazuya",
""
]
] | This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features into motion cost calculations. The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios. The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers. |
1610.03738 | Daniel Wesierski | Daniel Wesierski | Exploring the Entire Regularization Path for the Asymmetric Cost Linear
Support Vector Machine | 8 pages, 2 figures | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an algorithm for exploring the entire regularization path of
asymmetric-cost linear support vector machines. Empirical evidence suggests the
predictive power of support vector machines depends on the regularization
parameters of the training algorithms. The algorithms exploring the entire
regularization paths have been proposed for single-cost support vector machines
thereby providing the complete knowledge on the behavior of the trained model
over the hyperparameter space. Considering the problem in two-dimensional
hyperparameter space though enables our algorithm to maintain greater
flexibility in dealing with special cases and sheds light on problems
encountered by algorithms building the paths in one-dimensional spaces. We
demonstrate two-dimensional regularization paths for linear support vector
machines that we train on synthetic and real data.
| [
{
"created": "Wed, 12 Oct 2016 14:57:10 GMT",
"version": "v1"
}
] | 2016-10-13 | [
[
"Wesierski",
"Daniel",
""
]
] | We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms. The algorithms exploring the entire regularization paths have been proposed for single-cost support vector machines thereby providing the complete knowledge on the behavior of the trained model over the hyperparameter space. Considering the problem in two-dimensional hyperparameter space though enables our algorithm to maintain greater flexibility in dealing with special cases and sheds light on problems encountered by algorithms building the paths in one-dimensional spaces. We demonstrate two-dimensional regularization paths for linear support vector machines that we train on synthetic and real data. |
1504.05451 | Jing Yang | Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu | Adaptive Compressive Tracking via Online Vector Boosting Feature
Selection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, the compressive tracking (CT) method has attracted much attention
due to its high efficiency, but it cannot well deal with the large scale target
appearance variations due to its data-independent random projection matrix that
results in less discriminative features. To address this issue, in this paper
we propose an adaptive CT approach, which selects the most discriminative
features to design an effective appearance model. Our method significantly
improves CT in three aspects: Firstly, the most discriminative features are
selected via an online vector boosting method. Secondly, the object
representation is updated in an effective online manner, which preserves the
stable features while filtering out the noisy ones. Finally, a simple and
effective trajectory rectification approach is adopted that can make the
estimated location more accurate. Extensive experiments on the CVPR2013
tracking benchmark demonstrate the superior performance of our algorithm
compared over state-of-the-art tracking algorithms.
| [
{
"created": "Tue, 21 Apr 2015 14:55:07 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Apr 2015 01:27:08 GMT",
"version": "v2"
}
] | 2015-04-23 | [
[
"Liu",
"Qingshan",
""
],
[
"Yang",
"Jing",
""
],
[
"Zhang",
"Kaihua",
""
],
[
"Wu",
"Yi",
""
]
] | Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects: Firstly, the most discriminative features are selected via an online vector boosting method. Secondly, the object representation is updated in an effective online manner, which preserves the stable features while filtering out the noisy ones. Finally, a simple and effective trajectory rectification approach is adopted that can make the estimated location more accurate. Extensive experiments on the CVPR2013 tracking benchmark demonstrate the superior performance of our algorithm compared over state-of-the-art tracking algorithms. |
2111.04310 | Byeongjun Park | Byeongjun Park, Taekyung Kim, Hyojun Go, Changick Kim | Residual-Guided Learning Representation for Self-Supervised Monocular
Depth Estimation | 5 pages, 2 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Photometric consistency loss is one of the representative objective functions
commonly used for self-supervised monocular depth estimation. However, this
loss often causes unstable depth predictions in textureless or occluded regions
due to incorrect guidance. Recent self-supervised learning approaches tackle
this issue by utilizing feature representations explicitly learned from
auto-encoders, expecting better discriminability than the input image. Despite
the use of auto-encoded features, we observe that the method does not embed
features as discriminative as auto-encoded features. In this paper, we propose
residual guidance loss that enables the depth estimation network to embed the
discriminative feature by transferring the discriminability of auto-encoded
features. We conducted experiments on the KITTI benchmark and verified our
method's superiority and orthogonality on other state-of-the-art methods.
| [
{
"created": "Mon, 8 Nov 2021 07:44:31 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"Park",
"Byeongjun",
""
],
[
"Kim",
"Taekyung",
""
],
[
"Go",
"Hyojun",
""
],
[
"Kim",
"Changick",
""
]
] | Photometric consistency loss is one of the representative objective functions commonly used for self-supervised monocular depth estimation. However, this loss often causes unstable depth predictions in textureless or occluded regions due to incorrect guidance. Recent self-supervised learning approaches tackle this issue by utilizing feature representations explicitly learned from auto-encoders, expecting better discriminability than the input image. Despite the use of auto-encoded features, we observe that the method does not embed features as discriminative as auto-encoded features. In this paper, we propose residual guidance loss that enables the depth estimation network to embed the discriminative feature by transferring the discriminability of auto-encoded features. We conducted experiments on the KITTI benchmark and verified our method's superiority and orthogonality on other state-of-the-art methods. |
1912.12467 | Alcardo Alex Barakabitze | Alcardo Alex Barakabitze, Nabajeet Barman, Arslan Ahmad, Saman
Zadtootaghaj, Lingfen Sun, Maria G. Martini, Luigi Atzori | QoE Management of Multimedia Streaming Services in Future Networks: A
Tutorial and Survey | 42 pages, 21 figures, 10 tables | null | 10.1109/COMST.2019.2958784 | null | cs.NI cs.MM eess.SP | http://creativecommons.org/licenses/by/4.0/ | We provide in this paper a tutorial and a comprehensive survey of QoE
management solutions in current and future networks. We start with a high level
description of QoE management for multimedia services, which integrates QoE
modelling, monitoring, and optimization. This followed by a discussion of HTTP
Adaptive Streaming (HAS) solutions as the dominant technique for streaming
videos over the best-effort Internet. We then summarize the key elements in
SDN/NFV along with an overview of ongoing research projects, standardization
activities and use cases related to SDN, NFV, and other emerging applications.
We provide a survey of the state-of-the-art of QoE management techniques
categorized into three different groups: a) QoE-aware/driven strategies using
SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over
emerging architectures such as multi-access edge computing, cloud/fog
computing, and information-centric networking; and c) extended QoE management
approaches in new domains such as immersive augmented and virtual reality,
mulsemedia and video gaming applications. Based on the review, we present a
list of identified future QoE management challenges regarding emerging
multimedia applications, network management and orchestration, network slicing
and collaborative service management in softwarized networks. Finally, we
provide a discussion on future research directions with a focus on emerging
research areas in QoE management, such as QoE-oriented business models,
QoE-based big data strategies, and scalability issues in QoE optimization.
| [
{
"created": "Sat, 28 Dec 2019 14:50:48 GMT",
"version": "v1"
}
] | 2020-01-01 | [
[
"Barakabitze",
"Alcardo Alex",
""
],
[
"Barman",
"Nabajeet",
""
],
[
"Ahmad",
"Arslan",
""
],
[
"Zadtootaghaj",
"Saman",
""
],
[
"Sun",
"Lingfen",
""
],
[
"Martini",
"Maria G.",
""
],
[
"Atzori",
"Luigi",
""
]
] | We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization. |
cs/0702085 | Vincenzo Nicosia | V. Carchiolo, M. Malgeri, G. Mangioni and V. Nicosia | Social Behaviours Applied to P2P Systems: An efficient Algorithm for
Resource Organisation | 5 Pages; 8 Figures; Presented at COPS 2006 -- WETICE -- Manchester
(UK) | 15th IEEE International Workshops on Enabling Technologies:
Infrastructure for Collaborative Enterprises, 2006. WETICE '06. June 2006
Page(s):65 - 72 | null | null | cs.DC cs.IR | null | P2P systems are a great solution to the problem of distributing resources.
The main issue of P2P networks is that searching and retrieving resources
shared by peers is usually expensive and does not take into account
similarities among peers. In this paper we present preliminary simulations of
PROSA, a novel algorithm for P2P network structuring, inspired by social
behaviours. Peers in PROSA self--organise in social groups of similar peers,
called ``semantic--groups'', depending on the resources they are sharing. Such
a network smoothly evolves to a small--world graph, where queries for resources
are efficiently and effectively routed.
| [
{
"created": "Wed, 14 Feb 2007 11:53:14 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Carchiolo",
"V.",
""
],
[
"Malgeri",
"M.",
""
],
[
"Mangioni",
"G.",
""
],
[
"Nicosia",
"V.",
""
]
] | P2P systems are a great solution to the problem of distributing resources. The main issue of P2P networks is that searching and retrieving resources shared by peers is usually expensive and does not take into account similarities among peers. In this paper we present preliminary simulations of PROSA, a novel algorithm for P2P network structuring, inspired by social behaviours. Peers in PROSA self--organise in social groups of similar peers, called ``semantic--groups'', depending on the resources they are sharing. Such a network smoothly evolves to a small--world graph, where queries for resources are efficiently and effectively routed. |
0801.0677 | Jad Saklawi | Paul C. Attie | Finite-state concurrent programs can be expressed pairwise | 14 pages | null | null | null | cs.LO | null | We present a \emph{pairwise normal form} for finite-state shared memory
concurrent programs: all variables are shared between exactly two processes,
and the guards on transitions are conjunctions of conditions over this pairwise
shared state. This representation has been used to efficiently (in polynomial
time) synthesize and model-check correctness properties of concurrent programs.
Our main result is that any finite state concurrent program can be transformed
into pairwise normal form. Specifically, if $Q$ is an arbitrary finite-state
shared memory concurrent program, then there exists a finite-state shared
memory concurrent program $P$ expressed in pairwise normal form such that $P$
is strongly bisimilar to $Q$. Our result is constructive: we give an algorithm
for producing $P$, given $Q$.
| [
{
"created": "Fri, 4 Jan 2008 13:14:31 GMT",
"version": "v1"
}
] | 2008-01-07 | [
[
"Attie",
"Paul C.",
""
]
] | We present a \emph{pairwise normal form} for finite-state shared memory concurrent programs: all variables are shared between exactly two processes, and the guards on transitions are conjunctions of conditions over this pairwise shared state. This representation has been used to efficiently (in polynomial time) synthesize and model-check correctness properties of concurrent programs. Our main result is that any finite state concurrent program can be transformed into pairwise normal form. Specifically, if $Q$ is an arbitrary finite-state shared memory concurrent program, then there exists a finite-state shared memory concurrent program $P$ expressed in pairwise normal form such that $P$ is strongly bisimilar to $Q$. Our result is constructive: we give an algorithm for producing $P$, given $Q$. |
2212.04692 | Toshihiro Ota | Toshihiro Ota, Ryo Karakida | Attention in a family of Boltzmann machines emerging from modern
Hopfield networks | 15 pages, 3 figures. v2: added figures and various
corrections/improvements especially in Introduction and Section 3. Published
version | null | null | RIKEN-iTHEMS-Report-22 | cs.LG cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hopfield networks and Boltzmann machines (BMs) are fundamental energy-based
neural network models. Recent studies on modern Hopfield networks have broaden
the class of energy functions and led to a unified perspective on general
Hopfield networks including an attention module. In this letter, we consider
the BM counterparts of modern Hopfield networks using the associated energy
functions, and study their salient properties from a trainability perspective.
In particular, the energy function corresponding to the attention module
naturally introduces a novel BM, which we refer to as the attentional BM
(AttnBM). We verify that AttnBM has a tractable likelihood function and
gradient for certain special cases and is easy to train. Moreover, we reveal
the hidden connections between AttnBM and some single-layer models, namely the
Gaussian--Bernoulli restricted BM and the denoising autoencoder with softmax
units coming from denoising score matching. We also investigate BMs introduced
by other energy functions and show that the energy function of dense
associative memory models gives BMs belonging to Exponential Family Harmoniums.
| [
{
"created": "Fri, 9 Dec 2022 06:52:36 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Mar 2023 02:36:58 GMT",
"version": "v2"
}
] | 2023-03-30 | [
[
"Ota",
"Toshihiro",
""
],
[
"Karakida",
"Ryo",
""
]
] | Hopfield networks and Boltzmann machines (BMs) are fundamental energy-based neural network models. Recent studies on modern Hopfield networks have broaden the class of energy functions and led to a unified perspective on general Hopfield networks including an attention module. In this letter, we consider the BM counterparts of modern Hopfield networks using the associated energy functions, and study their salient properties from a trainability perspective. In particular, the energy function corresponding to the attention module naturally introduces a novel BM, which we refer to as the attentional BM (AttnBM). We verify that AttnBM has a tractable likelihood function and gradient for certain special cases and is easy to train. Moreover, we reveal the hidden connections between AttnBM and some single-layer models, namely the Gaussian--Bernoulli restricted BM and the denoising autoencoder with softmax units coming from denoising score matching. We also investigate BMs introduced by other energy functions and show that the energy function of dense associative memory models gives BMs belonging to Exponential Family Harmoniums. |
2404.15886 | Eman Alqahtani | Eman Alqahtani and Mustafa A. Mustafa | Privacy-Preserving Billing for Local Energy Markets (Long Version) | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a privacy-preserving billing protocol for local energy markets
(PBP-LEMs) that takes into account market participants' energy volume
deviations from their bids. PBP-LEMs enables a group of market entities to
jointly compute participants' bills in a decentralized and privacy-preserving
manner without sacrificing correctness. It also mitigates risks on individuals'
privacy arising from any potential internal collusion. We first propose a
novel, efficient, and privacy-preserving individual billing scheme, achieving
information-theoretic security, which serves as a building block. PBP-LEMs
utilizes this scheme, along with other techniques such as multiparty
computation, Pedersen commitments and inner product functional encryption, to
ensure data confidentiality and accuracy. Additionally, we present three
approaches, resulting in different levels of privacy and performance. We prove
that the protocol meets its security and privacy requirements and is feasible
for deployment in real LEMs. Our analysis also shows variations in overall
performance and identifies areas where overhead is concentrated based on the
applied approach.
| [
{
"created": "Wed, 24 Apr 2024 14:12:56 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Alqahtani",
"Eman",
""
],
[
"Mustafa",
"Mustafa A.",
""
]
] | We propose a privacy-preserving billing protocol for local energy markets (PBP-LEMs) that takes into account market participants' energy volume deviations from their bids. PBP-LEMs enables a group of market entities to jointly compute participants' bills in a decentralized and privacy-preserving manner without sacrificing correctness. It also mitigates risks on individuals' privacy arising from any potential internal collusion. We first propose a novel, efficient, and privacy-preserving individual billing scheme, achieving information-theoretic security, which serves as a building block. PBP-LEMs utilizes this scheme, along with other techniques such as multiparty computation, Pedersen commitments and inner product functional encryption, to ensure data confidentiality and accuracy. Additionally, we present three approaches, resulting in different levels of privacy and performance. We prove that the protocol meets its security and privacy requirements and is feasible for deployment in real LEMs. Our analysis also shows variations in overall performance and identifies areas where overhead is concentrated based on the applied approach. |
1302.6677 | Ashish Sabharwal | Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman | Taming the Curse of Dimensionality: Discrete Integration by Hashing and
Optimization | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Integration is affected by the curse of dimensionality and quickly becomes
intractable as the dimensionality of the problem grows. We propose a randomized
algorithm that, with high probability, gives a constant-factor approximation of
a general discrete integral defined over an exponentially large set. This
algorithm relies on solving only a small number of instances of a discrete
combinatorial optimization problem subject to randomly generated parity
constraints used as a hash function. As an application, we demonstrate that
with a small number of MAP queries we can efficiently approximate the partition
function of discrete graphical models, which can in turn be used, for instance,
for marginal computation or model selection.
| [
{
"created": "Wed, 27 Feb 2013 06:45:28 GMT",
"version": "v1"
}
] | 2013-02-28 | [
[
"Ermon",
"Stefano",
""
],
[
"Gomes",
"Carla P.",
""
],
[
"Sabharwal",
"Ashish",
""
],
[
"Selman",
"Bart",
""
]
] | Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm relies on solving only a small number of instances of a discrete combinatorial optimization problem subject to randomly generated parity constraints used as a hash function. As an application, we demonstrate that with a small number of MAP queries we can efficiently approximate the partition function of discrete graphical models, which can in turn be used, for instance, for marginal computation or model selection. |
2408.06707 | JunYong Choi | JunYong Choi, SeokYeong Lee, Haesol Park, Seung-Won Jung, Ig-Jae Kim,
Junghyun Cho | MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit
Lighting Representation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this paper, we propose a scene-level inverse rendering framework that uses
multi-view images to decompose the scene into geometry, SVBRDF, and 3D
spatially-varying lighting. While multi-view images have been widely used for
object-level inverse rendering, scene-level inverse rendering has primarily
been studied using single-view images due to the lack of a dataset containing
high dynamic range multi-view images with ground-truth geometry, material, and
spatially-varying lighting. To improve the quality of scene-level inverse
rendering, a novel framework called Multi-view Attention Inverse Rendering
(MAIR) was recently introduced. MAIR performs scene-level multi-view inverse
rendering by expanding the OpenRooms dataset, designing efficient pipelines to
handle multi-view images, and splitting spatially-varying lighting. Although
MAIR showed impressive results, its lighting representation is fixed to
spherical Gaussians, which limits its ability to render images realistically.
Consequently, MAIR cannot be directly used in applications such as material
editing. Moreover, its multi-view aggregation networks have difficulties
extracting rich features because they only focus on the mean and variance
between multi-view features. In this paper, we propose its extended version,
called MAIR++. MAIR++ addresses the aforementioned limitations by introducing
an implicit lighting representation that accurately captures the lighting
conditions of an image while facilitating realistic rendering. Furthermore, we
design a directional attention-based multi-view aggregation network to infer
more intricate relationships between views. Experimental results show that
MAIR++ not only achieves better performance than MAIR and single-view-based
methods, but also displays robust performance on unseen real-world scenes.
| [
{
"created": "Tue, 13 Aug 2024 08:04:23 GMT",
"version": "v1"
}
] | 2024-08-14 | [
[
"Choi",
"JunYong",
""
],
[
"Lee",
"SeokYeong",
""
],
[
"Park",
"Haesol",
""
],
[
"Jung",
"Seung-Won",
""
],
[
"Kim",
"Ig-Jae",
""
],
[
"Cho",
"Junghyun",
""
]
] | In this paper, we propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, SVBRDF, and 3D spatially-varying lighting. While multi-view images have been widely used for object-level inverse rendering, scene-level inverse rendering has primarily been studied using single-view images due to the lack of a dataset containing high dynamic range multi-view images with ground-truth geometry, material, and spatially-varying lighting. To improve the quality of scene-level inverse rendering, a novel framework called Multi-view Attention Inverse Rendering (MAIR) was recently introduced. MAIR performs scene-level multi-view inverse rendering by expanding the OpenRooms dataset, designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Although MAIR showed impressive results, its lighting representation is fixed to spherical Gaussians, which limits its ability to render images realistically. Consequently, MAIR cannot be directly used in applications such as material editing. Moreover, its multi-view aggregation networks have difficulties extracting rich features because they only focus on the mean and variance between multi-view features. In this paper, we propose its extended version, called MAIR++. MAIR++ addresses the aforementioned limitations by introducing an implicit lighting representation that accurately captures the lighting conditions of an image while facilitating realistic rendering. Furthermore, we design a directional attention-based multi-view aggregation network to infer more intricate relationships between views. Experimental results show that MAIR++ not only achieves better performance than MAIR and single-view-based methods, but also displays robust performance on unseen real-world scenes. |
1802.01920 | Vincent Neiger | Pascal Giorgi and Vincent Neiger | Certification of minimal approximant bases | ISSAC 2018. 8 pages, 3 algorithms, acmart sigconf style | null | null | null | cs.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a given computational problem, a certificate is a piece of data that one
(the prover) attaches to the output with the aim of allowing efficient
verification (by the verifier) that this output is correct. Here, we consider
the minimal approximant basis problem, for which the fastest known algorithms
output a polynomial matrix of dimensions $m \times m$ and average degree $D/m$
using $O\tilde{~}(m^\omega \frac{D}{m})$ field operations. We propose a
certificate which, for typical instances of the problem, is computed by the
prover using $O(m^\omega \frac{D}{m})$ additional field operations and allows
verification of the approximant basis by a Monte Carlo algorithm with cost
bound $O(m^\omega + m D)$.
