Search Results - (((((((want OR hands) OR granting) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Diabetes and cardiovascular disease : a guide to clinical management by Paneni, Francesco, Cosentino, Francesco

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Strategies for Teaching Fractions: Using Error Analysis for Interventionand Assessment. by Spangler, David B.

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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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Cognitive Electronic Warfare : An Artificial Intelligence Approach. by Haigh, Karen

Table of Contents: “…-- 1.5 Reader's Guide -- 1.6 Conclusion -- References -- 2 Objective Function -- 2.1 Observables That Describe the Environment -- 2.1.1 Clustering Environments -- 2.2 Control Parameters to Change Behavior -- 2.3 Metrics to Evaluate Performance -- 2.4 Creating a Utility Function -- 2.5 Utility Function Design Considerations -- 2.6 Conclusion -- References -- 3 ML Primer -- 3.1 Common ML Algorithms -- 3.1.1 SVMs -- 3.1.2 ANNs -- 3.2 Ensemble Methods -- 3.3 Hybrid ML -- 3.4 Open-Set Classification -- 3.5 Generalization and Meta-learning -- 3.6 Algorithmic Trade-Offs -- 3.7 Conclusion -- References -- 4 Electronic Support -- 4.1 Emitter Classification and Characterization -- 4.1.1 Feature Engineering and Behavior Characterization -- 4.1.2 Waveform Classification -- 4.1.3 SEI -- 4.2 Performance Estimation -- 4.3 Multi-Intelligence Data Fusion -- 4.3.1 Data Fusion Approaches -- 4.3.2 Example: 5G Multi-INT Data Fusion for Localization -- 4.3.3 Distributed-Data Fusion -- 4.4 Anomaly Detection -- 4.5 Causal Relationships -- 4.6 Intent Recognition -- 4.6.1 Automatic Target Recognition and Tracking -- 4.7 Conclusion -- References -- 5 EP and EA -- 5.1 Optimization -- 5.1.1 Multi-Objective Optimization -- 5.1.2 Searching Through the Performance Landscape -- 5.1.3 Optimization Metalearning -- 5.2 Scheduling -- 5.3 Anytime Algorithms -- 5.4 Distributed Optimization -- 5.5 Conclusion.…”
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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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Hack proofing your network

Table of Contents: “…</br><br> Knowing What To Expect in the Rest of This Book</br><br> Understanding the Current Legal Climate</br><br> Summary</br><br> Frequently Asked Questions</br><br>Chapter 2 The Laws of Security</br><br> Introduction</br><br> Knowing the Laws of Security</br><br> Client-Side Security Doesn't Work</br><br> You Cannot Securely Exchange Encryption Keys without a Shared Piece of Information</br><br> Malicious Code Cannot Be 100 Percent Protected against</br><br> Any Malicious Code Can Be Completely Morphed to Bypass Signature Detection</br><br> Firewalls Cannot Protect You 100 Percent from Attack</br><br> Social Engineering</br><br> Attacking Exposed Servers</br><br> Attacking the Firewall Directly</br><br> Client-Side Holes</br><br> Any IDS Can Be Evaded</br><br> Secret Cryptographic Algorithms Are Not Secure</br><br> If a Key Is Not Required, You Do Not Have Encryption-You Have Encoding</br><br> Passwords Cannot Be Securely Stored on the Client Unless There Is Another Password to Protect Them</br><br> In Order for a System to Begin to Be Considered Secure, It Must Undergo an Independent Security Audit</br><br> Security through Obscurity Does Not Work</br><br> Summary </br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 3 Classes of Attack</br><br> Introduction</br><br> Identifying and Understanding the Classes of Attack </br><br> Denial of Service</br><br> Information Leakage</br><br> Regular File Access</br><br> Misinformation</br><br> Special File/Database Access</br><br> Remote Arbitrary Code Execution</br><br> Elevation of Privileges</br><br> Identifying Methods of Testing for Vulnerabilities</br><br> Proof of Concept</br><br> Standard Research Techniques</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 4 Methodology</br><br> Introduction</br><br> Understanding Vulnerability Research Methodologies</br><br> Source Code Research</br><br> Binary Research</br><br> The Importance of Source Code Reviews</br><br> Searching Error-Prone Functions</br><br> Reverse Engineering Techniques</br><br> Disassemblers, Decompilers, and Debuggers</br><br> Black Box Testing</br><br> Chips</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 5 Diffing</br><br> Introduction</br><br> What Is Diffing?…”
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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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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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Creating wicked students : designing courses for a complex world by Hanstedt, Paul, 1965-

