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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2004.13354
|
Jinwoo Ahn
|
Jinwoo Ahn, Seungjin Lee, Jinhoon Lee, Yungwoo Ko, Donghyun Min,
Junghee Lee, Youngjae Kim
|
SGX-SSD: A Policy-based Versioning SSD with Intel SGX
|
7 pages, 4 figures
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper demonstrates that SSDs, which perform device-level versioning, can
be exposed to data tampering attacks when the retention time of data is less
than the malware's dwell time. To deal with that threat, we propose SGX-SSD, a
SGX-based versioning SSD which selectively preserves file history based on the
given policy. The proposed system adopts Intel SGX to implement the version
policy management system that is safe from high-privileged malware. Based on
the policy, only the necessary data is selectively preserved in SSD that
prevents files with less priority from wasting space and also ensures the
integrity of important files.
|
[
{
"created": "Tue, 28 Apr 2020 08:11:30 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Apr 2020 01:03:18 GMT",
"version": "v2"
}
] |
2020-04-30
|
[
[
"Ahn",
"Jinwoo",
""
],
[
"Lee",
"Seungjin",
""
],
[
"Lee",
"Jinhoon",
""
],
[
"Ko",
"Yungwoo",
""
],
[
"Min",
"Donghyun",
""
],
[
"Lee",
"Junghee",
""
],
[
"Kim",
"Youngjae",
""
]
] |
This paper demonstrates that SSDs, which perform device-level versioning, can be exposed to data tampering attacks when the retention time of data is less than the malware's dwell time. To deal with that threat, we propose SGX-SSD, a SGX-based versioning SSD which selectively preserves file history based on the given policy. The proposed system adopts Intel SGX to implement the version policy management system that is safe from high-privileged malware. Based on the policy, only the necessary data is selectively preserved in SSD that prevents files with less priority from wasting space and also ensures the integrity of important files.
|
1804.06454
|
Marco Baldi
|
Mohammad H. Tadayon, Alireza Tasdighi, Massimo Battaglioni, Marco
Baldi, Franco Chiaraluce
|
Efficient Search of Compact QC-LDPC and SC-LDPC Convolutional Codes with
Large Girth
|
4 pages, 3 figures, 1 table, accepted for publication in IEEE
Communications Letters
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a low-complexity method to find quasi-cyclic low-density
parity-check block codes with girth 10 or 12 and shorter length than those
designed through classical approaches. The method is extended to time-invariant
spatially coupled low-density parity-check convolutional codes, permitting to
achieve small syndrome former constraint lengths. Several numerical examples
are given to show its effectiveness.
|
[
{
"created": "Tue, 17 Apr 2018 19:47:42 GMT",
"version": "v1"
}
] |
2018-04-19
|
[
[
"Tadayon",
"Mohammad H.",
""
],
[
"Tasdighi",
"Alireza",
""
],
[
"Battaglioni",
"Massimo",
""
],
[
"Baldi",
"Marco",
""
],
[
"Chiaraluce",
"Franco",
""
]
] |
We propose a low-complexity method to find quasi-cyclic low-density parity-check block codes with girth 10 or 12 and shorter length than those designed through classical approaches. The method is extended to time-invariant spatially coupled low-density parity-check convolutional codes, permitting to achieve small syndrome former constraint lengths. Several numerical examples are given to show its effectiveness.
|
1710.09876
|
Samin Aref
|
Samin Aref, Andrew J. Mason, Mark C. Wilson
|
Computing the Line Index of Balance Using Integer Programming
Optimisation
|
Accepted author copy, 20 pages, 4 tables and 3 figures. This work is
followed up in another study with more focus on Operations Research aspects
of the topic that can be found in arXiv:1611.09030
| null | null | null |
cs.SI math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An important measure of signed graphs is the line index of balance which has
several applications in many fields. However, this graph-theoretic measure was
underused for decades because of the inherent complexity in its computation
which is closely related to solving NP-hard graph optimisation problems like
MAXCUT. We develop new quadratic and linear programming models to compute the
line index of balance exactly. Using the Gurobi integer programming
optimisation solver, we evaluate the line index of balance on real-world and
synthetic datasets. The synthetic data involves Erd\H{o}s-R\'{e}nyi graphs,
Barab\'{a}si-Albert graphs, and specially structured random graphs. We also use
well known datasets from the sociology literature, such as signed graphs
inferred from students' choice and rejection as well as datasets from the
biology literature including gene regulatory networks. The results show that
exact values of the line index of balance in relatively large signed graphs can
be efficiently computed using our suggested optimisation models. We find that
most real-world social networks and some biological networks have small line
index of balance which indicates that they are close to balanced.
|
[
{
"created": "Thu, 26 Oct 2017 19:09:57 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Feb 2018 00:34:19 GMT",
"version": "v2"
},
{
"created": "Wed, 7 Feb 2018 05:19:46 GMT",
"version": "v3"
}
] |
2018-02-08
|
[
[
"Aref",
"Samin",
""
],
[
"Mason",
"Andrew J.",
""
],
[
"Wilson",
"Mark C.",
""
]
] |
An important measure of signed graphs is the line index of balance which has several applications in many fields. However, this graph-theoretic measure was underused for decades because of the inherent complexity in its computation which is closely related to solving NP-hard graph optimisation problems like MAXCUT. We develop new quadratic and linear programming models to compute the line index of balance exactly. Using the Gurobi integer programming optimisation solver, we evaluate the line index of balance on real-world and synthetic datasets. The synthetic data involves Erd\H{o}s-R\'{e}nyi graphs, Barab\'{a}si-Albert graphs, and specially structured random graphs. We also use well known datasets from the sociology literature, such as signed graphs inferred from students' choice and rejection as well as datasets from the biology literature including gene regulatory networks. The results show that exact values of the line index of balance in relatively large signed graphs can be efficiently computed using our suggested optimisation models. We find that most real-world social networks and some biological networks have small line index of balance which indicates that they are close to balanced.
|
1903.00951
|
Babak Alipour
|
Babak Alipour, Leonardo Tonetto, Roozbeh Ketabi, Aaron Yi Ding, J\"org
Ott, Ahmed Helmy
|
Practical Prediction of Human Movements Across Device Types and
Spatiotemporal Granularities
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Understanding and predicting mobility are essential for the design and
evaluation of future mobile edge caching and networking. Consequently, research
on prediction of human mobility has drawn significant attention in the last
decade. Employing information-theoretic concepts and machine learning methods,
earlier research has shown evidence that human behavior can be highly
predictable.
Despite existing studies, more investigations are needed to capture intrinsic
mobility characteristics constraining predictability, and to explore more
dimensions (e.g. device types) and spatio-temporal granularities, especially
with the change in human behavior and technology. We analyze extensive
longitudinal datasets with fine spatial granularity (AP level) covering 16
months. The study reveals device type as an important factor affecting
predictability. Ultra-portable devices such as smartphones have "on-the-go"
mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use"
(dubbed "Cellos").
The goal of this study is to investigate practical prediction mechanisms to
quantify predictability as an aspect of human mobility modeling, across time,
space and device types. We apply our systematic analysis to wireless traces
from a large university campus. We compare several algorithms using varying
degrees of temporal and spatial granularity for the two modes of devices;
Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes
is less predictable than the mobility of Cellos. In addition, this pattern is
consistent across various spatio-temporal granularities, and for different
methods (Markov chains, neural networks/deep learning, entropy-based
estimators). This work substantiates the importance of predictability as an
essential aspect of human mobility, with direct application in predictive
caching, user behavior modeling and mobility simulations.
|
[
{
"created": "Sun, 3 Mar 2019 17:46:27 GMT",
"version": "v1"
}
] |
2019-03-05
|
[
[
"Alipour",
"Babak",
""
],
[
"Tonetto",
"Leonardo",
""
],
[
"Ketabi",
"Roozbeh",
""
],
[
"Ding",
"Aaron Yi",
""
],
[
"Ott",
"Jörg",
""
],
[
"Helmy",
"Ahmed",
""
]
] |
Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on prediction of human mobility has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, and to explore more dimensions (e.g. device types) and spatio-temporal granularities, especially with the change in human behavior and technology. We analyze extensive longitudinal datasets with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.
|
2109.00895
|
Yushan Zhu
|
Yushan Zhu, Huaixiao Tou, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu
Zhang and Huajun Chen
|
Knowledge Perceived Multi-modal Pretraining in E-commerce
|
Accepted to ACM MM 2021
| null |
10.1145/3474085.3475648
| null |
cs.CV cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we address multi-modal pretraining of product data in the
field of E-commerce. Current multi-modal pretraining methods proposed for image
and text modalities lack robustness in the face of modality-missing and
modality-noise, which are two pervasive problems of multi-modal product data in
real E-commerce scenarios. To this end, we propose a novel method, K3M, which
introduces knowledge modality in multi-modal pretraining to correct the noise
and supplement the missing of image and text modalities. The modal-encoding
layer extracts the features of each modality. The modal-interaction layer is
capable of effectively modeling the interaction of multiple modalities, where
an initial-interactive feature fusion model is designed to maintain the
independence of image modality and text modality, and a structure aggregation
module is designed to fuse the information of image, text, and knowledge
modalities. We pretrain K3M with three pretraining tasks, including masked
object modeling (MOM), masked language modeling (MLM), and link prediction
modeling (LPM). Experimental results on a real-world E-commerce dataset and a
series of product-based downstream tasks demonstrate that K3M achieves
significant improvements in performances than the baseline and state-of-the-art
methods when modality-noise or modality-missing exists.
|
[
{
"created": "Fri, 20 Aug 2021 08:01:28 GMT",
"version": "v1"
}
] |
2021-09-03
|
[
[
"Zhu",
"Yushan",
""
],
[
"Tou",
"Huaixiao",
""
],
[
"Zhang",
"Wen",
""
],
[
"Ye",
"Ganqiang",
""
],
[
"Chen",
"Hui",
""
],
[
"Zhang",
"Ningyu",
""
],
[
"Chen",
"Huajun",
""
]
] |
In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack robustness in the face of modality-missing and modality-noise, which are two pervasive problems of multi-modal product data in real E-commerce scenarios. To this end, we propose a novel method, K3M, which introduces knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interaction layer is capable of effectively modeling the interaction of multiple modalities, where an initial-interactive feature fusion model is designed to maintain the independence of image modality and text modality, and a structure aggregation module is designed to fuse the information of image, text, and knowledge modalities. We pretrain K3M with three pretraining tasks, including masked object modeling (MOM), masked language modeling (MLM), and link prediction modeling (LPM). Experimental results on a real-world E-commerce dataset and a series of product-based downstream tasks demonstrate that K3M achieves significant improvements in performances than the baseline and state-of-the-art methods when modality-noise or modality-missing exists.
|
1605.02041
|
David Guillermo Fajardo Ortiz
|
David Fajardo-Ortiz, Luis Duran, Laura Moreno, Hector Ochoa, Victor-M
Castano
|
Mapping knowledge translation and innovation processes in Cancer Drug
Development: the case of liposomal doxorubicin
| null |
Journal of Translational Medicine 2014, 12:227
|
10.1186/s12967-014-0227-9
| null |
cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We explored how the knowledge translation and innovation processes are
structured when they result in innovations, as in the case of liposomal
doxorubicin research. In order to map the processes, a literature network
analysis was made through Cytoscape and semantic analysis was performed by
GOPubmed which is based in the controlled vocabularies MeSH (Medical Subject
Headings) and GO (Gene Ontology). We found clusters related to different stages
of the technological development (invention, innovation and imitation) and the
knowledge translation process (preclinical, translational and clinical
research), and we were able to map the historic emergence of Doxil as a
paradigmatic nanodrug. This research could be a powerful methodological tool
for decision-making and innovation management in drug delivery research.
|
[
{
"created": "Tue, 12 Apr 2016 05:55:21 GMT",
"version": "v1"
}
] |
2016-05-09
|
[
[
"Fajardo-Ortiz",
"David",
""
],
[
"Duran",
"Luis",
""
],
[
"Moreno",
"Laura",
""
],
[
"Ochoa",
"Hector",
""
],
[
"Castano",
"Victor-M",
""
]
] |
We explored how the knowledge translation and innovation processes are structured when they result in innovations, as in the case of liposomal doxorubicin research. In order to map the processes, a literature network analysis was made through Cytoscape and semantic analysis was performed by GOPubmed which is based in the controlled vocabularies MeSH (Medical Subject Headings) and GO (Gene Ontology). We found clusters related to different stages of the technological development (invention, innovation and imitation) and the knowledge translation process (preclinical, translational and clinical research), and we were able to map the historic emergence of Doxil as a paradigmatic nanodrug. This research could be a powerful methodological tool for decision-making and innovation management in drug delivery research.
|
2108.11887
|
Lei Lei
|
Jiaju Qi, Qihao Zhou, Lei Lei, Kan Zheng
|
Federated Reinforcement Learning: Techniques, Applications, and Open
Challenges
| null |
Intelligence & Robotics. 2021; 1(1):18-57
|
10.20517/ir.2021.02
| null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a comprehensive survey of Federated Reinforcement
Learning (FRL), an emerging and promising field in Reinforcement Learning (RL).
Starting with a tutorial of Federated Learning (FL) and RL, we then focus on
the introduction of FRL as a new method with great potential by leveraging the
basic idea of FL to improve the performance of RL while preserving
data-privacy. According to the distribution characteristics of the agents in
the framework, FRL algorithms can be divided into two categories, i.e.
Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated
Reinforcement Learning (VFRL). We provide the detailed definitions of each
category by formulas, investigate the evolution of FRL from a technical
perspective, and highlight its advantages over previous RL algorithms. In
addition, the existing works on FRL are summarized by application fields,
including edge computing, communication, control optimization, and attack
detection. Finally, we describe and discuss several key research directions
that are crucial to solving the open problems within FRL.
|
[
{
"created": "Thu, 26 Aug 2021 16:22:49 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Oct 2021 19:02:03 GMT",
"version": "v2"
}
] |
2023-05-12
|
[
[
"Qi",
"Jiaju",
""
],
[
"Zhou",
"Qihao",
""
],
[
"Lei",
"Lei",
""
],
[
"Zheng",
"Kan",
""
]
] |
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e. Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.
|
2303.05946
|
Kyle Hart
|
Kyle M. Hart (1 and 2), Brendan Englot (2), Ryan P. O'Shea (1), John
D. Kelly (1), David Martinez (1) ((1) Naval Air Warfare Center Aircraft
Division Lakehurst, (2) Stevens Institute of Technology)
|
Monocular Simultaneous Localization and Mapping using Ground Textures
|
7 pages, 9 figures. To appear at ICRA 2023, London, UK. Distribution
Statement A: Approved for public release; distribution is unlimited, as
submitted under NAVAIR Public Release Authorization 2022-0586. The views
expressed here are those of the authors and do not reflect the official
policy or position of the U.S. Navy, Department of Defense, or U.S.
Government
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work has shown impressive localization performance using only images
of ground textures taken with a downward facing monocular camera. This provides
a reliable navigation method that is robust to feature sparse environments and
challenging lighting conditions. However, these localization methods require an
existing map for comparison. Our work aims to relax the need for a map by
introducing a full simultaneous localization and mapping (SLAM) system. By not
requiring an existing map, setup times are minimized and the system is more
robust to changing environments. This SLAM system uses a combination of several
techniques to accomplish this. Image keypoints are identified and projected
into the ground plane. These keypoints, visual bags of words, and several
threshold parameters are then used to identify overlapping images and revisited
areas. The system then uses robust M-estimators to estimate the transform
between robot poses with overlapping images and revisited areas. These
optimized estimates make up the map used for navigation. We show, through
experimental data, that this system performs reliably on many ground textures,
but not all.
|
[
{
"created": "Fri, 10 Mar 2023 14:27:31 GMT",
"version": "v1"
}
] |
2023-03-13
|
[
[
"Hart",
"Kyle M.",
"",
"1 and 2"
],
[
"Englot",
"Brendan",
""
],
[
"O'Shea",
"Ryan P.",
""
],
[
"Kelly",
"John D.",
""
],
[
"Martinez",
"David",
""
]
] |
Recent work has shown impressive localization performance using only images of ground textures taken with a downward facing monocular camera. This provides a reliable navigation method that is robust to feature sparse environments and challenging lighting conditions. However, these localization methods require an existing map for comparison. Our work aims to relax the need for a map by introducing a full simultaneous localization and mapping (SLAM) system. By not requiring an existing map, setup times are minimized and the system is more robust to changing environments. This SLAM system uses a combination of several techniques to accomplish this. Image keypoints are identified and projected into the ground plane. These keypoints, visual bags of words, and several threshold parameters are then used to identify overlapping images and revisited areas. The system then uses robust M-estimators to estimate the transform between robot poses with overlapping images and revisited areas. These optimized estimates make up the map used for navigation. We show, through experimental data, that this system performs reliably on many ground textures, but not all.
|
1405.1129
|
Vikram Krishnamurthy
|
Vikram Krishnamurthy and Omid Namvar Gharehshiran and Maziyar Hamdi
|
Interactive Sensing and Decision Making in Social Networks
|
Foundations and Trends in Signal Processing, Now Publishers, 2014
| null |
10.1561/2000000048
| null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The proliferation of social media such as real time microblogging and online
reputation systems facilitate real time sensing of social patterns and
behavior. In the last decade, sensing and decision making in social networks
have witnessed significant progress in the electrical engineering, computer
science, economics, finance, and sociology research communities. Research in
this area involves the interaction of dynamic random graphs, socio-economic
analysis, and statistical inference algorithms. This monograph provides a
survey, tutorial development, and discussion of four highly stylized examples:
social learning for interactive sensing; tracking the degree distribution of
social networks; sensing and information diffusion; and coordination of
decision making via game-theoretic learning. Each of the four examples is
motivated by practical examples, and comprises of a literature survey together
with careful problem formulation and mathematical analysis. Despite being
highly stylized, these examples provide a rich variety of models, algorithms
and analysis tools that are readily accessible to a signal processing,
control/systems theory, and applied mathematics audience.
|
[
{
"created": "Tue, 6 May 2014 02:21:24 GMT",
"version": "v1"
}
] |
2014-05-07
|
[
[
"Krishnamurthy",
"Vikram",
""
],
[
"Gharehshiran",
"Omid Namvar",
""
],
[
"Hamdi",
"Maziyar",
""
]
] |
The proliferation of social media such as real time microblogging and online reputation systems facilitate real time sensing of social patterns and behavior. In the last decade, sensing and decision making in social networks have witnessed significant progress in the electrical engineering, computer science, economics, finance, and sociology research communities. Research in this area involves the interaction of dynamic random graphs, socio-economic analysis, and statistical inference algorithms. This monograph provides a survey, tutorial development, and discussion of four highly stylized examples: social learning for interactive sensing; tracking the degree distribution of social networks; sensing and information diffusion; and coordination of decision making via game-theoretic learning. Each of the four examples is motivated by practical examples, and comprises of a literature survey together with careful problem formulation and mathematical analysis. Despite being highly stylized, these examples provide a rich variety of models, algorithms and analysis tools that are readily accessible to a signal processing, control/systems theory, and applied mathematics audience.
|
2404.09265
|
Mindaugas Budzys
|
Tanveer Khan, Mindaugas Budzys, Antonis Michalas
|
Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in
Split Learning
|
Accepted In Proceedings of the 29th ACM Symposium on Access Control
Models and Technologies (SACMAT '24)
| null | null | null |
cs.CR cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The popularity of Machine Learning (ML) makes the privacy of sensitive data
more imperative than ever. Collaborative learning techniques like Split
Learning (SL) aim to protect client data while enhancing ML processes. Though
promising, SL has been proved to be vulnerable to a plethora of attacks, thus
raising concerns about its effectiveness on data privacy. In this work, we
introduce a hybrid approach combining SL and Function Secret Sharing (FSS) to
ensure client data privacy. The client adds a random mask to the activation map
before sending it to the servers. The servers cannot access the original
function but instead work with shares generated using FSS. Consequently, during
both forward and backward propagation, the servers cannot reconstruct the
client's raw data from the activation map. Furthermore, through visual
invertibility, we demonstrate that the server is incapable of reconstructing
the raw image data from the activation map when using FSS. It enhances privacy
by reducing privacy leakage compared to other SL-based approaches where the
server can access client input information. Our approach also ensures security
against feature space hijacking attack, protecting sensitive information from
potential manipulation. Our protocols yield promising results, reducing
communication overhead by over 2x and training time by over 7x compared to the
same model with FSS, without any SL. Also, we show that our approach achieves
>96% accuracy and remains equivalent to the plaintext models.
|
[
{
"created": "Sun, 14 Apr 2024 14:14:31 GMT",
"version": "v1"
}
] |
2024-04-16
|
[
[
"Khan",
"Tanveer",
""
],
[
"Budzys",
"Mindaugas",
""
],
[
"Michalas",
"Antonis",
""
]
] |
The popularity of Machine Learning (ML) makes the privacy of sensitive data more imperative than ever. Collaborative learning techniques like Split Learning (SL) aim to protect client data while enhancing ML processes. Though promising, SL has been proved to be vulnerable to a plethora of attacks, thus raising concerns about its effectiveness on data privacy. In this work, we introduce a hybrid approach combining SL and Function Secret Sharing (FSS) to ensure client data privacy. The client adds a random mask to the activation map before sending it to the servers. The servers cannot access the original function but instead work with shares generated using FSS. Consequently, during both forward and backward propagation, the servers cannot reconstruct the client's raw data from the activation map. Furthermore, through visual invertibility, we demonstrate that the server is incapable of reconstructing the raw image data from the activation map when using FSS. It enhances privacy by reducing privacy leakage compared to other SL-based approaches where the server can access client input information. Our approach also ensures security against feature space hijacking attack, protecting sensitive information from potential manipulation. Our protocols yield promising results, reducing communication overhead by over 2x and training time by over 7x compared to the same model with FSS, without any SL. Also, we show that our approach achieves >96% accuracy and remains equivalent to the plaintext models.
|
2208.14925
|
Tim Schreiter
|
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu, Eduardo
Gutierrez Maestro, Lucas Morillo-Mendez, Andrey Rudenko, Tomasz P. Kucner,
Oscar Martinez Mozos, Martin Magnusson, Luigi Palmieri, Kai O. Arras, Achim
J. Lilienthal
|
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural,
Semantically-Rich and Contextualized
|
in SIRRW Workshop held in conjunction with 31st IEEE International
Conference on Robot & Human Interactive Communication, 29/08 - 02/09 2022,
Naples (Italy)
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rapid development of social robots stimulates active research in human motion
modeling, interpretation and prediction, proactive collision avoidance,
human-robot interaction and co-habitation in shared spaces. Modern approaches
to this end require high quality datasets for training and evaluation. However,
the majority of available datasets suffers from either inaccurate tracking data
or unnatural, scripted behavior of the tracked people. This paper attempts to
fill this gap by providing high quality tracking information from motion
capture, eye-gaze trackers and on-board robot sensors in a semantically-rich
environment. To induce natural behavior of the recorded participants, we
utilise loosely scripted task assignment, which induces the participants
navigate through the dynamic laboratory environment in a natural and purposeful
way. The motion dataset, presented in this paper, sets a high quality standard,
as the realistic and accurate data is enhanced with semantic information,
enabling development of new algorithms which rely not only on the tracking
information but also on contextual cues of the moving agents, static and
dynamic environment.
|
[
{
"created": "Wed, 31 Aug 2022 15:37:45 GMT",
"version": "v1"
}
] |
2022-09-01
|
[
[
"Schreiter",
"Tim",
""
],
[
"de Almeida",
"Tiago Rodrigues",
""
],
[
"Zhu",
"Yufei",
""
],
[
"Maestro",
"Eduardo Gutierrez",
""
],
[
"Morillo-Mendez",
"Lucas",
""
],
[
"Rudenko",
"Andrey",
""
],
[
"Kucner",
"Tomasz P.",
""
],
[
"Mozos",
"Oscar Martinez",
""
],
[
"Magnusson",
"Martin",
""
],
[
"Palmieri",
"Luigi",
""
],
[
"Arras",
"Kai O.",
""
],
[
"Lilienthal",
"Achim J.",
""
]
] |
Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.
|
2306.08935
|
Ritu Yadav
|
Ritu Yadav, Andrea Nascetti, Yifang Ban
|
Context-Aware Change Detection With Semi-Supervised Learning
|
Paper Accepted in IGARSS 2023
| null | null | null |
cs.CV cs.AI cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Change detection using earth observation data plays a vital role in
quantifying the impact of disasters in affected areas. While data sources like
Sentinel-2 provide rich optical information, they are often hindered by cloud
cover, limiting their usage in disaster scenarios. However, leveraging
pre-disaster optical data can offer valuable contextual information about the
area such as landcover type, vegetation cover, soil types, enabling a better
understanding of the disaster's impact. In this study, we develop a model to
assess the contribution of pre-disaster Sentinel-2 data in change detection
tasks, focusing on disaster-affected areas. The proposed Context-Aware Change
Detection Network (CACDN) utilizes a combination of pre-disaster Sentinel-2
data, pre and post-disaster Sentinel-1 data and ancillary Digital Elevation
Models (DEM) data. The model is validated on flood and landslide detection and
evaluated using three metrics: Area Under the Precision-Recall Curve (AUPRC),
Intersection over Union (IoU), and mean IoU. The preliminary results show
significant improvement (4\%, AUPRC, 3-7\% IoU, 3-6\% mean IoU) in model's
change detection capabilities when incorporated with pre-disaster optical data
reflecting the effectiveness of using contextual information for accurate flood
and landslide detection.
|
[
{
"created": "Thu, 15 Jun 2023 08:17:49 GMT",
"version": "v1"
}
] |
2023-06-16
|
[
[
"Yadav",
"Ritu",
""
],
[
"Nascetti",
"Andrea",
""
],
[
"Ban",
"Yifang",
""
]
] |
Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. While data sources like Sentinel-2 provide rich optical information, they are often hindered by cloud cover, limiting their usage in disaster scenarios. However, leveraging pre-disaster optical data can offer valuable contextual information about the area such as landcover type, vegetation cover, soil types, enabling a better understanding of the disaster's impact. In this study, we develop a model to assess the contribution of pre-disaster Sentinel-2 data in change detection tasks, focusing on disaster-affected areas. The proposed Context-Aware Change Detection Network (CACDN) utilizes a combination of pre-disaster Sentinel-2 data, pre and post-disaster Sentinel-1 data and ancillary Digital Elevation Models (DEM) data. The model is validated on flood and landslide detection and evaluated using three metrics: Area Under the Precision-Recall Curve (AUPRC), Intersection over Union (IoU), and mean IoU. The preliminary results show significant improvement (4\%, AUPRC, 3-7\% IoU, 3-6\% mean IoU) in model's change detection capabilities when incorporated with pre-disaster optical data reflecting the effectiveness of using contextual information for accurate flood and landslide detection.
|
2001.10494
|
Feiyang Cai
|
Feiyang Cai and Xenofon Koutsoukos
|
Real-time Out-of-distribution Detection in Learning-Enabled
Cyber-Physical Systems
|
Accepted by 11th International Conference on Cyber-Physical Systems
(ICCPS2020)
| null | null | null |
cs.LG cs.SY eess.SY stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cyber-physical systems (CPS) greatly benefit by using machine learning
components that can handle the uncertainty and variability of the real-world.
