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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2206.06620 | Weijie Chen | Rang Meng, Weijie Chen, Shicai Yang, Jie Song, Luojun Lin, Di Xie,
Shiliang Pu, Xinchao Wang, Mingli Song, Yueting Zhuang | Slimmable Domain Adaptation | To appear in CVPR 2022. Code is coming soon:
https://github.com/hikvision-research/SlimDA | IEEE/CVF Computer Vision and Pattern Recognition Conference
(CVPR), 2022 | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vanilla unsupervised domain adaptation methods tend to optimize the model
with fixed neural architecture, which is not very practical in real-world
scenarios since the target data is usually processed by different
resource-limited devices. It is therefore of great necessity to facilitate
architecture adaptation across various devices. In this paper, we introduce a
simple framework, Slimmable Domain Adaptation, to improve cross-domain
generalization with a weight-sharing model bank, from which models of different
capacities can be sampled to accommodate different accuracy-efficiency
trade-offs. The main challenge in this framework lies in simultaneously
boosting the adaptation performance of numerous models in the model bank. To
tackle this problem, we develop a Stochastic EnsEmble Distillation method to
fully exploit the complementary knowledge in the model bank for inter-model
interaction. Nevertheless, considering the optimization conflict between
inter-model interaction and intra-model adaptation, we augment the existing
bi-classifier domain confusion architecture into an Optimization-Separated
Tri-Classifier counterpart. After optimizing the model bank, architecture
adaptation is leveraged via our proposed Unsupervised Performance Evaluation
Metric. Under various resource constraints, our framework surpasses other
competing approaches by a very large margin on multiple benchmarks. It is also
worth emphasizing that our framework can preserve the performance improvement
against the source-only model even when the computing complexity is reduced to
$1/64$. Code will be available at https://github.com/hikvision-research/SlimDA.
| [
{
"created": "Tue, 14 Jun 2022 06:28:04 GMT",
"version": "v1"
}
] | 2022-06-15 | [
[
"Meng",
"Rang",
""
],
[
"Chen",
"Weijie",
""
],
[
"Yang",
"Shicai",
""
],
[
"Song",
"Jie",
""
],
[
"Lin",
"Luojun",
""
],
[
"Xie",
"Di",
""
],
[
"Pu",
"Shiliang",
""
],
[
"Wang",
"Xinchao",
""
],
[
"Song",
"Mingli",
""
],
[
"Zhuang",
"Yueting",
""
]
] | Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data is usually processed by different resource-limited devices. It is therefore of great necessity to facilitate architecture adaptation across various devices. In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs. The main challenge in this framework lies in simultaneously boosting the adaptation performance of numerous models in the model bank. To tackle this problem, we develop a Stochastic EnsEmble Distillation method to fully exploit the complementary knowledge in the model bank for inter-model interaction. Nevertheless, considering the optimization conflict between inter-model interaction and intra-model adaptation, we augment the existing bi-classifier domain confusion architecture into an Optimization-Separated Tri-Classifier counterpart. After optimizing the model bank, architecture adaptation is leveraged via our proposed Unsupervised Performance Evaluation Metric. Under various resource constraints, our framework surpasses other competing approaches by a very large margin on multiple benchmarks. It is also worth emphasizing that our framework can preserve the performance improvement against the source-only model even when the computing complexity is reduced to $1/64$. Code will be available at https://github.com/hikvision-research/SlimDA. |
1305.0513 | Ruoming Jin | Ruoming Jin, Yelong Shen, Lin Liu and Xue-wen Chen | Limiting the Neighborhood: De-Small-World Network for Outbreak
Prevention | 9 pages | null | null | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study a basic and practically important strategy to help
prevent and/or delay an outbreak in the context of network: limiting the
contact between individuals. In this paper, we introduce the average
neighborhood size as a new measure for the degree of being small-world and
utilize it to formally define the desmall- world network problem. We also prove
the NP-hardness of the general reachable pair cut problem and propose a greedy
edge betweenness based approach as the benchmark in selecting the candidate
edges for solving our problem. Furthermore, we transform the de-small-world
network problem as an OR-AND Boolean function maximization problem, which is
also an NP-hardness problem. In addition, we develop a numerical relaxation
approach to solve the Boolean function maximization and the de-small-world
problem. Also, we introduce the short-betweenness, which measures the edge
importance in terms of all short paths with distance no greater than a certain
threshold, and utilize it to speed up our numerical relaxation approach. The
experimental evaluation demonstrates the effectiveness and efficiency of our
approaches.
| [
{
"created": "Thu, 2 May 2013 17:03:58 GMT",
"version": "v1"
}
] | 2013-05-03 | [
[
"Jin",
"Ruoming",
""
],
[
"Shen",
"Yelong",
""
],
[
"Liu",
"Lin",
""
],
[
"Chen",
"Xue-wen",
""
]
] | In this work, we study a basic and practically important strategy to help prevent and/or delay an outbreak in the context of network: limiting the contact between individuals. In this paper, we introduce the average neighborhood size as a new measure for the degree of being small-world and utilize it to formally define the desmall- world network problem. We also prove the NP-hardness of the general reachable pair cut problem and propose a greedy edge betweenness based approach as the benchmark in selecting the candidate edges for solving our problem. Furthermore, we transform the de-small-world network problem as an OR-AND Boolean function maximization problem, which is also an NP-hardness problem. In addition, we develop a numerical relaxation approach to solve the Boolean function maximization and the de-small-world problem. Also, we introduce the short-betweenness, which measures the edge importance in terms of all short paths with distance no greater than a certain threshold, and utilize it to speed up our numerical relaxation approach. The experimental evaluation demonstrates the effectiveness and efficiency of our approaches. |
2405.07326 | Mahmood Ahmadi | Amirhossein Shahrokhi and Mahmood Ahmadi | Power Evaluation of IOT Application Layer Protocols | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by-sa/4.0/ | The Internet of Things has affected all aspects of daily life, and the number
of IoT devices is increasing day by day. According to forecasts, the number of
Internet of Things devices will reach one trillion devices by 2035. The
increase in the number of devices connected to the Internet will cause various
concerns. One of the most important concerns is the energy and power
consumption of these devices. Although Internet of Things modules are low in
energy consumption, their widespread and large-scale use has made the issue of
power consumption become the most important challenge in this field. For this
reason, it is necessary to use communication protocols that, in addition to
establishing efficient communication, impose minimal power consumption on the
network. In this paper, application layer protocols such as MQTT, MQTT-SN,
CoAP, and HTTP are simulated using the tools available in the Contiki operating
system, including COOJA and Powertrace, and they { are evaluated} and compared
with each other in terms of power consumption. According to the simulations
performed by the mentioned tools, the MQTT-SN protocol was the least consuming
protocol in terms of power consumption. After that, the CoAP protocol is
placed, and with a slight difference, the MQTT protocol, which consumes more
than MQTT-SN. Finally, the HTTP protocol consumes the most power, which makes
it unsuitable for communication in the Internet of Things
| [
{
"created": "Sun, 12 May 2024 16:23:52 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Shahrokhi",
"Amirhossein",
""
],
[
"Ahmadi",
"Mahmood",
""
]
] | The Internet of Things has affected all aspects of daily life, and the number of IoT devices is increasing day by day. According to forecasts, the number of Internet of Things devices will reach one trillion devices by 2035. The increase in the number of devices connected to the Internet will cause various concerns. One of the most important concerns is the energy and power consumption of these devices. Although Internet of Things modules are low in energy consumption, their widespread and large-scale use has made the issue of power consumption become the most important challenge in this field. For this reason, it is necessary to use communication protocols that, in addition to establishing efficient communication, impose minimal power consumption on the network. In this paper, application layer protocols such as MQTT, MQTT-SN, CoAP, and HTTP are simulated using the tools available in the Contiki operating system, including COOJA and Powertrace, and they { are evaluated} and compared with each other in terms of power consumption. According to the simulations performed by the mentioned tools, the MQTT-SN protocol was the least consuming protocol in terms of power consumption. After that, the CoAP protocol is placed, and with a slight difference, the MQTT protocol, which consumes more than MQTT-SN. Finally, the HTTP protocol consumes the most power, which makes it unsuitable for communication in the Internet of Things |
1703.01619 | Graham Neubig | Graham Neubig | Neural Machine Translation and Sequence-to-sequence Models: A Tutorial | 65 Pages | null | null | null | cs.CL cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This tutorial introduces a new and powerful set of techniques variously
called "neural machine translation" or "neural sequence-to-sequence models".
These techniques have been used in a number of tasks regarding the handling of
human language, and can be a powerful tool in the toolbox of anyone who wants
to model sequential data of some sort. The tutorial assumes that the reader
knows the basics of math and programming, but does not assume any particular
experience with neural networks or natural language processing. It attempts to
explain the intuition behind the various methods covered, then delves into them
with enough mathematical detail to understand them concretely, and culiminates
with a suggestion for an implementation exercise, where readers can test that
they understood the content in practice.
| [
{
"created": "Sun, 5 Mar 2017 16:10:11 GMT",
"version": "v1"
}
] | 2017-03-07 | [
[
"Neubig",
"Graham",
""
]
] | This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice. |
1105.3259 | Frank Nielsen | Frank Nielsen and Richard Nock | On R\'enyi and Tsallis entropies and divergences for exponential
families | 7 pages | Journal of Physics A: Mathematical and Theoretical, Volume 45
Number 3, 2012 | 10.1088/1751-8113/45/3/032003 | null | cs.IT cs.LG math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many common probability distributions in statistics like the Gaussian,
multinomial, Beta or Gamma distributions can be studied under the unified
framework of exponential families. In this paper, we prove that both R\'enyi
and Tsallis divergences of distributions belonging to the same exponential
family admit a generic closed form expression. Furthermore, we show that
R\'enyi and Tsallis entropies can also be calculated in closed-form for
sub-families including the Gaussian or exponential distributions, among others.
| [
{
"created": "Tue, 17 May 2011 02:05:32 GMT",
"version": "v1"
}
] | 2012-02-01 | [
[
"Nielsen",
"Frank",
""
],
[
"Nock",
"Richard",
""
]
] | Many common probability distributions in statistics like the Gaussian, multinomial, Beta or Gamma distributions can be studied under the unified framework of exponential families. In this paper, we prove that both R\'enyi and Tsallis divergences of distributions belonging to the same exponential family admit a generic closed form expression. Furthermore, we show that R\'enyi and Tsallis entropies can also be calculated in closed-form for sub-families including the Gaussian or exponential distributions, among others. |
1408.3639 | Ye Liang | Ye Liang | Solving Polynomial Equations with Equation Constraints: the
Zero-dimensional Case | null | null | null | null | cs.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A zero-dimensional polynomial ideal may have a lot of complex zeros. But
sometimes, only some of them are needed. In this paper, for a zero-dimensional
ideal $I$, we study its complex zeros that locate in another variety
$\textbf{V}(J)$ where $J$ is an arbitrary ideal.
The main problem is that for a point in $\textbf{V}(I) \cap
\textbf{V}(J)=\textbf{V}(I+J)$, its multiplicities w.r.t. $I$ and $I+J$ may be
different. Therefore, we cannot get the multiplicity of this point w.r.t. $I$
by studying $I + J$. A straightforward way is that first compute the points of
$\textbf{V}(I + J)$, then study their multiplicities w.r.t. $I$. But the former
step is difficult to realize exactly.
In this paper, we propose a natural geometric explanation of the localization
of a polynomial ring corresponding to a semigroup order. Then, based on this
view, using the standard basis method and the border basis method, we introduce
a way to compute the complex zeros of $I$ in $\textbf{V}(J)$ with their
multiplicities w.r.t. $I$. As an application, we compute the sum of Milnor
numbers of the singular points on a polynomial hypersurface and work out all
the singular points on the hypersurface with their Milnor numbers.
| [
{
"created": "Fri, 15 Aug 2014 20:03:21 GMT",
"version": "v1"
}
] | 2014-08-19 | [
[
"Liang",
"Ye",
""
]
] | A zero-dimensional polynomial ideal may have a lot of complex zeros. But sometimes, only some of them are needed. In this paper, for a zero-dimensional ideal $I$, we study its complex zeros that locate in another variety $\textbf{V}(J)$ where $J$ is an arbitrary ideal. The main problem is that for a point in $\textbf{V}(I) \cap \textbf{V}(J)=\textbf{V}(I+J)$, its multiplicities w.r.t. $I$ and $I+J$ may be different. Therefore, we cannot get the multiplicity of this point w.r.t. $I$ by studying $I + J$. A straightforward way is that first compute the points of $\textbf{V}(I + J)$, then study their multiplicities w.r.t. $I$. But the former step is difficult to realize exactly. In this paper, we propose a natural geometric explanation of the localization of a polynomial ring corresponding to a semigroup order. Then, based on this view, using the standard basis method and the border basis method, we introduce a way to compute the complex zeros of $I$ in $\textbf{V}(J)$ with their multiplicities w.r.t. $I$. As an application, we compute the sum of Milnor numbers of the singular points on a polynomial hypersurface and work out all the singular points on the hypersurface with their Milnor numbers. |
1407.4056 | Dinesh Ramasamy | Dinesh Ramasamy and Upamanyu Madhow | Scalable and Efficient Geographic Routing in Mobile Ad Hoc Wireless
Networks | IEEE Transactions on Information Theory | null | null | null | cs.NI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and evaluate a scalable position-publish and an accompanying
routing protocol which is efficient despite operating with imperfect
information regarding the destination's location. The traffic generated by our
position-publish protocol fits within the transport capacity of large mobile ad
hoc networks (MANETs) with constant communication bandwidth allocated for
routing overhead, even as the network size increases. The routing protocol
guarantees, with high probability, routes whose lengths are within a constant
"stretch" factor of the shortest path from source to destination. The key idea
underlying the scalability of the publish protocol is for each potential
destination node to send location updates (with frequency decaying with
distance) to a subset of network nodes, structured as annular regions around it
(the natural approach of updating circular regions in distance-dependent
fashion does not scale). The routing protocol must therefore account for the
fact that the source and/or relay nodes may not have estimates of the
destination's location (or may have stale estimates). Spatial and temporal
scaling of protocol parameters are chosen so as to guarantee scalability, route
reliability and route stretch, and these analytical design prescriptions are
verified using simulations.
| [
{
"created": "Tue, 15 Jul 2014 16:55:19 GMT",
"version": "v1"
}
] | 2014-07-16 | [
[
"Ramasamy",
"Dinesh",
""
],
[
"Madhow",
"Upamanyu",
""
]
] | We propose and evaluate a scalable position-publish and an accompanying routing protocol which is efficient despite operating with imperfect information regarding the destination's location. The traffic generated by our position-publish protocol fits within the transport capacity of large mobile ad hoc networks (MANETs) with constant communication bandwidth allocated for routing overhead, even as the network size increases. The routing protocol guarantees, with high probability, routes whose lengths are within a constant "stretch" factor of the shortest path from source to destination. The key idea underlying the scalability of the publish protocol is for each potential destination node to send location updates (with frequency decaying with distance) to a subset of network nodes, structured as annular regions around it (the natural approach of updating circular regions in distance-dependent fashion does not scale). The routing protocol must therefore account for the fact that the source and/or relay nodes may not have estimates of the destination's location (or may have stale estimates). Spatial and temporal scaling of protocol parameters are chosen so as to guarantee scalability, route reliability and route stretch, and these analytical design prescriptions are verified using simulations. |
2407.02651 | Majeed Kazemitabaar | Majeed Kazemitabaar, Jack Williams, Ian Drosos, Tovi Grossman, Austin
Henley, Carina Negreanu, Advait Sarkar | Improving Steering and Verification in AI-Assisted Data Analysis with
Interactive Task Decomposition | Published at UIST 2024; 19 pages, 9 figures, and 2 tables | Proceedings of the 37th Annual ACM Symposium on User Interface
Software and Technology (UIST 2024) | 10.1145/3654777.3676345 | null | cs.HC cs.AI | http://creativecommons.org/licenses/by/4.0/ | LLM-powered tools like ChatGPT Data Analysis, have the potential to help
users tackle the challenging task of data analysis programming, which requires
expertise in data processing, programming, and statistics. However, our
formative study (n=15) uncovered serious challenges in verifying AI-generated
results and steering the AI (i.e., guiding the AI system to produce the desired
output). We developed two contrasting approaches to address these challenges.
The first (Stepwise) decomposes the problem into step-by-step subgoals with
pairs of editable assumptions and code until task completion, while the second
(Phasewise) decomposes the entire problem into three editable, logical phases:
structured input/output assumptions, execution plan, and code. A controlled,
within-subjects experiment (n=18) compared these systems against a
conversational baseline. Users reported significantly greater control with the
Stepwise and Phasewise systems, and found intervention, correction, and
verification easier, compared to the baseline. The results suggest design
guidelines and trade-offs for AI-assisted data analysis tools.
| [
{
"created": "Tue, 2 Jul 2024 20:33:50 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2024 15:56:00 GMT",
"version": "v2"
}
] | 2024-08-02 | [
[
"Kazemitabaar",
"Majeed",
""
],
[
"Williams",
"Jack",
""
],
[
"Drosos",
"Ian",
""
],
[
"Grossman",
"Tovi",
""
],
[
"Henley",
"Austin",
""
],
[
"Negreanu",
"Carina",
""
],
[
"Sarkar",
"Advait",
""
]
] | LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing, programming, and statistics. However, our formative study (n=15) uncovered serious challenges in verifying AI-generated results and steering the AI (i.e., guiding the AI system to produce the desired output). We developed two contrasting approaches to address these challenges. The first (Stepwise) decomposes the problem into step-by-step subgoals with pairs of editable assumptions and code until task completion, while the second (Phasewise) decomposes the entire problem into three editable, logical phases: structured input/output assumptions, execution plan, and code. A controlled, within-subjects experiment (n=18) compared these systems against a conversational baseline. Users reported significantly greater control with the Stepwise and Phasewise systems, and found intervention, correction, and verification easier, compared to the baseline. The results suggest design guidelines and trade-offs for AI-assisted data analysis tools. |
2304.07689 | Zhiyuan Li | Zhiyuan Li, Ziru Liu, Anna Zou, Anca L. Ralescu | Learning Empirical Bregman Divergence for Uncertain Distance
Representation | Accepted by IEEE FUSION 2023 | null | 10.23919/FUSION52260.2023.10224080 | null | cs.CV cs.AI cs.IT cs.LG math.IT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep metric learning techniques have been used for visual representation in
various supervised and unsupervised learning tasks through learning embeddings
of samples with deep networks. However, classic approaches, which employ a
fixed distance metric as a similarity function between two embeddings, may lead
to suboptimal performance for capturing the complex data distribution. The
Bregman divergence generalizes measures of various distance metrics and arises
throughout many fields of deep metric learning. In this paper, we first show
how deep metric learning loss can arise from the Bregman divergence. We then
introduce a novel method for learning empirical Bregman divergence directly
from data based on parameterizing the convex function underlying the Bregman
divergence with a deep learning setting. We further experimentally show that
our approach performs effectively on five popular public datasets compared to
other SOTA deep metric learning methods, particularly for pattern recognition
problems.
| [
{
"created": "Sun, 16 Apr 2023 04:16:28 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Apr 2023 01:22:50 GMT",
"version": "v2"
},
{
"created": "Mon, 15 May 2023 16:38:23 GMT",
"version": "v3"
}
] | 2023-08-30 | [
[
"Li",
"Zhiyuan",
""
],
[
"Liu",
"Ziru",
""
],
[
"Zou",
"Anna",
""
],
[
"Ralescu",
"Anca L.",
""
]
] | Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems. |
2205.13190 | Haitao Lin | Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing
Zong | Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via
Role Interactions | Accepted by ACL 2022 main conference | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Role-oriented dialogue summarization is to generate summaries for different
roles in the dialogue, e.g., merchants and consumers. Existing methods handle
this task by summarizing each role's content separately and thus are prone to
ignore the information from other roles. However, we believe that other roles'
content could benefit the quality of summaries, such as the omitted information
mentioned by other roles. Therefore, we propose a novel role interaction
enhanced method for role-oriented dialogue summarization. It adopts cross
attention and decoder self-attention interactions to interactively acquire
other roles' critical information. The cross attention interaction aims to
select other roles' critical dialogue utterances, while the decoder
self-attention interaction aims to obtain key information from other roles'
summaries. Experimental results have shown that our proposed method
significantly outperforms strong baselines on two public role-oriented dialogue
summarization datasets. Extensive analyses have demonstrated that other roles'
content could help generate summaries with more complete semantics and correct
topic structures.
| [
{
"created": "Thu, 26 May 2022 06:58:02 GMT",
"version": "v1"
}
] | 2022-05-27 | [
[
"Lin",
"Haitao",
""
],
[
"Zhu",
"Junnan",
""
],
[
"Xiang",
"Lu",
""
],
[
"Zhou",
"Yu",
""
],
[
"Zhang",
"Jiajun",
""
],
[
"Zong",
"Chengqing",
""
]
] | Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role's content separately and thus are prone to ignore the information from other roles. However, we believe that other roles' content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles' critical information. The cross attention interaction aims to select other roles' critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles' summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles' content could help generate summaries with more complete semantics and correct topic structures. |
2406.19878 | Ruben Interian | Ruben Interian | A political radicalization framework based on Moral Foundations Theory | null | null | null | null | cs.SI physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Moral Foundations Theory proposes that individuals with conflicting political
views base their behavior on different principles chosen from a small group of
universal moral foundations. This study proposes using a set of widely accepted
moral foundations (Fairness, Ingroup loyalty, Authority, and Purity) as proxies
to determine the degree of radicalization of online communities. The fifth
principle, Care, is generally surpassed by others, which are higher in the
radicalized groups' moral hierarchy. Moreover, the presented data-driven
methodological framework proposes an alternative way to measure whether a
community complies with some moral principle or foundation: not evaluating its
speech, but its behavior through interactions of its individuals, establishing
a bridge between structural features of the interaction network and the
intensity of communities' radicalization regarding the considered moral
foundations. Two foundations may be assessed using the network's structural
characteristics: Ingroup loyalty measured by group-level modularity, and
Authority evaluated using group domination for detecting potential hierarchical
substructures within the network. By analyzing the set of Pareto-optimal groups
regarding a multidimensional moral relevance scale, the most radicalized
communities are identified among those considered extreme in some of their
attitudes or views. The application of the proposed framework is illustrated
using real-world datasets. The radicalized communities' behavior exhibits
increasing isolation, and its authorities and leaders show growing domination
over their audience. There were also detected differences between users'
behavior and speech, showing that individuals tend to share more 'extreme'
ingroup content than that they publish: extreme views get more likes on social
media.
| [
{
"created": "Fri, 28 Jun 2024 12:36:06 GMT",
"version": "v1"
}
] | 2024-07-01 | [
[
"Interian",
"Ruben",
""
]
] | Moral Foundations Theory proposes that individuals with conflicting political views base their behavior on different principles chosen from a small group of universal moral foundations. This study proposes using a set of widely accepted moral foundations (Fairness, Ingroup loyalty, Authority, and Purity) as proxies to determine the degree of radicalization of online communities. The fifth principle, Care, is generally surpassed by others, which are higher in the radicalized groups' moral hierarchy. Moreover, the presented data-driven methodological framework proposes an alternative way to measure whether a community complies with some moral principle or foundation: not evaluating its speech, but its behavior through interactions of its individuals, establishing a bridge between structural features of the interaction network and the intensity of communities' radicalization regarding the considered moral foundations. Two foundations may be assessed using the network's structural characteristics: Ingroup loyalty measured by group-level modularity, and Authority evaluated using group domination for detecting potential hierarchical substructures within the network. By analyzing the set of Pareto-optimal groups regarding a multidimensional moral relevance scale, the most radicalized communities are identified among those considered extreme in some of their attitudes or views. The application of the proposed framework is illustrated using real-world datasets. The radicalized communities' behavior exhibits increasing isolation, and its authorities and leaders show growing domination over their audience. There were also detected differences between users' behavior and speech, showing that individuals tend to share more 'extreme' ingroup content than that they publish: extreme views get more likes on social media. |
2403.09176 | Byeongjun Park | Byeongjun Park, Hyojun Go, Jin-Young Kim, Sangmin Woo, Seokil Ham,
Changick Kim | Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse
Mixture-of-Experts | Project Page: https://byeongjun-park.github.io/Switch-DiT/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Diffusion models have achieved remarkable success across a range of
generative tasks. Recent efforts to enhance diffusion model architectures have
reimagined them as a form of multi-task learning, where each task corresponds
to a denoising task at a specific noise level. While these efforts have focused
on parameter isolation and task routing, they fall short of capturing detailed
inter-task relationships and risk losing semantic information, respectively. In
response, we introduce Switch Diffusion Transformer (Switch-DiT), which
establishes inter-task relationships between conflicting tasks without
compromising semantic information. To achieve this, we employ a sparse
mixture-of-experts within each transformer block to utilize semantic
information and facilitate handling conflicts in tasks through parameter
isolation. Additionally, we propose a diffusion prior loss, encouraging similar
tasks to share their denoising paths while isolating conflicting ones. Through
these, each transformer block contains a shared expert across all tasks, where
the common and task-specific denoising paths enable the diffusion model to
construct its beneficial way of synergizing denoising tasks. Extensive
experiments validate the effectiveness of our approach in improving both image
quality and convergence rate, and further analysis demonstrates that Switch-DiT
constructs tailored denoising paths across various generation scenarios.
