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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1804.00702 | Rodrigo Bruno | Rodrigo Bruno, Duarte Patr\'icio, Jos\'e Sim\~ao, Lu\'is Veiga and
Paulo Ferreira | ROLP: Runtime Object Lifetime Profiling for Big Data Memory Management | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Low latency services such as credit-card fraud detection and website targeted
advertisement rely on Big Data platforms (e.g., Lucene, Graphchi, Cassandra)
which run on top of memory managed runtimes, such as the JVM. These platforms,
however, suffer from unpredictable and unacceptably high pause times due to
inadequate memory management decisions (e.g., allocating objects with very
different lifetimes next to each other, resulting in memory fragmentation).
This leads to long and frequent application pause times, breaking Service Level
Agreements (SLAs). This problem has been previously identified and results show
that current memory management techniques are ill-suited for applications that
hold in memory massive amounts of middle to long-lived objects (which is the
case for a wide spectrum of Big Data applications).
Previous works try to reduce such application pauses by allocating objects
off-heap or in special allocation regions/generations, thus alleviating the
pressure on memory management. However, all these solutions require a
combination of programmer effort and knowledge, source code access, or off-line
profiling, with clear negative impact on programmer productivity and/or
application performance.
This paper presents ROLP, a runtime object lifetime profiling system. ROLP
profiles application code at runtime in order to identify which allocation
contexts create objects with middle to long lifetimes, given that such objects
need to be handled differently (regarding short-lived ones). This profiling
information greatly improves memory management decisions, leading to long tail
latencies reduction of up to 51% for Lucene, 85% for GraphChi, and 60% for
Cassandra, with negligible throughput and memory overhead. ROLP is implemented
for the OpenJDK 8 HotSpot JVM and it does not require any programmer effort or
source code access.
| [
{
"created": "Fri, 9 Mar 2018 16:53:44 GMT",
"version": "v1"
}
] | 2018-04-04 | [
[
"Bruno",
"Rodrigo",
""
],
[
"Patrício",
"Duarte",
""
],
[
"Simão",
"José",
""
],
[
"Veiga",
"Luís",
""
],
[
"Ferreira",
"Paulo",
""
]
] | Low latency services such as credit-card fraud detection and website targeted advertisement rely on Big Data platforms (e.g., Lucene, Graphchi, Cassandra) which run on top of memory managed runtimes, such as the JVM. These platforms, however, suffer from unpredictable and unacceptably high pause times due to inadequate memory management decisions (e.g., allocating objects with very different lifetimes next to each other, resulting in memory fragmentation). This leads to long and frequent application pause times, breaking Service Level Agreements (SLAs). This problem has been previously identified and results show that current memory management techniques are ill-suited for applications that hold in memory massive amounts of middle to long-lived objects (which is the case for a wide spectrum of Big Data applications). Previous works try to reduce such application pauses by allocating objects off-heap or in special allocation regions/generations, thus alleviating the pressure on memory management. However, all these solutions require a combination of programmer effort and knowledge, source code access, or off-line profiling, with clear negative impact on programmer productivity and/or application performance. This paper presents ROLP, a runtime object lifetime profiling system. ROLP profiles application code at runtime in order to identify which allocation contexts create objects with middle to long lifetimes, given that such objects need to be handled differently (regarding short-lived ones). This profiling information greatly improves memory management decisions, leading to long tail latencies reduction of up to 51% for Lucene, 85% for GraphChi, and 60% for Cassandra, with negligible throughput and memory overhead. ROLP is implemented for the OpenJDK 8 HotSpot JVM and it does not require any programmer effort or source code access. |
2202.08400 | Changxi You | Changxi You | Real Time Motion Planning Using Constrained Iterative Linear Quadratic
Regulator for On-Road Self-Driving | 10 pages with 10 figures and 2 tables | null | null | null | cs.RO math.OC | http://creativecommons.org/licenses/by/4.0/ | Collision avoidance is one of the most challenging tasks people need to
consider for developing the self-driving technology. In this paper we propose a
new spatiotemporal motion planning algorithm that efficiently solves a
constrained nonlinear optimal control problem using the iterative linear
quadratic regulator (iLQR), which takes into account the uncertain driving
behaviors of the traffic vehicles and minimizes the collision risks between the
self-driving vehicle (referred to as the "ego" vehicle) and the traffic
vehicles such that the ego vehicle is able to maintain sufficiently large
distances to all the surrounding vehicles for achieving the desired collision
avoidance maneuver in traffic. To this end, we introduce the concept of the
"collision polygon" for computing the minimum distances between the ego vehicle
and the traffic vehicles, and provide two different solutions for designing the
constraints of the motion planning problem by properly modeling the behaviors
of the traffic vehicles in order to evaluate the collision risk. Finally, the
iLQR motion planning algorithm is validated in multiple real-time tasks for
collision avoidance using both a simulator and a level-3 autonomous driving
test platform.
| [
{
"created": "Thu, 17 Feb 2022 01:50:44 GMT",
"version": "v1"
}
] | 2022-02-18 | [
[
"You",
"Changxi",
""
]
] | Collision avoidance is one of the most challenging tasks people need to consider for developing the self-driving technology. In this paper we propose a new spatiotemporal motion planning algorithm that efficiently solves a constrained nonlinear optimal control problem using the iterative linear quadratic regulator (iLQR), which takes into account the uncertain driving behaviors of the traffic vehicles and minimizes the collision risks between the self-driving vehicle (referred to as the "ego" vehicle) and the traffic vehicles such that the ego vehicle is able to maintain sufficiently large distances to all the surrounding vehicles for achieving the desired collision avoidance maneuver in traffic. To this end, we introduce the concept of the "collision polygon" for computing the minimum distances between the ego vehicle and the traffic vehicles, and provide two different solutions for designing the constraints of the motion planning problem by properly modeling the behaviors of the traffic vehicles in order to evaluate the collision risk. Finally, the iLQR motion planning algorithm is validated in multiple real-time tasks for collision avoidance using both a simulator and a level-3 autonomous driving test platform. |
2312.00561 | Tingting Ni | Tingting Ni, Maryam Kamgarpour | A safe exploration approach to constrained Markov decision processes | 37 pages, 3 figures | null | null | null | cs.LG math.OC | http://creativecommons.org/licenses/by/4.0/ | We consider discounted infinite horizon constrained Markov decision processes
(CMDPs) where the goal is to find an optimal policy that maximizes the expected
cumulative reward subject to expected cumulative constraints. Motivated by the
application of CMDPs in online learning of safety-critical systems, we focus on
developing a model-free and simulator-free algorithm that ensures constraint
satisfaction during learning. To this end, we develop an interior point
approach based on the log barrier function of the CMDP. Under the commonly
assumed conditions of Fisher non-degeneracy and bounded transfer error of the
policy parameterization, we establish the theoretical properties of the
algorithm. In particular, in contrast to existing CMDP approaches that ensure
policy feasibility only upon convergence, our algorithm guarantees the
feasibility of the policies during the learning process and converges to the
$\varepsilon$-optimal policy with a sample complexity of
$\tilde{\mathcal{O}}(\varepsilon^{-6})$. In comparison to the state-of-the-art
policy gradient-based algorithm, C-NPG-PDA, our algorithm requires an
additional $\mathcal{O}(\varepsilon^{-2})$ samples to ensure policy feasibility
during learning with the same Fisher non-degenerate parameterization.
| [
{
"created": "Fri, 1 Dec 2023 13:16:39 GMT",
"version": "v1"
},
{
"created": "Thu, 23 May 2024 14:20:16 GMT",
"version": "v2"
}
] | 2024-05-24 | [
[
"Ni",
"Tingting",
""
],
[
"Kamgarpour",
"Maryam",
""
]
] | We consider discounted infinite horizon constrained Markov decision processes (CMDPs) where the goal is to find an optimal policy that maximizes the expected cumulative reward subject to expected cumulative constraints. Motivated by the application of CMDPs in online learning of safety-critical systems, we focus on developing a model-free and simulator-free algorithm that ensures constraint satisfaction during learning. To this end, we develop an interior point approach based on the log barrier function of the CMDP. Under the commonly assumed conditions of Fisher non-degeneracy and bounded transfer error of the policy parameterization, we establish the theoretical properties of the algorithm. In particular, in contrast to existing CMDP approaches that ensure policy feasibility only upon convergence, our algorithm guarantees the feasibility of the policies during the learning process and converges to the $\varepsilon$-optimal policy with a sample complexity of $\tilde{\mathcal{O}}(\varepsilon^{-6})$. In comparison to the state-of-the-art policy gradient-based algorithm, C-NPG-PDA, our algorithm requires an additional $\mathcal{O}(\varepsilon^{-2})$ samples to ensure policy feasibility during learning with the same Fisher non-degenerate parameterization. |
2309.10776 | Eduard Fosch-Villaronga | Andreas Hauselmann, Alan M. Sears, Lex Zard and Eduard
Fosch-Villaronga | EU law and emotion data | 8 pages, 2023 11th International Conference on Affective Computing
and Intelligent Interaction (ACII) | null | null | null | cs.CY cs.HC | http://creativecommons.org/licenses/by/4.0/ | This article sheds light on legal implications and challenges surrounding
emotion data processing within the EU's legal framework. Despite the sensitive
nature of emotion data, the GDPR does not categorize it as special data,
resulting in a lack of comprehensive protection. The article also discusses the
nuances of different approaches to affective computing and their relevance to
the processing of special data under the GDPR. Moreover, it points to potential
tensions with data protection principles, such as fairness and accuracy. Our
article also highlights some of the consequences, including harm, that
processing of emotion data may have for individuals concerned. Additionally, we
discuss how the AI Act proposal intends to regulate affective computing.
Finally, the article outlines the new obligations and transparency requirements
introduced by the DSA for online platforms utilizing emotion data. Our article
aims at raising awareness among the affective computing community about the
applicable legal requirements when developing AC systems intended for the EU
market, or when working with study participants located in the EU. We also
stress the importance of protecting the fundamental rights of individuals even
when the law struggles to keep up with technological developments that capture
sensitive emotion data.
| [
{
"created": "Tue, 19 Sep 2023 17:25:02 GMT",
"version": "v1"
}
] | 2023-09-20 | [
[
"Hauselmann",
"Andreas",
""
],
[
"Sears",
"Alan M.",
""
],
[
"Zard",
"Lex",
""
],
[
"Fosch-Villaronga",
"Eduard",
""
]
] | This article sheds light on legal implications and challenges surrounding emotion data processing within the EU's legal framework. Despite the sensitive nature of emotion data, the GDPR does not categorize it as special data, resulting in a lack of comprehensive protection. The article also discusses the nuances of different approaches to affective computing and their relevance to the processing of special data under the GDPR. Moreover, it points to potential tensions with data protection principles, such as fairness and accuracy. Our article also highlights some of the consequences, including harm, that processing of emotion data may have for individuals concerned. Additionally, we discuss how the AI Act proposal intends to regulate affective computing. Finally, the article outlines the new obligations and transparency requirements introduced by the DSA for online platforms utilizing emotion data. Our article aims at raising awareness among the affective computing community about the applicable legal requirements when developing AC systems intended for the EU market, or when working with study participants located in the EU. We also stress the importance of protecting the fundamental rights of individuals even when the law struggles to keep up with technological developments that capture sensitive emotion data. |
1010.4108 | Lorenzo Orecchia | Lorenzo Orecchia and Nisheeth K. Vishnoi | Towards an SDP-based Approach to Spectral Methods: A Nearly-Linear-Time
Algorithm for Graph Partitioning and Decomposition | To appear in SODA 2011 | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider the following graph partitioning problem: The
input is an undirected graph $G=(V,E),$ a balance parameter $b \in (0,1/2]$ and
a target conductance value $\gamma \in (0,1).$ The output is a cut which, if
non-empty, is of conductance at most $O(f),$ for some function $f(G, \gamma),$
and which is either balanced or well correlated with all cuts of conductance at
most $\gamma.$ Spielman and Teng gave an $\tilde{O}(|E|/\gamma^{2})$-time
algorithm for $f= \sqrt{\gamma \log^{3}|V|}$ and used it to decompose graphs
into a collection of near-expanders. We present a new spectral algorithm for
this problem which runs in time $\tilde{O}(|E|/\gamma)$ for $f=\sqrt{\gamma}.$
Our result yields the first nearly-linear time algorithm for the classic
Balanced Separator problem that achieves the asymptotically optimal
approximation guarantee for spectral methods. Our method has the advantage of
being conceptually simple and relies on a primal-dual semidefinite-programming
SDP approach. We first consider a natural SDP relaxation for the Balanced
Separator problem. While it is easy to obtain from this SDP a certificate of
the fact that the graph has no balanced cut of conductance less than $\gamma,$
somewhat surprisingly, we can obtain a certificate for the stronger correlation
condition. This is achieved via a novel separation oracle for our SDP and by
appealing to Arora and Kale's framework to bound the running time. Our result
contains technical ingredients that may be of independent interest.
| [
{
"created": "Wed, 20 Oct 2010 06:37:28 GMT",
"version": "v1"
}
] | 2010-10-21 | [
[
"Orecchia",
"Lorenzo",
""
],
[
"Vishnoi",
"Nisheeth K.",
""
]
] | In this paper, we consider the following graph partitioning problem: The input is an undirected graph $G=(V,E),$ a balance parameter $b \in (0,1/2]$ and a target conductance value $\gamma \in (0,1).$ The output is a cut which, if non-empty, is of conductance at most $O(f),$ for some function $f(G, \gamma),$ and which is either balanced or well correlated with all cuts of conductance at most $\gamma.$ Spielman and Teng gave an $\tilde{O}(|E|/\gamma^{2})$-time algorithm for $f= \sqrt{\gamma \log^{3}|V|}$ and used it to decompose graphs into a collection of near-expanders. We present a new spectral algorithm for this problem which runs in time $\tilde{O}(|E|/\gamma)$ for $f=\sqrt{\gamma}.$ Our result yields the first nearly-linear time algorithm for the classic Balanced Separator problem that achieves the asymptotically optimal approximation guarantee for spectral methods. Our method has the advantage of being conceptually simple and relies on a primal-dual semidefinite-programming SDP approach. We first consider a natural SDP relaxation for the Balanced Separator problem. While it is easy to obtain from this SDP a certificate of the fact that the graph has no balanced cut of conductance less than $\gamma,$ somewhat surprisingly, we can obtain a certificate for the stronger correlation condition. This is achieved via a novel separation oracle for our SDP and by appealing to Arora and Kale's framework to bound the running time. Our result contains technical ingredients that may be of independent interest. |
2111.07753 | Saif Sidhik | Saif Sidhik, Mohan Sridharan, Dirk Ruiken | An Adaptive Framework for Reliable Trajectory Following in
Changing-Contact Robot Manipulation Tasks | 21 pages including references | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | We describe a framework for changing-contact robot manipulation tasks that
require the robot to make and break contacts with objects and surfaces. The
discontinuous interaction dynamics of such tasks make it difficult to construct
and use a single dynamics model or control strategy, and the highly non-linear
nature of the dynamics during contact changes can be damaging to the robot and
the objects. We present an adaptive control framework that enables the robot to
incrementally learn to predict contact changes in a changing contact task,
learn the interaction dynamics of the piece-wise continuous system, and provide
smooth and accurate trajectory tracking using a task-space variable impedance
controller. We experimentally compare the performance of our framework against
that of representative control methods to establish that the adaptive control
and incremental learning components of our framework are needed to achieve
smooth control in the presence of discontinuous dynamics in changing-contact
robot manipulation tasks.
| [
{
"created": "Mon, 15 Nov 2021 13:54:38 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Sidhik",
"Saif",
""
],
[
"Sridharan",
"Mohan",
""
],
[
"Ruiken",
"Dirk",
""
]
] | We describe a framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy, and the highly non-linear nature of the dynamics during contact changes can be damaging to the robot and the objects. We present an adaptive control framework that enables the robot to incrementally learn to predict contact changes in a changing contact task, learn the interaction dynamics of the piece-wise continuous system, and provide smooth and accurate trajectory tracking using a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control and incremental learning components of our framework are needed to achieve smooth control in the presence of discontinuous dynamics in changing-contact robot manipulation tasks. |
2207.00188 | Cheng Li | Cheng Li, Yangxin Liu | Rethinking Query-Key Pairwise Interactions in Vision Transformers | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vision Transformers have achieved state-of-the-art performance in many visual
tasks. Due to the quadratic computational and memory complexities of
self-attention, recent works either apply attention only to low-resolution
inputs or restrict the receptive field to a small local region. To overcome
these limitations, we propose key-only attention, which excludes query-key
pairwise interactions and uses a compute-efficient saliency-gate to obtain
attention weights, modeling local-global interactions in all stages. Key-only
attention has linear computational and memory complexities w.r.t input size. We
use alternate layout to hybridize convolution and attention layers instead of
grafting which is suggested by previous works, so that all stages can benefit
from both spatial attentions and convolutions. We leverage these improvements
to develop a new self-attention model family, LinGlos, which reach
state-of-the-art accuracies on the parameter-limited setting of ImageNet
classification benchmark, and outperform baselines significantly in downstream
tasks, e.g., COCO object detection and ADE20K semantic segmentation.
| [
{
"created": "Fri, 1 Jul 2022 03:36:49 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Jul 2022 02:23:46 GMT",
"version": "v2"
}
] | 2022-07-05 | [
[
"Li",
"Cheng",
""
],
[
"Liu",
"Yangxin",
""
]
] | Vision Transformers have achieved state-of-the-art performance in many visual tasks. Due to the quadratic computational and memory complexities of self-attention, recent works either apply attention only to low-resolution inputs or restrict the receptive field to a small local region. To overcome these limitations, we propose key-only attention, which excludes query-key pairwise interactions and uses a compute-efficient saliency-gate to obtain attention weights, modeling local-global interactions in all stages. Key-only attention has linear computational and memory complexities w.r.t input size. We use alternate layout to hybridize convolution and attention layers instead of grafting which is suggested by previous works, so that all stages can benefit from both spatial attentions and convolutions. We leverage these improvements to develop a new self-attention model family, LinGlos, which reach state-of-the-art accuracies on the parameter-limited setting of ImageNet classification benchmark, and outperform baselines significantly in downstream tasks, e.g., COCO object detection and ADE20K semantic segmentation. |
2310.19704 | Vittorio Mazzia | Vittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann,
Davide Bernardi | A Survey on Knowledge Editing of Neural Networks | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks are becoming increasingly pervasive in academia and
industry, matching and surpassing human performance on a wide variety of fields
and related tasks. However, just as humans, even the largest artificial neural
networks make mistakes, and once-correct predictions can become invalid as the
world progresses in time. Augmenting datasets with samples that account for
mistakes or up-to-date information has become a common workaround in practical
applications. However, the well-known phenomenon of catastrophic forgetting
poses a challenge in achieving precise changes in the implicitly memorized
knowledge of neural network parameters, often requiring a full model
re-training to achieve desired behaviors. That is expensive, unreliable, and
incompatible with the current trend of large self-supervised pre-training,
making it necessary to find more efficient and effective methods for adapting
neural network models to changing data. To address this need, knowledge editing
is emerging as a novel area of research that aims to enable reliable,
data-efficient, and fast changes to a pre-trained target model, without
affecting model behaviors on previously learned tasks. In this survey, we
provide a brief review of this recent artificial intelligence field of
research. We first introduce the problem of editing neural networks, formalize
it in a common framework and differentiate it from more notorious branches of
research such as continuous learning. Next, we provide a review of the most
relevant knowledge editing approaches and datasets proposed so far, grouping
works under four different families: regularization techniques, meta-learning,
direct model editing, and architectural strategies. Finally, we outline some
intersections with other fields of research and potential directions for future
works.
| [
{
"created": "Mon, 30 Oct 2023 16:29:47 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Dec 2023 09:16:36 GMT",
"version": "v2"
}
] | 2023-12-15 | [
[
"Mazzia",
"Vittorio",
""
],
[
"Pedrani",
"Alessandro",
""
],
[
"Caciolai",
"Andrea",
""
],
[
"Rottmann",
"Kay",
""
],
[
"Bernardi",
"Davide",
""
]
] | Deep neural networks are becoming increasingly pervasive in academia and industry, matching and surpassing human performance on a wide variety of fields and related tasks. However, just as humans, even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world progresses in time. Augmenting datasets with samples that account for mistakes or up-to-date information has become a common workaround in practical applications. However, the well-known phenomenon of catastrophic forgetting poses a challenge in achieving precise changes in the implicitly memorized knowledge of neural network parameters, often requiring a full model re-training to achieve desired behaviors. That is expensive, unreliable, and incompatible with the current trend of large self-supervised pre-training, making it necessary to find more efficient and effective methods for adapting neural network models to changing data. To address this need, knowledge editing is emerging as a novel area of research that aims to enable reliable, data-efficient, and fast changes to a pre-trained target model, without affecting model behaviors on previously learned tasks. In this survey, we provide a brief review of this recent artificial intelligence field of research. We first introduce the problem of editing neural networks, formalize it in a common framework and differentiate it from more notorious branches of research such as continuous learning. Next, we provide a review of the most relevant knowledge editing approaches and datasets proposed so far, grouping works under four different families: regularization techniques, meta-learning, direct model editing, and architectural strategies. Finally, we outline some intersections with other fields of research and potential directions for future works. |
1809.10372 | Sivakanth Gopi | Zeev Dvir, Sivakanth Gopi, Yuzhou Gu, Avi Wigderson | Spanoids - an abstraction of spanning structures, and a barrier for LCCs | Conference version to appear in ITCS 2019. arXiv:1810.02494 is merged
into the new version | null | null | null | cs.CC cs.DM cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a simple logical inference structure we call a
$\textsf{spanoid}$ (generalizing the notion of a matroid), which captures
well-studied problems in several areas. These include combinatorial geometry,
algebra (arrangements of hypersurfaces and ideals), statistical physics
(bootstrap percolation) and coding theory. We initiate a thorough investigation
of spanoids, from computational and structural viewpoints, focusing on
parameters relevant to the applications areas above and, in particular, to
questions regarding Locally Correctable Codes (LCCs).
One central parameter we study is the $\textsf{rank}$ of a spanoid, extending
the rank of a matroid and related to the dimension of codes. This leads to one
main application of our work, establishing the first known barrier to improving
the nearly 20-year old bound of Katz-Trevisan (KT) on the dimension of LCCs. On
the one hand, we prove that the KT bound (and its more recent refinements)
holds for the much more general setting of spanoid rank. On the other hand we
show that there exist (random) spanoids whose rank matches these bounds. Thus,
to significantly improve the known bounds one must step out of the spanoid
framework.
Another parameter we explore is the $\textsf{functional rank}$ of a spanoid,
which captures the possibility of turning a given spanoid into an actual code.
The question of the relationship between rank and functional rank is one of the
main questions we raise as it may reveal new avenues for constructing new LCCs
(perhaps even matching the KT bound). As a first step, we develop an entropy
relaxation of functional rank to create a small constant gap and amplify it by
tensoring to construct a spanoid whose functional rank is smaller than rank by
a polynomial factor. This is evidence that the entropy method we develop can
prove polynomially better bounds than KT-type methods on the dimension of LCCs.
| [
{
"created": "Thu, 27 Sep 2018 06:44:15 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Nov 2018 23:13:54 GMT",
"version": "v2"
}
] | 2018-11-22 | [
[
"Dvir",
"Zeev",
""
],
[
"Gopi",
"Sivakanth",
""
],
[
"Gu",
"Yuzhou",
""
],
[
"Wigderson",
"Avi",
""
]
] | We introduce a simple logical inference structure we call a $\textsf{spanoid}$ (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry, algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation) and coding theory. We initiate a thorough investigation of spanoids, from computational and structural viewpoints, focusing on parameters relevant to the applications areas above and, in particular, to questions regarding Locally Correctable Codes (LCCs). One central parameter we study is the $\textsf{rank}$ of a spanoid, extending the rank of a matroid and related to the dimension of codes. This leads to one main application of our work, establishing the first known barrier to improving the nearly 20-year old bound of Katz-Trevisan (KT) on the dimension of LCCs. On the one hand, we prove that the KT bound (and its more recent refinements) holds for the much more general setting of spanoid rank. On the other hand we show that there exist (random) spanoids whose rank matches these bounds. Thus, to significantly improve the known bounds one must step out of the spanoid framework. Another parameter we explore is the $\textsf{functional rank}$ of a spanoid, which captures the possibility of turning a given spanoid into an actual code. The question of the relationship between rank and functional rank is one of the main questions we raise as it may reveal new avenues for constructing new LCCs (perhaps even matching the KT bound). As a first step, we develop an entropy relaxation of functional rank to create a small constant gap and amplify it by tensoring to construct a spanoid whose functional rank is smaller than rank by a polynomial factor. This is evidence that the entropy method we develop can prove polynomially better bounds than KT-type methods on the dimension of LCCs. |
2012.15505 | Anthony David Blaom | Anthony D. Blaom and Sebastian J. Vollmer | Flexible model composition in machine learning and its implementation in
MLJ | 13 pages, 3 figures | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | A graph-based protocol called `learning networks' which combine assorted
machine learning models into meta-models is described. Learning networks are
shown to overcome several limitations of model composition as implemented in
the dominant machine learning platforms. After illustrating the protocol in
simple examples, a concise syntax for specifying a learning network,
implemented in the MLJ framework, is presented. Using the syntax, it is shown
that learning networks are are sufficiently flexible to include Wolpert's model
stacking, with out-of-sample predictions for the base learners.
| [
{
"created": "Thu, 31 Dec 2020 08:49:43 GMT",
"version": "v1"
}
] | 2021-01-01 | [
[
"Blaom",
"Anthony D.",
""
],
[
"Vollmer",
"Sebastian J.",
""
]
] | A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described. Learning networks are shown to overcome several limitations of model composition as implemented in the dominant machine learning platforms. After illustrating the protocol in simple examples, a concise syntax for specifying a learning network, implemented in the MLJ framework, is presented. Using the syntax, it is shown that learning networks are are sufficiently flexible to include Wolpert's model stacking, with out-of-sample predictions for the base learners. |
1408.3709 | Parama Bagchi | Parama Bagchi, Debotosh Bhattacharjee and Mita Nasipuri | Robust 3D face recognition in presence of pose and partial occlusions or
missing parts | the paper is of 15 pages, International Journal in Foundations of
Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a robust 3D face recognition system which can
handle pose as well as occlusions in real world. The system at first takes as
input, a 3D range image, simultaneously registers it using ICP(Iterative
Closest Point) algorithm. ICP used in this work, registers facial surfaces to a
common model by minimizing distances between a probe model and a gallery model.