Typical components such as deep neural networks, however, introduce new types
of hazards that may impact system safety. The system behavior depends on data
that are available only during runtime and may be different than the data used
for training. Out-of-distribution data may lead to a large error and compromise
safety. The paper considers the problem of efficiently detecting
out-of-distribution data in CPS control systems. Detection must be robust and
limit the number of false alarms while being computational efficient for
real-time monitoring. The proposed approach leverages inductive conformal
prediction and anomaly detection for developing a method that has a
well-calibrated false alarm rate. We use variational autoencoders and deep
support vector data description to learn models that can be used efficiently
compute the nonconformity of new inputs relative to the training set and enable
real-time detection of out-of-distribution high-dimensional inputs. We
demonstrate the method using an advanced emergency braking system and a
self-driving end-to-end controller implemented in an open source simulator for
self-driving cars. The simulation results show very small number of false
positives and detection delay while the execution time is comparable to the
execution time of the original machine learning components.
|
[
{
"created": "Tue, 28 Jan 2020 17:51:07 GMT",
"version": "v1"
}
] |
2020-01-29
|
[
[
"Cai",
"Feiyang",
""
],
[
"Koutsoukos",
"Xenofon",
""
]
] |
Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are available only during runtime and may be different than the data used for training. Out-of-distribution data may lead to a large error and compromise safety. The paper considers the problem of efficiently detecting out-of-distribution data in CPS control systems. Detection must be robust and limit the number of false alarms while being computational efficient for real-time monitoring. The proposed approach leverages inductive conformal prediction and anomaly detection for developing a method that has a well-calibrated false alarm rate. We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable real-time detection of out-of-distribution high-dimensional inputs. We demonstrate the method using an advanced emergency braking system and a self-driving end-to-end controller implemented in an open source simulator for self-driving cars. The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components.
|
2212.07811
|
Mike Thelwall Prof
|
Mike Thelwall, Kayvan Kousha, Mahshid Abdoli, Emma Stuart, Meiko
Makita, Paul Wilson, Jonathan Levitt
|
Do altmetric scores reflect article quality? Evidence from the UK
Research Excellence Framework 2021
| null |
Journal of the Association for Information Science and Technology,
74(5), 582-593 (2023)
|
10.1108/10.1002/asi.24751
| null |
cs.DL
|
http://creativecommons.org/licenses/by/4.0/
|
Altmetrics are web-based quantitative impact or attention indicators for
academic articles that have been proposed to supplement citation counts. This
article reports the first assessment of the extent to which mature altmetrics
from Altmetric.com and Mendeley associate with journal article quality. It
exploits expert norm-referenced peer review scores from the UK Research
Excellence Framework 2021 for 67,030+ journal articles in all fields
2014-17/18, split into 34 Units of Assessment (UoAs). The results show that
altmetrics are better indicators of research quality than previously thought,
although not as good as raw and field normalised Scopus citation counts.
Surprisingly, field normalising citation counts can reduce their strength as a
quality indicator for articles in a single field. For most UoAs, Mendeley
reader counts are the best, tweet counts are also a relatively strong indicator
in many fields, and Facebook, blogs and news citations are moderately strong
indicators in some UoAs, at least in the UK. In general, altmetrics are the
strongest indicators of research quality in the health and physical sciences
and weakest in the arts and humanities. The Altmetric Attention Score, although
hybrid, is almost as good as Mendeley reader counts as a quality indicator and
reflects more non-scholarly impacts.
|
[
{
"created": "Sun, 11 Dec 2022 05:40:35 GMT",
"version": "v1"
}
] |
2023-08-01
|
[
[
"Thelwall",
"Mike",
""
],
[
"Kousha",
"Kayvan",
""
],
[
"Abdoli",
"Mahshid",
""
],
[
"Stuart",
"Emma",
""
],
[
"Makita",
"Meiko",
""
],
[
"Wilson",
"Paul",
""
],
[
"Levitt",
"Jonathan",
""
]
] |
Altmetrics are web-based quantitative impact or attention indicators for academic articles that have been proposed to supplement citation counts. This article reports the first assessment of the extent to which mature altmetrics from Altmetric.com and Mendeley associate with journal article quality. It exploits expert norm-referenced peer review scores from the UK Research Excellence Framework 2021 for 67,030+ journal articles in all fields 2014-17/18, split into 34 Units of Assessment (UoAs). The results show that altmetrics are better indicators of research quality than previously thought, although not as good as raw and field normalised Scopus citation counts. Surprisingly, field normalising citation counts can reduce their strength as a quality indicator for articles in a single field. For most UoAs, Mendeley reader counts are the best, tweet counts are also a relatively strong indicator in many fields, and Facebook, blogs and news citations are moderately strong indicators in some UoAs, at least in the UK. In general, altmetrics are the strongest indicators of research quality in the health and physical sciences and weakest in the arts and humanities. The Altmetric Attention Score, although hybrid, is almost as good as Mendeley reader counts as a quality indicator and reflects more non-scholarly impacts.
|
1710.11213
|
Sahil Singla
|
Soheil Ehsani, MohammadTaghi Hajiaghayi, Thomas Kesselheim, and Sahil
Singla
|
Prophet Secretary for Combinatorial Auctions and Matroids
|
Preliminary version appeared in SODA 2018. This version improves the
writeup on Fixed-Threshold algorithms
| null | null | null |
cs.DS cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The secretary and the prophet inequality problems are central to the field of
Stopping Theory. Recently, there has been a lot of work in generalizing these
models to multiple items because of their applications in mechanism design. The
most important of these generalizations are to matroids and to combinatorial
auctions (extends bipartite matching). Kleinberg-Weinberg \cite{KW-STOC12} and
Feldman et al. \cite{feldman2015combinatorial} show that for adversarial
arrival order of random variables the optimal prophet inequalities give a
$1/2$-approximation. For many settings, however, it's conceivable that the
arrival order is chosen uniformly at random, akin to the secretary problem. For
such a random arrival model, we improve upon the $1/2$-approximation and obtain
$(1-1/e)$-approximation prophet inequalities for both matroids and
combinatorial auctions. This also gives improvements to the results of Yan
\cite{yan2011mechanism} and Esfandiari et al. \cite{esfandiari2015prophet} who
worked in the special cases where we can fully control the arrival order or
when there is only a single item.
Our techniques are threshold based. We convert our discrete problem into a
continuous setting and then give a generic template on how to dynamically
adjust these thresholds to lower bound the expected total welfare.
|
[
{
"created": "Mon, 30 Oct 2017 19:41:38 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Mar 2018 17:13:41 GMT",
"version": "v2"
}
] |
2018-03-20
|
[
[
"Ehsani",
"Soheil",
""
],
[
"Hajiaghayi",
"MohammadTaghi",
""
],
[
"Kesselheim",
"Thomas",
""
],
[
"Singla",
"Sahil",
""
]
] |
The secretary and the prophet inequality problems are central to the field of Stopping Theory. Recently, there has been a lot of work in generalizing these models to multiple items because of their applications in mechanism design. The most important of these generalizations are to matroids and to combinatorial auctions (extends bipartite matching). Kleinberg-Weinberg \cite{KW-STOC12} and Feldman et al. \cite{feldman2015combinatorial} show that for adversarial arrival order of random variables the optimal prophet inequalities give a $1/2$-approximation. For many settings, however, it's conceivable that the arrival order is chosen uniformly at random, akin to the secretary problem. For such a random arrival model, we improve upon the $1/2$-approximation and obtain $(1-1/e)$-approximation prophet inequalities for both matroids and combinatorial auctions. This also gives improvements to the results of Yan \cite{yan2011mechanism} and Esfandiari et al. \cite{esfandiari2015prophet} who worked in the special cases where we can fully control the arrival order or when there is only a single item. Our techniques are threshold based. We convert our discrete problem into a continuous setting and then give a generic template on how to dynamically adjust these thresholds to lower bound the expected total welfare.
|
2304.02841
|
Zhijie Deng
|
Zhijie Deng and Yucen Luo
|
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised semantic segmentation is a long-standing challenge in computer
vision with great significance. Spectral clustering is a theoretically grounded
solution to it where the spectral embeddings for pixels are computed to
construct distinct clusters. Despite recent progress in enhancing spectral
clustering with powerful pre-trained models, current approaches still suffer
from inefficiencies in spectral decomposition and inflexibility in applying
them to the test data. This work addresses these issues by casting spectral
clustering as a parametric approach that employs neural network-based
eigenfunctions to produce spectral embeddings. The outputs of the neural
eigenfunctions are further restricted to discrete vectors that indicate
clustering assignments directly. As a result, an end-to-end NN-based paradigm
of spectral clustering emerges. In practice, the neural eigenfunctions are
lightweight and take the features from pre-trained models as inputs, improving
training efficiency and unleashing the potential of pre-trained models for
dense prediction. We conduct extensive empirical studies to validate the
effectiveness of our approach and observe significant performance gains over
competitive baselines on Pascal Context, Cityscapes, and ADE20K benchmarks.
|
[
{
"created": "Thu, 6 Apr 2023 03:14:15 GMT",
"version": "v1"
}
] |
2023-04-07
|
[
[
"Deng",
"Zhijie",
""
],
[
"Luo",
"Yucen",
""
]
] |
Unsupervised semantic segmentation is a long-standing challenge in computer vision with great significance. Spectral clustering is a theoretically grounded solution to it where the spectral embeddings for pixels are computed to construct distinct clusters. Despite recent progress in enhancing spectral clustering with powerful pre-trained models, current approaches still suffer from inefficiencies in spectral decomposition and inflexibility in applying them to the test data. This work addresses these issues by casting spectral clustering as a parametric approach that employs neural network-based eigenfunctions to produce spectral embeddings. The outputs of the neural eigenfunctions are further restricted to discrete vectors that indicate clustering assignments directly. As a result, an end-to-end NN-based paradigm of spectral clustering emerges. In practice, the neural eigenfunctions are lightweight and take the features from pre-trained models as inputs, improving training efficiency and unleashing the potential of pre-trained models for dense prediction. We conduct extensive empirical studies to validate the effectiveness of our approach and observe significant performance gains over competitive baselines on Pascal Context, Cityscapes, and ADE20K benchmarks.
|
2108.06812
|
Nikolai Karpov
|
Nikolai Karpov, Qin Zhang
|
Batched Thompson Sampling for Multi-Armed Bandits
|
9 pages
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We study Thompson Sampling algorithms for stochastic multi-armed bandits in
the batched setting, in which we want to minimize the regret over a sequence of
arm pulls using a small number of policy changes (or, batches). We propose two
algorithms and demonstrate their effectiveness by experiments on both synthetic
and real datasets. We also analyze the proposed algorithms from the theoretical
aspect and obtain almost tight regret-batches tradeoffs for the two-arm case.
|
[
{
"created": "Sun, 15 Aug 2021 20:47:46 GMT",
"version": "v1"
}
] |
2021-08-17
|
[
[
"Karpov",
"Nikolai",
""
],
[
"Zhang",
"Qin",
""
]
] |
We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small number of policy changes (or, batches). We propose two algorithms and demonstrate their effectiveness by experiments on both synthetic and real datasets. We also analyze the proposed algorithms from the theoretical aspect and obtain almost tight regret-batches tradeoffs for the two-arm case.
|
2104.13456
|
Adrian {\L}a\'ncucki
|
Pawe{\l} Rychlikowski, Bart{\l}omiej Najdecki, Adrian {\L}a\'ncucki,
Adam Kaczmarek
|
Named Entity Recognition and Linking Augmented with Large-Scale
Structured Data
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we describe our submissions to the 2nd and 3rd SlavNER Shared
Tasks held at BSNLP 2019 and BSNLP 2021, respectively. The tasks focused on the
analysis of Named Entities in multilingual Web documents in Slavic languages
with rich inflection. Our solution takes advantage of large collections of both
unstructured and structured documents. The former serve as data for
unsupervised training of language models and embeddings of lexical units. The
latter refers to Wikipedia and its structured counterpart - Wikidata, our
source of lemmatization rules, and real-world entities. With the aid of those
resources, our system could recognize, normalize and link entities, while being
trained with only small amounts of labeled data.
|
[
{
"created": "Tue, 27 Apr 2021 20:10:18 GMT",
"version": "v1"
}
] |
2021-04-29
|
[
[
"Rychlikowski",
"Paweł",
""
],
[
"Najdecki",
"Bartłomiej",
""
],
[
"Łańcucki",
"Adrian",
""
],
[
"Kaczmarek",
"Adam",
""
]
] |
In this paper we describe our submissions to the 2nd and 3rd SlavNER Shared Tasks held at BSNLP 2019 and BSNLP 2021, respectively. The tasks focused on the analysis of Named Entities in multilingual Web documents in Slavic languages with rich inflection. Our solution takes advantage of large collections of both unstructured and structured documents. The former serve as data for unsupervised training of language models and embeddings of lexical units. The latter refers to Wikipedia and its structured counterpart - Wikidata, our source of lemmatization rules, and real-world entities. With the aid of those resources, our system could recognize, normalize and link entities, while being trained with only small amounts of labeled data.
|
2205.06910
|
Kanishka Misra
|
Kanishka Misra, Julia Taylor Rayz, Allyson Ettinger
|
A Property Induction Framework for Neural Language Models
|
CogSci 2022 camera ready version, with hyperref-compatible citations.
Code and Supplemental Material can be found in
https://github.com/kanishkamisra/lm-induction
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
To what extent can experience from language contribute to our conceptual
knowledge? Computational explorations of this question have shed light on the
ability of powerful neural language models (LMs) -- informed solely through
text input -- to encode and elicit information about concepts and properties.
To extend this line of research, we present a framework that uses
neural-network language models (LMs) to perform property induction -- a task in
which humans generalize novel property knowledge (has sesamoid bones) from one
or more concepts (robins) to others (sparrows, canaries). Patterns of property
induction observed in humans have shed considerable light on the nature and
organization of human conceptual knowledge. Inspired by this insight, we use
our framework to explore the property inductions of LMs, and find that they
show an inductive preference to generalize novel properties on the basis of
category membership, suggesting the presence of a taxonomic bias in their
representations.
|
[
{
"created": "Fri, 13 May 2022 22:05:49 GMT",
"version": "v1"
}
] |
2022-05-17
|
[
[
"Misra",
"Kanishka",
""
],
[
"Rayz",
"Julia Taylor",
""
],
[
"Ettinger",
"Allyson",
""
]
] |
To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural language models (LMs) -- informed solely through text input -- to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction -- a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrows, canaries). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.
|
0904.0352
|
Rami Puzis
|
Shlomi Dolev, Yuval Elovici, Rami Puzis, Polina Zilberman
|
Incremental Deployment of Network Monitors Based on Group Betweenness
Centrality
| null |
Information Processing Letters, 109(20), 1172-1176 (2009)
|
10.1016/j.ipl.2009.07.019
| null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In many applications we are required to increase the deployment of a
distributed monitoring system on an evolving network. In this paper we present
a new method for finding candidate locations for additional deployment in the
network. This method is based on the Group Betweenness Centrality (GBC) measure
that is used to estimate the influence of a group of nodes over the information
flow in the network. The new method assists in finding the location of k
additional monitors in the evolving network, such that the portion of
additional traffic covered is at least (1-1/e) of the optimal.
|
[
{
"created": "Thu, 2 Apr 2009 09:32:51 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Jul 2009 10:01:36 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Oct 2020 13:32:31 GMT",
"version": "v3"
}
] |
2020-10-05
|
[
[
"Dolev",
"Shlomi",
""
],
[
"Elovici",
"Yuval",
""
],
[
"Puzis",
"Rami",
""
],
[
"Zilberman",
"Polina",
""
]
] |
In many applications we are required to increase the deployment of a distributed monitoring system on an evolving network. In this paper we present a new method for finding candidate locations for additional deployment in the network. This method is based on the Group Betweenness Centrality (GBC) measure that is used to estimate the influence of a group of nodes over the information flow in the network. The new method assists in finding the location of k additional monitors in the evolving network, such that the portion of additional traffic covered is at least (1-1/e) of the optimal.
|
2306.02500
|
Taylor Webb
|
Taylor W. Webb, Shanka Subhra Mondal, Jonathan D. Cohen
|
Systematic Visual Reasoning through Object-Centric Relational
Abstraction
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Human visual reasoning is characterized by an ability to identify abstract
patterns from only a small number of examples, and to systematically generalize
those patterns to novel inputs. This capacity depends in large part on our
ability to represent complex visual inputs in terms of both objects and
relations. Recent work in computer vision has introduced models with the
capacity to extract object-centric representations, leading to the ability to
process multi-object visual inputs, but falling short of the systematic
generalization displayed by human reasoning. Other recent models have employed
inductive biases for relational abstraction to achieve systematic
generalization of learned abstract rules, but have generally assumed the
presence of object-focused inputs. Here, we combine these two approaches,
introducing Object-Centric Relational Abstraction (OCRA), a model that extracts
explicit representations of both objects and abstract relations, and achieves
strong systematic generalization in tasks (including a novel dataset,
CLEVR-ART, with greater visual complexity) involving complex visual displays.
|
[
{
"created": "Sun, 4 Jun 2023 22:47:17 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Nov 2023 22:22:44 GMT",
"version": "v2"
}
] |
2023-11-14
|
[
[
"Webb",
"Taylor W.",
""
],
[
"Mondal",
"Shanka Subhra",
""
],
[
"Cohen",
"Jonathan D.",
""
]
] |
Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays.
|
1909.04954
|
Philipp Mayr
|
Guillaume Cabanac, Ingo Frommholz, Philipp Mayr
|
Report on the 8th International Workshop on Bibliometric-enhanced
Information Retrieval (BIR 2019)
|
8 pages, report to appear in ACM SIGIR Forum
| null | null | null |
cs.IR cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR
tackled issues related to academic search, at the crossroads between
Information Retrieval and Bibliometrics. BIR is a hot topic investigated by
both academia (e.g., ArnetMiner, CiteSeerx, DocEar) and the industry (e.g.,
Google Scholar, Microsoft Academic Search, Semantic Scholar). This report
presents the 8th iteration of the one-day BIR workshop held at ECIR 2019 in
Cologne, Germany.
|
[
{
"created": "Wed, 11 Sep 2019 10:07:59 GMT",
"version": "v1"
}
] |
2019-09-12
|
[
[
"Cabanac",
"Guillaume",
""
],
[
"Frommholz",
"Ingo",
""
],
[
"Mayr",
"Philipp",
""
]
] |
The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackled issues related to academic search, at the crossroads between Information Retrieval and Bibliometrics. BIR is a hot topic investigated by both academia (e.g., ArnetMiner, CiteSeerx, DocEar) and the industry (e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar). This report presents the 8th iteration of the one-day BIR workshop held at ECIR 2019 in Cologne, Germany.
|
2204.02004
|
Chaim Baskin
|
Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin
|
Bimodal Distributed Binarized Neural Networks
| null | null | null | null |
cs.LG cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Binary Neural Networks (BNNs) are an extremely promising method to reduce
deep neural networks' complexity and power consumption massively. Binarization
techniques, however, suffer from ineligible performance degradation compared to
their full-precision counterparts.
Prior work mainly focused on strategies for sign function approximation
during forward and backward phases to reduce the quantization error during the
binarization process. In this work, we propose a Bi-Modal Distributed
binarization method (\methodname{}). That imposes bi-modal distribution of the
network weights by kurtosis regularization. The proposed method consists of a
training scheme that we call Weight Distribution Mimicking (WDM), which
efficiently imitates the full-precision network weight distribution to their
binary counterpart. Preserving this distribution during binarization-aware
training creates robust and informative binary feature maps and significantly
reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10
and ImageNet demonstrate the superiority of our method over current
state-of-the-art schemes. Our source code, experimental settings, training
logs, and binary models are available at
\url{https://github.com/BlueAnon/BD-BNN}.
|
[
{
"created": "Tue, 5 Apr 2022 06:07:05 GMT",
"version": "v1"
}
] |
2022-04-06
|
[
[
"Rozen",
"Tal",
""
],
[
"Kimhi",
"Moshe",
""
],
[
"Chmiel",
"Brian",
""
],
[
"Mendelson",
"Avi",
""
],
[
"Baskin",
"Chaim",
""
]
] |
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a Bi-Modal Distributed binarization method (\methodname{}). That imposes bi-modal distribution of the network weights by kurtosis regularization. The proposed method consists of a training scheme that we call Weight Distribution Mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and significantly reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate the superiority of our method over current state-of-the-art schemes. Our source code, experimental settings, training logs, and binary models are available at \url{https://github.com/BlueAnon/BD-BNN}.
|
2404.12703
|
Marius Kurz
|
Daniel Kempf, Marius Kurz, Marcel Blind, Patrick Kopper, Philipp
Offenh\"auser, Anna Schwarz, Spencer Starr, Jens Keim, Andrea Beck
|
GAL{\AE}XI: Solving complex compressible flows with high-order
discontinuous Galerkin methods on accelerator-based systems
|
19 pages, 12 figures, 3 tables. Code available at:
https://github.com/flexi-framework/galaexi
| null | null | null |
cs.MS cs.CE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This work presents GAL{\AE}XI as a novel, energy-efficient flow solver for
the simulation of compressible flows on unstructured meshes leveraging the
parallel computing power of modern Graphics Processing Units (GPUs). GAL{\AE}XI
implements the high-order Discontinuous Galerkin Spectral Element Method
(DGSEM) using shock capturing with a finite-volume subcell approach to ensure
the stability of the high-order scheme near shocks. This work provides details
on the general code design, the parallelization strategy, and the
implementation approach for the compute kernels with a focus on the element
local mappings between volume and surface data due to the unstructured mesh.
GAL{\AE}XI exhibits excellent strong scaling properties up to 1024 GPUs if each
GPU is assigned a minimum of one million degrees of freedom degrees of freedom.
To verify its implementation, a convergence study is performed that recovers
the theoretical order of convergence of the implemented numerical schemes.
Moreover, the solver is validated using both the incompressible and
compressible formulation of the Taylor-Green-Vortex at a Mach number of 0.1 and
1.25, respectively. A mesh convergence study shows that the results converge to
the high-fidelity reference solution and that the results match the original
CPU implementation. Finally, GAL{\AE}XI is applied to a large-scale
wall-resolved large eddy simulation of a linear cascade of the NASA Rotor 37.
Here, the supersonic region and shocks at the leading edge are captured
accurately and robustly by the implemented shock-capturing approach. It is
demonstrated that GAL{\AE}XI requires less than half of the energy to carry out
this simulation in comparison to the reference CPU implementation. This renders
GAL{\AE}XI as a potent tool for accurate and efficient simulations of
compressible flows in the realm of exascale computing and the associated new
HPC architectures.
|
[
{
"created": "Fri, 19 Apr 2024 08:21:05 GMT",
"version": "v1"
}
] |
2024-04-22
|
[
[
"Kempf",
"Daniel",
""
],
[
"Kurz",
"Marius",
""
],
[
"Blind",
"Marcel",
""
],
[
"Kopper",
"Patrick",
""
],
[
"Offenhäuser",
"Philipp",
""
],
[
"Schwarz",
"Anna",
""
],
[
"Starr",
"Spencer",
""
],
[
"Keim",
"Jens",
""
],
[
"Beck",
"Andrea",
""
]
] |
This work presents GAL{\AE}XI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of modern Graphics Processing Units (GPUs). GAL{\AE}XI implements the high-order Discontinuous Galerkin Spectral Element Method (DGSEM) using shock capturing with a finite-volume subcell approach to ensure the stability of the high-order scheme near shocks. This work provides details on the general code design, the parallelization strategy, and the implementation approach for the compute kernels with a focus on the element local mappings between volume and surface data due to the unstructured mesh. GAL{\AE}XI exhibits excellent strong scaling properties up to 1024 GPUs if each GPU is assigned a minimum of one million degrees of freedom degrees of freedom. To verify its implementation, a convergence study is performed that recovers the theoretical order of convergence of the implemented numerical schemes. Moreover, the solver is validated using both the incompressible and compressible formulation of the Taylor-Green-Vortex at a Mach number of 0.1 and 1.25, respectively. A mesh convergence study shows that the results converge to the high-fidelity reference solution and that the results match the original CPU implementation. Finally, GAL{\AE}XI is applied to a large-scale wall-resolved large eddy simulation of a linear cascade of the NASA Rotor 37. Here, the supersonic region and shocks at the leading edge are captured accurately and robustly by the implemented shock-capturing approach. It is demonstrated that GAL{\AE}XI requires less than half of the energy to carry out this simulation in comparison to the reference CPU implementation. This renders GAL{\AE}XI as a potent tool for accurate and efficient simulations of compressible flows in the realm of exascale computing and the associated new HPC architectures.
|
1007.2449
|
Kamran Karimi
|
Kamran Karimi
|
A Brief Introduction to Temporality and Causality
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Causality is a non-obvious concept that is often considered to be related to
temporality. In this paper we present a number of past and present approaches
to the definition of temporality and causality from philosophical, physical,
and computational points of view. We note that time is an important ingredient
in many relationships and phenomena. The topic is then divided into the two
main areas of temporal discovery, which is concerned with finding relations
that are stretched over time, and causal discovery, where a claim is made as to
the causal influence of certain events on others. We present a number of
computational tools used for attempting to automatically discover temporal and
causal relations in data.
|
[
{
"created": "Wed, 14 Jul 2010 22:41:30 GMT",
"version": "v1"
}
] |
2010-07-16
|
[
[
"Karimi",
"Kamran",
""
]
] |
Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.
|
2403.20195
|
Victor Silva Dos Santos
|
Victor Silva dos Santos, Erwan Gloaguen, Shiva Tirdad
|
Enhancing Lithological Mapping with Spatially Constrained Bayesian
Network (SCB-Net): An Approach for Field Data-Constrained Predictions with
Uncertainty Evaluation
|
17 pages, 3559 words, 14 figures
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Geological maps are an extremely valuable source of information for the Earth
sciences. They provide insights into mineral exploration, vulnerability to
natural hazards, and many other applications. These maps are created using
numerical or conceptual models that use geological observations to extrapolate
data. Geostatistical techniques have traditionally been used to generate
reliable predictions that take into account the spatial patterns inherent in
the data. However, as the number of auxiliary variables increases, these
methods become more labor-intensive. Additionally, traditional machine learning
methods often struggle with spatially correlated data and extracting valuable
non-linear information from geoscientific datasets. To address these
limitations, a new architecture called the Spatially Constrained Bayesian
Network (SCB-Net) has been developed. The SCB-Net aims to effectively exploit
the information from auxiliary variables while producing spatially constrained
predictions. It is made up of two parts, the first part focuses on learning
underlying patterns in the auxiliary variables while the second part integrates
ground-truth data and the learned embeddings from the first part. Moreover, to
assess model uncertainty, a technique called Monte Carlo dropout is used as a
Bayesian approximation. The SCB-Net has been applied to two selected areas in
northern Quebec, Canada, and has demonstrated its potential in generating
field-data-constrained lithological maps while allowing assessment of
prediction uncertainty for decision-making. This study highlights the promising
advancements of deep neural networks in geostatistics, particularly in handling
complex spatial feature learning tasks, leading to improved spatial information
techniques.
|
[
{
"created": "Fri, 29 Mar 2024 14:17:30 GMT",
"version": "v1"
}
] |
2024-04-01
|
[
[
"Santos",
"Victor Silva dos",
""
],
[
"Gloaguen",
"Erwan",
""
],
[
"Tirdad",
"Shiva",
""
]
] |
Geological maps are an extremely valuable source of information for the Earth sciences. They provide insights into mineral exploration, vulnerability to natural hazards, and many other applications. These maps are created using numerical or conceptual models that use geological observations to extrapolate data. Geostatistical techniques have traditionally been used to generate reliable predictions that take into account the spatial patterns inherent in the data. However, as the number of auxiliary variables increases, these methods become more labor-intensive. Additionally, traditional machine learning methods often struggle with spatially correlated data and extracting valuable non-linear information from geoscientific datasets. To address these limitations, a new architecture called the Spatially Constrained Bayesian Network (SCB-Net) has been developed. The SCB-Net aims to effectively exploit the information from auxiliary variables while producing spatially constrained predictions. It is made up of two parts, the first part focuses on learning underlying patterns in the auxiliary variables while the second part integrates ground-truth data and the learned embeddings from the first part. Moreover, to assess model uncertainty, a technique called Monte Carlo dropout is used as a Bayesian approximation. The SCB-Net has been applied to two selected areas in northern Quebec, Canada, and has demonstrated its potential in generating field-data-constrained lithological maps while allowing assessment of prediction uncertainty for decision-making. This study highlights the promising advancements of deep neural networks in geostatistics, particularly in handling complex spatial feature learning tasks, leading to improved spatial information techniques.
|
2006.03622
|
Saman Motamed
|
Saman Motamed and Patrik Rogalla and Farzad Khalvati
|
Data Augmentation using Generative Adversarial Networks (GANs) for
GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
| null | null | null | null |
cs.CV cs.LG eess.IV q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Successful training of convolutional neural networks (CNNs) requires a
substantial amount of data. With small datasets networks generalize poorly.