Besides theoretical interest, our motivation also comes from the fact that
approximant bases arise in most of the fastest known algorithms for linear
algebra over the univariate polynomials; thus, this work may help in designing
certificates for other polynomial matrix computations. Furthermore,
cryptographic challenges such as breaking records for discrete logarithm
computations or for integer factorization rely in particular on computing
minimal approximant bases for large instances: certificates can then be used to
provide reliable computation on outsourced and error-prone clusters.
| [
{
"created": "Tue, 6 Feb 2018 12:57:47 GMT",
"version": "v1"
},
{
"created": "Thu, 17 May 2018 20:35:04 GMT",
"version": "v2"
}
] | 2018-05-21 | [
[
"Giorgi",
"Pascal",
""
],
[
"Neiger",
"Vincent",
""
]
] | For a given computational problem, a certificate is a piece of data that one (the prover) attaches to the output with the aim of allowing efficient verification (by the verifier) that this output is correct. Here, we consider the minimal approximant basis problem, for which the fastest known algorithms output a polynomial matrix of dimensions $m \times m$ and average degree $D/m$ using $O\tilde{~}(m^\omega \frac{D}{m})$ field operations. We propose a certificate which, for typical instances of the problem, is computed by the prover using $O(m^\omega \frac{D}{m})$ additional field operations and allows verification of the approximant basis by a Monte Carlo algorithm with cost bound $O(m^\omega + m D)$. Besides theoretical interest, our motivation also comes from the fact that approximant bases arise in most of the fastest known algorithms for linear algebra over the univariate polynomials; thus, this work may help in designing certificates for other polynomial matrix computations. Furthermore, cryptographic challenges such as breaking records for discrete logarithm computations or for integer factorization rely in particular on computing minimal approximant bases for large instances: certificates can then be used to provide reliable computation on outsourced and error-prone clusters. |
1904.07154 | Jaehun Kim | Jaehun Kim, Juli\'an Urbano, Cynthia C. S. Liem, Alan Hanjalic | Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings | this work was accepted for publication in the "Frontiers in Applied
Mathematics and Statistics (Deep Learning: Status, Applications and
Algorithms)" | null | null | null | cs.LG cs.SD eess.AS stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Deep neural networks have frequently been used to directly learn
representations useful for a given task from raw input data. In terms of
overall performance metrics, machine learning solutions employing deep
representations frequently have been reported to greatly outperform those using
hand-crafted feature representations. At the same time, they may pick up on
aspects that are predominant in the data, yet not actually meaningful or
interpretable. In this paper, we therefore propose a systematic way to test the
trustworthiness of deep music representations, considering musical semantics.
The underlying assumption is that in case a deep representation is to be
trusted, distance consistency between known related points should be maintained
both in the input audio space and corresponding latent deep space. We generate
known related points through semantically meaningful transformations, both
considering imperceptible and graver transformations. Then, we examine within-
and between-space distance consistencies, both considering audio space and
latent embedded space, the latter either being a result of a conventional
feature extractor or a deep encoder. We illustrate how our method, as a
complement to task-specific performance, provides interpretable insight into
what a network may have captured from training data signals.
| [
{
"created": "Mon, 15 Apr 2019 16:08:41 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Oct 2019 21:42:36 GMT",
"version": "v2"
},
{
"created": "Thu, 17 Oct 2019 23:34:04 GMT",
"version": "v3"
}
] | 2019-10-21 | [
[
"Kim",
"Jaehun",
""
],
[
"Urbano",
"Julián",
""
],
[
"Liem",
"Cynthia C. S.",
""
],
[
"Hanjalic",
"Alan",
""
]
] | Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time, they may pick up on aspects that are predominant in the data, yet not actually meaningful or interpretable. In this paper, we therefore propose a systematic way to test the trustworthiness of deep music representations, considering musical semantics. The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space. We generate known related points through semantically meaningful transformations, both considering imperceptible and graver transformations. Then, we examine within- and between-space distance consistencies, both considering audio space and latent embedded space, the latter either being a result of a conventional feature extractor or a deep encoder. We illustrate how our method, as a complement to task-specific performance, provides interpretable insight into what a network may have captured from training data signals. |
2106.02809 | Kaihao Zhang | Lirong Zheng, Yanshan Li, Kaihao Zhang, Wenhan Luo | T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hazy images reduce the visibility of the image content, and haze will lead to
failure in handling subsequent computer vision tasks. In this paper, we address
the problem of image dehazing by proposing a dehazing network named T-Net,
which consists of a backbone network based on the U-Net architecture and a dual
attention module. And it can achieve multi-scale feature fusion by using skip
connections with a new fusion strategy. Furthermore, by repeatedly unfolding
the plain T-Net, Stack T-Net is proposed to take advantage of the dependence of
deep features across stages via a recursive strategy. In order to reduce
network parameters, the intra-stage recursive computation of ResNet is adopted
in our Stack T-Net. And we take both the stage-wise result and the original
hazy image as input to each T-Net and finally output the prediction of clean
image. Experimental results on both synthetic and real-world images demonstrate
that our plain T-Net and the advanced Stack T-Net perform favorably against the
state-of-the-art dehazing algorithms, and show that our Stack T-Net could
further improve the dehazing effect, demonstrating the effectiveness of the
recursive strategy.
| [
{
"created": "Sat, 5 Jun 2021 06:01:05 GMT",
"version": "v1"
}
] | 2021-06-08 | [
[
"Zheng",
"Lirong",
""
],
[
"Li",
"Yanshan",
""
],
[
"Zhang",
"Kaihao",
""
],
[
"Luo",
"Wenhan",
""
]
] | Hazy images reduce the visibility of the image content, and haze will lead to failure in handling subsequent computer vision tasks. In this paper, we address the problem of image dehazing by proposing a dehazing network named T-Net, which consists of a backbone network based on the U-Net architecture and a dual attention module. And it can achieve multi-scale feature fusion by using skip connections with a new fusion strategy. Furthermore, by repeatedly unfolding the plain T-Net, Stack T-Net is proposed to take advantage of the dependence of deep features across stages via a recursive strategy. In order to reduce network parameters, the intra-stage recursive computation of ResNet is adopted in our Stack T-Net. And we take both the stage-wise result and the original hazy image as input to each T-Net and finally output the prediction of clean image. Experimental results on both synthetic and real-world images demonstrate that our plain T-Net and the advanced Stack T-Net perform favorably against the state-of-the-art dehazing algorithms, and show that our Stack T-Net could further improve the dehazing effect, demonstrating the effectiveness of the recursive strategy. |
cs/0603067 | Priya Sivakumar | Priya Sivakumar | Implementing the Three-Stage Quantum Cryptography Protocol | 4 pages, 1 figure | null | null | null | cs.CR | null | We present simple implementations of Kak's three-stage quantum cryptography
protocol. The case where the transformation is applied to more than one qubit
at the same time is also considered.
| [
{
"created": "Thu, 16 Mar 2006 22:20:44 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Sivakumar",
"Priya",
""
]
] | We present simple implementations of Kak's three-stage quantum cryptography protocol. The case where the transformation is applied to more than one qubit at the same time is also considered. |
2209.06452 | Luigy Alex Machaca Arcana | Luigy Machaca, F. Oliver Sumari H, Jose Huaman, Esteban Clua, Joris
Guerin | TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly
Detection | 6 pages, 4 figures, Accepted on ICMLA 2022 | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Person Re-Identification (Re-ID) aims to search for a person of interest
(query) in a network of cameras. In the classic Re-ID setting the query is
sought in a gallery containing properly cropped images of entire bodies.
Recently, the live Re-ID setting was introduced to represent the practical
application context of Re-ID better. It consists in searching for the query in
short videos, containing whole scene frames. The initial live Re-ID baseline
used a pedestrian detector to build a large search gallery and a classic Re-ID
model to find the query in the gallery. However, the galleries generated were
too large and contained low-quality images, which decreased the live Re-ID
performance. Here, we present a new live Re-ID approach called TrADe, to
generate lower high-quality galleries. TrADe first uses a Tracking algorithm to
identify sequences of images of the same individual in the gallery. Following,
an Anomaly Detection model is used to select a single good representative of
each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011
dataset and shows significant improvements over the baseline.
| [
{
"created": "Wed, 14 Sep 2022 07:00:35 GMT",
"version": "v1"
}
] | 2022-09-15 | [
[
"Machaca",
"Luigy",
""
],
[
"H",
"F. Oliver Sumari",
""
],
[
"Huaman",
"Jose",
""
],
[
"Clua",
"Esteban",
""
],
[
"Guerin",
"Joris",
""
]
] | Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing properly cropped images of entire bodies. Recently, the live Re-ID setting was introduced to represent the practical application context of Re-ID better. It consists in searching for the query in short videos, containing whole scene frames. The initial live Re-ID baseline used a pedestrian detector to build a large search gallery and a classic Re-ID model to find the query in the gallery. However, the galleries generated were too large and contained low-quality images, which decreased the live Re-ID performance. Here, we present a new live Re-ID approach called TrADe, to generate lower high-quality galleries. TrADe first uses a Tracking algorithm to identify sequences of images of the same individual in the gallery. Following, an Anomaly Detection model is used to select a single good representative of each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011 dataset and shows significant improvements over the baseline. |
2105.00328 | Marshall Ho | Marshall Ho, Zhipeng Zhou, Judith He | When to Fold'em: How to answer Unanswerable questions | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | We present 3 different question-answering models trained on the SQuAD2.0
dataset -- BIDAF, DocumentQA and ALBERT Retro-Reader -- demonstrating the
improvement of language models in the past three years. Through our research in
fine-tuning pre-trained models for question-answering, we developed a novel
approach capable of achieving a 2% point improvement in SQuAD2.0 F1 in reduced
training time. Our method of re-initializing select layers of a
parameter-shared language model is simple yet empirically powerful.
| [
{
"created": "Sat, 1 May 2021 19:08:40 GMT",
"version": "v1"
}
] | 2021-05-04 | [
[
"Ho",
"Marshall",
""
],
[
"Zhou",
"Zhipeng",
""
],
[
"He",
"Judith",
""
]
] | We present 3 different question-answering models trained on the SQuAD2.0 dataset -- BIDAF, DocumentQA and ALBERT Retro-Reader -- demonstrating the improvement of language models in the past three years. Through our research in fine-tuning pre-trained models for question-answering, we developed a novel approach capable of achieving a 2% point improvement in SQuAD2.0 F1 in reduced training time. Our method of re-initializing select layers of a parameter-shared language model is simple yet empirically powerful. |
2302.07396 | Marcelo Magnasco | Marcelo O. Magnasco | Convolutional unitary or orthogonal recurrent neural networks | null | null | null | null | cs.LG cond-mat.stat-mech cs.AI q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Recurrent neural networks are extremely powerful yet hard to train. One of
their issues is the vanishing gradient problem, whereby propagation of training
signals may be exponentially attenuated, freezing training. Use of orthogonal
or unitary matrices, whose powers neither explode nor decay, has been proposed
to mitigate this issue, but their computational expense has hindered their use.
Here we show that in the specific case of convolutional RNNs, we can define a
convolutional exponential and that this operation transforms antisymmetric or
anti-Hermitian convolution kernels into orthogonal or unitary convolution
kernels. We explicitly derive FFT-based algorithms to compute the kernels and
their derivatives. The computational complexity of parametrizing this subspace
of orthogonal transformations is thus the same as the networks' iteration.
| [
{
"created": "Tue, 14 Feb 2023 23:36:21 GMT",
"version": "v1"
}
] | 2023-02-16 | [
[
"Magnasco",
"Marcelo O.",
""
]
] | Recurrent neural networks are extremely powerful yet hard to train. One of their issues is the vanishing gradient problem, whereby propagation of training signals may be exponentially attenuated, freezing training. Use of orthogonal or unitary matrices, whose powers neither explode nor decay, has been proposed to mitigate this issue, but their computational expense has hindered their use. Here we show that in the specific case of convolutional RNNs, we can define a convolutional exponential and that this operation transforms antisymmetric or anti-Hermitian convolution kernels into orthogonal or unitary convolution kernels. We explicitly derive FFT-based algorithms to compute the kernels and their derivatives. The computational complexity of parametrizing this subspace of orthogonal transformations is thus the same as the networks' iteration. |
2006.05203 | Travis LaCroix | Travis LaCroix and Aydin Mohseni | The Tragedy of the AI Commons | 40 Pages, 5 Figures | null | null | null | cs.CY cs.AI cs.GT cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Policy and guideline proposals for ethical artificial-intelligence research
have proliferated in recent years. These are supposed to guide the
socially-responsible development of AI for the common good. However, there
typically exist incentives for non-cooperation (i.e., non-adherence to such
policies and guidelines); and, these proposals often lack effective mechanisms
to enforce their own normative claims. The situation just described constitutes
a social dilemma; namely, a situation where no one has an individual incentive
to cooperate, though mutual cooperation would lead to the best outcome for all
involved. In this paper, we use stochastic evolutionary game dynamics to model
this social dilemma in the context of the ethical development of artificial
intelligence. This formalism allows us to isolate variables that may be
intervened upon, thus providing actionable suggestions for increased
cooperation amongst numerous stakeholders in AI. Our results show how
stochastic effects can help make cooperation viable in such a scenario. They
suggest that coordination for a common good should be attempted in smaller
groups in which the cost for cooperation is low, and the perceived risk of
failure is high. This provides insight into the conditions under which we
should expect such ethics proposals to be successful with regard to their
scope, scale, and content.
| [
{
"created": "Tue, 9 Jun 2020 12:01:01 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jan 2021 19:07:13 GMT",
"version": "v2"
}
] | 2021-01-20 | [
[
"LaCroix",
"Travis",
""
],
[
"Mohseni",
"Aydin",
""
]
] | Policy and guideline proposals for ethical artificial-intelligence research have proliferated in recent years. These are supposed to guide the socially-responsible development of AI for the common good. However, there typically exist incentives for non-cooperation (i.e., non-adherence to such policies and guidelines); and, these proposals often lack effective mechanisms to enforce their own normative claims. The situation just described constitutes a social dilemma; namely, a situation where no one has an individual incentive to cooperate, though mutual cooperation would lead to the best outcome for all involved. In this paper, we use stochastic evolutionary game dynamics to model this social dilemma in the context of the ethical development of artificial intelligence. This formalism allows us to isolate variables that may be intervened upon, thus providing actionable suggestions for increased cooperation amongst numerous stakeholders in AI. Our results show how stochastic effects can help make cooperation viable in such a scenario. They suggest that coordination for a common good should be attempted in smaller groups in which the cost for cooperation is low, and the perceived risk of failure is high. This provides insight into the conditions under which we should expect such ethics proposals to be successful with regard to their scope, scale, and content. |
2204.02181 | Hyeonbin Hwang | Hyeonbin Hwang, Soyeon Kim, Wei-Jin Park, Jiho Seo, Kyungtae Ko, Hyeon
Yeo | Vision Transformer Equipped with Neural Resizer on Facial Expression
Recognition Task | Accepted to IEEE ICASSP 2022 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | When it comes to wild conditions, Facial Expression Recognition is often
challenged with low-quality data and imbalanced, ambiguous labels. This field
has much benefited from CNN based approaches; however, CNN models have
structural limitation to see the facial regions in distant. As a remedy,
Transformer has been introduced to vision fields with global receptive field,
but requires adjusting input spatial size to the pretrained models to enjoy
their strong inductive bias at hands. We herein raise a question whether using
the deterministic interpolation method is enough to feed low-resolution data to
Transformer. In this work, we propose a novel training framework, Neural
Resizer, to support Transformer by compensating information and downscaling in
a data-driven manner trained with loss function balancing the noisiness and
imbalance. Experiments show our Neural Resizer with F-PDLS loss function
improves the performance with Transformer variants in general and nearly
achieves the state-of-the-art performance.
| [
{
"created": "Tue, 5 Apr 2022 13:04:04 GMT",
"version": "v1"
}
] | 2022-04-06 | [
[
"Hwang",
"Hyeonbin",
""
],
[
"Kim",
"Soyeon",
""
],
[
"Park",
"Wei-Jin",
""
],
[
"Seo",
"Jiho",
""
],
[
"Ko",
"Kyungtae",
""
],
[
"Yeo",
"Hyeon",
""
]
] | When it comes to wild conditions, Facial Expression Recognition is often challenged with low-quality data and imbalanced, ambiguous labels. This field has much benefited from CNN based approaches; however, CNN models have structural limitation to see the facial regions in distant. As a remedy, Transformer has been introduced to vision fields with global receptive field, but requires adjusting input spatial size to the pretrained models to enjoy their strong inductive bias at hands. We herein raise a question whether using the deterministic interpolation method is enough to feed low-resolution data to Transformer. In this work, we propose a novel training framework, Neural Resizer, to support Transformer by compensating information and downscaling in a data-driven manner trained with loss function balancing the noisiness and imbalance. Experiments show our Neural Resizer with F-PDLS loss function improves the performance with Transformer variants in general and nearly achieves the state-of-the-art performance. |
1712.01337 | Hsiao-Yu Tung | Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki | Self-supervised Learning of Motion Capture | Neural Information Processing Systems (NIPS) 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current state-of-the-art solutions for motion capture from a single camera
are optimization driven: they optimize the parameters of a 3D human model so
that its re-projection matches measurements in the video (e.g. person
segmentation, optical flow, keypoint detections etc.). Optimization models are
susceptible to local minima. This has been the bottleneck that forced using
clean green-screen like backgrounds at capture time, manual initialization, or
switching to multiple cameras as input resource. In this work, we propose a
learning based motion capture model for single camera input. Instead of
optimizing mesh and skeleton parameters directly, our model optimizes neural
network weights that predict 3D shape and skeleton configurations given a
monocular RGB video. Our model is trained using a combination of strong
supervision from synthetic data, and self-supervision from differentiable
rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c)
human-background segmentation, in an end-to-end framework. Empirically we show
our model combines the best of both worlds of supervised learning and test-time
optimization: supervised learning initializes the model parameters in the right
regime, ensuring good pose and surface initialization at test time, without
manual effort. Self-supervision by back-propagating through differentiable
rendering allows (unsupervised) adaptation of the model to the test data, and
offers much tighter fit than a pretrained fixed model. We show that the
proposed model improves with experience and converges to low-error solutions
where previous optimization methods fail.
| [
{
"created": "Mon, 4 Dec 2017 20:25:47 GMT",
"version": "v1"
}
] | 2017-12-06 | [
[
"Tung",
"Hsiao-Yu Fish",
""
],
[
"Tung",
"Hsiao-Wei",
""
],
[
"Yumer",
"Ersin",
""
],
[
"Fragkiadaki",
"Katerina",
""
]
] | Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection matches measurements in the video (e.g. person segmentation, optical flow, keypoint detections etc.). Optimization models are susceptible to local minima. This has been the bottleneck that forced using clean green-screen like backgrounds at capture time, manual initialization, or switching to multiple cameras as input resource. In this work, we propose a learning based motion capture model for single camera input. Instead of optimizing mesh and skeleton parameters directly, our model optimizes neural network weights that predict 3D shape and skeleton configurations given a monocular RGB video. Our model is trained using a combination of strong supervision from synthetic data, and self-supervision from differentiable rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c) human-background segmentation, in an end-to-end framework. Empirically we show our model combines the best of both worlds of supervised learning and test-time optimization: supervised learning initializes the model parameters in the right regime, ensuring good pose and surface initialization at test time, without manual effort. Self-supervision by back-propagating through differentiable rendering allows (unsupervised) adaptation of the model to the test data, and offers much tighter fit than a pretrained fixed model. We show that the proposed model improves with experience and converges to low-error solutions where previous optimization methods fail. |
2308.09284 | Shaleen Deep | Paraschos Koutris, Shaleen Deep | The Fine-Grained Complexity of CFL Reachability | Appeared in POPL 2023. Please note the erratum on the first page | null | null | null | cs.FL | http://creativecommons.org/licenses/by/4.0/ | Many problems in static program analysis can be modeled as the context-free
language (CFL) reachability problem on directed labeled graphs. The CFL
reachability problem can be generally solved in time $O(n^3)$, where $n$ is the
number of vertices in the graph, with some specific cases that can be solved
faster. In this work, we ask the following question: given a specific CFL, what
is the exact exponent in the monomial of the running time? In other words, for
which cases do we have linear, quadratic or cubic algorithms, and are there
problems with intermediate runtimes? This question is inspired by recent
efforts to classify classic problems in terms of their exact polynomial
complexity, known as {\em fine-grained complexity}. Although recent efforts
have shown some conditional lower bounds (mostly for the class of combinatorial
algorithms), a general picture of the fine-grained complexity landscape for CFL
reachability is missing.
