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1. |
Record Nr. |
UNINA9910140909703321 |
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Autore |
Neward Ted |
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Titolo |
Professional F# 2.0 / / Ted Neward [and three others] ; foreword by Scott Hanselman |
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Pubbl/distr/stampa |
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Indianapolis, Indiana : , : Wiley Publishing, Inc., , 2011 |
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©2011 |
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ISBN |
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1-282-88412-3 |
9786612884122 |
1-118-25744-8 |
1-118-00713-1 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (434 p.) |
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Collana |
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Disciplina |
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Soggetti |
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F♯ (Computer program language) |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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PROFESSIONAL F# 2.0; CONTENTS; FOREWORD; INTRODUCTION; PART 0: BEGINNINGS; CHAPTER 1: PRIMER; Setup; It's that Time of Year Again...; Strategy; The Delegate Strategy; Lambda Calculus (Briefly); Type Inference; Immutability; Expressions, not Statements; Summary; PART I: BASICS; CHAPTER 2: LEXICAL STRUCTURE; Comments; Identifiers; Preprocessor Directives; Significant Whitespace; Summary; CHAPTER 3: PRIMITIVE TYPES; Boolean; Numeric Types; Bitwise Operations; Floating-Point Types; Arithmetic Conversions; String and Character Types; Unit; Units of Measure Types; Literal Values; Summary |
CHAPTER 4: CONTROL FLOWBasic Decisions: if; Looping: while/do; Looping: for; Exceptions; try...with; try...finally; Raising and Throwing Exceptions; Defining New Exception Types; Summary; CHAPTER 5: COMPOSITE TYPES; Option Types; Option Functions; Tuples; Arrays; Array Construction; Array Access; Array Functions; Lists; List Construction; List Access; List Methods; Using Lists and Arrays; Sequences; Maps; Map Construction; Map Access; Map Functions; Sets; Summary; CHAPTER 6: PATTERN MATCHING; Basics; Pattern Types; Constant Patterns; Variable-Binding ("Named") Patterns; AND, OR |
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Patterns |
Literal PatternsTuple Patterns; as Patterns; List Patterns; Array Patterns; Discriminated Union Patterns; Record Patterns; Pattern Guards; Active Patterns; Single Case; Partial Case; Multi-Case; Summary; PART II: OBJECTS; CHAPTER 7: COMPLEX COMPOSITE TYPES; Type Abbreviations; Enum Types; Discriminated Union Types; Structs; Value Type Implicit Members; Structs and Pattern-Matching; Record Types; Record Type Implicit Members; Summary; CHAPTER 8: CLASSES; Basics; Fields; Constructors; Creating; Members; Properties; Methods; Static Members; Operator Overloading; Delegates and Events; Subscribing |
DelegatesDelegateEvents; Beyond DelegateEvents: Events; Access Modifiers; Type Extensions; Summary; CHAPTER 9: INHERITANCE; Basics; Fields and Constructors; Overriding; Abstract Members; Default; Casting; Upcasting; Downcasting; Flexible Types; Boxing and Unboxing; Interfaces; Implementation; Definition; Object Expressions; Summary; CHAPTER 10: GENERICS; Basics; Type Parameters; Type Constraints; Type Constraint; Equality Constraint; Comparison Constraint; Null Constraint; Constructor Constraint; Value Type and Reference Type Constraints; Other Constraints; Statically Resolved Type Parameters |
Explicit Member ConstraintSummary; CHAPTER 11: PACKAGING; Namespaces; Referencing a Namespace; Defining a Namespace; Modules; Referencing a Module; Dening a Module; Summary; CHAPTER 12: CUSTOM ATTRIBUTES; Using Custom Attributes; EntryPoint; Obsolete; Conditional; ParamArray; Struct, Class, AbstractClass, Interface, Literal, and Measure; Assembly Attributes; DefaultMember; Serializable, NonSerialized; AutoOpen; Other Attributes; Creation and Consumption; Creation; Consumption; Summary; PART III: FUNCTIONAL PROGRAMMING; CHAPTER 13: FUNCTIONS; Traditional Function Calls; Mathematical Functions |
Coming from C# |
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Sommario/riassunto |
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This is a book on the F# programming language. On the surface of things, that is an intuitively obvious statement, given the title of this book. However, despite the apparent redundancy in saying it aloud, the sentence above elegantly describes what this book is about: The authors are not attempting to teach developers how to accomplish tasks from other languages in this one, nor are they attempting to evangelize the language or its feature set or its use ""over"" other languages. They assume that you are considering this book because you have an interest in learning the F# language: |