Data Augmentation techniques improve the generalizability of neural networks by
using existing training data more effectively. Standard data augmentation
methods, however, produce limited plausible alternative data. Generative
Adversarial Networks (GANs) have been utilized to generate new data and improve
the performance of CNNs. Nevertheless, data augmentation techniques for
training GANs are under-explored compared to CNNs. In this work, we propose a
new GAN architecture for augmentation of chest X-rays for semi-supervised
detection of pneumonia and COVID-19 using generative models. We show that the
proposed GAN can be used to effectively augment data and improve classification
accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our
augmentation GAN model with Deep Convolutional GAN and traditional augmentation
methods (rotate, zoom, etc) on two different X-ray datasets and show our
GAN-based augmentation method surpasses other augmentation methods for training
a GAN in detecting anomalies in X-ray images.
|
[
{
"created": "Fri, 5 Jun 2020 18:30:20 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Jan 2021 20:27:04 GMT",
"version": "v2"
}
] |
2021-01-14
|
[
[
"Motamed",
"Saman",
""
],
[
"Rogalla",
"Patrik",
""
],
[
"Khalvati",
"Farzad",
""
]
] |
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.
|
2308.02950
|
Louis Vervoort
|
Louis Vervoort, Vitaliy Mizyakov, Anastasia Ugleva
|
A criterion for Artificial General Intelligence: hypothetic-deductive
reasoning, tested on ChatGPT
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We argue that a key reasoning skill that any advanced AI, say GPT-4, should
master in order to qualify as 'thinking machine', or AGI, is
hypothetic-deductive reasoning. Problem-solving or question-answering can quite
generally be construed as involving two steps: hypothesizing that a certain set
of hypotheses T applies to the problem or question at hand, and deducing the
solution or answer from T - hence the term hypothetic-deductive reasoning. An
elementary proxy of hypothetic-deductive reasoning is causal reasoning. We
propose simple tests for both types of reasoning, and apply them to ChatGPT.
Our study shows that, at present, the chatbot has a limited capacity for either
type of reasoning, as soon as the problems considered are somewhat complex.
However, we submit that if an AI would be capable of this type of reasoning in
a sufficiently wide range of contexts, it would be an AGI.
|
[
{
"created": "Sat, 5 Aug 2023 20:33:13 GMT",
"version": "v1"
}
] |
2023-08-08
|
[
[
"Vervoort",
"Louis",
""
],
[
"Mizyakov",
"Vitaliy",
""
],
[
"Ugleva",
"Anastasia",
""
]
] |
We argue that a key reasoning skill that any advanced AI, say GPT-4, should master in order to qualify as 'thinking machine', or AGI, is hypothetic-deductive reasoning. Problem-solving or question-answering can quite generally be construed as involving two steps: hypothesizing that a certain set of hypotheses T applies to the problem or question at hand, and deducing the solution or answer from T - hence the term hypothetic-deductive reasoning. An elementary proxy of hypothetic-deductive reasoning is causal reasoning. We propose simple tests for both types of reasoning, and apply them to ChatGPT. Our study shows that, at present, the chatbot has a limited capacity for either type of reasoning, as soon as the problems considered are somewhat complex. However, we submit that if an AI would be capable of this type of reasoning in a sufficiently wide range of contexts, it would be an AGI.
|
1812.10550
|
Huy-Hieu Pham
|
Huy-Hieu Pham and Louahdi Khoudour and Alain Crouzil and Pablo Zegers
and Sergio A. Velastin
|
Learning to Recognize 3D Human Action from A New Skeleton-based
Representation Using Deep Convolutional Neural Networks
|
This paper is a preprint of a paper published to IET Computer Vision.
The copy of the record will be available at the IET Digital Library
| null |
10.1049/iet-cvi.2018.5014
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recognizing human actions in untrimmed videos is an important challenging
task. An effective 3D motion representation and a powerful learning model are
two key factors influencing recognition performance. In this paper we introduce
a new skeleton-based representation for 3D action recognition in videos. The
key idea of the proposed representation is to transform 3D joint coordinates of
the human body carried in skeleton sequences into RGB images via a color
encoding process. By normalizing the 3D joint coordinates and dividing each
skeleton frame into five parts, where the joints are concatenated according to
the order of their physical connections, the color-coded representation is able
to represent spatio-temporal evolutions of complex 3D motions, independently of
the length of each sequence. We then design and train different Deep
Convolutional Neural Networks (D-CNNs) based on the Residual Network
architecture (ResNet) on the obtained image-based representations to learn 3D
motion features and classify them into classes. Our method is evaluated on two
widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very
large-scale dataset for 3D human action recognition. The experimental results
demonstrate that the proposed method outperforms previous state-of-the-art
approaches whilst requiring less computation for training and prediction.
|
[
{
"created": "Wed, 26 Dec 2018 21:47:08 GMT",
"version": "v1"
}
] |
2018-12-31
|
[
[
"Pham",
"Huy-Hieu",
""
],
[
"Khoudour",
"Louahdi",
""
],
[
"Crouzil",
"Alain",
""
],
[
"Zegers",
"Pablo",
""
],
[
"Velastin",
"Sergio A.",
""
]
] |
Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.
|
1602.08456
|
Masaki Ogura Dr.
|
Masaki Ogura and Victor M. Preciado
|
Epidemic Processes over Adaptive State-Dependent Networks
| null |
Phys. Rev. E 93, 062316 (2016)
|
10.1103/PhysRevE.93.062316
| null |
cs.SI math.PR physics.soc-ph q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study the dynamics of epidemic processes taking place in
adaptive networks of arbitrary topology. We focus our study on the adaptive
susceptible-infected-susceptible (ASIS) model, where healthy individuals are
allowed to temporarily cut edges connecting them to infected nodes in order to
prevent the spread of the infection. In this paper, we derive a closed-form
expression for a lower bound on the epidemic threshold of the ASIS model in
arbitrary networks with heterogeneous node and edge dynamics. For networks with
homogeneous node and edge dynamics, we show that the resulting \blue{lower
bound} is proportional to the epidemic threshold of the standard SIS model over
static networks, with a proportionality constant that depends on the adaptation
rates. Furthermore, based on our results, we propose an efficient algorithm to
optimally tune the adaptation rates in order to eradicate epidemic outbreaks in
arbitrary networks. We confirm the tightness of the proposed lower bounds with
several numerical simulations and compare our optimal adaptation rates with
popular centrality measures.
|
[
{
"created": "Fri, 26 Feb 2016 19:56:41 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Jun 2016 17:30:07 GMT",
"version": "v2"
}
] |
2016-06-29
|
[
[
"Ogura",
"Masaki",
""
],
[
"Preciado",
"Victor M.",
""
]
] |
In this paper, we study the dynamics of epidemic processes taking place in adaptive networks of arbitrary topology. We focus our study on the adaptive susceptible-infected-susceptible (ASIS) model, where healthy individuals are allowed to temporarily cut edges connecting them to infected nodes in order to prevent the spread of the infection. In this paper, we derive a closed-form expression for a lower bound on the epidemic threshold of the ASIS model in arbitrary networks with heterogeneous node and edge dynamics. For networks with homogeneous node and edge dynamics, we show that the resulting \blue{lower bound} is proportional to the epidemic threshold of the standard SIS model over static networks, with a proportionality constant that depends on the adaptation rates. Furthermore, based on our results, we propose an efficient algorithm to optimally tune the adaptation rates in order to eradicate epidemic outbreaks in arbitrary networks. We confirm the tightness of the proposed lower bounds with several numerical simulations and compare our optimal adaptation rates with popular centrality measures.
|
1802.08984
|
Kalev Alpernas
|
Kalev Alpernas (Tel Aviv University), Cormac Flanagan (UC Santa Cruz),
Sadjad Fouladi (Stanford University), Leonid Ryzhyk (VMware Research), Mooly
Sagiv (Tel Aviv University), Thomas Schmitz (UC Santa Cruz) and Keith
Winstein (Stanford University)
|
Secure Serverless Computing Using Dynamic Information Flow Control
| null | null | null | null |
cs.PL cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The rise of serverless computing provides an opportunity to rethink cloud
security. We present an approach for securing serverless systems using a novel
form of dynamic information flow control (IFC).
We show that in serverless applications, the termination channel found in
most existing IFC systems can be arbitrarily amplified via multiple concurrent
requests, necessitating a stronger termination-sensitive non-interference
guarantee, which we achieve using a combination of static labeling of
serverless processes and dynamic faceted labeling of persistent data.
We describe our implementation of this approach on top of JavaScript for AWS
Lambda and OpenWhisk serverless platforms, and present three realistic case
studies showing that it can enforce important IFC security properties with low
overhead.
|
[
{
"created": "Sun, 25 Feb 2018 10:36:56 GMT",
"version": "v1"
}
] |
2018-02-27
|
[
[
"Alpernas",
"Kalev",
"",
"Tel Aviv University"
],
[
"Flanagan",
"Cormac",
"",
"UC Santa Cruz"
],
[
"Fouladi",
"Sadjad",
"",
"Stanford University"
],
[
"Ryzhyk",
"Leonid",
"",
"VMware Research"
],
[
"Sagiv",
"Mooly",
"",
"Tel Aviv University"
],
[
"Schmitz",
"Thomas",
"",
"UC Santa Cruz"
],
[
"Winstein",
"Keith",
"",
"Stanford University"
]
] |
The rise of serverless computing provides an opportunity to rethink cloud security. We present an approach for securing serverless systems using a novel form of dynamic information flow control (IFC). We show that in serverless applications, the termination channel found in most existing IFC systems can be arbitrarily amplified via multiple concurrent requests, necessitating a stronger termination-sensitive non-interference guarantee, which we achieve using a combination of static labeling of serverless processes and dynamic faceted labeling of persistent data. We describe our implementation of this approach on top of JavaScript for AWS Lambda and OpenWhisk serverless platforms, and present three realistic case studies showing that it can enforce important IFC security properties with low overhead.
|
0906.5233
|
Toby Walsh
|
George Katsirelos, Sebastian Maneth, Nina Narodytska, Toby Walsh
|
Restricted Global Grammar Constraints
|
Proceedings of the 15th International Conference on Principles and
Practice of Constraint Programming, Lisbon, Portugal. September 2009
| null | null | null |
cs.AI cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the global GRAMMAR constraint over restricted classes of
context free grammars like deterministic and unambiguous context-free grammars.
We show that detecting disentailment for the GRAMMAR constraint in these cases
is as hard as parsing an unrestricted context free grammar.We also consider the
class of linear grammars and give a propagator that runs in quadratic time.
Finally, to demonstrate the use of linear grammars, we show that a weighted
linear GRAMMAR constraint can efficiently encode the EDITDISTANCE constraint,
and a conjunction of the EDITDISTANCE constraint and the REGULAR constraint
|
[
{
"created": "Mon, 29 Jun 2009 09:23:39 GMT",
"version": "v1"
}
] |
2009-06-30
|
[
[
"Katsirelos",
"George",
""
],
[
"Maneth",
"Sebastian",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Walsh",
"Toby",
""
]
] |
We investigate the global GRAMMAR constraint over restricted classes of context free grammars like deterministic and unambiguous context-free grammars. We show that detecting disentailment for the GRAMMAR constraint in these cases is as hard as parsing an unrestricted context free grammar.We also consider the class of linear grammars and give a propagator that runs in quadratic time. Finally, to demonstrate the use of linear grammars, we show that a weighted linear GRAMMAR constraint can efficiently encode the EDITDISTANCE constraint, and a conjunction of the EDITDISTANCE constraint and the REGULAR constraint
|
1006.5188
|
Nicola Di Mauro
|
Nicola Di Mauro and Teresa M.A. Basile and Stefano Ferilli and
Floriana Esposito
|
Feature Construction for Relational Sequence Learning
|
15 pages
| null | null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We tackle the problem of multi-class relational sequence learning using
relevant patterns discovered from a set of labelled sequences. To deal with
this problem, firstly each relational sequence is mapped into a feature vector
using the result of a feature construction method. Since, the efficacy of
sequence learning algorithms strongly depends on the features used to represent
the sequences, the second step is to find an optimal subset of the constructed
features leading to high classification accuracy. This feature selection task
has been solved adopting a wrapper approach that uses a stochastic local search
algorithm embedding a naive Bayes classifier. The performance of the proposed
method applied to a real-world dataset shows an improvement when compared to
other established methods, such as hidden Markov models, Fisher kernels and
conditional random fields for relational sequences.
|
[
{
"created": "Sun, 27 Jun 2010 08:56:11 GMT",
"version": "v1"
}
] |
2010-06-29
|
[
[
"Di Mauro",
"Nicola",
""
],
[
"Basile",
"Teresa M. A.",
""
],
[
"Ferilli",
"Stefano",
""
],
[
"Esposito",
"Floriana",
""
]
] |
We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly each relational sequence is mapped into a feature vector using the result of a feature construction method. Since, the efficacy of sequence learning algorithms strongly depends on the features used to represent the sequences, the second step is to find an optimal subset of the constructed features leading to high classification accuracy. This feature selection task has been solved adopting a wrapper approach that uses a stochastic local search algorithm embedding a naive Bayes classifier. The performance of the proposed method applied to a real-world dataset shows an improvement when compared to other established methods, such as hidden Markov models, Fisher kernels and conditional random fields for relational sequences.
|
1905.10077
|
Lixin Su
|
Lixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, and Xueqi Cheng
|
Controlling Risk of Web Question Answering
|
42nd International ACM SIGIR Conference on Research and Development
in Information Retrieval
| null |
10.1145/3331184.3331261
| null |
cs.IR cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Web question answering (QA) has become an indispensable component in modern
search systems, which can significantly improve users' search experience by
providing a direct answer to users' information need. This could be achieved by
applying machine reading comprehension (MRC) models over the retrieved passages
to extract answers with respect to the search query. With the development of
deep learning techniques, state-of-the-art MRC performances have been achieved
by recent deep methods. However, existing studies on MRC seldom address the
predictive uncertainty issue, i.e., how likely the prediction of an MRC model
is wrong, leading to uncontrollable risks in real-world Web QA applications. In
this work, we first conduct an in-depth investigation over the risk of Web QA.
We then introduce a novel risk control framework, which consists of a qualify
model for uncertainty estimation using the probe idea, and a decision model for
selectively output. For evaluation, we introduce risk-related metrics, rather
than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA.
The empirical results over both the real-world Web QA dataset and the academic
MRC benchmark collection demonstrate the effectiveness of our approach.
|
[
{
"created": "Fri, 24 May 2019 07:55:42 GMT",
"version": "v1"
},
{
"created": "Mon, 27 May 2019 02:24:32 GMT",
"version": "v2"
},
{
"created": "Thu, 11 Jul 2019 05:10:47 GMT",
"version": "v3"
}
] |
2019-07-12
|
[
[
"Su",
"Lixin",
""
],
[
"Guo",
"Jiafeng",
""
],
[
"Fan",
"Yixing",
""
],
[
"Lan",
"Yanyan",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.
|
2209.07220
|
Hyung-Il Kim
|
Hyung-Il Kim, Kimin Yun, Yong Man Ro
|
Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to
Face Misalignment
|
14 pages, 9 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For the past decades, face recognition (FR) has been actively studied in
computer vision and pattern recognition society. Recently, due to the advances
in deep learning, the FR technology shows high performance for most of the
benchmark datasets. However, when the FR algorithm is applied to a real-world
scenario, the performance has been known to be still unsatisfactory. This is
mainly attributed to the mismatch between training and testing sets. Among such
mismatches, face misalignment between training and testing faces is one of the
factors that hinder successful FR. To address this limitation, we propose a
face shape-guided deep feature alignment framework for FR robust to the face
misalignment. Based on a face shape prior (e.g., face keypoints), we train the
proposed deep network by introducing alignment processes, i.e., pixel and
feature alignments, between well-aligned and misaligned face images. Through
the pixel alignment process that decodes the aggregated feature extracted from
a face image and face shape prior, we add the auxiliary task to reconstruct the
well-aligned face image. Since the aggregated features are linked to the face
feature extraction network as a guide via the feature alignment process, we
train the robust face feature to the face misalignment. Even if the face shape
estimation is required in the training stage, the additional face alignment
process, which is usually incorporated in the conventional FR pipeline, is not
necessarily needed in the testing phase. Through the comparative experiments,
we validate the effectiveness of the proposed method for the face misalignment
with the FR datasets.
|
[
{
"created": "Thu, 15 Sep 2022 11:23:51 GMT",
"version": "v1"
}
] |
2022-09-16
|
[
[
"Kim",
"Hyung-Il",
""
],
[
"Yun",
"Kimin",
""
],
[
"Ro",
"Yong Man",
""
]
] |
For the past decades, face recognition (FR) has been actively studied in computer vision and pattern recognition society. Recently, due to the advances in deep learning, the FR technology shows high performance for most of the benchmark datasets. However, when the FR algorithm is applied to a real-world scenario, the performance has been known to be still unsatisfactory. This is mainly attributed to the mismatch between training and testing sets. Among such mismatches, face misalignment between training and testing faces is one of the factors that hinder successful FR. To address this limitation, we propose a face shape-guided deep feature alignment framework for FR robust to the face misalignment. Based on a face shape prior (e.g., face keypoints), we train the proposed deep network by introducing alignment processes, i.e., pixel and feature alignments, between well-aligned and misaligned face images. Through the pixel alignment process that decodes the aggregated feature extracted from a face image and face shape prior, we add the auxiliary task to reconstruct the well-aligned face image. Since the aggregated features are linked to the face feature extraction network as a guide via the feature alignment process, we train the robust face feature to the face misalignment. Even if the face shape estimation is required in the training stage, the additional face alignment process, which is usually incorporated in the conventional FR pipeline, is not necessarily needed in the testing phase. Through the comparative experiments, we validate the effectiveness of the proposed method for the face misalignment with the FR datasets.
|
2302.10441
|
Zhuohang Li
|
Zhuohang Li, Jiaxin Zhang, Jian Liu
|
Speech Privacy Leakage from Shared Gradients in Distributed Learning
| null | null | null | null |
cs.LG cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Distributed machine learning paradigms, such as federated learning, have been
recently adopted in many privacy-critical applications for speech analysis.
However, such frameworks are vulnerable to privacy leakage attacks from shared
gradients. Despite extensive efforts in the image domain, the exploration of
speech privacy leakage from gradients is quite limited. In this paper, we
explore methods for recovering private speech/speaker information from the
shared gradients in distributed learning settings. We conduct experiments on a
keyword spotting model with two different types of speech features to quantify
the amount of leaked information by measuring the similarity between the
original and recovered speech signals. We further demonstrate the feasibility
of inferring various levels of side-channel information, including speech
content and speaker identity, under the distributed learning framework without
accessing the user's data.
|
[
{
"created": "Tue, 21 Feb 2023 04:48:29 GMT",
"version": "v1"
}
] |
2023-02-22
|
[
[
"Li",
"Zhuohang",
""
],
[
"Zhang",
"Jiaxin",
""
],
[
"Liu",
"Jian",
""
]
] |
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks are vulnerable to privacy leakage attacks from shared gradients. Despite extensive efforts in the image domain, the exploration of speech privacy leakage from gradients is quite limited. In this paper, we explore methods for recovering private speech/speaker information from the shared gradients in distributed learning settings. We conduct experiments on a keyword spotting model with two different types of speech features to quantify the amount of leaked information by measuring the similarity between the original and recovered speech signals. We further demonstrate the feasibility of inferring various levels of side-channel information, including speech content and speaker identity, under the distributed learning framework without accessing the user's data.
|
2403.05732
|
Nitsan Soffair
|
Nitsan Soffair, Shie Mannor
|
Conservative DDPG -- Pessimistic RL without Ensemble
|
Paper do not ready
| null | null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
DDPG is hindered by the overestimation bias problem, wherein its
$Q$-estimates tend to overstate the actual $Q$-values. Traditional solutions to
this bias involve ensemble-based methods, which require significant
computational resources, or complex log-policy-based approaches, which are
difficult to understand and implement. In contrast, we propose a
straightforward solution using a $Q$-target and incorporating a behavioral
cloning (BC) loss penalty. This solution, acting as an uncertainty measure, can
be easily implemented with minimal code and without the need for an ensemble.