Our main contribution is lower bound results that pinpoint the exact running
time of several classes of CFLs or specific CFLs under widely believed lower
bound conjectures (Boolean Matrix Multiplication and $k$-Clique). We
particularly focus on the family of Dyck-$k$ languages (which are strings with
well-matched parentheses), a fundamental class of CFL reachability problems. We
present new lower bounds for the case of sparse input graphs where the number
of edges $m$ is the input parameter, a common setting in the database
literature. For this setting, we show a cubic lower bound for Andersen's
Pointer Analysis which significantly strengthens prior known results.
| [
{
"created": "Fri, 18 Aug 2023 03:52:27 GMT",
"version": "v1"
}
] | 2023-08-21 | [
[
"Koutris",
"Paraschos",
""
],
[
"Deep",
"Shaleen",
""
]
] | Many problems in static program analysis can be modeled as the context-free language (CFL) reachability problem on directed labeled graphs. The CFL reachability problem can be generally solved in time $O(n^3)$, where $n$ is the number of vertices in the graph, with some specific cases that can be solved faster. In this work, we ask the following question: given a specific CFL, what is the exact exponent in the monomial of the running time? In other words, for which cases do we have linear, quadratic or cubic algorithms, and are there problems with intermediate runtimes? This question is inspired by recent efforts to classify classic problems in terms of their exact polynomial complexity, known as {\em fine-grained complexity}. Although recent efforts have shown some conditional lower bounds (mostly for the class of combinatorial algorithms), a general picture of the fine-grained complexity landscape for CFL reachability is missing. Our main contribution is lower bound results that pinpoint the exact running time of several classes of CFLs or specific CFLs under widely believed lower bound conjectures (Boolean Matrix Multiplication and $k$-Clique). We particularly focus on the family of Dyck-$k$ languages (which are strings with well-matched parentheses), a fundamental class of CFL reachability problems. We present new lower bounds for the case of sparse input graphs where the number of edges $m$ is the input parameter, a common setting in the database literature. For this setting, we show a cubic lower bound for Andersen's Pointer Analysis which significantly strengthens prior known results. |
2312.14280 | Sepideh Koohfar | Sepideh Koohfar and Laura Dietz | Fine-grained Forecasting Models Via Gaussian Process Blurring Effect | 10 pages | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time series forecasting is a challenging task due to the existence of complex
and dynamic temporal dependencies. This can lead to incorrect predictions by
even the best forecasting models. Using more training data is one way to
improve the accuracy, but this source is often limited. In contrast, we are
building on successful denoising approaches for image generation by advocating
for an end-to-end forecasting and denoising paradigm.
We propose an end-to-end forecast-blur-denoise forecasting framework by
encouraging a division of labors between the forecasting and the denoising
models. The initial forecasting model is directed to focus on accurately
predicting the coarse-grained behavior, while the denoiser model focuses on
capturing the fine-grained behavior that is locally blurred by integrating a
Gaussian Process model. All three parts are interacting for the best end-to-end
performance. Our extensive experiments demonstrate that our proposed approach
is able to improve the forecasting accuracy of several state-of-the-art
forecasting models as well as several other denoising approaches.
| [
{
"created": "Thu, 21 Dec 2023 20:25:16 GMT",
"version": "v1"
}
] | 2023-12-25 | [
[
"Koohfar",
"Sepideh",
""
],
[
"Dietz",
"Laura",
""
]
] | Time series forecasting is a challenging task due to the existence of complex and dynamic temporal dependencies. This can lead to incorrect predictions by even the best forecasting models. Using more training data is one way to improve the accuracy, but this source is often limited. In contrast, we are building on successful denoising approaches for image generation by advocating for an end-to-end forecasting and denoising paradigm. We propose an end-to-end forecast-blur-denoise forecasting framework by encouraging a division of labors between the forecasting and the denoising models. The initial forecasting model is directed to focus on accurately predicting the coarse-grained behavior, while the denoiser model focuses on capturing the fine-grained behavior that is locally blurred by integrating a Gaussian Process model. All three parts are interacting for the best end-to-end performance. Our extensive experiments demonstrate that our proposed approach is able to improve the forecasting accuracy of several state-of-the-art forecasting models as well as several other denoising approaches. |
1812.07072 | Nathana\"el Fijalkow | Nathana\"el Fijalkow and Pawe{\l} Gawrychowski and Pierre Ohlmann | The complexity of mean payoff games using universal graphs | null | null | null | null | cs.GT cs.FL cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the computational complexity of solving mean payoff games. This
class of games can be seen as an extension of parity games, and they have
similar complexity status: in both cases solving them is in $\textbf{NP} \cap
\textbf{coNP}$ and not known to be in $\textbf{P}$. In a breakthrough result
Calude, Jain, Khoussainov, Li, and Stephan constructed in 2017 a
quasipolynomial time algorithm for solving parity games, which was quickly
followed by two other algorithms with the same complexity. Our objective is to
investigate how these techniques can be extended to the study of mean payoff
games.
The starting point is the notion of separating automata, which has been used
to present all three quasipolynomial time algorithms for parity games and gives
the best complexity to date. The notion naturally extends to mean payoff games
and yields a class of algorithms for solving mean payoff games. The
contribution of this paper is to prove tight bounds on the complexity of
algorithms in this class. We construct two new algorithms for solving mean
payoff games. Our first algorithm depends on the largest weight $N$ (in
absolute value) appearing in the graph and runs in sublinear time in $N$,
improving over the previously known linear dependence in $N$. Our second
algorithm runs in polynomial time for a fixed number $k$ of weights.
We complement our upper bounds by providing in both cases almost matching
lower bounds, showing the limitations of the separating automata approach. We
show that we cannot hope to improve on the dependence in $N$ nor break the
linear dependence in the exponent in the number $k$ of weights. In particular,
this shows that separating automata do not yield a quasipolynomial algorithm
for solving mean payoff games.
| [
{
"created": "Mon, 17 Dec 2018 22:13:33 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Feb 2019 19:04:26 GMT",
"version": "v2"
}
] | 2019-02-06 | [
[
"Fijalkow",
"Nathanaël",
""
],
[
"Gawrychowski",
"Paweł",
""
],
[
"Ohlmann",
"Pierre",
""
]
] | We study the computational complexity of solving mean payoff games. This class of games can be seen as an extension of parity games, and they have similar complexity status: in both cases solving them is in $\textbf{NP} \cap \textbf{coNP}$ and not known to be in $\textbf{P}$. In a breakthrough result Calude, Jain, Khoussainov, Li, and Stephan constructed in 2017 a quasipolynomial time algorithm for solving parity games, which was quickly followed by two other algorithms with the same complexity. Our objective is to investigate how these techniques can be extended to the study of mean payoff games. The starting point is the notion of separating automata, which has been used to present all three quasipolynomial time algorithms for parity games and gives the best complexity to date. The notion naturally extends to mean payoff games and yields a class of algorithms for solving mean payoff games. The contribution of this paper is to prove tight bounds on the complexity of algorithms in this class. We construct two new algorithms for solving mean payoff games. Our first algorithm depends on the largest weight $N$ (in absolute value) appearing in the graph and runs in sublinear time in $N$, improving over the previously known linear dependence in $N$. Our second algorithm runs in polynomial time for a fixed number $k$ of weights. We complement our upper bounds by providing in both cases almost matching lower bounds, showing the limitations of the separating automata approach. We show that we cannot hope to improve on the dependence in $N$ nor break the linear dependence in the exponent in the number $k$ of weights. In particular, this shows that separating automata do not yield a quasipolynomial algorithm for solving mean payoff games. |
1908.00381 | Anton Vladzymyrskyy | S.P. Morozov, A.V. Vladzymyrskyy, V.G. Klyashtornyy, A.E.
Andreychenko, N.S. Kulberg, V.A. Gombolevsky, K.A. Sergunova | Clinical acceptance of software based on artificial intelligence
technologies (radiology) | For correspondence: info@npcmr.ru, npcmr@zdrav.mos.ru. 28/1,
Srednyaya Kalitnikovskaya st., Moscow, 109029, Russia +7 (495) 276-04-36 | null | null | Preprint No. CDT-2019-1 | cs.AI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aim: provide a methodological framework for the process of clinical tests,
clinical acceptance, and scientific assessment of algorithms and software based
on the artificial intelligence (AI) technologies. Clinical tests are considered
as a preparation stage for the software registration as a medical product. The
authors propose approaches to evaluate accuracy and efficiency of the AI
algorithms for radiology.
| [
{
"created": "Thu, 1 Aug 2019 13:24:26 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Feb 2020 14:16:03 GMT",
"version": "v2"
}
] | 2020-02-28 | [
[
"Morozov",
"S. P.",
""
],
[
"Vladzymyrskyy",
"A. V.",
""
],
[
"Klyashtornyy",
"V. G.",
""
],
[
"Andreychenko",
"A. E.",
""
],
[
"Kulberg",
"N. S.",
""
],
[
"Gombolevsky",
"V. A.",
""
],
[
"Sergunova",
"K. A.",
""
]
] | Aim: provide a methodological framework for the process of clinical tests, clinical acceptance, and scientific assessment of algorithms and software based on the artificial intelligence (AI) technologies. Clinical tests are considered as a preparation stage for the software registration as a medical product. The authors propose approaches to evaluate accuracy and efficiency of the AI algorithms for radiology. |
2005.03853 | Rishi Sonthalia | Rishi Sonthalia, Anna C. Gilbert | Project and Forget: Solving Large-Scale Metric Constrained Problems | null | null | null | null | cs.LG math.OC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a set of dissimilarity measurements amongst data points, determining
what metric representation is most "consistent" with the input measurements or
the metric that best captures the relevant geometric features of the data is a
key step in many machine learning algorithms. Existing methods are restricted
to specific kinds of metrics or small problem sizes because of the large number
of metric constraints in such problems. In this paper, we provide an active set
algorithm, Project and Forget, that uses Bregman projections, to solve metric
constrained problems with many (possibly exponentially) inequality constraints.
We provide a theoretical analysis of \textsc{Project and Forget} and prove that
our algorithm converges to the global optimal solution and that the $L_2$
distance of the current iterate to the optimal solution decays asymptotically
at an exponential rate. We demonstrate that using our method we can solve large
problem instances of three types of metric constrained problems: general weight
correlation clustering, metric nearness, and metric learning; in each case,
out-performing the state of the art methods with respect to CPU times and
problem sizes.
| [
{
"created": "Fri, 8 May 2020 04:50:54 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Sep 2022 21:27:20 GMT",
"version": "v2"
}
] | 2022-09-28 | [
[
"Sonthalia",
"Rishi",
""
],
[
"Gilbert",
"Anna C.",
""
]
] | Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms. Existing methods are restricted to specific kinds of metrics or small problem sizes because of the large number of metric constraints in such problems. In this paper, we provide an active set algorithm, Project and Forget, that uses Bregman projections, to solve metric constrained problems with many (possibly exponentially) inequality constraints. We provide a theoretical analysis of \textsc{Project and Forget} and prove that our algorithm converges to the global optimal solution and that the $L_2$ distance of the current iterate to the optimal solution decays asymptotically at an exponential rate. We demonstrate that using our method we can solve large problem instances of three types of metric constrained problems: general weight correlation clustering, metric nearness, and metric learning; in each case, out-performing the state of the art methods with respect to CPU times and problem sizes. |
2206.10607 | Jeewon Jeon | Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung | MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from
Experience Replay Buffer | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider cooperative multi-agent reinforcement learning
(MARL) with sparse reward. To tackle this problem, we propose a novel method
named MASER: MARL with subgoals generated from experience replay buffer. Under
the widely-used assumption of centralized training with decentralized execution
and consistent Q-value decomposition for MARL, MASER automatically generates
proper subgoals for multiple agents from the experience replay buffer by
considering both individual Q-value and total Q-value. Then, MASER designs
individual intrinsic reward for each agent based on actionable representation
relevant to Q-learning so that the agents reach their subgoals while maximizing
the joint action value. Numerical results show that MASER significantly
outperforms StarCraft II micromanagement benchmark compared to other
state-of-the-art MARL algorithms.
| [
{
"created": "Mon, 20 Jun 2022 08:12:26 GMT",
"version": "v1"
}
] | 2022-06-23 | [
[
"Jeon",
"Jeewon",
""
],
[
"Kim",
"Woojun",
""
],
[
"Jung",
"Whiyoung",
""
],
[
"Sung",
"Youngchul",
""
]
] | In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward. To tackle this problem, we propose a novel method named MASER: MARL with subgoals generated from experience replay buffer. Under the widely-used assumption of centralized training with decentralized execution and consistent Q-value decomposition for MARL, MASER automatically generates proper subgoals for multiple agents from the experience replay buffer by considering both individual Q-value and total Q-value. Then, MASER designs individual intrinsic reward for each agent based on actionable representation relevant to Q-learning so that the agents reach their subgoals while maximizing the joint action value. Numerical results show that MASER significantly outperforms StarCraft II micromanagement benchmark compared to other state-of-the-art MARL algorithms. |
1802.05998 | Tomas Teijeiro | Tom\'as Teijeiro, Constantino A. Garc\'ia, Daniel Castro and Paulo
F\'elix | Abductive reasoning as the basis to reproduce expert criteria in ECG
Atrial Fibrillation identification | 15 pages, 6 figures, 6 tables | Physiological Measurement. 2018 Aug 31;39(8):084006 | 10.1088/1361-6579/aad7e4 | null | cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: This work aims at providing a new method for the automatic
detection of atrial fibrillation, other arrhythmia and noise on short single
lead ECG signals, emphasizing the importance of the interpretability of the
classification results.
Approach: A morphological and rhythm description of the cardiac behavior is
obtained by a knowledge-based interpretation of the signal using the
\textit{Construe} abductive framework. Then, a set of meaningful features are
extracted for each individual heartbeat and as a summary of the full record.
The feature distributions were used to elucidate the expert criteria underlying
the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual
partial relabeling to improve the consistency of the classification rules.
Finally, state-of-the-art machine learning methods are combined to provide an
answer on the basis of the feature values.
Main results: The proposal tied for the first place in the official stage of
the Challenge, with a combined $F_1$ score of 0.83, and was even improved in
the follow-up stage to 0.85 with a significant simplification of the model.
Significance: This approach demonstrates the potential of \textit{Construe}
to provide robust and valuable descriptions of temporal data even with
significant amounts of noise and artifacts. Also, we discuss the importance of
a consistent classification criteria in manually labeled training datasets, and
the fundamental advantages of knowledge-based approaches to formalize and
validate that criteria.
| [
{
"created": "Fri, 16 Feb 2018 16:06:42 GMT",
"version": "v1"
}
] | 2021-12-09 | [
[
"Teijeiro",
"Tomás",
""
],
[
"García",
"Constantino A.",
""
],
[
"Castro",
"Daniel",
""
],
[
"Félix",
"Paulo",
""
]
] | Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single lead ECG signals, emphasizing the importance of the interpretability of the classification results. Approach: A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the \textit{Construe} abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions were used to elucidate the expert criteria underlying the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the classification rules. Finally, state-of-the-art machine learning methods are combined to provide an answer on the basis of the feature values. Main results: The proposal tied for the first place in the official stage of the Challenge, with a combined $F_1$ score of 0.83, and was even improved in the follow-up stage to 0.85 with a significant simplification of the model. Significance: This approach demonstrates the potential of \textit{Construe} to provide robust and valuable descriptions of temporal data even with significant amounts of noise and artifacts. Also, we discuss the importance of a consistent classification criteria in manually labeled training datasets, and the fundamental advantages of knowledge-based approaches to formalize and validate that criteria. |
2012.02024 | Michael Heffels | Michael R. Heffels and Joaquin Vanschoren | Aerial Imagery Pixel-level Segmentation | 30 pages, 15 figures, 4 tables. Code available through GitHub repo at
https://github.com/mrheffels/aerial-imagery-segmentation | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Aerial imagery can be used for important work on a global scale.
Nevertheless, the analysis of this data using neural network architectures lags
behind the current state-of-the-art on popular datasets such as PASCAL VOC,
CityScapes and Camvid. In this paper we bridge the performance-gap between
these popular datasets and aerial imagery data. Little work is done on aerial
imagery with state-of-the-art neural network architectures in a multi-class
setting. Our experiments concerning data augmentation, normalisation, image
size and loss functions give insight into a high performance setup for aerial
imagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+
Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy
validation set. With this result, we clearly outperform the current publicly
available state-of-the-art validation set mIOU (65%) performance with 5%.
Furthermore, to our knowledge, there is no mIOU benchmark for the test set.
