Search Results - (((((((ant OR wanting) OR span) OR wants) OR cantor) OR anne) OR carter) OR anting) algorithms.

Experiencing architecture in the nineteenth century : buildings and society in the modern age

Table of Contents: “…'The pressing public want of the age': The arrival of the grand hotel'Bitter competition in the London hotel world': The problem of publicity; 'A wealthy man's private mansion': The assurance of exclusivity; The 'spirit of the time': Cosmopolitanism and heterosociability; Conclusion; Chapter 10: 'The fullest fountain of advancing civilization': Experiencing Anthony Trollope's House of Commons, 1852-82; Experiences of Parliament; Progressive architecture; A political theatre; Conclusion: Reading experiences.…”
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Microscopic image analysis for life science applications

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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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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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Anesthesia student survival guide : a case-based approach

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"Intuitive understanding of Kalman filtering with MATLAB" by Barreto, Armando, 1963-, Adjouadi, Malek, 1955-, Ortega, Francisco R., O-larnnithipong, Nonnarit

Table of Contents: “…-- 7.2 EACH ITERATION OF THE KALMAN FILTER SPANS "TWO TIMES" AND "TWO SPACES" -- 7.3 YET, IN PRACTICE ALL THE COMPUTATIONS ARE PERFORMED IN A SINGLE, "CURRENT" ITERATION-CLARIFICATION -- 7.4 MODEL OR MEASUREMENT? …”
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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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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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Handbook of power systems. II

Table of Contents: “…Cover -- Handbook of Power Systems II13; -- Preface of Volume II13; -- Contents of Volume II13; -- Contents13; of Volume I -- Contributors13; -- Part I Transmission and Distribution Modeling -- Recent Developments in Optimal Power Flow Modeling Techniques -- 1 Introduction -- 2 Physical Network Representation -- 3 Operational Constraints -- 4 Tap-Changing and Regulating Transformers -- 5 FACTS Devices -- 6 OPF Objective Functions and Formulations -- 7 OPF Solution Techniques -- 8 Numerical Examples -- 9 Conclusion -- References -- Algorithms for Finding Optimal Flows in Dynamic Networks -- 1 Optimal Dynamic Network Flow Models and Power Industry -- 2 Minimum Cost Dynamic Single: Commodity Flow Problems and Algorithms for Their Solving -- 3 Minimum Cost Dynamic Multicommodity Flow Problems and Algorithms for Their Solving -- References -- Signal Processing for Improving Power Quality -- 1 Wavelet-based Algorithm for Harmonics Analysis -- 2 Wavelet-based Algorithm for Nonstationary Power System Waveform Analysis -- 3 Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-term Load Forecast -- 4 Conclusions -- References -- Transmission Valuation Analysis based on Real Options with Price Spikes -- 1 Introduction -- 2 Behavior of Commodity Prices -- 3 Valuation of Obligations and Options -- 4 Valuation in the Presence of Spikes -- 5 Conclusions -- References -- Part II Forecasting in Energy -- Short-term Forecasting in Power Systems: A Guided Tour -- 1 Introduction -- 2 Electricity Load Forecasting -- 3 Wind Power Forecasting -- 4 Forecasting Electricity Prices -- 5 Conclusions -- References -- State-of-the-Art of Electricity Price Forecasting in a Grid Environment -- 1 Introduction -- 2 State-of-the-Art Techniques of Electricity Price Forecasting -- 3 Input8211;Output Specifications of Electricity Price Forecasting Techniques -- 4 Comparing Existing Statistical Techniques for Electricity Price Forecasting -- 5 Implementations of Electricity Price Forecasting in a Grid Environment -- 6 Conclusions -- References -- Modelling the Structure of Long-Term Electricity Forward Prices at Nord Pool -- 1 Introduction -- 2 Long-term Forward Price Process -- 3 Model Estimation -- 4 Conclusions -- References -- Hybrid Bottom-Up/Top-Down Modeling of Prices in Deregulated Wholesale Power Markets -- 1 Introduction -- 2 Top-Down Models for Electricity Price Forecasting -- 3 Hybrid Bottom-Up/Top-Down Modeling -- 4 A Hybrid Model for the New Zealand Electricity Market -- 5 A Hybrid Model for the Australian Electricity Market -- 6 Conclusions -- References -- Part III Energy Auctions and Markets -- Agent-based Modeling and Simulation of Competitive Wholesale Electricity Markets -- 1 Introduction -- 2 Agent-based Modeling and Simulation -- 3 Behavioral Modeling -- 4 Market Modeling -- 5 Conclusions -- References -- Futures Market Trading for Electricity Producers and Retailers -- 1 Introduction: Futures Market Trading -- 2 Producer Trading -- 3 Retailer Trading -- 4 Conclusions -- References -- A Decision Support System for Generation Planning and Operation in Electricity Markets -- 1 Introduction -- 2 Long-term Stochastic Market Planning Model -- 3 Medium-term Stochastic Hydrothermal Coordination Model -- 4 Medium-term Stochastic Simulation Model -- T$29828.…”
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Cryptography 101 : From Theory to Practice. by Oppliger, Rolf

