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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2303.01272
|
Sondre S{\o}rb{\o}
|
Sondre S{\o}rb{\o} and Massimiliano Ruocco
|
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly
Detection in Time Series
|
29 pages, 28 figures and tables
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The field of time series anomaly detection is constantly advancing, with
several methods available, making it a challenge to determine the most
appropriate method for a specific domain. The evaluation of these methods is
facilitated by the use of metrics, which vary widely in their properties.
Despite the existence of new evaluation metrics, there is limited agreement on
which metrics are best suited for specific scenarios and domain, and the most
commonly used metrics have faced criticism in the literature. This paper
provides a comprehensive overview of the metrics used for the evaluation of
time series anomaly detection methods, and also defines a taxonomy of these
based on how they are calculated. By defining a set of properties for
evaluation metrics and a set of specific case studies and experiments, twenty
metrics are analyzed and discussed in detail, highlighting the unique
suitability of each for specific tasks. Through extensive experimentation and
analysis, this paper argues that the choice of evaluation metric must be made
with care, taking into account the specific requirements of the task at hand.
|
[
{
"created": "Thu, 2 Mar 2023 13:58:06 GMT",
"version": "v1"
}
] |
2023-03-03
|
[
[
"Sørbø",
"Sondre",
""
],
[
"Ruocco",
"Massimiliano",
""
]
] |
The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of metrics, which vary widely in their properties. Despite the existence of new evaluation metrics, there is limited agreement on which metrics are best suited for specific scenarios and domain, and the most commonly used metrics have faced criticism in the literature. This paper provides a comprehensive overview of the metrics used for the evaluation of time series anomaly detection methods, and also defines a taxonomy of these based on how they are calculated. By defining a set of properties for evaluation metrics and a set of specific case studies and experiments, twenty metrics are analyzed and discussed in detail, highlighting the unique suitability of each for specific tasks. Through extensive experimentation and analysis, this paper argues that the choice of evaluation metric must be made with care, taking into account the specific requirements of the task at hand.
|
2202.10545
|
Yohan Beugin
|
Fran\c{c}ois Homps, Yohan Beugin, Romain Vuillemot
|
ReViVD: Exploration and Filtering of Trajectories in an Immersive
Environment using 3D Shapes
|
Accepted at IEEE Conference on Virtual Reality and 3D User Interfaces
(VR) 2020
|
2020 IEEE Conference on Virtual Reality and 3D User Interfaces
(VR)
|
10.1109/VR46266.2020.00096
| null |
cs.HC cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present ReViVD, a tool for exploring and filtering large trajectory-based
datasets using virtual reality. ReViVD's novelty lies in using simple 3D shapes
-- such as cuboids, spheres and cylinders -- as queries for users to select and
filter groups of trajectories. Building on this simple paradigm, more complex
queries can be created by combining previously made selection groups through a
system of user-created Boolean operations. We demonstrate the use of ReViVD in
different application domains, from GPS position tracking to simulated data
(e.g., turbulent particle flows and traffic simulation). Our results show the
ease of use and expressiveness of the 3D geometric shapes in a broad range of
exploratory tasks. ReViVD was found to be particularly useful for progressively
refining selections to isolate outlying behaviors. It also acts as a powerful
communication tool for conveying the structure of normally abstract datasets to
an audience.
|
[
{
"created": "Mon, 21 Feb 2022 21:58:41 GMT",
"version": "v1"
}
] |
2022-02-23
|
[
[
"Homps",
"François",
""
],
[
"Beugin",
"Yohan",
""
],
[
"Vuillemot",
"Romain",
""
]
] |
We present ReViVD, a tool for exploring and filtering large trajectory-based datasets using virtual reality. ReViVD's novelty lies in using simple 3D shapes -- such as cuboids, spheres and cylinders -- as queries for users to select and filter groups of trajectories. Building on this simple paradigm, more complex queries can be created by combining previously made selection groups through a system of user-created Boolean operations. We demonstrate the use of ReViVD in different application domains, from GPS position tracking to simulated data (e.g., turbulent particle flows and traffic simulation). Our results show the ease of use and expressiveness of the 3D geometric shapes in a broad range of exploratory tasks. ReViVD was found to be particularly useful for progressively refining selections to isolate outlying behaviors. It also acts as a powerful communication tool for conveying the structure of normally abstract datasets to an audience.
|
2408.07724
|
Jacob Miller
|
Kiran Smelser, Jacob Miller, Stephen Kobourov
|
"Normalized Stress" is Not Normalized: How to Interpret Stress Correctly
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Stress is among the most commonly employed quality metrics and optimization
criteria for dimension reduction projections of high dimensional data. Complex,
high dimensional data is ubiquitous across many scientific disciplines,
including machine learning, biology, and the social sciences. One of the
primary methods of visualizing these datasets is with two dimensional scatter
plots that visually capture some properties of the data. Because visually
determining the accuracy of these plots is challenging, researchers often use
quality metrics to measure projection accuracy or faithfulness to the full
data. One of the most commonly employed metrics, normalized stress, is
sensitive to uniform scaling of the projection, despite this act not
meaningfully changing anything about the projection. We investigate the effect
of scaling on stress and other distance based quality metrics analytically and
empirically by showing just how much the values change and how this affects
dimension reduction technique evaluations. We introduce a simple technique to
make normalized stress scale invariant and show that it accurately captures
expected behavior on a small benchmark.
|
[
{
"created": "Wed, 14 Aug 2024 13:42:47 GMT",
"version": "v1"
}
] |
2024-08-16
|
[
[
"Smelser",
"Kiran",
""
],
[
"Miller",
"Jacob",
""
],
[
"Kobourov",
"Stephen",
""
]
] |
Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visualizing these datasets is with two dimensional scatter plots that visually capture some properties of the data. Because visually determining the accuracy of these plots is challenging, researchers often use quality metrics to measure projection accuracy or faithfulness to the full data. One of the most commonly employed metrics, normalized stress, is sensitive to uniform scaling of the projection, despite this act not meaningfully changing anything about the projection. We investigate the effect of scaling on stress and other distance based quality metrics analytically and empirically by showing just how much the values change and how this affects dimension reduction technique evaluations. We introduce a simple technique to make normalized stress scale invariant and show that it accurately captures expected behavior on a small benchmark.
|
2002.00084
|
Seokki Lee
|
Seokki Lee, Bertram Ludaescher, Boris Glavic
|
Approximate Summaries for Why and Why-not Provenance (Extended Version)
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Why and why-not provenance have been studied extensively in recent years.
However, why-not provenance, and to a lesser degree why provenance, can be very
large resulting in severe scalability and usability challenges. In this paper,
we introduce a novel approximate summarization technique for provenance which
overcomes these challenges. Our approach uses patterns to encode (why-not)
provenance concisely. We develop techniques for efficiently computing
provenance summaries balancing informativeness, conciseness, and completeness.
To achieve scalability, we integrate sampling techniques into provenance
capture and summarization. Our approach is the first to scale to large datasets
and to generate comprehensive and meaningful summaries.
|
[
{
"created": "Fri, 31 Jan 2020 22:47:43 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Apr 2020 16:29:09 GMT",
"version": "v2"
}
] |
2020-04-28
|
[
[
"Lee",
"Seokki",
""
],
[
"Ludaescher",
"Bertram",
""
],
[
"Glavic",
"Boris",
""
]
] |
Why and why-not provenance have been studied extensively in recent years. However, why-not provenance, and to a lesser degree why provenance, can be very large resulting in severe scalability and usability challenges. In this paper, we introduce a novel approximate summarization technique for provenance which overcomes these challenges. Our approach uses patterns to encode (why-not) provenance concisely. We develop techniques for efficiently computing provenance summaries balancing informativeness, conciseness, and completeness. To achieve scalability, we integrate sampling techniques into provenance capture and summarization. Our approach is the first to scale to large datasets and to generate comprehensive and meaningful summaries.
|
2010.10649
|
Wei-Fan Chen
|
Wei-Fan Chen, Khalid Al-Khatib, Benno Stein and Henning Wachsmuth
|
Detecting Media Bias in News Articles using Gaussian Bias Distributions
| null |
EMNLP 2020 Findings
| null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Media plays an important role in shaping public opinion. Biased media can
influence people in undesirable directions and hence should be unmasked as
such. We observe that featurebased and neural text classification approaches
which rely only on the distribution of low-level lexical information fail to
detect media bias. This weakness becomes most noticeable for articles on new
events, where words appear in new contexts and hence their "bias
predictiveness" is unclear. In this paper, we therefore study how second-order
information about biased statements in an article helps to improve detection
effectiveness. In particular, we utilize the probability distributions of the
frequency, positions, and sequential order of lexical and informational
sentence-level bias in a Gaussian Mixture Model. On an existing media bias
dataset, we find that the frequency and positions of biased statements strongly
impact article-level bias, whereas their exact sequential order is secondary.
Using a standard model for sentence-level bias detection, we provide empirical
evidence that article-level bias detectors that use second-order information
clearly outperform those without.
|
[
{
"created": "Tue, 20 Oct 2020 22:20:49 GMT",
"version": "v1"
}
] |
2020-10-22
|
[
[
"Chen",
"Wei-Fan",
""
],
[
"Al-Khatib",
"Khalid",
""
],
[
"Stein",
"Benno",
""
],
[
"Wachsmuth",
"Henning",
""
]
] |
Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that featurebased and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their "bias predictiveness" is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.
|
2004.14648
|
Jifan Chen
|
Jifan Chen and Greg Durrett
|
Robust Question Answering Through Sub-part Alignment
|
NAACL 2021
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current textual question answering models achieve strong performance on
in-domain test sets, but often do so by fitting surface-level patterns in the
data, so they fail to generalize to out-of-distribution settings. To make a
more robust and understandable QA system, we model question answering as an
alignment problem. We decompose both the question and context into smaller
units based on off-the-shelf semantic representations (here, semantic roles),
and align the question to a subgraph of the context in order to find the
answer. We formulate our model as a structured SVM, with alignment scores
computed via BERT, and we can train end-to-end despite using beam search for
approximate inference. Our explicit use of alignments allows us to explore a
set of constraints with which we can prohibit certain types of bad model
behavior arising in cross-domain settings. Furthermore, by investigating
differences in scores across different potential answers, we can seek to
understand what particular aspects of the input lead the model to choose the
answer without relying on post-hoc explanation techniques. We train our model
on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets.
The results show that our model is more robust cross-domain than the standard
BERT QA model, and constraints derived from alignment scores allow us to
effectively trade off coverage and accuracy.
|
[
{
"created": "Thu, 30 Apr 2020 09:10:57 GMT",
"version": "v1"
},
{
"created": "Fri, 1 May 2020 23:58:37 GMT",
"version": "v2"
},
{
"created": "Mon, 19 Apr 2021 20:43:55 GMT",
"version": "v3"
}
] |
2021-04-21
|
[
[
"Chen",
"Jifan",
""
],
[
"Durrett",
"Greg",
""
]
] |
Current textual question answering models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns in the data, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference. Our explicit use of alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings. Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets. The results show that our model is more robust cross-domain than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.
|
2311.14741
|
Oliver Bendel
|
Oliver Bendel and Karim N'diaye
|
@ve: A Chatbot for Latin
|
15 pages
| null | null | null |
cs.CL cs.AI cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dead, extinct, and endangered languages have been preserved primarily through
audio conservation and the collection and digitization of scripts and have been
promoted through targeted language acquisition efforts. Another possibility
would be to build conversational agents that can master these languages. This
would provide an artificial, active conversational partner which has knowledge
of the vocabulary and grammar, and one learns with it in a different way. The
chatbot @ve, with which one can communicate in Latin, was developed in
2022/2023 based on GPT-3.0. It was additionally equipped with a manually
created knowledge base. After conceptual groundwork, this paper presents the
preparation and implementation of the project. In addition, it summarizes the
test that a Latin expert conducted with the chatbot. A critical discussion
elaborates advantages and disadvantages. @ve could be a new tool for teaching
Latin in a memorable and entertaining way through dialogue. However, the
present implementation is still too prone to glitches for stand-alone use -
i.e., without the accompaniment of a teacher. The use of GPT-4 could be a
solution as well as the extension of the knowledge base. In conclusion, it can
be argued that conversational agents are an innovative approach to promoting
and preserving languages.
|
[
{
"created": "Wed, 22 Nov 2023 09:06:11 GMT",
"version": "v1"
}
] |
2023-11-28
|
[
[
"Bendel",
"Oliver",
""
],
[
"N'diaye",
"Karim",
""
]
] |
Dead, extinct, and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar, and one learns with it in a different way. The chatbot @ve, with which one can communicate in Latin, was developed in 2022/2023 based on GPT-3.0. It was additionally equipped with a manually created knowledge base. After conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the test that a Latin expert conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @ve could be a new tool for teaching Latin in a memorable and entertaining way through dialogue. However, the present implementation is still too prone to glitches for stand-alone use - i.e., without the accompaniment of a teacher. The use of GPT-4 could be a solution as well as the extension of the knowledge base. In conclusion, it can be argued that conversational agents are an innovative approach to promoting and preserving languages.
|
1606.04011
|
Tianhua Xu
|
Tianhua Xu, Gunnar Jacobsen, Jie Li, Mark Leeson, Sergei Popov
|
Dynamic physical layer equalization in optical communication networks
| null | null | null | null |
cs.IT math.IT physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In optical transport networks, signal lightpaths between two terminal nodes
can be different due to current network conditions. Thus the transmission
distance and accumulated dispersion in the lightpath cannot be predicted.
Therefore, the adaptive compensation of dynamic dispersion is necessary in such
networks to enable flexible routing and switching. In this paper, we present a
detailed analysis on the adaptive dispersion compensation using the
least-mean-square (LMS) algorithm in coherent optical communication networks.
It is found that the variable-step-size LMS equalizer can achieve the same
performance with a lower complexity, compared to the traditional LMS algorithm.
|
[
{
"created": "Mon, 13 Jun 2016 16:46:26 GMT",
"version": "v1"
},
{
"created": "Tue, 16 May 2017 22:39:54 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Dec 2017 16:27:52 GMT",
"version": "v3"
},
{
"created": "Mon, 7 May 2018 19:07:27 GMT",
"version": "v4"
}
] |
2018-05-09
|
[
[
"Xu",
"Tianhua",
""
],
[
"Jacobsen",
"Gunnar",
""
],
[
"Li",
"Jie",
""
],
[
"Leeson",
"Mark",
""
],
[
"Popov",
"Sergei",
""
]
] |
In optical transport networks, signal lightpaths between two terminal nodes can be different due to current network conditions. Thus the transmission distance and accumulated dispersion in the lightpath cannot be predicted. Therefore, the adaptive compensation of dynamic dispersion is necessary in such networks to enable flexible routing and switching. In this paper, we present a detailed analysis on the adaptive dispersion compensation using the least-mean-square (LMS) algorithm in coherent optical communication networks. It is found that the variable-step-size LMS equalizer can achieve the same performance with a lower complexity, compared to the traditional LMS algorithm.
|
2009.08685
|
Trista Chen
|
Yu-Sheng Lin, Hung Chang Lu, Yang-Bin Tsao, Yi-Min Chih, Wei-Chao
Chen, Shao-Yi Chien
|
GrateTile: Efficient Sparse Tensor Tiling for CNN Processing
|
To be published at IEEE Workshop on Signal Processing System (SiPS
2020)
| null | null | null |
cs.LG cs.AR stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose GrateTile, an efficient, hardwarefriendly data storage scheme for
sparse CNN feature maps (activations). It divides data into uneven-sized
subtensors and, with small indexing overhead, stores them in a compressed yet
randomly accessible format. This design enables modern CNN accelerators to
fetch and decompressed sub-tensors on-the-fly in a tiled processing manner.
GrateTile is suitable for architectures that favor aligned, coalesced data
access, and only requires minimal changes to the overall architectural design.
We simulate GrateTile with state-of-the-art CNNs and show an average of 55%
DRAM bandwidth reduction while using only 0.6% of feature map size for indexing
storage.
|
[
{
"created": "Fri, 18 Sep 2020 08:31:41 GMT",
"version": "v1"
}
] |
2020-09-21
|
[
[
"Lin",
"Yu-Sheng",
""
],
[
"Lu",
"Hung Chang",
""
],
[
"Tsao",
"Yang-Bin",
""
],
[
"Chih",
"Yi-Min",
""
],
[
"Chen",
"Wei-Chao",
""
],
[
"Chien",
"Shao-Yi",
""
]
] |
We propose GrateTile, an efficient, hardwarefriendly data storage scheme for sparse CNN feature maps (activations). It divides data into uneven-sized subtensors and, with small indexing overhead, stores them in a compressed yet randomly accessible format. This design enables modern CNN accelerators to fetch and decompressed sub-tensors on-the-fly in a tiled processing manner. GrateTile is suitable for architectures that favor aligned, coalesced data access, and only requires minimal changes to the overall architectural design. We simulate GrateTile with state-of-the-art CNNs and show an average of 55% DRAM bandwidth reduction while using only 0.6% of feature map size for indexing storage.
|
1902.00526
|
Amir Saeidi
|
Amir Saeidi (Utrecht University, Netherlands), Jurriaan Hage (Utrecht
University, Netherlands), Ravi Khadka (Utrecht University, Netherlands),
Slinger Jansen (Utrecht University, Netherlands)
|
Applications of Multi-view Learning Approaches for Software
Comprehension
| null |
The Art, Science, and Engineering of Programming, 2019, Vol. 3,
Issue 3, Article 14
|
10.22152/programming-journal.org/2019/3/14
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Program comprehension concerns the ability of an individual to make an
understanding of an existing software system to extend or transform it.
Software systems comprise of data that are noisy and missing, which makes
program understanding even more difficult. A software system consists of
various views including the module dependency graph, execution logs,
evolutionary information and the vocabulary used in the source code, that
collectively defines the software system. Each of these views contain unique
and complementary information; together which can more accurately describe the
data. In this paper, we investigate various techniques for combining different
sources of information to improve the performance of a program comprehension
task. We employ state-of-the-art techniques from learning to 1) find a suitable
similarity function for each view, and 2) compare different multi-view learning
techniques to decompose a software system into high-level units and give
component-level recommendations for refactoring of the system, as well as
cross-view source code search. The experiments conducted on 10 relatively large
Java software systems show that by fusing knowledge from different views, we
can guarantee a lower bound on the quality of the modularization and even
improve upon it. We proceed by integrating different sources of information to
give a set of high-level recommendations as to how to refactor the software
system. Furthermore, we demonstrate how learning a joint subspace allows for
performing cross-modal retrieval across views, yielding results that are more
aligned with what the user intends by the query. The multi-view approaches
outlined in this paper can be employed for addressing problems in software
engineering that can be encoded in terms of a learning problem, such as
software bug prediction and feature location.
|
[
{
"created": "Fri, 1 Feb 2019 19:07:49 GMT",
"version": "v1"
}
] |
2019-02-05
|
[
[
"Saeidi",
"Amir",
"",
"Utrecht University, Netherlands"
],
[
"Hage",
"Jurriaan",
"",
"Utrecht\n University, Netherlands"
],
[
"Khadka",
"Ravi",
"",
"Utrecht University, Netherlands"
],
[
"Jansen",
"Slinger",
"",
"Utrecht University, Netherlands"
]
] |
Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location.
|
2202.11678
|
Andrew Wilson
|
Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew
Gordon Wilson
|
Bayesian Model Selection, the Marginal Likelihood, and Generalization
|
Extended version. Shorter ICML version available at
arXiv:2202.11678v2
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How do we compare between hypotheses that are entirely consistent with
observations? The marginal likelihood (aka Bayesian evidence), which represents
the probability of generating our observations from a prior, provides a
distinctive approach to this foundational question, automatically encoding
Occam's razor. Although it has been observed that the marginal likelihood can
overfit and is sensitive to prior assumptions, its limitations for
hyperparameter learning and discrete model comparison have not been thoroughly
investigated. We first revisit the appealing properties of the marginal
likelihood for learning constraints and hypothesis testing. We then highlight
the conceptual and practical issues in using the marginal likelihood as a proxy
for generalization. Namely, we show how marginal likelihood can be negatively
correlated with generalization, with implications for neural architecture
search, and can lead to both underfitting and overfitting in hyperparameter
learning. We also re-examine the connection between the marginal likelihood and
PAC-Bayes bounds and use this connection to further elucidate the shortcomings
of the marginal likelihood for model selection. We provide a partial remedy
through a conditional marginal likelihood, which we show is more aligned with
generalization, and practically valuable for large-scale hyperparameter
learning, such as in deep kernel learning.
|
[
{
"created": "Wed, 23 Feb 2022 18:38:16 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jun 2022 17:10:24 GMT",
"version": "v2"
},
{
"created": "Tue, 2 May 2023 01:27:39 GMT",
"version": "v3"
}
] |
2023-05-03
|
[
[
"Lotfi",
"Sanae",
""
],
[
"Izmailov",
"Pavel",
""
],
[
"Benton",
"Gregory",
""
],
[
"Goldblum",
"Micah",
""
],
[
"Wilson",
"Andrew Gordon",
""
]
] |
How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating our observations from a prior, provides a distinctive approach to this foundational question, automatically encoding Occam's razor. Although it has been observed that the marginal likelihood can overfit and is sensitive to prior assumptions, its limitations for hyperparameter learning and discrete model comparison have not been thoroughly investigated. We first revisit the appealing properties of the marginal likelihood for learning constraints and hypothesis testing. We then highlight the conceptual and practical issues in using the marginal likelihood as a proxy for generalization. Namely, we show how marginal likelihood can be negatively correlated with generalization, with implications for neural architecture search, and can lead to both underfitting and overfitting in hyperparameter learning. We also re-examine the connection between the marginal likelihood and PAC-Bayes bounds and use this connection to further elucidate the shortcomings of the marginal likelihood for model selection. We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.
|
2011.04345
|
Tamara AlShammari
|
Tamara Alshammari and Sumudu Samarakoon and Anis Elgabli and Mehdi
Bennis
|
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
|
6 pages, 5 figures, conference
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article deals with the problem of distributed machine learning, in which
agents update their models based on their local datasets, and aggregate the
updated models collaboratively and in a fully decentralized manner. In this
paper, we tackle the problem of information heterogeneity arising in
multi-agent networks where the placement of informative agents plays a crucial
role in the learning dynamics. Specifically, we propose BayGo, a novel fully
decentralized joint Bayesian learning and graph optimization framework with
proven fast convergence over a sparse graph. Under our framework, agents are
able to learn and communicate with the most informative agent to their own
learning. Unlike prior works, our framework assumes no prior knowledge of the
data distribution across agents nor does it assume any knowledge of the true
parameter of the system. The proposed alternating minimization based framework
ensures global connectivity in a fully decentralized way while minimizing the
number of communication links. We theoretically show that by optimizing the
proposed objective function, the estimation error of the posterior probability
distribution decreases exponentially at each iteration. Via extensive
simulations, we show that our framework achieves faster convergence and higher
accuracy compared to fully-connected and star topology graphs.
|
[
{
"created": "Mon, 9 Nov 2020 11:16:55 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Feb 2021 19:47:14 GMT",
"version": "v2"
}
] |
2021-02-23
|
[
[
"Alshammari",
"Tamara",
""
],
[
"Samarakoon",
"Sumudu",
""
],
[
"Elgabli",
"Anis",
""
],
[
"Bennis",
"Mehdi",
""
]
] |
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner. In this paper, we tackle the problem of information heterogeneity arising in multi-agent networks where the placement of informative agents plays a crucial role in the learning dynamics. Specifically, we propose BayGo, a novel fully decentralized joint Bayesian learning and graph optimization framework with proven fast convergence over a sparse graph. Under our framework, agents are able to learn and communicate with the most informative agent to their own learning. Unlike prior works, our framework assumes no prior knowledge of the data distribution across agents nor does it assume any knowledge of the true parameter of the system. The proposed alternating minimization based framework ensures global connectivity in a fully decentralized way while minimizing the number of communication links. We theoretically show that by optimizing the proposed objective function, the estimation error of the posterior probability distribution decreases exponentially at each iteration. Via extensive simulations, we show that our framework achieves faster convergence and higher accuracy compared to fully-connected and star topology graphs.
|
1906.08885
|
Philipp Koehn
|
Vishrav Chaudhary and Yuqing Tang and Francisco Guzm\'an and Holger
Schwenk and Philipp Koehn
|
Low-Resource Corpus Filtering using Multilingual Sentence Embeddings
|
6 pages, WMT 2019
|
Conference on Machine Translation (WMT) 2019
| null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we describe our submission to the WMT19 low-resource parallel
corpus filtering shared task. Our main approach is based on the LASER toolkit
(Language-Agnostic SEntence Representations), which uses an encoder-decoder
architecture trained on a parallel corpus to obtain multilingual sentence
representations. We then use the representations directly to score and filter
the noisy parallel sentences without additionally training a scoring function.
We contrast our approach to other promising methods and show that LASER yields
strong results. Finally, we produce an ensemble of different scoring methods
and obtain additional gains. Our submission achieved the best overall
performance for both the Nepali-English and Sinhala-English 1M tasks by a
margin of 1.3 and 1.4 BLEU respectively, as compared to the second best
systems. Moreover, our experiments show that this technique is promising for
low and even no-resource scenarios.
|
[
{
"created": "Thu, 20 Jun 2019 22:39:44 GMT",
"version": "v1"
}
] |
2019-06-24
|
[
[
"Chaudhary",
"Vishrav",
""
],
[
"Tang",
"Yuqing",
""
],
[
"Guzmán",
"Francisco",
""
],
[
"Schwenk",
"Holger",
""
],
[
"Koehn",
"Philipp",
""
]
] |
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.
|
1807.11161
|
Hao Min Liu
|
Hao-Min Liu, Yi-Hsuan Yang
|
Lead Sheet Generation and Arrangement by Conditional Generative
Adversarial Network
|
7 pages, 7 figures and 4 tables
| null | null | null |
cs.SD cs.AI cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Research on automatic music generation has seen great progress due to the
development of deep neural networks. However, the generation of
multi-instrument music of arbitrary genres still remains a challenge. Existing
research either works on lead sheets or multi-track piano-rolls found in MIDIs,
but both musical notations have their limits. In this work, we propose a new
task called lead sheet arrangement to avoid such limits. A new recurrent
convolutional generative model for the task is proposed, along with three new
symbolic-domain harmonic features to facilitate learning from unpaired lead
sheets and MIDIs. Our model can generate lead sheets and their arrangements of
eight-bar long. Audio samples of the generated result can be found at
https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk
|
[
{
"created": "Mon, 30 Jul 2018 03:48:04 GMT",
"version": "v1"
}
] |
2018-07-31
|
[
[
"Liu",
"Hao-Min",
""
],
[
"Yang",
"Yi-Hsuan",
""
]
] |
Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk
|
1903.07389
|
Hamid Karimi
|
Hamid Karimi and Jiliang Tang
|
Learning Hierarchical Discourse-level Structure for Fake News Detection
|
Accepted to 2019 Annual Conference of the North American Chapter of
the Association for Computational Linguistics June 2-7, 2019 Minneapolis, USA
| null | null | null |
cs.CL cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
On the one hand, nowadays, fake news articles are easily propagated through
various online media platforms and have become a grand threat to the
trustworthiness of information. On the other hand, our understanding of the
language of fake news is still minimal. Incorporating hierarchical
discourse-level structure of fake and real news articles is one crucial step
toward a better understanding of how these articles are structured.