| [
{
"created": "Thu, 14 Mar 2024 08:43:43 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Jul 2024 07:39:08 GMT",
"version": "v2"
}
] | 2024-07-11 | [
[
"Park",
"Byeongjun",
""
],
[
"Go",
"Hyojun",
""
],
[
"Kim",
"Jin-Young",
""
],
[
"Woo",
"Sangmin",
""
],
[
"Ham",
"Seokil",
""
],
[
"Kim",
"Changick",
""
]
] | Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of multi-task learning, where each task corresponds to a denoising task at a specific noise level. While these efforts have focused on parameter isolation and task routing, they fall short of capturing detailed inter-task relationships and risk losing semantic information, respectively. In response, we introduce Switch Diffusion Transformer (Switch-DiT), which establishes inter-task relationships between conflicting tasks without compromising semantic information. To achieve this, we employ a sparse mixture-of-experts within each transformer block to utilize semantic information and facilitate handling conflicts in tasks through parameter isolation. Additionally, we propose a diffusion prior loss, encouraging similar tasks to share their denoising paths while isolating conflicting ones. Through these, each transformer block contains a shared expert across all tasks, where the common and task-specific denoising paths enable the diffusion model to construct its beneficial way of synergizing denoising tasks. Extensive experiments validate the effectiveness of our approach in improving both image quality and convergence rate, and further analysis demonstrates that Switch-DiT constructs tailored denoising paths across various generation scenarios. |
1509.04252 | Andreas Kreienbuehl | Andreas Kreienbuehl and Arne Naegel and Daniel Ruprecht and Andreas
Vogel and Gabriel Wittum and Rolf Krause | Parareal convergence for 2D unsteady flow around a cylinder | 16 pages, 7 figures | null | null | null | cs.CE cs.DC cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this technical report we study the convergence of Parareal for 2D
incompressible flow around a cylinder for different viscosities. Two methods
are used as fine integrator: backward Euler and a fractional step method. It is
found that Parareal converges better for the implicit Euler, likely because it
under-resolves the fine-scale dynamics as a result of numerical diffusion.
| [
{
"created": "Mon, 14 Sep 2015 19:29:41 GMT",
"version": "v1"
}
] | 2015-09-15 | [
[
"Kreienbuehl",
"Andreas",
""
],
[
"Naegel",
"Arne",
""
],
[
"Ruprecht",
"Daniel",
""
],
[
"Vogel",
"Andreas",
""
],
[
"Wittum",
"Gabriel",
""
],
[
"Krause",
"Rolf",
""
]
] | In this technical report we study the convergence of Parareal for 2D incompressible flow around a cylinder for different viscosities. Two methods are used as fine integrator: backward Euler and a fractional step method. It is found that Parareal converges better for the implicit Euler, likely because it under-resolves the fine-scale dynamics as a result of numerical diffusion. |
2202.13026 | Flash Sheridan | Flash Sheridan | Static Analysis Deployment Pitfalls | null | Supplemental Proceedings of the 21st IEEE International Symposium
on Software Reliability Engineering, November 2010 | null | null | cs.SE cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Organizational, political, and configuration mistakes in the deployment of a
static source code analysis tool within a software development organization can
result in most of the value of the tool being lost, even while apparently
meeting management goals. A list of pitfalls encountered as a static analysis
consultant is presented, with discussion of techniques for avoiding or
mitigating them. This is part of a work in progress, tentatively entitled
"Handbook of Static Analysis Deployment."
| [
{
"created": "Sat, 26 Feb 2022 01:01:08 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Sheridan",
"Flash",
""
]
] | Organizational, political, and configuration mistakes in the deployment of a static source code analysis tool within a software development organization can result in most of the value of the tool being lost, even while apparently meeting management goals. A list of pitfalls encountered as a static analysis consultant is presented, with discussion of techniques for avoiding or mitigating them. This is part of a work in progress, tentatively entitled "Handbook of Static Analysis Deployment." |
2305.13913 | Yun Li | Yun Li, Hongwei Liu, Sihem Mesnager | Constructions of Constant Dimension Subspace Codes | This article was submitted to Designs, Codes and Cryptography on
November 22nd, 2022 | null | null | null | cs.IT math.CO math.IT | http://creativecommons.org/licenses/by/4.0/ | Subspace codes have important applications in random network coding. It is
interesting to construct subspace codes with both sizes, and the minimum
distances are as large as possible. In particular, cyclic constant dimension
subspaces codes have additional properties which can be used to make encoding
and decoding more efficient. In this paper, we construct large cyclic constant
dimension subspace codes with minimum distances $2k-2$ and $2k$. These codes
are contained in $\mathcal{G}_q(n, k)$, where $\mathcal{G}_q(n, k)$ denotes the
set of all $k$-dimensional subspaces of $\mathbb{F}_{q^n}$. Consequently, some
results in \cite{FW}, \cite{NXG}, and \cite{ZT} are extended.
| [
{
"created": "Tue, 23 May 2023 10:37:00 GMT",
"version": "v1"
}
] | 2023-05-24 | [
[
"Li",
"Yun",
""
],
[
"Liu",
"Hongwei",
""
],
[
"Mesnager",
"Sihem",
""
]
] | Subspace codes have important applications in random network coding. It is interesting to construct subspace codes with both sizes, and the minimum distances are as large as possible. In particular, cyclic constant dimension subspaces codes have additional properties which can be used to make encoding and decoding more efficient. In this paper, we construct large cyclic constant dimension subspace codes with minimum distances $2k-2$ and $2k$. These codes are contained in $\mathcal{G}_q(n, k)$, where $\mathcal{G}_q(n, k)$ denotes the set of all $k$-dimensional subspaces of $\mathbb{F}_{q^n}$. Consequently, some results in \cite{FW}, \cite{NXG}, and \cite{ZT} are extended. |
1704.02789 | Mahardhika Pratama Dr | Mahardhika Pratama, Plamen P. Angelov, Edwin Lughofer | Parsimonious Random Vector Functional Link Network for Data Streams | this paper is submitted for publication in Information Sciences | null | 10.1016/j.ins.2017.11.050 | null | cs.NE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of random vector functional link network (RVFLN) has provided a
breakthrough in the design of neural networks (NNs) since it conveys solid
theoretical justification of randomized learning. Existing works in RVFLN are
hardly scalable for data stream analytics because they are inherent to the
issue of complexity as a result of the absence of structural learning
scenarios. A novel class of RVLFN, namely parsimonious random vector functional
link network (pRVFLN), is proposed in this paper. pRVFLN features an open
structure paradigm where its network structure can be built from scratch and
can be automatically generated in accordance with degree of nonlinearity and
time-varying property of system being modelled. pRVFLN is equipped with
complexity reduction scenarios where inconsequential hidden nodes can be pruned
and input features can be dynamically selected. pRVFLN puts into perspective an
online active learning mechanism which expedites the training process and
relieves operator labelling efforts. In addition, pRVFLN introduces a
non-parametric type of hidden node, developed using an interval-valued data
cloud. The hidden node completely reflects the real data distribution and is
not constrained by a specific shape of the cluster. All learning procedures of
pRVFLN follow a strictly single-pass learning mode, which is applicable for an
online real-time deployment. The efficacy of pRVFLN was rigorously validated
through numerous simulations and comparisons with state-of-the art algorithms
where it produced the most encouraging numerical results. Furthermore, the
robustness of pRVFLN was investigated and a new conclusion is made to the scope
of random parameters where it plays vital role to the success of randomized
learning.
| [
{
"created": "Mon, 10 Apr 2017 10:24:34 GMT",
"version": "v1"
},
{
"created": "Sat, 6 May 2017 11:59:53 GMT",
"version": "v2"
}
] | 2018-02-06 | [
[
"Pratama",
"Mahardhika",
""
],
[
"Angelov",
"Plamen P.",
""
],
[
"Lughofer",
"Edwin",
""
]
] | The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of randomized learning. Existing works in RVFLN are hardly scalable for data stream analytics because they are inherent to the issue of complexity as a result of the absence of structural learning scenarios. A novel class of RVLFN, namely parsimonious random vector functional link network (pRVFLN), is proposed in this paper. pRVFLN features an open structure paradigm where its network structure can be built from scratch and can be automatically generated in accordance with degree of nonlinearity and time-varying property of system being modelled. pRVFLN is equipped with complexity reduction scenarios where inconsequential hidden nodes can be pruned and input features can be dynamically selected. pRVFLN puts into perspective an online active learning mechanism which expedites the training process and relieves operator labelling efforts. In addition, pRVFLN introduces a non-parametric type of hidden node, developed using an interval-valued data cloud. The hidden node completely reflects the real data distribution and is not constrained by a specific shape of the cluster. All learning procedures of pRVFLN follow a strictly single-pass learning mode, which is applicable for an online real-time deployment. The efficacy of pRVFLN was rigorously validated through numerous simulations and comparisons with state-of-the art algorithms where it produced the most encouraging numerical results. Furthermore, the robustness of pRVFLN was investigated and a new conclusion is made to the scope of random parameters where it plays vital role to the success of randomized learning. |
2106.00451 | Fan Huang | Fan Huang | Highlight Timestamp Detection Model for Comedy Videos via Multimodal
Sentiment Analysis | null | null | null | null | cs.CV cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, the videos on the Internet are prevailing. The precise and in-depth
understanding of the videos is a difficult but valuable problem for both
platforms and researchers. The existing video understand models do well in
object recognition tasks but currently still cannot understand the abstract and
contextual features like highlight humor frames in comedy videos. The current
industrial works are also mainly focused on the basic category classification
task based on the appearances of objects. The feature detection methods for the
abstract category remains blank. A data structure that includes the information
of video frames, audio spectrum and texts provide a new direction to explore.
The multimodal models are proposed to make this in-depth video understanding
mission possible. In this paper, we analyze the difficulties in abstract
understanding of videos and propose a multimodal structure to obtain
state-of-the-art performance in this field. Then we select several benchmarks
for multimodal video understanding and apply the most suitable model to find
the best performance. At last, we evaluate the overall spotlights and drawbacks
of the models and methods in this paper and point out the possible directions
for further improvements.
| [
{
"created": "Fri, 28 May 2021 08:39:19 GMT",
"version": "v1"
}
] | 2021-06-02 | [
[
"Huang",
"Fan",
""
]
] | Nowadays, the videos on the Internet are prevailing. The precise and in-depth understanding of the videos is a difficult but valuable problem for both platforms and researchers. The existing video understand models do well in object recognition tasks but currently still cannot understand the abstract and contextual features like highlight humor frames in comedy videos. The current industrial works are also mainly focused on the basic category classification task based on the appearances of objects. The feature detection methods for the abstract category remains blank. A data structure that includes the information of video frames, audio spectrum and texts provide a new direction to explore. The multimodal models are proposed to make this in-depth video understanding mission possible. In this paper, we analyze the difficulties in abstract understanding of videos and propose a multimodal structure to obtain state-of-the-art performance in this field. Then we select several benchmarks for multimodal video understanding and apply the most suitable model to find the best performance. At last, we evaluate the overall spotlights and drawbacks of the models and methods in this paper and point out the possible directions for further improvements. |
2401.14579 | Ying Dai | Kun Fu, and Ying Dai | Recognizing Multiple Ingredients in Food Images Using a
Single-Ingredient Classification Model | 9 pages, 21 figures, 6 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognizing food images presents unique challenges due to the variable
spatial layout and shape changes of ingredients with different cooking and
cutting methods. This study introduces an advanced approach for recognizing
ingredients segmented from food images. The method localizes the candidate
regions of the ingredients using the locating and sliding window techniques.
Then, these regions are assigned into ingredient classes using a CNN
(Convolutional Neural Network)-based single-ingredient classification model
trained on a dataset of single-ingredient images. To address the challenge of
processing speed in multi-ingredient recognition, a novel model pruning method
is proposed that enhances the efficiency of the classification model.
Subsequently, the multi-ingredient identification is achieved through a
decision-making scheme, incorporating two novel algorithms. The
single-ingredient image dataset, designed in accordance with the book entitled
"New Food Ingredients List FOODS 2021", encompasses 9982 images across 110
diverse categories, emphasizing variety in ingredient shapes. In addition, a
multi-ingredient image dataset is developed to rigorously evaluate the
performance of our approach. Experimental results validate the effectiveness of
our method, particularly highlighting its improved capability in recognizing
multiple ingredients. This marks a significant advancement in the field of food
image analysis.
| [
{
"created": "Fri, 26 Jan 2024 00:46:56 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Feb 2024 11:58:59 GMT",
"version": "v2"
},
{
"created": "Mon, 19 Feb 2024 01:43:00 GMT",
"version": "v3"
}
] | 2024-02-20 | [
[
"Fu",
"Kun",
""
],
[
"Dai",
"Ying",
""
]
] | Recognizing food images presents unique challenges due to the variable spatial layout and shape changes of ingredients with different cooking and cutting methods. This study introduces an advanced approach for recognizing ingredients segmented from food images. The method localizes the candidate regions of the ingredients using the locating and sliding window techniques. Then, these regions are assigned into ingredient classes using a CNN (Convolutional Neural Network)-based single-ingredient classification model trained on a dataset of single-ingredient images. To address the challenge of processing speed in multi-ingredient recognition, a novel model pruning method is proposed that enhances the efficiency of the classification model. Subsequently, the multi-ingredient identification is achieved through a decision-making scheme, incorporating two novel algorithms. The single-ingredient image dataset, designed in accordance with the book entitled "New Food Ingredients List FOODS 2021", encompasses 9982 images across 110 diverse categories, emphasizing variety in ingredient shapes. In addition, a multi-ingredient image dataset is developed to rigorously evaluate the performance of our approach. Experimental results validate the effectiveness of our method, particularly highlighting its improved capability in recognizing multiple ingredients. This marks a significant advancement in the field of food image analysis. |
1212.4931 | Anatolii Leukhin Nikolaevich | Anatolii Leukhin, Oscar Moreno and Andrew Tirkel | Secure CDMA Sequences | 10 pages, 8 figures | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single sequences like Legendre have high linear complexity. Known CDMA
families of sequences all have low complexities. We present a new method of
constructing CDMA sequence sets with the complexity of the Legendre from new
frequency hop patterns, and compare them with known sequences. These are the
first families whose normalized linear complexities do not asymptote to 0,
verified for lengths up to 6x108. The new constructions in array format are
also useful in watermarking images. We present a conjecture regarding the
recursion polynomials. We also have a method to reverse the process, and from
small Kasami/No-Kumar sequences we obtain a new family of 2n doubly periodic
(2n+1)x(2n-1) frequency hop patterns with correlation 2.
| [
{
"created": "Thu, 20 Dec 2012 06:04:04 GMT",
"version": "v1"
}
] | 2012-12-21 | [
[
"Leukhin",
"Anatolii",
""
],
[
"Moreno",
"Oscar",
""
],
[
"Tirkel",
"Andrew",
""
]
] | Single sequences like Legendre have high linear complexity. Known CDMA families of sequences all have low complexities. We present a new method of constructing CDMA sequence sets with the complexity of the Legendre from new frequency hop patterns, and compare them with known sequences. These are the first families whose normalized linear complexities do not asymptote to 0, verified for lengths up to 6x108. The new constructions in array format are also useful in watermarking images. We present a conjecture regarding the recursion polynomials. We also have a method to reverse the process, and from small Kasami/No-Kumar sequences we obtain a new family of 2n doubly periodic (2n+1)x(2n-1) frequency hop patterns with correlation 2. |
1904.09046 | Laurent Lessard | Laurent Lessard, Peter Seiler | Direct Synthesis of Iterative Algorithms With Bounds on Achievable
Worst-Case Convergence Rate | American Control Conference, 2020 | null | null | null | cs.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Iterative first-order methods such as gradient descent and its variants are
widely used for solving optimization and machine learning problems. There has
been recent interest in analytic or numerically efficient methods for computing
worst-case performance bounds for such algorithms, for example over the class
of strongly convex loss functions. A popular approach is to assume the
algorithm has a fixed size (fixed dimension, or memory) and that its structure
is parameterized by one or two hyperparameters, for example a learning rate and
a momentum parameter. Then, a Lyapunov function is sought to certify robust
stability and subsequent optimization can be performed to find optimal
hyperparameter tunings. In the present work, we instead fix the constraints
that characterize the loss function and apply techniques from robust control
synthesis to directly search over algorithms. This approach yields stronger
results than those previously available, since the bounds produced hold over
algorithms with an arbitrary, but finite, amount of memory rather than just
holding for algorithms with a prescribed structure.
| [
{
"created": "Fri, 19 Apr 2019 01:07:50 GMT",
"version": "v1"
},
{
"created": "Sat, 21 Mar 2020 03:18:18 GMT",
"version": "v2"
}
] | 2020-03-24 | [
[
"Lessard",
"Laurent",
""
],
[
"Seiler",
"Peter",
""
]
] | Iterative first-order methods such as gradient descent and its variants are widely used for solving optimization and machine learning problems. There has been recent interest in analytic or numerically efficient methods for computing worst-case performance bounds for such algorithms, for example over the class of strongly convex loss functions. A popular approach is to assume the algorithm has a fixed size (fixed dimension, or memory) and that its structure is parameterized by one or two hyperparameters, for example a learning rate and a momentum parameter. Then, a Lyapunov function is sought to certify robust stability and subsequent optimization can be performed to find optimal hyperparameter tunings. In the present work, we instead fix the constraints that characterize the loss function and apply techniques from robust control synthesis to directly search over algorithms. This approach yields stronger results than those previously available, since the bounds produced hold over algorithms with an arbitrary, but finite, amount of memory rather than just holding for algorithms with a prescribed structure. |
2309.09609 | Enzo Rucci | Manuel Costanzo, Enzo Rucci, Carlos Garc\'ia S\'anchez, Marcelo
Naiouf, Manuel Prieto-Mat\'ias | Comparing Performance and Portability between CUDA and SYCL for Protein
Database Search on NVIDIA, AMD, and Intel GPUs | This article was accepted for publication in 2023 IEEE 35th
International Symposium on Computer Architecture and High Performance
Computing (SBAC-PAD) | null | 10.1109/SBAC-PAD59825.2023.00023 | null | cs.PL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The heterogeneous computing paradigm has led to the need for portable and
efficient programming solutions that can leverage the capabilities of various
hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the
portability and performance of the SYCL and CUDA languages for one fundamental
bioinformatics application (Smith-Waterman protein database search) across
different GPU architectures, considering single and multi-GPU configurations
from different vendors. The experimental work showed that, while both CUDA and
SYCL versions achieve similar performance on NVIDIA devices, the latter
demonstrated remarkable code portability to other GPU architectures, such as
AMD and Intel. Furthermore, the architectural efficiency rates achieved on
these devices were superior in 3 of the 4 cases tested. This brief study
highlights the potential of SYCL as a viable solution for achieving both
performance and portability in the heterogeneous computing ecosystem.
| [
{
"created": "Mon, 18 Sep 2023 09:26:46 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Nov 2023 12:11:08 GMT",
"version": "v2"
}
] | 2023-11-13 | [
[
"Costanzo",
"Manuel",
""
],
[
"Rucci",
"Enzo",
""
],
[
"Sánchez",
"Carlos García",
""
],
[
"Naiouf",
"Marcelo",
""
],
[
"Prieto-Matías",
"Manuel",
""
]
] | The heterogeneous computing paradigm has led to the need for portable and efficient programming solutions that can leverage the capabilities of various hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the portability and performance of the SYCL and CUDA languages for one fundamental bioinformatics application (Smith-Waterman protein database search) across different GPU architectures, considering single and multi-GPU configurations from different vendors. The experimental work showed that, while both CUDA and SYCL versions achieve similar performance on NVIDIA devices, the latter demonstrated remarkable code portability to other GPU architectures, such as AMD and Intel. Furthermore, the architectural efficiency rates achieved on these devices were superior in 3 of the 4 cases tested. This brief study highlights the potential of SYCL as a viable solution for achieving both performance and portability in the heterogeneous computing ecosystem. |
2201.12023 | Zhuohan Li | Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen,
Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E.
Gonzalez, Ion Stoica | Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed
Deep Learning | OSDI 2022 | null | null | null | cs.LG cs.DC cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Alpa automates model-parallel training of large deep learning (DL) models by
generating execution plans that unify data, operator, and pipeline parallelism.
Existing model-parallel training systems either require users to manually
create a parallelization plan or automatically generate one from a limited
space of model parallelism configurations. They do not suffice to scale out
complex DL models on distributed compute devices. Alpa distributes the training
of large DL models by viewing parallelisms as two hierarchical levels:
inter-operator and intra-operator parallelisms. Based on it, Alpa constructs a
new hierarchical space for massive model-parallel execution plans. Alpa designs
a number of compilation passes to automatically derive efficient parallel
execution plans at each parallelism level. Alpa implements an efficient runtime
to orchestrate the two-level parallel execution on distributed compute devices.
Our evaluation shows Alpa generates parallelization plans that match or
outperform hand-tuned model-parallel training systems even on models they are
designed for. Unlike specialized systems, Alpa also generalizes to models with
heterogeneous architectures and models without manually-designed plans. Alpa's
source code is publicly available at https://github.com/alpa-projects/alpa
| [
{
"created": "Fri, 28 Jan 2022 10:13:35 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Jun 2022 09:18:24 GMT",
"version": "v2"
},
{
"created": "Tue, 28 Jun 2022 19:36:44 GMT",
"version": "v3"
}
] | 2022-06-30 | [
[
"Zheng",
"Lianmin",
""
],
[
"Li",
"Zhuohan",
""
],
[
"Zhang",
"Hao",
""
],
[
"Zhuang",
"Yonghao",
""
],
[
"Chen",
"Zhifeng",
""
],
[
"Huang",
"Yanping",
""
],
[
"Wang",
"Yida",
""
],
[
"Xu",
"Yuanzhong",
""
],
[
"Zhuo",
"Danyang",
""
],
[
"Xing",
"Eric P.",
""
],
[
"Gonzalez",
"Joseph E.",
""
],
[
"Stoica",
"Ion",
""
]
] | Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations. They do not suffice to scale out complex DL models on distributed compute devices. Alpa distributes the training of large DL models by viewing parallelisms as two hierarchical levels: inter-operator and intra-operator parallelisms. Based on it, Alpa constructs a new hierarchical space for massive model-parallel execution plans. Alpa designs a number of compilation passes to automatically derive efficient parallel execution plans at each parallelism level. Alpa implements an efficient runtime to orchestrate the two-level parallel execution on distributed compute devices. Our evaluation shows Alpa generates parallelization plans that match or outperform hand-tuned model-parallel training systems even on models they are designed for. Unlike specialized systems, Alpa also generalizes to models with heterogeneous architectures and models without manually-designed plans. Alpa's source code is publicly available at https://github.com/alpa-projects/alpa |
2307.02591 | Sunjae Kwon | Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee L. Sung, Joel
I. Reisman, Wenjun Li, Robert D. Kerns, William Becker, Hong Yu | ODD: A Benchmark Dataset for the Natural Language Processing based
Opioid Related Aberrant Behavior Detection | To be appeared at NAACL 2024 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Opioid related aberrant behaviors (ORABs) present novel risk factors for
opioid overdose. This paper introduces a novel biomedical natural language
processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an
expert-annotated dataset designed to identify ORABs from patients' EHR notes
and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2)
Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid
dependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous
System-related, and 9) Social Determinants of Health. We explored two
state-of-the-art natural language processing models (fine-tuning and
prompt-tuning approaches) to identify ORAB. Experimental results show that the
prompt-tuning models outperformed the fine-tuning models in most categories and
the gains were especially higher among uncommon categories (Suggested Aberrant
Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and
Medication Change). Although the best model achieved the highest 88.17% on
macro average area under precision recall curve, uncommon classes still have a
large room for performance improvement. ODD is publicly available.
| [
{
"created": "Wed, 5 Jul 2023 18:41:29 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jul 2023 00:47:23 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Feb 2024 17:40:03 GMT",
"version": "v3"
},
{
"created": "Fri, 22 Mar 2024 20:01:04 GMT",
"version": "v4"
}
] | 2024-03-26 | [
[
"Kwon",
"Sunjae",
""
],
[
"Wang",
"Xun",
""
],
[
"Liu",
"Weisong",
""
],
[
"Druhl",
"Emily",
""
],
[
"Sung",
"Minhee L.",
""
],
[
"Reisman",
"Joel I.",
""
],
[
"Li",
"Wenjun",
""
],
[
"Kerns",
"Robert D.",
""
],
[
"Becker",
"William",
""
],
[
"Yu",
"Hong",
""
]
] | Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. This paper introduces a novel biomedical natural language processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset designed to identify ORABs from patients' EHR notes and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid dependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous System-related, and 9) Social Determinants of Health. We explored two state-of-the-art natural language processing models (fine-tuning and prompt-tuning approaches) to identify ORAB. Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change). Although the best model achieved the highest 88.17% on macro average area under precision recall curve, uncommon classes still have a large room for performance improvement. ODD is publicly available. |
1306.1889 | Pradeep Singla | Aakash Gupta, Pradeep Singla, Jitendra Gupta, Nitin Maheshwari | An Improved Structure Of Reversible Adder And Subtractor | null | International Journal of Electronics and Computer Science
Engineering, Vol 2, No. 2, pp712-718, June 2013 | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In today's world everyday a new technology which is faster, smaller and more
complex than its predecessor is being developed. The increased number of
transistors packed onto a chip of a conventional system results in increased
power consumption that is why Reversible logic has drawn attention of
Researchers due to its less heat dissipating characteristics. Reversible logic
can be imposed over applications such as quantum computing, optical computing,
quantum dot cellular automata, low power VLSI circuits, DNA computing. This
paper presents the reversible combinational circuit of adder, subtractor and
parity preserving subtractor. The suggested circuit in this paper are designed
using Feynman, Double Feynman and MUX gates which are better than the existing
one in literature in terms of Quantum cost, Garbage output and Total logical
calculations.
| [
{
"created": "Sat, 8 Jun 2013 07:21:22 GMT",
"version": "v1"
}
] | 2013-06-11 | [
[
"Gupta",
"Aakash",
""
],
[
"Singla",
"Pradeep",
""
],
[
"Gupta",
"Jitendra",
""
],
[
"Maheshwari",
"Nitin",
""
]
] | In today's world everyday a new technology which is faster, smaller and more complex than its predecessor is being developed. The increased number of transistors packed onto a chip of a conventional system results in increased power consumption that is why Reversible logic has drawn attention of Researchers due to its less heat dissipating characteristics. Reversible logic can be imposed over applications such as quantum computing, optical computing, quantum dot cellular automata, low power VLSI circuits, DNA computing. This paper presents the reversible combinational circuit of adder, subtractor and parity preserving subtractor. The suggested circuit in this paper are designed using Feynman, Double Feynman and MUX gates which are better than the existing one in literature in terms of Quantum cost, Garbage output and Total logical calculations. |
2312.15935 | Romain Abraham | Romain Abraham (IDP), Jean-Fran\c{c}ois Delmas (CERMICS), Julien
Weibel (IDP, CERMICS) | Probability-graphons: Limits of large dense weighted graphs | null | null | null | null | cs.DM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce probability-graphons which are probability kernels that
generalize graphons to the case of weighted graphs. Probability-graphons appear
as the limit objects to study sequences of large weighted graphs whose
distribution of subgraph sampling converge. The edge-weights are taken from a
general Polish space, which also covers the case of decorated graphs. Here,
graphs can be either directed or undirected. Starting from a distance $d_m$
inducing the weak topology on measures, we define a cut distance on
probability-graphons, making it a Polish space, and study the properties of
this cut distance. In particular, we exhibit a tightness criterion for
probability-graphons related to relative compactness in the cut distance. We
also prove that under some conditions on the distance $d_m$, which are
satisfied for some well-know distances like the Prohorov distance, and the
Fortet-Mourier and Kantorovitch-Rubinstein norms, the topology induced by the
cut distance on the spaceof probability-graphons is independent from the choice
of $d_m$. Eventually, we prove that this topology coincides with the topology
induced by the convergence in distribution of the sampled subgraphs.
