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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2403.05055 | Yitao Zhu | Yitao Zhu, Sheng Wang, Mengjie Xu, Zixu Zhuang, Zhixin Wang, Kaidong
Wang, Han Zhang, Qian Wang | MUC: Mixture of Uncalibrated Cameras for Robust 3D Human Body
Reconstruction | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multiple cameras can provide multi-view video coverage of a person. It is
necessary to fuse multi-view data, e.g., for subsequent behavioral analysis,
while such fusion often relies on calibration of cameras in traditional
solutions. However, it is non-trivial to calibrate multiple cameras. In this
work, we propose a method to reconstruct 3D human body from multiple
uncalibrated camera views. First, we adopt a pre-trained human body encoder to
process each individual camera view, such that human body models and parameters
can be reconstructed for each view. Next, instead of simply averaging models
across views, we train a network to determine the weights of individual views
for their fusion, based on the parameters estimated for joints and hands of
human body as well as camera positions. Further, we turn to the mesh surface of
human body for dynamic fusion, such that facial expression can be seamlessly
integrated into the model of human body. Our method has demonstrated superior
performance in reconstructing human body upon two public datasets. More
importantly, our method can flexibly support ad-hoc deployment of an arbitrary
number of cameras, which has significant potential in related applications. We
will release source code upon acceptance of the paper.
| [
{
"created": "Fri, 8 Mar 2024 05:03:25 GMT",
"version": "v1"
}
] | 2024-03-11 | [
[
"Zhu",
"Yitao",
""
],
[
"Wang",
"Sheng",
""
],
[
"Xu",
"Mengjie",
""
],
[
"Zhuang",
"Zixu",
""
],
[
"Wang",
"Zhixin",
""
],
[
"Wang",
"Kaidong",
""
],
[
"Zhang",
"Han",
""
],
[
"Wang",
"Qian",
""
]
] | Multiple cameras can provide multi-view video coverage of a person. It is necessary to fuse multi-view data, e.g., for subsequent behavioral analysis, while such fusion often relies on calibration of cameras in traditional solutions. However, it is non-trivial to calibrate multiple cameras. In this work, we propose a method to reconstruct 3D human body from multiple uncalibrated camera views. First, we adopt a pre-trained human body encoder to process each individual camera view, such that human body models and parameters can be reconstructed for each view. Next, instead of simply averaging models across views, we train a network to determine the weights of individual views for their fusion, based on the parameters estimated for joints and hands of human body as well as camera positions. Further, we turn to the mesh surface of human body for dynamic fusion, such that facial expression can be seamlessly integrated into the model of human body. Our method has demonstrated superior performance in reconstructing human body upon two public datasets. More importantly, our method can flexibly support ad-hoc deployment of an arbitrary number of cameras, which has significant potential in related applications. We will release source code upon acceptance of the paper. |
1906.08936 | Maofan Yin | Team Rocket, Maofan Yin, Kevin Sekniqi, Robbert van Renesse, and Emin
G\"un Sirer | Scalable and Probabilistic Leaderless BFT Consensus through
Metastability | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a family of leaderless Byzantine fault tolerance
protocols, built around a metastable mechanism via network subsampling. These
protocols provide a strong probabilistic safety guarantee in the presence of
Byzantine adversaries while their concurrent and leaderless nature enables them
to achieve high throughput and scalability. Unlike blockchains that rely on
proof-of-work, they are quiescent and green. Unlike traditional consensus
protocols where one or more nodes typically process linear bits in the number
of total nodes per decision, no node processes more than logarithmic bits. It
does not require accurate knowledge of all participants and exposes new
possible tradeoffs and improvements in safety and liveness for building
consensus protocols.
The paper describes the Snow protocol family, analyzes its guarantees, and
describes how it can be used to construct the core of an internet-scale
electronic payment system called Avalanche, which is evaluated in a large scale
deployment. Experiments demonstrate that the system can achieve high throughput
(3400 tps), provide low confirmation latency (1.35 sec), and scale well
compared to existing systems that deliver similar functionality. For our
implementation and setup, the bottleneck of the system is in transaction
verification.
| [
{
"created": "Fri, 21 Jun 2019 03:55:19 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Aug 2020 14:54:44 GMT",
"version": "v2"
}
] | 2020-08-25 | [
[
"Rocket",
"Team",
""
],
[
"Yin",
"Maofan",
""
],
[
"Sekniqi",
"Kevin",
""
],
[
"van Renesse",
"Robbert",
""
],
[
"Sirer",
"Emin Gün",
""
]
] | This paper introduces a family of leaderless Byzantine fault tolerance protocols, built around a metastable mechanism via network subsampling. These protocols provide a strong probabilistic safety guarantee in the presence of Byzantine adversaries while their concurrent and leaderless nature enables them to achieve high throughput and scalability. Unlike blockchains that rely on proof-of-work, they are quiescent and green. Unlike traditional consensus protocols where one or more nodes typically process linear bits in the number of total nodes per decision, no node processes more than logarithmic bits. It does not require accurate knowledge of all participants and exposes new possible tradeoffs and improvements in safety and liveness for building consensus protocols. The paper describes the Snow protocol family, analyzes its guarantees, and describes how it can be used to construct the core of an internet-scale electronic payment system called Avalanche, which is evaluated in a large scale deployment. Experiments demonstrate that the system can achieve high throughput (3400 tps), provide low confirmation latency (1.35 sec), and scale well compared to existing systems that deliver similar functionality. For our implementation and setup, the bottleneck of the system is in transaction verification. |
1905.02428 | Tobias Kaminski | Tobias Kaminski | Integrated Algorithms for HEX-Programs and Applications in Machine
Learning | 7 pages, submitted for the Doctoral Consortium at the 15th
International Conference on Logic Programming and Non-monotonic Reasoning
(LPNMR 2019) | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper summarizes my doctoral research on evaluation algorithms for
HEX-programs, which extend Answer Set Programming with means for interfacing
external computations. The focus is on integrating different subprocesses of
HEX-evaluation, such as solving and external calls as well as grounding, and on
applications of HEX-programs in the area of Machine Learning.
| [
{
"created": "Tue, 7 May 2019 09:22:36 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"Kaminski",
"Tobias",
""
]
] | This paper summarizes my doctoral research on evaluation algorithms for HEX-programs, which extend Answer Set Programming with means for interfacing external computations. The focus is on integrating different subprocesses of HEX-evaluation, such as solving and external calls as well as grounding, and on applications of HEX-programs in the area of Machine Learning. |
1903.03019 | Nalin Asanka Gamagedara Arachchilage | Matt Dixon, Nalin Asanka Gamagedara Arachchilage, James Nicholson | Engaging Users with Educational Games: The Case of Phishing | 4 | CHI '19 Extended Abstracts on Human Factors in Computing Systems
Proceedings (CHI 2019), 2019 | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phishing continues to be a difficult problem for individuals and
organisations. Educational games and simulations have been increasingly
acknowledged as enormous and powerful teaching tools, yet little work has
examined how to engage users with these games. We explore this problem by
conducting workshops with 9 younger adults and reporting on their expectations
for cybersecurity educational games. We find a disconnect between casual and
serious gamers, where casual gamers prefer simple games incorporating humour
while serious gamers demand a congruent narrative or storyline. Importantly,
both demographics agree that educational games should prioritise gameplay over
information provision - i.e. the game should be a game with educational
content. We discuss the implications for educational games developers.
| [
{
"created": "Thu, 7 Mar 2019 16:12:14 GMT",
"version": "v1"
}
] | 2019-03-08 | [
[
"Dixon",
"Matt",
""
],
[
"Arachchilage",
"Nalin Asanka Gamagedara",
""
],
[
"Nicholson",
"James",
""
]
] | Phishing continues to be a difficult problem for individuals and organisations. Educational games and simulations have been increasingly acknowledged as enormous and powerful teaching tools, yet little work has examined how to engage users with these games. We explore this problem by conducting workshops with 9 younger adults and reporting on their expectations for cybersecurity educational games. We find a disconnect between casual and serious gamers, where casual gamers prefer simple games incorporating humour while serious gamers demand a congruent narrative or storyline. Importantly, both demographics agree that educational games should prioritise gameplay over information provision - i.e. the game should be a game with educational content. We discuss the implications for educational games developers. |
1710.06194 | Da Chen | Da Chen, Laurent D. Cohen | A New Coherence-Penalized Minimal Path Model with Application to Retinal
Vessel Centerline Delineation | null | null | null | null | cs.CG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new minimal path model for minimally interactive
retinal vessel centerline extraction. The main contribution lies at the
construction of a novel coherence-penalized Riemannian metric in a lifted
space, dependently of the local geometry of tubularity and an external
scalar-valued reference feature map. The globally minimizing curves associated
to the proposed metric favour to pass through a set of retinal vessel segments
with low variations of the feature map, thus can avoid the short branches
combination problem and shortcut problem, commonly suffered by the existing
minimal path models in the application of retinal imaging. We validate our
model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR
datasets, showing that our model indeed get promising results.
| [
{
"created": "Tue, 17 Oct 2017 10:23:57 GMT",
"version": "v1"
}
] | 2017-10-18 | [
[
"Chen",
"Da",
""
],
[
"Cohen",
"Laurent D.",
""
]
] | In this paper, we propose a new minimal path model for minimally interactive retinal vessel centerline extraction. The main contribution lies at the construction of a novel coherence-penalized Riemannian metric in a lifted space, dependently of the local geometry of tubularity and an external scalar-valued reference feature map. The globally minimizing curves associated to the proposed metric favour to pass through a set of retinal vessel segments with low variations of the feature map, thus can avoid the short branches combination problem and shortcut problem, commonly suffered by the existing minimal path models in the application of retinal imaging. We validate our model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR datasets, showing that our model indeed get promising results. |
2202.05895 | Farhad Shirani Chaharsooghi | M. Shariatnasab, F. Shirani and Z. Anwar | Privacy Limits in Power-Law Bipartite Networks under Active
Fingerprinting Attacks | null | null | null | null | cs.SI cs.DB cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work considers the fundamental privacy limits under active
fingerprinting attacks in power-law bipartite networks. The scenario arises
naturally in social network analysis, tracking user mobility in wireless
networks, and forensics applications, among others. A stochastic growing
network generation model -- called the popularity-based model -- is
investigated, where the bipartite network is generated iteratively, and in each
iteration vertices attract new edges based on their assigned popularity values.
It is shown that using the appropriate choice of initial popularity values, the
node degree distribution follows a power-law distribution with arbitrary
parameter $\alpha>2$, i.e. fraction of nodes with degree $d$ is proportional to
$d^{-\alpha}$. An active fingerprinting deanonymization attack strategy called
the augmented information threshold attack strategy (A-ITS) is proposed which
uses the attacker's knowledge of the node degree distribution along with the
concept of information values for deanonymization. Sufficient conditions for
the success of the A-ITS, based on network parameters, are derived. It is shown
through simulations that the proposed attack significantly outperforms the
state-of-the-art attack strategies.
| [
{
"created": "Fri, 11 Feb 2022 20:31:09 GMT",
"version": "v1"
}
] | 2022-02-15 | [
[
"Shariatnasab",
"M.",
""
],
[
"Shirani",
"F.",
""
],
[
"Anwar",
"Z.",
""
]
] | This work considers the fundamental privacy limits under active fingerprinting attacks in power-law bipartite networks. The scenario arises naturally in social network analysis, tracking user mobility in wireless networks, and forensics applications, among others. A stochastic growing network generation model -- called the popularity-based model -- is investigated, where the bipartite network is generated iteratively, and in each iteration vertices attract new edges based on their assigned popularity values. It is shown that using the appropriate choice of initial popularity values, the node degree distribution follows a power-law distribution with arbitrary parameter $\alpha>2$, i.e. fraction of nodes with degree $d$ is proportional to $d^{-\alpha}$. An active fingerprinting deanonymization attack strategy called the augmented information threshold attack strategy (A-ITS) is proposed which uses the attacker's knowledge of the node degree distribution along with the concept of information values for deanonymization. Sufficient conditions for the success of the A-ITS, based on network parameters, are derived. It is shown through simulations that the proposed attack significantly outperforms the state-of-the-art attack strategies. |
1908.02575 | Oscar Correa | Oscar Correa and Jeffrey Chan and Vinh Nguyen | Alternative Blockmodelling | 56 pages, 23 figures | null | null | null | cs.SI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many approaches have been proposed to discover clusters within networks.
Community finding field encompasses approaches which try to discover clusters
where nodes are tightly related within them but loosely related with nodes of
other clusters. However, a community network configuration is not the only
possible latent structure in a graph. Core-periphery and hierarchical network
configurations are valid structures to discover in a relational dataset. On the
other hand, a network is not completely explained by only knowing the
membership of each node. A high level view of the inter-cluster relationships
is needed. Blockmodelling techniques deal with these two issues. Firstly,
blockmodelling allows finding any network configuration besides to the
well-known community structure. Secondly, blockmodelling is a summary
representation of a network which regards not only membership of nodes but also
relations between clusters. Finally, a unique summary representation of a
network is unlikely. Networks might hide more than one blockmodel. Therefore,
our proposed problem aims to discover a secondary blockmodel representation of
a network that is of good quality and dissimilar with respect to a given
blockmodel. Our methodology is presented through two approaches, (a) inclusion
of cannot-link constraints and (b) dissimilarity between image matrices. Both
approaches are based on non-negative matrix factorisation NMF which fits the
blockmodelling representation. The evaluation of these two approaches regards
quality and dissimilarity of the discovered alternative blockmodel as these are
the requirements of the problem.
| [
{
"created": "Sat, 27 Jul 2019 06:49:47 GMT",
"version": "v1"
}
] | 2019-08-08 | [
[
"Correa",
"Oscar",
""
],
[
"Chan",
"Jeffrey",
""
],
[
"Nguyen",
"Vinh",
""
]
] | Many approaches have been proposed to discover clusters within networks. Community finding field encompasses approaches which try to discover clusters where nodes are tightly related within them but loosely related with nodes of other clusters. However, a community network configuration is not the only possible latent structure in a graph. Core-periphery and hierarchical network configurations are valid structures to discover in a relational dataset. On the other hand, a network is not completely explained by only knowing the membership of each node. A high level view of the inter-cluster relationships is needed. Blockmodelling techniques deal with these two issues. Firstly, blockmodelling allows finding any network configuration besides to the well-known community structure. Secondly, blockmodelling is a summary representation of a network which regards not only membership of nodes but also relations between clusters. Finally, a unique summary representation of a network is unlikely. Networks might hide more than one blockmodel. Therefore, our proposed problem aims to discover a secondary blockmodel representation of a network that is of good quality and dissimilar with respect to a given blockmodel. Our methodology is presented through two approaches, (a) inclusion of cannot-link constraints and (b) dissimilarity between image matrices. Both approaches are based on non-negative matrix factorisation NMF which fits the blockmodelling representation. The evaluation of these two approaches regards quality and dissimilarity of the discovered alternative blockmodel as these are the requirements of the problem. |
1909.06319 | Yang Li | Yang Li, Shoaib Akbar, Junier B. Oliva | Flow Models for Arbitrary Conditional Likelihoods | null | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the dependencies among features of a dataset is at the core of
most unsupervised learning tasks. However, a majority of generative modeling
approaches are focused solely on the joint distribution $p(x)$ and utilize
models where it is intractable to obtain the conditional distribution of some
arbitrary subset of features $x_u$ given the rest of the observed covariates
$x_o$: $p(x_u \mid x_o)$. Traditional conditional approaches provide a model
for a fixed set of covariates conditioned on another fixed set of observed
covariates. Instead, in this work we develop a model that is capable of
yielding all conditional distributions $p(x_u \mid x_o)$ (for arbitrary $x_u$)
via tractable conditional likelihoods. We propose a novel extension of (change
of variables based) flow generative models, arbitrary conditioning flow models
(AC-Flow), that can be conditioned on arbitrary subsets of observed covariates,
which was previously infeasible. We apply AC-Flow to the imputation of
features, and also develop a unified platform for both multiple and single
imputation by introducing an auxiliary objective that provides a principled
single "best guess" for flow models. Extensive empirical evaluations show that
our models achieve state-of-the-art performance in both single and multiple
imputation across image inpainting and feature imputation in synthetic and
real-world datasets. Code is available at https://github.com/lupalab/ACFlow.
| [
{
"created": "Fri, 13 Sep 2019 16:35:17 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Aug 2020 13:30:33 GMT",
"version": "v2"
}
] | 2020-08-07 | [
[
"Li",
"Yang",
""
],
[
"Akbar",
"Shoaib",
""
],
[
"Oliva",
"Junier B.",
""
]
] | Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$. Traditional conditional approaches provide a model for a fixed set of covariates conditioned on another fixed set of observed covariates. Instead, in this work we develop a model that is capable of yielding all conditional distributions $p(x_u \mid x_o)$ (for arbitrary $x_u$) via tractable conditional likelihoods. We propose a novel extension of (change of variables based) flow generative models, arbitrary conditioning flow models (AC-Flow), that can be conditioned on arbitrary subsets of observed covariates, which was previously infeasible. We apply AC-Flow to the imputation of features, and also develop a unified platform for both multiple and single imputation by introducing an auxiliary objective that provides a principled single "best guess" for flow models. Extensive empirical evaluations show that our models achieve state-of-the-art performance in both single and multiple imputation across image inpainting and feature imputation in synthetic and real-world datasets. Code is available at https://github.com/lupalab/ACFlow. |
1301.2683 | Josef Urban | Josef Urban | BliStr: The Blind Strategymaker | null | null | null | null | cs.AI cs.LG cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BliStr is a system that automatically develops strategies for E prover on a
large set of problems. The main idea is to interleave (i) iterated
low-timelimit local search for new strategies on small sets of similar easy
problems with (ii) higher-timelimit evaluation of the new strategies on all
problems. The accumulated results of the global higher-timelimit runs are used
to define and evolve the notion of "similar easy problems", and to control the
selection of the next strategy to be improved. The technique was used to
significantly strengthen the set of E strategies used by the MaLARea, PS-E,
E-MaLeS, and E systems in the CASC@Turing 2012 competition, particularly in the
Mizar division. Similar improvement was obtained on the problems created from
the Flyspeck corpus.
| [
{
"created": "Sat, 12 Jan 2013 13:02:21 GMT",
"version": "v1"
},
{
"created": "Wed, 28 May 2014 12:54:41 GMT",
"version": "v2"
}
] | 2014-05-29 | [
[
"Urban",
"Josef",
""
]
] | BliStr is a system that automatically develops strategies for E prover on a large set of problems. The main idea is to interleave (i) iterated low-timelimit local search for new strategies on small sets of similar easy problems with (ii) higher-timelimit evaluation of the new strategies on all problems. The accumulated results of the global higher-timelimit runs are used to define and evolve the notion of "similar easy problems", and to control the selection of the next strategy to be improved. The technique was used to significantly strengthen the set of E strategies used by the MaLARea, PS-E, E-MaLeS, and E systems in the CASC@Turing 2012 competition, particularly in the Mizar division. Similar improvement was obtained on the problems created from the Flyspeck corpus. |
1504.02843 | Riccardo Sven Risuleo | Riccardo Sven Risuleo, Marco Molinari, Giulio Bottegal, H{\aa}kan
Hjalmarsson, Karl H. Johansson | A benchmark for data-based office modeling: challenges related to CO$_2$
dynamics | 14 pages, accepted for publication to IFAC SysId 2015 | null | 10.1016/j.ifacol.2015.12.304 | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a benchmark consisting of a set of synthetic
measurements relative to an office environment simulated with the software
IDA-ICE. The simulated environment reproduces a laboratory at the KTH-EES Smart
Building, equipped with a building management system. The data set contains
records collected over a period of several days. The signals to CO$_2$
concentration, mechanical ventilation airflows, air infiltrations and
occupancy. Information on door and window opening is also available. This
benchmark is intended for testing data-based modeling techniques. The ultimate
goal is the development of models to improve the forecast and control of
environmental variables. Among the numerous challenges related to this
framework, we point out the problem of occupancy estimation using information
on CO$_2$ concentration. This can be seen as a blind identification problem.
For benchmarking purposes, we present two different identification approaches:
a baseline overparametrization method and a kernel-based method.
| [
{
"created": "Sat, 11 Apr 2015 06:31:24 GMT",
"version": "v1"
},
{
"created": "Thu, 19 May 2016 09:12:14 GMT",
"version": "v2"
}
] | 2016-05-20 | [
[
"Risuleo",
"Riccardo Sven",
""
],
[
"Molinari",
"Marco",
""
],
[
"Bottegal",
"Giulio",
""
],
[
"Hjalmarsson",
"Håkan",
""
],
[
"Johansson",
"Karl H.",
""
]
] | This paper describes a benchmark consisting of a set of synthetic measurements relative to an office environment simulated with the software IDA-ICE. The simulated environment reproduces a laboratory at the KTH-EES Smart Building, equipped with a building management system. The data set contains records collected over a period of several days. The signals to CO$_2$ concentration, mechanical ventilation airflows, air infiltrations and occupancy. Information on door and window opening is also available. This benchmark is intended for testing data-based modeling techniques. The ultimate goal is the development of models to improve the forecast and control of environmental variables. Among the numerous challenges related to this framework, we point out the problem of occupancy estimation using information on CO$_2$ concentration. This can be seen as a blind identification problem. For benchmarking purposes, we present two different identification approaches: a baseline overparametrization method and a kernel-based method. |
1207.0805 | Pallavali Radha Krishna Reddy | G.Geethu Lakshmi | Anatomical Structure Segmentation in Liver MRI Images | Withdrawn by author for final modification | null | null | null | cs.CV | http://creativecommons.org/licenses/by/3.0/ | Segmentation of medical images is a challenging task owing to their
complexity. A standard segmentation problem within Magnetic Resonance Imaging
(MRI) is the task of labeling voxels according to their tissue type. Image
segmentation provides volumetric quantification of liver area and thus helps in
the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice,
Hemochromatosis etc.This work deals with comparison of segmentation by applying
Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and
Gradient Vector Flow Snake Algorithm.The results are compared using the
parameters such as Number of pixels correctly classified, and percentage of
area segmented.
| [
{
"created": "Tue, 3 Jul 2012 14:32:20 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Jul 2012 10:48:33 GMT",
"version": "v2"
},
{
"created": "Sat, 30 Mar 2013 05:25:58 GMT",
"version": "v3"
}
] | 2013-04-02 | [
[
"Lakshmi",
"G. Geethu",
""
]
] | Segmentation of medical images is a challenging task owing to their complexity. A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labeling voxels according to their tissue type. Image segmentation provides volumetric quantification of liver area and thus helps in the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice, Hemochromatosis etc.This work deals with comparison of segmentation by applying Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and Gradient Vector Flow Snake Algorithm.The results are compared using the parameters such as Number of pixels correctly classified, and percentage of area segmented. |
2404.01843 | Wangguandong Zheng | Wangguandong Zheng, Haifeng Xia, Rui Chen, Ming Shao, Siyu Xia,
Zhengming Ding | Sketch3D: Style-Consistent Guidance for Sketch-to-3D Generation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, image-to-3D approaches have achieved significant results with a
natural image as input. However, it is not always possible to access these
enriched color input samples in practical applications, where only sketches are
available. Existing sketch-to-3D researches suffer from limitations in broad
applications due to the challenges of lacking color information and multi-view
content. To overcome them, this paper proposes a novel generation paradigm
Sketch3D to generate realistic 3D assets with shape aligned with the input
sketch and color matching the textual description. Concretely, Sketch3D first
instantiates the given sketch in the reference image through the
shape-preserving generation process. Second, the reference image is leveraged
to deduce a coarse 3D Gaussian prior, and multi-view style-consistent guidance
images are generated based on the renderings of the 3D Gaussians. Finally,
three strategies are designed to optimize 3D Gaussians, i.e., structural
optimization via a distribution transfer mechanism, color optimization with a
straightforward MSE loss and sketch similarity optimization with a CLIP-based
geometric similarity loss. Extensive visual comparisons and quantitative
analysis illustrate the advantage of our Sketch3D in generating realistic 3D
assets while preserving consistency with the input.
| [
{
"created": "Tue, 2 Apr 2024 11:03:24 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Apr 2024 04:17:32 GMT",
"version": "v2"
}
] | 2024-04-09 | [
[
"Zheng",
"Wangguandong",
""
],
[
"Xia",
"Haifeng",
""
],
[
"Chen",
"Rui",
""
],
[
"Shao",
"Ming",
""
],
[
"Xia",
"Siyu",
""
],
[
"Ding",
"Zhengming",
""
]
] | Recently, image-to-3D approaches have achieved significant results with a natural image as input. However, it is not always possible to access these enriched color input samples in practical applications, where only sketches are available. Existing sketch-to-3D researches suffer from limitations in broad applications due to the challenges of lacking color information and multi-view content. To overcome them, this paper proposes a novel generation paradigm Sketch3D to generate realistic 3D assets with shape aligned with the input sketch and color matching the textual description. Concretely, Sketch3D first instantiates the given sketch in the reference image through the shape-preserving generation process. Second, the reference image is leveraged to deduce a coarse 3D Gaussian prior, and multi-view style-consistent guidance images are generated based on the renderings of the 3D Gaussians. Finally, three strategies are designed to optimize 3D Gaussians, i.e., structural optimization via a distribution transfer mechanism, color optimization with a straightforward MSE loss and sketch similarity optimization with a CLIP-based geometric similarity loss. Extensive visual comparisons and quantitative analysis illustrate the advantage of our Sketch3D in generating realistic 3D assets while preserving consistency with the input. |
2306.13773 | Stephen Pasteris | Stephen Pasteris, Chris Hicks, Vasilios Mavroudis | Nearest Neighbour with Bandit Feedback | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we adapt the nearest neighbour rule to the contextual bandit
problem. Our algorithm handles the fully adversarial setting in which no
assumptions at all are made about the data-generation process. When combined
with a sufficiently fast data-structure for (perhaps approximate) adaptive
nearest neighbour search, such as a navigating net, our algorithm is extremely
efficient - having a per trial running time polylogarithmic in both the number
of trials and actions, and taking only quasi-linear space. We give generic
regret bounds for our algorithm and further analyse them when applied to the
stochastic bandit problem in euclidean space. We note that our algorithm can
also be applied to the online classification problem.