Nevertheless, this has rarely been investigated in the fake news detection
domain and faces tremendous challenges. First, existing methods for capturing
discourse-level structure rely on annotated corpora which are not available for
fake news datasets. Second, how to extract out useful information from such
discovered structures is another challenge. To address these challenges, we
propose Hierarchical Discourse-level Structure for Fake news detection. HDSF
learns and constructs a discourse-level structure for fake/real news articles
in an automated and data-driven manner. Moreover, we identify insightful
structure-related properties, which can explain the discovered structures and
boost our understating of fake news. Conducted experiments show the
effectiveness of the proposed approach. Further structural analysis suggests
that real and fake news present substantial differences in the hierarchical
discourse-level structures.
|
[
{
"created": "Wed, 27 Feb 2019 00:03:17 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Mar 2019 01:15:14 GMT",
"version": "v2"
},
{
"created": "Tue, 2 Apr 2019 16:18:00 GMT",
"version": "v3"
},
{
"created": "Thu, 4 Apr 2019 02:38:36 GMT",
"version": "v4"
},
{
"created": "Fri, 5 Apr 2019 17:39:05 GMT",
"version": "v5"
},
{
"created": "Wed, 10 Apr 2019 14:20:53 GMT",
"version": "v6"
}
] |
2019-04-11
|
[
[
"Karimi",
"Hamid",
""
],
[
"Tang",
"Jiliang",
""
]
] |
On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges. First, existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets. Second, how to extract out useful information from such discovered structures is another challenge. To address these challenges, we propose Hierarchical Discourse-level Structure for Fake news detection. HDSF learns and constructs a discourse-level structure for fake/real news articles in an automated and data-driven manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Conducted experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structures.
|
0905.0197
|
Victor Marek
|
V.W. Marek and J.B. Remmel
|
An Application of Proof-Theory in Answer Set Programming
|
22 pages. Short version was published in ICLP08. New version slightly
shorter than the previous version
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We apply proof-theoretic techniques in answer Set Programming. The main
results include: 1. A characterization of continuity properties of
Gelfond-Lifschitz operator for logic program. 2. A propositional
characterization of stable models of logic programs (without referring to loop
formulas.
|
[
{
"created": "Sat, 2 May 2009 10:43:30 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Jan 2010 20:12:14 GMT",
"version": "v2"
}
] |
2010-01-11
|
[
[
"Marek",
"V. W.",
""
],
[
"Remmel",
"J. B.",
""
]
] |
We apply proof-theoretic techniques in answer Set Programming. The main results include: 1. A characterization of continuity properties of Gelfond-Lifschitz operator for logic program. 2. A propositional characterization of stable models of logic programs (without referring to loop formulas.
|
1801.08024
|
Grigori Fursin
|
Grigori Fursin, Anton Lokhmotov, Dmitry Savenko and Eben Upton
|
A Collective Knowledge workflow for collaborative research into
multi-objective autotuning and machine learning techniques
|
Interactive CK report: http://cKnowledge.org/rpi-crowd-tuning ; CK
repository with artifacts:
https://github.com/ctuning/ck-rpi-optimization-results ; FigShare data
archive: https://doi.org/10.6084/m9.figshare.5789007.v2
| null | null | null |
cs.HC cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Developing efficient software and hardware has never been harder whether it
is for a tiny IoT device or an Exascale supercomputer. Apart from the ever
growing design and optimization complexity, there exist even more fundamental
problems such as lack of interdisciplinary knowledge required for effective
software/hardware co-design, and a growing technology transfer gap between
academia and industry.
We introduce our new educational initiative to tackle these problems by
developing Collective Knowledge (CK), a unified experimental framework for
computer systems research and development. We use CK to teach the community how
to make their research artifacts and experimental workflows portable,
reproducible, customizable and reusable while enabling sustainable R&D and
facilitating technology transfer. We also demonstrate how to redesign
multi-objective autotuning and machine learning as a portable and extensible CK
workflow. Such workflows enable researchers to experiment with different
applications, data sets and tools; crowdsource experimentation across diverse
platforms; share experimental results, models, visualizations; gradually expose
more design and optimization choices using a simple JSON API; and ultimately
build upon each other's findings.
As the first practical step, we have implemented customizable compiler
autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi
3 devices, reduced the execution time and code size by up to 40%, and applied
machine learning to predict optimizations. We hope such approach will help
teach students how to build upon each others' work to enable efficient and
self-optimizing software/hardware/model stack for emerging workloads.
|
[
{
"created": "Fri, 19 Jan 2018 15:30:39 GMT",
"version": "v1"
}
] |
2018-01-25
|
[
[
"Fursin",
"Grigori",
""
],
[
"Lokhmotov",
"Anton",
""
],
[
"Savenko",
"Dmitry",
""
],
[
"Upton",
"Eben",
""
]
] |
Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer. Apart from the ever growing design and optimization complexity, there exist even more fundamental problems such as lack of interdisciplinary knowledge required for effective software/hardware co-design, and a growing technology transfer gap between academia and industry. We introduce our new educational initiative to tackle these problems by developing Collective Knowledge (CK), a unified experimental framework for computer systems research and development. We use CK to teach the community how to make their research artifacts and experimental workflows portable, reproducible, customizable and reusable while enabling sustainable R&D and facilitating technology transfer. We also demonstrate how to redesign multi-objective autotuning and machine learning as a portable and extensible CK workflow. Such workflows enable researchers to experiment with different applications, data sets and tools; crowdsource experimentation across diverse platforms; share experimental results, models, visualizations; gradually expose more design and optimization choices using a simple JSON API; and ultimately build upon each other's findings. As the first practical step, we have implemented customizable compiler autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi 3 devices, reduced the execution time and code size by up to 40%, and applied machine learning to predict optimizations. We hope such approach will help teach students how to build upon each others' work to enable efficient and self-optimizing software/hardware/model stack for emerging workloads.
|
1504.04123
|
Pietro Tesi
|
G. Battistelli and P. Tesi
|
Switching Control for Parameter Identifiability of Uncertain Systems
| null | null | null | null |
cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper considers the problem of identifying the parameters of an
uncertain linear system by means of feedback control. The problem is approached
by considering time-varying controllers. It is shown that even when the
uncertainty set is not finite, parameter identifiability can be generically
ensured by switching among a finite number of linear time-invariant
controllers. The results are shown to have several implications, ranging from
fault detection and isolation to adaptive and supervisory control. Practical
aspects of the problem are also discussed in details.
|
[
{
"created": "Thu, 16 Apr 2015 08:00:20 GMT",
"version": "v1"
}
] |
2015-04-17
|
[
[
"Battistelli",
"G.",
""
],
[
"Tesi",
"P.",
""
]
] |
This paper considers the problem of identifying the parameters of an uncertain linear system by means of feedback control. The problem is approached by considering time-varying controllers. It is shown that even when the uncertainty set is not finite, parameter identifiability can be generically ensured by switching among a finite number of linear time-invariant controllers. The results are shown to have several implications, ranging from fault detection and isolation to adaptive and supervisory control. Practical aspects of the problem are also discussed in details.
|
2108.05196
|
Peter Zaspel
|
Drishti Maharjan and Peter Zaspel
|
Towards data-driven filters in Paraview
| null | null | null | null |
cs.LG cs.GR cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent progress in scientific visualization has expanded the scope of
visualization from being merely a way of presentation to an analysis and
discovery tool. A given visualization result is usually generated by applying a
series of transformations or filters to the underlying data. Nowadays, such
filters use deterministic algorithms to process the data. In this work, we aim
at extending this methodology towards data-driven filters, thus filters that
expose the abilities of pre-trained machine learning models to the
visualization system. The use of such data-driven filters is of particular
interest in fields like segmentation, classification, etc., where machine
learning models regularly outperform existing algorithmic approaches. To
showcase this idea, we couple Paraview, the well-known flow visualization tool,
with PyTorch, a deep learning framework. Paraview is extended by plugins that
allow users to load pre-trained models of their choice in the form of newly
developed filters. The filters transform the input data by feeding it into the
model and then provide the model's output as input to the remaining
visualization pipeline. A series of simplistic use cases for segmentation and
classification on image and fluid data is presented to showcase the technical
applicability of such data-driven transformations in Paraview for future
complex analysis tasks.
|
[
{
"created": "Wed, 11 Aug 2021 13:02:22 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Aug 2021 08:10:47 GMT",
"version": "v2"
}
] |
2021-08-13
|
[
[
"Maharjan",
"Drishti",
""
],
[
"Zaspel",
"Peter",
""
]
] |
Recent progress in scientific visualization has expanded the scope of visualization from being merely a way of presentation to an analysis and discovery tool. A given visualization result is usually generated by applying a series of transformations or filters to the underlying data. Nowadays, such filters use deterministic algorithms to process the data. In this work, we aim at extending this methodology towards data-driven filters, thus filters that expose the abilities of pre-trained machine learning models to the visualization system. The use of such data-driven filters is of particular interest in fields like segmentation, classification, etc., where machine learning models regularly outperform existing algorithmic approaches. To showcase this idea, we couple Paraview, the well-known flow visualization tool, with PyTorch, a deep learning framework. Paraview is extended by plugins that allow users to load pre-trained models of their choice in the form of newly developed filters. The filters transform the input data by feeding it into the model and then provide the model's output as input to the remaining visualization pipeline. A series of simplistic use cases for segmentation and classification on image and fluid data is presented to showcase the technical applicability of such data-driven transformations in Paraview for future complex analysis tasks.
|
2306.02291
|
Shijie Chang
|
Shijie Chang, Zeqi Hao, Ben Kang, Xiaoqi Zhao, Jiawen Zhu, Zhenyu
Chen, Lihe Zhang, Lu Zhang, Huchuan Lu
|
3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic
Segmentation on VSPW
|
3rd Place Solution for CVPR 2023 PVUW VSS Track
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce 3rd place solution for PVUW2023 VSS track.
Semantic segmentation is a fundamental task in computer vision with numerous
real-world applications. We have explored various image-level visual backbones
and segmentation heads to tackle the problem of video semantic segmentation.
Through our experimentation, we find that InternImage-H as the backbone and
Mask2former as the segmentation head achieves the best performance. In
addition, we explore two post-precessing methods: CascadePSP and Segment
Anything Model (SAM). Ultimately, our approach obtains 62.60\% and 64.84\% mIoU
on the VSPW test set1 and final test set, respectively, securing the third
position in the PVUW2023 VSS track.
|
[
{
"created": "Sun, 4 Jun 2023 07:50:38 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Jun 2023 01:49:09 GMT",
"version": "v2"
}
] |
2023-06-07
|
[
[
"Chang",
"Shijie",
""
],
[
"Hao",
"Zeqi",
""
],
[
"Kang",
"Ben",
""
],
[
"Zhao",
"Xiaoqi",
""
],
[
"Zhu",
"Jiawen",
""
],
[
"Chen",
"Zhenyu",
""
],
[
"Zhang",
"Lihe",
""
],
[
"Zhang",
"Lu",
""
],
[
"Lu",
"Huchuan",
""
]
] |
In this paper, we introduce 3rd place solution for PVUW2023 VSS track. Semantic segmentation is a fundamental task in computer vision with numerous real-world applications. We have explored various image-level visual backbones and segmentation heads to tackle the problem of video semantic segmentation. Through our experimentation, we find that InternImage-H as the backbone and Mask2former as the segmentation head achieves the best performance. In addition, we explore two post-precessing methods: CascadePSP and Segment Anything Model (SAM). Ultimately, our approach obtains 62.60\% and 64.84\% mIoU on the VSPW test set1 and final test set, respectively, securing the third position in the PVUW2023 VSS track.
|
1509.08634
|
Takayuki Osogami
|
Takayuki Osogami and Makoto Otsuka
|
Learning dynamic Boltzmann machines with spike-timing dependent
plasticity
|
Preliminary and substantially different version of the paper appeared
in http://www.nature.com/articles/srep14149
| null | null | null |
cs.NE cs.AI cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a particularly structured Boltzmann machine, which we refer to as
a dynamic Boltzmann machine (DyBM), as a stochastic model of a
multi-dimensional time-series. The DyBM can have infinitely many layers of
units but allows exact and efficient inference and learning when its parameters
have a proposed structure. This proposed structure is motivated by postulates
and observations, from biological neural networks, that the synaptic weight is
strengthened or weakened, depending on the timing of spikes (i.e., spike-timing
dependent plasticity or STDP). We show that the learning rule of updating the
parameters of the DyBM in the direction of maximizing the likelihood of given
time-series can be interpreted as STDP with long term potentiation and long
term depression. The learning rule has a guarantee of convergence and can be
performed in a distributed matter (i.e., local in space) with limited memory
(i.e., local in time).
|
[
{
"created": "Tue, 29 Sep 2015 08:30:12 GMT",
"version": "v1"
}
] |
2015-09-30
|
[
[
"Osogami",
"Takayuki",
""
],
[
"Otsuka",
"Makoto",
""
]
] |
We propose a particularly structured Boltzmann machine, which we refer to as a dynamic Boltzmann machine (DyBM), as a stochastic model of a multi-dimensional time-series. The DyBM can have infinitely many layers of units but allows exact and efficient inference and learning when its parameters have a proposed structure. This proposed structure is motivated by postulates and observations, from biological neural networks, that the synaptic weight is strengthened or weakened, depending on the timing of spikes (i.e., spike-timing dependent plasticity or STDP). We show that the learning rule of updating the parameters of the DyBM in the direction of maximizing the likelihood of given time-series can be interpreted as STDP with long term potentiation and long term depression. The learning rule has a guarantee of convergence and can be performed in a distributed matter (i.e., local in space) with limited memory (i.e., local in time).
|
1901.04713
|
Chien-Sheng Wu
|
Chien-Sheng Wu, Richard Socher, Caiming Xiong
|
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
|
ICLR 2019
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
End-to-end task-oriented dialogue is challenging since knowledge bases are
usually large, dynamic and hard to incorporate into a learning framework. We
propose the global-to-local memory pointer (GLMP) networks to address this
issue. In our model, a global memory encoder and a local memory decoder are
proposed to share external knowledge. The encoder encodes dialogue history,
modifies global contextual representation, and generates a global memory
pointer. The decoder first generates a sketch response with unfilled slots.
Next, it passes the global memory pointer to filter the external knowledge for
relevant information, then instantiates the slots via the local memory
pointers. We empirically show that our model can improve copy accuracy and
mitigate the common out-of-vocabulary problem. As a result, GLMP is able to
improve over the previous state-of-the-art models in both simulated bAbI
Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on
automatic and human evaluation.
|
[
{
"created": "Tue, 15 Jan 2019 08:55:53 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Mar 2019 05:13:11 GMT",
"version": "v2"
}
] |
2019-04-01
|
[
[
"Wu",
"Chien-Sheng",
""
],
[
"Socher",
"Richard",
""
],
[
"Xiong",
"Caiming",
""
]
] |
End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge. The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer. The decoder first generates a sketch response with unfilled slots. Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers. We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem. As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.
|
2003.08915
|
Olivier Nicole
|
Olivier Nicole, Matthieu Lemerre, S\'ebastien Bardin, Xavier Rival
|
Automatically Proving Microkernels Free from Privilege Escalation from
their Executable
|
19 pages, 11 figures, submitted to IEEE Symposium on Security and
Privacy 2021
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Operating system kernels are the security keystone of most computer systems,
as they provide the core protection mechanisms. Kernels are in particular
responsible for their own security, i.e. they must prevent untrusted user tasks
from reaching their level of privilege. We demonstrate that proving such
absence of privilege escalation is a pre-requisite for any definitive security
proof of the kernel. While prior OS kernel formal verifications were performed
either on source code or crafted kernels, with manual or semi-automated methods
requiring significant human efforts in annotations or proofs, we show that it
is possible to compute such kernel security proofs using fully-automated
methods and starting from the executable code of an existing microkernel with
no modification, thus formally verifying absence of privilege escalation with
high confidence for a low cost. We applied our method on two embedded
microkernels, including the industrial kernel AnonymOS: with only 58 lines of
annotation and less than 10 minutes of computation, our method finds a
vulnerability in a first (buggy) version of AnonymOS and verifies absence of
privilege escalation in a second (secure) version.
|
[
{
"created": "Thu, 19 Mar 2020 17:28:36 GMT",
"version": "v1"
}
] |
2020-03-20
|
[
[
"Nicole",
"Olivier",
""
],
[
"Lemerre",
"Matthieu",
""
],
[
"Bardin",
"Sébastien",
""
],
[
"Rival",
"Xavier",
""
]
] |
Operating system kernels are the security keystone of most computer systems, as they provide the core protection mechanisms. Kernels are in particular responsible for their own security, i.e. they must prevent untrusted user tasks from reaching their level of privilege. We demonstrate that proving such absence of privilege escalation is a pre-requisite for any definitive security proof of the kernel. While prior OS kernel formal verifications were performed either on source code or crafted kernels, with manual or semi-automated methods requiring significant human efforts in annotations or proofs, we show that it is possible to compute such kernel security proofs using fully-automated methods and starting from the executable code of an existing microkernel with no modification, thus formally verifying absence of privilege escalation with high confidence for a low cost. We applied our method on two embedded microkernels, including the industrial kernel AnonymOS: with only 58 lines of annotation and less than 10 minutes of computation, our method finds a vulnerability in a first (buggy) version of AnonymOS and verifies absence of privilege escalation in a second (secure) version.
|
1302.6809
|
Dan Geiger
|
Dan Geiger, Azaria Paz, Judea Pearl
|
On Testing Whether an Embedded Bayesian Network Represents a Probability
Model
|
Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994)
| null | null |
UAI-P-1994-PG-244-252
|
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Testing the validity of probabilistic models containing unmeasured (hidden)
variables is shown to be a hard task. We show that the task of testing whether
models are structurally incompatible with the data at hand, requires an
exponential number of independence evaluations, each of the form: "X is
conditionally independent of Y, given Z." In contrast, a linear number of such
evaluations is required to test a standard Bayesian network (one per vertex).
On the positive side, we show that if a network with hidden variables G has a
tree skeleton, checking whether G represents a given probability model P
requires the polynomial number of such independence evaluations. Moreover, we
provide an algorithm that efficiently constructs a tree-structured Bayesian
network (with hidden variables) that represents P if such a network exists, and
further recognizes when such a network does not exist.
|
[
{
"created": "Wed, 27 Feb 2013 14:16:13 GMT",
"version": "v1"
}
] |
2013-02-28
|
[
[
"Geiger",
"Dan",
""
],
[
"Paz",
"Azaria",
""
],
[
"Pearl",
"Judea",
""
]
] |
Testing the validity of probabilistic models containing unmeasured (hidden) variables is shown to be a hard task. We show that the task of testing whether models are structurally incompatible with the data at hand, requires an exponential number of independence evaluations, each of the form: "X is conditionally independent of Y, given Z." In contrast, a linear number of such evaluations is required to test a standard Bayesian network (one per vertex). On the positive side, we show that if a network with hidden variables G has a tree skeleton, checking whether G represents a given probability model P requires the polynomial number of such independence evaluations. Moreover, we provide an algorithm that efficiently constructs a tree-structured Bayesian network (with hidden variables) that represents P if such a network exists, and further recognizes when such a network does not exist.
|
1906.09573
|
Yang Ai
|
Yang Ai, Zhen-Hua Ling
|
A Neural Vocoder with Hierarchical Generation of Amplitude and Phase
Spectra for Statistical Parametric Speech Synthesis
|
Published in IEEE Transactions on Audio, Speech and Language
Processing
| null |
10.1109/TASLP.2020.2970241
| null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a neural vocoder named HiNet which reconstructs speech
waveforms from acoustic features by predicting amplitude and phase spectra
hierarchically. Different from existing neural vocoders such as WaveNet,
SampleRNN and WaveRNN which directly generate waveform samples using single
neural networks, the HiNet vocoder is composed of an amplitude spectrum
predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a simple DNN
model which predicts log amplitude spectra (LAS) from acoustic features. The
predicted LAS are sent into the PSP for phase recovery. Considering the issue
of phase warping and the difficulty of phase modeling, the PSP is constructed
by concatenating a neural source-filter (NSF) waveform generator with a phase
extractor. We also introduce generative adversarial networks (GANs) into both
ASP and PSP. Finally, the outputs of ASP and PSP are combined to reconstruct
speech waveforms by short-time Fourier synthesis. Since there are no
autoregressive structures in both predictors, the HiNet vocoder can generate
speech waveforms with high efficiency. Objective and subjective experimental
results show that our proposed HiNet vocoder achieves better naturalness of
reconstructed speech than the conventional STRAIGHT vocoder, a 16-bit WaveNet
vocoder using open source implementation and an NSF vocoder with similar
complexity to the PSP and obtains similar performance with a 16-bit WaveRNN
vocoder. We also find that the performance of HiNet is insensitive to the
complexity of the neural waveform generator in PSP to some extend. After
simplifying its model structure, the time consumed for generating 1s waveforms
of 16kHz speech using a GPU can be further reduced from 0.34s to 0.19s without
significant quality degradation.
|
[
{
"created": "Sun, 23 Jun 2019 10:01:33 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Feb 2020 11:05:33 GMT",
"version": "v2"
}
] |
2020-02-06
|
[
[
"Ai",
"Yang",
""
],
[
"Ling",
"Zhen-Hua",
""
]
] |
This paper presents a neural vocoder named HiNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra hierarchically. Different from existing neural vocoders such as WaveNet, SampleRNN and WaveRNN which directly generate waveform samples using single neural networks, the HiNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a simple DNN model which predicts log amplitude spectra (LAS) from acoustic features. The predicted LAS are sent into the PSP for phase recovery. Considering the issue of phase warping and the difficulty of phase modeling, the PSP is constructed by concatenating a neural source-filter (NSF) waveform generator with a phase extractor. We also introduce generative adversarial networks (GANs) into both ASP and PSP. Finally, the outputs of ASP and PSP are combined to reconstruct speech waveforms by short-time Fourier synthesis. Since there are no autoregressive structures in both predictors, the HiNet vocoder can generate speech waveforms with high efficiency. Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder, a 16-bit WaveNet vocoder using open source implementation and an NSF vocoder with similar complexity to the PSP and obtains similar performance with a 16-bit WaveRNN vocoder. We also find that the performance of HiNet is insensitive to the complexity of the neural waveform generator in PSP to some extend. After simplifying its model structure, the time consumed for generating 1s waveforms of 16kHz speech using a GPU can be further reduced from 0.34s to 0.19s without significant quality degradation.
|
1303.1571
|
Rodrigo de Lamare
|
Lei Wang and Rodrigo C. de Lamare
|
Reduced-rank Adaptive Constrained Constant Modulus Beamforming
Algorithms based on Joint Iterative Optimization of Filters
|
4 figures
|
DSP, 2011
| null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a reduced-rank scheme for adaptive beamforming based on
the constrained joint iterative optimization of filters. We employ this scheme
to devise two novel reduced-rank adaptive algorithms according to the constant
modulus (CM) criterion with different constraints. The first devised algorithm
is formulated as a constrained joint iterative optimization of a projection
matrix and a reduced-rank filter with respect to the CM criterion subject to a
constraint on the array response. The constrained constant modulus (CCM)
expressions for the projection matrix and the reduced-rank weight vector are
derived, and a low-complexity adaptive algorithm is presented to jointly
estimate them for implementation. The second proposed algorithm is extended
from the first one and implemented according to the CM criterion subject to a
constraint on the array response and an orthogonal constraint on the projection
matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal
constraint and improve the performance. Simulation results are given to show
superior performance of the proposed algorithms in comparison with existing
methods.
|
[
{
"created": "Wed, 6 Mar 2013 23:12:24 GMT",
"version": "v1"
}
] |
2013-03-08
|
[
[
"Wang",
"Lei",
""
],
[
"de Lamare",
"Rodrigo C.",
""
]
] |
This paper proposes a reduced-rank scheme for adaptive beamforming based on the constrained joint iterative optimization of filters. We employ this scheme to devise two novel reduced-rank adaptive algorithms according to the constant modulus (CM) criterion with different constraints. The first devised algorithm is formulated as a constrained joint iterative optimization of a projection matrix and a reduced-rank filter with respect to the CM criterion subject to a constraint on the array response. The constrained constant modulus (CCM) expressions for the projection matrix and the reduced-rank weight vector are derived, and a low-complexity adaptive algorithm is presented to jointly estimate them for implementation. The second proposed algorithm is extended from the first one and implemented according to the CM criterion subject to a constraint on the array response and an orthogonal constraint on the projection matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal constraint and improve the performance. Simulation results are given to show superior performance of the proposed algorithms in comparison with existing methods.
|
1812.01083
|
Jacqueline Brixey
|
Jacqueline Brixey, Ramesh Manuvinakurike, Nham Le, Tuan Lai, Walter
Chang, Trung Bui
|
A System for Automated Image Editing from Natural Language Commands
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents the task of modifying images in an image editing program
using natural language written commands. We utilize a corpus of over 6000 image
edit text requests to alter real world images collected via crowdsourcing. A
novel framework composed of actions and entities to map a user's natural
language request to executable commands in an image editing program is
described. We resolve previously labeled annotator disagreement through a
voting process and complete annotation of the corpus. We experimented with
different machine learning models and found that the LSTM, the SVM, and the
bidirectional LSTM-CRF joint models are the best to detect image editing
actions and associated entities in a given utterance.
|
[
{
"created": "Mon, 3 Dec 2018 21:12:31 GMT",
"version": "v1"
}
] |
2018-12-05
|
[
[
"Brixey",
"Jacqueline",
""
],
[
"Manuvinakurike",
"Ramesh",
""
],
[
"Le",
"Nham",
""
],
[
"Lai",
"Tuan",
""
],
[
"Chang",
"Walter",
""
],
[
"Bui",
"Trung",
""
]
] |
This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework composed of actions and entities to map a user's natural language request to executable commands in an image editing program is described. We resolve previously labeled annotator disagreement through a voting process and complete annotation of the corpus. We experimented with different machine learning models and found that the LSTM, the SVM, and the bidirectional LSTM-CRF joint models are the best to detect image editing actions and associated entities in a given utterance.
|
2109.04399
|
Corinna Hertweck
|
Corinna Hertweck and Tim R\"az
|
Gradual (In)Compatibility of Fairness Criteria
|
Code available on GitHub:
https://github.com/hcorinna/gradual-compatibility, extended version of paper
accepted to AAAI'22
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Impossibility results show that important fairness measures (independence,
separation, sufficiency) cannot be satisfied at the same time under reasonable
assumptions. This paper explores whether we can satisfy and/or improve these
fairness measures simultaneously to a certain degree. We introduce
information-theoretic formulations of the fairness measures and define degrees
of fairness based on these formulations. The information-theoretic formulations
suggest unexplored theoretical relations between the three fairness measures.
