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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2007.13442 | Michal Valko | Pierre M\'enard, Omar Darwiche Domingues, Anders Jonsson, Emilie
Kaufmann, Edouard Leurent, Michal Valko | Fast active learning for pure exploration in reinforcement learning | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Realistic environments often provide agents with very limited feedback. When
the environment is initially unknown, the feedback, in the beginning, can be
completely absent, and the agents may first choose to devote all their effort
on exploring efficiently. The exploration remains a challenge while it has been
addressed with many hand-tuned heuristics with different levels of generality
on one side, and a few theoretically-backed exploration strategies on the
other. Many of them are incarnated by intrinsic motivation and in particular
explorations bonuses. A common rule of thumb for exploration bonuses is to use
$1/\sqrt{n}$ bonus that is added to the empirical estimates of the reward,
where $n$ is a number of times this particular state (or a state-action pair)
was visited. We show that, surprisingly, for a pure-exploration objective of
reward-free exploration, bonuses that scale with $1/n$ bring faster learning
rates, improving the known upper bounds with respect to the dependence on the
horizon $H$. Furthermore, we show that with an improved analysis of the
stopping time, we can improve by a factor $H$ the sample complexity in the
best-policy identification setting, which is another pure-exploration
objective, where the environment provides rewards but the agent is not
penalized for its behavior during the exploration phase.
| [
{
"created": "Mon, 27 Jul 2020 11:28:32 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Oct 2020 17:15:28 GMT",
"version": "v2"
}
] | 2020-10-13 | [
[
"Ménard",
"Pierre",
""
],
[
"Domingues",
"Omar Darwiche",
""
],
[
"Jonsson",
"Anders",
""
],
[
"Kaufmann",
"Emilie",
""
],
[
"Leurent",
"Edouard",
""
],
[
"Valko",
"Michal",
""
]
] | Realistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on exploring efficiently. The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically-backed exploration strategies on the other. Many of them are incarnated by intrinsic motivation and in particular explorations bonuses. A common rule of thumb for exploration bonuses is to use $1/\sqrt{n}$ bonus that is added to the empirical estimates of the reward, where $n$ is a number of times this particular state (or a state-action pair) was visited. We show that, surprisingly, for a pure-exploration objective of reward-free exploration, bonuses that scale with $1/n$ bring faster learning rates, improving the known upper bounds with respect to the dependence on the horizon $H$. Furthermore, we show that with an improved analysis of the stopping time, we can improve by a factor $H$ the sample complexity in the best-policy identification setting, which is another pure-exploration objective, where the environment provides rewards but the agent is not penalized for its behavior during the exploration phase. |
2303.01639 | Jun Rekimoto | Jun Rekimoto | WESPER: Zero-shot and Realtime Whisper to Normal Voice Conversion for
Whisper-based Speech Interactions | ACM CHI 2023 paper | Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (CHI '23), April 23--28, 2023 | 10.1145/3544548.3580706 | null | cs.SD cs.HC eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognizing whispered speech and converting it to normal speech creates many
possibilities for speech interaction. Because the sound pressure of whispered
speech is significantly lower than that of normal speech, it can be used as a
semi-silent speech interaction in public places without being audible to
others. Converting whispers to normal speech also improves the speech quality
for people with speech or hearing impairments. However, conventional speech
conversion techniques do not provide sufficient conversion quality or require
speaker-dependent datasets consisting of pairs of whispered and normal speech
utterances. To address these problems, we propose WESPER, a zero-shot,
real-time whisper-to-normal speech conversion mechanism based on
self-supervised learning. WESPER consists of a speech-to-unit (STU) encoder,
which generates hidden speech units common to both whispered and normal speech,
and a unit-to-speech (UTS) decoder, which reconstructs speech from the encoded
speech units. Unlike the existing methods, this conversion is user-independent
and does not require a paired dataset for whispered and normal speech. The UTS
decoder can reconstruct speech in any target speaker's voice from speech units,
and it requires only an unlabeled target speaker's speech data. We confirmed
that the quality of the speech converted from a whisper was improved while
preserving its natural prosody. Additionally, we confirmed the effectiveness of
the proposed approach to perform speech reconstruction for people with speech
or hearing disabilities. (project page: http://lab.rekimoto.org/projects/wesper
)
| [
{
"created": "Fri, 3 Mar 2023 00:10:25 GMT",
"version": "v1"
}
] | 2023-03-06 | [
[
"Rekimoto",
"Jun",
""
]
] | Recognizing whispered speech and converting it to normal speech creates many possibilities for speech interaction. Because the sound pressure of whispered speech is significantly lower than that of normal speech, it can be used as a semi-silent speech interaction in public places without being audible to others. Converting whispers to normal speech also improves the speech quality for people with speech or hearing impairments. However, conventional speech conversion techniques do not provide sufficient conversion quality or require speaker-dependent datasets consisting of pairs of whispered and normal speech utterances. To address these problems, we propose WESPER, a zero-shot, real-time whisper-to-normal speech conversion mechanism based on self-supervised learning. WESPER consists of a speech-to-unit (STU) encoder, which generates hidden speech units common to both whispered and normal speech, and a unit-to-speech (UTS) decoder, which reconstructs speech from the encoded speech units. Unlike the existing methods, this conversion is user-independent and does not require a paired dataset for whispered and normal speech. The UTS decoder can reconstruct speech in any target speaker's voice from speech units, and it requires only an unlabeled target speaker's speech data. We confirmed that the quality of the speech converted from a whisper was improved while preserving its natural prosody. Additionally, we confirmed the effectiveness of the proposed approach to perform speech reconstruction for people with speech or hearing disabilities. (project page: http://lab.rekimoto.org/projects/wesper ) |
2305.19818 | Marina Munkhoeva | Marina Munkhoeva, Ivan Oseledets | Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in
Self-Supervised Learning | 12 pages, 3 figures | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-supervised methods received tremendous attention thanks to their
seemingly heuristic approach to learning representations that respect the
semantics of the data without any apparent supervision in the form of labels. A
growing body of literature is already being published in an attempt to build a
coherent and theoretically grounded understanding of the workings of a zoo of
losses used in modern self-supervised representation learning methods. In this
paper, we attempt to provide an understanding from the perspective of a Laplace
operator and connect the inductive bias stemming from the augmentation process
to a low-rank matrix completion problem. To this end, we leverage the results
from low-rank matrix completion to provide theoretical analysis on the
convergence of modern SSL methods and a key property that affects their
downstream performance.
| [
{
"created": "Wed, 31 May 2023 13:02:06 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Oct 2023 15:45:09 GMT",
"version": "v2"
}
] | 2023-10-31 | [
[
"Munkhoeva",
"Marina",
""
],
[
"Oseledets",
"Ivan",
""
]
] | Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any apparent supervision in the form of labels. A growing body of literature is already being published in an attempt to build a coherent and theoretically grounded understanding of the workings of a zoo of losses used in modern self-supervised representation learning methods. In this paper, we attempt to provide an understanding from the perspective of a Laplace operator and connect the inductive bias stemming from the augmentation process to a low-rank matrix completion problem. To this end, we leverage the results from low-rank matrix completion to provide theoretical analysis on the convergence of modern SSL methods and a key property that affects their downstream performance. |
1902.06440 | Anas El Ankouri | Anas El Ankouri (IMT Atlantique), Luiz Neto, Ali Sanhaji, Sylvain
Barthomeuf, Hugues Le Bras, Bertrand Le Guyader, Abdelatif Chagdali, Minqi
Wang, N. Genay, K. Grzybowski, Sophie Durel, P. Chanclou | Experimental Demonstration of Real-time PDCP-RLC V-RAN Split
Transmission over Fixed XGS-PON access | null | ECOC 2018 (EUROPEAN CONFERENCE OF OPTICAL COMMUNICATION), Sep
2018, ROME, Italy | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we experimentally assess the transmission of a PDCP-RLC
virtualised RAN split interface through a commercial XGS-PON system. We
investigate the impacts of DBA on the uplink and packet jitter on the downlink.
| [
{
"created": "Mon, 18 Feb 2019 07:59:03 GMT",
"version": "v1"
}
] | 2019-02-19 | [
[
"Ankouri",
"Anas El",
"",
"IMT Atlantique"
],
[
"Neto",
"Luiz",
""
],
[
"Sanhaji",
"Ali",
""
],
[
"Barthomeuf",
"Sylvain",
""
],
[
"Bras",
"Hugues Le",
""
],
[
"Guyader",
"Bertrand Le",
""
],
[
"Chagdali",
"Abdelatif",
""
],
[
"Wang",
"Minqi",
""
],
[
"Genay",
"N.",
""
],
[
"Grzybowski",
"K.",
""
],
[
"Durel",
"Sophie",
""
],
[
"Chanclou",
"P.",
""
]
] | In this work, we experimentally assess the transmission of a PDCP-RLC virtualised RAN split interface through a commercial XGS-PON system. We investigate the impacts of DBA on the uplink and packet jitter on the downlink. |
0708.0505 | Luca Di Gaspero PhD | Luca Di Gaspero, Andrea Roli | A preliminary analysis on metaheuristics methods applied to the
Haplotype Inference Problem | 22 pages, 4 figures Technical Report: DEIS - Alma Mater Studiorum,
University of Bologna no. DEIS-LIA-006-07 | null | null | DEIS-LIA-006-07 | cs.AI cs.CE cs.DM q-bio.QM | null | Haplotype Inference is a challenging problem in bioinformatics that consists
in inferring the basic genetic constitution of diploid organisms on the basis
of their genotype. This information allows researchers to perform association
studies for the genetic variants involved in diseases and the individual
responses to therapeutic agents.
A notable approach to the problem is to encode it as a combinatorial problem
(under certain hypotheses, such as the pure parsimony criterion) and to solve
it using off-the-shelf combinatorial optimization techniques. The main methods
applied to Haplotype Inference are either simple greedy heuristic or exact
methods (Integer Linear Programming, Semidefinite Programming, SAT encoding)
that, at present, are adequate only for moderate size instances.
We believe that metaheuristic and hybrid approaches could provide a better
scalability. Moreover, metaheuristics can be very easily combined with problem
specific heuristics and they can also be integrated with tree-based search
techniques, thus providing a promising framework for hybrid systems in which a
good trade-off between effectiveness and efficiency can be reached.
In this paper we illustrate a feasibility study of the approach and discuss
some relevant design issues, such as modeling and design of approximate solvers
that combine constructive heuristics, local search-based improvement strategies
and learning mechanisms. Besides the relevance of the Haplotype Inference
problem itself, this preliminary analysis is also an interesting case study
because the formulation of the problem poses some challenges in modeling and
hybrid metaheuristic solver design that can be generalized to other problems.
| [
{
"created": "Fri, 3 Aug 2007 12:49:21 GMT",
"version": "v1"
}
] | 2007-08-06 | [
[
"Di Gaspero",
"Luca",
""
],
[
"Roli",
"Andrea",
""
]
] | Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search techniques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modeling and design of approximate solvers that combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype Inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modeling and hybrid metaheuristic solver design that can be generalized to other problems. |
1702.02047 | Ziyuan Gao | Ziyuan Gao, Christoph Ries, Hans Ulrich Simon and Sandra Zilles | Preference-based Teaching | 35 pages | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new model of teaching named "preference-based teaching" and a
corresponding complexity parameter---the preference-based teaching dimension
(PBTD)---representing the worst-case number of examples needed to teach any
concept in a given concept class. Although the PBTD coincides with the
well-known recursive teaching dimension (RTD) on finite classes, it is
radically different on infinite ones: the RTD becomes infinite already for
trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to
reasonably small values for a wide collection of infinite classes including
classes consisting of so-called closed sets w.r.t. a given closure operator,
including various classes related to linear sets over $\mathbb{N}_0$ (whose RTD
had been studied quite recently) and including the class of Euclidean
half-spaces. On top of presenting these concrete results, we provide the reader
with a theoretical framework (of a combinatorial flavor) which helps to derive
bounds on the PBTD.
| [
{
"created": "Mon, 6 Feb 2017 18:40:32 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Feb 2017 11:37:57 GMT",
"version": "v2"
}
] | 2017-02-09 | [
[
"Gao",
"Ziyuan",
""
],
[
"Ries",
"Christoph",
""
],
[
"Simon",
"Hans Ulrich",
""
],
[
"Zilles",
"Sandra",
""
]
] | We introduce a new model of teaching named "preference-based teaching" and a corresponding complexity parameter---the preference-based teaching dimension (PBTD)---representing the worst-case number of examples needed to teach any concept in a given concept class. Although the PBTD coincides with the well-known recursive teaching dimension (RTD) on finite classes, it is radically different on infinite ones: the RTD becomes infinite already for trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to reasonably small values for a wide collection of infinite classes including classes consisting of so-called closed sets w.r.t. a given closure operator, including various classes related to linear sets over $\mathbb{N}_0$ (whose RTD had been studied quite recently) and including the class of Euclidean half-spaces. On top of presenting these concrete results, we provide the reader with a theoretical framework (of a combinatorial flavor) which helps to derive bounds on the PBTD. |
2306.06088 | Alexandre Binninger | Alexandre Binninger, Amir Hertz, Olga Sorkine-Hornung, Daniel
Cohen-Or, Raja Giryes | SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling | 25 pages, 24 figures | null | null | null | cs.GR cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | We present SENS, a novel method for generating and editing 3D models from
hand-drawn sketches, including those of abstract nature. Our method allows
users to quickly and easily sketch a shape, and then maps the sketch into the
latent space of a part-aware neural implicit shape architecture. SENS analyzes
the sketch and encodes its parts into ViT patch encoding, subsequently feeding
them into a transformer decoder that converts them to shape embeddings suitable
for editing 3D neural implicit shapes. SENS provides intuitive sketch-based
generation and editing, and also succeeds in capturing the intent of the user's
sketch to generate a variety of novel and expressive 3D shapes, even from
abstract and imprecise sketches. Additionally, SENS supports refinement via
part reconstruction, allowing for nuanced adjustments and artifact removal. It
also offers part-based modeling capabilities, enabling the combination of
features from multiple sketches to create more complex and customized 3D
shapes. We demonstrate the effectiveness of our model compared to the
state-of-the-art using objective metric evaluation criteria and a user study,
both indicating strong performance on sketches with a medium level of
abstraction. Furthermore, we showcase our method's intuitive sketch-based shape
editing capabilities, and validate it through a usability study.
| [
{
"created": "Fri, 9 Jun 2023 17:50:53 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Feb 2024 13:35:34 GMT",
"version": "v2"
}
] | 2024-02-22 | [
[
"Binninger",
"Alexandre",
""
],
[
"Hertz",
"Amir",
""
],
[
"Sorkine-Hornung",
"Olga",
""
],
[
"Cohen-Or",
"Daniel",
""
],
[
"Giryes",
"Raja",
""
]
] | We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part-aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, subsequently feeding them into a transformer decoder that converts them to shape embeddings suitable for editing 3D neural implicit shapes. SENS provides intuitive sketch-based generation and editing, and also succeeds in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract and imprecise sketches. Additionally, SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal. It also offers part-based modeling capabilities, enabling the combination of features from multiple sketches to create more complex and customized 3D shapes. We demonstrate the effectiveness of our model compared to the state-of-the-art using objective metric evaluation criteria and a user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase our method's intuitive sketch-based shape editing capabilities, and validate it through a usability study. |
2204.02720 | Jan Maty\'a\v{s} K\v{r}i\v{s}\v{t}an | V\'aclav Bla\v{z}ej, Jan Maty\'a\v{s} K\v{r}i\v{s}\v{t}an, Tom\'a\v{s}
Valla | Efficient attack sequences in m-eternal domination | null | null | null | null | cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the m-eternal domination problem from the perspective of the
attacker. For many graph classes, the minimum required number of guards to
defend eternally is known. By definition, if the defender has less than the
required number of guards, then there exists a sequence of attacks that ensures
the attacker's victory. Little is known about such sequences of attacks, in
particular, no bound on its length is known.
We show that if the game is played on a tree $T$ on $n$ vertices and the
defender has less than the necessary number of guards, then the attacker can
win in at most $n$ turns. Furthermore, we present an efficient procedure that
produces such an attacking strategy.
| [
{
"created": "Wed, 6 Apr 2022 10:50:08 GMT",
"version": "v1"
}
] | 2022-04-07 | [
[
"Blažej",
"Václav",
""
],
[
"Křišťan",
"Jan Matyáš",
""
],
[
"Valla",
"Tomáš",
""
]
] | We study the m-eternal domination problem from the perspective of the attacker. For many graph classes, the minimum required number of guards to defend eternally is known. By definition, if the defender has less than the required number of guards, then there exists a sequence of attacks that ensures the attacker's victory. Little is known about such sequences of attacks, in particular, no bound on its length is known. We show that if the game is played on a tree $T$ on $n$ vertices and the defender has less than the necessary number of guards, then the attacker can win in at most $n$ turns. Furthermore, we present an efficient procedure that produces such an attacking strategy. |
2006.08606 | Akbar Siami Namin | Shuvalaxmi Dass and Akbar Siami Namin | Vulnerability Coverage as an Adequacy Testing Criterion | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mainstream software applications and tools are the configurable platforms
with an enormous number of parameters along with their values. Certain settings
and possible interactions between these parameters may harden (or soften) the
security and robustness of these applications against some known
vulnerabilities. However, the large number of vulnerabilities reported and
associated with these tools make the exhaustive testing of these tools
infeasible against these vulnerabilities infeasible. As an instance of general
software testing problem, the research question to address is whether the
system under test is robust and secure against these vulnerabilities. This
paper introduces the idea of ``vulnerability coverage,'' a concept to
adequately test a given application for a certain classes of vulnerabilities,
as reported by the National Vulnerability Database (NVD). The deriving idea is
to utilize the Common Vulnerability Scoring System (CVSS) as a means to measure
the fitness of test inputs generated by evolutionary algorithms and then
through pattern matching identify vulnerabilities that match the generated
vulnerability vectors and then test the system under test for those identified
vulnerabilities. We report the performance of two evolutionary algorithms
(i.e., Genetic Algorithms and Particle Swarm Optimization) in generating the
vulnerability pattern vectors.
| [
{
"created": "Sun, 14 Jun 2020 15:53:10 GMT",
"version": "v1"
}
] | 2020-06-17 | [
[
"Dass",
"Shuvalaxmi",
""
],
[
"Namin",
"Akbar Siami",
""
]
] | Mainstream software applications and tools are the configurable platforms with an enormous number of parameters along with their values. Certain settings and possible interactions between these parameters may harden (or soften) the security and robustness of these applications against some known vulnerabilities. However, the large number of vulnerabilities reported and associated with these tools make the exhaustive testing of these tools infeasible against these vulnerabilities infeasible. As an instance of general software testing problem, the research question to address is whether the system under test is robust and secure against these vulnerabilities. This paper introduces the idea of ``vulnerability coverage,'' a concept to adequately test a given application for a certain classes of vulnerabilities, as reported by the National Vulnerability Database (NVD). The deriving idea is to utilize the Common Vulnerability Scoring System (CVSS) as a means to measure the fitness of test inputs generated by evolutionary algorithms and then through pattern matching identify vulnerabilities that match the generated vulnerability vectors and then test the system under test for those identified vulnerabilities. We report the performance of two evolutionary algorithms (i.e., Genetic Algorithms and Particle Swarm Optimization) in generating the vulnerability pattern vectors. |
2102.10375 | Chaochao Li | Chaochao Li, Mingliang Xu | Hybrid-driven Trajectory Prediction Based on Group Emotion | null | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a hybrid-driven trajectory prediction method based on group
emotion. The data driven and model driven methods are combined to make a
compromise between the controllability, generality, and efficiency of the
method on the basis of simulating more real crowd movements. A hybrid driven
method is proposed to improve the reliability of the calculation results based
on real crowd data, and ensure the controllability of the model. It reduces the
dependence of our model on real data and realizes the complementary advantages
of these two kinds of methods. In addition, we divide crowd into groups based
on human relations in society. So our method can calculate the movements in
different scales. We predict individual movement trajectories according to the
trajectories of group and fully consider the influence of the group movement
state on the individual movements. Besides we also propose a group emotion
calculation method and our method also considers the effect of group emotion on
crowd movements.
| [
{
"created": "Sat, 20 Feb 2021 15:52:39 GMT",
"version": "v1"
}
] | 2021-02-23 | [
[
"Li",
"Chaochao",
""
],
[
"Xu",
"Mingliang",
""
]
] | We present a hybrid-driven trajectory prediction method based on group emotion. The data driven and model driven methods are combined to make a compromise between the controllability, generality, and efficiency of the method on the basis of simulating more real crowd movements. A hybrid driven method is proposed to improve the reliability of the calculation results based on real crowd data, and ensure the controllability of the model. It reduces the dependence of our model on real data and realizes the complementary advantages of these two kinds of methods. In addition, we divide crowd into groups based on human relations in society. So our method can calculate the movements in different scales. We predict individual movement trajectories according to the trajectories of group and fully consider the influence of the group movement state on the individual movements. Besides we also propose a group emotion calculation method and our method also considers the effect of group emotion on crowd movements. |
2106.03441 | Shengqiang Zhang | Shengqiang Zhang, Xingxing Zhang, Hangbo Bao, Furu Wei | Attention Temperature Matters in Abstractive Summarization Distillation | Accepted in ACL 2022 Main conference | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent progress of abstractive text summarization largely relies on large
pre-trained sequence-to-sequence Transformer models, which are computationally
expensive. This paper aims to distill these large models into smaller ones for
faster inference and minimal performance loss. Pseudo-labeling based methods
are popular in sequence-to-sequence model distillation. In this paper, we find
simply manipulating attention temperatures in Transformers can make pseudo
labels easier to learn for student models. Our experiments on three
summarization datasets show our proposed method consistently improves over
vanilla pseudo-labeling based methods. We also find that both the pseudo labels
and summaries produced by our students are shorter and more abstractive. Our
code is available at \url{https://github.com/Shengqiang-Zhang/plate}.
| [
{
"created": "Mon, 7 Jun 2021 09:18:21 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Jun 2021 03:09:45 GMT",
"version": "v2"
},
{
"created": "Tue, 1 Mar 2022 14:27:55 GMT",
"version": "v3"
}
] | 2022-03-02 | [
[
"Zhang",
"Shengqiang",
""
],
[
"Zhang",
"Xingxing",
""
],
[
"Bao",
"Hangbo",
""
],
[
"Wei",
"Furu",
""
]
] | Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into smaller ones for faster inference and minimal performance loss. Pseudo-labeling based methods are popular in sequence-to-sequence model distillation. In this paper, we find simply manipulating attention temperatures in Transformers can make pseudo labels easier to learn for student models. Our experiments on three summarization datasets show our proposed method consistently improves over vanilla pseudo-labeling based methods. We also find that both the pseudo labels and summaries produced by our students are shorter and more abstractive. Our code is available at \url{https://github.com/Shengqiang-Zhang/plate}. |
1904.07348 | Prashanta Saha | Prashanta Saha and Upulee Kanewala | Fault Detection Effectiveness of Metamorphic Relations Developed for
Testing Supervised Classifiers | 8 pages, AITesting 2019 | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In machine learning, supervised classifiers are used to obtain predictions
for unlabeled data by inferring prediction functions using labeled data.
Supervised classifiers are widely applied in domains such as computational
biology, computational physics and healthcare to make critical decisions.
However, it is often hard to test supervised classifiers since the expected
answers are unknown. This is commonly known as the \emph{oracle problem} and
metamorphic testing (MT) has been used to test such programs. In MT,
metamorphic relations (MRs) are developed from intrinsic characteristics of the
software under test (SUT). These MRs are used to generate test data and to
verify the correctness of the test results without the presence of a test
oracle. Effectiveness of MT heavily depends on the MRs used for testing. In
this paper we have conducted an extensive empirical study to evaluate the fault
detection effectiveness of MRs that have been used in multiple previous studies
to test supervised classifiers. Our study uses a total of 709 reachable mutants
generated by multiple mutation engines and uses data sets with varying
characteristics to test the SUT. Our results reveal that only 14.8\% of these
mutants are detected using the MRs and that the fault detection effectiveness
of these MRs do not scale with the increased number of mutants when compared to
what was reported in previous studies.
| [
{
"created": "Mon, 15 Apr 2019 22:23:32 GMT",
"version": "v1"
}
] | 2019-04-17 | [
[
"Saha",
"Prashanta",
""
],
[
"Kanewala",
"Upulee",
""
]
] | In machine learning, supervised classifiers are used to obtain predictions for unlabeled data by inferring prediction functions using labeled data. Supervised classifiers are widely applied in domains such as computational biology, computational physics and healthcare to make critical decisions. However, it is often hard to test supervised classifiers since the expected answers are unknown. This is commonly known as the \emph{oracle problem} and metamorphic testing (MT) has been used to test such programs. In MT, metamorphic relations (MRs) are developed from intrinsic characteristics of the software under test (SUT). These MRs are used to generate test data and to verify the correctness of the test results without the presence of a test oracle. Effectiveness of MT heavily depends on the MRs used for testing. In this paper we have conducted an extensive empirical study to evaluate the fault detection effectiveness of MRs that have been used in multiple previous studies to test supervised classifiers. Our study uses a total of 709 reachable mutants generated by multiple mutation engines and uses data sets with varying characteristics to test the SUT. Our results reveal that only 14.8\% of these mutants are detected using the MRs and that the fault detection effectiveness of these MRs do not scale with the increased number of mutants when compared to what was reported in previous studies. |
2311.12886 | Zhenghao Zhang | Zuozhuo Dai and Zhenghao Zhang and Yao Yao and Bingxue Qiu and Siyu
Zhu and Long Qin and Weizhi Wang | AnimateAnything: Fine-Grained Open Domain Image Animation with Motion
Guidance | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Image animation is a key task in computer vision which aims to generate
dynamic visual content from static image. Recent image animation methods employ
neural based rendering technique to generate realistic animations. Despite
these advancements, achieving fine-grained and controllable image animation
guided by text remains challenging, particularly for open-domain images
captured in diverse real environments. In this paper, we introduce an open
domain image animation method that leverages the motion prior of video
diffusion model. Our approach introduces targeted motion area guidance and
motion strength guidance, enabling precise control the movable area and its
motion speed. This results in enhanced alignment between the animated visual
elements and the prompting text, thereby facilitating a fine-grained and
interactive animation generation process for intricate motion sequences. We
validate the effectiveness of our method through rigorous experiments on an
open-domain dataset, with the results showcasing its superior performance.
