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Showing 20 of 757 academic articles
Bengt Jönsson • ACM Transactions on Programming Languages and Systems
(1994)
We present a method for specification and verification of distributed systems that communicate via asynchronous message passing. The method handles both safety and liveness properties. It is compositional, i.e., a specification of a composi...
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Open Access
DOI: https://doi.org/10.1145/174662.174665
Andreas Bolfing • Oxford University Press eBooks
(2020)
Abstract Chapter 5 considers distributed systems by their properties. The first section studies the classification of software systems, which is usually distinguished in centralized, decentralized and distributed systems. It studies the dif...
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DOI: https://doi.org/10.1093/oso/9780198862840.003.0005
Koushik Sen, Abhay Vardhan, Gul Agha, Grigore Roşu • ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
(2004)
This paper presents a variant of past time linear temporal logic, called PT-DTL, suitable for expressing temporal properties of distributed systems. The formulae in this logic are given with respect to a particular process and are interpret...
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DOI: https://doi.org/10.1109/icse.2004.1317464
Héctor García-Molina, Daniel Barbará • Journal of the ACM
(1985)
In a distributed system, one strategy for achieving mutual exclusion of groups of nodes without communication is to assign to each node a number of votes. Only a group with a majority of votes can execute the critical operations, and mutual...
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Open Access
DOI: https://doi.org/10.1145/4221.4223
Ioannis Georgiadis, Jeff Magee, Jeff Kramer • Workshop on Self-healing systems
(2002)
A self-organising software architecture is one in which components automatically configure their interaction in a way that is compatible with an overall architectural specification. The objective is to minimise the degree of explicit manage...
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DOI: https://doi.org/10.1145/582128.582135
Harish K. Pillai, Shiva Shankar • SIAM Journal on Control and Optimization
(1999)
This paper develops a theory of control for distributed systems (i.e., those defined by systems of constant coefficient partial differential operators) via the behavioral approach of Willems. The study here is algebraic in the sense that it...
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DOI: https://doi.org/10.1137/s0363012997321784
Daniel Conte de Leon, Georgios Michail Makrakis, Constantinos Kolias • Wiley eBooks
(2021)
This chapter describes fundamental concepts of security and cybersecurity within the context of Industrial Control Systems (ICSs) and Cyber–Physical Systems (CPSs) and their control subsystems. It identifies the fundamental properties of a ...
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DOI: https://doi.org/10.1002/9781119660446.ch7
Leslie F. Sikos • Knowledge and Information Systems
(2023)
Abstract Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist s...
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DOI: https://doi.org/10.1007/s10115-023-01860-3
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications
(2022)
Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...
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DOI: https://doi.org/10.1007/s12243-022-00926-7
Haim Sompolinsky • Physics Today
(1988)
A neural network is a large, highly interconnected assembly of simple elements. The elements, called neurons, are usually two-state devices that switch from one state to the other when their input exceeds a specific threshold value. In this...
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DOI: https://doi.org/10.1063/1.881142
Jianlin Cheng, Zheng Wang, Gianluca Pollastri • International Joint Conference on Neural Networks (IJCNN)
(2008)
Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...
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Open Access
DOI: https://doi.org/10.1109/ijcnn.2008.4633963
Igor I. Baskin, David Alan Winkler, Igor V. Tetko • Expert Opinion on Drug Discovery
(2016)
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...
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DOI: https://doi.org/10.1080/17460441.2016.1201262
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler et al. • arXiv (Cornell University)
(2018)
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1806.08734
Hongsik Jeong, Luping Shi • Journal of Physics D Applied Physics
(2018)
Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...
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DOI: https://doi.org/10.1088/1361-6463/aae223
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Dong-Ling Deng, Xiaopeng Li, S. Das Sarma • Physical Review X
(2017)
Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...
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DOI: https://doi.org/10.1103/physrevx.7.021021
Daniel J. Amit, Hanoch Gutfreund, Haim Sompolinsky • Physical Review A
(1985)
Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glas...
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DOI: https://doi.org/10.1103/physreva.32.1007
Chris Bishop • Choice Reviews Online
(1994)
From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...
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DOI: https://doi.org/10.5860/choice.31-5500
Chris Bishop (1995)
Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes technique...
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DOI: https://doi.org/10.1093/oso/9780198538493.001.0001