| [
{
"created": "Tue, 26 Dec 2023 07:59:59 GMT",
"version": "v1"
}
] | 2023-12-27 | [
[
"Abraham",
"Romain",
"",
"IDP"
],
[
"Delmas",
"Jean-François",
"",
"CERMICS"
],
[
"Weibel",
"Julien",
"",
"IDP, CERMICS"
]
] | We introduce probability-graphons which are probability kernels that generalize graphons to the case of weighted graphs. Probability-graphons appear as the limit objects to study sequences of large weighted graphs whose distribution of subgraph sampling converge. The edge-weights are taken from a general Polish space, which also covers the case of decorated graphs. Here, graphs can be either directed or undirected. Starting from a distance $d_m$ inducing the weak topology on measures, we define a cut distance on probability-graphons, making it a Polish space, and study the properties of this cut distance. In particular, we exhibit a tightness criterion for probability-graphons related to relative compactness in the cut distance. We also prove that under some conditions on the distance $d_m$, which are satisfied for some well-know distances like the Prohorov distance, and the Fortet-Mourier and Kantorovitch-Rubinstein norms, the topology induced by the cut distance on the spaceof probability-graphons is independent from the choice of $d_m$. Eventually, we prove that this topology coincides with the topology induced by the convergence in distribution of the sampled subgraphs. |
1707.06381 | Suhwan Lim | Suhwan Lim, Jong-Ho Bae, Jai-Ho Eum, Sungtae Lee, Chul-Heung Kim,
Dongseok Kwon, Byung-Gook Park, Jong-Ho Lee | Adaptive Learning Rule for Hardware-based Deep Neural Networks Using
Electronic Synapse Devices | null | Neural Comput. Appl. (2018) | 10.1007/s00521-018-3659-y | null | cs.NE cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a learning rule based on a back-propagation (BP)
algorithm that can be applied to a hardware-based deep neural network (HW-DNN)
using electronic devices that exhibit discrete and limited conductance
characteristics. This adaptive learning rule, which enables forward, backward
propagation, as well as weight updates in hardware, is helpful during the
implementation of power-efficient and high-speed deep neural networks. In
simulations using a three-layer perceptron network, we evaluate the learning
performance according to various conductance responses of electronic synapse
devices and weight-updating methods. It is shown that the learning accuracy is
comparable to that obtained when using a software-based BP algorithm when the
electronic synapse device has a linear conductance response with a high dynamic
range. Furthermore, the proposed unidirectional weight-updating method is
suitable for electronic synapse devices which have nonlinear and finite
conductance responses. Because this weight-updating method can compensate the
demerit of asymmetric weight updates, we can obtain better accuracy compared to
other methods. This adaptive learning rule, which can be applied to full
hardware implementation, can also compensate the degradation of learning
accuracy due to the probable device-to-device variation in an actual electronic
synapse device.
| [
{
"created": "Thu, 20 Jul 2017 06:10:36 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Aug 2017 11:42:23 GMT",
"version": "v2"
}
] | 2018-08-02 | [
[
"Lim",
"Suhwan",
""
],
[
"Bae",
"Jong-Ho",
""
],
[
"Eum",
"Jai-Ho",
""
],
[
"Lee",
"Sungtae",
""
],
[
"Kim",
"Chul-Heung",
""
],
[
"Kwon",
"Dongseok",
""
],
[
"Park",
"Byung-Gook",
""
],
[
"Lee",
"Jong-Ho",
""
]
] | In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron network, we evaluate the learning performance according to various conductance responses of electronic synapse devices and weight-updating methods. It is shown that the learning accuracy is comparable to that obtained when using a software-based BP algorithm when the electronic synapse device has a linear conductance response with a high dynamic range. Furthermore, the proposed unidirectional weight-updating method is suitable for electronic synapse devices which have nonlinear and finite conductance responses. Because this weight-updating method can compensate the demerit of asymmetric weight updates, we can obtain better accuracy compared to other methods. This adaptive learning rule, which can be applied to full hardware implementation, can also compensate the degradation of learning accuracy due to the probable device-to-device variation in an actual electronic synapse device. |
2104.08253 | Luyu Gao | Luyu Gao, Jamie Callan | Condenser: a Pre-training Architecture for Dense Retrieval | EMNLP 2021 | null | null | null | cs.CL cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-trained Transformer language models (LM) have become go-to text
representation encoders. Prior research fine-tunes deep LMs to encode text
sequences such as sentences and passages into single dense vector
representations for efficient text comparison and retrieval. However, dense
encoders require a lot of data and sophisticated techniques to effectively
train and suffer in low data situations. This paper finds a key reason is that
standard LMs' internal attention structure is not ready-to-use for dense
encoders, which needs to aggregate text information into the dense
representation. We propose to pre-train towards dense encoder with a novel
Transformer architecture, Condenser, where LM prediction CONditions on DENSE
Representation. Our experiments show Condenser improves over standard LM by
large margins on various text retrieval and similarity tasks.
| [
{
"created": "Fri, 16 Apr 2021 17:36:44 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Sep 2021 18:07:10 GMT",
"version": "v2"
}
] | 2021-09-22 | [
[
"Gao",
"Luyu",
""
],
[
"Callan",
"Jamie",
""
]
] | Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval. However, dense encoders require a lot of data and sophisticated techniques to effectively train and suffer in low data situations. This paper finds a key reason is that standard LMs' internal attention structure is not ready-to-use for dense encoders, which needs to aggregate text information into the dense representation. We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. Our experiments show Condenser improves over standard LM by large margins on various text retrieval and similarity tasks. |
1711.03543 | Anush Sankaran | Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, Senthil
Mani | DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers | AAAI2018 | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With an abundance of research papers in deep learning, reproducibility or
adoption of the existing works becomes a challenge. This is due to the lack of
open source implementations provided by the authors. Further, re-implementing
research papers in a different library is a daunting task. To address these
challenges, we propose a novel extensible approach, DLPaper2Code, to extract
and understand deep learning design flow diagrams and tables available in a
research paper and convert them to an abstract computational graph. The
extracted computational graph is then converted into execution ready source
code in both Keras and Caffe, in real-time. An arXiv-like website is created
where the automatically generated designs is made publicly available for 5,000
research papers. The generated designs could be rated and edited using an
intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our
approach, we create a simulated dataset with over 216,000 valid design
visualizations using a manually defined grammar. Experiments on the simulated
dataset show that the proposed framework provide more than $93\%$ accuracy in
flow diagram content extraction.
| [
{
"created": "Thu, 9 Nov 2017 10:00:19 GMT",
"version": "v1"
}
] | 2017-11-13 | [
[
"Sethi",
"Akshay",
""
],
[
"Sankaran",
"Anush",
""
],
[
"Panwar",
"Naveen",
""
],
[
"Khare",
"Shreya",
""
],
[
"Mani",
"Senthil",
""
]
] | With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid design visualizations using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than $93\%$ accuracy in flow diagram content extraction. |
2303.12739 | Raoul Sch\"onhof | Jannes Elstner and Raoul G. C. Sch\"onhof and Steffen Tauber and Marco
F Huber | Optimizing CAD Models with Latent Space Manipulation | null | null | null | null | cs.CV cs.AI cs.CE cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | When it comes to the optimization of CAD models in the automation domain,
neural networks currently play only a minor role. Optimizing abstract features
such as automation capability is challenging, since they can be very difficult
to simulate, are too complex for rule-based systems, and also have little to no
data available for machine-learning methods. On the other hand, image
manipulation methods that can manipulate abstract features in images such as
StyleCLIP have seen much success. They rely on the latent space of pretrained
generative adversarial networks, and could therefore also make use of the vast
amount of unlabeled CAD data. In this paper, we show that such an approach is
also suitable for optimizing abstract automation-related features of CAD parts.
We achieved this by extending StyleCLIP to work with CAD models in the form of
voxel models, which includes using a 3D StyleGAN and a custom classifier.
Finally, we demonstrate the ability of our system for the optimiziation of
automation-related features by optimizing the grabability of various CAD
models. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the
responsibility of the scientific committee of the 33rd CIRP Design Conference.
| [
{
"created": "Thu, 9 Mar 2023 08:25:09 GMT",
"version": "v1"
}
] | 2023-03-23 | [
[
"Elstner",
"Jannes",
""
],
[
"Schönhof",
"Raoul G. C.",
""
],
[
"Tauber",
"Steffen",
""
],
[
"Huber",
"Marco F",
""
]
] | When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capability is challenging, since they can be very difficult to simulate, are too complex for rule-based systems, and also have little to no data available for machine-learning methods. On the other hand, image manipulation methods that can manipulate abstract features in images such as StyleCLIP have seen much success. They rely on the latent space of pretrained generative adversarial networks, and could therefore also make use of the vast amount of unlabeled CAD data. In this paper, we show that such an approach is also suitable for optimizing abstract automation-related features of CAD parts. We achieved this by extending StyleCLIP to work with CAD models in the form of voxel models, which includes using a 3D StyleGAN and a custom classifier. Finally, we demonstrate the ability of our system for the optimiziation of automation-related features by optimizing the grabability of various CAD models. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 33rd CIRP Design Conference. |
1707.01204 | Santosh Vempala | Manuel Blum and Santosh Vempala | The Complexity of Human Computation: A Concrete Model with an
Application to Passwords | null | null | null | null | cs.HC cs.CC cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | What can humans compute in their heads? We are thinking of a variety of
Crypto Protocols, games like Sudoku, Crossword Puzzles, Speed Chess, and so on.
The intent of this paper is to apply the ideas and methods of theoretical
computer science to better understand what humans can compute in their heads.
For example, can a person compute a function in their head so that an
eavesdropper with a powerful computer --- who sees the responses to random
input --- still cannot infer responses to new inputs? To address such
questions, we propose a rigorous model of human computation and associated
measures of complexity. We apply the model and measures first and foremost to
the problem of (1) humanly computable password generation, and then consider
related problems of (2) humanly computable "one-way functions" and (3) humanly
computable "pseudorandom generators".
The theory of Human Computability developed here plays by different rules
than standard computability, and this takes some getting used to. For reasons
to be made clear, the polynomial versus exponential time divide of modern
computability theory is irrelevant to human computation. In human
computability, the step-counts for both humans and computers must be more
concrete. Specifically, we restrict the adversary to at most 10^24 (Avogadro
number of) steps. An alternate view of this work is that it deals with the
analysis of algorithms and counting steps for the case that inputs are small as
opposed to the usual case of inputs large-in-the-limit.
| [
{
"created": "Wed, 5 Jul 2017 03:25:52 GMT",
"version": "v1"
}
] | 2017-07-06 | [
[
"Blum",
"Manuel",
""
],
[
"Vempala",
"Santosh",
""
]
] | What can humans compute in their heads? We are thinking of a variety of Crypto Protocols, games like Sudoku, Crossword Puzzles, Speed Chess, and so on. The intent of this paper is to apply the ideas and methods of theoretical computer science to better understand what humans can compute in their heads. For example, can a person compute a function in their head so that an eavesdropper with a powerful computer --- who sees the responses to random input --- still cannot infer responses to new inputs? To address such questions, we propose a rigorous model of human computation and associated measures of complexity. We apply the model and measures first and foremost to the problem of (1) humanly computable password generation, and then consider related problems of (2) humanly computable "one-way functions" and (3) humanly computable "pseudorandom generators". The theory of Human Computability developed here plays by different rules than standard computability, and this takes some getting used to. For reasons to be made clear, the polynomial versus exponential time divide of modern computability theory is irrelevant to human computation. In human computability, the step-counts for both humans and computers must be more concrete. Specifically, we restrict the adversary to at most 10^24 (Avogadro number of) steps. An alternate view of this work is that it deals with the analysis of algorithms and counting steps for the case that inputs are small as opposed to the usual case of inputs large-in-the-limit. |
2204.02601 | Yanyang Li | Yanyang Li, Fuli Luo, Runxin Xu, Songfang Huang, Fei Huang, Liwei Wang | Probing Structured Pruning on Multilingual Pre-trained Models: Settings,
Algorithms, and Efficiency | ACL 2022 Main Conference, Camera-ready version | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structured pruning has been extensively studied on monolingual pre-trained
language models and is yet to be fully evaluated on their multilingual
counterparts. This work investigates three aspects of structured pruning on
multilingual pre-trained language models: settings, algorithms, and efficiency.
Experiments on nine downstream tasks show several counter-intuitive phenomena:
for settings, individually pruning for each language does not induce a better
result; for algorithms, the simplest method performs the best; for efficiency,
a fast model does not imply that it is also small. To facilitate the comparison
on all sparsity levels, we present Dynamic Sparsification, a simple approach
that allows training the model once and adapting to different model sizes at
inference. We hope this work fills the gap in the study of structured pruning
on multilingual pre-trained models and sheds light on future research.
| [
{
"created": "Wed, 6 Apr 2022 06:29:52 GMT",
"version": "v1"
}
] | 2022-04-07 | [
[
"Li",
"Yanyang",
""
],
[
"Luo",
"Fuli",
""
],
[
"Xu",
"Runxin",
""
],
[
"Huang",
"Songfang",
""
],
[
"Huang",
"Fei",
""
],
[
"Wang",
"Liwei",
""
]
] | Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigates three aspects of structured pruning on multilingual pre-trained language models: settings, algorithms, and efficiency. Experiments on nine downstream tasks show several counter-intuitive phenomena: for settings, individually pruning for each language does not induce a better result; for algorithms, the simplest method performs the best; for efficiency, a fast model does not imply that it is also small. To facilitate the comparison on all sparsity levels, we present Dynamic Sparsification, a simple approach that allows training the model once and adapting to different model sizes at inference. We hope this work fills the gap in the study of structured pruning on multilingual pre-trained models and sheds light on future research. |
2009.08704 | Aythami Morales | Alejandro Pe\~na and Julian Fierrez and Agata Lapedriza and Aythami
Morales | Learning Emotional-Blinded Face Representations | IAPR Intl. Conf. on Pattern Recognition, 2020 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose two face representations that are blind to facial expressions
associated to emotional responses. This work is in part motivated by new
international regulations for personal data protection, which enforce data
controllers to protect any kind of sensitive information involved in automatic
processes. The advances in Affective Computing have contributed to improve
human-machine interfaces but, at the same time, the capacity to monitorize
emotional responses triggers potential risks for humans, both in terms of
fairness and privacy. We propose two different methods to learn these
expression-blinded facial features. We show that it is possible to eliminate
information related to emotion recognition tasks, while the performance of
subject verification, gender recognition, and ethnicity classification are just
slightly affected. We also present an application to train fairer classifiers
in a case study of attractiveness classification with respect to a protected
facial expression attribute. The results demonstrate that it is possible to
reduce emotional information in the face representation while retaining
competitive performance in other face-based artificial intelligence tasks.
| [
{
"created": "Fri, 18 Sep 2020 09:24:10 GMT",
"version": "v1"
}
] | 2020-09-21 | [
[
"Peña",
"Alejandro",
""
],
[
"Fierrez",
"Julian",
""
],
[
"Lapedriza",
"Agata",
""
],
[
"Morales",
"Aythami",
""
]
] | We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which enforce data controllers to protect any kind of sensitive information involved in automatic processes. The advances in Affective Computing have contributed to improve human-machine interfaces but, at the same time, the capacity to monitorize emotional responses triggers potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these expression-blinded facial features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers in a case study of attractiveness classification with respect to a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks. |
2009.04177 | Ke Zhang | Ke Zhang, Yukun Su, Xiwang Guo, Liang Qi, and Zhenbing Zhao | MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism | 12 pages, 10 figures | null | null | null | cs.CV cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Facial attribute editing has mainly two objectives: 1) translating image from
a source domain to a target one, and 2) only changing the facial regions
related to a target attribute and preserving the attribute-excluding details.
In this work, we propose a Multi-attention U-Net-based Generative Adversarial
Network (MU-GAN). First, we replace a classic convolutional encoder-decoder
with a symmetric U-Net-like structure in a generator, and then apply an
additive attention mechanism to build attention-based U-Net connections for
adaptively transferring encoder representations to complement a decoder with
attribute-excluding detail and enhance attribute editing ability. Second, a
self-attention mechanism is incorporated into convolutional layers for modeling
long-range and multi-level dependencies across image regions. experimental
results indicate that our method is capable of balancing attribute editing
ability and details preservation ability, and can decouple the correlation
among attributes. It outperforms the state-of-the-art methods in terms of
attribute manipulation accuracy and image quality.
| [
{
"created": "Wed, 9 Sep 2020 09:25:04 GMT",
"version": "v1"
}
] | 2020-09-10 | [
[
"Zhang",
"Ke",
""
],
[
"Su",
"Yukun",
""
],
[
"Guo",
"Xiwang",
""
],
[
"Qi",
"Liang",
""
],
[
"Zhao",
"Zhenbing",
""
]
] | Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a Multi-attention U-Net-based Generative Adversarial Network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability. Second, a self-attention mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions. experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability, and can decouple the correlation among attributes. It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality. |
1706.06936 | Kushagra Singhal | Kushagra Singhal, Daniel Cullina, Negar Kiyavash | Significance of Side Information in the Graph Matching Problem | null | null | null | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Percolation based graph matching algorithms rely on the availability of seed
vertex pairs as side information to efficiently match users across networks.
Although such algorithms work well in practice, there are other types of side
information available which are potentially useful to an attacker. In this
paper, we consider the problem of matching two correlated graphs when an
attacker has access to side information, either in the form of community labels
or an imperfect initial matching. In the former case, we propose a naive graph
matching algorithm by introducing the community degree vectors which harness
the information from community labels in an efficient manner. Furthermore, we
analyze a variant of the basic percolation algorithm proposed in literature for
graphs with community structure. In the latter case, we propose a novel
percolation algorithm with two thresholds which uses an imperfect matching as
input to match correlated graphs.
We evaluate the proposed algorithms on synthetic as well as real world
datasets using various experiments. The experimental results demonstrate the
importance of communities as side information especially when the number of
seeds is small and the networks are weakly correlated.
| [
{
"created": "Wed, 21 Jun 2017 14:42:19 GMT",
"version": "v1"
}
] | 2017-06-22 | [
[
"Singhal",
"Kushagra",
""
],
[
"Cullina",
"Daniel",
""
],
[
"Kiyavash",
"Negar",
""
]
] | Percolation based graph matching algorithms rely on the availability of seed vertex pairs as side information to efficiently match users across networks. Although such algorithms work well in practice, there are other types of side information available which are potentially useful to an attacker. In this paper, we consider the problem of matching two correlated graphs when an attacker has access to side information, either in the form of community labels or an imperfect initial matching. In the former case, we propose a naive graph matching algorithm by introducing the community degree vectors which harness the information from community labels in an efficient manner. Furthermore, we analyze a variant of the basic percolation algorithm proposed in literature for graphs with community structure. In the latter case, we propose a novel percolation algorithm with two thresholds which uses an imperfect matching as input to match correlated graphs. We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of seeds is small and the networks are weakly correlated. |
1809.08311 | Carl Pearson | Carl Pearson and Abdul Dakkak and Cheng Li and Sarah Hashash and
Jinjun Xiong and Wen-mei Hwu | SCOPE: C3SR Systems Characterization and Benchmarking Framework | 8 pages, draft | null | null | null | cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This report presents the design of the Scope infrastructure for extensible
and portable benchmarking. Improvements in high- performance computing systems
rely on coordination across different levels of system abstraction. Developing
and defining accurate performance measurements is necessary at all levels of
the system hierarchy, and should be as accessible as possible to developers
with different backgrounds. The Scope project aims to lower the barrier to
entry for developing performance benchmarks by providing a software
architecture that allows benchmarks to be developed independently, by providing
useful C/C++ abstractions and utilities, and by providing a Python package for
generating publication-quality plots of resulting measurements.
| [
{
"created": "Tue, 18 Sep 2018 20:25:44 GMT",
"version": "v1"
}
] | 2018-09-25 | [
[
"Pearson",
"Carl",
""
],
[
"Dakkak",
"Abdul",
""
],
[
"Li",
"Cheng",
""
],
[
"Hashash",
"Sarah",
""
],
[
"Xiong",
"Jinjun",
""
],
[
"Hwu",
"Wen-mei",
""
]
] | This report presents the design of the Scope infrastructure for extensible and portable benchmarking. Improvements in high- performance computing systems rely on coordination across different levels of system abstraction. Developing and defining accurate performance measurements is necessary at all levels of the system hierarchy, and should be as accessible as possible to developers with different backgrounds. The Scope project aims to lower the barrier to entry for developing performance benchmarks by providing a software architecture that allows benchmarks to be developed independently, by providing useful C/C++ abstractions and utilities, and by providing a Python package for generating publication-quality plots of resulting measurements. |
cs/0009001 | Andrei N. Soklakov | Andrei N. Soklakov (Royal Holloway, University of London) | Complexity analysis for algorithmically simple strings | 10 pages | null | null | null | cs.LG | null | Given a reference computer, Kolmogorov complexity is a well defined function
on all binary strings. In the standard approach, however, only the asymptotic
properties of such functions are considered because they do not depend on the
reference computer. We argue that this approach can be more useful if it is
refined to include an important practical case of simple binary strings.
Kolmogorov complexity calculus may be developed for this case if we restrict
the class of available reference computers. The interesting problem is to
define a class of computers which is restricted in a {\it natural} way modeling
the real-life situation where only a limited class of computers is physically
available to us. We give an example of what such a natural restriction might
look like mathematically, and show that under such restrictions some error
terms, even logarithmic in complexity, can disappear from the standard
complexity calculus.
Keywords: Kolmogorov complexity; Algorithmic information theory.
| [
{
"created": "Tue, 5 Sep 2000 18:54:58 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jun 2001 03:22:43 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Feb 2002 01:51:09 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Soklakov",
"Andrei N.",
"",
"Royal Holloway, University of London"
]
] | Given a reference computer, Kolmogorov complexity is a well defined function on all binary strings. In the standard approach, however, only the asymptotic properties of such functions are considered because they do not depend on the reference computer. We argue that this approach can be more useful if it is refined to include an important practical case of simple binary strings. Kolmogorov complexity calculus may be developed for this case if we restrict the class of available reference computers. The interesting problem is to define a class of computers which is restricted in a {\it natural} way modeling the real-life situation where only a limited class of computers is physically available to us. We give an example of what such a natural restriction might look like mathematically, and show that under such restrictions some error terms, even logarithmic in complexity, can disappear from the standard complexity calculus. Keywords: Kolmogorov complexity; Algorithmic information theory. |
2305.20009 | Samuel Stanton | Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro
Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew
Gordon Wilson | Protein Design with Guided Discrete Diffusion | null | Advances in Neural Information Processing Systems 36, December
10-16, 2023 | null | null | cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A popular approach to protein design is to combine a generative model with a
discriminative model for conditional sampling. The generative model samples
plausible sequences while the discriminative model guides a search for
sequences with high fitness. Given its broad success in conditional sampling,
classifier-guided diffusion modeling is a promising foundation for protein
design, leading many to develop guided diffusion models for structure with
inverse folding to recover sequences. In this work, we propose diffusioN
Optimized Sampling (NOS), a guidance method for discrete diffusion models that
follows gradients in the hidden states of the denoising network. NOS makes it
possible to perform design directly in sequence space, circumventing
significant limitations of structure-based methods, including scarce data and
challenging inverse design. Moreover, we use NOS to generalize LaMBO, a
Bayesian optimization procedure for sequence design that facilitates multiple
objectives and edit-based constraints. The resulting method, LaMBO-2, enables
discrete diffusions and stronger performance with limited edits through a novel
application of saliency maps. We apply LaMBO-2 to a real-world protein design
task, optimizing antibodies for higher expression yield and binding affinity to
several therapeutic targets under locality and developability constraints,
attaining a 99% expression rate and 40% binding rate in exploratory in vitro
experiments.
| [
{
"created": "Wed, 31 May 2023 16:31:24 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Dec 2023 05:09:38 GMT",
"version": "v2"
}
] | 2023-12-13 | [
[
"Gruver",
"Nate",
""
],
[
"Stanton",
"Samuel",
""
],
[
"Frey",
"Nathan C.",
""
],
[
"Rudner",
"Tim G. J.",
""
],
[
"Hotzel",
"Isidro",
""
],
[
"Lafrance-Vanasse",
"Julien",
""
],
[
"Rajpal",
"Arvind",
""
],
[
"Cho",
"Kyunghyun",
""
],
[
"Wilson",
"Andrew Gordon",
""
]
] | A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising foundation for protein design, leading many to develop guided diffusion models for structure with inverse folding to recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models that follows gradients in the hidden states of the denoising network. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods, including scarce data and challenging inverse design. Moreover, we use NOS to generalize LaMBO, a Bayesian optimization procedure for sequence design that facilitates multiple objectives and edit-based constraints. The resulting method, LaMBO-2, enables discrete diffusions and stronger performance with limited edits through a novel application of saliency maps. We apply LaMBO-2 to a real-world protein design task, optimizing antibodies for higher expression yield and binding affinity to several therapeutic targets under locality and developability constraints, attaining a 99% expression rate and 40% binding rate in exploratory in vitro experiments. |
2305.00208 | Abdul Karim Gizzini | Abdul Karim Gizzini, Marwa Chafii | Deep Learning Based Channel Estimation in High Mobility Communications
Using Bi-RNN Networks | Accepted for presentation at IEEE 2023 IEEE International Conference
on Communications (ICC), 28 May - 01 June 2023, Rome, Italy | null | null | null | cs.IT cs.AI math.IT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Doubly-selective channel estimation represents a key element in ensuring
communication reliability in wireless systems. Due to the impact of multi-path
propagation and Doppler interference in dynamic environments, doubly-selective
channel estimation becomes challenging. Conventional channel estimation schemes
encounter performance degradation in high mobility scenarios due to the usage
of limited training pilots. Recently, deep learning (DL) has been utilized for
doubly-selective channel estimation, where convolutional neural network (CNN)
networks are employed in the frame-by-frame (FBF) channel estimation. However,
CNN-based estimators require high complexity, making them impractical in
real-case scenarios. For this purpose, we overcome this issue by proposing an
optimized and robust bi-directional recurrent neural network (Bi-RNN) based
channel estimator to accurately estimate the doubly-selective channel,
especially in high mobility scenarios. The proposed estimator is based on
performing end-to-end interpolation using gated recurrent unit (GRU) unit.