Project page can be found at https://animationai.github.io/AnimateAnything.
| [
{
"created": "Tue, 21 Nov 2023 03:47:54 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Dec 2023 05:43:53 GMT",
"version": "v2"
}
] | 2023-12-06 | [
[
"Dai",
"Zuozhuo",
""
],
[
"Zhang",
"Zhenghao",
""
],
[
"Yao",
"Yao",
""
],
[
"Qiu",
"Bingxue",
""
],
[
"Zhu",
"Siyu",
""
],
[
"Qin",
"Long",
""
],
[
"Wang",
"Weizhi",
""
]
] | Image animation is a key task in computer vision which aims to generate dynamic visual content from static image. Recent image animation methods employ neural based rendering technique to generate realistic animations. Despite these advancements, achieving fine-grained and controllable image animation guided by text remains challenging, particularly for open-domain images captured in diverse real environments. In this paper, we introduce an open domain image animation method that leverages the motion prior of video diffusion model. Our approach introduces targeted motion area guidance and motion strength guidance, enabling precise control the movable area and its motion speed. This results in enhanced alignment between the animated visual elements and the prompting text, thereby facilitating a fine-grained and interactive animation generation process for intricate motion sequences. We validate the effectiveness of our method through rigorous experiments on an open-domain dataset, with the results showcasing its superior performance. Project page can be found at https://animationai.github.io/AnimateAnything. |
2304.10770 | Shanchuan Wan | Shanchuan Wan, Yujin Tang, Yingtao Tian, Tomoyuki Kaneko | DEIR: Efficient and Robust Exploration through
Discriminative-Model-Based Episodic Intrinsic Rewards | Accepted as a conference paper to the 32nd International Joint
Conference on Artificial Intelligence (IJCAI-23) | null | null | null | cs.LG cs.AI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exploration is a fundamental aspect of reinforcement learning (RL), and its
effectiveness is a deciding factor in the performance of RL algorithms,
especially when facing sparse extrinsic rewards. Recent studies have shown the
effectiveness of encouraging exploration with intrinsic rewards estimated from
novelties in observations. However, there is a gap between the novelty of an
observation and an exploration, as both the stochasticity in the environment
and the agent's behavior may affect the observation. To evaluate exploratory
behaviors accurately, we propose DEIR, a novel method in which we theoretically
derive an intrinsic reward with a conditional mutual information term that
principally scales with the novelty contributed by agent explorations, and then
implement the reward with a discriminative forward model. Extensive experiments
on both standard and advanced exploration tasks in MiniGrid show that DEIR
quickly learns a better policy than the baselines. Our evaluations on ProcGen
demonstrate both the generalization capability and the general applicability of
our intrinsic reward. Our source code is available at
https://github.com/swan-utokyo/deir.
| [
{
"created": "Fri, 21 Apr 2023 06:39:38 GMT",
"version": "v1"
},
{
"created": "Thu, 18 May 2023 15:42:27 GMT",
"version": "v2"
}
] | 2023-05-19 | [
[
"Wan",
"Shanchuan",
""
],
[
"Tang",
"Yujin",
""
],
[
"Tian",
"Yingtao",
""
],
[
"Kaneko",
"Tomoyuki",
""
]
] | Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards. Recent studies have shown the effectiveness of encouraging exploration with intrinsic rewards estimated from novelties in observations. However, there is a gap between the novelty of an observation and an exploration, as both the stochasticity in the environment and the agent's behavior may affect the observation. To evaluate exploratory behaviors accurately, we propose DEIR, a novel method in which we theoretically derive an intrinsic reward with a conditional mutual information term that principally scales with the novelty contributed by agent explorations, and then implement the reward with a discriminative forward model. Extensive experiments on both standard and advanced exploration tasks in MiniGrid show that DEIR quickly learns a better policy than the baselines. Our evaluations on ProcGen demonstrate both the generalization capability and the general applicability of our intrinsic reward. Our source code is available at https://github.com/swan-utokyo/deir. |
2305.04079 | Jakob Svennevik Notland | Jakob Svennevik Notland and Mariusz Nowostawski and Jingyue Li | An Empirical Study on Governance in Bitcoin's Consensus Evolution | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Blockchain systems run consensus rules as code to agree on the state of the
distributed ledger and secure the network. Changing these rules can be risky
and challenging. In addition, it can often be controversial and take much
effort to make all the necessary participants agree to adopt a change.
Arguably, Bitcoin has seen centralisation tendencies in pools and in
development. However, how these tendencies influence blockchain governance has
received minimal community and academic attention. Our study analyses the
governmental structures in a blockchain by looking into the history of Bitcoin.
We investigate the process of changing consensus rules through a grounded
theory analysis comprising quantitative and qualitative data from 34 consensus
forks in Bitcoin and Bitcoin Cash. The results reveal the decentralised
behaviour in Bitcoin and blockchain. Our results are in contrast to related
work, emphasising centralisation among miners and developers. Furthermore, our
results show how the consensus-driven deployment techniques and governance of
consensus rules are intertwined.
| [
{
"created": "Sat, 6 May 2023 15:57:13 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Feb 2024 16:20:18 GMT",
"version": "v2"
}
] | 2024-02-15 | [
[
"Notland",
"Jakob Svennevik",
""
],
[
"Nowostawski",
"Mariusz",
""
],
[
"Li",
"Jingyue",
""
]
] | Blockchain systems run consensus rules as code to agree on the state of the distributed ledger and secure the network. Changing these rules can be risky and challenging. In addition, it can often be controversial and take much effort to make all the necessary participants agree to adopt a change. Arguably, Bitcoin has seen centralisation tendencies in pools and in development. However, how these tendencies influence blockchain governance has received minimal community and academic attention. Our study analyses the governmental structures in a blockchain by looking into the history of Bitcoin. We investigate the process of changing consensus rules through a grounded theory analysis comprising quantitative and qualitative data from 34 consensus forks in Bitcoin and Bitcoin Cash. The results reveal the decentralised behaviour in Bitcoin and blockchain. Our results are in contrast to related work, emphasising centralisation among miners and developers. Furthermore, our results show how the consensus-driven deployment techniques and governance of consensus rules are intertwined. |
1805.07866 | Yingyezhe Jin | Yingyezhe Jin, Wenrui Zhang and Peng Li | Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking
Neural Networks | 11 pages, 5 figures. Published at NeurIPS (Neural Information
Processing System) 2018. Code available:
https://github.com/jinyyy666/mm-bp-snn | null | null | null | cs.NE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiking neural networks (SNNs) are positioned to enable spatio-temporal
information processing and ultra-low power event-driven neuromorphic hardware.
However, SNNs are yet to reach the same performances of conventional deep
artificial neural networks (ANNs), a long-standing challenge due to complex
dynamics and non-differentiable spike events encountered in training. The
existing SNN error backpropagation (BP) methods are limited in terms of
scalability, lack of proper handling of spiking discontinuities, and/or
mismatch between the rate-coded loss function and computed gradient. We present
a hybrid macro/micro level backpropagation (HM2-BP) algorithm for training
multi-layer SNNs. The temporal effects are precisely captured by the proposed
spike-train level post-synaptic potential (S-PSP) at the microscopic level. The
rate-coded errors are defined at the macroscopic level, computed and
back-propagated across both macroscopic and microscopic levels. Different from
existing BP methods, HM2-BP directly computes the gradient of the rate-coded
loss function w.r.t tunable parameters. We evaluate the proposed HM2-BP
algorithm by training deep fully connected and convolutional SNNs based on the
static MNIST [14] and dynamic neuromorphic N-MNIST [26]. HM2-BP achieves an
accuracy level of 99.49% and 98.88% for MNIST and N-MNIST, respectively,
outperforming the best reported performances obtained from the existing SNN BP
algorithms. Furthermore, the HM2-BP produces the highest accuracies based on
SNNs for the EMNIST [3] dataset, and leads to high recognition accuracy for the
16-speaker spoken English letters of TI46 Corpus [16], a challenging
patio-temporal speech recognition benchmark for which no prior success based on
SNNs was reported. It also achieves competitive performances surpassing those
of conventional deep learning models when dealing with asynchronous spiking
streams.
| [
{
"created": "Mon, 21 May 2018 02:04:30 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Sep 2018 05:32:05 GMT",
"version": "v2"
},
{
"created": "Mon, 22 Oct 2018 06:34:07 GMT",
"version": "v3"
},
{
"created": "Fri, 26 Oct 2018 03:47:02 GMT",
"version": "v4"
},
{
"created": "Wed, 12 Dec 2018 04:44:45 GMT",
"version": "v5"
},
{
"created": "Sat, 19 Jan 2019 16:43:59 GMT",
"version": "v6"
}
] | 2019-01-23 | [
[
"Jin",
"Yingyezhe",
""
],
[
"Zhang",
"Wenrui",
""
],
[
"Li",
"Peng",
""
]
] | Spiking neural networks (SNNs) are positioned to enable spatio-temporal information processing and ultra-low power event-driven neuromorphic hardware. However, SNNs are yet to reach the same performances of conventional deep artificial neural networks (ANNs), a long-standing challenge due to complex dynamics and non-differentiable spike events encountered in training. The existing SNN error backpropagation (BP) methods are limited in terms of scalability, lack of proper handling of spiking discontinuities, and/or mismatch between the rate-coded loss function and computed gradient. We present a hybrid macro/micro level backpropagation (HM2-BP) algorithm for training multi-layer SNNs. The temporal effects are precisely captured by the proposed spike-train level post-synaptic potential (S-PSP) at the microscopic level. The rate-coded errors are defined at the macroscopic level, computed and back-propagated across both macroscopic and microscopic levels. Different from existing BP methods, HM2-BP directly computes the gradient of the rate-coded loss function w.r.t tunable parameters. We evaluate the proposed HM2-BP algorithm by training deep fully connected and convolutional SNNs based on the static MNIST [14] and dynamic neuromorphic N-MNIST [26]. HM2-BP achieves an accuracy level of 99.49% and 98.88% for MNIST and N-MNIST, respectively, outperforming the best reported performances obtained from the existing SNN BP algorithms. Furthermore, the HM2-BP produces the highest accuracies based on SNNs for the EMNIST [3] dataset, and leads to high recognition accuracy for the 16-speaker spoken English letters of TI46 Corpus [16], a challenging patio-temporal speech recognition benchmark for which no prior success based on SNNs was reported. It also achieves competitive performances surpassing those of conventional deep learning models when dealing with asynchronous spiking streams. |
2104.01778 | Yuan Gong | Yuan Gong, Yu-An Chung, James Glass | AST: Audio Spectrogram Transformer | Accepted at Interspeech 2021. Code at
https://github.com/YuanGongND/ast | null | null | null | cs.SD cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the past decade, convolutional neural networks (CNNs) have been widely
adopted as the main building block for end-to-end audio classification models,
which aim to learn a direct mapping from audio spectrograms to corresponding
labels. To better capture long-range global context, a recent trend is to add a
self-attention mechanism on top of the CNN, forming a CNN-attention hybrid
model. However, it is unclear whether the reliance on a CNN is necessary, and
if neural networks purely based on attention are sufficient to obtain good
performance in audio classification. In this paper, we answer the question by
introducing the Audio Spectrogram Transformer (AST), the first
convolution-free, purely attention-based model for audio classification. We
evaluate AST on various audio classification benchmarks, where it achieves new
state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50,
and 98.1% accuracy on Speech Commands V2.
| [
{
"created": "Mon, 5 Apr 2021 05:26:29 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Apr 2021 20:29:37 GMT",
"version": "v2"
},
{
"created": "Thu, 8 Jul 2021 20:16:28 GMT",
"version": "v3"
}
] | 2021-07-12 | [
[
"Gong",
"Yuan",
""
],
[
"Chung",
"Yu-An",
""
],
[
"Glass",
"James",
""
]
] | In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2. |
1807.05490 | Zehong Cao Dr. | Shiming Chen, Yisong Wang, Chin-Teng Lin, Weiping Ding, Zehong Cao | Semi-supervised Feature Learning For Improving Writer Identification | This manuscript is submitting to Information Science | Information Sciences (Volume 482, May 2019, Pages 156-170) | 10.1016/j.ins.2019.01.024 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data augmentation is usually used by supervised learning approaches for
offline writer identification, but such approaches require extra training data
and potentially lead to overfitting errors. In this study, a semi-supervised
feature learning pipeline was proposed to improve the performance of writer
identification by training with extra unlabeled data and the original labeled
data simultaneously. Specifically, we proposed a weighted label smoothing
regularization (WLSR) method for data augmentation, which assigned the weighted
uniform label distribution to the extra unlabeled data. The WLSR method could
regularize the convolutional neural network (CNN) baseline to allow more
discriminative features to be learned to represent the properties of different
writing styles. The experimental results on well-known benchmark datasets
(ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning
approach could significantly improve the baseline measurement and perform
competitively with existing writer identification approaches. Our findings
provide new insights into offline write identification.
| [
{
"created": "Sun, 15 Jul 2018 05:18:20 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Aug 2018 02:08:15 GMT",
"version": "v2"
},
{
"created": "Sat, 6 Oct 2018 15:06:38 GMT",
"version": "v3"
}
] | 2019-05-28 | [
[
"Chen",
"Shiming",
""
],
[
"Wang",
"Yisong",
""
],
[
"Lin",
"Chin-Teng",
""
],
[
"Ding",
"Weiping",
""
],
[
"Cao",
"Zehong",
""
]
] | Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we proposed a weighted label smoothing regularization (WLSR) method for data augmentation, which assigned the weighted uniform label distribution to the extra unlabeled data. The WLSR method could regularize the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach could significantly improve the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline write identification. |
1211.5873 | EPTCS | Franck Cassez (NICTA), Ralf Huuck (NICTA and UNSW), Gerwin Klein
(NICTA and UNSW), Bastian Schlich (ABB) | Proceedings Seventh Conference on Systems Software Verification | null | EPTCS 102, 2012 | 10.4204/EPTCS.102 | null | cs.SE cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This volume contains the papers accepted at the 7th Systems Software
Verification Conference (SSV 2012), held in Sydney, November 28-30, 2012.
The aim of SSV workshops and conference series is to bring together
researchers and developers from both academia and industry who are facing real
software and real problems with the goal of finding real, applicable solutions.
| [
{
"created": "Mon, 26 Nov 2012 07:33:19 GMT",
"version": "v1"
}
] | 2012-11-27 | [
[
"Cassez",
"Franck",
"",
"NICTA"
],
[
"Huuck",
"Ralf",
"",
"NICTA and UNSW"
],
[
"Klein",
"Gerwin",
"",
"NICTA and UNSW"
],
[
"Schlich",
"Bastian",
"",
"ABB"
]
] | This volume contains the papers accepted at the 7th Systems Software Verification Conference (SSV 2012), held in Sydney, November 28-30, 2012. The aim of SSV workshops and conference series is to bring together researchers and developers from both academia and industry who are facing real software and real problems with the goal of finding real, applicable solutions. |
2209.04747 | Radu Tudor Ionescu | Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah | Diffusion Models in Vision: A Survey | Accepted in IEEE Transactions on Pattern Analysis and Machine
Intelligence. 25 pages, 3 figures | null | 10.1109/TPAMI.2023.3261988 | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Denoising diffusion models represent a recent emerging topic in computer
vision, demonstrating remarkable results in the area of generative modeling. A
diffusion model is a deep generative model that is based on two stages, a
forward diffusion stage and a reverse diffusion stage. In the forward diffusion
stage, the input data is gradually perturbed over several steps by adding
Gaussian noise. In the reverse stage, a model is tasked at recovering the
original input data by learning to gradually reverse the diffusion process,
step by step. Diffusion models are widely appreciated for the quality and
diversity of the generated samples, despite their known computational burdens,
i.e. low speeds due to the high number of steps involved during sampling. In
this survey, we provide a comprehensive review of articles on denoising
diffusion models applied in vision, comprising both theoretical and practical
contributions in the field. First, we identify and present three generic
diffusion modeling frameworks, which are based on denoising diffusion
probabilistic models, noise conditioned score networks, and stochastic
differential equations. We further discuss the relations between diffusion
models and other deep generative models, including variational auto-encoders,
generative adversarial networks, energy-based models, autoregressive models and
normalizing flows. Then, we introduce a multi-perspective categorization of
diffusion models applied in computer vision. Finally, we illustrate the current
limitations of diffusion models and envision some interesting directions for
future research.
| [
{
"created": "Sat, 10 Sep 2022 22:00:30 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Oct 2022 08:26:17 GMT",
"version": "v2"
},
{
"created": "Tue, 20 Dec 2022 09:49:30 GMT",
"version": "v3"
},
{
"created": "Thu, 23 Mar 2023 11:42:58 GMT",
"version": "v4"
},
{
"created": "Sat, 1 Apr 2023 14:27:33 GMT",
"version": "v5"
}
] | 2023-04-04 | [
[
"Croitoru",
"Florinel-Alin",
""
],
[
"Hondru",
"Vlad",
""
],
[
"Ionescu",
"Radu Tudor",
""
],
[
"Shah",
"Mubarak",
""
]
] | Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research. |
2405.13319 | Denys Katerenchuk | Denys Katerenchuk and Rivka Levitan | ''You should probably read this'': Hedge Detection in Text | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans express ideas, beliefs, and statements through language. The manner of
expression can carry information indicating the author's degree of confidence
in their statement. Understanding the certainty level of a claim is crucial in
areas such as medicine, finance, engineering, and many others where errors can
lead to disastrous results. In this work, we apply a joint model that leverages
words and part-of-speech tags to improve hedge detection in text and achieve a
new top score on the CoNLL-2010 Wikipedia corpus.
| [
{
"created": "Wed, 22 May 2024 03:25:35 GMT",
"version": "v1"
}
] | 2024-05-24 | [
[
"Katerenchuk",
"Denys",
""
],
[
"Levitan",
"Rivka",
""
]
] | Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus. |
2010.15110 | Gintare Karolina Dziugaite | Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh
Kharaghani, Daniel M. Roy, Surya Ganguli | Deep learning versus kernel learning: an empirical study of loss
landscape geometry and the time evolution of the Neural Tangent Kernel | 19 pages, 19 figures, In Advances in Neural Information Processing
Systems 34 (NeurIPS 2020) | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In suitably initialized wide networks, small learning rates transform deep
neural networks (DNNs) into neural tangent kernel (NTK) machines, whose
training dynamics is well-approximated by a linear weight expansion of the
network at initialization. Standard training, however, diverges from its
linearization in ways that are poorly understood. We study the relationship
between the training dynamics of nonlinear deep networks, the geometry of the
loss landscape, and the time evolution of a data-dependent NTK. We do so
through a large-scale phenomenological analysis of training, synthesizing
diverse measures characterizing loss landscape geometry and NTK dynamics. In
multiple neural architectures and datasets, we find these diverse measures
evolve in a highly correlated manner, revealing a universal picture of the deep
learning process. In this picture, deep network training exhibits a highly
chaotic rapid initial transient that within 2 to 3 epochs determines the final
linearly connected basin of low loss containing the end point of training.
During this chaotic transient, the NTK changes rapidly, learning useful
features from the training data that enables it to outperform the standard
initial NTK by a factor of 3 in less than 3 to 4 epochs. After this rapid
chaotic transient, the NTK changes at constant velocity, and its performance
matches that of full network training in 15% to 45% of training time. Overall,
our analysis reveals a striking correlation between a diverse set of metrics
over training time, governed by a rapid chaotic to stable transition in the
first few epochs, that together poses challenges and opportunities for the
development of more accurate theories of deep learning.
| [
{
"created": "Wed, 28 Oct 2020 17:53:01 GMT",
"version": "v1"
}
] | 2020-10-29 | [
[
"Fort",
"Stanislav",
""
],
[
"Dziugaite",
"Gintare Karolina",
""
],
[
"Paul",
"Mansheej",
""
],
[
"Kharaghani",
"Sepideh",
""
],
[
"Roy",
"Daniel M.",
""
],
[
"Ganguli",
"Surya",
""
]
] | In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) machines, whose training dynamics is well-approximated by a linear weight expansion of the network at initialization. Standard training, however, diverges from its linearization in ways that are poorly understood. We study the relationship between the training dynamics of nonlinear deep networks, the geometry of the loss landscape, and the time evolution of a data-dependent NTK. We do so through a large-scale phenomenological analysis of training, synthesizing diverse measures characterizing loss landscape geometry and NTK dynamics. In multiple neural architectures and datasets, we find these diverse measures evolve in a highly correlated manner, revealing a universal picture of the deep learning process. In this picture, deep network training exhibits a highly chaotic rapid initial transient that within 2 to 3 epochs determines the final linearly connected basin of low loss containing the end point of training. During this chaotic transient, the NTK changes rapidly, learning useful features from the training data that enables it to outperform the standard initial NTK by a factor of 3 in less than 3 to 4 epochs. After this rapid chaotic transient, the NTK changes at constant velocity, and its performance matches that of full network training in 15% to 45% of training time. Overall, our analysis reveals a striking correlation between a diverse set of metrics over training time, governed by a rapid chaotic to stable transition in the first few epochs, that together poses challenges and opportunities for the development of more accurate theories of deep learning. |
2102.12575 | Yuanpeng He | Yuanpeng He | Ordinal relative belief entropy | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Specially customised Entropies are widely applied in measuring the degree of
uncertainties existing in the frame of discernment. However, all of these
entropies regard the frame as a whole that has already been determined which
dose not conform to actual situations. In real life, everything comes in an
order, so how to measure uncertainties of the dynamic process of determining
sequence of propositions contained in a frame of discernment is still an open
issue and no related research has been proceeded. Therefore, a novel ordinal
entropy to measure uncertainties of the frame of discernment considering the
order of confirmation of propositions is proposed in this paper. Compared with
traditional entropies, it manifests effects on degree of uncertainty brought by
orders of propositions existing in a frame of discernment. Besides, some
numerical examples are provided to verify the correctness and validity of the
proposed entropy in this paper.
| [
{
"created": "Sun, 21 Feb 2021 04:17:04 GMT",
"version": "v1"
}
] | 2021-02-26 | [
[
"He",
"Yuanpeng",
""
]
] | Specially customised Entropies are widely applied in measuring the degree of uncertainties existing in the frame of discernment. However, all of these entropies regard the frame as a whole that has already been determined which dose not conform to actual situations. In real life, everything comes in an order, so how to measure uncertainties of the dynamic process of determining sequence of propositions contained in a frame of discernment is still an open issue and no related research has been proceeded. Therefore, a novel ordinal entropy to measure uncertainties of the frame of discernment considering the order of confirmation of propositions is proposed in this paper. Compared with traditional entropies, it manifests effects on degree of uncertainty brought by orders of propositions existing in a frame of discernment. Besides, some numerical examples are provided to verify the correctness and validity of the proposed entropy in this paper. |
2001.01697 | Ashiqur KhudaBukhsh Ashiqur Rahman KhudaBukhsh | Rupak Sarkar, Hirak Sarkar, Sayantan Mahinder, Ashiqur R. KhudaBukhsh | Social Media Attributions in the Context of Water Crisis | null | null | null | null | cs.CY cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attribution of natural disasters/collective misfortune is a widely-studied
political science problem. However, such studies are typically survey-centric
or rely on a handful of experts to weigh in on the matter. In this paper, we
explore how can we use social media data and an AI-driven approach to
complement traditional surveys and automatically extract attribution factors.
We focus on the most-recent Chennai water crisis which started off as a
regional issue but rapidly escalated into a discussion topic with global
importance following alarming water-crisis statistics. Specifically, we present
a novel prediction task of attribution tie detection which identifies the
factors held responsible for the crisis (e.g., poor city planning, exploding
population etc.). On a challenging data set constructed from YouTube comments
(72,098 comments posted by 43,859 users on 623 relevant videos to the crisis),
we present a neural classifier to extract attribution ties that achieved a
reasonable performance (Accuracy: 81.34\% on attribution detection and 71.19\%
on attribution resolution).
| [
{
"created": "Mon, 6 Jan 2020 18:20:09 GMT",
"version": "v1"
}
] | 2020-01-07 | [
[
"Sarkar",
"Rupak",
""
],
[
"Sarkar",
"Hirak",
""
],
[
"Mahinder",
"Sayantan",
""
],
[
"KhudaBukhsh",
"Ashiqur R.",
""
]
] | Attribution of natural disasters/collective misfortune is a widely-studied political science problem. However, such studies are typically survey-centric or rely on a handful of experts to weigh in on the matter. In this paper, we explore how can we use social media data and an AI-driven approach to complement traditional surveys and automatically extract attribution factors. We focus on the most-recent Chennai water crisis which started off as a regional issue but rapidly escalated into a discussion topic with global importance following alarming water-crisis statistics. Specifically, we present a novel prediction task of attribution tie detection which identifies the factors held responsible for the crisis (e.g., poor city planning, exploding population etc.). On a challenging data set constructed from YouTube comments (72,098 comments posted by 43,859 users on 623 relevant videos to the crisis), we present a neural classifier to extract attribution ties that achieved a reasonable performance (Accuracy: 81.34\% on attribution detection and 71.19\% on attribution resolution). |
1609.08265 | Sudhir R. Ghorpade | Sudhir R. Ghorpade and Prasant Singh | Minimum Distance and the Minimum Weight Codewords of Schubert Codes | 26 pages; Slightly revised version; to appear in Finite Fields Appl | Finite Fields Appl. 49 (2018), 1-28 | 10.1016/j.ffa.2017.08.014 | null | cs.IT math.AG math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider linear codes associated to Schubert varieties in Grassmannians. A
formula for the minimum distance of these codes was conjectured in 2000 and
after having been established in various special cases, it was proved in 2008
by Xiang. We give an alternative proof of this formula. Further, we propose a
characterization of the minimum weight codewords of Schubert codes by
introducing the notion of Schubert decomposable elements of certain exterior
powers. It is shown that codewords corresponding to Schubert decomposable
elements are of minimum weight and also that the converse is true in many
cases. A lower bound, and in some cases, an exact formula, for the number of
minimum weight codewords of Schubert codes is also given. From a geometric
point of view, these results correspond to determining the maximum number of
$\mathbb{F}_q$-rational points that can lie on a hyperplane section of a
Schubert variety in a Grassmannian with its nondegenerate embedding in a
projective subspace of the Pl\"ucker projective space, and also the number of
hyperplanes for which the maximum is attained.
| [
{
"created": "Tue, 27 Sep 2016 05:46:33 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Sep 2017 10:37:51 GMT",
"version": "v2"
}
] | 2018-01-30 | [
[
"Ghorpade",
"Sudhir R.",
""
],
[
"Singh",
"Prasant",
""
]
] | We consider linear codes associated to Schubert varieties in Grassmannians. A formula for the minimum distance of these codes was conjectured in 2000 and after having been established in various special cases, it was proved in 2008 by Xiang. We give an alternative proof of this formula. Further, we propose a characterization of the minimum weight codewords of Schubert codes by introducing the notion of Schubert decomposable elements of certain exterior powers. It is shown that codewords corresponding to Schubert decomposable elements are of minimum weight and also that the converse is true in many cases. A lower bound, and in some cases, an exact formula, for the number of minimum weight codewords of Schubert codes is also given. From a geometric point of view, these results correspond to determining the maximum number of $\mathbb{F}_q$-rational points that can lie on a hyperplane section of a Schubert variety in a Grassmannian with its nondegenerate embedding in a projective subspace of the Pl\"ucker projective space, and also the number of hyperplanes for which the maximum is attained. |
2205.11558 | Sreejan Kumar | Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh,
Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen,
Karthik Narasimhan, Thomas L. Griffiths | Using Natural Language and Program Abstractions to Instill Human
Inductive Biases in Machines | In Proceedings of the 36th Conference on Neural Information
Processing Systems (NeurIPS 2022), winner of Outstanding Paper Award | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Strong inductive biases give humans the ability to quickly learn to perform a
variety of tasks. Although meta-learning is a method to endow neural networks
with useful inductive biases, agents trained by meta-learning may sometimes
acquire very different strategies from humans. We show that co-training these
agents on predicting representations from natural language task descriptions
and programs induced to generate such tasks guides them toward more human-like
inductive biases. Human-generated language descriptions and program induction
models that add new learned primitives both contain abstract concepts that can
compress description length. Co-training on these representations result in
more human-like behavior in downstream meta-reinforcement learning agents than
less abstract controls (synthetic language descriptions, program induction
without learned primitives), suggesting that the abstraction supported by these
representations is key.