Our empirical findings strongly support the superiority of Conservative DDPG
over DDPG across various MuJoCo and Bullet tasks. We consistently observe
better performance in all evaluated tasks and even competitive or superior
performance compared to TD3 and TD7, all achieved with significantly reduced
computational requirements.
|
[
{
"created": "Fri, 8 Mar 2024 23:59:38 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Jun 2024 19:40:48 GMT",
"version": "v2"
}
] |
2024-06-04
|
[
[
"Soffair",
"Nitsan",
""
],
[
"Mannor",
"Shie",
""
]
] |
DDPG is hindered by the overestimation bias problem, wherein its $Q$-estimates tend to overstate the actual $Q$-values. Traditional solutions to this bias involve ensemble-based methods, which require significant computational resources, or complex log-policy-based approaches, which are difficult to understand and implement. In contrast, we propose a straightforward solution using a $Q$-target and incorporating a behavioral cloning (BC) loss penalty. This solution, acting as an uncertainty measure, can be easily implemented with minimal code and without the need for an ensemble. Our empirical findings strongly support the superiority of Conservative DDPG over DDPG across various MuJoCo and Bullet tasks. We consistently observe better performance in all evaluated tasks and even competitive or superior performance compared to TD3 and TD7, all achieved with significantly reduced computational requirements.
|
2102.06930
|
Kleanthis Avramidis
|
Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis, Athanasia
Zlatintsi and Petros Maragos
|
Deep Convolutional and Recurrent Networks for Polyphonic Instrument
Classification from Monophonic Raw Audio Waveforms
|
5 pages, 4 figures, 6 tables, to be published in the Proc. of the
46th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2021) @ Toronto, Ontario, Canada
| null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Sound Event Detection and Audio Classification tasks are traditionally
addressed through time-frequency representations of audio signals such as
spectrograms. However, the emergence of deep neural networks as efficient
feature extractors has enabled the direct use of audio signals for
classification purposes. In this paper, we attempt to recognize musical
instruments in polyphonic audio by only feeding their raw waveforms into deep
learning models. Various recurrent and convolutional architectures
incorporating residual connections are examined and parameterized in order to
build end-to-end classi-fiers with low computational cost and only minimal
preprocessing. We obtain competitive classification scores and useful
instrument-wise insight through the IRMAS test set, utilizing a parallel
CNN-BiGRU model with multiple residual connections, while maintaining a
significantly reduced number of trainable parameters.
|
[
{
"created": "Sat, 13 Feb 2021 13:44:46 GMT",
"version": "v1"
}
] |
2021-02-16
|
[
[
"Avramidis",
"Kleanthis",
""
],
[
"Kratimenos",
"Agelos",
""
],
[
"Garoufis",
"Christos",
""
],
[
"Zlatintsi",
"Athanasia",
""
],
[
"Maragos",
"Petros",
""
]
] |
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient feature extractors has enabled the direct use of audio signals for classification purposes. In this paper, we attempt to recognize musical instruments in polyphonic audio by only feeding their raw waveforms into deep learning models. Various recurrent and convolutional architectures incorporating residual connections are examined and parameterized in order to build end-to-end classi-fiers with low computational cost and only minimal preprocessing. We obtain competitive classification scores and useful instrument-wise insight through the IRMAS test set, utilizing a parallel CNN-BiGRU model with multiple residual connections, while maintaining a significantly reduced number of trainable parameters.
|
1711.09408
|
Jonathan Zhu
|
Jonathan J. H. Zhu, Hexin Chen, Tai-Quan Peng, Xiao Fan Liu and
Haixing Dai
|
How to Measure Sessions of Mobile Device Use? Quantification,
Evaluation, and Applications
|
Preprint of forthcoming article in Mobile Media & Communication
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Research on mobile phone use often starts with a question of "How much time
users spend on using their phones?". The question involves an equal-length
measure that captures the duration of mobile phone use but does not tackle the
other temporal characteristics of user behavior, such as frequency, timing, and
sequence. In the study, we proposed a variable-length measure called "session"
to uncover the unmeasured temporal characteristics. We use an open source data
to demonstrate how to quantify sessions, aggregate the sessions to higher units
of analysis within and across users, evaluate the results, and apply the
measure for theoretical or practical purposes.
|
[
{
"created": "Sun, 26 Nov 2017 15:32:22 GMT",
"version": "v1"
}
] |
2017-11-28
|
[
[
"Zhu",
"Jonathan J. H.",
""
],
[
"Chen",
"Hexin",
""
],
[
"Peng",
"Tai-Quan",
""
],
[
"Liu",
"Xiao Fan",
""
],
[
"Dai",
"Haixing",
""
]
] |
Research on mobile phone use often starts with a question of "How much time users spend on using their phones?". The question involves an equal-length measure that captures the duration of mobile phone use but does not tackle the other temporal characteristics of user behavior, such as frequency, timing, and sequence. In the study, we proposed a variable-length measure called "session" to uncover the unmeasured temporal characteristics. We use an open source data to demonstrate how to quantify sessions, aggregate the sessions to higher units of analysis within and across users, evaluate the results, and apply the measure for theoretical or practical purposes.
|
2110.09234
|
Martha Barnard
|
Martha Barnard (1), Radhika Iyer (1 and 2), Sara Y. Del Valle (1),
Ashlynn R. Daughton (1) ((1) A-1 Information Systems and Modeling, Los Alamos
National Lab, Los Alamos, NM, USA, (2) Department of Political Science and
Department of Computing, Data Science, and Society, University of California,
Berkeley, Berkeley, CA, USA)
|
Impact of COVID-19 Policies and Misinformation on Social Unrest
|
21 pages, 9 figures
| null | null |
LA-UR-21-29745
|
cs.CY cs.LG stat.AP
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The novel coronavirus disease (COVID-19) pandemic has impacted every corner
of earth, disrupting governments and leading to socioeconomic instability. This
crisis has prompted questions surrounding how different sectors of society
interact and influence each other during times of change and stress. Given the
unprecedented economic and societal impacts of this pandemic, many new data
sources have become available, allowing us to quantitatively explore these
associations. Understanding these relationships can help us better prepare for
future disasters and mitigate the impacts. Here, we focus on the interplay
between social unrest (protests), health outcomes, public health orders, and
misinformation in eight countries of Western Europe and four regions of the
United States. We created 1-3 week forecasts of both a binary protest metric
for identifying times of high protest activity and the overall protest counts
over time. We found that for all regions, except Belgium, at least one feature
from our various data streams was predictive of protests. However, the accuracy
of the protest forecasts varied by country, that is, for roughly half of the
countries analyzed, our forecasts outperform a na\"ive model. These mixed
results demonstrate the potential of diverse data streams to predict a topic as
volatile as protests as well as the difficulties of predicting a situation that
is as rapidly evolving as a pandemic.
|
[
{
"created": "Thu, 7 Oct 2021 16:05:10 GMT",
"version": "v1"
}
] |
2021-10-19
|
[
[
"Barnard",
"Martha",
"",
"1 and 2"
],
[
"Iyer",
"Radhika",
"",
"1 and 2"
],
[
"Del Valle",
"Sara Y.",
""
],
[
"Daughton",
"Ashlynn R.",
""
]
] |
The novel coronavirus disease (COVID-19) pandemic has impacted every corner of earth, disrupting governments and leading to socioeconomic instability. This crisis has prompted questions surrounding how different sectors of society interact and influence each other during times of change and stress. Given the unprecedented economic and societal impacts of this pandemic, many new data sources have become available, allowing us to quantitatively explore these associations. Understanding these relationships can help us better prepare for future disasters and mitigate the impacts. Here, we focus on the interplay between social unrest (protests), health outcomes, public health orders, and misinformation in eight countries of Western Europe and four regions of the United States. We created 1-3 week forecasts of both a binary protest metric for identifying times of high protest activity and the overall protest counts over time. We found that for all regions, except Belgium, at least one feature from our various data streams was predictive of protests. However, the accuracy of the protest forecasts varied by country, that is, for roughly half of the countries analyzed, our forecasts outperform a na\"ive model. These mixed results demonstrate the potential of diverse data streams to predict a topic as volatile as protests as well as the difficulties of predicting a situation that is as rapidly evolving as a pandemic.
|
2102.04317
|
Shuquan Ye
|
Shuquan Ye, Dongdong Chen, Songfang Han, Ziyu Wan, Jing Liao
|
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
|
To appear at TVCG
| null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Point cloud upsampling is vital for the quality of the mesh in
three-dimensional reconstruction. Recent research on point cloud upsampling has
achieved great success due to the development of deep learning. However, the
existing methods regard point cloud upsampling of different scale factors as
independent tasks. Thus, the methods need to train a specific model for each
scale factor, which is both inefficient and impractical for storage and
computation in real applications. To address this limitation, in this work, we
propose a novel method called ``Meta-PU" to firstly support point cloud
upsampling of arbitrary scale factors with a single model. In the Meta-PU
method, besides the backbone network consisting of residual graph convolution
(RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC
blocks dynamically, and a farthest sampling block is adopted to sample
different numbers of points. Together, these two blocks enable our Meta-PU to
continuously upsample the point cloud with arbitrary scale factors by using
only a single model. In addition, the experiments reveal that training on
multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even
outperforms the existing methods trained for a specific scale factor only.
|
[
{
"created": "Mon, 8 Feb 2021 16:21:48 GMT",
"version": "v1"
}
] |
2021-02-09
|
[
[
"Ye",
"Shuquan",
""
],
[
"Chen",
"Dongdong",
""
],
[
"Han",
"Songfang",
""
],
[
"Wan",
"Ziyu",
""
],
[
"Liao",
"Jing",
""
]
] |
Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this work, we propose a novel method called ``Meta-PU" to firstly support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model. In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
|
1607.04557
|
Alfonso Cevallos
|
Alfonso Cevallos, Friedrich Eisenbrand, Rico Zenklusen
|
Local Search for Max-Sum Diversification
| null | null | null | null |
cs.DS cs.CG cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We provide simple and fast polynomial time approximation schemes (PTASs) for
several variants of the max-sum diversification problem which, in its most
basic form, is as follows: Given n points p_1,...,p_n in R^d and an integer k,
select k points such that the average Euclidean distance between these points
is maximized. This problem commonly appears in information retrieval and
web-search in order to select a diverse set of points from the input. In this
context, it has recently received a lot of attention.
We present new techniques to analyze natural local search algorithms. This
leads to a (1-O(1/k))-approximation for distances of negative type, even
subject to any matroid constraint of rank k, in time O(n k^2 log k), when
assuming that distance evaluations and calls to the independence oracle are
constant time. Negative type distances include as special cases Euclidean
distances and many further natural distances. Our result easily transforms into
a PTAS and improves on the only previously known PTAS for this setting, which
relies on convex optimization techniques in an n-dimensional space and is
impractical for large data sets. In contrast, our procedure has an (optimal)
linear dependence on n.
Using generalized exchange properties of matroid intersection, we show that a
PTAS can be obtained for matroid intersection constraints as well. Moreover,
our techniques, being based on local search, are conceptually simple and allow
for various extensions. In particular, we get asymptotically optimal
O(1)-approximations when combining the classic dispersion function with a
monotone submodular objective, which is a very common class of functions to
measure diversity and relevance. This result leverages recent advances on local
search techniques based on proxy functions to obtain optimal approximations for
monotone submodular function maximization subject to a matroid constraint.
|
[
{
"created": "Fri, 15 Jul 2016 15:38:02 GMT",
"version": "v1"
}
] |
2016-07-18
|
[
[
"Cevallos",
"Alfonso",
""
],
[
"Eisenbrand",
"Friedrich",
""
],
[
"Zenklusen",
"Rico",
""
]
] |
We provide simple and fast polynomial time approximation schemes (PTASs) for several variants of the max-sum diversification problem which, in its most basic form, is as follows: Given n points p_1,...,p_n in R^d and an integer k, select k points such that the average Euclidean distance between these points is maximized. This problem commonly appears in information retrieval and web-search in order to select a diverse set of points from the input. In this context, it has recently received a lot of attention. We present new techniques to analyze natural local search algorithms. This leads to a (1-O(1/k))-approximation for distances of negative type, even subject to any matroid constraint of rank k, in time O(n k^2 log k), when assuming that distance evaluations and calls to the independence oracle are constant time. Negative type distances include as special cases Euclidean distances and many further natural distances. Our result easily transforms into a PTAS and improves on the only previously known PTAS for this setting, which relies on convex optimization techniques in an n-dimensional space and is impractical for large data sets. In contrast, our procedure has an (optimal) linear dependence on n. Using generalized exchange properties of matroid intersection, we show that a PTAS can be obtained for matroid intersection constraints as well. Moreover, our techniques, being based on local search, are conceptually simple and allow for various extensions. In particular, we get asymptotically optimal O(1)-approximations when combining the classic dispersion function with a monotone submodular objective, which is a very common class of functions to measure diversity and relevance. This result leverages recent advances on local search techniques based on proxy functions to obtain optimal approximations for monotone submodular function maximization subject to a matroid constraint.
|
1706.06322
|
Mohamd Sultan
|
Mohamad T. Sultan and Salim M. Zaki
|
Evaluation of energy consumption of reactive and proactive routing
protocols in MANET
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mobile Ad hoc Network (MANET) is a distributed, infrastructure-less and
decentralized network. A routing protocol in MANET is used to find routes
between mobile nodes to facilitate communication within the network. Numerous
routing protocols have been proposed for MANET. Those routing protocols are
designed to adaptively accommodate for dynamic unpredictable changes in
network's topology. The mobile nodes in MANET are often powered by limited
batteries and network lifetime relies heavily on the energy consumption of
nodes. In consequence, the lack of a mobile node can lead to network
partitioning. In this paper we analyse, evaluate and measure the energy
efficiency of three prominent MANET routing protocols namely DSR, AODV and OLSR
in addition to modified protocols. These routing protocols follow the reactive
and the proactive routing schemes. A discussion and comparison highlighting
their particular merits and drawbacks are also presented. Evaluation study and
simulations are performed using NS-2 and its accompanying tools for analysis
and investigation of results.
|
[
{
"created": "Tue, 20 Jun 2017 08:56:12 GMT",
"version": "v1"
}
] |
2017-06-21
|
[
[
"Sultan",
"Mohamad T.",
""
],
[
"Zaki",
"Salim M.",
""
]
] |
Mobile Ad hoc Network (MANET) is a distributed, infrastructure-less and decentralized network. A routing protocol in MANET is used to find routes between mobile nodes to facilitate communication within the network. Numerous routing protocols have been proposed for MANET. Those routing protocols are designed to adaptively accommodate for dynamic unpredictable changes in network's topology. The mobile nodes in MANET are often powered by limited batteries and network lifetime relies heavily on the energy consumption of nodes. In consequence, the lack of a mobile node can lead to network partitioning. In this paper we analyse, evaluate and measure the energy efficiency of three prominent MANET routing protocols namely DSR, AODV and OLSR in addition to modified protocols. These routing protocols follow the reactive and the proactive routing schemes. A discussion and comparison highlighting their particular merits and drawbacks are also presented. Evaluation study and simulations are performed using NS-2 and its accompanying tools for analysis and investigation of results.
|
1606.05839
|
EPTCS
|
Olivier Danvy (University of Aarhus), Ugo de'Liguoro (Universit\`a di
Torino)
|
Proceedings of the Workshop on Continuations
| null |
EPTCS 212, 2016
|
10.4204/EPTCS.212
| null |
cs.PL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The notion of continuation is ubiquitous in many different areas of computer
science, including systems programming, programming languages, algorithmics,
semantics, logic, and constructive mathematics. In fact the concept of
continuation nicely realizes sophisticated control mechanisms, which are widely
used in a variety of applications. Since we cannot escape control features, it
becomes a challenge to provide them with sound reasoning principles.
Indeed there is much research activity on understanding, representing, and
reasoning about elaborated non-local control structures, in particular in
declarative programming languages such as functional and logic languages. The
proceedings of the Workshop on Continuations 2015, held in London in April
2015, illustrate some of the afore mentioned topics and hopefully they will
inspire further research work on the subject.
|
[
{
"created": "Sun, 19 Jun 2016 07:25:03 GMT",
"version": "v1"
}
] |
2016-06-21
|
[
[
"Danvy",
"Olivier",
"",
"University of Aarhus"
],
[
"de'Liguoro",
"Ugo",
"",
"Università di\n Torino"
]
] |
The notion of continuation is ubiquitous in many different areas of computer science, including systems programming, programming languages, algorithmics, semantics, logic, and constructive mathematics. In fact the concept of continuation nicely realizes sophisticated control mechanisms, which are widely used in a variety of applications. Since we cannot escape control features, it becomes a challenge to provide them with sound reasoning principles. Indeed there is much research activity on understanding, representing, and reasoning about elaborated non-local control structures, in particular in declarative programming languages such as functional and logic languages. The proceedings of the Workshop on Continuations 2015, held in London in April 2015, illustrate some of the afore mentioned topics and hopefully they will inspire further research work on the subject.
|
2001.00784
|
Dong Liu
|
Dong Liu, Chengjian Sun, Chenyang Yang, Lajos Hanzo
|
Optimizing Wireless Systems Using Unsupervised and
Reinforced-Unsupervised Deep Learning
|
To appear in IEEE Network Magazine
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Resource allocation and transceivers in wireless networks are usually
designed by solving optimization problems subject to specific constraints,
which can be formulated as variable or functional optimization. If the
objective and constraint functions of a variable optimization problem can be
derived, standard numerical algorithms can be applied for finding the optimal
solution, which however incur high computational cost when the dimension of the
variable is high. To reduce the on-line computational complexity, learning the
optimal solution as a function of the environment's status by deep neural
networks (DNNs) is an effective approach. DNNs can be trained under the
supervision of optimal solutions, which however, is not applicable to the
scenarios without models or for functional optimization where the optimal
solutions are hard to obtain. If the objective and constraint functions are
unavailable, reinforcement learning can be applied to find the solution of a
functional optimization problem, which is however not tailored to optimization
problems in wireless networks. In this article, we introduce unsupervised and
reinforced-unsupervised learning frameworks for solving both variable and
functional optimization problems without the supervision of the optimal
solutions. When the mathematical model of the environment is completely known
and the distribution of environment's status is known or unknown, we can invoke
unsupervised learning algorithm. When the mathematical model of the environment
is incomplete, we introduce reinforced-unsupervised learning algorithms that
learn the model by interacting with the environment. Our simulation results
confirm the applicability of these learning frameworks by taking a user
association problem as an example.
|
[
{
"created": "Fri, 3 Jan 2020 11:01:52 GMT",
"version": "v1"
}
] |
2020-01-06
|
[
[
"Liu",
"Dong",
""
],
[
"Sun",
"Chengjian",
""
],
[
"Yang",
"Chenyang",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical algorithms can be applied for finding the optimal solution, which however incur high computational cost when the dimension of the variable is high. To reduce the on-line computational complexity, learning the optimal solution as a function of the environment's status by deep neural networks (DNNs) is an effective approach. DNNs can be trained under the supervision of optimal solutions, which however, is not applicable to the scenarios without models or for functional optimization where the optimal solutions are hard to obtain. If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems without the supervision of the optimal solutions. When the mathematical model of the environment is completely known and the distribution of environment's status is known or unknown, we can invoke unsupervised learning algorithm. When the mathematical model of the environment is incomplete, we introduce reinforced-unsupervised learning algorithms that learn the model by interacting with the environment. Our simulation results confirm the applicability of these learning frameworks by taking a user association problem as an example.
|
2105.06807
|
Ruoxi Chen
|
Jinyin Chen, Ruoxi Chen, Haibin Zheng, Zhaoyan Ming, Wenrong Jiang and
Chen Cui
|
Salient Feature Extractor for Adversarial Defense on Deep Neural
Networks
| null | null | null | null |
cs.CV cs.AI cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent years have witnessed unprecedented success achieved by deep learning
models in the field of computer vision. However, their vulnerability towards
carefully crafted adversarial examples has also attracted the increasing
attention of researchers. Motivated by the observation that adversarial
examples are due to the non-robust feature learned from the original dataset by
models, we propose the concepts of salient feature(SF) and trivial feature(TF).
The former represents the class-related feature, while the latter is usually
adopted to mislead the model. We extract these two features with coupled
generative adversarial network model and put forward a novel detection and
defense method named salient feature extractor (SFE) to defend against
adversarial attacks. Concretely, detection is realized by separating and
comparing the difference between SF and TF of the input. At the same time,
correct labels are obtained by re-identifying SF to reach the purpose of
defense. Extensive experiments are carried out on MNIST, CIFAR-10, and ImageNet
datasets where SFE shows state-of-the-art results in effectiveness and
efficiency compared with baselines. Furthermore, we provide an interpretable
understanding of the defense and detection process.
|
[
{
"created": "Fri, 14 May 2021 12:56:06 GMT",
"version": "v1"
}
] |
2021-05-17
|
[
[
"Chen",
"Jinyin",
""
],
[
"Chen",
"Ruoxi",
""
],
[
"Zheng",
"Haibin",
""
],
[
"Ming",
"Zhaoyan",
""
],
[
"Jiang",
"Wenrong",
""
],
[
"Cui",
"Chen",
""
]
] |
Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully crafted adversarial examples has also attracted the increasing attention of researchers. Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF). The former represents the class-related feature, while the latter is usually adopted to mislead the model. We extract these two features with coupled generative adversarial network model and put forward a novel detection and defense method named salient feature extractor (SFE) to defend against adversarial attacks. Concretely, detection is realized by separating and comparing the difference between SF and TF of the input. At the same time, correct labels are obtained by re-identifying SF to reach the purpose of defense. Extensive experiments are carried out on MNIST, CIFAR-10, and ImageNet datasets where SFE shows state-of-the-art results in effectiveness and efficiency compared with baselines. Furthermore, we provide an interpretable understanding of the defense and detection process.
|
1407.1429
|
Mohammad Nassiry
|
Mohammad Nassiry, Muriati Mukhtar
|
Business types classification via e-commerce stage model in oil industry
in Iran
| null | null | null | null |
cs.OH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since the strategies and plans for e-commerce development are different for
different industries and since the oil industry is one of the most important
industries in Iran, the scope of this research is thus confined to that of the
oil industry in Iran. The main aim of this study is to identify and classify
the different features of e-commerce development stages and features based on
the different business types present in companies in the oil industry in Iran.
In order to achieve both of these objectives a questionnaire was developed and
administered online. The questionnaire was distributed to forty representatives
working in different companies. The collected data was classified and sorted
and the priority e-commerce features was classified and displayed as triangles
for each business type. Furthermore, the experts were asked to indicate the
features which they implemented in their companies in order to know the most
used features in each stage. The results of this study give an insight to the
practice of e-commerce for Iranian oil companies and can be used to strategize
future directions for the industry in terms of e- commerce.
|
[
{
"created": "Sat, 5 Jul 2014 19:33:10 GMT",
"version": "v1"
}
] |
2014-07-08
|
[
[
"Nassiry",
"Mohammad",
""
],
[
"Mukhtar",
"Muriati",
""
]
] |
Since the strategies and plans for e-commerce development are different for different industries and since the oil industry is one of the most important industries in Iran, the scope of this research is thus confined to that of the oil industry in Iran. The main aim of this study is to identify and classify the different features of e-commerce development stages and features based on the different business types present in companies in the oil industry in Iran. In order to achieve both of these objectives a questionnaire was developed and administered online. The questionnaire was distributed to forty representatives working in different companies. The collected data was classified and sorted and the priority e-commerce features was classified and displayed as triangles for each business type. Furthermore, the experts were asked to indicate the features which they implemented in their companies in order to know the most used features in each stage. The results of this study give an insight to the practice of e-commerce for Iranian oil companies and can be used to strategize future directions for the industry in terms of e- commerce.
|
1701.03305
|
Masahito Hayashi
|
Ryo Yaguchi and Masahito Hayashi
|
Finite-Length Bounds for Joint Source-Channel Coding with Markovian
Source and Additive Channel Noise to Achieve Large and Moderate Deviation
Bounds
|
This paper and arXiv:1701.03290 address joint source-channel coding
with markovian source. While arXiv:1701.03290 discusses the second order
analysis, this paper discusses finite-length bounds as well as large and
moderate deviation bounds. Hence, there is no overlap between these two
papers
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We derive novel upper and lower finite-length bounds of the error probability
in joint source-channel coding when the source obeys an ergodic Markov process
and the channel is a Markovian additive channel or a Markovian conditional
additive channel. These bounds are tight in the large and moderate deviation
regimes.
|
[
{
"created": "Thu, 12 Jan 2017 11:09:57 GMT",
"version": "v1"
},
{
"created": "Tue, 2 May 2017 03:39:26 GMT",
"version": "v2"
}
] |
2017-05-03
|
[
[
"Yaguchi",
"Ryo",
""
],
[
"Hayashi",
"Masahito",
""
]
] |
We derive novel upper and lower finite-length bounds of the error probability in joint source-channel coding when the source obeys an ergodic Markov process and the channel is a Markovian additive channel or a Markovian conditional additive channel. These bounds are tight in the large and moderate deviation regimes.
|
2402.11724
|
Jianling Wang
|
Jianling Wang, Haokai Lu, James Caverlee, Ed Chi and Minmin Chen
|
Large Language Models as Data Augmenters for Cold-Start Item
Recommendation
| null | null | null | null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The reasoning and generalization capabilities of LLMs can help us better
understand user preferences and item characteristics, offering exciting
prospects to enhance recommendation systems. Though effective while user-item
interactions are abundant, conventional recommendation systems struggle to
recommend cold-start items without historical interactions. To address this, we
propose utilizing LLMs as data augmenters to bridge the knowledge gap on
cold-start items during training. We employ LLMs to infer user preferences for
cold-start items based on textual description of user historical behaviors and
new item descriptions. The augmented training signals are then incorporated
into learning the downstream recommendation models through an auxiliary
pairwise loss. Through experiments on public Amazon datasets, we demonstrate
that LLMs can effectively augment the training signals for cold-start items,
leading to significant improvements in cold-start item recommendation for
various recommendation models.
|
[
{
"created": "Sun, 18 Feb 2024 22:29:04 GMT",
"version": "v1"
}
] |
2024-02-20
|
[
[
"Wang",
"Jianling",
""
],
[
"Lu",
"Haokai",
""
],
[
"Caverlee",
"James",
""
],
[
"Chi",
"Ed",
""
],
[
"Chen",
"Minmin",
""
]
] |
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To address this, we propose utilizing LLMs as data augmenters to bridge the knowledge gap on cold-start items during training. We employ LLMs to infer user preferences for cold-start items based on textual description of user historical behaviors and new item descriptions. The augmented training signals are then incorporated into learning the downstream recommendation models through an auxiliary pairwise loss. Through experiments on public Amazon datasets, we demonstrate that LLMs can effectively augment the training signals for cold-start items, leading to significant improvements in cold-start item recommendation for various recommendation models.
|
1904.09763
|
Seungjun Jung
|
Seungjun Jung, Muhammad Abul Hasan and Changick Kim
|
Water-Filling: An Efficient Algorithm for Digitized Document Shadow
Removal
|
Accepted at Asian Conference on Computer Vision (2018)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novel algorithm to rectify illumination of the
digitized documents by eliminating shading artifacts. Firstly, a topographic
surface of an input digitized document is created using luminance value of each
pixel. Then the shading artifact on the document is estimated by simulating an
immersion process. The simulation of the immersion process is modeled using a
novel diffusion equation with an iterative update rule. After estimating the
shading artifacts, the digitized document is reconstructed using the Lambertian
surface model. In order to evaluate the performance of the proposed algorithm,
we conduct rigorous experiments on a set of digitized documents which is
generated using smartphones under challenging lighting conditions. According to
the experimental results, it is found that the proposed method produces
promising illumination correction results and outperforms the results of the
state-of-the-art methods.
|
[
{
"created": "Mon, 22 Apr 2019 08:01:27 GMT",
"version": "v1"
},
{
"created": "Thu, 2 May 2019 18:44:59 GMT",
"version": "v2"
}
] |
2019-05-06
|
[
[
"Jung",
"Seungjun",
""
],
[
"Hasan",
"Muhammad Abul",
""
],
[
"Kim",
"Changick",
""
]
] |
In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts. Firstly, a topographic surface of an input digitized document is created using luminance value of each pixel. Then the shading artifact on the document is estimated by simulating an immersion process. The simulation of the immersion process is modeled using a novel diffusion equation with an iterative update rule. After estimating the shading artifacts, the digitized document is reconstructed using the Lambertian surface model. In order to evaluate the performance of the proposed algorithm, we conduct rigorous experiments on a set of digitized documents which is generated using smartphones under challenging lighting conditions. According to the experimental results, it is found that the proposed method produces promising illumination correction results and outperforms the results of the state-of-the-art methods.
|
1605.07515
|
Michael Roth
|
Michael Roth, Mirella Lapata
|
Neural Semantic Role Labeling with Dependency Path Embeddings
|
Camera-ready ACL paper
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces a novel model for semantic role labeling that makes use
of neural sequence modeling techniques. Our approach is motivated by the
observation that complex syntactic structures and related phenomena, such as
nested subordinations and nominal predicates, are not handled well by existing
models. Our model treats such instances as sub-sequences of lexicalized
dependency paths and learns suitable embedding representations. We
experimentally demonstrate that such embeddings can improve results over
previous state-of-the-art semantic role labelers, and showcase qualitative
improvements obtained by our method.
|
[
{
"created": "Tue, 24 May 2016 15:54:48 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jul 2016 09:08:51 GMT",
"version": "v2"
}
] |
2016-07-19
|
[
[
"Roth",
"Michael",
""
],
[
"Lapata",
"Mirella",
""
]
] |
This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models. Our model treats such instances as sub-sequences of lexicalized dependency paths and learns suitable embedding representations. We experimentally demonstrate that such embeddings can improve results over previous state-of-the-art semantic role labelers, and showcase qualitative improvements obtained by our method.
|
1707.00513
|
Chao Zhang
|
Chao Zhang, Vineeth Varma, Samson Lasaulce, Raphael Visoz
|
Interference Coordination via Power Domain Channel Estimation
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A novel technique is proposed which enables each transmitter to acquire
global channel state information (CSI) from the sole knowledge of individual
received signal power measurements, which makes dedicated feedback or
inter-transmitter signaling channels unnecessary. To make this possible, we
resort to a completely new technique whose key idea is to exploit the transmit
power levels as symbols to embed information and the observed interference as a
communication channel the transmitters can use to exchange coordination
information. Although the used technique allows any kind of {low-rate}
information to be exchanged among the transmitters, the focus here is to
exchange local CSI. The proposed procedure also comprises a phase which allows
local CSI to be estimated. Once an estimate of global CSI is acquired by the
transmitters, it can be used to optimize any utility function which depends on
it. While algorithms which use the same type of measurements such as the
iterative water-filling algorithm (IWFA) implement the sequential best-response
dynamics (BRD) applied to individual utilities, here, thanks to the
availability of global CSI, the BRD can be applied to the sum-utility.