Extensive numerical experiments demonstrate that the developed Bi-GRU estimator
significantly outperforms the recently proposed CNN-based estimators in
different mobility scenarios, while substantially reducing the overall
computational complexity.
| [
{
"created": "Sat, 29 Apr 2023 09:20:28 GMT",
"version": "v1"
}
] | 2023-05-02 | [
[
"Gizzini",
"Abdul Karim",
""
],
[
"Chafii",
"Marwa",
""
]
] | Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional channel estimation schemes encounter performance degradation in high mobility scenarios due to the usage of limited training pilots. Recently, deep learning (DL) has been utilized for doubly-selective channel estimation, where convolutional neural network (CNN) networks are employed in the frame-by-frame (FBF) channel estimation. However, CNN-based estimators require high complexity, making them impractical in real-case scenarios. For this purpose, we overcome this issue by proposing an optimized and robust bi-directional recurrent neural network (Bi-RNN) based channel estimator to accurately estimate the doubly-selective channel, especially in high mobility scenarios. The proposed estimator is based on performing end-to-end interpolation using gated recurrent unit (GRU) unit. Extensive numerical experiments demonstrate that the developed Bi-GRU estimator significantly outperforms the recently proposed CNN-based estimators in different mobility scenarios, while substantially reducing the overall computational complexity. |
2405.11574 | Yash Sanjay Bhalgat | Manan Shah, Yash Bhalgat | Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for
Multi-Label Image Classification | Reproducibility study | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | This report is a reproducibility study of the paper "CDUL: CLIP-Driven
Unsupervised Learning for Multi-Label Image Classification" (Abdelfattah et al,
ICCV 2023). Our report makes the following contributions: (1) We provide a
reproducible, well commented and open-sourced code implementation for the
entire method specified in the original paper. (2) We try to verify the
effectiveness of the novel aggregation strategy which uses the CLIP model to
initialize the pseudo labels for the subsequent unsupervised multi-label image
classification task. (3) We try to verify the effectiveness of the
gradient-alignment training method specified in the original paper, which is
used to update the network parameters and pseudo labels. The code can be found
at https://github.com/cs-mshah/CDUL
| [
{
"created": "Sun, 19 May 2024 14:48:19 GMT",
"version": "v1"
}
] | 2024-05-21 | [
[
"Shah",
"Manan",
""
],
[
"Bhalgat",
"Yash",
""
]
] | This report is a reproducibility study of the paper "CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification" (Abdelfattah et al, ICCV 2023). Our report makes the following contributions: (1) We provide a reproducible, well commented and open-sourced code implementation for the entire method specified in the original paper. (2) We try to verify the effectiveness of the novel aggregation strategy which uses the CLIP model to initialize the pseudo labels for the subsequent unsupervised multi-label image classification task. (3) We try to verify the effectiveness of the gradient-alignment training method specified in the original paper, which is used to update the network parameters and pseudo labels. The code can be found at https://github.com/cs-mshah/CDUL |
2405.20715 | Diabul Haque | Diabul Haque | Transforming Japan Real Estate | null | null | null | null | cs.CE econ.EM q-fin.ST | http://creativecommons.org/licenses/by/4.0/ | The Japanese real estate market, valued over 35 trillion USD, offers
significant investment opportunities. Accurate rent and price forecasting could
provide a substantial competitive edge. This paper explores using alternative
data variables to predict real estate performance in 1100 Japanese
municipalities. A comprehensive house price index was created, covering all
municipalities from 2005 to the present, using a dataset of over 5 million
transactions. This core dataset was enriched with economic factors spanning
decades, allowing for price trajectory predictions.
The findings show that alternative data variables can indeed forecast real
estate performance effectively. Investment signals based on these variables
yielded notable returns with low volatility. For example, the net migration
ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5.
Taxable income growth and new dwellings ratio also performed well, with
annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of
0.9), respectively. When combined with transformer models to predict
risk-adjusted returns 4 years in advance, the model achieved an R-squared score
of 0.28, explaining nearly 30% of the variation in future municipality prices.
These results highlight the potential of alternative data variables in real
estate investment. They underscore the need for further research to identify
more predictive factors. Nonetheless, the evidence suggests that such data can
provide valuable insights into real estate price drivers, enabling more
informed investment decisions in the Japanese market.
| [
{
"created": "Fri, 31 May 2024 09:12:28 GMT",
"version": "v1"
}
] | 2024-06-03 | [
[
"Haque",
"Diabul",
""
]
] | The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, covering all municipalities from 2005 to the present, using a dataset of over 5 million transactions. This core dataset was enriched with economic factors spanning decades, allowing for price trajectory predictions. The findings show that alternative data variables can indeed forecast real estate performance effectively. Investment signals based on these variables yielded notable returns with low volatility. For example, the net migration ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5. Taxable income growth and new dwellings ratio also performed well, with annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of 0.9), respectively. When combined with transformer models to predict risk-adjusted returns 4 years in advance, the model achieved an R-squared score of 0.28, explaining nearly 30% of the variation in future municipality prices. These results highlight the potential of alternative data variables in real estate investment. They underscore the need for further research to identify more predictive factors. Nonetheless, the evidence suggests that such data can provide valuable insights into real estate price drivers, enabling more informed investment decisions in the Japanese market. |
2209.11345 | Marcos V. Conde | Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte | Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and
Restoration | European Conference on Computer Vision (ECCV 2022) Workshops | null | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Compression plays an important role on the efficient transmission and storage
of images and videos through band-limited systems such as streaming services,
virtual reality or videogames. However, compression unavoidably leads to
artifacts and the loss of the original information, which may severely degrade
the visual quality. For these reasons, quality enhancement of compressed images
has become a popular research topic. While most state-of-the-art image
restoration methods are based on convolutional neural networks, other
transformers-based methods such as SwinIR, show impressive performance on these
tasks.
In this paper, we explore the novel Swin Transformer V2, to improve SwinIR
for image super-resolution, and in particular, the compressed input scenario.
Using this method we can tackle the major issues in training transformer vision
models, such as training instability, resolution gaps between pre-training and
fine-tuning, and hunger on data. We conduct experiments on three representative
tasks: JPEG compression artifacts removal, image super-resolution (classical
and lightweight), and compressed image super-resolution. Experimental results
demonstrate that our method, Swin2SR, can improve the training convergence and
performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on
Super-Resolution of Compressed Image and Video".
| [
{
"created": "Thu, 22 Sep 2022 23:25:08 GMT",
"version": "v1"
}
] | 2022-09-26 | [
[
"Conde",
"Marcos V.",
""
],
[
"Choi",
"Ui-Jin",
""
],
[
"Burchi",
"Maxime",
""
],
[
"Timofte",
"Radu",
""
]
] | Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video". |
1905.05408 | Kyunghwan Son | Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi | QTRAN: Learning to Factorize with Transformation for Cooperative
Multi-Agent Reinforcement Learning | 18 pages; Accepted to ICML 2019 | null | null | null | cs.LG cs.AI cs.MA stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore value-based solutions for multi-agent reinforcement learning
(MARL) tasks in the centralized training with decentralized execution (CTDE)
regime popularized recently. However, VDN and QMIX are representative examples
that use the idea of factorization of the joint action-value function into
individual ones for decentralized execution. VDN and QMIX address only a
fraction of factorizable MARL tasks due to their structural constraint in
factorization such as additivity and monotonicity. In this paper, we propose a
new factorization method for MARL, QTRAN, which is free from such structural
constraints and takes on a new approach to transforming the original joint
action-value function into an easily factorizable one, with the same optimal
actions. QTRAN guarantees more general factorization than VDN or QMIX, thus
covering a much wider class of MARL tasks than does previous methods. Our
experiments for the tasks of multi-domain Gaussian-squeeze and modified
predator-prey demonstrate QTRAN's superior performance with especially larger
margins in games whose payoffs penalize non-cooperative behavior more
aggressively.
| [
{
"created": "Tue, 14 May 2019 06:29:51 GMT",
"version": "v1"
}
] | 2019-05-15 | [
[
"Son",
"Kyunghwan",
""
],
[
"Kim",
"Daewoo",
""
],
[
"Kang",
"Wan Ju",
""
],
[
"Hostallero",
"David Earl",
""
],
[
"Yi",
"Yung",
""
]
] | We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones for decentralized execution. VDN and QMIX address only a fraction of factorizable MARL tasks due to their structural constraint in factorization such as additivity and monotonicity. In this paper, we propose a new factorization method for MARL, QTRAN, which is free from such structural constraints and takes on a new approach to transforming the original joint action-value function into an easily factorizable one, with the same optimal actions. QTRAN guarantees more general factorization than VDN or QMIX, thus covering a much wider class of MARL tasks than does previous methods. Our experiments for the tasks of multi-domain Gaussian-squeeze and modified predator-prey demonstrate QTRAN's superior performance with especially larger margins in games whose payoffs penalize non-cooperative behavior more aggressively. |
1803.04074 | Susana Vidrio-Bar\'on | Susana B. Vidrio Bar\'on, Andrew W. Luse, Anthony M. Townsend | Development of a culturally-oriented website usability evaluation | 15th Americas Conference on Information Systems 2009 | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the uni-cultural studies of website usability have matured, the paucity of
cross-cultural studies of usability become increasingly apparent. Moving toward
these cross-cultural studies will require the development of a new tool to
assess website usability in the context of cultural dimensions. This paper
introduces the preliminary results from the first phase of this project and
then presents the proposed method for the research in progress that
specifically is directed to the development and quantitative evaluation of a
measurement scale of a culture sensitive measurement of website usability. The
recognition of the need to develop this scale resulted from the identification
of culture-related shortcomings of previous measurement tools that have been
used widely within the Management of Information Systems (MIS) literature.
| [
{
"created": "Mon, 12 Mar 2018 00:39:08 GMT",
"version": "v1"
}
] | 2018-03-13 | [
[
"Barón",
"Susana B. Vidrio",
""
],
[
"Luse",
"Andrew W.",
""
],
[
"Townsend",
"Anthony M.",
""
]
] | As the uni-cultural studies of website usability have matured, the paucity of cross-cultural studies of usability become increasingly apparent. Moving toward these cross-cultural studies will require the development of a new tool to assess website usability in the context of cultural dimensions. This paper introduces the preliminary results from the first phase of this project and then presents the proposed method for the research in progress that specifically is directed to the development and quantitative evaluation of a measurement scale of a culture sensitive measurement of website usability. The recognition of the need to develop this scale resulted from the identification of culture-related shortcomings of previous measurement tools that have been used widely within the Management of Information Systems (MIS) literature. |
1603.02776 | Yang Liu | Yang Liu, Sujian Li, Xiaodong Zhang and Zhifang Sui | Implicit Discourse Relation Classification via Multi-Task Neural
Networks | This is the pre-print version of a paper accepted by AAAI-16 | null | null | null | cs.CL cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually, under different discourse annotation frameworks, there exist multiple
corpora which have internal connections. To exploit the combination of
different discourse corpora, we design related discourse classification tasks
specific to a corpus, and propose a novel Convolutional Neural Network embedded
multi-task learning system to synthesize these tasks by learning both unique
and shared representations for each task. The experimental results on the PDTB
implicit discourse relation classification task demonstrate that our model
achieves significant gains over baseline systems.
| [
{
"created": "Wed, 9 Mar 2016 03:13:37 GMT",
"version": "v1"
}
] | 2016-03-10 | [
[
"Liu",
"Yang",
""
],
[
"Li",
"Sujian",
""
],
[
"Zhang",
"Xiaodong",
""
],
[
"Sui",
"Zhifang",
""
]
] | Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems. |
2105.10325 | Jana Kierdorf | Jana Kierdorf, Immanuel Weber, Anna Kicherer, Laura Zabawa, Lukas
Drees, Ribana Roscher | Behind the leaves -- Estimation of occluded grapevine berries with
conditional generative adversarial networks | 45 pages, 18 figures, 1 table | null | 10.3389/frai.2022.830026 | null | cs.CV cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | The need for accurate yield estimates for viticulture is becoming more
important due to increasing competition in the wine market worldwide. One of
the most promising methods to estimate the harvest is berry counting, as it can
be approached non-destructively, and its process can be automated. In this
article, we present a method that addresses the challenge of occluded berries
with leaves to obtain a more accurate estimate of the number of berries that
will enable a better estimate of the harvest. We use generative adversarial
networks, a deep learning-based approach that generates a likely scenario
behind the leaves exploiting learned patterns from images with non-occluded
berries. Our experiments show that the estimate of the number of berries after
applying our method is closer to the manually counted reference. In contrast to
applying a factor to the berry count, our approach better adapts to local
conditions by directly involving the appearance of the visible berries.
Furthermore, we show that our approach can identify which areas in the image
should be changed by adding new berries without explicitly requiring
information about hidden areas.
| [
{
"created": "Fri, 21 May 2021 12:57:48 GMT",
"version": "v1"
}
] | 2022-03-28 | [
[
"Kierdorf",
"Jana",
""
],
[
"Weber",
"Immanuel",
""
],
[
"Kicherer",
"Anna",
""
],
[
"Zabawa",
"Laura",
""
],
[
"Drees",
"Lukas",
""
],
[
"Roscher",
"Ribana",
""
]
] | The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas. |
2102.04621 | Xinchen Liu | Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu,
Xiaoping Zhang, Tao Mei | TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain
Gait Recognition | Accepted by ISCAS 2021. 5 pages, 2 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gait, i.e., the movement pattern of human limbs during locomotion, is a
promising biometric for the identification of persons. Despite significant
improvement in gait recognition with deep learning, existing studies still
neglect a more practical but challenging scenario -- unsupervised cross-domain
gait recognition which aims to learn a model on a labeled dataset then adapts
it to an unlabeled dataset. Due to the domain shift and class gap, directly
applying a model trained on one source dataset to other target datasets usually
obtains very poor results. Therefore, this paper proposes a Transferable
Neighborhood Discovery (TraND) framework to bridge the domain gap for
unsupervised cross-domain gait recognition. To learn effective prior knowledge
for gait representation, we first adopt a backbone network pre-trained on the
labeled source data in a supervised manner. Then we design an end-to-end
trainable approach to automatically discover the confident neighborhoods of
unlabeled samples in the latent space. During training, the class consistency
indicator is adopted to select confident neighborhoods of samples based on
their entropy measurements. Moreover, we explore a high-entropy-first neighbor
selection strategy, which can effectively transfer prior knowledge to the
target domain. Our method achieves state-of-the-art results on two public
datasets, i.e., CASIA-B and OU-LP.
| [
{
"created": "Tue, 9 Feb 2021 03:07:07 GMT",
"version": "v1"
}
] | 2021-02-10 | [
[
"Zheng",
"Jinkai",
""
],
[
"Liu",
"Xinchen",
""
],
[
"Yan",
"Chenggang",
""
],
[
"Zhang",
"Jiyong",
""
],
[
"Liu",
"Wu",
""
],
[
"Zhang",
"Xiaoping",
""
],
[
"Mei",
"Tao",
""
]
] | Gait, i.e., the movement pattern of human limbs during locomotion, is a promising biometric for the identification of persons. Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset. Due to the domain shift and class gap, directly applying a model trained on one source dataset to other target datasets usually obtains very poor results. Therefore, this paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. To learn effective prior knowledge for gait representation, we first adopt a backbone network pre-trained on the labeled source data in a supervised manner. Then we design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. During training, the class consistency indicator is adopted to select confident neighborhoods of samples based on their entropy measurements. Moreover, we explore a high-entropy-first neighbor selection strategy, which can effectively transfer prior knowledge to the target domain. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP. |
1105.2934 | Ludo Waltman | Ludo Waltman, Nees Jan van Eck and Anthony F.J. van Raan | Universality of citation distributions revisited | null | null | null | null | cs.DL physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Radicchi, Fortunato, and Castellano [arXiv:0806.0974, PNAS 105(45), 17268]
claim that, apart from a scaling factor, all fields of science are
characterized by the same citation distribution. We present a large-scale
validation study of this universality-of-citation-distributions claim. Our
analysis shows that claiming citation distributions to be universal for all
fields of science is not warranted. Although many fields indeed seem to have
fairly similar citation distributions, there are quite some exceptions as well.
We also briefly discuss the consequences of our findings for the measurement of
scientific impact using citation-based bibliometric indicators.
| [
{
"created": "Sun, 15 May 2011 09:03:04 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jul 2011 21:28:10 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Aug 2011 17:19:16 GMT",
"version": "v3"
}
] | 2011-08-31 | [
[
"Waltman",
"Ludo",
""
],
[
"van Eck",
"Nees Jan",
""
],
[
"van Raan",
"Anthony F. J.",
""
]
] | Radicchi, Fortunato, and Castellano [arXiv:0806.0974, PNAS 105(45), 17268] claim that, apart from a scaling factor, all fields of science are characterized by the same citation distribution. We present a large-scale validation study of this universality-of-citation-distributions claim. Our analysis shows that claiming citation distributions to be universal for all fields of science is not warranted. Although many fields indeed seem to have fairly similar citation distributions, there are quite some exceptions as well. We also briefly discuss the consequences of our findings for the measurement of scientific impact using citation-based bibliometric indicators. |
1906.07523 | Emre Yilmaz | Emre Y{\i}lmaz, Samuel Cohen, Xianghu Yue, David van Leeuwen, Haizhou
Li | Multi-Graph Decoding for Code-Switching ASR | Accepted for publication at Interspeech 2019 | null | null | null | cs.CL cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the FAME! Project, a code-switching (CS) automatic speech recognition
(ASR) system for Frisian-Dutch speech is developed that can accurately
transcribe the local broadcaster's bilingual archives with CS speech. This
archive contains recordings with monolingual Frisian and Dutch speech segments
as well as Frisian-Dutch CS speech, hence the recognition performance on
monolingual segments is also vital for accurate transcriptions. In this work,
we propose a multi-graph decoding and rescoring strategy using bilingual and
monolingual graphs together with a unified acoustic model for CS ASR. The
proposed decoding scheme gives the freedom to design and employ alternative
search spaces for each (monolingual or bilingual) recognition task and enables
the effective use of monolingual resources of the high-resourced mixed language
in low-resourced CS scenarios. In our scenario, Dutch is the high-resourced and
Frisian is the low-resourced language. We therefore use additional monolingual
Dutch text resources to improve the Dutch language model (LM) and compare the
performance of single- and multi-graph CS ASR systems on Dutch segments using
larger Dutch LMs. The ASR results show that the proposed approach outperforms
baseline single-graph CS ASR systems, providing better performance on the
monolingual Dutch segments without any accuracy loss on monolingual Frisian and
code-mixed segments.
| [
{
"created": "Tue, 18 Jun 2019 12:24:32 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Jun 2019 07:07:08 GMT",
"version": "v2"
}
] | 2019-07-01 | [
[
"Yılmaz",
"Emre",
""
],
[
"Cohen",
"Samuel",
""
],
[
"Yue",
"Xianghu",
""
],
[
"van Leeuwen",
"David",
""
],
[
"Li",
"Haizhou",
""
]
] | In the FAME! Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's bilingual archives with CS speech. This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions. In this work, we propose a multi-graph decoding and rescoring strategy using bilingual and monolingual graphs together with a unified acoustic model for CS ASR. The proposed decoding scheme gives the freedom to design and employ alternative search spaces for each (monolingual or bilingual) recognition task and enables the effective use of monolingual resources of the high-resourced mixed language in low-resourced CS scenarios. In our scenario, Dutch is the high-resourced and Frisian is the low-resourced language. We therefore use additional monolingual Dutch text resources to improve the Dutch language model (LM) and compare the performance of single- and multi-graph CS ASR systems on Dutch segments using larger Dutch LMs. The ASR results show that the proposed approach outperforms baseline single-graph CS ASR systems, providing better performance on the monolingual Dutch segments without any accuracy loss on monolingual Frisian and code-mixed segments. |
1708.09597 | Cunxi Yu | Cunxi Yu, Mihir Choudhury, Andrew Sullivan, Maciej Ciesielski | Advanced Datapath Synthesis using Graph Isomorphism | 6 pages, 8 figures. To appear in 2017 IEEE/ACM International
Conference on Computer-Aided Design (ICCAD'17) | null | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an advanced DAG-based algorithm for datapath synthesis
that targets area minimization using logic-level resource sharing. The problem
of identifying common specification logic is formulated using unweighted graph
isomorphism problem, in contrast to a weighted graph isomorphism using AIGs. In
the context of gate-level datapath circuits, our algorithm solves the un-
weighted graph isomorphism problem in linear time. The experiments are
conducted within an industrial synthesis flow that includes the complete
high-level synthesis, logic synthesis and placement and route procedures.
Experimental results show a significant runtime improvements compared to the
existing datapath synthesis algorithms.
| [
{
"created": "Thu, 31 Aug 2017 07:34:00 GMT",
"version": "v1"
}
] | 2017-09-01 | [
[
"Yu",
"Cunxi",
""
],
[
"Choudhury",
"Mihir",
""
],
[
"Sullivan",
"Andrew",
""
],
[
"Ciesielski",
"Maciej",
""
]
] | This paper presents an advanced DAG-based algorithm for datapath synthesis that targets area minimization using logic-level resource sharing. The problem of identifying common specification logic is formulated using unweighted graph isomorphism problem, in contrast to a weighted graph isomorphism using AIGs. In the context of gate-level datapath circuits, our algorithm solves the un- weighted graph isomorphism problem in linear time. The experiments are conducted within an industrial synthesis flow that includes the complete high-level synthesis, logic synthesis and placement and route procedures. Experimental results show a significant runtime improvements compared to the existing datapath synthesis algorithms. |
1802.05568 | Bin Guo | Yi Ouyang, Bin Guo, Xinjiang Lu, Qi Han, Tong Guo, Zhiwen Yu | CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using
Heterogeneous Crowdsourced Data | null | null | null | null | cs.HC cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, bike-sharing systems have been deployed in many cities,
which provide an economical lifestyle. With the prevalence of bike-sharing
systems, a lot of companies join the market, leading to increasingly fierce
competition. To be competitive, bike-sharing companies and app developers need
to make strategic decisions for mobile apps development. Therefore, it is
significant to predict and compare the popularity of different bike-sharing
apps. However, existing works mostly focus on predicting the popularity of a
single app, the popularity contest among different apps has not been explored
yet. In this paper, we aim to forecast the popularity contest between Mobike
and Ofo, two most popular bike-sharing apps in China. We develop
CompetitiveBike, a system to predict the popularity contest among bike-sharing
apps. Moreover, we conduct experiments on real-world datasets collected from 11
app stores and Sina Weibo, and the experiments demonstrate the effectiveness of
our approach.
| [
{
"created": "Thu, 15 Feb 2018 14:36:09 GMT",
"version": "v1"
}
] | 2018-02-16 | [
[
"Ouyang",
"Yi",
""
],
[
"Guo",
"Bin",
""
],
[
"Lu",
"Xinjiang",
""
],
[
"Han",
"Qi",
""
],
[
"Guo",
"Tong",
""
],
[
"Yu",
"Zhiwen",
""
]
] | In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach. |
2402.16194 | Omama Hamad | Omama Hamad, Ali Hamdi, Khaled Shaban | ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and
Emotion Modeling | Accepted to the LREC-COLING 2024 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | Effective feature representations play a critical role in enhancing the
performance of text generation models that rely on deep neural networks.