However the performance of ICP relies heavily on the initial conditions. Hence,
it is necessary to provide an initial registration, which will be improved
iteratively and finally converge to the best alignment possible. Once the faces
are registered, the occlusions are automatically extracted by thresholding the
depth map values of the 3D image. After the occluded regions are detected,
restoration is done by Principal Component Analysis (PCA). The restored images,
after the removal of occlusions, are then fed to the recognition system for
classification purpose. Features are extracted from the reconstructed
non-occluded face images in the form of face normals. The experimental results
which were obtained on the occluded facial images from the Bosphorus 3D face
database, illustrate that our occlusion compensation scheme has attained a
recognition accuracy of 91.30%.
| [
{
"created": "Sat, 16 Aug 2014 06:43:30 GMT",
"version": "v1"
}
] | 2014-08-19 | [
[
"Bagchi",
"Parama",
""
],
[
"Bhattacharjee",
"Debotosh",
""
],
[
"Nasipuri",
"Mita",
""
]
] | In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions in real world. The system at first takes as input, a 3D range image, simultaneously registers it using ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model by minimizing distances between a probe model and a gallery model. However the performance of ICP relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will be improved iteratively and finally converge to the best alignment possible. Once the faces are registered, the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored images, after the removal of occlusions, are then fed to the recognition system for classification purpose. Features are extracted from the reconstructed non-occluded face images in the form of face normals. The experimental results which were obtained on the occluded facial images from the Bosphorus 3D face database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of 91.30%. |
1611.07800 | Ehsan Abbasnejad M | Ehsan Abbasnejad, Anthony Dick, Anton van den Hengel | Infinite Variational Autoencoder for Semi-Supervised Learning | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an infinite variational autoencoder (VAE) whose capacity
adapts to suit the input data. This is achieved using a mixture model where the
mixing coefficients are modeled by a Dirichlet process, allowing us to
integrate over the coefficients when performing inference. Critically, this
then allows us to automatically vary the number of autoencoders in the mixture
based on the data. Experiments show the flexibility of our method, particularly
for semi-supervised learning, where only a small number of training samples are
available.
| [
{
"created": "Wed, 23 Nov 2016 13:59:57 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Nov 2016 01:28:08 GMT",
"version": "v2"
}
] | 2016-11-28 | [
[
"Abbasnejad",
"Ehsan",
""
],
[
"Dick",
"Anthony",
""
],
[
"Hengel",
"Anton van den",
""
]
] | This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available. |
2208.00094 | Yulong Cao | Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar,
Chaowei Xiao, Marco Pavone | Robust Trajectory Prediction against Adversarial Attacks | null | null | null | null | cs.LG cs.AI cs.CR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trajectory prediction using deep neural networks (DNNs) is an essential
component of autonomous driving (AD) systems. However, these methods are
vulnerable to adversarial attacks, leading to serious consequences such as
collisions. In this work, we identify two key ingredients to defend trajectory
prediction models against adversarial attacks including (1) designing effective
adversarial training methods and (2) adding domain-specific data augmentation
to mitigate the performance degradation on clean data. We demonstrate that our
method is able to improve the performance by 46% on adversarial data and at the
cost of only 3% performance degradation on clean data, compared to the model
trained with clean data. Additionally, compared to existing robust methods, our
method can improve performance by 21% on adversarial examples and 9% on clean
data. Our robust model is evaluated with a planner to study its downstream
impacts. We demonstrate that our model can significantly reduce the severe
accident rates (e.g., collisions and off-road driving).
| [
{
"created": "Fri, 29 Jul 2022 22:35:05 GMT",
"version": "v1"
}
] | 2022-08-02 | [
[
"Cao",
"Yulong",
""
],
[
"Xu",
"Danfei",
""
],
[
"Weng",
"Xinshuo",
""
],
[
"Mao",
"Zhuoqing",
""
],
[
"Anandkumar",
"Anima",
""
],
[
"Xiao",
"Chaowei",
""
],
[
"Pavone",
"Marco",
""
]
] | Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks including (1) designing effective adversarial training methods and (2) adding domain-specific data augmentation to mitigate the performance degradation on clean data. We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data. Additionally, compared to existing robust methods, our method can improve performance by 21% on adversarial examples and 9% on clean data. Our robust model is evaluated with a planner to study its downstream impacts. We demonstrate that our model can significantly reduce the severe accident rates (e.g., collisions and off-road driving). |
2210.03292 | Hongrui Gao | Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan | Unsupervised Semantic Representation Learning of Scientific Literature
Based on Graph Attention Mechanism and Maximum Mutual Information | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning.
| [
{
"created": "Fri, 7 Oct 2022 02:48:14 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Jan 2023 09:25:18 GMT",
"version": "v2"
}
] | 2023-01-31 | [
[
"Gao",
"Hongrui",
""
],
[
"Li",
"Yawen",
""
],
[
"Liang",
"Meiyu",
""
],
[
"Guan",
"Zeli",
""
]
] | Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features make the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. Therefore, the correlations between vertex features can be better integrated into the model. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning. |
2207.12259 | AmirPouya Hemmasian | AmirPouya Hemmasian, Francis Ogoke, Parand Akbari, Jonathan Malen,
Jack Beuth, Amir Barati Farimani | Surrogate Modeling of Melt Pool Thermal Field using Deep Learning | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Powder-based additive manufacturing has transformed the manufacturing
industry over the last decade. In Laser Powder Bed Fusion, a specific part is
built in an iterative manner in which two-dimensional cross-sections are formed
on top of each other by melting and fusing the proper areas of the powder bed.
In this process, the behavior of the melt pool and its thermal field has a very
important role in predicting the quality of the manufactured part and its
possible defects. However, the simulation of such a complex phenomenon is
usually very time-consuming and requires huge computational resources. Flow-3D
is one of the software packages capable of executing such simulations using
iterative numerical solvers. In this work, we create three datasets of
single-trail processes using Flow-3D and use them to train a convolutional
neural network capable of predicting the behavior of the three-dimensional
thermal field of the melt pool solely by taking three parameters as input:
laser power, laser velocity, and time step. The CNN achieves a relative Root
Mean Squared Error of 2% to 3% for the temperature field and an average
Intersection over Union score of 80% to 90% in predicting the melt pool area.
Moreover, since time is included as one of the inputs of the model, the thermal
field can be instantly obtained for any arbitrary time step without the need to
iterate and compute all the steps
| [
{
"created": "Mon, 25 Jul 2022 15:27:16 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Aug 2022 21:16:44 GMT",
"version": "v2"
}
] | 2022-08-08 | [
[
"Hemmasian",
"AmirPouya",
""
],
[
"Ogoke",
"Francis",
""
],
[
"Akbari",
"Parand",
""
],
[
"Malen",
"Jonathan",
""
],
[
"Beuth",
"Jack",
""
],
[
"Farimani",
"Amir Barati",
""
]
] | Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in which two-dimensional cross-sections are formed on top of each other by melting and fusing the proper areas of the powder bed. In this process, the behavior of the melt pool and its thermal field has a very important role in predicting the quality of the manufactured part and its possible defects. However, the simulation of such a complex phenomenon is usually very time-consuming and requires huge computational resources. Flow-3D is one of the software packages capable of executing such simulations using iterative numerical solvers. In this work, we create three datasets of single-trail processes using Flow-3D and use them to train a convolutional neural network capable of predicting the behavior of the three-dimensional thermal field of the melt pool solely by taking three parameters as input: laser power, laser velocity, and time step. The CNN achieves a relative Root Mean Squared Error of 2% to 3% for the temperature field and an average Intersection over Union score of 80% to 90% in predicting the melt pool area. Moreover, since time is included as one of the inputs of the model, the thermal field can be instantly obtained for any arbitrary time step without the need to iterate and compute all the steps |
2206.08722 | J\"ames M\'en\'etrey | J\"ames M\'en\'etrey, Marcelo Pasin, Pascal Felber, Valerio Schiavoni | WaTZ: A Trusted WebAssembly Runtime Environment with Remote Attestation
for TrustZone | This publication incorporates results from the VEDLIoT project, which
received funding from the European Union's Horizon 2020 research and
innovation programme under grant agreement No 957197 | ICDCS'22: Proceedings of the 42nd IEEE International Conference on
Distributed Computing Systems, July 2022 | 10.1109/ICDCS54860.2022.00116 | null | cs.CR cs.DC cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | WebAssembly (Wasm) is a novel low-level bytecode format that swiftly gained
popularity for its efficiency, versatility and security, with near-native
performance. Besides, trusted execution environments (TEEs) shield critical
software assets against compromised infrastructures. However, TEEs do not
guarantee the code to be trustworthy or that it was not tampered with. Instead,
one relies on remote attestation to assess the code before execution. This
paper describes WaTZ, which is (i) an efficient and secure runtime for trusted
execution of Wasm code for Arm's TrustZone TEE, and (ii) a lightweight remote
attestation system optimised for Wasm applications running in TrustZone, as it
lacks built-in mechanisms for attestation. The remote attestation protocol is
formally verified using a state-of-the-art analyser and model checker. Our
extensive evaluation of Arm-based hardware uses synthetic and real-world
benchmarks, illustrating typical tasks IoT devices achieve. WaTZ's execution
speed is on par with Wasm runtimes in the normal world and reaches roughly half
the speed of native execution, which is compensated by the additional security
guarantees and the interoperability offered by Wasm. WaTZ is open-source and
available on GitHub along with instructions to reproduce our experiments.
| [
{
"created": "Fri, 17 Jun 2022 12:19:48 GMT",
"version": "v1"
},
{
"created": "Wed, 17 May 2023 15:04:34 GMT",
"version": "v2"
}
] | 2023-05-18 | [
[
"Ménétrey",
"Jämes",
""
],
[
"Pasin",
"Marcelo",
""
],
[
"Felber",
"Pascal",
""
],
[
"Schiavoni",
"Valerio",
""
]
] | WebAssembly (Wasm) is a novel low-level bytecode format that swiftly gained popularity for its efficiency, versatility and security, with near-native performance. Besides, trusted execution environments (TEEs) shield critical software assets against compromised infrastructures. However, TEEs do not guarantee the code to be trustworthy or that it was not tampered with. Instead, one relies on remote attestation to assess the code before execution. This paper describes WaTZ, which is (i) an efficient and secure runtime for trusted execution of Wasm code for Arm's TrustZone TEE, and (ii) a lightweight remote attestation system optimised for Wasm applications running in TrustZone, as it lacks built-in mechanisms for attestation. The remote attestation protocol is formally verified using a state-of-the-art analyser and model checker. Our extensive evaluation of Arm-based hardware uses synthetic and real-world benchmarks, illustrating typical tasks IoT devices achieve. WaTZ's execution speed is on par with Wasm runtimes in the normal world and reaches roughly half the speed of native execution, which is compensated by the additional security guarantees and the interoperability offered by Wasm. WaTZ is open-source and available on GitHub along with instructions to reproduce our experiments. |
2012.14740 | Lei Cui | Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang,
Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou | LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document
Understanding | ACL 2021 main conference | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Pre-training of text and layout has proved effective in a variety of
visually-rich document understanding tasks due to its effective model
architecture and the advantage of large-scale unlabeled scanned/digital-born
documents. We propose LayoutLMv2 architecture with new pre-training tasks to
model the interaction among text, layout, and image in a single multi-modal
framework. Specifically, with a two-stream multi-modal Transformer encoder,
LayoutLMv2 uses not only the existing masked visual-language modeling task but
also the new text-image alignment and text-image matching tasks, which make it
better capture the cross-modality interaction in the pre-training stage.
Meanwhile, it also integrates a spatial-aware self-attention mechanism into the
Transformer architecture so that the model can fully understand the relative
positional relationship among different text blocks. Experiment results show
that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new
state-of-the-art results on a wide variety of downstream visually-rich document
understanding tasks, including FUNSD (0.7895 $\to$ 0.8420), CORD (0.9493 $\to$
0.9601), SROIE (0.9524 $\to$ 0.9781), Kleister-NDA (0.8340 $\to$ 0.8520),
RVL-CDIP (0.9443 $\to$ 0.9564), and DocVQA (0.7295 $\to$ 0.8672). We made our
model and code publicly available at \url{https://aka.ms/layoutlmv2}.
| [
{
"created": "Tue, 29 Dec 2020 13:01:52 GMT",
"version": "v1"
},
{
"created": "Thu, 6 May 2021 07:02:57 GMT",
"version": "v2"
},
{
"created": "Tue, 11 May 2021 06:42:33 GMT",
"version": "v3"
},
{
"created": "Mon, 10 Jan 2022 04:08:10 GMT",
"version": "v4"
}
] | 2022-01-11 | [
[
"Xu",
"Yang",
""
],
[
"Xu",
"Yiheng",
""
],
[
"Lv",
"Tengchao",
""
],
[
"Cui",
"Lei",
""
],
[
"Wei",
"Furu",
""
],
[
"Wang",
"Guoxin",
""
],
[
"Lu",
"Yijuan",
""
],
[
"Florencio",
"Dinei",
""
],
[
"Zhang",
"Cha",
""
],
[
"Che",
"Wanxiang",
""
],
[
"Zhang",
"Min",
""
],
[
"Zhou",
"Lidong",
""
]
] | Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 $\to$ 0.8420), CORD (0.9493 $\to$ 0.9601), SROIE (0.9524 $\to$ 0.9781), Kleister-NDA (0.8340 $\to$ 0.8520), RVL-CDIP (0.9443 $\to$ 0.9564), and DocVQA (0.7295 $\to$ 0.8672). We made our model and code publicly available at \url{https://aka.ms/layoutlmv2}. |
2407.11421 | Junhao Chen | Junhao Chen, Shengding Hu, Zhiyuan Liu, Maosong Sun | States Hidden in Hidden States: LLMs Emerge Discrete State
Representations Implicitly | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) exhibit various emergent abilities. Among these
abilities, some might reveal the internal working mechanisms of models. In this
paper, we uncover a novel emergent capability in models: the intrinsic ability
to perform extended sequences of calculations without relying on
chain-of-thought step-by-step solutions. Remarkably, the most advanced models
can directly output the results of two-digit number additions with lengths
extending up to 15 addends. We hypothesize that the model emerges Implicit
Discrete State Representations (IDSRs) within its hidden states and performs
symbolic calculations internally. To test this hypothesis, we design a sequence
of experiments that look into the hidden states. Specifically, we first confirm
that IDSRs exist. Then, we provide interesting observations about the formation
of IDSRs from layer, digit, and sequence perspectives. Finally, we confirm that
models indeed use IDSRs to produce the final answers. However, we also discover
that these state representations are far from lossless in current open-sourced
models, leading to inaccuracies in their final performance. Our work presents a
novel exploration of LLMs' symbolic calculation abilities and the underlying
mechanisms.
| [
{
"created": "Tue, 16 Jul 2024 06:27:22 GMT",
"version": "v1"
}
] | 2024-07-17 | [
[
"Chen",
"Junhao",
""
],
[
"Hu",
"Shengding",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"Sun",
"Maosong",
""
]
] | Large Language Models (LLMs) exhibit various emergent abilities. Among these abilities, some might reveal the internal working mechanisms of models. In this paper, we uncover a novel emergent capability in models: the intrinsic ability to perform extended sequences of calculations without relying on chain-of-thought step-by-step solutions. Remarkably, the most advanced models can directly output the results of two-digit number additions with lengths extending up to 15 addends. We hypothesize that the model emerges Implicit Discrete State Representations (IDSRs) within its hidden states and performs symbolic calculations internally. To test this hypothesis, we design a sequence of experiments that look into the hidden states. Specifically, we first confirm that IDSRs exist. Then, we provide interesting observations about the formation of IDSRs from layer, digit, and sequence perspectives. Finally, we confirm that models indeed use IDSRs to produce the final answers. However, we also discover that these state representations are far from lossless in current open-sourced models, leading to inaccuracies in their final performance. Our work presents a novel exploration of LLMs' symbolic calculation abilities and the underlying mechanisms. |
2012.05756 | Lingda Wang | Lingda Wang, Bingcong Li, Huozhi Zhou, Georgios B. Giannakis, Lav R.
Varshney, Zhizhen Zhao | Adversarial Linear Contextual Bandits with Graph-Structured Side
Observations | fix some typos | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the adversarial graphical contextual bandits, a variant of
adversarial multi-armed bandits that leverage two categories of the most common
side information: \emph{contexts} and \emph{side observations}. In this
setting, a learning agent repeatedly chooses from a set of $K$ actions after
being presented with a $d$-dimensional context vector. The agent not only
incurs and observes the loss of the chosen action, but also observes the losses
of its neighboring actions in the observation structures, which are encoded as
a series of feedback graphs. This setting models a variety of applications in
social networks, where both contexts and graph-structured side observations are
available. Two efficient algorithms are developed based on \texttt{EXP3}. Under
mild conditions, our analysis shows that for undirected feedback graphs the
first algorithm, \texttt{EXP3-LGC-U}, achieves the regret of order
$\mathcal{O}(\sqrt{(K+\alpha(G)d)T\log{K}})$ over the time horizon $T$, where
$\alpha(G)$ is the average \emph{independence number} of the feedback graphs. A
slightly weaker result is presented for the directed graph setting as well. The
second algorithm, \texttt{EXP3-LGC-IX}, is developed for a special class of
problems, for which the regret is reduced to
$\mathcal{O}(\sqrt{\alpha(G)dT\log{K}\log(KT)})$ for both directed as well as
undirected feedback graphs. Numerical tests corroborate the efficiency of
proposed algorithms.
| [
{
"created": "Thu, 10 Dec 2020 15:40:07 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Dec 2020 01:52:23 GMT",
"version": "v2"
},
{
"created": "Wed, 17 Feb 2021 01:58:52 GMT",
"version": "v3"
}
] | 2021-02-18 | [
[
"Wang",
"Lingda",
""
],
[
"Li",
"Bingcong",
""
],
[
"Zhou",
"Huozhi",
""
],
[
"Giannakis",
"Georgios B.",
""
],
[
"Varshney",
"Lav R.",
""
],
[
"Zhao",
"Zhizhen",
""
]
] | This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: \emph{contexts} and \emph{side observations}. In this setting, a learning agent repeatedly chooses from a set of $K$ actions after being presented with a $d$-dimensional context vector. The agent not only incurs and observes the loss of the chosen action, but also observes the losses of its neighboring actions in the observation structures, which are encoded as a series of feedback graphs. This setting models a variety of applications in social networks, where both contexts and graph-structured side observations are available. Two efficient algorithms are developed based on \texttt{EXP3}. Under mild conditions, our analysis shows that for undirected feedback graphs the first algorithm, \texttt{EXP3-LGC-U}, achieves the regret of order $\mathcal{O}(\sqrt{(K+\alpha(G)d)T\log{K}})$ over the time horizon $T$, where $\alpha(G)$ is the average \emph{independence number} of the feedback graphs. A slightly weaker result is presented for the directed graph setting as well. The second algorithm, \texttt{EXP3-LGC-IX}, is developed for a special class of problems, for which the regret is reduced to $\mathcal{O}(\sqrt{\alpha(G)dT\log{K}\log(KT)})$ for both directed as well as undirected feedback graphs. Numerical tests corroborate the efficiency of proposed algorithms. |
2007.08688 | Qisheng Zhang | Qisheng Zhang, Abdullah Zubair Mohammed, Zelin Wan, Jin-Hee Cho,
Terrence J. Moore | Diversity-By-Design for Dependable and Secure Cyber-Physical Systems: A
Survey | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diversity-based security approaches have been studied for several decades
since the 1970's. The concept of diversity-by-design emerged in the 1980's and,
since then, diversity-based system design research has been explored to build
more secure and dependable systems. In this work, we are particularly
interested in providing an in-depth, comprehensive survey of existing
diversity-based approaches, insights, and future work directions for those who
want to conduct research on developing secure and dependable cyber-physical
systems (CPSs) using diversity as a system design feature. To be specific, this
survey paper provides: (i) The common concept of diversity based on a
multidisciplinary study of diversity from nine different fields along with the
historical evolution of diversity-by-design for security; (ii) The design
principles of diversity-based approaches; (iii) The key benefits and caveats of
using diversity-by-design; (iv) The key concerns of CPS environments in
introducing diversity-by-design; (v) A variety of existing diversity-based
approaches based on five different classifications; (vi) The types of attacks
mitigated by existing diversity-based approaches; (vii) The overall trends of
evaluation methodologies used in diversity-based approaches, in terms of
metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps
identified from this extensive survey.
| [
{
"created": "Thu, 16 Jul 2020 23:25:36 GMT",
"version": "v1"
}
] | 2020-07-20 | [
[
"Zhang",
"Qisheng",
""
],
[
"Mohammed",
"Abdullah Zubair",
""
],
[
"Wan",
"Zelin",
""
],
[
"Cho",
"Jin-Hee",
""
],
[
"Moore",
"Terrence J.",
""
]
] | Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey. |
2111.03212 | Jiangwei Liu | Jiangwei Liu, Liangyu Min and Xiaohong Huang | An overview of event extraction and its applications | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | With the rapid development of information technology, online platforms have
produced enormous text resources. As a particular form of Information
Extraction (IE), Event Extraction (EE) has gained increasing popularity due to
its ability to automatically extract events from human language. However, there
are limited literature surveys on event extraction. Existing review works
either spend much effort describing the details of various approaches or focus
on a particular field. This study provides a comprehensive overview of the
state-of-the-art event extraction methods and their applications from text,
including closed-domain and open-domain event extraction. A trait of this
survey is that it provides an overview in moderate complexity, avoiding
involving too many details of particular approaches. This study focuses on
discussing the common characters, application fields, advantages, and
disadvantages of representative works, ignoring the specificities of individual
approaches. Finally, we summarize the common issues, current solutions, and
future research directions. We hope this work could help researchers and
practitioners obtain a quick overview of recent event extraction.
| [
{
"created": "Fri, 5 Nov 2021 01:37:47 GMT",
"version": "v1"
}
] | 2021-11-08 | [
[
"Liu",
"Jiangwei",
""
],
[
"Min",
"Liangyu",
""
],
[
"Huang",
"Xiaohong",
""
]
] | With the rapid development of information technology, online platforms have produced enormous text resources. As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language. However, there are limited literature surveys on event extraction. Existing review works either spend much effort describing the details of various approaches or focus on a particular field. This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction. A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches. This study focuses on discussing the common characters, application fields, advantages, and disadvantages of representative works, ignoring the specificities of individual approaches. Finally, we summarize the common issues, current solutions, and future research directions. We hope this work could help researchers and practitioners obtain a quick overview of recent event extraction. |
2002.10035 | Xianmang He | Xianmang He, Yindong Chen, Zusheng Zhang | Improving the Linkage Construction with Echelon-Ferrers for
Constant-Dimension Codes | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Echelon-Ferrers is an important method to improve lower bounds for
constant-dimension codes, which can be applied on various parameters. Fagang Li
[12] combined the linkage construction and echelon-Ferrers to obtain some new
lower bounds of constant-dimension codes. In this letter, we generalize this
linkage construction to obtain new lower bounds.
| [
{
"created": "Mon, 24 Feb 2020 01:57:57 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Mar 2020 16:43:18 GMT",
"version": "v2"
},
{
"created": "Thu, 30 Jul 2020 17:58:14 GMT",
"version": "v3"
}
] | 2020-07-31 | [
[
"He",
"Xianmang",
""
],
[
"Chen",
"Yindong",
""
],
[
"Zhang",
"Zusheng",
""
]
] | Echelon-Ferrers is an important method to improve lower bounds for constant-dimension codes, which can be applied on various parameters. Fagang Li [12] combined the linkage construction and echelon-Ferrers to obtain some new lower bounds of constant-dimension codes. In this letter, we generalize this linkage construction to obtain new lower bounds. |
2210.00765 | Xiaoqi Zhao | Hongsheng Wang, Xiaoqi Zhao, Youwei Pang, Jinqing Qi | Few-Shot Segmentation via Rich Prototype Generation and Recurrent
Prediction Enhancement | Accepted in PRCV 2022 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prototype learning and decoder construction are the keys for few-shot
segmentation. However, existing methods use only a single prototype generation
mode, which can not cope with the intractable problem of objects with various
scales. Moreover, the one-way forward propagation adopted by previous methods
may cause information dilution from registered features during the decoding
process. In this research, we propose a rich prototype generation module (RPGM)
and a recurrent prediction enhancement module (RPEM) to reinforce the prototype
learning paradigm and build a unified memory-augmented decoder for few-shot
segmentation, respectively. Specifically, the RPGM combines superpixel and
K-means clustering to generate rich prototype features with complementary scale
relationships and adapt the scale gap between support and query images. The
RPEM utilizes the recurrent mechanism to design a round-way propagation
decoder. In this way, registered features can provide object-aware information
continuously. Experiments show that our method consistently outperforms other
competitors on two popular benchmarks PASCAL-${{5}^{i}}$ and COCO-${{20}^{i}}$.
| [
{
"created": "Mon, 3 Oct 2022 08:46:52 GMT",
"version": "v1"
}
] | 2022-10-04 | [
[
"Wang",
"Hongsheng",
""
],
[
"Zhao",
"Xiaoqi",
""
],
[
"Pang",
"Youwei",
""
],
[
"Qi",
"Jinqing",
""
]
] | Prototype learning and decoder construction are the keys for few-shot segmentation. However, existing methods use only a single prototype generation mode, which can not cope with the intractable problem of objects with various scales. Moreover, the one-way forward propagation adopted by previous methods may cause information dilution from registered features during the decoding process. In this research, we propose a rich prototype generation module (RPGM) and a recurrent prediction enhancement module (RPEM) to reinforce the prototype learning paradigm and build a unified memory-augmented decoder for few-shot segmentation, respectively. Specifically, the RPGM combines superpixel and K-means clustering to generate rich prototype features with complementary scale relationships and adapt the scale gap between support and query images. The RPEM utilizes the recurrent mechanism to design a round-way propagation decoder. In this way, registered features can provide object-aware information continuously. Experiments show that our method consistently outperforms other competitors on two popular benchmarks PASCAL-${{5}^{i}}$ and COCO-${{20}^{i}}$. |
2403.13248 | Zhengqing Yuan | Zhengqing Yuan, Ruoxi Chen, Zhaoxu Li, Haolong Jia, Lifang He, Chi
Wang, Lichao Sun | Mora: Enabling Generalist Video Generation via A Multi-Agent Framework | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sora is the first large-scale generalist video generation model that garnered
significant attention across society. Since its launch by OpenAI in February
2024, no other video generation models have paralleled {Sora}'s performance or
its capacity to support a broad spectrum of video generation tasks.