Extensive numerical results show that significant gains can be obtained and,
this, by requiring no additional online signaling.
|
[
{
"created": "Wed, 14 Jun 2017 08:04:12 GMT",
"version": "v1"
}
] |
2017-07-04
|
[
[
"Zhang",
"Chao",
""
],
[
"Varma",
"Vineeth",
""
],
[
"Lasaulce",
"Samson",
""
],
[
"Visoz",
"Raphael",
""
]
] |
A novel technique is proposed which enables each transmitter to acquire global channel state information (CSI) from the sole knowledge of individual received signal power measurements, which makes dedicated feedback or inter-transmitter signaling channels unnecessary. To make this possible, we resort to a completely new technique whose key idea is to exploit the transmit power levels as symbols to embed information and the observed interference as a communication channel the transmitters can use to exchange coordination information. Although the used technique allows any kind of {low-rate} information to be exchanged among the transmitters, the focus here is to exchange local CSI. The proposed procedure also comprises a phase which allows local CSI to be estimated. Once an estimate of global CSI is acquired by the transmitters, it can be used to optimize any utility function which depends on it. While algorithms which use the same type of measurements such as the iterative water-filling algorithm (IWFA) implement the sequential best-response dynamics (BRD) applied to individual utilities, here, thanks to the availability of global CSI, the BRD can be applied to the sum-utility. Extensive numerical results show that significant gains can be obtained and, this, by requiring no additional online signaling.
|
2306.01310
|
Jaeseung Heo
|
Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn, Dongwoo Kim
|
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Data augmentation plays a critical role in improving model performance across
various domains, but it becomes challenging with graph data due to their
complex and irregular structure. To address this issue, we propose EPIC (Edit
Path Interpolation via learnable Cost), a novel interpolation-based method for
augmenting graph datasets. To interpolate between two graphs lying in an
irregular domain, EPIC leverages the concept of graph edit distance,
constructing an edit path that represents the transformation process between
two graphs via edit operations. Moreover, our method introduces a
context-sensitive cost model that accounts for the importance of specific edit
operations formulated through a learning framework. This allows for a more
nuanced transformation process, where the edit distance is not merely
count-based but reflects meaningful graph attributes. With randomly sampled
graphs from the edit path, we enrich the training set to enhance the
generalization capability of classification models. Experimental evaluations
across several benchmark datasets demonstrate that our approach outperforms
existing augmentation techniques in many tasks.
|
[
{
"created": "Fri, 2 Jun 2023 07:19:07 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Jun 2024 05:54:38 GMT",
"version": "v2"
}
] |
2024-06-05
|
[
[
"Heo",
"Jaeseung",
""
],
[
"Lee",
"Seungbeom",
""
],
[
"Ahn",
"Sungsoo",
""
],
[
"Kim",
"Dongwoo",
""
]
] |
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process between two graphs via edit operations. Moreover, our method introduces a context-sensitive cost model that accounts for the importance of specific edit operations formulated through a learning framework. This allows for a more nuanced transformation process, where the edit distance is not merely count-based but reflects meaningful graph attributes. With randomly sampled graphs from the edit path, we enrich the training set to enhance the generalization capability of classification models. Experimental evaluations across several benchmark datasets demonstrate that our approach outperforms existing augmentation techniques in many tasks.
|
2405.06001
|
Yushi Huang
|
Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chentao Lv, Yunchen
Zhang, Xianglong Liu, Dacheng Tao
|
LLMC: Benchmarking Large Language Model Quantization with a Versatile
Compression Toolkit
| null | null | null | null |
cs.LG cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Recent advancements in large language models (LLMs) are propelling us toward
artificial general intelligence with their remarkable emergent abilities and
reasoning capabilities. However, the substantial computational and memory
requirements limit the widespread adoption. Quantization, a key compression
technique, can effectively mitigate these demands by compressing and
accelerating LLMs, albeit with potential risks to accuracy. Numerous studies
have aimed to minimize the accuracy loss associated with quantization. However,
their quantization configurations vary from each other and cannot be fairly
compared. In this paper, we present LLMC, a plug-and-play compression toolkit,
to fairly and systematically explore the impact of quantization. LLMC
integrates dozens of algorithms, models, and hardwares, offering high
extensibility from integer to floating-point quantization, from LLM to
vision-language (VLM) model, from fixed-bit to mixed precision, and from
quantization to sparsification. Powered by this versatile toolkit, our
benchmark covers three key aspects: calibration data, algorithms (three
strategies), and data formats, providing novel insights and detailed analyses
for further research and practical guidance for users. Our toolkit is available
at \href{LLMC}{https://github.com/ModelTC/llmc}.
|
[
{
"created": "Thu, 9 May 2024 11:49:05 GMT",
"version": "v1"
},
{
"created": "Sat, 20 Jul 2024 07:29:51 GMT",
"version": "v2"
}
] |
2024-07-23
|
[
[
"Gong",
"Ruihao",
""
],
[
"Yong",
"Yang",
""
],
[
"Gu",
"Shiqiao",
""
],
[
"Huang",
"Yushi",
""
],
[
"Lv",
"Chentao",
""
],
[
"Zhang",
"Yunchen",
""
],
[
"Liu",
"Xianglong",
""
],
[
"Tao",
"Dacheng",
""
]
] |
Recent advancements in large language models (LLMs) are propelling us toward artificial general intelligence with their remarkable emergent abilities and reasoning capabilities. However, the substantial computational and memory requirements limit the widespread adoption. Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating LLMs, albeit with potential risks to accuracy. Numerous studies have aimed to minimize the accuracy loss associated with quantization. However, their quantization configurations vary from each other and cannot be fairly compared. In this paper, we present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. LLMC integrates dozens of algorithms, models, and hardwares, offering high extensibility from integer to floating-point quantization, from LLM to vision-language (VLM) model, from fixed-bit to mixed precision, and from quantization to sparsification. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats, providing novel insights and detailed analyses for further research and practical guidance for users. Our toolkit is available at \href{LLMC}{https://github.com/ModelTC/llmc}.
|
2110.07888
|
Xingcheng Fu
|
Xingcheng Fu, Jianxin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang
Wang, Jiajun Tan, Hao Peng and Philip S. Yu
|
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
| null | null |
10.1109/ICDM51629.2021.00021
| null |
cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph Neural Networks (GNNs) have been widely studied in various graph data
mining tasks. Most existingGNNs embed graph data into Euclidean space and thus
are less effective to capture the ubiquitous hierarchical structures in
real-world networks. Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to
hyperbolic space and thus are more effective to capture the hierarchical
structures of graphs in node representation learning. In hyperbolic geometry,
the graph hierarchical structure can be reflected by the curvatures of the
hyperbolic space, and different curvatures can model different hierarchical
structures of a graph. However, most existing HGNNs manually set the curvature
to a fixed value for simplicity, which achieves a suboptimal performance of
graph learning due to the complex and diverse hierarchical structures of the
graphs. To resolve this problem, we propose an Adaptive Curvature Exploration
Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal
curvature according to the input graph and downstream tasks. Specifically,
ACE-HGNN exploits a multi-agent reinforcement learning framework and contains
two agents, ACE-Agent andHGNN-Agent for learning the curvature and node
representations, respectively. The two agents are updated by a NashQ-leaning
algorithm collaboratively, seeking the optimal hyperbolic space indexed by the
curvature. Extensive experiments on multiple real-world graph datasets
demonstrate a significant and consistent performance improvement in model
quality with competitive performance and good generalization ability.
|
[
{
"created": "Fri, 15 Oct 2021 07:18:57 GMT",
"version": "v1"
}
] |
2022-03-04
|
[
[
"Fu",
"Xingcheng",
""
],
[
"Li",
"Jianxin",
""
],
[
"Wu",
"Jia",
""
],
[
"Sun",
"Qingyun",
""
],
[
"Ji",
"Cheng",
""
],
[
"Wang",
"Senzhang",
""
],
[
"Tan",
"Jiajun",
""
],
[
"Peng",
"Hao",
""
],
[
"Yu",
"Philip S.",
""
]
] |
Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existingGNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent andHGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a NashQ-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
|
2101.06829
|
Tianxing He
|
Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl
|
Joint Energy-based Model Training for Better Calibrated Natural Language
Understanding Models
| null |
EACL 2021
| null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we explore joint energy-based model (EBM) training during the
finetuning of pretrained text encoders (e.g., Roberta) for natural language
understanding (NLU) tasks. Our experiments show that EBM training can help the
model reach a better calibration that is competitive to strong baselines, with
little or no loss in accuracy. We discuss three variants of energy functions
(namely scalar, hidden, and sharp-hidden) that can be defined on top of a text
encoder, and compare them in experiments. Due to the discreteness of text data,
we adopt noise contrastive estimation (NCE) to train the energy-based model. To
make NCE training more effective, we train an auto-regressive noise model with
the masked language model (MLM) objective.
|
[
{
"created": "Mon, 18 Jan 2021 01:41:31 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Feb 2021 18:36:31 GMT",
"version": "v2"
}
] |
2021-02-22
|
[
[
"He",
"Tianxing",
""
],
[
"McCann",
"Bryan",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Hosseini-Asl",
"Ehsan",
""
]
] |
In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. Our experiments show that EBM training can help the model reach a better calibration that is competitive to strong baselines, with little or no loss in accuracy. We discuss three variants of energy functions (namely scalar, hidden, and sharp-hidden) that can be defined on top of a text encoder, and compare them in experiments. Due to the discreteness of text data, we adopt noise contrastive estimation (NCE) to train the energy-based model. To make NCE training more effective, we train an auto-regressive noise model with the masked language model (MLM) objective.
|
2201.01182
|
Kshitija Taywade
|
Kshitija Taywade, Brent Harrison, Adib Bagh
|
Modelling Cournot Games as Multi-agent Multi-armed Bandits
|
12 pages. arXiv admin note: text overlap with arXiv:2201.00486
| null | null | null |
cs.GT cs.AI cs.LG cs.MA econ.EM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the use of a multi-agent multi-armed bandit (MA-MAB) setting
for modeling repeated Cournot oligopoly games, where the firms acting as agents
choose from the set of arms representing production quantity (a discrete
value). Agents interact with separate and independent bandit problems. In this
formulation, each agent makes sequential choices among arms to maximize its own
reward. Agents do not have any information about the environment; they can only
see their own rewards after taking an action. However, the market demand is a
stationary function of total industry output, and random entry or exit from the
market is not allowed. Given these assumptions, we found that an
$\epsilon$-greedy approach offers a more viable learning mechanism than other
traditional MAB approaches, as it does not require any additional knowledge of
the system to operate. We also propose two novel approaches that take advantage
of the ordered action space: $\epsilon$-greedy+HL and $\epsilon$-greedy+EL.
These new approaches help firms to focus on more profitable actions by
eliminating less profitable choices and hence are designed to optimize the
exploration. We use computer simulations to study the emergence of various
equilibria in the outcomes and do the empirical analysis of joint cumulative
regrets.
|
[
{
"created": "Sat, 1 Jan 2022 22:02:47 GMT",
"version": "v1"
}
] |
2022-01-05
|
[
[
"Taywade",
"Kshitija",
""
],
[
"Harrison",
"Brent",
""
],
[
"Bagh",
"Adib",
""
]
] |
We investigate the use of a multi-agent multi-armed bandit (MA-MAB) setting for modeling repeated Cournot oligopoly games, where the firms acting as agents choose from the set of arms representing production quantity (a discrete value). Agents interact with separate and independent bandit problems. In this formulation, each agent makes sequential choices among arms to maximize its own reward. Agents do not have any information about the environment; they can only see their own rewards after taking an action. However, the market demand is a stationary function of total industry output, and random entry or exit from the market is not allowed. Given these assumptions, we found that an $\epsilon$-greedy approach offers a more viable learning mechanism than other traditional MAB approaches, as it does not require any additional knowledge of the system to operate. We also propose two novel approaches that take advantage of the ordered action space: $\epsilon$-greedy+HL and $\epsilon$-greedy+EL. These new approaches help firms to focus on more profitable actions by eliminating less profitable choices and hence are designed to optimize the exploration. We use computer simulations to study the emergence of various equilibria in the outcomes and do the empirical analysis of joint cumulative regrets.
|
2407.13071
|
Vatsal Vinay Parikh
|
Vatsal Vinay Parikh
|
Analysing the Public Discourse around OpenAI's Text-To-Video Model
'Sora' using Topic Modeling
| null | null | null | null |
cs.CY cs.CL cs.IR cs.LG cs.SI
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The recent introduction of OpenAI's text-to-video model Sora has sparked
widespread public discourse across online communities. This study aims to
uncover the dominant themes and narratives surrounding Sora by conducting topic
modeling analysis on a corpus of 1,827 Reddit comments from five relevant
subreddits (r/OpenAI, r/technology, r/singularity, r/vfx, and r/ChatGPT). The
comments were collected over a two-month period following Sora's announcement
in February 2024. After preprocessing the data, Latent Dirichlet Allocation
(LDA) was employed to extract four key topics: 1) AI Impact and Trends in Sora
Discussions, 2) Public Opinion and Concerns about Sora, 3) Artistic Expression
and Video Creation with Sora, and 4) Sora's Applications in Media and
Entertainment. Visualizations including word clouds, bar charts, and t-SNE
clustering provided insights into the importance of topic keywords and the
distribution of comments across topics. The results highlight prominent
narratives around Sora's potential impact on industries and employment, public
sentiment and ethical concerns, creative applications, and use cases in the
media and entertainment sectors. While limited to Reddit data within a specific
timeframe, this study offers a framework for understanding public perceptions
of emerging generative AI technologies through online discourse analysis.
|
[
{
"created": "Thu, 30 May 2024 01:55:30 GMT",
"version": "v1"
}
] |
2024-07-19
|
[
[
"Parikh",
"Vatsal Vinay",
""
]
] |
The recent introduction of OpenAI's text-to-video model Sora has sparked widespread public discourse across online communities. This study aims to uncover the dominant themes and narratives surrounding Sora by conducting topic modeling analysis on a corpus of 1,827 Reddit comments from five relevant subreddits (r/OpenAI, r/technology, r/singularity, r/vfx, and r/ChatGPT). The comments were collected over a two-month period following Sora's announcement in February 2024. After preprocessing the data, Latent Dirichlet Allocation (LDA) was employed to extract four key topics: 1) AI Impact and Trends in Sora Discussions, 2) Public Opinion and Concerns about Sora, 3) Artistic Expression and Video Creation with Sora, and 4) Sora's Applications in Media and Entertainment. Visualizations including word clouds, bar charts, and t-SNE clustering provided insights into the importance of topic keywords and the distribution of comments across topics. The results highlight prominent narratives around Sora's potential impact on industries and employment, public sentiment and ethical concerns, creative applications, and use cases in the media and entertainment sectors. While limited to Reddit data within a specific timeframe, this study offers a framework for understanding public perceptions of emerging generative AI technologies through online discourse analysis.
|
1904.13279
|
Tim Pfeifer
|
Tim Pfeifer and Peter Protzel
|
Incrementally Learned Mixture Models for GNSS Localization
|
8 pages, 5 figures, published in proceedings of IEEE Intelligent
Vehicles Symposium (IV) 2019
| null |
10.1109/IVS.2019.8813847
| null |
cs.RO eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
GNSS localization is an important part of today's autonomous systems,
although it suffers from non-Gaussian errors caused by non-line-of-sight
effects. Recent methods are able to mitigate these effects by including the
corresponding distributions in the sensor fusion algorithm. However, these
approaches require prior knowledge about the sensor's distribution, which is
often not available. We introduce a novel sensor fusion algorithm based on
variational Bayesian inference, that is able to approximate the true
distribution with a Gaussian mixture model and to learn its parametrization
online. The proposed Incremental Variational Mixture algorithm automatically
adapts the number of mixture components to the complexity of the measurement's
error distribution. We compare the proposed algorithm against current
state-of-the-art approaches using a collection of open access real world
datasets and demonstrate its superior localization accuracy.
|
[
{
"created": "Tue, 30 Apr 2019 14:39:00 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Mar 2020 11:27:11 GMT",
"version": "v2"
}
] |
2020-03-20
|
[
[
"Pfeifer",
"Tim",
""
],
[
"Protzel",
"Peter",
""
]
] |
GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the sensor fusion algorithm. However, these approaches require prior knowledge about the sensor's distribution, which is often not available. We introduce a novel sensor fusion algorithm based on variational Bayesian inference, that is able to approximate the true distribution with a Gaussian mixture model and to learn its parametrization online. The proposed Incremental Variational Mixture algorithm automatically adapts the number of mixture components to the complexity of the measurement's error distribution. We compare the proposed algorithm against current state-of-the-art approaches using a collection of open access real world datasets and demonstrate its superior localization accuracy.
|
2011.01584
|
Li-Yang Tan
|
Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
|
Estimating decision tree learnability with polylogarithmic sample
complexity
|
25 pages, to appear in NeurIPS 2020
| null | null | null |
cs.LG cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that top-down decision tree learning heuristics are amenable to
highly efficient learnability estimation: for monotone target functions, the
error of the decision tree hypothesis constructed by these heuristics can be
estimated with polylogarithmically many labeled examples, exponentially smaller
than the number necessary to run these heuristics, and indeed, exponentially
smaller than information-theoretic minimum required to learn a good decision
tree. This adds to a small but growing list of fundamental learning algorithms
that have been shown to be amenable to learnability estimation.
En route to this result, we design and analyze sample-efficient minibatch
versions of top-down decision tree learning heuristics and show that they
achieve the same provable guarantees as the full-batch versions. We further
give "active local" versions of these heuristics: given a test point $x^\star$,
we show how the label $T(x^\star)$ of the decision tree hypothesis $T$ can be
computed with polylogarithmically many labeled examples, exponentially smaller
than the number necessary to learn $T$.
|
[
{
"created": "Tue, 3 Nov 2020 09:26:27 GMT",
"version": "v1"
}
] |
2020-11-04
|
[
[
"Blanc",
"Guy",
""
],
[
"Gupta",
"Neha",
""
],
[
"Lange",
"Jane",
""
],
[
"Tan",
"Li-Yang",
""
]
] |
We show that top-down decision tree learning heuristics are amenable to highly efficient learnability estimation: for monotone target functions, the error of the decision tree hypothesis constructed by these heuristics can be estimated with polylogarithmically many labeled examples, exponentially smaller than the number necessary to run these heuristics, and indeed, exponentially smaller than information-theoretic minimum required to learn a good decision tree. This adds to a small but growing list of fundamental learning algorithms that have been shown to be amenable to learnability estimation. En route to this result, we design and analyze sample-efficient minibatch versions of top-down decision tree learning heuristics and show that they achieve the same provable guarantees as the full-batch versions. We further give "active local" versions of these heuristics: given a test point $x^\star$, we show how the label $T(x^\star)$ of the decision tree hypothesis $T$ can be computed with polylogarithmically many labeled examples, exponentially smaller than the number necessary to learn $T$.
|
2009.06560
|
Lily Xu
|
Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind
Tambe
|
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
|
Published at AAAI 2021. 9 pages (paper and references), 3 page
appendix. 6 figures and 1 table
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Conservation efforts in green security domains to protect wildlife and
forests are constrained by the limited availability of defenders (i.e.,
patrollers), who must patrol vast areas to protect from attackers (e.g.,
poachers or illegal loggers). Defenders must choose how much time to spend in
each region of the protected area, balancing exploration of infrequently
visited regions and exploitation of known hotspots. We formulate the problem as
a stochastic multi-armed bandit, where each action represents a patrol
strategy, enabling us to guarantee the rate of convergence of the patrolling
policy. However, a naive bandit approach would compromise short-term
performance for long-term optimality, resulting in animals poached and forests
destroyed. To speed up performance, we leverage smoothness in the reward
function and decomposability of actions. We show a synergy between
Lipschitz-continuity and decomposition as each aids the convergence of the
other. In doing so, we bridge the gap between combinatorial and Lipschitz
bandits, presenting a no-regret approach that tightens existing guarantees
while optimizing for short-term performance. We demonstrate that our algorithm,
LIZARD, improves performance on real-world poaching data from Cambodia.
|
[
{
"created": "Mon, 14 Sep 2020 16:40:44 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Dec 2020 05:35:48 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Apr 2024 13:51:17 GMT",
"version": "v3"
}
] |
2024-04-29
|
[
[
"Xu",
"Lily",
""
],
[
"Bondi",
"Elizabeth",
""
],
[
"Fang",
"Fei",
""
],
[
"Perrault",
"Andrew",
""
],
[
"Wang",
"Kai",
""
],
[
"Tambe",
"Milind",
""
]
] |
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.
|
2212.00222
|
Madelyn Shapiro
|
Emilie Purvine, Davis Brown, Brett Jefferson, Cliff Joslyn, Brenda
Praggastis, Archit Rathore, Madelyn Shapiro, Bei Wang, Youjia Zhou
|
Experimental Observations of the Topology of Convolutional Neural
Network Activations
|
Accepted at AAAI 2023. This version includes supplementary material
| null | null | null |
cs.LG cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
Topological data analysis (TDA) is a branch of computational mathematics,
bridging algebraic topology and data science, that provides compact,
noise-robust representations of complex structures. Deep neural networks (DNNs)
learn millions of parameters associated with a series of transformations
defined by the model architecture, resulting in high-dimensional,
difficult-to-interpret internal representations of input data. As DNNs become
more ubiquitous across multiple sectors of our society, there is increasing
recognition that mathematical methods are needed to aid analysts, researchers,
and practitioners in understanding and interpreting how these models' internal
representations relate to the final classification. In this paper, we apply
cutting edge techniques from TDA with the goal of gaining insight into the
interpretability of convolutional neural networks used for image
classification. We use two common TDA approaches to explore several methods for
modeling hidden-layer activations as high-dimensional point clouds, and provide
experimental evidence that these point clouds capture valuable structural
information about the model's process. First, we demonstrate that a distance
metric based on persistent homology can be used to quantify meaningful
differences between layers, and we discuss these distances in the broader
context of existing representational similarity metrics for neural network
interpretability. Second, we show that a mapper graph can provide semantic
insight into how these models organize hierarchical class knowledge at each
layer. These observations demonstrate that TDA is a useful tool to help deep
learning practitioners unlock the hidden structures of their models.
|
[
{
"created": "Thu, 1 Dec 2022 02:05:44 GMT",
"version": "v1"
}
] |
2022-12-02
|
[
[
"Purvine",
"Emilie",
""
],
[
"Brown",
"Davis",
""
],
[
"Jefferson",
"Brett",
""
],
[
"Joslyn",
"Cliff",
""
],
[
"Praggastis",
"Brenda",
""
],
[
"Rathore",
"Archit",
""
],
[
"Shapiro",
"Madelyn",
""
],
[
"Wang",
"Bei",
""
],
[
"Zhou",
"Youjia",
""
]
] |
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture, resulting in high-dimensional, difficult-to-interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper, we apply cutting edge techniques from TDA with the goal of gaining insight into the interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden-layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers, and we discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight into how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.
|
1404.3660
|
Ren\'e van Bevern
|
Ren\'e van Bevern, Sepp Hartung, Andr\'e Nichterlein, Manuel Sorge
|
Constant-factor approximations for Capacitated Arc Routing without
triangle inequality
| null |
Operations Research Letters 42(4):290--292, 2014
|
10.1016/j.orl.2014.05.002
| null |
cs.DS cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given an undirected graph with edge costs and edge demands, the Capacitated
Arc Routing problem (CARP) asks for minimum-cost routes for equal-capacity
vehicles so as to satisfy all demands. Constant-factor polynomial-time
approximation algorithms were proposed for CARP with triangle inequality, while
CARP was claimed to be NP-hard to approximate within any constant factor in
general. Correcting this claim, we show that any factor {\alpha} approximation
for CARP with triangle inequality yields a factor {\alpha} approximation for
the general CARP.
|
[
{
"created": "Mon, 14 Apr 2014 17:28:19 GMT",
"version": "v1"
}
] |
2014-07-15
|
[
[
"van Bevern",
"René",
""
],
[
"Hartung",
"Sepp",
""
],
[
"Nichterlein",
"André",
""
],
[
"Sorge",
"Manuel",
""
]
] |
Given an undirected graph with edge costs and edge demands, the Capacitated Arc Routing problem (CARP) asks for minimum-cost routes for equal-capacity vehicles so as to satisfy all demands. Constant-factor polynomial-time approximation algorithms were proposed for CARP with triangle inequality, while CARP was claimed to be NP-hard to approximate within any constant factor in general. Correcting this claim, we show that any factor {\alpha} approximation for CARP with triangle inequality yields a factor {\alpha} approximation for the general CARP.
|
2105.01747
|
Pradeep Kr. Banerjee
|
Pradeep Kr. Banerjee, Guido Mont\'ufar
|
Information Complexity and Generalization Bounds
|
To appear in 2021 IEEE International Symposium on Information Theory
(ISIT); 23 pages
| null |
10.1109/ISIT45174.2021.9517960
| null |
cs.LG cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We present a unifying picture of PAC-Bayesian and mutual information-based
upper bounds on the generalization error of randomized learning algorithms. As
we show, Tong Zhang's information exponential inequality (IEI) gives a general
recipe for constructing bounds of both flavors. We show that several important
results in the literature can be obtained as simple corollaries of the IEI
under different assumptions on the loss function. Moreover, we obtain new
bounds for data-dependent priors and unbounded loss functions. Optimizing the
bounds gives rise to variants of the Gibbs algorithm, for which we discuss two
practical examples for learning with neural networks, namely, Entropy- and
PAC-Bayes- SGD. Further, we use an Occam's factor argument to show a
PAC-Bayesian bound that incorporates second-order curvature information of the
training loss.
|
[
{
"created": "Tue, 4 May 2021 20:37:57 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Oct 2021 02:02:45 GMT",
"version": "v2"
}
] |
2021-10-26
|
[
[
"Banerjee",
"Pradeep Kr.",
""
],
[
"Montúfar",
"Guido",
""
]
] |
We present a unifying picture of PAC-Bayesian and mutual information-based upper bounds on the generalization error of randomized learning algorithms. As we show, Tong Zhang's information exponential inequality (IEI) gives a general recipe for constructing bounds of both flavors. We show that several important results in the literature can be obtained as simple corollaries of the IEI under different assumptions on the loss function. Moreover, we obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing the bounds gives rise to variants of the Gibbs algorithm, for which we discuss two practical examples for learning with neural networks, namely, Entropy- and PAC-Bayes- SGD. Further, we use an Occam's factor argument to show a PAC-Bayesian bound that incorporates second-order curvature information of the training loss.