| [
{
"created": "Mon, 23 May 2022 18:17:58 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Oct 2022 12:32:49 GMT",
"version": "v2"
},
{
"created": "Sun, 5 Feb 2023 18:44:46 GMT",
"version": "v3"
}
] | 2023-02-07 | [
[
"Kumar",
"Sreejan",
""
],
[
"Correa",
"Carlos G.",
""
],
[
"Dasgupta",
"Ishita",
""
],
[
"Marjieh",
"Raja",
""
],
[
"Hu",
"Michael Y.",
""
],
[
"Hawkins",
"Robert D.",
""
],
[
"Daw",
"Nathaniel D.",
""
],
[
"Cohen",
"Jonathan D.",
""
],
[
"Narasimhan",
"Karthik",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key. |
1207.4147 | Nathanael Hyafil | Nathanael Hyafil, Craig Boutilier | Regret Minimizing Equilibria and Mechanisms for Games with Strict Type
Uncertainty | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-268-277 | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mechanism design has found considerable application to the construction of
agent-interaction protocols. In the standard setting, the type (e.g., utility
function) of an agent is not known by other agents, nor is it known by the
mechanism designer. When this uncertainty is quantified probabilistically, a
mechanism induces a game of incomplete information among the agents. However,
in many settings, uncertainty over utility functions cannot easily be
quantified. We consider the problem of incomplete information games in which
type uncertainty is strict or unquantified. We propose the use of minimax
regret as a decision criterion in such games, a robust approach for dealing
with type uncertainty. We define minimax-regret equilibria and prove that these
exist in mixed strategies for finite games. We also consider the problem of
mechanism design in this framework by adopting minimax regret as an
optimization criterion for the designer itself, and study automated
optimization of such mechanisms.
| [
{
"created": "Wed, 11 Jul 2012 14:55:55 GMT",
"version": "v1"
}
] | 2012-07-19 | [
[
"Hyafil",
"Nathanael",
""
],
[
"Boutilier",
"Craig",
""
]
] | Mechanism design has found considerable application to the construction of agent-interaction protocols. In the standard setting, the type (e.g., utility function) of an agent is not known by other agents, nor is it known by the mechanism designer. When this uncertainty is quantified probabilistically, a mechanism induces a game of incomplete information among the agents. However, in many settings, uncertainty over utility functions cannot easily be quantified. We consider the problem of incomplete information games in which type uncertainty is strict or unquantified. We propose the use of minimax regret as a decision criterion in such games, a robust approach for dealing with type uncertainty. We define minimax-regret equilibria and prove that these exist in mixed strategies for finite games. We also consider the problem of mechanism design in this framework by adopting minimax regret as an optimization criterion for the designer itself, and study automated optimization of such mechanisms. |
2105.10216 | Simon Walk | Matthias W\"olbitsch, Thomas Hasler, Patrick Kasper, Denis Helic,
Simon Walk | RFID-based Article-to-Fixture Predictions in Real-World Fashion Stores | Extended version of conference submission to IEEE RFID | null | null | null | cs.IR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In recent years, Radio Frequency Identification (RFID) technology has been
applied to improve numerous processes, such as inventory management in retail
stores. However, automatic localization of RFID-tagged goods in stores is still
a challenging problem. To address this issue, we equip fixtures (e.g., shelves)
with reference tags and use data we collect during RFID-based stocktakes to map
articles to fixtures. Knowing the location of goods enables the implementation
of several practical applications, such as automated Money Mapping (i.e., a
heat map of sales across fixtures). Specifically, we conduct controlled lab
experiments and a case-study in two fashion retail stores to evaluate our
article-to-fixture prediction approaches. The approaches are based on
calculating distances between read event time series using DTW, and clustering
of read events using DBSCAN. We find that, read events collected during
RFID-based stocktakes can be used to assign articles to fixtures with an
accuracy of more than 90%. Additionally, we conduct a pilot to investigate the
challenges related to the integration of such a localization system in the
day-to-day business of retail stores. Hence, in this paper we present an
exploratory venture into novel and practical RFID-based applications in fashion
retails stores, beyond the scope of stock management.
| [
{
"created": "Fri, 21 May 2021 09:12:36 GMT",
"version": "v1"
}
] | 2021-05-24 | [
[
"Wölbitsch",
"Matthias",
""
],
[
"Hasler",
"Thomas",
""
],
[
"Kasper",
"Patrick",
""
],
[
"Helic",
"Denis",
""
],
[
"Walk",
"Simon",
""
]
] | In recent years, Radio Frequency Identification (RFID) technology has been applied to improve numerous processes, such as inventory management in retail stores. However, automatic localization of RFID-tagged goods in stores is still a challenging problem. To address this issue, we equip fixtures (e.g., shelves) with reference tags and use data we collect during RFID-based stocktakes to map articles to fixtures. Knowing the location of goods enables the implementation of several practical applications, such as automated Money Mapping (i.e., a heat map of sales across fixtures). Specifically, we conduct controlled lab experiments and a case-study in two fashion retail stores to evaluate our article-to-fixture prediction approaches. The approaches are based on calculating distances between read event time series using DTW, and clustering of read events using DBSCAN. We find that, read events collected during RFID-based stocktakes can be used to assign articles to fixtures with an accuracy of more than 90%. Additionally, we conduct a pilot to investigate the challenges related to the integration of such a localization system in the day-to-day business of retail stores. Hence, in this paper we present an exploratory venture into novel and practical RFID-based applications in fashion retails stores, beyond the scope of stock management. |
2110.05679 | Xuechen Li | Xuechen Li, Florian Tram\`er, Percy Liang, Tatsunori Hashimoto | Large Language Models Can Be Strong Differentially Private Learners | 31 pages; update ethics statement to clarify benefits and potential
long-term harms | null | null | null | cs.LG cs.CL | http://creativecommons.org/licenses/by/4.0/ | Differentially Private (DP) learning has seen limited success for building
large deep learning models of text, and straightforward attempts at applying
Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have
resulted in large performance drops and high computational overhead. We show
that this performance drop can be mitigated with (1) the use of large
pretrained language models; (2) non-standard hyperparameters that suit DP
optimization; and (3) fine-tuning objectives which are aligned with the
pretraining procedure. With the above, we obtain NLP models that outperform
state-of-the-art DP-trained models under the same privacy budget and strong
non-private baselines -- by directly fine-tuning pretrained models with DP
optimization on moderately-sized corpora. To address the computational
challenge of running DP-SGD with large Transformers, we propose a memory saving
technique that allows clipping in DP-SGD to run without instantiating
per-example gradients for any linear layer in the model. The technique enables
privately training Transformers with almost the same memory cost as non-private
training at a modest run-time overhead. Contrary to conventional wisdom that DP
optimization fails at learning high-dimensional models (due to noise that
scales with dimension) empirical results reveal that private learning with
pretrained language models doesn't tend to suffer from dimension-dependent
performance degradation. Code to reproduce results can be found at
https://github.com/lxuechen/private-transformers.
| [
{
"created": "Tue, 12 Oct 2021 01:45:27 GMT",
"version": "v1"
},
{
"created": "Sun, 10 Jul 2022 20:48:32 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Jul 2022 01:30:31 GMT",
"version": "v3"
},
{
"created": "Mon, 18 Jul 2022 01:42:10 GMT",
"version": "v4"
},
{
"created": "Wed, 12 Oct 2022 05:25:28 GMT",
"version": "v5"
},
{
"created": "Thu, 10 Nov 2022 18:42:34 GMT",
"version": "v6"
}
] | 2022-11-11 | [
[
"Li",
"Xuechen",
""
],
[
"Tramèr",
"Florian",
""
],
[
"Liang",
"Percy",
""
],
[
"Hashimoto",
"Tatsunori",
""
]
] | Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and straightforward attempts at applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead. We show that this performance drop can be mitigated with (1) the use of large pretrained language models; (2) non-standard hyperparameters that suit DP optimization; and (3) fine-tuning objectives which are aligned with the pretraining procedure. With the above, we obtain NLP models that outperform state-of-the-art DP-trained models under the same privacy budget and strong non-private baselines -- by directly fine-tuning pretrained models with DP optimization on moderately-sized corpora. To address the computational challenge of running DP-SGD with large Transformers, we propose a memory saving technique that allows clipping in DP-SGD to run without instantiating per-example gradients for any linear layer in the model. The technique enables privately training Transformers with almost the same memory cost as non-private training at a modest run-time overhead. Contrary to conventional wisdom that DP optimization fails at learning high-dimensional models (due to noise that scales with dimension) empirical results reveal that private learning with pretrained language models doesn't tend to suffer from dimension-dependent performance degradation. Code to reproduce results can be found at https://github.com/lxuechen/private-transformers. |
2303.14821 | Michael Walter | Mich\`ele Vergne and Michael Walter | Moment cone membership for quivers in strongly polynomial time | 7 pages | null | null | null | cs.CC math.CO math.RT math.SG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this note we observe that membership in moment cones of spaces of quiver
representations can be decided in strongly polynomial time, for any acyclic
quiver. This generalizes a recent result by Chindris-Collins-Kline for
bipartite quivers. Their approach was to construct "multiplicity polytopes"
with a geometric realization similar to the Knutson-Tao polytopes for tensor
product multiplicities. Here we show that a less geometric but straightforward
variant of their construction leads to such a multiplicity polytope for any
acyclic quiver. Tardos' strongly polynomial time algorithm for combinatorial
linear programming along with the saturation property then implies that moment
cone membership can be decided in strongly polynomial time. The analogous
question for semi-invariants remains open.
| [
{
"created": "Sun, 26 Mar 2023 21:11:05 GMT",
"version": "v1"
}
] | 2023-03-28 | [
[
"Vergne",
"Michèle",
""
],
[
"Walter",
"Michael",
""
]
] | In this note we observe that membership in moment cones of spaces of quiver representations can be decided in strongly polynomial time, for any acyclic quiver. This generalizes a recent result by Chindris-Collins-Kline for bipartite quivers. Their approach was to construct "multiplicity polytopes" with a geometric realization similar to the Knutson-Tao polytopes for tensor product multiplicities. Here we show that a less geometric but straightforward variant of their construction leads to such a multiplicity polytope for any acyclic quiver. Tardos' strongly polynomial time algorithm for combinatorial linear programming along with the saturation property then implies that moment cone membership can be decided in strongly polynomial time. The analogous question for semi-invariants remains open. |
0710.4751 | EDA Publishing Association | Lars Wehmeyer, Peter Marwedel | Influence of Memory Hierarchies on Predictability for Time Constrained
Embedded Software | Submitted on behalf of EDAA (http://www.edaa.com/) | Dans Design, Automation and Test in Europe - DATE'05, Munich :
Allemagne (2005) | null | null | cs.AR | null | Safety-critical embedded systems having to meet real-time constraints are
expected to be highly predictable in order to guarantee at design time that
certain timing deadlines will always be met. This requirement usually prevents
designers from utilizing caches due to their highly dynamic, thus hardly
predictable behavior. The integration of scratchpad memories represents an
alternative approach which allows the system to benefit from a performance gain
comparable to that of caches while at the same time maintaining predictability.
In this work, we compare the impact of scratchpad memories and caches on worst
case execution time (WCET) analysis results. We show that caches, despite
requiring complex techniques, can have a negative impact on the predicted WCET,
while the estimated WCET for scratchpad memories scales with the achieved
Performance gain at no extra analysis cost.
| [
{
"created": "Thu, 25 Oct 2007 09:51:11 GMT",
"version": "v1"
}
] | 2011-11-09 | [
[
"Wehmeyer",
"Lars",
""
],
[
"Marwedel",
"Peter",
""
]
] | Safety-critical embedded systems having to meet real-time constraints are expected to be highly predictable in order to guarantee at design time that certain timing deadlines will always be met. This requirement usually prevents designers from utilizing caches due to their highly dynamic, thus hardly predictable behavior. The integration of scratchpad memories represents an alternative approach which allows the system to benefit from a performance gain comparable to that of caches while at the same time maintaining predictability. In this work, we compare the impact of scratchpad memories and caches on worst case execution time (WCET) analysis results. We show that caches, despite requiring complex techniques, can have a negative impact on the predicted WCET, while the estimated WCET for scratchpad memories scales with the achieved Performance gain at no extra analysis cost. |
2401.00978 | Ke Li | Shuang Li, Ke Li, Wei Li, Ming Yang | Evolutionary Alternating Direction Method of Multipliers for Constrained
Multi-Objective Optimization with Unknown Constraints | 29 pages, 17 figures | null | null | COLALab Report #2024002 | cs.NE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Constrained multi-objective optimization problems (CMOPs) pervade real-world
applications in science, engineering, and design. Constraint violation has been
a building block in designing evolutionary multi-objective optimization
algorithms for solving constrained multi-objective optimization problems.
However, in certain scenarios, constraint functions might be unknown or
inadequately defined, making constraint violation unattainable and potentially
misleading for conventional constrained evolutionary multi-objective
optimization algorithms. To address this issue, we present the first of its
kind evolutionary optimization framework, inspired by the principles of the
alternating direction method of multipliers that decouples objective and
constraint functions. This framework tackles CMOPs with unknown constraints by
reformulating the original problem into an additive form of two subproblems,
each of which is allotted a dedicated evolutionary population. Notably, these
two populations operate towards complementary evolutionary directions during
their optimization processes. In order to minimize discrepancy, their
evolutionary directions alternate, aiding the discovery of feasible solutions.
Comparative experiments conducted against five state-of-the-art constrained
evolutionary multi-objective optimization algorithms, on 120 benchmark test
problem instances with varying properties, as well as two real-world
engineering optimization problems, demonstrate the effectiveness and
superiority of our proposed framework. Its salient features include faster
convergence and enhanced resilience to various Pareto front shapes.
| [
{
"created": "Tue, 2 Jan 2024 00:38:20 GMT",
"version": "v1"
}
] | 2024-01-03 | [
[
"Li",
"Shuang",
""
],
[
"Li",
"Ke",
""
],
[
"Li",
"Wei",
""
],
[
"Yang",
"Ming",
""
]
] | Constrained multi-objective optimization problems (CMOPs) pervade real-world applications in science, engineering, and design. Constraint violation has been a building block in designing evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems. However, in certain scenarios, constraint functions might be unknown or inadequately defined, making constraint violation unattainable and potentially misleading for conventional constrained evolutionary multi-objective optimization algorithms. To address this issue, we present the first of its kind evolutionary optimization framework, inspired by the principles of the alternating direction method of multipliers that decouples objective and constraint functions. This framework tackles CMOPs with unknown constraints by reformulating the original problem into an additive form of two subproblems, each of which is allotted a dedicated evolutionary population. Notably, these two populations operate towards complementary evolutionary directions during their optimization processes. In order to minimize discrepancy, their evolutionary directions alternate, aiding the discovery of feasible solutions. Comparative experiments conducted against five state-of-the-art constrained evolutionary multi-objective optimization algorithms, on 120 benchmark test problem instances with varying properties, as well as two real-world engineering optimization problems, demonstrate the effectiveness and superiority of our proposed framework. Its salient features include faster convergence and enhanced resilience to various Pareto front shapes. |
2208.02244 | Colin Topping | Colin Topping, Ola Michalec, Awais Rashid | Contrasting global approaches for identifying and managing cybersecurity
risks in supply chains | 8 pages, 2 figures | null | null | null | cs.CR cs.CY | http://creativecommons.org/licenses/by/4.0/ | Supply chains are increasingly targeted by threat actors. Using a recent
taxonomy, we contrast the diverse levels of detail given by national
authorities. The threat is commonly acknowledged, but guidance is disjointed.
NIST SP 800-161 aligns closely with the taxonomy and offers a potential pathway
towards a common set of principles.
| [
{
"created": "Wed, 3 Aug 2022 17:50:16 GMT",
"version": "v1"
}
] | 2022-08-04 | [
[
"Topping",
"Colin",
""
],
[
"Michalec",
"Ola",
""
],
[
"Rashid",
"Awais",
""
]
] | Supply chains are increasingly targeted by threat actors. Using a recent taxonomy, we contrast the diverse levels of detail given by national authorities. The threat is commonly acknowledged, but guidance is disjointed. NIST SP 800-161 aligns closely with the taxonomy and offers a potential pathway towards a common set of principles. |
2401.00315 | Yifan Su | Yifan Su, Rishi Veerapaneni, Jiaoyang Li | Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders
for More Efficient Multi-Agent Path Finding Plan Execution | Accepted by AAAI-2024 | null | null | null | cs.AI cs.MA cs.RO | http://creativecommons.org/licenses/by/4.0/ | The Multi-Agent Path Finding (MAPF) problem involves planning collision-free
paths for multiple agents in a shared environment. The majority of MAPF solvers
rely on the assumption that an agent can arrive at a specific location at a
specific timestep. However, real-world execution uncertainties can cause agents
to deviate from this assumption, leading to collisions and deadlocks. Prior
research solves this problem by having agents follow a Temporal Plan Graph
(TPG), enforcing a consistent passing order at every location as defined in the
MAPF plan. However, we show that TPGs are overly strict because, in some
circumstances, satisfying the passing order requires agents to wait
unnecessarily, leading to longer execution time. To overcome this issue, we
introduce a new graphical representation called a Bidirectional Temporal Plan
Graph (BTPG), which allows switching passing orders during execution to avoid
unnecessary waiting time. We design two anytime algorithms for constructing a
BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following
BTPGs consistently outperforms following TPGs, reducing unnecessary waits by
8-20%.
| [
{
"created": "Sat, 30 Dec 2023 20:23:27 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jan 2024 01:23:49 GMT",
"version": "v2"
}
] | 2024-01-09 | [
[
"Su",
"Yifan",
""
],
[
"Veerapaneni",
"Rishi",
""
],
[
"Li",
"Jiaoyang",
""
]
] | The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%. |
2204.01193 | Nu Hoang | Thien-Nu Hoang, Daehee Kim | Detecting In-vehicle Intrusion via Semi-supervised Learning-based
Convolutional Adversarial Autoencoders | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | With the development of autonomous vehicle technology, the controller area
network (CAN) bus has become the de facto standard for an in-vehicle
communication system because of its simplicity and efficiency. However, without
any encryption and authentication mechanisms, the in-vehicle network using the
CAN protocol is susceptible to a wide range of attacks. Many studies, which are
mostly based on machine learning, have proposed installing an intrusion
detection system (IDS) for anomaly detection in the CAN bus system. Although
machine learning methods have many advantages for IDS, previous models usually
require a large amount of labeled data, which results in high time and labor
costs. To handle this problem, we propose a novel semi-supervised
learning-based convolutional adversarial autoencoder model in this paper. The
proposed model combines two popular deep learning models: autoencoder and
generative adversarial networks. First, the model is trained with unlabeled
data to learn the manifolds of normal and attack patterns. Then, only a small
number of labeled samples are used in supervised training. The proposed model
can detect various kinds of message injection attacks, such as DoS, fuzzy, and
spoofing, as well as unknown attacks. The experimental results show that the
proposed model achieves the highest F1 score of 0.99 and a low error rate of
0.1\% with limited labeled data compared to other supervised methods. In
addition, we show that the model can meet the real-time requirement by
analyzing the model complexity in terms of the number of trainable parameters
and inference time. This study successfully reduced the number of model
parameters by five times and the inference time by eight times, compared to a
state-of-the-art model.
| [
{
"created": "Mon, 4 Apr 2022 00:50:27 GMT",
"version": "v1"
}
] | 2022-04-05 | [
[
"Hoang",
"Thien-Nu",
""
],
[
"Kim",
"Daehee",
""
]
] | With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication system because of its simplicity and efficiency. However, without any encryption and authentication mechanisms, the in-vehicle network using the CAN protocol is susceptible to a wide range of attacks. Many studies, which are mostly based on machine learning, have proposed installing an intrusion detection system (IDS) for anomaly detection in the CAN bus system. Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. The proposed model combines two popular deep learning models: autoencoder and generative adversarial networks. First, the model is trained with unlabeled data to learn the manifolds of normal and attack patterns. Then, only a small number of labeled samples are used in supervised training. The proposed model can detect various kinds of message injection attacks, such as DoS, fuzzy, and spoofing, as well as unknown attacks. The experimental results show that the proposed model achieves the highest F1 score of 0.99 and a low error rate of 0.1\% with limited labeled data compared to other supervised methods. In addition, we show that the model can meet the real-time requirement by analyzing the model complexity in terms of the number of trainable parameters and inference time. This study successfully reduced the number of model parameters by five times and the inference time by eight times, compared to a state-of-the-art model. |
2110.10151 | Miko Stulajter | Miko M. Stulajter and Ronald M. Caplan and Jon A. Linker | Can Fortran's 'do concurrent' replace directives for accelerated
computing? | 18 pages, 2 figures, Accepted for publication at WACCPD 2021 | null | null | null | cs.MS cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, there has been growing interest in using standard language
constructs (e.g. C++'s Parallel Algorithms and Fortran's do concurrent) for
accelerated computing as an alternative to directive-based APIs (e.g. OpenMP
and OpenACC). These constructs have the potential to be more portable, and some
compilers already (or have plans to) support such standards. Here, we look at
the current capabilities, portability, and performance of replacing directives
with Fortran's do concurrent using a mini-app that currently implements OpenACC
for GPU-acceleration and OpenMP for multi-core CPU parallelism. We replace as
many directives as possible with do concurrent, testing various configurations
and compiler options within three major compilers: GNU's gfortran, NVIDIA's
nvfortran, and Intel's ifort. We find that with the right compiler versions and
flags, many directives can be replaced without loss of performance or
portability, and, in the case of nvfortran, they can all be replaced. We
discuss limitations that may apply to more complicated codes and future
language additions that may mitigate them. The software and Singularity
containers are publicly provided to allow the results to be reproduced.
| [
{
"created": "Mon, 18 Oct 2021 23:01:07 GMT",
"version": "v1"
}
] | 2021-10-22 | [
[
"Stulajter",
"Miko M.",
""
],
[
"Caplan",
"Ronald M.",
""
],
[
"Linker",
"Jon A.",
""
]
] | Recently, there has been growing interest in using standard language constructs (e.g. C++'s Parallel Algorithms and Fortran's do concurrent) for accelerated computing as an alternative to directive-based APIs (e.g. OpenMP and OpenACC). These constructs have the potential to be more portable, and some compilers already (or have plans to) support such standards. Here, we look at the current capabilities, portability, and performance of replacing directives with Fortran's do concurrent using a mini-app that currently implements OpenACC for GPU-acceleration and OpenMP for multi-core CPU parallelism. We replace as many directives as possible with do concurrent, testing various configurations and compiler options within three major compilers: GNU's gfortran, NVIDIA's nvfortran, and Intel's ifort. We find that with the right compiler versions and flags, many directives can be replaced without loss of performance or portability, and, in the case of nvfortran, they can all be replaced. We discuss limitations that may apply to more complicated codes and future language additions that may mitigate them. The software and Singularity containers are publicly provided to allow the results to be reproduced. |
2006.05044 | Baocheng Zhu | Baocheng Zhu, Shijun Wang and James Zhang | Neural Physicist: Learning Physical Dynamics from Image Sequences | 19 pages, 20 figures | null | null | null | cs.LG cs.AI stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present a novel architecture named Neural Physicist (NeurPhy) to learn
physical dynamics directly from image sequences using deep neural networks. For
any physical system, given the global system parameters, the time evolution of
states is governed by the underlying physical laws. How to learn meaningful
system representations in an end-to-end way and estimate accurate state
transition dynamics facilitating long-term prediction have been long-standing
challenges. In this paper, by leveraging recent progresses in representation
learning and state space models (SSMs), we propose NeurPhy, which uses
variational auto-encoder (VAE) to extract underlying Markovian dynamic state at
each time step, neural process (NP) to extract the global system parameters,
and a non-linear non-recurrent stochastic state space model to learn the
physical dynamic transition. We apply NeurPhy to two physical experimental
environments, i.e., damped pendulum and planetary orbits motion, and achieve
promising results. Our model can not only extract the physically meaningful
state representations, but also learn the state transition dynamics enabling
long-term predictions for unseen image sequences. Furthermore, from the
manifold dimension of the latent state space, we can easily identify the degree
of freedom (DoF) of the underlying physical systems.
| [
{
"created": "Tue, 9 Jun 2020 04:36:51 GMT",
"version": "v1"
}
] | 2020-06-11 | [
[
"Zhu",
"Baocheng",
""
],
[
"Wang",
"Shijun",
""
],
[
"Zhang",
"James",
""
]
] | We present a novel architecture named Neural Physicist (NeurPhy) to learn physical dynamics directly from image sequences using deep neural networks. For any physical system, given the global system parameters, the time evolution of states is governed by the underlying physical laws. How to learn meaningful system representations in an end-to-end way and estimate accurate state transition dynamics facilitating long-term prediction have been long-standing challenges. In this paper, by leveraging recent progresses in representation learning and state space models (SSMs), we propose NeurPhy, which uses variational auto-encoder (VAE) to extract underlying Markovian dynamic state at each time step, neural process (NP) to extract the global system parameters, and a non-linear non-recurrent stochastic state space model to learn the physical dynamic transition. We apply NeurPhy to two physical experimental environments, i.e., damped pendulum and planetary orbits motion, and achieve promising results. Our model can not only extract the physically meaningful state representations, but also learn the state transition dynamics enabling long-term predictions for unseen image sequences. Furthermore, from the manifold dimension of the latent state space, we can easily identify the degree of freedom (DoF) of the underlying physical systems. |
1612.00478 | Noranart Vesdapunt | Jonathan Shen, Noranart Vesdapunt, Vishnu N. Boddeti, Kris M. Kitani | In Teacher We Trust: Learning Compressed Models for Pedestrian Detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep convolutional neural networks continue to advance the state-of-the-art
in many domains as they grow bigger and more complex. It has been observed that
many of the parameters of a large network are redundant, allowing for the
possibility of learning a smaller network that mimics the outputs of the large
network through a process called Knowledge Distillation. We show, however, that
standard Knowledge Distillation is not effective for learning small models for
the task of pedestrian detection. To improve this process, we introduce a
higher-dimensional hint layer to increase information flow. We also estimate
the variance in the outputs of the large network and propose a loss function to
incorporate this uncertainty. Finally, we attempt to boost the complexity of
the small network without increasing its size by using as input hand-designed
features that have been demonstrated to be effective for pedestrian detection.
