Relevant bibliographies by topics / Stochastic processing networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stochastic processing networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Stochastic processing networks"

1

Williams, Ruth J. "Stochastic Processing Networks." Annual Review of Statistics and Its Application 3, no. 1 (2016): 323–45. http://dx.doi.org/10.1146/annurev-statistics-010814-020141.

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Shah, Devavrat. "Message-passing in stochastic processing networks." Surveys in Operations Research and Management Science 16, no. 2 (2011): 83–104. http://dx.doi.org/10.1016/j.sorms.2011.03.002.

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Dai, J. G., and Wuqin Lin. "Maximum Pressure Policies in Stochastic Processing Networks." Operations Research 53, no. 2 (2005): 197–218. http://dx.doi.org/10.1287/opre.1040.0170.

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Kella, Offer, and Ward Whitt. "Linear stochastic fluid networks." Journal of Applied Probability 36, no. 01 (1999): 244–60. http://dx.doi.org/10.1017/s0021900200017009.

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We introduce open stochastic fluid networks that can be regarded as continuous analogues or fluid limits of open networks of infinite-server queues. Random exogenous input may come to any of the queues. At each queue, a c.d.f.-valued stochastic process governs the proportion of the input processed by a given time after arrival. The routeing may be deterministic (a specified sequence of successive queue visits) or proportional, i.e. a stochastic transition matrix may govern the proportion of the output routed from one queue to another. This stochastic fluid network with deterministic c.d.f.s go
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Kella, Offer, and Ward Whitt. "Linear stochastic fluid networks." Journal of Applied Probability 36, no. 1 (1999): 244–60. http://dx.doi.org/10.1239/jap/1032374245.

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We introduce open stochastic fluid networks that can be regarded as continuous analogues or fluid limits of open networks of infinite-server queues. Random exogenous input may come to any of the queues. At each queue, a c.d.f.-valued stochastic process governs the proportion of the input processed by a given time after arrival. The routeing may be deterministic (a specified sequence of successive queue visits) or proportional, i.e. a stochastic transition matrix may govern the proportion of the output routed from one queue to another. This stochastic fluid network with deterministic c.d.f.s go
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Gao, Zhan, Elvin Isufi, and Alejandro Ribeiro. "Stochastic Graph Neural Networks." IEEE Transactions on Signal Processing 69 (2021): 4428–43. http://dx.doi.org/10.1109/tsp.2021.3092336.

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Ziv, Etay, Ilya Nemenman, and Chris H. Wiggins. "Optimal Signal Processing in Small Stochastic Biochemical Networks." PLoS ONE 2, no. 10 (2007): e1077. http://dx.doi.org/10.1371/journal.pone.0001077.

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Bassamboo, Achal, J. Michael Harrison, and Assaf Zeevi. "Pointwise Stationary Fluid Models for Stochastic Processing Networks." Manufacturing & Service Operations Management 11, no. 1 (2009): 70–89. http://dx.doi.org/10.1287/msom.1070.0195.

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Yuan, Yuan, Kui Wu, Weijia Jia, and Yuming Jiang. "Performance of Acyclic Stochastic Networks with Network Coding." IEEE Transactions on Parallel and Distributed Systems 22, no. 7 (2011): 1238–45. http://dx.doi.org/10.1109/tpds.2010.192.

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Afèche, Philipp. "Delay performance in stochastic processing networks with priority service." Operations Research Letters 31, no. 5 (2003): 390–400. http://dx.doi.org/10.1016/s0167-6377(03)00021-x.

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Dissertations / Theses on the topic "Stochastic processing networks"

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Lin, Wuqin. "Dynamic Control in Stochastic Processing Networks." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7105.