|
2310.15928
|
Claire Chen
|
Carlota Par\'es Morlans, Claire Chen, Yijia Weng, Michelle Yi, Yuying
Huang, Nick Heppert, Linqi Zhou, Leonidas Guibas, Jeannette Bohg
|
AO-Grasp: Articulated Object Grasp Generation
|
Project website: https://stanford-iprl-lab.github.io/ao-grasp
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps
that enable robots to interact with articulated objects, such as opening and
closing cabinets and appliances. AO-Grasp consists of two main contributions:
the AO-Grasp Model and the AO-Grasp Dataset. Given a segmented partial point
cloud of a single articulated object, the AO-Grasp Model predicts the best
grasp points on the object with an Actionable Grasp Point Predictor. Then, it
finds corresponding grasp orientations for each of these points, resulting in
stable and actionable grasp proposals. We train the AO-Grasp Model on our new
AO-Grasp Dataset, which contains 78K actionable parallel-jaw grasps on
synthetic articulated objects. In simulation, AO-Grasp achieves a 45.0 % grasp
success rate, whereas the highest performing baseline achieves a 35.0% success
rate. Additionally, we evaluate AO-Grasp on 120 real-world scenes of objects
with varied geometries, articulation axes, and joint states, where AO-Grasp
produces successful grasps on 67.5% of scenes, while the baseline only produces
successful grasps on 33.3% of scenes. To the best of our knowledge, AO-Grasp is
the first method for generating 6 DoF grasps on articulated objects directly
from partial point clouds without requiring part detection or hand-designed
grasp heuristics. Project website: https://stanford-iprl-lab.github.io/ao-grasp
|
[
{
"created": "Tue, 24 Oct 2023 15:26:57 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Mar 2024 17:36:33 GMT",
"version": "v2"
}
] |
2024-03-19
|
[
[
"Morlans",
"Carlota Parés",
""
],
[
"Chen",
"Claire",
""
],
[
"Weng",
"Yijia",
""
],
[
"Yi",
"Michelle",
""
],
[
"Huang",
"Yuying",
""
],
[
"Heppert",
"Nick",
""
],
[
"Zhou",
"Linqi",
""
],
[
"Guibas",
"Leonidas",
""
],
[
"Bohg",
"Jeannette",
""
]
] |
We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps that enable robots to interact with articulated objects, such as opening and closing cabinets and appliances. AO-Grasp consists of two main contributions: the AO-Grasp Model and the AO-Grasp Dataset. Given a segmented partial point cloud of a single articulated object, the AO-Grasp Model predicts the best grasp points on the object with an Actionable Grasp Point Predictor. Then, it finds corresponding grasp orientations for each of these points, resulting in stable and actionable grasp proposals. We train the AO-Grasp Model on our new AO-Grasp Dataset, which contains 78K actionable parallel-jaw grasps on synthetic articulated objects. In simulation, AO-Grasp achieves a 45.0 % grasp success rate, whereas the highest performing baseline achieves a 35.0% success rate. Additionally, we evaluate AO-Grasp on 120 real-world scenes of objects with varied geometries, articulation axes, and joint states, where AO-Grasp produces successful grasps on 67.5% of scenes, while the baseline only produces successful grasps on 33.3% of scenes. To the best of our knowledge, AO-Grasp is the first method for generating 6 DoF grasps on articulated objects directly from partial point clouds without requiring part detection or hand-designed grasp heuristics. Project website: https://stanford-iprl-lab.github.io/ao-grasp
|
2406.17070
|
Dimitris Chytas
|
Dimitris Chytas, Nithin Raveendran, Bane Vasi\'c
|
Collective Bit Flipping-Based Decoding of Quantum LDPC Codes
|
13 pages, 12 figures
| null | null | null |
cs.IT math.IT quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum low-density parity-check (QLDPC) codes have been proven to achieve
higher minimum distances at higher code rates than surface codes. However, this
family of codes imposes stringent latency requirements and poor performance
under iterative decoding, especially when the variable degree is low. In this
work, we improve both the error correction performance and decoding latency of
variable degree-3 (dv-3) QLDPC codes under iterative decoding. Firstly, we
perform a detailed analysis of the structure of a well-known family of QLDPC
codes, i.e., hypergraph product-based codes. Then, we propose a decoding
approach that stems from the knowledge of harmful configurations apparent in
these codes. Our decoding scheme is based on applying a modified version of bit
flipping (BF) decoding, namely two-bit bit flipping (TBF) decoding, which adds
more degrees of freedom to BF decoding. The granularity offered by TBF decoding
helps us design sets of decoders that operate in parallel and can collectively
decode error patterns appearing in harmful configurations of the code, thus
addressing both the latency and performance requirements. Finally, simulation
results demonstrate that the proposed decoding scheme surpasses other iterative
decoding approaches for various dv-3 QLDPC codes.
|
[
{
"created": "Mon, 24 Jun 2024 18:51:48 GMT",
"version": "v1"
}
] |
2024-06-26
|
[
[
"Chytas",
"Dimitris",
""
],
[
"Raveendran",
"Nithin",
""
],
[
"Vasić",
"Bane",
""
]
] |
Quantum low-density parity-check (QLDPC) codes have been proven to achieve higher minimum distances at higher code rates than surface codes. However, this family of codes imposes stringent latency requirements and poor performance under iterative decoding, especially when the variable degree is low. In this work, we improve both the error correction performance and decoding latency of variable degree-3 (dv-3) QLDPC codes under iterative decoding. Firstly, we perform a detailed analysis of the structure of a well-known family of QLDPC codes, i.e., hypergraph product-based codes. Then, we propose a decoding approach that stems from the knowledge of harmful configurations apparent in these codes. Our decoding scheme is based on applying a modified version of bit flipping (BF) decoding, namely two-bit bit flipping (TBF) decoding, which adds more degrees of freedom to BF decoding. The granularity offered by TBF decoding helps us design sets of decoders that operate in parallel and can collectively decode error patterns appearing in harmful configurations of the code, thus addressing both the latency and performance requirements. Finally, simulation results demonstrate that the proposed decoding scheme surpasses other iterative decoding approaches for various dv-3 QLDPC codes.
|
2104.10845
|
Li Zhang
|
Yuxuan Chen, Li Zhang, Shijian Li, Gang Pan
|
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Optimization of deep learning algorithms to approach Nash Equilibrium remains
a significant problem in imperfect information games, e.g. StarCraft and poker.
Neural Fictitious Self-Play (NFSP) has provided an effective way to learn
approximate Nash Equilibrium without prior domain knowledge in imperfect
information games. However, optimality gap was left as an optimization problem
of NFSP and by solving the problem, the performance of NFSP could be improved.
In this study, focusing on the optimality gap of NFSP, we have proposed a new
method replacing NFSP's best response computation with regret matching method.
The new algorithm can make the optimality gap converge to zero as it iterates,
thus converge faster than original NFSP. We have conduct experiments on three
typical environments of perfect-information games and imperfect information
games in OpenSpiel and all showed that our new algorithm performances better
than original NFSP.
|
[
{
"created": "Thu, 22 Apr 2021 03:24:23 GMT",
"version": "v1"
}
] |
2021-04-23
|
[
[
"Chen",
"Yuxuan",
""
],
[
"Zhang",
"Li",
""
],
[
"Li",
"Shijian",
""
],
[
"Pan",
"Gang",
""
]
] |
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e.g. StarCraft and poker. Neural Fictitious Self-Play (NFSP) has provided an effective way to learn approximate Nash Equilibrium without prior domain knowledge in imperfect information games. However, optimality gap was left as an optimization problem of NFSP and by solving the problem, the performance of NFSP could be improved. In this study, focusing on the optimality gap of NFSP, we have proposed a new method replacing NFSP's best response computation with regret matching method. The new algorithm can make the optimality gap converge to zero as it iterates, thus converge faster than original NFSP. We have conduct experiments on three typical environments of perfect-information games and imperfect information games in OpenSpiel and all showed that our new algorithm performances better than original NFSP.
|
2405.08709
|
Amirreza Zamani
|
Amirreza Zamani, Sajad Daei, Tobias J. Oechtering, Mikael Skoglund
|
Multi-Task Private Semantic Communication
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study a multi-task private semantic communication problem, in which an
encoder has access to an information source arbitrarily correlated with some
latent private data. A user has $L$ tasks with priorities. The encoder designs
a message to be revealed which is called the semantic of the information
source. Due to the privacy constraints the semantic can not be disclosed
directly and the encoder adds noise to produce disclosed data. The goal is to
design the disclosed data that maximizes the weighted sum of the utilities
achieved by the user while satisfying a privacy constraint on the private data.
In this work, we first consider a single-task scenario and design the added
noise utilizing various methods including the extended versions of the
Functional Representation Lemma, Strong Functional Representation Lemma, and
separation technique. We then study the multi-task scenario and derive a simple
design of the source semantics. We show that in the multi-task scenario the
main problem can be divided into multiple parallel single-task problems.
|
[
{
"created": "Tue, 14 May 2024 15:50:49 GMT",
"version": "v1"
}
] |
2024-05-15
|
[
[
"Zamani",
"Amirreza",
""
],
[
"Daei",
"Sajad",
""
],
[
"Oechtering",
"Tobias J.",
""
],
[
"Skoglund",
"Mikael",
""
]
] |
We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has $L$ tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to the privacy constraints the semantic can not be disclosed directly and the encoder adds noise to produce disclosed data. The goal is to design the disclosed data that maximizes the weighted sum of the utilities achieved by the user while satisfying a privacy constraint on the private data. In this work, we first consider a single-task scenario and design the added noise utilizing various methods including the extended versions of the Functional Representation Lemma, Strong Functional Representation Lemma, and separation technique. We then study the multi-task scenario and derive a simple design of the source semantics. We show that in the multi-task scenario the main problem can be divided into multiple parallel single-task problems.
|
2402.08897
|
Adam Seewald
|
Adam Seewald, Marvin Chanc\'an, Connor M. McCann, Seonghoon Noh, Omeed
Fallahi, Hector Castillo, Ian Abraham, Aaron M. Dollar
|
RB5 Low-Cost Explorer: Implementing Autonomous Long-Term Exploration on
Low-Cost Robotic Hardware
|
7 pages, 5 figures, ICRA'24
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This systems paper presents the implementation and design of RB5, a wheeled
robot for autonomous long-term exploration with fewer and cheaper sensors.
Requiring just an RGB-D camera and low-power computing hardware, the system
consists of an experimental platform with rocker-bogie suspension. It operates
in unknown and GPS-denied environments and on indoor and outdoor terrains. The
exploration consists of a methodology that extends frontier- and sampling-based
exploration with a path-following vector field and a state-of-the-art SLAM
algorithm. The methodology allows the robot to explore its surroundings at
lower update frequencies, enabling the use of lower-performing and lower-cost
hardware while still retaining good autonomous performance. The approach
further consists of a methodology to interact with a remotely located human
operator based on an inexpensive long-range and low-power communication
technology from the internet-of-things domain (i.e., LoRa) and a customized
communication protocol. The results and the feasibility analysis show the
possible applications and limitations of the approach.
|
[
{
"created": "Wed, 14 Feb 2024 02:07:04 GMT",
"version": "v1"
}
] |
2024-02-15
|
[
[
"Seewald",
"Adam",
""
],
[
"Chancán",
"Marvin",
""
],
[
"McCann",
"Connor M.",
""
],
[
"Noh",
"Seonghoon",
""
],
[
"Fallahi",
"Omeed",
""
],
[
"Castillo",
"Hector",
""
],
[
"Abraham",
"Ian",
""
],
[
"Dollar",
"Aaron M.",
""
]
] |
This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor and outdoor terrains. The exploration consists of a methodology that extends frontier- and sampling-based exploration with a path-following vector field and a state-of-the-art SLAM algorithm. The methodology allows the robot to explore its surroundings at lower update frequencies, enabling the use of lower-performing and lower-cost hardware while still retaining good autonomous performance. The approach further consists of a methodology to interact with a remotely located human operator based on an inexpensive long-range and low-power communication technology from the internet-of-things domain (i.e., LoRa) and a customized communication protocol. The results and the feasibility analysis show the possible applications and limitations of the approach.
|
2312.07553
|
Joon Hyun Jeong
|
Joonhyun Jeong
|
Hijacking Context in Large Multi-modal Models
|
Technical Report. Preprint
|
ICLR 2024 Workshop on Reliable and Responsible Foundation Models
| null | null |
cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, Large Multi-modal Models (LMMs) have demonstrated their ability to
understand the visual contents of images given the instructions regarding the
images. Built upon the Large Language Models (LLMs), LMMs also inherit their
abilities and characteristics such as in-context learning where a coherent
sequence of images and texts are given as the input prompt. However, we
identify a new limitation of off-the-shelf LMMs where a small fraction of
incoherent images or text descriptions mislead LMMs to only generate biased
output about the hijacked context, not the originally intended context. To
address this, we propose a pre-filtering method that removes irrelevant
contexts via GPT-4V, based on its robustness towards distribution shift within
the contexts. We further investigate whether replacing the hijacked visual and
textual contexts with the correlated ones via GPT-4V and text-to-image models
can help yield coherent responses.
|
[
{
"created": "Thu, 7 Dec 2023 11:23:29 GMT",
"version": "v1"
},
{
"created": "Mon, 13 May 2024 10:42:05 GMT",
"version": "v2"
}
] |
2024-05-14
|
[
[
"Jeong",
"Joonhyun",
""
]
] |
Recently, Large Multi-modal Models (LMMs) have demonstrated their ability to understand the visual contents of images given the instructions regarding the images. Built upon the Large Language Models (LLMs), LMMs also inherit their abilities and characteristics such as in-context learning where a coherent sequence of images and texts are given as the input prompt. However, we identify a new limitation of off-the-shelf LMMs where a small fraction of incoherent images or text descriptions mislead LMMs to only generate biased output about the hijacked context, not the originally intended context. To address this, we propose a pre-filtering method that removes irrelevant contexts via GPT-4V, based on its robustness towards distribution shift within the contexts. We further investigate whether replacing the hijacked visual and textual contexts with the correlated ones via GPT-4V and text-to-image models can help yield coherent responses.
|
2405.13365
|
Zavareh Bozorgasl
|
Zavareh Bozorgasl and Hao Chen
|
Clipped Uniform Quantizers for Communication-Efficient Federated
Learning
|
Work in progress
| null | null | null |
cs.LG cs.MA eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces an approach to employ clipped uniform quantization in
federated learning settings, aiming to enhance model efficiency by reducing
communication overhead without compromising accuracy. By employing optimal
clipping thresholds and adaptive quantization schemes, our method significantly
curtails the bit requirements for model weight transmissions between clients
and the server. We explore the implications of symmetric clipping and uniform
quantization on model performance, highlighting the utility of stochastic
quantization to mitigate quantization artifacts and improve model robustness.
Through extensive simulations on the MNIST dataset, our results demonstrate
that the proposed method achieves near full-precision performance while
ensuring substantial communication savings. Specifically, our approach
facilitates efficient weight averaging based on quantization errors,
effectively balancing the trade-off between communication efficiency and model
accuracy. The comparative analysis with conventional quantization methods
further confirms the superiority of our technique.
|
[
{
"created": "Wed, 22 May 2024 05:48:25 GMT",
"version": "v1"
}
] |
2024-05-24
|
[
[
"Bozorgasl",
"Zavareh",
""
],
[
"Chen",
"Hao",
""
]
] |
This paper introduces an approach to employ clipped uniform quantization in federated learning settings, aiming to enhance model efficiency by reducing communication overhead without compromising accuracy. By employing optimal clipping thresholds and adaptive quantization schemes, our method significantly curtails the bit requirements for model weight transmissions between clients and the server. We explore the implications of symmetric clipping and uniform quantization on model performance, highlighting the utility of stochastic quantization to mitigate quantization artifacts and improve model robustness. Through extensive simulations on the MNIST dataset, our results demonstrate that the proposed method achieves near full-precision performance while ensuring substantial communication savings. Specifically, our approach facilitates efficient weight averaging based on quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. The comparative analysis with conventional quantization methods further confirms the superiority of our technique.
|
2303.01295
|
Antonio Guerriero
|
Antonio Guerriero, Roberto Pietrantuono, Stefano Russo
|
Iterative Assessment and Improvement of DNN Operational Accuracy
|
Paper accepted at 45th International Conference on Software
Engineering (ICSE'23 NIER), May 2023
| null |
10.1109/ICSE-NIER58687.2023.00014
| null |
cs.LG cs.AI cs.CV cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Deep Neural Networks (DNN) are nowadays largely adopted in many application
domains thanks to their human-like, or even superhuman, performance in specific
tasks. However, due to unpredictable/unconsidered operating conditions,
unexpected failures show up on field, making the performance of a DNN in
operation very different from the one estimated prior to release. In the life
cycle of DNN systems, the assessment of accuracy is typically addressed in two
ways: offline, via sampling of operational inputs, or online, via
pseudo-oracles. The former is considered more expensive due to the need for
manual labeling of the sampled inputs. The latter is automatic but less
accurate. We believe that emerging iterative industrial-strength life cycle
models for Machine Learning systems, like MLOps, offer the possibility to
leverage inputs observed in operation not only to provide faithful estimates of
a DNN accuracy, but also to improve it through remodeling/retraining actions.
We propose DAIC (DNN Assessment and Improvement Cycle), an approach which
combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling
techniques to estimate and improve the operational accuracy of a DNN in the
iterations of its life cycle. Preliminary results show the benefits of
combining the two approaches and integrating them in the DNN life cycle.
|
[
{
"created": "Thu, 2 Mar 2023 14:21:54 GMT",
"version": "v1"
}
] |
2024-03-27
|
[
[
"Guerriero",
"Antonio",
""
],
[
"Pietrantuono",
"Roberto",
""
],
[
"Russo",
"Stefano",
""
]
] |
Deep Neural Networks (DNN) are nowadays largely adopted in many application domains thanks to their human-like, or even superhuman, performance in specific tasks. However, due to unpredictable/unconsidered operating conditions, unexpected failures show up on field, making the performance of a DNN in operation very different from the one estimated prior to release. In the life cycle of DNN systems, the assessment of accuracy is typically addressed in two ways: offline, via sampling of operational inputs, or online, via pseudo-oracles. The former is considered more expensive due to the need for manual labeling of the sampled inputs. The latter is automatic but less accurate. We believe that emerging iterative industrial-strength life cycle models for Machine Learning systems, like MLOps, offer the possibility to leverage inputs observed in operation not only to provide faithful estimates of a DNN accuracy, but also to improve it through remodeling/retraining actions. We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling techniques to estimate and improve the operational accuracy of a DNN in the iterations of its life cycle. Preliminary results show the benefits of combining the two approaches and integrating them in the DNN life cycle.
|
1406.5988
|
Luca Sanguinetti
|
Luca Sanguinetti, Aris L. Moustakas, Emil Bjornson, and Merouane
Debbah
|
Large System Analysis of the Energy Consumption Distribution in
Multi-User MIMO Systems with Mobility
|
8 figures, 2 tables, to appear on IEEE Transactions on Wireless
Communications
| null |
10.1109/TWC.2014.2372761
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we consider the downlink of a single-cell multi-user MIMO
system in which the base station (BS) makes use of $N$ antennas to communicate
with $K$ single-antenna user equipments (UEs). The UEs move around in the cell
according to a random walk mobility model. We aim at determining the energy
consumption distribution when different linear precoding techniques are used at
the BS to guarantee target rates within a finite time interval $T$. The
analysis is conducted in the asymptotic regime where $N$ and $K$ grow large
with fixed ratio under the assumption of perfect channel state information
(CSI). Both recent and standard results from large system analysis are used to
provide concise formulae for the asymptotic transmit powers and beamforming
vectors for all considered schemes. These results are eventually used to
provide a deterministic approximation of the energy consumption and to study
its fluctuations around this value in the form of a central limit theorem.
Closed-form expressions for the asymptotic means and variances are given.
Numerical results are used to validate the accuracy of the theoretical analysis
and to make comparisons. We show how the results can be used to approximate the
probability that a battery-powered BS runs out of energy and also to design the
cell radius for minimizing the energy consumption per unit area. The imperfect
CSI case is also briefly considered.
|
[
{
"created": "Mon, 23 Jun 2014 17:18:15 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Jan 2015 08:31:45 GMT",
"version": "v2"
}
] |
2016-11-18
|
[
[
"Sanguinetti",
"Luca",
""
],
[
"Moustakas",
"Aris L.",
""
],
[
"Bjornson",
"Emil",
""
],
[
"Debbah",
"Merouane",
""
]
] |
In this work, we consider the downlink of a single-cell multi-user MIMO system in which the base station (BS) makes use of $N$ antennas to communicate with $K$ single-antenna user equipments (UEs). The UEs move around in the cell according to a random walk mobility model. We aim at determining the energy consumption distribution when different linear precoding techniques are used at the BS to guarantee target rates within a finite time interval $T$. The analysis is conducted in the asymptotic regime where $N$ and $K$ grow large with fixed ratio under the assumption of perfect channel state information (CSI). Both recent and standard results from large system analysis are used to provide concise formulae for the asymptotic transmit powers and beamforming vectors for all considered schemes. These results are eventually used to provide a deterministic approximation of the energy consumption and to study its fluctuations around this value in the form of a central limit theorem. Closed-form expressions for the asymptotic means and variances are given. Numerical results are used to validate the accuracy of the theoretical analysis and to make comparisons. We show how the results can be used to approximate the probability that a battery-powered BS runs out of energy and also to design the cell radius for minimizing the energy consumption per unit area. The imperfect CSI case is also briefly considered.
|
1911.00616
|
Eduardo Soares Mr
|
Eduardo Soares, Plamen Angelov
|
Novelty Detection and Learning from Extremely Weak Supervision
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we offer a method and algorithm, which make possible fully
autonomous (unsupervised) detection of new classes, and learning following a
very parsimonious training priming (few labeled data samples only). Moreover,
new unknown classes may appear at a later stage and the proposed xClass method
and algorithm are able to successfully discover this and learn from the data
autonomously. Furthermore, the features (inputs to the classifier) are
automatically sub-selected by the algorithm based on the accumulated data
density per feature per class. As a result, a highly efficient, lean,
human-understandable, autonomously self-learning model (which only needs an
extremely parsimonious priming) emerges from the data. To validate our proposal
we tested it on two challenging problems, including imbalanced Caltech-101 data
set and iRoads dataset. Not only we achieved higher precision, but, more
significantly, we only used a single class beforehand, while other methods used
all the available classes) and we generated interpretable models with smaller
number of features used, through extremely weak and weak supervision.
|
[
{
"created": "Fri, 1 Nov 2019 23:51:08 GMT",
"version": "v1"
}
] |
2019-11-05
|
[
[
"Soares",
"Eduardo",
""
],
[
"Angelov",
"Plamen",
""
]
] |
In this paper we offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training priming (few labeled data samples only). Moreover, new unknown classes may appear at a later stage and the proposed xClass method and algorithm are able to successfully discover this and learn from the data autonomously. Furthermore, the features (inputs to the classifier) are automatically sub-selected by the algorithm based on the accumulated data density per feature per class. As a result, a highly efficient, lean, human-understandable, autonomously self-learning model (which only needs an extremely parsimonious priming) emerges from the data. To validate our proposal we tested it on two challenging problems, including imbalanced Caltech-101 data set and iRoads dataset. Not only we achieved higher precision, but, more significantly, we only used a single class beforehand, while other methods used all the available classes) and we generated interpretable models with smaller number of features used, through extremely weak and weak supervision.
|
2203.11903
|
Chace Lee
|
Chace Lee, Angelica Willis, Christina Chen, Marcin Sieniek, Akib
Uddin, Jonny Wong, Rory Pilgrim, Katherine Chou, Daniel Tse, Shravya Shetty,
Ryan G. Gomes
|
Enabling faster and more reliable sonographic assessment of gestational
age through machine learning
| null | null | null | null |
cs.LG cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fetal ultrasounds are an essential part of prenatal care and can be used to
estimate gestational age (GA). Accurate GA assessment is important for
providing appropriate prenatal care throughout pregnancy and identifying
complications such as fetal growth disorders. Since derivation of GA from
manual fetal biometry measurements (head, abdomen, femur) are
operator-dependent and time-consuming, there have been a number of research
efforts focused on using artificial intelligence (AI) models to estimate GA
using standard biometry images, but there is still room to improve the accuracy
and reliability of these AI systems for widescale adoption. To improve GA
estimates, without significant change to provider workflows, we leverage AI to
interpret standard plane ultrasound images as well as 'fly-to' ultrasound
videos, which are 5-10s videos automatically recorded as part of the standard
of care before the still image is captured. We developed and validated three AI
models: an image model using standard plane images, a video model using fly-to
videos, and an ensemble model (combining both image and video). All three were
statistically superior to standard fetal biometry-based GA estimates derived by
expert sonographers, the ensemble model has the lowest mean absolute error
(MAE) compared to the clinical standard fetal biometry (mean difference: -1.51
$\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404
participants. We showed that our models outperform standard biometry by a more
substantial margin on fetuses that were small for GA. Our AI models have the
potential to empower trained operators to estimate GA with higher accuracy
while reducing the amount of time required and user variability in measurement
acquisition.
|
[
{
"created": "Tue, 22 Mar 2022 17:15:56 GMT",
"version": "v1"
}
] |
2022-03-23
|
[
[
"Lee",
"Chace",
""
],
[
"Willis",
"Angelica",
""
],
[
"Chen",
"Christina",
""
],
[
"Sieniek",
"Marcin",
""
],
[
"Uddin",
"Akib",
""
],
[
"Wong",
"Jonny",
""
],
[
"Pilgrim",
"Rory",
""
],
[
"Chou",
"Katherine",
""
],
[
"Tse",
"Daniel",
""
],
[
"Shetty",
"Shravya",
""
],
[
"Gomes",
"Ryan G.",
""
]
] |
Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal care throughout pregnancy and identifying complications such as fetal growth disorders. Since derivation of GA from manual fetal biometry measurements (head, abdomen, femur) are operator-dependent and time-consuming, there have been a number of research efforts focused on using artificial intelligence (AI) models to estimate GA using standard biometry images, but there is still room to improve the accuracy and reliability of these AI systems for widescale adoption. To improve GA estimates, without significant change to provider workflows, we leverage AI to interpret standard plane ultrasound images as well as 'fly-to' ultrasound videos, which are 5-10s videos automatically recorded as part of the standard of care before the still image is captured. We developed and validated three AI models: an image model using standard plane images, a video model using fly-to videos, and an ensemble model (combining both image and video). All three were statistically superior to standard fetal biometry-based GA estimates derived by expert sonographers, the ensemble model has the lowest mean absolute error (MAE) compared to the clinical standard fetal biometry (mean difference: -1.51 $\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404 participants. We showed that our models outperform standard biometry by a more substantial margin on fetuses that were small for GA. Our AI models have the potential to empower trained operators to estimate GA with higher accuracy while reducing the amount of time required and user variability in measurement acquisition.
|
2210.00891
|
Enzo Tartaglione
|
Enzo Tartaglione
|
Information Removal at the bottleneck in Deep Neural Networks
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning models are nowadays broadly deployed to solve an incredibly
large variety of tasks. Commonly, leveraging over the availability of "big
data", deep neural networks are trained as black-boxes, minimizing an objective
function at its output. This however does not allow control over the
propagation of some specific features through the model, like gender or race,
for solving some an uncorrelated task. This raises issues either in the privacy
domain (considering the propagation of unwanted information) and of bias
(considering that these features are potentially used to solve the given task).
