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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1910.04277 | Daniel Campos | Daniel Campos, Zoe Konrad | Experiments in Inferring Social Networks of Diffusion | null | null | null | null | cs.SI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information diffusion is a fundamental process that takes place over
networks. While it is rarely realistic to observe the individual transmissions
of the information diffusion process, it is typically possible to observe when
individuals first publish the information. We look specifically at previously
published algorithm NETINF that probabilistically identifies the optimal
network that best explains the observed infection times. We explore how the
algorithm could perform on a range of intrinsically different social and
information network topologies, from news blogs and websites to Twitter to
Reddit.
| [
{
"created": "Wed, 9 Oct 2019 22:13:25 GMT",
"version": "v1"
}
] | 2019-10-11 | [
[
"Campos",
"Daniel",
""
],
[
"Konrad",
"Zoe",
""
]
] | Information diffusion is a fundamental process that takes place over networks. While it is rarely realistic to observe the individual transmissions of the information diffusion process, it is typically possible to observe when individuals first publish the information. We look specifically at previously published algorithm NETINF that probabilistically identifies the optimal network that best explains the observed infection times. We explore how the algorithm could perform on a range of intrinsically different social and information network topologies, from news blogs and websites to Twitter to Reddit. |
1602.02174 | Haris Aziz | Haris Aziz | Participation Incentives in Randomized Social Choice | corrected one proposition from previous version | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When aggregating preferences of agents via voting, two desirable goals are to
identify outcomes that are Pareto optimal and to incentivize agents to
participate in the voting process. We consider participation notions as
formalized by Brandl, Brandt, and Hofbauer (2015) and study how far efficiency
and participation are achievable by randomized social choice functions in
particular when agents' preferences are downward lexicographic (DL) or satisfy
stochastic dominance (SD). Our results include the followings ones: we prove
formal relations between the participation notions with respect to SD and DL
and we show that the maximal recursive rule satisfies very strong participation
with respect to both SD and DL.
| [
{
"created": "Fri, 5 Feb 2016 22:00:07 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Nov 2016 22:39:39 GMT",
"version": "v2"
}
] | 2016-11-10 | [
[
"Aziz",
"Haris",
""
]
] | When aggregating preferences of agents via voting, two desirable goals are to identify outcomes that are Pareto optimal and to incentivize agents to participate in the voting process. We consider participation notions as formalized by Brandl, Brandt, and Hofbauer (2015) and study how far efficiency and participation are achievable by randomized social choice functions in particular when agents' preferences are downward lexicographic (DL) or satisfy stochastic dominance (SD). Our results include the followings ones: we prove formal relations between the participation notions with respect to SD and DL and we show that the maximal recursive rule satisfies very strong participation with respect to both SD and DL. |
2002.03491 | Xiaoming Chen | Xiaoming Chen, Derrick Wing Kwan Ng, Wei Yu, Erik G. Larsson, Naofal
Al-Dhahir, Robert Schober | Massive Access for 5G and Beyond | 22 pages, 8 fugures, 6 tables | IEEE Journal on Selected Areas in Communications, 2020 | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Massive access, also known as massive connectivity or massive machine-type
communication (mMTC), is one of the main use cases of the fifth-generation (5G)
and beyond 5G (B5G) wireless networks. A typical application of massive access
is the cellular Internet of Things (IoT). Different from conventional
human-type communication, massive access aims at realizing efficient and
reliable communications for a massive number of IoT devices. Hence, the main
characteristics of massive access include low power, massive connectivity, and
broad coverage, which require new concepts, theories, and paradigms for the
design of next-generation cellular networks. This paper presents a
comprehensive survey of aspects of massive access design for B5G wireless
networks. Specifically, we provide a detailed review of massive access from the
perspectives of theory, protocols, techniques, coverage, energy, and security.
Furthermore, several future research directions and challenges are identified.
| [
{
"created": "Mon, 10 Feb 2020 01:31:22 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Aug 2020 03:21:26 GMT",
"version": "v2"
}
] | 2020-08-04 | [
[
"Chen",
"Xiaoming",
""
],
[
"Ng",
"Derrick Wing Kwan",
""
],
[
"Yu",
"Wei",
""
],
[
"Larsson",
"Erik G.",
""
],
[
"Al-Dhahir",
"Naofal",
""
],
[
"Schober",
"Robert",
""
]
] | Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of aspects of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified. |
2312.04193 | Adri\'an Bazaga | Adri\'an Bazaga, Pietro Li\`o, Gos Micklem | Language Model Knowledge Distillation for Efficient Question Answering
in Spanish | ICLR 2024 Tiny Paper (6 pages, 2 tables) | null | null | null | cs.CL cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Recent advances in the development of pre-trained Spanish language models has
led to significant progress in many Natural Language Processing (NLP) tasks,
such as question answering. However, the lack of efficient models imposes a
barrier for the adoption of such models in resource-constrained environments.
Therefore, smaller distilled models for the Spanish language could be proven to
be highly scalable and facilitate their further adoption on a variety of tasks
and scenarios. In this work, we take one step in this direction by developing
SpanishTinyRoBERTa, a compressed language model based on RoBERTa for efficient
question answering in Spanish. To achieve this, we employ knowledge
distillation from a large model onto a lighter model that allows for a wider
implementation, even in areas with limited computational resources, whilst
attaining negligible performance sacrifice. Our experiments show that the dense
distilled model can still preserve the performance of its larger counterpart,
while significantly increasing inference speedup. This work serves as a
starting point for further research and investigation of model compression
efforts for Spanish language models across various NLP tasks.
| [
{
"created": "Thu, 7 Dec 2023 10:21:22 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Mar 2024 17:44:27 GMT",
"version": "v2"
}
] | 2024-03-19 | [
[
"Bazaga",
"Adrián",
""
],
[
"Liò",
"Pietro",
""
],
[
"Micklem",
"Gos",
""
]
] | Recent advances in the development of pre-trained Spanish language models has led to significant progress in many Natural Language Processing (NLP) tasks, such as question answering. However, the lack of efficient models imposes a barrier for the adoption of such models in resource-constrained environments. Therefore, smaller distilled models for the Spanish language could be proven to be highly scalable and facilitate their further adoption on a variety of tasks and scenarios. In this work, we take one step in this direction by developing SpanishTinyRoBERTa, a compressed language model based on RoBERTa for efficient question answering in Spanish. To achieve this, we employ knowledge distillation from a large model onto a lighter model that allows for a wider implementation, even in areas with limited computational resources, whilst attaining negligible performance sacrifice. Our experiments show that the dense distilled model can still preserve the performance of its larger counterpart, while significantly increasing inference speedup. This work serves as a starting point for further research and investigation of model compression efforts for Spanish language models across various NLP tasks. |
1610.05725 | Anatoly Plotnikov | Anatoly D. Plotnikov | Polynomial-time algorithm for determining the graph isomorphism (v.2) | 13 pages, 11 figures | American Journal of Information Science and Computer Engineering,
Vol. 3, No. 6, 2017, pp. 71-76 | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop the methodology of positioning graph vertices relative to each
other to solve the problem of determining isomorphism of two undirected graphs.
Based on the position of the vertex in one of the graphs, it is determined the
corresponding vertex in the other graph.
For the selected vertex of the undirected graph, we define the neighborhoods
of the vertices. Next, we construct the auxiliary directed graph, spawned by
the selected vertex. The vertices of the digraph are positioned by special
characteristics --- vectors, which locate each vertex of the digraph relative
the found neighborhoods.
This enabled to develop the algorithm for determining graph isomorphism, the
runing time of which is equal to $O(n^4)$.
| [
{
"created": "Wed, 27 Apr 2016 20:06:12 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Oct 2016 21:03:20 GMT",
"version": "v2"
}
] | 2018-02-13 | [
[
"Plotnikov",
"Anatoly D.",
""
]
] | We develop the methodology of positioning graph vertices relative to each other to solve the problem of determining isomorphism of two undirected graphs. Based on the position of the vertex in one of the graphs, it is determined the corresponding vertex in the other graph. For the selected vertex of the undirected graph, we define the neighborhoods of the vertices. Next, we construct the auxiliary directed graph, spawned by the selected vertex. The vertices of the digraph are positioned by special characteristics --- vectors, which locate each vertex of the digraph relative the found neighborhoods. This enabled to develop the algorithm for determining graph isomorphism, the runing time of which is equal to $O(n^4)$. |
2304.07261 | Qingyue Yang | Qingyue Yang, Hongjing Niu, Pengfei Xia, Wei Zhang, Bin Li | Frequency Decomposition to Tap the Potential of Single Domain for
Generalization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain generalization (DG), aiming at models able to work on multiple unseen
domains, is a must-have characteristic of general artificial intelligence. DG
based on single source domain training data is more challenging due to the lack
of comparable information to help identify domain invariant features. In this
paper, it is determined that the domain invariant features could be contained
in the single source domain training samples, then the task is to find proper
ways to extract such domain invariant features from the single source domain
samples. An assumption is made that the domain invariant features are closely
related to the frequency. Then, a new method that learns through multiple
frequency domains is proposed. The key idea is, dividing the frequency domain
of each original image into multiple subdomains, and learning features in the
subdomain by a designed two branches network. In this way, the model is
enforced to learn features from more samples of the specifically limited
spectrum, which increases the possibility of obtaining the domain invariant
features that might have previously been defiladed by easily learned features.
Extensive experimental investigation reveals that 1) frequency decomposition
can help the model learn features that are difficult to learn. 2) the proposed
method outperforms the state-of-the-art methods of single-source domain
generalization.
| [
{
"created": "Fri, 14 Apr 2023 17:15:47 GMT",
"version": "v1"
}
] | 2023-04-17 | [
[
"Yang",
"Qingyue",
""
],
[
"Niu",
"Hongjing",
""
],
[
"Xia",
"Pengfei",
""
],
[
"Zhang",
"Wei",
""
],
[
"Li",
"Bin",
""
]
] | Domain generalization (DG), aiming at models able to work on multiple unseen domains, is a must-have characteristic of general artificial intelligence. DG based on single source domain training data is more challenging due to the lack of comparable information to help identify domain invariant features. In this paper, it is determined that the domain invariant features could be contained in the single source domain training samples, then the task is to find proper ways to extract such domain invariant features from the single source domain samples. An assumption is made that the domain invariant features are closely related to the frequency. Then, a new method that learns through multiple frequency domains is proposed. The key idea is, dividing the frequency domain of each original image into multiple subdomains, and learning features in the subdomain by a designed two branches network. In this way, the model is enforced to learn features from more samples of the specifically limited spectrum, which increases the possibility of obtaining the domain invariant features that might have previously been defiladed by easily learned features. Extensive experimental investigation reveals that 1) frequency decomposition can help the model learn features that are difficult to learn. 2) the proposed method outperforms the state-of-the-art methods of single-source domain generalization. |
1510.01098 | Boshra Rajaei | Boshra Rajaei, Eric W. Tramel, Sylvain Gigan, Florent Krzakala,
Laurent Daudet | Intensity-only optical compressive imaging using a multiply scattering
material and a double phase retrieval approach | null | Proceedings of the 2016 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP) pages: 4054 - 4058 | 10.1109/ICASSP.2016.7472439 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the problem of compressive imaging is addressed using natural
randomization by means of a multiply scattering medium. To utilize the medium
in this way, its corresponding transmission matrix must be estimated. To
calibrate the imager, we use a digital micromirror device (DMD) as a simple,
cheap, and high-resolution binary intensity modulator. We propose a phase
retrieval algorithm which is well adapted to intensity-only measurements on the
camera, and to the input binary intensity patterns, both to estimate the
complex transmission matrix as well as image reconstruction. We demonstrate
promising experimental results for the proposed algorithm using the MNIST
dataset of handwritten digits as example images.
| [
{
"created": "Mon, 5 Oct 2015 11:07:30 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jan 2016 14:35:44 GMT",
"version": "v2"
}
] | 2016-08-26 | [
[
"Rajaei",
"Boshra",
""
],
[
"Tramel",
"Eric W.",
""
],
[
"Gigan",
"Sylvain",
""
],
[
"Krzakala",
"Florent",
""
],
[
"Daudet",
"Laurent",
""
]
] | In this paper, the problem of compressive imaging is addressed using natural randomization by means of a multiply scattering medium. To utilize the medium in this way, its corresponding transmission matrix must be estimated. To calibrate the imager, we use a digital micromirror device (DMD) as a simple, cheap, and high-resolution binary intensity modulator. We propose a phase retrieval algorithm which is well adapted to intensity-only measurements on the camera, and to the input binary intensity patterns, both to estimate the complex transmission matrix as well as image reconstruction. We demonstrate promising experimental results for the proposed algorithm using the MNIST dataset of handwritten digits as example images. |
2407.05419 | Nafisa Hussain | Nafisa Hussain | Multimodal Language Models for Domain-Specific Procedural Video
Summarization | 6 pages, 3 figures | null | null | null | cs.CV cs.IR | http://creativecommons.org/publicdomain/zero/1.0/ | Videos serve as a powerful medium to convey ideas, tell stories, and provide
detailed instructions, especially through long-format tutorials. Such tutorials
are valuable for learning new skills at one's own pace, yet they can be
overwhelming due to their length and dense content. Viewers often seek specific
information, like precise measurements or step-by-step execution details,
making it essential to extract and summarize key segments efficiently. An
intelligent, time-sensitive video assistant capable of summarizing and
detecting highlights in long videos is highly sought after. Recent advancements
in Multimodal Large Language Models offer promising solutions to develop such
an assistant. Our research explores the use of multimodal models to enhance
video summarization and step-by-step instruction generation within specific
domains. These models need to understand temporal events and relationships
among actions across video frames. Our approach focuses on fine-tuning TimeChat
to improve its performance in specific domains: cooking and medical procedures.
By training the model on domain-specific datasets like Tasty for cooking and
MedVidQA for medical procedures, we aim to enhance its ability to generate
concise, accurate summaries of instructional videos. We curate and restructure
these datasets to create high-quality video-centric instruction data. Our
findings indicate that when finetuned on domain-specific procedural data,
TimeChat can significantly improve the extraction and summarization of key
instructional steps in long-format videos. This research demonstrates the
potential of specialized multimodal models to assist with practical tasks by
providing personalized, step-by-step guidance tailored to the unique aspects of
each domain.
| [
{
"created": "Sun, 7 Jul 2024 15:50:46 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Hussain",
"Nafisa",
""
]
] | Videos serve as a powerful medium to convey ideas, tell stories, and provide detailed instructions, especially through long-format tutorials. Such tutorials are valuable for learning new skills at one's own pace, yet they can be overwhelming due to their length and dense content. Viewers often seek specific information, like precise measurements or step-by-step execution details, making it essential to extract and summarize key segments efficiently. An intelligent, time-sensitive video assistant capable of summarizing and detecting highlights in long videos is highly sought after. Recent advancements in Multimodal Large Language Models offer promising solutions to develop such an assistant. Our research explores the use of multimodal models to enhance video summarization and step-by-step instruction generation within specific domains. These models need to understand temporal events and relationships among actions across video frames. Our approach focuses on fine-tuning TimeChat to improve its performance in specific domains: cooking and medical procedures. By training the model on domain-specific datasets like Tasty for cooking and MedVidQA for medical procedures, we aim to enhance its ability to generate concise, accurate summaries of instructional videos. We curate and restructure these datasets to create high-quality video-centric instruction data. Our findings indicate that when finetuned on domain-specific procedural data, TimeChat can significantly improve the extraction and summarization of key instructional steps in long-format videos. This research demonstrates the potential of specialized multimodal models to assist with practical tasks by providing personalized, step-by-step guidance tailored to the unique aspects of each domain. |
2310.16866 | Benjamin Chung | Benjamin Chung | A Type System for Julia | PhD thesis | null | null | null | cs.PL | http://creativecommons.org/licenses/by-sa/4.0/ | The Julia programming language was designed to fill the needs of scientific
computing by combining the benefits of productivity and performance languages.
Julia allows users to write untyped scripts easily without needing to worry
about many implementation details, as do other productivity languages. If one
just wants to get the work done-regardless of how efficient or general the
program might be, such a paradigm is ideal. Simultaneously, Julia also allows
library developers to write efficient generic code that can run as fast as
implementations in performance languages such as C or Fortran. This combination
of user-facing ease and library developer-facing performance has proven quite
attractive, and the language has increasing adoption.
With adoption comes combinatorial challenges to correctness. Multiple
dispatch -- Julia's key mechanism for abstraction -- allows many libraries to
compose "out of the box." However, it creates bugs where one library's
requirements do not match what another provides. Typing could address this at
the cost of Julia's flexibility for scripting.
I developed a "best of both worlds" solution: gradual typing for Julia. My
system forms the core of a gradual type system for Julia, laying the foundation
for improving the correctness of Julia programs while not getting in the way of
script writers. My framework allows methods to be individually typed or
untyped, allowing users to write untyped code that interacts with typed library
code and vice versa. Typed methods then get a soundness guarantee that is
robust in the presence of both dynamically typed code and dynamically generated
definitions. I additionally describe protocols, a mechanism for typing
abstraction over concrete implementation that accommodates one common pattern
in Julia libraries, and describe its implementation into my typed Julia
framework.
| [
{
"created": "Wed, 25 Oct 2023 10:55:21 GMT",
"version": "v1"
}
] | 2023-10-27 | [
[
"Chung",
"Benjamin",
""
]
] | The Julia programming language was designed to fill the needs of scientific computing by combining the benefits of productivity and performance languages. Julia allows users to write untyped scripts easily without needing to worry about many implementation details, as do other productivity languages. If one just wants to get the work done-regardless of how efficient or general the program might be, such a paradigm is ideal. Simultaneously, Julia also allows library developers to write efficient generic code that can run as fast as implementations in performance languages such as C or Fortran. This combination of user-facing ease and library developer-facing performance has proven quite attractive, and the language has increasing adoption. With adoption comes combinatorial challenges to correctness. Multiple dispatch -- Julia's key mechanism for abstraction -- allows many libraries to compose "out of the box." However, it creates bugs where one library's requirements do not match what another provides. Typing could address this at the cost of Julia's flexibility for scripting. I developed a "best of both worlds" solution: gradual typing for Julia. My system forms the core of a gradual type system for Julia, laying the foundation for improving the correctness of Julia programs while not getting in the way of script writers. My framework allows methods to be individually typed or untyped, allowing users to write untyped code that interacts with typed library code and vice versa. Typed methods then get a soundness guarantee that is robust in the presence of both dynamically typed code and dynamically generated definitions. I additionally describe protocols, a mechanism for typing abstraction over concrete implementation that accommodates one common pattern in Julia libraries, and describe its implementation into my typed Julia framework. |
2307.00936 | Xiaoshuang Liang | Yunyou Huang, Xianglong Guan, Xiangjiang Lu, Xiaoshuang Liang, Xiuxia
Miao, Jiyue Xie, Wenjing Liu, Li Ma, Suqin Tang, Zhifei Zhang, and Jianfeng
Zhan | OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's
Disease Diagnosis | Alzheimer's Disease, Abnormal Patterns, Open-set Recognition,
OpenAPMax | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Alzheimer's disease (AD) cannot be reversed, but early diagnosis will
significantly benefit patients' medical treatment and care. In recent works, AD
diagnosis has the primary assumption that all categories are known a prior -- a
closed-set classification problem, which contrasts with the open-set
recognition problem. This assumption hinders the application of the model in
natural clinical settings. Although many open-set recognition technologies have
been proposed in other fields, they are challenging to use for AD diagnosis
directly since 1) AD is a degenerative disease of the nervous system with
similar symptoms at each stage, and it is difficult to distinguish from its
pre-state, and 2) diversified strategies for AD diagnosis are challenging to
model uniformly. In this work, inspired by the concerns of clinicians during
diagnosis, we propose an open-set recognition model, OpenAPMax, based on the
anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first
obtains the abnormal pattern of each patient relative to each known category
through statistics or a literature search, clusters the patients' abnormal
pattern, and finally, uses extreme value theory (EVT) to model the distance
between each patient's abnormal pattern and the center of their category and
modify the classification probability. We evaluate the performance of the
proposed method with recent open-set recognition, where we obtain
state-of-the-art results.
| [
{
"created": "Mon, 3 Jul 2023 11:21:09 GMT",
"version": "v1"
}
] | 2023-07-04 | [
[
"Huang",
"Yunyou",
""
],
[
"Guan",
"Xianglong",
""
],
[
"Lu",
"Xiangjiang",
""
],
[
"Liang",
"Xiaoshuang",
""
],
[
"Miao",
"Xiuxia",
""
],
[
"Xie",
"Jiyue",
""
],
[
"Liu",
"Wenjing",
""
],
[
"Ma",
"Li",
""
],
[
"Tang",
"Suqin",
""
],
[
"Zhang",
"Zhifei",
""
],
[
"Zhan",
"Jianfeng",
""
]
] | Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior -- a closed-set classification problem, which contrasts with the open-set recognition problem. This assumption hinders the application of the model in natural clinical settings. Although many open-set recognition technologies have been proposed in other fields, they are challenging to use for AD diagnosis directly since 1) AD is a degenerative disease of the nervous system with similar symptoms at each stage, and it is difficult to distinguish from its pre-state, and 2) diversified strategies for AD diagnosis are challenging to model uniformly. In this work, inspired by the concerns of clinicians during diagnosis, we propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first obtains the abnormal pattern of each patient relative to each known category through statistics or a literature search, clusters the patients' abnormal pattern, and finally, uses extreme value theory (EVT) to model the distance between each patient's abnormal pattern and the center of their category and modify the classification probability. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results. |
2012.14642 | Le Qi | Le Qi, Yu Zhang, Qingyu Yin, Ting Liu | Multiple Structural Priors Guided Self Attention Network for Language
Understanding | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Self attention networks (SANs) have been widely utilized in recent NLP
studies. Unlike CNNs or RNNs, standard SANs are usually position-independent,
and thus are incapable of capturing the structural priors between sequences of
words. Existing studies commonly apply one single mask strategy on SANs for
incorporating structural priors while failing at modeling more abundant
structural information of texts. In this paper, we aim at introducing multiple
types of structural priors into SAN models, proposing the Multiple Structural
Priors Guided Self Attention Network (MS-SAN) that transforms different
structural priors into different attention heads by using a novel multi-mask
based multi-head attention mechanism. In particular, we integrate two
categories of structural priors, including the sequential order and the
relative position of words. For the purpose of capturing the latent
hierarchical structure of the texts, we extract these information not only from
the word contexts but also from the dependency syntax trees. Experimental
results on two tasks show that MS-SAN achieves significant improvements against
other strong baselines.
| [
{
"created": "Tue, 29 Dec 2020 07:30:03 GMT",
"version": "v1"
}
] | 2021-01-01 | [
[
"Qi",
"Le",
""
],
[
"Zhang",
"Yu",
""
],
[
"Yin",
"Qingyu",
""
],
[
"Liu",
"Ting",
""
]
] | Self attention networks (SANs) have been widely utilized in recent NLP studies. Unlike CNNs or RNNs, standard SANs are usually position-independent, and thus are incapable of capturing the structural priors between sequences of words. Existing studies commonly apply one single mask strategy on SANs for incorporating structural priors while failing at modeling more abundant structural information of texts. In this paper, we aim at introducing multiple types of structural priors into SAN models, proposing the Multiple Structural Priors Guided Self Attention Network (MS-SAN) that transforms different structural priors into different attention heads by using a novel multi-mask based multi-head attention mechanism. In particular, we integrate two categories of structural priors, including the sequential order and the relative position of words. For the purpose of capturing the latent hierarchical structure of the texts, we extract these information not only from the word contexts but also from the dependency syntax trees. Experimental results on two tasks show that MS-SAN achieves significant improvements against other strong baselines. |
2202.09221 | Stefan Scherzinger | Stefan Scherzinger, Pascal Becker, Arne Roennau and R\"udiger Dillmann | Motion Macro Programming on Assistive Robotic Manipulators: Three Skill
Types for Everyday Tasks | 8 pages, 10 figures, accepted to the IEEE 20th International
Conference on Ubiquitous Robots (UR 2023), Honolulu, USA | null | null | null | cs.RO | http://creativecommons.org/licenses/by-sa/4.0/ | Assistive robotic manipulators are becoming increasingly important for people
with disabilities. Teleoperating the manipulator in mundane tasks is part of
their daily lives. Instead of steering the robot through all actions, applying
self-recorded motion macros could greatly facilitate repetitive tasks. Dynamic
Movement Primitives (DMP) are a powerful method for skill learning via
teleoperation. For this use case, however, they need simple heuristics to
specify where to start, stop, and parameterize a skill without a background in
computer science and academic sensor setups for autonomous perception. To
achieve this goal, this paper provides the concept of local, global, and hybrid
skills that form a modular basis for composing single-handed tasks of daily
living. These skills are specified implicitly and can easily be programmed by
users themselves, requiring only their basic robotic manipulator. The paper
contributes all details for robot-agnostic implementations. Experiments
validate the developed methods for exemplary tasks, such as scratching an itchy
spot, sorting objects on a desk, and feeding a piggy bank with coins. The paper
is accompanied by an open-source implementation at
https://github.com/fzi-forschungszentrum-informatik/ArNe
| [
{
"created": "Fri, 18 Feb 2022 14:41:20 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Apr 2023 11:47:28 GMT",
"version": "v2"
},
{
"created": "Fri, 12 May 2023 14:14:09 GMT",
"version": "v3"
}
] | 2023-05-15 | [
[
"Scherzinger",
"Stefan",
""
],
[
"Becker",
"Pascal",
""
],
[
"Roennau",
"Arne",
""
],
[
"Dillmann",
"Rüdiger",
""
]
] | Assistive robotic manipulators are becoming increasingly important for people with disabilities. Teleoperating the manipulator in mundane tasks is part of their daily lives. Instead of steering the robot through all actions, applying self-recorded motion macros could greatly facilitate repetitive tasks. Dynamic Movement Primitives (DMP) are a powerful method for skill learning via teleoperation. For this use case, however, they need simple heuristics to specify where to start, stop, and parameterize a skill without a background in computer science and academic sensor setups for autonomous perception. To achieve this goal, this paper provides the concept of local, global, and hybrid skills that form a modular basis for composing single-handed tasks of daily living. These skills are specified implicitly and can easily be programmed by users themselves, requiring only their basic robotic manipulator. The paper contributes all details for robot-agnostic implementations. Experiments validate the developed methods for exemplary tasks, such as scratching an itchy spot, sorting objects on a desk, and feeding a piggy bank with coins. The paper is accompanied by an open-source implementation at https://github.com/fzi-forschungszentrum-informatik/ArNe |
1809.00832 | Eunji Jeong | Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon
Chun | Improving the Expressiveness of Deep Learning Frameworks with Recursion | Appeared in EuroSys 2018. 13 pages, 11 figures | EuroSys 2018: Thirteenth EuroSys Conference, April 23-26, 2018,
Porto, Portugal | 10.1145/3190508.3190530 | null | cs.LG cs.AI cs.CL stat.ML | http://creativecommons.org/licenses/by/4.0/ | Recursive neural networks have widely been used by researchers to handle
applications with recursively or hierarchically structured data. However,
embedded control flow deep learning frameworks such as TensorFlow, Theano,
Caffe2, and MXNet fail to efficiently represent and execute such neural
networks, due to lack of support for recursion. In this paper, we add recursion
to the programming model of existing frameworks by complementing their design
with recursive execution of dataflow graphs as well as additional APIs for
recursive definitions. Unlike iterative implementations, which can only
understand the topological index of each node in recursive data structures, our
recursive implementation is able to exploit the recursive relationships between
nodes for efficient execution based on parallel computation. We present an
implementation on TensorFlow and evaluation results with various recursive
neural network models, showing that our recursive implementation not only
conveys the recursive nature of recursive neural networks better than other
implementations, but also uses given resources more effectively to reduce
training and inference time.
| [
{
"created": "Tue, 4 Sep 2018 08:31:21 GMT",
"version": "v1"
}
] | 2018-09-05 | [
[
"Jeong",
"Eunji",
""
],
[
"Jeong",
"Joo Seong",
""
],
[
"Kim",
"Soojeong",
""
],
[
"Yu",
"Gyeong-In",
""
],
[
"Chun",
"Byung-Gon",
""
]
] | Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of support for recursion. In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions. Unlike iterative implementations, which can only understand the topological index of each node in recursive data structures, our recursive implementation is able to exploit the recursive relationships between nodes for efficient execution based on parallel computation. We present an implementation on TensorFlow and evaluation results with various recursive neural network models, showing that our recursive implementation not only conveys the recursive nature of recursive neural networks better than other implementations, but also uses given resources more effectively to reduce training and inference time. |
2102.08818 | Priyanshu Kumar | Aadarsh Singh and Priyanshu Kumar | SciDr at SDU-2020: IDEAS -- Identifying and Disambiguating Everyday
Acronyms for Scientific Domain | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | We present our systems submitted for the shared tasks of Acronym
Identification (AI) and Acronym Disambiguation (AD) held under Workshop on SDU.