Additionally, there are only a few fully published video generation models,
with the majority being closed-source. To address this gap, this paper proposes
a new multi-agent framework Mora, which incorporates several advanced visual AI
agents to replicate generalist video generation demonstrated by Sora. In
particular, Mora can utilize multiple visual agents and successfully mimic
Sora's video generation capabilities in various tasks, such as (1)
text-to-video generation, (2) text-conditional image-to-video generation, (3)
extend generated videos, (4) video-to-video editing, (5) connect videos and (6)
simulate digital worlds. Our extensive experimental results show that Mora
achieves performance that is proximate to that of Sora in various tasks.
However, there exists an obvious performance gap between our work and Sora when
assessed holistically. In summary, we hope this project can guide the future
trajectory of video generation through collaborative AI agents.
| [
{
"created": "Wed, 20 Mar 2024 02:19:21 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Mar 2024 12:43:56 GMT",
"version": "v2"
}
] | 2024-03-25 | [
[
"Yuan",
"Zhengqing",
""
],
[
"Chen",
"Ruoxi",
""
],
[
"Li",
"Zhaoxu",
""
],
[
"Jia",
"Haolong",
""
],
[
"He",
"Lifang",
""
],
[
"Wang",
"Chi",
""
],
[
"Sun",
"Lichao",
""
]
] | Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents. |
2109.04027 | Zilin Si | Zilin Si, Wenzhen Yuan | Taxim: An Example-based Simulation Model for GelSight Tactile Sensors | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Simulation is widely used in robotics for system verification and large-scale
data collection. However, simulating sensors, including tactile sensors, has
been a long-standing challenge. In this paper, we propose Taxim, a realistic
and high-speed simulation model for a vision-based tactile sensor, GelSight. A
GelSight sensor uses a piece of soft elastomer as the medium of contact and
embeds optical structures to capture the deformation of the elastomer, which
infers the geometry and forces applied at the contact surface. We propose an
example-based method for simulating GelSight: we simulate the optical response
to the deformation with a polynomial look-up table. This table maps the
deformed geometries to pixel intensity sampled by the embedded camera. In order
to simulate the surface markers' motion that is caused by the surface stretch
of the elastomer, we apply the linear elastic deformation theory and the
superposition principle. The simulation model is calibrated with less than 100
data points from a real sensor. The example-based approach enables the model to
easily migrate to other GelSight sensors or its variations. To the best of our
knowledge, our simulation framework is the first to incorporate marker motion
field simulation that derives from elastomer deformation together with the
optical simulation, creating a comprehensive and computationally efficient
tactile simulation framework. Experiments reveal that our optical simulation
has the lowest pixel-wise intensity errors compared to prior work and can run
online with CPU computing. Our code and supplementary materials are
open-sourced at https://github.com/CMURoboTouch/Taxim.
| [
{
"created": "Thu, 9 Sep 2021 04:22:27 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Dec 2021 17:02:43 GMT",
"version": "v2"
}
] | 2021-12-15 | [
[
"Si",
"Zilin",
""
],
[
"Yuan",
"Wenzhen",
""
]
] | Simulation is widely used in robotics for system verification and large-scale data collection. However, simulating sensors, including tactile sensors, has been a long-standing challenge. In this paper, we propose Taxim, a realistic and high-speed simulation model for a vision-based tactile sensor, GelSight. A GelSight sensor uses a piece of soft elastomer as the medium of contact and embeds optical structures to capture the deformation of the elastomer, which infers the geometry and forces applied at the contact surface. We propose an example-based method for simulating GelSight: we simulate the optical response to the deformation with a polynomial look-up table. This table maps the deformed geometries to pixel intensity sampled by the embedded camera. In order to simulate the surface markers' motion that is caused by the surface stretch of the elastomer, we apply the linear elastic deformation theory and the superposition principle. The simulation model is calibrated with less than 100 data points from a real sensor. The example-based approach enables the model to easily migrate to other GelSight sensors or its variations. To the best of our knowledge, our simulation framework is the first to incorporate marker motion field simulation that derives from elastomer deformation together with the optical simulation, creating a comprehensive and computationally efficient tactile simulation framework. Experiments reveal that our optical simulation has the lowest pixel-wise intensity errors compared to prior work and can run online with CPU computing. Our code and supplementary materials are open-sourced at https://github.com/CMURoboTouch/Taxim. |
1909.03772 | Nicolai Anton Lynnerup | Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam | A Survey on Reproducibility by Evaluating Deep Reinforcement Learning
Algorithms on Real-World Robots | Appears in Proceedings of the Third Conference on Robot Learning
(CoRL 2019). Companion source code at
https://github.com/dti-research/SenseActExperiments/ | null | null | null | cs.LG cs.AI cs.RO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As reinforcement learning (RL) achieves more success in solving complex
tasks, more care is needed to ensure that RL research is reproducible and that
algorithms herein can be compared easily and fairly with minimal bias. RL
results are, however, notoriously hard to reproduce due to the algorithms'
intrinsic variance, the environments' stochasticity, and numerous (potentially
unreported) hyper-parameters. In this work we investigate the many issues
leading to irreproducible research and how to manage those. We further show how
to utilise a rigorous and standardised evaluation approach for easing the
process of documentation, evaluation and fair comparison of different
algorithms, where we emphasise the importance of choosing the right measurement
metrics and conducting proper statistics on the results, for unbiased reporting
of the results.
| [
{
"created": "Mon, 9 Sep 2019 11:33:09 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Sep 2019 07:42:00 GMT",
"version": "v2"
}
] | 2019-09-12 | [
[
"Lynnerup",
"Nicolai A.",
""
],
[
"Nolling",
"Laura",
""
],
[
"Hasle",
"Rasmus",
""
],
[
"Hallam",
"John",
""
]
] | As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, the environments' stochasticity, and numerous (potentially unreported) hyper-parameters. In this work we investigate the many issues leading to irreproducible research and how to manage those. We further show how to utilise a rigorous and standardised evaluation approach for easing the process of documentation, evaluation and fair comparison of different algorithms, where we emphasise the importance of choosing the right measurement metrics and conducting proper statistics on the results, for unbiased reporting of the results. |
1302.0540 | Harris Georgiou | Harris V. Georgiou, Michael E. Mavroforakis | A game-theoretic framework for classifier ensembles using weighted
majority voting with local accuracy estimates | 21 pages, 9 tables, 1 figure, 68 references | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/3.0/ | In this paper, a novel approach for the optimal combination of binary
classifiers is proposed. The classifier combination problem is approached from
a Game Theory perspective. The proposed framework of adapted weighted majority
rules (WMR) is tested against common rank-based, Bayesian and simple majority
models, as well as two soft-output averaging rules. Experiments with ensembles
of Support Vector Machines (SVM), Ordinary Binary Tree Classifiers (OBTC) and
weighted k-nearest-neighbor (w/k-NN) models on benchmark datasets indicate that
this new adaptive WMR model, employing local accuracy estimators and the
analytically computed optimal weights outperform all the other simple
combination rules.
| [
{
"created": "Sun, 3 Feb 2013 22:12:52 GMT",
"version": "v1"
}
] | 2013-02-05 | [
[
"Georgiou",
"Harris V.",
""
],
[
"Mavroforakis",
"Michael E.",
""
]
] | In this paper, a novel approach for the optimal combination of binary classifiers is proposed. The classifier combination problem is approached from a Game Theory perspective. The proposed framework of adapted weighted majority rules (WMR) is tested against common rank-based, Bayesian and simple majority models, as well as two soft-output averaging rules. Experiments with ensembles of Support Vector Machines (SVM), Ordinary Binary Tree Classifiers (OBTC) and weighted k-nearest-neighbor (w/k-NN) models on benchmark datasets indicate that this new adaptive WMR model, employing local accuracy estimators and the analytically computed optimal weights outperform all the other simple combination rules. |
1709.10052 | Marc Zeitoun | Thomas Place and Marc Zeitoun | Adding successor: A transfer theorem for separation and covering | null | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a class C of word languages, the C-separation problem asks for an
algorithm that, given as input two regular languages, decides whether there
exists a third language in C containing the first language, while being
disjoint from the second. Separation is usually investigated as a means to
obtain a deep understanding of the class C.
In the paper, we are mainly interested in classes defined by logical
formalisms. Such classes are often built on top of each other: given some
logic, one builds a stronger one by adding new predicates to its signature. A
natural construction is to enrich a logic with the successor relation. In this
paper, we present a transfer result applying to this construction: we show that
for suitable logically defined classes, separation for the logic enriched with
the successor relation reduces to separation for the original logic. Our
theorem also applies to a problem that is stronger than separation: covering.
Moreover, we actually present two reductions: one for languages of finite words
and the other for languages of infinite words.
| [
{
"created": "Thu, 28 Sep 2017 16:40:03 GMT",
"version": "v1"
}
] | 2017-09-29 | [
[
"Place",
"Thomas",
""
],
[
"Zeitoun",
"Marc",
""
]
] | Given a class C of word languages, the C-separation problem asks for an algorithm that, given as input two regular languages, decides whether there exists a third language in C containing the first language, while being disjoint from the second. Separation is usually investigated as a means to obtain a deep understanding of the class C. In the paper, we are mainly interested in classes defined by logical formalisms. Such classes are often built on top of each other: given some logic, one builds a stronger one by adding new predicates to its signature. A natural construction is to enrich a logic with the successor relation. In this paper, we present a transfer result applying to this construction: we show that for suitable logically defined classes, separation for the logic enriched with the successor relation reduces to separation for the original logic. Our theorem also applies to a problem that is stronger than separation: covering. Moreover, we actually present two reductions: one for languages of finite words and the other for languages of infinite words. |
1706.02275 | Ryan Lowe T. | Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | null | null | null | null | cs.LG cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore deep reinforcement learning methods for multi-agent domains. We
begin by analyzing the difficulty of traditional algorithms in the multi-agent
case: Q-learning is challenged by an inherent non-stationarity of the
environment, while policy gradient suffers from a variance that increases as
the number of agents grows. We then present an adaptation of actor-critic
methods that considers action policies of other agents and is able to
successfully learn policies that require complex multi-agent coordination.
Additionally, we introduce a training regimen utilizing an ensemble of policies
for each agent that leads to more robust multi-agent policies. We show the
strength of our approach compared to existing methods in cooperative as well as
competitive scenarios, where agent populations are able to discover various
physical and informational coordination strategies.
| [
{
"created": "Wed, 7 Jun 2017 17:35:00 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Jun 2017 22:18:54 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Jan 2018 23:37:25 GMT",
"version": "v3"
},
{
"created": "Sat, 14 Mar 2020 20:33:00 GMT",
"version": "v4"
}
] | 2020-03-17 | [
[
"Lowe",
"Ryan",
""
],
[
"Wu",
"Yi",
""
],
[
"Tamar",
"Aviv",
""
],
[
"Harb",
"Jean",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Mordatch",
"Igor",
""
]
] | We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies. |
1308.1042 | Kafui Monu Dr. | Kafui Monu and Paul Ralph | Beyond Gamification: Implications of Purposeful Games for the
Information Systems Discipline | null | null | null | null | cs.CY | http://creativecommons.org/licenses/by/3.0/ | Gamification is an emerging design principle for information systems where
game design elements are applied to non-game contexts. IS researchers have
suggested that the IS discipline must study this area but there are other
applications such as serious games, and simulations that also use games in
non-game contexts. Specifically, the management field has been using games and
simulations for years and these applications are now being supported by
information systems. We propose in this paper that we must think beyond
gamification, towards other uses of games in non-gaming contexts, which we call
purposeful gaming.
In this paper we identify how the IS discipline can adapt to purposeful
gaming. Specifically, we show how IT artifacts, IS design, and IS theories can
be used in the purposeful gaming area. We also provide three conceptual
dimensions of purposeful gaming that can aid IS practitioners and researchers
to classify and understand purposeful games.
| [
{
"created": "Fri, 2 Aug 2013 16:08:31 GMT",
"version": "v1"
}
] | 2013-08-06 | [
[
"Monu",
"Kafui",
""
],
[
"Ralph",
"Paul",
""
]
] | Gamification is an emerging design principle for information systems where game design elements are applied to non-game contexts. IS researchers have suggested that the IS discipline must study this area but there are other applications such as serious games, and simulations that also use games in non-game contexts. Specifically, the management field has been using games and simulations for years and these applications are now being supported by information systems. We propose in this paper that we must think beyond gamification, towards other uses of games in non-gaming contexts, which we call purposeful gaming. In this paper we identify how the IS discipline can adapt to purposeful gaming. Specifically, we show how IT artifacts, IS design, and IS theories can be used in the purposeful gaming area. We also provide three conceptual dimensions of purposeful gaming that can aid IS practitioners and researchers to classify and understand purposeful games. |
2311.09704 | Leo Freitas | Leo Freitas | International System of Quantities library in VDM | 14 pages,, 1 figure, 21st Overture Workshop, Lubeck 2023 | null | null | OVT21/2023/01 | cs.SE | http://creativecommons.org/licenses/by/4.0/ | The International Systems of Quantities (ISQ) standard was published in 1960
to tame the wide diversity of measurement systems being developed across the
world, such as the centimetre-gram-second versus the meter-kilogram-second for
example. Such a standard is highly motivated by the potential of ``trivial''
(rather error-prone) mistakes in converting between incompatible units. There
have been such accidents in space missions, medical devices, etc. Thus,
rendering modelling or simulation experiments unusable or unsafe. We address
this problem by providing a \textbf{SAFE}-ISQ VDM-library that is: Simple,
Accurate, Fast, and Effective. It extends an ecosystem of other VDM
mathematical toolkit extensions, which include a translation and proof
environment for VDM in Isabelle at https://github.com/leouk/VDM_Toolkit.
| [
{
"created": "Thu, 16 Nov 2023 09:29:02 GMT",
"version": "v1"
}
] | 2023-11-17 | [
[
"Freitas",
"Leo",
""
]
] | The International Systems of Quantities (ISQ) standard was published in 1960 to tame the wide diversity of measurement systems being developed across the world, such as the centimetre-gram-second versus the meter-kilogram-second for example. Such a standard is highly motivated by the potential of ``trivial'' (rather error-prone) mistakes in converting between incompatible units. There have been such accidents in space missions, medical devices, etc. Thus, rendering modelling or simulation experiments unusable or unsafe. We address this problem by providing a \textbf{SAFE}-ISQ VDM-library that is: Simple, Accurate, Fast, and Effective. It extends an ecosystem of other VDM mathematical toolkit extensions, which include a translation and proof environment for VDM in Isabelle at https://github.com/leouk/VDM_Toolkit. |
2307.08412 | Arnab Mukherjee Mr. | Arnab Mukherjee, Souvik Majumdar, Anup Kumar Kolya, Saborni Nandi | A Privacy-Preserving Blockchain-based E-voting System | null | null | null | null | cs.CR cs.DC | http://creativecommons.org/licenses/by/4.0/ | Within a modern democratic nation, elections play a significant role in the
nation's functioning. However, with the existing infrastructure for conducting
elections using Electronic Voting Systems (EVMs), many loopholes exist, which
illegitimate entities might leverage to cast false votes or even tamper with
the EVMs after the voting session is complete. The need of the hour is to
introduce a robust, auditable, transparent, and tamper-proof e-voting system,
enabling a more reliable and fair election process. To address such concerns,
we propose a novel solution for blockchain-based e-voting, focusing on the
security and privacy aspects of the e-voting process. We consider the security
risks and loopholes and aim to preserve the anonymity of the voters while
ensuring that illegitimate votes are properly handled. Additionally, we develop
a prototype as a proof of concept using the Ethereum blockchain platform.
Finally, we perform experiments to demonstrate the performance of the system.
| [
{
"created": "Mon, 17 Jul 2023 11:48:39 GMT",
"version": "v1"
}
] | 2023-07-18 | [
[
"Mukherjee",
"Arnab",
""
],
[
"Majumdar",
"Souvik",
""
],
[
"Kolya",
"Anup Kumar",
""
],
[
"Nandi",
"Saborni",
""
]
] | Within a modern democratic nation, elections play a significant role in the nation's functioning. However, with the existing infrastructure for conducting elections using Electronic Voting Systems (EVMs), many loopholes exist, which illegitimate entities might leverage to cast false votes or even tamper with the EVMs after the voting session is complete. The need of the hour is to introduce a robust, auditable, transparent, and tamper-proof e-voting system, enabling a more reliable and fair election process. To address such concerns, we propose a novel solution for blockchain-based e-voting, focusing on the security and privacy aspects of the e-voting process. We consider the security risks and loopholes and aim to preserve the anonymity of the voters while ensuring that illegitimate votes are properly handled. Additionally, we develop a prototype as a proof of concept using the Ethereum blockchain platform. Finally, we perform experiments to demonstrate the performance of the system. |
2304.00627 | Felicitas H\"ormann | Felicitas H\"ormann and Hannes Bartz and Anna-Lena Horlemann | Distinguishing and Recovering Generalized Linearized Reed-Solomon Codes | 20 pages, published in the proceedings of CBCrypto 2022 | null | 10.1007/978-3-031-29689-5_1 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the distinguishability of linearized Reed-Solomon (LRS) codes by
defining and analyzing analogs of the square-code and the Overbeck
distinguisher for classical Reed-Solomon and Gabidulin codes, respectively. Our
main results show that the square-code distinguisher works for generalized
linearized Reed-Solomon (GLRS) codes defined with the trivial automorphism,
whereas the Overbeck-type distinguisher can handle LRS codes in the general
setting. We further show how to recover defining code parameters from any
generator matrix of such codes in the zero-derivation case. For other choices
of automorphisms and derivations simulations indicate that these distinguishers
and recovery algorithms do not work. The corresponding LRS and GLRS codes might
hence be of interest for code-based cryptography.
| [
{
"created": "Sun, 2 Apr 2023 20:58:50 GMT",
"version": "v1"
}
] | 2023-04-04 | [
[
"Hörmann",
"Felicitas",
""
],
[
"Bartz",
"Hannes",
""
],
[
"Horlemann",
"Anna-Lena",
""
]
] | We study the distinguishability of linearized Reed-Solomon (LRS) codes by defining and analyzing analogs of the square-code and the Overbeck distinguisher for classical Reed-Solomon and Gabidulin codes, respectively. Our main results show that the square-code distinguisher works for generalized linearized Reed-Solomon (GLRS) codes defined with the trivial automorphism, whereas the Overbeck-type distinguisher can handle LRS codes in the general setting. We further show how to recover defining code parameters from any generator matrix of such codes in the zero-derivation case. For other choices of automorphisms and derivations simulations indicate that these distinguishers and recovery algorithms do not work. The corresponding LRS and GLRS codes might hence be of interest for code-based cryptography. |
2010.04072 | Giulia Orr\`u | Giulia Orr\`u, Marco Micheletto, Julian Fierrez, Gian Luca Marcialis | Are Adaptive Face Recognition Systems still Necessary? Experiments on
the APE Dataset | Preprint version of a paper accepted at IPAS 2020 (Fourth IEEE
International Conference on Image Processing, Applications and Systems) | 2020 IEEE 4th International Conference on Image Processing,
Applications and Systems (IPAS), Genova, Italy, 2020, pp. 77-82 | 10.1109/IPAS50080.2020.9334946 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last five years, deep learning methods, in particular CNN, have
attracted considerable attention in the field of face-based recognition,
achieving impressive results. Despite this progress, it is not yet clear
precisely to what extent deep features are able to follow all the intra-class
variations that the face can present over time. In this paper we investigate
the performance the performance improvement of face recognition systems by
adopting self updating strategies of the face templates. For that purpose, we
evaluate the performance of a well-known deep-learning face representation,
namely, FaceNet, on a dataset that we generated explicitly conceived to embed
intra-class variations of users on a large time span of captures: the
APhotoEveryday (APE) dataset. Moreover, we compare these deep features with
handcrafted features extracted using the BSIF algorithm. In both cases, we
evaluate various template update strategies, in order to detect the most useful
for such kind of features. Experimental results show the effectiveness of
"optimized" self-update methods with respect to systems without update or
random selection of templates.
| [
{
"created": "Thu, 8 Oct 2020 15:45:55 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Oct 2020 14:36:11 GMT",
"version": "v2"
}
] | 2021-02-04 | [
[
"Orrù",
"Giulia",
""
],
[
"Micheletto",
"Marco",
""
],
[
"Fierrez",
"Julian",
""
],
[
"Marcialis",
"Gian Luca",
""
]
] | In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results. Despite this progress, it is not yet clear precisely to what extent deep features are able to follow all the intra-class variations that the face can present over time. In this paper we investigate the performance the performance improvement of face recognition systems by adopting self updating strategies of the face templates. For that purpose, we evaluate the performance of a well-known deep-learning face representation, namely, FaceNet, on a dataset that we generated explicitly conceived to embed intra-class variations of users on a large time span of captures: the APhotoEveryday (APE) dataset. Moreover, we compare these deep features with handcrafted features extracted using the BSIF algorithm. In both cases, we evaluate various template update strategies, in order to detect the most useful for such kind of features. Experimental results show the effectiveness of "optimized" self-update methods with respect to systems without update or random selection of templates. |
2310.01916 | Huayu Guo | Huayu Guo, Dongheng Chen, and Bruno Bentzen | Verified completeness in Henkin-style for intuitionistic propositional
logic | null | Joint proceedings of the Third International Workshop on Logics
for New-Generation Artificial Intelligence and the International Workshop on
Logic, AI and Law, pp.36-48, 2023 | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | This paper presents a formalization of the classical proof of completeness in
Henkin-style developed by Troelstra and van Dalen for intuitionistic logic with
respect to Kripke models. The completeness proof incorporates their insights in
a fresh and elegant manner that is better suited for mechanization. We discuss
details of our implementation in the Lean theorem prover with emphasis on the
prime extension lemma and construction of the canonical model. Our
implementation is restricted to a system of intuitionistic propositional logic
with implication, conjunction, disjunction, and falsity given in terms of a
Hilbert-style axiomatization. As far as we know, our implementation is the
first verified Henkin-style proof of completeness for intuitionistic logic
following Troelstra and van Dalen's method in the literature. The full source
code can be found online at https://github.com/bbentzen/ipl.