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2. |
Record Nr. |
UNISA996464393403316 |
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Titolo |
PRICAI 2021 : trends in artificial intelligence : 18th Pacific Rim international conference on artificial intelligence, PRICAI 2021, Hanoi, Vietnam, November 8-12, 2021 proceedings / / edited by Duc Nghia Pham [and three others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (457 pages) |
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Collana |
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Lecture Notes in Computer Science Ser. ; ; v.13033 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part III -- Reinforcement Learning -- Consistency Regularization for Ensemble Model Based Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Method -- 4.1 Model Discrepancy and Consistency -- 4.2 Model Learning -- 4.3 Implementation -- 5 Experiments -- 5.1 Comparative Evaluation -- 5.2 Effects of Consistency Regularization -- 5.3 Ablation Study -- 6 Conclusions -- References -- Detecting and Learning Against Unknown Opponents for Automated Negotiations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Negotiation Settings -- 3.2 Bayes Policy Reuse -- 4 Agent Design -- 4.1 Deep Reinforcement Learning Based Learning Module -- 4.2 Policy Reuse Mechanism -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Performance Against ANAC Winning Agents -- 5.3 New Opponent Detection and Learning -- 6 Conclusion -- References -- Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Goal-Oriented Reinforcement Learning -- 2.3 Deep Deterministic Policy Gradient -- 2.4 Determinantal Point Processes -- 3 Related Work -- 4 Methodology -- 4.1 Diversity-Based Trajectory Selection -- 4.2 Diversity-Based Goal Selection -- 5 Experiments -- 5.1 Environments -- 5.2 Training Settings -- 5.3 Benchmark Results -- 5.4 Ablation |
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Studies -- 5.5 Time Complexity -- 6 Conclusion -- References -- Off-Policy Training for Truncated TD() Boosted Soft Actor-Critic -- 1 Introduction -- 2 Related Work -- 2.1 TD Learning and Multi-step Methods -- 2.2 TD() and Eligibility Traces -- 3 Preliminaries -- 3.1 MDPs and Temporal Difference Learning -- 3.2 Multi-step Algorithms and TD() -- 4 Soft Actor-Critic with Truncated TD () -- 4.1 Off-Policy Truncated TD() -- 4.2 Soft Actor-Critic with Truncated TD(). |
4.3 SAC() Training -- 5 Experiments -- 5.1 Evaluation of SAC() -- 5.2 Ablation Study -- 6 Discussion -- References -- Adaptive Warm-Start MCTS in AlphaZero-Like Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Warm-Start AlphaZero Self-play -- 3.1 The Algorithm Framework -- 3.2 MCTS -- 3.3 MCTS Enhancements -- 4 Adaptive Warm-Start Switch Method -- 5 Experimental Setup -- 6 Results -- 6.1 MCTS Vs MCTS Enhancements -- 6.2 Fixed I Tuning -- 6.3 Adaptive Warm-Start Switch -- 7 Discussion and Conclusion -- References -- Batch-Constraint Inverse Reinforcement Learning -- 1 Introduction -- 2 Offline Inverse Reinforcement Learning -- 3 Method -- 3.1 Feature Expectation Approximation -- 3.2 Policy Optimization with BRL -- 3.3 Batch-Constraint Inverse Reinforcement Learning Algorithm (BCIRL) -- 4 Experiments -- 4.1 Standard Control Environments -- 4.2 Gridworld Example -- 5 Conclusion -- References -- KG-RL: A Knowledge-Guided Reinforcement Learning for Massive Battle Games -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Rule-Mix -- 3.2 Plan-Extend -- 4 Experiment Setup -- 4.1 Environment -- 4.2 Human Knowledge Based Module Design -- 4.3 Experiment Settings -- 5 Experimental Results -- 5.1 Battle Game -- 5.2 Comparison of Training Process -- 5.3 Model Differences -- 5.4 The Influence of Different Decisions and Action Modules -- 5.5 Discussion -- 6 Conclusion -- References -- Vision and Perception -- A Semi-supervised Defect Detection Method Based on Image Inpainting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Architecture -- 3.2 Loss Function -- 4 Experiments -- 4.1 Preparations -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions -- References -- ANF: Attention-Based Noise Filtering Strategy for Unsupervised Few-Shot Classification -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Dictionary Noises. |