Hence, we also propose a new benchmark on the DroneDeploy test set using the
best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%.
| [
{
"created": "Thu, 3 Dec 2020 16:09:09 GMT",
"version": "v1"
}
] | 2020-12-04 | [
[
"Heffels",
"Michael R.",
""
],
[
"Vanschoren",
"Joaquin",
""
]
] | Aerial imagery can be used for important work on a global scale. Nevertheless, the analysis of this data using neural network architectures lags behind the current state-of-the-art on popular datasets such as PASCAL VOC, CityScapes and Camvid. In this paper we bridge the performance-gap between these popular datasets and aerial imagery data. Little work is done on aerial imagery with state-of-the-art neural network architectures in a multi-class setting. Our experiments concerning data augmentation, normalisation, image size and loss functions give insight into a high performance setup for aerial imagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+ Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy validation set. With this result, we clearly outperform the current publicly available state-of-the-art validation set mIOU (65%) performance with 5%. Furthermore, to our knowledge, there is no mIOU benchmark for the test set. Hence, we also propose a new benchmark on the DroneDeploy test set using the best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%. |
2104.08404 | Junghoon Kim | Junghoon Kim and Bruno Clerckx | Wireless Information and Power Transfer for IoT: Pulse Position
Modulation, Integrated Receiver, and Experimental Validation | submitted for publication | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simultaneous wireless information and power transfer (SWIPT) has emerged as a
viable technique to energize and connect low-power autonomous devices and
enable future Internet of Things (IoT). A major challenge of SWIPT is the
energy consumption of the receiver of such low-power devices. An attractive
low-power solution consists in an integrated information decoder (ID) and
energy harvester (EH) architecture for SWIPT receiver (IntRx) where the
received RF signal is first rectified before being used for information
decoding. Such architecture eliminates the need for energy-consuming RF
components such as local oscillators and mixers. This paper proposes a novel
modulation and demodulation method for the IntRx SWIPT architecture based on
pulse position modulation (PPM) where information is encoded in the position of
the pulse. The new method transmits high amplitude pulses to increase the
Peak-to-Average Power Ratio (PAPR) of the transmit signal and exploits the EH's
nonlinearity so as to boost the harvested DC power. Simultaneously, the
information can be decoded from the rectifier signal by simply finding the
position of the pulse in a certain symbol duration. We have analyzed both the
information and the power transfer performance of the newly proposed PPM for
IntRx SWIPT theoretically, numerically, and experimentally. To that end, we
have established a SWIPT system testbed in an indoor environment by prototyping
a base station to transfer information-power signal and the IntRx SWIPT
receiver including ID and EH blocks. The performance evaluation of the PPM was
carried out in various conditions, and the results have been compared and
contrasted to conventional signals. Theoretical, numerical, and experimental
results highlight the significant benefits of the proposed PPM scheme to
enhance the power transfer performance and operate information decoding with
low-power consumption.
| [
{
"created": "Fri, 16 Apr 2021 23:25:51 GMT",
"version": "v1"
}
] | 2021-04-20 | [
[
"Kim",
"Junghoon",
""
],
[
"Clerckx",
"Bruno",
""
]
] | Simultaneous wireless information and power transfer (SWIPT) has emerged as a viable technique to energize and connect low-power autonomous devices and enable future Internet of Things (IoT). A major challenge of SWIPT is the energy consumption of the receiver of such low-power devices. An attractive low-power solution consists in an integrated information decoder (ID) and energy harvester (EH) architecture for SWIPT receiver (IntRx) where the received RF signal is first rectified before being used for information decoding. Such architecture eliminates the need for energy-consuming RF components such as local oscillators and mixers. This paper proposes a novel modulation and demodulation method for the IntRx SWIPT architecture based on pulse position modulation (PPM) where information is encoded in the position of the pulse. The new method transmits high amplitude pulses to increase the Peak-to-Average Power Ratio (PAPR) of the transmit signal and exploits the EH's nonlinearity so as to boost the harvested DC power. Simultaneously, the information can be decoded from the rectifier signal by simply finding the position of the pulse in a certain symbol duration. We have analyzed both the information and the power transfer performance of the newly proposed PPM for IntRx SWIPT theoretically, numerically, and experimentally. To that end, we have established a SWIPT system testbed in an indoor environment by prototyping a base station to transfer information-power signal and the IntRx SWIPT receiver including ID and EH blocks. The performance evaluation of the PPM was carried out in various conditions, and the results have been compared and contrasted to conventional signals. Theoretical, numerical, and experimental results highlight the significant benefits of the proposed PPM scheme to enhance the power transfer performance and operate information decoding with low-power consumption. |
2204.03341 | Tung Kieu | Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao,
Feiteng Huang, Kai Zheng | Robust and Explainable Autoencoders for Unsupervised Time Series Outlier
Detection---Extended Version | This paper has been accepted by IEEE ICDE 2022 | null | null | null | cs.LG cs.DB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Time series data occurs widely, and outlier detection is a fundamental
problem in data mining, which has numerous applications. Existing
autoencoder-based approaches deliver state-of-the-art performance on
challenging real-world data but are vulnerable to outliers and exhibit low
explainability. To address these two limitations, we propose robust and
explainable unsupervised autoencoder frameworks that decompose an input time
series into a clean time series and an outlier time series using autoencoders.
Improved explainability is achieved because clean time series are better
explained with easy-to-understand patterns such as trends and periodicities. We
provide insight into this by means of a post-hoc explainability analysis and
empirical studies. In addition, since outliers are separated from clean time
series iteratively, our approach offers improved robustness to outliers, which
in turn improves accuracy. We evaluate our approach on five real-world datasets
and report improvements over the state-of-the-art approaches in terms of
robustness and explainability.
This is an extended version of "Robust and Explainable Autoencoders for
Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.
| [
{
"created": "Thu, 7 Apr 2022 10:24:12 GMT",
"version": "v1"
}
] | 2022-04-08 | [
[
"Kieu",
"Tung",
""
],
[
"Yang",
"Bin",
""
],
[
"Guo",
"Chenjuan",
""
],
[
"Jensen",
"Christian S.",
""
],
[
"Zhao",
"Yan",
""
],
[
"Huang",
"Feiteng",
""
],
[
"Zheng",
"Kai",
""
]
] | Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised autoencoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. Improved explainability is achieved because clean time series are better explained with easy-to-understand patterns such as trends and periodicities. We provide insight into this by means of a post-hoc explainability analysis and empirical studies. In addition, since outliers are separated from clean time series iteratively, our approach offers improved robustness to outliers, which in turn improves accuracy. We evaluate our approach on five real-world datasets and report improvements over the state-of-the-art approaches in terms of robustness and explainability. This is an extended version of "Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022. |
1701.04626 | Simone Bova | Simone Bova and Stefan Szeider | Circuit Treewidth, Sentential Decision, and Query Compilation | null | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evaluation of a query over a probabilistic database boils down to
computing the probability of a suitable Boolean function, the lineage of the
query over the database. The method of query compilation approaches the task in
two stages: first, the query lineage is implemented (compiled) in a circuit
form where probability computation is tractable; and second, the desired
probability is computed over the compiled circuit. A basic theoretical quest in
query compilation is that of identifying pertinent classes of queries whose
lineages admit compact representations over increasingly succinct, tractable
circuit classes. Fostering previous work by Jha and Suciu (2012) and Petke and
Razgon (2013), we focus on queries whose lineages admit circuit implementations
with small treewidth, and investigate their compilability within tame classes
of decision diagrams. In perfect analogy with the characterization of bounded
circuit pathwidth by bounded OBDD width, we show that a class of Boolean
functions has bounded circuit treewidth if and only if it has bounded SDD
width. Sentential decision diagrams (SDDs) are central in knowledge
compilation, being essentially as tractable as OBDDs but exponentially more
succinct. By incorporating constant width SDDs and polynomial size SDDs, we
refine the panorama of query compilation for unions of conjunctive queries with
and without inequalities.
| [
{
"created": "Tue, 17 Jan 2017 11:34:07 GMT",
"version": "v1"
}
] | 2017-01-18 | [
[
"Bova",
"Simone",
""
],
[
"Szeider",
"Stefan",
""
]
] | The evaluation of a query over a probabilistic database boils down to computing the probability of a suitable Boolean function, the lineage of the query over the database. The method of query compilation approaches the task in two stages: first, the query lineage is implemented (compiled) in a circuit form where probability computation is tractable; and second, the desired probability is computed over the compiled circuit. A basic theoretical quest in query compilation is that of identifying pertinent classes of queries whose lineages admit compact representations over increasingly succinct, tractable circuit classes. Fostering previous work by Jha and Suciu (2012) and Petke and Razgon (2013), we focus on queries whose lineages admit circuit implementations with small treewidth, and investigate their compilability within tame classes of decision diagrams. In perfect analogy with the characterization of bounded circuit pathwidth by bounded OBDD width, we show that a class of Boolean functions has bounded circuit treewidth if and only if it has bounded SDD width. Sentential decision diagrams (SDDs) are central in knowledge compilation, being essentially as tractable as OBDDs but exponentially more succinct. By incorporating constant width SDDs and polynomial size SDDs, we refine the panorama of query compilation for unions of conjunctive queries with and without inequalities. |
1611.04144 | Xuanpeng Li | Xuanpeng Li and Rachid Belaroussi | Semi-Dense 3D Semantic Mapping from Monocular SLAM | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The bundle of geometry and appearance in computer vision has proven to be a
promising solution for robots across a wide variety of applications. Stereo
cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and
trajectory tracking in a dense way. However, they lack flexibility of seamless
switch between different scaled environments, i.e., indoor and outdoor scenes.
In addition, semantic information are still hard to acquire in a 3D mapping. We
address this challenge by combining the state-of-art deep learning method and
semi-dense Simultaneous Localisation and Mapping (SLAM) based on video stream
from a monocular camera. In our approach, 2D semantic information are
transferred to 3D mapping via correspondence between connective Keyframes with
spatial consistency. There is no need to obtain a semantic segmentation for
each frame in a sequence, so that it could achieve a reasonable computation
time. We evaluate our method on indoor/outdoor datasets and lead to an
improvement in the 2D semantic labelling over baseline single frame
predictions.
| [
{
"created": "Sun, 13 Nov 2016 15:31:31 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Li",
"Xuanpeng",
""
],
[
"Belaroussi",
"Rachid",
""
]
] | The bundle of geometry and appearance in computer vision has proven to be a promising solution for robots across a wide variety of applications. Stereo cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and trajectory tracking in a dense way. However, they lack flexibility of seamless switch between different scaled environments, i.e., indoor and outdoor scenes. In addition, semantic information are still hard to acquire in a 3D mapping. We address this challenge by combining the state-of-art deep learning method and semi-dense Simultaneous Localisation and Mapping (SLAM) based on video stream from a monocular camera. In our approach, 2D semantic information are transferred to 3D mapping via correspondence between connective Keyframes with spatial consistency. There is no need to obtain a semantic segmentation for each frame in a sequence, so that it could achieve a reasonable computation time. We evaluate our method on indoor/outdoor datasets and lead to an improvement in the 2D semantic labelling over baseline single frame predictions. |
2211.17107 | Ruturaj Ghatage | Sharvi Endait, Ruturaj Ghatage, Prof. DD Kadam | Handling and extracting key entities from customer conversations using
Speech recognition and Named Entity recognition | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this modern era of technology with e-commerce developing at a rapid pace,
it is very important to understand customer requirements and details from a
business conversation. It is very crucial for customer retention and
satisfaction. Extracting key insights from these conversations is very
important when it comes to developing their product or solving their issue.
Understanding customer feedback, responses, and important details of the
product are essential and it would be done using Named entity recognition
(NER). For extracting the entities we would be converting the conversations to
text using the optimal speech-to-text model. The model would be a two-stage
network in which the conversation is converted to text. Then, suitable entities
are extracted using robust techniques using a NER BERT transformer model. This
will aid in the enrichment of customer experience when there is an issue which
is faced by them. If a customer faces a problem he will call and register his
complaint. The model will then extract the key features from this conversation
which will be necessary to look into the problem. These features would include
details like the order number, and the exact problem. All these would be
extracted directly from the conversation and this would reduce the effort of
going through the conversation again.
| [
{
"created": "Mon, 28 Nov 2022 06:41:29 GMT",
"version": "v1"
}
] | 2022-12-01 | [
[
"Endait",
"Sharvi",
""
],
[
"Ghatage",
"Ruturaj",
""
],
[
"Kadam",
"Prof. DD",
""
]
] | In this modern era of technology with e-commerce developing at a rapid pace, it is very important to understand customer requirements and details from a business conversation. It is very crucial for customer retention and satisfaction. Extracting key insights from these conversations is very important when it comes to developing their product or solving their issue. Understanding customer feedback, responses, and important details of the product are essential and it would be done using Named entity recognition (NER). For extracting the entities we would be converting the conversations to text using the optimal speech-to-text model. The model would be a two-stage network in which the conversation is converted to text. Then, suitable entities are extracted using robust techniques using a NER BERT transformer model. This will aid in the enrichment of customer experience when there is an issue which is faced by them. If a customer faces a problem he will call and register his complaint. The model will then extract the key features from this conversation which will be necessary to look into the problem. These features would include details like the order number, and the exact problem. All these would be extracted directly from the conversation and this would reduce the effort of going through the conversation again. |
2405.02583 | Xiangqi Kong | Xiangqi Kong, Yang Xing, Antonios Tsourdos, Ziyue Wang, Weisi Guo,
Adolfo Perrusquia, Andreas Wikander | Explainable Interface for Human-Autonomy Teaming: A Survey | 45 pages, 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Nowadays, large-scale foundation models are being increasingly integrated
into numerous safety-critical applications, including human-autonomy teaming
(HAT) within transportation, medical, and defence domains. Consequently, the
inherent 'black-box' nature of these sophisticated deep neural networks
heightens the significance of fostering mutual understanding and trust between
humans and autonomous systems. To tackle the transparency challenges in HAT,
this paper conducts a thoughtful study on the underexplored domain of
Explainable Interface (EI) in HAT systems from a human-centric perspective,
thereby enriching the existing body of research in Explainable Artificial
Intelligence (XAI). We explore the design, development, and evaluation of EI
within XAI-enhanced HAT systems. To do so, we first clarify the distinctions
between these concepts: EI, explanations and model explainability, aiming to
provide researchers and practitioners with a structured understanding. Second,
we contribute to a novel framework for EI, addressing the unique challenges in
HAT. Last, our summarized evaluation framework for ongoing EI offers a holistic
perspective, encompassing model performance, human-centered factors, and group
task objectives. Based on extensive surveys across XAI, HAT, psychology, and
Human-Computer Interaction (HCI), this review offers multiple novel insights
into incorporating XAI into HAT systems and outlines future directions.
| [
{
"created": "Sat, 4 May 2024 06:35:38 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Kong",
"Xiangqi",
""
],
[
"Xing",
"Yang",
""
],
[
"Tsourdos",
"Antonios",
""
],
[
"Wang",
"Ziyue",
""
],
[
"Guo",
"Weisi",
""
],
[
"Perrusquia",
"Adolfo",
""
],
[
"Wikander",
"Andreas",
""
]
] | Nowadays, large-scale foundation models are being increasingly integrated into numerous safety-critical applications, including human-autonomy teaming (HAT) within transportation, medical, and defence domains. Consequently, the inherent 'black-box' nature of these sophisticated deep neural networks heightens the significance of fostering mutual understanding and trust between humans and autonomous systems. To tackle the transparency challenges in HAT, this paper conducts a thoughtful study on the underexplored domain of Explainable Interface (EI) in HAT systems from a human-centric perspective, thereby enriching the existing body of research in Explainable Artificial Intelligence (XAI). We explore the design, development, and evaluation of EI within XAI-enhanced HAT systems. To do so, we first clarify the distinctions between these concepts: EI, explanations and model explainability, aiming to provide researchers and practitioners with a structured understanding. Second, we contribute to a novel framework for EI, addressing the unique challenges in HAT. Last, our summarized evaluation framework for ongoing EI offers a holistic perspective, encompassing model performance, human-centered factors, and group task objectives. Based on extensive surveys across XAI, HAT, psychology, and Human-Computer Interaction (HCI), this review offers multiple novel insights into incorporating XAI into HAT systems and outlines future directions. |
1007.0571 | Vikram Krishnamurthy | Vikram Krishnamurthy | Quickest Detection with Social Learning: Interaction of local and global
decision makers | null | null | null | null | cs.GT cs.IT math.IT stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider how local and global decision policies interact in stopping time
problems such as quickest time change detection. Individual agents make myopic
local decisions via social learning, that is, each agent records a private
observation of a noisy underlying state process, selfishly optimizes its local
utility and then broadcasts its local decision. Given these local decisions,
how can a global decision maker achieve quickest time change detection when the
underlying state changes according to a phase-type distribution? The paper
presents four results. First, using Blackwell dominance of measures, it is
shown that the optimal cost incurred in social learning based quickest
detection is always larger than that of classical quickest detection. Second,
it is shown that in general the optimal decision policy for social learning
based quickest detection is characterized by multiple thresholds within the
space of Bayesian distributions. Third, using lattice programming and
stochastic dominance, sufficient conditions are given for the optimal decision
policy to consist of a single linear hyperplane, or, more generally, a
threshold curve. Estimation of the optimal linear approximation to this
threshold curve is formulated as a simulation-based stochastic optimization
problem. Finally, the paper shows that in multi-agent sensor management with
quickest detection, where each agent views the world according to its prior,
the optimal policy has a similar structure to social learning.
| [
{
"created": "Sun, 4 Jul 2010 17:06:38 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Aug 2011 09:01:38 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Mar 2012 18:55:34 GMT",
"version": "v3"
}
] | 2012-03-05 | [
[
"Krishnamurthy",
"Vikram",
""
]
] | We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, that is, each agent records a private observation of a noisy underlying state process, selfishly optimizes its local utility and then broadcasts its local decision. Given these local decisions, how can a global decision maker achieve quickest time change detection when the underlying state changes according to a phase-type distribution? The paper presents four results. First, using Blackwell dominance of measures, it is shown that the optimal cost incurred in social learning based quickest detection is always larger than that of classical quickest detection. Second, it is shown that in general the optimal decision policy for social learning based quickest detection is characterized by multiple thresholds within the space of Bayesian distributions. Third, using lattice programming and stochastic dominance, sufficient conditions are given for the optimal decision policy to consist of a single linear hyperplane, or, more generally, a threshold curve. Estimation of the optimal linear approximation to this threshold curve is formulated as a simulation-based stochastic optimization problem. Finally, the paper shows that in multi-agent sensor management with quickest detection, where each agent views the world according to its prior, the optimal policy has a similar structure to social learning. |
2408.07272 | Junxuan Li | Junxuan Li, Ryan Wickman, Sahil Bhatnagar, Raj Kumar Maity, Arko
Mukherjee | NL2OR: Solve Complex Operations Research Problems Using Natural Language
Inputs | null | null | null | null | cs.AI cs.HC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Operations research (OR) uses mathematical models to enhance decision-making,
but developing these models requires expert knowledge and can be
time-consuming. Automated mathematical programming (AMP) has emerged to
simplify this process, but existing systems have limitations. This paper
introduces a novel methodology that uses recent advances in Large Language
Model (LLM) to create and edit OR solutions from non-expert user queries
expressed using Natural Language. This reduces the need for domain expertise
and the time to formulate a problem. The paper presents an end-to-end pipeline,
named NL2OR, that generates solutions to OR problems from natural language
input, and shares experimental results on several important OR problems.
| [
{
"created": "Wed, 14 Aug 2024 03:42:53 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Li",
"Junxuan",
""
],
[
"Wickman",
"Ryan",
""
],
[
"Bhatnagar",
"Sahil",
""
],
[
"Maity",
"Raj Kumar",
""
],
[
"Mukherjee",
"Arko",
""
]
] | Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems. |
2112.01098 | Surabhi Gupta | Surabhi Gupta, Ashwath Shetty, Avinash Sharma | Attention based Occlusion Removal for Hybrid Telepresence Systems | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Traditionally, video conferencing is a widely adopted solution for
telecommunication, but a lack of immersiveness comes inherently due to the 2D
nature of facial representation. The integration of Virtual Reality (VR) in a
communication/telepresence system through Head Mounted Displays (HMDs) promises
to provide users a much better immersive experience. However, HMDs cause
hindrance by blocking the facial appearance and expressions of the user. To
overcome these issues, we propose a novel attention-enabled encoder-decoder
architecture for HMD de-occlusion. We also propose to train our person-specific
model using short videos (1-2 minutes) of the user, captured in varying
appearances, and demonstrated generalization to unseen poses and appearances of
the user. We report superior qualitative and quantitative results over
state-of-the-art methods. We also present applications of this approach to
hybrid video teleconferencing using existing animation and 3D face
reconstruction pipelines.
| [
{
"created": "Thu, 2 Dec 2021 10:18:22 GMT",
"version": "v1"
}
] | 2021-12-03 | [
[
"Gupta",
"Surabhi",
""
],
[
"Shetty",
"Ashwath",
""
],
[
"Sharma",
"Avinash",
""
]
] | Traditionally, video conferencing is a widely adopted solution for telecommunication, but a lack of immersiveness comes inherently due to the 2D nature of facial representation. The integration of Virtual Reality (VR) in a communication/telepresence system through Head Mounted Displays (HMDs) promises to provide users a much better immersive experience. However, HMDs cause hindrance by blocking the facial appearance and expressions of the user. To overcome these issues, we propose a novel attention-enabled encoder-decoder architecture for HMD de-occlusion. We also propose to train our person-specific model using short videos (1-2 minutes) of the user, captured in varying appearances, and demonstrated generalization to unseen poses and appearances of the user. We report superior qualitative and quantitative results over state-of-the-art methods. We also present applications of this approach to hybrid video teleconferencing using existing animation and 3D face reconstruction pipelines. |
2109.00301 | Pedro Henrique Martins | Pedro Henrique Martins and Zita Marinho and Andr\'e F. T. Martins | $\infty$-former: Infinite Memory Transformer | ACL 2022 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transformers are unable to model long-term memories effectively, since the
amount of computation they need to perform grows with the context length. While
variations of efficient transformers have been proposed, they all have a finite
memory capacity and are forced to drop old information. In this paper, we
propose the $\infty$-former, which extends the vanilla transformer with an
unbounded long-term memory. By making use of a continuous-space attention
mechanism to attend over the long-term memory, the $\infty$-former's attention
complexity becomes independent of the context length, trading off memory length
with precision. In order to control where precision is more important,
$\infty$-former maintains "sticky memories" being able to model arbitrarily
long contexts while keeping the computation budget fixed. Experiments on a
synthetic sorting task, language modeling, and document grounded dialogue
generation demonstrate the $\infty$-former's ability to retain information from
long sequences.
| [
{
"created": "Wed, 1 Sep 2021 10:51:58 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Sep 2021 10:12:03 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Mar 2022 10:37:54 GMT",
"version": "v3"
}
] | 2022-03-28 | [
[
"Martins",
"Pedro Henrique",
""
],
[
"Marinho",
"Zita",
""
],
[
"Martins",
"André F. T.",
""
]
] | Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information. In this paper, we propose the $\infty$-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the $\infty$-former's attention complexity becomes independent of the context length, trading off memory length with precision. In order to control where precision is more important, $\infty$-former maintains "sticky memories" being able to model arbitrarily long contexts while keeping the computation budget fixed. Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the $\infty$-former's ability to retain information from long sequences. |
2402.01079 | David OBrien | David OBrien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan | Data-Driven Evidence-Based Syntactic Sugar Design | 12 pages, 12 figures, to be published in ICSE'24 | null | 10.1145/3597503.3639580 | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Programming languages are essential tools for developers, and their evolution
plays a crucial role in supporting the activities of developers. One instance
of programming language evolution is the introduction of syntactic sugars,
which are additional syntax elements that provide alternative, more readable
code constructs. However, the process of designing and evolving a programming
language has traditionally been guided by anecdotal experiences and intuition.