In the experimental part, we use the information-theoretic expressions as
regularizers to obtain fairness-regularized predictors for three standard
datasets. Our experiments show that a) fairness regularization directly
increases fairness measures, in line with existing work, and b) some fairness
regularizations indirectly increase other fairness measures, as suggested by
our theoretical findings. This establishes that it is possible to increase the
degree to which some fairness measures are satisfied at the same time -- some
fairness measures are gradually compatible.
|
[
{
"created": "Thu, 9 Sep 2021 16:37:30 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Mar 2022 18:03:52 GMT",
"version": "v2"
}
] |
2022-03-21
|
[
[
"Hertweck",
"Corinna",
""
],
[
"Räz",
"Tim",
""
]
] |
Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.
|
1807.04001
|
Ismail Elezi
|
Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Durr
and Thilo Stadelmann
|
Learning Neural Models for End-to-End Clustering
|
Accepted for publication on ANNPR 2018
| null | null | null |
cs.LG cs.AI cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a novel end-to-end neural network architecture that, once trained,
directly outputs a probabilistic clustering of a batch of input examples in one
pass. It estimates a distribution over the number of clusters $k$, and for each
$1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster
assignment for each data point. The network is trained in advance in a
supervised fashion on separate data to learn grouping by any perceptual
similarity criterion based on pairwise labels (same/different group). It can
then be applied to different data containing different groups. We demonstrate
promising performance on high-dimensional data like images (COIL-100) and
speech (TIMIT). We call this ``learning to cluster'' and show its conceptual
difference to deep metric learning, semi-supervise clustering and other related
approaches while having the advantage of performing learnable clustering fully
end-to-end.
|
[
{
"created": "Wed, 11 Jul 2018 08:45:45 GMT",
"version": "v1"
}
] |
2018-07-12
|
[
[
"Meier",
"Benjamin Bruno",
""
],
[
"Elezi",
"Ismail",
""
],
[
"Amirian",
"Mohammadreza",
""
],
[
"Durr",
"Oliver",
""
],
[
"Stadelmann",
"Thilo",
""
]
] |
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters $k$, and for each $1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this ``learning to cluster'' and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.
|
1608.07670
|
Snehanshu Saha
|
Sobin CC, Snehanshu Saha, Vaskar Raychoudhury, Hategekimana Fidele and
Sumana Sinha
|
CISER: An Amoebiasis inspired Model for Epidemic Message Propagation in
DTN
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Delay Tolerant Networks (DTNs) are sparse mobile networks, which experiences
frequent disruptions in connectivity among nodes. Usually, DTN follows
store-carry-and forward mechanism for message forwarding, in which a node store
and carry the message until it finds an appropriate relay node to forward
further in the network. So, The efficiency of DTN routing protocol relies on
the intelligent selection of a relay node from a set of encountered nodes.
Although there are plenty of DTN routing schemes proposed in the literature
based on different strategies of relay selection, there are not many
mathematical models proposed to study the behavior of message forwarding in
DTN. In this paper, we have proposed a novel epidemic model, called as CISER
model, for message propagation in DTN, based on Amoebiasis disease propagation
in human population. The proposed CISER model is an extension of SIR epidemic
model with additional states to represent the resource constrained behavior of
nodes in DTN. Experimental results using both synthetic and real-world traces
show that the proposed model improves the routing performance metrics, such as
delivery ratio, overhead ratio and delivery delay compared to SIR model.
|
[
{
"created": "Sat, 27 Aug 2016 07:20:39 GMT",
"version": "v1"
}
] |
2016-08-30
|
[
[
"CC",
"Sobin",
""
],
[
"Saha",
"Snehanshu",
""
],
[
"Raychoudhury",
"Vaskar",
""
],
[
"Fidele",
"Hategekimana",
""
],
[
"Sinha",
"Sumana",
""
]
] |
Delay Tolerant Networks (DTNs) are sparse mobile networks, which experiences frequent disruptions in connectivity among nodes. Usually, DTN follows store-carry-and forward mechanism for message forwarding, in which a node store and carry the message until it finds an appropriate relay node to forward further in the network. So, The efficiency of DTN routing protocol relies on the intelligent selection of a relay node from a set of encountered nodes. Although there are plenty of DTN routing schemes proposed in the literature based on different strategies of relay selection, there are not many mathematical models proposed to study the behavior of message forwarding in DTN. In this paper, we have proposed a novel epidemic model, called as CISER model, for message propagation in DTN, based on Amoebiasis disease propagation in human population. The proposed CISER model is an extension of SIR epidemic model with additional states to represent the resource constrained behavior of nodes in DTN. Experimental results using both synthetic and real-world traces show that the proposed model improves the routing performance metrics, such as delivery ratio, overhead ratio and delivery delay compared to SIR model.
|
2312.02339
|
Derek Lim
|
Derek Lim and Joshua Robinson and Stefanie Jegelka and Haggai Maron
|
Expressive Sign Equivariant Networks for Spectral Geometric Learning
|
NeurIPS 2023 Spotlight
| null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work has shown the utility of developing machine learning models that
respect the structure and symmetries of eigenvectors. These works promote sign
invariance, since for any eigenvector v the negation -v is also an eigenvector.
However, we show that sign invariance is theoretically limited for tasks such
as building orthogonally equivariant models and learning node positional
encodings for link prediction in graphs. In this work, we demonstrate the
benefits of sign equivariance for these tasks. To obtain these benefits, we
develop novel sign equivariant neural network architectures. Our models are
based on a new analytic characterization of sign equivariant polynomials and
thus inherit provable expressiveness properties. Controlled synthetic
experiments show that our networks can achieve the theoretically predicted
benefits of sign equivariant models. Code is available at
https://github.com/cptq/Sign-Equivariant-Nets.
|
[
{
"created": "Mon, 4 Dec 2023 20:48:18 GMT",
"version": "v1"
}
] |
2023-12-06
|
[
[
"Lim",
"Derek",
""
],
[
"Robinson",
"Joshua",
""
],
[
"Jegelka",
"Stefanie",
""
],
[
"Maron",
"Haggai",
""
]
] |
Recent work has shown the utility of developing machine learning models that respect the structure and symmetries of eigenvectors. These works promote sign invariance, since for any eigenvector v the negation -v is also an eigenvector. However, we show that sign invariance is theoretically limited for tasks such as building orthogonally equivariant models and learning node positional encodings for link prediction in graphs. In this work, we demonstrate the benefits of sign equivariance for these tasks. To obtain these benefits, we develop novel sign equivariant neural network architectures. Our models are based on a new analytic characterization of sign equivariant polynomials and thus inherit provable expressiveness properties. Controlled synthetic experiments show that our networks can achieve the theoretically predicted benefits of sign equivariant models. Code is available at https://github.com/cptq/Sign-Equivariant-Nets.
|
2308.03648
|
Richard Nock
|
Richard Nock and Mathieu Guillame-Bert
|
Generative Forests
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Tabular data represents one of the most prevalent form of data. When it comes
to data generation, many approaches would learn a density for the data
generation process, but would not necessarily end up with a sampler, even less
so being exact with respect to the underlying density. A second issue is on
models: while complex modeling based on neural nets thrives in image or text
generation (etc.), less is known for powerful generative models on tabular
data. A third problem is the visible chasm on tabular data between training
algorithms for supervised learning with remarkable properties (e.g. boosting),
and a comparative lack of guarantees when it comes to data generation. In this
paper, we tackle the three problems, introducing new tree-based generative
models convenient for density modeling and tabular data generation that improve
on modeling capabilities of recent proposals, and a training algorithm which
simplifies the training setting of previous approaches and displays
boosting-compliant convergence. This algorithm has the convenient property to
rely on a supervised training scheme that can be implemented by a few tweaks to
the most popular induction scheme for decision tree induction with two classes.
Experiments are provided on missing data imputation and comparing generated
data to real data, displaying the quality of the results obtained by our
approach, in particular against state of the art.
|
[
{
"created": "Mon, 7 Aug 2023 14:58:53 GMT",
"version": "v1"
}
] |
2023-08-08
|
[
[
"Nock",
"Richard",
""
],
[
"Guillame-Bert",
"Mathieu",
""
]
] |
Tabular data represents one of the most prevalent form of data. When it comes to data generation, many approaches would learn a density for the data generation process, but would not necessarily end up with a sampler, even less so being exact with respect to the underlying density. A second issue is on models: while complex modeling based on neural nets thrives in image or text generation (etc.), less is known for powerful generative models on tabular data. A third problem is the visible chasm on tabular data between training algorithms for supervised learning with remarkable properties (e.g. boosting), and a comparative lack of guarantees when it comes to data generation. In this paper, we tackle the three problems, introducing new tree-based generative models convenient for density modeling and tabular data generation that improve on modeling capabilities of recent proposals, and a training algorithm which simplifies the training setting of previous approaches and displays boosting-compliant convergence. This algorithm has the convenient property to rely on a supervised training scheme that can be implemented by a few tweaks to the most popular induction scheme for decision tree induction with two classes. Experiments are provided on missing data imputation and comparing generated data to real data, displaying the quality of the results obtained by our approach, in particular against state of the art.
|
2108.09134
|
Jed Mills
|
Jed Mills, Jia Hu, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang
|
Accelerating Federated Learning with a Global Biased Optimiser
| null | null | null | null |
cs.LG cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Federated Learning (FL) is a recent development in distributed machine
learning that collaboratively trains models without training data leaving
client devices, preserving data privacy. In real-world FL, the training set is
distributed over clients in a highly non-Independent and Identically
Distributed (non-IID) fashion, harming model convergence speed and final
performance. To address this challenge, we propose a novel, generalised
approach for incorporating adaptive optimisation into FL with the Federated
Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by employing
a set of global biased optimiser values during training, reducing
'client-drift' from non-IID data whilst benefiting from adaptive optimisation.
We show that in FedGBO, updates to the global model can be reformulated as
centralised training using biased gradients and optimiser updates, and apply
this framework to prove FedGBO's convergence on nonconvex objectives when using
the momentum-SGD (SGDm) optimiser. We also conduct extensive experiments using
4 FL benchmark datasets (CIFAR100, Sent140, FEMNIST, Shakespeare) and 3 popular
optimisers (SGDm, RMSProp, Adam) to compare FedGBO against six state-of-the-art
FL algorithms. The results demonstrate that FedGBO displays superior or
competitive performance across the datasets whilst having low data-upload and
computational costs, and provide practical insights into the trade-offs
associated with different adaptive-FL algorithms and optimisers.
|
[
{
"created": "Fri, 20 Aug 2021 12:08:44 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Sep 2021 10:38:22 GMT",
"version": "v2"
},
{
"created": "Wed, 5 Oct 2022 21:27:40 GMT",
"version": "v3"
}
] |
2022-10-07
|
[
[
"Mills",
"Jed",
""
],
[
"Hu",
"Jia",
""
],
[
"Min",
"Geyong",
""
],
[
"Jin",
"Rui",
""
],
[
"Zheng",
"Siwei",
""
],
[
"Wang",
"Jin",
""
]
] |
Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data privacy. In real-world FL, the training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, harming model convergence speed and final performance. To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by employing a set of global biased optimiser values during training, reducing 'client-drift' from non-IID data whilst benefiting from adaptive optimisation. We show that in FedGBO, updates to the global model can be reformulated as centralised training using biased gradients and optimiser updates, and apply this framework to prove FedGBO's convergence on nonconvex objectives when using the momentum-SGD (SGDm) optimiser. We also conduct extensive experiments using 4 FL benchmark datasets (CIFAR100, Sent140, FEMNIST, Shakespeare) and 3 popular optimisers (SGDm, RMSProp, Adam) to compare FedGBO against six state-of-the-art FL algorithms. The results demonstrate that FedGBO displays superior or competitive performance across the datasets whilst having low data-upload and computational costs, and provide practical insights into the trade-offs associated with different adaptive-FL algorithms and optimisers.
|
2405.03870
|
Widad Elouataoui
|
Widad Elouataoui
|
AI-Driven Frameworks for Enhancing Data Quality in Big Data Ecosystems:
Error_Detection, Correction, and Metadata Integration
|
Doctoral thesis
| null | null | null |
cs.AI cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The widespread adoption of big data has ushered in a new era of data-driven
decision-making, transforming numerous industries and sectors. However, the
efficacy of these decisions hinges on the quality of the underlying data. Poor
data quality can result in inaccurate analyses and deceptive conclusions.
Managing the vast volume, velocity, and variety of data sources presents
significant challenges, heightening the importance of addressing big data
quality issues. While there has been increased attention from both academia and
industry, current approaches often lack comprehensiveness and universality.
They tend to focus on limited metrics, neglecting other dimensions of data
quality. Moreover, existing methods are often context-specific, limiting their
applicability across different domains. There is a clear need for intelligent,
automated approaches leveraging artificial intelligence (AI) for advanced data
quality corrections.
To bridge these gaps, this Ph.D. thesis proposes a novel set of
interconnected frameworks aimed at enhancing big data quality comprehensively.
Firstly, we introduce new quality metrics and a weighted scoring system for
precise data quality assessment. Secondly, we present a generic framework for
detecting various quality anomalies using AI models. Thirdly, we propose an
innovative framework for correcting detected anomalies through predictive
modeling. Additionally, we address metadata quality enhancement within big data
ecosystems. These frameworks are rigorously tested on diverse datasets,
demonstrating their efficacy in improving big data quality. Finally, the thesis
concludes with insights and suggestions for future research directions.
|
[
{
"created": "Mon, 6 May 2024 21:36:45 GMT",
"version": "v1"
}
] |
2024-05-08
|
[
[
"Elouataoui",
"Widad",
""
]
] |
The widespread adoption of big data has ushered in a new era of data-driven decision-making, transforming numerous industries and sectors. However, the efficacy of these decisions hinges on the quality of the underlying data. Poor data quality can result in inaccurate analyses and deceptive conclusions. Managing the vast volume, velocity, and variety of data sources presents significant challenges, heightening the importance of addressing big data quality issues. While there has been increased attention from both academia and industry, current approaches often lack comprehensiveness and universality. They tend to focus on limited metrics, neglecting other dimensions of data quality. Moreover, existing methods are often context-specific, limiting their applicability across different domains. There is a clear need for intelligent, automated approaches leveraging artificial intelligence (AI) for advanced data quality corrections. To bridge these gaps, this Ph.D. thesis proposes a novel set of interconnected frameworks aimed at enhancing big data quality comprehensively. Firstly, we introduce new quality metrics and a weighted scoring system for precise data quality assessment. Secondly, we present a generic framework for detecting various quality anomalies using AI models. Thirdly, we propose an innovative framework for correcting detected anomalies through predictive modeling. Additionally, we address metadata quality enhancement within big data ecosystems. These frameworks are rigorously tested on diverse datasets, demonstrating their efficacy in improving big data quality. Finally, the thesis concludes with insights and suggestions for future research directions.
|
2107.03068
|
Hajime Taira
|
Hajime Taira, Koki Onbe, Naoyuki Miyashita, Masatoshi Okutomi
|
Video-Based Camera Localization Using Anchor View Detection and
Recursive 3D Reconstruction
|
This paper have been accepted and will be appeared in the proceedings
of 17th International Conference on Machine Vision Applications (MVA2021)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we introduce a new camera localization strategy designed for
image sequences captured in challenging industrial situations such as
industrial parts inspection. To deal with peculiar appearances that hurt
standard 3D reconstruction pipeline, we exploit pre-knowledge of the scene by
selecting key frames in the sequence (called as anchors) which are roughly
connected to a certain location. Our method then seek the location of each
frame in time-order, while recursively updating an augmented 3D model which can
provide current camera location and surrounding 3D structure. In an experiment
on a practical industrial situation, our method can localize over 99% frames in
the input sequence, whereas standard localization methods fail to reconstruct a
complete camera trajectory.
|
[
{
"created": "Wed, 7 Jul 2021 08:13:33 GMT",
"version": "v1"
}
] |
2021-07-08
|
[
[
"Taira",
"Hajime",
""
],
[
"Onbe",
"Koki",
""
],
[
"Miyashita",
"Naoyuki",
""
],
[
"Okutomi",
"Masatoshi",
""
]
] |
In this paper we introduce a new camera localization strategy designed for image sequences captured in challenging industrial situations such as industrial parts inspection. To deal with peculiar appearances that hurt standard 3D reconstruction pipeline, we exploit pre-knowledge of the scene by selecting key frames in the sequence (called as anchors) which are roughly connected to a certain location. Our method then seek the location of each frame in time-order, while recursively updating an augmented 3D model which can provide current camera location and surrounding 3D structure. In an experiment on a practical industrial situation, our method can localize over 99% frames in the input sequence, whereas standard localization methods fail to reconstruct a complete camera trajectory.
|
2407.11867
|
Zikui Cai
|
Zikui Cai, Yaoteng Tan, M. Salman Asif
|
Single Layer Single Gradient Unlearning
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Machine unlearning methods seek to revise pretrained models such that effects
of certain training samples can be removed. In addition to effective erasure,
low computational cost and general utility retention are also highly desirable.
Existing unlearning methods usually involve iterative updates over the model
parameters, which incurs a high computational cost. In this work, we propose an
efficient method that only requires a one-time gradient computation, with which
we modify only a single layer of model parameters. Specifically, we first
identify a small number of model layers that lie on the Pareto front of high
forget importance and low retain influence as critical layers. Then we search
for a suitable step size and take a step along the gradient direction of a
single critical layer while keeping other layers frozen. This method is highly
modular and can be used to unlearn multiple concepts simultaneously in a
controllable manner. We demonstrate the effectiveness and efficiency of this
method on various models including CLIP, stable diffusion, and VLMs, surpassing
other state-of-the-art methods.
|
[
{
"created": "Tue, 16 Jul 2024 15:52:36 GMT",
"version": "v1"
}
] |
2024-07-17
|
[
[
"Cai",
"Zikui",
""
],
[
"Tan",
"Yaoteng",
""
],
[
"Asif",
"M. Salman",
""
]
] |
Machine unlearning methods seek to revise pretrained models such that effects of certain training samples can be removed. In addition to effective erasure, low computational cost and general utility retention are also highly desirable. Existing unlearning methods usually involve iterative updates over the model parameters, which incurs a high computational cost. In this work, we propose an efficient method that only requires a one-time gradient computation, with which we modify only a single layer of model parameters. Specifically, we first identify a small number of model layers that lie on the Pareto front of high forget importance and low retain influence as critical layers. Then we search for a suitable step size and take a step along the gradient direction of a single critical layer while keeping other layers frozen. This method is highly modular and can be used to unlearn multiple concepts simultaneously in a controllable manner. We demonstrate the effectiveness and efficiency of this method on various models including CLIP, stable diffusion, and VLMs, surpassing other state-of-the-art methods.
|
1703.09876
|
Hongge Chen
|
Hongge Chen, Duane Boning and Zheng Zhang
|
Efficient Spatial Variation Characterization via Matrix Completion
| null | null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a novel method to estimate and characterize spatial
variations on dies or wafers. This new technique exploits recent developments
in matrix completion, enabling estimation of spatial variation across wafers or
dies with a small number of randomly picked sampling points while still
achieving fairly high accuracy. This new approach can be easily generalized,
including for estimation of mixed spatial and structure or device type
information.
|
[
{
"created": "Wed, 29 Mar 2017 03:48:14 GMT",
"version": "v1"
}
] |
2017-03-30
|
[
[
"Chen",
"Hongge",
""
],
[
"Boning",
"Duane",
""
],
[
"Zhang",
"Zheng",
""
]
] |
In this paper, we propose a novel method to estimate and characterize spatial variations on dies or wafers. This new technique exploits recent developments in matrix completion, enabling estimation of spatial variation across wafers or dies with a small number of randomly picked sampling points while still achieving fairly high accuracy. This new approach can be easily generalized, including for estimation of mixed spatial and structure or device type information.
|
1903.00719
|
Lukas Pfannschmidt
|
Lukas Pfannschmidt, Christina G\"opfert, Ursula Neumann, Dominik
Heider, Barbara Hammer
|
FRI -- Feature Relevance Intervals for Interpretable and Interactive
Data Exploration
|
Addition of IEEE copyright notice. Accepted for CIBCB 2019
(https://cibcb2019.icas.xyz/)
| null |
10.1109/CIBCB.2019.8791489
| null |
cs.LG cs.IR stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing feature selection methods are insufficient for analytic
purposes as soon as high dimensional data or redundant sensor signals are dealt
with since features can be selected due to spurious effects or correlations
rather than causal effects. To support the finding of causal features in
biomedical experiments, we hereby present FRI, an open source Python library
that can be used to identify all-relevant variables in linear classification
and (ordinal) regression problems. Using the recently proposed feature
relevance method, FRI is able to provide the base for further general
experimentation or in specific can facilitate the search for alternative
biomarkers. It can be used in an interactive context, by providing model
manipulation and visualization methods, or in a batch process as a filter
method.
|
[
{
"created": "Sat, 2 Mar 2019 15:16:15 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2019 17:21:03 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Jun 2019 14:41:04 GMT",
"version": "v3"
}
] |
2019-08-13
|
[
[
"Pfannschmidt",
"Lukas",
""
],
[
"Göpfert",
"Christina",
""
],
[
"Neumann",
"Ursula",
""
],
[
"Heider",
"Dominik",
""
],
[
"Hammer",
"Barbara",
""
]
] |
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method.
|
2112.05000
|
Sebastian U. Stich
|
Yehao Liu and Matteo Pagliardini and Tatjana Chavdarova and Sebastian
U. Stich
|
The Peril of Popular Deep Learning Uncertainty Estimation Methods
|
Presented at the Bayesian Deep Learning Workshop at NeurIPS 2021
| null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP),
Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to
improve the interpretability of machine learning models by assigning an
estimated uncertainty value to each of their prediction outputs. However, since
too high uncertainty estimates can have fatal consequences in practice, this
paper analyzes the above techniques.
Firstly, we show that GP methods always yield high uncertainty estimates on
out of distribution (OOD) data. Secondly, we show on a 2D toy example that both
BNNs and MCDropout do not give high uncertainty estimates on OOD samples.
Finally, we show empirically that this pitfall of BNNs and MCDropout holds on
real world datasets as well. Our insights (i) raise awareness for the more
cautious use of currently popular UE methods in Deep Learning, (ii) encourage
the development of UE methods that approximate GP-based methods -- instead of
BNNs and MCDropout, and (iii) our empirical setups can be used for verifying
the OOD performances of any other UE method. The source code is available at
https://github.com/epfml/uncertainity-estimation.
|
[
{
"created": "Thu, 9 Dec 2021 15:47:21 GMT",
"version": "v1"
}
] |
2021-12-10
|
[
[
"Liu",
"Yehao",
""
],
[
"Pagliardini",
"Matteo",
""
],
[
"Chavdarova",
"Tatjana",
""
],
[
"Stich",
"Sebastian U.",
""
]
] |
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to improve the interpretability of machine learning models by assigning an estimated uncertainty value to each of their prediction outputs. However, since too high uncertainty estimates can have fatal consequences in practice, this paper analyzes the above techniques. Firstly, we show that GP methods always yield high uncertainty estimates on out of distribution (OOD) data. Secondly, we show on a 2D toy example that both BNNs and MCDropout do not give high uncertainty estimates on OOD samples. Finally, we show empirically that this pitfall of BNNs and MCDropout holds on real world datasets as well. Our insights (i) raise awareness for the more cautious use of currently popular UE methods in Deep Learning, (ii) encourage the development of UE methods that approximate GP-based methods -- instead of BNNs and MCDropout, and (iii) our empirical setups can be used for verifying the OOD performances of any other UE method. The source code is available at https://github.com/epfml/uncertainity-estimation.
|
1810.12894
|
Yuri Burda
|
Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
|
Exploration by Random Network Distillation
| null | null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce an exploration bonus for deep reinforcement learning methods
that is easy to implement and adds minimal overhead to the computation
performed. The bonus is the error of a neural network predicting features of
the observations given by a fixed randomly initialized neural network. We also
introduce a method to flexibly combine intrinsic and extrinsic rewards. We find
that the random network distillation (RND) bonus combined with this increased
flexibility enables significant progress on several hard exploration Atari
games. In particular we establish state of the art performance on Montezuma's
Revenge, a game famously difficult for deep reinforcement learning methods. To
the best of our knowledge, this is the first method that achieves better than
average human performance on this game without using demonstrations or having
access to the underlying state of the game, and occasionally completes the
first level.
|
[
{
"created": "Tue, 30 Oct 2018 17:44:42 GMT",
"version": "v1"
}
] |
2018-10-31
|
[
[
"Burda",
"Yuri",
""
],
[
"Edwards",
"Harrison",
""
],
[
"Storkey",
"Amos",
""
],
[
"Klimov",
"Oleg",
""
]
] |
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.
|
1809.00926
|
Charith Perera
|
Mahmoud Barhamgi, Charith Perera, Chirine Ghedira, Djamal Benslimane
|
User-centric Privacy Engineering for the Internet of Things
|
12 Pages
|
IEEE Cloud Computing, 2018
| null | null |
cs.NI cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
User privacy concerns are widely regarded as a key obstacle to the success of
modern smart cyber-physical systems. In this paper, we analyse, through an
example, some of the requirements that future data collection architectures of
these systems should implement to provide effective privacy protection for
users. Then, we give an example of how these requirements can be implemented in
a smart home scenario. Our example architecture allows the user to balance the
privacy risks with the potential benefits and take a practical decision
determining the extent of the sharing. Based on this example architecture, we
identify a number of challenges that must be addressed by future data
processing systems in order to achieve effective privacy management for smart
cyber-physical systems.
|
[
{
"created": "Tue, 4 Sep 2018 12:53:59 GMT",
"version": "v1"
}
] |
2018-09-10
|
[
[
"Barhamgi",
"Mahmoud",
""
],
[
"Perera",
"Charith",
""
],
[
"Ghedira",
"Chirine",
""
],
[
"Benslimane",
"Djamal",
""
]
] |
User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.
|
2012.11011
|
Theodore Omtzigt
|
E. Theodore L. Omtzigt, Peter Gottschling, Mark Seligman, William Zorn
|
Universal Numbers Library: design and implementation of a
high-performance reproducible number systems library
|
7 pages, 4 figures
| null | null | null |
cs.CE cs.MS
|
http://creativecommons.org/licenses/by/4.0/
|
With the proliferation of embedded systems requiring intelligent behavior,
custom number systems to optimize performance per Watt of the entire system
become essential components for successful commercial products. We present the
Universal Number Library, a high-performance number systems library that
includes arbitrary integer, decimal, fixed-point, floating-point, and
introduces two tapered floating-point types, posit and valid, that support
reproducible arithmetic computation in arbitrary concurrency environments. We
discuss the design of the Universal library as a run-time for application
development, and as a platform for application-driven hardware validation. The
library implementation is described, and examples are provided to show
educational examples to elucidate the number system properties, and how
specialization is used to yield very high-performance emulation on existing
x86, ARM, and POWER processors. We will highlight the integration of the
library in larger application environments in computational science and
engineering to enable multi-precision and adaptive precision algorithms to
improve performance and efficiency of large scale and real-time applications.
