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检索条件"任意字段=10th International Workshop on Algorithms and Data Structures"

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Monte Carlo Tree Search for Cross-Stratum Optimization of Survivable Inter-data Center Elastic Optical Network

Monte Carlo Tree Search for Cross-Stratum Optimization of Su...

引用

international workshop on Reliable Networks Design and Modeling (RNDM)

作者： Michał Aibin Krzysztof Walkowiak Department of Computing British Columbia Institute of Technology Vancouver British Columbia Canada Department of Systems and Computer Networks Faculty of Electronics Wroclaw University of Science and Technology Poland

In last few years, cloud computing and other services based on data centers have evolved from an emerging technology to a recognized approach that is gaining broad acceptance and deployment. therefore, there is a significant need to provide efficient and reliable operation of inter-data center networks based on optical technologies. In this article, we focus on cross stratum optimization of an inter-data center elastic optical network with additional survivability requirements. We propose a novel optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for simulation of future traffic to improve the performance of the network regarding blocking probability and operational cost. We evaluate the performance of the proposed method and other reference algorithms under various network scenarios, using representative topologies and real data provided by Amazon Web Services. the main conclusion is that the approach based on the MCTS algorithm enables better coordination of resource allocation in both strata, which results in lower blocking of requests and lower cost taking into account the survivability requirements, in comparison to other algorithms.

关键词： Optical fiber networks data centers Optimization Cloud computing Monte Carlo methods Modulation Resource management

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Accurate data and context management in message-passing programs 12th

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12th international workshop on Languages and Compilers for Parallel Computing, LCPC 1999

作者： Chakrabarti, Dhruva R. Banerjee, Prithviraj Center for Parallel and Distributed Computing ECE Dept Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States

ISBN: (纸本)9783540678588

this paper presents a novel scheme for maintaining accurate information about distributed data in message-passing programs. the ability to maintain dynamically the data-to-processor mapping as well as the program contexts at which state changes occur enable a variety of sophisticated optimizations. the algorithms described in this paper are based on the static single assignment (SSA) form of message-passing programs which can be used for performing many of the classical compiler optimizations during automatic parallelization as well as for analyzing user-written message-passing programs. Reaching de_nition analysis is performed on SSA-structures for determining a suitable communication point. the scheme addresses possible optimizations and shows how appropriate representation of the data structures can substantially reduce the associated overheads. Our scheme uniformly handles arbitrary subscripts in array references and can handle general reducible control flow. Experimental results for a number of benchmarks on an IBM SP-2 show a conspicuous reduction in inter-processor communication as well as a marked improvement in the total run-times. We have observed up to around 10-25% reduction in total run-times in our SSA-based schemes compared to non-SSA-based schemes on 16 processors. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Program compilers

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Integrated management of 10G-PON network element using NETCONF and OpenFlow

Integrated management of 10G-PON network element using NETCO...

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international Conference on Network and Service Management

作者： André Berti Sassi Manuel Santos Gavidia Eder Leão Fernandes Marcelo Ribeiro Nascimento Converged Networks Corporate Department CPqD Research and Development Center in Telecommunications Campinas Brazil

this paper describes an integrated management architecture and solution for controlling network elements composed of controller, optional aggregation switch and a variable number of optical line terminals (OLTs), including multiple OLTs based on different technologies and presenting distinct features. the YANG language was used to model configuration and state data manipulated by the Network Configuration Protocol (NETCONF). the system allows the configuration of OpenFlow switches, providing greater protocol flexibility and forwarding algorithms to the aggregation network. the architecture has been implemented and tested using a real Gigabit-capable Passive Optical Network (GPON), a stacked GPON and switch equipment in a variety of scenarios.

关键词： Passive optical networks Switches Optical network units data models Ports (Computers) Software

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Optimizing strategies for the exploration of social networks and associated data collections

Optimizing strategies for the exploration of social networks...