| [
{
"created": "Tue, 3 Oct 2023 09:45:43 GMT",
"version": "v1"
}
] | 2023-10-04 | [
[
"Guo",
"Huayu",
""
],
[
"Chen",
"Dongheng",
""
],
[
"Bentzen",
"Bruno",
""
]
] | This paper presents a formalization of the classical proof of completeness in Henkin-style developed by Troelstra and van Dalen for intuitionistic logic with respect to Kripke models. The completeness proof incorporates their insights in a fresh and elegant manner that is better suited for mechanization. We discuss details of our implementation in the Lean theorem prover with emphasis on the prime extension lemma and construction of the canonical model. Our implementation is restricted to a system of intuitionistic propositional logic with implication, conjunction, disjunction, and falsity given in terms of a Hilbert-style axiomatization. As far as we know, our implementation is the first verified Henkin-style proof of completeness for intuitionistic logic following Troelstra and van Dalen's method in the literature. The full source code can be found online at https://github.com/bbentzen/ipl. |
1405.5206 | Hao Li | Xiaohui Huang, Xing Hu, Weichang Jiang, Zhi Yang, Hao Li | Application of Multilayer Feedforward Neural Networks in Predicting Tree
Height and Forest Stock Volume of Chinese Fir | null | null | null | null | cs.CE | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Wood increment is critical information in forestry management. Previous
studies used mathematics models to describe complex growing pattern of forest
stand, in order to determine the dynamic status of growing forest stand in
multiple conditions. In our research, we aimed at studying non-linear
relationships to establish precise and robust Artificial Neural Networks (ANN)
models to predict the precise values of tree height and forest stock volume
based on data of Chinese fir. Results show that Multilayer Feedforward Neural
Networks with 4 nodes (MLFN-4) can predict the tree height with the lowest RMS
error (1.77); Multilayer Feedforward Neural Networks with 7 nodes (MLFN-7) can
predict the forest stock volume with the lowest RMS error (4.95). The training
and testing process have proved that our models are precise and robust.
| [
{
"created": "Tue, 20 May 2014 19:52:43 GMT",
"version": "v1"
}
] | 2014-05-21 | [
[
"Huang",
"Xiaohui",
""
],
[
"Hu",
"Xing",
""
],
[
"Jiang",
"Weichang",
""
],
[
"Yang",
"Zhi",
""
],
[
"Li",
"Hao",
""
]
] | Wood increment is critical information in forestry management. Previous studies used mathematics models to describe complex growing pattern of forest stand, in order to determine the dynamic status of growing forest stand in multiple conditions. In our research, we aimed at studying non-linear relationships to establish precise and robust Artificial Neural Networks (ANN) models to predict the precise values of tree height and forest stock volume based on data of Chinese fir. Results show that Multilayer Feedforward Neural Networks with 4 nodes (MLFN-4) can predict the tree height with the lowest RMS error (1.77); Multilayer Feedforward Neural Networks with 7 nodes (MLFN-7) can predict the forest stock volume with the lowest RMS error (4.95). The training and testing process have proved that our models are precise and robust. |
2404.17508 | Matthew England Dr | Dorian Florescu and Matthew England | Constrained Neural Networks for Interpretable Heuristic Creation to
Optimise Computer Algebra Systems | Accepted for presentation at ICMS 2024 | null | null | null | cs.SC cs.LG | http://creativecommons.org/licenses/by/4.0/ | We present a new methodology for utilising machine learning technology in
symbolic computation research. We explain how a well known human-designed
heuristic to make the choice of variable ordering in cylindrical algebraic
decomposition may be represented as a constrained neural network. This allows
us to then use machine learning methods to further optimise the heuristic,
leading to new networks of similar size, representing new heuristics of similar
complexity as the original human-designed one. We present this as a form of
ante-hoc explainability for use in computer algebra development.
| [
{
"created": "Fri, 26 Apr 2024 16:20:04 GMT",
"version": "v1"
}
] | 2024-04-29 | [
[
"Florescu",
"Dorian",
""
],
[
"England",
"Matthew",
""
]
] | We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development. |
1911.06791 | Francesco Quinzan | Vanja Dosko\v{c} and Tobias Friedrich and Andreas G\"obel and Frank
Neumann and Aneta Neumann and Francesco Quinzan | Non-Monotone Submodular Maximization with Multiple Knapsacks in Static
and Dynamic Settings | null | null | null | null | cs.LG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of maximizing a non-monotone submodular function under
multiple knapsack constraints. We propose a simple discrete greedy algorithm to
approach this problem, and prove that it yields strong approximation guarantees
for functions with bounded curvature. In contrast to other heuristics, this
requires no problem relaxation to continuous domains and it maintains a
constant-factor approximation guarantee in the problem size. In the case of a
single knapsack, our analysis suggests that the standard greedy can be used in
non-monotone settings.
Additionally, we study this problem in a dynamic setting, by which knapsacks
change during the optimization process. We modify our greedy algorithm to avoid
a complete restart at each constraint update. This modification retains the
approximation guarantees of the static case.
We evaluate our results experimentally on a video summarization and sensor
placement task. We show that our proposed algorithm competes with the
state-of-the-art in static settings. Furthermore, we show that in dynamic
settings with tight computational time budget, our modified greedy yields
significant improvements over starting the greedy from scratch, in terms of the
solution quality achieved.
| [
{
"created": "Fri, 15 Nov 2019 18:22:46 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Nov 2019 20:20:10 GMT",
"version": "v2"
},
{
"created": "Tue, 18 Feb 2020 10:55:31 GMT",
"version": "v3"
}
] | 2020-02-19 | [
[
"Doskoč",
"Vanja",
""
],
[
"Friedrich",
"Tobias",
""
],
[
"Göbel",
"Andreas",
""
],
[
"Neumann",
"Frank",
""
],
[
"Neumann",
"Aneta",
""
],
[
"Quinzan",
"Francesco",
""
]
] | We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature. In contrast to other heuristics, this requires no problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size. In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings. Additionally, we study this problem in a dynamic setting, by which knapsacks change during the optimization process. We modify our greedy algorithm to avoid a complete restart at each constraint update. This modification retains the approximation guarantees of the static case. We evaluate our results experimentally on a video summarization and sensor placement task. We show that our proposed algorithm competes with the state-of-the-art in static settings. Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved. |
2307.11332 | Reza Sameni | Reza Sameni | Beyond Convergence: Identifiability of Machine Learning and Deep
Learning Models | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine learning (ML) and deep learning models are extensively used for
parameter optimization and regression problems. However, not all inverse
problems in ML are ``identifiable,'' indicating that model parameters may not
be uniquely determined from the available data and the data model's
input-output relationship. In this study, we investigate the notion of model
parameter identifiability through a case study focused on parameter estimation
from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics
model, we generate synthetic data representing diverse gait patterns and
conditions. Employing a deep neural network, we attempt to estimate
subject-wise parameters, including mass, stiffness, and equilibrium leg length.
The results show that while certain parameters can be identified from the
observation data, others remain unidentifiable, highlighting that
unidentifiability is an intrinsic limitation of the experimental setup,
necessitating a change in data collection and experimental scenarios. Beyond
this specific case study, the concept of identifiability has broader
implications in ML and deep learning. Addressing unidentifiability requires
proven identifiable models (with theoretical support), multimodal data fusion
techniques, and advancements in model-based machine learning. Understanding and
resolving unidentifiability challenges will lead to more reliable and accurate
applications across diverse domains, transcending mere model convergence and
enhancing the reliability of machine learning models.
| [
{
"created": "Fri, 21 Jul 2023 03:40:53 GMT",
"version": "v1"
}
] | 2023-07-24 | [
[
"Sameni",
"Reza",
""
]
] | Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics model, we generate synthetic data representing diverse gait patterns and conditions. Employing a deep neural network, we attempt to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable, highlighting that unidentifiability is an intrinsic limitation of the experimental setup, necessitating a change in data collection and experimental scenarios. Beyond this specific case study, the concept of identifiability has broader implications in ML and deep learning. Addressing unidentifiability requires proven identifiable models (with theoretical support), multimodal data fusion techniques, and advancements in model-based machine learning. Understanding and resolving unidentifiability challenges will lead to more reliable and accurate applications across diverse domains, transcending mere model convergence and enhancing the reliability of machine learning models. |
2408.07408 | Fabian Egidy | Fabian Egidy and Christian Gla{\ss}er | Oracle without Optimal Proof Systems outside Nondeterministic
Subexponential Time | This version presents preliminary results. The findings and methods
described herein are part of ongoing research and are subject to revision. As
such, this document is a Work in Progress | null | null | null | cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the existence of optimal proof systems for sets outside of
$\mathrm{NP}$. Currently, no set $L \notin \mathrm{NP}$ is known that has
optimal proof systems. Our main result shows that this is not surprising,
because we can rule out relativizable proofs of optimality for all sets outside
$\mathrm{NTIME}(t)$ where $t$ is slightly superpolynomial. We construct an
oracle $O$, such that for any set $L \subseteq \Sigma^*$ at least one of the
following two properties holds: $L$ does not have optimal proof systems
relative to $O$. $L \in \mathrm{UTIME}^O(2^{2(\log
n)^{8+4\log(\log(\log(n)))}})$. The runtime bound is slightly superpolynomial.
So there is no relativizable proof showing that a complex set has optimal proof
systems. Hence, searching for non-trivial optimal proof systems with
relativizable methods can only be successful (if at all) in a narrow range
above $\mathrm{NP}$.
| [
{
"created": "Wed, 14 Aug 2024 09:25:29 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Egidy",
"Fabian",
""
],
[
"Glaßer",
"Christian",
""
]
] | We study the existence of optimal proof systems for sets outside of $\mathrm{NP}$. Currently, no set $L \notin \mathrm{NP}$ is known that has optimal proof systems. Our main result shows that this is not surprising, because we can rule out relativizable proofs of optimality for all sets outside $\mathrm{NTIME}(t)$ where $t$ is slightly superpolynomial. We construct an oracle $O$, such that for any set $L \subseteq \Sigma^*$ at least one of the following two properties holds: $L$ does not have optimal proof systems relative to $O$. $L \in \mathrm{UTIME}^O(2^{2(\log n)^{8+4\log(\log(\log(n)))}})$. The runtime bound is slightly superpolynomial. So there is no relativizable proof showing that a complex set has optimal proof systems. Hence, searching for non-trivial optimal proof systems with relativizable methods can only be successful (if at all) in a narrow range above $\mathrm{NP}$. |
1008.5325 | Danny Bickson | Danny Bickson and Carlos Guestrin | Inference with Multivariate Heavy-Tails in Linear Models | In Neural Information Processing System (NIPS) 2010, Dec. 2010,
Vancouver, Canada | null | null | null | cs.LG cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heavy-tailed distributions naturally occur in many real life problems.
Unfortunately, it is typically not possible to compute inference in closed-form
in graphical models which involve such heavy-tailed distributions.
In this work, we propose a novel simple linear graphical model for
independent latent random variables, called linear characteristic model (LCM),
defined in the characteristic function domain. Using stable distributions, a
heavy-tailed family of distributions which is a generalization of Cauchy,
L\'evy and Gaussian distributions, we show for the first time, how to compute
both exact and approximate inference in such a linear multivariate graphical
model. LCMs are not limited to stable distributions, in fact LCMs are always
defined for any random variables (discrete, continuous or a mixture of both).
We provide a realistic problem from the field of computer networks to
demonstrate the applicability of our construction. Other potential application
is iterative decoding of linear channels with non-Gaussian noise.
| [
{
"created": "Tue, 31 Aug 2010 14:31:57 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Nov 2010 15:26:53 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Nov 2010 16:14:02 GMT",
"version": "v3"
},
{
"created": "Mon, 21 Mar 2011 15:54:54 GMT",
"version": "v4"
}
] | 2011-03-22 | [
[
"Bickson",
"Danny",
""
],
[
"Guestrin",
"Carlos",
""
]
] | Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy-tailed distributions. In this work, we propose a novel simple linear graphical model for independent latent random variables, called linear characteristic model (LCM), defined in the characteristic function domain. Using stable distributions, a heavy-tailed family of distributions which is a generalization of Cauchy, L\'evy and Gaussian distributions, we show for the first time, how to compute both exact and approximate inference in such a linear multivariate graphical model. LCMs are not limited to stable distributions, in fact LCMs are always defined for any random variables (discrete, continuous or a mixture of both). We provide a realistic problem from the field of computer networks to demonstrate the applicability of our construction. Other potential application is iterative decoding of linear channels with non-Gaussian noise. |
2210.07970 | Peter Xenopoulos | Senan Hogan-Hennessy, Peter Xenopoulos, Claudio Silva | Market Interventions in a Large-Scale Virtual Economy | null | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Massively multiplayer online role-playing games often contain sophisticated
in-game economies. Many important real-world economic phenomena, such as
inflation, economic growth, and business cycles, are also present in these
virtual economies. One major difference between real-world and virtual
economies is the ease and frequency by which a policymaker, in this case, a
game developer, can introduce economic shocks. These economic shocks, typically
implemented with game updates or signaled through community channels, provide
fertile ground to study the effects of economic interventions on markets. In
this work, we study the effect of in-game economic market interventions,
namely, a transaction tax and an item sink, in Old School RuneScape. Using
causal inference methods, we find that the tax did not meaningfully affect the
trading volume of items at the tax boundaries and that the item sink
contributed to the inflation of luxury good prices, without reducing trade
volume. Furthermore, we find evidence that the illicit gold trading market was
relatively unaffected by the implemented market interventions. Our findings
yield useful insights not only into the effect of market interventions in
virtual economies but also for real-world markets.
| [
{
"created": "Fri, 14 Oct 2022 17:08:29 GMT",
"version": "v1"
}
] | 2022-10-17 | [
[
"Hogan-Hennessy",
"Senan",
""
],
[
"Xenopoulos",
"Peter",
""
],
[
"Silva",
"Claudio",
""
]
] | Massively multiplayer online role-playing games often contain sophisticated in-game economies. Many important real-world economic phenomena, such as inflation, economic growth, and business cycles, are also present in these virtual economies. One major difference between real-world and virtual economies is the ease and frequency by which a policymaker, in this case, a game developer, can introduce economic shocks. These economic shocks, typically implemented with game updates or signaled through community channels, provide fertile ground to study the effects of economic interventions on markets. In this work, we study the effect of in-game economic market interventions, namely, a transaction tax and an item sink, in Old School RuneScape. Using causal inference methods, we find that the tax did not meaningfully affect the trading volume of items at the tax boundaries and that the item sink contributed to the inflation of luxury good prices, without reducing trade volume. Furthermore, we find evidence that the illicit gold trading market was relatively unaffected by the implemented market interventions. Our findings yield useful insights not only into the effect of market interventions in virtual economies but also for real-world markets. |
2310.17551 | Jing Yao | Xiaoyuan Yi, Jing Yao, Xiting Wang and Xing Xie | Unpacking the Ethical Value Alignment in Big Models | null | null | null | null | cs.CY cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Big models have greatly advanced AI's ability to understand, generate, and
manipulate information and content, enabling numerous applications. However, as
these models become increasingly integrated into everyday life, their inherent
ethical values and potential biases pose unforeseen risks to society. This
paper provides an overview of the risks and challenges associated with big
models, surveys existing AI ethics guidelines, and examines the ethical
implications arising from the limitations of these models. Taking a normative
ethics perspective, we propose a reassessment of recent normative guidelines,
highlighting the importance of collaborative efforts in academia to establish a
unified and universal AI ethics framework. Furthermore, we investigate the
moral inclinations of current mainstream LLMs using the Moral Foundation
theory, analyze existing alignment algorithms, and outline the unique
challenges encountered in aligning ethical values within them. To address these
challenges, we introduce a novel conceptual paradigm for aligning the ethical
values of big models and discuss promising research directions for alignment
criteria, evaluation, and method, representing an initial step towards the
interdisciplinary construction of the ethically aligned AI
This paper is a modified English version of our Chinese paper
https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended
to help non-Chinese native speakers better understand our work.
| [
{
"created": "Thu, 26 Oct 2023 16:45:40 GMT",
"version": "v1"
}
] | 2023-10-27 | [
[
"Yi",
"Xiaoyuan",
""
],
[
"Yao",
"Jing",
""
],
[
"Wang",
"Xiting",
""
],
[
"Xie",
"Xing",
""
]
] | Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications. However, as these models become increasingly integrated into everyday life, their inherent ethical values and potential biases pose unforeseen risks to society. This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. Taking a normative ethics perspective, we propose a reassessment of recent normative guidelines, highlighting the importance of collaborative efforts in academia to establish a unified and universal AI ethics framework. Furthermore, we investigate the moral inclinations of current mainstream LLMs using the Moral Foundation theory, analyze existing alignment algorithms, and outline the unique challenges encountered in aligning ethical values within them. To address these challenges, we introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method, representing an initial step towards the interdisciplinary construction of the ethically aligned AI This paper is a modified English version of our Chinese paper https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended to help non-Chinese native speakers better understand our work. |
1811.07818 | Amarnath R | BV Divyashree, Amarnath R, Naveen M, G Hemantha Kumar | Novel approach to locate region of interest in mammograms for Breast
cancer | ROI, breast cancer, mammographic images, segmentation, entropy, quad
tree | International Journal of Intelligent Systems and Applications in
Engineering.(ISSN:2147-6799) Vol 6, No 3 (2018) | 10.18201/ijisae.2018644775 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Locating region of interest for breast cancer masses in the mammographic
image is a challenging problem in medical image processing. In this research
work, the keen idea is to efficiently extract suspected mass region for further
examination. In particular to this fact breast boundary segmentation on sliced
rgb image using modified intensity based approach followed by quad tree based
division to spot out suspicious area are proposed in the paper. To evaluate the
performance DDSM standard dataset are experimented and achieved acceptable
accuracy.
| [
{
"created": "Thu, 1 Nov 2018 11:01:40 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Divyashree",
"BV",
""
],
[
"R",
"Amarnath",
""
],
[
"M",
"Naveen",
""
],
[
"Kumar",
"G Hemantha",
""
]
] | Locating region of interest for breast cancer masses in the mammographic image is a challenging problem in medical image processing. In this research work, the keen idea is to efficiently extract suspected mass region for further examination. In particular to this fact breast boundary segmentation on sliced rgb image using modified intensity based approach followed by quad tree based division to spot out suspicious area are proposed in the paper. To evaluate the performance DDSM standard dataset are experimented and achieved acceptable accuracy. |
1004.3887 | Uwe Aickelin | William Wilson, Phil Birkin, Uwe Aickelin | Motif Detection Inspired by Immune Memory | 12 pages, 4 figures, (ICARIS2007), | Proceedings of the 6th International Conference on Artificial
Immune Systems (ICARIS2007), Lecture Notes in Computer Science 4628, Santos,
Brazil, 2007, p 276-287 | null | null | cs.AI cs.NE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The search for patterns or motifs in data represents an area of key interest
to many researchers. In this paper we present the Motif Tracking Algorithm, a
novel immune inspired pattern identification tool that is able to identify
variable length unknown motifs which repeat within time series data. The
algorithm searches from a completely neutral perspective that is independent of
the data being analysed and the underlying motifs. In this paper we test the
flexibility of the motif tracking algorithm by applying it to the search for
patterns in two industrial data sets. The algorithm is able to identify a
population of motifs successfully in both cases, and the value of these motifs
is discussed.
| [
{
"created": "Thu, 22 Apr 2010 10:55:23 GMT",
"version": "v1"
}
] | 2010-07-05 | [
[
"Wilson",
"William",
""
],
[
"Birkin",
"Phil",
""
],
[
"Aickelin",
"Uwe",
""
]
] | The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a completely neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of motifs successfully in both cases, and the value of these motifs is discussed. |
2207.02368 | Nurendra Choudhary | Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy | Text Enriched Sparse Hyperbolic Graph Convolutional Networks | Preprint under review. 13 pages, 10 figures, 6 tables | null | null | null | cs.IR cs.LG cs.SI | http://creativecommons.org/licenses/by-sa/4.0/ | Heterogeneous networks, which connect informative nodes containing text with
different edge types, are routinely used to store and process information in
various real-world applications. Graph Neural Networks (GNNs) and their
hyperbolic variants provide a promising approach to encode such networks in a
low-dimensional latent space through neighborhood aggregation and hierarchical
feature extraction, respectively. However, these approaches typically ignore
metapath structures and the available semantic information. Furthermore, these
approaches are sensitive to the noise present in the training data. To tackle
these limitations, in this paper, we propose Text Enriched Sparse Hyperbolic
Graph Convolution Network (TESH-GCN) to capture the graph's metapath structures
using semantic signals and further improve prediction in large heterogeneous
graphs. In TESH-GCN, we extract semantic node information, which successively
acts as a connection signal to extract relevant nodes' local neighborhood and
graph-level metapath features from the sparse adjacency tensor in a
reformulated hyperbolic graph convolution layer. These extracted features in
conjunction with semantic features from the language model (for robustness) are
used for the final downstream task. Experiments on various heterogeneous graph
datasets show that our model outperforms the current state-of-the-art
approaches by a large margin on the task of link prediction. We also report a
reduction in both the training time and model parameters compared to the
existing hyperbolic approaches through a reformulated hyperbolic graph
convolution. Furthermore, we illustrate the robustness of our model by
experimenting with different levels of simulated noise in both the graph
structure and text, and also, present a mechanism to explain TESH-GCN's
prediction by analyzing the extracted metapaths.
| [
{
"created": "Wed, 6 Jul 2022 00:23:35 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Jul 2022 04:58:49 GMT",
"version": "v2"
}
] | 2022-07-08 | [
[
"Choudhary",
"Nurendra",
""
],
[
"Rao",
"Nikhil",
""
],
[
"Subbian",
"Karthik",
""
],
[
"Reddy",
"Chandan K.",
""
]
] | Heterogeneous networks, which connect informative nodes containing text with different edge types, are routinely used to store and process information in various real-world applications. Graph Neural Networks (GNNs) and their hyperbolic variants provide a promising approach to encode such networks in a low-dimensional latent space through neighborhood aggregation and hierarchical feature extraction, respectively. However, these approaches typically ignore metapath structures and the available semantic information. Furthermore, these approaches are sensitive to the noise present in the training data. To tackle these limitations, in this paper, we propose Text Enriched Sparse Hyperbolic Graph Convolution Network (TESH-GCN) to capture the graph's metapath structures using semantic signals and further improve prediction in large heterogeneous graphs. In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer. These extracted features in conjunction with semantic features from the language model (for robustness) are used for the final downstream task. Experiments on various heterogeneous graph datasets show that our model outperforms the current state-of-the-art approaches by a large margin on the task of link prediction. We also report a reduction in both the training time and model parameters compared to the existing hyperbolic approaches through a reformulated hyperbolic graph convolution. Furthermore, we illustrate the robustness of our model by experimenting with different levels of simulated noise in both the graph structure and text, and also, present a mechanism to explain TESH-GCN's prediction by analyzing the extracted metapaths. |
1508.02774 | Thomas M. Breuel | Thomas M. Breuel | Benchmarking of LSTM Networks | null | null | null | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LSTM (Long Short-Term Memory) recurrent neural networks have been highly
successful in a number of application areas. This technical report describes
the use of the MNIST and UW3 databases for benchmarking LSTM networks and
explores the effect of different architectural and hyperparameter choices on
performance. Significant findings include: (1) LSTM performance depends
smoothly on learning rates, (2) batching and momentum has no significant effect
on performance, (3) softmax training outperforms least square training, (4)
peephole units are not useful, (5) the standard non-linearities (tanh and
sigmoid) perform best, (6) bidirectional training combined with CTC performs
better than other methods.
| [
{
"created": "Tue, 11 Aug 2015 23:31:49 GMT",
"version": "v1"
}
] | 2016-10-31 | [
[
"Breuel",
"Thomas M.",
""
]
] | LSTM (Long Short-Term Memory) recurrent neural networks have been highly successful in a number of application areas. This technical report describes the use of the MNIST and UW3 databases for benchmarking LSTM networks and explores the effect of different architectural and hyperparameter choices on performance. Significant findings include: (1) LSTM performance depends smoothly on learning rates, (2) batching and momentum has no significant effect on performance, (3) softmax training outperforms least square training, (4) peephole units are not useful, (5) the standard non-linearities (tanh and sigmoid) perform best, (6) bidirectional training combined with CTC performs better than other methods. |
0912.3852 | Sathish Gopalakrishnan | Sathish Gopalakrishnan | Sharp utilization thresholds for some real-time scheduling problems | null | null | null | null | cs.PF cs.DM cs.OS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scheduling policies for real-time systems exhibit threshold behavior that is
related to the utilization of the task set they schedule, and in some cases
this threshold is sharp. For the rate monotonic scheduling policy, we show that
periodic workload with utilization less than a threshold $U_{RM}^{*}$ can be
scheduled almost surely and that all workload with utilization greater than
$U_{RM}^{*}$ is almost surely not schedulable. We study such sharp threshold
behavior in the context of processor scheduling using static task priorities,
not only for periodic real-time tasks but for aperiodic real-time tasks as
well. The notion of a utilization threshold provides a simple schedulability
test for most real-time applications. These results improve our understanding
of scheduling policies and provide an interesting characterization of the
typical behavior of policies. The threshold is sharp (small deviations around
the threshold cause schedulability, as a property, to appear or disappear) for
most policies; this is a happy consequence that can be used to address the
limitations of existing utilization-based tests for schedulability. We
demonstrate the use of such an approach for balancing power consumption with
the need to meet deadlines in web servers.