| [
{
"created": "Fri, 23 Jun 2023 20:09:01 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Aug 2023 20:19:16 GMT",
"version": "v2"
},
{
"created": "Thu, 7 Mar 2024 21:07:35 GMT",
"version": "v3"
}
] | 2024-03-11 | [
[
"Pasteris",
"Stephen",
""
],
[
"Hicks",
"Chris",
""
],
[
"Mavroudis",
"Vasilios",
""
]
] | In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. When combined with a sufficiently fast data-structure for (perhaps approximate) adaptive nearest neighbour search, such as a navigating net, our algorithm is extremely efficient - having a per trial running time polylogarithmic in both the number of trials and actions, and taking only quasi-linear space. We give generic regret bounds for our algorithm and further analyse them when applied to the stochastic bandit problem in euclidean space. We note that our algorithm can also be applied to the online classification problem. |
2209.05070 | Xiangyu Wang | Anjun Chen, Xiangyu Wang, Shaohao Zhu, Yanxu Li, Jiming Chen, Qi Ye | mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for
Millimeter Wave Radar | Accepted to ACM Multimedia 2022, Project Page:
https://chen3110.github.io/mmbody/index.html | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Millimeter Wave (mmWave) Radar is gaining popularity as it can work in
adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has
explored the possibility of reconstructing 3D skeletons or meshes from the
noisy and sparse mmWave Radar signals. However, it is unclear how accurately we
can reconstruct the 3D body from the mmWave signals across scenes and how it
performs compared with cameras, which are important aspects needed to be
considered when either using mmWave radars alone or combining them with
cameras. To answer these questions, an automatic 3D body annotation system is
first designed and built up with multiple sensors to collect a large-scale
dataset. The dataset consists of synchronized and calibrated mmWave radar point
clouds and RGB(D) images in different scenes and skeleton/mesh annotations for
humans in the scenes. With this dataset, we train state-of-the-art methods with
inputs from different sensors and test them in various scenarios. The results
demonstrate that 1) despite the noise and sparsity of the generated point
clouds, the mmWave radar can achieve better reconstruction accuracy than the
RGB camera but worse than the depth camera; 2) the reconstruction from the
mmWave radar is affected by adverse weather conditions moderately while the
RGB(D) camera is severely affected. Further, analysis of the dataset and the
results shadow insights on improving the reconstruction from the mmWave radar
and the combination of signals from different sensors.
| [
{
"created": "Mon, 12 Sep 2022 08:00:31 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Apr 2023 03:07:03 GMT",
"version": "v2"
},
{
"created": "Thu, 21 Sep 2023 10:11:03 GMT",
"version": "v3"
}
] | 2023-09-22 | [
[
"Chen",
"Anjun",
""
],
[
"Wang",
"Xiangyu",
""
],
[
"Zhu",
"Shaohao",
""
],
[
"Li",
"Yanxu",
""
],
[
"Chen",
"Jiming",
""
],
[
"Ye",
"Qi",
""
]
] | Millimeter Wave (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWave Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors. |
2202.09892 | Alec Farid | Michelle Ho and Alec Farid and Anirudha Majumdar | Towards a Framework for Comparing the Complexity of Robotic Tasks | null | null | null | null | cs.RO cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are motivated by the problem of comparing the complexity of one robotic
task relative to another. To this end, we define a notion of reduction that
formalizes the following intuition: Task 1 reduces to Task 2 if we can
efficiently transform any policy that solves Task 2 into a policy that solves
Task 1. We further define a quantitative measure of the relative complexity
between any two tasks for a given robot. We prove useful properties of our
notion of reduction (e.g., reflexivity, transitivity, and antisymmetry) and
relative complexity measure (e.g., nonnegativity and monotonicity). In
addition, we propose practical algorithms for estimating the relative
complexity measure. We illustrate our framework for comparing robotic tasks
using (i) examples where one can analytically establish reductions, and (ii)
reinforcement learning examples where the proposed algorithm can estimate the
relative complexity between tasks.
| [
{
"created": "Sun, 20 Feb 2022 19:12:24 GMT",
"version": "v1"
},
{
"created": "Sun, 29 May 2022 21:54:55 GMT",
"version": "v2"
},
{
"created": "Fri, 24 Jun 2022 16:47:02 GMT",
"version": "v3"
}
] | 2022-06-27 | [
[
"Ho",
"Michelle",
""
],
[
"Farid",
"Alec",
""
],
[
"Majumdar",
"Anirudha",
""
]
] | We are motivated by the problem of comparing the complexity of one robotic task relative to another. To this end, we define a notion of reduction that formalizes the following intuition: Task 1 reduces to Task 2 if we can efficiently transform any policy that solves Task 2 into a policy that solves Task 1. We further define a quantitative measure of the relative complexity between any two tasks for a given robot. We prove useful properties of our notion of reduction (e.g., reflexivity, transitivity, and antisymmetry) and relative complexity measure (e.g., nonnegativity and monotonicity). In addition, we propose practical algorithms for estimating the relative complexity measure. We illustrate our framework for comparing robotic tasks using (i) examples where one can analytically establish reductions, and (ii) reinforcement learning examples where the proposed algorithm can estimate the relative complexity between tasks. |
2210.02843 | Runmin Cong | Runmin Cong, Qinwei Lin, Chen Zhang, Chongyi Li, Xiaochun Cao,
Qingming Huang, and Yao Zhao | CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient
Object Detection | Accepted by IEEE Transactions on Image Processing 2022, 16 pages, 11
figures | null | 10.1109/TIP.2022.3216198 | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Focusing on the issue of how to effectively capture and utilize
cross-modality information in RGB-D salient object detection (SOD) task, we
present a convolutional neural network (CNN) model, named CIR-Net, based on the
novel cross-modality interaction and refinement. For the cross-modality
interaction, 1) a progressive attention guided integration unit is proposed to
sufficiently integrate RGB-D feature representations in the encoder stage, and
2) a convergence aggregation structure is proposed, which flows the RGB and
depth decoding features into the corresponding RGB-D decoding streams via an
importance gated fusion unit in the decoder stage. For the cross-modality
refinement, we insert a refinement middleware structure between the encoder and
the decoder, in which the RGB, depth, and RGB-D encoder features are further
refined by successively using a self-modality attention refinement unit and a
cross-modality weighting refinement unit. At last, with the gradually refined
features, we predict the saliency map in the decoder stage. Extensive
experiments on six popular RGB-D SOD benchmarks demonstrate that our network
outperforms the state-of-the-art saliency detectors both qualitatively and
quantitatively.
| [
{
"created": "Thu, 6 Oct 2022 11:59:19 GMT",
"version": "v1"
}
] | 2022-11-23 | [
[
"Cong",
"Runmin",
""
],
[
"Lin",
"Qinwei",
""
],
[
"Zhang",
"Chen",
""
],
[
"Li",
"Chongyi",
""
],
[
"Cao",
"Xiaochun",
""
],
[
"Huang",
"Qingming",
""
],
[
"Zhao",
"Yao",
""
]
] | Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively. |
2403.18791 | Tianfu Wang | Tianfu Wang, Guosheng Hu, Hongguang Wang | Object Pose Estimation via the Aggregation of Diffusion Features | Accepted to CVPR2024 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating the pose of objects from images is a crucial task of 3D scene
understanding, and recent approaches have shown promising results on very large
benchmarks. However, these methods experience a significant performance drop
when dealing with unseen objects. We believe that it results from the limited
generalizability of image features. To address this problem, we have an
in-depth analysis on the features of diffusion models, e.g. Stable Diffusion,
which hold substantial potential for modeling unseen objects. Based on this
analysis, we then innovatively introduce these diffusion features for object
pose estimation. To achieve this, we propose three distinct architectures that
can effectively capture and aggregate diffusion features of different
granularity, greatly improving the generalizability of object pose estimation.
Our approach outperforms the state-of-the-art methods by a considerable margin
on three popular benchmark datasets, LM, O-LM, and T-LESS. In particular, our
method achieves higher accuracy than the previous best arts on unseen objects:
98.2% vs. 93.5% on Unseen LM, 85.9% vs. 76.3% on Unseen O-LM, showing the
strong generalizability of our method. Our code is released at
https://github.com/Tianfu18/diff-feats-pose.
| [
{
"created": "Wed, 27 Mar 2024 17:35:24 GMT",
"version": "v1"
},
{
"created": "Sat, 1 Jun 2024 15:25:47 GMT",
"version": "v2"
}
] | 2024-06-04 | [
[
"Wang",
"Tianfu",
""
],
[
"Hu",
"Guosheng",
""
],
[
"Wang",
"Hongguang",
""
]
] | Estimating the pose of objects from images is a crucial task of 3D scene understanding, and recent approaches have shown promising results on very large benchmarks. However, these methods experience a significant performance drop when dealing with unseen objects. We believe that it results from the limited generalizability of image features. To address this problem, we have an in-depth analysis on the features of diffusion models, e.g. Stable Diffusion, which hold substantial potential for modeling unseen objects. Based on this analysis, we then innovatively introduce these diffusion features for object pose estimation. To achieve this, we propose three distinct architectures that can effectively capture and aggregate diffusion features of different granularity, greatly improving the generalizability of object pose estimation. Our approach outperforms the state-of-the-art methods by a considerable margin on three popular benchmark datasets, LM, O-LM, and T-LESS. In particular, our method achieves higher accuracy than the previous best arts on unseen objects: 98.2% vs. 93.5% on Unseen LM, 85.9% vs. 76.3% on Unseen O-LM, showing the strong generalizability of our method. Our code is released at https://github.com/Tianfu18/diff-feats-pose. |
1205.4778 | Matthias W\"ahlisch | Matthias W\"ahlisch, Thomas C. Schmidt, Markus Vahlenkamp | Backscatter from the Data Plane --- Threats to Stability and Security in
Information-Centric Networking | 15 pages | Computer Networks, Vol. 57, No. 16, pp. 3192-3206, Elsevier, Nov.
2013 | 10.1016/j.comnet.2013.07.009 | null | cs.NI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information-centric networking proposals attract much attention in the
ongoing search for a future communication paradigm of the Internet. Replacing
the host-to-host connectivity by a data-oriented publish/subscribe service
eases content distribution and authentication by concept, while eliminating
threats from unwanted traffic at an end host as are common in today's Internet.
However, current approaches to content routing heavily rely on data-driven
protocol events and thereby introduce a strong coupling of the control to the
data plane in the underlying routing infrastructure. In this paper, threats to
the stability and security of the content distribution system are analyzed in
theory and practical experiments. We derive relations between state resources
and the performance of routers and demonstrate how this coupling can be misused
in practice. We discuss new attack vectors present in its current state of
development, as well as possibilities and limitations to mitigate them.
| [
{
"created": "Tue, 22 May 2012 00:24:13 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Sep 2012 22:22:41 GMT",
"version": "v2"
}
] | 2013-11-12 | [
[
"Wählisch",
"Matthias",
""
],
[
"Schmidt",
"Thomas C.",
""
],
[
"Vahlenkamp",
"Markus",
""
]
] | Information-centric networking proposals attract much attention in the ongoing search for a future communication paradigm of the Internet. Replacing the host-to-host connectivity by a data-oriented publish/subscribe service eases content distribution and authentication by concept, while eliminating threats from unwanted traffic at an end host as are common in today's Internet. However, current approaches to content routing heavily rely on data-driven protocol events and thereby introduce a strong coupling of the control to the data plane in the underlying routing infrastructure. In this paper, threats to the stability and security of the content distribution system are analyzed in theory and practical experiments. We derive relations between state resources and the performance of routers and demonstrate how this coupling can be misused in practice. We discuss new attack vectors present in its current state of development, as well as possibilities and limitations to mitigate them. |
2301.08245 | Pierluigi Zama Ramirez | Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi, Matteo Poggi,
Samuele Salti, Stefano Mattoccia, Luigi Di Stefano | Booster: a Benchmark for Depth from Images of Specular and Transparent
Surfaces | Extension of the paper "Open Challenges in Deep Stereo: the Booster
Dataset" presented at CVPR 2022. Accepted at TPAMI | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating depth from images nowadays yields outstanding results, both in
terms of in-domain accuracy and generalization. However, we identify two main
challenges that remain open in this field: dealing with non-Lambertian
materials and effectively processing high-resolution images. Purposely, we
propose a novel dataset that includes accurate and dense ground-truth labels at
high resolution, featuring scenes containing several specular and transparent
surfaces. Our acquisition pipeline leverages a novel deep space-time stereo
framework, enabling easy and accurate labeling with sub-pixel precision. The
dataset is composed of 606 samples collected in 85 different scenes, each
sample includes both a high-resolution pair (12 Mpx) as well as an unbalanced
stereo pair (Left: 12 Mpx, Right: 1.1 Mpx), typical of modern mobile devices
that mount sensors with different resolutions. Additionally, we provide
manually annotated material segmentation masks and 15K unlabeled samples. The
dataset is composed of a train set and two test sets, the latter devoted to the
evaluation of stereo and monocular depth estimation networks. Our experiments
highlight the open challenges and future research directions in this field.
| [
{
"created": "Thu, 19 Jan 2023 18:59:28 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Oct 2023 17:58:14 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Jan 2024 14:02:58 GMT",
"version": "v3"
}
] | 2024-01-31 | [
[
"Ramirez",
"Pierluigi Zama",
""
],
[
"Costanzino",
"Alex",
""
],
[
"Tosi",
"Fabio",
""
],
[
"Poggi",
"Matteo",
""
],
[
"Salti",
"Samuele",
""
],
[
"Mattoccia",
"Stefano",
""
],
[
"Di Stefano",
"Luigi",
""
]
] | Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lambertian materials and effectively processing high-resolution images. Purposely, we propose a novel dataset that includes accurate and dense ground-truth labels at high resolution, featuring scenes containing several specular and transparent surfaces. Our acquisition pipeline leverages a novel deep space-time stereo framework, enabling easy and accurate labeling with sub-pixel precision. The dataset is composed of 606 samples collected in 85 different scenes, each sample includes both a high-resolution pair (12 Mpx) as well as an unbalanced stereo pair (Left: 12 Mpx, Right: 1.1 Mpx), typical of modern mobile devices that mount sensors with different resolutions. Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. The dataset is composed of a train set and two test sets, the latter devoted to the evaluation of stereo and monocular depth estimation networks. Our experiments highlight the open challenges and future research directions in this field. |
2011.06198 | Eric Le Ferrand | \'Eric Le Ferrand, Steven Bird, Laurent Besacier | Enabling Interactive Transcription in an Indigenous Community | inproceedings Coling 2020 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel transcription workflow which combines spoken term
detection and human-in-the-loop, together with a pilot experiment. This work is
grounded in an almost zero-resource scenario where only a few terms have so far
been identified, involving two endangered languages. We show that in the early
stages of transcription, when the available data is insufficient to train a
robust ASR system, it is possible to take advantage of the transcription of a
small number of isolated words in order to bootstrap the transcription of a
speech collection.
| [
{
"created": "Thu, 12 Nov 2020 04:41:35 GMT",
"version": "v1"
}
] | 2020-11-13 | [
[
"Ferrand",
"Éric Le",
""
],
[
"Bird",
"Steven",
""
],
[
"Besacier",
"Laurent",
""
]
] | We propose a novel transcription workflow which combines spoken term detection and human-in-the-loop, together with a pilot experiment. This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages. We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection. |
2107.12940 | Mark Koren | Mark Koren and Ahmed Nassar and Mykel J. Kochenderfer | Finding Failures in High-Fidelity Simulation using Adaptive Stress
Testing and the Backward Algorithm | Accepted to IROS 2021 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Validating the safety of autonomous systems generally requires the use of
high-fidelity simulators that adequately capture the variability of real-world
scenarios. However, it is generally not feasible to exhaustively search the
space of simulation scenarios for failures. Adaptive stress testing (AST) is a
method that uses reinforcement learning to find the most likely failure of a
system. AST with a deep reinforcement learning solver has been shown to be
effective in finding failures across a range of different systems. This
approach generally involves running many simulations, which can be very
expensive when using a high-fidelity simulator. To improve efficiency, we
present a method that first finds failures in a low-fidelity simulator. It then
uses the backward algorithm, which trains a deep neural network policy using a
single expert demonstration, to adapt the low-fidelity failures to
high-fidelity. We have created a series of autonomous vehicle validation case
studies that represent some of the ways low-fidelity and high-fidelity
simulators can differ, such as time discretization. We demonstrate in a variety
of case studies that this new AST approach is able to find failures with
significantly fewer high-fidelity simulation steps than are needed when just
running AST directly in high-fidelity. As a proof of concept, we also
demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art
high-fidelity simulator for finding failures in autonomous vehicles.
| [
{
"created": "Tue, 27 Jul 2021 16:54:04 GMT",
"version": "v1"
}
] | 2021-07-28 | [
[
"Koren",
"Mark",
""
],
[
"Nassar",
"Ahmed",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] | Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles. |
1910.05522 | Hassan Khosravi | Hassan Khosravi, Kirsty Kitto, Joseph Jay Williams | RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning
Activities | To be published by the Journal of Learning Analytics | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a platform called RiPPLE (Recommendation in Personalised
Peer-Learning Environments) that recommends personalized learning activities to
students based on their knowledge state from a pool of crowdsourced learning
activities that are generated by educators and the students themselves. RiPPLE
integrates insights from crowdsourcing, learning sciences, and adaptive
learning, aiming to narrow the gap between these large bodies of research while
providing a practical platform-based implementation that instructors can easily
use in their courses. This paper provides a design overview of RiPPLE, which
can be employed as a standalone tool or embedded into any learning management
system (LMS) or online platform that supports the Learning Tools
Interoperability (LTI) standard. The platform has been evaluated based on a
pilot in an introductory course with 453 students at The University of
Queensland. Initial results suggest that the use of the \name platform led to
measurable learning gains and that students perceived the platform as
beneficially supporting their learning.
| [
{
"created": "Sat, 12 Oct 2019 07:42:52 GMT",
"version": "v1"
}
] | 2019-10-15 | [
[
"Khosravi",
"Hassan",
""
],
[
"Kitto",
"Kirsty",
""
],
[
"Williams",
"Joseph Jay",
""
]
] | This paper presents a platform called RiPPLE (Recommendation in Personalised Peer-Learning Environments) that recommends personalized learning activities to students based on their knowledge state from a pool of crowdsourced learning activities that are generated by educators and the students themselves. RiPPLE integrates insights from crowdsourcing, learning sciences, and adaptive learning, aiming to narrow the gap between these large bodies of research while providing a practical platform-based implementation that instructors can easily use in their courses. This paper provides a design overview of RiPPLE, which can be employed as a standalone tool or embedded into any learning management system (LMS) or online platform that supports the Learning Tools Interoperability (LTI) standard. The platform has been evaluated based on a pilot in an introductory course with 453 students at The University of Queensland. Initial results suggest that the use of the \name platform led to measurable learning gains and that students perceived the platform as beneficially supporting their learning. |
2302.10351 | Georgios Kissas | Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris | Variational Autoencoding Neural Operators | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Unsupervised learning with functional data is an emerging paradigm of machine
learning research with applications to computer vision, climate modeling and
physical systems. A natural way of modeling functional data is by learning
operators between infinite dimensional spaces, leading to discretization
invariant representations that scale independently of the sample grid
resolution. Here we present Variational Autoencoding Neural Operators (VANO), a
general strategy for making a large class of operator learning architectures
act as variational autoencoders. For this purpose, we provide a novel rigorous
mathematical formulation of the variational objective in function spaces for
training. VANO first maps an input function to a distribution over a latent
space using a parametric encoder and then decodes a sample from the latent
distribution to reconstruct the input, as in classic variational autoencoders.
We test VANO with different model set-ups and architecture choices for a
variety of benchmarks. We start from a simple Gaussian random field where we
can analytically track what the model learns and progressively transition to
more challenging benchmarks including modeling phase separation in
Cahn-Hilliard systems and real world satellite data for measuring Earth surface
deformation.
| [
{
"created": "Mon, 20 Feb 2023 22:34:43 GMT",
"version": "v1"
}
] | 2023-02-22 | [
[
"Seidman",
"Jacob H.",
""
],
[
"Kissas",
"Georgios",
""
],
[
"Pappas",
"George J.",
""
],
[
"Perdikaris",
"Paris",
""
]
] | Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems. A natural way of modeling functional data is by learning operators between infinite dimensional spaces, leading to discretization invariant representations that scale independently of the sample grid resolution. Here we present Variational Autoencoding Neural Operators (VANO), a general strategy for making a large class of operator learning architectures act as variational autoencoders. For this purpose, we provide a novel rigorous mathematical formulation of the variational objective in function spaces for training. VANO first maps an input function to a distribution over a latent space using a parametric encoder and then decodes a sample from the latent distribution to reconstruct the input, as in classic variational autoencoders. We test VANO with different model set-ups and architecture choices for a variety of benchmarks. We start from a simple Gaussian random field where we can analytically track what the model learns and progressively transition to more challenging benchmarks including modeling phase separation in Cahn-Hilliard systems and real world satellite data for measuring Earth surface deformation. |
2305.14215 | Ziru Chen | Chang-You Tai, Ziru Chen, Tianshu Zhang, Xiang Deng and Huan Sun | Exploring Chain-of-Thought Style Prompting for Text-to-SQL | EMNLP 2023 main; long paper | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In-context learning with large language models (LLMs) has recently caught
increasing attention due to its superior few-shot performance on various tasks.
However, its performance on text-to-SQL parsing still has much room for
improvement. In this paper, we hypothesize that a crucial aspect of LLMs to
improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we
systematically study how to enhance LLMs' reasoning ability through chain of
thought (CoT) style prompting, including the original chain-of-thought
prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023).
Our experiments demonstrate that iterative prompting as in Zhou et al. (2023)
may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps
tends to have more error propagation issues. Based on these findings, we
propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2
and 6.5 point absolute gains on the Spider development set and the Spider
Realistic set, respectively, compared to the standard prompting method without
reasoning steps; 2.4 and 1.5 point absolute gains, compared to the
least-to-most prompting method.
| [
{
"created": "Tue, 23 May 2023 16:32:36 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Oct 2023 15:21:38 GMT",
"version": "v2"
}
] | 2023-10-30 | [
[
"Tai",
"Chang-You",
""
],
[
"Chen",
"Ziru",
""
],
[
"Zhang",
"Tianshu",
""
],
[
"Deng",
"Xiang",
""
],
[
"Sun",
"Huan",
""
]
] | In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method. |
2206.10697 | Simona Maggio | Simona Maggio, Victor Bouvier and L\'eo Dreyfus-Schmidt | Performance Prediction Under Dataset Shift | Published at ICPR | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ML models deployed in production often have to face unknown domain changes,
fundamentally different from their training settings. Performance prediction
models carry out the crucial task of measuring the impact of these changes on
model performance. We study the generalization capabilities of various
performance prediction models to new domains by learning on generated synthetic
perturbations. Empirical validation on a benchmark of ten tabular datasets
shows that models based upon state-of-the-art shift detection metrics are not
expressive enough to generalize to unseen domains, while Error Predictors bring
a consistent improvement in performance prediction under shift. We additionally
propose a natural and effortless uncertainty estimation of the predicted
accuracy that ensures reliable use of performance predictors. Our
implementation is available at https:
//github.com/dataiku-research/performance_prediction_under_shift.
| [
{
"created": "Tue, 21 Jun 2022 19:40:58 GMT",
"version": "v1"
}
] | 2022-06-23 | [
[
"Maggio",
"Simona",
""
],
[
"Bouvier",
"Victor",
""
],
[
"Dreyfus-Schmidt",
"Léo",
""
]
] | ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings. Performance prediction models carry out the crucial task of measuring the impact of these changes on model performance. We study the generalization capabilities of various performance prediction models to new domains by learning on generated synthetic perturbations. Empirical validation on a benchmark of ten tabular datasets shows that models based upon state-of-the-art shift detection metrics are not expressive enough to generalize to unseen domains, while Error Predictors bring a consistent improvement in performance prediction under shift. We additionally propose a natural and effortless uncertainty estimation of the predicted accuracy that ensures reliable use of performance predictors. Our implementation is available at https: //github.com/dataiku-research/performance_prediction_under_shift. |
1810.13195 | Yacine Ouzrout | Thtiya Manakitsirisuthi, Yacine Ouzrout (LIESP), Abdelaziz Bouras
(LIESP) | A multi-agent system for managing the product lifecycle sustainability | null | International Conference on Software, Knowledge and Application,
Oct 2009, Fez, Morocco. pp.8, 2009 | null | null | cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The international competitive market causes the increasing of shorten product
life cycle and product development process with the improvement in term of
time, cost and quality while increasing the waste generation. Product life
cycle sustainability can reduce waste, conserve resources, use recycling
materials, design product for easy disassembly and avoid using hazardous
material. This paper proposes a knowledge management architecture, based on a
multi-agent system, which focuses on the "sustainability" in order to manage
knowledge in each stage of the product lifecycle, and particularly in the
recovery process. The aim of this research work is to make the link between a
decision-making system based on the agent's knowledge about the sustainability
(environmental norms, rules...) and a PLM (Product Lifecycle Management)
system. The software Agents will help the decision makers in each stage of the
lifecycle and make them take into account the environmental impact of their
decisions.
| [
{
"created": "Wed, 31 Oct 2018 10:17:33 GMT",
"version": "v1"
}
] | 2018-11-01 | [
[
"Manakitsirisuthi",
"Thtiya",
"",
"LIESP"
],
[
"Ouzrout",
"Yacine",
"",
"LIESP"
],
[
"Bouras",
"Abdelaziz",
"",
"LIESP"
]
] | The international competitive market causes the increasing of shorten product life cycle and product development process with the improvement in term of time, cost and quality while increasing the waste generation. Product life cycle sustainability can reduce waste, conserve resources, use recycling materials, design product for easy disassembly and avoid using hazardous material. This paper proposes a knowledge management architecture, based on a multi-agent system, which focuses on the "sustainability" in order to manage knowledge in each stage of the product lifecycle, and particularly in the recovery process. The aim of this research work is to make the link between a decision-making system based on the agent's knowledge about the sustainability (environmental norms, rules...) and a PLM (Product Lifecycle Management) system. The software Agents will help the decision makers in each stage of the lifecycle and make them take into account the environmental impact of their decisions. |
1309.1516 | Shih-Chun Lin | Shih-Chun Lin and Cheng-Liang Lin | On Secrecy Capacity of Fast Fading MIMOME Wiretap Channels With
Statistical CSIT | submitted to IEEE Transactions on Wireless Communications | null | 10.1109/TWC.2014.041714.11654 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider secure transmissions in ergodic Rayleigh
fast-faded multiple-input multiple-output multiple-antenna-eavesdropper
(MIMOME) wiretap channels with only statistical channel state information at
the transmitter (CSIT). When the legitimate receiver has more (or equal)
antennas than the eavesdropper, we prove the first MIMOME secrecy capacity with
partial CSIT by establishing a new secrecy capacity upper-bound. The key step
is to form an MIMOME degraded channel by dividing the legitimate receiver's
channel matrix into two submatrices, and setting one of the submatrices to be
the same as the eavesdropper's channel matrix. Next, under the total power
constraint over all transmit antennas, we analytically solve the channel-input
covariance matrix optimization problem to fully characterize the MIMOME secrecy
capacity. Typically, the MIMOME optimization problems are non-concave. However,
thank to the proposed degraded channel, we can transform the stochastic MIMOME
optimization problem to be a Schur-concave one and then find its solution.