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A stochastic processing network is a system that takes materials of various kinds as inputs, and uses processing resources to produce other materials as outputs. Such a network provides a powerful abstraction of a wide range of real world, complex systems, including semiconductor wafer fabrication facilities, networks of data switches, and large-scale call centers. Key performance measures of a stochastic processing network include throughput, cycle time, and holding cost. The network performance can dramatically be affected by the choice of operational policies. We propose a family of operat
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Zhong, Yuan Ph D. Massachusetts Institute of Technology Operations Research Center. "Resource allocation in stochastic processing networks : performance and scaling." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77828.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 189-193).<br>This thesis addresses the design and analysis of resource allocation policies in largescale stochastic systems, motivated by examples such as the Internet, cloud facilities, wireless networks, etc. A canonical framework for modeling many such systems is provided by "stochastic processing networks" (SPN) (Harrison [28, 29]). In this context, the key operational challenge is efficient
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Rising, Barry John Paul. "Hardware architectures for stochastic bit-stream neural networks : design and implementation." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326219.

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Ling, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems’ natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in
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Rogge-Solti, Andreas, Ronny S. Mans, der Aalst Wil M. P. van, and Mathias Weske. "Repairing event logs using stochastic process models." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6679/.

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Companies strive to improve their business processes in order to remain competitive. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect. I
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Liu, Kai. "Detecting stochastic motifs in network and sequence data for human behavior analysis." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/60.

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With the recent advent of Web 2.0, mobile computing, and pervasive sensing technologies, human activities can readily be logged, leaving digital traces of di.erent forms. For instance, human communication activities recorded in online social networks allow user interactions to be represented as “network” data. Also, human daily activities can be tracked in a smart house, where the log of sensor triggering events can be represented as “sequence” data. This thesis research aims to develop computational data mining algorithms using the generative modeling approach to extract salient patterns (mot
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Brown, Louise Eleanor. "Asymptotic behaviour of an overloading queueing network with resource pooling." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25644.

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Rogge-Solti, Andreas. "Probabilistic Estimation of Unobserved Process Events." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7042/.

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Organizations try to gain competitive advantages, and to increase customer satisfaction. To ensure the quality and efficiency of their business processes, they perform business process management. An important part of process management that happens on the daily operational level is process controlling. A prerequisite of controlling is process monitoring, i.e., keeping track of the performed activities in running process instances. Only by process monitoring can business analysts detect delays and react to deviations from the expected or guaranteed performance of a process instance. To enable
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Aguiar, Eliane Martins de. "Aplicação do Word2vec e do Gradiente descendente dstocástico em tradução automática." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/16798.

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Submitted by Eliane Martins de Aguiar (elianemart@gmail.com) on 2016-08-01T21:03:09Z No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2016-08-03T20:29:34Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5)<br>Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-08-23T20:12:35Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 606
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Dionigi, Pierfrancesco. "A random matrix theory approach to complex networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18513/.

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Si presenta un approccio matematico formale ai complex networks tramite l'uso della Random Matrix Theory (RMT). La legge del semicerchio di Wigner viene presentata come una generalizzazione del Teorema del Limite Centrale per determinati ensemble di matrici random. Sono presentati inoltre i principali metodi per calcolare la distribuzione spettrale delle matrici random e se ne sottolineano le differenze. Si è poi studiato come la RMT sia collegata alla Free Probability. Si è studiato come due tipi di grafi random apparentemente uguali, posseggono proprietà spettrali differenti analizzando le l
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Books on the topic "Stochastic processing networks"

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Nicol, David M. Parallel discrete-event simulation of FCFS stochastic queueing networks. ICASE, 1988.

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Shen, Bo. Nonlinear Stochastic Systems with Incomplete Information: Filtering and Control. Springer London, 2013.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Stochastic and neural methods in signal processing, image processing, and computer vision: 24-26 July 1991, San Diego, California. SPIE, 1991.

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Su-shing, Chen, Society of Photo-optical Instrumentation Engineers., and Society for Industrial and Applied Mathematics., eds. Neural and stochastic methods in image and signal processing: 20-23 July 1992, San Diego, California. SPIE, 1992.

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Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing III: 28-29 July 1994, San Diego, California. SPIE, 1994.

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Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing II: 12-13 July 1993, San Diego, California. SPIE, 1993.