| [
{
"created": "Sat, 19 Dec 2009 01:18:05 GMT",
"version": "v1"
}
] | 2009-12-22 | [
[
"Gopalakrishnan",
"Sathish",
""
]
] | Scheduling policies for real-time systems exhibit threshold behavior that is related to the utilization of the task set they schedule, and in some cases this threshold is sharp. For the rate monotonic scheduling policy, we show that periodic workload with utilization less than a threshold $U_{RM}^{*}$ can be scheduled almost surely and that all workload with utilization greater than $U_{RM}^{*}$ is almost surely not schedulable. We study such sharp threshold behavior in the context of processor scheduling using static task priorities, not only for periodic real-time tasks but for aperiodic real-time tasks as well. The notion of a utilization threshold provides a simple schedulability test for most real-time applications. These results improve our understanding of scheduling policies and provide an interesting characterization of the typical behavior of policies. The threshold is sharp (small deviations around the threshold cause schedulability, as a property, to appear or disappear) for most policies; this is a happy consequence that can be used to address the limitations of existing utilization-based tests for schedulability. We demonstrate the use of such an approach for balancing power consumption with the need to meet deadlines in web servers. |
2303.08463 | Yizhe Wang | Congqi Cao, Yizhe Wang, Yue Lu, Xin Zhang and Yanning Zhang | Co-Occurrence Matters: Learning Action Relation for Temporal Action
Localization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal action localization (TAL) is a prevailing task due to its great
application potential. Existing works in this field mainly suffer from two
weaknesses: (1) They often neglect the multi-label case and only focus on
temporal modeling. (2) They ignore the semantic information in class labels and
only use the visual information. To solve these problems, we propose a novel
Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence
relationship between actions. Besides the visual information, it further
utilizes the semantic embeddings of class labels to model the co-occurrence
relationship. The CORM works in a plug-and-play manner and can be easily
incorporated with the existing sequence models. By considering both visual and
semantic co-occurrence, our method achieves high multi-label relationship
modeling capacity. Meanwhile, existing datasets in TAL always focus on
low-semantic atomic actions. Thus we construct a challenging multi-label
dataset UCF-Crime-TAL that focuses on high-semantic actions by annotating the
UCF-Crime dataset at frame level and considering the semantic overlap of
different events. Extensive experiments on two commonly used TAL datasets,
\textit{i.e.}, MultiTHUMOS and TSU, and our newly proposed UCF-Crime-TAL
demenstrate the effectiveness of the proposed CORM, which achieves
state-of-the-art performance on these datasets.
| [
{
"created": "Wed, 15 Mar 2023 09:07:04 GMT",
"version": "v1"
}
] | 2023-03-16 | [
[
"Cao",
"Congqi",
""
],
[
"Wang",
"Yizhe",
""
],
[
"Lu",
"Yue",
""
],
[
"Zhang",
"Xin",
""
],
[
"Zhang",
"Yanning",
""
]
] | Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling. (2) They ignore the semantic information in class labels and only use the visual information. To solve these problems, we propose a novel Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence relationship between actions. Besides the visual information, it further utilizes the semantic embeddings of class labels to model the co-occurrence relationship. The CORM works in a plug-and-play manner and can be easily incorporated with the existing sequence models. By considering both visual and semantic co-occurrence, our method achieves high multi-label relationship modeling capacity. Meanwhile, existing datasets in TAL always focus on low-semantic atomic actions. Thus we construct a challenging multi-label dataset UCF-Crime-TAL that focuses on high-semantic actions by annotating the UCF-Crime dataset at frame level and considering the semantic overlap of different events. Extensive experiments on two commonly used TAL datasets, \textit{i.e.}, MultiTHUMOS and TSU, and our newly proposed UCF-Crime-TAL demenstrate the effectiveness of the proposed CORM, which achieves state-of-the-art performance on these datasets. |
2307.14068 | Bo Zhou | Long Liu, Bo Zhou, Zhipeng Zhao, Zening Liu | Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge
from related source domains to an unlabeled target domain. While recent MUDA
methods have shown promising results, most focus on aligning the overall
feature distributions across source domains, which can lead to negative effects
due to redundant features within each domain. Moreover, there is a significant
performance gap between MUDA and supervised methods. To address these
challenges, we propose a novel approach called Dynamic Domain Discrepancy
Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish
a multi-source dynamic modulation mechanism during the training process based
on the degree of distribution differences between source and target domains.
This mechanism controls the alignment level of features between each source
domain and the target domain, effectively leveraging the local advantageous
feature information within the source domains. Additionally, we propose a
Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a
guided dynamic boundary loss to design an efficient query function for
selecting important samples. This strategy achieves improved generalization to
the target domain with minimal sampling costs. We extensively evaluate our
proposed method on commonly used domain adaptation datasets, comparing it
against existing UDA and ADA methods. The experimental results unequivocally
demonstrate the superiority of our approach.
| [
{
"created": "Wed, 26 Jul 2023 09:40:19 GMT",
"version": "v1"
}
] | 2023-07-27 | [
[
"Liu",
"Long",
""
],
[
"Zhou",
"Bo",
""
],
[
"Zhao",
"Zhipeng",
""
],
[
"Liu",
"Zening",
""
]
] | Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the overall feature distributions across source domains, which can lead to negative effects due to redundant features within each domain. Moreover, there is a significant performance gap between MUDA and supervised methods. To address these challenges, we propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish a multi-source dynamic modulation mechanism during the training process based on the degree of distribution differences between source and target domains. This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains. Additionally, we propose a Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a guided dynamic boundary loss to design an efficient query function for selecting important samples. This strategy achieves improved generalization to the target domain with minimal sampling costs. We extensively evaluate our proposed method on commonly used domain adaptation datasets, comparing it against existing UDA and ADA methods. The experimental results unequivocally demonstrate the superiority of our approach. |
cs/0410061 | Vincenzo Pallotta | Vincenzo Pallotta, Hatem Ghorbel, Patrick Ruch, Giovanni Coray | An argumentative annotation schema for meeting discussions | 4 pages | Procedings of the LREC 2004 international conference, 26-28 May
2004, Lisbon, Portugal. Pages 1003-1006 | null | null | cs.CL cs.DL cs.IR | null | In this article, we are interested in the annotation of transcriptions of
human-human dialogue taken from meeting records. We first propose a meeting
content model where conversational acts are interpreted with respect to their
argumentative force and their role in building the argumentative structure of
the meeting discussion. Argumentation in dialogue describes the way
participants take part in the discussion and argue their standpoints. Then, we
propose an annotation scheme based on such an argumentative dialogue model as
well as the evaluation of its adequacy. The obtained higher-level semantic
annotations are exploited in the conceptual indexing of the information
contained in meeting discussions.
| [
{
"created": "Mon, 25 Oct 2004 01:38:07 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Pallotta",
"Vincenzo",
""
],
[
"Ghorbel",
"Hatem",
""
],
[
"Ruch",
"Patrick",
""
],
[
"Coray",
"Giovanni",
""
]
] | In this article, we are interested in the annotation of transcriptions of human-human dialogue taken from meeting records. We first propose a meeting content model where conversational acts are interpreted with respect to their argumentative force and their role in building the argumentative structure of the meeting discussion. Argumentation in dialogue describes the way participants take part in the discussion and argue their standpoints. Then, we propose an annotation scheme based on such an argumentative dialogue model as well as the evaluation of its adequacy. The obtained higher-level semantic annotations are exploited in the conceptual indexing of the information contained in meeting discussions. |
2110.02848 | Awni Hannun | Shubho Sengupta, Vineel Pratap, Awni Hannun | Parallel Composition of Weighted Finite-State Transducers | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finite-state transducers (FSTs) are frequently used in speech recognition.
Transducer composition is an essential operation for combining different
sources of information at different granularities. However, composition is also
one of the more computationally expensive operations. Due to the heterogeneous
structure of FSTs, parallel algorithms for composition are suboptimal in
efficiency, generality, or both. We propose an algorithm for parallel
composition and implement it on graphics processing units. We benchmark our
parallel algorithm on the composition of random graphs and the composition of
graphs commonly used in speech recognition. The parallel composition scales
better with the size of the input graphs and for large graphs can be as much as
10 to 30 times faster than a sequential CPU algorithm.
| [
{
"created": "Wed, 6 Oct 2021 15:19:00 GMT",
"version": "v1"
}
] | 2021-10-07 | [
[
"Sengupta",
"Shubho",
""
],
[
"Pratap",
"Vineel",
""
],
[
"Hannun",
"Awni",
""
]
] | Finite-state transducers (FSTs) are frequently used in speech recognition. Transducer composition is an essential operation for combining different sources of information at different granularities. However, composition is also one of the more computationally expensive operations. Due to the heterogeneous structure of FSTs, parallel algorithms for composition are suboptimal in efficiency, generality, or both. We propose an algorithm for parallel composition and implement it on graphics processing units. We benchmark our parallel algorithm on the composition of random graphs and the composition of graphs commonly used in speech recognition. The parallel composition scales better with the size of the input graphs and for large graphs can be as much as 10 to 30 times faster than a sequential CPU algorithm. |
2209.10073 | Anqi Zhu | Anqi Zhu, Qiuhong Ke, Mingming Gong and James Bailey | Adaptive Local-Component-aware Graph Convolutional Network for One-shot
Skeleton-based Action Recognition | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Skeleton-based action recognition receives increasing attention because the
skeleton representations reduce the amount of training data by eliminating
visual information irrelevant to actions. To further improve the sample
efficiency, meta-learning-based one-shot learning solutions were developed for
skeleton-based action recognition. These methods find the nearest neighbor
according to the similarity between instance-level global average embedding.
However, such measurement holds unstable representativity due to inadequate
generalized learning on local invariant and noisy features, while intuitively,
more fine-grained recognition usually relies on determining key local body
movements. To address this limitation, we present the Adaptive
Local-Component-aware Graph Convolutional Network, which replaces the
comparison metric with a focused sum of similarity measurements on aligned
local embedding of action-critical spatial/temporal segments. Comprehensive
one-shot experiments on the public benchmark of NTU-RGB+D 120 indicate that our
method provides a stronger representation than the global embedding and helps
our model reach state-of-the-art.
| [
{
"created": "Wed, 21 Sep 2022 02:33:07 GMT",
"version": "v1"
}
] | 2022-09-22 | [
[
"Zhu",
"Anqi",
""
],
[
"Ke",
"Qiuhong",
""
],
[
"Gong",
"Mingming",
""
],
[
"Bailey",
"James",
""
]
] | Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions. To further improve the sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods find the nearest neighbor according to the similarity between instance-level global average embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on local invariant and noisy features, while intuitively, more fine-grained recognition usually relies on determining key local body movements. To address this limitation, we present the Adaptive Local-Component-aware Graph Convolutional Network, which replaces the comparison metric with a focused sum of similarity measurements on aligned local embedding of action-critical spatial/temporal segments. Comprehensive one-shot experiments on the public benchmark of NTU-RGB+D 120 indicate that our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art. |
1711.09012 | Ekram Hossain | Shermila Ranadheera, Setareh Maghsudi, and Ekram Hossain | Mobile Edge Computation Offloading Using Game Theory and Reinforcement
Learning | null | null | null | null | cs.GT cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the ever-increasing popularity of resource-hungry and
delay-constrained mobile applications, the computation and storage capabilities
of remote cloud has partially migrated towards the mobile edge, giving rise to
the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the
close proximity to the end-users to provide services at reduced latency and
lower energy costs, they suffer from limitations in computational and radio
resources, which calls for fair efficient resource management in the MEC
servers. The problem is however challenging due to the ultra-high density,
distributed nature, and intrinsic randomness of next generation wireless
networks. In this article, we focus on the application of game theory and
reinforcement learning for efficient distributed resource management in MEC, in
particular, for computation offloading. We briefly review the cutting-edge
research and discuss future challenges. Furthermore, we develop a
game-theoretical model for energy-efficient distributed edge server activation
and study several learning techniques. Numerical results are provided to
illustrate the performance of these distributed learning techniques. Also, open
research issues in the context of resource management in MEC servers are
discussed.
| [
{
"created": "Mon, 20 Nov 2017 04:01:18 GMT",
"version": "v1"
}
] | 2017-11-27 | [
[
"Ranadheera",
"Shermila",
""
],
[
"Maghsudi",
"Setareh",
""
],
[
"Hossain",
"Ekram",
""
]
] | Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources, which calls for fair efficient resource management in the MEC servers. The problem is however challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. In this article, we focus on the application of game theory and reinforcement learning for efficient distributed resource management in MEC, in particular, for computation offloading. We briefly review the cutting-edge research and discuss future challenges. Furthermore, we develop a game-theoretical model for energy-efficient distributed edge server activation and study several learning techniques. Numerical results are provided to illustrate the performance of these distributed learning techniques. Also, open research issues in the context of resource management in MEC servers are discussed. |
2004.13843 | Ram G Athreya | Ram G Athreya, Srividya Bansal, Axel-Cyrille Ngonga Ngomo, Ricardo
Usbeck | Template-based Question Answering using Recursive Neural Networks | null | null | null | null | cs.CL cs.DB cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a neural network-based approach to automatically learn and
classify natural language questions into its corresponding template using
recursive neural networks. An obvious advantage of using neural networks is the
elimination of the need for laborious feature engineering that can be
cumbersome and error-prone. The input question is encoded into a vector
representation. The model is trained and evaluated on the LC-QuAD dataset
(Large-scale Complex Question Answering Dataset). The LC-QuAD queries are
annotated based on 38 unique templates that the model attempts to classify. The
resulting model is evaluated against both the LC-QuAD dataset and the 7th
Question Answering Over Linked Data (QALD-7) dataset. The recursive neural
network achieves template classification accuracy of 0.828 on the LC-QuAD
dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most
likely templates were considered the model achieves an accuracy of 0.945 on the
LC-QuAD dataset and 0.786 on the QALD-7 dataset. After slot filling, the
overall system achieves a macro F-score 0.419 on the LC-QuAD dataset and a
macro F-score of 0.417 on the QALD-7 dataset.
| [
{
"created": "Fri, 3 Apr 2020 18:14:39 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jun 2020 00:26:26 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Jun 2020 01:41:26 GMT",
"version": "v3"
}
] | 2020-06-11 | [
[
"Athreya",
"Ram G",
""
],
[
"Bansal",
"Srividya",
""
],
[
"Ngomo",
"Axel-Cyrille Ngonga",
""
],
[
"Usbeck",
"Ricardo",
""
]
] | We propose a neural network-based approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination of the need for laborious feature engineering that can be cumbersome and error-prone. The input question is encoded into a vector representation. The model is trained and evaluated on the LC-QuAD dataset (Large-scale Complex Question Answering Dataset). The LC-QuAD queries are annotated based on 38 unique templates that the model attempts to classify. The resulting model is evaluated against both the LC-QuAD dataset and the 7th Question Answering Over Linked Data (QALD-7) dataset. The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset. After slot filling, the overall system achieves a macro F-score 0.419 on the LC-QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset. |
2112.04368 | Sahan Bulathwela | Sahan Bulathwela, Mar\'ia P\'erez-Ortiz, Emine Yilmaz, John
Shawe-Taylor | Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation
Systems | Presented at the First International Workshop on Joint Use of
Probabilistic Graphical Models and Ontology at Conference on Knowledge Graph
and Semantic Web 2021 | null | null | null | cs.IR cs.AI cs.CY stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In informational recommenders, many challenges arise from the need to handle
the semantic and hierarchical structure between knowledge areas. This work aims
to advance towards building a state-aware educational recommendation system
that incorporates semantic relatedness between knowledge topics, propagating
latent information across semantically related topics. We introduce a novel
learner model that exploits this semantic relatedness between knowledge
components in learning resources using the Wikipedia link graph, with the aim
to better predict learner engagement and latent knowledge in a lifelong
learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive
knowledge representation while leveraging Bayesian machine learning to improve
the predictive performance of the educational engagement. Our experiments with
a large dataset demonstrate that this new semantic version of TrueLearn
algorithm achieves statistically significant improvements in terms of
predictive performance with a simple extension that adds semantic awareness to
the model.
| [
{
"created": "Wed, 8 Dec 2021 16:23:27 GMT",
"version": "v1"
}
] | 2021-12-09 | [
[
"Bulathwela",
"Sahan",
""
],
[
"Pérez-Ortiz",
"María",
""
],
[
"Yilmaz",
"Emine",
""
],
[
"Shawe-Taylor",
"John",
""
]
] | In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model. |
2305.09857 | Vipul Raheja | Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang | CoEdIT: Text Editing by Task-Specific Instruction Tuning | Accepted to EMNLP 2023 (Findings). 18 pages, 13 tables, 2 figures | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce CoEdIT, a state-of-the-art text editing system for writing
assistance. CoEdIT takes instructions from the user specifying the attributes
of the desired text, such as "Make the sentence simpler" or "Write it in a more
neutral style," and outputs the edited text. We present a large language model
fine-tuned on a diverse collection of task-specific instructions for text
editing (a total of 82K instructions). Our model (1) achieves state-of-the-art
performance on various text editing benchmarks, (2) is competitive with
publicly available largest-sized LLMs trained on instructions while being
nearly 60x smaller, (3) is capable of generalizing to unseen edit instructions,
and (4) exhibits abilities to generalize to composite instructions containing
different combinations of edit actions. Through extensive qualitative and
quantitative analysis, we show that writers prefer the edits suggested by
CoEdIT relative to other state-of-the-art text editing models. Our code, data,
and models are publicly available at https://github.com/vipulraheja/coedit.
| [
{
"created": "Wed, 17 May 2023 00:05:24 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Oct 2023 23:17:13 GMT",
"version": "v2"
}
] | 2023-10-25 | [
[
"Raheja",
"Vipul",
""
],
[
"Kumar",
"Dhruv",
""
],
[
"Koo",
"Ryan",
""
],
[
"Kang",
"Dongyeop",
""
]
] | We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions). Our model (1) achieves state-of-the-art performance on various text editing benchmarks, (2) is competitive with publicly available largest-sized LLMs trained on instructions while being nearly 60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits abilities to generalize to composite instructions containing different combinations of edit actions. Through extensive qualitative and quantitative analysis, we show that writers prefer the edits suggested by CoEdIT relative to other state-of-the-art text editing models. Our code, data, and models are publicly available at https://github.com/vipulraheja/coedit. |
2009.13905 | Jacopo Amidei | Jacopo Amidei | Aligning Intraobserver Agreement by Transitivity | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Annotation reproducibility and accuracy rely on good consistency within
annotators. We propose a novel method for measuring within annotator
consistency or annotator Intraobserver Agreement (IA). The proposed approach is
based on transitivity, a measure that has been thoroughly studied in the
context of rational decision-making. The transitivity measure, in contrast with
the commonly used test-retest strategy for annotator IA, is less sensitive to
the several types of bias introduced by the test-retest strategy. We present a
representation theorem to the effect that relative judgement data that meet
transitivity can be mapped to a scale (in terms of measurement theory). We also
discuss a further application of transitivity as part of data collection design
for addressing the problem of the quadratic complexity of data collection of
relative judgements.
| [
{
"created": "Tue, 29 Sep 2020 09:55:04 GMT",
"version": "v1"
}
] | 2020-09-30 | [
[
"Amidei",
"Jacopo",
""
]
] | Annotation reproducibility and accuracy rely on good consistency within annotators. We propose a novel method for measuring within annotator consistency or annotator Intraobserver Agreement (IA). The proposed approach is based on transitivity, a measure that has been thoroughly studied in the context of rational decision-making. The transitivity measure, in contrast with the commonly used test-retest strategy for annotator IA, is less sensitive to the several types of bias introduced by the test-retest strategy. We present a representation theorem to the effect that relative judgement data that meet transitivity can be mapped to a scale (in terms of measurement theory). We also discuss a further application of transitivity as part of data collection design for addressing the problem of the quadratic complexity of data collection of relative judgements. |
1303.5243 | Antonios Argyriou | Antonios Argyriou | Link Scheduling for Multiple Multicast Sessions in Distributed Wireless
Networks | null | IEEE Wireless Communications Letters 2013 | 10.1109/WCL.2013.040513.120924 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this letter we investigate link scheduling algorithms for throughput
maximization in multicast wireless networks. According to our system model,
each source node transmits to a multicast group that resides one hop away. We
adopt the physical interference model to reflect the aggregate signal to
interference and noise ratio (SINR) at each node of the multicast group. We
present an ILP formulation of the aforementioned problem. The basic feature of
the problem formulation is that it decomposes the single multicast session into
the corresponding point-to-point links. The rationale is that a solution
algorithm has more flexibility regarding the scheduling options for individual
nodes. The extended MILP problem that also considers power control is solved
with LP relaxation. Performance results for both the ILP and MILP problems are
obtained for different traffic loads and different number of nodes per
multicast group.
| [
{
"created": "Thu, 21 Mar 2013 12:38:10 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Argyriou",
"Antonios",
""
]
] | In this letter we investigate link scheduling algorithms for throughput maximization in multicast wireless networks. According to our system model, each source node transmits to a multicast group that resides one hop away. We adopt the physical interference model to reflect the aggregate signal to interference and noise ratio (SINR) at each node of the multicast group. We present an ILP formulation of the aforementioned problem. The basic feature of the problem formulation is that it decomposes the single multicast session into the corresponding point-to-point links. The rationale is that a solution algorithm has more flexibility regarding the scheduling options for individual nodes. The extended MILP problem that also considers power control is solved with LP relaxation. Performance results for both the ILP and MILP problems are obtained for different traffic loads and different number of nodes per multicast group. |
2209.00686 | Marco Zaffalon | Enrique Miranda and Marco Zaffalon | Nonlinear desirability theory | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Desirability can be understood as an extension of Anscombe and Aumann's
Bayesian decision theory to sets of expected utilities. At the core of
desirability lies an assumption of linearity of the scale in which rewards are
measured. It is a traditional assumption used to derive the expected utility
model, which clashes with a general representation of rational decision making,
though. Allais has, in particular, pointed this out in 1953 with his famous
paradox. We note that the utility scale plays the role of a closure operator
when we regard desirability as a logical theory. This observation enables us to
extend desirability to the nonlinear case by letting the utility scale be
represented via a general closure operator. The new theory directly expresses
rewards in actual nonlinear currency (money), much in Savage's spirit, while
arguably weakening the founding assumptions to a minimum. We characterise the
main properties of the new theory both from the perspective of sets of gambles
and of their lower and upper prices (previsions). We show how Allais paradox
finds a solution in the new theory, and discuss the role of sets of
probabilities in the theory.
| [
{
"created": "Thu, 1 Sep 2022 18:44:29 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Nov 2022 11:57:06 GMT",
"version": "v2"
}
] | 2022-11-21 | [
[
"Miranda",
"Enrique",
""
],
[
"Zaffalon",
"Marco",
""
]
] | Desirability can be understood as an extension of Anscombe and Aumann's Bayesian decision theory to sets of expected utilities. At the core of desirability lies an assumption of linearity of the scale in which rewards are measured. It is a traditional assumption used to derive the expected utility model, which clashes with a general representation of rational decision making, though. Allais has, in particular, pointed this out in 1953 with his famous paradox. We note that the utility scale plays the role of a closure operator when we regard desirability as a logical theory. This observation enables us to extend desirability to the nonlinear case by letting the utility scale be represented via a general closure operator. The new theory directly expresses rewards in actual nonlinear currency (money), much in Savage's spirit, while arguably weakening the founding assumptions to a minimum. We characterise the main properties of the new theory both from the perspective of sets of gambles and of their lower and upper prices (previsions). We show how Allais paradox finds a solution in the new theory, and discuss the role of sets of probabilities in the theory. |
0905.2386 | Joel Ratsaby | Joel Ratsaby | Combinatorial information distance | null | null | null | null | cs.DM cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Let $|A|$ denote the cardinality of a finite set $A$. For any real number $x$
define $t(x)=x$ if $x\geq1$ and 1 otherwise. For any finite sets $A,B$ let
$\delta(A,B)$ $=$ $\log_{2}(t(|B\cap\bar{A}||A|))$. We define {This appears as
Technical Report # arXiv:0905.2386v4. A shorter version appears in the {Proc.
of Mini-Conference on Applied Theoretical Computer Science (MATCOS-10)},
Slovenia, Oct. 13-14, 2010.} a new cobinatorial distance $d(A,B)$ $=$
$\max\{\delta(A,B),\delta(B,A)\} $ which may be applied to measure the distance
between binary strings of different lengths. The distance is based on a
classical combinatorial notion of information introduced by Kolmogorov.