We will demonstrate the integration of the Universal library into a
high-performance reproducible linear algebra run-time. We will conclude with
the roadmap of additional functionality of the library as we are targeting new
application domains, such as Software Defined Radio, instrumentation, sensor
fusion, and model-predictive control.
|
[
{
"created": "Sun, 20 Dec 2020 20:07:57 GMT",
"version": "v1"
}
] |
2020-12-22
|
[
[
"Omtzigt",
"E. Theodore L.",
""
],
[
"Gottschling",
"Peter",
""
],
[
"Seligman",
"Mark",
""
],
[
"Zorn",
"William",
""
]
] |
With the proliferation of embedded systems requiring intelligent behavior, custom number systems to optimize performance per Watt of the entire system become essential components for successful commercial products. We present the Universal Number Library, a high-performance number systems library that includes arbitrary integer, decimal, fixed-point, floating-point, and introduces two tapered floating-point types, posit and valid, that support reproducible arithmetic computation in arbitrary concurrency environments. We discuss the design of the Universal library as a run-time for application development, and as a platform for application-driven hardware validation. The library implementation is described, and examples are provided to show educational examples to elucidate the number system properties, and how specialization is used to yield very high-performance emulation on existing x86, ARM, and POWER processors. We will highlight the integration of the library in larger application environments in computational science and engineering to enable multi-precision and adaptive precision algorithms to improve performance and efficiency of large scale and real-time applications. We will demonstrate the integration of the Universal library into a high-performance reproducible linear algebra run-time. We will conclude with the roadmap of additional functionality of the library as we are targeting new application domains, such as Software Defined Radio, instrumentation, sensor fusion, and model-predictive control.
|
2009.05413
|
Michael Neuder
|
Michael Neuder, Daniel J. Moroz, Rithvik Rao, David C. Parkes
|
Defending Against Malicious Reorgs in Tezos Proof-of-Stake
|
To appear in the second ACM conference on Advances in Financial
Technology (AFT'20)
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Blockchains are intended to be immutable, so an attacker who is able to
delete transactions through a chain reorganization (a malicious reorg) can
perform a profitable double-spend attack. We study the rate at which an
attacker can execute reorgs in the Tezos Proof-of-Stake protocol. As an
example, an attacker with 40% of the staking power is able to execute a
20-block malicious reorg at an average rate of once per day, and the attack
probability increases super-linearly as the staking power grows beyond 40%.
Moreover, an attacker of the Tezos protocol knows in advance when an attack
opportunity will arise, and can use this knowledge to arrange transactions to
double-spend. We show that in particular cases, the Tezos protocol can be
adjusted to protect against deep reorgs. For instance, we demonstrate protocol
parameters that reduce the rate of length-20 reorg opportunities for a 40%
attacker by two orders of magnitude. We also observe a trade-off between
optimizing for robustness to deep reorgs (costly deviations that may be net
profitable because they enable double-spends) and robustness to selfish mining
(mining deviations that result in typically short reorgs that are profitable
even without double-spends). That is, the parameters that optimally protect
against one make the other attack easy. Finally, we develop a method that
monitors the Tezos blockchain health with respect to malicious reorgs using
only publicly available information.
|
[
{
"created": "Fri, 11 Sep 2020 12:58:51 GMT",
"version": "v1"
}
] |
2020-09-14
|
[
[
"Neuder",
"Michael",
""
],
[
"Moroz",
"Daniel J.",
""
],
[
"Rao",
"Rithvik",
""
],
[
"Parkes",
"David C.",
""
]
] |
Blockchains are intended to be immutable, so an attacker who is able to delete transactions through a chain reorganization (a malicious reorg) can perform a profitable double-spend attack. We study the rate at which an attacker can execute reorgs in the Tezos Proof-of-Stake protocol. As an example, an attacker with 40% of the staking power is able to execute a 20-block malicious reorg at an average rate of once per day, and the attack probability increases super-linearly as the staking power grows beyond 40%. Moreover, an attacker of the Tezos protocol knows in advance when an attack opportunity will arise, and can use this knowledge to arrange transactions to double-spend. We show that in particular cases, the Tezos protocol can be adjusted to protect against deep reorgs. For instance, we demonstrate protocol parameters that reduce the rate of length-20 reorg opportunities for a 40% attacker by two orders of magnitude. We also observe a trade-off between optimizing for robustness to deep reorgs (costly deviations that may be net profitable because they enable double-spends) and robustness to selfish mining (mining deviations that result in typically short reorgs that are profitable even without double-spends). That is, the parameters that optimally protect against one make the other attack easy. Finally, we develop a method that monitors the Tezos blockchain health with respect to malicious reorgs using only publicly available information.
|
1909.10111
|
Michael Posa
|
Yu-Ming Chen and Michael Posa
|
Optimal Reduced-order Modeling of Bipedal Locomotion
|
Submitted to ICRA 2020
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
State-of-the-art approaches to legged locomotion are widely dependent on the
use of models like the linear inverted pendulum (LIP) and the spring-loaded
inverted pendulum (SLIP), popular because their simplicity enables a wide array
of tools for planning, control, and analysis. However, they inevitably limit
the ability to execute complex tasks or agile maneuvers. In this work, we aim
to automatically synthesize models that remain low-dimensional but retain the
capabilities of the high-dimensional system. For example, if one were to
restore a small degree of complexity to LIP, SLIP, or a similar model, our
approach discovers the form of that additional complexity which optimizes
performance. In this paper, we define a class of reduced-order models and
provide an algorithm for optimization within this class. To demonstrate our
method, we optimize models for walking at a range of speeds and ground
inclines, for both a five-link model and the Cassie bipedal robot.
|
[
{
"created": "Mon, 23 Sep 2019 01:13:11 GMT",
"version": "v1"
}
] |
2019-09-24
|
[
[
"Chen",
"Yu-Ming",
""
],
[
"Posa",
"Michael",
""
]
] |
State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of tools for planning, control, and analysis. However, they inevitably limit the ability to execute complex tasks or agile maneuvers. In this work, we aim to automatically synthesize models that remain low-dimensional but retain the capabilities of the high-dimensional system. For example, if one were to restore a small degree of complexity to LIP, SLIP, or a similar model, our approach discovers the form of that additional complexity which optimizes performance. In this paper, we define a class of reduced-order models and provide an algorithm for optimization within this class. To demonstrate our method, we optimize models for walking at a range of speeds and ground inclines, for both a five-link model and the Cassie bipedal robot.
|
2206.11302
|
Brendon Boldt
|
Brendon Boldt, David Mortensen
|
Recommendations for Systematic Research on Emergent Language
|
10 pages
| null | null | null |
cs.MA cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Emergent language is unique among fields within the discipline of machine
learning for its open-endedness, not obviously presenting well-defined problems
to be solved. As a result, the current research in the field has largely been
exploratory: focusing on establishing new problems, techniques, and phenomena.
Yet after these problems have been established, subsequent progress requires
research which can measurably demonstrate how it improves on prior approaches.
This type of research is what we call systematic research; in this paper, we
illustrate this mode of research specifically for emergent language. We first
identify the overarching goals of emergent language research, categorizing them
as either science or engineering. Using this distinction, we present core
methodological elements of science and engineering, analyze their role in
current emergent language research, and recommend how to apply these elements.
|
[
{
"created": "Wed, 22 Jun 2022 18:10:44 GMT",
"version": "v1"
}
] |
2022-06-24
|
[
[
"Boldt",
"Brendon",
""
],
[
"Mortensen",
"David",
""
]
] |
Emergent language is unique among fields within the discipline of machine learning for its open-endedness, not obviously presenting well-defined problems to be solved. As a result, the current research in the field has largely been exploratory: focusing on establishing new problems, techniques, and phenomena. Yet after these problems have been established, subsequent progress requires research which can measurably demonstrate how it improves on prior approaches. This type of research is what we call systematic research; in this paper, we illustrate this mode of research specifically for emergent language. We first identify the overarching goals of emergent language research, categorizing them as either science or engineering. Using this distinction, we present core methodological elements of science and engineering, analyze their role in current emergent language research, and recommend how to apply these elements.
|
1306.1947
|
Wan Fokkink
|
Wan Fokkink, Dick Grune, Brinio Hond, Peter Rutgers
|
Detecting Useless Transitions in Pushdown Automata
| null | null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pushdown automata may contain transitions that are never used in any
accepting run of the automaton. We present an algorithm for detecting such
useless transitions. A finite automaton that captures the possible stack
content during runs of the pushdown automaton, is first constructed in a
forward procedure to determine which transitions are reachable, and then
employed in a backward procedure to determine which of these transitions can
lead to a final stat
|
[
{
"created": "Sat, 8 Jun 2013 19:10:45 GMT",
"version": "v1"
}
] |
2013-06-11
|
[
[
"Fokkink",
"Wan",
""
],
[
"Grune",
"Dick",
""
],
[
"Hond",
"Brinio",
""
],
[
"Rutgers",
"Peter",
""
]
] |
Pushdown automata may contain transitions that are never used in any accepting run of the automaton. We present an algorithm for detecting such useless transitions. A finite automaton that captures the possible stack content during runs of the pushdown automaton, is first constructed in a forward procedure to determine which transitions are reachable, and then employed in a backward procedure to determine which of these transitions can lead to a final stat
|
1803.10654
|
Najoua Essoukri Ben Amara
|
Ines Baccouche, Sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua
Essoukri Ben Amara
|
Implementation of an Improved Coulomb-Counting Algorithm Based on a
Piecewise SOC-OCV Relationship for SOC Estimation of Li-IonBattery
| null | null | null | null |
cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Considering the expanding useofembedded devices equipped with rechargeable
batteries, especially Li-ionbatteries that have higher power and energy
density, the battery management systemis becomingincreasingly important.
Infact, theestimationaccuracy of the amount of the remaining charges is
critical as it affects the device operational autonomy.Therefore, the battery
State-Of-Charge (SOC) is defined to indicate its estimated available charge. In
this paper, a solution isproposed for Li-ion battery SOC estimation based on an
enhanced Coulomb-counting algorithm to be implemented formultimedia
applications.However,the Coulomb-counting algorithm suffers from cumulative
errors due to the initial SOC andtheerrors ofmeasurements
uncertainties,thereforeto overcome these limitations,we use the
Open-CircuitVoltage (OCV),thushavinga piecewise linear SOC-OCV relationship
andperformingperiodic re-calibration of the battery capacity. Thissolutionis
implementedand validated on a hardware platform based onthePIC18F MCU family.
The measured resultsarecorrelated withthetheoretical ones; they have shown a
reliable estimation since accuracy is less than 2%.
|
[
{
"created": "Tue, 27 Mar 2018 16:55:59 GMT",
"version": "v1"
}
] |
2018-03-29
|
[
[
"Baccouche",
"Ines",
""
],
[
"Jemmali",
"Sabeur",
""
],
[
"Mlayah",
"Asma",
""
],
[
"Manai",
"Bilal",
""
],
[
"Amara",
"Najoua Essoukri Ben",
""
]
] |
Considering the expanding useofembedded devices equipped with rechargeable batteries, especially Li-ionbatteries that have higher power and energy density, the battery management systemis becomingincreasingly important. Infact, theestimationaccuracy of the amount of the remaining charges is critical as it affects the device operational autonomy.Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available charge. In this paper, a solution isproposed for Li-ion battery SOC estimation based on an enhanced Coulomb-counting algorithm to be implemented formultimedia applications.However,the Coulomb-counting algorithm suffers from cumulative errors due to the initial SOC andtheerrors ofmeasurements uncertainties,thereforeto overcome these limitations,we use the Open-CircuitVoltage (OCV),thushavinga piecewise linear SOC-OCV relationship andperformingperiodic re-calibration of the battery capacity. Thissolutionis implementedand validated on a hardware platform based onthePIC18F MCU family. The measured resultsarecorrelated withthetheoretical ones; they have shown a reliable estimation since accuracy is less than 2%.
|
2010.02029
|
Quan Zhang
|
Quan Zhang, Huangjie Zheng, Mingyuan Zhou
|
MCMC-Interactive Variational Inference
|
25 pages, 7 figures, 3 tables
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Leveraging well-established MCMC strategies, we propose MCMC-interactive
variational inference (MIVI) to not only estimate the posterior in a time
constrained manner, but also facilitate the design of MCMC transitions.
Constructing a variational distribution followed by a short Markov chain that
has parameters to learn, MIVI takes advantage of the complementary properties
of variational inference and MCMC to encourage mutual improvement. On one hand,
with the variational distribution locating high posterior density regions, the
Markov chain is optimized within the variational inference framework to
efficiently target the posterior despite a small number of transitions. On the
other hand, the optimized Markov chain with considerable flexibility guides the
variational distribution towards the posterior and alleviates its
underestimation of uncertainty. Furthermore, we prove the optimized Markov
chain in MIVI admits extrapolation, which means its marginal distribution gets
closer to the true posterior as the chain grows. Therefore, the Markov chain
can be used separately as an efficient MCMC scheme. Experiments show that MIVI
not only accurately and efficiently approximates the posteriors but also
facilitates designs of stochastic gradient MCMC and Gibbs sampling transitions.
|
[
{
"created": "Fri, 2 Oct 2020 17:43:20 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Dec 2022 20:07:54 GMT",
"version": "v2"
}
] |
2022-12-14
|
[
[
"Zhang",
"Quan",
""
],
[
"Zheng",
"Huangjie",
""
],
[
"Zhou",
"Mingyuan",
""
]
] |
Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions. Constructing a variational distribution followed by a short Markov chain that has parameters to learn, MIVI takes advantage of the complementary properties of variational inference and MCMC to encourage mutual improvement. On one hand, with the variational distribution locating high posterior density regions, the Markov chain is optimized within the variational inference framework to efficiently target the posterior despite a small number of transitions. On the other hand, the optimized Markov chain with considerable flexibility guides the variational distribution towards the posterior and alleviates its underestimation of uncertainty. Furthermore, we prove the optimized Markov chain in MIVI admits extrapolation, which means its marginal distribution gets closer to the true posterior as the chain grows. Therefore, the Markov chain can be used separately as an efficient MCMC scheme. Experiments show that MIVI not only accurately and efficiently approximates the posteriors but also facilitates designs of stochastic gradient MCMC and Gibbs sampling transitions.
|
1102.0850
|
Zoltan Esik
|
Zoltan Esik
|
Scattered context-free linear orderings
| null | null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that it is decidable in exponential time whether the lexicographic
ordering of a context-free language is scattered, or a well-ordering.
|
[
{
"created": "Fri, 4 Feb 2011 08:13:59 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Feb 2011 03:51:12 GMT",
"version": "v2"
}
] |
2015-03-18
|
[
[
"Esik",
"Zoltan",
""
]
] |
We show that it is decidable in exponential time whether the lexicographic ordering of a context-free language is scattered, or a well-ordering.
|
2406.04290
|
Trevor E. Carlson
|
Ali Hajiabadi, Trevor E. Carlson
|
Providing High-Performance Execution with a Sequential Contract for
Cryptographic Programs
|
17 pages, 7 figures, 4 tables
| null | null | null |
cs.CR cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Constant-time programming is a widely deployed approach to harden
cryptographic programs against side channel attacks. However, modern processors
violate the underlying assumptions of constant-time policies by speculatively
executing unintended paths of the program.
In this work, we propose Cassandra, a novel hardware-software mechanism to
protect constant-time cryptographic code against speculative control flow based
attacks. Cassandra explores the radical design point of disabling the branch
predictor and recording-and-replaying sequential control flow of the program.
Two key insights that enable our design are that (1) the sequential control
flow of a constant-time program is constant over different runs, and (2)
cryptographic programs are highly looped and their control flow patterns repeat
in a highly compressible way. These insights allow us to perform an offline
branch analysis that significantly compresses control flow traces. We add a
small component to a typical processor design, the Branch Trace Unit, to store
compressed traces and determine fetch redirections according to the sequential
model of the program. Moreover, we provide a formal security analysis and prove
that our methodology adheres to a strong security contract by design. Despite
providing a higher security guarantee, Cassandra counter-intuitively improves
performance by 1.77% by eliminating branch misprediction penalties.
|
[
{
"created": "Thu, 6 Jun 2024 17:34:48 GMT",
"version": "v1"
}
] |
2024-06-07
|
[
[
"Hajiabadi",
"Ali",
""
],
[
"Carlson",
"Trevor E.",
""
]
] |
Constant-time programming is a widely deployed approach to harden cryptographic programs against side channel attacks. However, modern processors violate the underlying assumptions of constant-time policies by speculatively executing unintended paths of the program. In this work, we propose Cassandra, a novel hardware-software mechanism to protect constant-time cryptographic code against speculative control flow based attacks. Cassandra explores the radical design point of disabling the branch predictor and recording-and-replaying sequential control flow of the program. Two key insights that enable our design are that (1) the sequential control flow of a constant-time program is constant over different runs, and (2) cryptographic programs are highly looped and their control flow patterns repeat in a highly compressible way. These insights allow us to perform an offline branch analysis that significantly compresses control flow traces. We add a small component to a typical processor design, the Branch Trace Unit, to store compressed traces and determine fetch redirections according to the sequential model of the program. Moreover, we provide a formal security analysis and prove that our methodology adheres to a strong security contract by design. Despite providing a higher security guarantee, Cassandra counter-intuitively improves performance by 1.77% by eliminating branch misprediction penalties.
|
2311.18085
|
Shubham Gandhi
|
Shubham Gandhi, Om Khare, Mihika Dravid, Mihika Sanghvi, Sunil Mane,
Aadesh Gajaralwar, Saloni Gandhi
|
Leveraging a Randomized Key Matrix to Enhance the Security of Symmetric
Substitution Ciphers
|
In Proceedings of the 10th IEEE Asia-Pacific Conference on Computer
Science and Data Engineering 2023 (CSDE)
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
An innovative strategy to enhance the security of symmetric substitution
ciphers is presented, through the implementation of a randomized key matrix
suitable for various file formats, including but not limited to binary and text
files. Despite their historical relevance, symmetric substitution ciphers have
been limited by vulnerabilities to cryptanalytic methods like frequency
analysis and known plaintext attacks. The aim of our research is to mitigate
these vulnerabilities by employing a polyalphabetic substitution strategy that
incorporates a distinct randomized key matrix. This matrix plays a pivotal role
in generating a unique random key, comprising characters, encompassing both
uppercase and lowercase letters, numeric, and special characters, to derive the
corresponding ciphertext. The effectiveness of the proposed methodology in
enhancing the security of conventional substitution methods for file encryption
and decryption is supported by comprehensive testing and analysis, which
encompass computational speed, frequency analysis, keyspace examination,
Kasiski test, entropy analysis, and the utilization of a large language model.
|
[
{
"created": "Wed, 29 Nov 2023 21:13:38 GMT",
"version": "v1"
}
] |
2023-12-01
|
[
[
"Gandhi",
"Shubham",
""
],
[
"Khare",
"Om",
""
],
[
"Dravid",
"Mihika",
""
],
[
"Sanghvi",
"Mihika",
""
],
[
"Mane",
"Sunil",
""
],
[
"Gajaralwar",
"Aadesh",
""
],
[
"Gandhi",
"Saloni",
""
]
] |
An innovative strategy to enhance the security of symmetric substitution ciphers is presented, through the implementation of a randomized key matrix suitable for various file formats, including but not limited to binary and text files. Despite their historical relevance, symmetric substitution ciphers have been limited by vulnerabilities to cryptanalytic methods like frequency analysis and known plaintext attacks. The aim of our research is to mitigate these vulnerabilities by employing a polyalphabetic substitution strategy that incorporates a distinct randomized key matrix. This matrix plays a pivotal role in generating a unique random key, comprising characters, encompassing both uppercase and lowercase letters, numeric, and special characters, to derive the corresponding ciphertext. The effectiveness of the proposed methodology in enhancing the security of conventional substitution methods for file encryption and decryption is supported by comprehensive testing and analysis, which encompass computational speed, frequency analysis, keyspace examination, Kasiski test, entropy analysis, and the utilization of a large language model.
|
1610.09064
|
Himabindu Lakkaraju
|
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
|
Identifying Unknown Unknowns in the Open World: Representations and
Policies for Guided Exploration
|
To appear in AAAI 2017; Presented at NIPS Workshop on Reliability in
ML, 2016
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Predictive models deployed in the real world may assign incorrect labels to
instances with high confidence. Such errors or unknown unknowns are rooted in
model incompleteness, and typically arise because of the mismatch between
training data and the cases encountered at test time. As the models are blind
to such errors, input from an oracle is needed to identify these failures. In
this paper, we formulate and address the problem of informed discovery of
unknown unknowns of any given predictive model where unknown unknowns occur due
to systematic biases in the training data. We propose a model-agnostic
methodology which uses feedback from an oracle to both identify unknown
unknowns and to intelligently guide the discovery. We employ a two-phase
approach which first organizes the data into multiple partitions based on the
feature similarity of instances and the confidence scores assigned by the
predictive model, and then utilizes an explore-exploit strategy for discovering
unknown unknowns across these partitions. We demonstrate the efficacy of our
framework by varying the underlying causes of unknown unknowns across various
applications. To the best of our knowledge, this paper presents the first
algorithmic approach to the problem of discovering unknown unknowns of
predictive models.
|
[
{
"created": "Fri, 28 Oct 2016 02:55:14 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Dec 2016 03:01:21 GMT",
"version": "v2"
},
{
"created": "Sat, 10 Dec 2016 06:02:38 GMT",
"version": "v3"
}
] |
2016-12-13
|
[
[
"Lakkaraju",
"Himabindu",
""
],
[
"Kamar",
"Ece",
""
],
[
"Caruana",
"Rich",
""
],
[
"Horvitz",
"Eric",
""
]
] |
Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch between training data and the cases encountered at test time. As the models are blind to such errors, input from an oracle is needed to identify these failures. In this paper, we formulate and address the problem of informed discovery of unknown unknowns of any given predictive model where unknown unknowns occur due to systematic biases in the training data. We propose a model-agnostic methodology which uses feedback from an oracle to both identify unknown unknowns and to intelligently guide the discovery. We employ a two-phase approach which first organizes the data into multiple partitions based on the feature similarity of instances and the confidence scores assigned by the predictive model, and then utilizes an explore-exploit strategy for discovering unknown unknowns across these partitions. We demonstrate the efficacy of our framework by varying the underlying causes of unknown unknowns across various applications. To the best of our knowledge, this paper presents the first algorithmic approach to the problem of discovering unknown unknowns of predictive models.
|
2101.04834
|
Doris Xin
|
Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, Aditya
Parameswaran
|
Whither AutoML? Understanding the Role of Automation in Machine Learning
Workflows
| null | null | null | null |
cs.HC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Efforts to make machine learning more widely accessible have led to a rapid
increase in Auto-ML tools that aim to automate the process of training and
deploying machine learning. To understand how Auto-ML tools are used in
practice today, we performed a qualitative study with participants ranging from
novice hobbyists to industry researchers who use Auto-ML tools. We present
insights into the benefits and deficiencies of existing tools, as well as the
respective roles of the human and automation in ML workflows. Finally, we
discuss design implications for the future of Auto-ML tool development. We
argue that instead of full automation being the ultimate goal of Auto-ML,
designers of these tools should focus on supporting a partnership between the
user and the Auto-ML tool. This means that a range of Auto-ML tools will need
to be developed to support varying user goals such as simplicity,
reproducibility, and reliability.
|
[
{
"created": "Wed, 13 Jan 2021 02:12:46 GMT",
"version": "v1"
}
] |
2021-01-14
|
[
[
"Xin",
"Doris",
""
],
[
"Wu",
"Eva Yiwei",
""
],
[
"Lee",
"Doris Jung-Lin",
""
],
[
"Salehi",
"Niloufar",
""
],
[
"Parameswaran",
"Aditya",
""
]
] |
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
|
2107.11878
|
Abhishek Aich
|
Abhishek Aich, Meng Zheng, Srikrishna Karanam, Terrence Chen, Amit K.