We succeed in training a model that contains $400\times$ fewer parameters than
the large network while outperforming AlexNet on the Caltech Pedestrian
Dataset.
| [
{
"created": "Thu, 1 Dec 2016 21:37:19 GMT",
"version": "v1"
}
] | 2016-12-05 | [
[
"Shen",
"Jonathan",
""
],
[
"Vesdapunt",
"Noranart",
""
],
[
"Boddeti",
"Vishnu N.",
""
],
[
"Kitani",
"Kris M.",
""
]
] | Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process, we introduce a higher-dimensional hint layer to increase information flow. We also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally, we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection. We succeed in training a model that contains $400\times$ fewer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset. |
2311.02405 | Yo-Seb Jeon | Seonjung Kim, Yongjeong Oh, and Yo-Seb Jeon | SplitMAC: Wireless Split Learning over Multiple Access Channels | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel split learning (SL) framework, referred to as
SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink
transmission over multiple access channels. The key strategy is to divide
devices into multiple groups and allow the devices within the same group to
simultaneously transmit their smashed data and device-side models over the
multiple access channels. The optimization problem of device grouping to
minimize SL latency is formulated, and the benefit of device grouping in
reducing the uplink latency of SL is theoretically derived. By examining a
two-device grouping case, two asymptotically-optimal algorithms are devised for
device grouping in low and high signal-to-noise ratio (SNR) scenarios,
respectively, while providing proofs of their optimality. By merging these
algorithms, a near-optimal device grouping algorithm is proposed to cover a
wide range of SNR. Our SL framework is also extended to consider practical
fading channels and to support a general group size. Simulation results
demonstrate that our SL framework with the proposed device grouping algorithm
is superior to existing SL frameworks in reducing SL latency.
| [
{
"created": "Sat, 4 Nov 2023 13:59:26 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Mar 2024 12:46:02 GMT",
"version": "v2"
}
] | 2024-03-20 | [
[
"Kim",
"Seonjung",
""
],
[
"Oh",
"Yongjeong",
""
],
[
"Jeon",
"Yo-Seb",
""
]
] | This paper presents a novel split learning (SL) framework, referred to as SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink transmission over multiple access channels. The key strategy is to divide devices into multiple groups and allow the devices within the same group to simultaneously transmit their smashed data and device-side models over the multiple access channels. The optimization problem of device grouping to minimize SL latency is formulated, and the benefit of device grouping in reducing the uplink latency of SL is theoretically derived. By examining a two-device grouping case, two asymptotically-optimal algorithms are devised for device grouping in low and high signal-to-noise ratio (SNR) scenarios, respectively, while providing proofs of their optimality. By merging these algorithms, a near-optimal device grouping algorithm is proposed to cover a wide range of SNR. Our SL framework is also extended to consider practical fading channels and to support a general group size. Simulation results demonstrate that our SL framework with the proposed device grouping algorithm is superior to existing SL frameworks in reducing SL latency. |
2406.02726 | Sanghyun Lee | Sanghyun Lee, Chanyoung Park | Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate traffic flow forecasting is a crucial research topic in
transportation management. However, it is a challenging problem due to rapidly
changing traffic conditions, high nonlinearity of traffic flow, and complex
spatial and temporal correlations of road networks. Most existing studies
either try to capture the spatial dependencies between roads using the same
semantic graph over different time steps, or assume all sensors on the roads
are equally likely to be connected regardless of the distance between them.
However, we observe that the spatial dependencies between roads indeed change
over time, and two distant roads are not likely to be helpful to each other
when predicting the traffic flow, both of which limit the performance of
existing studies. In this paper, we propose Temporal Graph Learning Recurrent
Neural Network (TGLRN) to address these problems. More precisely, to
effectively model the nature of time series, we leverage Recurrent Neural
Networks (RNNs) to dynamically construct a graph at each time step, thereby
capturing the time-evolving spatial dependencies between roads (i.e.,
microscopic view). Simultaneously, we provide the Adaptive Structure
Information to the model, ensuring that close and consecutive sensors are
considered to be more important for predicting the traffic flow (i.e.,
macroscopic view). Furthermore, to endow TGLRN with robustness, we introduce an
edge sampling strategy when constructing the graph at each time step, which
eventually leads to further improvements on the model performance. Experimental
results on four commonly used real-world benchmark datasets show the
effectiveness of TGLRN.
| [
{
"created": "Tue, 4 Jun 2024 19:08:40 GMT",
"version": "v1"
}
] | 2024-06-06 | [
[
"Lee",
"Sanghyun",
""
],
[
"Park",
"Chanyoung",
""
]
] | Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex spatial and temporal correlations of road networks. Most existing studies either try to capture the spatial dependencies between roads using the same semantic graph over different time steps, or assume all sensors on the roads are equally likely to be connected regardless of the distance between them. However, we observe that the spatial dependencies between roads indeed change over time, and two distant roads are not likely to be helpful to each other when predicting the traffic flow, both of which limit the performance of existing studies. In this paper, we propose Temporal Graph Learning Recurrent Neural Network (TGLRN) to address these problems. More precisely, to effectively model the nature of time series, we leverage Recurrent Neural Networks (RNNs) to dynamically construct a graph at each time step, thereby capturing the time-evolving spatial dependencies between roads (i.e., microscopic view). Simultaneously, we provide the Adaptive Structure Information to the model, ensuring that close and consecutive sensors are considered to be more important for predicting the traffic flow (i.e., macroscopic view). Furthermore, to endow TGLRN with robustness, we introduce an edge sampling strategy when constructing the graph at each time step, which eventually leads to further improvements on the model performance. Experimental results on four commonly used real-world benchmark datasets show the effectiveness of TGLRN. |
1808.06088 | Jingfeng Wu | Bing Yu, Jingfeng Wu, Jinwen Ma and Zhanxing Zhu | Tangent-Normal Adversarial Regularization for Semi-supervised Learning | CVPR 2019 | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compared with standard supervised learning, the key difficulty in
semi-supervised learning is how to make full use of the unlabeled data. A
recently proposed method, virtual adversarial training (VAT), smartly performs
adversarial training without label information to impose a local smoothness on
the classifier, which is especially beneficial to semi-supervised learning. In
this work, we propose tangent-normal adversarial regularization (TNAR) as an
extension of VAT by taking the data manifold into consideration. The proposed
TNAR is composed by two complementary parts, the tangent adversarial
regularization (TAR) and the normal adversarial regularization (NAR). In TAR,
VAT is applied along the tangent space of the data manifold, aiming to enforce
local invariance of the classifier on the manifold, while in NAR, VAT is
performed on the normal space orthogonal to the tangent space, intending to
impose robustness on the classifier against the noise causing the observed data
deviating from the underlying data manifold. Demonstrated by experiments on
both artificial and practical datasets, our proposed TAR and NAR complement
with each other, and jointly outperforms other state-of-the-art methods for
semi-supervised learning.
| [
{
"created": "Sat, 18 Aug 2018 14:30:57 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Nov 2018 13:44:47 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Mar 2019 14:57:07 GMT",
"version": "v3"
}
] | 2019-03-04 | [
[
"Yu",
"Bing",
""
],
[
"Wu",
"Jingfeng",
""
],
[
"Ma",
"Jinwen",
""
],
[
"Zhu",
"Zhanxing",
""
]
] | Compared with standard supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. A recently proposed method, virtual adversarial training (VAT), smartly performs adversarial training without label information to impose a local smoothness on the classifier, which is especially beneficial to semi-supervised learning. In this work, we propose tangent-normal adversarial regularization (TNAR) as an extension of VAT by taking the data manifold into consideration. The proposed TNAR is composed by two complementary parts, the tangent adversarial regularization (TAR) and the normal adversarial regularization (NAR). In TAR, VAT is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while in NAR, VAT is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Demonstrated by experiments on both artificial and practical datasets, our proposed TAR and NAR complement with each other, and jointly outperforms other state-of-the-art methods for semi-supervised learning. |
2211.12320 | Jun Liang | Jun Liang, Songsen Yu, Huan Yang | A Cross-Residual Learning for Image Recognition | After being added into fine training tricks and several key
components from the current SOTA, the performance of C-ResNet may can be
greatly improved | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ResNets and its variants play an important role in various fields of image
recognition. This paper gives another variant of ResNets, a kind of
cross-residual learning networks called C-ResNets, which has less computation
and parameters than ResNets. C-ResNets increases the information interaction
between modules by densifying jumpers and enriches the role of jumpers. In
addition, some meticulous designs on jumpers and channels counts can further
reduce the resource consumption of C-ResNets and increase its classification
performance. In order to test the effectiveness of C-ResNets, we use the same
hyperparameter settings as fine-tuned ResNets in the experiments.
We test our C-ResNets on datasets MNIST, FashionMnist, CIFAR-10, CIFAR-100,
CALTECH-101 and SVHN. Compared with fine-tuned ResNets, C-ResNets not only
maintains the classification performance, but also enormously reduces the
amount of calculations and parameters which greatly save the utilization rate
of GPUs and GPU memory resources. Therefore, our C-ResNets is competitive and
viable alternatives to ResNets in various scenarios. Code is available at
https://github.com/liangjunhello/C-ResNet
| [
{
"created": "Tue, 22 Nov 2022 15:12:55 GMT",
"version": "v1"
}
] | 2022-11-23 | [
[
"Liang",
"Jun",
""
],
[
"Yu",
"Songsen",
""
],
[
"Yang",
"Huan",
""
]
] | ResNets and its variants play an important role in various fields of image recognition. This paper gives another variant of ResNets, a kind of cross-residual learning networks called C-ResNets, which has less computation and parameters than ResNets. C-ResNets increases the information interaction between modules by densifying jumpers and enriches the role of jumpers. In addition, some meticulous designs on jumpers and channels counts can further reduce the resource consumption of C-ResNets and increase its classification performance. In order to test the effectiveness of C-ResNets, we use the same hyperparameter settings as fine-tuned ResNets in the experiments. We test our C-ResNets on datasets MNIST, FashionMnist, CIFAR-10, CIFAR-100, CALTECH-101 and SVHN. Compared with fine-tuned ResNets, C-ResNets not only maintains the classification performance, but also enormously reduces the amount of calculations and parameters which greatly save the utilization rate of GPUs and GPU memory resources. Therefore, our C-ResNets is competitive and viable alternatives to ResNets in various scenarios. Code is available at https://github.com/liangjunhello/C-ResNet |
2404.15721 | Ankit Vani | Ankit Vani, Bac Nguyen, Samuel Lavoie, Ranjay Krishna, Aaron Courville | SPARO: Selective Attention for Robust and Compositional Transformer
Encodings for Vision | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Selective attention helps us focus on task-relevant aspects in the constant
flood of our sensory input. This constraint in our perception allows us to
robustly generalize under distractions and to new compositions of perceivable
concepts. Transformers employ a similar notion of attention in their
architecture, but representation learning models with transformer backbones
like CLIP and DINO often fail to demonstrate robustness and compositionality.
We highlight a missing architectural prior: unlike human perception,
transformer encodings do not separately attend over individual concepts. In
response, we propose SPARO, a read-out mechanism that partitions encodings into
separately-attended slots, each produced by a single attention head. Using
SPARO with CLIP imparts an inductive bias that the vision and text modalities
are different views of a shared compositional world with the same corresponding
concepts. Using SPARO, we demonstrate improvements on downstream recognition,
robustness, retrieval, and compositionality benchmarks with CLIP (up to +14%
for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe
for ImageNet with DINO (+3% each). We also showcase a powerful ability to
intervene and select individual SPARO concepts to further improve downstream
task performance (up from +4% to +9% for SugarCrepe) and use this ability to
study the robustness of SPARO's representation structure. Finally, we provide
insights through ablation experiments and visualization of learned concepts.
| [
{
"created": "Wed, 24 Apr 2024 08:15:36 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Vani",
"Ankit",
""
],
[
"Nguyen",
"Bac",
""
],
[
"Lavoie",
"Samuel",
""
],
[
"Krishna",
"Ranjay",
""
],
[
"Courville",
"Aaron",
""
]
] | Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion of attention in their architecture, but representation learning models with transformer backbones like CLIP and DINO often fail to demonstrate robustness and compositionality. We highlight a missing architectural prior: unlike human perception, transformer encodings do not separately attend over individual concepts. In response, we propose SPARO, a read-out mechanism that partitions encodings into separately-attended slots, each produced by a single attention head. Using SPARO with CLIP imparts an inductive bias that the vision and text modalities are different views of a shared compositional world with the same corresponding concepts. Using SPARO, we demonstrate improvements on downstream recognition, robustness, retrieval, and compositionality benchmarks with CLIP (up to +14% for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe for ImageNet with DINO (+3% each). We also showcase a powerful ability to intervene and select individual SPARO concepts to further improve downstream task performance (up from +4% to +9% for SugarCrepe) and use this ability to study the robustness of SPARO's representation structure. Finally, we provide insights through ablation experiments and visualization of learned concepts. |
2301.11792 | Yunjie He | Yunjie He, Philip John Gorinski, Ieva Staliunaite, Pontus Stenetorp | Graph Attention with Hierarchies for Multi-hop Question Answering | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-hop QA (Question Answering) is the task of finding the answer to a
question across multiple documents. In recent years, a number of Deep
Learning-based approaches have been proposed to tackle this complex task, as
well as a few standard benchmarks to assess models Multi-hop QA capabilities.
In this paper, we focus on the well-established HotpotQA benchmark dataset,
which requires models to perform answer span extraction as well as support
sentence prediction. We present two extensions to the SOTA Graph Neural Network
(GNN) based model for HotpotQA, Hierarchical Graph Network (HGN): (i) we
complete the original hierarchical structure by introducing new edges between
the query and context sentence nodes; (ii) in the graph propagation step, we
propose a novel extension to Hierarchical Graph Attention Network GATH (Graph
ATtention with Hierarchies) that makes use of the graph hierarchy to update the
node representations in a sequential fashion. Experiments on HotpotQA
demonstrate the efficiency of the proposed modifications and support our
assumptions about the effects of model related variables.
| [
{
"created": "Fri, 27 Jan 2023 15:49:50 GMT",
"version": "v1"
}
] | 2023-01-30 | [
[
"He",
"Yunjie",
""
],
[
"Gorinski",
"Philip John",
""
],
[
"Staliunaite",
"Ieva",
""
],
[
"Stenetorp",
"Pontus",
""
]
] | Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents. In recent years, a number of Deep Learning-based approaches have been proposed to tackle this complex task, as well as a few standard benchmarks to assess models Multi-hop QA capabilities. In this paper, we focus on the well-established HotpotQA benchmark dataset, which requires models to perform answer span extraction as well as support sentence prediction. We present two extensions to the SOTA Graph Neural Network (GNN) based model for HotpotQA, Hierarchical Graph Network (HGN): (i) we complete the original hierarchical structure by introducing new edges between the query and context sentence nodes; (ii) in the graph propagation step, we propose a novel extension to Hierarchical Graph Attention Network GATH (Graph ATtention with Hierarchies) that makes use of the graph hierarchy to update the node representations in a sequential fashion. Experiments on HotpotQA demonstrate the efficiency of the proposed modifications and support our assumptions about the effects of model related variables. |
2305.17592 | Shubhendu Trivedi | Mircea Petrache, Shubhendu Trivedi | Approximation-Generalization Trade-offs under (Approximate) Group
Equivariance | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The explicit incorporation of task-specific inductive biases through symmetry
has emerged as a general design precept in the development of high-performance
machine learning models. For example, group equivariant neural networks have
demonstrated impressive performance across various domains and applications
such as protein and drug design. A prevalent intuition about such models is
that the integration of relevant symmetry results in enhanced generalization.
Moreover, it is posited that when the data and/or the model may only exhibit
$\textit{approximate}$ or $\textit{partial}$ symmetry, the optimal or
best-performing model is one where the model symmetry aligns with the data
symmetry. In this paper, we conduct a formal unified investigation of these
intuitions. To begin, we present general quantitative bounds that demonstrate
how models capturing task-specific symmetries lead to improved generalization.
In fact, our results do not require the transformations to be finite or even
form a group and can work with partial or approximate equivariance. Utilizing
this quantification, we examine the more general question of model
mis-specification i.e. when the model symmetries don't align with the data
symmetries. We establish, for a given symmetry group, a quantitative comparison
between the approximate/partial equivariance of the model and that of the data
distribution, precisely connecting model equivariance error and data
equivariance error. Our result delineates conditions under which the model
equivariance error is optimal, thereby yielding the best-performing model for
the given task and data.
| [
{
"created": "Sat, 27 May 2023 22:53:37 GMT",
"version": "v1"
}
] | 2023-05-30 | [
[
"Petrache",
"Mircea",
""
],
[
"Trivedi",
"Shubhendu",
""
]
] | The explicit incorporation of task-specific inductive biases through symmetry has emerged as a general design precept in the development of high-performance machine learning models. For example, group equivariant neural networks have demonstrated impressive performance across various domains and applications such as protein and drug design. A prevalent intuition about such models is that the integration of relevant symmetry results in enhanced generalization. Moreover, it is posited that when the data and/or the model may only exhibit $\textit{approximate}$ or $\textit{partial}$ symmetry, the optimal or best-performing model is one where the model symmetry aligns with the data symmetry. In this paper, we conduct a formal unified investigation of these intuitions. To begin, we present general quantitative bounds that demonstrate how models capturing task-specific symmetries lead to improved generalization. In fact, our results do not require the transformations to be finite or even form a group and can work with partial or approximate equivariance. Utilizing this quantification, we examine the more general question of model mis-specification i.e. when the model symmetries don't align with the data symmetries. We establish, for a given symmetry group, a quantitative comparison between the approximate/partial equivariance of the model and that of the data distribution, precisely connecting model equivariance error and data equivariance error. Our result delineates conditions under which the model equivariance error is optimal, thereby yielding the best-performing model for the given task and data. |
2206.11843 | Zhixuan Zhou | Kyrie Zhixuan Zhou, Bohui Shen, Franziska Zimmer, Chuanli Xia, Xin
Tong | More Than a Wife and a Mom: A Study of Mom Vlogging Practices in China | 26th ACM Conference On Computer-Supported Cooperative Work And Social
Computing | null | null | null | cs.HC cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mom vloggers are stay-at-home moms who record and share their daily life
through short videos. In this exploratory study, we aspire to understand mom
vloggers' motivations, practices, and challenges. Our mixed-methods inspection
contained interviews with 4 mom vloggers in China and a content analysis of mom
vlogs of 5 other mom vloggers. Mom vloggers' primary motivations are to make
money, record daily life, and seek their individual identities and values, well
meeting their financial and social needs after leaving their paid employment.
When creating vlog content, mom bloggers encounter various challenges, such as
a lack of video visibility, being stretched by both intensive motherhood and
heavy digital work, privacy and self-presentation concerns, and so on. Based on
the findings, we propose design implications toward resolving these challenges
and benefiting mom vloggers' experiences.
| [
{
"created": "Thu, 23 Jun 2022 17:18:52 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Sep 2023 07:40:10 GMT",
"version": "v2"
}
] | 2023-09-28 | [
[
"Zhou",
"Kyrie Zhixuan",
""
],
[
"Shen",
"Bohui",
""
],
[
"Zimmer",
"Franziska",
""
],
[
"Xia",
"Chuanli",
""
],
[
"Tong",
"Xin",
""
]
] | Mom vloggers are stay-at-home moms who record and share their daily life through short videos. In this exploratory study, we aspire to understand mom vloggers' motivations, practices, and challenges. Our mixed-methods inspection contained interviews with 4 mom vloggers in China and a content analysis of mom vlogs of 5 other mom vloggers. Mom vloggers' primary motivations are to make money, record daily life, and seek their individual identities and values, well meeting their financial and social needs after leaving their paid employment. When creating vlog content, mom bloggers encounter various challenges, such as a lack of video visibility, being stretched by both intensive motherhood and heavy digital work, privacy and self-presentation concerns, and so on. Based on the findings, we propose design implications toward resolving these challenges and benefiting mom vloggers' experiences. |
1802.02295 | Mengshi Zhang | Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, Sarfraz Khurshid | DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing | 7 pages | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While Deep Neural Networks (DNNs) have established the fundamentals of
DNN-based autonomous driving systems, they may exhibit erroneous behaviors and
cause fatal accidents. To resolve the safety issues of autonomous driving
systems, a recent set of testing techniques have been designed to automatically
generate test cases, e.g., new input images transformed from the original ones.
Unfortunately, many such generated input images often render inferior
authenticity, lacking accurate semantic information of the driving scenes and
hence compromising the resulting efficacy and reliability.
In this paper, we propose DeepRoad, an unsupervised framework to
automatically generate large amounts of accurate driving scenes to test the
consistency of DNN-based autonomous driving systems across different scenes. In
particular, DeepRoad delivers driving scenes with various weather conditions
(including those with rather extreme conditions) by applying the Generative
Adversarial Networks (GANs) along with the corresponding real-world weather
scenes. Moreover, we have implemented DeepRoad to test three well-recognized
DNN-based autonomous driving systems. Experimental results demonstrate that
DeepRoad can detect thousands of behavioral inconsistencies in these systems.
| [
{
"created": "Wed, 7 Feb 2018 03:18:44 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Mar 2018 02:30:58 GMT",
"version": "v2"
}
] | 2018-03-08 | [
[
"Zhang",
"Mengshi",
""
],
[
"Zhang",
"Yuqun",
""
],
[
"Zhang",
"Lingming",
""
],
[
"Liu",
"Cong",
""
],
[
"Khurshid",
"Sarfraz",
""
]
] | While Deep Neural Networks (DNNs) have established the fundamentals of DNN-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To resolve the safety issues of autonomous driving systems, a recent set of testing techniques have been designed to automatically generate test cases, e.g., new input images transformed from the original ones. Unfortunately, many such generated input images often render inferior authenticity, lacking accurate semantic information of the driving scenes and hence compromising the resulting efficacy and reliability. In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes. In particular, DeepRoad delivers driving scenes with various weather conditions (including those with rather extreme conditions) by applying the Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Moreover, we have implemented DeepRoad to test three well-recognized DNN-based autonomous driving systems. Experimental results demonstrate that DeepRoad can detect thousands of behavioral inconsistencies in these systems. |
2003.13256 | Tobias Glasmachers | Tobias Glasmachers, Oswin Krause | The Hessian Estimation Evolution Strategy | null | null | null | null | cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel black box optimization algorithm called Hessian Estimation
Evolution Strategy. The algorithm updates the covariance matrix of its sampling
distribution by directly estimating the curvature of the objective function.
This algorithm design is targeted at twice continuously differentiable
problems. For this, we extend the cumulative step-size adaptation algorithm of
the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance
matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We
also show that the algorithm is surprisingly robust when its core assumption of
a twice continuously differentiable objective function is violated. The
approach yields a new evolution strategy with competitive performance, and at
the same time it also offers an interesting alternative to the usual covariance
matrix update mechanism.
| [
{
"created": "Mon, 30 Mar 2020 08:01:16 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jun 2020 07:30:53 GMT",
"version": "v2"
}
] | 2020-06-11 | [
[
"Glasmachers",
"Tobias",
""
],
[
"Krause",
"Oswin",
""
]
] | We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism. |
1804.02528 | Iraklis Klampanos | Iraklis A. Klampanos, Athanasios Davvetas, Antonis Koukourikos,
Vangelis Karkaletsis | ANNETT-O: An Ontology for Describing Artificial Neural Network
Evaluation, Topology and Training | null | null | null | null | cs.AI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning models, while effective and versatile, are becoming
increasingly complex, often including multiple overlapping networks of
arbitrary depths, multiple objectives and non-intuitive training methodologies.
This makes it increasingly difficult for researchers and practitioners to
design, train and understand them. In this paper we present ANNETT-O, a
much-needed, generic and computer-actionable vocabulary for researchers and
practitioners to describe their deep learning configurations, training
procedures and experiments. The proposed ontology focuses on topological,
training and evaluation aspects of complex deep neural configurations, while
keeping peripheral entities more succinct. Knowledge bases implementing
ANNETT-O can support a wide variety of queries, providing relevant insights to
users. In addition to a detailed description of the ontology, we demonstrate
its suitability to the task via a number of hypothetical use-cases of
increasing complexity.
| [
{
"created": "Sat, 7 Apr 2018 07:56:29 GMT",
"version": "v1"
},
{
"created": "Thu, 10 May 2018 09:04:59 GMT",
"version": "v2"
}
] | 2018-05-11 | [
[
"Klampanos",
"Iraklis A.",
""
],
[
"Davvetas",
"Athanasios",
""
],
[
"Koukourikos",
"Antonis",
""
],
[
"Karkaletsis",
"Vangelis",
""
]
] | Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly difficult for researchers and practitioners to design, train and understand them. In this paper we present ANNETT-O, a much-needed, generic and computer-actionable vocabulary for researchers and practitioners to describe their deep learning configurations, training procedures and experiments. The proposed ontology focuses on topological, training and evaluation aspects of complex deep neural configurations, while keeping peripheral entities more succinct. Knowledge bases implementing ANNETT-O can support a wide variety of queries, providing relevant insights to users. In addition to a detailed description of the ontology, we demonstrate its suitability to the task via a number of hypothetical use-cases of increasing complexity. |
1211.6468 | Mike Stannett | Mike Stannett and Istv\'an N\'emeti | Using Isabelle to verify special relativity, with application to
hypercomputation theory | 14 pages, reformatted with minor corrections | Journal of Automated Reasoning, 52,4 (2014), 361-378 | 10.1007/s10817-013-9292-7 | null | cs.LO gr-qc | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logicians at the R\'enyi Mathematical Institute in Budapest have spent
several years developing versions of relativity theory (special, general, and
other variants) based wholly on first order logic, and have argued in favour of
the physical decidability, via exploitation of cosmological phenomena, of
formally undecidable questions such as the Halting Problem and the consistency
of set theory.
The Hungarian theories are very extensive, and their associated proofs are
intuitively very satisfying, but this brings its own risks since intuition can
sometimes be misleading. As part of a joint project, researchers at Sheffield
have recently started generating rigorous machine-verified versions of the
Hungarian proofs, so as to demonstrate the soundness of their work. In this
paper, we explain the background to the project and demonstrate an Isabelle
proof of the theorem "No inertial observer can travel faster than light".