| [
{
"created": "Thu, 14 May 2009 17:44:39 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Feb 2010 11:49:28 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Aug 2010 09:36:12 GMT",
"version": "v3"
},
{
"created": "Thu, 9 Sep 2010 19:54:28 GMT",
"version": "v4"
},
{
"created": "Sun, 17 Oct 2010 18:12:08 GMT",
"version": "v5"
}
] | 2010-10-19 | [
[
"Ratsaby",
"Joel",
""
]
] | Let $|A|$ denote the cardinality of a finite set $A$. For any real number $x$ define $t(x)=x$ if $x\geq1$ and 1 otherwise. For any finite sets $A,B$ let $\delta(A,B)$ $=$ $\log_{2}(t(|B\cap\bar{A}||A|))$. We define {This appears as Technical Report # arXiv:0905.2386v4. A shorter version appears in the {Proc. of Mini-Conference on Applied Theoretical Computer Science (MATCOS-10)}, Slovenia, Oct. 13-14, 2010.} a new cobinatorial distance $d(A,B)$ $=$ $\max\{\delta(A,B),\delta(B,A)\} $ which may be applied to measure the distance between binary strings of different lengths. The distance is based on a classical combinatorial notion of information introduced by Kolmogorov. |
2210.03841 | Polina Zablotskaia | Siddhartha Brahma, Polina Zablotskaia, David Mimno | Breaking BERT: Evaluating and Optimizing Sparsified Attention | Shorter version accepted to SNN2021 workshop | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Transformers allow attention between all pairs of tokens, but there is reason
to believe that most of these connections - and their quadratic time and memory
- may not be necessary. But which ones? We evaluate the impact of
sparsification patterns with a series of ablation experiments. First, we
compare masks based on syntax, lexical similarity, and token position to random
connections, and measure which patterns reduce performance the least. We find
that on three common finetuning tasks even using attention that is at least 78%
sparse can have little effect on performance if applied at later transformer
layers, but that applying sparsity throughout the network reduces performance
significantly. Second, we vary the degree of sparsity for three patterns
supported by previous work, and find that connections to neighbouring tokens
are the most significant. Finally, we treat sparsity as an optimizable
parameter, and present an algorithm to learn degrees of neighboring connections
that gives a fine-grained control over the accuracy-sparsity trade-off while
approaching the performance of existing methods.
| [
{
"created": "Fri, 7 Oct 2022 22:32:27 GMT",
"version": "v1"
}
] | 2022-10-11 | [
[
"Brahma",
"Siddhartha",
""
],
[
"Zablotskaia",
"Polina",
""
],
[
"Mimno",
"David",
""
]
] | Transformers allow attention between all pairs of tokens, but there is reason to believe that most of these connections - and their quadratic time and memory - may not be necessary. But which ones? We evaluate the impact of sparsification patterns with a series of ablation experiments. First, we compare masks based on syntax, lexical similarity, and token position to random connections, and measure which patterns reduce performance the least. We find that on three common finetuning tasks even using attention that is at least 78% sparse can have little effect on performance if applied at later transformer layers, but that applying sparsity throughout the network reduces performance significantly. Second, we vary the degree of sparsity for three patterns supported by previous work, and find that connections to neighbouring tokens are the most significant. Finally, we treat sparsity as an optimizable parameter, and present an algorithm to learn degrees of neighboring connections that gives a fine-grained control over the accuracy-sparsity trade-off while approaching the performance of existing methods. |
2406.18493 | Cynthia Kop | Cynthia Kop | A weakly monotonic, logically constrained, HORPO-variant | Technical report detailing an adaptation of the method in
https://link.springer.com/chapter/10.1007/978-3-031-57267-8_13 | null | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | In this short paper, we present a simple variant of the recursive path
ordering, specified for Logically Constrained Simply Typed Rewriting Systems
(LCSTRSs). This is a method for curried systems, without lambda but with
partially applied function symbols, which can deal with logical constraints. As
it is designed for use in the dependency pair framework, it is defined as
reduction pair, allowing weak monotonicity.
| [
{
"created": "Wed, 26 Jun 2024 16:56:18 GMT",
"version": "v1"
}
] | 2024-06-27 | [
[
"Kop",
"Cynthia",
""
]
] | In this short paper, we present a simple variant of the recursive path ordering, specified for Logically Constrained Simply Typed Rewriting Systems (LCSTRSs). This is a method for curried systems, without lambda but with partially applied function symbols, which can deal with logical constraints. As it is designed for use in the dependency pair framework, it is defined as reduction pair, allowing weak monotonicity. |
2201.12163 | Hiroshi Kajino | Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami | Biases in In Silico Evaluation of Molecular Optimization Methods and
Bias-Reduced Evaluation Methodology | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We are interested in in silico evaluation methodology for molecular
optimization methods. Given a sample of molecules and their properties of our
interest, we wish not only to train an agent that can find molecules optimized
with respect to the target property but also to evaluate its performance. A
common practice is to train a predictor of the target property on the sample
and use it for both training and evaluating the agent. We show that this
evaluator potentially suffers from two biases; one is due to misspecification
of the predictor and the other to reusing the same sample for training and
evaluation. We discuss bias reduction methods for each of the biases
comprehensively, and empirically investigate their effectiveness.
| [
{
"created": "Fri, 28 Jan 2022 14:53:14 GMT",
"version": "v1"
}
] | 2022-01-31 | [
[
"Kajino",
"Hiroshi",
""
],
[
"Miyaguchi",
"Kohei",
""
],
[
"Osogami",
"Takayuki",
""
]
] | We are interested in in silico evaluation methodology for molecular optimization methods. Given a sample of molecules and their properties of our interest, we wish not only to train an agent that can find molecules optimized with respect to the target property but also to evaluate its performance. A common practice is to train a predictor of the target property on the sample and use it for both training and evaluating the agent. We show that this evaluator potentially suffers from two biases; one is due to misspecification of the predictor and the other to reusing the same sample for training and evaluation. We discuss bias reduction methods for each of the biases comprehensively, and empirically investigate their effectiveness. |
2004.02432 | Jaeyeon Kang | Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, and Seon Joo
Kim | Deep Space-Time Video Upsampling Networks | ECCV2020 accepted | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video super-resolution (VSR) and frame interpolation (FI) are traditional
computer vision problems, and the performance have been improving by
incorporating deep learning recently. In this paper, we investigate the problem
of jointly upsampling videos both in space and time, which is becoming more
important with advances in display systems. One solution for this is to run VSR
and FI, one by one, independently. This is highly inefficient as heavy deep
neural networks (DNN) are involved in each solution. To this end, we propose an
end-to-end DNN framework for the space-time video upsampling by efficiently
merging VSR and FI into a joint framework. In our framework, a novel weighting
scheme is proposed to fuse input frames effectively without explicit motion
compensation for efficient processing of videos. The results show better
results both quantitatively and qualitatively, while reducing the computation
time (x7 faster) and the number of parameters (30%) compared to baselines.
| [
{
"created": "Mon, 6 Apr 2020 07:04:21 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Aug 2020 02:37:53 GMT",
"version": "v2"
}
] | 2020-08-11 | [
[
"Kang",
"Jaeyeon",
""
],
[
"Jo",
"Younghyun",
""
],
[
"Oh",
"Seoung Wug",
""
],
[
"Vajda",
"Peter",
""
],
[
"Kim",
"Seon Joo",
""
]
] | Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently. In this paper, we investigate the problem of jointly upsampling videos both in space and time, which is becoming more important with advances in display systems. One solution for this is to run VSR and FI, one by one, independently. This is highly inefficient as heavy deep neural networks (DNN) are involved in each solution. To this end, we propose an end-to-end DNN framework for the space-time video upsampling by efficiently merging VSR and FI into a joint framework. In our framework, a novel weighting scheme is proposed to fuse input frames effectively without explicit motion compensation for efficient processing of videos. The results show better results both quantitatively and qualitatively, while reducing the computation time (x7 faster) and the number of parameters (30%) compared to baselines. |
1304.7054 | Chetan Jhurani | Chetan Jhurani | Batched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs | null | null | null | null | cs.MS cs.DC math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an interface and an implementation for performing Kronecker
product actions on NVIDIA GPUs for multiple small 2-D matrices and 3-D arrays
processed in parallel as a batch. This method is suited to cases where the
Kronecker product component matrices are identical but the operands in a
matrix-free application vary in the batch. Any batched GEMM (General Matrix
Multiply) implementation, for example ours [1] or the one in cuBLAS, can also
be used for performing batched Kronecker products on GPUs. However, the
specialized implementation presented here is faster and uses less memory.
Partly this is because a simple GEMM based approach would require extra copies
to and from main memory. We focus on matrix sizes less than or equal to 16,
since these are the typical polynomial degrees in Finite Elements, but the
implementation can be easily extended for other sizes. We obtain 143 and 285
GFlop/s for single precision real when processing matrices of size 10 and 16,
respectively on NVIDIA Tesla K20c using CUDA 5.0. The corresponding speeds for
3-D array Kronecker products are 126 and 268 GFlop/s, respectively. Double
precision is easily supported using the C++ template mechanism.
| [
{
"created": "Fri, 26 Apr 2013 02:22:25 GMT",
"version": "v1"
}
] | 2013-04-29 | [
[
"Jhurani",
"Chetan",
""
]
] | We describe an interface and an implementation for performing Kronecker product actions on NVIDIA GPUs for multiple small 2-D matrices and 3-D arrays processed in parallel as a batch. This method is suited to cases where the Kronecker product component matrices are identical but the operands in a matrix-free application vary in the batch. Any batched GEMM (General Matrix Multiply) implementation, for example ours [1] or the one in cuBLAS, can also be used for performing batched Kronecker products on GPUs. However, the specialized implementation presented here is faster and uses less memory. Partly this is because a simple GEMM based approach would require extra copies to and from main memory. We focus on matrix sizes less than or equal to 16, since these are the typical polynomial degrees in Finite Elements, but the implementation can be easily extended for other sizes. We obtain 143 and 285 GFlop/s for single precision real when processing matrices of size 10 and 16, respectively on NVIDIA Tesla K20c using CUDA 5.0. The corresponding speeds for 3-D array Kronecker products are 126 and 268 GFlop/s, respectively. Double precision is easily supported using the C++ template mechanism. |
2308.05264 | Soumyaroop Nandi | Soumyaroop Nandi, Prem Natarajan, Wael Abd-Almageed | TrainFors: A Large Benchmark Training Dataset for Image Manipulation
Detection and Localization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evaluation datasets and metrics for image manipulation detection and
localization (IMDL) research have been standardized. But the training dataset
for such a task is still nonstandard. Previous researchers have used
unconventional and deviating datasets to train neural networks for detecting
image forgeries and localizing pixel maps of manipulated regions. For a fair
comparison, the training set, test set, and evaluation metrics should be
persistent. Hence, comparing the existing methods may not seem fair as the
results depend heavily on the training datasets as well as the model
architecture. Moreover, none of the previous works release the synthetic
training dataset used for the IMDL task. We propose a standardized benchmark
training dataset for image splicing, copy-move forgery, removal forgery, and
image enhancement forgery. Furthermore, we identify the problems with the
existing IMDL datasets and propose the required modifications. We also train
the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair
evaluation and report the actual performance of these methods under similar
conditions.
| [
{
"created": "Thu, 10 Aug 2023 00:26:34 GMT",
"version": "v1"
}
] | 2023-08-11 | [
[
"Nandi",
"Soumyaroop",
""
],
[
"Natarajan",
"Prem",
""
],
[
"Abd-Almageed",
"Wael",
""
]
] | The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized. But the training dataset for such a task is still nonstandard. Previous researchers have used unconventional and deviating datasets to train neural networks for detecting image forgeries and localizing pixel maps of manipulated regions. For a fair comparison, the training set, test set, and evaluation metrics should be persistent. Hence, comparing the existing methods may not seem fair as the results depend heavily on the training datasets as well as the model architecture. Moreover, none of the previous works release the synthetic training dataset used for the IMDL task. We propose a standardized benchmark training dataset for image splicing, copy-move forgery, removal forgery, and image enhancement forgery. Furthermore, we identify the problems with the existing IMDL datasets and propose the required modifications. We also train the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair evaluation and report the actual performance of these methods under similar conditions. |
2102.10960 | Guoqing Liu | Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li,
Nenghai Yu, Tie-Yan Liu | Return-Based Contrastive Representation Learning for Reinforcement
Learning | ICLR 2021 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, various auxiliary tasks have been proposed to accelerate
representation learning and improve sample efficiency in deep reinforcement
learning (RL). However, existing auxiliary tasks do not take the
characteristics of RL problems into consideration and are unsupervised. By
leveraging returns, the most important feedback signals in RL, we propose a
novel auxiliary task that forces the learnt representations to discriminate
state-action pairs with different returns. Our auxiliary loss is theoretically
justified to learn representations that capture the structure of a new form of
state-action abstraction, under which state-action pairs with similar return
distributions are aggregated together. In low data regime, our algorithm
outperforms strong baselines on complex tasks in Atari games and DeepMind
Control suite, and achieves even better performance when combined with existing
auxiliary tasks.
| [
{
"created": "Mon, 22 Feb 2021 13:04:18 GMT",
"version": "v1"
}
] | 2021-02-23 | [
[
"Liu",
"Guoqing",
""
],
[
"Zhang",
"Chuheng",
""
],
[
"Zhao",
"Li",
""
],
[
"Qin",
"Tao",
""
],
[
"Zhu",
"Jinhua",
""
],
[
"Li",
"Jian",
""
],
[
"Yu",
"Nenghai",
""
],
[
"Liu",
"Tie-Yan",
""
]
] | Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most important feedback signals in RL, we propose a novel auxiliary task that forces the learnt representations to discriminate state-action pairs with different returns. Our auxiliary loss is theoretically justified to learn representations that capture the structure of a new form of state-action abstraction, under which state-action pairs with similar return distributions are aggregated together. In low data regime, our algorithm outperforms strong baselines on complex tasks in Atari games and DeepMind Control suite, and achieves even better performance when combined with existing auxiliary tasks. |
2408.07479 | Luisa Coheur | Ana Sofia Evans, Helena Moniz and Lu\'isa Coheur | A Study on Bias Detection and Classification in Natural Language
Processing | 31 pages, 15 Tables, 4 Figures | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Human biases have been shown to influence the performance of models and
algorithms in various fields, including Natural Language Processing. While the
study of this phenomenon is garnering focus in recent years, the available
resources are still relatively scarce, often focusing on different forms or
manifestations of biases. The aim of our work is twofold: 1) gather
publicly-available datasets and determine how to better combine them to
effectively train models in the task of hate speech detection and
classification; 2) analyse the main issues with these datasets, such as
scarcity, skewed resources, and reliance on non-persistent data. We discuss
these issues in tandem with the development of our experiments, in which we
show that the combinations of different datasets greatly impact the models'
performance.
| [
{
"created": "Wed, 14 Aug 2024 11:49:24 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Evans",
"Ana Sofia",
""
],
[
"Moniz",
"Helena",
""
],
[
"Coheur",
"Luísa",
""
]
] | Human biases have been shown to influence the performance of models and algorithms in various fields, including Natural Language Processing. While the study of this phenomenon is garnering focus in recent years, the available resources are still relatively scarce, often focusing on different forms or manifestations of biases. The aim of our work is twofold: 1) gather publicly-available datasets and determine how to better combine them to effectively train models in the task of hate speech detection and classification; 2) analyse the main issues with these datasets, such as scarcity, skewed resources, and reliance on non-persistent data. We discuss these issues in tandem with the development of our experiments, in which we show that the combinations of different datasets greatly impact the models' performance. |
2008.13690 | Jussi Tohka | Jussi Tohka and Mark van Gils | Evaluation of machine learning algorithms for Health and Wellness
applications: a tutorial | To be published in Computers in Biology and Medicine | null | 10.1016/j.compbiomed.2021.104324 | null | cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Research on decision support applications in healthcare, such as those
related to diagnosis, prediction, treatment planning, etc., have seen
enormously increased interest recently. This development is thanks to the
increase in data availability as well as advances in artificial intelligence
and machine learning research. Highly promising research examples are published
daily. However, at the same time, there are some unrealistic expectations with
regards to the requirements for reliable development and objective validation
that is needed in healthcare settings. These expectations may lead to unmet
schedules and disappointments (or non-uptake) at the end-user side. It is the
aim of this tutorial to provide practical guidance on how to assess performance
reliably and efficiently and avoid common traps. Instead of giving a list of
do's and don't s, this tutorial tries to build a better understanding behind
these do's and don't s and presents both the most relevant performance
evaluation criteria as well as how to compute them. Along the way, we will
indicate common mistakes and provide references discussing various topics more
in-depth.
| [
{
"created": "Mon, 31 Aug 2020 15:50:51 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Mar 2021 16:40:09 GMT",
"version": "v2"
}
] | 2021-03-25 | [
[
"Tohka",
"Jussi",
""
],
[
"van Gils",
"Mark",
""
]
] | Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the increase in data availability as well as advances in artificial intelligence and machine learning research. Highly promising research examples are published daily. However, at the same time, there are some unrealistic expectations with regards to the requirements for reliable development and objective validation that is needed in healthcare settings. These expectations may lead to unmet schedules and disappointments (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these do's and don't s and presents both the most relevant performance evaluation criteria as well as how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth. |
1612.03382 | Sahar Yousefi | Sahar Yousefi, M.T. Manzuri Shalmani, Jeremy Lin, Marius Staring | A Novel Motion Detection Method Resistant to Severe Illumination Changes | null | null | 10.1109/TCSVT.2018.2885211 | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recently, there has been a considerable attention given to the motion
detection problem due to the explosive growth of its applications in video
analysis and surveillance systems. While the previous approaches can produce
good results, an accurate detection of motion remains a challenging task due to
the difficulties raised by illumination variations, occlusion, camouflage,
burst physical motion, dynamic texture, and environmental changes such as those
on climate changes, sunlight changes during a day, etc. In this paper, we
propose a novel per-pixel motion descriptor for both motion detection and
dynamic texture segmentation which outperforms the current methods in the
literature particularly in severe scenarios. The proposed descriptor is based
on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and
three-dimensional wavelet leader. In this approach, a feature vector is
extracted for each pixel by applying a novel three dimensional wavelet-based
motion descriptor. Then, the extracted features are clustered by a clustering
method such as well-known k-means algorithm or Gaussian Mixture Model (GMM).
The experimental results demonstrate the effectiveness of our proposed method
compared to the other motion detection approaches from the literature. The
application of the proposed method and additional experimental results for the
different datasets are available at
(http://dspl.ce.sharif.edu/motiondetector.html).
| [
{
"created": "Sun, 11 Dec 2016 07:50:00 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Feb 2017 05:38:03 GMT",
"version": "v2"
},
{
"created": "Mon, 8 May 2017 08:14:06 GMT",
"version": "v3"
},
{
"created": "Wed, 10 May 2017 05:57:40 GMT",
"version": "v4"
},
{
"created": "Thu, 5 Oct 2017 12:55:56 GMT",
"version": "v5"
},
{
"created": "Thu, 15 Mar 2018 13:29:46 GMT",
"version": "v6"
}
] | 2021-03-29 | [
[
"Yousefi",
"Sahar",
""
],
[
"Shalmani",
"M. T. Manzuri",
""
],
[
"Lin",
"Jeremy",
""
],
[
"Staring",
"Marius",
""
]
] | Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, an accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, burst physical motion, dynamic texture, and environmental changes such as those on climate changes, sunlight changes during a day, etc. In this paper, we propose a novel per-pixel motion descriptor for both motion detection and dynamic texture segmentation which outperforms the current methods in the literature particularly in severe scenarios. The proposed descriptor is based on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and three-dimensional wavelet leader. In this approach, a feature vector is extracted for each pixel by applying a novel three dimensional wavelet-based motion descriptor. Then, the extracted features are clustered by a clustering method such as well-known k-means algorithm or Gaussian Mixture Model (GMM). The experimental results demonstrate the effectiveness of our proposed method compared to the other motion detection approaches from the literature. The application of the proposed method and additional experimental results for the different datasets are available at (http://dspl.ce.sharif.edu/motiondetector.html). |
2211.05824 | Hassan Khan | Jason Ceci, Jonah Stegman, Hassan Khan | No Privacy in the Electronics Repair Industry | This paper has been accepted to appear at the 44th IEEE Symposium on
Security and Privacy (IEEE S&P 2023) | null | null | null | cs.CR cs.HC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Electronics repair and service providers offer a range of services to
computing device owners across North America -- from software installation to
hardware repair. Device owners obtain these services and leave their device
along with their access credentials at the mercy of technicians, which leads to
privacy concerns for owners' personal data. We conduct a comprehensive
four-part study to measure the state of privacy in the electronics repair
industry. First, through a field study with 18 service providers, we uncover
that most service providers do not have any privacy policy or controls to
safeguard device owners' personal data from snooping by technicians. Second, we
drop rigged devices for repair at 16 service providers and collect data on
widespread privacy violations by technicians, including snooping on personal
data, copying data off the device, and removing tracks of snooping activities.
Third, we conduct an online survey (n=112) to collect data on customers'
experiences when getting devices repaired. Fourth, we invite a subset of survey
respondents (n=30) for semi-structured interviews to establish a deeper
understanding of their experiences and identify potential solutions to curtail
privacy violations by technicians. We apply our findings to discuss possible
controls and actions different stakeholders and regulatory agencies should take
to improve the state of privacy in the repair industry.
| [
{
"created": "Thu, 10 Nov 2022 19:27:21 GMT",
"version": "v1"
}
] | 2022-11-14 | [
[
"Ceci",
"Jason",
""
],
[
"Stegman",
"Jonah",
""
],
[
"Khan",
"Hassan",
""
]
] | Electronics repair and service providers offer a range of services to computing device owners across North America -- from software installation to hardware repair. Device owners obtain these services and leave their device along with their access credentials at the mercy of technicians, which leads to privacy concerns for owners' personal data. We conduct a comprehensive four-part study to measure the state of privacy in the electronics repair industry. First, through a field study with 18 service providers, we uncover that most service providers do not have any privacy policy or controls to safeguard device owners' personal data from snooping by technicians. Second, we drop rigged devices for repair at 16 service providers and collect data on widespread privacy violations by technicians, including snooping on personal data, copying data off the device, and removing tracks of snooping activities. Third, we conduct an online survey (n=112) to collect data on customers' experiences when getting devices repaired. Fourth, we invite a subset of survey respondents (n=30) for semi-structured interviews to establish a deeper understanding of their experiences and identify potential solutions to curtail privacy violations by technicians. We apply our findings to discuss possible controls and actions different stakeholders and regulatory agencies should take to improve the state of privacy in the repair industry. |
2210.15926 | Hamid Fsian | Hamid Fsian, Vahid Mohammadi, Pierre Gouton, Saeid Minaei | Comparison of Stereo Matching Algorithms for the Development of
Disparity Map | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Stereo Matching is one of the classical problems in computer vision for the
extraction of 3D information but still controversial for accuracy and
processing costs. The use of matching techniques and cost functions is crucial
in the development of the disparity map. This paper presents a comparative
study of six different stereo matching algorithms including Block Matching
(BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP),
Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the
proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum
of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and
compared. The stereo images used in this study were from the Middlebury Stereo
Datasets provided with perfect and imperfect calibrations. Results show that
the selection of matching function is quite important and also depends on the
images properties. Results showed that the BP algorithm in most cases provided
better results getting accuracies over 95%.
| [
{
"created": "Fri, 28 Oct 2022 06:14:14 GMT",
"version": "v1"
}
] | 2022-10-31 | [
[
"Fsian",
"Hamid",
""
],
[
"Mohammadi",
"Vahid",
""
],
[
"Gouton",
"Pierre",
""
],
[
"Minaei",
"Saeid",
""
]
] | Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and compared. The stereo images used in this study were from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. Results show that the selection of matching function is quite important and also depends on the images properties. Results showed that the BP algorithm in most cases provided better results getting accuracies over 95%. |
1406.2255 | Ahmed El Shafie | Ahmed El Shafie, Tamer Khattab, Amr El-Keyi | Energy-Efficient Cooperative Cognitive Relaying Schemes for Cognitive
Radio Networks | null | null | null | null | cs.NI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a cognitive radio network in which a primary user (PU) may
cooperate with a cognitive radio user (i.e., a secondary user (SU)) for
transmissions of its data packets. The PU is assumed to be a buffered node
operating in a time-slotted fashion where the time is partitioned into
equal-length slots. We develop two schemes which involve cooperation between
primary and secondary users. To satisfy certain quality of service (QoS)
requirements, users share time slot duration and channel frequency bandwidth.
Moreover, the SU may leverage the primary feedback message to further increase
both its data rate and satisfy the PU QoS requirements. The proposed
cooperative schemes are designed such that the SU data rate is maximized under
the constraint that the PU average queueing delay is maintained less than the
average queueing delay in case of non-cooperative PU. In addition, the proposed
schemes guarantee the stability of the PU queue and maintain the average energy
emitted by the SU below a certain value. The proposed schemes also provide more
robust and potentially continuous service for SUs compared to the conventional
practice in cognitive networks where SUs transmit in the spectrum holes and
silence sessions of the PUs. We include primary source burstiness, sensing
errors, and feedback decoding errors to the analysis of our proposed
cooperative schemes. The optimization problems are solved offline and require a
simple 2-dimensional grid-based search over the optimization variables.