In this work we propose IRENE, a method to achieve information removal at the
bottleneck of deep neural networks, which explicitly minimizes the estimated
mutual information between the features to be kept ``private'' and the target.
Experiments on a synthetic dataset and on CelebA validate the effectiveness of
the proposed approach, and open the road towards the development of approaches
guaranteeing information removal in deep neural networks.
|
[
{
"created": "Fri, 30 Sep 2022 14:20:21 GMT",
"version": "v1"
}
] |
2022-10-04
|
[
[
"Tartaglione",
"Enzo",
""
]
] |
Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. Commonly, leveraging over the availability of "big data", deep neural networks are trained as black-boxes, minimizing an objective function at its output. This however does not allow control over the propagation of some specific features through the model, like gender or race, for solving some an uncorrelated task. This raises issues either in the privacy domain (considering the propagation of unwanted information) and of bias (considering that these features are potentially used to solve the given task). In this work we propose IRENE, a method to achieve information removal at the bottleneck of deep neural networks, which explicitly minimizes the estimated mutual information between the features to be kept ``private'' and the target. Experiments on a synthetic dataset and on CelebA validate the effectiveness of the proposed approach, and open the road towards the development of approaches guaranteeing information removal in deep neural networks.
|
1004.3566
|
Vishal Goyal
|
G. Murugesan, C. Chellappan
|
An Economic-based Resource Management and Scheduling for Grid Computing
Applications
|
International Journal of Computer Science Issues online at
http://ijcsi.org/articles/An-Economic-based-Resource-Management-and-Scheduling-for-Grid-Computing-Applications.php
|
IJCSI, Volume 7, Issue 2, March 2010
| null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Resource management and scheduling plays a crucial role in achieving high
utilization of resources in grid computing environments. Due to heterogeneity
of resources, scheduling an application is significantly complicated and
challenging task in grid system. Most of the researches in this area are mainly
focused on to improve the performance of the grid system. There were some
allocation model has been proposed based on divisible load theory with
different type of workloads and a single originating processor. In this paper
we introduce a new resource allocation model with multiple load originating
processors as an economic model. Solutions for an optimal allocation of
fraction of loads to nodes obtained to minimize the cost of the grid users via
linear programming approach. It is found that the resource allocation model can
efficiently and effectively allocate workloads to proper resources.
Experimental results showed that the proposed model obtained the better
solution in terms of cost and time.
|
[
{
"created": "Tue, 20 Apr 2010 20:32:31 GMT",
"version": "v1"
}
] |
2010-04-22
|
[
[
"Murugesan",
"G.",
""
],
[
"Chellappan",
"C.",
""
]
] |
Resource management and scheduling plays a crucial role in achieving high utilization of resources in grid computing environments. Due to heterogeneity of resources, scheduling an application is significantly complicated and challenging task in grid system. Most of the researches in this area are mainly focused on to improve the performance of the grid system. There were some allocation model has been proposed based on divisible load theory with different type of workloads and a single originating processor. In this paper we introduce a new resource allocation model with multiple load originating processors as an economic model. Solutions for an optimal allocation of fraction of loads to nodes obtained to minimize the cost of the grid users via linear programming approach. It is found that the resource allocation model can efficiently and effectively allocate workloads to proper resources. Experimental results showed that the proposed model obtained the better solution in terms of cost and time.
|
1912.12220
|
Do\u{g}analp Ergen\c{c}
|
Do\u{g}analp Ergen\c{c} and Ertan Onur
|
On Network Traffic Forecasting using Autoregressive Models
| null | null | null | null |
cs.NI eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various statistical analysis methods are studied for years to extract
accurate trends of network traffic and predict the future load mainly to
allocate required resources. Besides, many stochastic modeling techniques are
offered to represent fundamental characteristics of different types of network
traffic. In this study, we analyze autoregressive traffic forecasting
techniques considering their popularity and wide-use in the domain. In
comparison to similar works, we present important traffic characteristics and
discussions from the literature to create a self-consistent guidance along with
the survey. Then, we approach to techniques in the literature revealing which
network characteristics they can capture offering a characteristic-based
framework. Most importantly, we aim to fill the gap between the statistical
analysis of those methods and their relevance with networking by discussing
significant aspects and requirements for accurate forecasting from a
network-telemetric perspective.
|
[
{
"created": "Fri, 27 Dec 2019 16:26:25 GMT",
"version": "v1"
}
] |
2019-12-30
|
[
[
"Ergenç",
"Doğanalp",
""
],
[
"Onur",
"Ertan",
""
]
] |
Various statistical analysis methods are studied for years to extract accurate trends of network traffic and predict the future load mainly to allocate required resources. Besides, many stochastic modeling techniques are offered to represent fundamental characteristics of different types of network traffic. In this study, we analyze autoregressive traffic forecasting techniques considering their popularity and wide-use in the domain. In comparison to similar works, we present important traffic characteristics and discussions from the literature to create a self-consistent guidance along with the survey. Then, we approach to techniques in the literature revealing which network characteristics they can capture offering a characteristic-based framework. Most importantly, we aim to fill the gap between the statistical analysis of those methods and their relevance with networking by discussing significant aspects and requirements for accurate forecasting from a network-telemetric perspective.
|
2003.13165
|
Balakumar Sundaralingam
|
Balakumar Sundaralingam and Tucker Hermans
|
In-Hand Object-Dynamics Inference using Tactile Fingertips
|
Accepted at IEEE Transactions on Robotics (T-RO). Website:
https://sites.google.com/view/tactile-obj-dynamics
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Having the ability to estimate an object's properties through interaction
will enable robots to manipulate novel objects. Object's dynamics, specifically
the friction and inertial parameters have only been estimated in a lab
environment with precise and often external sensing. Could we infer an object's
dynamics in the wild with only the robot's sensors? In this paper, we explore
the estimation of dynamics of a grasped object in motion, with tactile force
sensing at multiple fingertips. Our estimation approach does not rely on torque
sensing to estimate the dynamics. To estimate friction, we develop a control
scheme to actively interact with the object until slip is detected. To robustly
perform the inertial estimation, we setup a factor graph that fuses all our
sensor measurements on physically consistent manifolds and perform inference.
We show that tactile fingertips enable in-hand dynamics estimation of low mass
objects.
|
[
{
"created": "Mon, 30 Mar 2020 00:12:11 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Jan 2021 04:37:38 GMT",
"version": "v2"
}
] |
2021-01-20
|
[
[
"Sundaralingam",
"Balakumar",
""
],
[
"Hermans",
"Tucker",
""
]
] |
Having the ability to estimate an object's properties through interaction will enable robots to manipulate novel objects. Object's dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise and often external sensing. Could we infer an object's dynamics in the wild with only the robot's sensors? In this paper, we explore the estimation of dynamics of a grasped object in motion, with tactile force sensing at multiple fingertips. Our estimation approach does not rely on torque sensing to estimate the dynamics. To estimate friction, we develop a control scheme to actively interact with the object until slip is detected. To robustly perform the inertial estimation, we setup a factor graph that fuses all our sensor measurements on physically consistent manifolds and perform inference. We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.
|
2302.07832
|
Aodong Li
|
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja
Rudolph
|
Deep Anomaly Detection under Labeling Budget Constraints
|
ICML 2023
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Selecting informative data points for expert feedback can significantly
improve the performance of anomaly detection (AD) in various contexts, such as
medical diagnostics or fraud detection. In this paper, we determine a set of
theoretical conditions under which anomaly scores generalize from labeled
queries to unlabeled data. Motivated by these results, we propose a data
labeling strategy with optimal data coverage under labeling budget constraints.
In addition, we propose a new learning framework for semi-supervised AD.
Extensive experiments on image, tabular, and video data sets show that our
approach results in state-of-the-art semi-supervised AD performance under
labeling budget constraints.
|
[
{
"created": "Wed, 15 Feb 2023 18:18:35 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Jul 2023 18:33:10 GMT",
"version": "v2"
}
] |
2023-07-06
|
[
[
"Li",
"Aodong",
""
],
[
"Qiu",
"Chen",
""
],
[
"Kloft",
"Marius",
""
],
[
"Smyth",
"Padhraic",
""
],
[
"Mandt",
"Stephan",
""
],
[
"Rudolph",
"Maja",
""
]
] |
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection. In this paper, we determine a set of theoretical conditions under which anomaly scores generalize from labeled queries to unlabeled data. Motivated by these results, we propose a data labeling strategy with optimal data coverage under labeling budget constraints. In addition, we propose a new learning framework for semi-supervised AD. Extensive experiments on image, tabular, and video data sets show that our approach results in state-of-the-art semi-supervised AD performance under labeling budget constraints.
|
1706.02337
|
Xiao Yang
|
Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee
Giles
|
Learning to Extract Semantic Structure from Documents Using Multimodal
Fully Convolutional Neural Network
|
CVPR 2017 Spotlight
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present an end-to-end, multimodal, fully convolutional network for
extracting semantic structures from document images. We consider document
semantic structure extraction as a pixel-wise segmentation task, and propose a
unified model that classifies pixels based not only on their visual appearance,
as in the traditional page segmentation task, but also on the content of
underlying text. Moreover, we propose an efficient synthetic document
generation process that we use to generate pretraining data for our network.
Once the network is trained on a large set of synthetic documents, we fine-tune
the network on unlabeled real documents using a semi-supervised approach. We
systematically study the optimum network architecture and show that both our
multimodal approach and the synthetic data pretraining significantly boost the
performance.
|
[
{
"created": "Wed, 7 Jun 2017 18:51:31 GMT",
"version": "v1"
}
] |
2017-06-09
|
[
[
"Yang",
"Xiao",
""
],
[
"Yumer",
"Ersin",
""
],
[
"Asente",
"Paul",
""
],
[
"Kraley",
"Mike",
""
],
[
"Kifer",
"Daniel",
""
],
[
"Giles",
"C. Lee",
""
]
] |
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance.
|
2403.02308
|
Yuchen Duan
|
Yuchen Duan, Weiyun Wang, Zhe Chen, Xizhou Zhu, Lewei Lu, Tong Lu, Yu
Qiao, Hongsheng Li, Jifeng Dai, Wenhai Wang
|
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like
Architectures
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformers have revolutionized computer vision and natural language
processing, but their high computational complexity limits their application in
high-resolution image processing and long-context analysis. This paper
introduces Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the
NLP field with necessary modifications for vision tasks. Similar to the Vision
Transformer (ViT), our model is designed to efficiently handle sparse inputs
and demonstrate robust global processing capabilities, while also scaling up
effectively, accommodating both large-scale parameters and extensive datasets.
Its distinctive advantage lies in its reduced spatial aggregation complexity,
which renders it exceptionally adept at processing high-resolution images
seamlessly, eliminating the necessity for windowing operations. Our evaluations
demonstrate that VRWKV surpasses ViT's performance in image classification and
has significantly faster speeds and lower memory usage processing
high-resolution inputs. In dense prediction tasks, it outperforms window-based
models, maintaining comparable speeds. These results highlight VRWKV's
potential as a more efficient alternative for visual perception tasks. Code is
released at \url{https://github.com/OpenGVLab/Vision-RWKV}.
|
[
{
"created": "Mon, 4 Mar 2024 18:46:20 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Mar 2024 15:43:08 GMT",
"version": "v2"
}
] |
2024-03-08
|
[
[
"Duan",
"Yuchen",
""
],
[
"Wang",
"Weiyun",
""
],
[
"Chen",
"Zhe",
""
],
[
"Zhu",
"Xizhou",
""
],
[
"Lu",
"Lewei",
""
],
[
"Lu",
"Tong",
""
],
[
"Qiao",
"Yu",
""
],
[
"Li",
"Hongsheng",
""
],
[
"Dai",
"Jifeng",
""
],
[
"Wang",
"Wenhai",
""
]
] |
Transformers have revolutionized computer vision and natural language processing, but their high computational complexity limits their application in high-resolution image processing and long-context analysis. This paper introduces Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the NLP field with necessary modifications for vision tasks. Similar to the Vision Transformer (ViT), our model is designed to efficiently handle sparse inputs and demonstrate robust global processing capabilities, while also scaling up effectively, accommodating both large-scale parameters and extensive datasets. Its distinctive advantage lies in its reduced spatial aggregation complexity, which renders it exceptionally adept at processing high-resolution images seamlessly, eliminating the necessity for windowing operations. Our evaluations demonstrate that VRWKV surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage processing high-resolution inputs. In dense prediction tasks, it outperforms window-based models, maintaining comparable speeds. These results highlight VRWKV's potential as a more efficient alternative for visual perception tasks. Code is released at \url{https://github.com/OpenGVLab/Vision-RWKV}.
|
2407.00024
|
Lang He Ph.D
|
Lang He, Kai Chen, Junnan Zhao, Yimeng Wang, Ercheng Pei, Haifeng
Chen, Jiewei Jiang, Shiqing Zhang, Jie Zhang, Zhongmin Wang, Tao He, Prayag
Tiwari
|
LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in
the Wild
| null | null | null | null |
cs.CV cs.AI cs.MM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Depression can significantly impact many aspects of an individual's life,
including their personal and social functioning, academic and work performance,
and overall quality of life. Many researchers within the field of affective
computing are adopting deep learning technology to explore potential patterns
related to the detection of depression. However, because of subjects' privacy
protection concerns, that data in this area is still scarce, presenting a
challenge for the deep discriminative models used in detecting depression. To
navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for
depression recognition in the wild is built. In LMVD, which has 1823 samples
with 214 hours of the 1475 participants captured from four multimedia platforms
(Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed
MDDformer to learn the non-verbal behaviors of individuals is proposed.
Extensive validations are performed on the LMVD dataset, demonstrating superior
performance for depression detection. We anticipate that the LMVD will
contribute a valuable function to the depression detection community. The data
and code will released at the link: https://github.com/helang818/LMVD/.
|
[
{
"created": "Thu, 9 May 2024 01:27:10 GMT",
"version": "v1"
}
] |
2024-07-02
|
[
[
"He",
"Lang",
""
],
[
"Chen",
"Kai",
""
],
[
"Zhao",
"Junnan",
""
],
[
"Wang",
"Yimeng",
""
],
[
"Pei",
"Ercheng",
""
],
[
"Chen",
"Haifeng",
""
],
[
"Jiang",
"Jiewei",
""
],
[
"Zhang",
"Shiqing",
""
],
[
"Zhang",
"Jie",
""
],
[
"Wang",
"Zhongmin",
""
],
[
"He",
"Tao",
""
],
[
"Tiwari",
"Prayag",
""
]
] |
Depression can significantly impact many aspects of an individual's life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects' privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://github.com/helang818/LMVD/.
|
1812.02615
|
Muhammad Junaid Farooq
|
Jin Shang and Muhammad Junaid Farooq and Quanyan Zhu
|
Real-Time Transmission Mechanism Design for Wireless IoT Sensors with
Energy Harvesting under Power Saving Mode
| null | null | null | null |
cs.SY eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Internet of things (IoT) comprises of wireless sensors and actuators
connected via access points to the Internet. Often, the sensing devices are
remotely deployed with limited battery power and are equipped with energy
harvesting equipment. These devices transmit real-time data to the base station
(BS), which is used in applications such as anomaly detection. Under sufficient
power availability, wireless transmissions from sensors can be scheduled at
regular time intervals to maintain real-time data acquisition. However, once
the battery is significantly depleted, the devices enter into power saving mode
and need to be more selective in transmitting information to the BS.
Transmitting a particular piece of sensed data consumes power while discarding
it may result in loss of utility at the BS. The goal is to design an optimal
dynamic policy which enables the device to decide whether to transmit or to
discard a piece of sensing data particularly under the power saving mode. This
will enable the sensor to prolong its operation while causing minimum loss of
utility to the application. We develop an analytical framework to capture the
utility of the IoT sensor transmissions and leverage dynamic programming based
approach to derive an optimal real-time transmission policy that is based on
the statistics of information arrival, the likelihood of harvested energy, and
designed lifetime of the sensors. Numerical results show that if the statistics
of future data valuation are accurately predicted, there is a significant
increase in utility obtained at the BS as well as the battery lifetime.
|
[
{
"created": "Thu, 6 Dec 2018 15:46:04 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Jan 2019 17:02:29 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Apr 2019 17:28:31 GMT",
"version": "v3"
}
] |
2019-04-09
|
[
[
"Shang",
"Jin",
""
],
[
"Farooq",
"Muhammad Junaid",
""
],
[
"Zhu",
"Quanyan",
""
]
] |
The Internet of things (IoT) comprises of wireless sensors and actuators connected via access points to the Internet. Often, the sensing devices are remotely deployed with limited battery power and are equipped with energy harvesting equipment. These devices transmit real-time data to the base station (BS), which is used in applications such as anomaly detection. Under sufficient power availability, wireless transmissions from sensors can be scheduled at regular time intervals to maintain real-time data acquisition. However, once the battery is significantly depleted, the devices enter into power saving mode and need to be more selective in transmitting information to the BS. Transmitting a particular piece of sensed data consumes power while discarding it may result in loss of utility at the BS. The goal is to design an optimal dynamic policy which enables the device to decide whether to transmit or to discard a piece of sensing data particularly under the power saving mode. This will enable the sensor to prolong its operation while causing minimum loss of utility to the application. We develop an analytical framework to capture the utility of the IoT sensor transmissions and leverage dynamic programming based approach to derive an optimal real-time transmission policy that is based on the statistics of information arrival, the likelihood of harvested energy, and designed lifetime of the sensors. Numerical results show that if the statistics of future data valuation are accurately predicted, there is a significant increase in utility obtained at the BS as well as the battery lifetime.
|
1803.11256
|
Alexander Kott
|
Alexander Kott
|
Challenges and Characteristics of Intelligent Autonomy for Internet of
Battle Things in Highly Adversarial Environments
|
This is a version of the paper that was presented at, and will appear
in the Proceedings of the 2018 Spring Symposium of AAAI, March 26-28, 2018,
Palo Alto, CA
| null | null | null |
cs.CY cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Numerous, artificially intelligent, networked things will populate the
battlefield of the future, operating in close collaboration with human
warfighters, and fighting as teams in highly adversarial environments. This
paper explores the characteristics, capabilities and intelligence required of
such a network of intelligent things and humans - Internet of Battle Things
(IOBT). It will experience unique challenges that are not yet well addressed by
the current generation of AI and machine learning.
|
[
{
"created": "Tue, 20 Mar 2018 22:15:14 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Apr 2018 19:36:14 GMT",
"version": "v2"
}
] |
2018-04-17
|
[
[
"Kott",
"Alexander",
""
]
] |
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This paper explores the characteristics, capabilities and intelligence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). It will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.
|
2303.09187
|
Zhongwei Qiu
|
Zhongwei Qiu, Yang Qiansheng, Jian Wang, Haocheng Feng, Junyu Han,
Errui Ding, Chang Xu, Dongmei Fu, Jingdong Wang
|
PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with
Progressive Video Transformers
|
CVPR2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing methods of multi-person video 3D human Pose and Shape Estimation
(PSE) typically adopt a two-stage strategy, which first detects human instances
in each frame and then performs single-person PSE with temporal model. However,
the global spatio-temporal context among spatial instances can not be captured.
In this paper, we propose a new end-to-end multi-person 3D Pose and Shape
estimation framework with progressive Video Transformer, termed PSVT. In PSVT,
a spatio-temporal encoder (STE) captures the global feature dependencies among
spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder
(STSD) capture the global dependencies between pose queries and feature tokens,
shape queries and feature tokens, respectively. To handle the variances of
objects as time proceeds, a novel scheme of progressive decoding is used to
update pose and shape queries at each frame. Besides, we propose a novel
pose-guided attention (PGA) for shape decoder to better predict shape
parameters. The two components strengthen the decoder of PSVT to improve
performance. Extensive experiments on the four datasets show that PSVT achieves
stage-of-the-art results.
|
[
{
"created": "Thu, 16 Mar 2023 09:55:43 GMT",
"version": "v1"
}
] |
2023-03-17
|
[
[
"Qiu",
"Zhongwei",
""
],
[
"Qiansheng",
"Yang",
""
],
[
"Wang",
"Jian",
""
],
[
"Feng",
"Haocheng",
""
],
[
"Han",
"Junyu",
""
],
[
"Ding",
"Errui",
""
],
[
"Xu",
"Chang",
""
],
[
"Fu",
"Dongmei",
""
],
[
"Wang",
"Jingdong",
""
]
] |
Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In this paper, we propose a new end-to-end multi-person 3D Pose and Shape estimation framework with progressive Video Transformer, termed PSVT. In PSVT, a spatio-temporal encoder (STE) captures the global feature dependencies among spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder (STSD) capture the global dependencies between pose queries and feature tokens, shape queries and feature tokens, respectively. To handle the variances of objects as time proceeds, a novel scheme of progressive decoding is used to update pose and shape queries at each frame. Besides, we propose a novel pose-guided attention (PGA) for shape decoder to better predict shape parameters. The two components strengthen the decoder of PSVT to improve performance. Extensive experiments on the four datasets show that PSVT achieves stage-of-the-art results.
|
2405.03251
|
Zhenmei Shi
|
Jiuxiang Gu, Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song
|
Exploring the Frontiers of Softmax: Provable Optimization, Applications
in Diffusion Model, and Beyond
|
53 pages
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The softmax activation function plays a crucial role in the success of large
language models (LLMs), particularly in the self-attention mechanism of the
widely adopted Transformer architecture. However, the underlying learning
dynamics that contribute to the effectiveness of softmax remain largely
unexplored. As a step towards better understanding, this paper provides a
theoretical study of the optimization and generalization properties of
two-layer softmax neural networks, providing theoretical insights into their
superior performance as other activation functions, such as ReLU and
exponential. Leveraging the Neural Tangent Kernel (NTK) framework, our analysis
reveals that the normalization effect of the softmax function leads to a good
perturbation property of the induced NTK matrix, resulting in a good convex
region of the loss landscape. Consequently, softmax neural networks can learn
the target function in the over-parametrization regime. To demonstrate the
broad applicability of our theoretical findings, we apply them to the task of
learning score estimation functions in diffusion models, a promising approach
for generative modeling. Our analysis shows that gradient-based algorithms can
learn the score function with a provable accuracy. Our work provides a deeper
understanding of the effectiveness of softmax neural networks and their
potential in various domains, paving the way for further advancements in
natural language processing and beyond.
|
[
{
"created": "Mon, 6 May 2024 08:15:29 GMT",
"version": "v1"
}
] |
2024-05-07
|
[
[
"Gu",
"Jiuxiang",
""
],
[
"Li",
"Chenyang",
""
],
[
"Liang",
"Yingyu",
""
],
[
"Shi",
"Zhenmei",
""
],
[
"Song",
"Zhao",
""
]
] |
The softmax activation function plays a crucial role in the success of large language models (LLMs), particularly in the self-attention mechanism of the widely adopted Transformer architecture. However, the underlying learning dynamics that contribute to the effectiveness of softmax remain largely unexplored. As a step towards better understanding, this paper provides a theoretical study of the optimization and generalization properties of two-layer softmax neural networks, providing theoretical insights into their superior performance as other activation functions, such as ReLU and exponential. Leveraging the Neural Tangent Kernel (NTK) framework, our analysis reveals that the normalization effect of the softmax function leads to a good perturbation property of the induced NTK matrix, resulting in a good convex region of the loss landscape. Consequently, softmax neural networks can learn the target function in the over-parametrization regime. To demonstrate the broad applicability of our theoretical findings, we apply them to the task of learning score estimation functions in diffusion models, a promising approach for generative modeling. Our analysis shows that gradient-based algorithms can learn the score function with a provable accuracy. Our work provides a deeper understanding of the effectiveness of softmax neural networks and their potential in various domains, paving the way for further advancements in natural language processing and beyond.
|
2402.10102
|
Irina Ar\'evalo
|
Jose L. Salmeron and Irina Ar\'evalo
|
A privacy-preserving, distributed and cooperative FCM-based learning
approach for Cancer Research
|
Rough Sets: International Joint Conference, IJCRS 2020
| null | null | null |
cs.AI cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Distributed Artificial Intelligence is attracting interest day by day. In
this paper, the authors introduce an innovative methodology for distributed
learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a
privacy-preserving way. The authors design a training scheme for collaborative
FCM learning that offers data privacy compliant with the current regulation.