However, current approaches suffer from several drawbacks, such as the
inability to capture the deep semantics of language and sensitivity to minor
input variations, resulting in significant changes in the generated text. In
this paper, we present a novel solution to these challenges by employing a
mixture of experts, multiple encoders, to offer distinct perspectives on the
emotional state of the user's utterance while simultaneously enhancing
performance. We propose an end-to-end model architecture called ASEM that
performs emotion analysis on top of sentiment analysis for open-domain
chatbots, enabling the generation of empathetic responses that are fluent and
relevant. In contrast to traditional attention mechanisms, the proposed model
employs a specialized attention strategy that uniquely zeroes in on sentiment
and emotion nuances within the user's utterance. This ensures the generation of
context-rich representations tailored to the underlying emotional tone and
sentiment intricacies of the text. Our approach outperforms existing methods
for generating empathetic embeddings, providing empathetic and diverse
responses. The performance of our proposed model significantly exceeds that of
existing models, enhancing emotion detection accuracy by 6.2% and lexical
diversity by 1.4%.
| [
{
"created": "Sun, 25 Feb 2024 20:36:51 GMT",
"version": "v1"
}
] | 2024-02-27 | [
[
"Hamad",
"Omama",
""
],
[
"Hamdi",
"Ali",
""
],
[
"Shaban",
"Khaled",
""
]
] | Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user's utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user's utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2% and lexical diversity by 1.4%. |
1801.07555 | Hongkai Wen | Yiran Shen, Fengyuan Yang, Bowen Du, Weitao Xu, Chengwen Luo, Hongkai
Wen | Shake-n-Shack: Enabling Secure Data Exchange Between Smart Wearables via
Handshakes | To appear in PerCom'18 | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since ancient Greece, handshaking has been commonly practiced between two
people as a friendly gesture to express trust and respect, or form a mutual
agreement. In this paper, we show that such physical contact can be used to
bootstrap secure cyber contact between the smart devices worn by users. The key
observation is that during handshaking, although belonged to two different
users, the two hands involved in the shaking events are often rigidly
connected, and therefore exhibit very similar motion patterns. We propose a
novel Shake-n-Shack system, which harvests motion data during user handshaking
from the wrist worn smart devices such as smartwatches or fitness bands, and
exploits the matching motion patterns to generate symmetric keys on both
parties. The generated keys can be then used to establish a secure
communication channel for exchanging data between devices. This provides a much
more natural and user-friendly alternative for many applications, e.g.
exchanging/sharing contact details, friending on social networks, or even
making payments, since it doesn't involve extra bespoke hardware, nor require
the users to perform pre-defined gestures. We implement the proposed
Shake-n-Shack system on off-the-shelf smartwatches, and extensive evaluation
shows that it can reliably generate 128-bit symmetric keys just after around 1s
of handshaking (with success rate >99%), and is resilient to real-time
mimicking attacks: in our experiments the Equal Error Rate (EER) is only 1.6%
on average. We also show that the proposed Shake-n-Shack system can be
extremely lightweight, and is able to run in-situ on the resource-constrained
smartwatches without incurring excessive resource consumption.
| [
{
"created": "Tue, 23 Jan 2018 14:23:13 GMT",
"version": "v1"
}
] | 2018-01-24 | [
[
"Shen",
"Yiran",
""
],
[
"Yang",
"Fengyuan",
""
],
[
"Du",
"Bowen",
""
],
[
"Xu",
"Weitao",
""
],
[
"Luo",
"Chengwen",
""
],
[
"Wen",
"Hongkai",
""
]
] | Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this paper, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel Shake-n-Shack system, which harvests motion data during user handshaking from the wrist worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g. exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn't involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed Shake-n-Shack system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate >99%), and is resilient to real-time mimicking attacks: in our experiments the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed Shake-n-Shack system can be extremely lightweight, and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption. |
2011.13495 | Zhizhong Han | Baorui Ma and Zhizhong Han and Yu-Shen Liu and Matthias Zwicker | Neural-Pull: Learning Signed Distance Functions from Point Clouds by
Learning to Pull Space onto Surfaces | To appear at ICML2021. Code and data are available at
https://github.com/mabaorui/NeuralPull | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reconstructing continuous surfaces from 3D point clouds is a fundamental
operation in 3D geometry processing. Several recent state-of-the-art methods
address this problem using neural networks to learn signed distance functions
(SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that
is simple and leads to high quality SDFs. Specifically, we train a neural
network to pull query 3D locations to their closest points on the surface using
the predicted signed distance values and the gradient at the query locations,
both of which are computed by the network itself. The pulling operation moves
each query location with a stride given by the distance predicted by the
network. Based on the sign of the distance, this may move the query location
along or against the direction of the gradient of the SDF. This is a
differentiable operation that allows us to update the signed distance value and
the gradient simultaneously during training. Our outperforming results under
widely used benchmarks demonstrate that we can learn SDFs more accurately and
flexibly for surface reconstruction and single image reconstruction than the
state-of-the-art methods.
| [
{
"created": "Thu, 26 Nov 2020 23:18:10 GMT",
"version": "v1"
},
{
"created": "Sun, 23 May 2021 17:54:34 GMT",
"version": "v2"
}
] | 2021-05-25 | [
[
"Ma",
"Baorui",
""
],
[
"Han",
"Zhizhong",
""
],
[
"Liu",
"Yu-Shen",
""
],
[
"Zwicker",
"Matthias",
""
]
] | Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neural networks to learn signed distance functions (SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that is simple and leads to high quality SDFs. Specifically, we train a neural network to pull query 3D locations to their closest points on the surface using the predicted signed distance values and the gradient at the query locations, both of which are computed by the network itself. The pulling operation moves each query location with a stride given by the distance predicted by the network. Based on the sign of the distance, this may move the query location along or against the direction of the gradient of the SDF. This is a differentiable operation that allows us to update the signed distance value and the gradient simultaneously during training. Our outperforming results under widely used benchmarks demonstrate that we can learn SDFs more accurately and flexibly for surface reconstruction and single image reconstruction than the state-of-the-art methods. |
1002.3187 | Seyed Hamed Hassani | S. Hamed Hassani, Kasra Alishahi, Rudiger Urbanke | On the scaling of Polar Codes: II. The behavior of un-polarized channels | Submitted to ISIT 2010 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide upper and lower bounds on the escape rate of the Bhattacharyya
process corresponding to polar codes and transmission over the the binary
erasure channel. More precisely, we bound the exponent of the number of
sub-channels whose Bhattacharyya constant falls in a fixed interval $[a,b]$.
Mathematically this can be stated as bounding the limit $\lim_{n \to \infty}
\frac{1}{n} \ln \mathbb{P}(Z_n \in [a,b])$, where $Z_n$ is the Bhattacharyya
process. The quantity $\mathbb{P}(Z_n \in [a,b])$ represents the fraction of
sub-channels that are still un-polarized at time $n$.
| [
{
"created": "Wed, 17 Feb 2010 03:55:40 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Feb 2010 07:54:04 GMT",
"version": "v2"
}
] | 2010-02-18 | [
[
"Hassani",
"S. Hamed",
""
],
[
"Alishahi",
"Kasra",
""
],
[
"Urbanke",
"Rudiger",
""
]
] | We provide upper and lower bounds on the escape rate of the Bhattacharyya process corresponding to polar codes and transmission over the the binary erasure channel. More precisely, we bound the exponent of the number of sub-channels whose Bhattacharyya constant falls in a fixed interval $[a,b]$. Mathematically this can be stated as bounding the limit $\lim_{n \to \infty} \frac{1}{n} \ln \mathbb{P}(Z_n \in [a,b])$, where $Z_n$ is the Bhattacharyya process. The quantity $\mathbb{P}(Z_n \in [a,b])$ represents the fraction of sub-channels that are still un-polarized at time $n$. |
1709.07758 | Farhana Ferdousi Liza | Farhana Ferdousi Liza and Marek Grzes | Improving Language Modelling with Noise-contrastive estimation | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural language models do not scale well when the vocabulary is large.
Noise-contrastive estimation (NCE) is a sampling-based method that allows for
fast learning with large vocabularies. Although NCE has shown promising
performance in neural machine translation, it was considered to be an
unsuccessful approach for language modelling. A sufficient investigation of the
hyperparameters in the NCE-based neural language models was also missing. In
this paper, we showed that NCE can be a successful approach in neural language
modelling when the hyperparameters of a neural network are tuned appropriately.
We introduced the 'search-then-converge' learning rate schedule for NCE and
designed a heuristic that specifies how to use this schedule. The impact of the
other important hyperparameters, such as the dropout rate and the weight
initialisation range, was also demonstrated. We showed that appropriate tuning
of NCE-based neural language models outperforms the state-of-the-art
single-model methods on a popular benchmark.
| [
{
"created": "Fri, 22 Sep 2017 13:59:17 GMT",
"version": "v1"
}
] | 2017-09-25 | [
[
"Liza",
"Farhana Ferdousi",
""
],
[
"Grzes",
"Marek",
""
]
] | Neural language models do not scale well when the vocabulary is large. Noise-contrastive estimation (NCE) is a sampling-based method that allows for fast learning with large vocabularies. Although NCE has shown promising performance in neural machine translation, it was considered to be an unsuccessful approach for language modelling. A sufficient investigation of the hyperparameters in the NCE-based neural language models was also missing. In this paper, we showed that NCE can be a successful approach in neural language modelling when the hyperparameters of a neural network are tuned appropriately. We introduced the 'search-then-converge' learning rate schedule for NCE and designed a heuristic that specifies how to use this schedule. The impact of the other important hyperparameters, such as the dropout rate and the weight initialisation range, was also demonstrated. We showed that appropriate tuning of NCE-based neural language models outperforms the state-of-the-art single-model methods on a popular benchmark. |
1711.06616 | Omid Haji Maghsoudi | Omid Haji Maghsoudi | Superpixels Based Segmentation and SVM Based Classification Method to
Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine
the entire GI trace. During an examination, it captures more than 55,000
frames. Reviewing all these images is time-consuming and prone to human error.
It has been a challenge to develop intelligent methods assisting physicians to
review the frames. The WCE frames are captured in 8-bit color depths which
provides enough a color range to detect abnormalities. Here, superpixel based
methods are proposed to segment five diseases including: bleeding, Crohn's
disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixels
methods are compared to provide semantic segmentation of these prolific
diseases: simple linear iterative clustering (SLIC) and quick shift (QS). The
segmented superpixels were classified into two classes (normal and abnormal) by
support vector machine (SVM) using texture and color features. For both
superpixel methods, the accuracy, specificity, sensitivity, and precision
(SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC was
dramatically faster than QS.
| [
{
"created": "Fri, 17 Nov 2017 16:25:34 GMT",
"version": "v1"
}
] | 2017-11-20 | [
[
"Maghsoudi",
"Omid Haji",
""
]
] | Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine the entire GI trace. During an examination, it captures more than 55,000 frames. Reviewing all these images is time-consuming and prone to human error. It has been a challenge to develop intelligent methods assisting physicians to review the frames. The WCE frames are captured in 8-bit color depths which provides enough a color range to detect abnormalities. Here, superpixel based methods are proposed to segment five diseases including: bleeding, Crohn's disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixels methods are compared to provide semantic segmentation of these prolific diseases: simple linear iterative clustering (SLIC) and quick shift (QS). The segmented superpixels were classified into two classes (normal and abnormal) by support vector machine (SVM) using texture and color features. For both superpixel methods, the accuracy, specificity, sensitivity, and precision (SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC was dramatically faster than QS. |
2104.13255 | Ting-Wu Chin | Ting-Wu Chin, Diana Marculescu, Ari S. Morcos | Width Transfer: On the (In)variance of Width Optimization | Full paper accepted at CVPR Workshops 2021; a 4-page abridged version
is accepted at ICLR 2021 NAS Workshop | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Optimizing the channel counts for different layers of a CNN has shown great
promise in improving the efficiency of CNNs at test-time. However, these
methods often introduce large computational overhead (e.g., an additional 2x
FLOPs of standard training). Minimizing this overhead could therefore
significantly speed up training. In this work, we propose width transfer, a
technique that harnesses the assumptions that the optimized widths (or channel
counts) are regular across sizes and depths. We show that width transfer works
well across various width optimization algorithms and networks. Specifically,
we can achieve up to 320x reduction in width optimization overhead without
compromising the top-1 accuracy on ImageNet, making the additional cost of
width optimization negligible relative to initial training. Our findings not
only suggest an efficient way to conduct width optimization but also highlight
that the widths that lead to better accuracy are invariant to various aspects
of network architectures and training data.
| [
{
"created": "Sat, 24 Apr 2021 19:51:53 GMT",
"version": "v1"
}
] | 2021-04-28 | [
[
"Chin",
"Ting-Wu",
""
],
[
"Marculescu",
"Diana",
""
],
[
"Morcos",
"Ari S.",
""
]
] | Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large computational overhead (e.g., an additional 2x FLOPs of standard training). Minimizing this overhead could therefore significantly speed up training. In this work, we propose width transfer, a technique that harnesses the assumptions that the optimized widths (or channel counts) are regular across sizes and depths. We show that width transfer works well across various width optimization algorithms and networks. Specifically, we can achieve up to 320x reduction in width optimization overhead without compromising the top-1 accuracy on ImageNet, making the additional cost of width optimization negligible relative to initial training. Our findings not only suggest an efficient way to conduct width optimization but also highlight that the widths that lead to better accuracy are invariant to various aspects of network architectures and training data. |
1704.02958 | Arturs Backurs | Arturs Backurs, Piotr Indyk, Ludwig Schmidt | On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel
Methods and Neural Networks | null | null | null | null | cs.CC cs.DS cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Empirical risk minimization (ERM) is ubiquitous in machine learning and
underlies most supervised learning methods. While there has been a large body
of work on algorithms for various ERM problems, the exact computational
complexity of ERM is still not understood. We address this issue for multiple
popular ERM problems including kernel SVMs, kernel ridge regression, and
training the final layer of a neural network. In particular, we give
conditional hardness results for these problems based on complexity-theoretic
assumptions such as the Strong Exponential Time Hypothesis. Under these
assumptions, we show that there are no algorithms that solve the aforementioned
ERM problems to high accuracy in sub-quadratic time. We also give similar
hardness results for computing the gradient of the empirical loss, which is the
main computational burden in many non-convex learning tasks.
| [
{
"created": "Mon, 10 Apr 2017 17:26:41 GMT",
"version": "v1"
}
] | 2017-04-11 | [
[
"Backurs",
"Arturs",
""
],
[
"Indyk",
"Piotr",
""
],
[
"Schmidt",
"Ludwig",
""
]
] | Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there has been a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel SVMs, kernel ridge regression, and training the final layer of a neural network. In particular, we give conditional hardness results for these problems based on complexity-theoretic assumptions such as the Strong Exponential Time Hypothesis. Under these assumptions, we show that there are no algorithms that solve the aforementioned ERM problems to high accuracy in sub-quadratic time. We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks. |
2003.14058 | Yuan Gao | Yuan Gao, Haoping Bai, Zequn Jie, Jiayi Ma, Kui Jia, and Wei Liu | MTL-NAS: Task-Agnostic Neural Architecture Search towards
General-Purpose Multi-Task Learning | Accepted to CVPR2020. The first two authors contribute equally | IEEE Conference on Computer Vision and Pattern Recognition, 2020 | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose to incorporate neural architecture search (NAS) into
general-purpose multi-task learning (GP-MTL). Existing NAS methods typically
define different search spaces according to different tasks. In order to adapt
to different task combinations (i.e., task sets), we disentangle the GP-MTL
networks into single-task backbones (optionally encode the task priors), and a
hierarchical and layerwise features sharing/fusing scheme across them. This
enables us to design a novel and general task-agnostic search space, which
inserts cross-task edges (i.e., feature fusion connections) into fixed
single-task network backbones. Moreover, we also propose a novel single-shot
gradient-based search algorithm that closes the performance gap between the
searched architectures and the final evaluation architecture. This is realized
with a minimum entropy regularization on the architecture weights during the
search phase, which makes the architecture weights converge to near-discrete
values and therefore achieves a single model. As a result, our searched model
can be directly used for evaluation without (re-)training from scratch. We
perform extensive experiments using different single-task backbones on various
task sets, demonstrating the promising performance obtained by exploiting the
hierarchical and layerwise features, as well as the desirable generalizability
to different i) task sets and ii) single-task backbones. The code of our paper
is available at https://github.com/bhpfelix/MTLNAS.
| [
{
"created": "Tue, 31 Mar 2020 09:49:14 GMT",
"version": "v1"
}
] | 2020-04-01 | [
[
"Gao",
"Yuan",
""
],
[
"Bai",
"Haoping",
""
],
[
"Jie",
"Zequn",
""
],
[
"Ma",
"Jiayi",
""
],
[
"Jia",
"Kui",
""
],
[
"Liu",
"Wei",
""
]
] | We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL). Existing NAS methods typically define different search spaces according to different tasks. In order to adapt to different task combinations (i.e., task sets), we disentangle the GP-MTL networks into single-task backbones (optionally encode the task priors), and a hierarchical and layerwise features sharing/fusing scheme across them. This enables us to design a novel and general task-agnostic search space, which inserts cross-task edges (i.e., feature fusion connections) into fixed single-task network backbones. Moreover, we also propose a novel single-shot gradient-based search algorithm that closes the performance gap between the searched architectures and the final evaluation architecture. This is realized with a minimum entropy regularization on the architecture weights during the search phase, which makes the architecture weights converge to near-discrete values and therefore achieves a single model. As a result, our searched model can be directly used for evaluation without (re-)training from scratch. We perform extensive experiments using different single-task backbones on various task sets, demonstrating the promising performance obtained by exploiting the hierarchical and layerwise features, as well as the desirable generalizability to different i) task sets and ii) single-task backbones. The code of our paper is available at https://github.com/bhpfelix/MTLNAS. |
1203.2511 | Victor Seal | Victor Seal, Arnab Raha, Shovan Maity, Souvik Kr Mitra, Amitava
Mukherjee and Mrinal Kanti Naskar | A Simple Flood Forecasting Scheme Using Wireless Sensor Networks | 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.2012 | null | 10.5121/ijasuc.2012.3105 | null | cs.LG cs.CE cs.NI cs.SY stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.
| [
{
"created": "Fri, 9 Mar 2012 18:08:34 GMT",
"version": "v1"
}
] | 2012-03-13 | [
[
"Seal",
"Victor",
""
],
[
"Raha",
"Arnab",
""
],
[
"Maity",
"Shovan",
""
],
[
"Mitra",
"Souvik Kr",
""
],
[
"Mukherjee",
"Amitava",
""
],
[
"Naskar",
"Mrinal Kanti",
""
]
] | This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level. |
1412.8185 | Yuliya Boyarinova | Yakiv O. Kalinovsky, Yuliya E. Boyarinova, Alina S. Turenko, Yana V.
Khitsko | Generalized quaternions and their relations with Grassmann-Clifford
procedure of doubling | arXiv admin note: substantial text overlap with arXiv:1409.3193 | null | null | null | cs.NA math.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The class of non-commutative hypercomplex number systems (HNS) of
4-dimension, constructed by using of non-commutative Grassmann-Clifford
procedure of doubling of 2-dimensional systems is investigated in the article
and established here are their relationships with the generalized quaternions.
Algorithms of performance of operations and methods of algebraic
characteristics calculation in them, such as conjugation, normalization, a type
of zero divisors are investigated. The considered arithmetic and algebraic
operations and procedures in this class HNS allow to use these HNS in
mathematical modeling.
| [
{
"created": "Sun, 28 Dec 2014 16:44:30 GMT",
"version": "v1"
}
] | 2014-12-30 | [
[
"Kalinovsky",
"Yakiv O.",
""
],
[
"Boyarinova",
"Yuliya E.",
""
],
[
"Turenko",
"Alina S.",
""
],
[
"Khitsko",
"Yana V.",
""
]
] | The class of non-commutative hypercomplex number systems (HNS) of 4-dimension, constructed by using of non-commutative Grassmann-Clifford procedure of doubling of 2-dimensional systems is investigated in the article and established here are their relationships with the generalized quaternions. Algorithms of performance of operations and methods of algebraic characteristics calculation in them, such as conjugation, normalization, a type of zero divisors are investigated. The considered arithmetic and algebraic operations and procedures in this class HNS allow to use these HNS in mathematical modeling. |
2309.06877 | Xinyang Yu | Zhenguang Liu, Xinyang Yu, Ruili Wang, Shuai Ye, Zhe Ma, Jianfeng
Dong, Sifeng He, Feng Qian, Xiaobo Zhang, Roger Zimmermann, Lei Yang | Video Infringement Detection via Feature Disentanglement and Mutual
Information Maximization | This paper is accepted by ACM MM 2023 | null | null | null | cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The self-media era provides us tremendous high quality videos. Unfortunately,
frequent video copyright infringements are now seriously damaging the interests
and enthusiasm of video creators. Identifying infringing videos is therefore a
compelling task. Current state-of-the-art methods tend to simply feed
high-dimensional mixed video features into deep neural networks and count on
the networks to extract useful representations. Despite its simplicity, this
paradigm heavily relies on the original entangled features and lacks
constraints guaranteeing that useful task-relevant semantics are extracted from
the features.
In this paper, we seek to tackle the above challenges from two aspects: (1)
We propose to disentangle an original high-dimensional feature into multiple
sub-features, explicitly disentangling the feature into exclusive
lower-dimensional components. We expect the sub-features to encode
non-overlapping semantics of the original feature and remove redundant
information.
(2) On top of the disentangled sub-features, we further learn an auxiliary
feature to enhance the sub-features. We theoretically analyzed the mutual
information between the label and the disentangled features, arriving at a loss
that maximizes the extraction of task-relevant information from the original
feature.
Extensive experiments on two large-scale benchmark datasets (i.e., SVD and
VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale
SVD dataset and also sets the new state-of-the-art on the VCSL benchmark
dataset. Our code and model have been released at
https://github.com/yyyooooo/DMI/, hoping to contribute to the community.
| [
{
"created": "Wed, 13 Sep 2023 10:53:12 GMT",
"version": "v1"
}
] | 2023-09-14 | [
[
"Liu",
"Zhenguang",
""
],
[
"Yu",
"Xinyang",
""
],
[
"Wang",
"Ruili",
""
],
[
"Ye",
"Shuai",
""
],
[
"Ma",
"Zhe",
""
],
[
"Dong",
"Jianfeng",
""
],
[
"He",
"Sifeng",
""
],
[
"Qian",
"Feng",
""
],
[
"Zhang",
"Xiaobo",
""
],
[
"Zimmermann",
"Roger",
""
],
[
"Yang",
"Lei",
""
]
] | The self-media era provides us tremendous high quality videos. Unfortunately, frequent video copyright infringements are now seriously damaging the interests and enthusiasm of video creators. Identifying infringing videos is therefore a compelling task. Current state-of-the-art methods tend to simply feed high-dimensional mixed video features into deep neural networks and count on the networks to extract useful representations. Despite its simplicity, this paradigm heavily relies on the original entangled features and lacks constraints guaranteeing that useful task-relevant semantics are extracted from the features. In this paper, we seek to tackle the above challenges from two aspects: (1) We propose to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and remove redundant information. (2) On top of the disentangled sub-features, we further learn an auxiliary feature to enhance the sub-features. We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature. Extensive experiments on two large-scale benchmark datasets (i.e., SVD and VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset. Our code and model have been released at https://github.com/yyyooooo/DMI/, hoping to contribute to the community. |
1610.07563 | Jinbo Bi | Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun | On Multiplicative Multitask Feature Learning | Advances in Neural Information Processing Systems 2014 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a general framework of multiplicative multitask feature
learning which decomposes each task's model parameters into a multiplication of
two components. One of the components is used across all tasks and the other
component is task-specific. Several previous methods have been proposed as
special cases of our framework. We study the theoretical properties of this
framework when different regularization conditions are applied to the two
decomposed components. We prove that this framework is mathematically
equivalent to the widely used multitask feature learning methods that are based
on a joint regularization of all model parameters, but with a more general form
of regularizers. Further, an analytical formula is derived for the across-task
component as related to the task-specific component for all these regularizers,
leading to a better understanding of the shrinkage effect. Study of this
framework motivates new multitask learning algorithms. We propose two new
learning formulations by varying the parameters in the proposed framework.
Empirical studies have revealed the relative advantages of the two new
formulations by comparing with the state of the art, which provides instructive
insights into the feature learning problem with multiple tasks.
| [
{
"created": "Mon, 24 Oct 2016 19:27:52 GMT",
"version": "v1"
}
] | 2016-10-25 | [
[
"Wang",
"Xin",
""
],
[
"Bi",
"Jinbo",
""
],
[
"Yu",
"Shipeng",
""
],
[
"Sun",
"Jiangwen",
""
]
] | We investigate a general framework of multiplicative multitask feature learning which decomposes each task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods have been proposed as special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effect. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. Empirical studies have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. |
2106.02320 | Gengwei Zhang | Gengwei Zhang, Guoliang Kang, Yi Yang, Yunchao Wei | Few-Shot Segmentation via Cycle-Consistent Transformer | Advances in Neural Information Processing Systems (NeurIPS), 2021.
Project: https://github.com/GengDavid/CyCTR | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Few-shot segmentation aims to train a segmentation model that can fast adapt
to novel classes with few exemplars. The conventional training paradigm is to
learn to make predictions on query images conditioned on the features from
support images. Previous methods only utilized the semantic-level prototypes of
support images as conditional information. These methods cannot utilize all
pixel-wise support information for the query predictions, which is however
critical for the segmentation task. In this paper, we focus on utilizing
pixel-wise relationships between support and query images to facilitate the
few-shot segmentation task. We design a novel Cycle-Consistent TRansformer
(CyCTR) module to aggregate pixel-wise support features into query ones. CyCTR
performs cross-attention between features from different images, i.e. support
and query images. We observe that there may exist unexpected irrelevant
pixel-level support features. Directly performing cross-attention may aggregate
these features from support to query and bias the query features. Thus, we
propose using a novel cycle-consistent attention mechanism to filter out
possible harmful support features and encourage query features to attend to the
most informative pixels from support images. Experiments on all few-shot
segmentation benchmarks demonstrate that our proposed CyCTR leads to remarkable
improvement compared to previous state-of-the-art methods. Specifically, on
Pascal-$5^i$ and COCO-$20^i$ datasets, we achieve 67.5% and 45.6% mIoU for
5-shot segmentation, outperforming previous state-of-the-art methods by 5.6%
and 7.1% respectively.
| [
{
"created": "Fri, 4 Jun 2021 07:57:48 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Oct 2021 11:50:27 GMT",
"version": "v2"
},
{
"created": "Tue, 21 Dec 2021 07:24:53 GMT",
"version": "v3"
},
{
"created": "Tue, 8 Mar 2022 00:20:03 GMT",
"version": "v4"
}
] | 2022-03-09 | [
[
"Zhang",
"Gengwei",
""
],
[
"Kang",
"Guoliang",
""
],
[
"Yang",
"Yi",
""
],
[
"Wei",
"Yunchao",
""
]
] | Few-shot segmentation aims to train a segmentation model that can fast adapt to novel classes with few exemplars. The conventional training paradigm is to learn to make predictions on query images conditioned on the features from support images. Previous methods only utilized the semantic-level prototypes of support images as conditional information. These methods cannot utilize all pixel-wise support information for the query predictions, which is however critical for the segmentation task. In this paper, we focus on utilizing pixel-wise relationships between support and query images to facilitate the few-shot segmentation task. We design a novel Cycle-Consistent TRansformer (CyCTR) module to aggregate pixel-wise support features into query ones. CyCTR performs cross-attention between features from different images, i.e. support and query images. We observe that there may exist unexpected irrelevant pixel-level support features. Directly performing cross-attention may aggregate these features from support to query and bias the query features. Thus, we propose using a novel cycle-consistent attention mechanism to filter out possible harmful support features and encourage query features to attend to the most informative pixels from support images. Experiments on all few-shot segmentation benchmarks demonstrate that our proposed CyCTR leads to remarkable improvement compared to previous state-of-the-art methods. Specifically, on Pascal-$5^i$ and COCO-$20^i$ datasets, we achieve 67.5% and 45.6% mIoU for 5-shot segmentation, outperforming previous state-of-the-art methods by 5.6% and 7.1% respectively. |
1204.5431 | Mohammad Tofighi | Mohammad Tofighi and Hashem Kalbkhani and Mahrokh G. Shayesteh and
Mehdi Ghasemzadeh | Robust Head Pose Estimation Using Contourlet Transform | 5 pages, conference paper | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating pose of the head is an important preprocessing step in many
pattern recognition and computer vision systems such as face recognition. Since
the performance of the face recognition systems is greatly affected by the
poses of the face, how to estimate the accurate pose of the face in human face
image is still a challenging problem. In this paper, we represent a novel
method for head pose estimation. To enhance the efficiency of the estimation we
use contourlet transform for feature extraction. Contourlet transform is
multi-resolution, multi-direction transform. In order to reduce the feature
space dimension and obtain appropriate features we use LDA (Linear Discriminant
Analysis) and PCA (Principal Component Analysis) to remove ineffcient features.