We mainly experiment with BERT and SciBERT. In addition, we assess the
effectiveness of "BIOless" tagging and blending along with the prowess of
ensembling in AI. For AD, we formulate the problem as a span prediction task,
experiment with different training techniques and also leverage the use of
external data. Our systems rank 11th and 3rd in AI and AD tasks respectively.
| [
{
"created": "Wed, 17 Feb 2021 15:24:50 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Mar 2021 13:34:34 GMT",
"version": "v2"
}
] | 2021-03-09 | [
[
"Singh",
"Aadarsh",
""
],
[
"Kumar",
"Priyanshu",
""
]
] | We present our systems submitted for the shared tasks of Acronym Identification (AI) and Acronym Disambiguation (AD) held under Workshop on SDU. We mainly experiment with BERT and SciBERT. In addition, we assess the effectiveness of "BIOless" tagging and blending along with the prowess of ensembling in AI. For AD, we formulate the problem as a span prediction task, experiment with different training techniques and also leverage the use of external data. Our systems rank 11th and 3rd in AI and AD tasks respectively. |
2405.15570 | Jakob Struye | Jakob Struye, Filip Lemic, Jeroen Famaey | Multi-Gigabit Interactive Extended Reality over Millimeter-Wave: An
End-to-End System Approach | Accepted at IEEE International Symposium on Personal, Indoor and
Mobile Radio Communications (PIMRC) 2024 | null | null | null | cs.NI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Achieving high-quality wireless interactive Extended Reality (XR) will
require multi-gigabit throughput at extremely low latency. The Millimeter-Wave
(mmWave) frequency bands, between 24 and 300GHz, can achieve such extreme
performance. However, maintaining a consistently high Quality of Experience
with highly mobile users is challenging, as mmWave communications are
inherently directional. In this work, we present and evaluate an end-to-end
approach to such a mmWave-based mobile XR system. We perform a highly realistic
simulation of the system, incorporating accurate XR data traffic, detailed
mmWave propagation models and actual user motion. We evaluate the impact of the
beamforming strategy and frequency on the overall performance. In addition, we
provide the first system-level evaluation of the CoVRage algorithm, a proactive
and spatially aware user-side beamforming approach designed specifically for
highly mobile XR environments.
| [
{
"created": "Fri, 24 May 2024 14:03:16 GMT",
"version": "v1"
}
] | 2024-05-27 | [
[
"Struye",
"Jakob",
""
],
[
"Lemic",
"Filip",
""
],
[
"Famaey",
"Jeroen",
""
]
] | Achieving high-quality wireless interactive Extended Reality (XR) will require multi-gigabit throughput at extremely low latency. The Millimeter-Wave (mmWave) frequency bands, between 24 and 300GHz, can achieve such extreme performance. However, maintaining a consistently high Quality of Experience with highly mobile users is challenging, as mmWave communications are inherently directional. In this work, we present and evaluate an end-to-end approach to such a mmWave-based mobile XR system. We perform a highly realistic simulation of the system, incorporating accurate XR data traffic, detailed mmWave propagation models and actual user motion. We evaluate the impact of the beamforming strategy and frequency on the overall performance. In addition, we provide the first system-level evaluation of the CoVRage algorithm, a proactive and spatially aware user-side beamforming approach designed specifically for highly mobile XR environments. |
2210.16352 | Thomas Plagemann | Thomas Plagemann (1), Vera Goebel (1), Matthias Hollick (2), Boris
Koldehofe (3) ((1) University of Oslo, (2) Technical University of Darmstadt,
(3) University of Groningen) | Towards Privacy Engineering for Real-Time Analytics in the
Human-Centered Internet of Things | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Big data applications offer smart solutions to many urgent societal
challenges, such as health care, traffic coordination, energy management, etc.
The basic premise for these applications is "the more data the better". The
focus often lies on sensing infrastructures in the public realm that produce an
ever-increasing amount of data. Yet, any smartphone and smartwatch owner could
be a continuous source of valuable data and contribute to many useful big data
applications. However, such data can reveal a lot of sensitive information,
like the current location or the heart rate of the owner of such devices.
Protection of personal data is important in our society and for example
manifested in the EU General Data Protection Regulation (GDPR). However,
privacy protection and useful big data applications are hard to bring together,
particularly in the human-centered IoT. Implementing proper privacy protection
requires skills that are typically not in the focus of data analysts and big
data developers. Thus, many individuals tend to share none of their data if in
doubt whether it will be properly protected. There exist excellent privacy
solutions between the "all or nothing" approach. For example, instead of
continuously publishing the current location of individuals one might aggregate
this data and only publish information of how many individuals are in a certain
area of the city. Thus, personal data is not revealed, while useful information
for certain applications like traffic coordination is retained. The goal of the
Parrot project is to provide tools for real-time data analysis applications
that leverage this "middle ground". Data analysts should only be required to
specify their data needs, and end-users can select the privacy requirements for
their data as well as the applications and end-users they want to share their
data with.
| [
{
"created": "Fri, 28 Oct 2022 18:39:51 GMT",
"version": "v1"
}
] | 2022-11-01 | [
[
"Plagemann",
"Thomas",
""
],
[
"Goebel",
"Vera",
""
],
[
"Hollick",
"Matthias",
""
],
[
"Koldehofe",
"Boris",
""
]
] | Big data applications offer smart solutions to many urgent societal challenges, such as health care, traffic coordination, energy management, etc. The basic premise for these applications is "the more data the better". The focus often lies on sensing infrastructures in the public realm that produce an ever-increasing amount of data. Yet, any smartphone and smartwatch owner could be a continuous source of valuable data and contribute to many useful big data applications. However, such data can reveal a lot of sensitive information, like the current location or the heart rate of the owner of such devices. Protection of personal data is important in our society and for example manifested in the EU General Data Protection Regulation (GDPR). However, privacy protection and useful big data applications are hard to bring together, particularly in the human-centered IoT. Implementing proper privacy protection requires skills that are typically not in the focus of data analysts and big data developers. Thus, many individuals tend to share none of their data if in doubt whether it will be properly protected. There exist excellent privacy solutions between the "all or nothing" approach. For example, instead of continuously publishing the current location of individuals one might aggregate this data and only publish information of how many individuals are in a certain area of the city. Thus, personal data is not revealed, while useful information for certain applications like traffic coordination is retained. The goal of the Parrot project is to provide tools for real-time data analysis applications that leverage this "middle ground". Data analysts should only be required to specify their data needs, and end-users can select the privacy requirements for their data as well as the applications and end-users they want to share their data with. |
2002.07287 | Andrej Sajenko | Frank Kammer, Johannes Meintrup, and Andrej Sajenko | Sorting and Ranking of Self-Delimiting Numbers with Applications to
Outerplanar Graph Isomorphism | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Assume that an $N$-bit sequence $S$ of $k$ numbers encoded as Elias gamma
codes is given as input. We present space-efficient algorithms for sorting,
dense ranking and competitive ranking on $S$ in the word RAM model with word
size $\Omega(\log N)$ bits. Our algorithms run in $O(k + \frac{N}{\log N})$
time and use $O(N)$ bits. The sorting algorithm returns the given numbers in
sorted order, stored within a bit-vector of $N$ bits, whereas our ranking
algorithms construct data structures that allow us subsequently to return the
dense/competitive rank of each number $x$ in $S$ in constant time. For numbers
$x \in \mathbb{N}$ with $x > N$ we require the position $p_x$ of $x$ as the
input for our dense-/competitive-rank data structure. As an application of our
algorithms above we give an algorithm for tree isomorphism, which runs in
$O(n)$ time and uses $O(n)$ bits on $n$-node trees. Finally, we generalize our
result for tree isomorphism to forests and outerplanar graphs, while
maintaining a space-usage of $O(n)$ bits. The previous best linear-time
algorithms for trees, forests and outerplanar graph isomorphism all use
$\Theta(n \log n)$ bits.
| [
{
"created": "Mon, 17 Feb 2020 22:39:00 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jun 2020 10:17:08 GMT",
"version": "v2"
},
{
"created": "Thu, 2 May 2024 15:22:21 GMT",
"version": "v3"
}
] | 2024-05-03 | [
[
"Kammer",
"Frank",
""
],
[
"Meintrup",
"Johannes",
""
],
[
"Sajenko",
"Andrej",
""
]
] | Assume that an $N$-bit sequence $S$ of $k$ numbers encoded as Elias gamma codes is given as input. We present space-efficient algorithms for sorting, dense ranking and competitive ranking on $S$ in the word RAM model with word size $\Omega(\log N)$ bits. Our algorithms run in $O(k + \frac{N}{\log N})$ time and use $O(N)$ bits. The sorting algorithm returns the given numbers in sorted order, stored within a bit-vector of $N$ bits, whereas our ranking algorithms construct data structures that allow us subsequently to return the dense/competitive rank of each number $x$ in $S$ in constant time. For numbers $x \in \mathbb{N}$ with $x > N$ we require the position $p_x$ of $x$ as the input for our dense-/competitive-rank data structure. As an application of our algorithms above we give an algorithm for tree isomorphism, which runs in $O(n)$ time and uses $O(n)$ bits on $n$-node trees. Finally, we generalize our result for tree isomorphism to forests and outerplanar graphs, while maintaining a space-usage of $O(n)$ bits. The previous best linear-time algorithms for trees, forests and outerplanar graph isomorphism all use $\Theta(n \log n)$ bits. |
2308.13785 | Minheng Ni | Minheng Ni, Chenfei Wu, Xiaodong Wang, Shengming Yin, Lijuan Wang,
Zicheng Liu, Nan Duan | ORES: Open-vocabulary Responsible Visual Synthesis | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Avoiding synthesizing specific visual concepts is an essential challenge in
responsible visual synthesis. However, the visual concept that needs to be
avoided for responsible visual synthesis tends to be diverse, depending on the
region, context, and usage scenarios. In this work, we formalize a new task,
Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model
is able to avoid forbidden visual concepts while allowing users to input any
desired content. To address this problem, we present a Two-stage Intervention
(TIN) framework. By introducing 1) rewriting with learnable instruction through
a large-scale language model (LLM) and 2) synthesizing with prompt intervention
on a diffusion synthesis model, it can effectively synthesize images avoiding
any concepts but following the user's query as much as possible. To evaluate on
ORES, we provide a publicly available dataset, baseline models, and benchmark.
Experimental results demonstrate the effectiveness of our method in reducing
risks of image generation. Our work highlights the potential of LLMs in
responsible visual synthesis. Our code and dataset is public available.
| [
{
"created": "Sat, 26 Aug 2023 06:47:34 GMT",
"version": "v1"
}
] | 2023-08-29 | [
[
"Ni",
"Minheng",
""
],
[
"Wu",
"Chenfei",
""
],
[
"Wang",
"Xiaodong",
""
],
[
"Yin",
"Shengming",
""
],
[
"Wang",
"Lijuan",
""
],
[
"Liu",
"Zicheng",
""
],
[
"Duan",
"Nan",
""
]
] | Avoiding synthesizing specific visual concepts is an essential challenge in responsible visual synthesis. However, the visual concept that needs to be avoided for responsible visual synthesis tends to be diverse, depending on the region, context, and usage scenarios. In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content. To address this problem, we present a Two-stage Intervention (TIN) framework. By introducing 1) rewriting with learnable instruction through a large-scale language model (LLM) and 2) synthesizing with prompt intervention on a diffusion synthesis model, it can effectively synthesize images avoiding any concepts but following the user's query as much as possible. To evaluate on ORES, we provide a publicly available dataset, baseline models, and benchmark. Experimental results demonstrate the effectiveness of our method in reducing risks of image generation. Our work highlights the potential of LLMs in responsible visual synthesis. Our code and dataset is public available. |
2001.05288 | Joseph Tassone | Joseph Tassone, Salimur Choudhury | A Comprehensive Survey on the Ambulance Routing and Location Problems | 30 pages,7 figures,16 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this research, an extensive literature review was performed on the recent
developments of the ambulance routing problem (ARP) and ambulance location
problem (ALP). Both are respective modifications of the vehicle routing problem
(VRP) and maximum covering problem (MCP), with modifications to objective
functions and constraints. Although alike, a key distinction is emergency
service systems (EMS) are considered critical and the optimization of these has
become all the more important as a result. Similar to their parent problems,
these are NP-hard and must resort to approximations if the space size is too
large. Much of the current work has simply been on modifying existing systems
through simulation to achieve a more acceptable result. There has been attempts
towards using meta-heuristics, though practical experimentation is lacking when
compared to VRP or MCP. The contributions of this work are a comprehensive
survey of current methodologies, summarized models, and suggested future
improvements.
| [
{
"created": "Fri, 10 Jan 2020 05:33:11 GMT",
"version": "v1"
}
] | 2020-01-16 | [
[
"Tassone",
"Joseph",
""
],
[
"Choudhury",
"Salimur",
""
]
] | In this research, an extensive literature review was performed on the recent developments of the ambulance routing problem (ARP) and ambulance location problem (ALP). Both are respective modifications of the vehicle routing problem (VRP) and maximum covering problem (MCP), with modifications to objective functions and constraints. Although alike, a key distinction is emergency service systems (EMS) are considered critical and the optimization of these has become all the more important as a result. Similar to their parent problems, these are NP-hard and must resort to approximations if the space size is too large. Much of the current work has simply been on modifying existing systems through simulation to achieve a more acceptable result. There has been attempts towards using meta-heuristics, though practical experimentation is lacking when compared to VRP or MCP. The contributions of this work are a comprehensive survey of current methodologies, summarized models, and suggested future improvements. |
2312.16552 | Jakub Mosinski | Jakub Mosi\'nski, Piotr Bili\'nski, Thomas Merritt, Abdelhamid Ezzerg,
Daniel Korzekwa | AE-Flow: AutoEncoder Normalizing Flow | ICASSP 2023 | null | null | null | cs.SD cs.LG eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently normalizing flows have been gaining traction in text-to-speech (TTS)
and voice conversion (VC) due to their state-of-the-art (SOTA) performance.
Normalizing flows are unsupervised generative models. In this paper, we
introduce supervision to the training process of normalizing flows, without the
need for parallel data. We call this training paradigm AutoEncoder Normalizing
Flow (AE-Flow). It adds a reconstruction loss forcing the model to use
information from the conditioning to reconstruct an audio sample. Our goal is
to understand the impact of each component and find the right combination of
the negative log-likelihood (NLL) and the reconstruction loss in training
normalizing flows with coupling blocks. For that reason we will compare
flow-based mapping model trained with: (i) NLL loss, (ii) NLL and
reconstruction losses, as well as (iii) reconstruction loss only. Additionally,
we compare our model with SOTA VC baseline. The models are evaluated in terms
of naturalness, speaker similarity, intelligibility in many-to-many and
many-to-any VC settings. The results show that the proposed training paradigm
systematically improves speaker similarity and naturalness when compared to
regular training methods of normalizing flows. Furthermore, we show that our
method improves speaker similarity and intelligibility over the
state-of-the-art.
| [
{
"created": "Wed, 27 Dec 2023 12:29:21 GMT",
"version": "v1"
}
] | 2023-12-29 | [
[
"Mosiński",
"Jakub",
""
],
[
"Biliński",
"Piotr",
""
],
[
"Merritt",
"Thomas",
""
],
[
"Ezzerg",
"Abdelhamid",
""
],
[
"Korzekwa",
"Daniel",
""
]
] | Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, without the need for parallel data. We call this training paradigm AutoEncoder Normalizing Flow (AE-Flow). It adds a reconstruction loss forcing the model to use information from the conditioning to reconstruct an audio sample. Our goal is to understand the impact of each component and find the right combination of the negative log-likelihood (NLL) and the reconstruction loss in training normalizing flows with coupling blocks. For that reason we will compare flow-based mapping model trained with: (i) NLL loss, (ii) NLL and reconstruction losses, as well as (iii) reconstruction loss only. Additionally, we compare our model with SOTA VC baseline. The models are evaluated in terms of naturalness, speaker similarity, intelligibility in many-to-many and many-to-any VC settings. The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows. Furthermore, we show that our method improves speaker similarity and intelligibility over the state-of-the-art. |
1211.2361 | Hossein Jahandideh | Hossein Jahandideh, Ardavan Asef-Vaziri, Mohammad Modarres | Genetic Algorithm for Designing a Convenient Facility Layout for a
Circular Flow Path | Accepted to the 2013 IEEE Symposium Series on Computational
Intelligence: Swarm Intelligence Symposium. This paper has been withdrawn by
the author, by the request of the supervisor, to be updated, fixed, and
combined with other papers | null | null | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a heuristic for designing facility layouts that are
convenient for designing a unidirectional loop for material handling. We use
genetic algorithm where the objective function and crossover and mutation
operators have all been designed specifically for this purpose. Our design is
made under flexible bay structure and comparisons are made with other layouts
from the literature that were designed under flexible bay structure.
| [
{
"created": "Sun, 11 Nov 2012 00:26:22 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Mar 2013 06:09:29 GMT",
"version": "v2"
}
] | 2013-03-25 | [
[
"Jahandideh",
"Hossein",
""
],
[
"Asef-Vaziri",
"Ardavan",
""
],
[
"Modarres",
"Mohammad",
""
]
] | In this paper, we present a heuristic for designing facility layouts that are convenient for designing a unidirectional loop for material handling. We use genetic algorithm where the objective function and crossover and mutation operators have all been designed specifically for this purpose. Our design is made under flexible bay structure and comparisons are made with other layouts from the literature that were designed under flexible bay structure. |
2304.10611 | Minghui Zhang | Minghui Zhang, Alex Sokolov, Weixin Cai, Si-Qing Chen | Joint Repetition Suppression and Content Moderation of Large Language
Models | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Natural language generation (NLG) is one of the most impactful fields in NLP,
and recent years have witnessed its evolution brought about by large language
models (LLMs). As the key instrument for writing assistance applications, they
are generally prone to replicating or extending offensive content provided in
the input. In low-resource data regime, they can also lead to repetitive
outputs. Usually, offensive content and repetitions are mitigated with post-hoc
methods, including n-gram level blocklists, top-k and nucleus sampling. In this
paper, we apply non-exact repetition suppression using token and sequence level
unlikelihood loss, and further explore the framework of unlikelihood training
objective in order to jointly endow the model with abilities to avoid
generating offensive words and phrases from the beginning. Finally, with
comprehensive experiments, we demonstrate that our proposed methods work
exceptionally in controlling the repetition and content quality of LLM outputs.
| [
{
"created": "Thu, 20 Apr 2023 19:17:49 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Jun 2023 18:16:29 GMT",
"version": "v2"
}
] | 2023-06-07 | [
[
"Zhang",
"Minghui",
""
],
[
"Sokolov",
"Alex",
""
],
[
"Cai",
"Weixin",
""
],
[
"Chen",
"Si-Qing",
""
]
] | Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs). As the key instrument for writing assistance applications, they are generally prone to replicating or extending offensive content provided in the input. In low-resource data regime, they can also lead to repetitive outputs. Usually, offensive content and repetitions are mitigated with post-hoc methods, including n-gram level blocklists, top-k and nucleus sampling. In this paper, we apply non-exact repetition suppression using token and sequence level unlikelihood loss, and further explore the framework of unlikelihood training objective in order to jointly endow the model with abilities to avoid generating offensive words and phrases from the beginning. Finally, with comprehensive experiments, we demonstrate that our proposed methods work exceptionally in controlling the repetition and content quality of LLM outputs. |
1111.7051 | Arup Pal | Arup Kumar Pal, G.P. Biswas and S. Mukhopadhyay | Design of Image Cryptosystem by Simultaneous VQ-Compression and
Shuffling of Codebook and Index Matrix | null | The International journal of Multimedia & Its Applications (IJMA),
Vol.1, No.1, November 2009 | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The popularity of Internet usage although increases exponentially, it is
incapable of providing the security for exchange of confidential data between
the users. As a result, several cryptosystems for encryption of data and images
have been developed for secured transmission over Internet. In this work, a
scheme for Image encryption/decryption based on Vector Quantization (VQ) has
been proposed that concurrently encodes the images for compression and shuffles
the codebook and the index matrix using pseudorandom sequences for encryption.
The processing time of the proposed scheme is much less than the other
cryptosystems, because it does not use any traditional cryptographic
operations, and instead it performs swapping between the contents of the
codebook with respect to a random sequence, which resulted an indirect
shuffling of the contents of the index matrix. It may be noted that the
security of the proposed cryptosystem depends on the generation and the
exchange of the random sequences used. Since the generation of truly random
sequences are not practically feasible, we simulate the proposed scheme using
MATLAB, where its operators like rand(method, seed), randperm(n) has been used
to generate pseudorandom sequences and it has been seen that the proposed
cryptosystem shows the expected performance.
| [
{
"created": "Wed, 30 Nov 2011 05:36:51 GMT",
"version": "v1"
}
] | 2011-12-01 | [
[
"Pal",
"Arup Kumar",
""
],
[
"Biswas",
"G. P.",
""
],
[
"Mukhopadhyay",
"S.",
""
]
] | The popularity of Internet usage although increases exponentially, it is incapable of providing the security for exchange of confidential data between the users. As a result, several cryptosystems for encryption of data and images have been developed for secured transmission over Internet. In this work, a scheme for Image encryption/decryption based on Vector Quantization (VQ) has been proposed that concurrently encodes the images for compression and shuffles the codebook and the index matrix using pseudorandom sequences for encryption. The processing time of the proposed scheme is much less than the other cryptosystems, because it does not use any traditional cryptographic operations, and instead it performs swapping between the contents of the codebook with respect to a random sequence, which resulted an indirect shuffling of the contents of the index matrix. It may be noted that the security of the proposed cryptosystem depends on the generation and the exchange of the random sequences used. Since the generation of truly random sequences are not practically feasible, we simulate the proposed scheme using MATLAB, where its operators like rand(method, seed), randperm(n) has been used to generate pseudorandom sequences and it has been seen that the proposed cryptosystem shows the expected performance. |
2103.12198 | Jacob Nogas | Joseph Jay Williams, Jacob Nogas, Nina Deliu, Hammad Shaikh, Sofia S.
