LEADER 01981nam 2200553 a 450 001 9910457707903321 005 20210802223555.0 010 $a1-61470-486-4 035 $a(CKB)2550000000059347 035 $a(EBL)3020610 035 $a(SSID)ssj0000569713 035 $a(PQKBManifestationID)12234933 035 $a(PQKBTitleCode)TC0000569713 035 $a(PQKBWorkID)10587437 035 $a(PQKB)10484467 035 $a(MiAaPQ)EBC3020610 035 $a(Au-PeEL)EBL3020610 035 $a(CaPaEBR)ebr10678015 035 $a(OCoLC)757394141 035 $a(EXLCZ)992550000000059347 100 $a20090827d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aOrganized crime in the U.S$b[electronic resource] /$fWesley B. Knowles, editor 210 $aNew York $cNova Science Publishers$d2010 215 $a1 online resource (119 p.) 225 1 $aCriminal justice, law enforcement and corrections series 300 $aDescription based upon print version of record. 311 $a1-60741-524-0 320 $aIncludes bibliographical references and index. 327 $aOrganized crime in the United States : trends and issues for Congress -- Impact on U.S. of Asian transnational crime -- African organized crime -- Asian organized crime -- Balkan organized crime -- Eurasian organized crime -- Italian organized crime -- Middle Eastern organized crime. 410 0$aCriminal justice, law enforcement and corrections. 606 $aOrganized crime$zUnited States 606 $aNoncitizen criminals$zUnited States 608 $aElectronic books. 615 0$aOrganized crime 615 4$aNoncitizen criminals 676 $a364.1060973 701 $aKnowles$b Wesley B$0871326 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910457707903321 996 $aOrganized crime in the U.S$91945194 997 $aUNINA LEADER 10444nam 22012135 450 001 996503570003316 005 20231206231930.0 010 $a3-11-078594-3 024 7 $a10.1515/9783110785944 035 $a(OCoLC)1356994849 035 $a(MiAaPQ)EBC7156125 035 $a(Au-PeEL)EBL7156125 035 $a(OCoLC)1357015184 035 $a(CKB)5580000000492226 035 $a(EXLCZ)995580000000492226 100 $a20230103h20232023 fy 0 101 0 $aeng 135 $aurcn#|||mna|a 181 $ctxt$2rdacontent 181 $csti$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning under resource constraints$iFundamentals /$fedited by Katharina Morik and Peter Marwedel 205 $a1st ed. 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2023] 210 4$d©2023 215 $a1 online resource (xiii, 491 pages) $cillustrations (chiefly colour) 225 1 $aDe Gruyter STEM ;$vVolume 1/3 300 $a"Part of the multi-volume work Machine Learning under Resource Constraints. In the series De Gruyter STEM."--Provided by publisher. 300 $a"Final report of CRC 876". 300 $a"Also of interest: Volume 2, Machine Learning under Resource Constraints. Discovery in Physics, Morik, Rhode (Eds.), 2023, ISBN 978-3-11-078595-1, e-ISBN 978-3-11-078596-8 ; Volume 3, Machine Learning under Resource Constraints. Applications, Morik, Rahnenführer, Wietfeld (Eds.), 2023, ISBN 978-3-11-078597-5, e-ISBN 978-3-11-078598-2."--Page ii. 311 08$a3-11-078593-5 320 $aIncludes bibliographical references (pages 437-483) and index. 327 $g1$tIntroduction /$rKatharina Morik, Jian-Jia Chen --$g1.1$tEmbedded Systems and Sustainability --$g1.2$tThe Energy Consumption of Machine Learning --$g1.3$tMemory Demands of Machine Learning --$g1.4$tStructure of this Book --$g2$tData Gathering and Resource Measuring --$g2.1$tDeclarative Stream-Based Acquisition and Processing of OS Data with kCQL /$rChristoph Borchert, Jochen Streicher, Alexander Lochmann,Olaf Spinczyk --$g2.2$tPhyNetLab Test Bed /$rMojtaba Masoudinejad, Markus Buschhoff --$g2.3$tZero-Power/Low-Power Sensing /$rAndres Gomez, Lars Suter, Simon Mayer --$g3 Streaming Data, Small Devices --$g3.1$tSummary Extraction from Streams /$rSebastian Buschjäger, Katharina Morik --$g3.2$tCoresets and Sketches for Regression Problems on Data Streams and Distributed Data /$rAlexander Munteanu --$g4$tStructured Data --$g4.1$tSpatio-Temporal Random Fields /$rNico Piatkowski, Katharina Morik --$g4.2$tThe Weisfeiler-Leman Method for Machine Learning with Graphs /$rNils Kriege, Christopher Morris --$g4.3$tDeep Graph Representation Learning /$rMatthias Fey, Frank Weichert --$g4.4$tHigh-Quality Parallel Max-Cut Approximation Algorithms for Shared Memory /$rNico Bertram, Jonas Ellert, Johannes Fischer --$g4.5$tMillions of Formulas /$rLukas Pfahler --$g5$tCluster Analysis --$g5.1$tSparse Partitioning Around Medoids /$rLars Lenssen, Erich Schubert --$g5.2$tClustering of Polygonal Curves and Time Series /$rAmer Krivo?ija --$g5.3$tData Aggregation for Hierarchical Clustering /$rErich Schubert, Andreas Lang --$g5.4$tMatrix Factorization with Binary Constraints /$rSibylle Hess$g6$tHardware-Aware Execution --$g6.1$tFPGA-Based Backpropagation Engine for Feed-Forward Neural Networks /$rWayne Luk, Ce Guo --$g6.2$tProcessor-Specific Code Transformation /$rHenning Funke, Jens Teubner --$g6.3$tExtreme Multicore Classification /$rErik Schultheis, Rohit Babbar --$g6.4$tOptimization of ML on Modern Multicore Systems /$rHelena Kotthaus, Peter Marwedel --$t7 Memory Awareness --$g7.1$tEfficient Memory Footprint Reduction /$rHelena Kotthaus, Peter Marwedel --$g7.2$tMachine Learning Based on Emerging Memories /$rMikail Yayla, Sebastian Buschjäger, Hussam Amrouch --$g7.3$tCache-Friendly Execution of Tree Ensembles /$rSebastian Buschjäger, Kuan-Hsun Chen --$g8$tCommunication Awareness --$g8.1$tTiming-Predictable Learning and Multiprocessor Synchronization /$rKuan-Hsun Chen, Junjie Shi --$g8.2$tCommunication Architecture for Heterogeneous Hardware /$rHenning Funke, Jens Teubner --$g9$tEnergy Awareness --$g9.1$tInteger Exponential Families /$rNico Piatkowski --$g9.2$tPower Consumption Analysis and Uplink Transmission Power /$rRobert Falkenberg. 