Then, we apply different classifiers such as k-nearest neighborhood (knn) and
minimum distance. We use the public available FERET database to evaluate the
performance of proposed method. Simulation results indicate the superior
robustness of the proposed method.
| [
{
"created": "Tue, 24 Apr 2012 17:08:04 GMT",
"version": "v1"
},
{
"created": "Sat, 12 May 2012 13:56:32 GMT",
"version": "v2"
}
] | 2012-05-15 | [
[
"Tofighi",
"Mohammad",
""
],
[
"Kalbkhani",
"Hashem",
""
],
[
"Shayesteh",
"Mahrokh G.",
""
],
[
"Ghasemzadeh",
"Mehdi",
""
]
] | Estimating pose of the head is an important preprocessing step in many pattern recognition and computer vision systems such as face recognition. Since the performance of the face recognition systems is greatly affected by the poses of the face, how to estimate the accurate pose of the face in human face image is still a challenging problem. In this paper, we represent a novel method for head pose estimation. To enhance the efficiency of the estimation we use contourlet transform for feature extraction. Contourlet transform is multi-resolution, multi-direction transform. In order to reduce the feature space dimension and obtain appropriate features we use LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) to remove ineffcient features. Then, we apply different classifiers such as k-nearest neighborhood (knn) and minimum distance. We use the public available FERET database to evaluate the performance of proposed method. Simulation results indicate the superior robustness of the proposed method. |
2401.00547 | Avvaru Ch Madhusudanarao | A Ch Madhusudanarao, Rahul Singh | On Learning for Ambiguous Chance Constrained Problems | We have "not considered the uniform bound" for violation
probabilities corresponding to the set of distributions in the ambiguity set | null | null | null | cs.LG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study chance constrained optimization problems $\min_x f(x)$ s.t.
$P(\left\{ \theta: g(x,\theta)\le 0 \right\})\ge 1-\epsilon$ where $\epsilon\in
(0,1)$ is the violation probability, when the distribution $P$ is not known to
the decision maker (DM). When the DM has access to a set of distributions
$\mathcal{U}$ such that $P$ is contained in $\mathcal{U}$, then the problem is
known as the ambiguous chance-constrained problem \cite{erdougan2006ambiguous}.
We study ambiguous chance-constrained problem for the case when $\mathcal{U}$
is of the form $\left\{\mu:\frac{\mu (y)}{\nu(y)}\leq C, \forall y\in\Theta,
\mu(y)\ge 0\right\}$, where $\nu$ is a ``reference distribution.'' We show that
in this case the original problem can be ``well-approximated'' by a sampled
problem in which $N$ i.i.d. samples of $\theta$ are drawn from $\nu$, and the
original constraint is replaced with $g(x,\theta_i)\le 0,~i=1,2,\ldots,N$. We
also derive the sample complexity associated with this approximation, i.e., for
$\epsilon,\delta>0$ the number of samples which must be drawn from $\nu$ so
that with a probability greater than $1-\delta$ (over the randomness of $\nu$),
the solution obtained by solving the sampled program yields an
$\epsilon$-feasible solution for the original chance constrained problem.
| [
{
"created": "Sun, 31 Dec 2023 17:25:43 GMT",
"version": "v1"
},
{
"created": "Sun, 11 Feb 2024 06:07:17 GMT",
"version": "v2"
}
] | 2024-02-13 | [
[
"Madhusudanarao",
"A Ch",
""
],
[
"Singh",
"Rahul",
""
]
] | We study chance constrained optimization problems $\min_x f(x)$ s.t. $P(\left\{ \theta: g(x,\theta)\le 0 \right\})\ge 1-\epsilon$ where $\epsilon\in (0,1)$ is the violation probability, when the distribution $P$ is not known to the decision maker (DM). When the DM has access to a set of distributions $\mathcal{U}$ such that $P$ is contained in $\mathcal{U}$, then the problem is known as the ambiguous chance-constrained problem \cite{erdougan2006ambiguous}. We study ambiguous chance-constrained problem for the case when $\mathcal{U}$ is of the form $\left\{\mu:\frac{\mu (y)}{\nu(y)}\leq C, \forall y\in\Theta, \mu(y)\ge 0\right\}$, where $\nu$ is a ``reference distribution.'' We show that in this case the original problem can be ``well-approximated'' by a sampled problem in which $N$ i.i.d. samples of $\theta$ are drawn from $\nu$, and the original constraint is replaced with $g(x,\theta_i)\le 0,~i=1,2,\ldots,N$. We also derive the sample complexity associated with this approximation, i.e., for $\epsilon,\delta>0$ the number of samples which must be drawn from $\nu$ so that with a probability greater than $1-\delta$ (over the randomness of $\nu$), the solution obtained by solving the sampled program yields an $\epsilon$-feasible solution for the original chance constrained problem. |
2101.00790 | Amir K. Khandani Dr. | Amir K. Khandani | Achieving Capacity Region of 2-users Weak GIC by Enlarging the Core in a
Nested Set of Polymatroids (continuation of arXiv:2012.07820 "Optimality of
Gaussian in Enlarging HK Rate Region, and its Overlap with ...") | 20 pages, 4 figures | null | null | null | cs.IT math.CO math.IT | http://creativecommons.org/publicdomain/zero/1.0/ | This article shows that achieving capacity region of a 2-users weak Gaussian
Interference Channel (GIC) is equivalent to enlarging the core in a nested set
of Polymatroids (each equivalent to capacity region of a multiple-access
channel) through maximizing a minimum rate, then projecting along its
orthogonal span and continuing recursively. This formulation relies on defining
dummy private messages to capture the effect of interference in GIC. It follows
that relying on independent Gaussian random code-books is optimum, and the
corresponding solution corresponds to achieving the boundary in HK constraints.
| [
{
"created": "Mon, 4 Jan 2021 06:07:56 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Jan 2021 18:45:29 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Jan 2021 05:38:25 GMT",
"version": "v3"
},
{
"created": "Thu, 28 Jan 2021 15:58:16 GMT",
"version": "v4"
},
{
"created": "Mon, 1 Feb 2021 23:33:22 GMT",
"version": "v5"
}
] | 2021-02-03 | [
[
"Khandani",
"Amir K.",
""
]
] | This article shows that achieving capacity region of a 2-users weak Gaussian Interference Channel (GIC) is equivalent to enlarging the core in a nested set of Polymatroids (each equivalent to capacity region of a multiple-access channel) through maximizing a minimum rate, then projecting along its orthogonal span and continuing recursively. This formulation relies on defining dummy private messages to capture the effect of interference in GIC. It follows that relying on independent Gaussian random code-books is optimum, and the corresponding solution corresponds to achieving the boundary in HK constraints. |
2109.13325 | Chen Quan | Chen Quan, Baocheng Geng, Yunghsiang S. Han and Pramod K. Varshney | Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence
of Strategic Attacks | null | null | null | null | cs.CR eess.SP | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper employs an audit bit based mechanism to mitigate the effect of
Byzantine attacks. In this framework, the optimal attacking strategy for
intelligent attackers is investigated for the traditional audit bit based
scheme (TAS) to evaluate the robustness of the system. We show that it is
possible for an intelligent attacker to degrade the performance of TAS to the
system without audit bits. To enhance the robustness of the system in the
presence of intelligent attackers, we propose an enhanced audit bit based
scheme (EAS). The optimal fusion rule for the proposed scheme is derived and
the detection performance of the system is evaluated via the probability of
error for the system. Simulation results show that the proposed EAS improves
the robustness and the detection performance of the system. Moreover, based on
EAS, another new scheme called the reduced audit bit based scheme (RAS) is
proposed which further improves system performance. We derive the new optimal
fusion rule and the simulation results show that RAS outperforms EAS and TAS in
terms of both robustness and detection performance of the system. Then, we
extend the proposed RAS for a wide-area cluster based distributed wireless
sensor networks (CWSNs). Simulation results show that the proposed RAS
significantly reduces the communication overhead between the sensors and the
FC, which prolongs the lifetime of the network.
| [
{
"created": "Mon, 27 Sep 2021 19:58:26 GMT",
"version": "v1"
}
] | 2021-09-29 | [
[
"Quan",
"Chen",
""
],
[
"Geng",
"Baocheng",
""
],
[
"Han",
"Yunghsiang S.",
""
],
[
"Varshney",
"Pramod K.",
""
]
] | This paper employs an audit bit based mechanism to mitigate the effect of Byzantine attacks. In this framework, the optimal attacking strategy for intelligent attackers is investigated for the traditional audit bit based scheme (TAS) to evaluate the robustness of the system. We show that it is possible for an intelligent attacker to degrade the performance of TAS to the system without audit bits. To enhance the robustness of the system in the presence of intelligent attackers, we propose an enhanced audit bit based scheme (EAS). The optimal fusion rule for the proposed scheme is derived and the detection performance of the system is evaluated via the probability of error for the system. Simulation results show that the proposed EAS improves the robustness and the detection performance of the system. Moreover, based on EAS, another new scheme called the reduced audit bit based scheme (RAS) is proposed which further improves system performance. We derive the new optimal fusion rule and the simulation results show that RAS outperforms EAS and TAS in terms of both robustness and detection performance of the system. Then, we extend the proposed RAS for a wide-area cluster based distributed wireless sensor networks (CWSNs). Simulation results show that the proposed RAS significantly reduces the communication overhead between the sensors and the FC, which prolongs the lifetime of the network. |
2206.05257 | Kamran Alipour | Kamran Alipour, Aditya Lahiri, Ehsan Adeli, Babak Salimi, Michael
Pazzani | Explaining Image Classifiers Using Contrastive Counterfactuals in
Generative Latent Spaces | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite their high accuracies, modern complex image classifiers cannot be
trusted for sensitive tasks due to their unknown decision-making process and
potential biases. Counterfactual explanations are very effective in providing
transparency for these black-box algorithms. Nevertheless, generating
counterfactuals that can have a consistent impact on classifier outputs and yet
expose interpretable feature changes is a very challenging task. We introduce a
novel method to generate causal and yet interpretable counterfactual
explanations for image classifiers using pretrained generative models without
any re-training or conditioning. The generative models in this technique are
not bound to be trained on the same data as the target classifier. We use this
framework to obtain contrastive and causal sufficiency and necessity scores as
global explanations for black-box classifiers. On the task of face attribute
classification, we show how different attributes influence the classifier
output by providing both causal and contrastive feature attributions, and the
corresponding counterfactual images.
| [
{
"created": "Fri, 10 Jun 2022 17:54:46 GMT",
"version": "v1"
}
] | 2022-06-13 | [
[
"Alipour",
"Kamran",
""
],
[
"Lahiri",
"Aditya",
""
],
[
"Adeli",
"Ehsan",
""
],
[
"Salimi",
"Babak",
""
],
[
"Pazzani",
"Michael",
""
]
] | Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these black-box algorithms. Nevertheless, generating counterfactuals that can have a consistent impact on classifier outputs and yet expose interpretable feature changes is a very challenging task. We introduce a novel method to generate causal and yet interpretable counterfactual explanations for image classifiers using pretrained generative models without any re-training or conditioning. The generative models in this technique are not bound to be trained on the same data as the target classifier. We use this framework to obtain contrastive and causal sufficiency and necessity scores as global explanations for black-box classifiers. On the task of face attribute classification, we show how different attributes influence the classifier output by providing both causal and contrastive feature attributions, and the corresponding counterfactual images. |
2001.04767 | Ulderico Fugacci | Ulderico Fugacci, Claudia Landi, Hanife Varl{\i} | Critical Sets of PL and Discrete Morse Theory: a Correspondence | In this version, we have fixed some minor typos | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Piecewise-linear (PL) Morse theory and discrete Morse theory are used in
shape analysis tasks to investigate the topological features of discretized
spaces. In spite of their common origin in smooth Morse theory, various notions
of critical points have been given in the literature for the discrete setting,
making a clear understanding of the relationships occurring between them not
obvious. This paper aims at providing equivalence results about critical points
of the two discretized Morse theories. First of all, we prove the equivalence
of the existing notions of PL critical points. Next, under an optimality
condition called relative perfectness, we show a dimension agnostic
correspondence between the set of PL critical points and that of discrete
critical simplices of the combinatorial approach. Finally, we show how a
relatively perfect discrete gradient vector field can be algorithmically built
up to dimension 3. This way, we guarantee a formal and operative connection
between critical sets in the PL and discrete theories.
| [
{
"created": "Tue, 14 Jan 2020 13:34:19 GMT",
"version": "v1"
},
{
"created": "Sun, 19 Jan 2020 09:16:18 GMT",
"version": "v2"
},
{
"created": "Fri, 8 May 2020 12:15:32 GMT",
"version": "v3"
},
{
"created": "Mon, 18 May 2020 16:38:15 GMT",
"version": "v4"
}
] | 2020-05-19 | [
[
"Fugacci",
"Ulderico",
""
],
[
"Landi",
"Claudia",
""
],
[
"Varlı",
"Hanife",
""
]
] | Piecewise-linear (PL) Morse theory and discrete Morse theory are used in shape analysis tasks to investigate the topological features of discretized spaces. In spite of their common origin in smooth Morse theory, various notions of critical points have been given in the literature for the discrete setting, making a clear understanding of the relationships occurring between them not obvious. This paper aims at providing equivalence results about critical points of the two discretized Morse theories. First of all, we prove the equivalence of the existing notions of PL critical points. Next, under an optimality condition called relative perfectness, we show a dimension agnostic correspondence between the set of PL critical points and that of discrete critical simplices of the combinatorial approach. Finally, we show how a relatively perfect discrete gradient vector field can be algorithmically built up to dimension 3. This way, we guarantee a formal and operative connection between critical sets in the PL and discrete theories. |
2006.14117 | Shuai Zhang | Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong | Fast Learning of Graph Neural Networks with Guaranteed Generalizability:
One-hidden-layer Case | null | International Conference on Machine Learning (ICML 2020) | null | null | cs.LG eess.SP math.OC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although graph neural networks (GNNs) have made great progress recently on
learning from graph-structured data in practice, their theoretical guarantee on
generalizability remains elusive in the literature. In this paper, we provide a
theoretically-grounded generalizability analysis of GNNs with one hidden layer
for both regression and binary classification problems. Under the assumption
that there exists a ground-truth GNN model (with zero generalization error),
the objective of GNN learning is to estimate the ground-truth GNN parameters
from the training data. To achieve this objective, we propose a learning
algorithm that is built on tensor initialization and accelerated gradient
descent. We then show that the proposed learning algorithm converges to the
ground-truth GNN model for the regression problem, and to a model sufficiently
close to the ground-truth for the binary classification problem. Moreover, for
both cases, the convergence rate of the proposed learning algorithm is proven
to be linear and faster than the vanilla gradient descent algorithm. We further
explore the relationship between the sample complexity of GNNs and their
underlying graph properties. Lastly, we provide numerical experiments to
demonstrate the validity of our analysis and the effectiveness of the proposed
learning algorithm for GNNs.
| [
{
"created": "Thu, 25 Jun 2020 00:45:52 GMT",
"version": "v1"
}
] | 2020-06-26 | [
[
"Zhang",
"Shuai",
""
],
[
"Wang",
"Meng",
""
],
[
"Liu",
"Sijia",
""
],
[
"Chen",
"Pin-Yu",
""
],
[
"Xiong",
"Jinjun",
""
]
] | Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains elusive in the literature. In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems. Under the assumption that there exists a ground-truth GNN model (with zero generalization error), the objective of GNN learning is to estimate the ground-truth GNN parameters from the training data. To achieve this objective, we propose a learning algorithm that is built on tensor initialization and accelerated gradient descent. We then show that the proposed learning algorithm converges to the ground-truth GNN model for the regression problem, and to a model sufficiently close to the ground-truth for the binary classification problem. Moreover, for both cases, the convergence rate of the proposed learning algorithm is proven to be linear and faster than the vanilla gradient descent algorithm. We further explore the relationship between the sample complexity of GNNs and their underlying graph properties. Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. |
2209.13429 | Yongchan Kwon | Yongchan Kwon, James Zou | WeightedSHAP: analyzing and improving Shapley based feature attributions | null | NeurIPS2022 | null | null | cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Shapley value is a popular approach for measuring the influence of individual
features. While Shapley feature attribution is built upon desiderata from game
theory, some of its constraints may be less natural in certain machine learning
settings, leading to unintuitive model interpretation. In particular, the
Shapley value uses the same weight for all marginal contributions -- i.e. it
gives the same importance when a large number of other features are given
versus when a small number of other features are given. This property can be
problematic if larger feature sets are more or less informative than smaller
feature sets. Our work performs a rigorous analysis of the potential
limitations of Shapley feature attribution. We identify simple settings where
the Shapley value is mathematically suboptimal by assigning larger attributions
for less influential features. Motivated by this observation, we propose
WeightedSHAP, which generalizes the Shapley value and learns which marginal
contributions to focus directly from data. On several real-world datasets, we
demonstrate that the influential features identified by WeightedSHAP are better
able to recapitulate the model's predictions compared to the features
identified by the Shapley value.
| [
{
"created": "Tue, 27 Sep 2022 14:34:07 GMT",
"version": "v1"
}
] | 2022-09-28 | [
[
"Kwon",
"Yongchan",
""
],
[
"Zou",
"James",
""
]
] | Shapley value is a popular approach for measuring the influence of individual features. While Shapley feature attribution is built upon desiderata from game theory, some of its constraints may be less natural in certain machine learning settings, leading to unintuitive model interpretation. In particular, the Shapley value uses the same weight for all marginal contributions -- i.e. it gives the same importance when a large number of other features are given versus when a small number of other features are given. This property can be problematic if larger feature sets are more or less informative than smaller feature sets. Our work performs a rigorous analysis of the potential limitations of Shapley feature attribution. We identify simple settings where the Shapley value is mathematically suboptimal by assigning larger attributions for less influential features. Motivated by this observation, we propose WeightedSHAP, which generalizes the Shapley value and learns which marginal contributions to focus directly from data. On several real-world datasets, we demonstrate that the influential features identified by WeightedSHAP are better able to recapitulate the model's predictions compared to the features identified by the Shapley value. |
1709.00596 | D\"om\"ot\"or P\'alv\"olgyi | D\"om\"ot\"or P\'alv\"olgyi | Complexity of Domination in Triangulated Plane Graphs | null | null | null | null | cs.CC math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We prove that for a triangulated plane graph it is NP-complete to determine
its domination number and its power domination number.
| [
{
"created": "Sat, 2 Sep 2017 15:21:07 GMT",
"version": "v1"
}
] | 2017-09-05 | [
[
"Pálvölgyi",
"Dömötör",
""
]
] | We prove that for a triangulated plane graph it is NP-complete to determine its domination number and its power domination number. |
1509.08086 | Arvind Kumar | Arvind Kumar, Adarsh Anand, Pankaj Kumar Garg and Mohini Agarwal | Optimal Release Time Decision from Fuzzy Mathematical Programming
Perspective | 10 Pages. arXiv admin note: substantial overlap with text by other
authors
http://archive.org/stream/Software_Reliability_Assessment_with_OR_Applications/Software_Reliability_Assessment_with_OR_Applications_djvu.txt | International Journal of Pure and Applied Mathematics, Volume 103
No. 2 2015, 359-376 | 10.12732/ijpam.v103i2.19 | null | cs.AI math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Demand for high software reliability requires rigorous testing followed by
requirement of robust modeling techniques for software quality prediction. On
one side, firms have to steadily manage the reliability by testing it
vigorously, the optimal release time determination is their biggest concern. In
past many models have been developed and much research has been devoted towards
assessment of release time of software. However, majority of the work deals in
crisp study. This paper addresses the problem of release time prediction using
fuzzy Logic. Here we have formulated a Fuzzy release time problem considering
the cost of testing under the impact of warranty period. Results show that
fuzzy model has good adaptability.
| [
{
"created": "Sun, 27 Sep 2015 11:41:05 GMT",
"version": "v1"
}
] | 2015-09-30 | [
[
"Kumar",
"Arvind",
""
],
[
"Anand",
"Adarsh",
""
],
[
"Garg",
"Pankaj Kumar",
""
],
[
"Agarwal",
"Mohini",
""
]
] | Demand for high software reliability requires rigorous testing followed by requirement of robust modeling techniques for software quality prediction. On one side, firms have to steadily manage the reliability by testing it vigorously, the optimal release time determination is their biggest concern. In past many models have been developed and much research has been devoted towards assessment of release time of software. However, majority of the work deals in crisp study. This paper addresses the problem of release time prediction using fuzzy Logic. Here we have formulated a Fuzzy release time problem considering the cost of testing under the impact of warranty period. Results show that fuzzy model has good adaptability. |
2204.14213 | Dallas Card | Junshen K. Chen and Dallas Card and Dan Jurafsky | Modular Domain Adaptation | Findings of ACL (2022) | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Off-the-shelf models are widely used by computational social science
researchers to measure properties of text, such as sentiment. However, without
access to source data it is difficult to account for domain shift, which
represents a threat to validity. Here, we treat domain adaptation as a modular
process that involves separate model producers and model consumers, and show
how they can independently cooperate to facilitate more accurate measurements
of text. We introduce two lightweight techniques for this scenario, and
demonstrate that they reliably increase out-of-domain accuracy on four
multi-domain text classification datasets when used with linear and contextual
embedding models. We conclude with recommendations for model producers and
consumers, and release models and replication code to accompany this paper.
| [
{
"created": "Tue, 26 Apr 2022 22:08:58 GMT",
"version": "v1"
}
] | 2022-05-02 | [
[
"Chen",
"Junshen K.",
""
],
[
"Card",
"Dallas",
""
],
[
"Jurafsky",
"Dan",
""
]
] | Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment. However, without access to source data it is difficult to account for domain shift, which represents a threat to validity. Here, we treat domain adaptation as a modular process that involves separate model producers and model consumers, and show how they can independently cooperate to facilitate more accurate measurements of text. We introduce two lightweight techniques for this scenario, and demonstrate that they reliably increase out-of-domain accuracy on four multi-domain text classification datasets when used with linear and contextual embedding models. We conclude with recommendations for model producers and consumers, and release models and replication code to accompany this paper. |
2402.05467 | Guangyu Shen | Guangyu Shen, Siyuan Cheng, Kaiyuan Zhang, Guanhong Tao, Shengwei An,
Lu Yan, Zhuo Zhang, Shiqing Ma, Xiangyu Zhang | Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation
and Echopraxia | null | null | null | null | cs.AI cs.CL cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have become prevalent across diverse sectors,
transforming human life with their extraordinary reasoning and comprehension
abilities. As they find increased use in sensitive tasks, safety concerns have
gained widespread attention. Extensive efforts have been dedicated to aligning
LLMs with human moral principles to ensure their safe deployment. Despite their
potential, recent research indicates aligned LLMs are prone to specialized
jailbreaking prompts that bypass safety measures to elicit violent and harmful
content. The intrinsic discrete nature and substantial scale of contemporary
LLMs pose significant challenges in automatically generating diverse,
efficient, and potent jailbreaking prompts, representing a continuous obstacle.
In this paper, we introduce RIPPLE (Rapid Optimization via Subconscious
Exploitation and Echopraxia), a novel optimization-based method inspired by two
psychological concepts: subconsciousness and echopraxia, which describe the
processes of the mind that occur without conscious awareness and the
involuntary mimicry of actions, respectively. Evaluations across 6 open-source
LLMs and 4 commercial LLM APIs show RIPPLE achieves an average Attack Success
Rate of 91.5\%, outperforming five current methods by up to 47.0\% with an 8x
reduction in overhead. Furthermore, it displays significant transferability and
stealth, successfully evading established detection mechanisms. The code of our
work is available at
\url{https://github.com/SolidShen/RIPPLE_official/tree/official}
| [
{
"created": "Thu, 8 Feb 2024 07:56:49 GMT",
"version": "v1"
}
] | 2024-02-09 | [
[
"Shen",
"Guangyu",
""
],
[
"Cheng",
"Siyuan",
""
],
[
"Zhang",
"Kaiyuan",
""
],
[
"Tao",
"Guanhong",
""
],
[
"An",
"Shengwei",
""
],
[
"Yan",
"Lu",
""
],
[
"Zhang",
"Zhuo",
""
],
[
"Ma",
"Shiqing",
""
],
[
"Zhang",
"Xiangyu",
""
]
] | Large Language Models (LLMs) have become prevalent across diverse sectors, transforming human life with their extraordinary reasoning and comprehension abilities. As they find increased use in sensitive tasks, safety concerns have gained widespread attention. Extensive efforts have been dedicated to aligning LLMs with human moral principles to ensure their safe deployment. Despite their potential, recent research indicates aligned LLMs are prone to specialized jailbreaking prompts that bypass safety measures to elicit violent and harmful content. The intrinsic discrete nature and substantial scale of contemporary LLMs pose significant challenges in automatically generating diverse, efficient, and potent jailbreaking prompts, representing a continuous obstacle. In this paper, we introduce RIPPLE (Rapid Optimization via Subconscious Exploitation and Echopraxia), a novel optimization-based method inspired by two psychological concepts: subconsciousness and echopraxia, which describe the processes of the mind that occur without conscious awareness and the involuntary mimicry of actions, respectively. Evaluations across 6 open-source LLMs and 4 commercial LLM APIs show RIPPLE achieves an average Attack Success Rate of 91.5\%, outperforming five current methods by up to 47.0\% with an 8x reduction in overhead. Furthermore, it displays significant transferability and stealth, successfully evading established detection mechanisms. The code of our work is available at \url{https://github.com/SolidShen/RIPPLE_official/tree/official} |
2407.06293 | Xin Liu | Xin Liu, Xingchen Liu, Paul Witherell | A Framework for Simulating the Path-level Residual Stress in the Laser
Powder Bed Fusion Process | null | null | null | null | cs.CE physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Laser Powder Bed Fusion (LPBF) additive manufacturing has revolutionized
industries with its capability to create intricate and customized components.
The LPBF process uses moving heat sources to melt and solidify metal powders.