Villar, Audrey Durand, Anna Rafferty | Challenges in Statistical Analysis of Data Collected by a Bandit
Algorithm: An Empirical Exploration in Applications to Adaptively Randomized
Experiments | null | null | null | null | cs.LG stat.AP | http://creativecommons.org/licenses/by/4.0/ | Multi-armed bandit algorithms have been argued for decades as useful for
adaptively randomized experiments. In such experiments, an algorithm varies
which arms (e.g. alternative interventions to help students learn) are assigned
to participants, with the goal of assigning higher-reward arms to as many
participants as possible. We applied the bandit algorithm Thompson Sampling
(TS) to run adaptive experiments in three university classes. Instructors saw
great value in trying to rapidly use data to give their students in the
experiments better arms (e.g. better explanations of a concept). Our
deployment, however, illustrated a major barrier for scientists and
practitioners to use such adaptive experiments: a lack of quantifiable insight
into how much statistical analysis of specific real-world experiments is
impacted (Pallmann et al, 2018; FDA, 2019), compared to traditional uniform
random assignment. We therefore use our case study of the ubiquitous two-arm
binary reward setting to empirically investigate the impact of using Thompson
Sampling instead of uniform random assignment. In this setting, using common
statistical hypothesis tests, we show that collecting data with TS can as much
as double the False Positive Rate (FPR; incorrectly reporting differences when
none exist) and the False Negative Rate (FNR; failing to report differences
when they exist)...
| [
{
"created": "Mon, 22 Mar 2021 22:05:18 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Mar 2021 14:44:02 GMT",
"version": "v2"
}
] | 2021-03-29 | [
[
"Williams",
"Joseph Jay",
""
],
[
"Nogas",
"Jacob",
""
],
[
"Deliu",
"Nina",
""
],
[
"Shaikh",
"Hammad",
""
],
[
"Villar",
"Sofia S.",
""
],
[
"Durand",
"Audrey",
""
],
[
"Rafferty",
"Anna",
""
]
] | Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative interventions to help students learn) are assigned to participants, with the goal of assigning higher-reward arms to as many participants as possible. We applied the bandit algorithm Thompson Sampling (TS) to run adaptive experiments in three university classes. Instructors saw great value in trying to rapidly use data to give their students in the experiments better arms (e.g. better explanations of a concept). Our deployment, however, illustrated a major barrier for scientists and practitioners to use such adaptive experiments: a lack of quantifiable insight into how much statistical analysis of specific real-world experiments is impacted (Pallmann et al, 2018; FDA, 2019), compared to traditional uniform random assignment. We therefore use our case study of the ubiquitous two-arm binary reward setting to empirically investigate the impact of using Thompson Sampling instead of uniform random assignment. In this setting, using common statistical hypothesis tests, we show that collecting data with TS can as much as double the False Positive Rate (FPR; incorrectly reporting differences when none exist) and the False Negative Rate (FNR; failing to report differences when they exist)... |
1911.04020 | Ya Xiao | Ya Xiao, Qingying Hao, Danfeng (Daphne) Yao | Neural Cryptanalysis: Metrics, Methodology, and Applications in CPS
Ciphers | 8 pages, 8 figures, The 2019 IEEE Conference on Dependable and Secure
Computing | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real-world cyber-physical systems (CPS) use proprietary cipher
algorithms. In this work, we describe an easy-to-use black-box security
evaluation approach to measure the strength of proprietary ciphers without
having to know the algorithms. We quantify the strength of a cipher by
measuring how difficult it is for a neural network to mimic the cipher
algorithm. We define new metrics (e.g., cipher match rate, training data
complexity and training time complexity) that are computed from neural networks
to quantitatively represent the cipher strength. This measurement approach
allows us to directly compare the security of ciphers. Our experimental
demonstration utilizes fully connected neural networks with multiple parallel
binary classifiers at the output layer. The results show that when compared
with round-reduced DES, the security strength of Hitag2 (a popular stream
cipher used in the keyless entry of modern cars) is weaker than 3-round DES.
| [
{
"created": "Mon, 11 Nov 2019 00:36:38 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Nov 2019 19:45:11 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Nov 2019 02:05:35 GMT",
"version": "v3"
}
] | 2019-11-27 | [
[
"Xiao",
"Ya",
"",
"Daphne"
],
[
"Hao",
"Qingying",
"",
"Daphne"
],
[
"Danfeng",
"",
"",
"Daphne"
],
[
"Yao",
"",
""
]
] | Many real-world cyber-physical systems (CPS) use proprietary cipher algorithms. In this work, we describe an easy-to-use black-box security evaluation approach to measure the strength of proprietary ciphers without having to know the algorithms. We quantify the strength of a cipher by measuring how difficult it is for a neural network to mimic the cipher algorithm. We define new metrics (e.g., cipher match rate, training data complexity and training time complexity) that are computed from neural networks to quantitatively represent the cipher strength. This measurement approach allows us to directly compare the security of ciphers. Our experimental demonstration utilizes fully connected neural networks with multiple parallel binary classifiers at the output layer. The results show that when compared with round-reduced DES, the security strength of Hitag2 (a popular stream cipher used in the keyless entry of modern cars) is weaker than 3-round DES. |
2312.01432 | Andrzej Ruszczy\'nski | Zhengqi Lin and Andrzej Ruszczynski | Fast Dual Subgradient Optimization of the Integrated Transportation
Distance Between Stochastic Kernels | arXiv admin note: text overlap with arXiv:2311.06645 | null | null | null | cs.LG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A generalization of the Wasserstein metric, the integrated transportation
distance, establishes a novel distance between probability kernels of Markov
systems. This metric serves as the foundation for an efficient approximation
technique, enabling the replacement of the original system's kernel with a
kernel with a discrete support of limited cardinality. To facilitate practical
implementation, we present a specialized dual algorithm capable of constructing
these approximate kernels quickly and efficiently, without requiring
computationally expensive matrix operations. Finally, we demonstrate the
efficacy of our method through several illustrative examples, showcasing its
utility in practical scenarios. This advancement offers new possibilities for
the streamlined analysis and manipulation of stochastic systems represented by
kernels.
| [
{
"created": "Sun, 3 Dec 2023 15:44:17 GMT",
"version": "v1"
}
] | 2023-12-07 | [
[
"Lin",
"Zhengqi",
""
],
[
"Ruszczynski",
"Andrzej",
""
]
] | A generalization of the Wasserstein metric, the integrated transportation distance, establishes a novel distance between probability kernels of Markov systems. This metric serves as the foundation for an efficient approximation technique, enabling the replacement of the original system's kernel with a kernel with a discrete support of limited cardinality. To facilitate practical implementation, we present a specialized dual algorithm capable of constructing these approximate kernels quickly and efficiently, without requiring computationally expensive matrix operations. Finally, we demonstrate the efficacy of our method through several illustrative examples, showcasing its utility in practical scenarios. This advancement offers new possibilities for the streamlined analysis and manipulation of stochastic systems represented by kernels. |
1810.02452 | Elham Havvaei | David Eppstein and Elham Havvaei | Parameterized Leaf Power Recognition via Embedding into Graph Products | null | Algorithmica 82 (8): 2337-2359, 2020 | 10.1007/s00453-020-00720-8 | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The $k$-leaf power graph $G$ of a tree $T$ is a graph whose vertices are the
leaves of $T$ and whose edges connect pairs of leaves at unweighted distance at
most~$k$ in $T$. Recognition of the $k$-leaf power graphs for $k \geq 7$ is
still an open problem. In this paper, we provide two algorithms for this
problem for sparse leaf power graphs. Our results shows that the problem of
recognizing these graphs is fixed-parameter tractable when parameterized both
by $k$ and by the degeneracy of the given graph. To prove this, we first
describe how to embed the leaf root of a leaf power graph into a product of the
graph with a cycle graph. We bound the treewidth of the resulting product in
terms of $k$ and the degeneracy of $G$. The first presented algorithm uses
methods based on monadic second-order logic (MSO$_2$) to recognize the
existence of a leaf power as a subgraph of the product graph. Using the same
embedding in the product graph, the second algorithm presents a dynamic
programming approach to solve the problem and provide a better dependence on
the parameters.
| [
{
"created": "Thu, 4 Oct 2018 23:08:03 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Oct 2019 22:49:21 GMT",
"version": "v2"
},
{
"created": "Sun, 31 May 2020 22:43:32 GMT",
"version": "v3"
}
] | 2020-08-11 | [
[
"Eppstein",
"David",
""
],
[
"Havvaei",
"Elham",
""
]
] | The $k$-leaf power graph $G$ of a tree $T$ is a graph whose vertices are the leaves of $T$ and whose edges connect pairs of leaves at unweighted distance at most~$k$ in $T$. Recognition of the $k$-leaf power graphs for $k \geq 7$ is still an open problem. In this paper, we provide two algorithms for this problem for sparse leaf power graphs. Our results shows that the problem of recognizing these graphs is fixed-parameter tractable when parameterized both by $k$ and by the degeneracy of the given graph. To prove this, we first describe how to embed the leaf root of a leaf power graph into a product of the graph with a cycle graph. We bound the treewidth of the resulting product in terms of $k$ and the degeneracy of $G$. The first presented algorithm uses methods based on monadic second-order logic (MSO$_2$) to recognize the existence of a leaf power as a subgraph of the product graph. Using the same embedding in the product graph, the second algorithm presents a dynamic programming approach to solve the problem and provide a better dependence on the parameters. |
2106.12978 | Georgios Damaskinos | Alessandro Solbiati, Kevin Heffernan, Georgios Damaskinos, Shivani
Poddar, Shubham Modi, Jacques Cali | Unsupervised Topic Segmentation of Meetings with BERT Embeddings | null | null | null | null | cs.LG cs.CL | http://creativecommons.org/licenses/by/4.0/ | Topic segmentation of meetings is the task of dividing multi-person meeting
transcripts into topic blocks. Supervised approaches to the problem have proven
intractable due to the difficulties in collecting and accurately annotating
large datasets. In this paper we show how previous unsupervised topic
segmentation methods can be improved using pre-trained neural architectures. We
introduce an unsupervised approach based on BERT embeddings that achieves a
15.5% reduction in error rate over existing unsupervised approaches applied to
two popular datasets for meeting transcripts.
| [
{
"created": "Thu, 24 Jun 2021 12:54:43 GMT",
"version": "v1"
}
] | 2021-06-25 | [
[
"Solbiati",
"Alessandro",
""
],
[
"Heffernan",
"Kevin",
""
],
[
"Damaskinos",
"Georgios",
""
],
[
"Poddar",
"Shivani",
""
],
[
"Modi",
"Shubham",
""
],
[
"Cali",
"Jacques",
""
]
] | Topic segmentation of meetings is the task of dividing multi-person meeting transcripts into topic blocks. Supervised approaches to the problem have proven intractable due to the difficulties in collecting and accurately annotating large datasets. In this paper we show how previous unsupervised topic segmentation methods can be improved using pre-trained neural architectures. We introduce an unsupervised approach based on BERT embeddings that achieves a 15.5% reduction in error rate over existing unsupervised approaches applied to two popular datasets for meeting transcripts. |
2002.05245 | Shengxin Liu | Xiaohui Bei, Shengxin Liu, Xinhang Lu, Hongao Wang | Maximin Fairness with Mixed Divisible and Indivisible Goods | Appears in the 35th AAAI Conference on Artificial Intelligence
(AAAI), 2021 | Autonomous Agents and Multi-Agent Systems, 35(2):34 (2021) | 10.1007/s10458-021-09517-7 | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study fair resource allocation when the resources contain a mixture of
divisible and indivisible goods, focusing on the well-studied fairness notion
of maximin share fairness (MMS). With only indivisible goods, a full MMS
allocation may not exist, but a constant multiplicative approximate allocation
always does. We analyze how the MMS approximation guarantee would be affected
when the resources to be allocated also contain divisible goods. In particular,
we show that the worst-case MMS approximation guarantee with mixed goods is no
worse than that with only indivisible goods. However, there exist problem
instances to which adding some divisible resources would strictly decrease the
MMS approximation ratio of the instance. On the algorithmic front, we propose a
constructive algorithm that will always produce an $\alpha$-MMS allocation for
any number of agents, where $\alpha$ takes values between $1/2$ and $1$ and is
a monotone increasing function determined by how agents value the divisible
goods relative to their MMS values.
| [
{
"created": "Wed, 12 Feb 2020 21:37:38 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Dec 2020 15:32:36 GMT",
"version": "v2"
},
{
"created": "Thu, 1 Jul 2021 13:05:31 GMT",
"version": "v3"
}
] | 2021-07-02 | [
[
"Bei",
"Xiaohui",
""
],
[
"Liu",
"Shengxin",
""
],
[
"Lu",
"Xinhang",
""
],
[
"Wang",
"Hongao",
""
]
] | We study fair resource allocation when the resources contain a mixture of divisible and indivisible goods, focusing on the well-studied fairness notion of maximin share fairness (MMS). With only indivisible goods, a full MMS allocation may not exist, but a constant multiplicative approximate allocation always does. We analyze how the MMS approximation guarantee would be affected when the resources to be allocated also contain divisible goods. In particular, we show that the worst-case MMS approximation guarantee with mixed goods is no worse than that with only indivisible goods. However, there exist problem instances to which adding some divisible resources would strictly decrease the MMS approximation ratio of the instance. On the algorithmic front, we propose a constructive algorithm that will always produce an $\alpha$-MMS allocation for any number of agents, where $\alpha$ takes values between $1/2$ and $1$ and is a monotone increasing function determined by how agents value the divisible goods relative to their MMS values. |
1301.3641 | Ryan Kiros | Ryan Kiros | Training Neural Networks with Stochastic Hessian-Free Optimization | 11 pages, ICLR 2013 | null | null | null | cs.LG cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hessian-free (HF) optimization has been successfully used for training deep
autoencoders and recurrent networks. HF uses the conjugate gradient algorithm
to construct update directions through curvature-vector products that can be
computed on the same order of time as gradients. In this paper we exploit this
property and study stochastic HF with gradient and curvature mini-batches
independent of the dataset size. We modify Martens' HF for these settings and
integrate dropout, a method for preventing co-adaptation of feature detectors,
to guard against overfitting. Stochastic Hessian-free optimization gives an
intermediary between SGD and HF that achieves competitive performance on both
classification and deep autoencoder experiments.
| [
{
"created": "Wed, 16 Jan 2013 10:10:23 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Mar 2013 05:51:37 GMT",
"version": "v2"
},
{
"created": "Wed, 1 May 2013 06:57:50 GMT",
"version": "v3"
}
] | 2013-05-02 | [
[
"Kiros",
"Ryan",
""
]
] | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property and study stochastic HF with gradient and curvature mini-batches independent of the dataset size. We modify Martens' HF for these settings and integrate dropout, a method for preventing co-adaptation of feature detectors, to guard against overfitting. Stochastic Hessian-free optimization gives an intermediary between SGD and HF that achieves competitive performance on both classification and deep autoencoder experiments. |
2403.17064 | Stefan Andreas Baumann | Stefan Andreas Baumann and Felix Krause and Michael Neumayr and Nick
Stracke and Vincent Tao Hu and Bj\"orn Ommer | Continuous, Subject-Specific Attribute Control in T2I Models by
Identifying Semantic Directions | Project page: https://compvis.github.io/attribute-control | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, advances in text-to-image (T2I) diffusion models have
substantially elevated the quality of their generated images. However,
achieving fine-grained control over attributes remains a challenge due to the
limitations of natural language prompts (such as no continuous set of
intermediate descriptions existing between ``person'' and ``old person''). Even
though many methods were introduced that augment the model or generation
process to enable such control, methods that do not require a fixed reference
image are limited to either enabling global fine-grained attribute expression
control or coarse attribute expression control localized to specific subjects,
not both simultaneously. We show that there exist directions in the commonly
used token-level CLIP text embeddings that enable fine-grained subject-specific
control of high-level attributes in text-to-image models. Based on this
observation, we introduce one efficient optimization-free and one robust
optimization-based method to identify these directions for specific attributes
from contrastive text prompts. We demonstrate that these directions can be used
to augment the prompt text input with fine-grained control over attributes of
specific subjects in a compositional manner (control over multiple attributes
of a single subject) without having to adapt the diffusion model. Project page:
https://compvis.github.io/attribute-control. Code is available at
https://github.com/CompVis/attribute-control.
| [
{
"created": "Mon, 25 Mar 2024 18:00:42 GMT",
"version": "v1"
}
] | 2024-03-27 | [
[
"Baumann",
"Stefan Andreas",
""
],
[
"Krause",
"Felix",
""
],
[
"Neumayr",
"Michael",
""
],
[
"Stracke",
"Nick",
""
],
[
"Hu",
"Vincent Tao",
""
],
[
"Ommer",
"Björn",
""
]
] | In recent years, advances in text-to-image (T2I) diffusion models have substantially elevated the quality of their generated images. However, achieving fine-grained control over attributes remains a challenge due to the limitations of natural language prompts (such as no continuous set of intermediate descriptions existing between ``person'' and ``old person''). Even though many methods were introduced that augment the model or generation process to enable such control, methods that do not require a fixed reference image are limited to either enabling global fine-grained attribute expression control or coarse attribute expression control localized to specific subjects, not both simultaneously. We show that there exist directions in the commonly used token-level CLIP text embeddings that enable fine-grained subject-specific control of high-level attributes in text-to-image models. Based on this observation, we introduce one efficient optimization-free and one robust optimization-based method to identify these directions for specific attributes from contrastive text prompts. We demonstrate that these directions can be used to augment the prompt text input with fine-grained control over attributes of specific subjects in a compositional manner (control over multiple attributes of a single subject) without having to adapt the diffusion model. Project page: https://compvis.github.io/attribute-control. Code is available at https://github.com/CompVis/attribute-control. |
2007.11246 | Mehdi Teimouri | Mehdi Teimouri, Zahra Seyedghorban, Fatemeh Amirjani | Fragments-Expert: A Graphical User Interface MATLAB Toolbox for
Classification of File Fragments | 47 Pages, 34 Figures, and 3 Tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The classification of file fragments of various file formats is an essential
task in various applications such as firewalls, intrusion detection systems,
anti-viruses, web content filtering, and digital forensics. However, the
community lacks a suitable software tool that can integrate major methods for
feature extraction from file fragments and classification among various file
formats. In this paper, we present Fragments-Expert that is a graphical user
interface MATLAB toolbox for the classification of file fragments. It provides
users with 22 categories of features extracted from file fragments. These
features can be employed by 7 categories of machine learning algorithms for the
task of classification among various file formats.
| [
{
"created": "Wed, 22 Jul 2020 08:03:02 GMT",
"version": "v1"
}
] | 2020-07-23 | [
[
"Teimouri",
"Mehdi",
""
],
[
"Seyedghorban",
"Zahra",
""
],
[
"Amirjani",
"Fatemeh",
""
]
] | The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, anti-viruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this paper, we present Fragments-Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 22 categories of features extracted from file fragments. These features can be employed by 7 categories of machine learning algorithms for the task of classification among various file formats. |
2305.04429 | Yang Wu | Yang Wu, Yanyan Zhao, Zhongyang Li, Bing Qin, Kai Xiong | Improving Cross-Task Generalization with Step-by-Step Instructions | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Instruction tuning has been shown to be able to improve cross-task
generalization of language models. However, it is still challenging for
language models to complete the target tasks following the instructions, as the
instructions are general and lack intermediate steps. To address this problem,
we propose to incorporate the step-by-step instructions to help language models
to decompose the tasks, which can provide the detailed and specific procedures
for completing the target tasks. The step-by-step instructions are obtained
automatically by prompting ChatGPT, which are further combined with the
original instructions to tune language models. The extensive experiments on
SUP-NATINST show that the high-quality step-by-step instructions can improve
cross-task generalization across different model sizes. Moreover, the further
analysis indicates the importance of the order of steps of the step-by-step
instruction for the improvement. To facilitate future research, we release the
step-by-step instructions and their human quality evaluation results.
| [
{
"created": "Mon, 8 May 2023 02:50:41 GMT",
"version": "v1"
}
] | 2023-05-09 | [
[
"Wu",
"Yang",
""
],
[
"Zhao",
"Yanyan",
""
],
[
"Li",
"Zhongyang",
""
],
[
"Qin",
"Bing",
""
],
[
"Xiong",
"Kai",
""
]
] | Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target tasks following the instructions, as the instructions are general and lack intermediate steps. To address this problem, we propose to incorporate the step-by-step instructions to help language models to decompose the tasks, which can provide the detailed and specific procedures for completing the target tasks. The step-by-step instructions are obtained automatically by prompting ChatGPT, which are further combined with the original instructions to tune language models. The extensive experiments on SUP-NATINST show that the high-quality step-by-step instructions can improve cross-task generalization across different model sizes. Moreover, the further analysis indicates the importance of the order of steps of the step-by-step instruction for the improvement. To facilitate future research, we release the step-by-step instructions and their human quality evaluation results. |
2310.04020 | Anand Kulkarni Dr | Anand J Kulkarni, Ishaan R Kale, Apoorva Shastri, Aayush Khandekar | Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired
Metaheuristic Algorithm | 46 Pages, 11 Figures, 24 Tables | null | null | null | cs.NE | http://creativecommons.org/licenses/by/4.0/ | In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is
proposed. It is inspired from the biological behaviour of the snails. Snails
continuously travels to find food and a mate, leaving behind a trail of mucus
that serves as a guide for their return. Snails tend to navigate by following
the available trails on the ground and responding to cues from nearby shelter
homes. The proposed SHMS algorithm is investigated by solving several unimodal
and multimodal functions. The solutions are validated using standard
statistical tests such as two-sided and pairwise signed rank Wilcoxon test and
Friedman rank test. The solution obtained from the SHMS algorithm exhibited
superior robustness as well as search space exploration capabilities within the
less computational cost. The real-world application of SHMS algorithm is
successfully demonstrated in the engineering design domain by solving three
cases of design and economic optimization shell and tube heat exchanger
problem. The objective function value and other statistical results obtained
using SHMS algorithm are compared with other well-known metaheuristic
algorithms.
| [
{
"created": "Fri, 6 Oct 2023 05:18:48 GMT",
"version": "v1"
}
] | 2023-10-09 | [
[
"Kulkarni",
"Anand J",
""
],
[
"Kale",
"Ishaan R",
""
],
[
"Shastri",
"Apoorva",
""
],
[
"Khandekar",
"Aayush",
""
]
] | In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travels to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to cues from nearby shelter homes. The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The solution obtained from the SHMS algorithm exhibited superior robustness as well as search space exploration capabilities within the less computational cost. The real-world application of SHMS algorithm is successfully demonstrated in the engineering design domain by solving three cases of design and economic optimization shell and tube heat exchanger problem. The objective function value and other statistical results obtained using SHMS algorithm are compared with other well-known metaheuristic algorithms. |
1705.07460 | Min Xu | Min Xu | Experience enrichment based task independent reward model | 4 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For most reinforcement learning approaches, the learning is performed by
maximizing an accumulative reward that is expectedly and manually defined for
specific tasks. However, in real world, rewards are emergent phenomena from the
complex interactions between agents and environments. In this paper, we propose
an implicit generic reward model for reinforcement learning. Unlike those
rewards that are manually defined for specific tasks, such implicit reward is
task independent. It only comes from the deviation from the agents' previous
experiences.
| [
{
"created": "Sun, 21 May 2017 15:19:20 GMT",
"version": "v1"
}
] | 2017-05-23 | [
[
"Xu",
"Min",
""
]
] | For most reinforcement learning approaches, the learning is performed by maximizing an accumulative reward that is expectedly and manually defined for specific tasks. However, in real world, rewards are emergent phenomena from the complex interactions between agents and environments. In this paper, we propose an implicit generic reward model for reinforcement learning. Unlike those rewards that are manually defined for specific tasks, such implicit reward is task independent. It only comes from the deviation from the agents' previous experiences. |
2303.14321 | Daniel Lemire | Daniel Lemire | Exact Short Products From Truncated Multipliers | Software at https://github.com/lemire/exactshortlib | Computer Journal 67 (4), 2024 | 10.1093/comjnl/bxad077 | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | We sometimes need to compute the most significant digits of the product of
small integers with a multiplier requiring much storage: e.g., a large integer
(e.g., $5^{100}$) or an irrational number ($\pi$). We only need to access the
most significant digits of the multiplier-as long as the integers are
sufficiently small. We provide an efficient algorithm to compute the range of
integers given a truncated multiplier and a desired number of digits.
| [
{
"created": "Sat, 25 Mar 2023 01:26:00 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Lemire",
"Daniel",
""
]
] | We sometimes need to compute the most significant digits of the product of small integers with a multiplier requiring much storage: e.g., a large integer (e.g., $5^{100}$) or an irrational number ($\pi$). We only need to access the most significant digits of the multiplier-as long as the integers are sufficiently small. We provide an efficient algorithm to compute the range of integers given a truncated multiplier and a desired number of digits. |
2206.06836 | ali hassan | Amine Mrabet, Ali Hassan, Patrice Darmon (Umanis) | "hasSignification()": une nouvelle fonction de distance pour soutenir la
d\'etection de donn\'ees personnelles | in French language | null | null | null | cs.CL cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today with Big Data and data lakes, we are faced of a mass of data that is
very difficult to manage it manually. The protection of personal data in this
context requires an automatic analysis for data discovery. Storing the names of
attributes already analyzed in a knowledge base could optimize this automatic
discovery. To have a better knowledge base, we should not store any attributes
whose name does not make sense. In this article, to check if the name of an
attribute has a meaning, we propose a solution that calculate the distances
between this name and the words in a dictionary. Our studies on the distance
functions like N-Gram, Jaro-Winkler and Levenshtein show limits to set an
acceptance threshold for an attribute in the knowledge base. In order to
overcome these limitations, our solution aims to strengthen the score
calculation by using an exponential function based on the longest sequence. In
addition, a double scan in dictionary is also proposed in order to process the
attributes which have a compound name.
| [
{
"created": "Tue, 14 Jun 2022 13:31:26 GMT",
"version": "v1"
}
] | 2022-06-15 | [
[
"Mrabet",
"Amine",
"",
"Umanis"
],
[
"Hassan",
"Ali",
"",
"Umanis"
],
[
"Darmon",
"Patrice",
"",
"Umanis"
]
] | Today with Big Data and data lakes, we are faced of a mass of data that is very difficult to manage it manually. The protection of personal data in this context requires an automatic analysis for data discovery. Storing the names of attributes already analyzed in a knowledge base could optimize this automatic discovery. To have a better knowledge base, we should not store any attributes whose name does not make sense. In this article, to check if the name of an attribute has a meaning, we propose a solution that calculate the distances between this name and the words in a dictionary. Our studies on the distance functions like N-Gram, Jaro-Winkler and Levenshtein show limits to set an acceptance threshold for an attribute in the knowledge base. In order to overcome these limitations, our solution aims to strengthen the score calculation by using an exponential function based on the longest sequence. In addition, a double scan in dictionary is also proposed in order to process the attributes which have a compound name. |
1403.5618 | P\"ar-Ola Zander | Shahadat Hossein, Par-Ola Zander, Md. Kamal, Linkon Chowdhury | Belief-Rule-Based Expert Systems for Evaluation of E- Government: A Case
Study | Accepted with no Changes for Wiley Expert Systems | null | null | null | cs.AI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Little knowledge exists on the impact and results associated with
e-government projects in many specific use domains. Therefore it is necessary
to evaluate the efficiency and effectiveness of e-government systems. Since the
development of e-government is a continuous process of improvement, it requires
continuous evaluation of the overall e-government system as well as evaluation
of its various dimensions such as determinants, characteristics and results.
E-government development is often complex with multiple stakeholders, large
user bases and complex goals. Consequently, even experts have difficulties in
evaluating these systems, especially in an integrated and comprehensive way as
well as on an aggregate level. Expert systems are a candidate solution to
evaluate such complex e-government systems. However, it is difficult for expert
systems to cope with uncertain evaluation data that are vague, inconsistent,
highly subjective or in other ways challenging to formalize. This paper
presents an approach that can handle uncertainty in e-government evaluation:
The combination of Belief Rule Base (BRB) knowledge representation and
Evidential Reasoning (ES). This approach is illustrated with a concrete
prototype, known as Belief Rule Based Expert System (BRBES) and put to use in
the local e-government of Bangladesh. The results have been compared with a
recently developed method of evaluating e-Government, and it is shown that the
results of BRBES are more accurate and reliable. BRBES can be used to identify
the factors that need to be improved to achieve the overall aim of an
e-government project. In addition, various "what if" scenarios can be generated
and developers and managers can get a forecast of the outcomes. In this way,
the system can be used to facilitate decision making processes under
uncertainty.
| [
{
"created": "Sat, 22 Mar 2014 05:56:26 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Mar 2015 09:35:48 GMT",
"version": "v2"
}
] | 2015-03-10 | [
[
"Hossein",
"Shahadat",
""
],
[
"Zander",
"Par-Ola",
""
],
[
"Kamal",
"Md.",
""
],
[
"Chowdhury",
"Linkon",
""
]
] | Little knowledge exists on the impact and results associated with e-government projects in many specific use domains. Therefore it is necessary to evaluate the efficiency and effectiveness of e-government systems. Since the development of e-government is a continuous process of improvement, it requires continuous evaluation of the overall e-government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E-government development is often complex with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e-government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways challenging to formalize. This paper presents an approach that can handle uncertainty in e-government evaluation: The combination of Belief Rule Base (BRB) knowledge representation and Evidential Reasoning (ES). This approach is illustrated with a concrete prototype, known as Belief Rule Based Expert System (BRBES) and put to use in the local e-government of Bangladesh. The results have been compared with a recently developed method of evaluating e-Government, and it is shown that the results of BRBES are more accurate and reliable. BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e-government project. In addition, various "what if" scenarios can be generated and developers and managers can get a forecast of the outcomes. In this way, the system can be used to facilitate decision making processes under uncertainty. |
2307.01689 | Yuval Dagan | Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis,
Maxwell Fishelson | Online Learning and Solving Infinite Games with an ERM Oracle | In COLT2023 | null | null | null | cs.LG cs.AI cs.GT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While ERM suffices to attain near-optimal generalization error in the
stochastic learning setting, this is not known to be the case in the online
learning setting, where algorithms for general concept classes rely on
computationally inefficient oracles such as the Standard Optimal Algorithm
(SOA). In this work, we propose an algorithm for online binary classification
setting that relies solely on ERM oracle calls, and show that it has finite
regret in the realizable setting and sublinearly growing regret in the agnostic
setting. We bound the regret in terms of the Littlestone and threshold
dimensions of the underlying concept class.