Table of Contents: “…Intro -- Cryptography 101: From Theory to Practice -- Contents -- Foreword -- Preface -- References -- Acknowledgments -- Chapter 1 Introduction -- 1.1 CRYPTOLOGY -- 1.2 CRYPTOGRAPHIC SYSTEMS -- 1.2.1 Classes of Cryptographic Systems -- 1.2.2 Secure Cryptographic Systems -- 1.3 HISTORICAL BACKGROUND INFORMATION -- 1.4 OUTLINE OF THE BOOK -- References -- Chapter 2 Cryptographic Systems -- 2.1 UNKEYED CRYPTOSYSTEMS -- 2.1.1 Random Generators -- 2.1.2 Random Functions -- 2.1.3 One-Way Functions -- 2.1.4 Cryptographic Hash Functions -- 2.2 SECRET KEY CRYPTOSYSTEMS -- 2.2.1 Pseudorandom Generators -- 2.2.2 Pseudorandom Functions -- 2.2.3 Symmetric Encryption -- 2.2.4 Message Authentication -- 2.2.5 Authenticated Encryption -- 2.3 PUBLIC KEY CRYPTOSYSTEMS -- 2.3.1 Key Establishment -- 2.3.2 Asymmetric Encryption Systems -- 2.4 FINAL REMARKS -- References -- Part I UNKEYEDC RYPTOSYSTEMS -- Chapter 3 Random Generators -- 3.1 INTRODUCTION -- 3.2 REALIZATIONS AND IMPLEMENTATIONS -- 3.2.1 Hardware-Based Random Generators -- 3.2.2 Software-Based Random Generators -- 3.2.3 Deskewing Techniques -- 3.3 STATISTICAL RANDOMNESS TESTING -- References -- Chapter 4 Random Functions -- 4.1 INTRODUCTION -- 4.2 IMPLEMENTATION -- 4.3 FINAL REMARKS -- Chapter 5 One-Way Functions -- 5.1 INTRODUCTION -- 5.2 CANDIDATE ONE-WAY FUNCTIONS -- 5.2.1 Discrete Exponentiation Function -- 5.2.2 RSA Function -- 5.2.3 Modular Square Function -- 5.3 INTEGER FACTORIZATION ALGORITHMS -- 5.3.1 Special-Purpose Algorithms -- 5.3.2 General-Purpose Algorithms -- 5.3.3 State of the Art -- 5.4 ALGORITHMS FOR COMPUTING DISCRETE LOGARITHMS -- 5.4.1 Generic Algorithms -- 5.4.2 Nongeneric (Special-Purpose) Algorithms -- 5.4.3 State of the Art -- 5.5 ELLIPTIC CURVE CRYPTOGRAPHY -- 5.6 FINAL REMARKS -- References -- Chapter 6 Cryptographic Hash Functions -- 6.1 INTRODUCTION.…”
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There's Something about Gödel : The Complete Guide to the Incompleteness Theorem. by Berto, Francesco

Table of Contents: “…. -- 6 ... and the unsatisfied logicists, Frege and Russell -- 7 Bits of set theory -- 8 The Abstraction Principle -- 9 Bytes of set theory -- 10 Properties, relations, functions, that is, sets again -- 11 Calculating, computing, enumerating, that is, the notion of algorithm…”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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FX barrier options : a comprehensive guide for industry quants by Dadachanji, Zareer

Table of Contents: “…Stupid; 4.10 Five things we want from a model; 4.11 Stochastic volatility (SV) models; 4.11.1 SABR model; 4.11.2 Heston model; 4.12 Mixed local/stochastic volatility (lsv) models; 4.12.1 Term structure of volatility of volatility; 4.13 Other models and methods; 4.13.1 Uncertain Volatility (UV) models; 4.13.2 Jump-diffusion models; 4.13.3 Vanna-volga methods; 5 Smile Risk Management; 5.1 Black-Scholes with term structure; 5.2 Local volatility model; 5.3 Spot risk under smile models; 5.4 Theta risk under smile models; 5.5 Mixed local/stochastic volatility models; 5.6 Static hedging; 5.7 Managing risk across businesses; 6 Numerical Methods; 6.1 Finite-difference (FD) methods; 6.1.1 Grid geometry; 6.1.2 Finite-difference schemes; 6.2 Monte Carlo (MC) methods; 6.2.1 Monte Carlo schedules; 6.2.2 Monte Carlo algorithms; 6.2.3 Variance reduction; 6.2.4 The Brownian Bridge; 6.2.5 Early…”
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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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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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Oracle Primavera P6 version 8 : project and portfolio management : a comprehensive guide to managing projects, resources, and portfolios using Primavera P6, through version 8.2 by Williams, Daniel L.

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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications by Lantz, Brett

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Smart buildings, smart communities and demand response

Table of Contents: “…Demand Response in Smart Zero Energy Buildings and Grids / Nikos Kampelis -- DR in Smart and Near-zero Energy Buildings: The Leaf Community / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- Performance of Industrial and Residential Near-zero Energy Buildings / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response / Nikos Kampelis, Nikolaos Sifakis, Denia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions / Nikos Kampelis, Elisavet Tsekeri, Denia Kolokotsa, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Conclusions and Recommendations.…”
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Handbook of Mixture Analysis

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Numerical linear algebra with applications : using MATLAB by Ford, William H.

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