Roy-Chowdhury, Ziyan Wu
|
Spatio-Temporal Representation Factorization for Video-based Person
Re-Identification
|
Accepted at IEEE ICCV 2021, Includes Supplementary Material
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Despite much recent progress in video-based person re-identification (re-ID),
the current state-of-the-art still suffers from common real-world challenges
such as appearance similarity among various people, occlusions, and frame
misalignment. To alleviate these problems, we propose Spatio-Temporal
Representation Factorization (STRF), a flexible new computational unit that can
be used in conjunction with most existing 3D convolutional neural network
architectures for re-ID. The key innovations of STRF over prior work include
explicit pathways for learning discriminative temporal and spatial features,
with each component further factorized to capture complementary person-specific
appearance and motion information. Specifically, temporal factorization
comprises two branches, one each for static features (e.g., the color of
clothes) that do not change much over time, and dynamic features (e.g., walking
patterns) that change over time. Further, spatial factorization also comprises
two branches to learn both global (coarse segments) as well as local (finer
segments) appearance features, with the local features particularly useful in
cases of occlusion or spatial misalignment. These two factorization operations
taken together result in a modular architecture for our parameter-wise light
STRF unit that can be plugged in between any two 3D convolutional layers,
resulting in an end-to-end learning framework. We empirically show that STRF
improves performance of various existing baseline architectures while
demonstrating new state-of-the-art results using standard person re-ID
evaluation protocols on three benchmarks.
|
[
{
"created": "Sun, 25 Jul 2021 19:29:37 GMT",
"version": "v1"
},
{
"created": "Sun, 15 Aug 2021 01:49:08 GMT",
"version": "v2"
}
] |
2021-08-17
|
[
[
"Aich",
"Abhishek",
""
],
[
"Zheng",
"Meng",
""
],
[
"Karanam",
"Srikrishna",
""
],
[
"Chen",
"Terrence",
""
],
[
"Roy-Chowdhury",
"Amit K.",
""
],
[
"Wu",
"Ziyan",
""
]
] |
Despite much recent progress in video-based person re-identification (re-ID), the current state-of-the-art still suffers from common real-world challenges such as appearance similarity among various people, occlusions, and frame misalignment. To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. The key innovations of STRF over prior work include explicit pathways for learning discriminative temporal and spatial features, with each component further factorized to capture complementary person-specific appearance and motion information. Specifically, temporal factorization comprises two branches, one each for static features (e.g., the color of clothes) that do not change much over time, and dynamic features (e.g., walking patterns) that change over time. Further, spatial factorization also comprises two branches to learn both global (coarse segments) as well as local (finer segments) appearance features, with the local features particularly useful in cases of occlusion or spatial misalignment. These two factorization operations taken together result in a modular architecture for our parameter-wise light STRF unit that can be plugged in between any two 3D convolutional layers, resulting in an end-to-end learning framework. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results using standard person re-ID evaluation protocols on three benchmarks.
|
0709.4671
|
Ruoheng Liu
|
Ruoheng Liu and H. Vincent Poor
|
Secrecy Capacity Region of a Multi-Antenna Gaussian Broadcast Channel
with Confidential Messages
|
Submitted to the IEEE Transactions on Information Theory
| null | null | null |
cs.IT math.IT
| null |
In wireless data networks, communication is particularly susceptible to
eavesdropping due to its broadcast nature. Security and privacy systems have
become critical for wireless providers and enterprise networks. This paper
considers the problem of secret communication over the Gaussian broadcast
channel, where a multi-antenna transmitter sends independent confidential
messages to two users with information-theoretic secrecy. That is, each user
would like to obtain its own confidential message in a reliable and safe
manner. This communication model is referred to as the multi-antenna Gaussian
broadcast channel with confidential messages (MGBC-CM). Under this
communication scenario, a secret dirty-paper coding scheme and the
corresponding achievable secrecy rate region are first developed based on
Gaussian codebooks. Next, a computable Sato-type outer bound on the secrecy
capacity region is provided for the MGBC-CM. Furthermore, the Sato-type outer
bound prove to be consistent with the boundary of the secret dirty-paper coding
achievable rate region, and hence, the secrecy capacity region of the MGBC-CM
is established. Finally, two numerical examples demonstrate that both users can
achieve positive rates simultaneously under the information-theoretic secrecy
requirement.
|
[
{
"created": "Fri, 28 Sep 2007 19:10:03 GMT",
"version": "v1"
}
] |
2007-10-01
|
[
[
"Liu",
"Ruoheng",
""
],
[
"Poor",
"H. Vincent",
""
]
] |
In wireless data networks, communication is particularly susceptible to eavesdropping due to its broadcast nature. Security and privacy systems have become critical for wireless providers and enterprise networks. This paper considers the problem of secret communication over the Gaussian broadcast channel, where a multi-antenna transmitter sends independent confidential messages to two users with information-theoretic secrecy. That is, each user would like to obtain its own confidential message in a reliable and safe manner. This communication model is referred to as the multi-antenna Gaussian broadcast channel with confidential messages (MGBC-CM). Under this communication scenario, a secret dirty-paper coding scheme and the corresponding achievable secrecy rate region are first developed based on Gaussian codebooks. Next, a computable Sato-type outer bound on the secrecy capacity region is provided for the MGBC-CM. Furthermore, the Sato-type outer bound prove to be consistent with the boundary of the secret dirty-paper coding achievable rate region, and hence, the secrecy capacity region of the MGBC-CM is established. Finally, two numerical examples demonstrate that both users can achieve positive rates simultaneously under the information-theoretic secrecy requirement.
|
1702.03791
|
Hong Yu
|
Hong Yu, Zheng-Hua Tan, Zhanyu Ma, Jun Guo
|
DNN Filter Bank Cepstral Coefficients for Spoofing Detection
| null | null | null | null |
cs.SD cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of speech synthesis techniques, automatic speaker
verification systems face the serious challenge of spoofing attack. In order to
improve the reliability of speaker verification systems, we develop a new
filter bank based cepstral feature, deep neural network filter bank cepstral
coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The
deep neural network filter bank is automatically generated by training a filter
bank neural network (FBNN) using natural and synthetic speech. By adding
restrictions on the training rules, the learned weight matrix of FBNN is
band-limited and sorted by frequency, similar to the normal filter bank. Unlike
the manually designed filter bank, the learned filter bank has different filter
shapes in different channels, which can capture the differences between natural
and synthetic speech more effectively. The experimental results on the ASVspoof
{2015} database show that the Gaussian mixture model maximum-likelihood
(GMM-ML) classifier trained by the new feature performs better than the
state-of-the-art linear frequency cepstral coefficients (LFCC) based
classifier, especially on detecting unknown attacks.
|
[
{
"created": "Mon, 13 Feb 2017 14:44:17 GMT",
"version": "v1"
}
] |
2017-02-14
|
[
[
"Yu",
"Hong",
""
],
[
"Tan",
"Zheng-Hua",
""
],
[
"Ma",
"Zhanyu",
""
],
[
"Guo",
"Jun",
""
]
] |
With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The deep neural network filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band-limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof {2015} database show that the Gaussian mixture model maximum-likelihood (GMM-ML) classifier trained by the new feature performs better than the state-of-the-art linear frequency cepstral coefficients (LFCC) based classifier, especially on detecting unknown attacks.
|
2012.00483
|
Markus Leippold
|
Francesco S. Varini and Jordan Boyd-Graber and Massimiliano Ciaramita
and Markus Leippold
|
ClimaText: A Dataset for Climate Change Topic Detection
|
Accepted for the Tackling Climate Change with Machine Learning
Workshop at NeurIPS 2020
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Climate change communication in the mass media and other textual sources may
affect and shape public perception. Extracting climate change information from
these sources is an important task, e.g., for filtering content and
e-discovery, sentiment analysis, automatic summarization, question-answering,
and fact-checking. However, automating this process is a challenge, as climate
change is a complex, fast-moving, and often ambiguous topic with scarce
resources for popular text-based AI tasks. In this paper, we introduce
\textsc{ClimaText}, a dataset for sentence-based climate change topic
detection, which we make publicly available. We explore different approaches to
identify the climate change topic in various text sources. We find that popular
keyword-based models are not adequate for such a complex and evolving task.
Context-based algorithms like BERT \cite{devlin2018bert} can detect, in
addition to many trivial cases, a variety of complex and implicit topic
patterns. Nevertheless, our analysis reveals a great potential for improvement
in several directions, such as, e.g., capturing the discussion on indirect
effects of climate change. Hence, we hope this work can serve as a good
starting point for further research on this topic.
|
[
{
"created": "Tue, 1 Dec 2020 13:42:37 GMT",
"version": "v1"
},
{
"created": "Sat, 2 Jan 2021 16:13:06 GMT",
"version": "v2"
}
] |
2021-01-05
|
[
[
"Varini",
"Francesco S.",
""
],
[
"Boyd-Graber",
"Jordan",
""
],
[
"Ciaramita",
"Massimiliano",
""
],
[
"Leippold",
"Markus",
""
]
] |
Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.
|
1601.06453
|
Or Ordentlich
|
Or Ordentlich
|
Novel Lower Bounds on the Entropy Rate of Binary Hidden Markov Processes
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, Samorodnitsky proved a strengthened version of Mrs. Gerber's Lemma,
where the output entropy of a binary symmetric channel is bounded in terms of
the average entropy of the input projected on a random subset of coordinates.
Here, this result is applied for deriving novel lower bounds on the entropy
rate of binary hidden Markov processes. For symmetric underlying Markov
processes, our bound improves upon the best known bound in the very noisy
regime. The nonsymmetric case is also considered, and explicit bounds are
derived for Markov processes that satisfy the $(1,\infty)$-RLL constraint.
|
[
{
"created": "Mon, 25 Jan 2016 00:19:36 GMT",
"version": "v1"
},
{
"created": "Mon, 9 May 2016 21:24:26 GMT",
"version": "v2"
}
] |
2016-05-11
|
[
[
"Ordentlich",
"Or",
""
]
] |
Recently, Samorodnitsky proved a strengthened version of Mrs. Gerber's Lemma, where the output entropy of a binary symmetric channel is bounded in terms of the average entropy of the input projected on a random subset of coordinates. Here, this result is applied for deriving novel lower bounds on the entropy rate of binary hidden Markov processes. For symmetric underlying Markov processes, our bound improves upon the best known bound in the very noisy regime. The nonsymmetric case is also considered, and explicit bounds are derived for Markov processes that satisfy the $(1,\infty)$-RLL constraint.
|
2310.06762
|
Xiao Wang
|
Xiao Wang, Yuansen Zhang, Tianze Chen, Songyang Gao, Senjie Jin,
Xianjun Yang, Zhiheng Xi, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xuanjing
Huang
|
TRACE: A Comprehensive Benchmark for Continual Learning in Large
Language Models
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aligned large language models (LLMs) demonstrate exceptional capabilities in
task-solving, following instructions, and ensuring safety. However, the
continual learning aspect of these aligned LLMs has been largely overlooked.
Existing continual learning benchmarks lack sufficient challenge for leading
aligned LLMs, owing to both their simplicity and the models' potential exposure
during instruction tuning. In this paper, we introduce TRACE, a novel benchmark
designed to evaluate continual learning in LLMs. TRACE consists of 8 distinct
datasets spanning challenging tasks including domain-specific tasks,
multilingual capabilities, code generation, and mathematical reasoning. All
datasets are standardized into a unified format, allowing for effortless
automatic evaluation of LLMs. Our experiments show that after training on
TRACE, aligned LLMs exhibit significant declines in both general ability and
instruction-following capabilities. For example, the accuracy of llama2-chat
13B on gsm8k dataset declined precipitously from 28.8\% to 2\% after training
on our datasets. This highlights the challenge of finding a suitable tradeoff
between achieving performance on specific tasks while preserving the original
prowess of LLMs. Empirical findings suggest that tasks inherently equipped with
reasoning paths contribute significantly to preserving certain capabilities of
LLMs against potential declines. Motivated by this, we introduce the
Reasoning-augmented Continual Learning (RCL) approach. RCL integrates
task-specific cues with meta-rationales, effectively reducing catastrophic
forgetting in LLMs while expediting convergence on novel tasks.
|
[
{
"created": "Tue, 10 Oct 2023 16:38:49 GMT",
"version": "v1"
}
] |
2023-10-11
|
[
[
"Wang",
"Xiao",
""
],
[
"Zhang",
"Yuansen",
""
],
[
"Chen",
"Tianze",
""
],
[
"Gao",
"Songyang",
""
],
[
"Jin",
"Senjie",
""
],
[
"Yang",
"Xianjun",
""
],
[
"Xi",
"Zhiheng",
""
],
[
"Zheng",
"Rui",
""
],
[
"Zou",
"Yicheng",
""
],
[
"Gui",
"Tao",
""
],
[
"Zhang",
"Qi",
""
],
[
"Huang",
"Xuanjing",
""
]
] |
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of these aligned LLMs has been largely overlooked. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs, owing to both their simplicity and the models' potential exposure during instruction tuning. In this paper, we introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs. TRACE consists of 8 distinct datasets spanning challenging tasks including domain-specific tasks, multilingual capabilities, code generation, and mathematical reasoning. All datasets are standardized into a unified format, allowing for effortless automatic evaluation of LLMs. Our experiments show that after training on TRACE, aligned LLMs exhibit significant declines in both general ability and instruction-following capabilities. For example, the accuracy of llama2-chat 13B on gsm8k dataset declined precipitously from 28.8\% to 2\% after training on our datasets. This highlights the challenge of finding a suitable tradeoff between achieving performance on specific tasks while preserving the original prowess of LLMs. Empirical findings suggest that tasks inherently equipped with reasoning paths contribute significantly to preserving certain capabilities of LLMs against potential declines. Motivated by this, we introduce the Reasoning-augmented Continual Learning (RCL) approach. RCL integrates task-specific cues with meta-rationales, effectively reducing catastrophic forgetting in LLMs while expediting convergence on novel tasks.
|
2403.12830
|
Chenglong Wang
|
Cheng-Long Wang, Qi Li, Zihang Xiang, Yinzhi Cao, and Di Wang
|
Towards Lifecycle Unlearning Commitment Management: Measuring
Sample-level Approximate Unlearning Completeness
| null | null | null | null |
cs.LG cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
By adopting a more flexible definition of unlearning and adjusting the model
distribution to simulate training without the targeted data, approximate
machine unlearning provides a less resource-demanding alternative to the more
laborious exact unlearning methods. Yet, the unlearning completeness of target
samples-even when the approximate algorithms are executed faithfully without
external threats-remains largely unexamined, raising questions about those
approximate algorithms' ability to fulfill their commitment of unlearning
during the lifecycle.
In this paper, we introduce the task of Lifecycle Unlearning Commitment
Management (LUCM) for approximate unlearning and outline its primary
challenges. We propose an efficient metric designed to assess the sample-level
unlearning completeness. Our empirical results demonstrate its superiority over
membership inference techniques in two key areas: the strong correlation of its
measurements with unlearning completeness across various unlearning tasks, and
its computational efficiency, making it suitable for real-time applications.
Additionally, we show that this metric is able to serve as a tool for
monitoring unlearning anomalies throughout the unlearning lifecycle, including
both under-unlearning and over-unlearning.
We apply this metric to evaluate the unlearning commitments of current
approximate algorithms. Our analysis, conducted across multiple unlearning
benchmarks, reveals that these algorithms inconsistently fulfill their
unlearning commitments due to two main issues: 1) unlearning new data can
significantly affect the unlearning utility of previously requested data, and
2) approximate algorithms fail to ensure equitable unlearning utility across
different groups. These insights emphasize the crucial importance of LUCM
throughout the unlearning lifecycle. We will soon open-source our newly
developed benchmark.
|
[
{
"created": "Tue, 19 Mar 2024 15:37:27 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2024 23:20:41 GMT",
"version": "v2"
}
] |
2024-05-02
|
[
[
"Wang",
"Cheng-Long",
""
],
[
"Li",
"Qi",
""
],
[
"Xiang",
"Zihang",
""
],
[
"Cao",
"Yinzhi",
""
],
[
"Wang",
"Di",
""
]
] |
By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a less resource-demanding alternative to the more laborious exact unlearning methods. Yet, the unlearning completeness of target samples-even when the approximate algorithms are executed faithfully without external threats-remains largely unexamined, raising questions about those approximate algorithms' ability to fulfill their commitment of unlearning during the lifecycle. In this paper, we introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning and outline its primary challenges. We propose an efficient metric designed to assess the sample-level unlearning completeness. Our empirical results demonstrate its superiority over membership inference techniques in two key areas: the strong correlation of its measurements with unlearning completeness across various unlearning tasks, and its computational efficiency, making it suitable for real-time applications. Additionally, we show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle, including both under-unlearning and over-unlearning. We apply this metric to evaluate the unlearning commitments of current approximate algorithms. Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups. These insights emphasize the crucial importance of LUCM throughout the unlearning lifecycle. We will soon open-source our newly developed benchmark.
|
1603.03727
|
Hongwei Xi
|
Hongwei Xi and Zhiqiang Ren and Hanwen Wu and William Blair
|
Session Types in a Linearly Typed Multi-Threaded Lambda-Calculus
|
This is the original version of the paper on supporting programming
with dyadic session types in ATS
| null | null | null |
cs.PL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a formalization of session types in a multi-threaded
lambda-calculus (MTLC) equipped with a linear type system, establishing for the
MTLC both type preservation and global progress. The latter (global progress)
implies that the evaluation of a well-typed program in the MTLC can never reach
a deadlock. As this formulated MTLC can be readily embedded into ATS, a
full-fledged language with a functional programming core that supports both
dependent types (of DML-style) and linear types, we obtain a direct
implementation of session types in ATS. In addition, we gain immediate support
for a form of dependent session types based on this embedding into ATS.
Compared to various existing formalizations of session types, we see the one
given in this paper is unique in its closeness to concrete implementation. In
particular, we report such an implementation ready for practical use that
generates Erlang code from well-typed ATS source (making use of session types),
thus taking great advantage of the infrastructural support for distributed
computing in Erlang.
|
[
{
"created": "Fri, 11 Mar 2016 19:15:03 GMT",
"version": "v1"
}
] |
2016-03-14
|
[
[
"Xi",
"Hongwei",
""
],
[
"Ren",
"Zhiqiang",
""
],
[
"Wu",
"Hanwen",
""
],
[
"Blair",
"William",
""
]
] |
We present a formalization of session types in a multi-threaded lambda-calculus (MTLC) equipped with a linear type system, establishing for the MTLC both type preservation and global progress. The latter (global progress) implies that the evaluation of a well-typed program in the MTLC can never reach a deadlock. As this formulated MTLC can be readily embedded into ATS, a full-fledged language with a functional programming core that supports both dependent types (of DML-style) and linear types, we obtain a direct implementation of session types in ATS. In addition, we gain immediate support for a form of dependent session types based on this embedding into ATS. Compared to various existing formalizations of session types, we see the one given in this paper is unique in its closeness to concrete implementation. In particular, we report such an implementation ready for practical use that generates Erlang code from well-typed ATS source (making use of session types), thus taking great advantage of the infrastructural support for distributed computing in Erlang.
|
2112.13301
|
Rajagopal Venkatesaramani
|
Rajagopal Venkatesaramani, Zhiyu Wan, Bradley A. Malin, Yevgeniy
Vorobeychik
|
Defending Against Membership Inference Attacks on Beacon Services
| null | null | null | null |
cs.CR q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
Large genomic datasets are now created through numerous activities, including
recreational genealogical investigations, biomedical research, and clinical
care. At the same time, genomic data has become valuable for reuse beyond their
initial point of collection, but privacy concerns often hinder access. Over the
past several years, Beacon services have emerged to broaden accessibility to
such data. These services enable users to query for the presence of a
particular minor allele in a private dataset, information that can help care
providers determine if genomic variation is spurious or has some known clinical
indication. However, various studies have shown that even this limited access
model can leak if individuals are members in the underlying dataset. Several
approaches for mitigating this vulnerability have been proposed, but they are
limited in that they 1) typically rely on heuristics and 2) offer probabilistic
privacy guarantees, but neglect utility. In this paper, we present a novel
algorithmic framework to ensure privacy in a Beacon service setting with a
minimal number of query response flips (e.g., changing a positive response to a
negative). Specifically, we represent this problem as combinatorial
optimization in both the batch setting (where queries arrive all at once), as
well as the online setting (where queries arrive sequentially). The former
setting has been the primary focus in prior literature, whereas real Beacons
allow sequential queries, motivating the latter investigation. We present
principled algorithms in this framework with both privacy and, in some cases,
worst-case utility guarantees. Moreover, through an extensive experimental
evaluation, we show that the proposed approaches significantly outperform the
state of the art in terms of privacy and utility.
|
[
{
"created": "Sat, 25 Dec 2021 23:33:44 GMT",
"version": "v1"
}
] |
2021-12-28
|
[
[
"Venkatesaramani",
"Rajagopal",
""
],
[
"Wan",
"Zhiyu",
""
],
[
"Malin",
"Bradley A.",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
]
] |
Large genomic datasets are now created through numerous activities, including recreational genealogical investigations, biomedical research, and clinical care. At the same time, genomic data has become valuable for reuse beyond their initial point of collection, but privacy concerns often hinder access. Over the past several years, Beacon services have emerged to broaden accessibility to such data. These services enable users to query for the presence of a particular minor allele in a private dataset, information that can help care providers determine if genomic variation is spurious or has some known clinical indication. However, various studies have shown that even this limited access model can leak if individuals are members in the underlying dataset. Several approaches for mitigating this vulnerability have been proposed, but they are limited in that they 1) typically rely on heuristics and 2) offer probabilistic privacy guarantees, but neglect utility. In this paper, we present a novel algorithmic framework to ensure privacy in a Beacon service setting with a minimal number of query response flips (e.g., changing a positive response to a negative). Specifically, we represent this problem as combinatorial optimization in both the batch setting (where queries arrive all at once), as well as the online setting (where queries arrive sequentially). The former setting has been the primary focus in prior literature, whereas real Beacons allow sequential queries, motivating the latter investigation. We present principled algorithms in this framework with both privacy and, in some cases, worst-case utility guarantees. Moreover, through an extensive experimental evaluation, we show that the proposed approaches significantly outperform the state of the art in terms of privacy and utility.
|
2307.06563
|
Geoffrey Goodell
|
Ryan Bowler, Chris Speed, Geoffrey Goodell
|
Money: Who Has a Stake in the Most Value-Centric Common Design Material?
|
19 pages, 1 figure
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Money is more than just a numeric value. It embodies trust and moral gravity,
and it offers flexible ways to transact. However, the emergence of Central Bank
Digital Currency (CBDC) is set to bring about a drastic change in the future of
money. This paper invites designers to reflect on their role in shaping
material and immaterial monetary change. In this rapidly changing landscape,
design could be instrumental in uncovering and showcasing the diverse values
that money holds for different stakeholders. Understanding these diversities
could promote a more equitable and inclusive financial, social, and global
landscape within emergent forms of cash-like digital currency. Without such
consideration, certain forms of money we have come to know could disappear,
along with the values people hold upon them. We report on semi-structured
interviews with stakeholders who have current knowledge or involvement in the
emerging field of Central Bank Digital Currency (CBDC). Our research indicates
that this new form of money presents both challenges and opportunities for
designers. Specifically, we emphasise the potential for Central Bank Digital
Currency (CBDC) to either positively or negatively reform values through its
design. By considering time, reflecting present values, and promoting inclusion
in its deployment, we can strive to ensure that Central Bank Digital Currency
(CBDC) represents the diverse needs and perspectives of its users.
|
[
{
"created": "Thu, 13 Jul 2023 05:30:41 GMT",
"version": "v1"
}
] |
2023-07-14
|
[
[
"Bowler",
"Ryan",
""
],
[
"Speed",
"Chris",
""
],
[
"Goodell",
"Geoffrey",
""
]
] |
Money is more than just a numeric value. It embodies trust and moral gravity, and it offers flexible ways to transact. However, the emergence of Central Bank Digital Currency (CBDC) is set to bring about a drastic change in the future of money. This paper invites designers to reflect on their role in shaping material and immaterial monetary change. In this rapidly changing landscape, design could be instrumental in uncovering and showcasing the diverse values that money holds for different stakeholders. Understanding these diversities could promote a more equitable and inclusive financial, social, and global landscape within emergent forms of cash-like digital currency. Without such consideration, certain forms of money we have come to know could disappear, along with the values people hold upon them. We report on semi-structured interviews with stakeholders who have current knowledge or involvement in the emerging field of Central Bank Digital Currency (CBDC). Our research indicates that this new form of money presents both challenges and opportunities for designers. Specifically, we emphasise the potential for Central Bank Digital Currency (CBDC) to either positively or negatively reform values through its design. By considering time, reflecting present values, and promoting inclusion in its deployment, we can strive to ensure that Central Bank Digital Currency (CBDC) represents the diverse needs and perspectives of its users.
|
2209.07943
|
Mirza Fuad Adnan
|
Mirza Fuad Adnan, Nadim Ahmed, Imrez Ishraque, Md. Sifath Al Amin, Md.