Recent advances in tools and methodologies for mining open-source repositories
have enabled developers to make data-driven software engineering decisions. In
light of this, this paper proposes an approach for motivating data-driven
programming evolution by applying frequent subgraph mining techniques to a
large dataset of 166,827,154 open-source Java methods. The dataset is mined by
generalizing Java control-flow graphs to capture broad programming language
usages and instances of duplication. Frequent subgraphs are then extracted to
identify potentially impactful opportunities for new syntactic sugars. Our
diverse results demonstrate the benefits of the proposed technique by
identifying new syntactic sugars involving a variety of programming constructs
that could be implemented in Java, thus simplifying frequent code idioms. This
approach can potentially provide valuable insights for Java language designers,
and serve as a proof-of-concept for data-driven programming language design and
evolution.
| [
{
"created": "Fri, 2 Feb 2024 00:35:14 GMT",
"version": "v1"
}
] | 2024-02-05 | [
[
"OBrien",
"David",
""
],
[
"Dyer",
"Robert",
""
],
[
"Nguyen",
"Tien N.",
""
],
[
"Rajan",
"Hridesh",
""
]
] | Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of syntactic sugars, which are additional syntax elements that provide alternative, more readable code constructs. However, the process of designing and evolving a programming language has traditionally been guided by anecdotal experiences and intuition. Recent advances in tools and methodologies for mining open-source repositories have enabled developers to make data-driven software engineering decisions. In light of this, this paper proposes an approach for motivating data-driven programming evolution by applying frequent subgraph mining techniques to a large dataset of 166,827,154 open-source Java methods. The dataset is mined by generalizing Java control-flow graphs to capture broad programming language usages and instances of duplication. Frequent subgraphs are then extracted to identify potentially impactful opportunities for new syntactic sugars. Our diverse results demonstrate the benefits of the proposed technique by identifying new syntactic sugars involving a variety of programming constructs that could be implemented in Java, thus simplifying frequent code idioms. This approach can potentially provide valuable insights for Java language designers, and serve as a proof-of-concept for data-driven programming language design and evolution. |
2106.08389 | Anne Collin | Anne Collin, Amitai Y. Bin-Nun, Radboud Duintjer Tebbens | Plane and Sample: Maximizing Information about Autonomous Vehicle
Performance using Submodular Optimization | 8 pages, 8 figures. Accepted for publication at the 2021 IEEE
International Intelligent Transportation Systems Conference (ITSC) | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As autonomous vehicles (AVs) take on growing Operational Design Domains
(ODDs), they need to go through a systematic, transparent, and scalable
evaluation process to demonstrate their benefits to society. Current scenario
sampling techniques for AV performance evaluation usually focus on a specific
functionality, such as lane changing, and do not accommodate a transfer of
information about an AV system from one ODD to the next. In this paper, we
reformulate the scenario sampling problem across ODDs and functionalities as a
submodular optimization problem. To do so, we abstract AV performance as a
Bayesian Hierarchical Model, which we use to infer information gained by
revealing performance in new scenarios. We propose the information gain as a
measure of scenario relevance and evaluation progress. Furthermore, we leverage
the submodularity, or diminishing returns, property of the information gain not
only to find a near-optimal scenario set, but also to propose a stopping
criterion for an AV performance evaluation campaign. We find that we only need
to explore about 7.5% of the scenario space to meet this criterion, a 23%
improvement over Latin Hypercube Sampling.
| [
{
"created": "Tue, 15 Jun 2021 19:35:30 GMT",
"version": "v1"
}
] | 2021-06-17 | [
[
"Collin",
"Anne",
""
],
[
"Bin-Nun",
"Amitai Y.",
""
],
[
"Tebbens",
"Radboud Duintjer",
""
]
] | As autonomous vehicles (AVs) take on growing Operational Design Domains (ODDs), they need to go through a systematic, transparent, and scalable evaluation process to demonstrate their benefits to society. Current scenario sampling techniques for AV performance evaluation usually focus on a specific functionality, such as lane changing, and do not accommodate a transfer of information about an AV system from one ODD to the next. In this paper, we reformulate the scenario sampling problem across ODDs and functionalities as a submodular optimization problem. To do so, we abstract AV performance as a Bayesian Hierarchical Model, which we use to infer information gained by revealing performance in new scenarios. We propose the information gain as a measure of scenario relevance and evaluation progress. Furthermore, we leverage the submodularity, or diminishing returns, property of the information gain not only to find a near-optimal scenario set, but also to propose a stopping criterion for an AV performance evaluation campaign. We find that we only need to explore about 7.5% of the scenario space to meet this criterion, a 23% improvement over Latin Hypercube Sampling. |
1812.03556 | Kamran Keykhosravi | Kamran Keykhosravi, Marco Secondini, Giuseppe Durisi and Erik Agrell | How to Increase the Achievable Information Rate by Per-Channel
Dispersion Compensation | null | IEEE/OSA Journal of Lightwave Technology, vol. 37, no. 10, pp.
2443-2451, May 2019 | 10.1109/JLT.2019.2907311 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deploying periodic inline chromatic dispersion compensation enables reducing
the complexity of the digital back propagation (DBP) algorithm. However,
compared with nondispersion-managed (NDM) links, dispersion-managed (DM) ones
suffer a stronger cross-phase modulation (XPM). Utilizing per-channel
dispersion-managed (CDM) links (e.g., using fiber Bragg grating) allows for a
complexity reduction of DBP, while abating XPM compared to DM links. In this
paper, we show for the first time that CDM links enable also a more effective
XPM compensation compared to NDM ones, allowing a higher achievable information
rate (AIR). This is explained by resorting to the frequency-resolved
logarithmic perturbation model and showing that per-channel dispersion
compensation increases the frequency correlation of the distortions induced by
XPM over the channel bandwidth, making them more similar to a conventional
phase noise. We compare the performance (in terms of the AIR) of a DM, an NDM,
and a CDM link, considering two types of mismatched receivers: one neglects the
XPM phase distortion and the other compensates for it. With the former, the CDM
link is inferior to the NDM one due to an increased in-band signal--noise
interaction. However, with the latter, a higher AIR is obtained with the CDM
link than with the NDM one owing to a higher XPM frequency correlation. The DM
link has the lowest AIR for both receivers because of a stronger XPM.
| [
{
"created": "Sun, 9 Dec 2018 20:50:52 GMT",
"version": "v1"
}
] | 2024-01-25 | [
[
"Keykhosravi",
"Kamran",
""
],
[
"Secondini",
"Marco",
""
],
[
"Durisi",
"Giuseppe",
""
],
[
"Agrell",
"Erik",
""
]
] | Deploying periodic inline chromatic dispersion compensation enables reducing the complexity of the digital back propagation (DBP) algorithm. However, compared with nondispersion-managed (NDM) links, dispersion-managed (DM) ones suffer a stronger cross-phase modulation (XPM). Utilizing per-channel dispersion-managed (CDM) links (e.g., using fiber Bragg grating) allows for a complexity reduction of DBP, while abating XPM compared to DM links. In this paper, we show for the first time that CDM links enable also a more effective XPM compensation compared to NDM ones, allowing a higher achievable information rate (AIR). This is explained by resorting to the frequency-resolved logarithmic perturbation model and showing that per-channel dispersion compensation increases the frequency correlation of the distortions induced by XPM over the channel bandwidth, making them more similar to a conventional phase noise. We compare the performance (in terms of the AIR) of a DM, an NDM, and a CDM link, considering two types of mismatched receivers: one neglects the XPM phase distortion and the other compensates for it. With the former, the CDM link is inferior to the NDM one due to an increased in-band signal--noise interaction. However, with the latter, a higher AIR is obtained with the CDM link than with the NDM one owing to a higher XPM frequency correlation. The DM link has the lowest AIR for both receivers because of a stronger XPM. |
1210.6241 | Mael Le Treust | Ma\"el Le Treust, Samson Lasaulce | Transforming Monitoring Structures with Resilient Encoders. Application
to Repeated Games | Springer, Dynamic Games and Applications, 2012 | null | 10.1007/s13235-012-0058-3 | null | cs.IT cs.GT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important feature of a dynamic game is its monitoring structure namely,
what the players effectively see from the played actions. We consider games
with arbitrary monitoring structures. One of the purposes of this paper is to
know to what extent an encoder, who perfectly observes the played actions and
sends a complementary public signal to the players, can establish perfect
monitoring for all the players. To reach this goal, the main technical problem
to be solved at the encoder is to design a source encoder which compresses the
action profile in the most concise manner possible. A special feature of this
encoder is that the multi-dimensional signal (namely, the action profiles) to
be encoded is assumed to comprise a component whose probability distribution is
not known to the encoder and the decoder has a side information (the private
signals received by the players when the encoder is off). This new framework
appears to be both of game-theoretical and information-theoretical interest. In
particular, it is useful for designing certain types of encoders that are
resilient to single deviations and provide an equilibrium utility region in the
proposed setting; it provides a new type of constraints to compress an
information source (i.e., a random variable). Regarding the first aspect, we
apply the derived result to the repeated prisoner's dilemma.
| [
{
"created": "Tue, 23 Oct 2012 14:14:17 GMT",
"version": "v1"
}
] | 2012-10-24 | [
[
"Treust",
"Maël Le",
""
],
[
"Lasaulce",
"Samson",
""
]
] | An important feature of a dynamic game is its monitoring structure namely, what the players effectively see from the played actions. We consider games with arbitrary monitoring structures. One of the purposes of this paper is to know to what extent an encoder, who perfectly observes the played actions and sends a complementary public signal to the players, can establish perfect monitoring for all the players. To reach this goal, the main technical problem to be solved at the encoder is to design a source encoder which compresses the action profile in the most concise manner possible. A special feature of this encoder is that the multi-dimensional signal (namely, the action profiles) to be encoded is assumed to comprise a component whose probability distribution is not known to the encoder and the decoder has a side information (the private signals received by the players when the encoder is off). This new framework appears to be both of game-theoretical and information-theoretical interest. In particular, it is useful for designing certain types of encoders that are resilient to single deviations and provide an equilibrium utility region in the proposed setting; it provides a new type of constraints to compress an information source (i.e., a random variable). Regarding the first aspect, we apply the derived result to the repeated prisoner's dilemma. |
1104.3497 | Shih-Chun Lin | Pin-Hsun Lin, Shih-Chun Lin, Hsuan-Jung Su, and Y.-W. Peter Hong | Clean relaying aided cognitive radio under the coexistence constraint | 30 pages | null | 10.1109/TWC.2012.092712.120005 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the interference-mitigation based cognitive radio where the
primary and secondary users can coexist at the same time and frequency bands,
under the constraint that the rate of the primary user (PU) must remain the
same with a single-user decoder. To meet such a coexistence constraint, the
relaying from the secondary user (SU) can help the PU's transmission under the
interference from the SU. However, the relayed signal in the known dirty paper
coding (DPC) based scheme is interfered by the SU's signal, and is not "clean".
In this paper, under the half-duplex constraints, we propose two new
transmission schemes aided by the clean relaying from the SU's transmitter and
receiver without interference from the SU. We name them as the clean
transmitter relaying (CT) and clean transmitter-receiver relaying (CTR) aided
cognitive radio, respectively. The rate and multiplexing gain performances of
CT and CTR in fading channels with various availabilities of the channel state
information at the transmitters (CSIT) are studied. Our CT generalizes the
celebrated DPC based scheme proposed previously. With full CSIT, the
multiplexing gain of the CTR is proved to be better (or no less) than that of
the previous DPC based schemes. This is because the silent period for decoding
the PU's messages for the DPC may not be necessary in the CTR. With only the
statistics of CSIT, we further prove that the CTR outperforms the rate
performance of the previous scheme in fast Rayleigh fading channels. The
numerical examples also show that in a large class of channels, the proposed CT
and CTR provide significant rate gains over the previous scheme with small
complexity penalties.
| [
{
"created": "Mon, 18 Apr 2011 14:33:58 GMT",
"version": "v1"
}
] | 2012-12-24 | [
[
"Lin",
"Pin-Hsun",
""
],
[
"Lin",
"Shih-Chun",
""
],
[
"Su",
"Hsuan-Jung",
""
],
[
"Hong",
"Y. -W. Peter",
""
]
] | We consider the interference-mitigation based cognitive radio where the primary and secondary users can coexist at the same time and frequency bands, under the constraint that the rate of the primary user (PU) must remain the same with a single-user decoder. To meet such a coexistence constraint, the relaying from the secondary user (SU) can help the PU's transmission under the interference from the SU. However, the relayed signal in the known dirty paper coding (DPC) based scheme is interfered by the SU's signal, and is not "clean". In this paper, under the half-duplex constraints, we propose two new transmission schemes aided by the clean relaying from the SU's transmitter and receiver without interference from the SU. We name them as the clean transmitter relaying (CT) and clean transmitter-receiver relaying (CTR) aided cognitive radio, respectively. The rate and multiplexing gain performances of CT and CTR in fading channels with various availabilities of the channel state information at the transmitters (CSIT) are studied. Our CT generalizes the celebrated DPC based scheme proposed previously. With full CSIT, the multiplexing gain of the CTR is proved to be better (or no less) than that of the previous DPC based schemes. This is because the silent period for decoding the PU's messages for the DPC may not be necessary in the CTR. With only the statistics of CSIT, we further prove that the CTR outperforms the rate performance of the previous scheme in fast Rayleigh fading channels. The numerical examples also show that in a large class of channels, the proposed CT and CTR provide significant rate gains over the previous scheme with small complexity penalties. |
2104.11693 | Yiming Zhao | Yiming Zhao, Xinming Huang and Ziming Zhang | Deep Lucas-Kanade Homography for Multimodal Image Alignment | Accepted by CVPR2021, codelink:
https://github.com/placeforyiming/CVPR21-Deep-Lucas-Kanade-Homography | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating homography to align image pairs captured by different sensors or
image pairs with large appearance changes is an important and general challenge
for many computer vision applications. In contrast to others, we propose a
generic solution to pixel-wise align multimodal image pairs by extending the
traditional Lucas-Kanade algorithm with networks. The key contribution in our
method is how we construct feature maps, named as deep Lucas-Kanade feature map
(DLKFM). The learned DLKFM can spontaneously recognize invariant features under
various appearance-changing conditions. It also has two nice properties for the
Lucas-Kanade algorithm: (1) The template feature map keeps brightness
consistency with the input feature map, thus the color difference is very small
while they are well-aligned. (2) The Lucas-Kanade objective function built on
DLKFM has a smooth landscape around ground truth homography parameters, so the
iterative solution of the Lucas-Kanade can easily converge to the ground truth.
With those properties, directly updating the Lucas-Kanade algorithm on our
feature maps will precisely align image pairs with large appearance changes. We
share the datasets, code, and demo video online.
| [
{
"created": "Thu, 22 Apr 2021 04:11:29 GMT",
"version": "v1"
}
] | 2021-04-26 | [
[
"Zhao",
"Yiming",
""
],
[
"Huang",
"Xinming",
""
],
[
"Zhang",
"Ziming",
""
]
] | Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision applications. In contrast to others, we propose a generic solution to pixel-wise align multimodal image pairs by extending the traditional Lucas-Kanade algorithm with networks. The key contribution in our method is how we construct feature maps, named as deep Lucas-Kanade feature map (DLKFM). The learned DLKFM can spontaneously recognize invariant features under various appearance-changing conditions. It also has two nice properties for the Lucas-Kanade algorithm: (1) The template feature map keeps brightness consistency with the input feature map, thus the color difference is very small while they are well-aligned. (2) The Lucas-Kanade objective function built on DLKFM has a smooth landscape around ground truth homography parameters, so the iterative solution of the Lucas-Kanade can easily converge to the ground truth. With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes. We share the datasets, code, and demo video online. |
1907.10709 | Giulio Siracusano Dr. | Giulio Siracusano, Francesca Garesc\`i, Giovanni Finocchio, Riccardo
Tomasello, Francesco Lamonaca, Carmelo Scuro, Mario Carpentieri, Massimo
Chiappini and Aurelio La Corte | Automatic crack classification by exploiting statistical event
descriptors for Deep Learning | 19 pages, 2 tables, 9 figures | null | null | null | cs.LG eess.SP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In modern building infrastructures, the chance to devise adaptive and
unsupervised data-driven health monitoring systems is gaining in popularity due
to the large availability of big data from low-cost sensors with communication
capabilities and advanced modeling tools such as Deep Learning. The main
purpose of this paper is to combine deep neural networks with Bidirectional
Long Short Term Memory and advanced statistical analysis involving
Instantaneous Frequency and Spectral Kurtosis to develop an accurate
classification tool for tensile, shear and mixed modes originated from acoustic
emission events (cracks). We investigated on effective event descriptors to
capture the unique characteristics from the different types of modes. Tests on
experimental results confirm that this method achieves promising classification
among different crack events and can impact on the design of future on
structural health monitoring (SHM) technologies. This approach is effective to
classify incipient damages with 92% of accuracy, which is advantageous to plan
maintenance.
| [
{
"created": "Wed, 24 Jul 2019 20:39:49 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Nov 2021 17:01:13 GMT",
"version": "v2"
}
] | 2021-11-29 | [
[
"Siracusano",
"Giulio",
""
],
[
"Garescì",
"Francesca",
""
],
[
"Finocchio",
"Giovanni",
""
],
[
"Tomasello",
"Riccardo",
""
],
[
"Lamonaca",
"Francesco",
""
],
[
"Scuro",
"Carmelo",
""
],
[
"Carpentieri",
"Mario",
""
],
[
"Chiappini",
"Massimo",
""
],
[
"La Corte",
"Aurelio",
""
]
] | In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this paper is to combine deep neural networks with Bidirectional Long Short Term Memory and advanced statistical analysis involving Instantaneous Frequency and Spectral Kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from acoustic emission events (cracks). We investigated on effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of future on structural health monitoring (SHM) technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance. |
2112.14192 | Xudong Li | Xudong Li, Ye Fan, Rugui Yao, Peng Wang, Nan Qi, Xiaoya Zuo | Robust Security Analysis Based on Random Geometry Theory for
Satellite-Terrestrial-Vehicle Network | The theoretical analysis in the original manuscript is insufficient,
and the system model is not convincing. With the consideration of these
flaws, we decide to withdraw our work for further improvement | null | null | null | cs.IT cs.CL math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Driven by B5G and 6G technologies, multi-network fusion is an indispensable
tendency for future communications. In this paper, we focus on and analyze the
\emph{security performance} (SP) of the \emph{satellite-terrestrial downlink
transmission} (STDT). Here, the STDT is composed of a satellite network and a
vehicular network with a legitimate mobile receiver and an mobile eavesdropper
distributing. To theoretically analyze the SP of this system from the
perspective of mobile terminals better, the random geometry theory is adopted,
which assumes that both terrestrial vehicles are distributed stochastically in
one beam of the satellite. Furthermore, based on this theory, the closed-form
analytical expressions for two crucial and specific indicators in the STDT are
derived, respectively, the secrecy outage probability and the ergodic secrecy
capacity. Additionally, several related variables restricting the SP of the
STDT are discussed, and specific schemes are presented to enhance the SP. Then,
the asymptotic property is investigated in the high signal-to-noise ratio
scenario, and accurate and asymptotic closed-form expressions are given.