3.2 Direct Noise Filter -- 3.3 Attention-Based Noise Filter -- 3.4 Dynamic Momentum Updating -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Visualization of Filter Results -- 4.5 Ablation Studies -- 4.6 Traditional Feature Descriptor -- 5 Conclusions -- References -- Asymmetric Mutual Learning for Unsupervised Cross-Domain Person Re-identification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Structure of Asymmetric Mutual Learning -- 3.2 Merging Clusters Algorithm -- 3.3 Similarity Weighted Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- Collaborative Positional-Motion Excitation Module for Efficient Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Action Recognition -- 2.2 CNN-Based Approaches -- 2.3 Temporal Modeling in Action Recognition -- 2.4 Attention Mechanisms -- 3 Approach -- 3.1 Architecture of CPME -- 3.2 CPME Network -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 Improving the Baseline 2D CNN-Approach -- 4.4 Comparison with the State of the Art -- 5 Conclusion -- References -- Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 GCN for Skeleton Action Recognition -- 2.2 |
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Motion Tempo Modeling -- 3 Method -- 3.1 Multi-neighborhood Graph Attention Module -- 3.2 Motion Tempo Modeling -- 4 Experiments -- 4.1 Datasets -- 4.2 Training Details -- 4.3 Ablation Study -- 4.4 Comparisons with the State-of-the-Art Methods -- 5 Conclusion -- References -- Learning to Synthesize and Remove Rain Unsupervisedly -- 1 Introduction -- 2 Related Work -- 2.1 Single Image Deraining Methods -- 2.2 Rain Synthesis Methods. |
2.3 Generative Adversarial Networks -- 3 SAA-CycleGAN -- 3.1 Overview -- 3.2 Deraining Process -- 3.3 Rain Synthesis Process -- 3.4 Objective Function -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Rain Synthesis Results -- 4.3 Deraining Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Object Bounding Box-Aware Embedding for Point Cloud Instance Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning Methods on Point Cloud -- 2.2 Instance Segmentation on Point Cloud -- 3 Method -- 3.1 Network Framework -- 3.2 Bounding Box Prediction Branch -- 3.3 Instance Segmentation Branch -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Ablation Study -- 4.3 Comparison with State-of-the-Art Approaches -- 5 Conclusion -- References -- Objects as Extreme Points -- 1 Introduction -- 1.1 Key-Point-Based Prediction -- 1.2 Dense Prediction -- 1.3 Motivation -- 2 Related Work -- 2.1 Anchor-Free Object Detection -- 2.2 Localization and Classification Spatial Misalignment -- 2.3 Regression Loss -- 3 Method -- 3.1 Positive Sampling with Dynamic Radius -- 3.2 Network Outputs -- 3.3 EIoU Loss -- 3.4 EIoU Predictor -- 3.5 Optimization -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 State-of-the-Art Comparisons -- 5 Conclusion -- References -- Occlusion-Aware Facial Expression Recognition Based Region Re-weight Network -- 1 Introduction -- 2 Related Work -- 2.1 FER Methods Against Occlusions -- 2.2 Sparse Representation -- 3 Proposed Method -- 3.1 Overview of Region Re-weight Network -- 3.2 Occlusion-Aware Module -- 3.3 Block-Loss Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Visualization of the Blocks Selected by OAM -- 4.4 Ablation Studies Evaluation -- 4.5 Results and Comparison -- 5 Conclusion -- References. |
Online Multi-Object Tracking with Pose-Guided Object Location and Dual Self-Attention Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Soft-Pose-NMS Object Detection Strategy -- 3.2 Feature Extraction with Dual Self-Attention Network -- 3.3 Data Association and Trajectory Management -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Performance on MOT Benchmark Datasets -- 4.3 Ablation Studies -- 5 Conclusions -- References -- Random Walk Erasing with Attention Calibration for Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Video Action Recognition -- 2.2 Motion Occlusion in Video -- 3 Approach -- 3.1 Network Overview -- 3.2 Random Walk Erasing Module -- 3.3 Attention Calibration Module -- 4 Experiments -- 4.1 Datasets and Implementations -- 4.2 Main Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- RGB-D Based Visual Navigation Using Direction Estimation Module -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Task Definition -- 3.2 3D Geometry -- 3.3 Visual and Spatial Features of Objects -- 3.4 Direction Estimation Module -- 3.5 Actor-Critic Policy Network -- 4 Experiment -- 4.1 Dataset and Evaluation -- 4.2 Experiment Setup and Comparison Methods -- 4.3 Training Details -- 4.4 Results and Analysis -- 4.5 Ablation Study -- 5 Conclusion -- References -- Semi-supervised Single Image Deraining with Discrete Wavelet Transform -- 1 Introduction -- 2 Related Works -- 3 Semi-supervised Image Deraining by DWT -- 3.1 Methodology Overview -- 3.2 Residual |
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Attentive Network Architecture -- 3.3 Discriminator by DWT for Semi-supervised Method -- 4 Experimental Results -- 4.1 Datasets and Measurements -- 4.2 Implementation Details -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 5 Conclusion -- References -- Simple Light-Weight Network for Human Pose Estimation -- 1 Introduction -- 2 Methodology. |
2.1 Adaptive Convolution. |
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