Sumit Hasan
|
Traffic Congestion Prediction using Deep Convolutional Neural Networks:
A Color-coding Approach
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The traffic video data has become a critical factor in confining the state of
traffic congestion due to the recent advancements in computer vision. This work
proposes a unique technique for traffic video classification using a
color-coding scheme before training the traffic data in a Deep convolutional
neural network. At first, the video data is transformed into an imagery data
set; then, the vehicle detection is performed using the You Only Look Once
algorithm. A color-coded scheme has been adopted to transform the imagery
dataset into a binary image dataset. These binary images are fed to a Deep
Convolutional Neural Network. Using the UCSD dataset, we have obtained a
classification accuracy of 98.2%.
|
[
{
"created": "Fri, 16 Sep 2022 14:02:20 GMT",
"version": "v1"
}
] |
2022-09-19
|
[
[
"Adnan",
"Mirza Fuad",
""
],
[
"Ahmed",
"Nadim",
""
],
[
"Ishraque",
"Imrez",
""
],
[
"Amin",
"Md. Sifath Al",
""
],
[
"Hasan",
"Md. Sumit",
""
]
] |
The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a Deep Convolutional Neural Network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%.
|
1809.03254
|
Jakub Michaliszyn
|
Jakub Michaliszyn
|
Elementary Multimodal Logics
| null | null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study multimodal logics over universally first-order definable classes of
frames. We show that even for bimodal logics, there are universal Horn formulas
that define set of frames such that the satisfiability problem is undecidable,
even if one or two of the binary relations are transitive.
|
[
{
"created": "Mon, 10 Sep 2018 12:01:13 GMT",
"version": "v1"
}
] |
2018-09-11
|
[
[
"Michaliszyn",
"Jakub",
""
]
] |
We study multimodal logics over universally first-order definable classes of frames. We show that even for bimodal logics, there are universal Horn formulas that define set of frames such that the satisfiability problem is undecidable, even if one or two of the binary relations are transitive.
|
2303.02880
|
Yan Qin
|
Yan Qin, Yong Liang Guan, and Chau Yuen
|
Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
|
IEEE TVT has accepted this paper
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Through advancement of the Vehicle-to-Everything (V2X) network, road safety,
energy consumption, and traffic efficiency can be significantly improved. An
accurate vehicle trajectory prediction benefits communication traffic
management and network resource allocation for the real-time application of the
V2X network. Recurrent neural networks and their variants have been reported in
recent research to predict vehicle mobility. However, the spatial attribute of
vehicle movement behavior has been overlooked, resulting in incomplete
information utilization. To bridge this gap, we put forward for the first time
a hierarchical trajectory prediction structure using the capsule neural network
(CapsNet) with three sequential components. First, the geographic information
is transformed into a grid map presentation, describing vehicle mobility
distribution spatially and temporally. Second, CapsNet serves as the core model
to embed local temporal and global spatial correlation through hierarchical
capsules. Finally, extensive experiments conducted on actual taxi mobility data
collected in Porto city (Portugal) and Singapore show that the proposed method
outperforms the state-of-the-art methods.
|
[
{
"created": "Mon, 6 Mar 2023 04:15:29 GMT",
"version": "v1"
}
] |
2023-03-07
|
[
[
"Qin",
"Yan",
""
],
[
"Guan",
"Yong Liang",
""
],
[
"Yuen",
"Chau",
""
]
] |
Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods.
|
1912.03135
|
Preslav Nakov
|
Francisco Guzman, Shafiq Joty, Lluis Marquez, Preslav Nakov
|
Pairwise Neural Machine Translation Evaluation
|
machine translation evaluation, machine translation, pairwise
ranking, learning to rank. arXiv admin note: substantial text overlap with
arXiv:1710.02095
|
Conference of the Association for Computational Linguistics
(ACL'2015)
| null | null |
cs.CL cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel framework for machine translation evaluation using neural
networks in a pairwise setting, where the goal is to select the better
translation from a pair of hypotheses, given the reference translation. In this
framework, lexical, syntactic and semantic information from the reference and
the two hypotheses is compacted into relatively small distributed vector
representations, and fed into a multi-layer neural network that models the
interaction between each of the hypotheses and the reference, as well as
between the two hypotheses. These compact representations are in turn based on
word and sentence embeddings, which are learned using neural networks. The
framework is flexible, allows for efficient learning and classification, and
yields correlation with humans that rivals the state of the art.
|
[
{
"created": "Thu, 5 Dec 2019 05:17:05 GMT",
"version": "v1"
}
] |
2019-12-09
|
[
[
"Guzman",
"Francisco",
""
],
[
"Joty",
"Shafiq",
""
],
[
"Marquez",
"Lluis",
""
],
[
"Nakov",
"Preslav",
""
]
] |
We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.
|
1805.10383
|
Christopher Jenkins
|
Christopher Jenkins, Aaron Stump
|
Spine-local Type Inference
|
Submitted to IFL'18 (Implementation and Application of Functional
Languages)
| null | null | null |
cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present spine-local type inference, a partial type inference system for
inferring omitted type annotations for System F terms based on local type
inference. Local type inference relies on bidirectional inference rules to
propagate type information into and out of adjacent nodes of the AST and
restricts type-argument inference to occur only within a single node.
Spine-local inference relaxes the restriction on type-argument inference by
allowing it to occur only within an {application spine and improves upon it by
using contextual type-argument inference. As our goal is to explore the design
space of local type inference, we show that, relative to other variants,
spine-local type inference enables desirable features such as first-class
curried applications, partial type applications, and the ability to infer types
for some terms not otherwise possible. Our approach enjoys usual properties of
a bidirectional system of having a specification for our inference algorithm
and predictable requirements for typing annotations, and in particular
maintains some the advantages of local type inference such as a relatively
simple implementation and a tendency to produce good-quality error messages
when type inference fails.
|
[
{
"created": "Fri, 25 May 2018 22:44:08 GMT",
"version": "v1"
}
] |
2018-05-29
|
[
[
"Jenkins",
"Christopher",
""
],
[
"Stump",
"Aaron",
""
]
] |
We present spine-local type inference, a partial type inference system for inferring omitted type annotations for System F terms based on local type inference. Local type inference relies on bidirectional inference rules to propagate type information into and out of adjacent nodes of the AST and restricts type-argument inference to occur only within a single node. Spine-local inference relaxes the restriction on type-argument inference by allowing it to occur only within an {application spine and improves upon it by using contextual type-argument inference. As our goal is to explore the design space of local type inference, we show that, relative to other variants, spine-local type inference enables desirable features such as first-class curried applications, partial type applications, and the ability to infer types for some terms not otherwise possible. Our approach enjoys usual properties of a bidirectional system of having a specification for our inference algorithm and predictable requirements for typing annotations, and in particular maintains some the advantages of local type inference such as a relatively simple implementation and a tendency to produce good-quality error messages when type inference fails.
|
2210.06333
|
Max Chumley
|
Max M. Chumley, Melih C. Yesilli, Jisheng Chen, Firas A. Khasawneh,
Yang Guo
|
Pattern Characterization Using Topological Data Analysis: Application to
Piezo Vibration Striking Treatment
|
Updated 6/9/23 to include changes from the review process. Main
updates: redefined roundness score to be consistent with the outputs from the
depth score (percentage), all quantities defined in terms of radius instead
of diameter, added noise study to demonstrate noise robustness of the scores
| null |
10.1016/j.precisioneng.2023.05.005
| null |
cs.CG math.AT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantifying patterns in visual or tactile textures provides important
information about the process or phenomena that generated these patterns. In
manufacturing, these patterns can be intentionally introduced as a design
feature, or they can be a byproduct of a specific process. Since surface
texture has significant impact on the mechanical properties and the longevity
of the workpiece, it is important to develop tools for quantifying surface
patterns and, when applicable, comparing them to their nominal counterparts.
While existing tools may be able to indicate the existence of a pattern, they
typically do not provide more information about the pattern structure, or how
much it deviates from a nominal pattern. Further, prior works do not provide
automatic or algorithmic approaches for quantifying other pattern
characteristics such as depths' consistency, and variations in the pattern
motifs at different level sets. This paper leverages persistent homology from
Topological Data Analysis (TDA) to derive noise-robust scores for quantifying
motifs' depth and roundness in a pattern. Specifically, sublevel persistence is
used to derive scores that quantify the consistency of indentation depths at
any level set in Piezo Vibration Striking Treatment (PVST) surfaces. Moreover,
we combine sublevel persistence with the distance transform to quantify the
consistency of the indentation radii, and to compare them with the nominal
ones. Although the tool in our PVST experiments had a semi-spherical profile,
we present a generalization of our approach to tools/motifs of arbitrary shapes
thus making our method applicable to other pattern-generating manufacturing
processes.
|
[
{
"created": "Wed, 12 Oct 2022 15:53:23 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Jun 2023 22:26:13 GMT",
"version": "v2"
}
] |
2023-06-13
|
[
[
"Chumley",
"Max M.",
""
],
[
"Yesilli",
"Melih C.",
""
],
[
"Chen",
"Jisheng",
""
],
[
"Khasawneh",
"Firas A.",
""
],
[
"Guo",
"Yang",
""
]
] |
Quantifying patterns in visual or tactile textures provides important information about the process or phenomena that generated these patterns. In manufacturing, these patterns can be intentionally introduced as a design feature, or they can be a byproduct of a specific process. Since surface texture has significant impact on the mechanical properties and the longevity of the workpiece, it is important to develop tools for quantifying surface patterns and, when applicable, comparing them to their nominal counterparts. While existing tools may be able to indicate the existence of a pattern, they typically do not provide more information about the pattern structure, or how much it deviates from a nominal pattern. Further, prior works do not provide automatic or algorithmic approaches for quantifying other pattern characteristics such as depths' consistency, and variations in the pattern motifs at different level sets. This paper leverages persistent homology from Topological Data Analysis (TDA) to derive noise-robust scores for quantifying motifs' depth and roundness in a pattern. Specifically, sublevel persistence is used to derive scores that quantify the consistency of indentation depths at any level set in Piezo Vibration Striking Treatment (PVST) surfaces. Moreover, we combine sublevel persistence with the distance transform to quantify the consistency of the indentation radii, and to compare them with the nominal ones. Although the tool in our PVST experiments had a semi-spherical profile, we present a generalization of our approach to tools/motifs of arbitrary shapes thus making our method applicable to other pattern-generating manufacturing processes.
|
2407.13363
|
Chang Liu
|
Chang Liu, Giulia Rizzoli, Pietro Zanuttigh, Fu Li, Yi Niu
|
Learning from the Web: Language Drives Weakly-Supervised Incremental
Learning for Semantic Segmentation
|
ECCV 2024
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current weakly-supervised incremental learning for semantic segmentation
(WILSS) approaches only consider replacing pixel-level annotations with
image-level labels, while the training images are still from well-designed
datasets. In this work, we argue that widely available web images can also be
considered for the learning of new classes. To achieve this, firstly we
introduce a strategy to select web images which are similar to previously seen
examples in the latent space using a Fourier-based domain discriminator. Then,
an effective caption-driven reharsal strategy is proposed to preserve
previously learnt classes. To our knowledge, this is the first work to rely
solely on web images for both the learning of new concepts and the preservation
of the already learned ones in WILSS. Experimental results show that the
proposed approach can reach state-of-the-art performances without using
manually selected and annotated data in the incremental steps.
|
[
{
"created": "Thu, 18 Jul 2024 10:14:49 GMT",
"version": "v1"
}
] |
2024-07-19
|
[
[
"Liu",
"Chang",
""
],
[
"Rizzoli",
"Giulia",
""
],
[
"Zanuttigh",
"Pietro",
""
],
[
"Li",
"Fu",
""
],
[
"Niu",
"Yi",
""
]
] |
Current weakly-supervised incremental learning for semantic segmentation (WILSS) approaches only consider replacing pixel-level annotations with image-level labels, while the training images are still from well-designed datasets. In this work, we argue that widely available web images can also be considered for the learning of new classes. To achieve this, firstly we introduce a strategy to select web images which are similar to previously seen examples in the latent space using a Fourier-based domain discriminator. Then, an effective caption-driven reharsal strategy is proposed to preserve previously learnt classes. To our knowledge, this is the first work to rely solely on web images for both the learning of new concepts and the preservation of the already learned ones in WILSS. Experimental results show that the proposed approach can reach state-of-the-art performances without using manually selected and annotated data in the incremental steps.
|
1509.05618
|
Ioannis Krikidis
|
Ioannis Krikidis
|
Relay Selection in Wireless Powered Cooperative Networks with Energy
Storage
|
IEEE Journal Selected Areas on Communications- Special Issue on Green
Communications and Networking
| null |
10.1109/JSAC.2015.2479015
| null |
cs.IT cs.NI math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper deals with the problem of relay selection in wireless powered
cooperative networks, where spatially random relays are equipped with energy
storage devices e.g., batteries. In contrast to conventional techniques and in
order to reduce complexity, the relay nodes can either harvest energy from the
source signal (in case of uncharged battery) or attempt to decode and forward
it (in case of charged battery). Several relay selection schemes that
correspond to different state information requirements and implementation
complexities are proposed. The charging/discharging behavior of the battery is
modeled as a two-state Markov chain and analytical expressions for the
steady-state distribution and the outage probability performance are derived
for each relay selection scheme. We prove that energy storage significantly
affects the performance of the system and results in a zeroth diversity gain at
high signal-to-noise ratios; the convergence floors depend on the steady-state
distribution of the battery and are derived in closed-form by using appropriate
approximations. The proposed relay selection schemes are generalized to a
large-scale network with multiple access points (APs), where relays assist the
closest AP and suffer from multi-user interference.
|
[
{
"created": "Fri, 18 Sep 2015 13:22:43 GMT",
"version": "v1"
}
] |
2016-11-17
|
[
[
"Krikidis",
"Ioannis",
""
]
] |
This paper deals with the problem of relay selection in wireless powered cooperative networks, where spatially random relays are equipped with energy storage devices e.g., batteries. In contrast to conventional techniques and in order to reduce complexity, the relay nodes can either harvest energy from the source signal (in case of uncharged battery) or attempt to decode and forward it (in case of charged battery). Several relay selection schemes that correspond to different state information requirements and implementation complexities are proposed. The charging/discharging behavior of the battery is modeled as a two-state Markov chain and analytical expressions for the steady-state distribution and the outage probability performance are derived for each relay selection scheme. We prove that energy storage significantly affects the performance of the system and results in a zeroth diversity gain at high signal-to-noise ratios; the convergence floors depend on the steady-state distribution of the battery and are derived in closed-form by using appropriate approximations. The proposed relay selection schemes are generalized to a large-scale network with multiple access points (APs), where relays assist the closest AP and suffer from multi-user interference.
|
1703.00356
|
Renata Khasanova
|
Renata Khasanova and Pascal Frossard
|
Graph-based Isometry Invariant Representation Learning
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning transformation invariant representations of visual data is an
important problem in computer vision. Deep convolutional networks have
demonstrated remarkable results for image and video classification tasks.
However, they have achieved only limited success in the classification of
images that undergo geometric transformations. In this work we present a novel
Transformation Invariant Graph-based Network (TIGraNet), which learns
graph-based features that are inherently invariant to isometric transformations
such as rotation and translation of input images. In particular, images are
represented as signals on graphs, which permits to replace classical
convolution and pooling layers in deep networks with graph spectral convolution
and dynamic graph pooling layers that together contribute to invariance to
isometric transformations. Our experiments show high performance on rotated and
translated images from the test set compared to classical architectures that
are very sensitive to transformations in the data. The inherent invariance
properties of our framework provide key advantages, such as increased
resiliency to data variability and sustained performance with limited training
sets.
|
[
{
"created": "Wed, 1 Mar 2017 15:51:13 GMT",
"version": "v1"
}
] |
2017-03-02
|
[
[
"Khasanova",
"Renata",
""
],
[
"Frossard",
"Pascal",
""
]
] |
Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited success in the classification of images that undergo geometric transformations. In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images. In particular, images are represented as signals on graphs, which permits to replace classical convolution and pooling layers in deep networks with graph spectral convolution and dynamic graph pooling layers that together contribute to invariance to isometric transformations. Our experiments show high performance on rotated and translated images from the test set compared to classical architectures that are very sensitive to transformations in the data. The inherent invariance properties of our framework provide key advantages, such as increased resiliency to data variability and sustained performance with limited training sets.
|
2004.01300
|
Sabur Baidya
|
Sabur Baidya, Peyman Tehrani and Marco Levorato
|
Data-Driven Path Selection for Real-Time Video Streaming at the Network
Edge
|
This article has been accepted for publication in the IEEE
International Conference on Communications (ICC) Workshop on "Edge Machine
Learning for 5G Mobile Networks and Beyond (EML5G)" 2020
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a framework for the dynamic selection of the
wireless channels used to deliver information-rich data streams to edge
servers. The approach we propose is data-driven, where a predictor, whose
output informs the decision making of the channel selector, is built from
available data on the transformation imposed by the network on previously
transmitted packets. The proposed technique is contextualized to real-time
video streaming for immediate processing. The core of our framework is the
notion of probes, that is, short bursts of packets transmitted over unused
channels to acquire information while generating a controlled impact on other
active links. Results indicate a high accuracy of the prediction output and a
significant improvement in terms of received video quality when the prediction
output is used to dynamically select the used channel for transmission.
|
[
{
"created": "Thu, 2 Apr 2020 23:08:00 GMT",
"version": "v1"
}
] |
2020-04-06
|
[
[
"Baidya",
"Sabur",
""
],
[
"Tehrani",
"Peyman",
""
],
[
"Levorato",
"Marco",
""
]
] |
In this paper, we present a framework for the dynamic selection of the wireless channels used to deliver information-rich data streams to edge servers. The approach we propose is data-driven, where a predictor, whose output informs the decision making of the channel selector, is built from available data on the transformation imposed by the network on previously transmitted packets. The proposed technique is contextualized to real-time video streaming for immediate processing. The core of our framework is the notion of probes, that is, short bursts of packets transmitted over unused channels to acquire information while generating a controlled impact on other active links. Results indicate a high accuracy of the prediction output and a significant improvement in terms of received video quality when the prediction output is used to dynamically select the used channel for transmission.
|
cs/0512100
|
Giorgi Japaridze
|
Giorgi Japaridze
|
The logic of interactive Turing reduction
| null |
Journal of Symbolic Logic 72 (2007), pp. 243-276
|
10.2178/jsl/1174668394
| null |
cs.LO cs.AI math.LO
| null |
The paper gives a soundness and completeness proof for the implicative
fragment of intuitionistic calculus with respect to the semantics of
computability logic, which understands intuitionistic implication as
interactive algorithmic reduction. This concept -- more precisely, the
associated concept of reducibility -- is a generalization of Turing
reducibility from the traditional, input/output sorts of problems to
computational tasks of arbitrary degrees of interactivity. See
http://www.cis.upenn.edu/~giorgi/cl.html for a comprehensive online source on
computability logic.
|
[
{
"created": "Wed, 28 Dec 2005 07:25:57 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Dec 2005 22:03:06 GMT",
"version": "v2"
},
{
"created": "Fri, 3 Feb 2006 05:44:27 GMT",
"version": "v3"
},
{
"created": "Sat, 11 Feb 2006 15:38:42 GMT",
"version": "v4"
}
] |
2011-04-15
|
[
[
"Japaridze",
"Giorgi",
""
]
] |
The paper gives a soundness and completeness proof for the implicative fragment of intuitionistic calculus with respect to the semantics of computability logic, which understands intuitionistic implication as interactive algorithmic reduction. This concept -- more precisely, the associated concept of reducibility -- is a generalization of Turing reducibility from the traditional, input/output sorts of problems to computational tasks of arbitrary degrees of interactivity. See http://www.cis.upenn.edu/~giorgi/cl.html for a comprehensive online source on computability logic.
|
2309.09228
|
Nikola Jedli\v{c}kov\'a
|
Nikola Jedli\v{c}kov\'a, Jan Kratochv\'il
|
Hamiltonian path and Hamiltonian cycle are solvable in polynomial time
in graphs of bounded independence number
| null | null | null | null |
cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A Hamiltonian path (a Hamiltonian cycle) in a graph is a path (a cycle,
respectively) that traverses all of its vertices. The problems of deciding
their existence in an input graph are well-known to be NP-complete, in fact,
they belong to the first problems shown to be computationally hard when the
theory of NP-completeness was being developed. A lot of research has been
devoted to the complexity of Hamiltonian path and Hamiltonian cycle problems
for special graph classes, yet only a handful of positive results are known.
The complexities of both of these problems have been open even for $4K_1$-free
graphs, i.e., graphs of independence number at most $3$. We answer this
question in the general setting of graphs of bounded independence number.
We also consider a newly introduced problem called
\emph{Hamiltonian-$\ell$-Linkage} which is related to the notions of a path
cover and of a linkage in a graph. This problem asks if given $\ell$ pairs of
vertices in an input graph can be connected by disjoint paths that altogether
traverse all vertices of the graph. For $\ell=1$, Hamiltonian-1-Linkage asks
for existence of a Hamiltonian path connecting a given pair of vertices. Our
main result reads that for every pair of integers $k$ and $\ell$, the
Hamiltonian-$\ell$-Linkage problem is polynomial time solvable for graphs of
independence number not exceeding $k$.
|
[
{
"created": "Sun, 17 Sep 2023 09:59:47 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Apr 2024 16:58:02 GMT",
"version": "v2"
}
] |
2024-04-10
|
[
[
"Jedličková",
"Nikola",
""
],
[
"Kratochvíl",
"Jan",
""
]
] |
A Hamiltonian path (a Hamiltonian cycle) in a graph is a path (a cycle, respectively) that traverses all of its vertices. The problems of deciding their existence in an input graph are well-known to be NP-complete, in fact, they belong to the first problems shown to be computationally hard when the theory of NP-completeness was being developed. A lot of research has been devoted to the complexity of Hamiltonian path and Hamiltonian cycle problems for special graph classes, yet only a handful of positive results are known. The complexities of both of these problems have been open even for $4K_1$-free graphs, i.e., graphs of independence number at most $3$. We answer this question in the general setting of graphs of bounded independence number. We also consider a newly introduced problem called \emph{Hamiltonian-$\ell$-Linkage} which is related to the notions of a path cover and of a linkage in a graph. This problem asks if given $\ell$ pairs of vertices in an input graph can be connected by disjoint paths that altogether traverse all vertices of the graph. For $\ell=1$, Hamiltonian-1-Linkage asks for existence of a Hamiltonian path connecting a given pair of vertices. Our main result reads that for every pair of integers $k$ and $\ell$, the Hamiltonian-$\ell$-Linkage problem is polynomial time solvable for graphs of independence number not exceeding $k$.
|
2101.12691
|
Tao Wang
|
Tao Wang, Xiangrui Yang, Gianni Antichi, Anirudh Sivaraman, Aurojit
Panda
|
Isolation mechanisms for high-speed packet-processing pipelines
| null |
The 19th USENIX Symposium on Networked Systems Design and
Implementation (NSDI '22), 2022
| null | null |
cs.NI cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data-plane programmability is now mainstream. As we find more use cases,
deployments need to be able to run multiple packet-processing modules in a
single device. These are likely to be developed by independent teams, either
within the same organization or from multiple organizations. Therefore, we need
isolation mechanisms to ensure that modules on the same device do not interfere
with each other. This paper presents Menshen, an extension of the
Reconfigurable Match Tables (RMT) pipeline that enforces isolation between
different packet-processing modules. Menshen is comprised of a set of
lightweight hardware primitives and an extension to the open source P4-16
reference compiler that act in conjunction to meet this goal. We have
prototyped Menshen on two FPGA platforms (NetFPGA and Corundum). We show that
our design provides isolation, and allows new modules to be loaded without
impacting the ones already running. Finally, we demonstrate that feasibility of
implementing Menshen on ASICs by using the FreePDK45nm technology library and
the Synopsys DC synthesis software, showing that our design meets timing at a
1GHz clock frequency and needs approximately 6% additional chip area. We have
open sourced the code for Menshen's hardware and software at
https://isolation.quest/.
|
[
{
"created": "Fri, 29 Jan 2021 17:21:27 GMT",
"version": "v1"
},
{
"created": "Mon, 10 May 2021 20:48:20 GMT",
"version": "v2"
},
{
"created": "Fri, 17 Sep 2021 03:45:01 GMT",
"version": "v3"
},
{
"created": "Wed, 2 Mar 2022 17:26:01 GMT",
"version": "v4"
}
] |
2022-04-19
|
[
[
"Wang",
"Tao",
""
],
[
"Yang",
"Xiangrui",
""
],
[
"Antichi",
"Gianni",
""
],
[
"Sivaraman",
"Anirudh",
""
],
[
"Panda",
"Aurojit",
""
]
] |
Data-plane programmability is now mainstream. As we find more use cases, deployments need to be able to run multiple packet-processing modules in a single device. These are likely to be developed by independent teams, either within the same organization or from multiple organizations. Therefore, we need isolation mechanisms to ensure that modules on the same device do not interfere with each other. This paper presents Menshen, an extension of the Reconfigurable Match Tables (RMT) pipeline that enforces isolation between different packet-processing modules. Menshen is comprised of a set of lightweight hardware primitives and an extension to the open source P4-16 reference compiler that act in conjunction to meet this goal. We have prototyped Menshen on two FPGA platforms (NetFPGA and Corundum). We show that our design provides isolation, and allows new modules to be loaded without impacting the ones already running. Finally, we demonstrate that feasibility of implementing Menshen on ASICs by using the FreePDK45nm technology library and the Synopsys DC synthesis software, showing that our design meets timing at a 1GHz clock frequency and needs approximately 6% additional chip area. We have open sourced the code for Menshen's hardware and software at https://isolation.quest/.
|
2301.03512
|
Julian Schmidt
|
Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian
Jordan, Daniel Frank, Steffen Staab, Klaus Dietmayer
|
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural
Networks
|
Thomas Monninger and Julian Schmidt are co-first authors. The order
was determined alphabetically
|
IEEE Robotics and Automation Letters (RA-L), 2023
|
10.1109/LRA.2023.3234771
| null |
cs.CV cs.AI cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding traffic scenes requires considering heterogeneous information
about dynamic agents and the static infrastructure. In this work we propose
SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs
and to reason about these graphs using a heterogeneous Graph Neural Network
encoder and task-specific decoders. The heterogeneous graphs, whose structures
are defined by an ontology, consist of different nodes with type-specific node
features and different relations with type-specific edge features. In order to
exploit all the information given by these graphs, we propose to use cascaded
layers of graph convolution. The result is an encoding of the scene.
Task-specific decoders can be applied to predict desired attributes of the
scene. Extensive evaluation on two diverse binary node classification tasks
show the main strength of this methodology: despite being generic, it even
manages to outperform task-specific baselines. The further application of our
methodology to the task of node classification in various knowledge graphs
shows its transferability to other domains.
|
[
{
"created": "Mon, 9 Jan 2023 17:05:28 GMT",
"version": "v1"
}
] |
2023-01-10
|
[
[
"Monninger",
"Thomas",
""
],
[
"Schmidt",
"Julian",
""
],
[
"Rupprecht",
"Jan",
""
],
[
"Raba",
"David",
""
],
[
"Jordan",
"Julian",
""
],
[
"Frank",
"Daniel",
""
],
[
"Staab",
"Steffen",
""
],
[
"Dietmayer",
"Klaus",
""
]
] |
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains.
|
1502.02481
|
Shahbaz Khan
|
Surender Baswana, Shreejit Ray Chaudhury, Keerti Choudhary and Shahbaz
Khan
|
Dynamic DFS Tree in Undirected Graphs: breaking the $O(m)$ barrier
|
27 pages, SODA 2016
| null |
10.1137/1.9781611974331.ch52
| null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Depth first search (DFS) tree is a fundamental data structure for solving
various problems in graphs. It is well known that it takes $O(m+n)$ time to
build a DFS tree for a given undirected graph $G=(V,E)$ on $n$ vertices and $m$
edges. We address the problem of maintaining a DFS tree when the graph is
undergoing {\em updates} (insertion and deletion of vertices or edges). We
present the following results for this problem.