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Shen, Bo, Zidong Wang, and Huisheng Shu. Nonlinear Stochastic Systems with Incomplete Information. Springer, 2013.

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R, Dougherty Edward, and Society of Photo-optical Instrumentation Engineers., eds. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. SPIE, 1995.

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Book chapters on the topic "Stochastic processing networks"

1

Dai, Jim, and Otis B. Jennings. "Stability of General Processing Networks." In Stochastic Modeling and Optimization. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21757-4_7.

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Chen, Hong. "A Brownian Model of Stochastic Processing Networks." In Stochastic Modeling and Optimization. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21757-4_6.

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Shu, Zhan, and James Lam. "Delay-Dependent Exponential Estimates of Stochastic Neural Networks with Time Delay." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_38.

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Oyedotun, Oyebade K., Abd El Rahman Shabayek, Djamila Aouada, and Björn Ottersten. "Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_3.

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Zhu, Song, Yi Shen, and Guici Chen. "Robust Stability of Stochastic Neural Networks with Interval Discrete and Distributed Delays." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_29.

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Viazovychenko, Yuliia, and Oleksiy Larin. "Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66717-7_55.

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Maravall, Darío, and Javier de Lope. "Neuro Granular Networks with Self-learning Stochastic Connections: Fusion of Neuro Granular Networks and Learning Automata Theory." In Advances in Neuro-Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_125.

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Tang, Tong Boon, Hsin Chen, and Alan F. Murray. "Adaptive Stochastic Classifier for Noisy pH-ISFET Measurements." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_76.

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Hsu, Wei-jen, and Zsehong Tsai. "A Call Admission Control Algorithm Based on Stochastic Performance Bound for Wireless Networks." In Advances in Multimedia Information Processing — PCM 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36228-2_83.

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Shikalgar, Arifa, and Shefali Sonavane. "An Enhanced Stochastic Gradient Descent Variance Reduced Ascension Optimization Algorithm for Deep Neural Networks." In Applied Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4029-5_38.

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Conference papers on the topic "Stochastic processing networks"

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Gao, Zhan, Elvin Isufi, and Alejandro Ribeiro. "Stochastic Graph Neural Networks." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054424.

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Lyshevski, Sergey Edward, Vlad Shmerko, Marina Alexandra Lyshevski, and Svetlana Yanushchkevich. "Neuronal Processing, Reconfigurable Neural Networks and Stochastic Computing." In 2008 8th IEEE Conference on Nanotechnology (NANO). IEEE, 2008. http://dx.doi.org/10.1109/nano.2008.216.

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Ouabi, Othmane-Latif, Radmila Pribic, and Sorin Olaru. "Stochastic Complex-valued Neural Networks for Radar." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287425.

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Calvo-Fullana, Miguel, Javier Matamoros, Carles Anton-Haro, and Alejandro Ribeiro. "Stochastic backpressure in energy harvesting networks." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952852.

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Jiang, Libin, and Jean Walrand. "Stable and utility-maximizing scheduling for stochastic processing networks." In 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2009. http://dx.doi.org/10.1109/allerton.2009.5394870.

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Zheng, Jun, Li Yu, and Peng Yang. "Throughput analysis of cognitive radio networks via stochastic network calculus." In 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2014. http://dx.doi.org/10.1109/wcsp.2014.6992170.

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Hajiramezanali, Ehsan, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, and Xiaoning Qian. "Semi-Implicit Stochastic Recurrent Neural Networks." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053491.

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Zaki, Ahmed, Arun Venkitaraman, Saikat Chatterjee, and Lars K. Rasmussen. "Distributed greedy sparse learning over doubly stochastic networks." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081229.

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Touzene, A. "Approximated tensor sum preconditioner for stochastic automata networks." In Proceedings 20th IEEE International Parallel & Distributed Processing Symposium. IEEE, 2006. http://dx.doi.org/10.1109/ipdps.2006.1639649.

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Leung, H. C., I. L. Heherington, and V. W. Zue. "Speech recognition using stochastic segment neural networks." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.225834.

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