Besides total power constraint, we also investigate the secrecy capacity when
the transmitter is subject to the practical per-antenna power constraint. The
corresponding optimization problem is even more difficult since it is not
Schuar-concave. Under the two power constraints considered, the corresponding
MIMOME secrecy capacities can both scale with the signal-to-noise ratios (SNR)
when the difference between numbers of antennas at legitimate receiver and
eavesdropper are large enough. However, when the legitimate receiver and
eavesdropper have a single antenna each, such SNR scalings do not exist for
both cases.
| [
{
"created": "Fri, 6 Sep 2013 01:32:11 GMT",
"version": "v1"
}
] | 2014-05-13 | [
[
"Lin",
"Shih-Chun",
""
],
[
"Lin",
"Cheng-Liang",
""
]
] | In this paper, we consider secure transmissions in ergodic Rayleigh fast-faded multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) wiretap channels with only statistical channel state information at the transmitter (CSIT). When the legitimate receiver has more (or equal) antennas than the eavesdropper, we prove the first MIMOME secrecy capacity with partial CSIT by establishing a new secrecy capacity upper-bound. The key step is to form an MIMOME degraded channel by dividing the legitimate receiver's channel matrix into two submatrices, and setting one of the submatrices to be the same as the eavesdropper's channel matrix. Next, under the total power constraint over all transmit antennas, we analytically solve the channel-input covariance matrix optimization problem to fully characterize the MIMOME secrecy capacity. Typically, the MIMOME optimization problems are non-concave. However, thank to the proposed degraded channel, we can transform the stochastic MIMOME optimization problem to be a Schur-concave one and then find its solution. Besides total power constraint, we also investigate the secrecy capacity when the transmitter is subject to the practical per-antenna power constraint. The corresponding optimization problem is even more difficult since it is not Schuar-concave. Under the two power constraints considered, the corresponding MIMOME secrecy capacities can both scale with the signal-to-noise ratios (SNR) when the difference between numbers of antennas at legitimate receiver and eavesdropper are large enough. However, when the legitimate receiver and eavesdropper have a single antenna each, such SNR scalings do not exist for both cases. |
2309.05388 | Seong Hun Lee | Seong Hun Lee, Javier Civera | Robust Single Rotation Averaging Revisited | null | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we propose a novel method for robust single rotation averaging
that can efficiently handle an extremely large fraction of outliers. Our
approach is to minimize the total truncated least unsquared deviations (TLUD)
cost of geodesic distances. The proposed algorithm consists of three steps:
First, we consider each input rotation as a potential initial solution and
choose the one that yields the least sum of truncated chordal deviations. Next,
we obtain the inlier set using the initial solution and compute its chordal
$L_2$-mean. Finally, starting from this estimate, we iteratively compute the
geodesic $L_1$-mean of the inliers using the Weiszfeld algorithm on $SO(3)$. An
extensive evaluation shows that our method is robust against up to 99% outliers
given a sufficient number of accurate inliers, outperforming the current state
of the art.
| [
{
"created": "Mon, 11 Sep 2023 11:35:17 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Feb 2024 22:49:08 GMT",
"version": "v2"
},
{
"created": "Mon, 26 Feb 2024 23:10:27 GMT",
"version": "v3"
},
{
"created": "Wed, 28 Feb 2024 12:14:48 GMT",
"version": "v4"
}
] | 2024-02-29 | [
[
"Lee",
"Seong Hun",
""
],
[
"Civera",
"Javier",
""
]
] | In this work, we propose a novel method for robust single rotation averaging that can efficiently handle an extremely large fraction of outliers. Our approach is to minimize the total truncated least unsquared deviations (TLUD) cost of geodesic distances. The proposed algorithm consists of three steps: First, we consider each input rotation as a potential initial solution and choose the one that yields the least sum of truncated chordal deviations. Next, we obtain the inlier set using the initial solution and compute its chordal $L_2$-mean. Finally, starting from this estimate, we iteratively compute the geodesic $L_1$-mean of the inliers using the Weiszfeld algorithm on $SO(3)$. An extensive evaluation shows that our method is robust against up to 99% outliers given a sufficient number of accurate inliers, outperforming the current state of the art. |
2203.13883 | Sara Abdali | Sara Abdali, Sina shaham, Bhaskar Krishnamachari | Multi-modal Misinformation Detection: Approaches, Challenges and
Opportunities | null | null | null | null | cs.LG cs.AI cs.CV cs.CY cs.MM cs.SI | http://creativecommons.org/licenses/by-sa/4.0/ | As social media platforms are evolving from text-based forums into
multi-modal environments, the nature of misinformation in social media is also
transforming accordingly. Taking advantage of the fact that visual modalities
such as images and videos are more favorable and attractive to the users and
textual contents are sometimes skimmed carelessly, misinformation spreaders
have recently targeted contextual connections between the modalities e.g., text
and image. Hence many researchers have developed automatic techniques for
detecting possible cross-modal discordance in web-based content. We analyze,
categorize and identify existing approaches in addition to challenges and
shortcomings they face in order to unearth new research opportunities in the
field of multi-modal misinformation detection.
| [
{
"created": "Fri, 25 Mar 2022 19:45:33 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Apr 2022 21:03:13 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Jul 2022 21:55:37 GMT",
"version": "v3"
},
{
"created": "Tue, 23 Jan 2024 03:54:48 GMT",
"version": "v4"
},
{
"created": "Wed, 24 Jan 2024 01:50:22 GMT",
"version": "v5"
},
{
"created": "Wed, 27 Mar 2024 23:27:58 GMT",
"version": "v6"
}
] | 2024-03-29 | [
[
"Abdali",
"Sara",
""
],
[
"shaham",
"Sina",
""
],
[
"Krishnamachari",
"Bhaskar",
""
]
] | As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to the users and textual contents are sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. Hence many researchers have developed automatic techniques for detecting possible cross-modal discordance in web-based content. We analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection. |
2306.10724 | Hamed Hemati | Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian Borth | Partial Hypernetworks for Continual Learning | Accepted to the 2nd Conference on Lifelong Learning Agents (CoLLAs),
2023 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Hypernetworks mitigate forgetting in continual learning (CL) by generating
task-dependent weights and penalizing weight changes at a meta-model level.
Unfortunately, generating all weights is not only computationally expensive for
larger architectures, but also, it is not well understood whether generating
all model weights is necessary. Inspired by latent replay methods in CL, we
propose partial weight generation for the final layers of a model using
hypernetworks while freezing the initial layers. With this objective, we first
answer the question of how many layers can be frozen without compromising the
final performance. Through several experiments, we empirically show that the
number of layers that can be frozen is proportional to the distributional
similarity in the CL stream. Then, to demonstrate the effectiveness of
hypernetworks, we show that noisy streams can significantly impact the
performance of latent replay methods, leading to increased forgetting when
features from noisy experiences are replayed with old samples. In contrast,
partial hypernetworks are more robust to noise by maintaining accuracy on
previous experiences. Finally, we conduct experiments on the split CIFAR-100
and TinyImagenet benchmarks and compare different versions of partial
hypernetworks to latent replay methods. We conclude that partial weight
generation using hypernetworks is a promising solution to the problem of
forgetting in neural networks. It can provide an effective balance between
computation and final test accuracy in CL streams.
| [
{
"created": "Mon, 19 Jun 2023 06:49:10 GMT",
"version": "v1"
}
] | 2023-06-21 | [
[
"Hemati",
"Hamed",
""
],
[
"Lomonaco",
"Vincenzo",
""
],
[
"Bacciu",
"Davide",
""
],
[
"Borth",
"Damian",
""
]
] | Hypernetworks mitigate forgetting in continual learning (CL) by generating task-dependent weights and penalizing weight changes at a meta-model level. Unfortunately, generating all weights is not only computationally expensive for larger architectures, but also, it is not well understood whether generating all model weights is necessary. Inspired by latent replay methods in CL, we propose partial weight generation for the final layers of a model using hypernetworks while freezing the initial layers. With this objective, we first answer the question of how many layers can be frozen without compromising the final performance. Through several experiments, we empirically show that the number of layers that can be frozen is proportional to the distributional similarity in the CL stream. Then, to demonstrate the effectiveness of hypernetworks, we show that noisy streams can significantly impact the performance of latent replay methods, leading to increased forgetting when features from noisy experiences are replayed with old samples. In contrast, partial hypernetworks are more robust to noise by maintaining accuracy on previous experiences. Finally, we conduct experiments on the split CIFAR-100 and TinyImagenet benchmarks and compare different versions of partial hypernetworks to latent replay methods. We conclude that partial weight generation using hypernetworks is a promising solution to the problem of forgetting in neural networks. It can provide an effective balance between computation and final test accuracy in CL streams. |
1202.1458 | Adam Williamson | Adam R. Williamson, Tsung-Yi Chen, and Richard D. Wesel | A Rate-Compatible Sphere-Packing Analysis of Feedback Coding with
Limited Retransmissions | To be published at the 2012 IEEE International Symposium on
Information Theory, Cambridge, MA, USA. Updated to incorporate reviewers'
comments and add new figures | null | 10.1109/ISIT.2012.6284061 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work by Polyanskiy et al. and Chen et al. has excited new interest in
using feedback to approach capacity with low latency. Polyanskiy showed that
feedback identifying the first symbol at which decoding is successful allows
capacity to be approached with surprisingly low latency. This paper uses Chen's
rate-compatible sphere-packing (RCSP) analysis to study what happens when
symbols must be transmitted in packets, as with a traditional hybrid ARQ
system, and limited to relatively few (six or fewer) incremental transmissions.
Numerical optimizations find the series of progressively growing cumulative
block lengths that enable RCSP to approach capacity with the minimum possible
latency. RCSP analysis shows that five incremental transmissions are sufficient
to achieve 92% of capacity with an average block length of fewer than 101
symbols on the AWGN channel with SNR of 2.0 dB.
The RCSP analysis provides a decoding error trajectory that specifies the
decoding error rate for each cumulative block length. Though RCSP is an
idealization, an example tail-biting convolutional code matches the RCSP
decoding error trajectory and achieves 91% of capacity with an average block
length of 102 symbols on the AWGN channel with SNR of 2.0 dB. We also show how
RCSP analysis can be used in cases where packets have deadlines associated with
them (leading to an outage probability).
| [
{
"created": "Tue, 7 Feb 2012 16:31:54 GMT",
"version": "v1"
},
{
"created": "Mon, 21 May 2012 01:19:20 GMT",
"version": "v2"
}
] | 2016-11-15 | [
[
"Williamson",
"Adam R.",
""
],
[
"Chen",
"Tsung-Yi",
""
],
[
"Wesel",
"Richard D.",
""
]
] | Recent work by Polyanskiy et al. and Chen et al. has excited new interest in using feedback to approach capacity with low latency. Polyanskiy showed that feedback identifying the first symbol at which decoding is successful allows capacity to be approached with surprisingly low latency. This paper uses Chen's rate-compatible sphere-packing (RCSP) analysis to study what happens when symbols must be transmitted in packets, as with a traditional hybrid ARQ system, and limited to relatively few (six or fewer) incremental transmissions. Numerical optimizations find the series of progressively growing cumulative block lengths that enable RCSP to approach capacity with the minimum possible latency. RCSP analysis shows that five incremental transmissions are sufficient to achieve 92% of capacity with an average block length of fewer than 101 symbols on the AWGN channel with SNR of 2.0 dB. The RCSP analysis provides a decoding error trajectory that specifies the decoding error rate for each cumulative block length. Though RCSP is an idealization, an example tail-biting convolutional code matches the RCSP decoding error trajectory and achieves 91% of capacity with an average block length of 102 symbols on the AWGN channel with SNR of 2.0 dB. We also show how RCSP analysis can be used in cases where packets have deadlines associated with them (leading to an outage probability). |
1803.08634 | Zhifei Mao | Zhifei Mao, Yuming Jiang, Xiaoqiang Di, and Yordanos Woldeyohannes | Joint Head Selection and Airtime Allocation for Data Dissemination in
Mobile Social Networks | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mobile social networks (MSNs) enable people with similar interests to
interact without Internet access. By forming a temporary group, users can
disseminate their data to other interested users in proximity with short-range
communication technologies. However, due to user mobility, airtime available
for users in the same group to disseminate data is limited. In addition, for
practical consideration, a star network topology among users in the group is
expected. For the former, unfair airtime allocation among the users will
undermine their willingness to participate in MSNs. For the latter, a group
head is required to connect other users. These two problems have to be properly
addressed to enable real implementation and adoption of MSNs. To this aim, we
propose a Nash bargaining-based joint head selection and airtime allocation
scheme for data dissemination within the group. Specifically, the bargaining
game of joint head selection and airtime allocation is first formulated. Then,
Nash bargaining solution (NBS) based optimization problems are proposed for a
homogeneous case and a more general heterogeneous case. For both cases, the
existence of solution to the optimization problem is proved, which guarantees
Pareto optimality and proportional fairness. Next, an algorithm, allowing
distributed implementation, for join head selection and airtime allocation is
introduced. Finally, numerical results are presented to evaluate the
performance, validate intuitions and derive insights of the proposed scheme.
| [
{
"created": "Fri, 23 Mar 2018 01:53:34 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Jan 2019 21:51:50 GMT",
"version": "v2"
}
] | 2019-01-10 | [
[
"Mao",
"Zhifei",
""
],
[
"Jiang",
"Yuming",
""
],
[
"Di",
"Xiaoqiang",
""
],
[
"Woldeyohannes",
"Yordanos",
""
]
] | Mobile social networks (MSNs) enable people with similar interests to interact without Internet access. By forming a temporary group, users can disseminate their data to other interested users in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a Nash bargaining-based joint head selection and airtime allocation scheme for data dissemination within the group. Specifically, the bargaining game of joint head selection and airtime allocation is first formulated. Then, Nash bargaining solution (NBS) based optimization problems are proposed for a homogeneous case and a more general heterogeneous case. For both cases, the existence of solution to the optimization problem is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm, allowing distributed implementation, for join head selection and airtime allocation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme. |
1709.10237 | Biswadip Dey | Kevin S. Galloway and Biswadip Dey | Beacon-referenced Mutual Pursuit in Three Dimensions | null | null | null | null | cs.SY cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by station-keeping applications in various unmanned settings, this
paper introduces a steering control law for a pair of agents operating in the
vicinity of a fixed beacon in a three-dimensional environment. This feedback
law is a modification of the previously studied three-dimensional constant
bearing (CB) pursuit law, in the sense that it incorporates an additional term
to allocate attention to the beacon. We investigate the behavior of the
closed-loop dynamics for a two agent mutual pursuit system in which each agent
employs the beacon-referenced CB pursuit law with regards to the other agent
and a stationary beacon. Under certain assumptions on the associated control
parameters, we demonstrate that this problem admits circling equilibria wherein
the agents move on circular orbits with a common radius, in planes
perpendicular to a common axis passing through the beacon. As the common radius
and distances from the beacon are determined by choice of parameters in the
feedback law, this approach provides a means to engineer desired formations in
a three-dimensional setting.
| [
{
"created": "Fri, 29 Sep 2017 04:52:57 GMT",
"version": "v1"
}
] | 2017-10-02 | [
[
"Galloway",
"Kevin S.",
""
],
[
"Dey",
"Biswadip",
""
]
] | Motivated by station-keeping applications in various unmanned settings, this paper introduces a steering control law for a pair of agents operating in the vicinity of a fixed beacon in a three-dimensional environment. This feedback law is a modification of the previously studied three-dimensional constant bearing (CB) pursuit law, in the sense that it incorporates an additional term to allocate attention to the beacon. We investigate the behavior of the closed-loop dynamics for a two agent mutual pursuit system in which each agent employs the beacon-referenced CB pursuit law with regards to the other agent and a stationary beacon. Under certain assumptions on the associated control parameters, we demonstrate that this problem admits circling equilibria wherein the agents move on circular orbits with a common radius, in planes perpendicular to a common axis passing through the beacon. As the common radius and distances from the beacon are determined by choice of parameters in the feedback law, this approach provides a means to engineer desired formations in a three-dimensional setting. |
1302.7082 | Meena Kabilan | A.Meena, K.Raja | K Means Segmentation of Alzheimers Disease in PET scan datasets: An
implementation | International Joint Conference on Advances in Signal Processing and
Information Technology, SPIT2012 | LNICST, ISSN:1867 To 8211 pp. 158 To 162, 2012 | null | null | cs.CV cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Positron Emission Tomography (PET) scan image requires expertise in the
segmentation where clustering algorithm plays an important role in the
automation process. The algorithm optimization is concluded based on the
performance, quality and number of clusters extracted. This paper is proposed
to study the commonly used K Means clustering algorithm and to discuss a brief
list of toolboxes for reproducing and extending works presented in medical
image analysis. This work is compiled using AForge .NET framework in windows
environment and MATrix LABoratory (MATLAB 7.0.1)
| [
{
"created": "Thu, 28 Feb 2013 04:50:31 GMT",
"version": "v1"
}
] | 2013-03-01 | [
[
"Meena",
"A.",
""
],
[
"Raja",
"K.",
""
]
] | The Positron Emission Tomography (PET) scan image requires expertise in the segmentation where clustering algorithm plays an important role in the automation process. The algorithm optimization is concluded based on the performance, quality and number of clusters extracted. This paper is proposed to study the commonly used K Means clustering algorithm and to discuss a brief list of toolboxes for reproducing and extending works presented in medical image analysis. This work is compiled using AForge .NET framework in windows environment and MATrix LABoratory (MATLAB 7.0.1) |
2204.12039 | Yuqing Liu | Yuqing Liu, Qi Jia, Jian Zhang, Xin Fan, Shanshe Wang, Siwei Ma, Wen
Gao | Learning Weighting Map for Bit-Depth Expansion within a Rational Range | This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bit-depth expansion (BDE) is one of the emerging technologies to display high
bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods
have no unified solution for various BDE situations, and directly learn a
mapping for each pixel from LBD image to the desired value in HBD image, which
may change the given high-order bits and lead to a huge deviation from the
ground truth. In this paper, we design a bit restoration network (BRNet) to
learn a weight for each pixel, which indicates the ratio of the replenished
value within a rational range, invoking an accurate solution without modifying
the given high-order bit information. To make the network adaptive for any
bit-depth degradation, we investigate the issue in an optimization perspective
and train the network under progressive training strategy for better
performance. Moreover, we employ Wasserstein distance as a visual quality
indicator to evaluate the difference of color distribution between restored
image and the ground truth. Experimental results show our method can restore
colorful images with fewer artifacts and false contours, and outperforms
state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein
distance. The source code will be made available at
https://github.com/yuqing-liu-dut/bit-depth-expansion
| [
{
"created": "Tue, 26 Apr 2022 02:27:39 GMT",
"version": "v1"
}
] | 2022-04-27 | [
[
"Liu",
"Yuqing",
""
],
[
"Jia",
"Qi",
""
],
[
"Zhang",
"Jian",
""
],
[
"Fan",
"Xin",
""
],
[
"Wang",
"Shanshe",
""
],
[
"Ma",
"Siwei",
""
],
[
"Gao",
"Wen",
""
]
] | Bit-depth expansion (BDE) is one of the emerging technologies to display high bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth. In this paper, we design a bit restoration network (BRNet) to learn a weight for each pixel, which indicates the ratio of the replenished value within a rational range, invoking an accurate solution without modifying the given high-order bit information. To make the network adaptive for any bit-depth degradation, we investigate the issue in an optimization perspective and train the network under progressive training strategy for better performance. Moreover, we employ Wasserstein distance as a visual quality indicator to evaluate the difference of color distribution between restored image and the ground truth. Experimental results show our method can restore colorful images with fewer artifacts and false contours, and outperforms state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein distance. The source code will be made available at https://github.com/yuqing-liu-dut/bit-depth-expansion |
1006.0386 | Haitham Rashwan | Haitham Rashwan, Ernst M. Gabidulin, Bahram Honary | A Smart Approach for GPT Cryptosystem Based on Rank Codes | 5 pages. to appear in Proceedings of IEEE ISIT2010 | null | 10.1109/ISIT.2010.5513549 | #1223: ISIT 2010 | cs.IT cs.CR math.IT | http://creativecommons.org/licenses/by/3.0/ | The concept of Public- key cryptosystem was innovated by McEliece's
cryptosystem. The public key cryptosystem based on rank codes was presented in
1991 by Gabidulin -Paramonov-Trejtakov(GPT). The use of rank codes in
cryptographic applications is advantageous since it is practically impossible
to utilize combinatoric decoding. This has enabled using public keys of a
smaller size. Respective structural attacks against this system were proposed
by Gibson and recently by Overbeck. Overbeck's attacks break many versions of
the GPT cryptosystem and are turned out to be either polynomial or exponential
depending on parameters of the cryptosystem. In this paper, we introduce a new
approach, called the Smart approach, which is based on a proper choice of the
distortion matrix X. The Smart approach allows for withstanding all known
attacks even if the column scrambler matrix P over the base field Fq.
| [
{
"created": "Wed, 2 Jun 2010 14:18:25 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Rashwan",
"Haitham",
""
],
[
"Gabidulin",
"Ernst M.",
""
],
[
"Honary",
"Bahram",
""
]
] | The concept of Public- key cryptosystem was innovated by McEliece's cryptosystem. The public key cryptosystem based on rank codes was presented in 1991 by Gabidulin -Paramonov-Trejtakov(GPT). The use of rank codes in cryptographic applications is advantageous since it is practically impossible to utilize combinatoric decoding. This has enabled using public keys of a smaller size. Respective structural attacks against this system were proposed by Gibson and recently by Overbeck. Overbeck's attacks break many versions of the GPT cryptosystem and are turned out to be either polynomial or exponential depending on parameters of the cryptosystem. In this paper, we introduce a new approach, called the Smart approach, which is based on a proper choice of the distortion matrix X. The Smart approach allows for withstanding all known attacks even if the column scrambler matrix P over the base field Fq. |
2311.10832 | Elaheh Jafarigol | Elaheh Jafarigol, Theodore Trafalis, Talayeh Razzaghi, Mona Zamankhani | Exploring Machine Learning Models for Federated Learning: A Review of
Approaches, Performance, and Limitations | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of individuals'
data. Federated learning lays the groundwork for collaborative research in
areas where the data is sensitive. Federated learning has several implications
for real-world problems. In times of crisis, when real-time decision-making is
critical, federated learning allows multiple entities to work collectively
without sharing sensitive data. This distributed approach enables us to
leverage information from multiple sources and gain more diverse insights. This
paper is a systematic review of the literature on privacy-preserving machine
learning in the last few years based on the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have
presented an extensive review of supervised/unsupervised machine learning
algorithms, ensemble methods, meta-heuristic approaches, blockchain technology,
and reinforcement learning used in the framework of federated learning, in
addition to an overview of federated learning applications. This paper reviews
the literature on the components of federated learning and its applications in
the last few years. The main purpose of this work is to provide researchers and
practitioners with a comprehensive overview of federated learning from the
machine learning point of view. A discussion of some open problems and future
research directions in federated learning is also provided.
| [
{
"created": "Fri, 17 Nov 2023 19:23:21 GMT",
"version": "v1"
}
] | 2023-11-21 | [
[
"Jafarigol",
"Elaheh",
""
],
[
"Trafalis",
"Theodore",
""
],
[
"Razzaghi",
"Talayeh",
""
],
[
"Zamankhani",
"Mona",
""
]
] | In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided. |
2207.12710 | Christoffer Loeffler | Christoffer Loeffler, Kion Fallah, Stefano Fenu, Dario Zanca, Bjoern
Eskofier, Christopher John Rozell, Christopher Mutschler | Active Learning of Ordinal Embeddings: A User Study on Football Data | 23 pages, 17 figures | Transactions on Machine Learning Research 04/2023
https://openreview.net/forum?id=oq3tx5kinu | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans innately measure distance between instances in an unlabeled dataset
using an unknown similarity function. Distance metrics can only serve as proxy
for similarity in information retrieval of similar instances. Learning a good
similarity function from human annotations improves the quality of retrievals.
This work uses deep metric learning to learn these user-defined similarity
functions from few annotations for a large football trajectory dataset. We
adapt an entropy-based active learning method with recent work from triplet
mining to collect easy-to-answer but still informative annotations from human
participants and use them to train a deep convolutional network that
generalizes to unseen samples. Our user study shows that our approach improves
the quality of the information retrieval compared to a previous deep metric
learning approach that relies on a Siamese network. Specifically, we shed light
on the strengths and weaknesses of passive sampling heuristics and active
learners alike by analyzing the participants' response efficacy. To this end,
we collect accuracy, algorithmic time complexity, the participants' fatigue and
time-to-response, qualitative self-assessment and statements, as well as the
effects of mixed-expertise annotators and their consistency on model
performance and transfer-learning.
| [
{
"created": "Tue, 26 Jul 2022 07:55:23 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Nov 2022 09:49:18 GMT",
"version": "v2"
}
] | 2023-04-25 | [
[
"Loeffler",
"Christoffer",
""
],
[
"Fallah",
"Kion",
""
],
[
"Fenu",
"Stefano",
""
],
[
"Zanca",
"Dario",
""
],
[
"Eskofier",
"Bjoern",
""
],
[
"Rozell",
"Christopher John",
""
],
[
"Mutschler",
"Christopher",
""
]
] | Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning. |
1808.07910 | Nicolas Ford | Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, and George E. Dahl | The Importance of Generation Order in Language Modeling | null | null | null | null | cs.LG cs.CL stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural language models are a critical component of state-of-the-art systems
for machine translation, summarization, audio transcription, and other tasks.
