“…OECD360 przedstawia najnowsze analizy i dane z kluczowych publikacji OECD. Liczne materiały graficzne ilustrują główne aktualne kwestie. …”

“…Although traditional texts present isolated algorithms and data structures, they do not provide a unifying structure and offer little guidance on how to appropriately select among them. …”

“… A timeless, easy-to-read guide on life-long investment principles that can help any investor succeed The Elements of Investing has a single-minded goal: to teach the principles of investing in the same pared-to-bone manner that Professor William Strunk Jr. once taught composition to students at Harvard, using his classic little book, The Elements of Style. With great daring, Ellis and Malkiel imagined their own Little Red Schoolhouse course in investing for every investor around the world-and then penned this book. …”

“…In Trend Trading Set-Ups, Little reveals the data behind qualified trends and utilizes a novel concept involving time to calculate trend failure probability rates…”

“…Because of its inherently stealthy nature, scientists have paid little attention to IWB, allowing us to know very little about it. …”

“…On the other hand, a little improvement in the quality of specialty polymers can lead to substantial increase in economic profits…”

“…The Trouble with Ed Schools: Little Harm, Little HelpNotes References Index…”

Tabla de Contenidos: “…Données préliminaires concernant les litiges soumis aux tribunaux dans certains pays de l'OCDE -- B. …”

Tabla de Contenidos: “…-- 1.2.3 You want to enjoy the view a little longer. But not too much longer -- 1.2.4 If you just gotta reject a null hypothesis... -- 1.2.5 Where's the equivalent of traditional test X in this book? …”

Tabla de Contenidos: “…4 Learning Convolutional Neural Networks for Object Detection with Very Little Training Data -- 4.1 Introduction -- 4.2 Fundamentals -- 4.2.1 Types of Learning -- 4.2.2 Convolutional Neural Networks -- 4.2.2.1 Arti cial neuron -- 4.2.2.2 Arti cial neural network -- 4.2.2.3 Training -- 4.2.2.4 Convolutional neural networks -- 4.2.3 Random Forests -- 4.2.3.1 Decision tree -- 4.2.3.2 Random forest -- 4.3 Related Work -- 4.4 Traf c Sign Detection -- 4.4.1 Feature Learning -- 4.4.2 Random Forest Classi cation -- 4.4.3 RF to NN Mapping -- 4.4.4 Fully Convolutional Network -- 4.4.5 Bounding Box Prediction -- 4.5 Localization -- 4.6 Clustering -- 4.7 Dataset -- 4.7.1 Data Capturing -- 4.7.2 Filtering -- 4.8 Experiments -- 4.8.1 Training and Test Data -- 4.8.2 Classi cation -- 4.8.3 Object Detection -- 4.8.4 Computation Time -- 4.8.5 Precision of Localizations -- 4.9 Conclusion -- Acknowledgment -- References -- 5 Multimodal Fusion Architectures for Pedestrian Detection -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Visible Pedestrian Detection -- 5.2.2 Infrared Pedestrian Detection -- 5.2.3 Multimodal Pedestrian Detection -- 5.3 Proposed Method -- 5.3.1 Multimodal Feature Learning/Fusion -- 5.3.2 Multimodal Pedestrian Detection -- 5.3.2.1 Baseline DNN model -- 5.3.2.2 Scene-aware DNN model -- 5.3.3 Multimodal Segmentation Supervision -- 5.4 Experimental Results and Discussion -- 5.4.1 Dataset and Evaluation Metric -- 5.4.2 Implementation Details -- 5.4.3 Evaluation of Multimodal Feature Fusion -- 5.4.4 Evaluation of Multimodal Pedestrian Detection Networks -- 5.4.5 Evaluation of Multimodal Segmentation Supervision Networks -- 5.4.6 Comparison with State-of-the-Art Multimodal Pedestrian Detection Methods -- 5.5 Conclusion -- Acknowledgment -- References -- 6 Multispectral Person Re-Identi cation Using GAN for Color-to-Thermal Image Translation…”

Tabla de Contenidos: “…-- 7.1.2 Measurement of test adequacy -- 7.1.3 Test enhancement using measurements of adequacy -- 7.1.4 Infeasibility and test adequacy -- 7.1.5 Error detection and test enhancement -- 7.1.6 Single and multiple executions -- 7.2 Adequacy Criteria Based on Control Flow -- 7.2.1 Statement and block coverage -- 7.2.2 Conditions and decisions -- 7.2.3 Decision coverage -- 7.2.4 Condition coverage -- 7.2.5 Condition/decision coverage -- 7.2.6 Multiple condition coverage -- 7.2.7 Linear code sequence and jump (LCSAJ) coverage -- 7.2.8 Modified condition/decision coverage -- 7.2.9 MC/DC adequate tests for compound conditions -- 7.2.10 Definition of MC/DC coverage -- 7.2.11 Minimal MC/DC tests -- 7.2.12 Error detection and MC/DC adequacy -- 7.2.13 Short-circuit evaluation and infeasibility -- 7.2.14 Basis path coverage -- 7.2.15 Tracing test cases to requirements -- 7.3 Concepts From Data Flow -- 7.3.1 Definitions and uses -- 7.3.2 C-use and p-use -- 7.3.3 Global and local definitions and uses -- 7.3.4 Data flow graph -- 7.3.5 Def-clear paths -- 7.3.6 Def-use pairs -- 7.3.7 Def-use chains -- 7.3.8 A little optimization -- 7.3.9 Data contexts and ordered data contexts -- 7.4 Adequacy Criteria Based on Data Flow -- 7.4.1 c-use coverage…”