The fast melting and cooling leads to residual stress, which critically affects
the part quality. Currently, the computational intensity of accurately
simulating the residual stress on the path scale remains a significant
challenge, limiting our understanding of the LPBF processes.
This paper presents a framework for simulating the LPBF process residual
stress based on the path-level thermal history. Compared with the existing
approaches, the path-level simulation requires discretization only to capture
the scanning path rather than the details of the melt pools, thus requiring
less dense mesh and is more computationally efficient. We develop this
framework by introducing a new concept termed effective thermal strain to
capture the anisotropic thermal strain near and around the melt pool. We
validate our approach with the high-fidelity results from the literature. We
use the proposed approach to simulate various single-island scanning patterns
and layers with multiple full and trimmed islands. We further investigate the
influence of the path-level thermal history and the layer shape on the residual
stress by analyzing their simulation results.
| [
{
"created": "Wed, 10 Apr 2024 17:28:43 GMT",
"version": "v1"
}
] | 2024-07-10 | [
[
"Liu",
"Xin",
""
],
[
"Liu",
"Xingchen",
""
],
[
"Witherell",
"Paul",
""
]
] | Laser Powder Bed Fusion (LPBF) additive manufacturing has revolutionized industries with its capability to create intricate and customized components. The LPBF process uses moving heat sources to melt and solidify metal powders. The fast melting and cooling leads to residual stress, which critically affects the part quality. Currently, the computational intensity of accurately simulating the residual stress on the path scale remains a significant challenge, limiting our understanding of the LPBF processes. This paper presents a framework for simulating the LPBF process residual stress based on the path-level thermal history. Compared with the existing approaches, the path-level simulation requires discretization only to capture the scanning path rather than the details of the melt pools, thus requiring less dense mesh and is more computationally efficient. We develop this framework by introducing a new concept termed effective thermal strain to capture the anisotropic thermal strain near and around the melt pool. We validate our approach with the high-fidelity results from the literature. We use the proposed approach to simulate various single-island scanning patterns and layers with multiple full and trimmed islands. We further investigate the influence of the path-level thermal history and the layer shape on the residual stress by analyzing their simulation results. |
2405.18483 | Mengyi Shan | Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao, Tao Liu, Ifeoma Nwogu,
Guo-Jun Qi, and Mitch Hill | Towards Open Domain Text-Driven Synthesis of Multi-Person Motions | ECCV 2024. Project page: https://shanmy.github.io/Multi-Motion/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This work aims to generate natural and diverse group motions of multiple
humans from textual descriptions. While single-person text-to-motion generation
is extensively studied, it remains challenging to synthesize motions for more
than one or two subjects from in-the-wild prompts, mainly due to the lack of
available datasets. In this work, we curate human pose and motion datasets by
estimating pose information from large-scale image and video datasets. Our
models use a transformer-based diffusion framework that accommodates multiple
datasets with any number of subjects or frames. Experiments explore both
generation of multi-person static poses and generation of multi-person motion
sequences. To our knowledge, our method is the first to generate multi-subject
motion sequences with high diversity and fidelity from a large variety of
textual prompts.
| [
{
"created": "Tue, 28 May 2024 18:00:06 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jul 2024 07:55:43 GMT",
"version": "v2"
}
] | 2024-07-16 | [
[
"Shan",
"Mengyi",
""
],
[
"Dong",
"Lu",
""
],
[
"Han",
"Yutao",
""
],
[
"Yao",
"Yuan",
""
],
[
"Liu",
"Tao",
""
],
[
"Nwogu",
"Ifeoma",
""
],
[
"Qi",
"Guo-Jun",
""
],
[
"Hill",
"Mitch",
""
]
] | This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While single-person text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human pose and motion datasets by estimating pose information from large-scale image and video datasets. Our models use a transformer-based diffusion framework that accommodates multiple datasets with any number of subjects or frames. Experiments explore both generation of multi-person static poses and generation of multi-person motion sequences. To our knowledge, our method is the first to generate multi-subject motion sequences with high diversity and fidelity from a large variety of textual prompts. |
2401.09656 | Tan Chen | Tan Chen, Jintao Yan, Yuxuan Sun, Sheng Zhou, Deniz G\"und\"uz,
Zhisheng Niu | Mobility Accelerates Learning: Convergence Analysis on Hierarchical
Federated Learning in Vehicular Networks | Submitted to IEEE for possible publication | null | null | null | cs.LG cs.AI cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical federated learning (HFL) enables distributed training of models
across multiple devices with the help of several edge servers and a cloud edge
server in a privacy-preserving manner. In this paper, we consider HFL with
highly mobile devices, mainly targeting at vehicular networks. Through
convergence analysis, we show that mobility influences the convergence speed by
both fusing the edge data and shuffling the edge models. While mobility is
usually considered as a challenge from the perspective of communication, we
prove that it increases the convergence speed of HFL with edge-level
heterogeneous data, since more diverse data can be incorporated. Furthermore,
we demonstrate that a higher speed leads to faster convergence, since it
accelerates the fusion of data. Simulation results show that mobility increases
the model accuracy of HFL by up to 15.1% when training a convolutional neural
network on the CIFAR-10 dataset.
| [
{
"created": "Thu, 18 Jan 2024 00:09:54 GMT",
"version": "v1"
}
] | 2024-01-19 | [
[
"Chen",
"Tan",
""
],
[
"Yan",
"Jintao",
""
],
[
"Sun",
"Yuxuan",
""
],
[
"Zhou",
"Sheng",
""
],
[
"Gündüz",
"Deniz",
""
],
[
"Niu",
"Zhisheng",
""
]
] | Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner. In this paper, we consider HFL with highly mobile devices, mainly targeting at vehicular networks. Through convergence analysis, we show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. While mobility is usually considered as a challenge from the perspective of communication, we prove that it increases the convergence speed of HFL with edge-level heterogeneous data, since more diverse data can be incorporated. Furthermore, we demonstrate that a higher speed leads to faster convergence, since it accelerates the fusion of data. Simulation results show that mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network on the CIFAR-10 dataset. |
1006.1382 | Majid Fozunbal | Majid Fozunbal | On Regret of Parametric Mismatch in Minimum Mean Square Error Estimation | 5 Pages, 2 figures, International Symposium on Information Theory
(ISIT), June 2010 | null | null | HPL-2010-10 | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the effect of parametric mismatch in minimum mean square
error (MMSE) estimation. In particular, we consider the problem of estimating
the input signal from the output of an additive white Gaussian channel whose
gain is fixed, but unknown. The input distribution is known, and the estimation
process consists of two algorithms. First, a channel estimator blindly
estimates the channel gain using past observations. Second, a mismatched MMSE
estimator, optimized for the estimated channel gain, estimates the input
signal. We analyze the regret, i.e., the additional mean square error, that is
raised in this process. We derive upper-bounds on both absolute and relative
regrets. Bounds are expressed in terms of the Fisher information. We also study
regret for unbiased, efficient channel estimators, and derive a simple
trade-off between Fisher information and relative regret. This trade-off shows
that the product of a certain function of relative regret and Fisher
information equals the signal-to-noise ratio, independent of the input
distribution. The trade-off relation implies that higher Fisher information
results to smaller expected relative regret.
| [
{
"created": "Mon, 7 Jun 2010 21:47:09 GMT",
"version": "v1"
}
] | 2010-06-09 | [
[
"Fozunbal",
"Majid",
""
]
] | This paper studies the effect of parametric mismatch in minimum mean square error (MMSE) estimation. In particular, we consider the problem of estimating the input signal from the output of an additive white Gaussian channel whose gain is fixed, but unknown. The input distribution is known, and the estimation process consists of two algorithms. First, a channel estimator blindly estimates the channel gain using past observations. Second, a mismatched MMSE estimator, optimized for the estimated channel gain, estimates the input signal. We analyze the regret, i.e., the additional mean square error, that is raised in this process. We derive upper-bounds on both absolute and relative regrets. Bounds are expressed in terms of the Fisher information. We also study regret for unbiased, efficient channel estimators, and derive a simple trade-off between Fisher information and relative regret. This trade-off shows that the product of a certain function of relative regret and Fisher information equals the signal-to-noise ratio, independent of the input distribution. The trade-off relation implies that higher Fisher information results to smaller expected relative regret. |
2103.10668 | Ramin Shahbazi | Ramin Shahbazi, Rishab Sharma, Fatemeh H. Fard | API2Com: On the Improvement of Automatically Generated Code Comments
Using API Documentations | null | null | null | null | cs.SE cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Code comments can help in program comprehension and are considered as
important artifacts to help developers in software maintenance. However, the
comments are mostly missing or are outdated, specially in complex software
projects. As a result, several automatic comment generation models are
developed as a solution. The recent models explore the integration of external
knowledge resources such as Unified Modeling Language class diagrams to improve
the generated comments. In this paper, we propose API2Com, a model that
leverages the Application Programming Interface Documentations (API Docs) as a
knowledge resource for comment generation. The API Docs include the description
of the methods in more details and therefore, can provide better context in the
generated comments. The API Docs are used along with the code snippets and
Abstract Syntax Trees in our model. We apply the model on a large Java dataset
of over 130,000 methods and evaluate it using both Transformer and RNN-base
architectures. Interestingly, when API Docs are used, the performance increase
is negligible. We therefore run different experiments to reason about the
results. For methods that only contain one API, adding API Docs improves the
results by 4% BLEU score on average (BLEU score is an automatic evaluation
metric used in machine translation). However, as the number of APIs that are
used in a method increases, the performance of the model in generating comments
decreases due to long documentations used in the input. Our results confirm
that the API Docs can be useful in generating better comments, but, new
techniques are required to identify the most informative ones in a method
rather than using all documentations simultaneously.
| [
{
"created": "Fri, 19 Mar 2021 07:29:40 GMT",
"version": "v1"
}
] | 2021-03-22 | [
[
"Shahbazi",
"Ramin",
""
],
[
"Sharma",
"Rishab",
""
],
[
"Fard",
"Fatemeh H.",
""
]
] | Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex software projects. As a result, several automatic comment generation models are developed as a solution. The recent models explore the integration of external knowledge resources such as Unified Modeling Language class diagrams to improve the generated comments. In this paper, we propose API2Com, a model that leverages the Application Programming Interface Documentations (API Docs) as a knowledge resource for comment generation. The API Docs include the description of the methods in more details and therefore, can provide better context in the generated comments. The API Docs are used along with the code snippets and Abstract Syntax Trees in our model. We apply the model on a large Java dataset of over 130,000 methods and evaluate it using both Transformer and RNN-base architectures. Interestingly, when API Docs are used, the performance increase is negligible. We therefore run different experiments to reason about the results. For methods that only contain one API, adding API Docs improves the results by 4% BLEU score on average (BLEU score is an automatic evaluation metric used in machine translation). However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input. Our results confirm that the API Docs can be useful in generating better comments, but, new techniques are required to identify the most informative ones in a method rather than using all documentations simultaneously. |
2103.01843 | Nikolaus Demmel | Nikolaus Demmel, Christiane Sommer, Daniel Cremers, Vladyslav Usenko | Square Root Bundle Adjustment for Large-Scale Reconstruction | Accepted to CVPR 2021. Updated version corresponding to CVPR
camera-ready. Formatting changes and minor tweaks to fit page requirements | null | 10.1109/CVPR46437.2021.01155 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new formulation for the bundle adjustment problem which relies
on nullspace marginalization of landmark variables by QR decomposition. Our
approach, which we call square root bundle adjustment, is algebraically
equivalent to the commonly used Schur complement trick, improves the numeric
stability of computations, and allows for solving large-scale bundle adjustment
problems with single-precision floating-point numbers. We show in real-world
experiments with the BAL datasets that even in single precision the proposed
solver achieves on average equally accurate solutions compared to Schur
complement solvers using double precision. It runs significantly faster, but
can require larger amounts of memory on dense problems. The proposed
formulation relies on simple linear algebra operations and opens the way for
efficient implementations of bundle adjustment on hardware platforms optimized
for single-precision linear algebra processing.
| [
{
"created": "Tue, 2 Mar 2021 16:26:20 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Mar 2021 23:50:04 GMT",
"version": "v2"
}
] | 2021-11-23 | [
[
"Demmel",
"Nikolaus",
""
],
[
"Sommer",
"Christiane",
""
],
[
"Cremers",
"Daniel",
""
],
[
"Usenko",
"Vladyslav",
""
]
] | We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing. |
1212.6883 | Vivek Nittoor | Vivek S. Nittoor, Reiji Suda | Partition Parameters for Girth Maximum (m, r) BTUs | null | null | null | null | cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the calculation of the optimal partition parameters such
that the girth maximum (m, r) Balanced Tanner Unit lies in family of BTUs
specified by them using a series of proved results and thus creates a framework
for specifying a search problem for finding the girth maximum (m, r) BTU.
Several open questions for girth maximum (m, r) BTU have been raised.
| [
{
"created": "Mon, 31 Dec 2012 12:53:32 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Jan 2013 15:06:22 GMT",
"version": "v2"
}
] | 2013-01-23 | [
[
"Nittoor",
"Vivek S.",
""
],
[
"Suda",
"Reiji",
""
]
] | This paper describes the calculation of the optimal partition parameters such that the girth maximum (m, r) Balanced Tanner Unit lies in family of BTUs specified by them using a series of proved results and thus creates a framework for specifying a search problem for finding the girth maximum (m, r) BTU. Several open questions for girth maximum (m, r) BTU have been raised. |
1907.12182 | Zhenlong Li Dr. | Zhenlong Li | Geospatial Big Data Handling with High Performance Computing: Current
Approaches and Future Directions | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geospatial big data plays a major role in the era of big data, as most data
today are inherently spatial, collected with ubiquitous location-aware sensors.
Efficiently collecting, managing, storing, and analyzing geospatial data
streams enables development of new decision-support systems and provides
unprecedented opportunities for business, science, and engineering. However,
handling the "Vs" (volume, variety, velocity, veracity, and value) of big data
is a challenging task. This is especially true for geospatial big data, since
the massive datasets must be analyzed in the context of space and time. High
performance computing (HPC) provides an essential solution to geospatial big
data challenges. This chapter first summarizes four key aspects for handling
geospatial big data with HPC and then briefly reviews existing HPC-related
platforms and tools for geospatial big data processing. Lastly, future research
directions in using HPC for geospatial big data handling are discussed.
| [
{
"created": "Mon, 29 Jul 2019 02:37:43 GMT",
"version": "v1"
}
] | 2019-07-30 | [
[
"Li",
"Zhenlong",
""
]
] | Geospatial big data plays a major role in the era of big data, as most data today are inherently spatial, collected with ubiquitous location-aware sensors. Efficiently collecting, managing, storing, and analyzing geospatial data streams enables development of new decision-support systems and provides unprecedented opportunities for business, science, and engineering. However, handling the "Vs" (volume, variety, velocity, veracity, and value) of big data is a challenging task. This is especially true for geospatial big data, since the massive datasets must be analyzed in the context of space and time. High performance computing (HPC) provides an essential solution to geospatial big data challenges. This chapter first summarizes four key aspects for handling geospatial big data with HPC and then briefly reviews existing HPC-related platforms and tools for geospatial big data processing. Lastly, future research directions in using HPC for geospatial big data handling are discussed. |
1902.09782 | Qingyan Duan | Qingyan Duan and Lei Zhang | BoostGAN for Occlusive Profile Face Frontalization and Recognition | 9 pages, 7 figures, 7 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are many facts affecting human face recognition, such as pose,
occlusion, illumination, age, etc. First and foremost are large pose and
occlusion problems, which can even result in more than 10% performance
degradation. Pose-invariant feature representation and face frontalization with
generative adversarial networks (GAN) have been widely used to solve the pose
problem. However, the synthesis and recognition of occlusive but profile faces
is still an uninvestigated problem. To address this issue, in this paper, we
aim to contribute an effective solution on how to recognize occlusive but
profile faces, even with facial keypoint region (e.g. eyes, nose, etc.)
corrupted. Specifically, we propose a boosting Generative Adversarial Network
(BoostGAN) for de-occlusion, frontalization, and recognition of faces. Upon the
assumption that facial occlusion is partial and incomplete, multiple patch
occluded images are fed as inputs for knowledge boosting, such as identity and
texture information. A new aggregation structure composed of a deep GAN for
coarse face synthesis and a shallow boosting net for fine face generation is
further designed. Exhaustive experiments demonstrate that the proposed approach
not only presents clear perceptual photo-realistic results but also shows
state-of-the-art recognition performance for occlusive but profile faces.
| [
{
"created": "Tue, 26 Feb 2019 07:59:47 GMT",
"version": "v1"
}
] | 2019-02-27 | [
[
"Duan",
"Qingyan",
""
],
[
"Zhang",
"Lei",
""
]
] | There are many facts affecting human face recognition, such as pose, occlusion, illumination, age, etc. First and foremost are large pose and occlusion problems, which can even result in more than 10% performance degradation. Pose-invariant feature representation and face frontalization with generative adversarial networks (GAN) have been widely used to solve the pose problem. However, the synthesis and recognition of occlusive but profile faces is still an uninvestigated problem. To address this issue, in this paper, we aim to contribute an effective solution on how to recognize occlusive but profile faces, even with facial keypoint region (e.g. eyes, nose, etc.) corrupted. Specifically, we propose a boosting Generative Adversarial Network (BoostGAN) for de-occlusion, frontalization, and recognition of faces. Upon the assumption that facial occlusion is partial and incomplete, multiple patch occluded images are fed as inputs for knowledge boosting, such as identity and texture information. A new aggregation structure composed of a deep GAN for coarse face synthesis and a shallow boosting net for fine face generation is further designed. Exhaustive experiments demonstrate that the proposed approach not only presents clear perceptual photo-realistic results but also shows state-of-the-art recognition performance for occlusive but profile faces. |
2212.05560 | Priya Shukla | Ankit Kumar, Priya Shukla, Vandana Kushwaha and G.C. Nandi | Context-aware 6D Pose Estimation of Known Objects using RGB-D data | null | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by/4.0/ | 6D object pose estimation has been a research topic in the field of computer
vision and robotics. Many modern world applications like robot grasping,
manipulation, autonomous navigation etc, require the correct pose of objects
present in a scene to perform their specific task. It becomes even harder when
the objects are placed in a cluttered scene and the level of occlusion is high.
Prior works have tried to overcome this problem but could not achieve accuracy
that can be considered reliable in real-world applications. In this paper, we
present an architecture that, unlike prior work, is context-aware. It utilizes
the context information available to us about the objects. Our proposed
architecture treats the objects separately according to their types i.e;
symmetric and non-symmetric. A deeper estimator and refiner network pair is
used for non-symmetric objects as compared to symmetric due to their intrinsic
differences. Our experiments show an enhancement in the accuracy of about 3.2%
over the LineMOD dataset, which is considered a benchmark for pose estimation
in the occluded and cluttered scenes, against the prior state-of-the-art
DenseFusion. Our results also show that the inference time we got is sufficient
for real-time usage.
| [
{
"created": "Sun, 11 Dec 2022 18:01:01 GMT",
"version": "v1"
}
] | 2022-12-13 | [
[
"Kumar",
"Ankit",
""
],
[
"Shukla",
"Priya",
""
],
[
"Kushwaha",
"Vandana",
""
],
[
"Nandi",
"G. C.",
""
]
] | 6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects are placed in a cluttered scene and the level of occlusion is high. Prior works have tried to overcome this problem but could not achieve accuracy that can be considered reliable in real-world applications. In this paper, we present an architecture that, unlike prior work, is context-aware. It utilizes the context information available to us about the objects. Our proposed architecture treats the objects separately according to their types i.e; symmetric and non-symmetric. A deeper estimator and refiner network pair is used for non-symmetric objects as compared to symmetric due to their intrinsic differences. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset, which is considered a benchmark for pose estimation in the occluded and cluttered scenes, against the prior state-of-the-art DenseFusion. Our results also show that the inference time we got is sufficient for real-time usage. |
1606.05943 | EPTCS | Roly Perera (University of Glasgow), Julien Lange (Imperial College
London), Simon J. Gay (University of Glasgow) | Multiparty Compatibility for Concurrent Objects | In Proceedings PLACES 2016, arXiv:1606.05403 | EPTCS 211, 2016, pp. 73-82 | 10.4204/EPTCS.211.8 | null | cs.PL cs.DC cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objects and actors are communicating state machines, offering and consuming
different services at different points in their lifecycle. Two complementary
challenges arise when programming such systems. When objects interact, their
state machines must be "compatible", so that services are requested only when
they are available. Dually, when objects refine other objects, their state
machines must be "compliant", so that services are honoured whenever they are
promised.
In this paper we show how the idea of multiparty compatibility from the
session types literature can be applied to both of these problems. We present
an untyped language in which concurrent objects are checked automatically for
compatibility and compliance. For simple objects, checking can be exhaustive
and has the feel of a type system. More complex objects can be partially
validated via test cases, leading to a methodology closer to continuous
testing. Our proof-of-concept implementation is limited in some important
respects, but demonstrates the potential value of the approach and the
relationship to existing software development practices.
| [
{
"created": "Mon, 20 Jun 2016 01:09:44 GMT",
"version": "v1"
}
] | 2016-06-21 | [
[
"Perera",
"Roly",
"",
"University of Glasgow"
],
[
"Lange",
"Julien",
"",
"Imperial College\n London"
],
[
"Gay",
"Simon J.",
"",
"University of Glasgow"
]
] | Objects and actors are communicating state machines, offering and consuming different services at different points in their lifecycle. Two complementary challenges arise when programming such systems. When objects interact, their state machines must be "compatible", so that services are requested only when they are available. Dually, when objects refine other objects, their state machines must be "compliant", so that services are honoured whenever they are promised. In this paper we show how the idea of multiparty compatibility from the session types literature can be applied to both of these problems. We present an untyped language in which concurrent objects are checked automatically for compatibility and compliance. For simple objects, checking can be exhaustive and has the feel of a type system. More complex objects can be partially validated via test cases, leading to a methodology closer to continuous testing. Our proof-of-concept implementation is limited in some important respects, but demonstrates the potential value of the approach and the relationship to existing software development practices. |
2211.14672 | Mohammad Javad Sojdeh | Mohammad Javad Sojdeh, Mehdi Letafati, Seyed Pooya Shariatpanahi,
Babak Hossein Khalaj | Multi-Transmitter Coded Caching with Secure Delivery over Linear
Networks -- Extended Version | null | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper, we consider multiple cache-enabled end-users connected to
multiple transmitters through a linear network. We also prevent a totally
passive eavesdropper, who sniffs the packets in the delivery phase, from
obtaining any information about the original files in cache-aided networks.
Three different secure centralized multi-transmitter coded caching scenarios
namely, secure multi-transmitter coded caching, secure multi-transmitter coded
caching with reduced subpacketization, and secure multi-transmitter coded
caching with reduced feedback, are considered and closed-form coding delay and
secret shared key storage expressions are provided. As our security guarantee,
we show that the delivery phase does not reveal any information to the
eavesdropper using the mutual information metric. Moreover, we investigate the
secure decentralized multi-transmitter coded caching scenario, in which there
is no cooperation between the clients and transmitters during the cache content
placement phase and study its performance compared to the centralized scheme.
We analyze the system's performance in terms of Coding Delay and guarantee the
security of our presented schemes using the Mutual Information metric.