These language models are almost universally autoregressive in nature,
generating sentences one token at a time from left to right. This paper studies
the influence of token generation order on model quality via a novel two-pass
language model that produces partially-filled sentence "templates" and then
fills in missing tokens. We compare various strategies for structuring these
two passes and observe a surprisingly large variation in model quality. We find
the most effective strategy generates function words in the first pass followed
by content words in the second. We believe these experimental results justify a
more extensive investigation of generation order for neural language models.
| [
{
"created": "Thu, 23 Aug 2018 19:17:24 GMT",
"version": "v1"
}
] | 2018-08-27 | [
[
"Ford",
"Nicolas",
""
],
[
"Duckworth",
"Daniel",
""
],
[
"Norouzi",
"Mohammad",
""
],
[
"Dahl",
"George E.",
""
]
] | Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks. These language models are almost universally autoregressive in nature, generating sentences one token at a time from left to right. This paper studies the influence of token generation order on model quality via a novel two-pass language model that produces partially-filled sentence "templates" and then fills in missing tokens. We compare various strategies for structuring these two passes and observe a surprisingly large variation in model quality. We find the most effective strategy generates function words in the first pass followed by content words in the second. We believe these experimental results justify a more extensive investigation of generation order for neural language models. |
2007.05906 | Khawar Islam Mr | Khawar Islam, Uzma Afzal | Framework for Passenger Seat Availability Using Face Detection in
Passenger Bus | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Advancements in Intelligent Transportation System (IES) improve passenger
traveling by providing information systems for bus arrival time and counting
the number of passengers and buses in cities. Passengers still face bus waiting
and seat unavailability issues which have adverse effects on traffic management
and controlling authority. We propose a Face Detection based Framework (FDF) to
determine passenger seat availability in a camera-equipped bus through face
detection which is based on background subtraction to count empty, filled, and
total seats. FDF has an integrated smartphone Passenger Application (PA) to
identify the nearest bus stop. We evaluate FDF in a live test environment and
results show that it gives 90% accuracy. We believe our results have the
potential to address traffic management concerns and assist passengers to save
their valuable time
| [
{
"created": "Sun, 12 Jul 2020 04:31:28 GMT",
"version": "v1"
}
] | 2020-07-14 | [
[
"Islam",
"Khawar",
""
],
[
"Afzal",
"Uzma",
""
]
] | Advancements in Intelligent Transportation System (IES) improve passenger traveling by providing information systems for bus arrival time and counting the number of passengers and buses in cities. Passengers still face bus waiting and seat unavailability issues which have adverse effects on traffic management and controlling authority. We propose a Face Detection based Framework (FDF) to determine passenger seat availability in a camera-equipped bus through face detection which is based on background subtraction to count empty, filled, and total seats. FDF has an integrated smartphone Passenger Application (PA) to identify the nearest bus stop. We evaluate FDF in a live test environment and results show that it gives 90% accuracy. We believe our results have the potential to address traffic management concerns and assist passengers to save their valuable time |
1205.5055 | Matthew Anderson | Matthew Anderson, Maciej Brodowicz, Hartmut Kaiser, Bryce
Adelstein-Lelbach, and Thomas Sterling | Neutron Star Evolutions using Tabulated Equations of State with a New
Execution Model | 9 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1110.1131 | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The addition of nuclear and neutrino physics to general relativistic fluid
codes allows for a more realistic description of hot nuclear matter in neutron
star and black hole systems. This additional microphysics requires that each
processor have access to large tables of data, such as equations of state, and
in large simulations the memory required to store these tables locally can
become excessive unless an alternative execution model is used. In this work we
present relativistic fluid evolutions of a neutron star obtained using a
message driven multi-threaded execution model known as ParalleX. These neutron
star simulations would require substantial memory overhead dedicated entirely
to the equation of state table if using a more traditional execution model. We
introduce a ParalleX component based on Futures for accessing large tables of
data, including out-of-core sized tables, which does not require substantial
memory overhead and effectively hides any increased network latency.
| [
{
"created": "Tue, 22 May 2012 20:46:11 GMT",
"version": "v1"
}
] | 2012-05-24 | [
[
"Anderson",
"Matthew",
""
],
[
"Brodowicz",
"Maciej",
""
],
[
"Kaiser",
"Hartmut",
""
],
[
"Adelstein-Lelbach",
"Bryce",
""
],
[
"Sterling",
"Thomas",
""
]
] | The addition of nuclear and neutrino physics to general relativistic fluid codes allows for a more realistic description of hot nuclear matter in neutron star and black hole systems. This additional microphysics requires that each processor have access to large tables of data, such as equations of state, and in large simulations the memory required to store these tables locally can become excessive unless an alternative execution model is used. In this work we present relativistic fluid evolutions of a neutron star obtained using a message driven multi-threaded execution model known as ParalleX. These neutron star simulations would require substantial memory overhead dedicated entirely to the equation of state table if using a more traditional execution model. We introduce a ParalleX component based on Futures for accessing large tables of data, including out-of-core sized tables, which does not require substantial memory overhead and effectively hides any increased network latency. |
2202.08901 | Anubrata Das | Li Shi, Nilavra Bhattacharya, Anubrata Das, Matthew Lease, Jacek
Gwidzka | The Effects of Interactive AI Design on User Behavior: An Eye-tracking
Study of Fact-checking COVID-19 Claims | null | Published in ACM SIGIR Conference on Human Information Interaction
and Retrieval (CHIIR 2022), March 14 --- 18, 2022, Regensburg, Germany | 10.1145/3498366.3505786 | null | cs.HC cs.CL cs.IR | http://creativecommons.org/licenses/by/4.0/ | We conducted a lab-based eye-tracking study to investigate how the
interactivity of an AI-powered fact-checking system affects user interactions,
such as dwell time, attention, and mental resources involved in using the
system. A within-subject experiment was conducted, where participants used an
interactive and a non-interactive version of a mock AI fact-checking system and
rated their perceived correctness of COVID-19 related claims. We collected
web-page interactions, eye-tracking data, and mental workload using NASA-TLX.
We found that the presence of the affordance of interactively manipulating the
AI system's prediction parameters affected users' dwell times, and
eye-fixations on AOIs, but not mental workload. In the interactive system,
participants spent the most time evaluating claims' correctness, followed by
reading news. This promising result shows a positive role of interactivity in a
mixed-initiative AI-powered system.
| [
{
"created": "Thu, 17 Feb 2022 21:08:57 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Mar 2022 20:47:34 GMT",
"version": "v2"
}
] | 2022-03-16 | [
[
"Shi",
"Li",
""
],
[
"Bhattacharya",
"Nilavra",
""
],
[
"Das",
"Anubrata",
""
],
[
"Lease",
"Matthew",
""
],
[
"Gwidzka",
"Jacek",
""
]
] | We conducted a lab-based eye-tracking study to investigate how the interactivity of an AI-powered fact-checking system affects user interactions, such as dwell time, attention, and mental resources involved in using the system. A within-subject experiment was conducted, where participants used an interactive and a non-interactive version of a mock AI fact-checking system and rated their perceived correctness of COVID-19 related claims. We collected web-page interactions, eye-tracking data, and mental workload using NASA-TLX. We found that the presence of the affordance of interactively manipulating the AI system's prediction parameters affected users' dwell times, and eye-fixations on AOIs, but not mental workload. In the interactive system, participants spent the most time evaluating claims' correctness, followed by reading news. This promising result shows a positive role of interactivity in a mixed-initiative AI-powered system. |
2404.09842 | Tao Wu | Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang | STMixer: A One-Stage Sparse Action Detector | Extended version of the paper arXiv:2303.15879 presented at CVPR
2023. Accepted by TPAMI 2024 | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Traditional video action detectors typically adopt the two-stage pipeline,
where a person detector is first employed to generate actor boxes and then 3D
RoIAlign is used to extract actor-specific features for classification. This
detection paradigm requires multi-stage training and inference, and the feature
sampling is constrained inside the box, failing to effectively leverage richer
context information outside. Recently, a few query-based action detectors have
been proposed to predict action instances in an end-to-end manner. However,
they still lack adaptability in feature sampling and decoding, thus suffering
from the issues of inferior performance or slower convergence. In this paper,
we propose two core designs for a more flexible one-stage sparse action
detector. First, we present a query-based adaptive feature sampling module,
which endows the detector with the flexibility of mining a group of
discriminative features from the entire spatio-temporal domain. Second, we
devise a decoupled feature mixing module, which dynamically attends to and
mixes video features along the spatial and temporal dimensions respectively for
better feature decoding. Based on these designs, we instantiate two detection
pipelines, that is, STMixer-K for keyframe action detection and STMixer-T for
action tubelet detection. Without bells and whistles, our STMixer detectors
obtain state-of-the-art results on five challenging spatio-temporal action
detection benchmarks for keyframe action detection or action tube detection.
| [
{
"created": "Mon, 15 Apr 2024 14:52:02 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Wu",
"Tao",
""
],
[
"Cao",
"Mengqi",
""
],
[
"Gao",
"Ziteng",
""
],
[
"Wu",
"Gangshan",
""
],
[
"Wang",
"Limin",
""
]
] | Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for classification. This detection paradigm requires multi-stage training and inference, and the feature sampling is constrained inside the box, failing to effectively leverage richer context information outside. Recently, a few query-based action detectors have been proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling and decoding, thus suffering from the issues of inferior performance or slower convergence. In this paper, we propose two core designs for a more flexible one-stage sparse action detector. First, we present a query-based adaptive feature sampling module, which endows the detector with the flexibility of mining a group of discriminative features from the entire spatio-temporal domain. Second, we devise a decoupled feature mixing module, which dynamically attends to and mixes video features along the spatial and temporal dimensions respectively for better feature decoding. Based on these designs, we instantiate two detection pipelines, that is, STMixer-K for keyframe action detection and STMixer-T for action tubelet detection. Without bells and whistles, our STMixer detectors obtain state-of-the-art results on five challenging spatio-temporal action detection benchmarks for keyframe action detection or action tube detection. |
2209.05243 | Christofer Fellicious | Christofer Fellicious, Stewart Sentanoe, Michael Granitzer, Hans P.
Reiser | SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap
Dump | null | null | null | null | cs.CR cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Digital forensics is the process of extracting, preserving, and documenting
evidence in digital devices. A commonly used method in digital forensics is to
extract data from the main memory of a digital device. However, the main
challenge is identifying the important data to be extracted. Several pieces of
crucial information reside in the main memory, like usernames, passwords, and
cryptographic keys such as SSH session keys. In this paper, we propose
SmartKex, a machine-learning assisted method to extract session keys from heap
memory snapshots of an OpenSSH process. In addition, we release an openly
available dataset and the corresponding toolchain for creating additional data.
Finally, we compare SmartKex with naive brute-force methods and empirically
show that SmartKex can extract the session keys with high accuracy and high
throughput. With the provided resources, we intend to strengthen the research
on the intersection between digital forensics, cybersecurity, and machine
learning.
| [
{
"created": "Mon, 12 Sep 2022 13:36:54 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Sep 2022 08:50:56 GMT",
"version": "v2"
}
] | 2022-09-14 | [
[
"Fellicious",
"Christofer",
""
],
[
"Sentanoe",
"Stewart",
""
],
[
"Granitzer",
"Michael",
""
],
[
"Reiser",
"Hans P.",
""
]
] | Digital forensics is the process of extracting, preserving, and documenting evidence in digital devices. A commonly used method in digital forensics is to extract data from the main memory of a digital device. However, the main challenge is identifying the important data to be extracted. Several pieces of crucial information reside in the main memory, like usernames, passwords, and cryptographic keys such as SSH session keys. In this paper, we propose SmartKex, a machine-learning assisted method to extract session keys from heap memory snapshots of an OpenSSH process. In addition, we release an openly available dataset and the corresponding toolchain for creating additional data. Finally, we compare SmartKex with naive brute-force methods and empirically show that SmartKex can extract the session keys with high accuracy and high throughput. With the provided resources, we intend to strengthen the research on the intersection between digital forensics, cybersecurity, and machine learning. |
1311.2495 | Moritz Hardt | Moritz Hardt and Eric Price | The Noisy Power Method: A Meta Algorithm with Applications | NIPS 2014 | null | null | null | cs.DS cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide a new robust convergence analysis of the well-known power method
for computing the dominant singular vectors of a matrix that we call the noisy
power method. Our result characterizes the convergence behavior of the
algorithm when a significant amount noise is introduced after each
matrix-vector multiplication. The noisy power method can be seen as a
meta-algorithm that has recently found a number of important applications in a
broad range of machine learning problems including alternating minimization for
matrix completion, streaming principal component analysis (PCA), and
privacy-preserving spectral analysis. Our general analysis subsumes several
existing ad-hoc convergence bounds and resolves a number of open problems in
multiple applications including streaming PCA and privacy-preserving singular
vector computation.
| [
{
"created": "Mon, 11 Nov 2013 16:47:25 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Sep 2014 19:17:32 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Dec 2014 21:53:05 GMT",
"version": "v3"
},
{
"created": "Tue, 3 Feb 2015 23:43:37 GMT",
"version": "v4"
}
] | 2015-02-05 | [
[
"Hardt",
"Moritz",
""
],
[
"Price",
"Eric",
""
]
] | We provide a new robust convergence analysis of the well-known power method for computing the dominant singular vectors of a matrix that we call the noisy power method. Our result characterizes the convergence behavior of the algorithm when a significant amount noise is introduced after each matrix-vector multiplication. The noisy power method can be seen as a meta-algorithm that has recently found a number of important applications in a broad range of machine learning problems including alternating minimization for matrix completion, streaming principal component analysis (PCA), and privacy-preserving spectral analysis. Our general analysis subsumes several existing ad-hoc convergence bounds and resolves a number of open problems in multiple applications including streaming PCA and privacy-preserving singular vector computation. |
1905.08526 | Yukiko Yamauchi | Yukiko Yamauchi and Masafumi Yamashita | Coding theory for noiseless channels realized by anonymous oblivious
mobile robots | null | null | null | null | cs.DC cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an information transmission scheme by a swarm of anonymous
oblivious mobile robots on a graph. The swarm of robots travel from a sender
vertex to a receiver vertex to transmit a symbol generated at the sender. The
codeword for a symbol is a pair of an initial configuration at the sender and a
set of terminal configurations at the receiver. The set of such codewords forms
a code. We analyze the performance of the proposed scheme in terms of its code
size and transmission delay. We first demonstrate that a lower bound of the
transmission delay depends on the size of the swarm, and the code size is upper
bounded by an exponent of the size of the swarm. We then give two algorithms
for a swarm of a fixed size. The first algorithm realizes a near optimal code
size with a large transmission delay. The second algorithm realizes an optimal
transmission delay with a smaller code size. We then consider information
transmission by swarms of different sizes and present upper bounds of the
expected swarm size by the two algorithms. We also present lower bounds by
Shannon's lemma and noiseless coding theorem.
| [
{
"created": "Tue, 21 May 2019 10:02:49 GMT",
"version": "v1"
}
] | 2019-05-22 | [
[
"Yamauchi",
"Yukiko",
""
],
[
"Yamashita",
"Masafumi",
""
]
] | We propose an information transmission scheme by a swarm of anonymous oblivious mobile robots on a graph. The swarm of robots travel from a sender vertex to a receiver vertex to transmit a symbol generated at the sender. The codeword for a symbol is a pair of an initial configuration at the sender and a set of terminal configurations at the receiver. The set of such codewords forms a code. We analyze the performance of the proposed scheme in terms of its code size and transmission delay. We first demonstrate that a lower bound of the transmission delay depends on the size of the swarm, and the code size is upper bounded by an exponent of the size of the swarm. We then give two algorithms for a swarm of a fixed size. The first algorithm realizes a near optimal code size with a large transmission delay. The second algorithm realizes an optimal transmission delay with a smaller code size. We then consider information transmission by swarms of different sizes and present upper bounds of the expected swarm size by the two algorithms. We also present lower bounds by Shannon's lemma and noiseless coding theorem. |
2406.15888 | Khai Le-Duc | Khai Le-Duc, Khai-Nguyen Nguyen, Long Vo-Dang, Truong-Son Hy | Real-time Speech Summarization for Medical Conversations | Interspeech 2024 | null | null | null | cs.CL cs.AI cs.LG cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | In doctor-patient conversations, identifying medically relevant information
is crucial, posing the need for conversation summarization. In this work, we
propose the first deployable real-time speech summarization system for
real-world applications in industry, which generates a local summary after
every N speech utterances within a conversation and a global summary after the
end of a conversation. Our system could enhance user experience from a business
standpoint, while also reducing computational costs from a technical
perspective. Secondly, we present VietMed-Sum which, to our knowledge, is the
first speech summarization dataset for medical conversations. Thirdly, we are
the first to utilize LLM and human annotators collaboratively to create gold
standard and synthetic summaries for medical conversation summarization.
Finally, we present baseline results of state-of-the-art models on VietMed-Sum.
All code, data (English-translated and Vietnamese) and models are available
online: https://github.com/leduckhai/MultiMed
| [
{
"created": "Sat, 22 Jun 2024 16:37:51 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Le-Duc",
"Khai",
""
],
[
"Nguyen",
"Khai-Nguyen",
""
],
[
"Vo-Dang",
"Long",
""
],
[
"Hy",
"Truong-Son",
""
]
] | In doctor-patient conversations, identifying medically relevant information is crucial, posing the need for conversation summarization. In this work, we propose the first deployable real-time speech summarization system for real-world applications in industry, which generates a local summary after every N speech utterances within a conversation and a global summary after the end of a conversation. Our system could enhance user experience from a business standpoint, while also reducing computational costs from a technical perspective. Secondly, we present VietMed-Sum which, to our knowledge, is the first speech summarization dataset for medical conversations. Thirdly, we are the first to utilize LLM and human annotators collaboratively to create gold standard and synthetic summaries for medical conversation summarization. Finally, we present baseline results of state-of-the-art models on VietMed-Sum. All code, data (English-translated and Vietnamese) and models are available online: https://github.com/leduckhai/MultiMed |
2304.11718 | Ihab Bendidi | Ihab Bendidi, Adrien Bardes, Ethan Cohen, Alexis Lamiable, Guillaume
Bollot, Auguste Genovesio | No Free Lunch in Self Supervised Representation Learning | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Self-supervised representation learning in computer vision relies heavily on
hand-crafted image transformations to learn meaningful and invariant features.
However few extensive explorations of the impact of transformation design have
been conducted in the literature. In particular, the dependence of downstream
performances to transformation design has been established, but not studied in
depth. In this work, we explore this relationship, its impact on a domain other
than natural images, and show that designing the transformations can be viewed
as a form of supervision. First, we demonstrate that not only do
transformations have an effect on downstream performance and relevance of
clustering, but also that each category in a supervised dataset can be impacted
in a different way. Following this, we explore the impact of transformation
design on microscopy images, a domain where the difference between classes is
more subtle and fuzzy than in natural images. In this case, we observe a
greater impact on downstream tasks performances. Finally, we demonstrate that
transformation design can be leveraged as a form of supervision, as careful
selection of these by a domain expert can lead to a drastic increase in
performance on a given downstream task.
| [
{
"created": "Sun, 23 Apr 2023 18:14:19 GMT",
"version": "v1"
}
] | 2023-04-25 | [
[
"Bendidi",
"Ihab",
""
],
[
"Bardes",
"Adrien",
""
],
[
"Cohen",
"Ethan",
""
],
[
"Lamiable",
"Alexis",
""
],
[
"Bollot",
"Guillaume",
""
],
[
"Genovesio",
"Auguste",
""
]
] | Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features. However few extensive explorations of the impact of transformation design have been conducted in the literature. In particular, the dependence of downstream performances to transformation design has been established, but not studied in depth. In this work, we explore this relationship, its impact on a domain other than natural images, and show that designing the transformations can be viewed as a form of supervision. First, we demonstrate that not only do transformations have an effect on downstream performance and relevance of clustering, but also that each category in a supervised dataset can be impacted in a different way. Following this, we explore the impact of transformation design on microscopy images, a domain where the difference between classes is more subtle and fuzzy than in natural images. In this case, we observe a greater impact on downstream tasks performances. Finally, we demonstrate that transformation design can be leveraged as a form of supervision, as careful selection of these by a domain expert can lead to a drastic increase in performance on a given downstream task. |
2107.04683 | Isma\"el Jecker | Isma\"el Jecker, Nicolas Mazzocchi, Petra Wolf | Decomposing Permutation Automata | null | null | null | null | cs.FL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A deterministic finite automaton (DFA) is composite if its language can be
decomposed into an intersection of languages of smaller DFAs. Otherwise, A is
prime. This notion of primality was introduced by Kupferman and Mosheiff in
2013, and while they proved that we can decide whether a DFA is composite, the
precise complexity of this problem is still open, with a doubly-exponential gap
between the upper and lower bounds. In this work, we focus on permutation DFAs,
i.e., those for which the transition monoid is a group. We provide an NP
algorithm to decide whether a permutation DFA is composite, and show that the
difficulty of this problem comes from the number of non-accepting states of the
instance: we give a fixed-parameter tractable algorithm with the number of
rejecting states as the parameter. Moreover, we investigate the class of
commutative permutation DFAs. Their structural properties allow us to decide
compositionality in NLOGSPACE, and even in LOGSPACE if the alphabet size is
fixed. Despite this low complexity, we show that complex behaviors still arise
in this class: we provide a family of composite DFAs each requiring
polynomially many factors with respect to its size. We also consider the
variant of the problem that asks whether a DFA is k-factor composite, that is,
decomposable into k smaller DFAs, for some given integer k. We show that, for
commutative permutation DFAs, restricting the number of factors makes the
decision computationally harder, and yields a problem with tight bounds: it is
NP-complete. Finally, we show that in general, this problem is in PSPACE, and
it is in LOGSPACE for DFAs with a singleton alphabet.
| [
{
"created": "Fri, 9 Jul 2021 21:20:39 GMT",
"version": "v1"
}
] | 2021-07-13 | [
[
"Jecker",
"Ismaël",
""
],
[
"Mazzocchi",
"Nicolas",
""
],
[
"Wolf",
"Petra",
""
]
] | A deterministic finite automaton (DFA) is composite if its language can be decomposed into an intersection of languages of smaller DFAs. Otherwise, A is prime. This notion of primality was introduced by Kupferman and Mosheiff in 2013, and while they proved that we can decide whether a DFA is composite, the precise complexity of this problem is still open, with a doubly-exponential gap between the upper and lower bounds. In this work, we focus on permutation DFAs, i.e., those for which the transition monoid is a group. We provide an NP algorithm to decide whether a permutation DFA is composite, and show that the difficulty of this problem comes from the number of non-accepting states of the instance: we give a fixed-parameter tractable algorithm with the number of rejecting states as the parameter. Moreover, we investigate the class of commutative permutation DFAs. Their structural properties allow us to decide compositionality in NLOGSPACE, and even in LOGSPACE if the alphabet size is fixed. Despite this low complexity, we show that complex behaviors still arise in this class: we provide a family of composite DFAs each requiring polynomially many factors with respect to its size. We also consider the variant of the problem that asks whether a DFA is k-factor composite, that is, decomposable into k smaller DFAs, for some given integer k. We show that, for commutative permutation DFAs, restricting the number of factors makes the decision computationally harder, and yields a problem with tight bounds: it is NP-complete. Finally, we show that in general, this problem is in PSPACE, and it is in LOGSPACE for DFAs with a singleton alphabet. |
2104.10319 | Frederico Araujo | Frederico Araujo and Dhilung Kirat and Xiaokui Shu and Teryl Taylor
and Jiyong Jang | Evidential Cyber Threat Hunting | 5 pages, SDM AI4CS 2021 | In Proceedings of the 2021 SIAM AI/ML for Cybersecurity Workshop
(AI4CS) | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by/4.0/ | A formal cyber reasoning framework for automating the threat hunting process
is described. The new cyber reasoning methodology introduces an operational
semantics that operates over three subspaces -- knowledge, hypothesis, and
action -- to enable human-machine co-creation of threat hypotheses and
protective recommendations. An implementation of this framework shows that the
approach is practical and can be used to generalize evidence-based
multi-criteria threat investigations.
| [
{
"created": "Wed, 21 Apr 2021 02:38:29 GMT",
"version": "v1"
}
] | 2021-04-22 | [
[
"Araujo",
"Frederico",
""
],
[
"Kirat",
"Dhilung",
""
],
[
"Shu",
"Xiaokui",
""
],
[
"Taylor",
"Teryl",
""
],
[
"Jang",
"Jiyong",
""
]
] | A formal cyber reasoning framework for automating the threat hunting process is described. The new cyber reasoning methodology introduces an operational semantics that operates over three subspaces -- knowledge, hypothesis, and action -- to enable human-machine co-creation of threat hypotheses and protective recommendations. An implementation of this framework shows that the approach is practical and can be used to generalize evidence-based multi-criteria threat investigations. |
2009.06724 | Mourad Oulghelou | M. Oulghelou, C. Beghein, C. Allery | Data-Driven Optimization Approach for Inverse Problems : Application to
Turbulent Mixed-Convection Flows | null | null | null | null | cs.CE physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimal control of turbulent mixed-convection flows has attracted
considerable attention from researchers. Numerical algorithms such as Genetic
Algorithms (GAs) are powerful tools that allow to perform global optimization.
These algorithms are particularly of great interest in complex optimization
problems where cost functionals may lack smoothness and regularity. In
turbulent flow optimization, the hybridization of GA with high fidelity
Computational Fluid Dynamics (CFD) is extremely demanding in terms of
computational time and memory storage. Thus, alternative approaches aiming to
alleviate these requirements are of great interest. Nowadays, data driven
approaches gained attention due to their potential in predicting flow solutions
based only on preexisting data. In the present paper, we propose a near-real
time data-driven genetic algorithm (DDGA) for inverse parameter identification
problems involving turbulent flows. In this optimization framework, the
parametrized flow data are used in their reduced form obtained by the POD
(Proper Orthogonal Decomposition) and solutions prediction is made by
interpolating the temporal and the spatial POD subspaces through a recently
developed Riemannian barycentric interpolation. The validation of the proposed
optimization approach is carried out in the parameter identification problem of
the turbulent mixed-convection flow in a cavity. The objective is to determine
the inflow temperature and inflow velocity corresponding to a given temperature
distribution in a restricted area of the spatial domain. The results show that
the proposed genetic programming optimization framework is able to deliver good
approximations of the optimal solutions within less than two minutes.
| [
{
"created": "Thu, 10 Sep 2020 18:08:18 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Sep 2020 07:38:26 GMT",
"version": "v2"
}
] | 2020-09-25 | [
[
"Oulghelou",
"M.",
""
],
[
"Beghein",
"C.",
""
],
[
"Allery",
"C.",
""
]
] | Optimal control of turbulent mixed-convection flows has attracted considerable attention from researchers. Numerical algorithms such as Genetic Algorithms (GAs) are powerful tools that allow to perform global optimization. These algorithms are particularly of great interest in complex optimization problems where cost functionals may lack smoothness and regularity. In turbulent flow optimization, the hybridization of GA with high fidelity Computational Fluid Dynamics (CFD) is extremely demanding in terms of computational time and memory storage. Thus, alternative approaches aiming to alleviate these requirements are of great interest. Nowadays, data driven approaches gained attention due to their potential in predicting flow solutions based only on preexisting data. In the present paper, we propose a near-real time data-driven genetic algorithm (DDGA) for inverse parameter identification problems involving turbulent flows. In this optimization framework, the parametrized flow data are used in their reduced form obtained by the POD (Proper Orthogonal Decomposition) and solutions prediction is made by interpolating the temporal and the spatial POD subspaces through a recently developed Riemannian barycentric interpolation. The validation of the proposed optimization approach is carried out in the parameter identification problem of the turbulent mixed-convection flow in a cavity. The objective is to determine the inflow temperature and inflow velocity corresponding to a given temperature distribution in a restricted area of the spatial domain. The results show that the proposed genetic programming optimization framework is able to deliver good approximations of the optimal solutions within less than two minutes. |
1903.09054 | Dan Wang | Dan Wang, Xu Chen | An Optimal Stable Selective Model Inversion for Nonminimum-phase Systems | We are withdrawing this draft. Some technical issues need resolving | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stably inverting a dynamic system model is the foundation of numerous servo
designs. Existing inversion techniques have provided accurate model
approximations that are often highly effective in feedforward controls.