(a) Fault tolerant DFS tree: There exists a data structure of size ${O}(m
~polylog~ n)$ such that given any set ${\cal F}$ of failed vertices or edges, a
DFS tree of the graph $G\setminus {\cal F}$ can be reported in ${O}(n|{\cal F}|
~polylog~ n)$ time.
(b) Fully dynamic DFS tree: There exists a fully dynamic algorithm for
maintaining a DFS tree that takes worst case ${O}(\sqrt{mn} ~polylog~ n)$ time
per update for any arbitrary online sequence of updates.
(c) Incremental DFS tree: Given any arbitrary online sequence of edge
insertions, we can maintain a DFS tree in ${O}(n ~polylog~ n)$ worst case time
per edge insertion.
These are the first $o(m)$ worst case time results for maintaining a DFS tree
in a dynamic environment. Moreover, our fully dynamic algorithm provides, in a
seamless manner, the first deterministic algorithm with $O(1)$ query time and
$o(m)$ worst case update time for the dynamic subgraph connectivity,
biconnectivity, and 2-edge connectivity.
|
[
{
"created": "Mon, 9 Feb 2015 13:36:20 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Apr 2015 10:11:02 GMT",
"version": "v2"
},
{
"created": "Mon, 28 Dec 2015 17:34:08 GMT",
"version": "v3"
},
{
"created": "Wed, 7 Feb 2018 15:42:54 GMT",
"version": "v4"
}
] |
2018-02-08
|
[
[
"Baswana",
"Surender",
""
],
[
"Chaudhury",
"Shreejit Ray",
""
],
[
"Choudhary",
"Keerti",
""
],
[
"Khan",
"Shahbaz",
""
]
] |
Depth first search (DFS) tree is a fundamental data structure for solving various problems in graphs. It is well known that it takes $O(m+n)$ time to build a DFS tree for a given undirected graph $G=(V,E)$ on $n$ vertices and $m$ edges. We address the problem of maintaining a DFS tree when the graph is undergoing {\em updates} (insertion and deletion of vertices or edges). We present the following results for this problem. (a) Fault tolerant DFS tree: There exists a data structure of size ${O}(m ~polylog~ n)$ such that given any set ${\cal F}$ of failed vertices or edges, a DFS tree of the graph $G\setminus {\cal F}$ can be reported in ${O}(n|{\cal F}| ~polylog~ n)$ time. (b) Fully dynamic DFS tree: There exists a fully dynamic algorithm for maintaining a DFS tree that takes worst case ${O}(\sqrt{mn} ~polylog~ n)$ time per update for any arbitrary online sequence of updates. (c) Incremental DFS tree: Given any arbitrary online sequence of edge insertions, we can maintain a DFS tree in ${O}(n ~polylog~ n)$ worst case time per edge insertion. These are the first $o(m)$ worst case time results for maintaining a DFS tree in a dynamic environment. Moreover, our fully dynamic algorithm provides, in a seamless manner, the first deterministic algorithm with $O(1)$ query time and $o(m)$ worst case update time for the dynamic subgraph connectivity, biconnectivity, and 2-edge connectivity.
|
1105.6010
|
Tayeb Bouhadiba
|
Tayeb Bouhadiba (INRIA Grenoble Rh\^one-Alpes / LIG Laboratoire
d'Informatique de Grenoble), Quentin Sabah (INRIA Grenoble Rh\^one-Alpes /
LIG Laboratoire d'Informatique de Grenoble), Gwena\"el Delaval (INRIA
Grenoble Rh\^one-Alpes / LIG Laboratoire d'Informatique de Grenoble), \'Eric
Rutten (INRIA Grenoble Rh\^one-Alpes / LIG Laboratoire d'Informatique de
Grenoble)
|
Synchronous Control of Reconfiguration in Fractal Component-based
Systems -- a Case Study
| null |
N° RR-7631 (2011)
| null |
RR-7631, RR-7631
|
cs.SE cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the context of component-based embedded systems, the management of dynamic
reconfiguration in adaptive systems is an increasingly important feature. The
Fractal component-based framework, and its industrial instantiation MIND,
provide for support for control operations in the lifecycle of components.
Nevertheless, the use of complex and integrated architectures make the
management of this reconfiguration operations difficult to handle by
programmers. To address this issue, we propose to use Synchronous languages,
which are a complete approach to the design of reactive systems, based on
behavior models in the form of transition systems. Furthermore, the design of
closed-loop reactive managers of reconfigurations can benefit from formal tools
like Discrete Controller Synthesis. In this paper we describe an approach to
concretely integrate synchronous reconfiguration managers in Fractal
component-based systems. We describe how to model the state space of the
control problem, and how to specify the control objectives. We describe the
implementation of the resulting manager with the Fractal/Cecilia programming
environment, taking advantage of the Comete distributed middleware. We
illustrate and validate it with the case study of the Comanche HTTP server on a
multi-core execution platform.
|
[
{
"created": "Mon, 30 May 2011 14:46:00 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jun 2011 11:49:30 GMT",
"version": "v2"
}
] |
2011-06-07
|
[
[
"Bouhadiba",
"Tayeb",
"",
"INRIA Grenoble Rhône-Alpes / LIG Laboratoire\n d'Informatique de Grenoble"
],
[
"Sabah",
"Quentin",
"",
"INRIA Grenoble Rhône-Alpes /\n LIG Laboratoire d'Informatique de Grenoble"
],
[
"Delaval",
"Gwenaël",
"",
"INRIA\n Grenoble Rhône-Alpes / LIG Laboratoire d'Informatique de Grenoble"
],
[
"Rutten",
"Éric",
"",
"INRIA Grenoble Rhône-Alpes / LIG Laboratoire d'Informatique de\n Grenoble"
]
] |
In the context of component-based embedded systems, the management of dynamic reconfiguration in adaptive systems is an increasingly important feature. The Fractal component-based framework, and its industrial instantiation MIND, provide for support for control operations in the lifecycle of components. Nevertheless, the use of complex and integrated architectures make the management of this reconfiguration operations difficult to handle by programmers. To address this issue, we propose to use Synchronous languages, which are a complete approach to the design of reactive systems, based on behavior models in the form of transition systems. Furthermore, the design of closed-loop reactive managers of reconfigurations can benefit from formal tools like Discrete Controller Synthesis. In this paper we describe an approach to concretely integrate synchronous reconfiguration managers in Fractal component-based systems. We describe how to model the state space of the control problem, and how to specify the control objectives. We describe the implementation of the resulting manager with the Fractal/Cecilia programming environment, taking advantage of the Comete distributed middleware. We illustrate and validate it with the case study of the Comanche HTTP server on a multi-core execution platform.
|
2105.15100
|
Suman Kumar
|
Suman Kumar, Kazi Amanul Islam Siddiqui, Mukesh Kumary
|
Skin-Health Monitoring system using a Wireless Body Area Network
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A new class of sensing paradigm known as lab-onskin where stretchable and
flexible smart sensor devices are integrated into the skin, provides direct
monitoring and diagnostic interfaces to the body. Distributed lab-on-skin
wireless sensors have the ability to provide continuous long term assessment of
the skin health. This paper proposes a distributed skin health monitoring
system using a wireless body area network. The system is responsive to the
dynamic changes in the skin health, and remotely reports on the same. The
proposed algorithm detects the abnormal skin and creates an energy efficient
data aggregation tree covering the affected area while putting the unnecessary
sensors to sleep mode. The algorithm responds to the changing conditions of the
skin by dynamically adapting the size and shape of the monitoring trees to that
of the abnormal skin areas thus providing a comprehensive monitoring.
Simulation results demonstrate the application and utility of the proposed
algorithm for changing wound shapes and sizes.
|
[
{
"created": "Thu, 15 Apr 2021 20:32:54 GMT",
"version": "v1"
}
] |
2021-06-01
|
[
[
"Kumar",
"Suman",
""
],
[
"Siddiqui",
"Kazi Amanul Islam",
""
],
[
"Kumary",
"Mukesh",
""
]
] |
A new class of sensing paradigm known as lab-onskin where stretchable and flexible smart sensor devices are integrated into the skin, provides direct monitoring and diagnostic interfaces to the body. Distributed lab-on-skin wireless sensors have the ability to provide continuous long term assessment of the skin health. This paper proposes a distributed skin health monitoring system using a wireless body area network. The system is responsive to the dynamic changes in the skin health, and remotely reports on the same. The proposed algorithm detects the abnormal skin and creates an energy efficient data aggregation tree covering the affected area while putting the unnecessary sensors to sleep mode. The algorithm responds to the changing conditions of the skin by dynamically adapting the size and shape of the monitoring trees to that of the abnormal skin areas thus providing a comprehensive monitoring. Simulation results demonstrate the application and utility of the proposed algorithm for changing wound shapes and sizes.
|
2302.08135
|
Youjia Zhang
|
Youjia Zhang, Pingzhong Tang
|
A Truthful Referral Auction Over Networks
| null | null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper studies a mechanism design problem over a network, where agents
can only participate by referrals. The Bulow-Klemberer theorem proposes that
expanding the number of participants is a more effective approach to increase
revenue than modifying the auction format. However, agents lack the motivation
to invite others because doing so intensifies competition among them. On the
other hand, misreporting social networks is also a common problem that can
reduce revenue. Examples of misreporting include Sybil attacks (an agent
pretending to be multiple bidders) and coalition groups (multiple agents
pretending to be an agent). To address these challenges, we introduce a novel
mechanism called the Truthful Referral Diffusion Mechanism (TRDM). TRDM
incentivizes agents to report their social networks truthfully, and some of
them are rewarded by the seller for improving revenue. In spite of the fact
that some agents overbid in TRDM, the revenue is fixed, and it is higher than
the revenue of any mechanism without referrals. TRDM is budget-balanced
(non-negative revenue) and generates an efficient outcome (maximized social
welfare), making it attractive for both the seller and the buyers as it
improves revenue and reward.
|
[
{
"created": "Thu, 16 Feb 2023 08:06:55 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Feb 2023 11:51:42 GMT",
"version": "v2"
},
{
"created": "Thu, 16 Mar 2023 07:51:52 GMT",
"version": "v3"
}
] |
2023-03-17
|
[
[
"Zhang",
"Youjia",
""
],
[
"Tang",
"Pingzhong",
""
]
] |
This paper studies a mechanism design problem over a network, where agents can only participate by referrals. The Bulow-Klemberer theorem proposes that expanding the number of participants is a more effective approach to increase revenue than modifying the auction format. However, agents lack the motivation to invite others because doing so intensifies competition among them. On the other hand, misreporting social networks is also a common problem that can reduce revenue. Examples of misreporting include Sybil attacks (an agent pretending to be multiple bidders) and coalition groups (multiple agents pretending to be an agent). To address these challenges, we introduce a novel mechanism called the Truthful Referral Diffusion Mechanism (TRDM). TRDM incentivizes agents to report their social networks truthfully, and some of them are rewarded by the seller for improving revenue. In spite of the fact that some agents overbid in TRDM, the revenue is fixed, and it is higher than the revenue of any mechanism without referrals. TRDM is budget-balanced (non-negative revenue) and generates an efficient outcome (maximized social welfare), making it attractive for both the seller and the buyers as it improves revenue and reward.
|
2307.08364
|
Lennart Purucker
|
Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel,
Bernd Bischl, Holger Hoos
|
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc
Ensemble Selection in AutoML
|
10 pages main paper, 24 pages references and appendix, 4 figures, 16
subfigures, 13 tables, to be published in: International Conference on
Automated Machine Learning 2023; affiliations corrected. arXiv admin note:
text overlap with arXiv:2307.00286
| null | null | null |
cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Automated machine learning (AutoML) systems commonly ensemble models post hoc
to improve predictive performance, typically via greedy ensemble selection
(GES). However, we believe that GES may not always be optimal, as it performs a
simple deterministic greedy search. In this work, we introduce two novel
population-based ensemble selection methods, QO-ES and QDO-ES, and compare them
to GES. While QO-ES optimises solely for predictive performance, QDO-ES also
considers the diversity of ensembles within the population, maintaining a
diverse set of well-performing ensembles during optimisation based on ideas of
quality diversity optimisation. The methods are evaluated using 71
classification datasets from the AutoML benchmark, demonstrating that QO-ES and
QDO-ES often outrank GES, albeit only statistically significant on validation
data. Our results further suggest that diversity can be beneficial for post hoc
ensembling but also increases the risk of overfitting.
|
[
{
"created": "Mon, 17 Jul 2023 10:02:01 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Aug 2023 16:09:56 GMT",
"version": "v2"
}
] |
2023-08-03
|
[
[
"Purucker",
"Lennart",
""
],
[
"Schneider",
"Lennart",
""
],
[
"Anastacio",
"Marie",
""
],
[
"Beel",
"Joeran",
""
],
[
"Bischl",
"Bernd",
""
],
[
"Hoos",
"Holger",
""
]
] |
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe that GES may not always be optimal, as it performs a simple deterministic greedy search. In this work, we introduce two novel population-based ensemble selection methods, QO-ES and QDO-ES, and compare them to GES. While QO-ES optimises solely for predictive performance, QDO-ES also considers the diversity of ensembles within the population, maintaining a diverse set of well-performing ensembles during optimisation based on ideas of quality diversity optimisation. The methods are evaluated using 71 classification datasets from the AutoML benchmark, demonstrating that QO-ES and QDO-ES often outrank GES, albeit only statistically significant on validation data. Our results further suggest that diversity can be beneficial for post hoc ensembling but also increases the risk of overfitting.
|
2111.13156
|
Ammarah Farooq
|
Ammarah Farooq, Muhammad Awais, Sara Ahmed, Josef Kittler
|
Global Interaction Modelling in Vision Transformer via Super Tokens
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
With the popularity of Transformer architectures in computer vision, the
research focus has shifted towards developing computationally efficient
designs. Window-based local attention is one of the major techniques being
adopted in recent works. These methods begin with very small patch size and
small embedding dimensions and then perform strided convolution (patch merging)
in order to reduce the feature map size and increase embedding dimensions,
hence, forming a pyramidal Convolutional Neural Network (CNN) like design. In
this work, we investigate local and global information modelling in
transformers by presenting a novel isotropic architecture that adopts local
windows and special tokens, called Super tokens, for self-attention.
Specifically, a single Super token is assigned to each image window which
captures the rich local details for that window. These tokens are then employed
for cross-window communication and global representation learning. Hence, most
of the learning is independent of the image patches $(N)$ in the higher layers,
and the class embedding is learned solely based on the Super tokens $(N/M^2)$
where $M^2$ is the window size. In standard image classification on
Imagenet-1K, the proposed Super tokens based transformer (STT-S25) achieves
83.5\% accuracy which is equivalent to Swin transformer (Swin-B) with circa
half the number of parameters (49M) and double the inference time throughput.
The proposed Super token transformer offers a lightweight and promising
backbone for visual recognition tasks.
|
[
{
"created": "Thu, 25 Nov 2021 16:22:57 GMT",
"version": "v1"
}
] |
2021-11-29
|
[
[
"Farooq",
"Ammarah",
""
],
[
"Awais",
"Muhammad",
""
],
[
"Ahmed",
"Sara",
""
],
[
"Kittler",
"Josef",
""
]
] |
With the popularity of Transformer architectures in computer vision, the research focus has shifted towards developing computationally efficient designs. Window-based local attention is one of the major techniques being adopted in recent works. These methods begin with very small patch size and small embedding dimensions and then perform strided convolution (patch merging) in order to reduce the feature map size and increase embedding dimensions, hence, forming a pyramidal Convolutional Neural Network (CNN) like design. In this work, we investigate local and global information modelling in transformers by presenting a novel isotropic architecture that adopts local windows and special tokens, called Super tokens, for self-attention. Specifically, a single Super token is assigned to each image window which captures the rich local details for that window. These tokens are then employed for cross-window communication and global representation learning. Hence, most of the learning is independent of the image patches $(N)$ in the higher layers, and the class embedding is learned solely based on the Super tokens $(N/M^2)$ where $M^2$ is the window size. In standard image classification on Imagenet-1K, the proposed Super tokens based transformer (STT-S25) achieves 83.5\% accuracy which is equivalent to Swin transformer (Swin-B) with circa half the number of parameters (49M) and double the inference time throughput. The proposed Super token transformer offers a lightweight and promising backbone for visual recognition tasks.
|
2304.08176
|
Saber Elsayed
|
Saber Elsayed
|
Towards Mitigating ChatGPT's Negative Impact on Education: Optimizing
Question Design through Bloom's Taxonomy
| null | null | null | null |
cs.CY cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The popularity of generative text AI tools in answering questions has led to
concerns regarding their potential negative impact on students' academic
performance and the challenges that educators face in evaluating student
learning. To address these concerns, this paper introduces an evolutionary
approach that aims to identify the best set of Bloom's taxonomy keywords to
generate questions that these tools have low confidence in answering. The
effectiveness of this approach is evaluated through a case study that uses
questions from a Data Structures and Representation course being taught at the
University of New South Wales in Canberra, Australia. The results demonstrate
that the optimization algorithm is able to find keywords from different
cognitive levels to create questions that ChatGPT has low confidence in
answering. This study is a step forward to offer valuable insights for
educators seeking to create more effective questions that promote critical
thinking among students.
|
[
{
"created": "Fri, 31 Mar 2023 00:01:59 GMT",
"version": "v1"
}
] |
2023-04-18
|
[
[
"Elsayed",
"Saber",
""
]
] |
The popularity of generative text AI tools in answering questions has led to concerns regarding their potential negative impact on students' academic performance and the challenges that educators face in evaluating student learning. To address these concerns, this paper introduces an evolutionary approach that aims to identify the best set of Bloom's taxonomy keywords to generate questions that these tools have low confidence in answering. The effectiveness of this approach is evaluated through a case study that uses questions from a Data Structures and Representation course being taught at the University of New South Wales in Canberra, Australia. The results demonstrate that the optimization algorithm is able to find keywords from different cognitive levels to create questions that ChatGPT has low confidence in answering. This study is a step forward to offer valuable insights for educators seeking to create more effective questions that promote critical thinking among students.
|
2209.13815
|
Yuntao Wang
|
Yuntao Wang, Zhou Su, Abderrahim Benslimane, Qichao Xu, Minghui Dai,
and Ruidong Li
|
A Learning-based Honeypot Game for Collaborative Defense in UAV Networks
|
Accepted by IEEE Globecom2022
| null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The proliferation of unmanned aerial vehicles (UAVs) opens up new
opportunities for on-demand service provisioning anywhere and anytime, but it
also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is
regarded as a promising lightweight defense to actively protect mobile Internet
of things, especially UAV networks. Existing works primarily focused on
honeypot design and attack pattern recognition, the incentive issue for
motivating UAVs' participation (e.g., sharing trapped attack data in honeypots)
to collaboratively resist distributed and sophisticated attacks is still
under-explored. This paper proposes a novel game-based collaborative defense
approach to address optimal, fair, and feasible incentive mechanism design, in
the presence of network dynamics and UAVs' multi-dimensional private
information (e.g., valid defense data (VDD) volume, communication delay, and
UAV cost). Specifically, we first develop a honeypot game between UAVs under
both partial and complete information asymmetry scenarios. We then devise a
contract-theoretic method to solve the optimal VDD-reward contract design
problem with partial information asymmetry, while ensuring truthfulness,
fairness, and computational efficiency. Furthermore, under complete information
asymmetry, we devise a reinforcement learning based distributed method to
dynamically design optimal contracts for distinct types of UAVs in the
fast-changing network. Experimental simulations show that the proposed scheme
can motivate UAV's collaboration in VDD sharing and enhance defensive
effectiveness, compared with existing solutions.
|
[
{
"created": "Wed, 28 Sep 2022 03:40:06 GMT",
"version": "v1"
}
] |
2022-09-29
|
[
[
"Wang",
"Yuntao",
""
],
[
"Su",
"Zhou",
""
],
[
"Benslimane",
"Abderrahim",
""
],
[
"Xu",
"Qichao",
""
],
[
"Dai",
"Minghui",
""
],
[
"Li",
"Ruidong",
""
]
] |
The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fairness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.
|
2202.03460
|
Ji Gao
|
Ji Gao, Sanjam Garg, Mohammad Mahmoody, Prashant Nalini Vasudevan
|
Deletion Inference, Reconstruction, and Compliance in Machine
(Un)Learning
|
Full version of a paper appearing in the 22nd Privacy Enhancing
Technologies Symposium (PETS 2022)
| null | null | null |
cs.LG cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Privacy attacks on machine learning models aim to identify the data that is
used to train such models. Such attacks, traditionally, are studied on static
models that are trained once and are accessible by the adversary. Motivated to
meet new legal requirements, many machine learning methods are recently
extended to support machine unlearning, i.e., updating models as if certain
examples are removed from their training sets, and meet new legal requirements.
However, privacy attacks could potentially become more devastating in this new
setting, since an attacker could now access both the original model before
deletion and the new model after the deletion. In fact, the very act of
deletion might make the deleted record more vulnerable to privacy attacks.
Inspired by cryptographic definitions and the differential privacy framework,
we formally study privacy implications of machine unlearning. We formalize
(various forms of) deletion inference and deletion reconstruction attacks, in
which the adversary aims to either identify which record is deleted or to
reconstruct (perhaps part of) the deleted records. We then present successful
deletion inference and reconstruction attacks for a variety of machine learning
models and tasks such as classification, regression, and language models.
Finally, we show that our attacks would provably be precluded if the schemes
satisfy (variants of) Deletion Compliance (Garg, Goldwasser, and Vasudevan,
Eurocrypt' 20).
|
[
{
"created": "Mon, 7 Feb 2022 19:02:58 GMT",
"version": "v1"
}
] |
2022-02-09
|
[
[
"Gao",
"Ji",
""
],
[
"Garg",
"Sanjam",
""
],
[
"Mahmoody",
"Mohammad",
""
],
[
"Vasudevan",
"Prashant Nalini",
""
]
] |
Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained once and are accessible by the adversary. Motivated to meet new legal requirements, many machine learning methods are recently extended to support machine unlearning, i.e., updating models as if certain examples are removed from their training sets, and meet new legal requirements. However, privacy attacks could potentially become more devastating in this new setting, since an attacker could now access both the original model before deletion and the new model after the deletion. In fact, the very act of deletion might make the deleted record more vulnerable to privacy attacks. Inspired by cryptographic definitions and the differential privacy framework, we formally study privacy implications of machine unlearning. We formalize (various forms of) deletion inference and deletion reconstruction attacks, in which the adversary aims to either identify which record is deleted or to reconstruct (perhaps part of) the deleted records. We then present successful deletion inference and reconstruction attacks for a variety of machine learning models and tasks such as classification, regression, and language models. Finally, we show that our attacks would provably be precluded if the schemes satisfy (variants of) Deletion Compliance (Garg, Goldwasser, and Vasudevan, Eurocrypt' 20).
|
1308.0686
|
Arpan Chattopadhyay
|
Arpan Chattopadhyay, Marceau Coupechoux, and Anurag Kumar
|
As-You-Go Deployment of a Wireless Network with On-Line Measurements and
Backtracking
|
16 pages; 6 figures; submitted
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We are motivated by the need, in some applications, for impromptu or
as-you-go deployment of wireless sensor networks. A person walks along a line,
making link quality measurements with the previous relay at equally spaced
locations, and deploys relays at some of these locations, so as to connect a
sensor placed on the line with a sink at the start of the line. In this paper,
we extend our earlier work on the problem (see [1]) to incorporate two new
aspects: (i) inclusion of path outage in the deployment objective, and (ii)
permitting the deployment agent to make measurements over several consecutive
steps before selecting a placement location among them (which we call
backtracking). We consider a light traffic regime, and formulate the problem as
a Markov decision process. Placement algorithms are obtained for two cases: (i)
the distance to the source is geometrically distributed with known mean, and
(ii) the average cost per step case. We motivate the per-step cost function in
terms of several known forwarding protocols for sleep-wake cycling wireless
sensor networks. We obtain the structures of the optimal policies for the
various formulations, and provide some sensitivity results about the policies
and the optimal values. We then provide a numerical study of the algorithms,
thus providing insights into the advantage of backtracking, and a comparison
with simple heuristic placement policies.
|
[
{
"created": "Sat, 3 Aug 2013 11:37:14 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Aug 2013 04:06:02 GMT",
"version": "v2"
}
] |
2013-08-23
|
[
[
"Chattopadhyay",
"Arpan",
""
],
[
"Coupechoux",
"Marceau",
""
],
[
"Kumar",
"Anurag",
""
]
] |
We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, making link quality measurements with the previous relay at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed on the line with a sink at the start of the line. In this paper, we extend our earlier work on the problem (see [1]) to incorporate two new aspects: (i) inclusion of path outage in the deployment objective, and (ii) permitting the deployment agent to make measurements over several consecutive steps before selecting a placement location among them (which we call backtracking). We consider a light traffic regime, and formulate the problem as a Markov decision process. Placement algorithms are obtained for two cases: (i) the distance to the source is geometrically distributed with known mean, and (ii) the average cost per step case. We motivate the per-step cost function in terms of several known forwarding protocols for sleep-wake cycling wireless sensor networks. We obtain the structures of the optimal policies for the various formulations, and provide some sensitivity results about the policies and the optimal values. We then provide a numerical study of the algorithms, thus providing insights into the advantage of backtracking, and a comparison with simple heuristic placement policies.
|
2008.09706
|
Pengjie Ren
|
Yangjun Zhang, Pengjie Ren, Maarten de Rijke
|
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data
and Methodology
|
under review at JASIST
| null | null | null |
cs.CL cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Conversational interfaces are increasingly popular as a way of connecting
people to information. Corpus-based conversational interfaces are able to
generate more diverse and natural responses than template-based or
retrieval-based agents. With their increased generative capacity of corpusbased
conversational agents comes the need to classify and filter out malevolent
responses that are inappropriate in terms of content and dialogue acts.