We obtain similar results for nonparametric games, where the ERM oracle can
be interpreted as a best response oracle, finding the best response of a player
to a given history of play of the other players. In this setting, we provide
learning algorithms that only rely on best response oracles and converge to
approximate-minimax equilibria in two-player zero-sum games and approximate
coarse correlated equilibria in multi-player general-sum games, as long as the
game has a bounded fat-threshold dimension. Our algorithms apply to both
binary-valued and real-valued games and can be viewed as providing
justification for the wide use of double oracle and multiple oracle algorithms
in the practice of solving large games.
| [
{
"created": "Tue, 4 Jul 2023 12:51:21 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jul 2023 11:16:54 GMT",
"version": "v2"
}
] | 2023-07-11 | [
[
"Assos",
"Angelos",
""
],
[
"Attias",
"Idan",
""
],
[
"Dagan",
"Yuval",
""
],
[
"Daskalakis",
"Constantinos",
""
],
[
"Fishelson",
"Maxwell",
""
]
] | While ERM suffices to attain near-optimal generalization error in the stochastic learning setting, this is not known to be the case in the online learning setting, where algorithms for general concept classes rely on computationally inefficient oracles such as the Standard Optimal Algorithm (SOA). In this work, we propose an algorithm for online binary classification setting that relies solely on ERM oracle calls, and show that it has finite regret in the realizable setting and sublinearly growing regret in the agnostic setting. We bound the regret in terms of the Littlestone and threshold dimensions of the underlying concept class. We obtain similar results for nonparametric games, where the ERM oracle can be interpreted as a best response oracle, finding the best response of a player to a given history of play of the other players. In this setting, we provide learning algorithms that only rely on best response oracles and converge to approximate-minimax equilibria in two-player zero-sum games and approximate coarse correlated equilibria in multi-player general-sum games, as long as the game has a bounded fat-threshold dimension. Our algorithms apply to both binary-valued and real-valued games and can be viewed as providing justification for the wide use of double oracle and multiple oracle algorithms in the practice of solving large games. |
1508.03725 | Mirco Musolesi | Veljko Pejovic, Neal Lathia, Cecilia Mascolo, Mirco Musolesi | Mobile-Based Experience Sampling for Behaviour Research | 20 pages, 2 figures | null | null | null | cs.HC cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Experience Sampling Method (ESM) introduces in-situ sampling of human
behaviour, and provides researchers and behavioural therapists with
ecologically valid and timely assessments of a person's psychological state.
This, in turn, opens up new opportunities for understanding behaviour at a
scale and granularity that was not possible just a few years ago. The practical
applications are many, such as the delivery of personalised and agile behaviour
interventions. Mobile computing devices represent a revolutionary platform for
improving ESM. They are an inseparable part of our daily lives, context-aware,
and can interact with people at suitable moments. Furthermore, these devices
are equipped with sensors, and can thus take part of the reporting burden off
the participant, and collect data automatically. The goal of this survey is to
discuss recent advancements in using mobile technologies for ESM (mESM), and
present our vision of the future of mobile experience sampling.
| [
{
"created": "Sat, 15 Aug 2015 12:15:38 GMT",
"version": "v1"
}
] | 2015-08-18 | [
[
"Pejovic",
"Veljko",
""
],
[
"Lathia",
"Neal",
""
],
[
"Mascolo",
"Cecilia",
""
],
[
"Musolesi",
"Mirco",
""
]
] | The Experience Sampling Method (ESM) introduces in-situ sampling of human behaviour, and provides researchers and behavioural therapists with ecologically valid and timely assessments of a person's psychological state. This, in turn, opens up new opportunities for understanding behaviour at a scale and granularity that was not possible just a few years ago. The practical applications are many, such as the delivery of personalised and agile behaviour interventions. Mobile computing devices represent a revolutionary platform for improving ESM. They are an inseparable part of our daily lives, context-aware, and can interact with people at suitable moments. Furthermore, these devices are equipped with sensors, and can thus take part of the reporting burden off the participant, and collect data automatically. The goal of this survey is to discuss recent advancements in using mobile technologies for ESM (mESM), and present our vision of the future of mobile experience sampling. |
1601.03278 | Longqi Yang | Longqi Yang, Diana Freed, Alex Wu, Judy Wu, JP Pollak, Deborah Estrin | Your Activities of Daily Living (YADL): An Image-based Survey Technique
for Patients with Arthritis | null | null | null | null | cs.CY cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Healthcare professionals use Activities of Daily Living (ADL) to characterize
a patient's functional status and to evaluate the effectiveness of treatment
plans. ADLs are traditionally measured using standardized text-based
questionnaires and the only form of personalization is in the form of question
branching logic. Pervasive smartphone adoption makes it feasible to consider
more frequent patient-reporting on ADLs. However, asking generic sets of
questions repeatedly introduces user burden and fatigue that threatens to
interfere with their utility. We introduce an approach called YADL (Your
Activities of Daily Living) which uses images of ADLs and personalization to
improve survey efficiency and the patient-experience. It offers several
potential benefits: wider coverage of ADLs, improved engagement, and accurate
capture of individual health situations. In this paper, we discuss our system
design and the wide applicability of the design process for survey tools in
healthcare and beyond. Interactions with with a small number of patients with
Arthritis throughout the design process have been promising and we share
detailed insights.
| [
{
"created": "Wed, 13 Jan 2016 15:27:58 GMT",
"version": "v1"
}
] | 2016-01-14 | [
[
"Yang",
"Longqi",
""
],
[
"Freed",
"Diana",
""
],
[
"Wu",
"Alex",
""
],
[
"Wu",
"Judy",
""
],
[
"Pollak",
"JP",
""
],
[
"Estrin",
"Deborah",
""
]
] | Healthcare professionals use Activities of Daily Living (ADL) to characterize a patient's functional status and to evaluate the effectiveness of treatment plans. ADLs are traditionally measured using standardized text-based questionnaires and the only form of personalization is in the form of question branching logic. Pervasive smartphone adoption makes it feasible to consider more frequent patient-reporting on ADLs. However, asking generic sets of questions repeatedly introduces user burden and fatigue that threatens to interfere with their utility. We introduce an approach called YADL (Your Activities of Daily Living) which uses images of ADLs and personalization to improve survey efficiency and the patient-experience. It offers several potential benefits: wider coverage of ADLs, improved engagement, and accurate capture of individual health situations. In this paper, we discuss our system design and the wide applicability of the design process for survey tools in healthcare and beyond. Interactions with with a small number of patients with Arthritis throughout the design process have been promising and we share detailed insights. |
2210.10207 | Denizalp Goktas | Denizalp Goktas and Amy Greenwald | Exploitability Minimization in Games and Beyond | null | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pseudo-games are a natural and well-known generalization of normal-form
games, in which the actions taken by each player affect not only the other
players' payoffs, as in games, but also the other players' strategy sets. The
solution concept par excellence for pseudo-games is the generalized Nash
equilibrium (GNE), i.e., a strategy profile at which each player's strategy is
feasible and no player can improve their payoffs by unilaterally deviating to
another strategy in the strategy set determined by the other players'
strategies. The computation of GNE in pseudo-games has long been a problem of
interest, due to applications in a wide variety of fields, from environmental
protection to logistics to telecommunications. Although computing GNE is
PPAD-hard in general, it is still of interest to try to compute them in
restricted classes of pseudo-games. One approach is to search for a strategy
profile that minimizes exploitability, i.e., the sum of the regrets across all
players. As exploitability is nondifferentiable in general, developing
efficient first-order methods that minimize it might not seem possible at first
glance. We observe, however, that the exploitability-minimization problem can
be recast as a min-max optimization problem, and thereby obtain polynomial-time
first-order methods to compute a refinement of GNE, namely the variational
equilibria (VE), in convex-concave cumulative regret pseudo-games with jointly
convex constraints. More generally, we also show that our methods find the
stationary points of the exploitability in polynomial time in Lipschitz-smooth
pseudo-games with jointly convex constraints. Finally, we demonstrate in
experiments that our methods not only outperform known algorithms, but that
even in pseudo-games where they are not guaranteed to converge to a GNE, they
may do so nonetheless, with proper initialization.
| [
{
"created": "Tue, 18 Oct 2022 23:21:57 GMT",
"version": "v1"
}
] | 2022-10-20 | [
[
"Goktas",
"Denizalp",
""
],
[
"Greenwald",
"Amy",
""
]
] | Pseudo-games are a natural and well-known generalization of normal-form games, in which the actions taken by each player affect not only the other players' payoffs, as in games, but also the other players' strategy sets. The solution concept par excellence for pseudo-games is the generalized Nash equilibrium (GNE), i.e., a strategy profile at which each player's strategy is feasible and no player can improve their payoffs by unilaterally deviating to another strategy in the strategy set determined by the other players' strategies. The computation of GNE in pseudo-games has long been a problem of interest, due to applications in a wide variety of fields, from environmental protection to logistics to telecommunications. Although computing GNE is PPAD-hard in general, it is still of interest to try to compute them in restricted classes of pseudo-games. One approach is to search for a strategy profile that minimizes exploitability, i.e., the sum of the regrets across all players. As exploitability is nondifferentiable in general, developing efficient first-order methods that minimize it might not seem possible at first glance. We observe, however, that the exploitability-minimization problem can be recast as a min-max optimization problem, and thereby obtain polynomial-time first-order methods to compute a refinement of GNE, namely the variational equilibria (VE), in convex-concave cumulative regret pseudo-games with jointly convex constraints. More generally, we also show that our methods find the stationary points of the exploitability in polynomial time in Lipschitz-smooth pseudo-games with jointly convex constraints. Finally, we demonstrate in experiments that our methods not only outperform known algorithms, but that even in pseudo-games where they are not guaranteed to converge to a GNE, they may do so nonetheless, with proper initialization. |
2309.16789 | Jayati Deshmukh | Balambiga Ayappane, Rohith Vaidyanathan, Srinath Srinivasa, Jayati
Deshmukh | Extensible Consent Management Architectures for Data Trusts | An earlier version of this paper was published in ISIC 2021 | null | null | null | cs.CY | http://creativecommons.org/licenses/by/4.0/ | Sensitive personal information of individuals and non-personal information of
organizations or communities often needs to be legitimately exchanged among
different stakeholders, to provide services, maintain public health, law and
order, and so on. While such exchanges are necessary, they also impose enormous
privacy and security challenges. Data protection laws like GDPR for personal
data and Indian Non-personal data protection draft specify conditions and the
\textit{legal capacity} in which personal and non-personal information can be
solicited and disseminated further. But there is a dearth of formalisms for
specifying legal capacities and jurisdictional boundaries, so that open-ended
exchange of such data can be implemented. This paper proposes an extensible
framework for consent management in Data Trusts in which data can flow across a
network through "role tunnels" established based on corresponding legal
capacities.
| [
{
"created": "Thu, 28 Sep 2023 18:28:50 GMT",
"version": "v1"
}
] | 2023-10-02 | [
[
"Ayappane",
"Balambiga",
""
],
[
"Vaidyanathan",
"Rohith",
""
],
[
"Srinivasa",
"Srinath",
""
],
[
"Deshmukh",
"Jayati",
""
]
] | Sensitive personal information of individuals and non-personal information of organizations or communities often needs to be legitimately exchanged among different stakeholders, to provide services, maintain public health, law and order, and so on. While such exchanges are necessary, they also impose enormous privacy and security challenges. Data protection laws like GDPR for personal data and Indian Non-personal data protection draft specify conditions and the \textit{legal capacity} in which personal and non-personal information can be solicited and disseminated further. But there is a dearth of formalisms for specifying legal capacities and jurisdictional boundaries, so that open-ended exchange of such data can be implemented. This paper proposes an extensible framework for consent management in Data Trusts in which data can flow across a network through "role tunnels" established based on corresponding legal capacities. |
1908.11431 | Yuxing Ma | Yuxing Ma, Audris Mockus, Beth Milhollin, Russel Zaretzki, Randy
Bradley, Bogdan Bichescu | A Methodology for Analyzing Uptake of Software Technologies Among
Developers | 5 figures, 15 pages | null | null | null | cs.SE | http://creativecommons.org/publicdomain/zero/1.0/ | Motivation: The question of what combination of attributes drives the
adoption of a particular software technology is critical to developers. It
determines both those technologies that receive wide support from the community
and those which may be abandoned, thus rendering developers' investments
worthless. Aim and Context: We model software technology adoption by developers
and provide insights on specific technology attributes that are associated with
better visibility among alternative technologies. Approach: We leverage social
contagion theory and statistical modeling to identify, define, and test
empirically measures that are likely to affect software adoption. More
specifically, we leverage a large collection of open source version control
repositories to construct a software dependency chain for a specific set of R
language source-code files. We formulate logistic regression models, to
investigate the combination of technological attributes that drive adoption
among competing data frame implementations in the R language: tidy and
data.table. We quantify key project attributes that might affect adoption and
also characteristics of developers making the selection. Results: We find that
a quick response to raised issues, a larger number of overall deployments, and
a larger number of high-quality StackExchange questions are associated with
higher adoption. Decision makers tend to adopt the technology that is closer to
them in the technical dependency network and in author collaborations networks
while meeting their performance needs. Future work: We hope that our
methodology encompassing social contagion that captures both rational and
irrational preferences and the elucidation of key measures from large
collections of version control data provides a general path toward increasing
visibility, driving better informed decisions, and producing more sustainable
and widely adopted software
| [
{
"created": "Thu, 29 Aug 2019 19:36:28 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Sep 2019 15:06:13 GMT",
"version": "v2"
}
] | 2019-09-05 | [
[
"Ma",
"Yuxing",
""
],
[
"Mockus",
"Audris",
""
],
[
"Milhollin",
"Beth",
""
],
[
"Zaretzki",
"Russel",
""
],
[
"Bradley",
"Randy",
""
],
[
"Bichescu",
"Bogdan",
""
]
] | Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers' investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source version control repositories to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, to investigate the combination of technological attributes that drive adoption among competing data frame implementations in the R language: tidy and data.table. We quantify key project attributes that might affect adoption and also characteristics of developers making the selection. Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-quality StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software |
2408.06814 | Vaghawan Prasad Ojha | Bishwash Khanal, Sanjay Rijal, Manish Awale and Vaghawan Ojha | Structure-preserving Planar Simplification for Indoor Environments | null | null | null | null | cs.CV cs.CG | http://creativecommons.org/licenses/by/4.0/ | This paper presents a novel approach for structure-preserving planar
simplification of indoor scene point clouds for both simulated and real-world
environments. Initially, the scene point cloud undergoes preprocessing steps,
including noise reduction and Manhattan world alignment, to ensure robustness
and coherence in subsequent analyses. We segment each captured scene into
structured (walls-ceiling-floor) and non-structured (indoor objects) scenes.
Leveraging a RANSAC algorithm, we extract primitive planes from the input point
cloud, facilitating the segmentation and simplification of the structured
scene. The best-fitting wall meshes are then generated from the primitives,
followed by adjacent mesh merging with the vertex-translation algorithm which
preserves the mesh layout. To accurately represent ceilings and floors, we
employ the mesh clipping algorithm which clips the ceiling and floor meshes
with respect to wall normals. In the case of indoor scenes, we apply a surface
reconstruction technique to enhance the fidelity. This paper focuses on the
intricate steps of the proposed scene simplification methodology, addressing
complex scenarios such as multi-story and slanted walls and ceilings. We also
conduct qualitative and quantitative performance comparisons against popular
surface reconstruction, shape approximation, and floorplan generation
approaches.
| [
{
"created": "Tue, 13 Aug 2024 11:10:26 GMT",
"version": "v1"
}
] | 2024-08-14 | [
[
"Khanal",
"Bishwash",
""
],
[
"Rijal",
"Sanjay",
""
],
[
"Awale",
"Manish",
""
],
[
"Ojha",
"Vaghawan",
""
]
] | This paper presents a novel approach for structure-preserving planar simplification of indoor scene point clouds for both simulated and real-world environments. Initially, the scene point cloud undergoes preprocessing steps, including noise reduction and Manhattan world alignment, to ensure robustness and coherence in subsequent analyses. We segment each captured scene into structured (walls-ceiling-floor) and non-structured (indoor objects) scenes. Leveraging a RANSAC algorithm, we extract primitive planes from the input point cloud, facilitating the segmentation and simplification of the structured scene. The best-fitting wall meshes are then generated from the primitives, followed by adjacent mesh merging with the vertex-translation algorithm which preserves the mesh layout. To accurately represent ceilings and floors, we employ the mesh clipping algorithm which clips the ceiling and floor meshes with respect to wall normals. In the case of indoor scenes, we apply a surface reconstruction technique to enhance the fidelity. This paper focuses on the intricate steps of the proposed scene simplification methodology, addressing complex scenarios such as multi-story and slanted walls and ceilings. We also conduct qualitative and quantitative performance comparisons against popular surface reconstruction, shape approximation, and floorplan generation approaches. |
2310.02227 | Parshin Shojaee | Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, Amir Barati Farimani | SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified
Pre-training | ICLR 2024 Spotlight Paper | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | In an era where symbolic mathematical equations are indispensable for
modeling complex natural phenomena, scientific inquiry often involves
collecting observations and translating them into mathematical expressions.
Recently, deep learning has emerged as a powerful tool for extracting insights
from data. However, existing models typically specialize in either numeric or
symbolic domains, and are usually trained in a supervised manner tailored to
specific tasks. This approach neglects the substantial benefits that could
arise from a task-agnostic multi-modal understanding between symbolic equations
and their numeric counterparts. To bridge the gap, we introduce SNIP, a
Symbolic-Numeric Integrated Pre-training model, which employs contrastive
learning between symbolic and numeric domains, enhancing their mutual
similarities in the embeddings. By performing latent space analysis, we observe
that SNIP provides cross-domain insights into the representations, revealing
that symbolic supervision enhances the embeddings of numeric data and vice
versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric
mathematical property prediction and numeric-to-symbolic equation discovery,
commonly known as symbolic regression. Results show that SNIP effectively
transfers to various tasks, consistently outperforming fully supervised
baselines and competing strongly with established task-specific methods,
especially in the low data regime scenarios where available data is limited.
Code and model are available at:
https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining
| [
{
"created": "Tue, 3 Oct 2023 17:32:44 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Oct 2023 13:53:04 GMT",
"version": "v2"
},
{
"created": "Fri, 15 Mar 2024 06:00:29 GMT",
"version": "v3"
}
] | 2024-03-18 | [
[
"Meidani",
"Kazem",
""
],
[
"Shojaee",
"Parshin",
""
],
[
"Reddy",
"Chandan K.",
""
],
[
"Farimani",
"Amir Barati",
""
]
] | In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic multi-modal understanding between symbolic equations and their numeric counterparts. To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings. By performing latent space analysis, we observe that SNIP provides cross-domain insights into the representations, revealing that symbolic supervision enhances the embeddings of numeric data and vice versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric mathematical property prediction and numeric-to-symbolic equation discovery, commonly known as symbolic regression. Results show that SNIP effectively transfers to various tasks, consistently outperforming fully supervised baselines and competing strongly with established task-specific methods, especially in the low data regime scenarios where available data is limited. Code and model are available at: https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining |
1908.06148 | Govind Mittal | Govind Mittal, Pawel Korus, Nasir Memon | FiFTy: Large-scale File Fragment Type Identification using Neural
Networks | Paper accepted for publication in the IEEE Transactions on
Information Forensics and Security | null | null | null | cs.CR cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present FiFTy, a modern file type identification tool for memory forensics
and data carving. In contrast to previous approaches based on hand-crafted
features, we design a compact neural network architecture, which uses a
trainable embedding space, akin to successful natural language processing
models. Our approach dispenses with explicit feature extraction which is a
bottleneck in legacy systems. We evaluate the proposed method on a novel
dataset with 75 file types - the most diverse and balanced dataset reported to
date. FiFTy consistently outperforms all baselines in terms of speed, accuracy
and individual misclassification rates. We achieved an average accuracy of
77.5% with processing speed of approx 38 sec/GB, which is better and more than
an order of magnitude faster than the previous state-of-the-art tool - Sceadan
(69% at 9 min/GB). Our tool and the corresponding dataset are available
publicly online.
| [
{
"created": "Fri, 16 Aug 2019 19:53:46 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jun 2020 05:13:26 GMT",
"version": "v2"
}
] | 2020-06-09 | [
[
"Mittal",
"Govind",
""
],
[
"Korus",
"Pawel",
""
],
[
"Memon",
"Nasir",
""
]
] | We present FiFTy, a modern file type identification tool for memory forensics and data carving. In contrast to previous approaches based on hand-crafted features, we design a compact neural network architecture, which uses a trainable embedding space, akin to successful natural language processing models. Our approach dispenses with explicit feature extraction which is a bottleneck in legacy systems. We evaluate the proposed method on a novel dataset with 75 file types - the most diverse and balanced dataset reported to date. FiFTy consistently outperforms all baselines in terms of speed, accuracy and individual misclassification rates. We achieved an average accuracy of 77.5% with processing speed of approx 38 sec/GB, which is better and more than an order of magnitude faster than the previous state-of-the-art tool - Sceadan (69% at 9 min/GB). Our tool and the corresponding dataset are available publicly online. |
1910.09495 | Saeed Reza Kheradpisheh | Saeed Reza Kheradpisheh and Timoth\'ee Masquelier | S4NN: temporal backpropagation for spiking neural networks with one
spike per neuron | null | International Journal of Neural Systems 2020 | 10.1142/S0129065720500276 | null | cs.NE cs.CV cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new supervised learning rule for multilayer spiking neural
networks (SNNs) that use a form of temporal coding known as rank-order-coding.
With this coding scheme, all neurons fire exactly one spike per stimulus, but
the firing order carries information. In particular, in the readout layer, the
first neuron to fire determines the class of the stimulus. We derive a new
learning rule for this sort of network, named S4NN, akin to traditional error
backpropagation, yet based on latencies. We show how approximated error
gradients can be computed backward in a feedforward network with any number of
layers. This approach reaches state-of-the-art performance with supervised
multi fully-connected layer SNNs: test accuracy of 97.4% for the MNIST dataset,
and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we
use, non-leaky integrate-and-fire, is much simpler than the one used in all
previous works. The source codes of the proposed S4NN are publicly available at
https://github.com/SRKH/S4NN.
| [
{
"created": "Mon, 21 Oct 2019 16:39:42 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Mar 2020 15:43:30 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Apr 2020 09:23:11 GMT",
"version": "v3"
},
{
"created": "Sat, 13 Jun 2020 10:33:19 GMT",
"version": "v4"
}
] | 2020-06-16 | [
[
"Kheradpisheh",
"Saeed Reza",
""
],
[
"Masquelier",
"Timothée",
""
]
] | We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, named S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximated error gradients can be computed backward in a feedforward network with any number of layers. This approach reaches state-of-the-art performance with supervised multi fully-connected layer SNNs: test accuracy of 97.4% for the MNIST dataset, and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we use, non-leaky integrate-and-fire, is much simpler than the one used in all previous works. The source codes of the proposed S4NN are publicly available at https://github.com/SRKH/S4NN. |
2207.11365 | Tushar Nagarajan | Tushar Nagarajan, Santhosh Kumar Ramakrishnan, Ruta Desai, James
Hillis, Kristen Grauman | EgoEnv: Human-centric environment representations from egocentric video | Published in NeurIPS 2023 (Oral) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | First-person video highlights a camera-wearer's activities in the context of
their persistent environment. However, current video understanding approaches
reason over visual features from short video clips that are detached from the
underlying physical space and capture only what is immediately visible. To
facilitate human-centric environment understanding, we present an approach that
links egocentric video and the environment by learning representations that are
predictive of the camera-wearer's (potentially unseen) local surroundings. We
train such models using videos from agents in simulated 3D environments where
the environment is fully observable, and test them on human-captured real-world
videos from unseen environments. On two human-centric video tasks, we show that
models equipped with our environment-aware features consistently outperform
their counterparts with traditional clip features. Moreover, despite being
trained exclusively on simulated videos, our approach successfully handles
real-world videos from HouseTours and Ego4D, and achieves state-of-the-art
results on the Ego4D NLQ challenge. Project page:
https://vision.cs.utexas.edu/projects/ego-env/
| [
{
"created": "Fri, 22 Jul 2022 22:39:57 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Dec 2022 16:39:40 GMT",
"version": "v2"
},
{
"created": "Thu, 9 Nov 2023 19:13:18 GMT",
"version": "v3"
}
] | 2023-11-13 | [
[
"Nagarajan",
"Tushar",
""
],
[
"Ramakrishnan",
"Santhosh Kumar",
""
],
[
"Desai",
"Ruta",
""
],
[
"Hillis",
"James",
""
],
[
"Grauman",
"Kristen",
""
]
] | First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and capture only what is immediately visible. To facilitate human-centric environment understanding, we present an approach that links egocentric video and the environment by learning representations that are predictive of the camera-wearer's (potentially unseen) local surroundings. We train such models using videos from agents in simulated 3D environments where the environment is fully observable, and test them on human-captured real-world videos from unseen environments. On two human-centric video tasks, we show that models equipped with our environment-aware features consistently outperform their counterparts with traditional clip features. Moreover, despite being trained exclusively on simulated videos, our approach successfully handles real-world videos from HouseTours and Ego4D, and achieves state-of-the-art results on the Ego4D NLQ challenge. Project page: https://vision.cs.utexas.edu/projects/ego-env/ |
2306.07650 | Yuchen Han | Yuchen Han, Chen Xu, Tong Xiao and Jingbo Zhu | Modality Adaption or Regularization? A Case Study on End-to-End Speech
Translation | ACL 2023 Main Conference | null | null | null | cs.CL cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-training and fine-tuning is a paradigm for alleviating the data scarcity
problem in end-to-end speech translation (E2E ST). The commonplace "modality
gap" between speech and text data often leads to inconsistent inputs between
pre-training and fine-tuning. However, we observe that this gap occurs in the
early stages of fine-tuning, but does not have a major impact on the final
performance. On the other hand, we find that there has another gap, which we
call the "capacity gap": high resource tasks (such as ASR and MT) always
require a large model to fit, when the model is reused for a low resource task
(E2E ST), it will get a sub-optimal performance due to the over-fitting. In a
case study, we find that the regularization plays a more important role than
the well-designed modality adaption method, which achieves 29.0 for en-de and
40.3 for en-fr on the MuST-C dataset. Code and models are available at
https://github.com/hannlp/TAB.
| [
{
"created": "Tue, 13 Jun 2023 09:42:48 GMT",
"version": "v1"
}
] | 2023-06-14 | [
[
"Han",
"Yuchen",
""
],
[
"Xu",
"Chen",
""
],
[
"Xiao",
"Tong",
""
],
[
"Zhu",
"Jingbo",
""
]
] | Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST). The commonplace "modality gap" between speech and text data often leads to inconsistent inputs between pre-training and fine-tuning. However, we observe that this gap occurs in the early stages of fine-tuning, but does not have a major impact on the final performance. On the other hand, we find that there has another gap, which we call the "capacity gap": high resource tasks (such as ASR and MT) always require a large model to fit, when the model is reused for a low resource task (E2E ST), it will get a sub-optimal performance due to the over-fitting. In a case study, we find that the regularization plays a more important role than the well-designed modality adaption method, which achieves 29.0 for en-de and 40.3 for en-fr on the MuST-C dataset. Code and models are available at https://github.com/hannlp/TAB. |
2211.16104 | Gianluca Curzi | Gianluca Curzi and Anupam Das | Non-uniform complexity via non-wellfounded proofs | null | null | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | Cyclic and non-wellfounded proofs are now increasingly employed to establish
metalogical results in a variety of settings, in particular for type systems
with forms of (co)induction. Under the Curry-Howard correspondence, a cyclic
proof can be seen as a typing derivation 'with loops', closer to low-level
machine models, and so comprise a highly expressive computational model that
nonetheless enjoys excellent metalogical properties.