However, when the inverse is implemented in a feedback system, additional
considerations are needed for assuring causality, closed-loop stability, and
robustness. In pursuit of bridging the gap between the best model matching and
a robust feedback performance under closed-loop constraints, this paper
provides a modern review of frequency-domain model inversion techniques and a
new treatment of unstable zeros. We provide first a pole-zero-map-based
intuitive inverse tuning for motion control systems. Then for general
nonminimum-phase and unstable systems, we propose an optimal inversion
algorithm that can attain model accuracy at the frequency regions of interest
and meanwhile constrain noise amplification elsewhere to guarantee system
robustness. The design goals are achieved by a multi-objective H infinity
formulation and all-pass factorization that consider model matching, causality
of transfer functions, frequency-domain gain constraints, and factorization of
unstable system modes in a unified scheme. The proposed algorithm is validated
on motion control systems and complex high-order systems.
| [
{
"created": "Thu, 21 Mar 2019 15:24:10 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Nov 2019 19:10:16 GMT",
"version": "v2"
}
] | 2019-11-19 | [
[
"Wang",
"Dan",
""
],
[
"Chen",
"Xu",
""
]
] | Stably inverting a dynamic system model is the foundation of numerous servo designs. Existing inversion techniques have provided accurate model approximations that are often highly effective in feedforward controls. However, when the inverse is implemented in a feedback system, additional considerations are needed for assuring causality, closed-loop stability, and robustness. In pursuit of bridging the gap between the best model matching and a robust feedback performance under closed-loop constraints, this paper provides a modern review of frequency-domain model inversion techniques and a new treatment of unstable zeros. We provide first a pole-zero-map-based intuitive inverse tuning for motion control systems. Then for general nonminimum-phase and unstable systems, we propose an optimal inversion algorithm that can attain model accuracy at the frequency regions of interest and meanwhile constrain noise amplification elsewhere to guarantee system robustness. The design goals are achieved by a multi-objective H infinity formulation and all-pass factorization that consider model matching, causality of transfer functions, frequency-domain gain constraints, and factorization of unstable system modes in a unified scheme. The proposed algorithm is validated on motion control systems and complex high-order systems. |
1808.09729 | Wouter Meulemans | Thom Castermans, Mereke van Garderen, Wouter Meulemans, Martin
N\"ollenburg and Xiaoru Yuan | Short Plane Supports for Spatial Hypergraphs | Appears in the Proceedings of the 26th International Symposium on
Graph Drawing and Network Visualization (GD 2018) | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A graph $G=(V,E)$ is a support of a hypergraph $H=(V,S)$ if every hyperedge
induces a connected subgraph in $G$. Supports are used for certain types of
hypergraph visualizations. In this paper we consider visualizing spatial
hypergraphs, where each vertex has a fixed location in the plane. This is the
case, e.g., when modeling set systems of geospatial locations as hypergraphs.
By applying established aesthetic quality criteria we are interested in finding
supports that yield plane straight-line drawings with minimum total edge length
on the input point set $V$. We first show, from a theoretical point of view,
that the problem is NP-hard already under rather mild conditions as well as a
negative approximability results. Therefore, the main focus of the paper lies
on practical heuristic algorithms as well as an exact, ILP-based approach for
computing short plane supports. We report results from computational
experiments that investigate the effect of requiring planarity and acyclicity
on the resulting support length. Further, we evaluate the performance and
trade-offs between solution quality and speed of several heuristics relative to
each other and compared to optimal solutions.
| [
{
"created": "Wed, 29 Aug 2018 11:12:55 GMT",
"version": "v1"
}
] | 2018-08-30 | [
[
"Castermans",
"Thom",
""
],
[
"van Garderen",
"Mereke",
""
],
[
"Meulemans",
"Wouter",
""
],
[
"Nöllenburg",
"Martin",
""
],
[
"Yuan",
"Xiaoru",
""
]
] | A graph $G=(V,E)$ is a support of a hypergraph $H=(V,S)$ if every hyperedge induces a connected subgraph in $G$. Supports are used for certain types of hypergraph visualizations. In this paper we consider visualizing spatial hypergraphs, where each vertex has a fixed location in the plane. This is the case, e.g., when modeling set systems of geospatial locations as hypergraphs. By applying established aesthetic quality criteria we are interested in finding supports that yield plane straight-line drawings with minimum total edge length on the input point set $V$. We first show, from a theoretical point of view, that the problem is NP-hard already under rather mild conditions as well as a negative approximability results. Therefore, the main focus of the paper lies on practical heuristic algorithms as well as an exact, ILP-based approach for computing short plane supports. We report results from computational experiments that investigate the effect of requiring planarity and acyclicity on the resulting support length. Further, we evaluate the performance and trade-offs between solution quality and speed of several heuristics relative to each other and compared to optimal solutions. |
2205.04093 | Abhinav Ramesh Kashyap | Abhinav Ramesh Kashyap, Devamanyu Hazarika, Min-Yen Kan, Roger
Zimmermann, Soujanya Poria | So Different Yet So Alike! Constrained Unsupervised Text Style Transfer | Accepted to ACL 2022 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Automatic transfer of text between domains has become popular in recent
times. One of its aims is to preserve the semantic content of text being
translated from source to target domain. However, it does not explicitly
maintain other attributes between the source and translated text, for e.g.,
text length and descriptiveness. Maintaining constraints in transfer has
several downstream applications, including data augmentation and de-biasing. We
introduce a method for such constrained unsupervised text style transfer by
introducing two complementary losses to the generative adversarial network
(GAN) family of models. Unlike the competing losses used in GANs, we introduce
cooperative losses where the discriminator and the generator cooperate and
reduce the same loss. The first is a contrastive loss and the second is a
classification loss, aiming to regularize the latent space further and bring
similar sentences across domains closer together. We demonstrate that such
training retains lexical, syntactic, and domain-specific constraints between
domains for multiple benchmark datasets, including ones where more than one
attribute change. We show that the complementary cooperative losses improve
text quality, according to both automated and human evaluation measures.
| [
{
"created": "Mon, 9 May 2022 07:46:40 GMT",
"version": "v1"
}
] | 2022-05-10 | [
[
"Kashyap",
"Abhinav Ramesh",
""
],
[
"Hazarika",
"Devamanyu",
""
],
[
"Kan",
"Min-Yen",
""
],
[
"Zimmermann",
"Roger",
""
],
[
"Poria",
"Soujanya",
""
]
] | Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content of text being translated from source to target domain. However, it does not explicitly maintain other attributes between the source and translated text, for e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and de-biasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss, aiming to regularize the latent space further and bring similar sentences across domains closer together. We demonstrate that such training retains lexical, syntactic, and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures. |
2009.08511 | Sudipta Banerjee | Sudipta Banerjee and Arun Ross | Smartphone Camera De-identification while Preserving Biometric Utility | null | Proc. of 10th IEEE International Conference on Biometrics: Theory,
Applications and Systems (BTAS), (Tampa, USA), September 2019 | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | The principle of Photo Response Non Uniformity (PRNU) is often exploited to
deduce the identity of the smartphone device whose camera or sensor was used to
acquire a certain image. In this work, we design an algorithm that perturbs a
face image acquired using a smartphone camera such that (a) sensor-specific
details pertaining to the smartphone camera are suppressed (sensor
anonymization); (b) the sensor pattern of a different device is incorporated
(sensor spoofing); and (c) biometric matching using the perturbed image is not
affected (biometric utility). We employ a simple approach utilizing Discrete
Cosine Transform to achieve the aforementioned objectives. Experiments
conducted on the MICHE-I and OULU-NPU datasets, which contain periocular and
facial data acquired using 12 smartphone cameras, demonstrate the efficacy of
the proposed de-identification algorithm on three different PRNU-based sensor
identification schemes. This work has application in sensor forensics and
personal privacy.
| [
{
"created": "Thu, 17 Sep 2020 19:48:43 GMT",
"version": "v1"
}
] | 2020-09-21 | [
[
"Banerjee",
"Sudipta",
""
],
[
"Ross",
"Arun",
""
]
] | The principle of Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image. In this work, we design an algorithm that perturbs a face image acquired using a smartphone camera such that (a) sensor-specific details pertaining to the smartphone camera are suppressed (sensor anonymization); (b) the sensor pattern of a different device is incorporated (sensor spoofing); and (c) biometric matching using the perturbed image is not affected (biometric utility). We employ a simple approach utilizing Discrete Cosine Transform to achieve the aforementioned objectives. Experiments conducted on the MICHE-I and OULU-NPU datasets, which contain periocular and facial data acquired using 12 smartphone cameras, demonstrate the efficacy of the proposed de-identification algorithm on three different PRNU-based sensor identification schemes. This work has application in sensor forensics and personal privacy. |
2002.02545 | Lichen Wang | Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu | Contradictory Structure Learning for Semi-supervised Domain Adaptation | 8 pages without citations | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current adversarial adaptation methods attempt to align the cross-domain
features, whereas two challenges remain unsolved: 1) the conditional
distribution mismatch and 2) the bias of the decision boundary towards the
source domain. To solve these challenges, we propose a novel framework for
semi-supervised domain adaptation by unifying the learning of opposite
structures (UODA). UODA consists of a generator and two classifiers (i.e., the
source-scattering classifier and the target-clustering classifier), which are
trained for contradictory purposes. The target-clustering classifier attempts
to cluster the target features to improve intra-class density and enlarge
inter-class divergence. Meanwhile, the source-scattering classifier is designed
to scatter the source features to enhance the decision boundary's smoothness.
Through the alternation of source-feature expansion and target-feature
clustering procedures, the target features are well-enclosed within the dilated
boundary of the corresponding source features. This strategy can make the
cross-domain features to be precisely aligned against the source bias
simultaneously. Moreover, to overcome the model collapse through training, we
progressively update the measurement of feature's distance and their
representation via an adversarial training paradigm. Extensive experiments on
the benchmarks of DomainNet and Office-home datasets demonstrate the
superiority of our approach over the state-of-the-art methods.
| [
{
"created": "Thu, 6 Feb 2020 22:58:20 GMT",
"version": "v1"
},
{
"created": "Sun, 14 Feb 2021 19:58:09 GMT",
"version": "v2"
}
] | 2021-02-16 | [
[
"Qin",
"Can",
""
],
[
"Wang",
"Lichen",
""
],
[
"Ma",
"Qianqian",
""
],
[
"Yin",
"Yu",
""
],
[
"Wang",
"Huan",
""
],
[
"Fu",
"Yun",
""
]
] | Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA). UODA consists of a generator and two classifiers (i.e., the source-scattering classifier and the target-clustering classifier), which are trained for contradictory purposes. The target-clustering classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence. Meanwhile, the source-scattering classifier is designed to scatter the source features to enhance the decision boundary's smoothness. Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features. This strategy can make the cross-domain features to be precisely aligned against the source bias simultaneously. Moreover, to overcome the model collapse through training, we progressively update the measurement of feature's distance and their representation via an adversarial training paradigm. Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the superiority of our approach over the state-of-the-art methods. |
2207.00288 | Miguel Suau | Miguel Suau, Jinke He, Mustafa Mert \c{C}elikok, Matthijs T. J. Spaan,
Frans A. Oliehoek | Distributed Influence-Augmented Local Simulators for Parallel MARL in
Large Networked Systems | null | null | null | null | cs.LG cs.MA | http://creativecommons.org/licenses/by/4.0/ | Due to its high sample complexity, simulation is, as of today, critical for
the successful application of reinforcement learning. Many real-world problems,
however, exhibit overly complex dynamics, which makes their full-scale
simulation computationally slow. In this paper, we show how to decompose large
networked systems of many agents into multiple local components such that we
can build separate simulators that run independently and in parallel. To
monitor the influence that the different local components exert on one another,
each of these simulators is equipped with a learned model that is periodically
trained on real trajectories. Our empirical results reveal that distributing
the simulation among different processes not only makes it possible to train
large multi-agent systems in just a few hours but also helps mitigate the
negative effects of simultaneous learning.
| [
{
"created": "Fri, 1 Jul 2022 09:33:33 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Mar 2024 08:36:33 GMT",
"version": "v2"
}
] | 2024-03-04 | [
[
"Suau",
"Miguel",
""
],
[
"He",
"Jinke",
""
],
[
"Çelikok",
"Mustafa Mert",
""
],
[
"Spaan",
"Matthijs T. J.",
""
],
[
"Oliehoek",
"Frans A.",
""
]
] | Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly complex dynamics, which makes their full-scale simulation computationally slow. In this paper, we show how to decompose large networked systems of many agents into multiple local components such that we can build separate simulators that run independently and in parallel. To monitor the influence that the different local components exert on one another, each of these simulators is equipped with a learned model that is periodically trained on real trajectories. Our empirical results reveal that distributing the simulation among different processes not only makes it possible to train large multi-agent systems in just a few hours but also helps mitigate the negative effects of simultaneous learning. |
1901.02636 | Jianan Zhang | Jianan Zhang, Hyang-Won Lee, Eytan Modiano | On the Robustness of Distributed Computing Networks | International Conference on the Design of Reliable Communication
Networks (DRCN) | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic flows in a distributed computing network require both transmission
and processing, and can be interdicted by removing either communication or
computation resources. We study the robustness of a distributed computing
network under the failures of communication links and computation nodes. We
define cut metrics that measure the connectivity, and show a non-zero gap
between the maximum flow and the minimum cut. Moreover, we study a network flow
interdiction problem that minimizes the maximum flow by removing communication
and computation resources within a given budget. We develop mathematical
programs to compute the optimal interdiction, and polynomial-time approximation
algorithms that achieve near-optimal interdiction in simulation.
| [
{
"created": "Wed, 9 Jan 2019 08:38:38 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Jan 2019 10:47:18 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Nov 2021 10:26:06 GMT",
"version": "v3"
}
] | 2021-11-29 | [
[
"Zhang",
"Jianan",
""
],
[
"Lee",
"Hyang-Won",
""
],
[
"Modiano",
"Eytan",
""
]
] | Traffic flows in a distributed computing network require both transmission and processing, and can be interdicted by removing either communication or computation resources. We study the robustness of a distributed computing network under the failures of communication links and computation nodes. We define cut metrics that measure the connectivity, and show a non-zero gap between the maximum flow and the minimum cut. Moreover, we study a network flow interdiction problem that minimizes the maximum flow by removing communication and computation resources within a given budget. We develop mathematical programs to compute the optimal interdiction, and polynomial-time approximation algorithms that achieve near-optimal interdiction in simulation. |
2107.05563 | Adnan Aijaz | Adnan Aijaz | Infrastructure-less Wireless Connectivity for Mobile Robotic Systems in
Logistics: Why Bluetooth Mesh Networking is Important? | To appear in IEEE ETFA 2021 | null | null | null | cs.RO cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mobile robots have disrupted the material handling industry which is
witnessing radical changes. The requirement for enhanced automation across
various industry segments often entails mobile robotic systems operating in
logistics facilities with little/no infrastructure. In such environments,
out-of-box low-cost robotic solutions are desirable. Wireless connectivity
plays a crucial role in successful operation of such mobile robotic systems. A
wireless mesh network of mobile robots is an attractive solution; however, a
number of system-level challenges create unique and stringent service
requirements. The focus of this paper is the role of Bluetooth mesh technology,
which is the latest addition to the Internet-of-Things (IoT) connectivity
landscape, in addressing the challenges of infrastructure-less connectivity for
mobile robotic systems. It articulates the key system-level design challenges
from communication, control, cooperation, coverage, security, and
navigation/localization perspectives, and explores different capabilities of
Bluetooth mesh technology for such challenges. It also provides performance
insights through real-world experimental evaluation of Bluetooth mesh while
investigating its differentiating features against competing solutions.
| [
{
"created": "Mon, 12 Jul 2021 16:34:04 GMT",
"version": "v1"
}
] | 2021-07-13 | [
[
"Aijaz",
"Adnan",
""
]
] | Mobile robots have disrupted the material handling industry which is witnessing radical changes. The requirement for enhanced automation across various industry segments often entails mobile robotic systems operating in logistics facilities with little/no infrastructure. In such environments, out-of-box low-cost robotic solutions are desirable. Wireless connectivity plays a crucial role in successful operation of such mobile robotic systems. A wireless mesh network of mobile robots is an attractive solution; however, a number of system-level challenges create unique and stringent service requirements. The focus of this paper is the role of Bluetooth mesh technology, which is the latest addition to the Internet-of-Things (IoT) connectivity landscape, in addressing the challenges of infrastructure-less connectivity for mobile robotic systems. It articulates the key system-level design challenges from communication, control, cooperation, coverage, security, and navigation/localization perspectives, and explores different capabilities of Bluetooth mesh technology for such challenges. It also provides performance insights through real-world experimental evaluation of Bluetooth mesh while investigating its differentiating features against competing solutions. |
1711.08191 | Alberto Molinari | Laura Bozzelli, Alberto Molinari, Angelo Montanari, Adriano Peron,
Pietro Sala | Interval vs. Point Temporal Logic Model Checking: an Expressiveness
Comparison | null | ACM Trans. Comput. Logic 20 (2018) 4:1-4:31 | 10.1145/3281028 | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last years, model checking with interval temporal logics is emerging
as a viable alternative to model checking with standard point-based temporal
logics, such as LTL, CTL, CTL*, and the like. The behavior of the system is
modeled by means of (finite) Kripke structures, as usual. However, while
temporal logics which are interpreted "point-wise" describe how the system
evolves state-by-state, and predicate properties of system states, those which
are interpreted "interval-wise" express properties of computation stretches,
spanning a sequence of states. A proposition letter is assumed to hold over a
computation stretch (interval) if and only if it holds over each component
state (homogeneity assumption). A natural question arises: is there any
advantage in replacing points by intervals as the primary temporal entities, or
is it just a matter of taste?
In this paper, we study the expressiveness of Halpern and Shoham's interval
temporal logic (HS) in model checking, in comparison with those of LTL, CTL,
and CTL*. To this end, we consider three semantic variants of HS: the
state-based one, introduced by Montanari et al., that allows time to branch
both in the past and in the future, the computation-tree-based one, that allows
time to branch in the future only, and the trace-based variant, that disallows
time to branch. These variants are compared among themselves and to the
aforementioned standard logics, getting a complete picture. In particular, we
show that HS with trace-based semantics is equivalent to LTL (but at least
exponentially more succinct), HS with computation-tree-based semantics is
equivalent to finitary CTL*, and HS with state-based semantics is incomparable
with all of them (LTL, CTL, and CTL*).
| [
{
"created": "Wed, 22 Nov 2017 09:33:35 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Sep 2018 14:50:28 GMT",
"version": "v2"
}
] | 2019-02-07 | [
[
"Bozzelli",
"Laura",
""
],
[
"Molinari",
"Alberto",
""
],
[
"Montanari",
"Angelo",
""
],
[
"Peron",
"Adriano",
""
],
[
"Sala",
"Pietro",
""
]
] | In the last years, model checking with interval temporal logics is emerging as a viable alternative to model checking with standard point-based temporal logics, such as LTL, CTL, CTL*, and the like. The behavior of the system is modeled by means of (finite) Kripke structures, as usual. However, while temporal logics which are interpreted "point-wise" describe how the system evolves state-by-state, and predicate properties of system states, those which are interpreted "interval-wise" express properties of computation stretches, spanning a sequence of states. A proposition letter is assumed to hold over a computation stretch (interval) if and only if it holds over each component state (homogeneity assumption). A natural question arises: is there any advantage in replacing points by intervals as the primary temporal entities, or is it just a matter of taste? In this paper, we study the expressiveness of Halpern and Shoham's interval temporal logic (HS) in model checking, in comparison with those of LTL, CTL, and CTL*. To this end, we consider three semantic variants of HS: the state-based one, introduced by Montanari et al., that allows time to branch both in the past and in the future, the computation-tree-based one, that allows time to branch in the future only, and the trace-based variant, that disallows time to branch. These variants are compared among themselves and to the aforementioned standard logics, getting a complete picture. In particular, we show that HS with trace-based semantics is equivalent to LTL (but at least exponentially more succinct), HS with computation-tree-based semantics is equivalent to finitary CTL*, and HS with state-based semantics is incomparable with all of them (LTL, CTL, and CTL*). |
2306.02317 | Alexandra Antonova | Alexandra Antonova, Evelina Bakhturina, Boris Ginsburg | SpellMapper: A non-autoregressive neural spellchecker for ASR
customization with candidate retrieval based on n-gram mappings | Accepted by INTERSPEECH 2023 | null | null | null | cs.CL cs.AI cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contextual spelling correction models are an alternative to shallow fusion to
improve automatic speech recognition (ASR) quality given user vocabulary. To
deal with large user vocabularies, most of these models include candidate
retrieval mechanisms, usually based on minimum edit distance between fragments
of ASR hypothesis and user phrases. However, the edit-distance approach is
slow, non-trainable, and may have low recall as it relies only on common
letters. We propose: 1) a novel algorithm for candidate retrieval, based on
misspelled n-gram mappings, which gives up to 90% recall with just the top 10
candidates on Spoken Wikipedia; 2) a non-autoregressive neural model based on
BERT architecture, where the initial transcript and ten candidates are combined
into one input. The experiments on Spoken Wikipedia show 21.4% word error rate
improvement compared to a baseline ASR system.
| [
{
"created": "Sun, 4 Jun 2023 10:00:12 GMT",
"version": "v1"
}
] | 2023-06-06 | [
[
"Antonova",
"Alexandra",
""
],
[
"Bakhturina",
"Evelina",
""
],
[
"Ginsburg",
"Boris",
""
]
] | Contextual spelling correction models are an alternative to shallow fusion to improve automatic speech recognition (ASR) quality given user vocabulary. To deal with large user vocabularies, most of these models include candidate retrieval mechanisms, usually based on minimum edit distance between fragments of ASR hypothesis and user phrases. However, the edit-distance approach is slow, non-trainable, and may have low recall as it relies only on common letters. We propose: 1) a novel algorithm for candidate retrieval, based on misspelled n-gram mappings, which gives up to 90% recall with just the top 10 candidates on Spoken Wikipedia; 2) a non-autoregressive neural model based on BERT architecture, where the initial transcript and ten candidates are combined into one input. The experiments on Spoken Wikipedia show 21.4% word error rate improvement compared to a baseline ASR system. |
2301.05499 | Vidit Vidit | Vidit Vidit, Martin Engilberge, Mathieu Salzmann | CLIP the Gap: A Single Domain Generalization Approach for Object
Detection | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Single Domain Generalization (SDG) tackles the problem of training a model on
a single source domain so that it generalizes to any unseen target domain.
While this has been well studied for image classification, the literature on
SDG object detection remains almost non-existent. To address the challenges of
simultaneously learning robust object localization and representation, we
propose to leverage a pre-trained vision-language model to introduce semantic
domain concepts via textual prompts. We achieve this via a semantic
augmentation strategy acting on the features extracted by the detector
backbone, as well as a text-based classification loss. Our experiments evidence
the benefits of our approach, outperforming by 10% the only existing SDG object
detection method, Single-DGOD [49], on their own diverse weather-driving
benchmark.
| [
{
"created": "Fri, 13 Jan 2023 12:01:18 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Mar 2023 13:35:22 GMT",
"version": "v2"
}
] | 2023-03-07 | [
[
"Vidit",
"Vidit",
""
],
[
"Engilberge",
"Martin",
""
],
[
"Salzmann",
"Mathieu",
""
]
] | Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remains almost non-existent. To address the challenges of simultaneously learning robust object localization and representation, we propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss. Our experiments evidence the benefits of our approach, outperforming by 10% the only existing SDG object detection method, Single-DGOD [49], on their own diverse weather-driving benchmark. |
2305.16333 | Zhuangqun Huang | Zhuangqun Huang, Gil Keren, Ziran Jiang, Shashank Jain, David
Goss-Grubbs, Nelson Cheng, Farnaz Abtahi, Duc Le, David Zhang, Antony
D'Avirro, Ethan Campbell-Taylor, Jessie Salas, Irina-Elena Veliche, Xi Chen | Text Generation with Speech Synthesis for ASR Data Augmentation | null | null | null | null | cs.CL cs.AI cs.LG eess.AS | http://creativecommons.org/licenses/by/4.0/ | Aiming at reducing the reliance on expensive human annotations, data
synthesis for Automatic Speech Recognition (ASR) has remained an active area of
research. While prior work mainly focuses on synthetic speech generation for
ASR data augmentation, its combination with text generation methods is
considerably less explored. In this work, we explore text augmentation for ASR
using large-scale pre-trained neural networks, and systematically compare those
to traditional text augmentation methods. The generated synthetic texts are
then converted to synthetic speech using a text-to-speech (TTS) system and
added to the ASR training data. In experiments conducted on three datasets, we
find that neural models achieve 9%-15% relative WER improvement and outperform
traditional methods. We conclude that text augmentation, particularly through
modern neural approaches, is a viable tool for improving the accuracy of ASR
systems.
| [
{
"created": "Mon, 22 May 2023 18:45:20 GMT",
"version": "v1"
}
] | 2023-05-29 | [
[
"Huang",
"Zhuangqun",
""
],
[
"Keren",
"Gil",
""
],
[
"Jiang",
"Ziran",
""
],
[
"Jain",
"Shashank",
""
],
[
"Goss-Grubbs",
"David",
""
],
[
"Cheng",
"Nelson",
""
],
[
"Abtahi",
"Farnaz",
""
],
[
"Le",
"Duc",
""
],
[
"Zhang",
"David",
""
],
[
"D'Avirro",
"Antony",
""
],
[
"Campbell-Taylor",
"Ethan",
""
],
[
"Salas",
"Jessie",
""
],
[
"Veliche",
"Irina-Elena",
""
],
[
"Chen",
"Xi",
""
]
] | Aiming at reducing the reliance on expensive human annotations, data synthesis for Automatic Speech Recognition (ASR) has remained an active area of research. While prior work mainly focuses on synthetic speech generation for ASR data augmentation, its combination with text generation methods is considerably less explored. In this work, we explore text augmentation for ASR using large-scale pre-trained neural networks, and systematically compare those to traditional text augmentation methods. The generated synthetic texts are then converted to synthetic speech using a text-to-speech (TTS) system and added to the ASR training data. In experiments conducted on three datasets, we find that neural models achieve 9%-15% relative WER improvement and outperform traditional methods. We conclude that text augmentation, particularly through modern neural approaches, is a viable tool for improving the accuracy of ASR systems. |
2207.10635 | Connor Wagaman | S\'ilvia Casacuberta, Michael Shoemate, Salil Vadhan, Connor Wagaman | Widespread Underestimation of Sensitivity in Differentially Private
Libraries and How to Fix It | Full version of the paper presented at ACM CCS 2022 and TPDP 2022 | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | We identify a new class of vulnerabilities in implementations of differential
privacy. Specifically, they arise when computing basic statistics such as sums,
thanks to discrepancies between the implemented arithmetic using finite data
types (namely, ints or floats) and idealized arithmetic over the reals or
integers. These discrepancies cause the sensitivity of the implemented
statistics (i.e., how much one individual's data can affect the result) to be
much larger than the sensitivity we expect. Consequently, essentially all
differential privacy libraries fail to introduce enough noise to meet the
requirements of differential privacy, and we show that this may be exploited in
realistic attacks that can extract individual-level information from private
query systems. In addition to presenting these vulnerabilities, we also provide
a number of solutions, which modify or constrain the way in which the sum is
implemented in order to recover the idealized or near-idealized bounds on
sensitivity.
| [
{
"created": "Thu, 21 Jul 2022 17:45:25 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Nov 2022 18:51:08 GMT",
"version": "v2"
}
] | 2022-11-11 | [
[
"Casacuberta",
"Sílvia",
""
],
[
"Shoemate",
"Michael",
""
],
[
"Vadhan",
"Salil",
""
],
[
"Wagaman",
"Connor",
""
]
] | We identify a new class of vulnerabilities in implementations of differential privacy. Specifically, they arise when computing basic statistics such as sums, thanks to discrepancies between the implemented arithmetic using finite data types (namely, ints or floats) and idealized arithmetic over the reals or integers. These discrepancies cause the sensitivity of the implemented statistics (i.e., how much one individual's data can affect the result) to be much larger than the sensitivity we expect. Consequently, essentially all differential privacy libraries fail to introduce enough noise to meet the requirements of differential privacy, and we show that this may be exploited in realistic attacks that can extract individual-level information from private query systems. In addition to presenting these vulnerabilities, we also provide a number of solutions, which modify or constrain the way in which the sum is implemented in order to recover the idealized or near-idealized bounds on sensitivity. |
2001.11819 | Dan Piponi | Dan Piponi, Dave Moore, Joshua V. Dillon | Joint Distributions for TensorFlow Probability | Based on extended abstract submitted to PROBPROG 2020 | null | null | null | cs.PL cs.LG stat.CO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central tenet of probabilistic programming is that a model is specified
exactly once in a canonical representation which is usable by inference
algorithms. We describe JointDistributions, a family of declarative
representations of directed graphical models in TensorFlow Probability.
| [
{
"created": "Wed, 22 Jan 2020 01:00:35 GMT",
"version": "v1"
}
] | 2020-02-03 | [
[
"Piponi",
"Dan",
""
],
[
"Moore",
"Dave",
""
],
[
"Dillon",
"Joshua V.",
""
]
] | A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability. |
2103.13267 | Iretiayo Akinola | Iretiayo Akinola, Zizhao Wang, and Peter Allen | CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based
RL | null | null | null | null | cs.RO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a vision-based reinforcement learning (RL) approach for
closed-loop trajectory generation in an arm reaching problem. Arm trajectory
generation is a fundamental robotics problem which entails finding
collision-free paths to move the robot's body (e.g. arm) in order to satisfy a
goal (e.g. place end-effector at a point).
