01820nam 2200577Ia 450 991045880350332120200520144314.01-282-48495-8978661248495790-272-8865-8(CKB)2670000000011486(OCoLC)593287185(CaPaEBR)ebrary10364086(SSID)ssj0000343456(PQKBManifestationID)11243037(PQKBTitleCode)TC0000343456(PQKBWorkID)10290878(PQKB)10707302(MiAaPQ)EBC623418(Au-PeEL)EBL623418(CaPaEBR)ebr10364086(CaONFJC)MIL248495(EXLCZ)99267000000001148620091016d2010 uy 0engurcn|||||||||txtccrThe syntactic licensing of ellipsis[electronic resource] /Lobke AelbrechtAmsterdam ;Philadelphia John Benjamins Pub. Co.20101 online resource (247 p.) Linguistik aktuell ;Bd. 149Bibliographic Level Mode of Issuance: Monograph90-272-5532-6 Includes bibliographical references and index.Linguistik aktuell ;Bd. 149.Grammar, Comparative and generalEllipsisGrammar, Comparative and generalSyntaxElectronic books.Grammar, Comparative and generalEllipsis.Grammar, Comparative and generalSyntax.415Aelbrecht Lobke920854MiAaPQMiAaPQMiAaPQBOOK9910458803503321The syntactic licensing of ellipsis2065421UNINA06036nam 2200457 450 99650346950331620230418132133.03-031-23498-7(MiAaPQ)EBC7157464(Au-PeEL)EBL7157464(CKB)25703769600041(PPN)268651159(EXLCZ)992570376960004120230418d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCloud computing - CLOUD 2022 15th international conference, held as part of the Services Conference Federation, SCF 2022, Honolulu, Hi, USA, December 10-14 2022, proceedings /Kejiang Ye and Liang-Jie ZhangCham, Switzerland :Springer,[2022]©20221 online resource (131 pages)Lecture Notes in Computer SciencePrint version: Ye, Kejiang Cloud Computing - CLOUD 2022 Cham : Springer,c2022 9783031234972 Intro -- Preface -- Organization -- Services Society -- Services Conference Federation (SCF) -- SCF 2023 Events -- Contents -- Performance Evaluation of Modified Best First Decreasing Algorithms for Dynamic Virtual Machine Placement in Cloud Computing -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 Performance Evaluation -- 4.1 Implementation Tools -- 4.2 Experimental Setup on CloudSim -- 4.3 Results from Experimentation with Modified Best First Decreasing Algorithms -- 4.4 Discussions -- 5 Conclusion and Recommendation -- References -- Towards an Efficient Client Selection System for Federated Learning -- 1 Introduction -- 2 Design -- 2.1 Overview -- 2.2 Architecture of Resource Management Trees -- 2.3 Selection of Available Clients -- 2.4 Real-Time Resource Management -- 3 Evaluations -- 4 Related Work -- 5 Conclusion -- References -- Hestia: A Cost-Effective Multi-dimensional Resource Utilization for Microservices Execution in the Cloud -- 1 Introduction -- 2 Related Work -- 3 Problems and Challenges -- 4 Framework -- 5 Hestia Algorithm -- 5.1 Algorithm Details -- 6 Implementation -- 6.1 Preparation -- 6.2 Detail -- 7 Evaluation -- 8 Conclusion -- References -- Optimizing Cache Accesses with Tensor Memory Format Search for Transformers in TVM -- 1 Introduction -- 2 Related Works -- 2.1 Image Transformers -- 2.2 GPU Architecture -- 2.3 Compiler Optimizations -- 3 Method -- 3.1 Preliminaries and Problem Formulation -- 3.2 Search Algorithm -- 4 Results -- 4.1 Inference Performance -- 4.2 Analyses -- 5 Conclusion -- References -- Improving Few-Shot Image Classification with Self-supervised Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Few-Shot Image Classification (FSIC) -- 2.2 Self-Supervised Learning (SSL) -- 2.3 Few-Shot Image Classification with Self-Supervised Learning -- 3 Methodology -- 3.1 Pre-training.3.2 Meta-training -- 4 Experimental Results -- 4.1 Datasets and Baseline -- 4.2 Implementation Details -- 4.3 Results -- 4.4 Analysis -- 5 Conclusion and Future Work -- References -- New Commonsense Views Inspired by Infants and Its Implications for Artificial Intelligence -- 1 Introduction -- 2 Literature Review -- 2.1 Disciplinary Differences in Commonsense -- 2.2 Subject Consensus of Commonsense -- 3 A New View of Commonsense Inspired by Infant Learning -- 3.1 Commonsense Learning Characteristics of Infants -- 3.2 A New View of Commonsense -- 4 The Inspiration of the New View of Commonsense to AI -- 4.1 Commonsense Acquisition and Representation -- 4.2 Commonsense Organization and Reasoning -- References -- Analysis of Data Micro-governance in Full Life Cycle Management of the Leased Assets -- 1 Introduction -- 1.1 Background -- 1.2 Pain Points -- 1.3 Purpose and Meaning -- 1.4 Achievements -- 2 Management Requirements Analysis and Platform Solutions -- 2.1 Management Needs of Financial Leased Assets -- 2.2 Related Technologies -- 2.3 Construction Objectives of the Management Platform -- 2.4 Brief Description of the Management Platform Architecture Design -- 3 Micro-governance in Platform Construction -- 3.1 The Concept and Significance of Micro-governance -- 3.2 Analysis of Data Characteristics and Management Difficulties of Leased Assets -- 3.3 Requirement Analysis of the Leased Assets Data Service -- 3.4 Practice of Micro-governance in the Online Management Platform of Leased Assets -- 3.5 Achievement and Value Realization of Micro-governance -- 3.6 Summary -- 4 Outlook and Thinking -- 4.1 Remote Intelligent Due Diligence -- 4.2 ESG Rating Study -- 4.3 Digital Scene Mining -- References -- How to Build an Efficient Data Team -- 1 Introduction -- 2 CDO and Organizational Capability -- 2.1 Team Capability -- 2.2 Team Thinking.2.3 Team Governance -- 3 How to Implement Organizational Capability -- 3.1 Establishment of a Chief Data Officer (CDO) System -- 3.2 Synergy of Four Key Groups -- 4 CDO Roadmap -- 4.1 IsCDO Roadmap Theory -- 4.2 Typical CDO Roadmap -- 5 Conclusion -- References -- A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment -- 1 Introduction -- 2 Related Work -- 3 OUR METHOD: NT-E-AR -- 3.1 NT-E-AR Architecture -- 3.2 Neural Transformation(NT) -- 3.3 Convolutional Long-Short Term Memory Network (ConvLSTM) -- 3.4 Autoregressive Long-Short Term Memory Network (LSTM) -- 3.5 Loss Function -- 4 Evaluation -- 4.1 Datasets -- 4.2 Baseline -- 4.3 Implementation Details -- 4.4 Comparison Results -- 5 Conclusion -- References -- Author Index.Lecture notes in computer science.Cloud computingCongressesCloud computing004.6782Ye Kejiang1272965Zhang Liang-JieMiAaPQMiAaPQMiAaPQBOOK996503469503316Cloud computing - CLOUD 20223088777UNISA