Numerical evaluations verify that security incurs a negligible cost in terms of
memory usage when the number of files and users are scaled up, in both
centralized and decentralized scenarios. Also, we numerically show that by
increasing the number of files and users, the secure coding delay of
centralized and decentralized schemes became asymptotically equal.
| [
{
"created": "Sat, 26 Nov 2022 21:57:45 GMT",
"version": "v1"
}
] | 2022-11-29 | [
[
"Sojdeh",
"Mohammad Javad",
""
],
[
"Letafati",
"Mehdi",
""
],
[
"Shariatpanahi",
"Seyed Pooya",
""
],
[
"Khalaj",
"Babak Hossein",
""
]
] | In this paper, we consider multiple cache-enabled end-users connected to multiple transmitters through a linear network. We also prevent a totally passive eavesdropper, who sniffs the packets in the delivery phase, from obtaining any information about the original files in cache-aided networks. Three different secure centralized multi-transmitter coded caching scenarios namely, secure multi-transmitter coded caching, secure multi-transmitter coded caching with reduced subpacketization, and secure multi-transmitter coded caching with reduced feedback, are considered and closed-form coding delay and secret shared key storage expressions are provided. As our security guarantee, we show that the delivery phase does not reveal any information to the eavesdropper using the mutual information metric. Moreover, we investigate the secure decentralized multi-transmitter coded caching scenario, in which there is no cooperation between the clients and transmitters during the cache content placement phase and study its performance compared to the centralized scheme. We analyze the system's performance in terms of Coding Delay and guarantee the security of our presented schemes using the Mutual Information metric. Numerical evaluations verify that security incurs a negligible cost in terms of memory usage when the number of files and users are scaled up, in both centralized and decentralized scenarios. Also, we numerically show that by increasing the number of files and users, the secure coding delay of centralized and decentralized schemes became asymptotically equal. |
2311.06204 | Md. Motahar Mahtab | Md. Motahar Mahtab, Monirul Haque, Mehedi Hasan and Farig Sadeque | BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection
on Bangla Clickbait Dataset | 8 pages, 3 figures, 5 tables, published in Recent Advances in Natural
Language Processing 2023 | null | 10.26615/978-954-452-092-2_081 | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Intentionally luring readers to click on a particular content by exploiting
their curiosity defines a title as clickbait. Although several studies focused
on detecting clickbait titles in English articles, low resource language like
Bangla has not been given adequate attention. To tackle clickbait titles in
Bangla, we have constructed the first Bangla clickbait detection dataset
containing 15,056 labeled news articles and 65,406 unlabelled news articles
extracted from clickbait dense news sites. Each article has been labeled by
three expert linguists and includes an article's title, body, and other
metadata. By incorporating labeled and unlabelled data, we finetune a
pretrained Bangla transformer model in an adversarial fashion using Semi
Supervised Generative Adversarial Networks (SS GANs). The proposed model acts
as a good baseline for this dataset, outperforming traditional neural network
models (LSTM, GRU, CNN) and linguistic feature based models. We expect that
this dataset and the detailed analysis and comparison of these clickbait
detection models will provide a fundamental basis for future research into
detecting clickbait titles in Bengali articles. We have released the
corresponding code and dataset.
| [
{
"created": "Fri, 10 Nov 2023 17:38:46 GMT",
"version": "v1"
}
] | 2023-11-13 | [
[
"Mahtab",
"Md. Motahar",
""
],
[
"Haque",
"Monirul",
""
],
[
"Hasan",
"Mehedi",
""
],
[
"Sadeque",
"Farig",
""
]
] | Intentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low resource language like Bangla has not been given adequate attention. To tackle clickbait titles in Bangla, we have constructed the first Bangla clickbait detection dataset containing 15,056 labeled news articles and 65,406 unlabelled news articles extracted from clickbait dense news sites. Each article has been labeled by three expert linguists and includes an article's title, body, and other metadata. By incorporating labeled and unlabelled data, we finetune a pretrained Bangla transformer model in an adversarial fashion using Semi Supervised Generative Adversarial Networks (SS GANs). The proposed model acts as a good baseline for this dataset, outperforming traditional neural network models (LSTM, GRU, CNN) and linguistic feature based models. We expect that this dataset and the detailed analysis and comparison of these clickbait detection models will provide a fundamental basis for future research into detecting clickbait titles in Bengali articles. We have released the corresponding code and dataset. |
1702.04956 | Aaron Gerow | Aaron Gerow, Mingyang Zhou, Stan Matwin, Feng Shi | Reflexive Regular Equivalence for Bipartite Data | A condensed version of this paper will appear in Proceedings of the
30th Canadian Conference on Artificial Intelligence, Edmonton, Alberta,
Canada | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bipartite data is common in data engineering and brings unique challenges,
particularly when it comes to clustering tasks that impose on strong structural
assumptions. This work presents an unsupervised method for assessing similarity
in bipartite data. Similar to some co-clustering methods, the method is based
on regular equivalence in graphs. The algorithm uses spectral properties of a
bipartite adjacency matrix to estimate similarity in both dimensions. The
method is reflexive in that similarity in one dimension is used to inform
similarity in the other. Reflexive regular equivalence can also use the
structure of transitivities -- in a network sense -- the contribution of which
is controlled by the algorithm's only free-parameter, $\alpha$. The method is
completely unsupervised and can be used to validate assumptions of
co-similarity, which are required but often untested, in co-clustering
analyses. Three variants of the method with different normalizations are tested
on synthetic data. The method is found to be robust to noise and well-suited to
asymmetric co-similar structure, making it particularly informative for cluster
analysis and recommendation in bipartite data of unknown structure. In
experiments, the convergence and speed of the algorithm are found to be stable
for different levels of noise. Real-world data from a network of malaria genes
are analyzed, where the similarity produced by the reflexive method is shown to
out-perform other measures' ability to correctly classify genes.
| [
{
"created": "Thu, 16 Feb 2017 13:29:30 GMT",
"version": "v1"
}
] | 2017-02-17 | [
[
"Gerow",
"Aaron",
""
],
[
"Zhou",
"Mingyang",
""
],
[
"Matwin",
"Stan",
""
],
[
"Shi",
"Feng",
""
]
] | Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose on strong structural assumptions. This work presents an unsupervised method for assessing similarity in bipartite data. Similar to some co-clustering methods, the method is based on regular equivalence in graphs. The algorithm uses spectral properties of a bipartite adjacency matrix to estimate similarity in both dimensions. The method is reflexive in that similarity in one dimension is used to inform similarity in the other. Reflexive regular equivalence can also use the structure of transitivities -- in a network sense -- the contribution of which is controlled by the algorithm's only free-parameter, $\alpha$. The method is completely unsupervised and can be used to validate assumptions of co-similarity, which are required but often untested, in co-clustering analyses. Three variants of the method with different normalizations are tested on synthetic data. The method is found to be robust to noise and well-suited to asymmetric co-similar structure, making it particularly informative for cluster analysis and recommendation in bipartite data of unknown structure. In experiments, the convergence and speed of the algorithm are found to be stable for different levels of noise. Real-world data from a network of malaria genes are analyzed, where the similarity produced by the reflexive method is shown to out-perform other measures' ability to correctly classify genes. |
cs/0312001 | Martin Lisewski | A. M. Lisewski | The concept of strong and weak virtual reality | 17 pages; several edits in v2 | Minds and Machines, 16 (2), 201-219 (2006) | 10.1007/s11023-006-9037-z | null | cs.LO nlin.AO physics.comp-ph | null | We approach the virtual reality phenomenon by studying its relationship to
set theory, and we investigate the case where this is done using the
wellfoundedness property of sets. Our hypothesis is that non-wellfounded sets
(hypersets) give rise to a different quality of virtual reality than do
familiar wellfounded sets. We initially provide an alternative approach to
virtual reality based on Sommerhoff's idea of first and second order
self-awareness; both categories of self-awareness are considered as necessary
conditions for consciousness in terms of higher cognitive functions. We then
introduce a representation of first and second order self-awareness through
sets, and assume that these sets, which we call events, originally form a
collection of wellfounded sets. Strong virtual reality characterizes virtual
reality environments which have the limited capacity to create only events
associated with wellfounded sets. In contrast, the more general concept of weak
virtual reality characterizes collections of virtual reality mediated events
altogether forming an entirety larger than any collection of wellfounded sets.
By giving reference to Aczel's hyperset theory we indicate that this definition
is not empty, because hypersets encompass wellfounded sets already. Moreover,
we argue that weak virtual reality could be realized in human history through
continued progress in computer technology. Finally, we reformulate our
characterization into a more general framework, and use Baltag's Structural
Theory of Sets (STS) to show that within this general hyperset theory
Sommerhoff's first and second order self-awareness as well as both concepts of
virtual reality admit a consistent mathematical representation.
| [
{
"created": "Sat, 29 Nov 2003 14:08:56 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Mar 2006 20:21:55 GMT",
"version": "v2"
},
{
"created": "Thu, 30 Mar 2006 22:38:13 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Lisewski",
"A. M.",
""
]
] | We approach the virtual reality phenomenon by studying its relationship to set theory, and we investigate the case where this is done using the wellfoundedness property of sets. Our hypothesis is that non-wellfounded sets (hypersets) give rise to a different quality of virtual reality than do familiar wellfounded sets. We initially provide an alternative approach to virtual reality based on Sommerhoff's idea of first and second order self-awareness; both categories of self-awareness are considered as necessary conditions for consciousness in terms of higher cognitive functions. We then introduce a representation of first and second order self-awareness through sets, and assume that these sets, which we call events, originally form a collection of wellfounded sets. Strong virtual reality characterizes virtual reality environments which have the limited capacity to create only events associated with wellfounded sets. In contrast, the more general concept of weak virtual reality characterizes collections of virtual reality mediated events altogether forming an entirety larger than any collection of wellfounded sets. By giving reference to Aczel's hyperset theory we indicate that this definition is not empty, because hypersets encompass wellfounded sets already. Moreover, we argue that weak virtual reality could be realized in human history through continued progress in computer technology. Finally, we reformulate our characterization into a more general framework, and use Baltag's Structural Theory of Sets (STS) to show that within this general hyperset theory Sommerhoff's first and second order self-awareness as well as both concepts of virtual reality admit a consistent mathematical representation. |
2205.09121 | Mahsa Yousefi | Mahsa Yousefi, Angeles Martinez | On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning | null | null | null | null | cs.LG math.OC | http://creativecommons.org/licenses/by/4.0/ | While first-order methods are popular for solving optimization problems that
arise in large-scale deep learning problems, they come with some acute
deficiencies. To diminish such shortcomings, there has been recent interest in
applying second-order methods such as quasi-Newton based methods which
construct Hessians approximations using only gradient information. The main
focus of our work is to study the behaviour of stochastic quasi-Newton
algorithms for training deep neural networks. We have analyzed the performance
of two well-known quasi-Newton updates, the limited memory
Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the Symmetric Rank One (SR1). This
study fills a gap concerning the real performance of both updates and analyzes
whether more efficient training is obtained when using the more robust BFGS
update or the cheaper SR1 formula which allows for indefinite Hessian
approximations and thus can potentially help to better navigate the
pathological saddle points present in the non-convex loss functions found in
deep learning. We present and discuss the results of an extensive experimental
study which includes the effect of batch normalization and network's
architecture, the limited memory parameter, the batch size and the type of
sampling strategy. we show that stochastic quasi-Newton optimizers are
efficient and able to outperform in some instances the well-known first-order
Adam optimizer run with the optimal combination of its numerous
hyperparameters.
| [
{
"created": "Wed, 18 May 2022 20:53:58 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Oct 2023 14:44:35 GMT",
"version": "v2"
}
] | 2023-10-05 | [
[
"Yousefi",
"Mahsa",
""
],
[
"Martinez",
"Angeles",
""
]
] | While first-order methods are popular for solving optimization problems that arise in large-scale deep learning problems, they come with some acute deficiencies. To diminish such shortcomings, there has been recent interest in applying second-order methods such as quasi-Newton based methods which construct Hessians approximations using only gradient information. The main focus of our work is to study the behaviour of stochastic quasi-Newton algorithms for training deep neural networks. We have analyzed the performance of two well-known quasi-Newton updates, the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the Symmetric Rank One (SR1). This study fills a gap concerning the real performance of both updates and analyzes whether more efficient training is obtained when using the more robust BFGS update or the cheaper SR1 formula which allows for indefinite Hessian approximations and thus can potentially help to better navigate the pathological saddle points present in the non-convex loss functions found in deep learning. We present and discuss the results of an extensive experimental study which includes the effect of batch normalization and network's architecture, the limited memory parameter, the batch size and the type of sampling strategy. we show that stochastic quasi-Newton optimizers are efficient and able to outperform in some instances the well-known first-order Adam optimizer run with the optimal combination of its numerous hyperparameters. |
2307.07134 | Zheng Gong | Qi Liu, Zheng Gong, Zhenya Huang, Chuanren Liu, Hengshu Zhu, Zhi Li,
Enhong Chen and Hui Xiong | Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning algorithms have become ubiquitous in a number of
applications (e.g. image classification). However, due to the insufficient
measurement of traditional metrics (e.g. the coarse-grained Accuracy of each
classifier), substantial gaps are usually observed between the real-world
performance of these algorithms and their scores in standardized evaluations.
In this paper, inspired by the psychometric theories from human measurement, we
propose a task-agnostic evaluation framework Camilla, where a multi-dimensional
diagnostic metric Ability is defined for collaboratively measuring the
multifaceted strength of each machine learning algorithm. Specifically, given
the response logs from different algorithms to data samples, we leverage
cognitive diagnosis assumptions and neural networks to learn the complex
interactions among algorithms, samples and the skills (explicitly or implicitly
pre-defined) of each sample. In this way, both the abilities of each algorithm
on multiple skills and some of the sample factors (e.g. sample difficulty) can
be simultaneously quantified. We conduct extensive experiments with hundreds of
machine learning algorithms on four public datasets, and our experimental
results demonstrate that Camilla not only can capture the pros and cons of each
algorithm more precisely, but also outperforms state-of-the-art baselines on
the metric reliability, rank consistency and rank stability.
| [
{
"created": "Fri, 14 Jul 2023 03:15:56 GMT",
"version": "v1"
}
] | 2023-07-17 | [
[
"Liu",
"Qi",
""
],
[
"Gong",
"Zheng",
""
],
[
"Huang",
"Zhenya",
""
],
[
"Liu",
"Chuanren",
""
],
[
"Zhu",
"Hengshu",
""
],
[
"Li",
"Zhi",
""
],
[
"Chen",
"Enhong",
""
],
[
"Xiong",
"Hui",
""
]
] | Machine learning algorithms have become ubiquitous in a number of applications (e.g. image classification). However, due to the insufficient measurement of traditional metrics (e.g. the coarse-grained Accuracy of each classifier), substantial gaps are usually observed between the real-world performance of these algorithms and their scores in standardized evaluations. In this paper, inspired by the psychometric theories from human measurement, we propose a task-agnostic evaluation framework Camilla, where a multi-dimensional diagnostic metric Ability is defined for collaboratively measuring the multifaceted strength of each machine learning algorithm. Specifically, given the response logs from different algorithms to data samples, we leverage cognitive diagnosis assumptions and neural networks to learn the complex interactions among algorithms, samples and the skills (explicitly or implicitly pre-defined) of each sample. In this way, both the abilities of each algorithm on multiple skills and some of the sample factors (e.g. sample difficulty) can be simultaneously quantified. We conduct extensive experiments with hundreds of machine learning algorithms on four public datasets, and our experimental results demonstrate that Camilla not only can capture the pros and cons of each algorithm more precisely, but also outperforms state-of-the-art baselines on the metric reliability, rank consistency and rank stability. |
2306.15898 | Marzieh Haghighi | Marzieh Haghighi, Mario C. Cruz, Erin Weisbart, Beth A. Cimini, Avtar
Singh, Julia Bauman, Maria E. Lozada, Sanam L. Kavari, James T. Neal, Paul C.
Blainey, Anne E. Carpenter and Shantanu Singh | Pseudo-Labeling Enhanced by Privileged Information and Its Application
to In Situ Sequencing Images | This paper has been accepted for publication at IJCAI 2023 | Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence (IJCAI), Main Track, Pages 4775-4784, 2023 | 10.24963/ijcai.2023/531 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Various strategies for label-scarce object detection have been explored by
the computer vision research community. These strategies mainly rely on
assumptions that are specific to natural images and not directly applicable to
the biological and biomedical vision domains. For example, most semi-supervised
learning strategies rely on a small set of labeled data as a confident source
of ground truth. In many biological vision applications, however, the ground
truth is unknown and indirect information might be available in the form of
noisy estimations or orthogonal evidence. In this work, we frame a crucial
problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing
(ISS) images - as a semi-supervised object detection (SSOD) problem. Our
proposed framework incorporates additional available sources of information
into a semi-supervised learning framework in the form of privileged
information. The privileged information is incorporated into the teacher's
pseudo-labeling in a teacher-student self-training iteration. Although the
available privileged information could be data domain specific, we have
introduced a general strategy of pseudo-labeling enhanced by privileged
information (PLePI) and exemplified the concept using ISS images, as well on
the COCO benchmark using extra evidence provided by CLIP.
| [
{
"created": "Wed, 28 Jun 2023 03:44:42 GMT",
"version": "v1"
}
] | 2023-09-25 | [
[
"Haghighi",
"Marzieh",
""
],
[
"Cruz",
"Mario C.",
""
],
[
"Weisbart",
"Erin",
""
],
[
"Cimini",
"Beth A.",
""
],
[
"Singh",
"Avtar",
""
],
[
"Bauman",
"Julia",
""
],
[
"Lozada",
"Maria E.",
""
],
[
"Kavari",
"Sanam L.",
""
],
[
"Neal",
"James T.",
""
],
[
"Blainey",
"Paul C.",
""
],
[
"Carpenter",
"Anne E.",
""
],
[
"Singh",
"Shantanu",
""
]
] | Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to natural images and not directly applicable to the biological and biomedical vision domains. For example, most semi-supervised learning strategies rely on a small set of labeled data as a confident source of ground truth. In many biological vision applications, however, the ground truth is unknown and indirect information might be available in the form of noisy estimations or orthogonal evidence. In this work, we frame a crucial problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing (ISS) images - as a semi-supervised object detection (SSOD) problem. Our proposed framework incorporates additional available sources of information into a semi-supervised learning framework in the form of privileged information. The privileged information is incorporated into the teacher's pseudo-labeling in a teacher-student self-training iteration. Although the available privileged information could be data domain specific, we have introduced a general strategy of pseudo-labeling enhanced by privileged information (PLePI) and exemplified the concept using ISS images, as well on the COCO benchmark using extra evidence provided by CLIP. |
1604.01595 | Kazuyuki Asada | Kazuyuki Asada and Naoki Kobayashi | On Word and Frontier Languages of Unsafe Higher-Order Grammars | null | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Higher-order grammars are extensions of regular and context-free grammars,
where non-terminals may take parameters. They have been extensively studied in
1980's, and restudied recently in the context of model checking and program
verification. We show that the class of unsafe order-(n+1) word languages
coincides with the class of frontier languages of unsafe order-n tree
languages. We use intersection types for transforming an order-(n+1) word
grammar to a corresponding order-n tree grammar. The result has been proved for
safe languages by Damm in 1982, but it has been open for unsafe languages, to
our knowledge. Various known results on higher-order grammars can be obtained
as almost immediate corollaries of our result.
| [
{
"created": "Wed, 6 Apr 2016 12:47:52 GMT",
"version": "v1"
},
{
"created": "Mon, 16 May 2016 11:49:15 GMT",
"version": "v2"
},
{
"created": "Fri, 20 May 2016 06:43:01 GMT",
"version": "v3"
}
] | 2016-05-23 | [
[
"Asada",
"Kazuyuki",
""
],
[
"Kobayashi",
"Naoki",
""
]
] | Higher-order grammars are extensions of regular and context-free grammars, where non-terminals may take parameters. They have been extensively studied in 1980's, and restudied recently in the context of model checking and program verification. We show that the class of unsafe order-(n+1) word languages coincides with the class of frontier languages of unsafe order-n tree languages. We use intersection types for transforming an order-(n+1) word grammar to a corresponding order-n tree grammar. The result has been proved for safe languages by Damm in 1982, but it has been open for unsafe languages, to our knowledge. Various known results on higher-order grammars can be obtained as almost immediate corollaries of our result. |
0805.0120 | Stephen Vavasis | Michael Biggs, Ali Ghodsi, Stephen Vavasis | Nonnegative Matrix Factorization via Rank-One Downdate | null | null | null | null | cs.IR cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nonnegative matrix factorization (NMF) was popularized as a tool for data
mining by Lee and Seung in 1999. NMF attempts to approximate a matrix with
nonnegative entries by a product of two low-rank matrices, also with
nonnegative entries. We propose an algorithm called rank-one downdate (R1D) for
computing a NMF that is partly motivated by singular value decomposition. This
algorithm computes the dominant singular values and vectors of adaptively
determined submatrices of a matrix. On each iteration, R1D extracts a rank-one
submatrix from the dataset according to an objective function. We establish a
theoretical result that maximizing this objective function corresponds to
correctly classifying articles in a nearly separable corpus. We also provide
computational experiments showing the success of this method in identifying
features in realistic datasets.
| [
{
"created": "Thu, 1 May 2008 17:59:44 GMT",
"version": "v1"
}
] | 2008-05-02 | [
[
"Biggs",
"Michael",
""
],
[
"Ghodsi",
"Ali",
""
],
[
"Vavasis",
"Stephen",
""
]
] | Nonnegative matrix factorization (NMF) was popularized as a tool for data mining by Lee and Seung in 1999. NMF attempts to approximate a matrix with nonnegative entries by a product of two low-rank matrices, also with nonnegative entries. We propose an algorithm called rank-one downdate (R1D) for computing a NMF that is partly motivated by singular value decomposition. This algorithm computes the dominant singular values and vectors of adaptively determined submatrices of a matrix. On each iteration, R1D extracts a rank-one submatrix from the dataset according to an objective function. We establish a theoretical result that maximizing this objective function corresponds to correctly classifying articles in a nearly separable corpus. We also provide computational experiments showing the success of this method in identifying features in realistic datasets. |
2402.12712 | Shitao Tang | Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang,
Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, Rakesh Ranjan | MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for
Single or Sparse-view 3D Object Reconstruction | 3D generation, project page: https://mvdiffusion-plusplus.github.io/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper presents a neural architecture MVDiffusion++ for 3D object
reconstruction that synthesizes dense and high-resolution views of an object
given one or a few images without camera poses. MVDiffusion++ achieves superior
flexibility and scalability with two surprisingly simple ideas: 1) A
``pose-free architecture'' where standard self-attention among 2D latent
features learns 3D consistency across an arbitrary number of conditional and
generation views without explicitly using camera pose information; and 2) A
``view dropout strategy'' that discards a substantial number of output views
during training, which reduces the training-time memory footprint and enables
dense and high-resolution view synthesis at test time. We use the Objaverse for
training and the Google Scanned Objects for evaluation with standard novel view
synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly
outperforms the current state of the arts. We also demonstrate a text-to-3D
application example by combining MVDiffusion++ with a text-to-image generative
model. The project page is at https://mvdiffusion-plusplus.github.io.
| [
{
"created": "Tue, 20 Feb 2024 04:25:57 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Mar 2024 17:58:05 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Apr 2024 04:11:58 GMT",
"version": "v3"
}
] | 2024-05-01 | [
[
"Tang",
"Shitao",
""
],
[
"Chen",
"Jiacheng",
""
],
[
"Wang",
"Dilin",
""
],
[
"Tang",
"Chengzhou",
""
],
[
"Zhang",
"Fuyang",
""
],
[
"Fan",
"Yuchen",
""
],
[
"Chandra",
"Vikas",
""
],
[
"Furukawa",
"Yasutaka",
""
],
[
"Ranjan",
"Rakesh",
""
]
] | This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time. We use the Objaverse for training and the Google Scanned Objects for evaluation with standard novel view synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining MVDiffusion++ with a text-to-image generative model. The project page is at https://mvdiffusion-plusplus.github.io. |
1204.4765 | Julius D'souza | Julius D'souza | String Trees | 5 pages | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A string-like compact data structure for unlabelled rooted trees is given
using 2n bits.
| [
{
"created": "Sat, 21 Apr 2012 00:36:28 GMT",
"version": "v1"
}
] | 2015-03-20 | [
[
"D'souza",
"Julius",
""
]
] | A string-like compact data structure for unlabelled rooted trees is given using 2n bits. |
1301.3865 | Tony S. Jebara | Tony S. Jebara, Tommi S. Jaakkola | Feature Selection and Dualities in Maximum Entropy Discrimination | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-291-300 | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incorporating feature selection into a classification or regression method
often carries a number of advantages. In this paper we formalize feature
selection specifically from a discriminative perspective of improving
classification/regression accuracy. The feature selection method is developed
as an extension to the recently proposed maximum entropy discrimination (MED)
framework. We describe MED as a flexible (Bayesian) regularization approach
that subsumes, e.g., support vector classification, regression and exponential
family models. For brevity, we restrict ourselves primarily to feature
selection in the context of linear classification/regression methods and
demonstrate that the proposed approach indeed carries substantial improvements
in practice. Moreover, we discuss and develop various extensions of feature
selection, including the problem of dealing with example specific but
unobserved degrees of freedom -- alignments or invariants.
| [
{
"created": "Wed, 16 Jan 2013 15:50:50 GMT",
"version": "v1"
}
] | 2013-01-18 | [
[
"Jebara",
"Tony S.",
""
],
[
"Jaakkola",
"Tommi S.",
""
]
] | Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving classification/regression accuracy. The feature selection method is developed as an extension to the recently proposed maximum entropy discrimination (MED) framework. We describe MED as a flexible (Bayesian) regularization approach that subsumes, e.g., support vector classification, regression and exponential family models. For brevity, we restrict ourselves primarily to feature selection in the context of linear classification/regression methods and demonstrate that the proposed approach indeed carries substantial improvements in practice. Moreover, we discuss and develop various extensions of feature selection, including the problem of dealing with example specific but unobserved degrees of freedom -- alignments or invariants. |
2209.08731 | Sandy Irani | Dorit Aharonov and Sandy Irani | Translationally Invariant Constraint Optimization Problems | 75 pages, 13 figures | null | null | null | cs.CC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the complexity of classical constraint satisfaction problems on a 2D
grid. Specifically, we consider the complexity of function versions of such
problems, with the additional restriction that the constraints are
translationally invariant, namely, the variables are located at the vertices of
a 2D grid and the constraint between every pair of adjacent variables is the
same in each dimension. The only input to the problem is thus the size of the
grid. This problem is equivalent to one of the most interesting problems in
classical physics, namely, computing the lowest energy of a classical system of
particles on the grid. We provide a tight characterization of the complexity of
this problem, and show that it is complete for the class $FP^{NEXP}$. Gottesman
and Irani (FOCS 2009) also studied classical translationally-invariant
constraint satisfaction problems; they show that the problem of deciding
whether the cost of the optimal solution is below a given threshold is
NEXP-complete. Our result is thus a strengthening of their result from the
decision version to the function version of the problem. Our result can also be
viewed as a generalization to the translationally invariant setting, of
Krentel's famous result from 1988, showing that the function version of SAT is
complete for the class $FP^{NP}$. An essential ingredient in the proof is a
study of the complexity of a gapped variant of the problem. We show that it is
NEXP-hard to approximate the cost of the optimal assignment to within an
additive error of $\Omega(N^{1/4})$, for an $N \times N$ grid. To the best of
our knowledge, no gapped result is known for CSPs on the grid, even in the
non-translationally invariant case. As a byproduct of our results, we also show
that a decision version of the optimization problem which asks whether the cost
of the optimal assignment is odd or even is also complete for $P^{NEXP}$.
| [
{
"created": "Mon, 19 Sep 2022 03:03:05 GMT",
"version": "v1"
}
] | 2022-09-20 | [
[
"Aharonov",
"Dorit",
""
],
[
"Irani",
"Sandy",
""
]
] | We study the complexity of classical constraint satisfaction problems on a 2D grid. Specifically, we consider the complexity of function versions of such problems, with the additional restriction that the constraints are translationally invariant, namely, the variables are located at the vertices of a 2D grid and the constraint between every pair of adjacent variables is the same in each dimension. The only input to the problem is thus the size of the grid. This problem is equivalent to one of the most interesting problems in classical physics, namely, computing the lowest energy of a classical system of particles on the grid. We provide a tight characterization of the complexity of this problem, and show that it is complete for the class $FP^{NEXP}$. Gottesman and Irani (FOCS 2009) also studied classical translationally-invariant constraint satisfaction problems; they show that the problem of deciding whether the cost of the optimal solution is below a given threshold is NEXP-complete. Our result is thus a strengthening of their result from the decision version to the function version of the problem. Our result can also be viewed as a generalization to the translationally invariant setting, of Krentel's famous result from 1988, showing that the function version of SAT is complete for the class $FP^{NP}$. An essential ingredient in the proof is a study of the complexity of a gapped variant of the problem. We show that it is NEXP-hard to approximate the cost of the optimal assignment to within an additive error of $\Omega(N^{1/4})$, for an $N \times N$ grid. To the best of our knowledge, no gapped result is known for CSPs on the grid, even in the non-translationally invariant case. As a byproduct of our results, we also show that a decision version of the optimization problem which asks whether the cost of the optimal assignment is odd or even is also complete for $P^{NEXP}$. |
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