引用

international workshop on Image Analysis for Multimedia Interactive Services, WIAMIS

作者： S. Marchand-Maillet E. Szekely E. Bruno Viper group-Department of Computer Science University of Geneva Carouge Switzerland

Multimedia data collections immersed into social networks may be explored from the point of view of varying documents and users characteristics. In this paper, we develop a unified model to embed documents and users into coherent structures from which to extract optimal subsets. the result is the definition of guiding navigation strategies of both the user and document networks, as a complement to classical search operations. An initial interface that may materialize such browsing over documents is demonstrated in the context of cultural heritage.

关键词： Navigation Context Social network services data mining Distance measurement Symmetric matrices Multimedia communication

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mODa 10 – Advances in Model-Oriented Design and Analysis: Proceedings of the 10th international workshop in Model-Oriented Design and Analysis Held in Łagów Lubuski, Poland, June 10–14, 2013 1

引用

丛书名： Contributions to Statistics

2013年

作者： Giacomo Aletti, Caterina May, Chiara Tommasi (auth.), Dariusz Ucinski, Anthony C. Atkinson, Maciej Patan (eds.)

ISBN: (数字)9783319002187

ISBN: (纸本)9783319002170;9783319002187

this book collects the proceedings of the 10th workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. the methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. this increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. this book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. the topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.

关键词：统计理论和方法为生命科学的统计,药,健康科学为工程,物理,计算机科学,化学和地球科学的统计统计和计算统计程序

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A new SHM strategy using in situ modal analysis processing to monitor civil engineering structures 10

A new SHM strategy using in situ modal analysis processing t...

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10th international Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021

作者： Fontan, Maxime Guerineau, Laurent Apave SA Immeuble Canopy 6 rue du Général Audran COURBEVOIE Cedex 92 412 France SERCEL 16 Rue de bel air Carquefou 44470 France

this article introduces an application of the SHM solution 'AP'Structure' which has been jointly developed by Apave and Sercel. An ageing and decommissioned concrete bridge built more than 70 years ago was monitored using both new and standard sensors, in order to perform an Operational Modal Analysis (OMA) on site. the collected raw data were compared and analyzed in the frequency domain, using several algorithms such as Stochastic Subspace Identification or Enhanced Frequency Domain Decomposition. the comparison of the results not only highlights the efficiency of the sensors in terms of accuracy of the identification of the dynamics properties, but also their operational effectiveness on site in order to improve the measurement ratio per day using an improved communication system. then the collected data were used to calibrate a Finite Element model of the bridge using the MAC matrix, so as to estimate the feasibility of using this structure as a pedestrian bridge. the comparison of the final results between two FE models developed either with and/or without OMA data highlights that the use of dynamics properties improves the knowledge of the structure and avoids inappropriate conclusions in terms of the use of the ageing bridge. Indeed, the application of the French regulations code to verify a pedestrian bridge has been applied and conclusions may be quite different according to whether the FE model is calibrated on modal basis or not. © 2021 international Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Decision making

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Analysis of Machine Learning algorithms in Big data Analytics

Analysis of Machine Learning Algorithms in Big Data Analytic...

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international Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Er. Sandeep Kaur Anand Kumar Shukla Chandigarh University Punjab India

ISBN: (纸本)9781665439718

Massive information being a brilliant resource of information and understanding from structures to the different end users. However, managing this much quantity of information wishes automation, thus leading to a fashion of statistics processing along with gadget mastering techniques. Inside the ict region, as in various areas of trade and evaluation, structures and equipment have been furnished and advanced in assisting the specialists to deal with the information and study from it routinely. Maximum of the systems returned from large corporations such as Microsoft or Google, or from the apache foundation’s incubators. this evaluation reveals gadget mastering algorithms in big records analytics, and gadget mastering challenges us to make selections where it may be no recognized "right course" for the specified trouble based on the previous training and tallies a number of the headmost used gear to analyze and model massive-statistics.