While classical methods typically require the model of the environment to
solve a planning, search or optimization problem, learning-based approaches
hold the promise of directly mapping from observations to robot actions.
However, learning a collision-avoidance policy using RL remains a challenge
for various reasons, including, but not limited to, partial observability, poor
exploration, low sample efficiency, and learning instabilities.
To address these challenges, we present a residual-RL method that leverages a
greedy goal-reaching RL policy as the base to improve exploration, and the base
policy is augmented with residual state-action values and residual actions
learned from images to avoid obstacles. Further more, we introduce novel
learning objectives and techniques to improve 3D understanding from multiple
image views and sample efficiency of our algorithm.
Compared to RL baselines, our method achieves superior performance in terms
of success rate.
| [
{
"created": "Wed, 24 Mar 2021 15:33:03 GMT",
"version": "v1"
}
] | 2021-03-25 | [
[
"Akinola",
"Iretiayo",
""
],
[
"Wang",
"Zizhao",
""
],
[
"Allen",
"Peter",
""
]
] | We propose a vision-based reinforcement learning (RL) approach for closed-loop trajectory generation in an arm reaching problem. Arm trajectory generation is a fundamental robotics problem which entails finding collision-free paths to move the robot's body (e.g. arm) in order to satisfy a goal (e.g. place end-effector at a point). While classical methods typically require the model of the environment to solve a planning, search or optimization problem, learning-based approaches hold the promise of directly mapping from observations to robot actions. However, learning a collision-avoidance policy using RL remains a challenge for various reasons, including, but not limited to, partial observability, poor exploration, low sample efficiency, and learning instabilities. To address these challenges, we present a residual-RL method that leverages a greedy goal-reaching RL policy as the base to improve exploration, and the base policy is augmented with residual state-action values and residual actions learned from images to avoid obstacles. Further more, we introduce novel learning objectives and techniques to improve 3D understanding from multiple image views and sample efficiency of our algorithm. Compared to RL baselines, our method achieves superior performance in terms of success rate. |
2011.08315 | Omid Ardakanian | Omid Hajihassani, Omid Ardakanian, Hamzeh Khazaei | Anonymizing Sensor Data on the Edge: A Representation Learning and
Transformation Approach | 25 pages, 11 figures; Title updated | ACM Transactions on Internet of Things 3 (2022) 1-26 | 10.1145/3485820 | null | cs.LG cs.AI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The abundance of data collected by sensors in Internet of Things (IoT)
devices, and the success of deep neural networks in uncovering hidden patterns
in time series data have led to mounting privacy concerns. This is because
private and sensitive information can be potentially learned from sensor data
by applications that have access to this data. In this paper, we aim to examine
the tradeoff between utility and privacy loss by learning low-dimensional
representations that are useful for data obfuscation. We propose deterministic
and probabilistic transformations in the latent space of a variational
autoencoder to synthesize time series data such that intrusive inferences are
prevented while desired inferences can still be made with sufficient accuracy.
In the deterministic case, we use a linear transformation to move the
representation of input data in the latent space such that the reconstructed
data is likely to have the same public attribute but a different private
attribute than the original input data. In the probabilistic case, we apply the
linear transformation to the latent representation of input data with some
probability. We compare our technique with autoencoder-based anonymization
techniques and additionally show that it can anonymize data in real time on
resource-constrained edge devices.
| [
{
"created": "Mon, 16 Nov 2020 22:32:30 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Aug 2021 22:21:43 GMT",
"version": "v2"
},
{
"created": "Fri, 27 Aug 2021 21:11:42 GMT",
"version": "v3"
}
] | 2022-06-02 | [
[
"Hajihassani",
"Omid",
""
],
[
"Ardakanian",
"Omid",
""
],
[
"Khazaei",
"Hamzeh",
""
]
] | The abundance of data collected by sensors in Internet of Things (IoT) devices, and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this paper, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices. |
2203.14695 | Daniel Russo | Daniel Russo | Recruiting Software Engineers on Prolific | null | The 1st International Workshop on Recruiting Participants for
Empirical Software Engineering, May 17, 2022, Pittsburgh, PA, USA | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recruiting participants for software engineering research has been a primary
concern of the human factors community. This is particularly true for
quantitative investigations that require a minimum sample size not to be
statistically underpowered. Traditional data collection techniques, such as
mailing lists, are highly doubtful due to self-selection biases. The
introduction of crowdsourcing platforms allows researchers to select informants
with the exact requirements foreseen by the study design, gather data in a
concise time frame, compensate their work with fair hourly pay, and most
importantly, have a high degree of control over the entire data collection
process. This experience report discusses our experience conducting sample
studies using Prolific, an academic crowdsourcing platform. Topics discussed
are the type of studies, selection processes, and power computation.
| [
{
"created": "Mon, 28 Mar 2022 12:49:27 GMT",
"version": "v1"
}
] | 2022-03-29 | [
[
"Russo",
"Daniel",
""
]
] | Recruiting participants for software engineering research has been a primary concern of the human factors community. This is particularly true for quantitative investigations that require a minimum sample size not to be statistically underpowered. Traditional data collection techniques, such as mailing lists, are highly doubtful due to self-selection biases. The introduction of crowdsourcing platforms allows researchers to select informants with the exact requirements foreseen by the study design, gather data in a concise time frame, compensate their work with fair hourly pay, and most importantly, have a high degree of control over the entire data collection process. This experience report discusses our experience conducting sample studies using Prolific, an academic crowdsourcing platform. Topics discussed are the type of studies, selection processes, and power computation. |
2402.15944 | Shuang Li | Gang Li, Qiuwei Li, Shuang Li, and Wu Angela Li | On A Class of Greedy Sparse Recovery Algorithms -- A High Dimensional
Approach | null | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/publicdomain/zero/1.0/ | Sparse signal recovery deals with finding the sparest solution of an
under-determined linear system $x = Qs$. In this paper, we propose a novel
greedy approach to addressing the challenges from such a problem. Such an
approach is based on a characterization of solutions to the system, which
allows us to work on the sparse recovery in the $s$-space directly with a given
measure. With $l_2$-based measure, two OMP-type algorithms are proposed, which
significantly outperform the classical OMP algorithm in terms of recovery
accuracy while maintaining comparable computational complexity. An $l_1$-based
algorithm, denoted as $\text{Alg}_{GBP}$ (greedy basis pursuit) algorithm, is
derived. Such an algorithm significantly outperforms the classical BP
algorithm. A CoSaMP-type algorithm is also proposed to further enhance the
performance of the two proposed OMP-type algorithms. The superior performance
of our proposed algorithms is demonstrated through extensive numerical
simulations using synthetic data as well as video signals, highlighting their
potential for various applications in compressed sensing and signal processing.
| [
{
"created": "Sun, 25 Feb 2024 01:05:39 GMT",
"version": "v1"
}
] | 2024-02-27 | [
[
"Li",
"Gang",
""
],
[
"Li",
"Qiuwei",
""
],
[
"Li",
"Shuang",
""
],
[
"Li",
"Wu Angela",
""
]
] | Sparse signal recovery deals with finding the sparest solution of an under-determined linear system $x = Qs$. In this paper, we propose a novel greedy approach to addressing the challenges from such a problem. Such an approach is based on a characterization of solutions to the system, which allows us to work on the sparse recovery in the $s$-space directly with a given measure. With $l_2$-based measure, two OMP-type algorithms are proposed, which significantly outperform the classical OMP algorithm in terms of recovery accuracy while maintaining comparable computational complexity. An $l_1$-based algorithm, denoted as $\text{Alg}_{GBP}$ (greedy basis pursuit) algorithm, is derived. Such an algorithm significantly outperforms the classical BP algorithm. A CoSaMP-type algorithm is also proposed to further enhance the performance of the two proposed OMP-type algorithms. The superior performance of our proposed algorithms is demonstrated through extensive numerical simulations using synthetic data as well as video signals, highlighting their potential for various applications in compressed sensing and signal processing. |
2405.15182 | Peihua Mai | Peihua Mai, Ran Yan, Yan Pang | RFLPA: A Robust Federated Learning Framework against Poisoning Attacks
with Secure Aggregation | 22 pages | null | null | null | cs.CR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning (FL) allows multiple devices to train a model
collaboratively without sharing their data. Despite its benefits, FL is
vulnerable to privacy leakage and poisoning attacks. To address the privacy
concern, secure aggregation (SecAgg) is often used to obtain the aggregation of
gradients on sever without inspecting individual user updates. Unfortunately,
existing defense strategies against poisoning attacks rely on the analysis of
local updates in plaintext, making them incompatible with SecAgg. To reconcile
the conflicts, we propose a robust federated learning framework against
poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the
cosine similarity between local updates and server updates to conduct robust
aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing
to achieve reduced communication cost of $O(M+N)$ per user, and design a novel
dot-product aggregation algorithm to resolve the issue of increased information
leakage. Our experimental results show that RFLPA significantly reduces
communication and computation overhead by over $75\%$ compared to the
state-of-the-art method, BREA, while maintaining competitive accuracy.
| [
{
"created": "Fri, 24 May 2024 03:31:10 GMT",
"version": "v1"
}
] | 2024-05-27 | [
[
"Mai",
"Peihua",
""
],
[
"Yan",
"Ran",
""
],
[
"Pang",
"Yan",
""
]
] | Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without inspecting individual user updates. Unfortunately, existing defense strategies against poisoning attacks rely on the analysis of local updates in plaintext, making them incompatible with SecAgg. To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the cosine similarity between local updates and server updates to conduct robust aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing to achieve reduced communication cost of $O(M+N)$ per user, and design a novel dot-product aggregation algorithm to resolve the issue of increased information leakage. Our experimental results show that RFLPA significantly reduces communication and computation overhead by over $75\%$ compared to the state-of-the-art method, BREA, while maintaining competitive accuracy. |
1909.06344 | Paul Emmerich | Paul Emmerich, Simon Ellmann, Fabian Bonk, Alex Egger, Esa\'u Garc\'ia
S\'anchez-Torija, Thomas G\"unzel, Sebastian Di Luzio, Alexandru Obada,
Maximilian Stadlmeier, Sebastian Voit, Georg Carle | The Case for Writing Network Drivers in High-Level Programming Languages | null | ACM/IEEE Symposium on Architectures for Networking and
Communications Systems (ANCS 2019), 2019 | null | null | cs.NI cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drivers are written in C or restricted subsets of C++ on all production-grade
server, desktop, and mobile operating systems. They account for 66% of the code
in Linux, but 39 out of 40 security bugs related to memory safety found in
Linux in 2017 are located in drivers. These bugs could have been prevented by
using high-level languages for drivers. We present user space drivers for the
Intel ixgbe 10 Gbit/s network cards implemented in Rust, Go, C#, Java, OCaml,
Haskell, Swift, JavaScript, and Python written from scratch in idiomatic style
for the respective languages. We quantify costs and benefits of using these
languages: High-level languages are safer (fewer bugs, more safety checks), but
run-time safety checks reduce throughput and garbage collection leads to
latency spikes. Out-of-order CPUs mitigate the cost of safety checks: Our Rust
driver executes 63% more instructions per packet but is only 4% slower than a
reference C implementation. Go's garbage collector keeps latencies below 100
$\mu$s even under heavy load. Other languages fare worse, but their unique
properties make for an interesting case study.
All implementations are available as free and open source at
https://github.com/ixy-languages/ixy-languages.
| [
{
"created": "Fri, 13 Sep 2019 17:41:43 GMT",
"version": "v1"
}
] | 2019-09-16 | [
[
"Emmerich",
"Paul",
""
],
[
"Ellmann",
"Simon",
""
],
[
"Bonk",
"Fabian",
""
],
[
"Egger",
"Alex",
""
],
[
"Sánchez-Torija",
"Esaú García",
""
],
[
"Günzel",
"Thomas",
""
],
[
"Di Luzio",
"Sebastian",
""
],
[
"Obada",
"Alexandru",
""
],
[
"Stadlmeier",
"Maximilian",
""
],
[
"Voit",
"Sebastian",
""
],
[
"Carle",
"Georg",
""
]
] | Drivers are written in C or restricted subsets of C++ on all production-grade server, desktop, and mobile operating systems. They account for 66% of the code in Linux, but 39 out of 40 security bugs related to memory safety found in Linux in 2017 are located in drivers. These bugs could have been prevented by using high-level languages for drivers. We present user space drivers for the Intel ixgbe 10 Gbit/s network cards implemented in Rust, Go, C#, Java, OCaml, Haskell, Swift, JavaScript, and Python written from scratch in idiomatic style for the respective languages. We quantify costs and benefits of using these languages: High-level languages are safer (fewer bugs, more safety checks), but run-time safety checks reduce throughput and garbage collection leads to latency spikes. Out-of-order CPUs mitigate the cost of safety checks: Our Rust driver executes 63% more instructions per packet but is only 4% slower than a reference C implementation. Go's garbage collector keeps latencies below 100 $\mu$s even under heavy load. Other languages fare worse, but their unique properties make for an interesting case study. All implementations are available as free and open source at https://github.com/ixy-languages/ixy-languages. |
1604.04772 | Thejaka Kanewala | Thejaka Amila Kanewala, Marcin Zalewski and Andrew Lumsdaine | Abstract Graph Machine | 10 pages, including Appendix and References | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An Abstract Graph Machine(AGM) is an abstract model for distributed memory
parallel stabilizing graph algorithms. A stabilizing algorithm starts from a
particular initial state and goes through series of different state changes
until it converges. The AGM adds work dependency to the stabilizing algorithm.
The work is processed within the processing function. All processes in the
system execute the same processing function. Before feeding work into the
processing function, work is ordered using a strict weak ordering relation. The
strict weak ordering relation divides work into equivalence classes, hence work
within a single equivalence class can be processed in parallel, but work in
different equivalence classes must be executed in the order they appear in
equivalence classes. The paper presents the AGM model, semantics and AGM models
for several existing distributed memory parallel graph algorithms.
| [
{
"created": "Sat, 16 Apr 2016 16:26:29 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Apr 2016 19:13:02 GMT",
"version": "v2"
}
] | 2016-04-29 | [
[
"Kanewala",
"Thejaka Amila",
""
],
[
"Zalewski",
"Marcin",
""
],
[
"Lumsdaine",
"Andrew",
""
]
] | An Abstract Graph Machine(AGM) is an abstract model for distributed memory parallel stabilizing graph algorithms. A stabilizing algorithm starts from a particular initial state and goes through series of different state changes until it converges. The AGM adds work dependency to the stabilizing algorithm. The work is processed within the processing function. All processes in the system execute the same processing function. Before feeding work into the processing function, work is ordered using a strict weak ordering relation. The strict weak ordering relation divides work into equivalence classes, hence work within a single equivalence class can be processed in parallel, but work in different equivalence classes must be executed in the order they appear in equivalence classes. The paper presents the AGM model, semantics and AGM models for several existing distributed memory parallel graph algorithms. |
2408.02623 | Duc Manh Nguyen Dang | Duc Manh Nguyen Dang, Viet Hang Duong, Jia Ching Wang, Nhan Bui Duc | YOWOv3: An Efficient and Generalized Framework for Human Action
Detection and Recognition | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this paper, we propose a new framework called YOWOv3, which is an improved
version of YOWOv2, designed specifically for the task of Human Action Detection
and Recognition. This framework is designed to facilitate extensive
experimentation with different configurations and supports easy customization
of various components within the model, reducing efforts required for
understanding and modifying the code. YOWOv3 demonstrates its superior
performance compared to YOWOv2 on two widely used datasets for Human Action
Detection and Recognition: UCF101-24 and AVAv2.2. Specifically, the predecessor
model YOWOv2 achieves an mAP of 85.2% and 20.3% on UCF101-24 and AVAv2.2,
respectively, with 109.7M parameters and 53.6 GFLOPs. In contrast, our model -
YOWOv3, with only 59.8M parameters and 39.8 GFLOPs, achieves an mAP of 88.33%
and 20.31% on UCF101-24 and AVAv2.2, respectively. The results demonstrate that
YOWOv3 significantly reduces the number of parameters and GFLOPs while still
achieving comparable performance.
| [
{
"created": "Mon, 5 Aug 2024 16:48:03 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Aug 2024 00:17:51 GMT",
"version": "v2"
}
] | 2024-08-12 | [
[
"Dang",
"Duc Manh Nguyen",
""
],
[
"Duong",
"Viet Hang",
""
],
[
"Wang",
"Jia Ching",
""
],
[
"Duc",
"Nhan Bui",
""
]
] | In this paper, we propose a new framework called YOWOv3, which is an improved version of YOWOv2, designed specifically for the task of Human Action Detection and Recognition. This framework is designed to facilitate extensive experimentation with different configurations and supports easy customization of various components within the model, reducing efforts required for understanding and modifying the code. YOWOv3 demonstrates its superior performance compared to YOWOv2 on two widely used datasets for Human Action Detection and Recognition: UCF101-24 and AVAv2.2. Specifically, the predecessor model YOWOv2 achieves an mAP of 85.2% and 20.3% on UCF101-24 and AVAv2.2, respectively, with 109.7M parameters and 53.6 GFLOPs. In contrast, our model - YOWOv3, with only 59.8M parameters and 39.8 GFLOPs, achieves an mAP of 88.33% and 20.31% on UCF101-24 and AVAv2.2, respectively. The results demonstrate that YOWOv3 significantly reduces the number of parameters and GFLOPs while still achieving comparable performance. |
2209.07529 | Haeyong Kang | Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid, Sung Ju
Hwang, Chang D. Yoo | On the Soft-Subnetwork for Few-shot Class Incremental Learning | The Eleventh International Conference on Learning Representations
(ICLR, 2023) | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes
that there exist smooth (non-binary) subnetworks within a dense network that
achieve the competitive performance of the dense network, we propose a few-shot
class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks
(SoftNet)}. Our objective is to learn a sequence of sessions incrementally,
where each session only includes a few training instances per class while
preserving the knowledge of the previously learned ones. SoftNet jointly learns
the model weights and adaptive non-binary soft masks at a base training session
in which each mask consists of the major and minor subnetwork; the former aims
to minimize catastrophic forgetting during training, and the latter aims to
avoid overfitting to a few samples in each new training session. We provide
comprehensive empirical validations demonstrating that our SoftNet effectively
tackles the few-shot incremental learning problem by surpassing the performance
of state-of-the-art baselines over benchmark datasets.
| [
{
"created": "Thu, 15 Sep 2022 04:54:02 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Mar 2023 12:21:06 GMT",
"version": "v2"
}
] | 2023-03-02 | [
[
"Kang",
"Haeyong",
""
],
[
"Yoon",
"Jaehong",
""
],
[
"Madjid",
"Sultan Rizky Hikmawan",
""
],
[
"Hwang",
"Sung Ju",
""
],
[
"Yoo",
"Chang D.",
""
]
] | Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftNet)}. Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets. |
2212.02997 | Evangelos Ververas | Evangelos Ververas, Polydefkis Gkagkos, Jiankang Deng, Michail
Christos Doukas, Jia Guo, Stefanos Zafeiriou | 3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from
Synthetic Views | 17 pages, 13 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Developing gaze estimation models that generalize well to unseen domains and
in-the-wild conditions remains a challenge with no known best solution. This is
mostly due to the difficulty of acquiring ground truth data that cover the
distribution of faces, head poses, and environments that exist in the real
world. Most recent methods attempt to close the gap between specific source and
target domains using domain adaptation. In this work, we propose to train
general gaze estimation models which can be directly employed in novel
environments without adaptation. To do so, we leverage the observation that
head, body, and hand pose estimation benefit from revising them as dense 3D
coordinate prediction, and similarly express gaze estimation as regression of
dense 3D eye meshes. To close the gap between image domains, we create a
large-scale dataset of diverse faces with gaze pseudo-annotations, which we
extract based on the 3D geometry of the scene, and design a multi-view
supervision framework to balance their effect during training. We test our
method in the task of gaze generalization, in which we demonstrate improvement
of up to 30% compared to state-of-the-art when no ground truth data are
available, and up to 10% when they are. The project material are available for
research purposes at https://github.com/Vagver/3DGazeNet.
| [
{
"created": "Tue, 6 Dec 2022 14:15:17 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Mar 2023 15:57:15 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Dec 2023 13:39:34 GMT",
"version": "v3"
}
] | 2023-12-15 | [
[
"Ververas",
"Evangelos",
""
],
[
"Gkagkos",
"Polydefkis",
""
],
[
"Deng",
"Jiankang",
""
],
[
"Doukas",
"Michail Christos",
""
],
[
"Guo",
"Jia",
""
],
[
"Zafeiriou",
"Stefanos",
""
]
] | Developing gaze estimation models that generalize well to unseen domains and in-the-wild conditions remains a challenge with no known best solution. This is mostly due to the difficulty of acquiring ground truth data that cover the distribution of faces, head poses, and environments that exist in the real world. Most recent methods attempt to close the gap between specific source and target domains using domain adaptation. In this work, we propose to train general gaze estimation models which can be directly employed in novel environments without adaptation. To do so, we leverage the observation that head, body, and hand pose estimation benefit from revising them as dense 3D coordinate prediction, and similarly express gaze estimation as regression of dense 3D eye meshes. To close the gap between image domains, we create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene, and design a multi-view supervision framework to balance their effect during training. We test our method in the task of gaze generalization, in which we demonstrate improvement of up to 30% compared to state-of-the-art when no ground truth data are available, and up to 10% when they are. The project material are available for research purposes at https://github.com/Vagver/3DGazeNet. |
2010.08056 | Yudi Dong | Yudi Dong and Yu-Dong Yao | IoT Platform for COVID-19 Prevention and Control: A Survey | 12 pages; Submitted to IEEE Internet of Things Journal | IEEE Access 2021 | 10.1109/ACCESS.2021.3068276 | null | cs.HC cs.AI cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a result of the worldwide transmission of severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has
evolved into an unprecedented pandemic. Currently, with unavailable
pharmaceutical treatments and vaccines, this novel coronavirus results in a
great impact on public health, human society, and global economy, which is
likely to last for many years. One of the lessons learned from the COVID-19
pandemic is that a long-term system with non-pharmaceutical interventions for
preventing and controlling new infectious diseases is desirable to be
implemented. Internet of things (IoT) platform is preferred to be utilized to
achieve this goal, due to its ubiquitous sensing ability and seamless
connectivity. IoT technology is changing our lives through smart healthcare,
smart home, and smart city, which aims to build a more convenient and
intelligent community. This paper presents how the IoT could be incorporated
into the epidemic prevention and control system. Specifically, we demonstrate a
potential fog-cloud combined IoT platform that can be used in the systematic
and intelligent COVID-19 prevention and control, which involves five
interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring,
Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and
SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art
literatures of these five interventions to present the capabilities of IoT in
countering against the current COVID-19 pandemic or future infectious disease
epidemics.
| [
{
"created": "Thu, 15 Oct 2020 22:43:03 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Oct 2020 18:04:14 GMT",
"version": "v2"
}
] | 2021-03-26 | [
[
"Dong",
"Yudi",
""
],
[
"Yao",
"Yu-Dong",
""
]
] | As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics. |
2003.05746 | Camille Bourgaux | Meghyn Bienvenu and Camille Bourgaux | Querying and Repairing Inconsistent Prioritized Knowledge Bases:
Complexity Analysis and Links with Abstract Argumentation | This is an extended version of a paper appearing at the 17th
International Conference on Principles of Knowledge Representation and
Reasoning (KR 2020). This version corrects the statement of Theorem 43
(missing hypothesis). 27 pages | null | null | null | cs.LO cs.AI cs.DB | http://creativecommons.org/licenses/by/4.0/ | In this paper, we explore the issue of inconsistency handling over
prioritized knowledge bases (KBs), which consist of an ontology, a set of
facts, and a priority relation between conflicting facts. In the database
setting, a closely related scenario has been studied and led to the definition
of three different notions of optimal repairs (global, Pareto, and completion)
of a prioritized inconsistent database. After transferring the notions of
globally-, Pareto- and completion-optimal repairs to our setting, we study the
data complexity of the core reasoning tasks: query entailment under
inconsistency-tolerant semantics based upon optimal repairs, existence of a
unique optimal repair, and enumeration of all optimal repairs. Our results
provide a nearly complete picture of the data complexity of these tasks for
ontologies formulated in common DL-Lite dialects. The second contribution of
our work is to clarify the relationship between optimal repairs and different
notions of extensions for (set-based) argumentation frameworks. Among our
results, we show that Pareto-optimal repairs correspond precisely to stable
extensions (and often also to preferred extensions), and we propose a novel
semantics for prioritized KBs which is inspired by grounded extensions and
enjoys favourable computational properties. Our study also yields some results
of independent interest concerning preference-based argumentation frameworks.
| [
{
"created": "Thu, 12 Mar 2020 12:38:37 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Jun 2020 16:15:30 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Jun 2024 06:42:55 GMT",
"version": "v3"
}
] | 2024-06-10 | [
[
"Bienvenu",
"Meghyn",
""
],
[
"Bourgaux",
"Camille",
""
]
] | In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of facts, and a priority relation between conflicting facts. In the database setting, a closely related scenario has been studied and led to the definition of three different notions of optimal repairs (global, Pareto, and completion) of a prioritized inconsistent database. After transferring the notions of globally-, Pareto- and completion-optimal repairs to our setting, we study the data complexity of the core reasoning tasks: query entailment under inconsistency-tolerant semantics based upon optimal repairs, existence of a unique optimal repair, and enumeration of all optimal repairs. Our results provide a nearly complete picture of the data complexity of these tasks for ontologies formulated in common DL-Lite dialects. The second contribution of our work is to clarify the relationship between optimal repairs and different notions of extensions for (set-based) argumentation frameworks. Among our results, we show that Pareto-optimal repairs correspond precisely to stable extensions (and often also to preferred extensions), and we propose a novel semantics for prioritized KBs which is inspired by grounded extensions and enjoys favourable computational properties. Our study also yields some results of independent interest concerning preference-based argumentation frameworks. |
2403.00157 | Ziqin Chen | Ziqin Chen and Yongqiang Wang | Privacy-Preserving Distributed Optimization and Learning | Accepted as a chapter in the Encyclopedia of Systems and Control
Engineering published by Elsevier | null | null | null | cs.LG cs.CR cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed optimization and learning has recently garnered great attention
due to its wide applications in sensor networks, smart grids, machine learning,
and so forth. Despite rapid development, existing distributed optimization and
learning algorithms require each agent to exchange messages with its neighbors,
which may expose sensitive information and raise significant privacy concerns.