In recent work, we showed how the cyclic proof setting can be further
employed to model computational complexity, yielding characterisations of the
polynomial time and elementary computable functions. These characterisations
are 'implicit', inspired by Bellantoni and Cook's famous algebra of safe
recursion, but exhibit greater expressivity thanks to the looping capacity of
cyclic proofs.
In this work we investigate the capacity for non-wellfounded proofs, where
finite presentability is relaxed, to model non-uniformity in complexity theory.
In particular, we present a characterisation of the class $\mathsf{FP/poly}$ of
functions computed by polynomial-size circuits. While relating
non-wellfoundedness to non-uniformity is a natural idea, the precise amount of
irregularity, informally speaking, required to capture $\mathsf{FP/poly}$ is
given by proof-level conditions novel to cyclic proof theory. Along the way, we
formalise some (presumably) folklore techniques for characterising non-uniform
classes in relativised function algebras with appropriate oracles.
| [
{
"created": "Tue, 29 Nov 2022 11:26:50 GMT",
"version": "v1"
}
] | 2022-11-30 | [
[
"Curzi",
"Gianluca",
""
],
[
"Das",
"Anupam",
""
]
] | Cyclic and non-wellfounded proofs are now increasingly employed to establish metalogical results in a variety of settings, in particular for type systems with forms of (co)induction. Under the Curry-Howard correspondence, a cyclic proof can be seen as a typing derivation 'with loops', closer to low-level machine models, and so comprise a highly expressive computational model that nonetheless enjoys excellent metalogical properties. In recent work, we showed how the cyclic proof setting can be further employed to model computational complexity, yielding characterisations of the polynomial time and elementary computable functions. These characterisations are 'implicit', inspired by Bellantoni and Cook's famous algebra of safe recursion, but exhibit greater expressivity thanks to the looping capacity of cyclic proofs. In this work we investigate the capacity for non-wellfounded proofs, where finite presentability is relaxed, to model non-uniformity in complexity theory. In particular, we present a characterisation of the class $\mathsf{FP/poly}$ of functions computed by polynomial-size circuits. While relating non-wellfoundedness to non-uniformity is a natural idea, the precise amount of irregularity, informally speaking, required to capture $\mathsf{FP/poly}$ is given by proof-level conditions novel to cyclic proof theory. Along the way, we formalise some (presumably) folklore techniques for characterising non-uniform classes in relativised function algebras with appropriate oracles. |
2310.14782 | Alexandra Volokhova | Alexandra Volokhova, Micha{\l} Koziarski, Alex Hern\'andez-Garc\'ia,
Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Al\'an
Aspuru-Guzik, Yoshua Bengio | Towards equilibrium molecular conformation generation with GFlowNets | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Sampling diverse, thermodynamically feasible molecular conformations plays a
crucial role in predicting properties of a molecule. In this paper we propose
to use GFlowNet for sampling conformations of small molecules from the
Boltzmann distribution, as determined by the molecule's energy. The proposed
approach can be used in combination with energy estimation methods of different
fidelity and discovers a diverse set of low-energy conformations for highly
flexible drug-like molecules. We demonstrate that GFlowNet can reproduce
molecular potential energy surfaces by sampling proportionally to the Boltzmann
distribution.
| [
{
"created": "Fri, 20 Oct 2023 15:41:50 GMT",
"version": "v1"
}
] | 2023-10-24 | [
[
"Volokhova",
"Alexandra",
""
],
[
"Koziarski",
"Michał",
""
],
[
"Hernández-García",
"Alex",
""
],
[
"Liu",
"Cheng-Hao",
""
],
[
"Miret",
"Santiago",
""
],
[
"Lemos",
"Pablo",
""
],
[
"Thiede",
"Luca",
""
],
[
"Yan",
"Zichao",
""
],
[
"Aspuru-Guzik",
"Alán",
""
],
[
"Bengio",
"Yoshua",
""
]
] | Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution. |
1811.08772 | Sean MacAvaney | Sean MacAvaney, Andrew Yates, Arman Cohan, Luca Soldaini, Kai Hui,
Nazli Goharian, Ophir Frieder | Overcoming low-utility facets for complex answer retrieval | This is a pre-print of an article published in Information Retrieval
Journal. The final authenticated version (including additional experimental
results, analysis, etc.) is available online at:
https://doi.org/10.1007/s10791-018-9343-0 | Information Retrieval Journal 2018 | 10.1007/s10791-018-9343-0 | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many questions cannot be answered simply; their answers must include numerous
nuanced details and additional context. Complex Answer Retrieval (CAR) is the
retrieval of answers to such questions. In their simplest form, these questions
are constructed from a topic entity (e.g., `cheese') and a facet (e.g., `health
effects'). While topic matching has been thoroughly explored, we observe that
some facets use general language that is unlikely to appear verbatim in
answers. We call these low-utility facets. In this work, we present an approach
to CAR that identifies and addresses low-utility facets. We propose two
estimators of facet utility. These include exploiting the hierarchical
structure of CAR queries and using facet frequency information from training
data. To improve the retrieval performance on low-utility headings, we also
include entity similarity scores using knowledge graph embeddings. We apply our
approaches to a leading neural ranking technique, and evaluate using the TREC
CAR dataset. We find that our approach perform significantly better than the
unmodified neural ranker and other leading CAR techniques. We also provide a
detailed analysis of our results, and verify that low-utility facets are indeed
more difficult to match, and that our approach improves the performance for
these difficult queries.
| [
{
"created": "Wed, 21 Nov 2018 15:09:00 GMT",
"version": "v1"
}
] | 2018-11-22 | [
[
"MacAvaney",
"Sean",
""
],
[
"Yates",
"Andrew",
""
],
[
"Cohan",
"Arman",
""
],
[
"Soldaini",
"Luca",
""
],
[
"Hui",
"Kai",
""
],
[
"Goharian",
"Nazli",
""
],
[
"Frieder",
"Ophir",
""
]
] | Many questions cannot be answered simply; their answers must include numerous nuanced details and additional context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. In their simplest form, these questions are constructed from a topic entity (e.g., `cheese') and a facet (e.g., `health effects'). While topic matching has been thoroughly explored, we observe that some facets use general language that is unlikely to appear verbatim in answers. We call these low-utility facets. In this work, we present an approach to CAR that identifies and addresses low-utility facets. We propose two estimators of facet utility. These include exploiting the hierarchical structure of CAR queries and using facet frequency information from training data. To improve the retrieval performance on low-utility headings, we also include entity similarity scores using knowledge graph embeddings. We apply our approaches to a leading neural ranking technique, and evaluate using the TREC CAR dataset. We find that our approach perform significantly better than the unmodified neural ranker and other leading CAR techniques. We also provide a detailed analysis of our results, and verify that low-utility facets are indeed more difficult to match, and that our approach improves the performance for these difficult queries. |
2012.03682 | Elnaz Soleimani | Elnaz Soleimani, Ghazaleh Khodabandelou, Abdelghani Chibani, Yacine
Amirat | Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based
Human Activity Recognition | null | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The performance of Human Activity Recognition (HAR) models, particularly deep
neural networks, is highly contingent upon the availability of the massive
amount of annotated training data which should be sufficiently labeled. Though,
data acquisition and manual annotation in the HAR domain are prohibitively
expensive due to skilled human resource requirements in both steps. Hence,
domain adaptation techniques have been proposed to adapt the knowledge from the
existing source of data. More recently, adversarial transfer learning methods
have shown very promising results in image classification, yet limited for
sensor-based HAR problems, which are still prone to the unfavorable effects of
the imbalanced distribution of samples. This paper presents a novel generic and
robust approach for semi-supervised domain adaptation in HAR, which capitalizes
on the advantages of the adversarial framework to tackle the shortcomings, by
leveraging knowledge from annotated samples exclusively from the source subject
and unlabeled ones of the target subject. Extensive subject translation
experiments are conducted on three large, middle, and small-size datasets with
different levels of imbalance to assess the robustness and effectiveness of the
proposed model to the scale as well as imbalance in the data. The results
demonstrate the effectiveness of our proposed algorithms over state-of-the-art
methods, which led in up to 13%, 4%, and 13% improvement of our high-level
activities recognition metrics for Opportunity, LISSI, and PAMAP2 datasets,
respectively. The LISSI dataset is the most challenging one owing to its less
populated and imbalanced distribution. Compared to the SA-GAN adversarial
domain adaptation method, the proposed approach enhances the final
classification performance with an average of 7.5% for the three datasets,
which emphasizes the effectiveness of micro-mini-batch training.
| [
{
"created": "Wed, 11 Nov 2020 12:16:23 GMT",
"version": "v1"
}
] | 2020-12-08 | [
[
"Soleimani",
"Elnaz",
""
],
[
"Khodabandelou",
"Ghazaleh",
""
],
[
"Chibani",
"Abdelghani",
""
],
[
"Amirat",
"Yacine",
""
]
] | The performance of Human Activity Recognition (HAR) models, particularly deep neural networks, is highly contingent upon the availability of the massive amount of annotated training data which should be sufficiently labeled. Though, data acquisition and manual annotation in the HAR domain are prohibitively expensive due to skilled human resource requirements in both steps. Hence, domain adaptation techniques have been proposed to adapt the knowledge from the existing source of data. More recently, adversarial transfer learning methods have shown very promising results in image classification, yet limited for sensor-based HAR problems, which are still prone to the unfavorable effects of the imbalanced distribution of samples. This paper presents a novel generic and robust approach for semi-supervised domain adaptation in HAR, which capitalizes on the advantages of the adversarial framework to tackle the shortcomings, by leveraging knowledge from annotated samples exclusively from the source subject and unlabeled ones of the target subject. Extensive subject translation experiments are conducted on three large, middle, and small-size datasets with different levels of imbalance to assess the robustness and effectiveness of the proposed model to the scale as well as imbalance in the data. The results demonstrate the effectiveness of our proposed algorithms over state-of-the-art methods, which led in up to 13%, 4%, and 13% improvement of our high-level activities recognition metrics for Opportunity, LISSI, and PAMAP2 datasets, respectively. The LISSI dataset is the most challenging one owing to its less populated and imbalanced distribution. Compared to the SA-GAN adversarial domain adaptation method, the proposed approach enhances the final classification performance with an average of 7.5% for the three datasets, which emphasizes the effectiveness of micro-mini-batch training. |
2211.06153 | Sareena Karapoola | Sareena Karapoola, Nikhilesh Singh, Chester Rebeiro, Kamakoti V | SUNDEW: An Ensemble of Predictors for Case-Sensitive Detection of
Malware | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Malware programs are diverse, with varying objectives, functionalities, and
threat levels ranging from mere pop-ups to financial losses. Consequently,
their run-time footprints across the system differ, impacting the optimal data
source (Network, Operating system (OS), Hardware) and features that are
instrumental to malware detection. Further, the variations in threat levels of
malware classes affect the user requirements for detection. Thus, the optimal
tuple of <data-source, features, user-requirements> is different for each
malware class, impacting the state-of-the-art detection solutions that are
agnostic to these subtle differences.
This paper presents SUNDEW, a framework to detect malware classes using their
optimal tuple of <data-source, features, user-requirements>. SUNDEW uses an
ensemble of specialized predictors, each trained with a particular data source
(network, OS, and hardware) and tuned for features and requirements of a
specific class. While the specialized ensemble with a holistic view across the
system improves detection, aggregating the independent conflicting inferences
from the different predictors is challenging. SUNDEW resolves such conflicts
with a hierarchical aggregation considering the threat-level, noise in the data
sources, and prior domain knowledge. We evaluate SUNDEW on a real-world dataset
of over 10,000 malware samples from 8 classes. It achieves an F1-Score of one
for most classes, with an average of 0.93 and a limited performance overhead of
1.5%.
| [
{
"created": "Fri, 11 Nov 2022 12:13:41 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Nov 2022 08:49:24 GMT",
"version": "v2"
}
] | 2022-11-15 | [
[
"Karapoola",
"Sareena",
""
],
[
"Singh",
"Nikhilesh",
""
],
[
"Rebeiro",
"Chester",
""
],
[
"V",
"Kamakoti",
""
]
] | Malware programs are diverse, with varying objectives, functionalities, and threat levels ranging from mere pop-ups to financial losses. Consequently, their run-time footprints across the system differ, impacting the optimal data source (Network, Operating system (OS), Hardware) and features that are instrumental to malware detection. Further, the variations in threat levels of malware classes affect the user requirements for detection. Thus, the optimal tuple of <data-source, features, user-requirements> is different for each malware class, impacting the state-of-the-art detection solutions that are agnostic to these subtle differences. This paper presents SUNDEW, a framework to detect malware classes using their optimal tuple of <data-source, features, user-requirements>. SUNDEW uses an ensemble of specialized predictors, each trained with a particular data source (network, OS, and hardware) and tuned for features and requirements of a specific class. While the specialized ensemble with a holistic view across the system improves detection, aggregating the independent conflicting inferences from the different predictors is challenging. SUNDEW resolves such conflicts with a hierarchical aggregation considering the threat-level, noise in the data sources, and prior domain knowledge. We evaluate SUNDEW on a real-world dataset of over 10,000 malware samples from 8 classes. It achieves an F1-Score of one for most classes, with an average of 0.93 and a limited performance overhead of 1.5%. |
1507.02563 | Wen Shen | Wen Shen and Cristina Lopes | Managing Autonomous Mobility on Demand Systems for Better Passenger
Experience | null | Proceedings of the 18th International Conference on Principles and
Practice of Multi-Agent Systems (PRIMA 2015). pp 20-35. Lecture Notes in
Computer Science, vol 9387. Springer | 10.1007/978-3-319-25524-8_2 | null | cs.AI cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous mobility on demand systems, though still in their infancy, have
very promising prospects in providing urban population with sustainable and
safe personal mobility in the near future. While much research has been
conducted on both autonomous vehicles and mobility on demand systems, to the
best of our knowledge, this is the first work that shows how to manage
autonomous mobility on demand systems for better passenger experience. We
introduce the Expand and Target algorithm which can be easily integrated with
three different scheduling strategies for dispatching autonomous vehicles. We
implement an agent-based simulation platform and empirically evaluate the
proposed approaches with the New York City taxi data. Experimental results
demonstrate that the algorithm significantly improve passengers' experience by
reducing the average passenger waiting time by up to 29.82% and increasing the
trip success rate by up to 7.65%.
| [
{
"created": "Thu, 9 Jul 2015 15:43:17 GMT",
"version": "v1"
}
] | 2017-11-23 | [
[
"Shen",
"Wen",
""
],
[
"Lopes",
"Cristina",
""
]
] | Autonomous mobility on demand systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future. While much research has been conducted on both autonomous vehicles and mobility on demand systems, to the best of our knowledge, this is the first work that shows how to manage autonomous mobility on demand systems for better passenger experience. We introduce the Expand and Target algorithm which can be easily integrated with three different scheduling strategies for dispatching autonomous vehicles. We implement an agent-based simulation platform and empirically evaluate the proposed approaches with the New York City taxi data. Experimental results demonstrate that the algorithm significantly improve passengers' experience by reducing the average passenger waiting time by up to 29.82% and increasing the trip success rate by up to 7.65%. |
2011.12713 | Nima Safari | N. Safari, S.M. Mazhari, C.Y. Chung, S.B. Ko | A Secure Deep Probabilistic Dynamic Thermal Line Rating Prediction | The work is accepted for publication in Journal of Modern Power
Systems and Clean Energy | null | null | null | cs.CR cs.LG eess.SP | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Accurate short-term prediction of overhead line (OHL) transmission ampacity
can directly affect the efficiency of power system operation and planning. Any
overestimation of the dynamic thermal line rating (DTLR) can lead to lifetime
degradation and failure of OHLs, safety hazards, etc. This paper presents a
secure yet sharp probabilistic prediction model for the hour-ahead forecasting
of the DTLR. The security of the proposed DTLR limits the frequency of DTLR
prediction exceeding the actual DTLR. The model is based on an augmented deep
learning architecture that makes use of a wide range of predictors, including
historical climatology data and latent variables obtained during DTLR
calculation. Furthermore, by introducing a customized cost function, the deep
neural network is trained to consider the DTLR security based on the required
probability of exceedance while minimizing deviations of the predicted DTLRs
from the actual values. The proposed probabilistic DTLR is developed and
verified using recorded experimental data. The simulation results validate the
superiority of the proposed DTLR compared to state-of-the-art prediction models
using well-known evaluation metrics.
| [
{
"created": "Sat, 21 Nov 2020 23:20:58 GMT",
"version": "v1"
}
] | 2020-11-26 | [
[
"Safari",
"N.",
""
],
[
"Mazhari",
"S. M.",
""
],
[
"Chung",
"C. Y.",
""
],
[
"Ko",
"S. B.",
""
]
] | Accurate short-term prediction of overhead line (OHL) transmission ampacity can directly affect the efficiency of power system operation and planning. Any overestimation of the dynamic thermal line rating (DTLR) can lead to lifetime degradation and failure of OHLs, safety hazards, etc. This paper presents a secure yet sharp probabilistic prediction model for the hour-ahead forecasting of the DTLR. The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR. The model is based on an augmented deep learning architecture that makes use of a wide range of predictors, including historical climatology data and latent variables obtained during DTLR calculation. Furthermore, by introducing a customized cost function, the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing deviations of the predicted DTLRs from the actual values. The proposed probabilistic DTLR is developed and verified using recorded experimental data. The simulation results validate the superiority of the proposed DTLR compared to state-of-the-art prediction models using well-known evaluation metrics. |
2002.10732 | Aamir Mahmood | Luca Beltramelli, Aamir Mahmood, Patrik \"Osterberg, and Mikael
Gidlund | LoRa beyond ALOHA: An Investigation of Alternative Random Access
Protocols | 10 pages, 9 figures, final version to appear in IEEE Transactions on
Industrial Informatics | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a stochastic geometry-based model to investigate alternative
medium access choices for LoRaWAN---a widely adopted low-power wide-area
networking (LPWAN) technology for the Internet-of-things (IoT). LoRaWAN
adoption is driven by its simplified network architecture, air interface, and
medium access. The physical layer, known as LoRa, provides quasi-orthogonal
virtual channels through spreading factors (SFs) and time-power capture gains.
However, the adopted pure ALOHA access mechanism suffers, in terms of
scalability, under the same-channel same-SF transmissions from a large number
of devices. In this paper, our objective is to explore access mechanisms
beyond-ALOHA for LoRaWAN. Using recent results on time- and power-capture
effects of LoRa, we develop a unified model for the comparative study of other
choices, i.e., slotted ALOHA and carrier-sense multiple access (CSMA). The
model includes the necessary design parameters of these access mechanisms, such
as guard time and synchronization accuracy for slotted ALOHA, carrier sensing
threshold for CSMA. It also accounts for the spatial interaction of devices in
annular-shaped regions, characteristic of LoRa, for CSMA. The performance
derived from the model in terms of coverage probability, channel throughput,
and energy efficiency are validated using Monte-Carlo simulations. Our analysis
shows that slotted ALOHA indeed has higher reliability than pure ALOHA but at
the cost of lower energy efficiency for low device densities. Whereas, CSMA
outperforms slotted ALOHA at smaller SFs in terms of reliability and energy
efficiency, with its performance degrading to pure ALOHA at higher SFs.
| [
{
"created": "Tue, 25 Feb 2020 08:36:05 GMT",
"version": "v1"
}
] | 2020-02-26 | [
[
"Beltramelli",
"Luca",
""
],
[
"Mahmood",
"Aamir",
""
],
[
"Österberg",
"Patrik",
""
],
[
"Gidlund",
"Mikael",
""
]
] | We present a stochastic geometry-based model to investigate alternative medium access choices for LoRaWAN---a widely adopted low-power wide-area networking (LPWAN) technology for the Internet-of-things (IoT). LoRaWAN adoption is driven by its simplified network architecture, air interface, and medium access. The physical layer, known as LoRa, provides quasi-orthogonal virtual channels through spreading factors (SFs) and time-power capture gains. However, the adopted pure ALOHA access mechanism suffers, in terms of scalability, under the same-channel same-SF transmissions from a large number of devices. In this paper, our objective is to explore access mechanisms beyond-ALOHA for LoRaWAN. Using recent results on time- and power-capture effects of LoRa, we develop a unified model for the comparative study of other choices, i.e., slotted ALOHA and carrier-sense multiple access (CSMA). The model includes the necessary design parameters of these access mechanisms, such as guard time and synchronization accuracy for slotted ALOHA, carrier sensing threshold for CSMA. It also accounts for the spatial interaction of devices in annular-shaped regions, characteristic of LoRa, for CSMA. The performance derived from the model in terms of coverage probability, channel throughput, and energy efficiency are validated using Monte-Carlo simulations. Our analysis shows that slotted ALOHA indeed has higher reliability than pure ALOHA but at the cost of lower energy efficiency for low device densities. Whereas, CSMA outperforms slotted ALOHA at smaller SFs in terms of reliability and energy efficiency, with its performance degrading to pure ALOHA at higher SFs. |
2106.04835 | Zichuan Lin | Zichuan Lin, Jing Huang, Bowen Zhou, Xiaodong He, Tengyu Ma | Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System | 13 pages | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on
dialog systems and found that improvement on individual components (e.g., NLU,
policy) in prior work may not necessarily bring benefit to pipeline systems in
system-wise evaluation. To improve the system-wise performance, in this paper,
we propose new joint system-wise optimization techniques for the pipeline
dialog system. First, we propose a new data augmentation approach which
automates the labeling process for NLU training. Second, we propose a novel
stochastic policy parameterization with Poisson distribution that enables
better exploration and offers a principled way to compute policy gradient.
Third, we propose a reward bonus to help policy explore successful dialogs. Our
approaches outperform the competitive pipeline systems from Takanobu et al.
(2020) by big margins of 12% success rate in automatic system-wise evaluation
and of 16% success rate in human evaluation on the standard multi-domain
benchmark dataset MultiWOZ 2.1, and also outperform the recent state-of-the-art
end-to-end trained model from DSTC9.
| [
{
"created": "Wed, 9 Jun 2021 06:44:57 GMT",
"version": "v1"
}
] | 2021-06-10 | [
[
"Lin",
"Zichuan",
""
],
[
"Huang",
"Jing",
""
],
[
"Zhou",
"Bowen",
""
],
[
"He",
"Xiaodong",
""
],
[
"Ma",
"Tengyu",
""
]
] | Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e.g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation. To improve the system-wise performance, in this paper, we propose new joint system-wise optimization techniques for the pipeline dialog system. First, we propose a new data augmentation approach which automates the labeling process for NLU training. Second, we propose a novel stochastic policy parameterization with Poisson distribution that enables better exploration and offers a principled way to compute policy gradient. Third, we propose a reward bonus to help policy explore successful dialogs. Our approaches outperform the competitive pipeline systems from Takanobu et al. (2020) by big margins of 12% success rate in automatic system-wise evaluation and of 16% success rate in human evaluation on the standard multi-domain benchmark dataset MultiWOZ 2.1, and also outperform the recent state-of-the-art end-to-end trained model from DSTC9. |
2310.16135 | Chenghao Yang | Chenghao Yang, Allyson Ettinger | Can You Follow Me? Testing Situational Understanding in ChatGPT | EMNLP 2023 Main Paper (Camera Ready) | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Understanding sentence meanings and updating information states appropriately
across time -- what we call "situational understanding" (SU) -- is a critical
ability for human-like AI agents. SU is essential in particular for chat
models, such as ChatGPT, to enable consistent, coherent, and effective dialogue
between humans and AI. Previous works have identified certain SU limitations in
non-chatbot Large Language models (LLMs), but the extent and causes of these
limitations are not well understood, and capabilities of current chat-based
models in this domain have not been explored. In this work we tackle these
questions, proposing a novel synthetic environment for SU testing which allows
us to do controlled and systematic testing of SU in chat-oriented models,
through assessment of models' ability to track and enumerate environment
states. Our environment also allows for close analysis of dynamics of model
performance, to better understand underlying causes for performance patterns.
We apply our test to ChatGPT, the state-of-the-art chatbot, and find that
despite the fundamental simplicity of the task, the model's performance
reflects an inability to retain correct environment states across time. Our
follow-up analyses suggest that performance degradation is largely because
ChatGPT has non-persistent in-context memory (although it can access the full
dialogue history) and it is susceptible to hallucinated updates -- including
updates that artificially inflate accuracies. Our findings suggest overall that
ChatGPT is not currently equipped for robust tracking of situation states, and
that trust in the impressive dialogue performance of ChatGPT comes with risks.