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Pulmonary arterial hypertension : diagnosis and evidence-based treatment

Table of Contents: “…Combination therapy in pulmonary arterial hypertension / Anne Keogh and Marius Hoeper -- Interventional and surgical modalities of treatment for pulmonary arterial hypertension / Julio Sandoval and Ramona Doyle -- End points and clinical trial design in pulmonary arterial hypertension : clinical and regulatory perspectives / Andrew J. …”
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Machine Learning with Swift : Artificial Intelligence for iOS. by Sosnovshchenko, Oleksandr

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Code : Collaborative Ownership and the Digital Economy. by Ghosh, Rishab Aiyer

Table of Contents: “…Oral Traditions, Indigenous Rights, and Valuable Old Knowledge -- 7 From Keeping "Nature's Secrets" to the Institutionalization of "Open Science" -- 8 Benefit Sharing: Experiments in Governance -- 9 Trust among the Algorithms: Ownership, Identity, and the Collaborative Stewardship of Information -- 10 Cooking-Pot Markets and Balanced Value Flows -- 11 Coase's Penguin, or, Linux and the Nature of the Firm -- 12 Paying for Public Goods -- 13 Fencing Off Ideas: Enclosure and the Disappearance of the Public Domain -- 14 A Renaissance of the Commons: How the New Sciences and Internet are Framing a New Global Identity and Order -- 15 Positive Intellectual Rights and Information Exchanges -- 16 Copyright and Globalization in the Age of Computer Networks -- Contributors -- Index.…”
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Pattern Recognition in Computational Molecular Biology : Techniques and Approaches by Elloumi, Mourad, Iliopoulos, Costas, Wang, Jason T. L., Zomaya, Albert Y.

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Mechanisms and games for dynamic spectrum allocation

Table of Contents: “…7.3.10 Other equilibrium concepts -- 7.4 Learning equilibria -- 7.4.1 Learning Nash equilibria -- 7.4.2 Learning epsilon-equilibrium -- 7.4.3 Learning coarse correlated equilibrium -- 7.4.4 Learning satisfaction equilibrium -- 7.4.5 Discussion -- 7.5 Conclusion -- References -- II Cognitive radio and sharing of unlicensed spectrum -- 8 Cooperation in cognitiveradio networks: from accessto monitoring -- 8.1 Introduction -- 8.1.1 Cooperation in cognitive radio: mutual benefits and costs -- 8.2 An overview of coalitional game theory -- 8.3 Cooperative spectrum exploration and exploitation -- 8.3.1 Motivation -- 8.3.2 Basic problem -- 8.3.3 Joint sensing and access as a cooperative game -- 8.3.4 Coalition formation algorithm for joint sensing and access -- 8.3.5 Numerical results -- 8.4 Cooperative primary user activity monitoring -- 8.4.1 Motivation -- 8.4.2 Primary user activity monitoring: basic model -- 8.4.3 Cooperative primary user monitoring -- 8.4.4 Numerical results -- 8.5 Summary -- Acknowledgements -- Copyright notice -- References -- 9 Cooperative cognitive radios with diffusion networks -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Basic tools in convex and matrix analysis -- 9.2.2 Graphs -- 9.3 Distributed spectrum sensing -- 9.4 Iterative consensus-based approaches -- 9.4.1 Average consensus algorithms -- 9.4.2 Acceleration techniques for iterative consensus algorithms -- 9.4.3 Empirical evaluation -- 9.5 Consensus techniques based on CoMAC -- 9.6 Adaptive distributed spectrum sensing based on adaptive subgradient techniques -- 9.6.1 Distributed detection with adaptive filters -- 9.6.2 Set-theoretic adaptive filters for distributed detection -- 9.6.3 Empirical evaluation -- 9.7 Channel probing -- 9.7.1 Introduction -- 9.7.2 Admissibility problem -- 9.7.3 Power and admission control algorithms.…”
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On the Shoulders of Giants : New Approaches to Numeracy.

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Heterogeneous computing with OpenCL

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Fundamental statistical inference : a computational approach by Paolella, Marc S.

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The smart city in a digital world by Mosco, Vincent

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