Finally, simulation results show that, under the precondition of guaranteeing
the reliability of the STDT, the asymptotic solutions outperform the
corresponding accurate results significantly in the effectiveness.
| [
{
"created": "Tue, 28 Dec 2021 15:46:28 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Jul 2022 09:44:03 GMT",
"version": "v2"
}
] | 2022-07-15 | [
[
"Li",
"Xudong",
""
],
[
"Fan",
"Ye",
""
],
[
"Yao",
"Rugui",
""
],
[
"Wang",
"Peng",
""
],
[
"Qi",
"Nan",
""
],
[
"Zuo",
"Xiaoya",
""
]
] | Driven by B5G and 6G technologies, multi-network fusion is an indispensable tendency for future communications. In this paper, we focus on and analyze the \emph{security performance} (SP) of the \emph{satellite-terrestrial downlink transmission} (STDT). Here, the STDT is composed of a satellite network and a vehicular network with a legitimate mobile receiver and an mobile eavesdropper distributing. To theoretically analyze the SP of this system from the perspective of mobile terminals better, the random geometry theory is adopted, which assumes that both terrestrial vehicles are distributed stochastically in one beam of the satellite. Furthermore, based on this theory, the closed-form analytical expressions for two crucial and specific indicators in the STDT are derived, respectively, the secrecy outage probability and the ergodic secrecy capacity. Additionally, several related variables restricting the SP of the STDT are discussed, and specific schemes are presented to enhance the SP. Then, the asymptotic property is investigated in the high signal-to-noise ratio scenario, and accurate and asymptotic closed-form expressions are given. Finally, simulation results show that, under the precondition of guaranteeing the reliability of the STDT, the asymptotic solutions outperform the corresponding accurate results significantly in the effectiveness. |
1810.12272 | Dimitrios Diochnos | Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody | Adversarial Risk and Robustness: General Definitions and Implications
for the Uniform Distribution | Full version of a work with the same title that will appear in NIPS
2018, 31 pages containing 5 figures, 1 table, 2 algorithms | null | null | null | cs.LG cs.CC cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study adversarial perturbations when the instances are uniformly
distributed over $\{0,1\}^n$. We study both "inherent" bounds that apply to any
problem and any classifier for such a problem as well as bounds that apply to
specific problems and specific hypothesis classes.
As the current literature contains multiple definitions of adversarial risk
and robustness, we start by giving a taxonomy for these definitions based on
their goals, we identify one of them as the one guaranteeing misclassification
by pushing the instances to the error region. We then study some classic
algorithms for learning monotone conjunctions and compare their adversarial
risk and robustness under different definitions by attacking the hypotheses
using instances drawn from the uniform distribution. We observe that sometimes
these definitions lead to significantly different bounds. Thus, this study
advocates for the use of the error-region definition, even though other
definitions, in other contexts, may coincide with the error-region definition.
Using the error-region definition of adversarial perturbations, we then study
inherent bounds on risk and robustness of any classifier for any classification
problem whose instances are uniformly distributed over $\{0,1\}^n$. Using the
isoperimetric inequality for the Boolean hypercube, we show that for initial
error $0.01$, there always exists an adversarial perturbation that changes
$O(\sqrt{n})$ bits of the instances to increase the risk to $0.5$, making
classifier's decisions meaningless. Furthermore, by also using the central
limit theorem we show that when $n\to \infty$, at most $c \cdot \sqrt{n}$ bits
of perturbations, for a universal constant $c< 1.17$, suffice for increasing
the risk to $0.5$, and the same $c \cdot \sqrt{n} $ bits of perturbations on
average suffice to increase the risk to $1$, hence bounding the robustness by
$c \cdot \sqrt{n}$.
| [
{
"created": "Mon, 29 Oct 2018 17:41:29 GMT",
"version": "v1"
}
] | 2018-10-30 | [
[
"Diochnos",
"Dimitrios I.",
""
],
[
"Mahloujifar",
"Saeed",
""
],
[
"Mahmoody",
"Mohammad",
""
]
] | We study adversarial perturbations when the instances are uniformly distributed over $\{0,1\}^n$. We study both "inherent" bounds that apply to any problem and any classifier for such a problem as well as bounds that apply to specific problems and specific hypothesis classes. As the current literature contains multiple definitions of adversarial risk and robustness, we start by giving a taxonomy for these definitions based on their goals, we identify one of them as the one guaranteeing misclassification by pushing the instances to the error region. We then study some classic algorithms for learning monotone conjunctions and compare their adversarial risk and robustness under different definitions by attacking the hypotheses using instances drawn from the uniform distribution. We observe that sometimes these definitions lead to significantly different bounds. Thus, this study advocates for the use of the error-region definition, even though other definitions, in other contexts, may coincide with the error-region definition. Using the error-region definition of adversarial perturbations, we then study inherent bounds on risk and robustness of any classifier for any classification problem whose instances are uniformly distributed over $\{0,1\}^n$. Using the isoperimetric inequality for the Boolean hypercube, we show that for initial error $0.01$, there always exists an adversarial perturbation that changes $O(\sqrt{n})$ bits of the instances to increase the risk to $0.5$, making classifier's decisions meaningless. Furthermore, by also using the central limit theorem we show that when $n\to \infty$, at most $c \cdot \sqrt{n}$ bits of perturbations, for a universal constant $c< 1.17$, suffice for increasing the risk to $0.5$, and the same $c \cdot \sqrt{n} $ bits of perturbations on average suffice to increase the risk to $1$, hence bounding the robustness by $c \cdot \sqrt{n}$. |
1906.09934 | Ioannis Chatzigiannakis | Chrysanthi Tziortzioti, Irene Mavrommati, Georgios Mylonas, Andrea
Vitaletti, Ioannis Chatzigiannakis | Scenarios for Educational and Game Activities using Internet of Things
Data | 14 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1805.09561 | null | null | null | cs.HC cs.CY | http://creativecommons.org/licenses/by/4.0/ | Raising awareness among young people and changing their behavior and habits
concerning energy usage and the environment is key to achieving a sustainable
planet. The goal to address the global climate problem requires informing the
population on their roles in mitigation actions and adaptation of sustainable
behaviors. Addressing climate change and achieve ambitious energy and climate
targets requires a change in citizen behavior and consumption practices. IoT
sensing and related scenario and practices, which address school children via
discovery, gamification, and educational activities, are examined in this
paper. Use of seawater sensors in STEM education, that has not previously been
addressed, is included in these educational scenaria.
| [
{
"created": "Thu, 20 Jun 2019 18:26:12 GMT",
"version": "v1"
}
] | 2019-06-25 | [
[
"Tziortzioti",
"Chrysanthi",
""
],
[
"Mavrommati",
"Irene",
""
],
[
"Mylonas",
"Georgios",
""
],
[
"Vitaletti",
"Andrea",
""
],
[
"Chatzigiannakis",
"Ioannis",
""
]
] | Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria. |
0804.2155 | Joseph Y. Halpern | Joseph Y. Halpern | From Qualitative to Quantitative Proofs of Security Properties Using
First-Order Conditional Logic | null | null | null | null | cs.CR cs.AI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A first-order conditional logic is considered, with semantics given by a
variant of epsilon-semantics, where p -> q means that Pr(q | p) approaches 1
super-polynomially --faster than any inverse polynomial. This type of
convergence is needed for reasoning about security protocols. A complete
axiomatization is provided for this semantics, and it is shown how a
qualitative proof of the correctness of a security protocol can be
automatically converted to a quantitative proof appropriate for reasoning about
concrete security.
| [
{
"created": "Mon, 14 Apr 2008 12:06:04 GMT",
"version": "v1"
}
] | 2008-12-18 | [
[
"Halpern",
"Joseph Y.",
""
]
] | A first-order conditional logic is considered, with semantics given by a variant of epsilon-semantics, where p -> q means that Pr(q | p) approaches 1 super-polynomially --faster than any inverse polynomial. This type of convergence is needed for reasoning about security protocols. A complete axiomatization is provided for this semantics, and it is shown how a qualitative proof of the correctness of a security protocol can be automatically converted to a quantitative proof appropriate for reasoning about concrete security. |
1911.11534 | Siyan Dong | Siyan Dong, Songyin Wu, Yixin Zhuang, Kai Xu, Shanghang Zhang, Baoquan
Chen | Decoupling Features and Coordinates for Few-shot RGB Relocalization | This is a very early initialization of a research project and
contains some out-of-date results and errors. A later version with
significant improvements has been published as a new paper. See
arXiv:2208.06933 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cross-scene model adaption is crucial for camera relocalization in real
scenarios. It is often preferable that a pre-learned model can be fast adapted
to a novel scene with as few training samples as possible. The existing
state-of-the-art approaches, however, can hardly support such few-shot scene
adaption due to the entangling of image feature extraction and scene coordinate
regression. To address this issue, we approach camera relocalization with a
decoupled solution where feature extraction, coordinate regression, and pose
estimation are performed separately. Our key insight is that feature encoder
used for coordinate regression should be learned by removing the distracting
factor of coordinate systems, such that feature encoder is learned from
multiple scenes for general feature representation and more important,
view-insensitive capability. With this feature prior, and combined with a
coordinate regressor, few-shot observations in a new scene are much easier to
connect with the 3D world than the one with existing integrated solution.
Experiments have shown the superiority of our approach compared to the
state-of-the-art methods, producing higher accuracy on several scenes with
diverse visual appearance and viewpoint distribution.
| [
{
"created": "Tue, 26 Nov 2019 13:57:39 GMT",
"version": "v1"
},
{
"created": "Sat, 1 Aug 2020 17:49:36 GMT",
"version": "v2"
},
{
"created": "Tue, 4 Aug 2020 10:29:36 GMT",
"version": "v3"
},
{
"created": "Tue, 16 Aug 2022 15:40:00 GMT",
"version": "v4"
}
] | 2022-08-17 | [
[
"Dong",
"Siyan",
""
],
[
"Wu",
"Songyin",
""
],
[
"Zhuang",
"Yixin",
""
],
[
"Xu",
"Kai",
""
],
[
"Zhang",
"Shanghang",
""
],
[
"Chen",
"Baoquan",
""
]
] | Cross-scene model adaption is crucial for camera relocalization in real scenarios. It is often preferable that a pre-learned model can be fast adapted to a novel scene with as few training samples as possible. The existing state-of-the-art approaches, however, can hardly support such few-shot scene adaption due to the entangling of image feature extraction and scene coordinate regression. To address this issue, we approach camera relocalization with a decoupled solution where feature extraction, coordinate regression, and pose estimation are performed separately. Our key insight is that feature encoder used for coordinate regression should be learned by removing the distracting factor of coordinate systems, such that feature encoder is learned from multiple scenes for general feature representation and more important, view-insensitive capability. With this feature prior, and combined with a coordinate regressor, few-shot observations in a new scene are much easier to connect with the 3D world than the one with existing integrated solution. Experiments have shown the superiority of our approach compared to the state-of-the-art methods, producing higher accuracy on several scenes with diverse visual appearance and viewpoint distribution. |
2010.13986 | Zhuqi Li | Zhuqi Li, Can Wu, Sigurd Wagner, James C. Sturm, Naveen Verma, Kyle
Jamieson | REITS: Reflective Surface for Intelligent Transportation Systems | null | null | 10.1145/3446382.3448650 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous vehicles are predicted to dominate the transportation industry in
the foreseeable future. Safety is one of the major challenges to the early
deployment of self-driving systems. To ensure safety, self-driving vehicles
must sense and detect humans, other vehicles, and road infrastructure
accurately, robustly, and timely. However, existing sensing techniques used by
self-driving vehicles may not be absolutely reliable. In this paper, we design
REITS, a system to improve the reliability of RF-based sensing modules for
autonomous vehicles. We conduct theoretical analysis on possible failures of
existing RF-based sensing systems. Based on the analysis, REITS adopts a
multi-antenna design, which enables constructive blind beamforming to return an
enhanced radar signal in the incident direction. REITS can also let the
existing radar system sense identification information by switching between
constructive beamforming state and destructive beamforming state. Preliminary
results show that REITS improves the detection distance of a self-driving car
radar by a factor of 3.63.
| [
{
"created": "Tue, 27 Oct 2020 01:45:09 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Nov 2020 15:57:35 GMT",
"version": "v2"
},
{
"created": "Tue, 2 Feb 2021 16:25:12 GMT",
"version": "v3"
}
] | 2021-02-03 | [
[
"Li",
"Zhuqi",
""
],
[
"Wu",
"Can",
""
],
[
"Wagner",
"Sigurd",
""
],
[
"Sturm",
"James C.",
""
],
[
"Verma",
"Naveen",
""
],
[
"Jamieson",
"Kyle",
""
]
] | Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. However, existing sensing techniques used by self-driving vehicles may not be absolutely reliable. In this paper, we design REITS, a system to improve the reliability of RF-based sensing modules for autonomous vehicles. We conduct theoretical analysis on possible failures of existing RF-based sensing systems. Based on the analysis, REITS adopts a multi-antenna design, which enables constructive blind beamforming to return an enhanced radar signal in the incident direction. REITS can also let the existing radar system sense identification information by switching between constructive beamforming state and destructive beamforming state. Preliminary results show that REITS improves the detection distance of a self-driving car radar by a factor of 3.63. |
1812.02428 | Sai Sri Sathya | Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra
and Santanu Bhattacharya | A Review of Homomorphic Encryption Libraries for Secure Computation | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we provide a survey of various libraries for homomorphic
encryption. We describe key features and trade-offs that should be considered
while choosing the right approach for secure computation. We then present a
comparison of six commonly available Homomorphic Encryption libraries - SEAL,
HElib, TFHE, Paillier, ELGamal and RSA across these identified features.
Support for different languages and real-life applications are also elucidated.
| [
{
"created": "Thu, 6 Dec 2018 09:55:24 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Dec 2018 06:06:35 GMT",
"version": "v2"
}
] | 2018-12-10 | [
[
"Sathya",
"Sai Sri",
""
],
[
"Vepakomma",
"Praneeth",
""
],
[
"Raskar",
"Ramesh",
""
],
[
"Ramachandra",
"Ranjan",
""
],
[
"Bhattacharya",
"Santanu",
""
]
] | In this paper we provide a survey of various libraries for homomorphic encryption. We describe key features and trade-offs that should be considered while choosing the right approach for secure computation. We then present a comparison of six commonly available Homomorphic Encryption libraries - SEAL, HElib, TFHE, Paillier, ELGamal and RSA across these identified features. Support for different languages and real-life applications are also elucidated. |
2406.17636 | Alexander Gambashidze | Alexander Gambashidze, Anton Kulikov, Yuriy Sosnin, Ilya Makarov | Aligning Diffusion Models with Noise-Conditioned Perception | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in human preference optimization, initially developed for
Language Models (LMs), have shown promise for text-to-image Diffusion Models,
enhancing prompt alignment, visual appeal, and user preference. Unlike LMs,
Diffusion Models typically optimize in pixel or VAE space, which does not align
well with human perception, leading to slower and less efficient training
during the preference alignment stage. We propose using a perceptual objective
in the U-Net embedding space of the diffusion model to address these issues.
Our approach involves fine-tuning Stable Diffusion 1.5 and XL using Direct
Preference Optimization (DPO), Contrastive Preference Optimization (CPO), and
supervised fine-tuning (SFT) within this embedding space. This method
significantly outperforms standard latent-space implementations across various
metrics, including quality and computational cost. For SDXL, our approach
provides 60.8\% general preference, 62.2\% visual appeal, and 52.1\% prompt
following against original open-sourced SDXL-DPO on the PartiPrompts dataset,
while significantly reducing compute. Our approach not only improves the
efficiency and quality of human preference alignment for diffusion models but
is also easily integrable with other optimization techniques. The training code
and LoRA weights will be available here:
https://huggingface.co/alexgambashidze/SDXL\_NCP-DPO\_v0.1
| [
{
"created": "Tue, 25 Jun 2024 15:21:50 GMT",
"version": "v1"
}
] | 2024-06-26 | [
[
"Gambashidze",
"Alexander",
""
],
[
"Kulikov",
"Anton",
""
],
[
"Sosnin",
"Yuriy",
""
],
[
"Makarov",
"Ilya",
""
]
] | Recent advancements in human preference optimization, initially developed for Language Models (LMs), have shown promise for text-to-image Diffusion Models, enhancing prompt alignment, visual appeal, and user preference. Unlike LMs, Diffusion Models typically optimize in pixel or VAE space, which does not align well with human perception, leading to slower and less efficient training during the preference alignment stage. We propose using a perceptual objective in the U-Net embedding space of the diffusion model to address these issues. Our approach involves fine-tuning Stable Diffusion 1.5 and XL using Direct Preference Optimization (DPO), Contrastive Preference Optimization (CPO), and supervised fine-tuning (SFT) within this embedding space. This method significantly outperforms standard latent-space implementations across various metrics, including quality and computational cost. For SDXL, our approach provides 60.8\% general preference, 62.2\% visual appeal, and 52.1\% prompt following against original open-sourced SDXL-DPO on the PartiPrompts dataset, while significantly reducing compute. Our approach not only improves the efficiency and quality of human preference alignment for diffusion models but is also easily integrable with other optimization techniques. The training code and LoRA weights will be available here: https://huggingface.co/alexgambashidze/SDXL\_NCP-DPO\_v0.1 |
2103.11874 | Zemin Sun | Zemin Sun, Yanheng Liu, Jian Wang, Guofa Li, Carie Anil, Keqiang Li,
Xinyu Guo, Geng Sun, Daxin Tian, Dongpu Cao | Applications of Game Theory in Vehicular Networks: A Survey | It has been published on "IEEE communications surveys and tutorials"
(https://ieeexplore.ieee.org/document/9524815) | IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp.