In this survey paper, we overview privacy-preserving distributed optimization
and learning methods. We first discuss cryptography, differential privacy, and
other techniques that can be used for privacy preservation and indicate their
pros and cons for privacy protection in distributed optimization and learning.
We believe that among these approaches, differential privacy is most promising
due to its low computational and communication complexities, which are
extremely appealing for modern learning based applications with high dimensions
of optimization variables. We then introduce several differential-privacy
algorithms that can simultaneously ensure privacy and optimization accuracy.
Moreover, we provide example applications in several machine learning problems
to confirm the real-world effectiveness of these algorithms. Finally, we
highlight some challenges in this research domain and discuss future
directions.
| [
{
"created": "Thu, 29 Feb 2024 22:18:05 GMT",
"version": "v1"
}
] | 2024-03-04 | [
[
"Chen",
"Ziqin",
""
],
[
"Wang",
"Yongqiang",
""
]
] | Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and raise significant privacy concerns. In this survey paper, we overview privacy-preserving distributed optimization and learning methods. We first discuss cryptography, differential privacy, and other techniques that can be used for privacy preservation and indicate their pros and cons for privacy protection in distributed optimization and learning. We believe that among these approaches, differential privacy is most promising due to its low computational and communication complexities, which are extremely appealing for modern learning based applications with high dimensions of optimization variables. We then introduce several differential-privacy algorithms that can simultaneously ensure privacy and optimization accuracy. Moreover, we provide example applications in several machine learning problems to confirm the real-world effectiveness of these algorithms. Finally, we highlight some challenges in this research domain and discuss future directions. |
2305.16793 | Xikun Jiang | Xikun Jiang, Chenhao Ying, Lei Li, Boris D\"udder, Haiqin Wu, Haiming
Jin and Yuan Luo | Incentive Mechanism for Uncertain Tasks under Differential Privacy | null | null | null | null | cs.GT cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing
paradigm due to its cost-effectiveness. This approach relies on platforms to
outsource tasks to participating workers when prompted by task publishers.
Although incentive mechanisms have been devised to foster widespread
participation in MCS, most of them focus only on static tasks (i.e., tasks for
which the timing and type are known in advance) and do not protect the privacy
of worker bids. In a dynamic and resource-constrained environment, tasks are
often uncertain (i.e., the platform lacks a priori knowledge about the tasks)
and worker bids may be vulnerable to inference attacks. This paper presents
HERALD*, an incentive mechanism that addresses these issues through the use of
uncertainty and hidden bids. Theoretical analysis reveals that HERALD*
satisfies a range of critical criteria, including truthfulness, individual
rationality, differential privacy, low computational complexity, and low social
cost. These properties are then corroborated through a series of evaluations.
| [
{
"created": "Fri, 26 May 2023 10:15:02 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Mar 2024 15:16:51 GMT",
"version": "v2"
}
] | 2024-03-07 | [
[
"Jiang",
"Xikun",
""
],
[
"Ying",
"Chenhao",
""
],
[
"Li",
"Lei",
""
],
[
"Düdder",
"Boris",
""
],
[
"Wu",
"Haiqin",
""
],
[
"Jin",
"Haiming",
""
],
[
"Luo",
"Yuan",
""
]
] | Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations. |
2007.00211 | Marc Law | Marc T. Law and Jos Stam | Ultrahyperbolic Representation Learning | NeurIPS 2020 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In machine learning, data is usually represented in a (flat) Euclidean space
where distances between points are along straight lines. Researchers have
recently considered more exotic (non-Euclidean) Riemannian manifolds such as
hyperbolic space which is well suited for tree-like data. In this paper, we
propose a representation living on a pseudo-Riemannian manifold of constant
nonzero curvature. It is a generalization of hyperbolic and spherical
geometries where the nondegenerate metric tensor need not be positive definite.
We provide the necessary learning tools in this geometry and extend
gradient-based optimization techniques. More specifically, we provide
closed-form expressions for distances via geodesics and define a descent
direction to minimize some objective function. Our novel framework is applied
to graph representations.
| [
{
"created": "Wed, 1 Jul 2020 03:49:24 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Oct 2020 15:15:44 GMT",
"version": "v2"
},
{
"created": "Mon, 26 Oct 2020 15:56:04 GMT",
"version": "v3"
},
{
"created": "Wed, 28 Oct 2020 14:10:44 GMT",
"version": "v4"
},
{
"created": "Mon, 11 Jan 2021 02:49:43 GMT",
"version": "v5"
}
] | 2021-01-12 | [
[
"Law",
"Marc T.",
""
],
[
"Stam",
"Jos",
""
]
] | In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exotic (non-Euclidean) Riemannian manifolds such as hyperbolic space which is well suited for tree-like data. In this paper, we propose a representation living on a pseudo-Riemannian manifold of constant nonzero curvature. It is a generalization of hyperbolic and spherical geometries where the nondegenerate metric tensor need not be positive definite. We provide the necessary learning tools in this geometry and extend gradient-based optimization techniques. More specifically, we provide closed-form expressions for distances via geodesics and define a descent direction to minimize some objective function. Our novel framework is applied to graph representations. |
1404.5828 | Dimitra Panagou | Dimitra Panagou | Motion planning and Collision Avoidance using Non-Gradient Vector
Fields. Technical Report | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel feedback method on the motion planning for
unicycle robots in environments with static obstacles, along with an extension
to the distributed planning and coordination in multi-robot systems. The method
employs a family of 2-dimensional analytic vector fields, whose integral curves
exhibit various patterns depending on the value of a parameter lambda. More
specifically, for an a priori known value of lambda, the vector field has a
unique singular point of dipole type and can be used to steer the unicycle to a
goal configuration. Furthermore, for the unique value of lambda that the vector
field has a continuum of singular points, the integral curves are used to
define flows around obstacles. An almost global feedback motion plan can then
be constructed by suitably blending attractive and repulsive vector fields in a
static obstacle environment. The method does not suffer from the appearance of
sinks (stable nodes) away from goal point. Compared to other similar methods
which are free of local minima, the proposed approach does not require any
parameter tuning to render the desired convergence properties. The paper also
addresses the extension of the method to the distributed coordination and
control of multiple robots, where each robot needs to navigate to a goal
configuration while avoiding collisions with the remaining robots, and while
using local information only. More specifically, based on the results which
apply to the single-robot case, a motion coordination protocol is presented
which guarantees the safety of the multi-robot system and the almost global
convergence of the robots to their goal configurations. The efficacy of the
proposed methodology is demonstrated via simulation results in static and
dynamic environments.
| [
{
"created": "Wed, 23 Apr 2014 14:12:36 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Apr 2014 15:55:14 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Oct 2014 20:55:13 GMT",
"version": "v3"
}
] | 2014-10-22 | [
[
"Panagou",
"Dimitra",
""
]
] | This paper presents a novel feedback method on the motion planning for unicycle robots in environments with static obstacles, along with an extension to the distributed planning and coordination in multi-robot systems. The method employs a family of 2-dimensional analytic vector fields, whose integral curves exhibit various patterns depending on the value of a parameter lambda. More specifically, for an a priori known value of lambda, the vector field has a unique singular point of dipole type and can be used to steer the unicycle to a goal configuration. Furthermore, for the unique value of lambda that the vector field has a continuum of singular points, the integral curves are used to define flows around obstacles. An almost global feedback motion plan can then be constructed by suitably blending attractive and repulsive vector fields in a static obstacle environment. The method does not suffer from the appearance of sinks (stable nodes) away from goal point. Compared to other similar methods which are free of local minima, the proposed approach does not require any parameter tuning to render the desired convergence properties. The paper also addresses the extension of the method to the distributed coordination and control of multiple robots, where each robot needs to navigate to a goal configuration while avoiding collisions with the remaining robots, and while using local information only. More specifically, based on the results which apply to the single-robot case, a motion coordination protocol is presented which guarantees the safety of the multi-robot system and the almost global convergence of the robots to their goal configurations. The efficacy of the proposed methodology is demonstrated via simulation results in static and dynamic environments. |
2210.05714 | Chenguang Huang | Chenguang Huang, Oier Mees, Andy Zeng, Wolfram Burgard | Visual Language Maps for Robot Navigation | Accepted at the 2023 IEEE International Conference on Robotics and
Automation (ICRA). Project page: https://vlmaps.github.io | null | null | null | cs.RO cs.AI cs.CL cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Grounding language to the visual observations of a navigating agent can be
performed using off-the-shelf visual-language models pretrained on
Internet-scale data (e.g., image captions). While this is useful for matching
images to natural language descriptions of object goals, it remains disjoint
from the process of mapping the environment, so that it lacks the spatial
precision of classic geometric maps. To address this problem, we propose
VLMaps, a spatial map representation that directly fuses pretrained
visual-language features with a 3D reconstruction of the physical world. VLMaps
can be autonomously built from video feed on robots using standard exploration
approaches and enables natural language indexing of the map without additional
labeled data. Specifically, when combined with large language models (LLMs),
VLMaps can be used to (i) translate natural language commands into a sequence
of open-vocabulary navigation goals (which, beyond prior work, can be spatial
by construction, e.g., "in between the sofa and TV" or "three meters to the
right of the chair") directly localized in the map, and (ii) can be shared
among multiple robots with different embodiments to generate new obstacle maps
on-the-fly (by using a list of obstacle categories). Extensive experiments
carried out in simulated and real world environments show that VLMaps enable
navigation according to more complex language instructions than existing
methods. Videos are available at https://vlmaps.github.io.
| [
{
"created": "Tue, 11 Oct 2022 18:13:20 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Oct 2022 09:37:38 GMT",
"version": "v2"
},
{
"created": "Mon, 17 Oct 2022 14:46:08 GMT",
"version": "v3"
},
{
"created": "Wed, 8 Mar 2023 10:30:41 GMT",
"version": "v4"
}
] | 2023-03-09 | [
[
"Huang",
"Chenguang",
""
],
[
"Mees",
"Oier",
""
],
[
"Zeng",
"Andy",
""
],
[
"Burgard",
"Wolfram",
""
]
] | Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image captions). While this is useful for matching images to natural language descriptions of object goals, it remains disjoint from the process of mapping the environment, so that it lacks the spatial precision of classic geometric maps. To address this problem, we propose VLMaps, a spatial map representation that directly fuses pretrained visual-language features with a 3D reconstruction of the physical world. VLMaps can be autonomously built from video feed on robots using standard exploration approaches and enables natural language indexing of the map without additional labeled data. Specifically, when combined with large language models (LLMs), VLMaps can be used to (i) translate natural language commands into a sequence of open-vocabulary navigation goals (which, beyond prior work, can be spatial by construction, e.g., "in between the sofa and TV" or "three meters to the right of the chair") directly localized in the map, and (ii) can be shared among multiple robots with different embodiments to generate new obstacle maps on-the-fly (by using a list of obstacle categories). Extensive experiments carried out in simulated and real world environments show that VLMaps enable navigation according to more complex language instructions than existing methods. Videos are available at https://vlmaps.github.io. |
2405.13238 | Peng Liu | Peng Liu, Nian Wang, Cong Xu, Ming Zhao, Bin Wang, Yi Ren | Enhancing User Interest based on Stream Clustering and Memory Networks
in Large-Scale Recommender Systems | null | null | null | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommender Systems (RSs) provide personalized recommendation service based
on user interest, which are widely used in various platforms. However, there
are lots of users with sparse interest due to lacking consumption behaviors,
which leads to poor recommendation results for them. This problem is widespread
in large-scale RSs and is particularly difficult to address. To solve this
problem, we propose a novel solution named User Interest Enhancement (UIE)
which enhances user interest including user profile and user history behavior
sequences using the enhancement vectors and personalized enhancement vector
generated based on stream clustering and memory networks from different
perspectives. UIE not only remarkably improves model performance on the users
with sparse interest but also significantly enhance model performance on other
users. UIE is an end-to-end solution which is easy to be implemented based on
ranking model. Moreover, we expand our solution and apply similar methods to
long-tail items, which also achieves excellent improvement. Furthermore, we
conduct extensive offline and online experiments in a large-scale industrial
RS. The results demonstrate that our model outperforms other models remarkably,
especially for the users with sparse interest. Until now, UIE has been fully
deployed in multiple large-scale RSs and achieved remarkable improvements.
| [
{
"created": "Tue, 21 May 2024 22:53:00 GMT",
"version": "v1"
},
{
"created": "Sun, 26 May 2024 23:18:53 GMT",
"version": "v2"
}
] | 2024-05-28 | [
[
"Liu",
"Peng",
""
],
[
"Wang",
"Nian",
""
],
[
"Xu",
"Cong",
""
],
[
"Zhao",
"Ming",
""
],
[
"Wang",
"Bin",
""
],
[
"Ren",
"Yi",
""
]
] | Recommender Systems (RSs) provide personalized recommendation service based on user interest, which are widely used in various platforms. However, there are lots of users with sparse interest due to lacking consumption behaviors, which leads to poor recommendation results for them. This problem is widespread in large-scale RSs and is particularly difficult to address. To solve this problem, we propose a novel solution named User Interest Enhancement (UIE) which enhances user interest including user profile and user history behavior sequences using the enhancement vectors and personalized enhancement vector generated based on stream clustering and memory networks from different perspectives. UIE not only remarkably improves model performance on the users with sparse interest but also significantly enhance model performance on other users. UIE is an end-to-end solution which is easy to be implemented based on ranking model. Moreover, we expand our solution and apply similar methods to long-tail items, which also achieves excellent improvement. Furthermore, we conduct extensive offline and online experiments in a large-scale industrial RS. The results demonstrate that our model outperforms other models remarkably, especially for the users with sparse interest. Until now, UIE has been fully deployed in multiple large-scale RSs and achieved remarkable improvements. |
1804.04076 | Faraz Saeedan | Faraz Saeedan, Nicolas Weber, Michael Goesele, Stefan Roth | Detail-Preserving Pooling in Deep Networks | To appear at CVPR 2018 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most convolutional neural networks use some method for gradually downscaling
the size of the hidden layers. This is commonly referred to as pooling, and is
applied to reduce the number of parameters, improve invariance to certain
distortions, and increase the receptive field size. Since pooling by nature is
a lossy process, it is crucial that each such layer maintains the portion of
the activations that is most important for the network's discriminability. Yet,
simple maximization or averaging over blocks, max or average pooling, or plain
downsampling in the form of strided convolutions are the standard. In this
paper, we aim to leverage recent results on image downscaling for the purposes
of deep learning. Inspired by the human visual system, which focuses on local
spatial changes, we propose detail-preserving pooling (DPP), an adaptive
pooling method that magnifies spatial changes and preserves important
structural detail. Importantly, its parameters can be learned jointly with the
rest of the network. We analyze some of its theoretical properties and show its
empirical benefits on several datasets and networks, where DPP consistently
outperforms previous pooling approaches.
| [
{
"created": "Wed, 11 Apr 2018 16:28:11 GMT",
"version": "v1"
}
] | 2018-04-13 | [
[
"Saeedan",
"Faraz",
""
],
[
"Weber",
"Nicolas",
""
],
[
"Goesele",
"Michael",
""
],
[
"Roth",
"Stefan",
""
]
] | Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size. Since pooling by nature is a lossy process, it is crucial that each such layer maintains the portion of the activations that is most important for the network's discriminability. Yet, simple maximization or averaging over blocks, max or average pooling, or plain downsampling in the form of strided convolutions are the standard. In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches. |
2310.19124 | Theodoros Plessas | Evangelia Panourgia (Athens University of Economics and Business),
Theodoros Plessas (Athens University of Economics and Business), Ilias
Balampanis (Athens University of Economics and Business), Diomidis Spinellis
(Athens University of Economics and Business, Delft University of Technology) | Good Tools are Half the Work: Tool Usage in Deep Learning Projects | null | null | null | null | cs.SE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rising popularity of deep learning (DL) methods and techniques has
invigorated interest in the topic of SE4DL (Software Engineering for Deep
Learning), the application of software engineering (SE) practices on deep
learning software. Despite the novel engineering challenges brought on by the
data-driven and non-deterministic paradigm of DL software, little work has been
invested into developing DL-targeted SE tools. On the other hand, tools
tackling non-SE issues specific to DL are actively used and referred to under
the umbrella term "MLOps (Machine Learning Operations) tools". Nevertheless,
the available literature supports the utility of conventional SE tooling in DL
software development. Building upon previous mining software repositories (MSR)
research on tool usage in open-source software works, we identify conventional
and MLOps tools adopted in popular applied DL projects that use Python as the
main programming language. About 63\% of the GitHub repositories we examined
contained at least one conventional SE tool. Software construction tools are
the most widely adopted, while the opposite applies to management and
maintenance tools. Relatively few MLOps tools were found to be use, with only
20 tools out of a sample of 74 used in at least one repository. The majority of
them were open-source rather than proprietary. One of these tools, TensorBoard,
was found to be adopted in about half of the repositories in our study.
Consequently, the widespread use of conventional SE tooling demonstrates its
relevance to DL software. Further research is recommended on the adoption of
MLOps tooling, focusing on the relevance of particular tool types, the
development of required tools, as well as ways to promote the use of already
available tools.
| [
{
"created": "Sun, 29 Oct 2023 19:21:33 GMT",
"version": "v1"
},
{
"created": "Tue, 28 May 2024 16:13:22 GMT",
"version": "v2"
}
] | 2024-05-29 | [
[
"Panourgia",
"Evangelia",
"",
"Athens University of Economics and Business"
],
[
"Plessas",
"Theodoros",
"",
"Athens University of Economics and Business"
],
[
"Balampanis",
"Ilias",
"",
"Athens University of Economics and Business"
],
[
"Spinellis",
"Diomidis",
"",
"Athens University of Economics and Business, Delft University of Technology"
]
] | The rising popularity of deep learning (DL) methods and techniques has invigorated interest in the topic of SE4DL (Software Engineering for Deep Learning), the application of software engineering (SE) practices on deep learning software. Despite the novel engineering challenges brought on by the data-driven and non-deterministic paradigm of DL software, little work has been invested into developing DL-targeted SE tools. On the other hand, tools tackling non-SE issues specific to DL are actively used and referred to under the umbrella term "MLOps (Machine Learning Operations) tools". Nevertheless, the available literature supports the utility of conventional SE tooling in DL software development. Building upon previous mining software repositories (MSR) research on tool usage in open-source software works, we identify conventional and MLOps tools adopted in popular applied DL projects that use Python as the main programming language. About 63\% of the GitHub repositories we examined contained at least one conventional SE tool. Software construction tools are the most widely adopted, while the opposite applies to management and maintenance tools. Relatively few MLOps tools were found to be use, with only 20 tools out of a sample of 74 used in at least one repository. The majority of them were open-source rather than proprietary. One of these tools, TensorBoard, was found to be adopted in about half of the repositories in our study. Consequently, the widespread use of conventional SE tooling demonstrates its relevance to DL software. Further research is recommended on the adoption of MLOps tooling, focusing on the relevance of particular tool types, the development of required tools, as well as ways to promote the use of already available tools. |
2408.08258 | Hossein Jafarinia | Hossein Jafarinia, Alireza Alipanah, Danial Hamdi, Saeed Razavi, Nahal
Mirzaie, Mohammad Hossein Rohban | Snuffy: Efficient Whole Slide Image Classifier | Accepted for ECCV 2024 | null | null | null | cs.CV cs.AI cs.LG cs.NE eess.IV | http://creativecommons.org/licenses/by/4.0/ | Whole Slide Image (WSI) classification with multiple instance learning (MIL)
in digital pathology faces significant computational challenges. Current
methods mostly rely on extensive self-supervised learning (SSL) for
satisfactory performance, requiring long training periods and considerable
computational resources. At the same time, no pre-training affects performance
due to domain shifts from natural images to WSIs. We introduce
\textbf{\textit{Snuffy}} architecture, a novel MIL-pooling method based on
sparse transformers that mitigates performance loss with limited pre-training
and enables continual few-shot pre-training as a competitive option. Our
sparsity pattern is tailored for pathology and is theoretically proven to be a
universal approximator with the tightest probabilistic sharp bound on the
number of layers for sparse transformers, to date. We demonstrate Snuffy's
effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior
WSI and patch-level accuracies. The code is available on
\url{https://github.com/jafarinia/snuffy}.
| [
{
"created": "Thu, 15 Aug 2024 16:59:15 GMT",
"version": "v1"
}
] | 2024-08-16 | [
[
"Jafarinia",
"Hossein",
""
],
[
"Alipanah",
"Alireza",
""
],
[
"Hamdi",
"Danial",
""
],
[
"Razavi",
"Saeed",
""
],
[
"Mirzaie",
"Nahal",
""
],
[
"Rohban",
"Mohammad Hossein",
""
]
] | Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce \textbf{\textit{Snuffy}} architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on \url{https://github.com/jafarinia/snuffy}. |
2209.00650 | Fran\c{c}ois Renaville | Fran\c{c}ois Renaville, Fabienne Prosmans, Isabelle Gilles | Analyse fonctionnelle de l'outil de gestion de planning LibStaffer | 17 pages, in French | Cahiers de la Documentation (2022)1/2, 6-17 | null | null | cs.HC | http://creativecommons.org/licenses/by-sa/4.0/ | LibStaffer is a staff scheduling tool for libraries provided by Springshare.
In this article, we present the analysis that we implemented to determine the
potential of LibStaffer in order to simplify staff scheduling in library
branches with various configurations, as well as in transversal units with a
focus on public services. As a starting point, we focused on the questions
available in Philippe Lenepveu and Marc Maisonneuve's work on scheduling tools
for libraries. We then enriched the initial list with questions that emerged
among several colleagues. After a two-month LibStaffer trial period, we were
able to answer those questions and were convinced to take out a subscription to
the tool.
--
LibStaffer est un outil de gestion de planning de service pour les
biblioth\`eques propos\'e par Springshare. Dans cet article, nous exposons
l'analyse que nous avons mise en {\oe}uvre pour d\'eterminer le potentiel de
LibStaffer en mati\`ere de simplification de la gestion de planning d'accueil,
sur des implantations de configurations diverses, ainsi que d'activit\'es
transversales de service au public. Nous nous sommes fond\'es sur les questions
\'etablies par Philippe Lenepveu et Marc Maisonneuve, dans leur ouvrage
consacr\'e aux logiciels de gestion de planning pour les biblioth\`eques, et
les avons enrichies par les interrogations de plusieurs coll\`egues.
B\'en\'eficier d'un test de LibStaffer, de pr\`es de deux mois, nous a permis
de r\'epondre \`a ces questions et nous a convaincus de prendre une
souscription \`a l'outil.
| [
{
"created": "Tue, 30 Aug 2022 06:39:24 GMT",
"version": "v1"
}
] | 2022-09-05 | [
[
"Renaville",
"François",
""
],
[
"Prosmans",
"Fabienne",
""
],
[
"Gilles",
"Isabelle",
""
]
] | LibStaffer is a staff scheduling tool for libraries provided by Springshare. In this article, we present the analysis that we implemented to determine the potential of LibStaffer in order to simplify staff scheduling in library branches with various configurations, as well as in transversal units with a focus on public services. As a starting point, we focused on the questions available in Philippe Lenepveu and Marc Maisonneuve's work on scheduling tools for libraries. We then enriched the initial list with questions that emerged among several colleagues. After a two-month LibStaffer trial period, we were able to answer those questions and were convinced to take out a subscription to the tool. -- LibStaffer est un outil de gestion de planning de service pour les biblioth\`eques propos\'e par Springshare. Dans cet article, nous exposons l'analyse que nous avons mise en {\oe}uvre pour d\'eterminer le potentiel de LibStaffer en mati\`ere de simplification de la gestion de planning d'accueil, sur des implantations de configurations diverses, ainsi que d'activit\'es transversales de service au public. Nous nous sommes fond\'es sur les questions \'etablies par Philippe Lenepveu et Marc Maisonneuve, dans leur ouvrage consacr\'e aux logiciels de gestion de planning pour les biblioth\`eques, et les avons enrichies par les interrogations de plusieurs coll\`egues. B\'en\'eficier d'un test de LibStaffer, de pr\`es de deux mois, nous a permis de r\'epondre \`a ces questions et nous a convaincus de prendre une souscription \`a l'outil. |
1901.06637 | Bin Li | Bin Li, Zesong Fei, and Yan Zhang | UAV Communications for 5G and Beyond: Recent Advances and Future Trends | 53 pages, 9 figures | null | 10.1109/JIOT.2018.2887086 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Providing ubiquitous connectivity to diverse device types is the key
challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are
expected to be an important component of the upcoming wireless networks that
can potentially facilitate wireless broadcast and support high rate
transmissions. Compared to the communications with fixed infrastructure, UAV
has salient attributes, such as flexible deployment, strong line-of-sight (LoS)
connection links, and additional design degrees of freedom with the controlled
mobility. In this paper, a comprehensive survey on UAV communication towards
5G/B5G wireless networks is presented. We first briefly introduce essential
background and the space-air-ground integrated networks, as well as discuss
related research challenges faced by the emerging integrated network
architecture. We then provide an exhaustive review of various 5G techniques
based on UAV platforms, which we categorize by different domains including
physical layer, network layer, and joint communication, computing and caching.