330 $a"Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters. Ranges from embedded systems to large computing clusters. Provides application of the methods in various domains of science and engineering."--Provided by publisher. 410 0$aDe Gruyter STEM ;$vvolume 1. 606 $aMachine learning 606 $aSCIENCE / Chemistry / General$2bisacsh 610 0$aArtificial Intelligence. 610 0$aBig Data and Machine Learning. 610 0$aCyber-physical systems. 610 0$aData mining for Ubiquitous System Software. 610 0$aEmbedded Systems and Machine Learning. 610 0$aHighly Distributed Data. 610 0$aML on Small devices. 610 0$aMachine learning for knowledge discovery. 610 0$aMachine learning in high-energy physics. 610 0$aResource-Aware Machine Learning. 610 0$aResource-Constrained Data Analysis. 615 0$aMachine learning. 615 7$aSCIENCE / Chemistry / General. 676 $a006.31 702 $aAmrouch$b Hussam$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBabbar$b Rohit$f1982-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBertram$b Nico$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBorchert$b Christoph$f1984-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBuschhoff$b Markus$f1974-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aBuschjäger$b Sebastian$f1990-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aChen$b Jian-Jia$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aChen$b Kuan-Hsun$f1989-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aEllert$b Jonas$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFalkenberg$b Robert$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFey$b Matthias$f1990-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFischer$b Johannes$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aFunke$b Henning$f1988-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aGomez$b Andres$f1986-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aGuo$b Ce$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aHeß$b Sibylle?$f1984-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aKotthaus$b Helena$f1984-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aKriege$b Nils Morten$f1983-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aKrivo?ija$b Amer$f1980-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aLang$b Andreas$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aLenssen$b Lars$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aLochmann$b Alexander$f1988-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aLuk$b Wayne$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMarwedel$b Peter$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMarwedel$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMasoudinejad$b Mojtaba$f1984-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMayer$b Simon$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMorik$b Katharina$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMorik$b Katharina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMorris$b Christopher$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aMunteanu$b Alexander$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aPfahler$b Lukas$f1991-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aPiatkowski$b Nico$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSchubert$b Erich$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSchultheis$b Erik$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aShi$b Junjie$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSpinczyk$b Olaf$f1970-$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aStreicher$b Jochen$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aSuter$b Lars$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aTeubner$b Jens$g(Jens Thilo)?$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aWeichert$b Frank$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 702 $aYayla$b Mikail$4ctb$4https://id.loc.gov/vocabulary/relators/ctb 801 0$bHUA 801 1$bHUA 801 2$bUMC 801 2$bDLC 801 2$bDE-B1597 801 2$bCaOWtU 906 $aBOOK 912 $a996503570003316 996 $aMachine Learning under Resource Constraints$93011774 997 $aUNISA