Previous studies on the topic of recognizing and classifying inappropriate
content are mostly focused on a certain category of malevolence or on single
sentences instead of an entire dialogue. In this paper, we define the task of
Malevolent Dialogue Response Detection and Classification (MDRDC). We make
three contributions to advance research on this task. First, we present a
Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled
multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical
classification task over this taxonomy. Third, we apply stateof-the-art text
classification methods to the MDRDC task and report on extensive experiments
aimed at assessing the performance of these approaches.
|
[
{
"created": "Fri, 21 Aug 2020 22:43:27 GMT",
"version": "v1"
}
] |
2020-08-25
|
[
[
"Zhang",
"Yangjun",
""
],
[
"Ren",
"Pengjie",
""
],
[
"de Rijke",
"Maarten",
""
]
] |
Conversational interfaces are increasingly popular as a way of connecting people to information. Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpusbased conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence or on single sentences instead of an entire dialogue. In this paper, we define the task of Malevolent Dialogue Response Detection and Classification (MDRDC). We make three contributions to advance research on this task. First, we present a Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical classification task over this taxonomy. Third, we apply stateof-the-art text classification methods to the MDRDC task and report on extensive experiments aimed at assessing the performance of these approaches.
|
2306.00488
|
Ruizhong Qiu
|
Ruizhong Qiu, Dingsu Wang, Lei Ying, H. Vincent Poor, Yifang Zhang,
Hanghang Tong
|
Reconstructing Graph Diffusion History from a Single Snapshot
|
Full version of the KDD 2023 paper (including the appendix)
| null |
10.1145/3580305.3599488
| null |
cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Diffusion on graphs is ubiquitous with numerous high-impact applications. In
these applications, complete diffusion histories play an essential role in
terms of identifying dynamical patterns, reflecting on precaution actions, and
forecasting intervention effects. Despite their importance, complete diffusion
histories are rarely available and are highly challenging to reconstruct due to
ill-posedness, explosive search space, and scarcity of training data. To date,
few methods exist for diffusion history reconstruction. They are exclusively
based on the maximum likelihood estimation (MLE) formulation and require to
know true diffusion parameters. In this paper, we study an even harder problem,
namely reconstructing Diffusion history from A single SnapsHot} (DASH), where
we seek to reconstruct the history from only the final snapshot without knowing
true diffusion parameters. We start with theoretical analyses that reveal a
fundamental limitation of the MLE formulation. We prove: (a) estimation error
of diffusion parameters is unavoidable due to NP-hardness of diffusion
parameter estimation, and (b) the MLE formulation is sensitive to estimation
error of diffusion parameters. To overcome the inherent limitation of the MLE
formulation, we propose a novel barycenter formulation: finding the barycenter
of the posterior distribution of histories, which is provably stable against
the estimation error of diffusion parameters. We further develop an effective
solver named DIffusion hiTting Times with Optimal proposal (DITTO) by reducing
the problem to estimating posterior expected hitting times via the
Metropolis--Hastings Markov chain Monte Carlo method (M--H MCMC) and employing
an unsupervised graph neural network to learn an optimal proposal to accelerate
the convergence of M--H MCMC. We conduct extensive experiments to demonstrate
the efficacy of the proposed method.
|
[
{
"created": "Thu, 1 Jun 2023 09:39:32 GMT",
"version": "v1"
},
{
"created": "Sun, 4 Jun 2023 21:25:25 GMT",
"version": "v2"
},
{
"created": "Sat, 1 Jul 2023 06:46:07 GMT",
"version": "v3"
},
{
"created": "Fri, 31 May 2024 23:25:07 GMT",
"version": "v4"
}
] |
2024-06-04
|
[
[
"Qiu",
"Ruizhong",
""
],
[
"Wang",
"Dingsu",
""
],
[
"Ying",
"Lei",
""
],
[
"Poor",
"H. Vincent",
""
],
[
"Zhang",
"Yifang",
""
],
[
"Tong",
"Hanghang",
""
]
] |
Diffusion on graphs is ubiquitous with numerous high-impact applications. In these applications, complete diffusion histories play an essential role in terms of identifying dynamical patterns, reflecting on precaution actions, and forecasting intervention effects. Despite their importance, complete diffusion histories are rarely available and are highly challenging to reconstruct due to ill-posedness, explosive search space, and scarcity of training data. To date, few methods exist for diffusion history reconstruction. They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters. In this paper, we study an even harder problem, namely reconstructing Diffusion history from A single SnapsHot} (DASH), where we seek to reconstruct the history from only the final snapshot without knowing true diffusion parameters. We start with theoretical analyses that reveal a fundamental limitation of the MLE formulation. We prove: (a) estimation error of diffusion parameters is unavoidable due to NP-hardness of diffusion parameter estimation, and (b) the MLE formulation is sensitive to estimation error of diffusion parameters. To overcome the inherent limitation of the MLE formulation, we propose a novel barycenter formulation: finding the barycenter of the posterior distribution of histories, which is provably stable against the estimation error of diffusion parameters. We further develop an effective solver named DIffusion hiTting Times with Optimal proposal (DITTO) by reducing the problem to estimating posterior expected hitting times via the Metropolis--Hastings Markov chain Monte Carlo method (M--H MCMC) and employing an unsupervised graph neural network to learn an optimal proposal to accelerate the convergence of M--H MCMC. We conduct extensive experiments to demonstrate the efficacy of the proposed method.
|
2309.02528
|
Jayanth Yetukuri
|
Ian Hardy, Jayanth Yetukuri and Yang Liu
|
Adaptive Adversarial Training Does Not Increase Recourse Costs
| null |
In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and
Society (AIES '23). Association for Computing Machinery, New York, NY, USA,
432 442
|
10.1145/3600211.3604704
| null |
cs.LG cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Recent work has connected adversarial attack methods and algorithmic recourse
methods: both seek minimal changes to an input instance which alter a model's
classification decision. It has been shown that traditional adversarial
training, which seeks to minimize a classifier's susceptibility to malicious
perturbations, increases the cost of generated recourse; with larger
adversarial training radii correlating with higher recourse costs. From the
perspective of algorithmic recourse, however, the appropriate adversarial
training radius has always been unknown. Another recent line of work has
motivated adversarial training with adaptive training radii to address the
issue of instance-wise variable adversarial vulnerability, showing success in
domains with unknown attack radii. This work studies the effects of adaptive
adversarial training on algorithmic recourse costs. We establish that the
improvements in model robustness induced by adaptive adversarial training show
little effect on algorithmic recourse costs, providing a potential avenue for
affordable robustness in domains where recoursability is critical.
|
[
{
"created": "Tue, 5 Sep 2023 18:40:22 GMT",
"version": "v1"
}
] |
2023-09-07
|
[
[
"Hardy",
"Ian",
""
],
[
"Yetukuri",
"Jayanth",
""
],
[
"Liu",
"Yang",
""
]
] |
Recent work has connected adversarial attack methods and algorithmic recourse methods: both seek minimal changes to an input instance which alter a model's classification decision. It has been shown that traditional adversarial training, which seeks to minimize a classifier's susceptibility to malicious perturbations, increases the cost of generated recourse; with larger adversarial training radii correlating with higher recourse costs. From the perspective of algorithmic recourse, however, the appropriate adversarial training radius has always been unknown. Another recent line of work has motivated adversarial training with adaptive training radii to address the issue of instance-wise variable adversarial vulnerability, showing success in domains with unknown attack radii. This work studies the effects of adaptive adversarial training on algorithmic recourse costs. We establish that the improvements in model robustness induced by adaptive adversarial training show little effect on algorithmic recourse costs, providing a potential avenue for affordable robustness in domains where recoursability is critical.
|
1806.11248
|
Rory Mitchell
|
Rory Mitchell, Andrey Adinets, Thejaswi Rao, Eibe Frank
|
XGBoost: Scalable GPU Accelerated Learning
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe the multi-GPU gradient boosting algorithm implemented in the
XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast,
scalable training on multi-GPU systems with all of the features of the XGBoost
library. We employ data compression techniques to minimise the usage of scarce
GPU memory while still allowing highly efficient implementation. Using our
algorithm we show that it is possible to process 115 million training instances
in under three minutes on a publicly available cloud computing instance. The
algorithm is implemented using end-to-end GPU parallelism, with prediction,
gradient calculation, feature quantisation, decision tree construction and
evaluation phases all computed on device.
|
[
{
"created": "Fri, 29 Jun 2018 02:05:32 GMT",
"version": "v1"
}
] |
2018-07-02
|
[
[
"Mitchell",
"Rory",
""
],
[
"Adinets",
"Andrey",
""
],
[
"Rao",
"Thejaswi",
""
],
[
"Frank",
"Eibe",
""
]
] |
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GPU systems with all of the features of the XGBoost library. We employ data compression techniques to minimise the usage of scarce GPU memory while still allowing highly efficient implementation. Using our algorithm we show that it is possible to process 115 million training instances in under three minutes on a publicly available cloud computing instance. The algorithm is implemented using end-to-end GPU parallelism, with prediction, gradient calculation, feature quantisation, decision tree construction and evaluation phases all computed on device.
|
2303.06987
|
Georges Gagnere
|
Georges Gagner\'e (INREV, UP8, UPL), Andy Lavender, C\'edric Plessiet
(INREV, AIAC, UP8, UPL), Tim White
|
Challenges of movement quality using motion capture in theatre
| null |
MOCO '18: 5th International Conference on Movement and Computing,
Jun 2018, Genoa, Italy. pp.1-6
|
10.1145/3212721.3212883
| null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe1 two case studies of AvatarStaging theatrical mixed reality
framework combining avatars and performers acting in an artistic context. We
outline a qualitative approach toward the condition for stage presence for the
avatars. We describe the motion control solutions we experimented with from the
perspective of building a protocol of avatar direction in a mixed reality
appropriate to live performance.
|
[
{
"created": "Mon, 13 Mar 2023 10:37:20 GMT",
"version": "v1"
}
] |
2023-03-14
|
[
[
"Gagneré",
"Georges",
"",
"INREV, UP8, UPL"
],
[
"Lavender",
"Andy",
"",
"INREV, AIAC, UP8, UPL"
],
[
"Plessiet",
"Cédric",
"",
"INREV, AIAC, UP8, UPL"
],
[
"White",
"Tim",
""
]
] |
We describe1 two case studies of AvatarStaging theatrical mixed reality framework combining avatars and performers acting in an artistic context. We outline a qualitative approach toward the condition for stage presence for the avatars. We describe the motion control solutions we experimented with from the perspective of building a protocol of avatar direction in a mixed reality appropriate to live performance.
|
2012.01468
|
Yuqi Ouyang
|
Yuqi Ouyang, Victor Sanchez
|
Video Anomaly Detection by Estimating Likelihood of Representations
|
Accepted to ICPR 2020
| null | null | null |
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video anomaly detection is a challenging task not only because it involves
solving many sub-tasks such as motion representation, object localization and
action recognition, but also because it is commonly considered as an
unsupervised learning problem that involves detecting outliers. Traditionally,
solutions to this task have focused on the mapping between video frames and
their low-dimensional features, while ignoring the spatial connections of those
features. Recent solutions focus on analyzing these spatial connections by
using hard clustering techniques, such as K-Means, or applying neural networks
to map latent features to a general understanding, such as action attributes.
In order to solve video anomaly in the latent feature space, we propose a deep
probabilistic model to transfer this task into a density estimation problem
where latent manifolds are generated by a deep denoising autoencoder and
clustered by expectation maximization. Evaluations on several benchmarks
datasets show the strengths of our model, achieving outstanding performance on
challenging datasets.
|
[
{
"created": "Wed, 2 Dec 2020 19:16:22 GMT",
"version": "v1"
}
] |
2020-12-04
|
[
[
"Ouyang",
"Yuqi",
""
],
[
"Sanchez",
"Victor",
""
]
] |
Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as an unsupervised learning problem that involves detecting outliers. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding, such as action attributes. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem where latent manifolds are generated by a deep denoising autoencoder and clustered by expectation maximization. Evaluations on several benchmarks datasets show the strengths of our model, achieving outstanding performance on challenging datasets.
|
1812.10265
|
Zheng Xin
|
Xin Zheng, Yanqing Guo, Huaibo Huang, Yi Li, Ran He
|
A Survey of Deep Facial Attribute Analysis
|
submitted to International Journal of Computer Vision (IJCV)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Facial attribute analysis has received considerable attention when deep
learning techniques made remarkable breakthroughs in this field over the past
few years. Deep learning based facial attribute analysis consists of two basic
sub-issues: facial attribute estimation (FAE), which recognizes whether facial
attributes are present in given images, and facial attribute manipulation
(FAM), which synthesizes or removes desired facial attributes. In this paper,
we provide a comprehensive survey of deep facial attribute analysis from the
perspectives of both estimation and manipulation. First, we summarize a general
pipeline that deep facial attribute analysis follows, which comprises two
stages: data preprocessing and model construction. Additionally, we introduce
the underlying theories of this two-stage pipeline for both FAE and FAM.
Second, the datasets and performance metrics commonly used in facial attribute
analysis are presented. Third, we create a taxonomy of state-of-the-art methods
and review deep FAE and FAM algorithms in detail. Furthermore, several
additional facial attribute related issues are introduced, as well as relevant
real-world applications. Finally, we discuss possible challenges and promising
future research directions.
|
[
{
"created": "Wed, 26 Dec 2018 09:24:07 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Mar 2019 06:58:40 GMT",
"version": "v2"
},
{
"created": "Sun, 27 Oct 2019 03:13:51 GMT",
"version": "v3"
}
] |
2019-10-29
|
[
[
"Zheng",
"Xin",
""
],
[
"Guo",
"Yanqing",
""
],
[
"Huang",
"Huaibo",
""
],
[
"Li",
"Yi",
""
],
[
"He",
"Ran",
""
]
] |
Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
|
1812.09383
|
Franziska Roesner
|
John Akers, Gagan Bansal, Gabriel Cadamuro, Christine Chen, Quanze
Chen, Lucy Lin, Phoebe Mulcaire, Rajalakshmi Nandakumar, Matthew Rockett,
Lucy Simko, John Toman, Tongshuang Wu, Eric Zeng, Bill Zorn, Franziska
Roesner
|
Technology-Enabled Disinformation: Summary, Lessons, and Recommendations
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Technology is increasingly used -- unintentionally (misinformation) or
intentionally (disinformation) -- to spread false information at scale, with
potentially broad-reaching societal effects. For example, technology enables
increasingly realistic false images and videos, and hyper-personal targeting
means different people may see different versions of reality. This report is
the culmination of a PhD-level special topics course
(https://courses.cs.washington.edu/courses/cse599b/18au/) in Computer Science &
Engineering at the University of Washington's Paul G. Allen School in the fall
of 2018. The goals of this course were to study (1) how technologies and
today's technical platforms enable and support the creation and spread of such
mis- and disinformation, as well as (2) how technical approaches could be used
to mitigate these issues. In this report, we summarize the space of
technology-enabled mis- and disinformation based on our investigations, and
then surface our lessons and recommendations for technologists, researchers,
platform designers, policymakers, and users.
|
[
{
"created": "Fri, 21 Dec 2018 21:46:34 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Jan 2019 14:35:55 GMT",
"version": "v2"
}
] |
2019-01-04
|
[
[
"Akers",
"John",
""
],
[
"Bansal",
"Gagan",
""
],
[
"Cadamuro",
"Gabriel",
""
],
[
"Chen",
"Christine",
""
],
[
"Chen",
"Quanze",
""
],
[
"Lin",
"Lucy",
""
],
[
"Mulcaire",
"Phoebe",
""
],
[
"Nandakumar",
"Rajalakshmi",
""
],
[
"Rockett",
"Matthew",
""
],
[
"Simko",
"Lucy",
""
],
[
"Toman",
"John",
""
],
[
"Wu",
"Tongshuang",
""
],
[
"Zeng",
"Eric",
""
],
[
"Zorn",
"Bill",
""
],
[
"Roesner",
"Franziska",
""
]
] |
Technology is increasingly used -- unintentionally (misinformation) or intentionally (disinformation) -- to spread false information at scale, with potentially broad-reaching societal effects. For example, technology enables increasingly realistic false images and videos, and hyper-personal targeting means different people may see different versions of reality. This report is the culmination of a PhD-level special topics course (https://courses.cs.washington.edu/courses/cse599b/18au/) in Computer Science & Engineering at the University of Washington's Paul G. Allen School in the fall of 2018. The goals of this course were to study (1) how technologies and today's technical platforms enable and support the creation and spread of such mis- and disinformation, as well as (2) how technical approaches could be used to mitigate these issues. In this report, we summarize the space of technology-enabled mis- and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform designers, policymakers, and users.
|
1602.06058
|
Sung-Il Pae
|
Sung-il Pae
|
Binarization Trees and Random Number Generation
|
8 pages
| null | null | null |
cs.DS cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An m-extracting procedure produces unbiased random bits from a loaded dice
with m faces. A binarization takes inputs from an m-faced dice and produce bit
sequences to be fed into a (binary) extracting procedure to obtain random bits.
Thus, binary extracting procedures give rise to an m-extracting procedure via a
binarization. An entropy- preserving binarization is to be called complete, and
such a procedure has been proposed by Zhou and Bruck. We show that there exist
complete binarizations in abundance as naturally arising from binary trees with
m leaves. The well-known leaf entropy theorem and a closely related structure
lemma play important roles in the arguments.
|
[
{
"created": "Fri, 19 Feb 2016 07:12:02 GMT",
"version": "v1"
},
{
"created": "Fri, 11 May 2018 21:58:45 GMT",
"version": "v2"
}
] |
2018-05-15
|
[
[
"Pae",
"Sung-il",
""
]
] |
An m-extracting procedure produces unbiased random bits from a loaded dice with m faces. A binarization takes inputs from an m-faced dice and produce bit sequences to be fed into a (binary) extracting procedure to obtain random bits. Thus, binary extracting procedures give rise to an m-extracting procedure via a binarization. An entropy- preserving binarization is to be called complete, and such a procedure has been proposed by Zhou and Bruck. We show that there exist complete binarizations in abundance as naturally arising from binary trees with m leaves. The well-known leaf entropy theorem and a closely related structure lemma play important roles in the arguments.
|
2405.15994
|
Junlin Wu
|
Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik
|
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning reliably safe autonomous control is one of the core problems in
trustworthy autonomy. However, training a controller that can be formally
verified to be safe remains a major challenge. We introduce a novel approach
for learning verified safe control policies in nonlinear neural dynamical
systems while maximizing overall performance. Our approach aims to achieve
safety in the sense of finite-horizon reachability proofs, and is comprised of
three key parts. The first is a novel curriculum learning scheme that
iteratively increases the verified safe horizon. The second leverages the
iterative nature of gradient-based learning to leverage incremental
verification, reusing information from prior verification runs. Finally, we
learn multiple verified initial-state-dependent controllers, an idea that is
especially valuable for more complex domains where learning a single universal
verified safe controller is extremely challenging. Our experiments on five safe
control problems demonstrate that our trained controllers can achieve verified
safety over horizons that are as much as an order of magnitude longer than
state-of-the-art baselines, while maintaining high reward, as well as a perfect
safety record over entire episodes.
|
[
{
"created": "Sat, 25 May 2024 00:35:39 GMT",
"version": "v1"
}
] |
2024-05-28
|
[
[
"Wu",
"Junlin",
""
],
[
"Zhang",
"Huan",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
]
] |
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a major challenge. We introduce a novel approach for learning verified safe control policies in nonlinear neural dynamical systems while maximizing overall performance. Our approach aims to achieve safety in the sense of finite-horizon reachability proofs, and is comprised of three key parts. The first is a novel curriculum learning scheme that iteratively increases the verified safe horizon. The second leverages the iterative nature of gradient-based learning to leverage incremental verification, reusing information from prior verification runs. Finally, we learn multiple verified initial-state-dependent controllers, an idea that is especially valuable for more complex domains where learning a single universal verified safe controller is extremely challenging. Our experiments on five safe control problems demonstrate that our trained controllers can achieve verified safety over horizons that are as much as an order of magnitude longer than state-of-the-art baselines, while maintaining high reward, as well as a perfect safety record over entire episodes.
|
2205.04816
|
Jiaqiang Zhang
|
Jiaqiang Zhang, Senzhang Wang, Songcan Chen
|
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly
Detection on Attributed Networks
|
Accepted at IJCAI-ECAI 2022
|
IJCAI2022
|
10.24963/ijcai.2022/330
| null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detecting abnormal nodes from attributed networks is of great importance in
many real applications, such as financial fraud detection and cyber security.
This task is challenging due to both the complex interactions between the
anomalous nodes with other counterparts and their inconsistency in terms of
attributes. This paper proposes a self-supervised learning framework that
jointly optimizes a multi-view contrastive learning-based module and an
attribute reconstruction-based module to more accurately detect anomalies on
attributed networks. Specifically, two contrastive learning views are firstly
established, which allow the model to better encode rich local and global
information related to the abnormality. Motivated by the attribute consistency
principle between neighboring nodes, a masked autoencoder-based reconstruction
module is also introduced to identify the nodes which have large reconstruction
errors, then are regarded as anomalies. Finally, the two complementary modules
are integrated for more accurately detecting the anomalous nodes. Extensive
experiments conducted on five benchmark datasets show our model outperforms
current state-of-the-art models.
|
[
{
"created": "Tue, 10 May 2022 11:35:32 GMT",
"version": "v1"
}
] |
2023-10-02
|
[
[
"Zhang",
"Jiaqiang",
""
],
[
"Wang",
"Senzhang",
""
],
[
"Chen",
"Songcan",
""
]
] |
Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
|
2112.14680
|
Sebastian Drost
|
Sebastian Drost, Arne Vogt, Christian Danowski-Buhren, Simon Jirka,
Verena Kirstein, Kian Pakzad, and Matthes Rieke
|
WaCoDiS: Automated Earth Observation Data Processing within an
Event-Driven Architecture for Water Monitoring
| null | null |
10.1016/j.cageo.2021.105003
| null |
cs.DC cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
To ensure an efficient and environmentally friendly water resource
management, water management associations need means for efficient water
monitoring as well as novel strategies to reduce the pollution of surface and
ground water. Traditionally, water management associations operate large sensor
networks to suffice their needs for hydrological and meteorological measurement
data to monitor and model physical processes within catchments. Implementing a
comprehensive monitoring system often suffers from sparse coverage of in-situ
data. Due to the evolvement of the Copernicus satellite platforms, the broader
availability of satellite data provides a great potential for deriving
complementary information from Earth Observation data. Although the number of
satellite data platforms that provide online processing environments is
growing, it is still a big challenge to integrate those platforms into
traditional workflows of users from environmental domains such as hydrology.
Thus, in this paper, we introduce a software architecture to facilitate the
generation of Earth Observation information targeted towards hydrology. The
presented WaCoDiS System comprises several microservices as well standardized
interfaces that enable a platform-independent processing of satellite data.
First, we discuss the contribution of Earth Observation data to water
monitoring and derive several challenges regarding the facilitation of
satellite data processing. We then describe our system design with a brief
overview about the different system components which form an automated
processing pipeline. The suitability of our system is proven as part of a
pre-operational deployment for a German water management association. In
addition, we demonstrate how our system is capable of integrating satellite
data platforms, using the Copernicus Data and Exploitation Platform -
Deutschland (CODE-DE) as a reference example.
|
[
{
"created": "Thu, 23 Dec 2021 15:37:10 GMT",
"version": "v1"
}
] |
2021-12-30
|
[
[
"Drost",
"Sebastian",
""
],
[
"Vogt",
"Arne",
""
],
[
"Danowski-Buhren",
"Christian",
""
],
[
"Jirka",
"Simon",
""
],
[
"Kirstein",
"Verena",
""
],
[
"Pakzad",
"Kian",
""
],
[
"Rieke",
"Matthes",
""
]
] |
To ensure an efficient and environmentally friendly water resource management, water management associations need means for efficient water monitoring as well as novel strategies to reduce the pollution of surface and ground water. Traditionally, water management associations operate large sensor networks to suffice their needs for hydrological and meteorological measurement data to monitor and model physical processes within catchments. Implementing a comprehensive monitoring system often suffers from sparse coverage of in-situ data. Due to the evolvement of the Copernicus satellite platforms, the broader availability of satellite data provides a great potential for deriving complementary information from Earth Observation data. Although the number of satellite data platforms that provide online processing environments is growing, it is still a big challenge to integrate those platforms into traditional workflows of users from environmental domains such as hydrology. Thus, in this paper, we introduce a software architecture to facilitate the generation of Earth Observation information targeted towards hydrology. The presented WaCoDiS System comprises several microservices as well standardized interfaces that enable a platform-independent processing of satellite data. First, we discuss the contribution of Earth Observation data to water monitoring and derive several challenges regarding the facilitation of satellite data processing. We then describe our system design with a brief overview about the different system components which form an automated processing pipeline. The suitability of our system is proven as part of a pre-operational deployment for a German water management association. In addition, we demonstrate how our system is capable of integrating satellite data platforms, using the Copernicus Data and Exploitation Platform - Deutschland (CODE-DE) as a reference example.
|
1403.3376
|
Xiang Gao
|
Xiang Gao, Ove Edfors, Fredrik Rusek, Fredrik Tufvesson
|
Massive MIMO performance evaluation based on measured propagation data
|
IEEE Transactions on Wireless Communications, 2015
| null |
10.1109/TWC.2015.2414413
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Massive MIMO, also known as very-large MIMO or large-scale antenna systems,
is a new technique that potentially can offer large network capacities in
multi-user scenarios. With a massive MIMO system, we consider the case where a
base station equipped with a large number of antenna elements simultaneously
serves multiple single-antenna users in the same time-frequency resource. So
far, investigations are mostly based on theoretical channels with independent
and identically distributed (i.i.d.) complex Gaussian coefficients, i.e.,
i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in
channels measured in real propagation environments. Channel measurements were
performed at 2.6 GHz using a virtual uniform linear array (ULA) which has a
physically large aperture, and a practical uniform cylindrical array (UCA)
which is more compact in size, both having 128 antenna ports. Based on
measurement data, we illustrate channel behavior of massive MIMO in three
representative propagation conditions, and evaluate the corresponding
performance. The investigation shows that the measured channels, for both array
types, allow us to achieve performance close to that in i.i.d. Rayleigh
channels. It is concluded that in real propagation environments we have
characteristics that can allow for efficient use of massive MIMO, i.e., the
theoretical advantages of this new technology can also be harvested in real
channels.
|
[
{
"created": "Thu, 13 Mar 2014 19:22:17 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Mar 2015 15:17:54 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Apr 2015 20:17:35 GMT",
"version": "v3"
}
] |
2016-11-17
|
[
[
"Gao",
"Xiang",
""
],
[
"Edfors",
"Ove",
""
],
[
"Rusek",
"Fredrik",
""
],
[
"Tufvesson",
"Fredrik",
""
]
] |
Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA) which has a physically large aperture, and a practical uniform cylindrical array (UCA) which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.
|
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