2660-2710, Fourthquarter 2021 | 10.1109/COMST.2021.3108466 | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the Internet of Things (IoT) era, vehicles and other intelligent
components in an intelligent transportation system (ITS) are connected, forming
Vehicular Networks (VNs) that provide efficient and secure traffic and
ubiquitous access to various applications. However, as the number of nodes in
ITS increases, it is challenging to satisfy a varied and large number of
service requests with different Quality of Service and security requirements in
highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for
limited network resources to achieve either an individual or a group's
objectives. Game Theory (GT), a theoretical framework designed for strategic
interactions among rational decision-makers sharing scarce resources, can be
used to model and analyze individual or group behaviors of communicating
entities in VNs. This paper primarily surveys the recent developments of GT in
solving various challenges of VNs. This survey starts with an introduction to
the background of VNs. A review of GT models studied in the VNs is then
introduced, including its basic concepts, classifications, and applicable
vehicular issues. After discussing the requirements of VNs and the motivation
of using GT, a comprehensive literature review on GT applications in dealing
with the challenges of current VNs is provided. Furthermore, recent
contributions of GT to VNs integrating with diverse emerging 5G technologies
are surveyed. Finally, the lessons learned are given, and several key research
challenges and possible solutions for applying GT in VNs are outlined.
| [
{
"created": "Mon, 22 Mar 2021 14:09:33 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jan 2022 13:22:20 GMT",
"version": "v2"
}
] | 2022-01-06 | [
[
"Sun",
"Zemin",
""
],
[
"Liu",
"Yanheng",
""
],
[
"Wang",
"Jian",
""
],
[
"Li",
"Guofa",
""
],
[
"Anil",
"Carie",
""
],
[
"Li",
"Keqiang",
""
],
[
"Guo",
"Xinyu",
""
],
[
"Sun",
"Geng",
""
],
[
"Tian",
"Daxin",
""
],
[
"Cao",
"Dongpu",
""
]
] | In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming Vehicular Networks (VNs) that provide efficient and secure traffic and ubiquitous access to various applications. However, as the number of nodes in ITS increases, it is challenging to satisfy a varied and large number of service requests with different Quality of Service and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve either an individual or a group's objectives. Game Theory (GT), a theoretical framework designed for strategic interactions among rational decision-makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including its basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs integrating with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions for applying GT in VNs are outlined. |
1701.08547 | Robert Lim | Robert V. Lim, Boyana Norris, Allen D. Malony | Autotuning GPU Kernels via Static and Predictive Analysis | null | null | null | null | cs.DC cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimizing the performance of GPU kernels is challenging for both human
programmers and code generators. For example, CUDA programmers must set thread
and block parameters for a kernel, but might not have the intuition to make a
good choice. Similarly, compilers can generate working code, but may miss
tuning opportunities by not targeting GPU models or performing code
transformations. Although empirical autotuning addresses some of these
challenges, it requires extensive experimentation and search for optimal code
variants. This research presents an approach for tuning CUDA kernels based on
static analysis that considers fine-grained code structure and the specific GPU
architecture features. Notably, our approach does not require any program runs
in order to discover near-optimal parameter settings. We demonstrate the
applicability of our approach in enabling code autotuners such as Orio to
produce competitive code variants comparable with empirical-based methods,
without the high cost of experiments.
| [
{
"created": "Mon, 30 Jan 2017 11:23:42 GMT",
"version": "v1"
},
{
"created": "Thu, 11 May 2017 22:27:32 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Jun 2017 11:25:02 GMT",
"version": "v3"
}
] | 2017-06-30 | [
[
"Lim",
"Robert V.",
""
],
[
"Norris",
"Boyana",
""
],
[
"Malony",
"Allen D.",
""
]
] | Optimizing the performance of GPU kernels is challenging for both human programmers and code generators. For example, CUDA programmers must set thread and block parameters for a kernel, but might not have the intuition to make a good choice. Similarly, compilers can generate working code, but may miss tuning opportunities by not targeting GPU models or performing code transformations. Although empirical autotuning addresses some of these challenges, it requires extensive experimentation and search for optimal code variants. This research presents an approach for tuning CUDA kernels based on static analysis that considers fine-grained code structure and the specific GPU architecture features. Notably, our approach does not require any program runs in order to discover near-optimal parameter settings. We demonstrate the applicability of our approach in enabling code autotuners such as Orio to produce competitive code variants comparable with empirical-based methods, without the high cost of experiments. |
2010.12176 | Yuxi Li | Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin | Delving into the Cyclic Mechanism in Semi-supervised Video Object
Segmentation | 13 pages, 10 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we address several inadequacies of current video object
segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the
standard semi-supervised process to produce more robust representations. By
relying on the accurate reference mask in the starting frame, we show that the
error propagation problem can be mitigated. Next, we introduce a simple
gradient correction module, which extends the offline pipeline to an online
method while maintaining the efficiency of the former. Finally we develop cycle
effective receptive field (cycle-ERF) based on gradient correction to provide a
new perspective into analyzing object-specific regions of interests. We conduct
comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS,
demonstrating that the cyclic mechanism is beneficial to segmentation quality.
| [
{
"created": "Fri, 23 Oct 2020 05:40:53 GMT",
"version": "v1"
}
] | 2020-10-26 | [
[
"Li",
"Yuxi",
""
],
[
"Xu",
"Ning",
""
],
[
"Peng",
"Jinlong",
""
],
[
"See",
"John",
""
],
[
"Lin",
"Weiyao",
""
]
] | In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introduce a simple gradient correction module, which extends the offline pipeline to an online method while maintaining the efficiency of the former. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality. |
1903.07840 | John Skinner | John Skinner, David Hall, Haoyang Zhang, Feras Dayoub, Niko
S\"underhauf | The Probabilistic Object Detection Challenge | 4 pages, workshop paper | null | null | null | cs.RO cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new challenge for computer and robotic vision, the first ACRV
Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object
detection is a new variation on traditional object detection tasks, requiring
estimates of spatial and semantic uncertainty. We extend the traditional
bounding box format of object detection to express spatial uncertainty using
gaussian distributions for the box corners. The challenge introduces a new test
dataset of video sequences, which are designed to more closely resemble the
kind of data available to a robotic system. We evaluate probabilistic
detections using a new probability-based detection quality (PDQ) measure. The
goal in creating this challenge is to draw the computer and robotic vision
communities together, toward applying object detection solutions for practical
robotics applications.
| [
{
"created": "Tue, 19 Mar 2019 05:18:52 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Apr 2019 00:58:24 GMT",
"version": "v2"
}
] | 2019-04-09 | [
[
"Skinner",
"John",
""
],
[
"Hall",
"David",
""
],
[
"Zhang",
"Haoyang",
""
],
[
"Dayoub",
"Feras",
""
],
[
"Sünderhauf",
"Niko",
""
]
] | We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new variation on traditional object detection tasks, requiring estimates of spatial and semantic uncertainty. We extend the traditional bounding box format of object detection to express spatial uncertainty using gaussian distributions for the box corners. The challenge introduces a new test dataset of video sequences, which are designed to more closely resemble the kind of data available to a robotic system. We evaluate probabilistic detections using a new probability-based detection quality (PDQ) measure. The goal in creating this challenge is to draw the computer and robotic vision communities together, toward applying object detection solutions for practical robotics applications. |
1808.04510 | Kanstantsin Pashkovich | Robert Chiang and Kanstantsin Pashkovich | On the approximability of the stable matching problem with ties of size
two | Added a detailed comparison with the approaches from the papers "On
the approximability of the stable marriage problem with one-sided ties." by
Bauckholt, Pashkovich, and Sanita and "Improved approximation algorithms for
two variants of the stable marriage problem with ties." by Huang and Kavitha.
(See Appendix) | null | null | null | cs.GT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The stable matching problem is one of the central problems of algorithmic
game theory. If participants are allowed to have ties, the problem of finding a
stable matching of maximum cardinality is an NP-hard problem, even when the
ties are of size two. Moreover, in this setting it is UGC-hard to provide an
approximation for the maximum cardinality stable matching problem with a
constant factor smaller than 4/3. In this paper, we give a tight analysis of an
approximation algorithm given by Huang and Kavitha for the maximum cardinality
stable matching problem with ties of size two, demonstrating an improved
4/3-approximation factor.
| [
{
"created": "Tue, 14 Aug 2018 02:47:05 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Feb 2019 21:59:31 GMT",
"version": "v2"
}
] | 2019-02-19 | [
[
"Chiang",
"Robert",
""
],
[
"Pashkovich",
"Kanstantsin",
""
]
] | The stable matching problem is one of the central problems of algorithmic game theory. If participants are allowed to have ties, the problem of finding a stable matching of maximum cardinality is an NP-hard problem, even when the ties are of size two. Moreover, in this setting it is UGC-hard to provide an approximation for the maximum cardinality stable matching problem with a constant factor smaller than 4/3. In this paper, we give a tight analysis of an approximation algorithm given by Huang and Kavitha for the maximum cardinality stable matching problem with ties of size two, demonstrating an improved 4/3-approximation factor. |
1603.00977 | EPTCS | Mahdi Amani (Universit\`a di Pisa, Pisa, Italy), Abbas Nowzari-Dalini
(UT, Tehran, Iran) | Generation, Ranking and Unranking of Ordered Trees with Degree Bounds | In Proceedings DCM 2015, arXiv:1603.00536 | EPTCS 204, 2016, pp. 31-45 | 10.4204/EPTCS.204.4 | null | cs.CC cs.DM cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of generating, ranking and unranking of unlabeled
ordered trees whose nodes have maximum degree of $\Delta$. This class of trees
represents a generalization of chemical trees. A chemical tree is an unlabeled
tree in which no node has degree greater than 4. By allowing up to $\Delta$
children for each node of chemical tree instead of 4, we will have a
generalization of chemical trees. Here, we introduce a new encoding over an
alphabet of size 4 for representing unlabeled ordered trees with maximum degree
of $\Delta$. We use this encoding for generating these trees in A-order with
constant average time and O(n) worst case time. Due to the given encoding, with
a precomputation of size and time O(n^2) (assuming $\Delta$ is constant), both
ranking and unranking algorithms are also designed taking O(n) and O(nlogn)
time complexities.
| [
{
"created": "Thu, 3 Mar 2016 05:33:50 GMT",
"version": "v1"
}
] | 2016-08-22 | [
[
"Amani",
"Mahdi",
"",
"Università di Pisa, Pisa, Italy"
],
[
"Nowzari-Dalini",
"Abbas",
"",
"UT, Tehran, Iran"
]
] | We study the problem of generating, ranking and unranking of unlabeled ordered trees whose nodes have maximum degree of $\Delta$. This class of trees represents a generalization of chemical trees. A chemical tree is an unlabeled tree in which no node has degree greater than 4. By allowing up to $\Delta$ children for each node of chemical tree instead of 4, we will have a generalization of chemical trees. Here, we introduce a new encoding over an alphabet of size 4 for representing unlabeled ordered trees with maximum degree of $\Delta$. We use this encoding for generating these trees in A-order with constant average time and O(n) worst case time. Due to the given encoding, with a precomputation of size and time O(n^2) (assuming $\Delta$ is constant), both ranking and unranking algorithms are also designed taking O(n) and O(nlogn) time complexities. |
2005.14253 | Nicholas FitzGerald | Thibault F\'evry, Nicholas FitzGerald, Livio Baldini Soares, Tom
Kwiatkowski | Empirical Evaluation of Pretraining Strategies for Supervised Entity
Linking | 11 pages, 8 figures, appearing at AKBC 2020 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present an entity linking model which combines a Transformer
architecture with large scale pretraining from Wikipedia links. Our model
achieves the state-of-the-art on two commonly used entity linking datasets:
96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand
what design choices are important for entity linking, including choices of
negative entity candidates, Transformer architecture, and input perturbations.
Lastly, we present promising results on more challenging settings such as
end-to-end entity linking and entity linking without in-domain training data.
| [
{
"created": "Thu, 28 May 2020 19:32:52 GMT",
"version": "v1"
}
] | 2020-06-01 | [
[
"Févry",
"Thibault",
""
],
[
"FitzGerald",
"Nicholas",
""
],
[
"Soares",
"Livio Baldini",
""
],
[
"Kwiatkowski",
"Tom",
""
]
] | In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-art on two commonly used entity linking datasets: 96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand what design choices are important for entity linking, including choices of negative entity candidates, Transformer architecture, and input perturbations. Lastly, we present promising results on more challenging settings such as end-to-end entity linking and entity linking without in-domain training data. |
2406.12746 | Miaoyu Li | Miaoyu Li, Haoxin Li, Zilin Du, and Boyang Li | Rationale-based Ensemble of Multiple QA Strategies for Zero-shot
Knowledge-based VQA | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge-based Visual Qustion-answering (K-VQA) necessitates the use of
background knowledge beyond what is depicted in the image. Current zero-shot
K-VQA methods usually translate an image to a single type of textual decision
context and use a text-based model to answer the question based on it, which
conflicts with the fact that K-VQA questions often require the combination of
multiple question-answering strategies. In light of this, we propose
Rationale-based Ensemble of Answer Context Tactics (REACT) to achieve a dynamic
ensemble of multiple question-answering tactics, comprising Answer Candidate
Generation (ACG) and Rationale-based Strategy Fusion (RSF). In ACG, we generate
three distinctive decision contexts to provide different strategies for each
question, resulting in the generation of three answer candidates. RSF generates
automatic and mechanistic rationales from decision contexts for each candidate,
allowing the model to select the correct answer from all candidates. We conduct
comprehensive experiments on the OK-VQA and A-OKVQA datasets, and our method
significantly outperforms state-of-the-art LLM-based baselines on all datasets.
| [
{
"created": "Tue, 18 Jun 2024 16:06:38 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Jun 2024 02:02:13 GMT",
"version": "v2"
},
{
"created": "Sun, 23 Jun 2024 03:06:42 GMT",
"version": "v3"
}
] | 2024-06-25 | [
[
"Li",
"Miaoyu",
""
],
[
"Li",
"Haoxin",
""
],
[
"Du",
"Zilin",
""
],
[
"Li",
"Boyang",
""
]
] | Knowledge-based Visual Qustion-answering (K-VQA) necessitates the use of background knowledge beyond what is depicted in the image. Current zero-shot K-VQA methods usually translate an image to a single type of textual decision context and use a text-based model to answer the question based on it, which conflicts with the fact that K-VQA questions often require the combination of multiple question-answering strategies. In light of this, we propose Rationale-based Ensemble of Answer Context Tactics (REACT) to achieve a dynamic ensemble of multiple question-answering tactics, comprising Answer Candidate Generation (ACG) and Rationale-based Strategy Fusion (RSF). In ACG, we generate three distinctive decision contexts to provide different strategies for each question, resulting in the generation of three answer candidates. RSF generates automatic and mechanistic rationales from decision contexts for each candidate, allowing the model to select the correct answer from all candidates. We conduct comprehensive experiments on the OK-VQA and A-OKVQA datasets, and our method significantly outperforms state-of-the-art LLM-based baselines on all datasets. |
1412.8501 | Eli A. Meirom | Eli A. Meirom, Shie Mannor, Ariel Orda | Formation Games of Reliable Networks | null | null | null | null | cs.GT cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We establish a network formation game for the Internet's Autonomous System
(AS) interconnection topology. The game includes different types of players,
accounting for the heterogeneity of ASs in the Internet. We incorporate
reliability considerations in the player's utility function, and analyze static
properties of the game as well as its dynamic evolution. We provide dynamic
analysis of its topological quantities, and explain the prevalence of some
"network motifs" in the Internet graph. We assess our predictions with
real-world data.
| [
{
"created": "Mon, 29 Dec 2014 22:38:53 GMT",
"version": "v1"
}
] | 2014-12-31 | [
[
"Meirom",
"Eli A.",
""
],
[
"Mannor",
"Shie",
""
],
[
"Orda",
"Ariel",
""
]
] | We establish a network formation game for the Internet's Autonomous System (AS) interconnection topology. The game includes different types of players, accounting for the heterogeneity of ASs in the Internet. We incorporate reliability considerations in the player's utility function, and analyze static properties of the game as well as its dynamic evolution. We provide dynamic analysis of its topological quantities, and explain the prevalence of some "network motifs" in the Internet graph. We assess our predictions with real-world data. |
1410.4207 | Claudio Criscione | Enrico Bazzoli, Claudio Criscione, Federico Maggi, Stefano Zanero | XSS Peeker: A Systematic Analysis of Cross-site Scripting Vulnerability
Scanners | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the first publication of the "OWASP Top 10" (2004), cross-site
scripting (XSS) vulnerabilities have always been among the top 5 web
application security bugs. Black-box vulnerability scanners are widely used in
the industry to reproduce (XSS) attacks automatically. In spite of the
technical sophistication and advancement, previous work showed that black-box
scanners miss a non-negligible portion of vulnerabilities, and report
non-existing, non-exploitable or uninteresting vulnerabilities. Unfortunately,
these results hold true even for XSS vulnerabilities, which are relatively
simple to trigger if compared, for instance, to logic flaws.
Black-box scanners have not been studied in depth on this vertical: knowing
precisely how scanners try to detect XSS can provide useful insights to
understand their limitations, to design better detection methods. In this
paper, we present and discuss the results of a detailed and systematic study on
6 black-box web scanners (both proprietary and open source) that we conducted
in coordination with the respective vendors. To this end, we developed an
automated tool to (1) extract the payloads used by each scanner, (2) distill
the "templates" that have originated each payload, (3) evaluate them according
to quality indicators, and (4) perform a cross-scanner analysis. Unlike
previous work, our testbed application, which contains a large set of XSS
vulnerabilities, including DOM XSS, was gradually retrofitted to accomodate for
the payloads that triggered no vulnerabilities.
Our analysis reveals a highly fragmented scenario. Scanners exhibit a wide
variety of distinct payloads, a non-uniform approach to fuzzing and mutating
the payloads, and a very diverse detection effectiveness.
| [
{
"created": "Wed, 15 Oct 2014 20:03:07 GMT",
"version": "v1"
}
] | 2014-10-17 | [
[
"Bazzoli",
"Enrico",
""
],
[
"Criscione",
"Claudio",
""
],
[
"Maggi",
"Federico",
""
],
[
"Zanero",
"Stefano",
""
]
] | Since the first publication of the "OWASP Top 10" (2004), cross-site scripting (XSS) vulnerabilities have always been among the top 5 web application security bugs. Black-box vulnerability scanners are widely used in the industry to reproduce (XSS) attacks automatically. In spite of the technical sophistication and advancement, previous work showed that black-box scanners miss a non-negligible portion of vulnerabilities, and report non-existing, non-exploitable or uninteresting vulnerabilities. Unfortunately, these results hold true even for XSS vulnerabilities, which are relatively simple to trigger if compared, for instance, to logic flaws. Black-box scanners have not been studied in depth on this vertical: knowing precisely how scanners try to detect XSS can provide useful insights to understand their limitations, to design better detection methods. In this paper, we present and discuss the results of a detailed and systematic study on 6 black-box web scanners (both proprietary and open source) that we conducted in coordination with the respective vendors. To this end, we developed an automated tool to (1) extract the payloads used by each scanner, (2) distill the "templates" that have originated each payload, (3) evaluate them according to quality indicators, and (4) perform a cross-scanner analysis. Unlike previous work, our testbed application, which contains a large set of XSS vulnerabilities, including DOM XSS, was gradually retrofitted to accomodate for the payloads that triggered no vulnerabilities. Our analysis reveals a highly fragmented scenario. Scanners exhibit a wide variety of distinct payloads, a non-uniform approach to fuzzing and mutating the payloads, and a very diverse detection effectiveness. |
2001.04484 | Luca Papariello | Luca Papariello, Alexandros Bampoulidis, Mihai Lupu | On the Replicability of Combining Word Embeddings and Retrieval Models | null | null | null | null | cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We replicate recent experiments attempting to demonstrate an attractive
hypothesis about the use of the Fisher kernel framework and mixture models for
aggregating word embeddings towards document representations and the use of
these representations in document classification, clustering, and retrieval.
Specifically, the hypothesis was that the use of a mixture model of von
Mises-Fisher (VMF) distributions instead of Gaussian distributions would be
beneficial because of the focus on cosine distances of both VMF and the vector
space model traditionally used in information retrieval. Previous experiments
had validated this hypothesis. Our replication was not able to validate it,
despite a large parameter scan space.
| [
{
"created": "Mon, 13 Jan 2020 19:01:07 GMT",
"version": "v1"
}
] | 2020-01-15 | [
[
"Papariello",
"Luca",
""
],
[
"Bampoulidis",
"Alexandros",
""
],
[
"Lupu",
"Mihai",
""
]
] | We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval. Specifically, the hypothesis was that the use of a mixture model of von Mises-Fisher (VMF) distributions instead of Gaussian distributions would be beneficial because of the focus on cosine distances of both VMF and the vector space model traditionally used in information retrieval. Previous experiments had validated this hypothesis. Our replication was not able to validate it, despite a large parameter scan space. |
2403.09547 | Jo\~ao Helis Bernardo | Jo\~ao Helis Bernardo, Daniel Alencar da Costa, S\'ergio Queiroz de
Medeiros, Uir\'a Kulesza | How do Machine Learning Projects use Continuous Integration Practices?