In addition, a great number of open research problems are outlined and
identified as possible future research directions.
| [
{
"created": "Sun, 20 Jan 2019 07:53:08 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Li",
"Bin",
""
],
[
"Fei",
"Zesong",
""
],
[
"Zhang",
"Yan",
""
]
] | Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions. |
1912.06986 | Ziyi Wang | Ziyi Wang, Zhaohao Wang, Yansong Xu, Bi Wu, and Weisheng Zhao | Erase-hidden and Drivability-improved Magnetic Non-Volatile Flip-Flops
with NAND-SPIN Devices | This article has been accepted in a future issue of IEEE Transactions
on Nanotechnology: Regular Papers | null | 10.1109/TNANO.2020.2999751 | null | cs.ET eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-volatile flip-flops (NVFFs) using power gating techniques promise to
overcome the soaring leakage power consumption issue with the scaling of CMOS
technology. Magnetic tunnel junction (MTJ) is a good candidate for constructing
the NVFF thanks to its low power, high speed, good CMOS compatibility, etc. In
this paper, we propose a novel magnetic NVFF based on an emerging memory device
called NAND-SPIN. The data writing of NAND-SPIN is achieved by successively
applying two unidirectional currents, which respectively generate the spin
orbit torque (SOT) and spin transfer torque (STT) for erase and programming
operations. This characteristic allows us to design an erase-hidden and
drivability-improved magnetic NVFF. Furthermore, more design flexibility could
be obtained since the backup operation of the proposed NVFF is not limited by
the inherent slave latch. Simulation results show that our proposed NVFF
achieves performance improvement in terms of power, delay and area, compared
with conventional slave-latch-driven SOT-NVFF designs.
| [
{
"created": "Sun, 15 Dec 2019 05:59:33 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jun 2020 03:17:51 GMT",
"version": "v2"
}
] | 2020-07-15 | [
[
"Wang",
"Ziyi",
""
],
[
"Wang",
"Zhaohao",
""
],
[
"Xu",
"Yansong",
""
],
[
"Wu",
"Bi",
""
],
[
"Zhao",
"Weisheng",
""
]
] | Non-volatile flip-flops (NVFFs) using power gating techniques promise to overcome the soaring leakage power consumption issue with the scaling of CMOS technology. Magnetic tunnel junction (MTJ) is a good candidate for constructing the NVFF thanks to its low power, high speed, good CMOS compatibility, etc. In this paper, we propose a novel magnetic NVFF based on an emerging memory device called NAND-SPIN. The data writing of NAND-SPIN is achieved by successively applying two unidirectional currents, which respectively generate the spin orbit torque (SOT) and spin transfer torque (STT) for erase and programming operations. This characteristic allows us to design an erase-hidden and drivability-improved magnetic NVFF. Furthermore, more design flexibility could be obtained since the backup operation of the proposed NVFF is not limited by the inherent slave latch. Simulation results show that our proposed NVFF achieves performance improvement in terms of power, delay and area, compared with conventional slave-latch-driven SOT-NVFF designs. |
1008.3845 | Kalyana Babu Nakshatrala | S. Srinivasan and K. B. Nakshatrala | A stabilized mixed formulation for unsteady Brinkman equation based on
the method of horizontal lines | null | null | null | null | cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a stabilized mixed formulation for unsteady
Brinkman equation. The formulation is systematically derived based on the
variational multiscale formalism and the method of horizontal lines. The
derivation does not need the assumption that the fine-scale variables do not
depend on the time, which is the case with the conventional derivation of
multiscale stabilized formulations for transient mixed problems. An expression
for the stabilization parameter is obtained in terms of a bubble function, and
appropriate bubble functions for various finite elements are also presented.
Under the proposed formulation, equal-order interpolation for the velocity and
pressure (which is computationally the most convenient) is stable.
Representative numerical results are presented to illustrate the performance of
the proposed formulation. Spatial and temporal convergence studies are also
performed, and the proposed formulation performed well.
| [
{
"created": "Thu, 15 Jul 2010 21:47:04 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Nov 2010 06:18:59 GMT",
"version": "v2"
}
] | 2010-12-01 | [
[
"Srinivasan",
"S.",
""
],
[
"Nakshatrala",
"K. B.",
""
]
] | In this paper, we present a stabilized mixed formulation for unsteady Brinkman equation. The formulation is systematically derived based on the variational multiscale formalism and the method of horizontal lines. The derivation does not need the assumption that the fine-scale variables do not depend on the time, which is the case with the conventional derivation of multiscale stabilized formulations for transient mixed problems. An expression for the stabilization parameter is obtained in terms of a bubble function, and appropriate bubble functions for various finite elements are also presented. Under the proposed formulation, equal-order interpolation for the velocity and pressure (which is computationally the most convenient) is stable. Representative numerical results are presented to illustrate the performance of the proposed formulation. Spatial and temporal convergence studies are also performed, and the proposed formulation performed well. |
2206.08918 | Nikos Zarifis | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis | Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent | null | null | null | null | cs.LG cs.DS math.ST stat.ML stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the fundamental problem of learning a single neuron, i.e., a
function of the form $\mathbf{x}\mapsto\sigma(\mathbf{w}\cdot\mathbf{x})$ for
monotone activations $\sigma:\mathbb{R}\mapsto\mathbb{R}$, with respect to the
$L_2^2$-loss in the presence of adversarial label noise. Specifically, we are
given labeled examples from a distribution $D$ on $(\mathbf{x},
y)\in\mathbb{R}^d \times \mathbb{R}$ such that there exists
$\mathbf{w}^\ast\in\mathbb{R}^d$ achieving $F(\mathbf{w}^\ast)=\epsilon$, where
$F(\mathbf{w})=\mathbf{E}_{(\mathbf{x},y)\sim D}[(\sigma(\mathbf{w}\cdot
\mathbf{x})-y)^2]$. The goal of the learner is to output a hypothesis vector
$\mathbf{w}$ such that $F(\mathbb{w})=C\, \epsilon$ with high probability,
where $C>1$ is a universal constant. As our main contribution, we give
efficient constant-factor approximate learners for a broad class of
distributions (including log-concave distributions) and activation functions.
Concretely, for the class of isotropic log-concave distributions, we obtain the
following important corollaries:
For the logistic activation, we obtain the first polynomial-time constant
factor approximation (even under the Gaussian distribution). Our algorithm has
sample complexity $\widetilde{O}(d/\epsilon)$, which is tight within
polylogarithmic factors.
For the ReLU activation, we give an efficient algorithm with sample
complexity $\tilde{O}(d\, \polylog(1/\epsilon))$. Prior to our work, the best
known constant-factor approximate learner had sample complexity
$\tilde{\Omega}(d/\epsilon)$.
In both of these settings, our algorithms are simple, performing
gradient-descent on the (regularized) $L_2^2$-loss. The correctness of our
algorithms relies on novel structural results that we establish, showing that
(essentially all) stationary points of the underlying non-convex loss are
approximately optimal.
| [
{
"created": "Fri, 17 Jun 2022 17:55:43 GMT",
"version": "v1"
}
] | 2022-06-20 | [
[
"Diakonikolas",
"Ilias",
""
],
[
"Kontonis",
"Vasilis",
""
],
[
"Tzamos",
"Christos",
""
],
[
"Zarifis",
"Nikos",
""
]
] | We study the fundamental problem of learning a single neuron, i.e., a function of the form $\mathbf{x}\mapsto\sigma(\mathbf{w}\cdot\mathbf{x})$ for monotone activations $\sigma:\mathbb{R}\mapsto\mathbb{R}$, with respect to the $L_2^2$-loss in the presence of adversarial label noise. Specifically, we are given labeled examples from a distribution $D$ on $(\mathbf{x}, y)\in\mathbb{R}^d \times \mathbb{R}$ such that there exists $\mathbf{w}^\ast\in\mathbb{R}^d$ achieving $F(\mathbf{w}^\ast)=\epsilon$, where $F(\mathbf{w})=\mathbf{E}_{(\mathbf{x},y)\sim D}[(\sigma(\mathbf{w}\cdot \mathbf{x})-y)^2]$. The goal of the learner is to output a hypothesis vector $\mathbf{w}$ such that $F(\mathbb{w})=C\, \epsilon$ with high probability, where $C>1$ is a universal constant. As our main contribution, we give efficient constant-factor approximate learners for a broad class of distributions (including log-concave distributions) and activation functions. Concretely, for the class of isotropic log-concave distributions, we obtain the following important corollaries: For the logistic activation, we obtain the first polynomial-time constant factor approximation (even under the Gaussian distribution). Our algorithm has sample complexity $\widetilde{O}(d/\epsilon)$, which is tight within polylogarithmic factors. For the ReLU activation, we give an efficient algorithm with sample complexity $\tilde{O}(d\, \polylog(1/\epsilon))$. Prior to our work, the best known constant-factor approximate learner had sample complexity $\tilde{\Omega}(d/\epsilon)$. In both of these settings, our algorithms are simple, performing gradient-descent on the (regularized) $L_2^2$-loss. The correctness of our algorithms relies on novel structural results that we establish, showing that (essentially all) stationary points of the underlying non-convex loss are approximately optimal. |
1906.05015 | Ming Zhu | Ming Zhu, Xiao-Yang Liu, and Anwar Walid | Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted
Vehicular Networks | 28 pages | null | null | null | cs.LG cs.AI cs.RO cs.SY eess.SY stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G
communication infrastructure in future smart cities. Hot spots easily appear in
road intersections, where effective communication among vehicles is
challenging. UAVs may serve as relays with the advantages of low price, easy
deployment, line-of-sight links, and flexible mobility. In this paper, we study
a UAV-assisted vehicular network where the UAV jointly adjusts its transmission
control (power and channel) and 3D flight to maximize the total throughput.
First, we formulate a Markov decision process (MDP) problem by modeling the
mobility of the UAV/vehicles and the state transitions. Secondly, we solve the
target problem using a deep reinforcement learning method, namely, the deep
deterministic policy gradient (DDPG), and propose three solutions with
different control objectives. Deep reinforcement learning methods obtain the
optimal policy through the interactions with the environment without knowing
the environment variables. Considering that environment variables in our
problem are unknown and unmeasurable, we choose a deep reinforcement learning
method to solve it. Moreover, considering the energy consumption of 3D flight,
we extend the proposed solutions to maximize the total throughput per unit
energy. To encourage or discourage the UAV's mobility according to its
prediction, the DDPG framework is modified, where the UAV adjusts its learning
rate automatically. Thirdly, in a simplified model with small state space and
action space, we verify the optimality of proposed algorithms. Comparing with
two baseline schemes, we demonstrate the effectiveness of proposed algorithms
in a realistic model.
| [
{
"created": "Wed, 12 Jun 2019 09:12:50 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Mar 2020 02:18:20 GMT",
"version": "v10"
},
{
"created": "Sat, 11 Feb 2023 10:54:11 GMT",
"version": "v11"
},
{
"created": "Tue, 14 Feb 2023 11:53:43 GMT",
"version": "v12"
},
{
"created": "Sun, 23 Jun 2019 08:58:02 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Jul 2019 12:25:15 GMT",
"version": "v3"
},
{
"created": "Mon, 8 Jul 2019 16:00:25 GMT",
"version": "v4"
},
{
"created": "Tue, 9 Jul 2019 01:47:49 GMT",
"version": "v5"
},
{
"created": "Fri, 12 Jul 2019 13:49:35 GMT",
"version": "v6"
},
{
"created": "Sat, 27 Jul 2019 03:50:11 GMT",
"version": "v7"
},
{
"created": "Mon, 12 Aug 2019 10:50:40 GMT",
"version": "v8"
},
{
"created": "Tue, 3 Sep 2019 14:29:54 GMT",
"version": "v9"
}
] | 2023-02-22 | [
[
"Zhu",
"Ming",
""
],
[
"Liu",
"Xiao-Yang",
""
],
[
"Walid",
"Anwar",
""
]
] | Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission control (power and channel) and 3D flight to maximize the total throughput. First, we formulate a Markov decision process (MDP) problem by modeling the mobility of the UAV/vehicles and the state transitions. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient (DDPG), and propose three solutions with different control objectives. Deep reinforcement learning methods obtain the optimal policy through the interactions with the environment without knowing the environment variables. Considering that environment variables in our problem are unknown and unmeasurable, we choose a deep reinforcement learning method to solve it. Moreover, considering the energy consumption of 3D flight, we extend the proposed solutions to maximize the total throughput per unit energy. To encourage or discourage the UAV's mobility according to its prediction, the DDPG framework is modified, where the UAV adjusts its learning rate automatically. Thirdly, in a simplified model with small state space and action space, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model. |
1912.06119 | Caglar Tunc | Caglar Tunc and Shivendra Panwar | Optimal Transmission Policies for Energy Harvesting Age of Information
Systems with Battery Recovery | Submitted for publication | null | null | null | cs.IT cs.NI eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an energy harvesting information update system where a sensor is
allowed to choose a transmission mode for each transmission, where each mode
consists of a transmission power-error pair. We also incorporate the battery
phenomenon called battery recovery effect where a battery replenishes the
deliverable energy if kept idle after discharge. For an energy-limited age of
information (AoI) system, this phenomenon gives rise to the interesting
trade-off of recovering energy after transmissions, at the cost of increased
AoI. Considering two metrics, namely peak-age hitting probability and average
age as the worst-case and average performance indicators, respectively, we
propose a framework that formulates the optimal transmission scheme selection
problem as a Markov Decision Process (MDP). We show that the gains obtained by
considering both battery dynamics and adjustable transmission power together
are much higher than the sum gain achieved if they are considered separately.
We also propose a simple methodology to optimize the system performance taking
into account worst-case and average performances jointly.
| [
{
"created": "Thu, 12 Dec 2019 18:34:17 GMT",
"version": "v1"
}
] | 2019-12-13 | [
[
"Tunc",
"Caglar",
""
],
[
"Panwar",
"Shivendra",
""
]
] | We consider an energy harvesting information update system where a sensor is allowed to choose a transmission mode for each transmission, where each mode consists of a transmission power-error pair. We also incorporate the battery phenomenon called battery recovery effect where a battery replenishes the deliverable energy if kept idle after discharge. For an energy-limited age of information (AoI) system, this phenomenon gives rise to the interesting trade-off of recovering energy after transmissions, at the cost of increased AoI. Considering two metrics, namely peak-age hitting probability and average age as the worst-case and average performance indicators, respectively, we propose a framework that formulates the optimal transmission scheme selection problem as a Markov Decision Process (MDP). We show that the gains obtained by considering both battery dynamics and adjustable transmission power together are much higher than the sum gain achieved if they are considered separately. We also propose a simple methodology to optimize the system performance taking into account worst-case and average performances jointly. |
cs/0007038 | Konstantinos Georgatos | Konstantinos Georgatos | Modal Logics for Topological Spaces | 25 pages, extened abstract of PHD Dissertation | null | null | null | cs.LO cs.AI | null | In this thesis we shall present two logical systems, MP and MP, for the
purpose of reasoning about knowledge and effort. These logical systems will be
interpreted in a spatial context and therefore, the abstract concepts of
knowledge and effort will be defined by concrete mathematical concepts.
| [
{
"created": "Wed, 26 Jul 2000 18:41:17 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Georgatos",
"Konstantinos",
""
]
] | In this thesis we shall present two logical systems, MP and MP, for the purpose of reasoning about knowledge and effort. These logical systems will be interpreted in a spatial context and therefore, the abstract concepts of knowledge and effort will be defined by concrete mathematical concepts. |
2202.10450 | Ignacio Carlucho | Reuth Mirsky and Ignacio Carlucho and Arrasy Rahman and Elliot Fosong
and William Macke and Mohan Sridharan and Peter Stone and Stefano V. Albrecht | A Survey of Ad Hoc Teamwork Research | European Conference on Multi-Agent Systems (EUMAS), 2022 | null | null | null | cs.MA cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ad hoc teamwork is the research problem of designing agents that can
collaborate with new teammates without prior coordination. This survey makes a
two-fold contribution: First, it provides a structured description of the
different facets of the ad hoc teamwork problem. Second, it discusses the
progress that has been made in the field so far, and identifies the immediate
and long-term open problems that need to be addressed in ad hoc teamwork.
| [
{
"created": "Wed, 16 Feb 2022 18:16:27 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Aug 2022 11:09:23 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Aug 2022 16:40:01 GMT",
"version": "v3"
}
] | 2022-08-17 | [
[
"Mirsky",
"Reuth",
""
],
[
"Carlucho",
"Ignacio",
""
],
[
"Rahman",
"Arrasy",
""
],
[
"Fosong",
"Elliot",
""
],
[
"Macke",
"William",
""
],
[
"Sridharan",
"Mohan",
""
],
[
"Stone",
"Peter",
""
],
[
"Albrecht",
"Stefano V.",
""
]
] | Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination. This survey makes a two-fold contribution: First, it provides a structured description of the different facets of the ad hoc teamwork problem. Second, it discusses the progress that has been made in the field so far, and identifies the immediate and long-term open problems that need to be addressed in ad hoc teamwork. |
2210.04883 | Nicolas Dufour | Nicolas Dufour, David Picard, Vicky Kalogeiton | SCAM! Transferring humans between images with Semantic Cross Attention
Modulation | Accepted at ECCV 2022 | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A large body of recent work targets semantically conditioned image
generation. Most such methods focus on the narrower task of pose transfer and
ignore the more challenging task of subject transfer that consists in not only
transferring the pose but also the appearance and background. In this work, we
introduce SCAM (Semantic Cross Attention Modulation), a system that encodes
rich and diverse information in each semantic region of the image (including
foreground and background), thus achieving precise generation with emphasis on
fine details. This is enabled by the Semantic Attention Transformer Encoder
that extracts multiple latent vectors for each semantic region, and the
corresponding generator that exploits these multiple latents by using semantic
cross attention modulation. It is trained only using a reconstruction setup,
while subject transfer is performed at test time. Our analysis shows that our
proposed architecture is successful at encoding the diversity of appearance in
each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD
datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new
state of the art on subject transfer.
| [
{
"created": "Mon, 10 Oct 2022 17:54:47 GMT",
"version": "v1"
}
] | 2022-10-11 | [
[
"Dufour",
"Nicolas",
""
],
[
"Picard",
"David",
""
],
[
"Kalogeiton",
"Vicky",
""
]
] | A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details. This is enabled by the Semantic Attention Transformer Encoder that extracts multiple latent vectors for each semantic region, and the corresponding generator that exploits these multiple latents by using semantic cross attention modulation. It is trained only using a reconstruction setup, while subject transfer is performed at test time. Our analysis shows that our proposed architecture is successful at encoding the diversity of appearance in each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new state of the art on subject transfer. |
2407.10114 | Roni Goldshmidt | Roni Goldshmidt, Miriam Horovicz | TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley
Value Estimation | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | As large language models (LLMs) become increasingly prevalent in critical
applications, the need for interpretable AI has grown. We introduce TokenSHAP,
a novel method for interpreting LLMs by attributing importance to individual
tokens or substrings within input prompts. This approach adapts Shapley values
from cooperative game theory to natural language processing, offering a
rigorous framework for understanding how different parts of an input contribute
to a model's response. TokenSHAP leverages Monte Carlo sampling for
computational efficiency, providing interpretable, quantitative measures of
token importance. We demonstrate its efficacy across diverse prompts and LLM
architectures, showing consistent improvements over existing baselines in
alignment with human judgments, faithfulness to model behavior, and
consistency.
Our method's ability to capture nuanced interactions between tokens provides
valuable insights into LLM behavior, enhancing model transparency, improving
prompt engineering, and aiding in the development of more reliable AI systems.
TokenSHAP represents a significant step towards the necessary interpretability
for responsible AI deployment, contributing to the broader goal of creating
more transparent, accountable, and trustworthy AI systems.
| [
{
"created": "Sun, 14 Jul 2024 08:07:50 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Jul 2024 08:59:07 GMT",
"version": "v2"
}
] | 2024-07-23 | [
[
"Goldshmidt",
"Roni",
""
],
[
"Horovicz",
"Miriam",
""
]
] | As large language models (LLMs) become increasingly prevalent in critical applications, the need for interpretable AI has grown. We introduce TokenSHAP, a novel method for interpreting LLMs by attributing importance to individual tokens or substrings within input prompts. This approach adapts Shapley values from cooperative game theory to natural language processing, offering a rigorous framework for understanding how different parts of an input contribute to a model's response. TokenSHAP leverages Monte Carlo sampling for computational efficiency, providing interpretable, quantitative measures of token importance. We demonstrate its efficacy across diverse prompts and LLM architectures, showing consistent improvements over existing baselines in alignment with human judgments, faithfulness to model behavior, and consistency. Our method's ability to capture nuanced interactions between tokens provides valuable insights into LLM behavior, enhancing model transparency, improving prompt engineering, and aiding in the development of more reliable AI systems. TokenSHAP represents a significant step towards the necessary interpretability for responsible AI deployment, contributing to the broader goal of creating more transparent, accountable, and trustworthy AI systems. |
1104.3469 | Yoo Chung | Yoo Chung and Dongman Lee | Probabilistic Analysis of Loss in Interface Adapter Chaining | 20 pages, 2 figures | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interface adapters allow applications written for one interface to be reused
with another interface without having to rewrite application code, and chaining
interface adapters can significantly reduce the development effort required to
create the adapters. However, interface adapters will often be unable to
convert interfaces perfectly, so there must be a way to analyze the loss from
interface adapter chains in order to improve the quality of interface
adaptation. This paper describes a probabilistic approach to analyzing loss in
interface adapter chains, which not only models whether a method can be adapted
but also how well methods can be adapted. We also show that probabilistic
optimal adapter chaining is an NP-complete problem, so we describe a greedy
algorithm which can construct an optimal interface adapter chain with
exponential time in the worst case.
| [
{
"created": "Mon, 18 Apr 2011 13:07:00 GMT",
"version": "v1"
}
] | 2011-04-19 | [
[
"Chung",
"Yoo",
""
],
[
"Lee",
"Dongman",
""
]
] | Interface adapters allow applications written for one interface to be reused with another interface without having to rewrite application code, and chaining interface adapters can significantly reduce the development effort required to create the adapters. However, interface adapters will often be unable to convert interfaces perfectly, so there must be a way to analyze the loss from interface adapter chains in order to improve the quality of interface adaptation. This paper describes a probabilistic approach to analyzing loss in interface adapter chains, which not only models whether a method can be adapted but also how well methods can be adapted. We also show that probabilistic optimal adapter chaining is an NP-complete problem, so we describe a greedy algorithm which can construct an optimal interface adapter chain with exponential time in the worst case. |
1607.03305 | Martin Cadik | Martin Cadik and Jan Vasicek and Michal Hradis and Filip Radenovic and
Ondrej Chum | Camera Elevation Estimation from a Single Mountain Landscape Photograph | null | In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors,
Proceedings of the British Machine Vision Conference (BMVC), pages
30.1-30.12. BMVA Press, September 2015 | 10.5244/C.29.30 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work addresses the problem of camera elevation estimation from a single
photograph in an outdoor environment. We introduce a new benchmark dataset of
one-hundred thousand images with annotated camera elevation called Alps100K. We
propose and experimentally evaluate two automatic data-driven approaches to
camera elevation estimation: one based on convolutional neural networks, the
other on local features. To compare the proposed methods to human performance,
an experiment with 100 subjects is conducted. The experimental results show
that both proposed approaches outperform humans and that the best result is
achieved by their combination.
| [
{
"created": "Tue, 12 Jul 2016 10:47:51 GMT",
"version": "v1"
}
] | 2016-07-13 | [
[
"Cadik",
"Martin",
""
],
[
"Vasicek",
"Jan",
""
],
[
"Hradis",
"Michal",
""
],
[
"Radenovic",
"Filip",
""
],
[
"Chum",
"Ondrej",
""
]
] | This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand images with annotated camera elevation called Alps100K. We propose and experimentally evaluate two automatic data-driven approaches to camera elevation estimation: one based on convolutional neural networks, the other on local features. To compare the proposed methods to human performance, an experiment with 100 subjects is conducted. The experimental results show that both proposed approaches outperform humans and that the best result is achieved by their combination. |
2206.09068 | Sukesh Adiga Vasudeva | Sukesh Adiga V, Jose Dolz, Herve Lombaert | Attention-based Dynamic Subspace Learners for Medical Image Analysis | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning similarity is a key aspect in medical image analysis, particularly
in recommendation systems or in uncovering the interpretation of anatomical
data in images. Most existing methods learn such similarities in the embedding
space over image sets using a single metric learner. Images, however, have a
variety of object attributes such as color, shape, or artifacts. Encoding such
attributes using a single metric learner is inadequate and may fail to
generalize. Instead, multiple learners could focus on separate aspects of these
attributes in subspaces of an overarching embedding. This, however, implies the
number of learners to be found empirically for each new dataset. This work,
Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by
removing the need of knowing apriori the number of learners and aggregating new
subspace learners during training. Furthermore, the visual interpretability of
such subspace learning is enforced by integrating an attention module into our
method. This integrated attention mechanism provides a visual insight of
discriminative image features that contribute to the clustering of image sets
and a visual explanation of the embedding features. The benefits of our
attention-based dynamic subspace learners are evaluated in the application of
image clustering, image retrieval, and weakly supervised segmentation. Our
method achieves competitive results with the performances of multiple learners
baselines and significantly outperforms the classification network in terms of
clustering and retrieval scores on three different public benchmark datasets.
Moreover, our attention maps offer a proxy-labels, which improves the
segmentation accuracy up to 15% in Dice scores when compared to
state-of-the-art interpretation techniques.
| [
{
"created": "Sat, 18 Jun 2022 00:44:40 GMT",
"version": "v1"
}
] | 2022-06-22 | [
[
"Adiga",
"Sukesh",
"V"
],
[
"Dolz",
"Jose",
""
],
[
"Lombaert",
"Herve",
""
]
] | Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of object attributes such as color, shape, or artifacts. Encoding such attributes using a single metric learner is inadequate and may fail to generalize. Instead, multiple learners could focus on separate aspects of these attributes in subspaces of an overarching embedding. This, however, implies the number of learners to be found empirically for each new dataset. This work, Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by removing the need of knowing apriori the number of learners and aggregating new subspace learners during training. Furthermore, the visual interpretability of such subspace learning is enforced by integrating an attention module into our method. This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features. The benefits of our attention-based dynamic subspace learners are evaluated in the application of image clustering, image retrieval, and weakly supervised segmentation. Our method achieves competitive results with the performances of multiple learners baselines and significantly outperforms the classification network in terms of clustering and retrieval scores on three different public benchmark datasets. Moreover, our attention maps offer a proxy-labels, which improves the segmentation accuracy up to 15% in Dice scores when compared to state-of-the-art interpretation techniques. |
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