Numerical results show the beneficial gains of the cooperative schemes in terms
of SU data rate and PU throughput, average PU queueing delay, and average PU
energy savings.
| [
{
"created": "Mon, 9 Jun 2014 17:40:21 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Jul 2014 21:25:45 GMT",
"version": "v2"
},
{
"created": "Thu, 26 Oct 2017 14:39:40 GMT",
"version": "v3"
}
] | 2017-10-27 | [
[
"Shafie",
"Ahmed El",
""
],
[
"Khattab",
"Tamer",
""
],
[
"El-Keyi",
"Amr",
""
]
] | We investigate a cognitive radio network in which a primary user (PU) may cooperate with a cognitive radio user (i.e., a secondary user (SU)) for transmissions of its data packets. The PU is assumed to be a buffered node operating in a time-slotted fashion where the time is partitioned into equal-length slots. We develop two schemes which involve cooperation between primary and secondary users. To satisfy certain quality of service (QoS) requirements, users share time slot duration and channel frequency bandwidth. Moreover, the SU may leverage the primary feedback message to further increase both its data rate and satisfy the PU QoS requirements. The proposed cooperative schemes are designed such that the SU data rate is maximized under the constraint that the PU average queueing delay is maintained less than the average queueing delay in case of non-cooperative PU. In addition, the proposed schemes guarantee the stability of the PU queue and maintain the average energy emitted by the SU below a certain value. The proposed schemes also provide more robust and potentially continuous service for SUs compared to the conventional practice in cognitive networks where SUs transmit in the spectrum holes and silence sessions of the PUs. We include primary source burstiness, sensing errors, and feedback decoding errors to the analysis of our proposed cooperative schemes. The optimization problems are solved offline and require a simple 2-dimensional grid-based search over the optimization variables. Numerical results show the beneficial gains of the cooperative schemes in terms of SU data rate and PU throughput, average PU queueing delay, and average PU energy savings. |
1005.0092 | Andrei Sukhov M | E.S. Sagatov, A.M. Sukhov, P. Calyam | Influence of distortions of key frames on video transfer in wireless
networks | 6 pages, 4 figures, 2 Tables | null | 10.1109/ISVC.2010.5656258 | null | cs.NI cs.MM | http://creativecommons.org/licenses/by/3.0/ | In this paper it is shown that for substantial increase of video quality in
wireless network it is necessary to execute two obligatory points on
modernization of the communication scheme. The player on the received part
should throw back automatically duplicated RTP packets, server of streaming
video should duplicate the packets containing the information of key frames.
Coefficients of the mathematical model describing video quality in wireless
network have been found for WiFi and 3G standards and codecs MPEG-2 and MPEG-4
(DivX). The special experimental technique which has allowed collecting and
processing the data has been developed for calculation of values of factors.
| [
{
"created": "Sat, 1 May 2010 17:15:36 GMT",
"version": "v1"
}
] | 2017-02-20 | [
[
"Sagatov",
"E. S.",
""
],
[
"Sukhov",
"A. M.",
""
],
[
"Calyam",
"P.",
""
]
] | In this paper it is shown that for substantial increase of video quality in wireless network it is necessary to execute two obligatory points on modernization of the communication scheme. The player on the received part should throw back automatically duplicated RTP packets, server of streaming video should duplicate the packets containing the information of key frames. Coefficients of the mathematical model describing video quality in wireless network have been found for WiFi and 3G standards and codecs MPEG-2 and MPEG-4 (DivX). The special experimental technique which has allowed collecting and processing the data has been developed for calculation of values of factors. |
2110.09829 | Ilir Kola | Ilir Kola, Pradeep K. Murukannaiah, Catholijn M. Jonker, M. Birna van
Riemsdijk | Towards Social Situation Awareness in Support Agents | 8 pages, 1 figure | null | 10.1109/MIS.2022.3163625 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial agents that support people in their daily activities (e.g.,
virtual coaches and personal assistants) are increasingly prevalent. Since many
daily activities are social in nature, support agents should understand a
user's social situation to offer comprehensive support. However, there are no
systematic approaches for developing support agents that are social situation
aware. We identify key requirements for a support agent to be social situation
aware and propose steps to realize those requirements. These steps are
presented through a conceptual architecture centered on two key ideas: (1)
conceptualizing social situation awareness as an instantiation of `general'
situation awareness, and (2) using situation taxonomies for such instantiation.
This enables support agents to represent a user's social situation, comprehend
its meaning, and assess its impact on the user's behavior. We discuss empirical
results supporting the effectiveness of the proposed approach and illustrate
how the architecture can be used in support agents through two use cases.
| [
{
"created": "Tue, 19 Oct 2021 10:35:46 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Oct 2021 06:20:46 GMT",
"version": "v2"
},
{
"created": "Mon, 4 Apr 2022 08:55:03 GMT",
"version": "v3"
}
] | 2022-04-05 | [
[
"Kola",
"Ilir",
""
],
[
"Murukannaiah",
"Pradeep K.",
""
],
[
"Jonker",
"Catholijn M.",
""
],
[
"van Riemsdijk",
"M. Birna",
""
]
] | Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approaches for developing support agents that are social situation aware. We identify key requirements for a support agent to be social situation aware and propose steps to realize those requirements. These steps are presented through a conceptual architecture centered on two key ideas: (1) conceptualizing social situation awareness as an instantiation of `general' situation awareness, and (2) using situation taxonomies for such instantiation. This enables support agents to represent a user's social situation, comprehend its meaning, and assess its impact on the user's behavior. We discuss empirical results supporting the effectiveness of the proposed approach and illustrate how the architecture can be used in support agents through two use cases. |
1406.0079 | Shashishekar Ramakrishna | Shashishekar Ramakrishna and Adrian Paschke | Bridging the gap between Legal Practitioners and Knowledge Engineers
using semi-formal KR | published in proceedings of the 8th International Workshop on Value
Modeling and Business Ontology, VMBO, Berlin, 2014 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of Structured English as a computation independent knowledge
representation format for non-technical users in business rules representation
has been proposed in OMGs Semantics and Business Vocabulary Representation
(SBVR). In the legal domain we face a similar problem. Formal representation
languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2
ontologies etc.) support the technical knowledge engineer and the automated
reasoning. But, they can be hardly used directly by the legal domain experts
who do not have a computer science background. In this paper we adapt the SBVR
Structured English approach for the legal domain and implement a
proof-of-concept, called KR4IPLaw, which enables legal domain experts to
represent their knowledge in Structured English in a computational independent
and hence, for them, more usable way. The benefit of this approach is that the
underlying pre-defined semantics of the Structured English approach makes
transformations into formal languages such as OASIS LegalRuleML and OWL2
ontologies possible. We exemplify our approach in the domain of patent law.
| [
{
"created": "Sat, 31 May 2014 14:16:30 GMT",
"version": "v1"
}
] | 2014-06-03 | [
[
"Ramakrishna",
"Shashishekar",
""
],
[
"Paschke",
"Adrian",
""
]
] | The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt the SBVR Structured English approach for the legal domain and implement a proof-of-concept, called KR4IPLaw, which enables legal domain experts to represent their knowledge in Structured English in a computational independent and hence, for them, more usable way. The benefit of this approach is that the underlying pre-defined semantics of the Structured English approach makes transformations into formal languages such as OASIS LegalRuleML and OWL2 ontologies possible. We exemplify our approach in the domain of patent law. |
1611.01853 | Aviv Yehezkel | Reuven Cohen, Liran Katzir and Aviv Yehezkel | MTS Sketch for Accurate Estimation of Set-Expression Cardinalities from
Small Samples | arXiv admin note: text overlap with arXiv:1508.06216 | null | null | null | cs.DB cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sketch-based streaming algorithms allow efficient processing of big data.
These algorithms use small fixed-size storage to store a summary ("sketch") of
the input data, and use probabilistic algorithms to estimate the desired
quantity. However, in many real-world applications it is impractical to collect
and process the entire data stream, the common practice is thus to sample and
process only a small part of it. While sampling is crucial for handling massive
data sets, it may reduce accuracy. In this paper we present a new framework
that can accurately estimate the cardinality of any set expression between any
number of streams using only a small sample of each stream. The proposed
framework consists of a new sketch, called Maximal-Term with Subsample (MTS),
and a family of algorithms that use this sketch. An example of a possible query
that can be efficiently answered using the proposed sketch is, How many
distinct tuples appear in tables $T_1$ and $T_2$, but not in $T_3$? The
algorithms presented in this paper answer such queries accurately, processing
only a small sample of the tuples in each table and using a constant amount of
memory. Such estimations are useful for the optimization of queries over very
large database systems. We show that all our algorithms are unbiased, and we
analyze their asymptotic variance.
| [
{
"created": "Sun, 6 Nov 2016 22:22:40 GMT",
"version": "v1"
}
] | 2016-11-08 | [
[
"Cohen",
"Reuven",
""
],
[
"Katzir",
"Liran",
""
],
[
"Yehezkel",
"Aviv",
""
]
] | Sketch-based streaming algorithms allow efficient processing of big data. These algorithms use small fixed-size storage to store a summary ("sketch") of the input data, and use probabilistic algorithms to estimate the desired quantity. However, in many real-world applications it is impractical to collect and process the entire data stream, the common practice is thus to sample and process only a small part of it. While sampling is crucial for handling massive data sets, it may reduce accuracy. In this paper we present a new framework that can accurately estimate the cardinality of any set expression between any number of streams using only a small sample of each stream. The proposed framework consists of a new sketch, called Maximal-Term with Subsample (MTS), and a family of algorithms that use this sketch. An example of a possible query that can be efficiently answered using the proposed sketch is, How many distinct tuples appear in tables $T_1$ and $T_2$, but not in $T_3$? The algorithms presented in this paper answer such queries accurately, processing only a small sample of the tuples in each table and using a constant amount of memory. Such estimations are useful for the optimization of queries over very large database systems. We show that all our algorithms are unbiased, and we analyze their asymptotic variance. |
1602.01038 | Mahmoud Ashour | Mahmoud Ashour and Amr El-Keyi | Interactive Multiple Model Estimation of Doubly-Selective Channels for
OFDM systems | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose an algorithm for channel estimation, acquisition
and tracking, for orthogonal frequency division multiplexing (OFDM) systems.
The proposed algorithm is suitable for vehicular communications that encounter
very high mobility. A preamble sequence is used to derive an initial estimate
of the channel using least squares (LS). The temporal variation of the channel
within one OFDM symbol is approximated by two complex exponential basis
expansion models (CE-BEM). One of the Fourier-based BEMs is intended to capture
the low frequencies in the channel (slow variations corresponding to low
Doppler), while the other is destined to capture high frequencies (fast
variations corresponding to high Doppler). Kalman filtering is employed to
track the BEM coefficients iteratively on an OFDM symbol-by-symbol basis. An
interactive multiple model (IMM) estimator is implemented to dynamically mix
the estimates obtained by the two Kalman filters, each of which matched to one
of the BEMs. Extensive numerical simulations are conducted to signify the gain
obtained by the proposed combining technique.
| [
{
"created": "Tue, 2 Feb 2016 18:41:37 GMT",
"version": "v1"
}
] | 2016-02-03 | [
[
"Ashour",
"Mahmoud",
""
],
[
"El-Keyi",
"Amr",
""
]
] | In this paper, we propose an algorithm for channel estimation, acquisition and tracking, for orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm is suitable for vehicular communications that encounter very high mobility. A preamble sequence is used to derive an initial estimate of the channel using least squares (LS). The temporal variation of the channel within one OFDM symbol is approximated by two complex exponential basis expansion models (CE-BEM). One of the Fourier-based BEMs is intended to capture the low frequencies in the channel (slow variations corresponding to low Doppler), while the other is destined to capture high frequencies (fast variations corresponding to high Doppler). Kalman filtering is employed to track the BEM coefficients iteratively on an OFDM symbol-by-symbol basis. An interactive multiple model (IMM) estimator is implemented to dynamically mix the estimates obtained by the two Kalman filters, each of which matched to one of the BEMs. Extensive numerical simulations are conducted to signify the gain obtained by the proposed combining technique. |
1207.0873 | EPTCS | Luca Bortolussi (University of Trieste), Vashti Galpin (University of
Edinburgh), Jane Hillston (University of Edinburgh) | Hybrid performance modelling of opportunistic networks | In Proceedings QAPL 2012, arXiv:1207.0559 | EPTCS 85, 2012, pp. 106-121 | 10.4204/EPTCS.85.8 | null | cs.SY cs.LO cs.NI cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.
| [
{
"created": "Wed, 4 Jul 2012 01:25:04 GMT",
"version": "v1"
}
] | 2012-07-05 | [
[
"Bortolussi",
"Luca",
"",
"University of Trieste"
],
[
"Galpin",
"Vashti",
"",
"University of\n Edinburgh"
],
[
"Hillston",
"Jane",
"",
"University of Edinburgh"
]
] | We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them. |
2003.00409 | Haokun Li | Haokun Li and Bican Xia | Solving Satisfiability of Polynomial Formulas By Sample-Cell Projection | null | null | null | null | cs.LO cs.AI cs.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new algorithm for deciding the satisfiability of polynomial formulas over
the reals is proposed. The key point of the algorithm is a new projection
operator, called sample-cell projection operator, custom-made for
Conflict-Driven Clause Learning (CDCL)-style search. Although the new operator
is also a CAD (Cylindrical Algebraic Decomposition)-like projection operator
which computes the cell (not necessarily cylindrical) containing a given sample
such that each polynomial from the problem is sign-invariant on the cell, it is
of singly exponential time complexity. The sample-cell projection operator can
efficiently guide CDCL-style search away from conflicting states. Experiments
show the effectiveness of the new algorithm.
| [
{
"created": "Sun, 1 Mar 2020 05:36:09 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Mar 2020 03:01:35 GMT",
"version": "v2"
}
] | 2020-03-05 | [
[
"Li",
"Haokun",
""
],
[
"Xia",
"Bican",
""
]
] | A new algorithm for deciding the satisfiability of polynomial formulas over the reals is proposed. The key point of the algorithm is a new projection operator, called sample-cell projection operator, custom-made for Conflict-Driven Clause Learning (CDCL)-style search. Although the new operator is also a CAD (Cylindrical Algebraic Decomposition)-like projection operator which computes the cell (not necessarily cylindrical) containing a given sample such that each polynomial from the problem is sign-invariant on the cell, it is of singly exponential time complexity. The sample-cell projection operator can efficiently guide CDCL-style search away from conflicting states. Experiments show the effectiveness of the new algorithm. |
2012.14058 | Yiming Liu | Yiming Liu, Erwu Liu, Rui Wang, Zhu Han, Binyu Lu | Asymptotic Achievability of the Cram\'er-Rao Lower Bound of Channel
Estimation for Reconfigurable Intelligent Surface Aided Communication Systems | 5 pages, 3 figures, 1 table. To be published in IEEE Wireless
Communications Letters | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To achieve the joint active and passive beamforming gains in the
reconfigurable intelligent surface assisted millimeter wave system, the
reflected cascade channel needs to be accurately estimated. Many strategies
have been proposed in the literature to solve this issue. However, whether the
Cram\'er-Rao lower bound (CRLB) of such estimation is achievable still remains
uncertain. To fill this gap, we first convert the channel estimation problem
into a sparse signal recovery problem by utilizing the properties of discrete
Fourier transform matrix and Kronecker product. Then, a joint typicality based
estimator is utilized to carry out the signal recovery task. We show that,
through both mathematical proofs and numerical simulations, the solution
proposed in this letter can in fact asymptotically achieve the CRLB.
| [
{
"created": "Mon, 28 Dec 2020 02:03:22 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Feb 2021 04:59:03 GMT",
"version": "v2"
},
{
"created": "Tue, 21 Sep 2021 05:36:25 GMT",
"version": "v3"
}
] | 2021-09-22 | [
[
"Liu",
"Yiming",
""
],
[
"Liu",
"Erwu",
""
],
[
"Wang",
"Rui",
""
],
[
"Han",
"Zhu",
""
],
[
"Lu",
"Binyu",
""
]
] | To achieve the joint active and passive beamforming gains in the reconfigurable intelligent surface assisted millimeter wave system, the reflected cascade channel needs to be accurately estimated. Many strategies have been proposed in the literature to solve this issue. However, whether the Cram\'er-Rao lower bound (CRLB) of such estimation is achievable still remains uncertain. To fill this gap, we first convert the channel estimation problem into a sparse signal recovery problem by utilizing the properties of discrete Fourier transform matrix and Kronecker product. Then, a joint typicality based estimator is utilized to carry out the signal recovery task. We show that, through both mathematical proofs and numerical simulations, the solution proposed in this letter can in fact asymptotically achieve the CRLB. |
1606.03021 | Minh-Duc Hua | Minh-Duc Hua, Jochen Trumpf, Tarek Hamel, Robert Mahony, and Pascal
Morin | Feature-based Recursive Observer Design for Homography Estimation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new algorithm for online estimation of a sequence of
homographies applicable to image sequences obtained from robotic vehicles
equipped with vision sensors. The approach taken exploits the underlying
Special Linear group structure of the set of homographies along with gyroscope
measurements and direct point-feature correspondences between images to develop
temporal filter for the homography estimate. Theoretical analysis and
experimental results are provided to demonstrate the robustness of the proposed
algorithm. The experimental results show excellent performance even in the case
of very fast camera motion (relative to frame rate), severe occlusion, and in
the presence of specular reflections.
| [
{
"created": "Thu, 9 Jun 2016 16:35:46 GMT",
"version": "v1"
}
] | 2016-06-10 | [
[
"Hua",
"Minh-Duc",
""
],
[
"Trumpf",
"Jochen",
""
],
[
"Hamel",
"Tarek",
""
],
[
"Mahony",
"Robert",
""
],
[
"Morin",
"Pascal",
""
]
] | This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with vision sensors. The approach taken exploits the underlying Special Linear group structure of the set of homographies along with gyroscope measurements and direct point-feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance even in the case of very fast camera motion (relative to frame rate), severe occlusion, and in the presence of specular reflections. |
0903.1146 | Toby Walsh | Toby Walsh | Breaking Value Symmetry | Principles and Practice of Constraint Programming - CP 2007, 13th
International Conference, CP 2007, Providence, RI, USA, September 23-27,
2007, Proceedings. Lecture Notes in Computer Science 4741 Springer 2007, ISBN
978-3-540-74969- | null | null | COMIC-2007-008 | cs.AI cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One common type of symmetry is when values are symmetric. For example, if we
are assigning colours (values) to nodes (variables) in a graph colouring
problem then we can uniformly interchange the colours throughout a colouring.
For a problem with value symmetries, all symmetric solutions can be eliminated
in polynomial time. However, as we show here, both static and dynamic methods
to deal with symmetry have computational limitations. With static methods,
pruning all symmetric values is NP-hard in general. With dynamic methods, we
can take exponential time on problems which static methods solve without
search.
| [
{
"created": "Fri, 6 Mar 2009 03:50:17 GMT",
"version": "v1"
}
] | 2009-03-09 | [
[
"Walsh",
"Toby",
""
]
] | One common type of symmetry is when values are symmetric. For example, if we are assigning colours (values) to nodes (variables) in a graph colouring problem then we can uniformly interchange the colours throughout a colouring. For a problem with value symmetries, all symmetric solutions can be eliminated in polynomial time. However, as we show here, both static and dynamic methods to deal with symmetry have computational limitations. With static methods, pruning all symmetric values is NP-hard in general. With dynamic methods, we can take exponential time on problems which static methods solve without search. |
1612.04459 | Xiaohu Ge | Xiaohu Ge, Jiaqi Chen, Songxue Ying, Min Chen | Energy and coverage efficiency trade-off in 5G small cell networks | Our work needs further polish | null | null | null | cs.NI cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | When small cells are densely deployed in the fifth generation (5G) cellular
networks, the base stations (BSs) switch-off strategy is an effective approach
for saving energy consumption considering changes of traffic load. In general,
the loss of coverage efficiency is an inevitable cost for cellular networks
adopting BSs switch-off strategies. Based on the BSs switch-off strategy, an
optimized energy density efficiency of hard core point process (HCPP) small
cell networks is proposed to trade off the energy and coverage efficiency.
Simulation results imply that the minimum active BS distance used for the BSs
switch-off strategy is recommended as 150 meters to achieve a tradeoff between
energy and coverage efficiency in 5G small cell networks.
| [
{
"created": "Wed, 14 Dec 2016 02:17:20 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jul 2017 10:54:52 GMT",
"version": "v2"
}
] | 2017-07-06 | [
[
"Ge",
"Xiaohu",
""
],
[
"Chen",
"Jiaqi",
""
],
[
"Ying",
"Songxue",
""
],
[
"Chen",
"Min",
""
]
] | When small cells are densely deployed in the fifth generation (5G) cellular networks, the base stations (BSs) switch-off strategy is an effective approach for saving energy consumption considering changes of traffic load. In general, the loss of coverage efficiency is an inevitable cost for cellular networks adopting BSs switch-off strategies. Based on the BSs switch-off strategy, an optimized energy density efficiency of hard core point process (HCPP) small cell networks is proposed to trade off the energy and coverage efficiency. Simulation results imply that the minimum active BS distance used for the BSs switch-off strategy is recommended as 150 meters to achieve a tradeoff between energy and coverage efficiency in 5G small cell networks. |
2105.05454 | Darja Smite | Darja Smite, Marius Mikalsen, Nils B. Moe, Viktoria Stray and Eriks
Klotins | From Collaboration to Solitude and Back: Remote Pair Programming during
COVID-19 | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Along with the increasing popularity of agile software development, software
work has become much more social than ever. Contemporary software teams rely on
a variety of collaborative practices, such as pair programming, the topic of
our study. Many agilists advocated the importance of collocation, face-to-face
interaction, and physical artefacts incorporated in the shared workspace, which
the COVID-19 pandemic made unavailable; most software companies around the
world were forced to send their engineers to work from home. As software
projects and teams overnight turned into dis-tributed collaborations, we
question what happened to the pair programming practice in the work-from-home
mode. This paper reports on a longitudinal study of remote pair programming in
two companies. We conducted 38 interviews with 30 engineers from Norway,
Sweden, and the USA, and used the results of a survey in one of the case
companies. Our study is unique as we collected the data longitudinally in
April/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming
has decreased and some interviewees report not pairing at all for almost a full
year. The experiences of those who paired vary from actively co-editing the
code by using special tools to more passively co-reading and discussing the
code and solutions by sharing the screen. Finally, we found that the interest
in and the use of PP over time, since the first months of forced work from home
to early 2021, has admittedly increased, also as a social practice.
| [
{
"created": "Wed, 12 May 2021 06:38:22 GMT",
"version": "v1"
}
] | 2021-05-13 | [
[
"Smite",
"Darja",
""
],
[
"Mikalsen",
"Marius",
""
],
[
"Moe",
"Nils B.",
""
],
[
"Stray",
"Viktoria",
""
],
[
"Klotins",
"Eriks",
""
]
] | Along with the increasing popularity of agile software development, software work has become much more social than ever. Contemporary software teams rely on a variety of collaborative practices, such as pair programming, the topic of our study. Many agilists advocated the importance of collocation, face-to-face interaction, and physical artefacts incorporated in the shared workspace, which the COVID-19 pandemic made unavailable; most software companies around the world were forced to send their engineers to work from home. As software projects and teams overnight turned into dis-tributed collaborations, we question what happened to the pair programming practice in the work-from-home mode. This paper reports on a longitudinal study of remote pair programming in two companies. We conducted 38 interviews with 30 engineers from Norway, Sweden, and the USA, and used the results of a survey in one of the case companies. Our study is unique as we collected the data longitudinally in April/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming has decreased and some interviewees report not pairing at all for almost a full year. The experiences of those who paired vary from actively co-editing the code by using special tools to more passively co-reading and discussing the code and solutions by sharing the screen. Finally, we found that the interest in and the use of PP over time, since the first months of forced work from home to early 2021, has admittedly increased, also as a social practice. |
2012.10695 | Quoc Phong Nguyen | Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet | An Information-Theoretic Framework for Unifying Active Learning Problems | 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Extended
version with derivations, 12 pages | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an information-theoretic framework for unifying active
learning problems: level set estimation (LSE), Bayesian optimization (BO), and
their generalized variant. We first introduce a novel active learning criterion
that subsumes an existing LSE algorithm and achieves state-of-the-art
performance in LSE problems with a continuous input domain. Then, by exploiting
the relationship between LSE and BO, we design a competitive
information-theoretic acquisition function for BO that has interesting
connections to upper confidence bound and max-value entropy search (MES). The
latter connection reveals a drawback of MES which has important implications on
not only MES but also on other MES-based acquisition functions. Finally, our
unifying information-theoretic framework can be applied to solve a generalized
problem of LSE and BO involving multiple level sets in a data-efficient manner.
