Search Results - (((((((kant OR want) OR mantis) OR when) OR cantor) OR anne) OR shared) OR hints) algorithms.

Parallel computing : technology trends

Table of Contents: “…Feedback-Driven Performance and Precision Tuning for Automatic Fixed Point Exploitation -- Parallel Programming -- A GPU-CUDA Framework for Solving a Two-Dimensional Inverse Anomalous Diffusion Problem -- Parallelization Strategies for GPU-Based Ant Colony Optimization Applied to TSP -- DBCSR: A Blocked Sparse Tensor Algebra Library -- Acceleration of Hydro Poro-Elastic Damage Simulation in a Shared-Memory Environment -- BERTHA and PyBERTHA: State of the Art for Full Four-Component Dirac-Kohn-Sham Calculations -- Prediction-Based Partitions Evaluation Algorithm for Resource Allocation…”
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Lead generation for dummies by Rothman, Dayna

Table of Contents: “…Creating an Internal Blogging ProgramChapter 9: Creating Lasting Relationships Through Social Media; Sharing on Social Media; Getting the Most out of Facebook; Leveraging Twitter; Engaging Through LinkedIn; Building Your Google+ Presence; Getting Visual with Pinterest; Attracting Attention with SlideShare; Chapter 10: Getting Found Through Search Engine Optimization; Maximizing Different Traffic Sources; Knowing Your Search Engines; Making the Most out of Google Algorithm Updates; Choosing Your Keywords; Perfecting On-Page SEO; Utilizing Links in a Natural Way.…”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…Infinite Two-Minute DebateIndian Debate Tournament; Winning by Losing; Minimising Selfishness; Lawrence of Arabia Collars a Leopard; A Bridge without Pillars; Advice from Shih Teng; Thales' Shady Measurement of Pyramids; The Cowpox Transmission Problem; Kant's Gloves; An Antipodal Algorithm; The Invisibility of Function Words; Necessary Waste; The Art of the Counterexample; The Philosophy of Scale Effects; Humble Exercise; Philosophy for the Eye; Synthetic A Priori Lies; Passive A Priori Deception; Crete Revisited; Less Lucky the Second Time?…”
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High performance computing on complex environments by Jeannot, Emmanuel

Table of Contents: “…Chapter 4: Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience4.1 Introduction; 4.2 Formulation of the Discrete Model; 4.3 Parallel Algorithms; 4.4 Computational Results; 4.5 Conclusions; Acknowledgments; References; Part III: Communication and Storage Considerations in High-Performance Computing; Chapter 5: An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing; 5.1 Introduction; 5.2 General Overview; 5.3 Formalization of the Problem; 5.4 Algorithmic Strategies for Topology Mapping; 5.5 Mapping Enforcement Techniques; 5.6 Survey of Solutions.…”
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Sentient Enterprise The Evolution of Business Decision-making.

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Kaplan's clinical hypertension by Kaplan, Norman M., 1931-2020, Victor, Ronald G., Flynn, Joseph T.

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Fundamentals of Parallel Computing. by Razdan, Sanjay

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Learning concurrency in Kotlin : build highly efficient and robust applications by Castiblanco Torres, Miguel Angel

Table of Contents: “…; Processes, threads, and coroutines; Processes; Threads; Coroutines; Putting things together; Introduction to concurrency; Concurrency is not parallelism; CPU-bound and I/O-bound; CPU-bound; I/O-bound; Concurrency versus parallelism in CPU-bound algorithms; Single-core execution; Parallel execution; Concurrency versus parallelism in I/O-bound algorithms; Why concurrency is often feared; Race conditions; Atomicity violation; Deadlocks; Livelocks…”
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Insect media : an archaeology of animals and technology by Parikka, Jussi, 1976-

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Design in Crisis : New Worlds, Philosophies and Practices. by Fry, Tony

Table of Contents: “…Sacrifices that do not work in a crisis -- 3. When design goes south: from decoloniality, through declassification to dessobons -- 4. …”
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Iterative learning control for multi-agent systems coordination by Yang, Shiping, 1987-, Xu, Jian-Xin, Li, Xuefang, 1985-, Shen, Dong, 1982-

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Communicating process architectures 2002. by World Occam And Transputer User Group. Technical Meeting (25th: 2002: University Of Reading, U. K.)

Table of Contents: “…A Communicating Threads (CT) Case Study: JIWYPrioritised Dynamic Communicating Processes -- Part I; Prioritised Dynamic Communicating Processes -- Part II; Implementing a Distributed Algorithm for Detection of Local Knots and Cycles in Directed Graphs; Author Index.…”
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Natural language processing with TensorFlow : teach language to machines using Python's deep learning library by Ganegedara, Thushan

Table of Contents: “…Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?…”
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Versatile Video Coding by Dominguez, Humberto Ochoa

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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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Stochastic Models in Reliability Engineering. by Cui, Lirong

Table of Contents: “…4.3.1 Run and Phrase -- 4.3.2 Multi-State Compression Algorithm -- 4.4 Proposed Multi-State Inference Algorithm -- 4.4.1 Rules for Calculating Intermediate Variables -- 4.4.2 Proposed Multi-State Inference Algorithm -- 4.5 Case Study -- 4.5.1 Case Background -- 4.5.2 Calculation and Analysis -- 4.6 Summary -- Appendix A -- Appendix B -- References -- Chapter 5 Reliability Analysis of Demand-Based Warm Standby System with Multi-State Common Bus -- 5.1 Introduction -- 5.2 Model Description for a DBWSS with Multi-State Common Bus Performance Sharing…”
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The book of Qt 4 : the art of building Qt applications by Molkentin, Daniel

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PRINCIPLES OF QUANTUM ARTIFICIAL INTELLIGENCE. by Wichert, Andreas

Table of Contents: “…Computation; 2.1 Entscheidungsproblem; 2.1.1 Cantor's diagonal argument; 2.1.2 Reductio ad absurdum; 2.2 Complexity Theory; 2.2.1 Decision problems; 2.2.2 P and NP; 2.3 Church-Turing Thesis; 2.3.1 Church-Turing-Deutsch principle; 2.4 Computers; 2.4.1 Analog computers; 2.4.2 Digital computers; 2.4.3 Von Neumann architecture; 3. …”
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The attacker's advantage : turning uncertainty into breakthrough opportunities by Charan, Ram

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Data mining techniques : for marketing, sales, and customer relationship management by Linoff, Gordon, Berry, Michael J. A.

Table of Contents: “…-- Data mining applications in marketing and customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificail neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making the data mean more -- Too much of a good thing? …”
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