This approach to physical theories and physical computability has several
pay-offs: (a) we can be certain our intuition hasn't led us astray (or if it
has, we can identify where this has happened); (b) we can identify which axioms
are specifically required in the proof of each theorem and to what extent those
axioms can be weakened (the fewer assumptions we make up-front, the stronger
the results); and (c) we can identify whether new formal proof techniques and
tactics are needed when tackling physical as opposed to mathematical theories.
| [
{
"created": "Tue, 27 Nov 2012 22:29:05 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Jan 2013 11:09:06 GMT",
"version": "v2"
}
] | 2018-03-30 | [
[
"Stannett",
"Mike",
""
],
[
"Németi",
"István",
""
]
] | Logicians at the R\'enyi Mathematical Institute in Budapest have spent several years developing versions of relativity theory (special, general, and other variants) based wholly on first order logic, and have argued in favour of the physical decidability, via exploitation of cosmological phenomena, of formally undecidable questions such as the Halting Problem and the consistency of set theory. The Hungarian theories are very extensive, and their associated proofs are intuitively very satisfying, but this brings its own risks since intuition can sometimes be misleading. As part of a joint project, researchers at Sheffield have recently started generating rigorous machine-verified versions of the Hungarian proofs, so as to demonstrate the soundness of their work. In this paper, we explain the background to the project and demonstrate an Isabelle proof of the theorem "No inertial observer can travel faster than light". This approach to physical theories and physical computability has several pay-offs: (a) we can be certain our intuition hasn't led us astray (or if it has, we can identify where this has happened); (b) we can identify which axioms are specifically required in the proof of each theorem and to what extent those axioms can be weakened (the fewer assumptions we make up-front, the stronger the results); and (c) we can identify whether new formal proof techniques and tactics are needed when tackling physical as opposed to mathematical theories. |
1911.07273 | Zhigang Chang | Zhigang Chang, Qin Zhou, Mingyang Yu, Shibao Zheng, Hua Yang, Tai-Pang
Wu | Distribution Context Aware Loss for Person Re-identification | IEEE VCIP | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To learn the optimal similarity function between probe and gallery images in
Person re-identification, effective deep metric learning methods have been
extensively explored to obtain discriminative feature embedding. However,
existing metric loss like triplet loss and its variants always emphasize
pair-wise relations but ignore the distribution context in feature space,
leading to inconsistency and sub-optimal. In fact, the similarity of one pair
not only decides the match of this pair, but also has potential impacts on
other sample pairs. In this paper, we propose a novel Distribution Context
Aware (DCA) loss based on triplet loss to combine both numerical similarity and
relation similarity in feature space for better clustering. Extensive
experiments on three benchmarks including Market-1501, DukeMTMC-reID and
MSMT17, evidence the favorable performance of our method against the
corresponding baseline and other state-of-the-art methods.
| [
{
"created": "Sun, 17 Nov 2019 16:28:35 GMT",
"version": "v1"
}
] | 2019-11-19 | [
[
"Chang",
"Zhigang",
""
],
[
"Zhou",
"Qin",
""
],
[
"Yu",
"Mingyang",
""
],
[
"Zheng",
"Shibao",
""
],
[
"Yang",
"Hua",
""
],
[
"Wu",
"Tai-Pang",
""
]
] | To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obtain discriminative feature embedding. However, existing metric loss like triplet loss and its variants always emphasize pair-wise relations but ignore the distribution context in feature space, leading to inconsistency and sub-optimal. In fact, the similarity of one pair not only decides the match of this pair, but also has potential impacts on other sample pairs. In this paper, we propose a novel Distribution Context Aware (DCA) loss based on triplet loss to combine both numerical similarity and relation similarity in feature space for better clustering. Extensive experiments on three benchmarks including Market-1501, DukeMTMC-reID and MSMT17, evidence the favorable performance of our method against the corresponding baseline and other state-of-the-art methods. |
2303.07154 | Yun-Da Tsai | Yun-Da Tsai, Tzu-Hsien Tsai, Shou-De Lin | Differential Good Arm Identification | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | This paper targets a variant of the stochastic multi-armed bandit problem
called good arm identification (GAI). GAI is a pure-exploration bandit problem
with the goal to output as many good arms using as few samples as possible,
where a good arm is defined as an arm whose expected reward is greater than a
given threshold. In this work, we propose DGAI - a differentiable good arm
identification algorithm to improve the sample complexity of the
state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that
the DGAI can further boost the performance of a general multi-arm bandit (MAB)
problem given a threshold as a prior knowledge to the arm set. Extensive
experiments confirm that our algorithm outperform the baseline algorithms
significantly in both synthetic and real world datasets for both GAI and MAB
tasks.
| [
{
"created": "Mon, 13 Mar 2023 14:28:21 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Aug 2023 04:09:23 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Feb 2024 00:24:32 GMT",
"version": "v3"
}
] | 2024-02-19 | [
[
"Tsai",
"Yun-Da",
""
],
[
"Tsai",
"Tzu-Hsien",
""
],
[
"Lin",
"Shou-De",
""
]
] | This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI). GAI is a pure-exploration bandit problem with the goal to output as many good arms using as few samples as possible, where a good arm is defined as an arm whose expected reward is greater than a given threshold. In this work, we propose DGAI - a differentiable good arm identification algorithm to improve the sample complexity of the state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that the DGAI can further boost the performance of a general multi-arm bandit (MAB) problem given a threshold as a prior knowledge to the arm set. Extensive experiments confirm that our algorithm outperform the baseline algorithms significantly in both synthetic and real world datasets for both GAI and MAB tasks. |
1112.3787 | Salvador Abreu | Dario Campagna, Beata Sarna-Starosta and Tom Schrijvers | Approximating Constraint Propagation in Datalog | Online Proceedings of the 11th International Colloquium on
Implementation of Constraint LOgic Programming Systems (CICLOPS 2011),
Lexington, KY, U.S.A., July 10, 2011 | null | null | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a technique exploiting Datalog with aggregates to improve the
performance of programs with arithmetic (in)equalities. Our approach employs a
source-to-source program transformation which approximates the propagation
technique from Constraint Programming. The experimental evaluation of the
approach shows good run time speed-ups on a range of non-recursive as well as
recursive programs. Furthermore, our technique improves upon the previously
reported in the literature constraint magic set transformation approach.
| [
{
"created": "Fri, 16 Dec 2011 12:26:59 GMT",
"version": "v1"
}
] | 2011-12-19 | [
[
"Campagna",
"Dario",
""
],
[
"Sarna-Starosta",
"Beata",
""
],
[
"Schrijvers",
"Tom",
""
]
] | We present a technique exploiting Datalog with aggregates to improve the performance of programs with arithmetic (in)equalities. Our approach employs a source-to-source program transformation which approximates the propagation technique from Constraint Programming. The experimental evaluation of the approach shows good run time speed-ups on a range of non-recursive as well as recursive programs. Furthermore, our technique improves upon the previously reported in the literature constraint magic set transformation approach. |
2405.13039 | Arnav Chavan | Arnav Chavan, Nahush Lele, Deepak Gupta | Surgical Feature-Space Decomposition of LLMs: Why, When and How? | Accepted at ACL 2024 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Low-rank approximations, of the weight and feature space can enhance the
performance of deep learning models, whether in terms of improving
generalization or reducing the latency of inference. However, there is no clear
consensus yet on \emph{how}, \emph{when} and \emph{why} these approximations
are helpful for large language models (LLMs). In this work, we empirically
study the efficacy of weight and feature space decomposition in
transformer-based LLMs. We demonstrate that surgical decomposition not only
provides critical insights into the trade-off between compression and language
modelling performance, but also sometimes enhances commonsense reasoning
performance of LLMs. Our empirical analysis identifies specific network
segments that intrinsically exhibit a low-rank structure. Furthermore, we
extend our investigation to the implications of low-rank approximations on
model bias. Overall, our findings offer a novel perspective on optimizing LLMs,
presenting the low-rank approximation not only as a tool for performance
enhancements, but also as a means to potentially rectify biases within these
models. Our code is available at
\href{https://github.com/nyunAI/SFSD-LLM}{GitHub}.
| [
{
"created": "Fri, 17 May 2024 07:34:03 GMT",
"version": "v1"
}
] | 2024-05-24 | [
[
"Chavan",
"Arnav",
""
],
[
"Lele",
"Nahush",
""
],
[
"Gupta",
"Deepak",
""
]
] | Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference. However, there is no clear consensus yet on \emph{how}, \emph{when} and \emph{why} these approximations are helpful for large language models (LLMs). In this work, we empirically study the efficacy of weight and feature space decomposition in transformer-based LLMs. We demonstrate that surgical decomposition not only provides critical insights into the trade-off between compression and language modelling performance, but also sometimes enhances commonsense reasoning performance of LLMs. Our empirical analysis identifies specific network segments that intrinsically exhibit a low-rank structure. Furthermore, we extend our investigation to the implications of low-rank approximations on model bias. Overall, our findings offer a novel perspective on optimizing LLMs, presenting the low-rank approximation not only as a tool for performance enhancements, but also as a means to potentially rectify biases within these models. Our code is available at \href{https://github.com/nyunAI/SFSD-LLM}{GitHub}. |
2109.13037 | Federico Bianchi | Federico Bianchi, Debora Nozza, Dirk Hovy | Language Invariant Properties in Natural Language Processing | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Meaning is context-dependent, but many properties of language (should) remain
the same even if we transform the context. For example, sentiment, entailment,
or speaker properties should be the same in a translation and original of a
text. We introduce language invariant properties: i.e., properties that should
not change when we transform text, and how they can be used to quantitatively
evaluate the robustness of transformation algorithms. We use translation and
paraphrasing as transformation examples, but our findings apply more broadly to
any transformation. Our results indicate that many NLP transformations change
properties like author characteristics, i.e., make them sound more male. We
believe that studying these properties will allow NLP to address both social
factors and pragmatic aspects of language. We also release an application suite
that can be used to evaluate the invariance of transformation applications.
| [
{
"created": "Mon, 27 Sep 2021 13:23:05 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Oct 2021 14:10:30 GMT",
"version": "v2"
}
] | 2021-10-04 | [
[
"Bianchi",
"Federico",
""
],
[
"Nozza",
"Debora",
""
],
[
"Hovy",
"Dirk",
""
]
] | Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties should be the same in a translation and original of a text. We introduce language invariant properties: i.e., properties that should not change when we transform text, and how they can be used to quantitatively evaluate the robustness of transformation algorithms. We use translation and paraphrasing as transformation examples, but our findings apply more broadly to any transformation. Our results indicate that many NLP transformations change properties like author characteristics, i.e., make them sound more male. We believe that studying these properties will allow NLP to address both social factors and pragmatic aspects of language. We also release an application suite that can be used to evaluate the invariance of transformation applications. |
2212.00479 | Hansang Lee | Hansang Lee, Haeil Lee, Helen Hong, and Junmo Kim | Noisy Label Classification using Label Noise Selection with Test-Time
Augmentation Cross-Entropy and NoiseMix Learning | Accepted at the 2nd MICCAI workshop on Data Augmentation, Labeling,
and Imperfections (DALI @ MICCAI 2022) | null | 10.1007/978-3-031-17027-0_8 | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As the size of the dataset used in deep learning tasks increases, the noisy
label problem, which is a task of making deep learning robust to the
incorrectly labeled data, has become an important task. In this paper, we
propose a method of learning noisy label data using the label noise selection
with test-time augmentation (TTA) cross-entropy and classifier learning with
the NoiseMix method. In the label noise selection, we propose TTA cross-entropy
by measuring the cross-entropy to predict the test-time augmented training
data. In the classifier learning, we propose the NoiseMix method based on MixUp
and BalancedMix methods by mixing the samples from the noisy and the clean
label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset,
the proposed TTA cross-entropy outperformed the conventional cross-entropy and
the TTA uncertainty in detecting label noise data in the label noise selection
process. Moreover, the proposed NoiseMix not only outperformed the
state-of-the-art methods in the classification performance but also showed the
most robustness to the label noise in the classifier learning.
| [
{
"created": "Thu, 1 Dec 2022 13:05:20 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jul 2024 05:28:13 GMT",
"version": "v2"
}
] | 2024-07-18 | [
[
"Lee",
"Hansang",
""
],
[
"Lee",
"Haeil",
""
],
[
"Hong",
"Helen",
""
],
[
"Kim",
"Junmo",
""
]
] | As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning. |
2003.05864 | Zhaoji Zhang | Zhaoji Zhang, Ying Li, Guanghui Song, Chau Yuen, and Yong Liang Guan | Random NOMA With Cross-Slot Successive Interference Cancellation Packet
Recovery | accepted by IEEE Wireless Communications Letters, 5 pages, 4 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional power-domain non-orthogonal multiple access (NOMA) relies on
precise power control, which requires real-time channel state information at
transmitters. This requirement severely limits its application to future
wireless communication systems. To address this problem, we consider NOMA
without power allocation, where we exploit the random channel fading and
opportunistically perform successive interference cancellation (SIC) detection.
To mitigate the multi-user interference, we propose a random NOMA where users
randomly transmit their data packets with a certain probability. Then a
cross-slot SIC packet recovery scheme is proposed to recover transmitted data
packets. We model the cross-slot SIC packet recovery as a Markov process, and
provide a throughput analysis, based on which the sum rate is maximized by
jointly optimizing the transmission probability and the encoding rate of users.
| [
{
"created": "Thu, 12 Mar 2020 15:56:06 GMT",
"version": "v1"
}
] | 2020-03-13 | [
[
"Zhang",
"Zhaoji",
""
],
[
"Li",
"Ying",
""
],
[
"Song",
"Guanghui",
""
],
[
"Yuen",
"Chau",
""
],
[
"Guan",
"Yong Liang",
""
]
] | Conventional power-domain non-orthogonal multiple access (NOMA) relies on precise power control, which requires real-time channel state information at transmitters. This requirement severely limits its application to future wireless communication systems. To address this problem, we consider NOMA without power allocation, where we exploit the random channel fading and opportunistically perform successive interference cancellation (SIC) detection. To mitigate the multi-user interference, we propose a random NOMA where users randomly transmit their data packets with a certain probability. Then a cross-slot SIC packet recovery scheme is proposed to recover transmitted data packets. We model the cross-slot SIC packet recovery as a Markov process, and provide a throughput analysis, based on which the sum rate is maximized by jointly optimizing the transmission probability and the encoding rate of users. |
1703.08985 | Michele Polese | Michele Polese, Rittwik Jana, Michele Zorzi | TCP in 5G mmWave Networks: Link Level Retransmissions and MP-TCP | 6 pages, 11 figures, accepted for presentation at the 2017 IEEE
Conference on Computer Communications Workshops (INFOCOM WKSHPS) | null | 10.1109/INFCOMW.2017.8116400 | null | cs.NI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MmWave communications, one of the cornerstones of future 5G mobile networks,
are characterized at the same time by a potential multi-gigabit capacity and by
a very dynamic channel, sensitive to blockage, wide fluctuations in the
received signal quality, and possibly also sudden link disruption. While the
performance of physical and MAC layer schemes that address these issues has
been thoroughly investigated in the literature, the complex interactions
between mmWave links and transport layer protocols such as TCP are still
relatively unexplored. This paper uses the ns-3 mmWave module, with its channel
model based on real measurements in New York City, to analyze the performance
of the Linux TCP/IP stack (i) with and without link-layer retransmissions,
showing that they are fundamental to reach a high TCP throughput on mmWave
links and (ii) with Multipath TCP (MP-TCP) over multiple LTE and mmWave links,
illustrating which are the throughput-optimal combinations of secondary paths
and congestion control algorithms in different conditions.
| [
{
"created": "Mon, 27 Mar 2017 09:50:20 GMT",
"version": "v1"
}
] | 2018-09-06 | [
[
"Polese",
"Michele",
""
],
[
"Jana",
"Rittwik",
""
],
[
"Zorzi",
"Michele",
""
]
] | MmWave communications, one of the cornerstones of future 5G mobile networks, are characterized at the same time by a potential multi-gigabit capacity and by a very dynamic channel, sensitive to blockage, wide fluctuations in the received signal quality, and possibly also sudden link disruption. While the performance of physical and MAC layer schemes that address these issues has been thoroughly investigated in the literature, the complex interactions between mmWave links and transport layer protocols such as TCP are still relatively unexplored. This paper uses the ns-3 mmWave module, with its channel model based on real measurements in New York City, to analyze the performance of the Linux TCP/IP stack (i) with and without link-layer retransmissions, showing that they are fundamental to reach a high TCP throughput on mmWave links and (ii) with Multipath TCP (MP-TCP) over multiple LTE and mmWave links, illustrating which are the throughput-optimal combinations of secondary paths and congestion control algorithms in different conditions. |
2210.16947 | Mohamed Suliman | Mohamed Suliman, Douglas Leith | Two Models are Better than One: Federated Learning Is Not Private For
Google GBoard Next Word Prediction | ESORICS 2023 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this paper we present new attacks against federated learning when used to
train natural language text models. We illustrate the effectiveness of the
attacks against the next word prediction model used in Google's GBoard app, a
widely used mobile keyboard app that has been an early adopter of federated
learning for production use. We demonstrate that the words a user types on
their mobile handset, e.g. when sending text messages, can be recovered with
high accuracy under a wide range of conditions and that counter-measures such a
use of mini-batches and adding local noise are ineffective. We also show that
the word order (and so the actual sentences typed) can be reconstructed with
high fidelity. This raises obvious privacy concerns, particularly since GBoard
is in production use.
| [
{
"created": "Sun, 30 Oct 2022 20:58:34 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Oct 2023 21:05:32 GMT",
"version": "v2"
}
] | 2023-10-11 | [
[
"Suliman",
"Mohamed",
""
],
[
"Leith",
"Douglas",
""
]
] | In this paper we present new attacks against federated learning when used to train natural language text models. We illustrate the effectiveness of the attacks against the next word prediction model used in Google's GBoard app, a widely used mobile keyboard app that has been an early adopter of federated learning for production use. We demonstrate that the words a user types on their mobile handset, e.g. when sending text messages, can be recovered with high accuracy under a wide range of conditions and that counter-measures such a use of mini-batches and adding local noise are ineffective. We also show that the word order (and so the actual sentences typed) can be reconstructed with high fidelity. This raises obvious privacy concerns, particularly since GBoard is in production use. |
2305.10736 | Chenhe Dong | Chenhe Dong, Yuexiang Xie, Yaliang Li, Ying Shen | Counterfactual Debiasing for Generating Factually Consistent Text
Summaries | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite substantial progress in abstractive text summarization to generate
fluent and informative texts, the factual inconsistency in the generated
summaries remains an important yet challenging problem to be solved. In this
paper, we construct causal graphs for abstractive text summarization and
identify the intrinsic causes of the factual inconsistency, i.e., the language
bias and irrelevancy bias, and further propose a debiasing framework, named
CoFactSum, to alleviate the causal effects of these biases by counterfactual
estimation. Specifically, the proposed CoFactSum provides two counterfactual
estimation strategies, i.e., Explicit Counterfactual Masking with an explicit
dynamic masking strategy, and Implicit Counterfactual Training with an implicit
discriminative cross-attention mechanism. Meanwhile, we design a Debiasing
Degree Adjustment mechanism to dynamically adapt the debiasing degree at each
decoding step. Extensive experiments on two widely-used summarization datasets
demonstrate the effectiveness of CoFactSum in enhancing the factual consistency
of generated summaries compared with several baselines.
| [
{
"created": "Thu, 18 May 2023 06:15:45 GMT",
"version": "v1"
}
] | 2023-05-19 | [
[
"Dong",
"Chenhe",
""
],
[
"Xie",
"Yuexiang",
""
],
[
"Li",
"Yaliang",
""
],
[
"Shen",
"Ying",
""
]
] | Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In this paper, we construct causal graphs for abstractive text summarization and identify the intrinsic causes of the factual inconsistency, i.e., the language bias and irrelevancy bias, and further propose a debiasing framework, named CoFactSum, to alleviate the causal effects of these biases by counterfactual estimation. Specifically, the proposed CoFactSum provides two counterfactual estimation strategies, i.e., Explicit Counterfactual Masking with an explicit dynamic masking strategy, and Implicit Counterfactual Training with an implicit discriminative cross-attention mechanism. Meanwhile, we design a Debiasing Degree Adjustment mechanism to dynamically adapt the debiasing degree at each decoding step. Extensive experiments on two widely-used summarization datasets demonstrate the effectiveness of CoFactSum in enhancing the factual consistency of generated summaries compared with several baselines. |
1810.04783 | Gopal Krishna Kamath | Sreelakshmi Manjunath, Gopal Krishna Kamath and Gaurav Raina | Stability, convergence, and limit cycles in some human physiological
processes | null | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical models for physiological processes aid qualitative understanding
of the impact of various parameters on the underlying process. We analyse two
such models for human physiological processes: the Mackey-Glass and the Lasota
equations, which model the change in the concentration of blood cells in the
human body. We first study the local stability of these models, and derive
bounds on various model parameters and the feedback delay for the concentration
to equilibrate. We then deduce conditions for non-oscillatory convergence of
the solutions, which could ensure that the blood cell concentration does not
oscillate. Further, we define the convergence characteristics of the solutions
which govern the rate at which the concentration equilibrates when the system
is stable. Owing to the possibility that physiological parameters can seldom be
estimated precisely, we also derive bounds for robust stability\textemdash
which enable one to ensure that the blood cell concentration equilibrates
despite parametric uncertainty. We also highlight that when the necessary and
sufficient condition for local stability is violated, the system transits into
instability via a Hopf bifurcation, leading to limit cycles in the blood cell
concentration. We then outline a framework to characterise the type of the Hopf
bifurcation and determine the asymptotic orbital stability of limit cycles. The
analysis is complemented with numerical examples, stability charts and
bifurcation diagrams. The insights into the dynamical properties of the
mathematical models may serve to guide the study of dynamical diseases.
| [
{
"created": "Tue, 9 Oct 2018 12:47:59 GMT",
"version": "v1"
}
] | 2018-10-12 | [
[
"Manjunath",
"Sreelakshmi",
""
],
[
"Kamath",
"Gopal Krishna",
""
],
[
"Raina",
"Gaurav",
""
]
] | Mathematical models for physiological processes aid qualitative understanding of the impact of various parameters on the underlying process. We analyse two such models for human physiological processes: the Mackey-Glass and the Lasota equations, which model the change in the concentration of blood cells in the human body. We first study the local stability of these models, and derive bounds on various model parameters and the feedback delay for the concentration to equilibrate. We then deduce conditions for non-oscillatory convergence of the solutions, which could ensure that the blood cell concentration does not oscillate. Further, we define the convergence characteristics of the solutions which govern the rate at which the concentration equilibrates when the system is stable. Owing to the possibility that physiological parameters can seldom be estimated precisely, we also derive bounds for robust stability\textemdash which enable one to ensure that the blood cell concentration equilibrates despite parametric uncertainty. We also highlight that when the necessary and sufficient condition for local stability is violated, the system transits into instability via a Hopf bifurcation, leading to limit cycles in the blood cell concentration. We then outline a framework to characterise the type of the Hopf bifurcation and determine the asymptotic orbital stability of limit cycles. The analysis is complemented with numerical examples, stability charts and bifurcation diagrams. The insights into the dynamical properties of the mathematical models may serve to guide the study of dynamical diseases. |
2405.08238 | Katie Seaborn | Takao Fujii, Katie Seaborn, Madeleine Steeds | Silver-Tongued and Sundry: Exploring Intersectional Pronouns with
ChatGPT | Honorable Mention award (top 5%) at CHI '24 | CHI '24: Proceedings of the CHI Conference on Human Factors in
Computing Systems (2024), Article No. 511, 1-14 | 10.1145/3613904.3642303 | null | cs.HC cs.AI cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | ChatGPT is a conversational agent built on a large language model. Trained on
a significant portion of human output, ChatGPT can mimic people to a degree. As
such, we need to consider what social identities ChatGPT simulates (or can be
designed to simulate). In this study, we explored the case of identity
simulation through Japanese first-person pronouns, which are tightly connected
to social identities in intersectional ways, i.e., intersectional pronouns. We
conducted a controlled online experiment where people from two regions in Japan
(Kanto and Kinki) witnessed interactions with ChatGPT using ten sets of
first-person pronouns. We discovered that pronouns alone can evoke perceptions
of social identities in ChatGPT at the intersections of gender, age, region,
and formality, with caveats. This work highlights the importance of pronoun use
for social identity simulation, provides a language-based methodology for
culturally-sensitive persona development, and advances the potential of
intersectional identities in intelligent agents.
| [
{
"created": "Mon, 13 May 2024 23:38:50 GMT",
"version": "v1"
}
] | 2024-05-15 | [
[
"Fujii",
"Takao",
""
],
[
"Seaborn",
"Katie",
""
],
[
"Steeds",
"Madeleine",
""
]
] | ChatGPT is a conversational agent built on a large language model. Trained on a significant portion of human output, ChatGPT can mimic people to a degree. As such, we need to consider what social identities ChatGPT simulates (or can be designed to simulate). In this study, we explored the case of identity simulation through Japanese first-person pronouns, which are tightly connected to social identities in intersectional ways, i.e., intersectional pronouns. We conducted a controlled online experiment where people from two regions in Japan (Kanto and Kinki) witnessed interactions with ChatGPT using ten sets of first-person pronouns. We discovered that pronouns alone can evoke perceptions of social identities in ChatGPT at the intersections of gender, age, region, and formality, with caveats. This work highlights the importance of pronoun use for social identity simulation, provides a language-based methodology for culturally-sensitive persona development, and advances the potential of intersectional identities in intelligent agents. |
1210.3846 | Igor Konnov | Annu John, Igor Konnov, Ulrich Schmid, Helmut Veith, Josef Widder | Counter Attack on Byzantine Generals: Parameterized Model Checking of
Fault-tolerant Distributed Algorithms | null | null | null | null | cs.LO cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce an automated parameterized verification method for
fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both
the number of processes and the assumed maximum number of Byzantine faulty
processes. At the center of our technique is a parametric interval abstraction
(PIA) where the interval boundaries are arithmetic expressions over parameters.