We empirically evaluate the performance of our proposed algorithms using
synthetic benchmark functions, a real-world dataset, and in hyperparameter
tuning of machine learning models.
| [
{
"created": "Sat, 19 Dec 2020 14:22:48 GMT",
"version": "v1"
}
] | 2020-12-22 | [
[
"Nguyen",
"Quoc Phong",
""
],
[
"Low",
"Bryan Kian Hsiang",
""
],
[
"Jaillet",
"Patrick",
""
]
] | This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active learning criterion that subsumes an existing LSE algorithm and achieves state-of-the-art performance in LSE problems with a continuous input domain. Then, by exploiting the relationship between LSE and BO, we design a competitive information-theoretic acquisition function for BO that has interesting connections to upper confidence bound and max-value entropy search (MES). The latter connection reveals a drawback of MES which has important implications on not only MES but also on other MES-based acquisition functions. Finally, our unifying information-theoretic framework can be applied to solve a generalized problem of LSE and BO involving multiple level sets in a data-efficient manner. We empirically evaluate the performance of our proposed algorithms using synthetic benchmark functions, a real-world dataset, and in hyperparameter tuning of machine learning models. |
1805.06665 | Bin He | Bin He, Yi Guan, Rui Dai | Classifying medical relations in clinical text via convolutional neural
networks | Accepted by Artificial Intelligence In Medicine | null | 10.1016/j.artmed.2018.05.001 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning research on relation classification has achieved solid
performance in the general domain. This study proposes a convolutional neural
network (CNN) architecture with a multi-pooling operation for medical relation
classification on clinical records and explores a loss function with a
category-level constraint matrix. Experiments using the 2010 i2b2/VA relation
corpus demonstrate these models, which do not depend on any external features,
outperform previous single-model methods and our best model is competitive with
the existing ensemble-based method.
| [
{
"created": "Thu, 17 May 2018 09:20:52 GMT",
"version": "v1"
}
] | 2018-05-18 | [
[
"He",
"Bin",
""
],
[
"Guan",
"Yi",
""
],
[
"Dai",
"Rui",
""
]
] | Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. |
1003.4146 | Michael Bommarito II | Michael J. Bommarito II, Daniel Martin Katz | A Mathematical Approach to the Study of the United States Code | 5 pages, 6 figures, 2 tables. | null | 10.1016/j.physa.2010.05.057 | null | cs.IR cs.CY cs.DL physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The United States Code (Code) is a document containing over 22 million words
that represents a large and important source of Federal statutory law. Scholars
and policy advocates often discuss the direction and magnitude of changes in
various aspects of the Code. However, few have mathematically formalized the
notions behind these discussions or directly measured the resulting
representations. This paper addresses the current state of the literature in
two ways. First, we formalize a representation of the United States Code as the
union of a hierarchical network and a citation network over vertices containing
the language of the Code. This representation reflects the fact that the Code
is a hierarchically organized document containing language and explicit
citations between provisions. Second, we use this formalization to measure
aspects of the Code as codified in October 2008, November 2009, and March 2010.
These measurements allow for a characterization of the actual changes in the
Code over time. Our findings indicate that in the recent past, the Code has
grown in its amount of structure, interdependence, and language.
| [
{
"created": "Mon, 22 Mar 2010 12:41:01 GMT",
"version": "v1"
}
] | 2015-05-18 | [
[
"Bommarito",
"Michael J.",
"II"
],
[
"Katz",
"Daniel Martin",
""
]
] | The United States Code (Code) is a document containing over 22 million words that represents a large and important source of Federal statutory law. Scholars and policy advocates often discuss the direction and magnitude of changes in various aspects of the Code. However, few have mathematically formalized the notions behind these discussions or directly measured the resulting representations. This paper addresses the current state of the literature in two ways. First, we formalize a representation of the United States Code as the union of a hierarchical network and a citation network over vertices containing the language of the Code. This representation reflects the fact that the Code is a hierarchically organized document containing language and explicit citations between provisions. Second, we use this formalization to measure aspects of the Code as codified in October 2008, November 2009, and March 2010. These measurements allow for a characterization of the actual changes in the Code over time. Our findings indicate that in the recent past, the Code has grown in its amount of structure, interdependence, and language. |
2305.04684 | Kazuki Osawa | Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, and Torsten
Hoefler | ASDL: A Unified Interface for Gradient Preconditioning in PyTorch | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Gradient preconditioning is a key technique to integrate the second-order
information into gradients for improving and extending gradient-based learning
algorithms. In deep learning, stochasticity, nonconvexity, and high
dimensionality lead to a wide variety of gradient preconditioning methods, with
implementation complexity and inconsistent performance and feasibility. We
propose the Automatic Second-order Differentiation Library (ASDL), an extension
library for PyTorch, which offers various implementations and a plug-and-play
unified interface for gradient preconditioning. ASDL enables the study and
structured comparison of a range of gradient preconditioning methods.
| [
{
"created": "Mon, 8 May 2023 12:59:49 GMT",
"version": "v1"
}
] | 2023-05-09 | [
[
"Osawa",
"Kazuki",
""
],
[
"Ishikawa",
"Satoki",
""
],
[
"Yokota",
"Rio",
""
],
[
"Li",
"Shigang",
""
],
[
"Hoefler",
"Torsten",
""
]
] | Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms. In deep learning, stochasticity, nonconvexity, and high dimensionality lead to a wide variety of gradient preconditioning methods, with implementation complexity and inconsistent performance and feasibility. We propose the Automatic Second-order Differentiation Library (ASDL), an extension library for PyTorch, which offers various implementations and a plug-and-play unified interface for gradient preconditioning. ASDL enables the study and structured comparison of a range of gradient preconditioning methods. |
1810.12737 | Ciriaco Andrea D'Angelo | Giovanni Abramo, Tindaro Cicero, Ciriaco Andrea D'Angelo | Should the research performance of scientists be distinguished by
gender? | null | Abramo, G., Cicero, T., D'Angelo, C.A. (2015). Should the research
performance of scientists be distinguished by gender? Journal of
Informetrics, 9(1), 25-38 | 10.1016/j.joi.2014.11.002 | null | cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The literature on gender differences in research performance seems to suggest
a gap between men and women, where the former outperform the latter. Whether
one agrees with the different factors proposed to explain the phenomenon, it is
worthwhile to verify if comparing the performance within each gender, rather
than without distinction, gives significantly different ranking lists. If there
were some structural factor that determined a penalty in performance of female
researchers compared to their male peers, then under conditions of equal
capacities of men and women, any comparative evaluations of individual
performance that fail to account for gender differences would lead to
distortion of the judgments in favor of men. In this work we measure the extent
of differences in rank between the two methods of comparing performance in each
field of the hard sciences: for professors in the Italian university system, we
compare the distributions of research performance for men and women and
subsequently the ranking lists with and without distinction by gender. The
results are of interest for the optimization of efficient selection in
formulation of recruitment, career advancement and incentive schemes.
| [
{
"created": "Tue, 30 Oct 2018 13:54:47 GMT",
"version": "v1"
}
] | 2018-10-31 | [
[
"Abramo",
"Giovanni",
""
],
[
"Cicero",
"Tindaro",
""
],
[
"D'Angelo",
"Ciriaco Andrea",
""
]
] | The literature on gender differences in research performance seems to suggest a gap between men and women, where the former outperform the latter. Whether one agrees with the different factors proposed to explain the phenomenon, it is worthwhile to verify if comparing the performance within each gender, rather than without distinction, gives significantly different ranking lists. If there were some structural factor that determined a penalty in performance of female researchers compared to their male peers, then under conditions of equal capacities of men and women, any comparative evaluations of individual performance that fail to account for gender differences would lead to distortion of the judgments in favor of men. In this work we measure the extent of differences in rank between the two methods of comparing performance in each field of the hard sciences: for professors in the Italian university system, we compare the distributions of research performance for men and women and subsequently the ranking lists with and without distinction by gender. The results are of interest for the optimization of efficient selection in formulation of recruitment, career advancement and incentive schemes. |
1511.03576 | Mohammad Khabbaz | Mohammad Khabbaz | DataGrinder: Fast, Accurate, Fully non-Parametric Classification
Approach Using 2D Convex Hulls | null | null | null | null | cs.DB cs.CG cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been a long time, since data mining technologies have made their ways
to the field of data management. Classification is one of the most important
data mining tasks for label prediction, categorization of objects into groups,
advertisement and data management. In this paper, we focus on the standard
classification problem which is predicting unknown labels in Euclidean space.
Most efforts in Machine Learning communities are devoted to methods that use
probabilistic algorithms which are heavy on Calculus and Linear Algebra. Most
of these techniques have scalability issues for big data, and are hardly
parallelizable if they are to maintain their high accuracies in their standard
form. Sampling is a new direction for improving scalability, using many small
parallel classifiers. In this paper, rather than conventional sampling methods,
we focus on a discrete classification algorithm with O(n) expected running
time. Our approach performs a similar task as sampling methods. However, we use
column-wise sampling of data, rather than the row-wise sampling used in the
literature. In either case, our algorithm is completely deterministic. Our
algorithm, proposes a way of combining 2D convex hulls in order to achieve high
classification accuracy as well as scalability in the same time. First, we
thoroughly describe and prove our O(n) algorithm for finding the convex hull of
a point set in 2D. Then, we show with experiments our classifier model built
based on this idea is very competitive compared with existing sophisticated
classification algorithms included in commercial statistical applications such
as MATLAB.
| [
{
"created": "Wed, 11 Nov 2015 17:06:35 GMT",
"version": "v1"
}
] | 2015-11-12 | [
[
"Khabbaz",
"Mohammad",
""
]
] | It has been a long time, since data mining technologies have made their ways to the field of data management. Classification is one of the most important data mining tasks for label prediction, categorization of objects into groups, advertisement and data management. In this paper, we focus on the standard classification problem which is predicting unknown labels in Euclidean space. Most efforts in Machine Learning communities are devoted to methods that use probabilistic algorithms which are heavy on Calculus and Linear Algebra. Most of these techniques have scalability issues for big data, and are hardly parallelizable if they are to maintain their high accuracies in their standard form. Sampling is a new direction for improving scalability, using many small parallel classifiers. In this paper, rather than conventional sampling methods, we focus on a discrete classification algorithm with O(n) expected running time. Our approach performs a similar task as sampling methods. However, we use column-wise sampling of data, rather than the row-wise sampling used in the literature. In either case, our algorithm is completely deterministic. Our algorithm, proposes a way of combining 2D convex hulls in order to achieve high classification accuracy as well as scalability in the same time. First, we thoroughly describe and prove our O(n) algorithm for finding the convex hull of a point set in 2D. Then, we show with experiments our classifier model built based on this idea is very competitive compared with existing sophisticated classification algorithms included in commercial statistical applications such as MATLAB. |
2109.13916 | Dan Hendrycks | Dan Hendrycks and Nicholas Carlini and John Schulman and Jacob
Steinhardt | Unsolved Problems in ML Safety | Position Paper | null | null | null | cs.LG cs.AI cs.CL cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning (ML) systems are rapidly increasing in size, are acquiring
new capabilities, and are increasingly deployed in high-stakes settings. As
with other powerful technologies, safety for ML should be a leading research
priority. In response to emerging safety challenges in ML, such as those
introduced by recent large-scale models, we provide a new roadmap for ML Safety
and refine the technical problems that the field needs to address. We present
four problems ready for research, namely withstanding hazards ("Robustness"),
identifying hazards ("Monitoring"), reducing inherent model hazards
("Alignment"), and reducing systemic hazards ("Systemic Safety"). Throughout,
we clarify each problem's motivation and provide concrete research directions.
| [
{
"created": "Tue, 28 Sep 2021 17:59:36 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Oct 2021 19:41:22 GMT",
"version": "v2"
},
{
"created": "Sat, 25 Dec 2021 19:27:40 GMT",
"version": "v3"
},
{
"created": "Fri, 29 Apr 2022 17:41:33 GMT",
"version": "v4"
},
{
"created": "Thu, 16 Jun 2022 21:12:42 GMT",
"version": "v5"
}
] | 2022-06-20 | [
[
"Hendrycks",
"Dan",
""
],
[
"Carlini",
"Nicholas",
""
],
[
"Schulman",
"John",
""
],
[
"Steinhardt",
"Jacob",
""
]
] | Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. We present four problems ready for research, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), reducing inherent model hazards ("Alignment"), and reducing systemic hazards ("Systemic Safety"). Throughout, we clarify each problem's motivation and provide concrete research directions. |
2402.17262 | Zhenhong Zhou | Zhenhong Zhou, Jiuyang Xiang, Haopeng Chen, Quan Liu, Zherui Li, Sen
Su | Speak Out of Turn: Safety Vulnerability of Large Language Models in
Multi-turn Dialogue | working in progress 23pages, 18 figures | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have been demonstrated to generate illegal or
unethical responses, particularly when subjected to "jailbreak." Research on
jailbreak has highlighted the safety issues of LLMs. However, prior studies
have predominantly focused on single-turn dialogue, ignoring the potential
complexities and risks presented by multi-turn dialogue, a crucial mode through
which humans derive information from LLMs. In this paper, we argue that humans
could exploit multi-turn dialogue to induce LLMs into generating harmful
information. LLMs may not intend to reject cautionary or borderline unsafe
queries, even if each turn is closely served for one malicious purpose in a
multi-turn dialogue. Therefore, by decomposing an unsafe query into several
sub-queries for multi-turn dialogue, we induced LLMs to answer harmful
sub-questions incrementally, culminating in an overall harmful response. Our
experiments, conducted across a wide range of LLMs, indicate current
inadequacies in the safety mechanisms of LLMs in multi-turn dialogue. Our
findings expose vulnerabilities of LLMs in complex scenarios involving
multi-turn dialogue, presenting new challenges for the safety of LLMs.
| [
{
"created": "Tue, 27 Feb 2024 07:11:59 GMT",
"version": "v1"
}
] | 2024-02-28 | [
[
"Zhou",
"Zhenhong",
""
],
[
"Xiang",
"Jiuyang",
""
],
[
"Chen",
"Haopeng",
""
],
[
"Liu",
"Quan",
""
],
[
"Li",
"Zherui",
""
],
[
"Su",
"Sen",
""
]
] | Large Language Models (LLMs) have been demonstrated to generate illegal or unethical responses, particularly when subjected to "jailbreak." Research on jailbreak has highlighted the safety issues of LLMs. However, prior studies have predominantly focused on single-turn dialogue, ignoring the potential complexities and risks presented by multi-turn dialogue, a crucial mode through which humans derive information from LLMs. In this paper, we argue that humans could exploit multi-turn dialogue to induce LLMs into generating harmful information. LLMs may not intend to reject cautionary or borderline unsafe queries, even if each turn is closely served for one malicious purpose in a multi-turn dialogue. Therefore, by decomposing an unsafe query into several sub-queries for multi-turn dialogue, we induced LLMs to answer harmful sub-questions incrementally, culminating in an overall harmful response. Our experiments, conducted across a wide range of LLMs, indicate current inadequacies in the safety mechanisms of LLMs in multi-turn dialogue. Our findings expose vulnerabilities of LLMs in complex scenarios involving multi-turn dialogue, presenting new challenges for the safety of LLMs. |
1404.1820 | Derrick Wing Kwan Ng | Derrick Wing Kwan Ng and Robert Schober | Max-min Fair Wireless Energy Transfer for Secure Multiuser Communication
Systems | 5 pages, invited paper, IEEE Information Theory Workshop 2014,
Hobart, Tasmania, Australia, Nov. 2014 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers max-min fairness for wireless energy transfer in a
downlink multiuser communication system. Our resource allocation design
maximizes the minimum harvested energy among multiple multiple-antenna energy
harvesting receivers (potential eavesdroppers) while providing quality of
service (QoS) for secure communication to multiple single-antenna information
receivers. In particular, the algorithm design is formulated as a non-convex
optimization problem which takes into account a minimum required
signal-to-interference-plus-noise ratio (SINR) constraint at the information
receivers and a constraint on the maximum tolerable channel capacity achieved
by the energy harvesting receivers for a given transmit power budget. The
proposed problem formulation exploits the dual use of artificial noise
generation for facilitating efficient wireless energy transfer and secure
communication. A semidefinite programming (SDP) relaxation approach is
exploited to obtain a global optimal solution of the considered problem.
Simulation results demonstrate the significant performance gain in harvested
energy that is achieved by the proposed optimal scheme compared to two simple
baseline schemes.
| [
{
"created": "Mon, 7 Apr 2014 15:53:18 GMT",
"version": "v1"
}
] | 2014-04-08 | [
[
"Ng",
"Derrick Wing Kwan",
""
],
[
"Schober",
"Robert",
""
]
] | This paper considers max-min fairness for wireless energy transfer in a downlink multiuser communication system. Our resource allocation design maximizes the minimum harvested energy among multiple multiple-antenna energy harvesting receivers (potential eavesdroppers) while providing quality of service (QoS) for secure communication to multiple single-antenna information receivers. In particular, the algorithm design is formulated as a non-convex optimization problem which takes into account a minimum required signal-to-interference-plus-noise ratio (SINR) constraint at the information receivers and a constraint on the maximum tolerable channel capacity achieved by the energy harvesting receivers for a given transmit power budget. The proposed problem formulation exploits the dual use of artificial noise generation for facilitating efficient wireless energy transfer and secure communication. A semidefinite programming (SDP) relaxation approach is exploited to obtain a global optimal solution of the considered problem. Simulation results demonstrate the significant performance gain in harvested energy that is achieved by the proposed optimal scheme compared to two simple baseline schemes. |
1608.07846 | Henry Kim | Henry M. Kim, Jackie Ho Nam Cheung, Marek Laskowski, Iryna Gel | Data Analytics using Ontologies of Management Theories: Towards
Implementing 'From Theory to Practice' | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore how computational ontologies can be impactful vis-a-vis the
developing discipline of "data science." We posit an approach wherein
management theories are represented as formal axioms, and then applied to draw
inferences about data that reside in corporate databases. That is, management
theories would be implemented as rules within a data analytics engine. We
demonstrate a case study development of such an ontology by formally
representing an accounting theory in First-Order Logic. Though quite
preliminary, the idea that an information technology, namely ontologies, can
potentially actualize the academic cliche, "From Theory to Practice," and be
applicable to the burgeoning domain of data analytics is novel and exciting.
| [
{
"created": "Sun, 28 Aug 2016 19:51:31 GMT",
"version": "v1"
}
] | 2016-08-30 | [
[
"Kim",
"Henry M.",
""
],
[
"Cheung",
"Jackie Ho Nam",
""
],
[
"Laskowski",
"Marek",
""
],
[
"Gel",
"Iryna",
""
]
] | We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliche, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting. |
2407.05339 | Jakob Mokander | Jakob M\"okander and Margi Sheth and Mimmi Gersbro-Sundler and Peder
Blomgren and Luciano Floridi | Challenges and Best Practices in Corporate AI Governance:Lessons from
the Biopharmaceutical Industry | null | Frontiers in Computer Science (2022) | 10.3389/fcomp.2022.1068361 | null | cs.CY cs.AI | http://creativecommons.org/licenses/by/4.0/ | While the use of artificial intelligence (AI) systems promises to bring
significant economic and social benefits, it is also coupled with ethical,
legal, and technical challenges. Business leaders thus face the question of how
to best reap the benefits of automation whilst managing the associated risks.
As a first step, many companies have committed themselves to various sets of
ethics principles aimed at guiding the design and use of AI systems. So far so
good. But how can well-intentioned ethical principles be translated into
effective practice? And what challenges await companies that attempt to
operationalize AI governance? In this article, we address these questions by
drawing on our first-hand experience of shaping and driving the roll-out of AI
governance within AstraZeneca, a biopharmaceutical company. The examples we
discuss highlight challenges that any organization attempting to operationalize
AI governance will have to face. These include questions concerning how to
define the material scope of AI governance, how to harmonize standards across
decentralized organizations, and how to measure the impact of specific AI
governance initiatives. By showcasing how AstraZeneca managed these operational
questions, we hope to provide project managers, CIOs, AI practitioners, and
data privacy officers responsible for designing and implementing AI governance
frameworks within other organizations with generalizable best practices. In
essence, companies seeking to operationalize AI governance are encouraged to
build on existing policies and governance structures, use pragmatic and
action-oriented terminology, focus on risk management in development and
procurement, and empower employees through continuous education and change
management.
| [
{
"created": "Sun, 7 Jul 2024 12:01:42 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Mökander",
"Jakob",
""
],
[
"Sheth",
"Margi",
""
],
[
"Gersbro-Sundler",
"Mimmi",
""
],
[
"Blomgren",
"Peder",
""
],
[
"Floridi",
"Luciano",
""
]
] | While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. |
1112.2516 | Jesper Schneider jws | Jesper W. Schneider | Caveats for using statistical significance tests in research assessments | Accepted version for Journal of Informetrics | null | 10.1016/j.joi.2012.08.005 | null | cs.DL stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper raises concerns about the advantages of using statistical
significance tests in research assessments as has recently been suggested in
the debate about proper normalization procedures for citation indicators.
Statistical significance tests are highly controversial and numerous criticisms
have been leveled against their use. Based on examples from articles by
proponents of the use statistical significance tests in research assessments,
we address some of the numerous problems with such tests. The issues
specifically discussed are the ritual practice of such tests, their dichotomous
application in decision making, the difference between statistical and
substantive significance, the implausibility of most null hypotheses, the
crucial assumption of randomness, as well as the utility of standard errors and
confidence intervals for inferential purposes. We argue that applying
statistical significance tests and mechanically adhering to their results is
highly problematic and detrimental to critical thinking. We claim that the use
of such tests do not provide any advantages in relation to citation indicators,
interpretations of them, or the decision making processes based upon them. On
the contrary their use may be harmful. Like many other critics, we generally
believe that statistical significance tests are over- and misused in the social
sciences including scientometrics and we encourage a reform on these matters.
| [
{
"created": "Mon, 12 Dec 2011 11:57:12 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Sep 2012 07:15:27 GMT",
"version": "v2"
}
] | 2012-09-26 | [
[
"Schneider",
"Jesper W.",
""
]
] | This paper raises concerns about the advantages of using statistical significance tests in research assessments as has recently been suggested in the debate about proper normalization procedures for citation indicators. Statistical significance tests are highly controversial and numerous criticisms have been leveled against their use. Based on examples from articles by proponents of the use statistical significance tests in research assessments, we address some of the numerous problems with such tests. The issues specifically discussed are the ritual practice of such tests, their dichotomous application in decision making, the difference between statistical and substantive significance, the implausibility of most null hypotheses, the crucial assumption of randomness, as well as the utility of standard errors and confidence intervals for inferential purposes. We argue that applying statistical significance tests and mechanically adhering to their results is highly problematic and detrimental to critical thinking. We claim that the use of such tests do not provide any advantages in relation to citation indicators, interpretations of them, or the decision making processes based upon them. On the contrary their use may be harmful. Like many other critics, we generally believe that statistical significance tests are over- and misused in the social sciences including scientometrics and we encourage a reform on these matters. |
2002.08562 | Dianbo Liu Dr | Dianbo Liu, Tim Miller | Federated pretraining and fine tuning of BERT using clinical notes from
multiple silos | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large scale contextual representation models, such as BERT, have
significantly advanced natural language processing (NLP) in recently years.
However, in certain area like healthcare, accessing diverse large scale text
data from multiple institutions is extremely challenging due to privacy and
regulatory reasons. In this article, we show that it is possible to both
pretrain and fine tune BERT models in a federated manner using clinical texts
from different silos without moving the data.
| [
{
"created": "Thu, 20 Feb 2020 04:14:35 GMT",
"version": "v1"
}
] | 2020-02-21 | [
[
"Liu",
"Dianbo",
""
],
[
"Miller",
"Tim",
""
]
] | Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcare, accessing diverse large scale text data from multiple institutions is extremely challenging due to privacy and regulatory reasons. In this article, we show that it is possible to both pretrain and fine tune BERT models in a federated manner using clinical texts from different silos without moving the data. |
2305.01275 | Peng-Tao Jiang | Peng-Tao Jiang, Yuqi Yang | Segment Anything is A Good Pseudo-label Generator for Weakly Supervised
Semantic Segmentation | Technical report | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Weakly supervised semantic segmentation with weak labels is a long-lived
ill-posed problem. Mainstream methods mainly focus on improving the quality of
pseudo labels. In this report, we attempt to explore the potential of 'prompt
to masks' from the powerful class-agnostic large segmentation model,
segment-anything. Specifically, different weak labels are used as prompts to
the segment-anything model, generating precise class masks. The class masks are
utilized to generate pseudo labels to train the segmentation networks. We have
conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments
demonstrate that segment-anything can serve as a good pseudo-label generator.
The code will be made publicly available.
| [
{
"created": "Tue, 2 May 2023 09:22:38 GMT",
"version": "v1"
}
] | 2023-05-03 | [
[
"Jiang",
"Peng-Tao",
""
],
[
"Yang",
"Yuqi",
""
]
] | Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential of 'prompt to masks' from the powerful class-agnostic large segmentation model, segment-anything. Specifically, different weak labels are used as prompts to the segment-anything model, generating precise class masks. The class masks are utilized to generate pseudo labels to train the segmentation networks. We have conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments demonstrate that segment-anything can serve as a good pseudo-label generator. The code will be made publicly available. |
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