关键词： Training Analytical models Machine learning algorithms Automation Gears Collaboration Big data

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Parallel frequent itemset mining on streaming data

Parallel frequent itemset mining on streaming data

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international Conference on Natural Computation (ICNC)

作者： Yanshan He Min Yue Electronic and Information Science Department Lanzhou Jiaotong University Lanzhou Gansu China Institute of Modern Physics Chinese Academy of Sciences Lanzhou Gansu China

ISBN: (纸本)9781479951529

Owing to the widely used of data stream, frequent itemset mining on data stream have received more attention. data stream is fast changing, massive, and potentially infinite. therefore, we have to establish new data structure and algorithm to mine it. On the base of our previous work, we propose a new paralleled frequent itemset mining algorithm for data stream based on sliding window, which is called PFIMSD. the algorithm compresses whole data in current window into PSD-trees on paralleled processor only by one-scan. Increment method is used to append or delete related branch on PSD-tree when window is sliding. the experiment shows PFIMSD algorithm has good performance on efficiency and expansibility.

关键词： data mining Algorithm design and analysis Itemsets data structures Approximation algorithms Parallel algorithms

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Connection machine vision-Replicated data structures

Connection machine vision-Replicated data structures

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international Conference on Pattern Recognition

作者： L.S. Davis L.T. Chen P.J. Narayanan Computer Vision Laboratory University of Maryland College Park MD USA

the problem of efficiently processing small data structures on massively parallel single-instruction multiple-data machines using replication methods is discussed. the problem stems from considerations of both multiresolution vision systems and focus of attention vision systems. A general framework for developing replicated algorithms, based on the four steps of embedding, distribution, decomposition, and collection, is described. A simple example is provided based on computing the histogram of a gray-level image. Replicated chain processing is discussed, and an efficient algorithm for ranking the elements in a chain in log (n) time on a concurrent write parallel random access machine is presented.< >

关键词： data structures Spatial resolution Image processing Image analysis Pixel Computer vision Machine vision Focusing Phase change random access memory Image resolution

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Identifying Partial Mouse Brain Microscopy Images from the Allen Reference Atlas Using a Contrastively Learned Semantic Space 10th

Identifying Partial Mouse Brain Microscopy Images from the A...

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10th international workshop on Biomedical Image Registration (WBIR)

作者： Antanavicius, Justinas Leiras, Roberto Selvan, Raghavendra Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Neurosci Copenhagen Denmark

ISBN: (纸本)9783031112034;9783031112027

Registering mouse brain microscopy images to a reference atlas is crucial to determine the locations of anatomical structures in the brain, which is an essential step for understanding the function of brain circuits. Most existing registration pipelines assume the identity of the reference plate - to which the image slice is to be registered - is known beforehand. this might not always be the case due to three main challenges in microscopy image data: missing image regions (partial data), different cutting angles compared to the atlas plates and a large number of high-resolution images to be identified. Manual identification of reference plates as an initial step requires highly experienced personnel and can be biased, tedious and resource intensive. On the other hand, registering images to all atlas plates can be slow, limiting the application of automated registration methods when dealing with high-resolution image data. this work proposes to perform the image identification by learning a low-dimensional space that captures the similarity between microscopy images and the reference atlas plates. We employ Convolutional Neural Networks (CNNs), in the Siamese network configuration, to first obtain low-dimensional embeddings of microscopy image data and atlas plates. these embeddings are contrasted with positive and negative examples in order to learn a semantically meaningful space that can be used for identifying corresponding 2D atlas plates. At inference, atlas plates that are closest to the microscopy image data in the learned embedding space are presented as candidates for registration. Our method achieved TOP-3 and TOP-5 accuracy of 83.3% and 100%, respectively, compared to the SimpleElastix-based baseline which obtained 25% in both the Top-3 and Top-5 accuracy (Source code is available at https://***/Justinas256/2d-mouse-brain-identification).

关键词： Image registration Mouse brain Partial data Deep learning

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