We release the codebase for reproducing our test environment, as well as all
prompts and API responses from ChatGPT, at
https://github.com/yangalan123/SituationalTesting.
| [
{
"created": "Tue, 24 Oct 2023 19:22:01 GMT",
"version": "v1"
}
] | 2023-10-26 | [
[
"Yang",
"Chenghao",
""
],
[
"Ettinger",
"Allyson",
""
]
] | Understanding sentence meanings and updating information states appropriately across time -- what we call "situational understanding" (SU) -- is a critical ability for human-like AI agents. SU is essential in particular for chat models, such as ChatGPT, to enable consistent, coherent, and effective dialogue between humans and AI. Previous works have identified certain SU limitations in non-chatbot Large Language models (LLMs), but the extent and causes of these limitations are not well understood, and capabilities of current chat-based models in this domain have not been explored. In this work we tackle these questions, proposing a novel synthetic environment for SU testing which allows us to do controlled and systematic testing of SU in chat-oriented models, through assessment of models' ability to track and enumerate environment states. Our environment also allows for close analysis of dynamics of model performance, to better understand underlying causes for performance patterns. We apply our test to ChatGPT, the state-of-the-art chatbot, and find that despite the fundamental simplicity of the task, the model's performance reflects an inability to retain correct environment states across time. Our follow-up analyses suggest that performance degradation is largely because ChatGPT has non-persistent in-context memory (although it can access the full dialogue history) and it is susceptible to hallucinated updates -- including updates that artificially inflate accuracies. Our findings suggest overall that ChatGPT is not currently equipped for robust tracking of situation states, and that trust in the impressive dialogue performance of ChatGPT comes with risks. We release the codebase for reproducing our test environment, as well as all prompts and API responses from ChatGPT, at https://github.com/yangalan123/SituationalTesting. |
2209.08189 | Andreas Mang | Naveen Himthani and Malte Brunn and Jae-Youn Kim and Miriam Schulte
and Andreas Mang and George Biros | CLAIRE -- Parallelized Diffeomorphic Image Registration for Large-Scale
Biomedical Imaging Applications | 32 pages, 9 tables, 8 figures | null | null | null | cs.CV cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the performance of CLAIRE -- a diffeomorphic multi-node, multi-GPU
image-registration algorithm, and software -- in large-scale biomedical imaging
applications with billions of voxels. At such resolutions, most existing
software packages for diffeomorphic image registration are prohibitively
expensive. As a result, practitioners first significantly downsample the
original images and then register them using existing tools. Our main
contribution is an extensive analysis of the impact of downsampling on
registration performance. We study this impact by comparing full-resolution
registrations obtained with CLAIRE to lower-resolution registrations for
synthetic and real-world imaging datasets. Our results suggest that
registration at full resolution can yield a superior registration quality --
but not always. For example, downsampling a synthetic image from $1024^3$ to
$256^3$ decreases the Dice coefficient from 92% to 79%. However, the
differences are less pronounced for noisy or low-contrast high-resolution
images. CLAIRE allows us not only to register images of clinically relevant
size in a few seconds but also to register images at unprecedented resolution
in a reasonable time. The highest resolution considered is CLARITY images of
size $2816\times3016\times1162$. To the best of our knowledge, this is the
first study on image registration quality at such resolutions.
| [
{
"created": "Fri, 16 Sep 2022 22:42:24 GMT",
"version": "v1"
}
] | 2022-09-20 | [
[
"Himthani",
"Naveen",
""
],
[
"Brunn",
"Malte",
""
],
[
"Kim",
"Jae-Youn",
""
],
[
"Schulte",
"Miriam",
""
],
[
"Mang",
"Andreas",
""
],
[
"Biros",
"George",
""
]
] | We study the performance of CLAIRE -- a diffeomorphic multi-node, multi-GPU image-registration algorithm, and software -- in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower-resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality -- but not always. For example, downsampling a synthetic image from $1024^3$ to $256^3$ decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low-contrast high-resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in a reasonable time. The highest resolution considered is CLARITY images of size $2816\times3016\times1162$. To the best of our knowledge, this is the first study on image registration quality at such resolutions. |
2208.08820 | Jiawei Li | Jiawei Li, Ru Zhang, Jianyi Liu, Gongshen Liu | LogKernel A Threat Hunting Approach Based on Behaviour Provenance Graph
and Graph Kernel Clustering | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | Cyber threat hunting is a proactive search process for hidden threats in the
organization's information system. It is a crucial component of active defense
against advanced persistent threats (APTs). However, most of the current threat
hunting methods rely on Cyber Threat Intelligence(CTI), which can find known
attacks but cannot find unknown attacks that have not been disclosed by CTI. In
this paper, we propose LogKernel, a threat hunting method based on graph kernel
clustering which can effectively separates attack behaviour from benign
activities. LogKernel first abstracts system audit logs into Behaviour
Provenance Graphs (BPGs), and then clusters graphs by embedding them into a
continuous space using a graph kernel. In particular, we design a new graph
kernel clustering method based on the characteristics of BPGs, which can
capture structure information and rich label information of the BPGs. To reduce
false positives, LogKernel further quantifies the threat of abnormal behaviour.
We evaluate LogKernel on the malicious dataset which includes seven simulated
attack scenarios and the DAPRA CADETS dataset which includes four attack
scenarios. The result shows that LogKernel can hunt all attack scenarios among
them, and compared to the state-of-the-art methods, it can find unknown
attacks.
| [
{
"created": "Thu, 18 Aug 2022 13:28:19 GMT",
"version": "v1"
}
] | 2022-08-19 | [
[
"Li",
"Jiawei",
""
],
[
"Zhang",
"Ru",
""
],
[
"Liu",
"Jianyi",
""
],
[
"Liu",
"Gongshen",
""
]
] | Cyber threat hunting is a proactive search process for hidden threats in the organization's information system. It is a crucial component of active defense against advanced persistent threats (APTs). However, most of the current threat hunting methods rely on Cyber Threat Intelligence(CTI), which can find known attacks but cannot find unknown attacks that have not been disclosed by CTI. In this paper, we propose LogKernel, a threat hunting method based on graph kernel clustering which can effectively separates attack behaviour from benign activities. LogKernel first abstracts system audit logs into Behaviour Provenance Graphs (BPGs), and then clusters graphs by embedding them into a continuous space using a graph kernel. In particular, we design a new graph kernel clustering method based on the characteristics of BPGs, which can capture structure information and rich label information of the BPGs. To reduce false positives, LogKernel further quantifies the threat of abnormal behaviour. We evaluate LogKernel on the malicious dataset which includes seven simulated attack scenarios and the DAPRA CADETS dataset which includes four attack scenarios. The result shows that LogKernel can hunt all attack scenarios among them, and compared to the state-of-the-art methods, it can find unknown attacks. |
0705.1364 | Mustaq Ahmed | Mustaq Ahmed and Anna Lubiw | An Approximation Algorithm for Shortest Descending Paths | 14 pages, 3 figures | null | null | CS-2007-14 | cs.CG cs.DS | null | A path from s to t on a polyhedral terrain is descending if the height of a
point p never increases while we move p along the path from s to t. No
efficient algorithm is known to find a shortest descending path (SDP) from s to
t in a polyhedral terrain. We give a simple approximation algorithm that solves
the SDP problem on general terrains. Our algorithm discretizes the terrain with
O(n^2 X / e) Steiner points so that after an O(n^2 X / e * log(n X /e))-time
preprocessing phase for a given vertex s, we can determine a (1+e)-approximate
SDP from s to any point v in O(n) time if v is either a vertex of the terrain
or a Steiner point, and in O(n X /e) time otherwise. Here n is the size of the
terrain, and X is a parameter of the geometry of the terrain.
| [
{
"created": "Wed, 9 May 2007 22:02:28 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ahmed",
"Mustaq",
""
],
[
"Lubiw",
"Anna",
""
]
] | A path from s to t on a polyhedral terrain is descending if the height of a point p never increases while we move p along the path from s to t. No efficient algorithm is known to find a shortest descending path (SDP) from s to t in a polyhedral terrain. We give a simple approximation algorithm that solves the SDP problem on general terrains. Our algorithm discretizes the terrain with O(n^2 X / e) Steiner points so that after an O(n^2 X / e * log(n X /e))-time preprocessing phase for a given vertex s, we can determine a (1+e)-approximate SDP from s to any point v in O(n) time if v is either a vertex of the terrain or a Steiner point, and in O(n X /e) time otherwise. Here n is the size of the terrain, and X is a parameter of the geometry of the terrain. |
1906.07630 | Ankur Kulkarni | Karan N. Chadha and Ankur A. Kulkarni | Aggregate Play and Welfare in Strategic Interactions on Networks | null | null | null | null | cs.GT math.CO math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent work by Bramoull\'{e} and Kranton, a model for the provision of
public goods on a network was presented and relations between equilibria of
such a game and properties of the network were established. This model was
further extended to include games with imperfect substitutability in
Bramoull\'{e} et al. The vast multiplicity of equilibria in such games along
with the drastic changes in equilibria with small changes in network structure,
makes it challenging for a system planner to estimate the maximum social
welfare of such a game or to devise interventions that enhance this welfare.
Our main results address this challenge by providing close approximations to
the maximum social welfare and the maximum aggregate play in terms of only
network characteristics such as the maximum degree and independence number. For
the special case when the underlying network is a tree, we derive formulae
which use only the number of nodes and their degrees. These results allow a
system planner to assess aggregate outcomes and design interventions for the
game, directly from the underlying graph structure, without enumerating all
equilibria of the game, thereby significantly simplifying the planner's
problem. A part of our results can be viewed as a logical extension of [7]
where the maximum weighted aggregate effort of the model in [2] was
characterized as the weighted independence number of the graph.
| [
{
"created": "Tue, 18 Jun 2019 15:06:07 GMT",
"version": "v1"
}
] | 2019-06-19 | [
[
"Chadha",
"Karan N.",
""
],
[
"Kulkarni",
"Ankur A.",
""
]
] | In recent work by Bramoull\'{e} and Kranton, a model for the provision of public goods on a network was presented and relations between equilibria of such a game and properties of the network were established. This model was further extended to include games with imperfect substitutability in Bramoull\'{e} et al. The vast multiplicity of equilibria in such games along with the drastic changes in equilibria with small changes in network structure, makes it challenging for a system planner to estimate the maximum social welfare of such a game or to devise interventions that enhance this welfare. Our main results address this challenge by providing close approximations to the maximum social welfare and the maximum aggregate play in terms of only network characteristics such as the maximum degree and independence number. For the special case when the underlying network is a tree, we derive formulae which use only the number of nodes and their degrees. These results allow a system planner to assess aggregate outcomes and design interventions for the game, directly from the underlying graph structure, without enumerating all equilibria of the game, thereby significantly simplifying the planner's problem. A part of our results can be viewed as a logical extension of [7] where the maximum weighted aggregate effort of the model in [2] was characterized as the weighted independence number of the graph. |
2206.07538 | Javier Laplaza | Javier Laplaza, Joan Jaume Oliver, Ram\'on Romero, Alberto Sanfeliu
and Ana\'is Garrell | Body Gesture Recognition to Control a Social Robot | null | null | null | null | cs.RO cs.CV cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this work, we propose a gesture based language to allow humans to interact
with robots using their body in a natural way. We have created a new gesture
detection model using neural networks and a custom dataset of humans performing
a set of body gestures to train our network. Furthermore, we compare body
gesture communication with other communication channels to acknowledge the
importance of adding this knowledge to robots. The presented approach is
extensively validated in diverse simulations and real-life experiments with
non-trained volunteers. This attains remarkable results and shows that it is a
valuable framework for social robotics applications, such as human robot
collaboration or human-robot interaction.
| [
{
"created": "Wed, 15 Jun 2022 13:49:22 GMT",
"version": "v1"
}
] | 2022-06-16 | [
[
"Laplaza",
"Javier",
""
],
[
"Oliver",
"Joan Jaume",
""
],
[
"Romero",
"Ramón",
""
],
[
"Sanfeliu",
"Alberto",
""
],
[
"Garrell",
"Anaís",
""
]
] | In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network. Furthermore, we compare body gesture communication with other communication channels to acknowledge the importance of adding this knowledge to robots. The presented approach is extensively validated in diverse simulations and real-life experiments with non-trained volunteers. This attains remarkable results and shows that it is a valuable framework for social robotics applications, such as human robot collaboration or human-robot interaction. |
2004.11055 | Alma Rahat PhD | Alma Rahat and Michael Wood | On Bayesian Search for the Feasible Space Under Computationally
Expensive Constraints | Accepted at The Sixth International Conference on Machine Learning,
Optimization, and Data Science. Main content 12 pages, a total of 19 pages
with supplementary. 3 Figures and 2 tables. Python code for Bayesian search
is available at: http://bitbucket.org/arahat/lod-2020 | null | null | null | cs.LG cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are often interested in identifying the feasible subset of a decision
space under multiple constraints to permit effective design exploration. If
determining feasibility required computationally expensive simulations, the
cost of exploration would be prohibitive. Bayesian search is data-efficient for
such problems: starting from a small dataset, the central concept is to use
Bayesian models of constraints with an acquisition function to locate promising
solutions that may improve predictions of feasibility when the dataset is
augmented. At the end of this sequential active learning approach with a
limited number of expensive evaluations, the models can accurately predict the
feasibility of any solution obviating the need for full simulations. In this
paper, we propose a novel acquisition function that combines the probability
that a solution lies at the boundary between feasible and infeasible spaces
(representing exploitation) and the entropy in predictions (representing
exploration). Experiments confirmed the efficacy of the proposed function.
| [
{
"created": "Thu, 23 Apr 2020 10:22:32 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Jun 2020 12:00:05 GMT",
"version": "v2"
}
] | 2020-06-25 | [
[
"Rahat",
"Alma",
""
],
[
"Wood",
"Michael",
""
]
] | We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective design exploration. If determining feasibility required computationally expensive simulations, the cost of exploration would be prohibitive. Bayesian search is data-efficient for such problems: starting from a small dataset, the central concept is to use Bayesian models of constraints with an acquisition function to locate promising solutions that may improve predictions of feasibility when the dataset is augmented. At the end of this sequential active learning approach with a limited number of expensive evaluations, the models can accurately predict the feasibility of any solution obviating the need for full simulations. In this paper, we propose a novel acquisition function that combines the probability that a solution lies at the boundary between feasible and infeasible spaces (representing exploitation) and the entropy in predictions (representing exploration). Experiments confirmed the efficacy of the proposed function. |
2208.02235 | Roman Orus | Raj Patel, Chia-Wei Hsing, Serkan Sahin, Saeed S. Jahromi, Samuel
Palmer, Shivam Sharma, Christophe Michel, Vincent Porte, Mustafa Abid,
Stephane Aubert, Pierre Castellani, Chi-Guhn Lee, Samuel Mugel, Roman Orus | Quantum-Inspired Tensor Neural Networks for Partial Differential
Equations | 14 pages, 11 figures, minimal changes | null | null | null | cs.LG cond-mat.str-el cs.AI physics.comp-ph quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partial Differential Equations (PDEs) are used to model a variety of
dynamical systems in science and engineering. Recent advances in deep learning
have enabled us to solve them in a higher dimension by addressing the curse of
dimensionality in new ways. However, deep learning methods are constrained by
training time and memory. To tackle these shortcomings, we implement Tensor
Neural Networks (TNN), a quantum-inspired neural network architecture that
leverages Tensor Network ideas to improve upon deep learning approaches. We
demonstrate that TNN provide significant parameter savings while attaining the
same accuracy as compared to the classical Dense Neural Network (DNN). In
addition, we also show how TNN can be trained faster than DNN for the same
accuracy. We benchmark TNN by applying them to solve parabolic PDEs,
specifically the Black-Scholes-Barenblatt equation, widely used in financial
pricing theory, empirically showing the advantages of TNN over DNN. Further
examples, such as the Hamilton-Jacobi-Bellman equation, are also discussed.
| [
{
"created": "Wed, 3 Aug 2022 17:41:11 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Aug 2022 08:07:10 GMT",
"version": "v2"
}
] | 2022-08-11 | [
[
"Patel",
"Raj",
""
],
[
"Hsing",
"Chia-Wei",
""
],
[
"Sahin",
"Serkan",
""
],
[
"Jahromi",
"Saeed S.",
""
],
[
"Palmer",
"Samuel",
""
],
[
"Sharma",
"Shivam",
""
],
[
"Michel",
"Christophe",
""
],
[
"Porte",
"Vincent",
""
],
[
"Abid",
"Mustafa",
""
],
[
"Aubert",
"Stephane",
""
],
[
"Castellani",
"Pierre",
""
],
[
"Lee",
"Chi-Guhn",
""
],
[
"Mugel",
"Samuel",
""
],
[
"Orus",
"Roman",
""
]
] | Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of dimensionality in new ways. However, deep learning methods are constrained by training time and memory. To tackle these shortcomings, we implement Tensor Neural Networks (TNN), a quantum-inspired neural network architecture that leverages Tensor Network ideas to improve upon deep learning approaches. We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. We benchmark TNN by applying them to solve parabolic PDEs, specifically the Black-Scholes-Barenblatt equation, widely used in financial pricing theory, empirically showing the advantages of TNN over DNN. Further examples, such as the Hamilton-Jacobi-Bellman equation, are also discussed. |
1905.05253 | Alexander Kott | Michael J. De Lucia, Allison Newcomb, Alexander Kott | Features and Operation of an Autonomous Agent for Cyber Defense | null | CSIAC Journal, v.7, n.1, April 2019, pp.6-13 | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ever increasing number of battlefield devices that are capable of
collecting, processing, storing, and communicating information are rapidly
becoming interconnected. The staggering number of connected devices on the
battlefield greatly increases the possibility that an adversary could find ways
to exploit hardware or software vulnerabilities, degrading or denying
Warfighters the assured and secure use of those devices. Autonomous software
agents will become necessities to manage, defend, and react to cyber threats in
the future battlespace. The number of connected devices increases
disproportionately to the number of cyber experts that could be available
within an operational environment. In this paper, an autonomous agent
capability and a scenario of how it could operate are proposed. The goal of
developing such capability is to increase the security posture of the Internet
of Battlefield Things and meet the challenges of an increasingly complex
battlefield. This paper describes an illustrative scenario in a notional use
case and discusses the challenges associated with such autonomous agents. We
conclude by offering ideas for potential research into developing autonomous
agents suitable for cyber defense in a battlefield environment.
| [
{
"created": "Mon, 13 May 2019 19:18:25 GMT",
"version": "v1"
}
] | 2019-05-15 | [
[
"De Lucia",
"Michael J.",
""
],
[
"Newcomb",
"Allison",
""
],
[
"Kott",
"Alexander",
""
]
] | An ever increasing number of battlefield devices that are capable of collecting, processing, storing, and communicating information are rapidly becoming interconnected. The staggering number of connected devices on the battlefield greatly increases the possibility that an adversary could find ways to exploit hardware or software vulnerabilities, degrading or denying Warfighters the assured and secure use of those devices. Autonomous software agents will become necessities to manage, defend, and react to cyber threats in the future battlespace. The number of connected devices increases disproportionately to the number of cyber experts that could be available within an operational environment. In this paper, an autonomous agent capability and a scenario of how it could operate are proposed. The goal of developing such capability is to increase the security posture of the Internet of Battlefield Things and meet the challenges of an increasingly complex battlefield. This paper describes an illustrative scenario in a notional use case and discusses the challenges associated with such autonomous agents. We conclude by offering ideas for potential research into developing autonomous agents suitable for cyber defense in a battlefield environment. |
2306.01072 | Niloy Saha | Niloy Saha, Nashid Shahriar, Raouf Boutaba and Aladdin Saleh | MonArch: Network Slice Monitoring Architecture for Cloud Native 5G
Deployments | Accepted at IEEE/IFIP NOMS 2023 | null | null | null | cs.NI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Automated decision making algorithms are expected to play a key role in
management and orchestration of network slices in 5G and beyond networks.
State-of-the-art algorithms for automated orchestration and management tend to
rely on data-driven methods which require a timely and accurate view of the
network. Accurately monitoring an end-to-end (E2E) network slice requires a
scalable monitoring architecture that facilitates collection and correlation of
data from various network segments comprising the slice. The state-of-the-art
on 5G monitoring mostly focuses on scalability, falling short in providing
explicit support for network slicing and computing network slice key
performance indicators (KPIs). To fill this gap, in this paper, we present
MonArch, a scalable monitoring architecture for 5G, which focuses on network
slice monitoring, slice KPI computation, and an application programming
interface (API) for specifying slice monitoring requests. We validate the
proposed architecture by implementing MonArch on a 5G testbed, and demonstrate
its capability to compute a network slice KPI (e.g., slice throughput). Our
evaluations show that MonArch does not significantly increase data ingestion
time when scaling the number of slices and that a 5-second monitoring interval
offers a good balance between monitoring overhead and accuracy.
| [
{
"created": "Thu, 1 Jun 2023 18:19:12 GMT",
"version": "v1"
}
] | 2023-06-05 | [
[
"Saha",
"Niloy",
""
],
[
"Shahriar",
"Nashid",
""
],
[
"Boutaba",
"Raouf",
""
],
[
"Saleh",
"Aladdin",
""
]
] | Automated decision making algorithms are expected to play a key role in management and orchestration of network slices in 5G and beyond networks. State-of-the-art algorithms for automated orchestration and management tend to rely on data-driven methods which require a timely and accurate view of the network. Accurately monitoring an end-to-end (E2E) network slice requires a scalable monitoring architecture that facilitates collection and correlation of data from various network segments comprising the slice. The state-of-the-art on 5G monitoring mostly focuses on scalability, falling short in providing explicit support for network slicing and computing network slice key performance indicators (KPIs). To fill this gap, in this paper, we present MonArch, a scalable monitoring architecture for 5G, which focuses on network slice monitoring, slice KPI computation, and an application programming interface (API) for specifying slice monitoring requests. We validate the proposed architecture by implementing MonArch on a 5G testbed, and demonstrate its capability to compute a network slice KPI (e.g., slice throughput). Our evaluations show that MonArch does not significantly increase data ingestion time when scaling the number of slices and that a 5-second monitoring interval offers a good balance between monitoring overhead and accuracy. |
2201.01688 | Daniel Diethei | Daniel Diethei, Ashley Colley, Julian Wienert, Johannes Sch\"oning | Different Length, Different Needs: Qualitative Analysis of Threads in
Online Health Communities | null | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online health communities provide a knowledge exchange platform for a wide
range of diseases and health conditions. Informational and emotional support
helps forum participants orient around health issues beyond in-person doctor
visits. So far, little is known about the relation between the level of
participation and participants' contributions in online health communities. To
gain insights on the issue, we analyzed 456 posts in 56 threads from the
Dermatology sub-forum of an online health community. While low participation
threads (short threads) revolved around solving an individual's health issue
through diagnosis suggestions and medical advice, participants in high
participation threads (long threads) built collective knowledge and a sense of
community, typically discussing chronic and rare conditions that medical
professionals were unfamiliar with or could not treat effectively. Our results
suggest that in short threads an individual's health issue is addressed, while
in long threads, sub-communities about specific rare and chronic diseases
emerge. This has implications for the user interface design of health forums,
which could be developed to better support community building elements, even in
short threads.
| [
{
"created": "Wed, 5 Jan 2022 16:32:28 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Jan 2022 13:28:25 GMT",
"version": "v2"
}
] | 2022-01-21 | [
[
"Diethei",
"Daniel",
""
],
[
"Colley",
"Ashley",
""
],
[
"Wienert",
"Julian",
""
],
[
"Schöning",
"Johannes",
""
]
] | Online health communities provide a knowledge exchange platform for a wide range of diseases and health conditions. Informational and emotional support helps forum participants orient around health issues beyond in-person doctor visits. So far, little is known about the relation between the level of participation and participants' contributions in online health communities. To gain insights on the issue, we analyzed 456 posts in 56 threads from the Dermatology sub-forum of an online health community. While low participation threads (short threads) revolved around solving an individual's health issue through diagnosis suggestions and medical advice, participants in high participation threads (long threads) built collective knowledge and a sense of community, typically discussing chronic and rare conditions that medical professionals were unfamiliar with or could not treat effectively. Our results suggest that in short threads an individual's health issue is addressed, while in long threads, sub-communities about specific rare and chronic diseases emerge. This has implications for the user interface design of health forums, which could be developed to better support community building elements, even in short threads. |
2407.14695 | Alejandro Leonardo Garc\'ia Navarro | Alejandro L. Garc\'ia Navarro, Nataliia Koneva, Alfonso
S\'anchez-Maci\'an, Jos\'e Alberto Hern\'andez | A Comprehensive Guide to Combining R and Python code for Data Science,
Machine Learning and Reinforcement Learning | null | null | null | null | cs.LG cs.PL | http://creativecommons.org/licenses/by/4.0/ | Python has gained widespread popularity in the fields of machine learning,
artificial intelligence, and data engineering due to its effectiveness and
extensive libraries. R, on its side, remains a dominant language for
statistical analysis and visualization. However, certain libraries have become
outdated, limiting their functionality and performance. Users can use Python's
advanced machine learning and AI capabilities alongside R's robust statistical
packages by combining these two programming languages. This paper explores
using R's reticulate package to call Python from R, providing practical
examples and highlighting scenarios where this integration enhances
productivity and analytical capabilities. With a few hello-world code snippets,
we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym
libraries for building Machine Learning, Deep Learning, and Reinforcement
Learning projects easily.
| [
{
"created": "Fri, 19 Jul 2024 23:01:48 GMT",
"version": "v1"
}
] | 2024-07-23 | [
[
"Navarro",
"Alejandro L. García",
""
],
[
"Koneva",
"Nataliia",
""
],
[
"Sánchez-Macián",
"Alfonso",
""
],
[
"Hernández",
"José Alberto",
""
]
] | Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visualization. However, certain libraries have become outdated, limiting their functionality and performance. Users can use Python's advanced machine learning and AI capabilities alongside R's robust statistical packages by combining these two programming languages. This paper explores using R's reticulate package to call Python from R, providing practical examples and highlighting scenarios where this integration enhances productivity and analytical capabilities. With a few hello-world code snippets, we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily. |
2007.03988 | Quanming Yao | Yu Liu and Quanming Yao and Yong Li | Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases | WWW 2020 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development of knowledge bases (KBs), link prediction task,
which completes KBs with missing facts, has been broadly studied in especially
binary relational KBs (a.k.a knowledge graph) with powerful tensor
decomposition related methods. However, the ubiquitous n-ary relational KBs
with higher-arity relational facts are paid less attention, in which existing
translation based and neural network based approaches have weak expressiveness
and high complexity in modeling various relations. Tensor decomposition has not
been considered for n-ary relational KBs, while directly extending tensor
decomposition related methods of binary relational KBs to the n-ary case does
not yield satisfactory results due to exponential model complexity and their
strong assumptions on binary relations. To generalize tensor decomposition for
n-ary relational KBs, in this work, we propose GETD, a generalized model based
on Tucker decomposition and Tensor Ring decomposition. The existing negative
sampling technique is also generalized to the n-ary case for GETD. In addition,
we theoretically prove that GETD is fully expressive to completely represent
any KBs. Extensive evaluations on two representative n-ary relational KB
datasets demonstrate the superior performance of GETD, significantly improving
the state-of-the-art methods by over 15\%. Moreover, GETD further obtains the
state-of-the-art results on the benchmark binary relational KB datasets.
| [
{
"created": "Wed, 8 Jul 2020 09:49:38 GMT",
"version": "v1"
}
] | 2020-07-09 | [
[
"Liu",
"Yu",
""
],
[
"Yao",
"Quanming",
""
],
[
"Li",
"Yong",
""
]
] | With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k.a knowledge graph) with powerful tensor decomposition related methods. However, the ubiquitous n-ary relational KBs with higher-arity relational facts are paid less attention, in which existing translation based and neural network based approaches have weak expressiveness and high complexity in modeling various relations. Tensor decomposition has not been considered for n-ary relational KBs, while directly extending tensor decomposition related methods of binary relational KBs to the n-ary case does not yield satisfactory results due to exponential model complexity and their strong assumptions on binary relations. To generalize tensor decomposition for n-ary relational KBs, in this work, we propose GETD, a generalized model based on Tucker decomposition and Tensor Ring decomposition. The existing negative sampling technique is also generalized to the n-ary case for GETD. In addition, we theoretically prove that GETD is fully expressive to completely represent any KBs. Extensive evaluations on two representative n-ary relational KB datasets demonstrate the superior performance of GETD, significantly improving the state-of-the-art methods by over 15\%. Moreover, GETD further obtains the state-of-the-art results on the benchmark binary relational KB datasets. |
cs/0403041 | Mingsheng Ying | Mingsheng Ying | A Theory of Computation Based on Quantum Logic (I) | null | Theoretical Computer Science 344(2-3): 134-207 (2005) | null | null | cs.LO | null | The (meta)logic underlying classical theory of computation is Boolean
(two-valued) logic. Quantum logic was proposed by Birkhoff and von Neumann as a
logic of quantum mechanics more than sixty years ago. The major difference
between Boolean logic and quantum logic is that the latter does not enjoy
distributivity in general. The rapid development of quantum computation in
recent years stimulates us to establish a theory of computation based on
quantum logic. The present paper is the first step toward such a new theory and
it focuses on the simplest models of computation, namely finite automata. It is
found that the universal validity of many properties of automata depend heavily
upon the distributivity of the underlying logic. This indicates that these
properties does not universally hold in the realm of quantum logic. On the
other hand, we show that a local validity of them can be recovered by imposing
a certain commutativity to the (atomic) statements about the automata under
consideration. This reveals an essential difference between the classical
theory of computation and the computation theory based on quantum logic.
| [
{
"created": "Mon, 29 Mar 2004 15:20:32 GMT",
"version": "v1"
}
] | 2013-04-02 | [
[
"Ying",
"Mingsheng",
""
]
] | The (meta)logic underlying classical theory of computation is Boolean (two-valued) logic. Quantum logic was proposed by Birkhoff and von Neumann as a logic of quantum mechanics more than sixty years ago. The major difference between Boolean logic and quantum logic is that the latter does not enjoy distributivity in general. The rapid development of quantum computation in recent years stimulates us to establish a theory of computation based on quantum logic. The present paper is the first step toward such a new theory and it focuses on the simplest models of computation, namely finite automata. It is found that the universal validity of many properties of automata depend heavily upon the distributivity of the underlying logic. This indicates that these properties does not universally hold in the realm of quantum logic. On the other hand, we show that a local validity of them can be recovered by imposing a certain commutativity to the (atomic) statements about the automata under consideration. This reveals an essential difference between the classical theory of computation and the computation theory based on quantum logic. |
2010.05514 | Luca Bedogni | Luca Bedogni, Shakila Khan Rumi, Flora Salim | Modelling Memory for Individual Re-identification in Decentralised
Mobile Contact Tracing Applications | null | null | null | null | cs.CY cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 2020 the coronavirus outbreak changed the lives of people worldwide. After
an initial time period in which it was unclear how to battle the virus, social
distancing has been recognised globally as an effective method to mitigate the
disease spread. This called for technological tools such as Mobile Contact
Tracing Applications (MCTA), which are used to digitally trace contacts among
people, and in case a positive case is found, people with the application
installed which had been in contact will be notified. De-centralised MCTA may
suffer from a novel kind of privacy attack, based on the memory of the human
beings, which upon notification of the application can identify who is the
positive individual responsible for the notification. Our results show that it
is indeed possible to identify positive people among the group of contacts of a
human being, and this is even easier when the sociability of the positive
individual is low. In practice, our simulation results show that identification
can be made with an accuracy of more than 90% depending on the scenario. We
also provide three mitigation strategies which can be implemented in
de-centralised MCTA and analyse which of the three are more effective in
limiting this novel kind of attack.
| [
{
"created": "Mon, 12 Oct 2020 08:10:54 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Nov 2020 08:59:54 GMT",
"version": "v2"
}
] | 2020-11-16 | [
[
"Bedogni",
"Luca",
""
],
[
"Rumi",
"Shakila Khan",
""
],
[
"Salim",
"Flora",
""
]
] | In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack. |
0909.1870 | David Eppstein | David Eppstein | Paired approximation problems and incompatible inapproximabilities | 13 pages, 3 figures. To appear at 21st ACM-SIAM Symp. Discrete
Algorithms (SODA 2010) | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers pairs of optimization problems that are defined from a
single input and for which it is desired to find a good approximation to either
one of the problems. In many instances, it is possible to efficiently find an
approximation of this type that is better than known inapproximability lower
bounds for either of the two individual optimization problems forming the pair.
