Search Results - (((((((want OR pants) OR wanting) OR markant) OR cantor) OR anne) OR share) OR hints) algorithms.

  1. 301

    Corporate strategy for dramatic productivity surge

    Published 2013
    Table of Contents: “…Instantaneous and spontaneous information sharing / Hiromichi Yasuoka -- ch. 7. The development of a three-minute battery charger / Toru Fujii -- ch. 8. …”
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    Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

    Published 2019
    Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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  6. 306

    Concise encyclopaedia of bioinformatics and computational biology 2e

    Published 2014
    Table of Contents: “….; Akaike Information Criterion; Pedro Larranaga and Concha Bielza; Algorithm; Matthew He; Alignment (Domain Alignment, Repeats Alignment); Jaap Heringa; Alignment Score; Laszlo Patthy; Allele-Sharing Methods (Non-parametric Linkage Analysis).…”
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  7. 307

    CUDA Application Design and Development. by Farber, Rob

    Published 2011
    Table of Contents: “…The Nsight Timeline Analysis -- The NVTX Tracing Library -- Scaling Behavior of the CUDA API -- Tuning and Analysis Utilities (TAU) -- Summary -- 4 The CUDA Execution Model -- GPU Architecture Overview -- Thread Scheduling: Orchestrating Performance and Parallelism via the Execution Configuration -- Relevant computeprof Values for a Warp -- Warp Divergence -- Guidelines for Warp Divergence -- Relevant computeprof Values for Warp Divergence -- Warp Scheduling and TLP -- Relevant computeprof Values for Occupancy -- ILP: Higher Performance at Lower Occupancy -- ILP Hides Arithmetic Latency -- ILP Hides Data Latency -- ILP in the Future -- Relevant computeprof Values for Instruction Rates -- Little's Law -- CUDA Tools to Identify Limiting Factors -- The nvcc Compiler -- Launch Bounds -- The Disassembler -- PTX Kernels -- GPU Emulators -- Summary -- 5 CUDA Memory -- The CUDA Memory Hierarchy -- GPU Memory -- L2 Cache -- Relevant computeprof Values for the L2 Cache -- L1 Cache -- Relevant computeprof Values for the L1 Cache -- CUDA Memory Types -- Registers -- Local memory -- Relevant computeprof Values for Local Memory Cache -- Shared Memory -- Relevant computeprof Values for Shared Memory -- Constant Memory -- Texture Memory -- Relevant computeprof Values for Texture Memory -- Global Memory -- Common Coalescing Use Cases -- Allocation of Global Memory -- Limiting Factors in the Design of Global Memory -- Relevant computeprof Values for Global Memory -- Summary -- 6 Efficiently Using GPU Memory -- Reduction -- The Reduction Template -- A Test Program for functionReduce.h -- Results -- Utilizing Irregular Data Structures -- Sparse Matrices and the CUSP Library -- Graph Algorithms -- SoA, AoS, and Other Structures -- Tiles and Stencils -- Summary -- 7 Techniques to Increase Parallelism -- CUDA Contexts Extend Parallelism -- Streams and Contexts.…”
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  13. 313

    Large Scale Network-Centric Distributed Systems

    Published 2014
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    Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

    Published 2022
    Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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    Frontiers In The Study Of Chaotic Dynamical Systems With Open Problems.

    Published 2011
    Table of Contents: “…Preface; Contents; Chapter 1 Problems with Lorenz's Modeling and the Algorithm of Chaos Doctrine; 1.1. Introduction; 1.2. …”
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    XML for DB2 information integration

    Published 2004
    Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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  19. 319

    Computational models of argument : Proceedings of COMMA 2012

    Published 2012
    Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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