Using PIA for both data abstraction and a new form of counter abstraction, we
reduce the parameterized problem to finite-state model checking. We demonstrate
the practical feasibility of our method by verifying several variants of the
well-known distributed algorithm by Srikanth and Toueg. Our semi-decision
procedures are complemented and motivated by an undecidability proof for FTDA
verification which holds even in the absence of interprocess communication. To
the best of our knowledge, this is the first paper to achieve parameterized
automated verification of Byzantine FTDA.
| [
{
"created": "Sun, 14 Oct 2012 21:31:23 GMT",
"version": "v1"
},
{
"created": "Sun, 3 Feb 2013 19:26:53 GMT",
"version": "v2"
}
] | 2013-02-05 | [
[
"John",
"Annu",
""
],
[
"Konnov",
"Igor",
""
],
[
"Schmid",
"Ulrich",
""
],
[
"Veith",
"Helmut",
""
],
[
"Widder",
"Josef",
""
]
] | We introduce an automated parameterized verification method for fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both the number of processes and the assumed maximum number of Byzantine faulty processes. At the center of our technique is a parametric interval abstraction (PIA) where the interval boundaries are arithmetic expressions over parameters. Using PIA for both data abstraction and a new form of counter abstraction, we reduce the parameterized problem to finite-state model checking. We demonstrate the practical feasibility of our method by verifying several variants of the well-known distributed algorithm by Srikanth and Toueg. Our semi-decision procedures are complemented and motivated by an undecidability proof for FTDA verification which holds even in the absence of interprocess communication. To the best of our knowledge, this is the first paper to achieve parameterized automated verification of Byzantine FTDA. |
1912.07319 | Micha{\l} Idzik | Micha{\l} Idzik | Multi-Objective Evolutionary Algorithms platform with support for
flexible hybridization tools | null | null | null | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Working with complex, high-level MOEA meta-models such as Multiobjec-tive
Optimization Hierarchic Genetic Strategy (MO-mHGS) with multi-deme support
usually requires dedicated implementation and configuration for each internal
(single-deme) algorithm variant. If we generalize meta-model, we can simplify
whole simulation process and bind any internal algorithm (we denote it as a
driver), without providing redundant meta-model implementations. This idea has
become a fundamental of Evogil platform. Our aim was to allow construct-ing
custom hybrid models or combine existing solutions in runtime simulation
environment. We define hybrid solution as a composition of a meta-model and a
driver (or multiple drivers). Meta-model uses drivers to perform evolutionary
calculations and process their results. Moreover, Evogil provides set of
ready-made solutions divided into two groups (multi-deme meta-models and
single-deme drivers), as well as processing tools (quality metrics, statistics
and plotting scripts), simulation management and results persistence layer.
| [
{
"created": "Mon, 16 Dec 2019 12:32:21 GMT",
"version": "v1"
}
] | 2019-12-17 | [
[
"Idzik",
"Michał",
""
]
] | Working with complex, high-level MOEA meta-models such as Multiobjec-tive Optimization Hierarchic Genetic Strategy (MO-mHGS) with multi-deme support usually requires dedicated implementation and configuration for each internal (single-deme) algorithm variant. If we generalize meta-model, we can simplify whole simulation process and bind any internal algorithm (we denote it as a driver), without providing redundant meta-model implementations. This idea has become a fundamental of Evogil platform. Our aim was to allow construct-ing custom hybrid models or combine existing solutions in runtime simulation environment. We define hybrid solution as a composition of a meta-model and a driver (or multiple drivers). Meta-model uses drivers to perform evolutionary calculations and process their results. Moreover, Evogil provides set of ready-made solutions divided into two groups (multi-deme meta-models and single-deme drivers), as well as processing tools (quality metrics, statistics and plotting scripts), simulation management and results persistence layer. |
2007.08860 | Rachmad Vidya Wicaksana Putra | Rachmad Vidya Wicaksana Putra, Muhammad Shafique | FSpiNN: An Optimization Framework for Memory- and Energy-Efficient
Spiking Neural Networks | To appear at the IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems (IEEE-TCAD), as part of the ESWEEK-TCAD
Special Issue, September 2020 | null | 10.1109/TCAD.2020.3013049 | null | cs.NE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiking Neural Networks (SNNs) are gaining interest due to their event-driven
processing which potentially consumes low power/energy computations in hardware
platforms, while offering unsupervised learning capability due to the
spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs
require a large memory footprint to achieve high accuracy, thereby making them
difficult to be deployed on embedded systems, for instance on battery-powered
mobile devices and IoT Edge nodes. Towards this, we propose FSpiNN, an
optimization framework for obtaining memory- and energy-efficient SNNs for
training and inference processing, with unsupervised learning capability while
maintaining accuracy. It is achieved by (1) reducing the computational
requirements of neuronal and STDP operations, (2) improving the accuracy of
STDP-based learning, (3) compressing the SNN through a fixed-point
quantization, and (4) incorporating the memory and energy requirements in the
optimization process. FSpiNN reduces the computational requirements by reducing
the number of neuronal operations, the STDP-based synaptic weight updates, and
the STDP complexity. To improve the accuracy of learning, FSpiNN employs
timestep-based synaptic weight updates, and adaptively determines the STDP
potentiation factor and the effective inhibition strength. The experimental
results show that, as compared to the state-of-the-art work, FSpiNN achieves
7.5x memory saving, and improves the energy-efficiency by 3.5x on average for
training and by 1.8x on average for inference, across MNIST and Fashion MNIST
datasets, with no accuracy loss for a network with 4900 excitatory neurons,
thereby enabling energy-efficient SNNs for edge devices/embedded systems.
| [
{
"created": "Fri, 17 Jul 2020 09:40:26 GMT",
"version": "v1"
}
] | 2023-03-06 | [
[
"Putra",
"Rachmad Vidya Wicaksana",
""
],
[
"Shafique",
"Muhammad",
""
]
] | Spiking Neural Networks (SNNs) are gaining interest due to their event-driven processing which potentially consumes low power/energy computations in hardware platforms, while offering unsupervised learning capability due to the spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs require a large memory footprint to achieve high accuracy, thereby making them difficult to be deployed on embedded systems, for instance on battery-powered mobile devices and IoT Edge nodes. Towards this, we propose FSpiNN, an optimization framework for obtaining memory- and energy-efficient SNNs for training and inference processing, with unsupervised learning capability while maintaining accuracy. It is achieved by (1) reducing the computational requirements of neuronal and STDP operations, (2) improving the accuracy of STDP-based learning, (3) compressing the SNN through a fixed-point quantization, and (4) incorporating the memory and energy requirements in the optimization process. FSpiNN reduces the computational requirements by reducing the number of neuronal operations, the STDP-based synaptic weight updates, and the STDP complexity. To improve the accuracy of learning, FSpiNN employs timestep-based synaptic weight updates, and adaptively determines the STDP potentiation factor and the effective inhibition strength. The experimental results show that, as compared to the state-of-the-art work, FSpiNN achieves 7.5x memory saving, and improves the energy-efficiency by 3.5x on average for training and by 1.8x on average for inference, across MNIST and Fashion MNIST datasets, with no accuracy loss for a network with 4900 excitatory neurons, thereby enabling energy-efficient SNNs for edge devices/embedded systems. |
2102.03011 | Oliver Wang | Felix Klose and Oliver Wang and Jean-Charles Bazin and Marcus Magnor
and Alexander Sorkine-Hornung | Sampling Based Scene-Space Video Processing | null | null | null | null | cs.CV cs.GR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Many compelling video processing effects can be achieved if per-pixel depth
information and 3D camera calibrations are known. However, the success of such
methods is highly dependent on the accuracy of this "scene-space" information.
We present a novel, sampling-based framework for processing video that enables
high-quality scene-space video effects in the presence of inevitable errors in
depth and camera pose estimation. Instead of trying to improve the explicit 3D
scene representation, the key idea of our method is to exploit the high
redundancy of approximate scene information that arises due to most scene
points being visible multiple times across many frames of video. Based on this
observation, we propose a novel pixel gathering and filtering approach. The
gathering step is general and collects pixel samples in scene-space, while the
filtering step is application-specific and computes a desired output video from
the gathered sample sets. Our approach is easily parallelizable and has been
implemented on GPU, allowing us to take full advantage of large volumes of
video data and facilitating practical runtimes on HD video using a standard
desktop computer. Our generic scene-space formulation is able to
comprehensively describe a multitude of video processing applications such as
denoising, deblurring, super resolution, object removal, computational shutter
functions, and other scene-space camera effects. We present results for various
casually captured, hand-held, moving, compressed, monocular videos depicting
challenging scenes recorded in uncontrolled environments.
| [
{
"created": "Fri, 5 Feb 2021 05:55:04 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Klose",
"Felix",
""
],
[
"Wang",
"Oliver",
""
],
[
"Bazin",
"Jean-Charles",
""
],
[
"Magnor",
"Marcus",
""
],
[
"Sorkine-Hornung",
"Alexander",
""
]
] | Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly dependent on the accuracy of this "scene-space" information. We present a novel, sampling-based framework for processing video that enables high-quality scene-space video effects in the presence of inevitable errors in depth and camera pose estimation. Instead of trying to improve the explicit 3D scene representation, the key idea of our method is to exploit the high redundancy of approximate scene information that arises due to most scene points being visible multiple times across many frames of video. Based on this observation, we propose a novel pixel gathering and filtering approach. The gathering step is general and collects pixel samples in scene-space, while the filtering step is application-specific and computes a desired output video from the gathered sample sets. Our approach is easily parallelizable and has been implemented on GPU, allowing us to take full advantage of large volumes of video data and facilitating practical runtimes on HD video using a standard desktop computer. Our generic scene-space formulation is able to comprehensively describe a multitude of video processing applications such as denoising, deblurring, super resolution, object removal, computational shutter functions, and other scene-space camera effects. We present results for various casually captured, hand-held, moving, compressed, monocular videos depicting challenging scenes recorded in uncontrolled environments. |
2205.14806 | Mayra Samaniego Mrs | Mayra Samaniego | Data Trust and IoT | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | People IoT surroundings have become valuable information sources that can
positively impact individuals and society. A user IoT data can be used for
different purposes. For instance, research and improvement of public services.
However, individuals lack the governance power to share their IoT data. Data
trust is a concept that brings opportunities to address data sharing in IoT.
This research reviews the idea of data trust. Then, we review IoT and its
unique characteristics that implement data trust a challenge. We further
discuss blockchain technology and how it can be used to enable data trust in
IoT. Finally, we introduce a blockchain-based solution for data trust in IoT.
| [
{
"created": "Mon, 30 May 2022 02:05:36 GMT",
"version": "v1"
}
] | 2022-05-31 | [
[
"Samaniego",
"Mayra",
""
]
] | People IoT surroundings have become valuable information sources that can positively impact individuals and society. A user IoT data can be used for different purposes. For instance, research and improvement of public services. However, individuals lack the governance power to share their IoT data. Data trust is a concept that brings opportunities to address data sharing in IoT. This research reviews the idea of data trust. Then, we review IoT and its unique characteristics that implement data trust a challenge. We further discuss blockchain technology and how it can be used to enable data trust in IoT. Finally, we introduce a blockchain-based solution for data trust in IoT. |
1905.01752 | Shivangi Srivastava | Shivangi Srivastava and John E. Vargas-Mu\~noz and Devis Tuia | Understanding urban landuse from the above and ground perspectives: a
deep learning, multimodal solution | null | Remote Sensing of Environment, 228, pages 129 - 143, 2019 | 10.1016/j.rse.2019.04.014 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Landuse characterization is important for urban planning. It is traditionally
performed with field surveys or manual photo interpretation, two practices that
are time-consuming and labor-intensive. Therefore, we aim to automate landuse
mapping at the urban-object level with a deep learning approach based on data
from multiple sources (or modalities). We consider two image modalities:
overhead imagery from Google Maps and ensembles of ground-based pictures
(side-views) per urban-object from Google Street View (GSV). These modalities
bring complementary visual information pertaining to the urban-objects. We
propose an end-to-end trainable model, which uses OpenStreetMap annotations as
labels. The model can accommodate a variable number of GSV pictures for the
ground-based branch and can also function in the absence of ground pictures at
prediction time. We test the effectiveness of our model over the area of
\^Ile-de-France, France, and test its generalization abilities on a set of
urban-objects from the city of Nantes, France. Our proposed multimodal
Convolutional Neural Network achieves considerably higher accuracies than
methods that use a single image modality, making it suitable for automatic
landuse map updates. Additionally, our approach could be easily scaled to
multiple cities, because it is based on data sources available for many cities
worldwide.
| [
{
"created": "Sun, 5 May 2019 21:36:59 GMT",
"version": "v1"
}
] | 2019-05-07 | [
[
"Srivastava",
"Shivangi",
""
],
[
"Vargas-Muñoz",
"John E.",
""
],
[
"Tuia",
"Devis",
""
]
] | Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of \^Ile-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide. |
2207.08997 | Neil Nie | Neil Nie, Samir Yitzhak Gadre, Kiana Ehsani, Shuran Song | Structure from Action: Learning Interactions for Articulated Object 3D
Structure Discovery | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We introduce Structure from Action (SfA), a framework to discover 3D part
geometry and joint parameters of unseen articulated objects via a sequence of
inferred interactions. Our key insight is that 3D interaction and perception
should be considered in conjunction to construct 3D articulated CAD models,
especially for categories not seen during training. By selecting informative
interactions, SfA discovers parts and reveals occluded surfaces, like the
inside of a closed drawer. By aggregating visual observations in 3D, SfA
accurately segments multiple parts, reconstructs part geometry, and infers all
joint parameters in a canonical coordinate frame. Our experiments demonstrate
that a SfA model trained in simulation can generalize to many unseen object
categories with diverse structures and to real-world objects. Empirically, SfA
outperforms a pipeline of state-of-the-art components by 25.4 3D IoU percentage
points on unseen categories, while matching already performant joint estimation
baselines.
| [
{
"created": "Tue, 19 Jul 2022 00:27:36 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Apr 2023 16:49:33 GMT",
"version": "v2"
}
] | 2023-04-10 | [
[
"Nie",
"Neil",
""
],
[
"Gadre",
"Samir Yitzhak",
""
],
[
"Ehsani",
"Kiana",
""
],
[
"Song",
"Shuran",
""
]
] | We introduce Structure from Action (SfA), a framework to discover 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions. Our key insight is that 3D interaction and perception should be considered in conjunction to construct 3D articulated CAD models, especially for categories not seen during training. By selecting informative interactions, SfA discovers parts and reveals occluded surfaces, like the inside of a closed drawer. By aggregating visual observations in 3D, SfA accurately segments multiple parts, reconstructs part geometry, and infers all joint parameters in a canonical coordinate frame. Our experiments demonstrate that a SfA model trained in simulation can generalize to many unseen object categories with diverse structures and to real-world objects. Empirically, SfA outperforms a pipeline of state-of-the-art components by 25.4 3D IoU percentage points on unseen categories, while matching already performant joint estimation baselines. |
2307.13679 | Luca Bennett | Luca A. Bennett and Zahraa S. Abdallah | RED CoMETS: An ensemble classifier for symbolically represented
multivariate time series | Accepted by AALTD 2023; fixed typos and minor error in Table 2 | In proceedings of the 8th Workshop on Advanced Analytics and
Learning on Temporal Data (AALTD 2023), pages 76-91, 2023 | 10.1007/978-3-031-49896-1_6 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multivariate time series classification is a rapidly growing research field
with practical applications in finance, healthcare, engineering, and more. The
complexity of classifying multivariate time series data arises from its high
dimensionality, temporal dependencies, and varying lengths. This paper
introduces a novel ensemble classifier called RED CoMETS (Random Enhanced
Co-eye for Multivariate Time Series), which addresses these challenges. RED
CoMETS builds upon the success of Co-eye, an ensemble classifier specifically
designed for symbolically represented univariate time series, and extends its
capabilities to handle multivariate data. The performance of RED CoMETS is
evaluated on benchmark datasets from the UCR archive, where it demonstrates
competitive accuracy when compared to state-of-the-art techniques in
multivariate settings. Notably, it achieves the highest reported accuracy in
the literature for the 'HandMovementDirection' dataset. Moreover, the proposed
method significantly reduces computation time compared to Co-eye, making it an
efficient and effective choice for multivariate time series classification.
| [
{
"created": "Tue, 25 Jul 2023 17:36:34 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Sep 2023 20:11:40 GMT",
"version": "v2"
}
] | 2024-02-06 | [
[
"Bennett",
"Luca A.",
""
],
[
"Abdallah",
"Zahraa S.",
""
]
] | Multivariate time series classification is a rapidly growing research field with practical applications in finance, healthcare, engineering, and more. The complexity of classifying multivariate time series data arises from its high dimensionality, temporal dependencies, and varying lengths. This paper introduces a novel ensemble classifier called RED CoMETS (Random Enhanced Co-eye for Multivariate Time Series), which addresses these challenges. RED CoMETS builds upon the success of Co-eye, an ensemble classifier specifically designed for symbolically represented univariate time series, and extends its capabilities to handle multivariate data. The performance of RED CoMETS is evaluated on benchmark datasets from the UCR archive, where it demonstrates competitive accuracy when compared to state-of-the-art techniques in multivariate settings. Notably, it achieves the highest reported accuracy in the literature for the 'HandMovementDirection' dataset. Moreover, the proposed method significantly reduces computation time compared to Co-eye, making it an efficient and effective choice for multivariate time series classification. |
2107.04011 | Jawad Haqbeen | J. Haqbeen, T. Ito, S. Sahab, R. Hadfi, T. Sato, S. Okuhara | Meeting the SDGs : Enabling the Goals by Cooperation with Crowd using a
Conversational AI Platform | 7 pages, 6 figures, 1 table, To appear as a conference paper at KICSS
2020 | null | null | null | cs.CY cs.CL | http://creativecommons.org/licenses/by/4.0/ | In this paper, we report about a large-scale online discussion with 1099
citizens on the Afghanistan Sustainable Development Goals.
| [
{
"created": "Wed, 9 Jun 2021 04:14:19 GMT",
"version": "v1"
}
] | 2021-07-09 | [
[
"Haqbeen",
"J.",
""
],
[
"Ito",
"T.",
""
],
[
"Sahab",
"S.",
""
],
[
"Hadfi",
"R.",
""
],
[
"Sato",
"T.",
""
],
[
"Okuhara",
"S.",
""
]
] | In this paper, we report about a large-scale online discussion with 1099 citizens on the Afghanistan Sustainable Development Goals. |
1704.02703 | Lei Bi | Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng | Automatic Liver Lesion Detection using Cascaded Deep Residual Networks | Submission for 2017 ISBI LiTS Challenge | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic segmentation of liver lesions is a fundamental requirement towards
the creation of computer aided diagnosis (CAD) and decision support systems
(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
features and a priori knowledge of the user. As such, these methods are
difficult to adopt within a clinical environment. Recently, deep learning
methods based on fully convolutional networks (FCNs) have been successful in
many segmentation problems primarily because they leverage a large labelled
dataset to hierarchically learn the features that best correspond to the
shallow visual appearance as well as the deep semantics of the areas to be
segmented. However, FCNs based on a 16 layer VGGNet architecture have limited
capacity to add additional layers. Therefore, it is challenging to learn more
discriminative features among different classes for FCNs. In this study, we
overcome these limitations using deep residual networks (ResNet) to segment
liver lesions. ResNet contain skip connections between convolutional layers,
which solved the problem of the training degradation of training accuracy in
very deep networks and thereby enables the use of additional layers for
learning more discriminative features. In addition, we achieve more precise
boundary definitions through a novel cascaded ResNet architecture with
multi-scale fusion to gradually learn and infer the boundaries of both the
liver and the liver lesions. Our proposed method achieved 4th place in the ISBI
2017 Liver Tumor Segmentation Challenge by the submission deadline.
| [
{
"created": "Mon, 10 Apr 2017 04:05:50 GMT",
"version": "v1"
},
{
"created": "Sun, 21 May 2017 02:58:40 GMT",
"version": "v2"
}
] | 2017-05-23 | [
[
"Bi",
"Lei",
""
],
[
"Kim",
"Jinman",
""
],
[
"Kumar",
"Ashnil",
""
],
[
"Feng",
"Dagan",
""
]
] | Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented. However, FCNs based on a 16 layer VGGNet architecture have limited capacity to add additional layers. Therefore, it is challenging to learn more discriminative features among different classes for FCNs. In this study, we overcome these limitations using deep residual networks (ResNet) to segment liver lesions. ResNet contain skip connections between convolutional layers, which solved the problem of the training degradation of training accuracy in very deep networks and thereby enables the use of additional layers for learning more discriminative features. In addition, we achieve more precise boundary definitions through a novel cascaded ResNet architecture with multi-scale fusion to gradually learn and infer the boundaries of both the liver and the liver lesions. Our proposed method achieved 4th place in the ISBI 2017 Liver Tumor Segmentation Challenge by the submission deadline. |
1208.3205 | Manas Gaur | Manas Gaur | Software Security analysis, static and dynamic testing in java and C
environment, a comparative study | the research paper consists of 11 figures and 7 tabular comparison | null | null | null | cs.CR cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main stretch in the paper is buffer overflow anomaly occurring in major
source codes, designed in various programming language. It describes the
various as to how to improve your code and increase its strength to withstand
security theft occurring at vulnerable areas in the code. The main language
used is JAVA, regarded as one of the most object oriented language still create
lot of error like stack overflow, illegal/inappropriate method overriding. I
used tools confined to JAVA to test as how weak points in the code can be
rectified before compiled. The byte code theft is difficult to be conquered, so
it's a better to get rid of it in the plain java code itself. The tools used in
the research are PMD(Programming mistake detector), it helps to detect line of
code that make pop out error in near future like defect in hashcode(memory
maps) overriding due to which the java code will not function correctly.
Another tool is FindBUGS which provide the tester of the code to analyze the
weak points in the code like infinite loop, unsynchronized wait, deadlock
situation, null referring and dereferencing. Another tool which provides the
base to above tools is JaCoCo code coverage analysis used to detect unreachable
part and unused conditions of the code which improves the space complexity and
helps in easy clarification of errors.
Through this paper, we design an algorithm to prevent the loss of data. The
main audience is the white box tester who might leave out essential line of
code like, index variables, infinite loop, and inappropriate hashcode in the
major source program. This algorithm serves to reduce the damage in case of
buffer overflow
| [
{
"created": "Wed, 15 Aug 2012 20:08:59 GMT",
"version": "v1"
}
] | 2012-08-17 | [
[
"Gaur",
"Manas",
""
]
] | The main stretch in the paper is buffer overflow anomaly occurring in major source codes, designed in various programming language. It describes the various as to how to improve your code and increase its strength to withstand security theft occurring at vulnerable areas in the code. The main language used is JAVA, regarded as one of the most object oriented language still create lot of error like stack overflow, illegal/inappropriate method overriding. I used tools confined to JAVA to test as how weak points in the code can be rectified before compiled. The byte code theft is difficult to be conquered, so it's a better to get rid of it in the plain java code itself. The tools used in the research are PMD(Programming mistake detector), it helps to detect line of code that make pop out error in near future like defect in hashcode(memory maps) overriding due to which the java code will not function correctly. Another tool is FindBUGS which provide the tester of the code to analyze the weak points in the code like infinite loop, unsynchronized wait, deadlock situation, null referring and dereferencing. Another tool which provides the base to above tools is JaCoCo code coverage analysis used to detect unreachable part and unused conditions of the code which improves the space complexity and helps in easy clarification of errors. Through this paper, we design an algorithm to prevent the loss of data. The main audience is the white box tester who might leave out essential line of code like, index variables, infinite loop, and inappropriate hashcode in the major source program. This algorithm serves to reduce the damage in case of buffer overflow |
1912.06185 | Himanshu Rai | Yichao Lu, Cheng Chang, Himanshu Rai, Guangwei Yu, Maksims Volkovs | Learning Effective Visual Relationship Detector on 1 GPU | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present our winning solution to the Open Images 2019 Visual Relationship
challenge. This is the largest challenge of its kind to date with nearly 9
million training images. Challenge task consists of detecting objects and
identifying relationships between them in complex scenes. Our solution has
three stages, first object detection model is fine-tuned for the challenge
classes using a novel weight transfer approach. Then, spatio-semantic and
visual relationship models are trained on candidate object pairs. Finally,
features and model predictions are combined to generate the final relationship
prediction. Throughout the challenge we focused on minimizing the hardware
requirements of our architecture. Specifically, our weight transfer approach
enables much faster optimization, allowing the entire architecture to be
trained on a single GPU in under two days. In addition to efficient
optimization, our approach also achieves superior accuracy winning first place
out of over 200 teams, and outperforming the second place team by over $5\%$ on
the held-out private leaderboard.
| [
{
"created": "Thu, 12 Dec 2019 19:59:41 GMT",
"version": "v1"
}
] | 2019-12-16 | [
[
"Lu",
"Yichao",
""
],
[
"Chang",
"Cheng",
""
],
[
"Rai",
"Himanshu",
""
],
[
"Yu",
"Guangwei",
""
],
[
"Volkovs",
"Maksims",
""
]
] | We present our winning solution to the Open Images 2019 Visual Relationship challenge. This is the largest challenge of its kind to date with nearly 9 million training images. Challenge task consists of detecting objects and identifying relationships between them in complex scenes. Our solution has three stages, first object detection model is fine-tuned for the challenge classes using a novel weight transfer approach. Then, spatio-semantic and visual relationship models are trained on candidate object pairs. Finally, features and model predictions are combined to generate the final relationship prediction. Throughout the challenge we focused on minimizing the hardware requirements of our architecture. Specifically, our weight transfer approach enables much faster optimization, allowing the entire architecture to be trained on a single GPU in under two days. In addition to efficient optimization, our approach also achieves superior accuracy winning first place out of over 200 teams, and outperforming the second place team by over $5\%$ on the held-out private leaderboard. |
2403.19935 | Daniel Oliveira Dantas | Artur Santos Nascimento, Valter Guilherme Silva de Souza, Daniel
Oliveira Dantas, Beatriz Trinch\~ao Andrade | CP HDR: A feature point detection and description library for LDR and
HDR images | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In computer vision, characteristics refer to image regions with unique
properties, such as corners, edges, textures, or areas with high contrast.
These regions can be represented through feature points (FPs). FP detection and
description are fundamental steps to many computer vision tasks. Most FP
detection and description methods use low dynamic range (LDR) images,
sufficient for most applications involving digital images. However, LDR images
may have saturated pixels in scenes with extreme light conditions, which
degrade FP detection. On the other hand, high dynamic range (HDR) images
usually present a greater dynamic range but FP detection algorithms do not take
advantage of all the information in such images. In this study, we present a
systematic review of image detection and description algorithms that use HDR
images as input. We developed a library called CP_HDR that implements the
Harris corner detector, SIFT detector and descriptor, and two modifications of
those algorithms specialized in HDR images, called SIFT for HDR (SfHDR) and
Harris for HDR (HfHDR). Previous studies investigated the use of HDR images in
FP detection, but we did not find studies investigating the use of HDR images
in FP description. Using uniformity, repeatability rate, mean average
precision, and matching rate metrics, we compared the performance of the CP_HDR
algorithms using LDR and HDR images. We observed an increase in the uniformity
of the distribution of FPs among the high-light, mid-light, and low-light areas
of the images. The results show that using HDR images as input to detection
algorithms improves performance and that SfHDR and HfHDR enhance FP
description.
| [
{
"created": "Fri, 29 Mar 2024 02:42:22 GMT",
"version": "v1"
}
] | 2024-04-01 | [
[
"Nascimento",
"Artur Santos",
""
],
[
"de Souza",
"Valter Guilherme Silva",
""
],
[
"Dantas",
"Daniel Oliveira",
""
],
[
"Andrade",
"Beatriz Trinchão",
""
]
] | In computer vision, characteristics refer to image regions with unique properties, such as corners, edges, textures, or areas with high contrast. These regions can be represented through feature points (FPs). FP detection and description are fundamental steps to many computer vision tasks. Most FP detection and description methods use low dynamic range (LDR) images, sufficient for most applications involving digital images. However, LDR images may have saturated pixels in scenes with extreme light conditions, which degrade FP detection. On the other hand, high dynamic range (HDR) images usually present a greater dynamic range but FP detection algorithms do not take advantage of all the information in such images. In this study, we present a systematic review of image detection and description algorithms that use HDR images as input. We developed a library called CP_HDR that implements the Harris corner detector, SIFT detector and descriptor, and two modifications of those algorithms specialized in HDR images, called SIFT for HDR (SfHDR) and Harris for HDR (HfHDR). Previous studies investigated the use of HDR images in FP detection, but we did not find studies investigating the use of HDR images in FP description. Using uniformity, repeatability rate, mean average precision, and matching rate metrics, we compared the performance of the CP_HDR algorithms using LDR and HDR images. We observed an increase in the uniformity of the distribution of FPs among the high-light, mid-light, and low-light areas of the images. The results show that using HDR images as input to detection algorithms improves performance and that SfHDR and HfHDR enhance FP description. |
1206.1969 | Iztok Fister | Iztok Fister Jr., Marjan Mernik, Iztok Fister, Dejan Hrn\v{c}i\v{c} | Implementation of EasyTime Formal Semantics using a LISA Compiler
Generator | null | null | null | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A manual measuring time tool in mass sporting competitions would not be
imaginable nowadays, because many modern disciplines, such as IRONMAN, last a
long-time and, therefore, demand additional reliability. Moreover, automatic
timing-devices based on RFID technology, have become cheaper. However, these
devices cannot operate as stand-alone because they need a computer measuring
system that is capable of processing incoming events, encoding the results,
assigning them to the correct competitor, sorting the results according to the
achieved times, and then providing a printout of the results. This article
presents the domain-specific language EasyTime, which enables the controlling
of an agent by writing the events within a database. It focuses, in particular,
on the implementation of EasyTime with a LISA tool that enables the automatic
construction of compilers from language specifications, using Attribute
Grammars.
| [
{
"created": "Sat, 9 Jun 2012 20:10:16 GMT",
"version": "v1"
}
] | 2012-06-12 | [
[
"Fister",
"Iztok",
"Jr."