An Empirical Study on GitHub Actions | 10 pages, Mining Software Repositories, MSR 2024 | null | null | null | cs.SE cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Continuous Integration (CI) is a well-established practice in traditional
software development, but its nuances in the domain of Machine Learning (ML)
projects remain relatively unexplored. Given the distinctive nature of ML
development, understanding how CI practices are adopted in this context is
crucial for tailoring effective approaches. In this study, we conduct a
comprehensive analysis of 185 open-source projects on GitHub (93 ML and 92
non-ML projects). Our investigation comprises both quantitative and qualitative
dimensions, aiming to uncover differences in CI adoption between ML and non-ML
projects. Our findings indicate that ML projects often require longer build
durations, and medium-sized ML projects exhibit lower test coverage compared to
non-ML projects. Moreover, small and medium-sized ML projects show a higher
prevalence of increasing build duration trends compared to their non-ML
counterparts. Additionally, our qualitative analysis illuminates the
discussions around CI in both ML and non-ML projects, encompassing themes like
CI Build Execution and Status, CI Testing, and CI Infrastructure. These
insights shed light on the unique challenges faced by ML projects in adopting
CI practices effectively.
| [
{
"created": "Thu, 14 Mar 2024 16:35:39 GMT",
"version": "v1"
}
] | 2024-03-15 | [
[
"Bernardo",
"João Helis",
""
],
[
"da Costa",
"Daniel Alencar",
""
],
[
"de Medeiros",
"Sérgio Queiroz",
""
],
[
"Kulesza",
"Uirá",
""
]
] | Continuous Integration (CI) is a well-established practice in traditional software development, but its nuances in the domain of Machine Learning (ML) projects remain relatively unexplored. Given the distinctive nature of ML development, understanding how CI practices are adopted in this context is crucial for tailoring effective approaches. In this study, we conduct a comprehensive analysis of 185 open-source projects on GitHub (93 ML and 92 non-ML projects). Our investigation comprises both quantitative and qualitative dimensions, aiming to uncover differences in CI adoption between ML and non-ML projects. Our findings indicate that ML projects often require longer build durations, and medium-sized ML projects exhibit lower test coverage compared to non-ML projects. Moreover, small and medium-sized ML projects show a higher prevalence of increasing build duration trends compared to their non-ML counterparts. Additionally, our qualitative analysis illuminates the discussions around CI in both ML and non-ML projects, encompassing themes like CI Build Execution and Status, CI Testing, and CI Infrastructure. These insights shed light on the unique challenges faced by ML projects in adopting CI practices effectively. |
1902.09103 | Tianwei Shen | Tianwei Shen, Zixin Luo, Lei Zhou, Hanyu Deng, Runze Zhang, Tian Fang,
Long Quan | Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation | Accepted by ICRA 2019 | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate relative pose is one of the key components in visual odometry (VO)
and simultaneous localization and mapping (SLAM). Recently, the self-supervised
learning framework that jointly optimizes the relative pose and target image
depth has attracted the attention of the community. Previous works rely on the
photometric error generated from depths and poses between adjacent frames,
which contains large systematic error under realistic scenes due to reflective
surfaces and occlusions. In this paper, we bridge the gap between geometric
loss and photometric loss by introducing the matching loss constrained by
epipolar geometry in a self-supervised framework. Evaluated on the KITTI
dataset, our method outperforms the state-of-the-art unsupervised ego-motion
estimation methods by a large margin. The code and data are available at
https://github.com/hlzz/DeepMatchVO.
| [
{
"created": "Mon, 25 Feb 2019 06:22:52 GMT",
"version": "v1"
}
] | 2019-02-26 | [
[
"Shen",
"Tianwei",
""
],
[
"Luo",
"Zixin",
""
],
[
"Zhou",
"Lei",
""
],
[
"Deng",
"Hanyu",
""
],
[
"Zhang",
"Runze",
""
],
[
"Fang",
"Tian",
""
],
[
"Quan",
"Long",
""
]
] | Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depths and poses between adjacent frames, which contains large systematic error under realistic scenes due to reflective surfaces and occlusions. In this paper, we bridge the gap between geometric loss and photometric loss by introducing the matching loss constrained by epipolar geometry in a self-supervised framework. Evaluated on the KITTI dataset, our method outperforms the state-of-the-art unsupervised ego-motion estimation methods by a large margin. The code and data are available at https://github.com/hlzz/DeepMatchVO. |
2305.19157 | Reza Faieghi | S. Mohammadreza Ebrahimi, Farid Norouzi, Hossein Dastres, Reza
Faieghi, Mehdi Naderi, Milad Malekzadeh | Sensor Fault Detection and Compensation with Performance Prescription
for Robotic Manipulators | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on sensor fault detection and compensation for robotic
manipulators. The proposed method features a new adaptive observer and a new
terminal sliding mode control law established on a second-order integral
sliding surface. The method enables sensor fault detection without the need to
know the bounds on fault value and/or its derivative. It also enables fast and
fixed-time fault-tolerant control whose performance can be prescribed
beforehand by defining funnel bounds on the tracking error. The ultimate
boundedness of the estimation errors for the proposed observer and the
fixed-time stability of the control system are shown using Lyapunov stability
analysis. The effectiveness of the proposed method is verified using numerical
simulations on two different robotic manipulators, and the results are compared
with existing methods. Our results demonstrate performance gains obtained by
the proposed method compared to the existing results.
| [
{
"created": "Tue, 30 May 2023 15:58:56 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Mar 2024 01:09:34 GMT",
"version": "v2"
}
] | 2024-03-20 | [
[
"Ebrahimi",
"S. Mohammadreza",
""
],
[
"Norouzi",
"Farid",
""
],
[
"Dastres",
"Hossein",
""
],
[
"Faieghi",
"Reza",
""
],
[
"Naderi",
"Mehdi",
""
],
[
"Malekzadeh",
"Milad",
""
]
] | This paper focuses on sensor fault detection and compensation for robotic manipulators. The proposed method features a new adaptive observer and a new terminal sliding mode control law established on a second-order integral sliding surface. The method enables sensor fault detection without the need to know the bounds on fault value and/or its derivative. It also enables fast and fixed-time fault-tolerant control whose performance can be prescribed beforehand by defining funnel bounds on the tracking error. The ultimate boundedness of the estimation errors for the proposed observer and the fixed-time stability of the control system are shown using Lyapunov stability analysis. The effectiveness of the proposed method is verified using numerical simulations on two different robotic manipulators, and the results are compared with existing methods. Our results demonstrate performance gains obtained by the proposed method compared to the existing results. |
2102.11649 | Rafa\"el Bocquet | Rafa\"el Bocquet and Ambrus Kaposi and Christian Sattler | Relative induction principles for type theories | null | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present new induction principles for the syntax of dependent type
theories, which we call relative induction principles. The result of the
induction principle relative to a functor F into the syntax is stable over the
codomain of F. We rely on the internal language of presheaf categories. In
order to combine the internal languages of multiple presheaf categories, we use
Dependent Right Adjoints and Multimodal Type Theory. Categorical gluing is used
to prove these induction principles, but it not visible in their statements,
which involve a notion of model without context extensions. As example
applications of these induction principles, we give short and boilerplate-free
proofs of canonicity and normalization for some small type theories, and sketch
proofs of other metatheoretic results.
| [
{
"created": "Tue, 23 Feb 2021 12:08:25 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Jul 2021 12:42:20 GMT",
"version": "v2"
}
] | 2021-07-20 | [
[
"Bocquet",
"Rafaël",
""
],
[
"Kaposi",
"Ambrus",
""
],
[
"Sattler",
"Christian",
""
]
] | We present new induction principles for the syntax of dependent type theories, which we call relative induction principles. The result of the induction principle relative to a functor F into the syntax is stable over the codomain of F. We rely on the internal language of presheaf categories. In order to combine the internal languages of multiple presheaf categories, we use Dependent Right Adjoints and Multimodal Type Theory. Categorical gluing is used to prove these induction principles, but it not visible in their statements, which involve a notion of model without context extensions. As example applications of these induction principles, we give short and boilerplate-free proofs of canonicity and normalization for some small type theories, and sketch proofs of other metatheoretic results. |
1811.05233 | Yuichi Kageyama | Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U-chupala, Yoshiki
Tanaka and Yuichi Kageyama | Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scaling the distributed deep learning to a massive GPU cluster level is
challenging due to the instability of the large mini-batch training and the
overhead of the gradient synchronization. We address the instability of the
large mini-batch training with batch-size control and label smoothing. We
address the overhead of the gradient synchronization with 2D-Torus all-reduce.
Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and
performs a series of collective operation in different orientations. These two
techniques are implemented with Neural Network Libraries (NNL). We have
successfully trained ImageNet/ResNet-50 in 122 seconds without significant
accuracy loss on ABCI cluster.
| [
{
"created": "Tue, 13 Nov 2018 11:52:04 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Mar 2019 09:18:09 GMT",
"version": "v2"
}
] | 2019-03-06 | [
[
"Mikami",
"Hiroaki",
""
],
[
"Suganuma",
"Hisahiro",
""
],
[
"U-chupala",
"Pongsakorn",
""
],
[
"Tanaka",
"Yoshiki",
""
],
[
"Kageyama",
"Yuichi",
""
]
] | Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster. |
2401.02636 | Haitao Wang | Haitao Wang | Algorithms for Computing Closest Points for Segments | Accepted to STACS 2024 | null | null | null | cs.CG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a set $P$ of $n$ points and a set $S$ of $n$ segments in the plane, we
consider the problem of computing for each segment of $S$ its closest point in
$P$. The previously best algorithm solves the problem in
$n^{4/3}2^{O(\log^*n)}$ time [Bespamyatnikh, 2003] and a lower bound (under a
somewhat restricted model) $\Omega(n^{4/3})$ has also been proved. In this
paper, we present an $O(n^{4/3})$ time algorithm and thus solve the problem
optimally (under the restricted model). In addition, we also present data
structures for solving the online version of the problem, i.e., given a query
segment (or a line as a special case), find its closest point in $P$. Our new
results improve the previous work.
| [
{
"created": "Fri, 5 Jan 2024 04:56:22 GMT",
"version": "v1"
}
] | 2024-01-08 | [
[
"Wang",
"Haitao",
""
]
] | Given a set $P$ of $n$ points and a set $S$ of $n$ segments in the plane, we consider the problem of computing for each segment of $S$ its closest point in $P$. The previously best algorithm solves the problem in $n^{4/3}2^{O(\log^*n)}$ time [Bespamyatnikh, 2003] and a lower bound (under a somewhat restricted model) $\Omega(n^{4/3})$ has also been proved. In this paper, we present an $O(n^{4/3})$ time algorithm and thus solve the problem optimally (under the restricted model). In addition, we also present data structures for solving the online version of the problem, i.e., given a query segment (or a line as a special case), find its closest point in $P$. Our new results improve the previous work. |
2303.12594 | Jie Luo | Jie Luo, Carlo Longhi and Agoston E. Eiben | A Comparative Study of Brain Reproduction Methods for Morphologically
Evolving Robots | 8 pages, ALife | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | In the most extensive robot evolution systems, both the bodies and the brains
of the robots undergo evolution and the brains of 'infant' robots are also
optimized by a learning process immediately after 'birth'. This paper is
concerned with the brain evolution mechanism in such a system. In particular,
we compare four options obtained by combining asexual or sexual brain
reproduction with Darwinian or Lamarckian evolution mechanisms. We conduct
experiments in simulation with a system of evolvable modular robots on two
different tasks. The results show that sexual reproduction of the robots'
brains is preferable in the Darwinian framework, but the effect is the opposite
in the Lamarckian system (both using the same infant learning method). Our
experiments suggest that the overall best option is asexual reproduction
combined with the Lamarckian framework, as it obtains better robots in terms of
fitness than the other three. Considering the evolved morphologies, the
different brain reproduction methods do not lead to differences. This result
indicates that the morphology of the robot is mainly determined by the task and
the environment, not by the brain reproduction methods.
| [
{
"created": "Wed, 22 Mar 2023 14:31:52 GMT",
"version": "v1"
},
{
"created": "Mon, 29 May 2023 07:23:32 GMT",
"version": "v2"
},
{
"created": "Tue, 30 May 2023 12:01:04 GMT",
"version": "v3"
}
] | 2023-05-31 | [
[
"Luo",
"Jie",
""
],
[
"Longhi",
"Carlo",
""
],
[
"Eiben",
"Agoston E.",
""
]
] | In the most extensive robot evolution systems, both the bodies and the brains of the robots undergo evolution and the brains of 'infant' robots are also optimized by a learning process immediately after 'birth'. This paper is concerned with the brain evolution mechanism in such a system. In particular, we compare four options obtained by combining asexual or sexual brain reproduction with Darwinian or Lamarckian evolution mechanisms. We conduct experiments in simulation with a system of evolvable modular robots on two different tasks. The results show that sexual reproduction of the robots' brains is preferable in the Darwinian framework, but the effect is the opposite in the Lamarckian system (both using the same infant learning method). Our experiments suggest that the overall best option is asexual reproduction combined with the Lamarckian framework, as it obtains better robots in terms of fitness than the other three. Considering the evolved morphologies, the different brain reproduction methods do not lead to differences. This result indicates that the morphology of the robot is mainly determined by the task and the environment, not by the brain reproduction methods. |
2401.13656 | Federico Cinus | Ernesto Colacrai, Federico Cinus, Gianmarco De Francisci Morales,
Michele Starnini | Navigating Multidimensional Ideologies with Reddit's Political Compass:
Economic Conflict and Social Affinity | null | null | null | null | cs.SI cs.CY physics.soc-ph stat.AP | http://creativecommons.org/licenses/by/4.0/ | The prevalent perspective in quantitative research on opinion dynamics
flattens the landscape of the online political discourse into a traditional
left--right dichotomy. While this approach helps simplify the analysis and
modeling effort, it also neglects the intrinsic multidimensional richness of
ideologies. In this study, we analyze social interactions on Reddit, under the
lens of a multi-dimensional ideological framework: the political compass. We
examine over 8 million comments posted on the subreddits /r/PoliticalCompass
and /r/PoliticalCompassMemes during 2020--2022. By leveraging their
self-declarations, we disentangle the ideological dimensions of users into
economic (left--right) and social (libertarian--authoritarian) axes. In
addition, we characterize users by their demographic attributes (age, gender,
and affluence).
We find significant homophily for interactions along the social axis of the
political compass and demographic attributes. Compared to a null model,
interactions among individuals of similar ideology surpass expectations by 6%.
In contrast, we uncover a significant heterophily along the economic axis:
left/right interactions exceed expectations by 10%. Furthermore, heterophilic
interactions are characterized by a higher language toxicity than homophilic
interactions, which hints at a conflictual discourse between every opposite
ideology. Our results help reconcile apparent contradictions in recent
literature, which found a superposition of homophilic and heterophilic
interactions in online political discussions. By disentangling such
interactions into the economic and social axes we pave the way for a deeper
understanding of opinion dynamics on social media.
| [
{
"created": "Wed, 24 Jan 2024 18:49:19 GMT",
"version": "v1"
}
] | 2024-01-25 | [
[
"Colacrai",
"Ernesto",
""
],
[
"Cinus",
"Federico",
""
],
[
"Morales",
"Gianmarco De Francisci",
""
],
[
"Starnini",
"Michele",
""
]
] | The prevalent perspective in quantitative research on opinion dynamics flattens the landscape of the online political discourse into a traditional left--right dichotomy. While this approach helps simplify the analysis and modeling effort, it also neglects the intrinsic multidimensional richness of ideologies. In this study, we analyze social interactions on Reddit, under the lens of a multi-dimensional ideological framework: the political compass. We examine over 8 million comments posted on the subreddits /r/PoliticalCompass and /r/PoliticalCompassMemes during 2020--2022. By leveraging their self-declarations, we disentangle the ideological dimensions of users into economic (left--right) and social (libertarian--authoritarian) axes. In addition, we characterize users by their demographic attributes (age, gender, and affluence). We find significant homophily for interactions along the social axis of the political compass and demographic attributes. Compared to a null model, interactions among individuals of similar ideology surpass expectations by 6%. In contrast, we uncover a significant heterophily along the economic axis: left/right interactions exceed expectations by 10%. Furthermore, heterophilic interactions are characterized by a higher language toxicity than homophilic interactions, which hints at a conflictual discourse between every opposite ideology. Our results help reconcile apparent contradictions in recent literature, which found a superposition of homophilic and heterophilic interactions in online political discussions. By disentangling such interactions into the economic and social axes we pave the way for a deeper understanding of opinion dynamics on social media. |
1807.06958 | Dimitar Nikolov | Dimitar Nikolov and Mounia Lalmas and Alessandro Flammini and Filippo
Menczer | Quantifying Biases in Online Information Exposure | 25 pages, 10 figures, to appear in the Journal of the Association for
Information Science and Technology (JASIST) | JASIST 70 (3): 218-229, 2019 | 10.1002/asi.24121 | null | cs.SI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our consumption of online information is mediated by filtering, ranking, and
recommendation algorithms that introduce unintentional biases as they attempt
to deliver relevant and engaging content. It has been suggested that our
reliance on online technologies such as search engines and social media may
limit exposure to diverse points of view and make us vulnerable to manipulation
by disinformation. In this paper, we mine a massive dataset of Web traffic to
quantify two kinds of bias: (i) homogeneity bias, which is the tendency to
consume content from a narrow set of information sources, and (ii) popularity
bias, which is the selective exposure to content from top sites. Our analysis
reveals different bias levels across several widely used Web platforms. Search
exposes users to a diverse set of sources, while social media traffic tends to
exhibit high popularity and homogeneity bias. When we focus our analysis on
traffic to news sites, we find higher levels of popularity bias, with smaller
differences across applications. Overall, our results quantify the extent to
which our choices of online systems confine us inside "social bubbles."
| [
{
"created": "Wed, 18 Jul 2018 14:19:49 GMT",
"version": "v1"
}
] | 2020-10-07 | [
[
"Nikolov",
"Dimitar",
""
],
[
"Lalmas",
"Mounia",
""
],
[
"Flammini",
"Alessandro",
""
],
[
"Menczer",
"Filippo",
""
]
] | Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this paper, we mine a massive dataset of Web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used Web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles." |
2406.02034 | Soha Hussein | Soha Hussein, Stephen McCamant, Mike Whalen | Generator-Based Fuzzers with Type-Based Targeted Mutation | Fixing rendering of figure | null | null | null | cs.SE | http://creativecommons.org/licenses/by-sa/4.0/ | As with any fuzzer, directing Generator-Based Fuzzers (GBF) to reach
particular code targets can increase the fuzzer's effectiveness. In previous
work, coverage-guided fuzzers used a mix of static analysis, taint analysis,
and constraint-solving approaches to address this problem. However, none of
these techniques were particularly crafted for GBF where input generators are
used to construct program inputs. The observation is that input generators
carry information about the input structure that is naturally present through
the typing composition of the program input.
In this paper, we introduce a type-based mutation heuristic, along with
constant string lookup, for Java GBF. Our key intuition is that if one can
identify which sub-part (types) of the input will likely influence the
branching decision, then focusing on mutating the choices of the generators
constructing these types is likely to achieve the desired coverages. We used
our technique to fuzz AWSLambda applications. Results compared to a baseline
GBF tool show an almost 20\% average improvement in application coverage, and
larger improvements when third-party code is included.
| [
{
"created": "Tue, 4 Jun 2024 07:20:13 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jun 2024 07:32:41 GMT",
"version": "v2"
}
] | 2024-06-13 | [
[
"Hussein",
"Soha",
""
],
[
"McCamant",
"Stephen",
""
],
[
"Whalen",
"Mike",
""
]
] | As with any fuzzer, directing Generator-Based Fuzzers (GBF) to reach particular code targets can increase the fuzzer's effectiveness. In previous work, coverage-guided fuzzers used a mix of static analysis, taint analysis, and constraint-solving approaches to address this problem. However, none of these techniques were particularly crafted for GBF where input generators are used to construct program inputs. The observation is that input generators carry information about the input structure that is naturally present through the typing composition of the program input. In this paper, we introduce a type-based mutation heuristic, along with constant string lookup, for Java GBF. Our key intuition is that if one can identify which sub-part (types) of the input will likely influence the branching decision, then focusing on mutating the choices of the generators constructing these types is likely to achieve the desired coverages. We used our technique to fuzz AWSLambda applications. Results compared to a baseline GBF tool show an almost 20\% average improvement in application coverage, and larger improvements when third-party code is included. |
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