This method is applied to a cancer detection problem, proving that the
performance of the model is improved by the Federated Learning process, and
obtaining similar results to the ones that can be found in the literature.
|
[
{
"created": "Thu, 15 Feb 2024 16:56:25 GMT",
"version": "v1"
}
] |
2024-02-16
|
[
[
"Salmeron",
"Jose L.",
""
],
[
"Arévalo",
"Irina",
""
]
] |
Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.
|
2003.04470
|
Vuong M. Ngo
|
V.M. Ngo, N.A. Le-Khac, and M.T. Kechadi
|
Data Warehouse and Decision Support on Integrated Crop Big Data
|
13 pages, 11 figures. arXiv admin note: text overlap with
arXiv:1905.12411
|
International Journal of Business Process Integration and
Management 2020 Vol.10 No.1
|
10.1504/IJBPIM.2020.113115
| null |
cs.DB cs.DC cs.LG cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, precision agriculture is becoming very popular. The
introduction of modern information and communication technologies for
collecting and processing Agricultural data revolutionise the agriculture
practises. This has started a while ago (early 20th century) and it is driven
by the low cost of collecting data about everything; from information on fields
such as seed, soil, fertiliser, pest, to weather data, drones and satellites
images. Specially, the agricultural data mining today is considered as Big Data
application in terms of volume, variety, velocity and veracity. Hence it leads
to challenges in processing vast amounts of complex and diverse information to
extract useful knowledge for the farmer, agronomist, and other businesses. It
is a key foundation to establishing a crop intelligence platform, which will
enable efficient resource management and high quality agronomy decision making
and recommendations. In this paper, we designed and implemented a continental
level agricultural data warehouse (ADW). ADW is characterised by its (1)
flexible schema; (2) data integration from real agricultural multi datasets;
(3) data science and business intelligent support; (4) high performance; (5)
high storage; (6) security; (7) governance and monitoring; (8) consistency,
availability and partition tolerant; (9) cloud compatibility. We also evaluate
the performance of ADW and present some complex queries to extract and return
necessary knowledge about crop management.
|
[
{
"created": "Tue, 10 Mar 2020 00:10:22 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Apr 2021 08:45:11 GMT",
"version": "v2"
}
] |
2021-04-13
|
[
[
"Ngo",
"V. M.",
""
],
[
"Le-Khac",
"N. A.",
""
],
[
"Kechadi",
"M. T.",
""
]
] |
In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting data about everything; from information on fields such as seed, soil, fertiliser, pest, to weather data, drones and satellites images. Specially, the agricultural data mining today is considered as Big Data application in terms of volume, variety, velocity and veracity. Hence it leads to challenges in processing vast amounts of complex and diverse information to extract useful knowledge for the farmer, agronomist, and other businesses. It is a key foundation to establishing a crop intelligence platform, which will enable efficient resource management and high quality agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse (ADW). ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility. We also evaluate the performance of ADW and present some complex queries to extract and return necessary knowledge about crop management.
|
1701.05013
|
Veronika Cheplygina
|
Veronika Cheplygina, Isabel Pino Pe\~na, Jesper Holst Pedersen, David
A. Lynch, Lauge S{\o}rensen, Marleen de Bruijne
|
Transfer learning for multi-center classification of chronic obstructive
pulmonary disease
|
Accepted at Journal of Biomedical and Health Informatics
| null |
10.1109/JBHI.2017.2769800
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chronic obstructive pulmonary disease (COPD) is a lung disease which can be
quantified using chest computed tomography (CT) scans. Recent studies have
shown that COPD can be automatically diagnosed using weakly supervised learning
of intensity and texture distributions. However, up till now such classifiers
have only been evaluated on scans from a single domain, and it is unclear
whether they would generalize across domains, such as different scanners or
scanning protocols. To address this problem, we investigate classification of
COPD in a multi-center dataset with a total of 803 scans from three different
centers, four different scanners, with heterogenous subject distributions. Our
method is based on Gaussian texture features, and a weighted logistic
classifier, which increases the weights of samples similar to the test data. We
show that Gaussian texture features outperform intensity features previously
used in multi-center classification tasks. We also show that a weighting
strategy based on a classifier that is trained to discriminate between scans
from different domains, can further improve the results. To encourage further
research into transfer learning methods for classification of COPD, upon
acceptance of the paper we will release two feature datasets used in this study
on http://bigr.nl/research/projects/copd
|
[
{
"created": "Wed, 18 Jan 2017 11:13:01 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Nov 2017 14:10:34 GMT",
"version": "v2"
}
] |
2017-11-27
|
[
[
"Cheplygina",
"Veronika",
""
],
[
"Peña",
"Isabel Pino",
""
],
[
"Pedersen",
"Jesper Holst",
""
],
[
"Lynch",
"David A.",
""
],
[
"Sørensen",
"Lauge",
""
],
[
"de Bruijne",
"Marleen",
""
]
] |
Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd
|
2212.11122
|
Parviz Ali
|
Parviz Ali
|
Diamond Abrasive Electroplated Surface Anomaly Detection using
Convolutional Neural Networks for Industrial Quality Inspection
| null | null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Electroplated diamond abrasive tools require nickel coating on a metal
surface for abrasive bonding and part functionality. The electroplated
nickel-coated abrasive tool is expected to have a high-quality part performance
by having a nickel coating thickness of between 50% to 60% of the abrasive
median diameter, uniformity of the nickel layer, abrasive distribution over the
electroplated surface, and bright gloss. Electroplating parameters are set
accordingly for this purpose. Industrial quality inspection for defects of
these abrasive electroplated parts with optical inspection instruments is
extremely challenging due to the diamond's light refraction, dispersion nature,
and reflective bright nickel surface. The difficulty posed by this challenge
requires parts to be quality inspected manually with an eye loupe that is
subjective and costly. In this study, we use a Convolutional Neural Network
(CNN) model in the production line to detect abrasive electroplated part
anomalies allowing us to fix or eliminate those parts or elements that are in
bad condition from the production chain and ultimately reduce manual quality
inspection cost. We used 744 samples to train our model. Our model successfully
identified over 99% of the parts with an anomaly. Keywords: Artificial
Intelligence, Anomaly Detection, Industrial Quality Inspection, Electroplating,
Diamond Abrasive Tool
|
[
{
"created": "Sun, 11 Dec 2022 20:14:18 GMT",
"version": "v1"
}
] |
2022-12-22
|
[
[
"Ali",
"Parviz",
""
]
] |
Electroplated diamond abrasive tools require nickel coating on a metal surface for abrasive bonding and part functionality. The electroplated nickel-coated abrasive tool is expected to have a high-quality part performance by having a nickel coating thickness of between 50% to 60% of the abrasive median diameter, uniformity of the nickel layer, abrasive distribution over the electroplated surface, and bright gloss. Electroplating parameters are set accordingly for this purpose. Industrial quality inspection for defects of these abrasive electroplated parts with optical inspection instruments is extremely challenging due to the diamond's light refraction, dispersion nature, and reflective bright nickel surface. The difficulty posed by this challenge requires parts to be quality inspected manually with an eye loupe that is subjective and costly. In this study, we use a Convolutional Neural Network (CNN) model in the production line to detect abrasive electroplated part anomalies allowing us to fix or eliminate those parts or elements that are in bad condition from the production chain and ultimately reduce manual quality inspection cost. We used 744 samples to train our model. Our model successfully identified over 99% of the parts with an anomaly. Keywords: Artificial Intelligence, Anomaly Detection, Industrial Quality Inspection, Electroplating, Diamond Abrasive Tool
|
1307.0214
|
Thijs Laarhoven
|
Thijs Laarhoven
|
Dynamic Traitor Tracing Schemes, Revisited
|
7 pages, 1 figure (6 subfigures), 1 table
|
IEEE Workshop on Information Forensics and Security (WIFS), pp.
191-196, 2013
|
10.1109/WIFS.2013.6707817
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We revisit recent results from the area of collusion-resistant traitor
tracing, and show how they can be combined and improved to obtain more
efficient dynamic traitor tracing schemes. In particular, we show how the
dynamic Tardos scheme of Laarhoven et al. can be combined with the optimized
score functions of Oosterwijk et al. to trace coalitions much faster. If the
attack strategy is known, in many cases the order of the code length goes down
from quadratic to linear in the number of colluders, while if the attack is not
known, we show how the interleaving defense may be used to catch all colluders
about twice as fast as in the dynamic Tardos scheme. Some of these results also
apply to the static traitor tracing setting where the attack strategy is known
in advance, and to group testing.
|
[
{
"created": "Sun, 30 Jun 2013 15:55:11 GMT",
"version": "v1"
}
] |
2016-11-17
|
[
[
"Laarhoven",
"Thijs",
""
]
] |
We revisit recent results from the area of collusion-resistant traitor tracing, and show how they can be combined and improved to obtain more efficient dynamic traitor tracing schemes. In particular, we show how the dynamic Tardos scheme of Laarhoven et al. can be combined with the optimized score functions of Oosterwijk et al. to trace coalitions much faster. If the attack strategy is known, in many cases the order of the code length goes down from quadratic to linear in the number of colluders, while if the attack is not known, we show how the interleaving defense may be used to catch all colluders about twice as fast as in the dynamic Tardos scheme. Some of these results also apply to the static traitor tracing setting where the attack strategy is known in advance, and to group testing.
|
1509.00721
|
Andrey Shchurov
|
Andrey A. Shchurov
|
A Multilayer Model of Computer Networks
|
5 pages, 4 figures. ISSN:2231-2803
|
International Journal of Computer Trends and Technology (IJCTT)
V26(1):12-16, August 2015
|
10.14445/22312803/IJCTT-V26P103
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The fundamental concept of applying the system methodology to network
analysis declares that network architecture should take into account services
and applications which this network provides and supports. This work introduces
a formal model of computer networks on the basis of the hierarchical multilayer
networks. In turn, individual layers are represented as multiplex networks. The
concept of layered networks provides conditions of top-down consistency of the
model. Next, we determined the necessary set of layers for network architecture
representation with regard to applying the system methodology to network
analysis.
|
[
{
"created": "Wed, 2 Sep 2015 14:34:53 GMT",
"version": "v1"
}
] |
2015-09-03
|
[
[
"Shchurov",
"Andrey A.",
""
]
] |
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the necessary set of layers for network architecture representation with regard to applying the system methodology to network analysis.
|
1811.00753
|
Kartik Ahuja
|
Kartik Ahuja, Mihaela van der Schaar
|
Risk-Stratify: Confident Stratification Of Patients Based On Risk
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A clinician desires to use a risk-stratification method that achieves
confident risk-stratification - the risk estimates of the different patients
reflect the true risks with a high probability. This allows him/her to use
these risks to make accurate predictions about prognosis and decisions about
screening, treatments for the current patient. We develop Risk-stratify - a two
phase algorithm that is designed to achieve confident risk-stratification. In
the first phase, we grow a tree to partition the covariate space. Each node in
the tree is split using statistical tests that determine if the risks of the
child nodes are different or not. The choice of the statistical tests depends
on whether the data is censored (Log-rank test) or not (U-test). The set of the
leaves of the tree form a partition. The risk distribution of patients that
belong to a leaf is different from the sibling leaf but not the rest of the
leaves. Therefore, some of the leaves that have similar underlying risks are
incorrectly specified to have different risks. In the second phase, we develop
a novel recursive graph decomposition approach to address this problem. We
merge the leaves of the tree that have similar risks to form new leaves that
form the final output. We apply Risk-stratify on a cohort of patients (with no
history of cardiovascular disease) from UK Biobank and assess their risk for
cardiovascular disease. Risk-stratify significantly improves
risk-stratification, i.e., a lower fraction of the groups have over/under
estimated risks (measured in terms of false discovery rate; 33% reduction) in
comparison to state-of-the-art methods for cardiovascular prediction (Random
forests, Cox model, etc.). We find that the Cox model significantly over
estimates the risk of 21,621 patients out of 216,211 patients. Risk-stratify
can accurately categorize 2,987 of these 21,621 patients as low-risk
individuals.
|
[
{
"created": "Fri, 2 Nov 2018 06:30:52 GMT",
"version": "v1"
}
] |
2018-11-05
|
[
[
"Ahuja",
"Kartik",
""
],
[
"van der Schaar",
"Mihaela",
""
]
] |
A clinician desires to use a risk-stratification method that achieves confident risk-stratification - the risk estimates of the different patients reflect the true risks with a high probability. This allows him/her to use these risks to make accurate predictions about prognosis and decisions about screening, treatments for the current patient. We develop Risk-stratify - a two phase algorithm that is designed to achieve confident risk-stratification. In the first phase, we grow a tree to partition the covariate space. Each node in the tree is split using statistical tests that determine if the risks of the child nodes are different or not. The choice of the statistical tests depends on whether the data is censored (Log-rank test) or not (U-test). The set of the leaves of the tree form a partition. The risk distribution of patients that belong to a leaf is different from the sibling leaf but not the rest of the leaves. Therefore, some of the leaves that have similar underlying risks are incorrectly specified to have different risks. In the second phase, we develop a novel recursive graph decomposition approach to address this problem. We merge the leaves of the tree that have similar risks to form new leaves that form the final output. We apply Risk-stratify on a cohort of patients (with no history of cardiovascular disease) from UK Biobank and assess their risk for cardiovascular disease. Risk-stratify significantly improves risk-stratification, i.e., a lower fraction of the groups have over/under estimated risks (measured in terms of false discovery rate; 33% reduction) in comparison to state-of-the-art methods for cardiovascular prediction (Random forests, Cox model, etc.). We find that the Cox model significantly over estimates the risk of 21,621 patients out of 216,211 patients. Risk-stratify can accurately categorize 2,987 of these 21,621 patients as low-risk individuals.
|
1812.03615
|
Isuru Godage
|
Jiahao Deng, Brandon H. Meng, Iyad Kanj, Isuru S. Godage
|
Near-optimal Smooth Path Planning for Multisection Continuum Arms
|
Submitted to 2019 IEEE International Conference on Soft Robotics
(RoboSoft 2019)
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the path planning problem for continuum-arm robots, in which we are
given a starting and an end point, and we need to compute a path for the tip of
the continuum arm between the two points. We consider both cases where
obstacles are present and where they are not. We demonstrate how to leverage
the continuum arm features to introduce a new model that enables a path
planning approach based on the configurations graph, for a continuum arm
consisting of three sections, each consisting of three muscle actuators. The
algorithm we apply to the configurations graph allows us to exploit parallelism
in the computation to obtain efficient implementation. We conducted extensive
tests, and the obtained results show the completeness of the proposed algorithm
under the considered discretizations, in both cases where obstacles are present
and where they are not. We compared our approach to the standard inverse
kinematics approach. While the inverse kinematics approach is much faster when
successful, our algorithm always succeeds in finding a path or reporting that
no path exists, compared to a roughly 70% success rate of the inverse
kinematics approach (when a path exists).
|
[
{
"created": "Mon, 10 Dec 2018 04:00:27 GMT",
"version": "v1"
}
] |
2018-12-11
|
[
[
"Deng",
"Jiahao",
""
],
[
"Meng",
"Brandon H.",
""
],
[
"Kanj",
"Iyad",
""
],
[
"Godage",
"Isuru S.",
""
]
] |
We study the path planning problem for continuum-arm robots, in which we are given a starting and an end point, and we need to compute a path for the tip of the continuum arm between the two points. We consider both cases where obstacles are present and where they are not. We demonstrate how to leverage the continuum arm features to introduce a new model that enables a path planning approach based on the configurations graph, for a continuum arm consisting of three sections, each consisting of three muscle actuators. The algorithm we apply to the configurations graph allows us to exploit parallelism in the computation to obtain efficient implementation. We conducted extensive tests, and the obtained results show the completeness of the proposed algorithm under the considered discretizations, in both cases where obstacles are present and where they are not. We compared our approach to the standard inverse kinematics approach. While the inverse kinematics approach is much faster when successful, our algorithm always succeeds in finding a path or reporting that no path exists, compared to a roughly 70% success rate of the inverse kinematics approach (when a path exists).
|
1011.6030
|
Bernard Cousin
|
Shadi Jawhar (IRISA), Bernard Cousin (IRISA)
|
Optical Multicast Routing Under Light Splitter Constraints
| null |
7th International Conference on Information Technology : New
Generations (ITNG 2010), Las Vegas : United States (2010)
|
10.1109/ITNG.2010.168
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
During the past few years, we have observed the emergence of new applications
that use multicast transmission. For a multicast routing algorithm to be
applicable in optical networks, it must route data only to group members,
optimize and maintain loop-free routes, and concentrate the routes on a subset
of network links. For an all-optical switch to play the role of a branching
router, it must be equipped with a light splitter. Light splitters are
expensive equipments and therefore it will be very expensive to implement
splitters on all optical switches. Optical light splitters are only implemented
on some optical switches. That limited availability of light splitters raises a
new problem when we want to implement multicast protocols in optical network
(because usual multicast protocols make the assumption that all nodes have
branching capabilities). Another issue is the knowledge of the locations of
light splitters in the optical network. Nodes in the network should be able to
identify the locations of light splitters scattered in the optical network so
it can construct multicast trees. These problems must be resolved by
implementing a multicast routing protocol that must take into consideration
that not all nodes can be branching node. As a result, a new signaling process
must be implemented so that light paths can be created, spanning from source to
the group members.
|
[
{
"created": "Sun, 28 Nov 2010 11:04:01 GMT",
"version": "v1"
}
] |
2010-11-30
|
[
[
"Jawhar",
"Shadi",
"",
"IRISA"
],
[
"Cousin",
"Bernard",
"",
"IRISA"
]
] |
During the past few years, we have observed the emergence of new applications that use multicast transmission. For a multicast routing algorithm to be applicable in optical networks, it must route data only to group members, optimize and maintain loop-free routes, and concentrate the routes on a subset of network links. For an all-optical switch to play the role of a branching router, it must be equipped with a light splitter. Light splitters are expensive equipments and therefore it will be very expensive to implement splitters on all optical switches. Optical light splitters are only implemented on some optical switches. That limited availability of light splitters raises a new problem when we want to implement multicast protocols in optical network (because usual multicast protocols make the assumption that all nodes have branching capabilities). Another issue is the knowledge of the locations of light splitters in the optical network. Nodes in the network should be able to identify the locations of light splitters scattered in the optical network so it can construct multicast trees. These problems must be resolved by implementing a multicast routing protocol that must take into consideration that not all nodes can be branching node. As a result, a new signaling process must be implemented so that light paths can be created, spanning from source to the group members.
|
2012.04733
|
Jiaqi Wang
|
Jiaqi Wang, Kai Chen, Rui Xu, Ziwei Liu, Chen Change Loy, Dahua Lin
|
CARAFE++: Unified Content-Aware ReAssembly of FEatures
|
Technical Report. Extended journal version of the conference paper
that appeared as arXiv:1905.02188
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Feature reassembly, i.e. feature downsampling and upsampling, is a key
operation in a number of modern convolutional network architectures, e.g.,
residual networks and feature pyramids. Its design is critical for dense
prediction tasks such as object detection and semantic/instance segmentation.
In this work, we propose unified Content-Aware ReAssembly of FEatures
(CARAFE++), a universal, lightweight and highly effective operator to fulfill
this goal. CARAFE++ has several appealing properties: (1) Unlike conventional
methods such as pooling and interpolation that only exploit sub-pixel
neighborhood, CARAFE++ aggregates contextual information within a large
receptive field. (2) Instead of using a fixed kernel for all samples (e.g.
convolution and deconvolution), CARAFE++ generates adaptive kernels on-the-fly
to enable instance-specific content-aware handling. (3) CARAFE++ introduces
little computational overhead and can be readily integrated into modern network
architectures. We conduct comprehensive evaluations on standard benchmarks in
object detection, instance/semantic segmentation and image inpainting. CARAFE++
shows consistent and substantial gains across all the tasks (2.5% APbox, 2.1%
APmask, 1.94% mIoU, 1.35 dB respectively) with negligible computational
overhead. It shows great potential to serve as a strong building block for
modern deep networks.
|
[
{
"created": "Mon, 7 Dec 2020 07:34:57 GMT",
"version": "v1"
}
] |
2020-12-10
|
[
[
"Wang",
"Jiaqi",
""
],
[
"Chen",
"Kai",
""
],
[
"Xu",
"Rui",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Loy",
"Chen Change",
""
],
[
"Lin",
"Dahua",
""
]
] |
Feature reassembly, i.e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e.g., residual networks and feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose unified Content-Aware ReAssembly of FEatures (CARAFE++), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE++ has several appealing properties: (1) Unlike conventional methods such as pooling and interpolation that only exploit sub-pixel neighborhood, CARAFE++ aggregates contextual information within a large receptive field. (2) Instead of using a fixed kernel for all samples (e.g. convolution and deconvolution), CARAFE++ generates adaptive kernels on-the-fly to enable instance-specific content-aware handling. (3) CARAFE++ introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and image inpainting. CARAFE++ shows consistent and substantial gains across all the tasks (2.5% APbox, 2.1% APmask, 1.94% mIoU, 1.35 dB respectively) with negligible computational overhead. It shows great potential to serve as a strong building block for modern deep networks.
|
1903.02639
|
Keegan Lensink
|
Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto
|
IMEXnet: A Forward Stable Deep Neural Network
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep convolutional neural networks have revolutionized many machine learning
and computer vision tasks, however, some remaining key challenges limit their
wider use. These challenges include improving the network's robustness to
perturbations of the input image and the limited ``field of view'' of
convolution operators. We introduce the IMEXnet that addresses these challenges
by adapting semi-implicit methods for partial differential equations. Compared
to similar explicit networks, such as residual networks, our network is more
stable, which has recently shown to reduce the sensitivity to small changes in
the input features and improve generalization. The addition of an implicit step
connects all pixels in each channel of the image and therefore addresses the
field of view problem while still being comparable to standard convolutions in
terms of the number of parameters and computational complexity. We also present
a new dataset for semantic segmentation and demonstrate the effectiveness of
our architecture using the NYU Depth dataset.
|
[
{
"created": "Wed, 6 Mar 2019 22:33:06 GMT",
"version": "v1"
},
{
"created": "Fri, 17 May 2019 21:45:28 GMT",
"version": "v2"
}
] |
2019-05-21
|
[
[
"Haber",
"Eldad",
""
],
[
"Lensink",
"Keegan",
""
],
[
"Treister",
"Eran",
""
],
[
"Ruthotto",
"Lars",
""
]
] |
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challenges include improving the network's robustness to perturbations of the input image and the limited ``field of view'' of convolution operators. We introduce the IMEXnet that addresses these challenges by adapting semi-implicit methods for partial differential equations. Compared to similar explicit networks, such as residual networks, our network is more stable, which has recently shown to reduce the sensitivity to small changes in the input features and improve generalization. The addition of an implicit step connects all pixels in each channel of the image and therefore addresses the field of view problem while still being comparable to standard convolutions in terms of the number of parameters and computational complexity. We also present a new dataset for semantic segmentation and demonstrate the effectiveness of our architecture using the NYU Depth dataset.
|
2404.14779
|
Cl\'ement Christophe
|
Cl\'ement Christophe, Praveen K Kanithi, Prateek Munjal, Tathagata
Raha, Nasir Hayat, Ronnie Rajan, Ahmed Al-Mahrooqi, Avani Gupta, Muhammad
Umar Salman, Gurpreet Gosal, Bhargav Kanakiya, Charles Chen, Natalia
Vassilieva, Boulbaba Ben Amor, Marco AF Pimentel, Shadab Khan
|
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs:
Full-Parameter vs. Parameter-Efficient Approaches
|
Published at AAAI 2024 Spring Symposium - Clinical Foundation Models
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
This study presents a comprehensive analysis and comparison of two
predominant fine-tuning methodologies - full-parameter fine-tuning and
parameter-efficient tuning - within the context of medical Large Language
Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2
architecture, specifically designed to enhance medical knowledge retrieval,
reasoning, and question-answering capabilities. Our experiments systematically
evaluate the effectiveness of these tuning strategies across various well-known
medical benchmarks. Notably, our medical LLM Med42 showed an accuracy level of
72% on the US Medical Licensing Examination (USMLE) datasets, setting a new
standard in performance for openly available medical LLMs. Through this
comparative analysis, we aim to identify the most effective and efficient
method for fine-tuning LLMs in the medical domain, thereby contributing
significantly to the advancement of AI-driven healthcare applications.
|
[
{
"created": "Tue, 23 Apr 2024 06:36:21 GMT",
"version": "v1"
}
] |
2024-04-24
|
[
[
"Christophe",
"Clément",
""
],
[
"Kanithi",
"Praveen K",
""
],
[
"Munjal",
"Prateek",
""
],
[
"Raha",
"Tathagata",
""
],
[
"Hayat",
"Nasir",
""
],
[
"Rajan",
"Ronnie",
""
],
[
"Al-Mahrooqi",
"Ahmed",
""
],
[
"Gupta",
"Avani",
""
],
[
"Salman",
"Muhammad Umar",
""
],
[
"Gosal",
"Gurpreet",
""
],
[
"Kanakiya",
"Bhargav",
""
],
[
"Chen",
"Charles",
""
],
[
"Vassilieva",
"Natalia",
""
],
[
"Amor",
"Boulbaba Ben",
""
],
[
"Pimentel",
"Marco AF",
""
],
[
"Khan",
"Shadab",
""
]
] |
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2 architecture, specifically designed to enhance medical knowledge retrieval, reasoning, and question-answering capabilities. Our experiments systematically evaluate the effectiveness of these tuning strategies across various well-known medical benchmarks. Notably, our medical LLM Med42 showed an accuracy level of 72% on the US Medical Licensing Examination (USMLE) datasets, setting a new standard in performance for openly available medical LLMs. Through this comparative analysis, we aim to identify the most effective and efficient method for fine-tuning LLMs in the medical domain, thereby contributing significantly to the advancement of AI-driven healthcare applications.
|
1812.03304
|
Peiyao Shen
|
Peiyao Shen, Xuebo Zhang and Yongchun Fang
|
Real-time Acceleration-continuous Path-constrained Trajectory Planning
With Built-in Tradability Between Cruise and Time-optimal Motions
|
12 pages, 19 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, a novel real-time acceleration-continuous path-constrained
trajectory planning algorithm is proposed with an appealing built-in
tradability mechanism between cruise motion and time-optimal motion. Different
from existing approaches, the proposed approach smoothens time-optimal
trajectories with bang-bang input structures to generate
acceleration-continuous trajectories while preserving the completeness
property. More importantly, a novel built-in tradability mechanism is proposed
and embedded into the trajectory planning framework, so that the proportion of
the cruise motion and time-optimal motion can be flexibly adjusted by changing
a user-specified functional parameter. Thus, the user can easily apply the
trajectory planning algorithm for various tasks with different requirements on
motion efficiency and cruise proportion. Moreover, it is shown that feasible
trajectories are computed more quickly than optimal trajectories. Rigorous
mathematical analysis and proofs are provided for these aforementioned results.
Comparative simulation and experimental results on omnidirectional wheeled
mobile robots demonstrate the capability of the proposed algorithm in terms of
flexible tunning between cruise and time-optimal motions, as well as higher
computational efficiency.
|
[
{
"created": "Sat, 8 Dec 2018 12:02:49 GMT",
"version": "v1"
}
] |
2018-12-11
|
[
[
"Shen",
"Peiyao",
""
],
[
"Zhang",
"Xuebo",
""
],
[
"Fang",
"Yongchun",
""
]
] |
In this paper, a novel real-time acceleration-continuous path-constrained trajectory planning algorithm is proposed with an appealing built-in tradability mechanism between cruise motion and time-optimal motion. Different from existing approaches, the proposed approach smoothens time-optimal trajectories with bang-bang input structures to generate acceleration-continuous trajectories while preserving the completeness property. More importantly, a novel built-in tradability mechanism is proposed and embedded into the trajectory planning framework, so that the proportion of the cruise motion and time-optimal motion can be flexibly adjusted by changing a user-specified functional parameter. Thus, the user can easily apply the trajectory planning algorithm for various tasks with different requirements on motion efficiency and cruise proportion. Moreover, it is shown that feasible trajectories are computed more quickly than optimal trajectories. Rigorous mathematical analysis and proofs are provided for these aforementioned results. Comparative simulation and experimental results on omnidirectional wheeled mobile robots demonstrate the capability of the proposed algorithm in terms of flexible tunning between cruise and time-optimal motions, as well as higher computational efficiency.
|
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