],
[
"Mernik",
"Marjan",
""
],
[
"Fister",
"Iztok",
""
],
[
"Hrnčič",
"Dejan",
""
]
] | A manual measuring time tool in mass sporting competitions would not be imaginable nowadays, because many modern disciplines, such as IRONMAN, last a long-time and, therefore, demand additional reliability. Moreover, automatic timing-devices based on RFID technology, have become cheaper. However, these devices cannot operate as stand-alone because they need a computer measuring system that is capable of processing incoming events, encoding the results, assigning them to the correct competitor, sorting the results according to the achieved times, and then providing a printout of the results. This article presents the domain-specific language EasyTime, which enables the controlling of an agent by writing the events within a database. It focuses, in particular, on the implementation of EasyTime with a LISA tool that enables the automatic construction of compilers from language specifications, using Attribute Grammars. |
2003.03645 | Nabiha Asghar | Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart, and Muhammad
Bilal Sheikh | Generating Emotionally Aligned Responses in Dialogues using Affect
Control Theory | null | null | null | null | cs.CL cs.AI cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art neural dialogue systems excel at syntactic and semantic
modelling of language, but often have a hard time establishing emotional
alignment with the human interactant during a conversation. In this work, we
bring Affect Control Theory (ACT), a socio-mathematical model of emotions for
human-human interactions, to the neural dialogue generation setting. ACT makes
predictions about how humans respond to emotional stimuli in social situations.
Due to this property, ACT and its derivative probabilistic models have been
successfully deployed in several applications of Human-Computer Interaction,
including empathetic tutoring systems, assistive healthcare devices and
two-person social dilemma games. We investigate how ACT can be used to develop
affect-aware neural conversational agents, which produce emotionally aligned
responses to prompts and take into consideration the affective identities of
the interactants.
| [
{
"created": "Sat, 7 Mar 2020 19:31:08 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Apr 2020 06:46:25 GMT",
"version": "v2"
}
] | 2020-04-17 | [
[
"Asghar",
"Nabiha",
""
],
[
"Kobyzev",
"Ivan",
""
],
[
"Hoey",
"Jesse",
""
],
[
"Poupart",
"Pascal",
""
],
[
"Sheikh",
"Muhammad Bilal",
""
]
] | State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation. In this work, we bring Affect Control Theory (ACT), a socio-mathematical model of emotions for human-human interactions, to the neural dialogue generation setting. ACT makes predictions about how humans respond to emotional stimuli in social situations. Due to this property, ACT and its derivative probabilistic models have been successfully deployed in several applications of Human-Computer Interaction, including empathetic tutoring systems, assistive healthcare devices and two-person social dilemma games. We investigate how ACT can be used to develop affect-aware neural conversational agents, which produce emotionally aligned responses to prompts and take into consideration the affective identities of the interactants. |
1809.00043 | Bin Han | Bin Han, Antonio De Domenico, Ghina Dandachi, Anastasios Drosou,
Dimitrios Tzovaras, Roberto Querio, Fabrizio Moggio, \"Omer Bulakci, Hans D.
Schotten | Admission and Congestion Control for 5G Network Slicing | Submitted to 2018 IEEE Conference on Standards for Communications and
Networking (CSCN) | 2018 IEEE Conference on Standards for Communications and
Networking (CSCN) | 10.1109/CSCN.2018.8581773 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network Slicing has been widely accepted as essential feature of future 5th
Generation (5G) mobile communication networks. Accounting the potentially dense
demand of network slices as a cloud service and the limited resource of mobile
network operators (MNOs), an efficient inter-slice management and orchestration
plays a key role in 5G networks. This calls advanced solutions for slice
admission and congestion control. This paper proposes a novel approach of
inter-slice control that well copes with existing pre-standardized 5G
architectures
| [
{
"created": "Fri, 31 Aug 2018 20:08:17 GMT",
"version": "v1"
}
] | 2021-11-30 | [
[
"Han",
"Bin",
""
],
[
"De Domenico",
"Antonio",
""
],
[
"Dandachi",
"Ghina",
""
],
[
"Drosou",
"Anastasios",
""
],
[
"Tzovaras",
"Dimitrios",
""
],
[
"Querio",
"Roberto",
""
],
[
"Moggio",
"Fabrizio",
""
],
[
"Bulakci",
"Ömer",
""
],
[
"Schotten",
"Hans D.",
""
]
] | Network Slicing has been widely accepted as essential feature of future 5th Generation (5G) mobile communication networks. Accounting the potentially dense demand of network slices as a cloud service and the limited resource of mobile network operators (MNOs), an efficient inter-slice management and orchestration plays a key role in 5G networks. This calls advanced solutions for slice admission and congestion control. This paper proposes a novel approach of inter-slice control that well copes with existing pre-standardized 5G architectures |
1302.1848 | Delgado Lopez-Cozar emilio | Emilio Delgado Lopez-Cozar, Manuel Ramirez Sanchez | H Index of History journals published in Spain according to Google
Scholar Metrics (2007-2011) | 7 pages, 2 tables | null | null | EC3 Working Papers 10 | cs.DL | http://creativecommons.org/licenses/by/3.0/ | Google Scholar Metrics (GSM), which was recently launched in April 2012,
features new bibliometric systems for gauging scientific journals by counting
the number of citations obtained in Google Scholar. This way, it opens new
possibilities for measuring journal impacts in the field of Humanities. The
present article intends to evaluate the scope of this tool through analysing
GSM searches, from the 5th through 6th of December 2012, of History journals
published in Spain. In sum, 69 journals were identified, accounting for only
24% of the History journals published in Spain. The ranges of H index values
for this field are so small that the ranking can no longer be said to show a
discriminating potential. In the light of this, we would like to propose a
change in the way Google Scholar Metrics is designed so that it could also
accommodate production and citation patterns in the particular field of
History, and, in a broader scope, in the area of Humanities as well.
| [
{
"created": "Thu, 7 Feb 2013 20:16:17 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Feb 2013 09:16:17 GMT",
"version": "v2"
}
] | 2013-02-21 | [
[
"Lopez-Cozar",
"Emilio Delgado",
""
],
[
"Sanchez",
"Manuel Ramirez",
""
]
] | Google Scholar Metrics (GSM), which was recently launched in April 2012, features new bibliometric systems for gauging scientific journals by counting the number of citations obtained in Google Scholar. This way, it opens new possibilities for measuring journal impacts in the field of Humanities. The present article intends to evaluate the scope of this tool through analysing GSM searches, from the 5th through 6th of December 2012, of History journals published in Spain. In sum, 69 journals were identified, accounting for only 24% of the History journals published in Spain. The ranges of H index values for this field are so small that the ranking can no longer be said to show a discriminating potential. In the light of this, we would like to propose a change in the way Google Scholar Metrics is designed so that it could also accommodate production and citation patterns in the particular field of History, and, in a broader scope, in the area of Humanities as well. |
1904.01784 | Yuning Chai | Yuning Chai | Patchwork: A Patch-wise Attention Network for Efficient Object Detection
and Segmentation in Video Streams | ICCV 2019 Camera Ready + Supplementary | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in single-frame object detection and segmentation techniques
have motivated a wide range of works to extend these methods to process video
streams. In this paper, we explore the idea of hard attention aimed for
latency-sensitive applications. Instead of reasoning about every frame
separately, our method selects and only processes a small sub-window of the
frame. Our technique then makes predictions for the full frame based on the
sub-windows from previous frames and the update from the current sub-window.
The latency reduction by this hard attention mechanism comes at the cost of
degraded accuracy. We made two contributions to address this. First, we propose
a specialized memory cell that recovers lost context when processing
sub-windows. Secondly, we adopt a Q-learning-based policy training strategy
that enables our approach to intelligently select the sub-windows such that the
staleness in the memory hurts the performance the least. Our experiments
suggest that our approach reduces the latency by approximately four times
without significantly sacrificing the accuracy on the ImageNet VID video object
detection dataset and the DAVIS video object segmentation dataset. We further
demonstrate that we can reinvest the saved computation into other parts of the
network, and thus resulting in an accuracy increase at a comparable
computational cost as the original system and beating other recently proposed
state-of-the-art methods in the low latency range.
| [
{
"created": "Wed, 3 Apr 2019 05:58:42 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Aug 2019 17:11:31 GMT",
"version": "v2"
}
] | 2019-08-21 | [
[
"Chai",
"Yuning",
""
]
] | Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams. In this paper, we explore the idea of hard attention aimed for latency-sensitive applications. Instead of reasoning about every frame separately, our method selects and only processes a small sub-window of the frame. Our technique then makes predictions for the full frame based on the sub-windows from previous frames and the update from the current sub-window. The latency reduction by this hard attention mechanism comes at the cost of degraded accuracy. We made two contributions to address this. First, we propose a specialized memory cell that recovers lost context when processing sub-windows. Secondly, we adopt a Q-learning-based policy training strategy that enables our approach to intelligently select the sub-windows such that the staleness in the memory hurts the performance the least. Our experiments suggest that our approach reduces the latency by approximately four times without significantly sacrificing the accuracy on the ImageNet VID video object detection dataset and the DAVIS video object segmentation dataset. We further demonstrate that we can reinvest the saved computation into other parts of the network, and thus resulting in an accuracy increase at a comparable computational cost as the original system and beating other recently proposed state-of-the-art methods in the low latency range. |
2003.07311 | Johannes C. Paetzold | Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov,
Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H.
Menze | clDice -- A Novel Topology-Preserving Loss Function for Tubular
Structure Segmentation | * The authors Suprosanna Shit and Johannes C. Paetzold contributed
equally to the work | null | 10.1109/CVPR46437.2021.01629 | CVPR 2021 | cs.CV cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate segmentation of tubular, network-like structures, such as vessels,
neurons, or roads, is relevant to many fields of research. For such structures,
the topology is their most important characteristic; particularly preserving
connectedness: in the case of vascular networks, missing a connected vessel
entirely alters the blood-flow dynamics. We introduce a novel similarity
measure termed centerlineDice (short clDice), which is calculated on the
intersection of the segmentation masks and their (morphological) skeleta. We
theoretically prove that clDice guarantees topology preservation up to homotopy
equivalence for binary 2D and 3D segmentation. Extending this, we propose a
computationally efficient, differentiable loss function (soft-clDice) for
training arbitrary neural segmentation networks. We benchmark the soft-clDice
loss on five public datasets, including vessels, roads and neurons (2D and 3D).
Training on soft-clDice leads to segmentation with more accurate connectivity
information, higher graph similarity, and better volumetric scores.
| [
{
"created": "Mon, 16 Mar 2020 16:27:49 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Mar 2020 20:45:16 GMT",
"version": "v2"
},
{
"created": "Sun, 29 Mar 2020 22:46:43 GMT",
"version": "v3"
},
{
"created": "Thu, 3 Dec 2020 19:53:43 GMT",
"version": "v4"
},
{
"created": "Mon, 29 Mar 2021 13:36:28 GMT",
"version": "v5"
},
{
"created": "Tue, 30 Mar 2021 11:51:21 GMT",
"version": "v6"
},
{
"created": "Fri, 15 Jul 2022 10:39:38 GMT",
"version": "v7"
}
] | 2022-07-18 | [
[
"Shit",
"Suprosanna",
""
],
[
"Paetzold",
"Johannes C.",
""
],
[
"Sekuboyina",
"Anjany",
""
],
[
"Ezhov",
"Ivan",
""
],
[
"Unger",
"Alexander",
""
],
[
"Zhylka",
"Andrey",
""
],
[
"Pluim",
"Josien P. W.",
""
],
[
"Bauer",
"Ulrich",
""
],
[
"Menze",
"Bjoern H.",
""
]
] | Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores. |
2002.11869 | Anurag Sarkar | Anurag Sarkar, Zhihan Yang, Seth Cooper | Controllable Level Blending between Games using Variational Autoencoders | 6 pages, 11 figures, Sixth Experimental AI in Games Workshop at AIIDE | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous work explored blending levels from existing games to create levels
for a new game that mixes properties of the original games. In this paper, we
use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs
are artificial neural networks that learn and use latent representations of
datasets to generate novel outputs. We train a VAE on level data from Super
Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning
both games. We then use this space to generate level segments that combine
properties of levels from both games. Moreover, by applying evolutionary search
in the latent space, we evolve level segments satisfying specific constraints.
We argue that these affordances make the VAE-based approach especially suitable
for co-creative level design and compare its performance with similar
generative models like the GAN and the VAE-GAN.
| [
{
"created": "Thu, 27 Feb 2020 01:38:35 GMT",
"version": "v1"
}
] | 2020-02-28 | [
[
"Sarkar",
"Anurag",
""
],
[
"Yang",
"Zhihan",
""
],
[
"Cooper",
"Seth",
""
]
] | Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolutionary search in the latent space, we evolve level segments satisfying specific constraints. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design and compare its performance with similar generative models like the GAN and the VAE-GAN. |
2204.04399 | Hieu Hughes Le-Au | Rubab Hussain, Rigo Vargas, Hieu Hughes Le-Au, Will Gass, Melissa
Fenn, Briseyda Serna-Marquez, Jongwook Woo | Crime Patterns in Los Angeles County Before and After Covid19
(2018-2021) | Keywords: Pandemic, Crime Rate Los Angeles, Data Analysis, Data
Science, Predictive Analysis | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | The objective of our research is to present the change in crime rates in Los
Angeles post-Covid19. Using data analysis with Geo-Mapping, bubbles, Marimekko,
and a time series charts, we can illustrate which areas have the largest crime
rate, and how it has changed. Through regression modeling, we can interpret
which locations may also have a correlation to crime versus income, race, type
of crime, and gender. The story will help to uncover whether the areas
associated with crime are due to demographic or income variance. In showing the
details of crimes in Los Angeles along with the factors at play we hope to see
a compelling relationship between crime rates and recent events from 2020 to
the present, along with changes in crime type trends during these periods. We
use Excel to clean the data for SAP SAC to model effectively, as well as
resources from other studies a comparison.
| [
{
"created": "Sat, 9 Apr 2022 06:03:05 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Hussain",
"Rubab",
""
],
[
"Vargas",
"Rigo",
""
],
[
"Le-Au",
"Hieu Hughes",
""
],
[
"Gass",
"Will",
""
],
[
"Fenn",
"Melissa",
""
],
[
"Serna-Marquez",
"Briseyda",
""
],
[
"Woo",
"Jongwook",
""
]
] | The objective of our research is to present the change in crime rates in Los Angeles post-Covid19. Using data analysis with Geo-Mapping, bubbles, Marimekko, and a time series charts, we can illustrate which areas have the largest crime rate, and how it has changed. Through regression modeling, we can interpret which locations may also have a correlation to crime versus income, race, type of crime, and gender. The story will help to uncover whether the areas associated with crime are due to demographic or income variance. In showing the details of crimes in Los Angeles along with the factors at play we hope to see a compelling relationship between crime rates and recent events from 2020 to the present, along with changes in crime type trends during these periods. We use Excel to clean the data for SAP SAC to model effectively, as well as resources from other studies a comparison. |
2112.05941 | Xinyi Zhang | Xinyi Zhang, Yukiyasu Domae, Weiwei Wan and Kensuke Harada | Learning Efficient Policies for Picking Entangled Wire Harnesses: An
Approach to Industrial Bin Picking | 8 pages, IEEE RA-L | null | 10.1109/LRA.2022.3222995 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wire harnesses are essential connecting components in manufacturing industry
but are challenging to be automated in industrial tasks such as bin picking.
They are long, flexible and tend to get entangled when randomly placed in a
bin. This makes it difficult for the robot to grasp a single one in dense
clutter. Besides, training or collecting data in simulation is challenging due
to the difficulties in modeling the combination of deformable and rigid
components for wire harnesses. In this work, instead of directly lifting wire
harnesses, we propose to grasp and extract the target following a circle-like
trajectory until it is untangled. We learn a policy from real-world data that
can infer grasps and separation actions from visual observation. Our policy
enables the robot to efficiently pick and separate entangled wire harnesses by
maximizing success rates and reducing execution time. To evaluate our policy,
we present a set of real-world experiments on picking wire harnesses. Our
policy achieves an overall 84.6% success rate compared with 49.2% in baseline.
We also evaluate the effectiveness of our policy under different clutter
scenarios using unseen types of wire harnesses. Results suggest that our
approach is feasible for handling wire harnesses in industrial bin picking.
| [
{
"created": "Sat, 11 Dec 2021 10:01:39 GMT",
"version": "v1"
},
{
"created": "Sun, 10 Jul 2022 11:38:49 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Nov 2022 07:13:01 GMT",
"version": "v3"
},
{
"created": "Sat, 7 Jan 2023 05:54:15 GMT",
"version": "v4"
}
] | 2023-01-10 | [
[
"Zhang",
"Xinyi",
""
],
[
"Domae",
"Yukiyasu",
""
],
[
"Wan",
"Weiwei",
""
],
[
"Harada",
"Kensuke",
""
]
] | Wire harnesses are essential connecting components in manufacturing industry but are challenging to be automated in industrial tasks such as bin picking. They are long, flexible and tend to get entangled when randomly placed in a bin. This makes it difficult for the robot to grasp a single one in dense clutter. Besides, training or collecting data in simulation is challenging due to the difficulties in modeling the combination of deformable and rigid components for wire harnesses. In this work, instead of directly lifting wire harnesses, we propose to grasp and extract the target following a circle-like trajectory until it is untangled. We learn a policy from real-world data that can infer grasps and separation actions from visual observation. Our policy enables the robot to efficiently pick and separate entangled wire harnesses by maximizing success rates and reducing execution time. To evaluate our policy, we present a set of real-world experiments on picking wire harnesses. Our policy achieves an overall 84.6% success rate compared with 49.2% in baseline. We also evaluate the effectiveness of our policy under different clutter scenarios using unseen types of wire harnesses. Results suggest that our approach is feasible for handling wire harnesses in industrial bin picking. |
1812.00769 | Aditya Gangrade | Aditya Gangrade, Praveen Venkatesh, Bobak Nazer and Venkatesh
Saligrama | Testing Changes in Communities for the Stochastic Block Model | Version 3 includes material on unbalanced but linearly sized
communities. This version is to appear in NeurIPS 2019 | null | null | null | cs.IT cs.LG cs.SI math.IT math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and analyze the problems of \textit{community goodness-of-fit and
two-sample testing} for stochastic block models (SBM), where changes arise due
to modification in community memberships of nodes. Motivated by practical
applications, we consider the challenging sparse regime, where expected node
degrees are constant, and the inter-community mean degree ($b$) scales
proportionally to intra-community mean degree ($a$). Prior work has sharply
characterized partial or full community recovery in terms of a "signal-to-noise
ratio" ($\mathrm{SNR}$) based on $a$ and $b$. For both problems, we propose
computationally-efficient tests that can succeed far beyond the regime where
recovery of community membership is even possible. Overall, for large changes,
$s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test
based on community recovery with $O(s)$ errors requires $\mathrm{SNR}=
\Theta(\log n)$. Conversely, in the small change regime, $s \ll \sqrt{n}$, via
an information-theoretic lower bound, we show that, surprisingly, no algorithm
can do better than the na\"ive algorithm that first estimates the community up
to $O(s)$ errors and then detects changes. We validate these phenomena
numerically on SBMs and on real-world datasets as well as Markov Random Fields
where we only observe node data rather than the existence of links.
| [
{
"created": "Thu, 29 Nov 2018 20:09:21 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jun 2019 05:12:21 GMT",
"version": "v2"
},
{
"created": "Thu, 31 Oct 2019 03:20:52 GMT",
"version": "v3"
}
] | 2019-11-01 | [
[
"Gangrade",
"Aditya",
""
],
[
"Venkatesh",
"Praveen",
""
],
[
"Nazer",
"Bobak",
""
],
[
"Saligrama",
"Venkatesh",
""
]
] | We propose and analyze the problems of \textit{community goodness-of-fit and two-sample testing} for stochastic block models (SBM), where changes arise due to modification in community memberships of nodes. Motivated by practical applications, we consider the challenging sparse regime, where expected node degrees are constant, and the inter-community mean degree ($b$) scales proportionally to intra-community mean degree ($a$). Prior work has sharply characterized partial or full community recovery in terms of a "signal-to-noise ratio" ($\mathrm{SNR}$) based on $a$ and $b$. For both problems, we propose computationally-efficient tests that can succeed far beyond the regime where recovery of community membership is even possible. Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$. Conversely, in the small change regime, $s \ll \sqrt{n}$, via an information-theoretic lower bound, we show that, surprisingly, no algorithm can do better than the na\"ive algorithm that first estimates the community up to $O(s)$ errors and then detects changes. We validate these phenomena numerically on SBMs and on real-world datasets as well as Markov Random Fields where we only observe node data rather than the existence of links. |
2107.03688 | Longyu Ma | Longyu Ma, Chiu-Wing Sham, Chun Yan Lo, and Xinchao Zhong | An Embedded Iris Recognition System Optimization using Dynamically
ReconfigurableDecoder with LDPC Codes | 8 pages, 6 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Extracting and analyzing iris textures for biometric recognition has been
extensively studied. As the transition of iris recognition from lab technology
to nation-scale applications, most systems are facing high complexity in either
time or space, leading to unfitness for embedded devices. In this paper, the
proposed design includes a minimal set of computer vision modules and
multi-mode QC-LDPC decoder which can alleviate variability and noise caused by
iris acquisition and follow-up process. Several classes of QC-LDPC code from
IEEE 802.16 are tested for the validity of accuracy improvement. Some of the
codes mentioned above are used for further QC-LDPC decoder quantization,
validation and comparison to each other. We show that we can apply Dynamic
Partial Reconfiguration technology to implement the multi-mode QC-LDPC decoder
for the iris recognition system. The results show that the implementation is
power-efficient and good for edge applications.
| [
{
"created": "Thu, 8 Jul 2021 09:04:11 GMT",
"version": "v1"
}
] | 2021-07-09 | [
[
"Ma",
"Longyu",
""
],
[
"Sham",
"Chiu-Wing",
""
],
[
"Lo",
"Chun Yan",
""
],
[
"Zhong",
"Xinchao",
""
]
] | Extracting and analyzing iris textures for biometric recognition has been extensively studied. As the transition of iris recognition from lab technology to nation-scale applications, most systems are facing high complexity in either time or space, leading to unfitness for embedded devices. In this paper, the proposed design includes a minimal set of computer vision modules and multi-mode QC-LDPC decoder which can alleviate variability and noise caused by iris acquisition and follow-up process. Several classes of QC-LDPC code from IEEE 802.16 are tested for the validity of accuracy improvement. Some of the codes mentioned above are used for further QC-LDPC decoder quantization, validation and comparison to each other. We show that we can apply Dynamic Partial Reconfiguration technology to implement the multi-mode QC-LDPC decoder for the iris recognition system. The results show that the implementation is power-efficient and good for edge applications. |
1202.5012 | Matthew Patitz | Jennifer E. Padilla and Matthew J. Patitz and Raul Pena and Robert T.
Schweller and Nadrian C. Seeman and Robert Sheline and Scott M. Summers and
Xingsi Zhong | Asynchronous Signal Passing for Tile Self-Assembly: Fuel Efficient
Computation and Efficient Assembly of Shapes | This version contains the appendices omitted from the version
appearing in the UCNC 2013 proceedings | null | null | null | cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we demonstrate the power of a model of tile self-assembly based
on active glues which can dynamically change state. We formulate the
Signal-passing Tile Assembly Model (STAM), based on the model of Padilla, Liu,
and Seeman to be asynchronous, allowing any action of turning a glue on or off,
attaching a new tile, or breaking apart an assembly to happen in any order.