In particular, we find either a $(1+\epsilon)$-approximation to $(1,2)$-TSP or
a $1/\epsilon$-approximation to maximum independent set, from a given graph, in
linear time. We show a similar paired approximation result for finding either a
coloring or a long path. However, no such tradeoff exists in some other cases:
for set cover and hitting set problems defined from a single set family, and
for clique and independent set problems on the same graph, it is not possible
to find an approximation when both problems are combined that is better than
the best approximation for either problem on its own.
| [
{
"created": "Thu, 10 Sep 2009 05:23:43 GMT",
"version": "v1"
}
] | 2009-09-11 | [
[
"Eppstein",
"David",
""
]
] | This paper considers pairs of optimization problems that are defined from a single input and for which it is desired to find a good approximation to either one of the problems. In many instances, it is possible to efficiently find an approximation of this type that is better than known inapproximability lower bounds for either of the two individual optimization problems forming the pair. In particular, we find either a $(1+\epsilon)$-approximation to $(1,2)$-TSP or a $1/\epsilon$-approximation to maximum independent set, from a given graph, in linear time. We show a similar paired approximation result for finding either a coloring or a long path. However, no such tradeoff exists in some other cases: for set cover and hitting set problems defined from a single set family, and for clique and independent set problems on the same graph, it is not possible to find an approximation when both problems are combined that is better than the best approximation for either problem on its own. |
2112.01917 | Guillermo Ortiz-Jimenez | Gizem Y\"uce, Guillermo Ortiz-Jim\'enez, Beril Besbinar, Pascal
Frossard | A Structured Dictionary Perspective on Implicit Neural Representations | Accepted to IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR) 2022 (26 pages, 16 figures) | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Implicit neural representations (INRs) have recently emerged as a promising
alternative to classical discretized representations of signals. Nevertheless,
despite their practical success, we still do not understand how INRs represent
signals. We propose a novel unified perspective to theoretically analyse INRs.
Leveraging results from harmonic analysis and deep learning theory, we show
that most INR families are analogous to structured signal dictionaries whose
atoms are integer harmonics of the set of initial mapping frequencies. This
structure allows INRs to express signals with an exponentially increasing
frequency support using a number of parameters that only grows linearly with
depth. We also explore the inductive bias of INRs exploiting recent results
about the empirical neural tangent kernel (NTK). Specifically, we show that the
eigenfunctions of the NTK can be seen as dictionary atoms whose inner product
with the target signal determines the final performance of their
reconstruction. In this regard, we reveal that meta-learning has a reshaping
effect on the NTK analogous to dictionary learning, building dictionary atoms
as a combination of the examples seen during meta-training. Our results permit
to design and tune novel INR architectures, but can also be of interest for the
wider deep learning theory community.
| [
{
"created": "Fri, 3 Dec 2021 14:00:52 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Mar 2022 16:03:32 GMT",
"version": "v2"
}
] | 2022-03-28 | [
[
"Yüce",
"Gizem",
""
],
[
"Ortiz-Jiménez",
"Guillermo",
""
],
[
"Besbinar",
"Beril",
""
],
[
"Frossard",
"Pascal",
""
]
] | Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a novel unified perspective to theoretically analyse INRs. Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies. This structure allows INRs to express signals with an exponentially increasing frequency support using a number of parameters that only grows linearly with depth. We also explore the inductive bias of INRs exploiting recent results about the empirical neural tangent kernel (NTK). Specifically, we show that the eigenfunctions of the NTK can be seen as dictionary atoms whose inner product with the target signal determines the final performance of their reconstruction. In this regard, we reveal that meta-learning has a reshaping effect on the NTK analogous to dictionary learning, building dictionary atoms as a combination of the examples seen during meta-training. Our results permit to design and tune novel INR architectures, but can also be of interest for the wider deep learning theory community. |
2401.02081 | Tianchen Liu | Tianchen Liu, Liang Wu, Bo An, Zaichen Zhang, Jian Dang and Jiangzhou
Wang | Performance Trade-off and Joint Waveform Design for MIMO-OFDM DFRC
Systems | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dual-functional radar-communication (DFRC) has attracted considerable
attention. This paper considers the frequency-selective multipath fading
environment and proposes DFRC waveform design strategies based on
multiple-input and multiple-output (MIMO) and orthogonal frequency division
multiplexing (OFDM) techniques. In the proposed waveform design strategies, the
Cramer-Rao bound (CRB) of the radar system, the inter-stream interference (ISI)
and the achievable rate of the communication system, are respectively
considered as the performance metrics. In this paper, we focus on the
performance trade-off between the radar system and the communication system,
and the optimization problems are formulated. In the ISI minimization based
waveform design strategy, the optimization problem is convex and can be easily
solved. In the achievable rate maximization based waveform design strategy, we
propose a water-filling (WF) and sequential quadratic programming (SQP) based
algorithm to derive the covariance matrix and the precoding matrix. Simulation
results validate the proposed DFRC waveform designs and show that the
achievable rate maximization based strategy has a better performance than the
ISI minimization based strategy.
| [
{
"created": "Thu, 4 Jan 2024 06:05:29 GMT",
"version": "v1"
}
] | 2024-01-05 | [
[
"Liu",
"Tianchen",
""
],
[
"Wu",
"Liang",
""
],
[
"An",
"Bo",
""
],
[
"Zhang",
"Zaichen",
""
],
[
"Dang",
"Jian",
""
],
[
"Wang",
"Jiangzhou",
""
]
] | Dual-functional radar-communication (DFRC) has attracted considerable attention. This paper considers the frequency-selective multipath fading environment and proposes DFRC waveform design strategies based on multiple-input and multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques. In the proposed waveform design strategies, the Cramer-Rao bound (CRB) of the radar system, the inter-stream interference (ISI) and the achievable rate of the communication system, are respectively considered as the performance metrics. In this paper, we focus on the performance trade-off between the radar system and the communication system, and the optimization problems are formulated. In the ISI minimization based waveform design strategy, the optimization problem is convex and can be easily solved. In the achievable rate maximization based waveform design strategy, we propose a water-filling (WF) and sequential quadratic programming (SQP) based algorithm to derive the covariance matrix and the precoding matrix. Simulation results validate the proposed DFRC waveform designs and show that the achievable rate maximization based strategy has a better performance than the ISI minimization based strategy. |
1304.2683 | Nan Yao | Yao Nan, Qian Feng and Sun Zuolei | Image Classification by Feature Dimension Reduction and Graph based
Ranking | 4 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dimensionality reduction (DR) of image features plays an important role in
image retrieval and classification tasks. Recently, two types of methods have
been proposed to improve the both the accuracy and efficiency for the
dimensionality reduction problem. One uses Non-negative matrix factorization
(NMF) to describe the image distribution on the space of base matrix. Another
one for dimension reduction trains a subspace projection matrix to project
original data space into some low-dimensional subspaces which have deep
architecture, so that the low-dimensional codes would be learned. At the same
time, the graph based similarity learning algorithm which tries to exploit
contextual information for improving the effectiveness of image rankings is
also proposed for image class and retrieval problem. In this paper, after above
two methods mentioned are utilized to reduce the high-dimensional features of
images respectively, we learn the graph based similarity for the image
classification problem. This paper compares the proposed approach with other
approaches on an image database.
| [
{
"created": "Tue, 9 Apr 2013 18:11:08 GMT",
"version": "v1"
}
] | 2013-04-10 | [
[
"Nan",
"Yao",
""
],
[
"Feng",
"Qian",
""
],
[
"Zuolei",
"Sun",
""
]
] | Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve the both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database. |
2002.06714 | Qiang Wang | Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, Jingbo Zhu | Multi-layer Representation Fusion for Neural Machine Translation | COLING 2018 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural machine translation systems require a number of stacked layers for
deep models. But the prediction depends on the sentence representation of the
top-most layer with no access to low-level representations. This makes it more
difficult to train the model and poses a risk of information loss to
prediction. In this paper, we propose a multi-layer representation fusion
(MLRF) approach to fusing stacked layers. In particular, we design three fusion
functions to learn a better representation from the stack. Experimental results
show that our approach yields improvements of 0.92 and 0.56 BLEU points over
the strong Transformer baseline on IWSLT German-English and NIST
Chinese-English MT tasks respectively. The result is new state-of-the-art in
German-English translation.
| [
{
"created": "Sun, 16 Feb 2020 23:53:07 GMT",
"version": "v1"
}
] | 2020-02-18 | [
[
"Wang",
"Qiang",
""
],
[
"Li",
"Fuxue",
""
],
[
"Xiao",
"Tong",
""
],
[
"Li",
"Yanyang",
""
],
[
"Li",
"Yinqiao",
""
],
[
"Zhu",
"Jingbo",
""
]
] | Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation. |
2206.00253 | Ning Luo | Ning Luo and Linlin Zhang | Intelligent UNIT LEVEL TEST Generator for Enhanced Software Quality | 10 pages, 6 figures | 8th International Conference on Software Engineering (SEC 2022) | null | null | cs.SE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Unit level test has been widely recognized as an important approach to
improve the software quality, as it can expose bugs earlier during the
development phase. However, manual unit level test development is often tedious
and insufficient. Also, it is hard for developers to precisely identify the
most error prone code block deserving the best test coverage by themselves. In
this paper, we present the automatic Unit level test framework we used for
intel media driver development. It can help us identify the most critical code
block, provide the test coverage recommendation, and automatically generate
>80% ULT code (~400K Lines of test code) as well as ~35% test cases (~7K test
cases) for intel media driver. It helps us to greatly shrink the average ULT
development effort from ~24 Man hours to ~3 Man hours per 1000 Lines of driver
source code.
| [
{
"created": "Wed, 1 Jun 2022 06:33:48 GMT",
"version": "v1"
}
] | 2022-06-02 | [
[
"Luo",
"Ning",
""
],
[
"Zhang",
"Linlin",
""
]
] | Unit level test has been widely recognized as an important approach to improve the software quality, as it can expose bugs earlier during the development phase. However, manual unit level test development is often tedious and insufficient. Also, it is hard for developers to precisely identify the most error prone code block deserving the best test coverage by themselves. In this paper, we present the automatic Unit level test framework we used for intel media driver development. It can help us identify the most critical code block, provide the test coverage recommendation, and automatically generate >80% ULT code (~400K Lines of test code) as well as ~35% test cases (~7K test cases) for intel media driver. It helps us to greatly shrink the average ULT development effort from ~24 Man hours to ~3 Man hours per 1000 Lines of driver source code. |
1907.09695 | Shivangi Srivastava | Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia | Adaptive Compression-based Lifelong Learning | Accepted at BMVC 2019 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of a deep learning model losing performance on a previously
learned task when fine-tuned to a new one is a phenomenon known as Catastrophic
forgetting. There are two major ways to mitigate this problem: either
preserving activations of the initial network during training with a new task;
or restricting the new network activations to remain close to the initial ones.
The latter approach falls under the denomination of lifelong learning, where
the model is updated in a way that it performs well on both old and new tasks,
without having access to the old task's training samples anymore. Recently,
approaches like pruning networks for freeing network capacity during sequential
learning of tasks have been gaining in popularity. Such approaches allow
learning small networks while making redundant parameters available for the
next tasks. The common problem encountered with these approaches is that the
pruning percentage is hard-coded, irrespective of the number of samples, of the
complexity of the learning task and of the number of classes in the dataset. We
propose a method based on Bayesian optimization to perform adaptive
compression/pruning of the network and show its effectiveness in lifelong
learning. Our method learns to perform heavy pruning for small and/or simple
datasets while using milder compression rates for large and/or complex data.
Experiments on classification and semantic segmentation demonstrate the
applicability of learning network compression, where we are able to effectively
preserve performances along sequences of tasks of varying complexity.
| [
{
"created": "Tue, 23 Jul 2019 04:58:52 GMT",
"version": "v1"
}
] | 2019-07-24 | [
[
"Srivastava",
"Shivangi",
""
],
[
"Berman",
"Maxim",
""
],
[
"Blaschko",
"Matthew B.",
""
],
[
"Tuia",
"Devis",
""
]
] | The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two major ways to mitigate this problem: either preserving activations of the initial network during training with a new task; or restricting the new network activations to remain close to the initial ones. The latter approach falls under the denomination of lifelong learning, where the model is updated in a way that it performs well on both old and new tasks, without having access to the old task's training samples anymore. Recently, approaches like pruning networks for freeing network capacity during sequential learning of tasks have been gaining in popularity. Such approaches allow learning small networks while making redundant parameters available for the next tasks. The common problem encountered with these approaches is that the pruning percentage is hard-coded, irrespective of the number of samples, of the complexity of the learning task and of the number of classes in the dataset. We propose a method based on Bayesian optimization to perform adaptive compression/pruning of the network and show its effectiveness in lifelong learning. Our method learns to perform heavy pruning for small and/or simple datasets while using milder compression rates for large and/or complex data. Experiments on classification and semantic segmentation demonstrate the applicability of learning network compression, where we are able to effectively preserve performances along sequences of tasks of varying complexity. |
2103.03011 | Chen-Huan Pi | Chen-Huan Pi, Kai-Chun Hu, Yu-Ting Huang, Stone Cheng | Reinforcement Learning Trajectory Generation and Control for Aggressive
Perching on Vertical Walls with Quadrotors | null | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Micro aerial vehicles are widely being researched and employed due to their
relative low operation costs and high flexibility in various applications. We
study the under-actuated quadrotor perching problem, designing a trajectory
planner and controller which generates feasible trajectories and drives
quadrotors to desired state in state space. This paper proposes a trajectory
generating and tracking method for quadrotor perching that takes the advantages
of reinforcement learning controller and traditional controller. The trained
low-level reinforcement learning controller would manipulate quadrotor toward
the perching point in simulation environment. Once the simulated quadrotor has
successfully perched, the relative trajectory information in simulation will be
sent to tracking controller on real quadrotor and start the actual perching
task. Generating feasible trajectories via the trained reinforcement learning
controller requires less time, and the traditional trajectory tracking
controller could easily be modified to control the quadrotor and mathematically
analysis its stability and robustness. We show that this approach permits the
control structure of trajectories and controllers enabling such aggressive
maneuvers perching on vertical surfaces with high precision.
| [
{
"created": "Thu, 4 Mar 2021 13:20:05 GMT",
"version": "v1"
}
] | 2021-03-05 | [
[
"Pi",
"Chen-Huan",
""
],
[
"Hu",
"Kai-Chun",
""
],
[
"Huang",
"Yu-Ting",
""
],
[
"Cheng",
"Stone",
""
]
] | Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. The trained low-level reinforcement learning controller would manipulate quadrotor toward the perching point in simulation environment. Once the simulated quadrotor has successfully perched, the relative trajectory information in simulation will be sent to tracking controller on real quadrotor and start the actual perching task. Generating feasible trajectories via the trained reinforcement learning controller requires less time, and the traditional trajectory tracking controller could easily be modified to control the quadrotor and mathematically analysis its stability and robustness. We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with high precision. |
2401.16937 | Magnus Andersson | Saqib Qamar, Abu Imran Baba, St\'ephane Verger, Magnus Andersson | Segmentation and Characterization of Macerated Fibers and Vessels Using
Deep Learning | 7 figures | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Wood comprises different cell types, such as fibers, tracheids and vessels,
defining its properties. Studying cells' shape, size, and arrangement in
microscopy images is crucial for understanding wood characteristics. Typically,
this involves macerating (soaking) samples in a solution to separate cells,
then spreading them on slides for imaging with a microscope that covers a wide
area, capturing thousands of cells. However, these cells often cluster and
overlap in images, making the segmentation difficult and time-consuming using
standard image-processing methods. In this work, we developed an automatic deep
learning segmentation approach that utilizes the one-stage YOLOv8 model for
fast and accurate segmentation and characterization of macerated fiber and
vessel form aspen trees in microscopy images. The model can analyze 32,640 x
25,920 pixels images and demonstrate effective cell detection and segmentation,
achieving a mAP_{0.5-0.95} of 78 %. To assess the model's robustness, we
examined fibers from a genetically modified tree line known for longer fibers.
The outcomes were comparable to previous manual measurements. Additionally, we
created a user-friendly web application for image analysis and provided the
code for use on Google Colab. By leveraging YOLOv8's advances, this work
provides a deep learning solution to enable efficient quantification and
analysis of wood cells suitable for practical applications.
| [
{
"created": "Tue, 30 Jan 2024 12:04:56 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Jun 2024 11:02:49 GMT",
"version": "v2"
}
] | 2024-06-19 | [
[
"Qamar",
"Saqib",
""
],
[
"Baba",
"Abu Imran",
""
],
[
"Verger",
"Stéphane",
""
],
[
"Andersson",
"Magnus",
""
]
] | Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Typically, this involves macerating (soaking) samples in a solution to separate cells, then spreading them on slides for imaging with a microscope that covers a wide area, capturing thousands of cells. However, these cells often cluster and overlap in images, making the segmentation difficult and time-consuming using standard image-processing methods. In this work, we developed an automatic deep learning segmentation approach that utilizes the one-stage YOLOv8 model for fast and accurate segmentation and characterization of macerated fiber and vessel form aspen trees in microscopy images. The model can analyze 32,640 x 25,920 pixels images and demonstrate effective cell detection and segmentation, achieving a mAP_{0.5-0.95} of 78 %. To assess the model's robustness, we examined fibers from a genetically modified tree line known for longer fibers. The outcomes were comparable to previous manual measurements. Additionally, we created a user-friendly web application for image analysis and provided the code for use on Google Colab. By leveraging YOLOv8's advances, this work provides a deep learning solution to enable efficient quantification and analysis of wood cells suitable for practical applications. |
2212.02941 | Shamil Mamedov | Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Ruan Viljoen,
Joschka Boedecker, Moritz Diehl, Jan Swevers | Safe Imitation Learning of Nonlinear Model Predictive Control for
Flexible Robots | Accepted to IROS 2024 | null | null | null | cs.RO cs.LG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Flexible robots may overcome some of the industry's major challenges, such as
enabling intrinsically safe human-robot collaboration and achieving a higher
payload-to-mass ratio. However, controlling flexible robots is complicated due
to their complex dynamics, which include oscillatory behavior and a
high-dimensional state space. Nonlinear model predictive control (NMPC) offers
an effective means to control such robots, but its significant computational
demand often limits its application in real-time scenarios. To enable fast
control of flexible robots, we propose a framework for a safe approximation of
NMPC using imitation learning and a predictive safety filter. Our framework
significantly reduces computation time while incurring a slight loss in
performance. Compared to NMPC, our framework shows more than an eightfold
improvement in computation time when controlling a three-dimensional flexible
robot arm in simulation, all while guaranteeing safety constraints. Notably,
our approach outperforms state-of-the-art reinforcement learning methods. The
development of fast and safe approximate NMPC holds the potential to accelerate
the adoption of flexible robots in industry. The project code is available at:
tinyurl.com/anmpc4fr
| [
{
"created": "Tue, 6 Dec 2022 12:54:08 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Sep 2023 07:34:32 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Aug 2024 20:40:17 GMT",
"version": "v3"
}
] | 2024-08-16 | [
[
"Mamedov",
"Shamil",
""
],
[
"Reiter",
"Rudolf",
""
],
[
"Azad",
"Seyed Mahdi Basiri",
""
],
[
"Viljoen",
"Ruan",
""
],
[
"Boedecker",
"Joschka",
""
],
[
"Diehl",
"Moritz",
""
],
[
"Swevers",
"Jan",
""
]
] | Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. Nonlinear model predictive control (NMPC) offers an effective means to control such robots, but its significant computational demand often limits its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. Compared to NMPC, our framework shows more than an eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms state-of-the-art reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry. The project code is available at: tinyurl.com/anmpc4fr |
2404.08008 | Kehua Feng | Kehua Feng, Keyan Ding, Kede Ma, Zhihua Wang, Qiang Zhang, Huajun Chen | Sample-Efficient Human Evaluation of Large Language Models via Maximum
Discrepancy Competition | 32 pages, 6 figures | null | null | null | cs.LG cs.CL cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The past years have witnessed a proliferation of large language models
(LLMs). Yet, automated and unbiased evaluation of LLMs is challenging due to
the inaccuracy of standard metrics in reflecting human preferences and the
inefficiency in sampling informative and diverse test examples. While human
evaluation remains the gold standard, it is expensive and time-consuming,
especially when dealing with a large number of testing samples. To address this
problem, we propose a sample-efficient human evaluation method based on MAximum
Discrepancy (MAD) competition. MAD automatically selects a small set of
informative and diverse instructions, each adapted to two LLMs, whose responses
are subject to three-alternative forced choice by human subjects. The pairwise
comparison results are then aggregated into a global ranking using the Elo
rating system. We select eight representative LLMs and compare them in terms of
four skills: knowledge understanding, mathematical reasoning, writing, and
coding. Experimental results show that the proposed method achieves a reliable
and sensible ranking of LLMs' capabilities, identifies their relative strengths
and weaknesses, and offers valuable insights for further LLM advancement.
| [
{
"created": "Wed, 10 Apr 2024 01:26:24 GMT",
"version": "v1"
}
] | 2024-04-15 | [
[
"Feng",
"Kehua",
""
],
[
"Ding",
"Keyan",
""
],
[
"Ma",
"Kede",
""
],
[
"Wang",
"Zhihua",
""
],
[
"Zhang",
"Qiang",
""
],
[
"Chen",
"Huajun",
""
]
] | The past years have witnessed a proliferation of large language models (LLMs). Yet, automated and unbiased evaluation of LLMs is challenging due to the inaccuracy of standard metrics in reflecting human preferences and the inefficiency in sampling informative and diverse test examples. While human evaluation remains the gold standard, it is expensive and time-consuming, especially when dealing with a large number of testing samples. To address this problem, we propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs, whose responses are subject to three-alternative forced choice by human subjects. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system. We select eight representative LLMs and compare them in terms of four skills: knowledge understanding, mathematical reasoning, writing, and coding. Experimental results show that the proposed method achieves a reliable and sensible ranking of LLMs' capabilities, identifies their relative strengths and weaknesses, and offers valuable insights for further LLM advancement. |
2109.10047 | Guosheng Feng | Guosheng Feng, Chunnan Wang, Hongzhi Wang | Search For Deep Graph Neural Networks | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current GNN-oriented NAS methods focus on the search for different layer
aggregate components with shallow and simple architectures, which are limited
by the 'over-smooth' problem. To further explore the benefits from structural
diversity and depth of GNN architectures, we propose a GNN generation pipeline
with a novel two-stage search space, which aims at automatically generating
high-performance while transferable deep GNN models in a block-wise manner.
Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple
flexible residual connection in our search space and apply identity mapping in
the basic GNN layers. For the search algorithm, we use deep-q-learning with
epsilon-greedy exploration strategy and reward reshaping. Extensive experiments
on real-world datasets show that our generated GNN models outperforms existing
manually designed and NAS-based ones.
| [
{
"created": "Tue, 21 Sep 2021 09:24:59 GMT",
"version": "v1"
}
] | 2021-09-22 | [
[
"Feng",
"Guosheng",
""
],
[
"Wang",
"Chunnan",
""
],
[
"Wang",
"Hongzhi",
""
]
] | Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which aims at automatically generating high-performance while transferable deep GNN models in a block-wise manner. Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple flexible residual connection in our search space and apply identity mapping in the basic GNN layers. For the search algorithm, we use deep-q-learning with epsilon-greedy exploration strategy and reward reshaping. Extensive experiments on real-world datasets show that our generated GNN models outperforms existing manually designed and NAS-based ones. |
1508.02479 | Heejin Choi | Heejin Choi, Yutaka Sasaki, Nathan Srebro | Normalized Hierarchical SVM | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present improved methods of using structured SVMs in a large-scale
hierarchical classification problem, that is when labels are leaves, or sets of
leaves, in a tree or a DAG. We examine the need to normalize both the
regularization and the margin and show how doing so significantly improves
performance, including allowing achieving state-of-the-art results where
unnormalized structured SVMs do not perform better than flat models. We also
describe a further extension of hierarchical SVMs that highlight the connection
between hierarchical SVMs and matrix factorization models.
| [
{
"created": "Tue, 11 Aug 2015 03:34:33 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Mar 2016 18:53:19 GMT",
"version": "v2"
}
] | 2016-03-07 | [
[
"Choi",
"Heejin",
""
],
[
"Sasaki",
"Yutaka",
""
],
[
"Srebro",
"Nathan",
""
]
] | We present improved methods of using structured SVMs in a large-scale hierarchical classification problem, that is when labels are leaves, or sets of leaves, in a tree or a DAG. We examine the need to normalize both the regularization and the margin and show how doing so significantly improves performance, including allowing achieving state-of-the-art results where unnormalized structured SVMs do not perform better than flat models. We also describe a further extension of hierarchical SVMs that highlight the connection between hierarchical SVMs and matrix factorization models. |
1408.1482 | Joseph Y. Halpern | Joseph Y. Halpern | Axiomatizing Causal Reasoning | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-202-210 | cs.AI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal models defined in terms of a collection of equations, as defined by
Pearl, are axiomatized here. Axiomatizations are provided for three
successively more general classes of causal models: (1) the class of recursive
theories (those without feedback), (2) the class of theories where the
solutions to the equations are unique, (3) arbitrary theories (where the
equations may not have solutions and, if they do, they are not necessarily
unique). It is shown that to reason about causality in the most general third
class, we must extend the language used by Galles and Pearl. In addition, the
complexity of the decision procedures is examined for all the languages and
classes of models considered.
| [
{
"created": "Thu, 7 Aug 2014 06:24:41 GMT",
"version": "v1"
}
] | 2014-08-08 | [
[
"Halpern",
"Joseph Y.",
""
]
] | Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl. In addition, the complexity of the decision procedures is examined for all the languages and classes of models considered. |
2104.12868 | Ali Akbar Sadat Asl | Ali Akbar Sadat Asl, Mohammad Mahdi Ershadi, Shahabeddin Sotudian,
Xingyu Li, Scott Dick | Fuzzy Expert Systems for Prediction of ICU Admission in Patients with
COVID-19 | null | null | 10.3233/IDT-200220 | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The pandemic COVID-19 disease has had a dramatic impact on almost all
countries around the world so that many hospitals have been overwhelmed with
Covid-19 cases. As medical resources are limited, deciding on the proper
allocation of these resources is a very crucial issue. Besides, uncertainty is
a major factor that can affect decisions, especially in medical fields. To cope
with this issue, we use fuzzy logic (FL) as one of the most suitable methods in
modeling systems with high uncertainty and complexity. We intend to make use of
the advantages of FL in decisions on cases that need to treat in ICU. In this
study, an interval type-2 fuzzy expert system is proposed for prediction of ICU
admission in COVID-19 patients. For this prediction task, we also developed an
adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these
fuzzy systems are compared to some well-known classification methods such as
Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest
Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS
models perform competitively in terms of accuracy and F-measure compared to the
other system modeling techniques.
| [
{
"created": "Thu, 22 Apr 2021 05:12:49 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Feb 2023 03:24:44 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Feb 2023 11:25:27 GMT",
"version": "v3"
}
] | 2023-02-09 | [
[
"Asl",
"Ali Akbar Sadat",
""
],
[
"Ershadi",
"Mohammad Mahdi",
""
],
[
"Sotudian",
"Shahabeddin",
""
],
[
"Li",
"Xingyu",
""
],
[
"Dick",
"Scott",
""
]
] | The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with Covid-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with this issue, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat in ICU. In this study, an interval type-2 fuzzy expert system is proposed for prediction of ICU admission in COVID-19 patients. For this prediction task, we also developed an adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these fuzzy systems are compared to some well-known classification methods such as Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other system modeling techniques. |
2111.06750 | Guannan Lou | Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng | STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural
Networks | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a spatial-temporal federated learning framework for graph neural
networks, namely STFL. The framework explores the underlying correlation of the
input spatial-temporal data and transform it to both node features and
adjacency matrix. The federated learning setting in the framework ensures data
privacy while achieving a good model generalization. Experiments results on the
sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph
prediction tasks.
| [
{
"created": "Fri, 12 Nov 2021 14:55:57 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jan 2022 08:38:21 GMT",
"version": "v2"
}
] | 2022-01-12 | [
[
"Lou",
"Guannan",
""
],
[
"Liu",
"Yuze",
""
],
[
"Zhang",
"Tiehua",
""
],
[
"Zheng",
"Xi",
""
]
] | We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph prediction tasks. |
1002.2294 | Jean-Marc Seigneur | Jean-Marc Seigneur, Xavier Titi | Reputation-based Telecommunication Network Selection | Published in the Proceedings of the 2009 IADIS e-Society
International Conference | null | null | null | cs.NI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, mobile users can switch between different available networks, for
example, nearby WiFi networks or their standard mobile operator network. Soon
it will be extended to other operators. However, unless telecommunication
operators can directly benefit from allowing a user to switch to another
operator, operators have an incentive to keep their network quality of service
confidential to avoid that their users decide to switch to another network. In
contrast, in a user-centric way, the users should be allowed to share their
observations regarding the networks that they have used. In this paper, we
present our work in progress towards attack-resistant sharing of quality of
service information and network provider reputation among mobile users.
| [
{
"created": "Thu, 11 Feb 2010 08:26:47 GMT",
"version": "v1"
}
] | 2010-02-12 | [
[
"Seigneur",
"Jean-Marc",
""
],
[
"Titi",
"Xavier",
""
]
] | Nowadays, mobile users can switch between different available networks, for example, nearby WiFi networks or their standard mobile operator network. Soon it will be extended to other operators. However, unless telecommunication operators can directly benefit from allowing a user to switch to another operator, operators have an incentive to keep their network quality of service confidential to avoid that their users decide to switch to another network. In contrast, in a user-centric way, the users should be allowed to share their observations regarding the networks that they have used. In this paper, we present our work in progress towards attack-resistant sharing of quality of service information and network provider reputation among mobile users. |
2104.14098 | S. Akshay | Preey Shah, Aman Bansal, S. Akshay and Supratik Chakraborty | A Normal Form Characterization for Efficient Boolean Skolem Function
Synthesis | Full version of conference paper accepted at LICS'2021 | null | null | null | cs.LO cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Boolean Skolem function synthesis concerns synthesizing outputs as Boolean
functions of inputs such that a relational specification between inputs and
outputs is satisfied. This problem, also known as Boolean functional synthesis,
has several applications, including design of safe controllers for autonomous
systems, certified QBF solving, cryptanalysis etc. Recently, complexity
theoretic hardness results have been shown for the problem, although several
algorithms proposed in the literature are known to work well in practice. This
dichotomy between theoretical hardness and practical efficacy has motivated the
research into normal forms or representations of input specifications that
permit efficient synthesis, thus explaining perhaps the efficacy of these
algorithms.
In this paper we go one step beyond this and ask if there exists a normal
form representation that can in fact precisely characterize "efficient"
synthesis. We present a normal form called SAUNF that precisely characterizes
tractable synthesis in the following sense: a specification is polynomial time
synthesizable iff it can be compiled to SAUNF in polynomial time. Additionally,
a specification admits a polynomial-sized functional solution iff there exists
a semantically equivalent polynomial-sized SAUNF representation. SAUNF is
exponentially more succinct than well-established normal forms like BDDs and
DNNFs, used in the context of AI problems, and strictly subsumes other more
recently proposed forms like SynNNF. It enjoys compositional properties that
are similar to those of DNNF. Thus, SAUNF provides the right trade-off in
knowledge representation for Boolean functional synthesis.
| [
{
"created": "Thu, 29 Apr 2021 04:16:41 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Jun 2021 12:52:38 GMT",
"version": "v2"
}
] | 2021-06-29 | [
[
"Shah",
"Preey",
""
],
[
"Bansal",
"Aman",
""
],
[
"Akshay",
"S.",
""
],
[
"Chakraborty",
"Supratik",
""
]
] | Boolean Skolem function synthesis concerns synthesizing outputs as Boolean functions of inputs such that a relational specification between inputs and outputs is satisfied. This problem, also known as Boolean functional synthesis, has several applications, including design of safe controllers for autonomous systems, certified QBF solving, cryptanalysis etc. Recently, complexity theoretic hardness results have been shown for the problem, although several algorithms proposed in the literature are known to work well in practice. This dichotomy between theoretical hardness and practical efficacy has motivated the research into normal forms or representations of input specifications that permit efficient synthesis, thus explaining perhaps the efficacy of these algorithms. In this paper we go one step beyond this and ask if there exists a normal form representation that can in fact precisely characterize "efficient" synthesis. We present a normal form called SAUNF that precisely characterizes tractable synthesis in the following sense: a specification is polynomial time synthesizable iff it can be compiled to SAUNF in polynomial time. Additionally, a specification admits a polynomial-sized functional solution iff there exists a semantically equivalent polynomial-sized SAUNF representation. SAUNF is exponentially more succinct than well-established normal forms like BDDs and DNNFs, used in the context of AI problems, and strictly subsumes other more recently proposed forms like SynNNF. It enjoys compositional properties that are similar to those of DNNF. Thus, SAUNF provides the right trade-off in knowledge representation for Boolean functional synthesis. |
2309.13908 | Jie Luo | Jie Luo, Jakub Tomczak, Karine Miras, Agoston E. Eiben | A comparison of controller architectures and learning mechanisms for
arbitrary robot morphologies | null | null | null | null | cs.RO cs.AI cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | The main question this paper addresses is: What combination of a robot
controller and a learning method should be used, if the morphology of the
learning robot is not known in advance? Our interest is rooted in the context
of morphologically evolving modular robots, but the question is also relevant
in general, for system designers interested in widely applicable solutions. We
perform an experimental comparison of three controller-and-learner
combinations: one approach where controllers are based on modelling animal
locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary
algorithm, a completely different method using Reinforcement Learning (RL) with
a neural network controller architecture, and a combination `in-between' where
controllers are neural networks and the learner is an evolutionary algorithm.
We apply these three combinations to a test suite of modular robots and compare
their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based
and RL-based options are outperformed by the in-between combination that is
more robust and efficient than the other two setups.
| [
{
"created": "Mon, 25 Sep 2023 07:11:43 GMT",
"version": "v1"
}
] | 2023-09-26 | [
[
"Luo",
"Jie",
""
],
[
"Tomczak",
"Jakub",
""
],
[
"Miras",
"Karine",
""
],
[
"Eiben",
"Agoston E.",
""
]
] | The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? Our interest is rooted in the context of morphologically evolving modular robots, but the question is also relevant in general, for system designers interested in widely applicable solutions. We perform an experimental comparison of three controller-and-learner combinations: one approach where controllers are based on modelling animal locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary algorithm, a completely different method using Reinforcement Learning (RL) with a neural network controller architecture, and a combination `in-between' where controllers are neural networks and the learner is an evolutionary algorithm. We apply these three combinations to a test suite of modular robots and compare their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based and RL-based options are outperformed by the in-between combination that is more robust and efficient than the other two setups. |
2101.07725 | Muhammad AL-Qurishi Dr | Majed Alrubaian, Muhammad Al-Qurishi, Sherif Omar and Mohamed A.
Mostafa | DeepTrust: A Deep Learning Approach for Measuring Social Media Users
Trustworthiness | 18 pages,6 figures | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Veracity of data posted on the microblog platforms has in recent years been a
subject of intensive study by professionals specializing in various fields of
informatics as well as sociology, particularly in the light of increasing
importance of online tools for news spreading. On Twitter and similar sites, it
is possible to report on ongoing situations globally with minimal delay, while
the cost of such reporting remains negligible. One of the most important
features of this social network is that content delivery can be customized to
allow users to focus only on news items covering subject matters they find
interesting. With this in mind, it becomes necessary to create verification
mechanisms that can ascertain whether the claims made on Twitter can be taken
seriously and prevent false content from spreading too far. This study
demonstrates an innovative System for verification of information that can
fulfill the role described above. The System is comprised of four mutually
connected modules: a legacy module, a trustworthiness classifier; a module
managing user authority, and a ranking procedure. All of the modules function
within an integrated framework and jointly contribute to an accurate
classification of messages and authors. Effectiveness of the solution was
evaluated empirically on a sample of Twitter users, with a strict 10-fold
evaluation procedure applied for each module. The findings indicate that the
solution successfully meets the primary objectives of the study and performs
its function as expected.
| [
{
"created": "Tue, 19 Jan 2021 16:55:32 GMT",
"version": "v1"
}
] | 2021-01-20 | [
[
"Alrubaian",
"Majed",
""
],
[
"Al-Qurishi",
"Muhammad",
""
],
[
"Omar",
"Sherif",
""
],
[
"Mostafa",
"Mohamed A.",
""
]
] | Veracity of data posted on the microblog platforms has in recent years been a subject of intensive study by professionals specializing in various fields of informatics as well as sociology, particularly in the light of increasing importance of online tools for news spreading. On Twitter and similar sites, it is possible to report on ongoing situations globally with minimal delay, while the cost of such reporting remains negligible. One of the most important features of this social network is that content delivery can be customized to allow users to focus only on news items covering subject matters they find interesting. With this in mind, it becomes necessary to create verification mechanisms that can ascertain whether the claims made on Twitter can be taken seriously and prevent false content from spreading too far. This study demonstrates an innovative System for verification of information that can fulfill the role described above. The System is comprised of four mutually connected modules: a legacy module, a trustworthiness classifier; a module managing user authority, and a ranking procedure. All of the modules function within an integrated framework and jointly contribute to an accurate classification of messages and authors. Effectiveness of the solution was evaluated empirically on a sample of Twitter users, with a strict 10-fold evaluation procedure applied for each module. The findings indicate that the solution successfully meets the primary objectives of the study and performs its function as expected. |
1607.05812 | Esmitt Ram\'irez | Juan Perozo, Mimia Lo Leung, Esmitt Ram\'irez | HoloMed: A Low-Cost Gesture-Based Holographic | English version of an accepted paper in the Proceedings of the 4th
Simposio Cient\'ifico y Tecnol\'ogico en Computaci\'on, 160-168. May 2016.
Original version in spanish
http://ccg.ciens.ucv.ve/~esmitt/publications/2016/SCTC2016.pdf | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During medicine studies, visualization of certain elements is common and
indispensable in order to get more information about the way they work.
Currently, we resort to the use of photographs -which are insufficient due to
being static- or tests in patients, which can be invasive or even risky.
Therefore, a low-cost approach is proposed by using a 3D visualization. This
paper presents a holographic system built with low-cost materials for teaching
obstetrics, where student interaction is performed by using voice and gestures.
Our solution, which we called HoloMed, is focused on the projection of a
euthocic normal delivery under a web-based infrastructure which also employs a
Kinect. HoloMed is divided in three (3) essential modules: a gesture analyzer,
a data server, and a holographic projection architecture, which can be executed
in several interconnected computers using different network protocols. Tests
used for determining the user's position, illumination factors, and response
times, demonstrate HoloMed's effectiveness as a low-cost system for teaching,
using a natural user interface and 3D images.
| [
{
"created": "Wed, 20 Jul 2016 04:00:44 GMT",
"version": "v1"
}
] | 2016-07-21 | [
[
"Perozo",
"Juan",
""
],
[
"Leung",
"Mimia Lo",
""
],
[
"Ramírez",
"Esmitt",
""
]
] | During medicine studies, visualization of certain elements is common and indispensable in order to get more information about the way they work. Currently, we resort to the use of photographs -which are insufficient due to being static- or tests in patients, which can be invasive or even risky. Therefore, a low-cost approach is proposed by using a 3D visualization. This paper presents a holographic system built with low-cost materials for teaching obstetrics, where student interaction is performed by using voice and gestures. Our solution, which we called HoloMed, is focused on the projection of a euthocic normal delivery under a web-based infrastructure which also employs a Kinect. HoloMed is divided in three (3) essential modules: a gesture analyzer, a data server, and a holographic projection architecture, which can be executed in several interconnected computers using different network protocols. Tests used for determining the user's position, illumination factors, and response times, demonstrate HoloMed's effectiveness as a low-cost system for teaching, using a natural user interface and 3D images. |
1704.01175 | Jens Braband | Jens Braband | Towards an IT Security Risk Assessment Framework for Railway Automation | 14 pages, 3 figures | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some recent incidents have shown that possibly the vulnerability of IT
systems in railway automation has been underestimated. Fortunately, so far,
almost only denial-of-service attacks were successful, but due to several
trends, such as the use of commercial IT and communication systems or
privatization, the threat potential could increase in the near future. However,
up to now, no harmonized IT security risk assessment framework for railway
automation exists. This paper defines an IT security risk assessment framework
which aims to separate IT security and safety requirements as well as
certification processes as far as possible. It builds on the well-known safety
and approval processes from IEC 62425 and integrates IT security requirements
based on the ISA99/IEC62443 standard series. While the detailed results are
related to railway automation the general concepts are also applicable to other
safety-critical application areas.
| [
{
"created": "Tue, 4 Apr 2017 20:33:04 GMT",
"version": "v1"
}
] | 2017-04-06 | [
[
"Braband",
"Jens",
""
]
] | Some recent incidents have shown that possibly the vulnerability of IT systems in railway automation has been underestimated. Fortunately, so far, almost only denial-of-service attacks were successful, but due to several trends, such as the use of commercial IT and communication systems or privatization, the threat potential could increase in the near future. However, up to now, no harmonized IT security risk assessment framework for railway automation exists. This paper defines an IT security risk assessment framework which aims to separate IT security and safety requirements as well as certification processes as far as possible. It builds on the well-known safety and approval processes from IEC 62425 and integrates IT security requirements based on the ISA99/IEC62443 standard series. While the detailed results are related to railway automation the general concepts are also applicable to other safety-critical application areas. |
2401.16832 | Kai Hartung | Panagiotis Pagonis and Kai Hartung and Di Wu and Munir Georges and
S\"oren Gr\"ottrup | Analysis of Knowledge Tracing performance on synthesised student data | Accepted at AI4AI Education workshop 2023 (
https://sme.uni-bamberg.de/ai4ai/ ) | null | null | null | cs.CY cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Knowledge Tracing (KT) aims to predict the future performance of students by
tracking the development of their knowledge states. Despite all the recent
progress made in this field, the application of KT models in education systems
is still restricted from the data perspectives: 1) limited access to real life
data due to data protection concerns, 2) lack of diversity in public datasets,
3) noises in benchmark datasets such as duplicate records. To resolve these
problems, we simulated student data with three statistical strategies based on
public datasets and tested their performance on two KT baselines. While we
observe only minor performance improvement with additional synthetic data, our
work shows that using only synthetic data for training can lead to similar
performance as real data.
| [
{
"created": "Tue, 30 Jan 2024 09:19:50 GMT",
"version": "v1"
}
] | 2024-01-31 | [
[
"Pagonis",
"Panagiotis",
""
],
[
"Hartung",
"Kai",
""
],
[
"Wu",
"Di",
""
],
[
"Georges",
"Munir",
""
],
[
"Gröttrup",
"Sören",
""
]
] | Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data. |
2110.04126 | Hannes St\"ark | Hannes St\"ark, Dominique Beaini, Gabriele Corso, Prudencio Tossou,
Christian Dallago, Stephan G\"unnemann, Pietro Li\`o | 3D Infomax improves GNNs for Molecular Property Prediction | 39th International Conference on Machine Learning (ICML 2022). Also
accepted at NeurIPS 2021 ML4PH, AI4S, and SSL workshops and as oral at ELLIS
ML4Molecules. 24 pages, 7 figures, 18 tables | 39th International Conference on Machine Learning (ICML 2022) | null | null | cs.LG cs.AI q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular property prediction is one of the fastest-growing applications of
deep learning with critical real-world impacts. Including 3D molecular
structure as input to learned models improves their performance for many
molecular tasks. However, this information is infeasible to compute at the
scale required by several real-world applications. We propose pre-training a
model to reason about the geometry of molecules given only their 2D molecular
graphs. Using methods from self-supervised learning, we maximize the mutual
information between 3D summary vectors and the representations of a Graph
Neural Network (GNN) such that they contain latent 3D information. During
fine-tuning on molecules with unknown geometry, the GNN still generates
implicit 3D information and can use it to improve downstream tasks. We show
that 3D pre-training provides significant improvements for a wide range of
properties, such as a 22% average MAE reduction on eight quantum mechanical
properties. Moreover, the learned representations can be effectively
transferred between datasets in different molecular spaces.
| [
{
"created": "Fri, 8 Oct 2021 13:30:49 GMT",
"version": "v1"
},
{
"created": "Sat, 27 Nov 2021 06:54:40 GMT",
"version": "v2"
},
{
"created": "Mon, 23 May 2022 21:48:48 GMT",
"version": "v3"
},
{
"created": "Sat, 4 Jun 2022 22:57:54 GMT",
"version": "v4"
}
] | 2022-06-07 | [
[
"Stärk",
"Hannes",
""
],
[
"Beaini",
"Dominique",
""
],
[
"Corso",
"Gabriele",
""
],
[
"Tossou",
"Prudencio",
""
],
[
"Dallago",
"Christian",
""
],
[
"Günnemann",
"Stephan",
""
],
[
"Liò",
"Pietro",
""
]
] | Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces. |
1811.00692 | Yuanpeng Li | Yuanpeng Li, Yi Yang, Jianyu Wang, Wei Xu | Zero-Shot Transfer VQA Dataset | null | null | null | null | cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acquiring a large vocabulary is an important aspect of human intelligence.
Onecommon approach for human to populating vocabulary is to learn words
duringreading or listening, and then use them in writing or speaking. This
ability totransfer from input to output is natural for human, but it is
difficult for machines.Human spontaneously performs this knowledge transfer in
complicated multimodaltasks, such as Visual Question Answering (VQA). In order
to approach human-levelArtificial Intelligence, we hope to equip machines with
such ability. Therefore, toaccelerate this research, we propose a newzero-shot
transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1.0 dataset in
the way that duringtraining, some words appear only in one module (i.e.
questions) but not in theother (i.e. answers). In this setting, an intelligent
model should understand andlearn the concepts from one module (i.e. questions),
and at test time, transfer themto the other (i.e. predict the concepts as
answers). We conduct evaluation on thisnew dataset using three existing
state-of-the-art VQA neural models. Experimentalresults show a significant drop
in performance on this dataset, indicating existingmethods do not address the
zero-shot transfer problem. Besides, our analysis findsthat this may be caused
by the implicit bias learned during training.
| [
{
"created": "Fri, 2 Nov 2018 01:02:49 GMT",
"version": "v1"
}
] | 2018-11-05 | [
[
"Li",
"Yuanpeng",
""
],
[
"Yang",
"Yi",
""
],
[
"Wang",
"Jianyu",
""
],
[
"Xu",
"Wei",
""
]
] | Acquiring a large vocabulary is an important aspect of human intelligence. Onecommon approach for human to populating vocabulary is to learn words duringreading or listening, and then use them in writing or speaking. This ability totransfer from input to output is natural for human, but it is difficult for machines.Human spontaneously performs this knowledge transfer in complicated multimodaltasks, such as Visual Question Answering (VQA). In order to approach human-levelArtificial Intelligence, we hope to equip machines with such ability. Therefore, toaccelerate this research, we propose a newzero-shot transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1.0 dataset in the way that duringtraining, some words appear only in one module (i.e. questions) but not in theother (i.e. answers). In this setting, an intelligent model should understand andlearn the concepts from one module (i.e. questions), and at test time, transfer themto the other (i.e. predict the concepts as answers). We conduct evaluation on thisnew dataset using three existing state-of-the-art VQA neural models. Experimentalresults show a significant drop in performance on this dataset, indicating existingmethods do not address the zero-shot transfer problem. Besides, our analysis findsthat this may be caused by the implicit bias learned during training. |
2109.06810 | Akash Patel | Akash Patel, Avijit Banerjee, Bjorn Lindqvist, Christoforos
Kanellakis, George Nikolakopoulos | Design and Model Predictive Control of Mars Coaxial Quadrotor | null | null | 10.1109/AERO53065.2022.9843799 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mars has been a prime candidate for planetary exploration of the solar system
because of the science discoveries that support chances of future habitation on
this planet. Martian caves and lava tubes like terrains, which consists of
uneven ground, poor visibility and confined space, makes it impossible for
wheel based rovers to navigate through these areas. In order to address these
limitations and advance the exploration capability in a Martian terrain, this
article presents the design and control of a novel coaxial quadrotor Micro
Aerial Vehicle (MAV). As it will be presented, the key contributions on the
design and control architecture of the proposed Mars coaxial quadrotor, are
introducing an alternative and more enhanced, from a control point of view
concept, when compared in terms of autonomy to Ingenuity. Based on the
presented design, the article will introduce the mathematical modelling and
automatic control framework of the vehicle that will consist of a linearised
model of a co-axial quadrotor and a corresponding Model Predictive Controller
(MPC) for the trajectory tracking. Among the many models, proposed for the
aerial flight on Mars, a reliable control architecture lacks in the related
state of the art. The MPC based closed loop responses of the proposed MAV will
be verified in different conditions during the flight with additional
disturbances, induced to replicate a real flight scenario. In order to further
validate the proposed control architecture and prove the efficacy of the
suggested design, the introduced Mars coaxial quadrotor and the MPC scheme will
be compared to a PID-type controller, similar to the Ingenuity helicopter's
control architecture for the position and the heading.
| [
{
"created": "Tue, 14 Sep 2021 16:45:10 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Oct 2021 11:01:58 GMT",
"version": "v2"
}
] | 2022-08-16 | [
[
"Patel",
"Akash",
""
],
[
"Banerjee",
"Avijit",
""
],
[
"Lindqvist",
"Bjorn",
""
],
[
"Kanellakis",
"Christoforos",
""
],
[
"Nikolakopoulos",
"George",
""
]
] | Mars has been a prime candidate for planetary exploration of the solar system because of the science discoveries that support chances of future habitation on this planet. Martian caves and lava tubes like terrains, which consists of uneven ground, poor visibility and confined space, makes it impossible for wheel based rovers to navigate through these areas. In order to address these limitations and advance the exploration capability in a Martian terrain, this article presents the design and control of a novel coaxial quadrotor Micro Aerial Vehicle (MAV). As it will be presented, the key contributions on the design and control architecture of the proposed Mars coaxial quadrotor, are introducing an alternative and more enhanced, from a control point of view concept, when compared in terms of autonomy to Ingenuity. Based on the presented design, the article will introduce the mathematical modelling and automatic control framework of the vehicle that will consist of a linearised model of a co-axial quadrotor and a corresponding Model Predictive Controller (MPC) for the trajectory tracking. Among the many models, proposed for the aerial flight on Mars, a reliable control architecture lacks in the related state of the art. The MPC based closed loop responses of the proposed MAV will be verified in different conditions during the flight with additional disturbances, induced to replicate a real flight scenario. In order to further validate the proposed control architecture and prove the efficacy of the suggested design, the introduced Mars coaxial quadrotor and the MPC scheme will be compared to a PID-type controller, similar to the Ingenuity helicopter's control architecture for the position and the heading. |
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