Within this highly generalized model we provide three new solutions to tile
self-assembly problems that have been addressed within the abstract Tile
Assembly Model and its variants, showing that signal passing tiles allow for
substantial improvement across multiple complexity metrics. Our first result
utilizes a recursive assembly process to achieve tile-type efficient assembly
of linear structures, using provably fewer tile types than what is possible in
standard tile assembly models. Our second system of signal-passing tiles
simulates any Turing machine with high fuel efficiency by using only a constant
number of tiles per computation step. Our third system assembles the discrete
Sierpinski triangle, demonstrating that this pattern can be strictly
self-assembled within the STAM. This result is of particular interest in that
it is known that this pattern cannot self-assemble within a number of well
studied tile self-assembly models. Notably, all of our constructions are at
temperature 1, further demonstrating that signal-passing confers the power to
bypass many restrictions found in standard tile assembly models.
| [
{
"created": "Wed, 22 Feb 2012 19:16:38 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Oct 2012 06:18:58 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Nov 2013 01:15:06 GMT",
"version": "v3"
}
] | 2015-03-20 | [
[
"Padilla",
"Jennifer E.",
""
],
[
"Patitz",
"Matthew J.",
""
],
[
"Pena",
"Raul",
""
],
[
"Schweller",
"Robert T.",
""
],
[
"Seeman",
"Nadrian C.",
""
],
[
"Sheline",
"Robert",
""
],
[
"Summers",
"Scott M.",
""
],
[
"Zhong",
"Xingsi",
""
]
] | In this paper we demonstrate the power of a model of tile self-assembly based on active glues which can dynamically change state. We formulate the Signal-passing Tile Assembly Model (STAM), based on the model of Padilla, Liu, and Seeman to be asynchronous, allowing any action of turning a glue on or off, attaching a new tile, or breaking apart an assembly to happen in any order. Within this highly generalized model we provide three new solutions to tile self-assembly problems that have been addressed within the abstract Tile Assembly Model and its variants, showing that signal passing tiles allow for substantial improvement across multiple complexity metrics. Our first result utilizes a recursive assembly process to achieve tile-type efficient assembly of linear structures, using provably fewer tile types than what is possible in standard tile assembly models. Our second system of signal-passing tiles simulates any Turing machine with high fuel efficiency by using only a constant number of tiles per computation step. Our third system assembles the discrete Sierpinski triangle, demonstrating that this pattern can be strictly self-assembled within the STAM. This result is of particular interest in that it is known that this pattern cannot self-assemble within a number of well studied tile self-assembly models. Notably, all of our constructions are at temperature 1, further demonstrating that signal-passing confers the power to bypass many restrictions found in standard tile assembly models. |
2301.10540 | David W. Romero | David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik
J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke | Modelling Long Range Dependencies in $N$D: From Task-Specific to a
General Purpose CNN | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Performant Convolutional Neural Network (CNN) architectures must be tailored
to specific tasks in order to consider the length, resolution, and
dimensionality of the input data. In this work, we tackle the need for
problem-specific CNN architectures. We present the Continuous Convolutional
Neural Network (CCNN): a single CNN able to process data of arbitrary
resolution, dimensionality and length without any structural changes. Its key
component are its continuous convolutional kernels which model long-range
dependencies at every layer, and thus remove the need of current CNN
architectures for task-dependent downsampling and depths. We showcase the
generality of our method by using the same architecture for tasks on sequential
($1{\rm D}$), visual ($2{\rm D}$) and point-cloud ($3{\rm D}$) data. Our CCNN
matches and often outperforms the current state-of-the-art across all tasks
considered.
| [
{
"created": "Wed, 25 Jan 2023 12:12:47 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Apr 2023 08:55:36 GMT",
"version": "v2"
}
] | 2023-04-18 | [
[
"Knigge",
"David M.",
""
],
[
"Romero",
"David W.",
""
],
[
"Gu",
"Albert",
""
],
[
"Gavves",
"Efstratios",
""
],
[
"Bekkers",
"Erik J.",
""
],
[
"Tomczak",
"Jakub M.",
""
],
[
"Hoogendoorn",
"Mark",
""
],
[
"Sonke",
"Jan-Jakob",
""
]
] | Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In this work, we tackle the need for problem-specific CNN architectures. We present the Continuous Convolutional Neural Network (CCNN): a single CNN able to process data of arbitrary resolution, dimensionality and length without any structural changes. Its key component are its continuous convolutional kernels which model long-range dependencies at every layer, and thus remove the need of current CNN architectures for task-dependent downsampling and depths. We showcase the generality of our method by using the same architecture for tasks on sequential ($1{\rm D}$), visual ($2{\rm D}$) and point-cloud ($3{\rm D}$) data. Our CCNN matches and often outperforms the current state-of-the-art across all tasks considered. |
2212.03404 | Lola Burgue\~no | Meriem Ben Chaaben and Lola Burgue\~no and Houari Sahraoui | Towards using Few-Shot Prompt Learning for Automating Model Completion | null | null | null | null | cs.SE cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We propose a simple yet a novel approach to improve completion in domain
modeling activities. Our approach exploits the power of large language models
by using few-shot prompt learning without the need to train or fine-tune those
models with large datasets that are scarce in this field. We implemented our
approach and tested it on the completion of static and dynamic domain diagrams.
Our initial evaluation shows that such an approach is effective and can be
integrated in different ways during the modeling activities.
| [
{
"created": "Wed, 7 Dec 2022 02:11:26 GMT",
"version": "v1"
}
] | 2022-12-08 | [
[
"Chaaben",
"Meriem Ben",
""
],
[
"Burgueño",
"Lola",
""
],
[
"Sahraoui",
"Houari",
""
]
] | We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities. |
2207.08391 | Hiep Nguyen | Hiep Nguyen, Lam Phan, Harikrishna Warrier and Yogesh Gupta | Federated Learning for Non-IID Data via Client Variance Reduction and
Adaptive Server Update | null | null | null | null | cs.LG cs.DC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Federated learning (FL) is an emerging technique used to collaboratively
train a global machine learning model while keeping the data localized on the
user devices. The main obstacle to FL's practical implementation is the
Non-Independent and Identical (Non-IID) data distribution across users, which
slows convergence and degrades performance. To tackle this fundamental issue,
we propose a method (ComFed) that enhances the whole training process on both
the client and server sides. The key idea of ComFed is to simultaneously
utilize client-variance reduction techniques to facilitate server aggregation
and global adaptive update techniques to accelerate learning. Our experiments
on the Cifar-10 classification task show that ComFed can improve
state-of-the-art algorithms dedicated to Non-IID data.
| [
{
"created": "Mon, 18 Jul 2022 05:58:19 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Jul 2022 10:28:52 GMT",
"version": "v2"
}
] | 2022-08-01 | [
[
"Nguyen",
"Hiep",
""
],
[
"Phan",
"Lam",
""
],
[
"Warrier",
"Harikrishna",
""
],
[
"Gupta",
"Yogesh",
""
]
] | Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments on the Cifar-10 classification task show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data. |
1706.02061 | Nir Levine | Nir Levine, Haggai Roitman, and Doron Cohen | An Extended Relevance Model for Session Search | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The session search task aims at best serving the user's information need
given her previous search behavior during the session. We propose an extended
relevance model that captures the user's dynamic information need in the
session. Our relevance modelling approach is directly driven by the user's
query reformulation (change) decisions and the estimate of how much the user's
search behavior affects such decisions. Overall, we demonstrate that, the
proposed approach significantly boosts session search performance.
| [
{
"created": "Wed, 7 Jun 2017 06:57:25 GMT",
"version": "v1"
}
] | 2017-06-08 | [
[
"Levine",
"Nir",
""
],
[
"Roitman",
"Haggai",
""
],
[
"Cohen",
"Doron",
""
]
] | The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance. |
1310.2665 | Emilio Ferrara | Emilio Ferrara, Mohsen JafariAsbagh, Onur Varol, Vahed Qazvinian,
Filippo Menczer, Alessandro Flammini | Clustering Memes in Social Media | Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 2013 | Advances in social networks analysis and mining (ASONAM), 2013
IEEE/ACM international conference on (pp. 548-555). IEEE | 10.1145/2492517.2492530 | null | cs.SI cs.CY physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.
| [
{
"created": "Thu, 10 Oct 2013 00:10:46 GMT",
"version": "v1"
}
] | 2017-03-07 | [
[
"Ferrara",
"Emilio",
""
],
[
"JafariAsbagh",
"Mohsen",
""
],
[
"Varol",
"Onur",
""
],
[
"Qazvinian",
"Vahed",
""
],
[
"Menczer",
"Filippo",
""
],
[
"Flammini",
"Alessandro",
""
]
] | The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data. |
2111.06230 | Vukosi Marivate | Mack Makgatho, Vukosi Marivate, Tshephisho Sefara, Valencia Wagner | Training Cross-Lingual embeddings for Setswana and Sepedi | Accepted (to appear) for the 2nd Workshop on Resources for African
Indigenous Languages | Vol. 3 No. 03 (2021): Proceedings of the 2nd workshop on Resources
for African Indigenous Language (RAIL) at DHASA 2021 | 10.55492/dhasa.v3i03.3822 | null | cs.CL stat.AP | http://creativecommons.org/licenses/by/4.0/ | African languages still lag in the advances of Natural Language Processing
techniques, one reason being the lack of representative data, having a
technique that can transfer information between languages can help mitigate
against the lack of data problem. This paper trains Setswana and Sepedi
monolingual word vectors and uses VecMap to create cross-lingual embeddings for
Setswana-Sepedi in order to do a cross-lingual transfer.
Word embeddings are word vectors that represent words as continuous floating
numbers where semantically similar words are mapped to nearby points in
n-dimensional space. The idea of word embeddings is based on the distribution
hypothesis that states, semantically similar words are distributed in similar
contexts (Harris, 1954).
Cross-lingual embeddings leverages monolingual embeddings by learning a
shared vector space for two separately trained monolingual vectors such that
words with similar meaning are represented by similar vectors. In this paper,
we investigate cross-lingual embeddings for Setswana-Sepedi monolingual word
vector. We use the unsupervised cross lingual embeddings in VecMap to train the
Setswana-Sepedi cross-language word embeddings. We evaluate the quality of the
Setswana-Sepedi cross-lingual word representation using a semantic evaluation
task. For the semantic similarity task, we translated the WordSim and SimLex
tasks into Setswana and Sepedi. We release this dataset as part of this work
for other researchers. We evaluate the intrinsic quality of the embeddings to
determine if there is improvement in the semantic representation of the word
embeddings.
| [
{
"created": "Thu, 11 Nov 2021 14:26:15 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Makgatho",
"Mack",
""
],
[
"Marivate",
"Vukosi",
""
],
[
"Sefara",
"Tshephisho",
""
],
[
"Wagner",
"Valencia",
""
]
] | African languages still lag in the advances of Natural Language Processing techniques, one reason being the lack of representative data, having a technique that can transfer information between languages can help mitigate against the lack of data problem. This paper trains Setswana and Sepedi monolingual word vectors and uses VecMap to create cross-lingual embeddings for Setswana-Sepedi in order to do a cross-lingual transfer. Word embeddings are word vectors that represent words as continuous floating numbers where semantically similar words are mapped to nearby points in n-dimensional space. The idea of word embeddings is based on the distribution hypothesis that states, semantically similar words are distributed in similar contexts (Harris, 1954). Cross-lingual embeddings leverages monolingual embeddings by learning a shared vector space for two separately trained monolingual vectors such that words with similar meaning are represented by similar vectors. In this paper, we investigate cross-lingual embeddings for Setswana-Sepedi monolingual word vector. We use the unsupervised cross lingual embeddings in VecMap to train the Setswana-Sepedi cross-language word embeddings. We evaluate the quality of the Setswana-Sepedi cross-lingual word representation using a semantic evaluation task. For the semantic similarity task, we translated the WordSim and SimLex tasks into Setswana and Sepedi. We release this dataset as part of this work for other researchers. We evaluate the intrinsic quality of the embeddings to determine if there is improvement in the semantic representation of the word embeddings. |
2109.11821 | Ming Liu | Ming Liu, Zhi Xue, Xiangjian He, and Jinjun Chen | SCADS: A Scalable Approach Using Spark in Cloud for Host-based Intrusion
Detection System with System Calls | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Following the current big data trend, the scale of real-time system call
traces generated by Linux applications in a contemporary data center may
increase excessively. Due to the deficiency of scalability, it is challenging
for traditional host-based intrusion detection systems deployed on every single
host to collect, maintain, and manipulate those large-scale accumulated system
call traces. It is inflexible to build data mining models on one physical host
that has static computing capability and limited storage capacity. To address
this issue, we propose SCADS, a corresponding solution using Apache Spark in
the Google cloud environment. A set of Spark algorithms are developed to
achieve the computational scalability. The experiment results demonstrate that
the efficiency of intrusion detection can be enhanced, which indicates that the
proposed method can apply to the design of next-generation host-based intrusion
detection systems with system calls.
| [
{
"created": "Fri, 24 Sep 2021 09:10:21 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Jan 2022 05:23:14 GMT",
"version": "v2"
}
] | 2022-01-10 | [
[
"Liu",
"Ming",
""
],
[
"Xue",
"Zhi",
""
],
[
"He",
"Xiangjian",
""
],
[
"Chen",
"Jinjun",
""
]
] | Following the current big data trend, the scale of real-time system call traces generated by Linux applications in a contemporary data center may increase excessively. Due to the deficiency of scalability, it is challenging for traditional host-based intrusion detection systems deployed on every single host to collect, maintain, and manipulate those large-scale accumulated system call traces. It is inflexible to build data mining models on one physical host that has static computing capability and limited storage capacity. To address this issue, we propose SCADS, a corresponding solution using Apache Spark in the Google cloud environment. A set of Spark algorithms are developed to achieve the computational scalability. The experiment results demonstrate that the efficiency of intrusion detection can be enhanced, which indicates that the proposed method can apply to the design of next-generation host-based intrusion detection systems with system calls. |
2407.03131 | Yanjie Cui | Yanjie Cui, Xiaohong Liu, Jing Liang, Yamin Fu | MVGT: A Multi-view Graph Transformer Based on Spatial Relations for EEG
Emotion Recognition | null | null | null | null | cs.NE cs.AI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electroencephalography (EEG), a medical imaging technique that captures scalp
electrical activity of brain structures via electrodes, has been widely used in
affective computing. The spatial domain of EEG is rich in affective
information. However, few of the existing studies have simultaneously analyzed
EEG signals from multiple perspectives of geometric and anatomical structures
in spatial domain. In this paper, we propose a multi-view Graph Transformer
(MVGT) based on spatial relations, which integrates information from the
temporal, frequency and spatial domains, including geometric and anatomical
structures, so as to enhance the expressive power of the model comprehensively.
We incorporate the spatial information of EEG channels into the model as
encoding, thereby improving its ability to perceive the spatial structure of
the channels. Meanwhile, experimental results based on publicly available
datasets demonstrate that our proposed model outperforms state-of-the-art
methods in recent years. In addition, the results also show that the MVGT could
extract information from multiple domains and capture inter-channel
relationships in EEG emotion recognition tasks effectively.
| [
{
"created": "Wed, 3 Jul 2024 14:13:00 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Jul 2024 13:11:53 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Aug 2024 09:21:47 GMT",
"version": "v3"
}
] | 2024-08-07 | [
[
"Cui",
"Yanjie",
""
],
[
"Liu",
"Xiaohong",
""
],
[
"Liang",
"Jing",
""
],
[
"Fu",
"Yamin",
""
]
] | Electroencephalography (EEG), a medical imaging technique that captures scalp electrical activity of brain structures via electrodes, has been widely used in affective computing. The spatial domain of EEG is rich in affective information. However, few of the existing studies have simultaneously analyzed EEG signals from multiple perspectives of geometric and anatomical structures in spatial domain. In this paper, we propose a multi-view Graph Transformer (MVGT) based on spatial relations, which integrates information from the temporal, frequency and spatial domains, including geometric and anatomical structures, so as to enhance the expressive power of the model comprehensively. We incorporate the spatial information of EEG channels into the model as encoding, thereby improving its ability to perceive the spatial structure of the channels. Meanwhile, experimental results based on publicly available datasets demonstrate that our proposed model outperforms state-of-the-art methods in recent years. In addition, the results also show that the MVGT could extract information from multiple domains and capture inter-channel relationships in EEG emotion recognition tasks effectively. |
1601.00082 | Geraldo A. Barbosa | Geraldo A. Barbosa | A wireless physically secure key distribution system | 6 pages,10 figures, 1 table | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A secure key distribution protocol protected by light's noise was introduced
in 2003 [Phys. Rev. A 68, 052307 (2003)]. That protocol utilized the shot noise
of light present in the optical channel (eg., an optical fiber) to restrict
information leaks to an adversary. An initial shared information between the
legitimate users allowed them to extract more information from the channel than
the one obtained by the adversary. That original paper recognized the need for
a privacy amplification step but no specific protocol was presented. More
recently that original idea was improved with a specific privacy amplification
protocol [arXiv:1406.1543v2 [cs.CR] 8 Jul 2015] while keeping the use of an
optical communication channel. This work merges main ideas of the protection
given by the light's noise in a protocol applied to wireless channels. The use
of a wireless channels together with recorded physical noise was introduced
from 2005 to 2007 (see eg, arXiv:quant-ph/0510011 v2 16 Nov 2005 and
arXiv:0705.2243v2 [quant-ph] 17 May 2007). This work improves those embrionary
ideas of wireless channels secured by recorded optical noise. The need for
specific optical channels is eliminated with the wireless variation and opens
up the possibility to apply the technique to mobile devices. This work
introduces this new scheme and calculates the associated security level.
| [
{
"created": "Fri, 1 Jan 2016 14:55:47 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jul 2016 20:06:45 GMT",
"version": "v2"
}
] | 2016-07-27 | [
[
"Barbosa",
"Geraldo A.",
""
]
] | A secure key distribution protocol protected by light's noise was introduced in 2003 [Phys. Rev. A 68, 052307 (2003)]. That protocol utilized the shot noise of light present in the optical channel (eg., an optical fiber) to restrict information leaks to an adversary. An initial shared information between the legitimate users allowed them to extract more information from the channel than the one obtained by the adversary. That original paper recognized the need for a privacy amplification step but no specific protocol was presented. More recently that original idea was improved with a specific privacy amplification protocol [arXiv:1406.1543v2 [cs.CR] 8 Jul 2015] while keeping the use of an optical communication channel. This work merges main ideas of the protection given by the light's noise in a protocol applied to wireless channels. The use of a wireless channels together with recorded physical noise was introduced from 2005 to 2007 (see eg, arXiv:quant-ph/0510011 v2 16 Nov 2005 and arXiv:0705.2243v2 [quant-ph] 17 May 2007). This work improves those embrionary ideas of wireless channels secured by recorded optical noise. The need for specific optical channels is eliminated with the wireless variation and opens up the possibility to apply the technique to mobile devices. This work introduces this new scheme and calculates the associated security level. |
2401.08123 | Xinni Jiang | Xinni Jiang, Zengsheng Kuang, Chunle Guo, Ruixun Zhang, Lei Cai, Xiao
Fan, Chongyi Li | The Devil is in the Details: Boosting Guided Depth Super-Resolution via
Rethinking Cross-Modal Alignment and Aggregation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Guided depth super-resolution (GDSR) involves restoring missing depth details
using the high-resolution RGB image of the same scene. Previous approaches have
struggled with the heterogeneity and complementarity of the multi-modal inputs,
and neglected the issues of modal misalignment, geometrical misalignment, and
feature selection. In this study, we rethink some essential components in GDSR
networks and propose a simple yet effective Dynamic Dual Alignment and
Aggregation network (D2A2). D2A2 mainly consists of 1) a dynamic dual alignment
module that adapts to alleviate the modal misalignment via a learnable domain
alignment block and geometrically align cross-modal features by learning the
offset; and 2) a mask-to-pixel feature aggregate module that uses the gated
mechanism and pixel attention to filter out irrelevant texture noise from RGB
features and combine the useful features with depth features. By combining the
strengths of RGB and depth features while minimizing disturbance introduced by
the RGB image, our method with simple reuse and redesign of basic components
achieves state-of-the-art performance on multiple benchmark datasets. The code
is available at https://github.com/JiangXinni/D2A2.
| [
{
"created": "Tue, 16 Jan 2024 05:37:08 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Jiang",
"Xinni",
""
],
[
"Kuang",
"Zengsheng",
""
],
[
"Guo",
"Chunle",
""
],
[
"Zhang",
"Ruixun",
""
],
[
"Cai",
"Lei",
""
],
[
"Fan",
"Xiao",
""
],
[
"Li",
"Chongyi",
""
]
] | Guided depth super-resolution (GDSR) involves restoring missing depth details using the high-resolution RGB image of the same scene. Previous approaches have struggled with the heterogeneity and complementarity of the multi-modal inputs, and neglected the issues of modal misalignment, geometrical misalignment, and feature selection. In this study, we rethink some essential components in GDSR networks and propose a simple yet effective Dynamic Dual Alignment and Aggregation network (D2A2). D2A2 mainly consists of 1) a dynamic dual alignment module that adapts to alleviate the modal misalignment via a learnable domain alignment block and geometrically align cross-modal features by learning the offset; and 2) a mask-to-pixel feature aggregate module that uses the gated mechanism and pixel attention to filter out irrelevant texture noise from RGB features and combine the useful features with depth features. By combining the strengths of RGB and depth features while minimizing disturbance introduced by the RGB image, our method with simple reuse and redesign of basic components achieves state-of-the-art performance on multiple benchmark datasets. The code is available at https://github.com/JiangXinni/D2A2. |
2403.12818 | Hugo Y\`eche | Hugo Y\`eche, Manuel Burger, Dinara Veshchezerova, Gunnar R\"atsch | Dynamic Survival Analysis for Early Event Prediction | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study advances Early Event Prediction (EEP) in healthcare through
Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk
localization into alarm policies to enhance clinical event metrics. By adapting
and evaluating DSA models against traditional EEP benchmarks, our research
demonstrates their ability to match EEP models on a time-step level and
significantly improve event-level metrics through a new alarm prioritization
scheme (up to 11% AuPRC difference). This approach represents a significant
step forward in predictive healthcare, providing a more nuanced and actionable
framework for early event prediction and management.
| [
{
"created": "Tue, 19 Mar 2024 15:17:23 GMT",
"version": "v1"
}
] | 2024-03-20 | [
[
"Yèche",
"Hugo",
""
],
[
"Burger",
"Manuel",
""
],
[
"Veshchezerova",
"Dinara",
""
],
[
"Rätsch",
"Gunnar",
""
]
] | This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics. By adapting and evaluating DSA models against traditional EEP benchmarks, our research demonstrates their ability to match EEP models on a time-step level and significantly improve event-level metrics through a new alarm prioritization scheme (up to 11% AuPRC difference). This approach represents a significant step forward in predictive healthcare, providing a more nuanced and actionable framework for early event prediction and management. |
2101.02415 | Ying Sheng | Yichao Zhou, Ying Sheng, Nguyen Vo, Nick Edmonds, Sandeep Tata | Simplified DOM Trees for Transferable Attribute Extraction from the Web | 10 pages, 9 figures | null | null | null | cs.LG cs.CL | http://creativecommons.org/licenses/by/4.0/ | There has been a steady need to precisely extract structured knowledge from
the web (i.e. HTML documents). Given a web page, extracting a structured object
along with various attributes of interest (e.g. price, publisher, author, and
genre for a book) can facilitate a variety of downstream applications such as
large-scale knowledge base construction, e-commerce product search, and
personalized recommendation. Considering each web page is rendered from an HTML
DOM tree, existing approaches formulate the problem as a DOM tree node tagging
task. However, they either rely on computationally expensive visual feature
engineering or are incapable of modeling the relationship among the tree nodes.
In this paper, we propose a novel transferable method, Simplified DOM Trees for
Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving
useful context for each node by leveraging the tree structure. We study two
challenging experimental settings: (i) intra-vertical few-shot extraction, and
(ii) cross-vertical fewshot extraction with out-of-domain knowledge, to
evaluate our approach. Extensive experiments on the SWDE public dataset show
that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1
score. We also find that utilizing knowledge from a different vertical
(cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a
further 1.37%.
| [
{
"created": "Thu, 7 Jan 2021 07:41:55 GMT",
"version": "v1"
}
] | 2021-01-08 | [
[
"Zhou",
"Yichao",
""
],
[
"Sheng",
"Ying",
""
],
[
"Vo",
"Nguyen",
""
],
[
"Edmonds",
"Nick",
""
],
[
"Tata",
"Sandeep",
""
]
] | There has been a steady need to precisely extract structured knowledge from the web (i.e. HTML documents). Given a web page, extracting a structured object along with various attributes of interest (e.g. price, publisher, author, and genre for a book) can facilitate a variety of downstream applications such as large-scale knowledge base construction, e-commerce product search, and personalized recommendation. Considering each web page is rendered from an HTML DOM tree, existing approaches formulate the problem as a DOM tree node tagging task. However, they either rely on computationally expensive visual feature engineering or are incapable of modeling the relationship among the tree nodes. In this paper, we propose a novel transferable method, Simplified DOM Trees for Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving useful context for each node by leveraging the tree structure. We study two challenging experimental settings: (i) intra-vertical few-shot extraction, and (ii) cross-vertical fewshot extraction with out-of-domain knowledge, to evaluate our approach. Extensive experiments on the SWDE public dataset show that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1 score. We also find that utilizing knowledge from a different vertical (cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a further 1.37%. |
2402.12144 | Shay Sapir | Asaf Petruschka, Shay Sapir and Elad Tzalik | Connectivity Labeling in Faulty Colored Graphs | shortened abstract for arxiv | null | null | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | Fault-tolerant connectivity labelings are schemes that, given an $n$-vertex
graph $G=(V,E)$ and $f\geq 1$, produce succinct yet informative labels for the
elements of the graph. Given only the labels of two vertices $u,v$ and of the
elements in a faulty-set $F$ with $|F|\leq f$, one can determine if $u,v$ are
connected in $G-F$, the surviving graph after removing $F$. For the edge or
vertex faults models, i.e., $F\subseteq E$ or $F\subseteq V$, a sequence of
recent work established schemes with $poly(f,\log n)$-bit labels. This paper
considers the color faults model, recently introduced in the context of
spanners [Petruschka, Sapir and Tzalik, ITCS'24], which accounts for known
correlations between failures. Here, the edges (or vertices) of the input $G$
are arbitrarily colored, and the faulty elements in $F$ are colors; a failing
color causes all edges (vertices) of that color to crash.
Our main contribution is settling the label length complexity for
connectivity under one color fault ($f=1$). The existing implicit solution, by
applying the state-of-the-art scheme for edge faults of [Dory and Parter,
PODC'21], might yield labels of $\Omega(n)$ bits. We provide a deterministic
scheme with labels of $\tilde{O}(\sqrt{n})$ bits in the worst case, and a
matching lower bound. Moreover, our scheme is universally optimal: even schemes
tailored to handle only colorings of one specific graph topology cannot produce
asymptotically smaller labels. We extend our labeling approach to yield a
routing scheme avoiding a single forbidden color. We also consider the
centralized setting, and show an $\tilde{O}(n)$-space oracle, answering
connectivity queries under one color fault in $\tilde{O}(1)$ time. Turning to
$f\geq 2$ color faults, we give a randomized labeling scheme with
$\tilde{O}(n^{1-1/2^f})$-bit labels, along with a lower bound of
$\Omega(n^{1-1/(f+1)})$ bits.
| [
{
"created": "Mon, 19 Feb 2024 13:53:13 GMT",
"version": "v1"
}
] | 2024-02-20 | [
[
"Petruschka",
"Asaf",
""
],
[
"Sapir",
"Shay",
""
],
[
"Tzalik",
"Elad",
""
]
] | Fault-tolerant connectivity labelings are schemes that, given an $n$-vertex graph $G=(V,E)$ and $f\geq 1$, produce succinct yet informative labels for the elements of the graph. Given only the labels of two vertices $u,v$ and of the elements in a faulty-set $F$ with $|F|\leq f$, one can determine if $u,v$ are connected in $G-F$, the surviving graph after removing $F$. For the edge or vertex faults models, i.e., $F\subseteq E$ or $F\subseteq V$, a sequence of recent work established schemes with $poly(f,\log n)$-bit labels. This paper considers the color faults model, recently introduced in the context of spanners [Petruschka, Sapir and Tzalik, ITCS'24], which accounts for known correlations between failures. Here, the edges (or vertices) of the input $G$ are arbitrarily colored, and the faulty elements in $F$ are colors; a failing color causes all edges (vertices) of that color to crash. Our main contribution is settling the label length complexity for connectivity under one color fault ($f=1$). The existing implicit solution, by applying the state-of-the-art scheme for edge faults of [Dory and Parter, PODC'21], might yield labels of $\Omega(n)$ bits. We provide a deterministic scheme with labels of $\tilde{O}(\sqrt{n})$ bits in the worst case, and a matching lower bound. Moreover, our scheme is universally optimal: even schemes tailored to handle only colorings of one specific graph topology cannot produce asymptotically smaller labels. We extend our labeling approach to yield a routing scheme avoiding a single forbidden color. We also consider the centralized setting, and show an $\tilde{O}(n)$-space oracle, answering connectivity queries under one color fault in $\tilde{O}(1)$ time. Turning to $f\geq 2$ color faults, we give a randomized labeling scheme with $\tilde{O}(n^{1-1/2^f})$-bit labels, along with a lower bound of $\Omega(n^{1-1/(f+1)})$ bits. |
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