Tabla de Contenidos: “…3.3 Types of Algorithms -- 3.3.1 Brute Force Algorithms -- 3.3.2 Divide and Conquer Algorithms -- 3.3.3 Dynamic Programming Algorithms -- 3.3.4 Greedy Algorithms -- 3.3.5 Branch and Bound Algorithms -- 3.3.6 Recursive Algorithms -- 3.3.7 Back Tracking Algorithms -- 3.3.8 Randomized Algorithms -- 3.3.9 Hill Climbing Algorithms -- 3.4 Performance Analysis -- 3.4.1 Properties of the Best Algorithms -- 3.5 Space Complexity -- 3.5.1 Instruction Space -- 3.5.2 Text Section of a Program -- 3.5.3 Data Space -- 3.5.4 Stack Space -- 3.5.5 Calculating the Instruction Space -- 3.5.6 Calculating the Data Space -- 3.5.7 Size of Data Section -- 3.5.8 Size of Rodata Section -- 3.5.9 Size of bss Section -- 3.6 Apriori Analysis -- 3.7 Asymptotic Notation -- 3.7.1 Big oh Notation (O) -- 3.7.2 Omega Notation (Ω) -- 3.7.3 Theta Notation (θ) -- 3.7.4 Little oh Notation(o) -- 3.8 Time Complexity -- 3.8.1 Time Complexity Analysis of Bubble Sort -- 3.8.2 Time Complexity Analysis of Selection Sort -- 3.9 Worst Case, Average Case and Best Case Complexity -- 3.9.1 Worst Case -- 3.9.2 Average Case -- 3.9.3 Best Case -- Summary -- Exercises -- Chapter 4 Arrays -- 4.1 Introduction -- 4.1.1 Array -- 4.2 Array Types -- 4.2.1 Single-dimensional Array -- 4.2.2 Multi-dimensional Array -- 4.2.3 N-dimensional Array -- 4.3 Array Representation -- 4.4 Initializing Arrays -- 4.5 Accessing Values of an Array -- 4.6 Array Operations -- 4.6.1 Traversing -- 4.6.2 Insertion -- 4.6.3 Deletion -- 4.6.4 Sorting -- 4.6.5 Searching -- 4.7 Arrays as Parameters -- 4.8 Character Sequences -- 4.9 Applications -- Summary -- Exercises -- Chapter 5 Linked List -- 5.1 Introduction -- 5.2 Representation of Linked List in Memory -- 5.2.1 Static Representation -- 5.2.2 Dynamic Representation -- 5.3 Singly Linked List -- 5.3.1 Operations -- 5.4 Circular Linked List -- 5.4.1 Merging of Two Circular Lists…”

Tabla de Contenidos: “…Def-use chains -- 6.3.8. A little optimization -- 6.3.9. Data contexts and ordered data contexts -- 6.4 Adequacy Criteria Based on Data Flow -- 6.4.1. c-use coverage -- 6.4.2. p-use coverage -- 6.4.3. all-uses coverage -- 6.4.4. k-dr chain coverage -- 6.4.5. …”

Tabla de Contenidos: “…7.2.3 Pseudocode -- Summary -- Exercises -- Chapter 8: Overview of C++ -- 8.1 Introduction -- 8.2 ANSI Standard -- 8.3 Object-oriented Technology -- 8.4 Disadvantages of Conventional Programming -- 8.5 Programming Paradigms -- 8.5.1 Monolithic Programming -- 8.5.2 Procedural Programming -- 8.5.3 Structured Programming Approach -- 8.6 Basic Concepts of C++ -- 8.7 Structure of C++ Program -- 8.7.1 Include Files Section -- 8.7.2 Class Declaration or Definition -- 8.7.3 Class Function Definitions -- 8.7.4 The main() Function -- 8.8 Creating Source File, Compiling and Linking in C++ -- 8.9 IDE and its Features -- 8.10 Character Set in C++ -- 8.11 Types of Tokens -- 8.12 Keywords -- 8.13 Identifiers -- 8.13.1 Variable Declaration and Initialization -- 8.14 Dynamic Initialization -- 8.15 Data Types in C++ -- 8.16 Basic Data Type -- 8.17 Derived Data Type -- 8.17.1 Pointers -- 8.17.2 Functions -- 8.17.3 Arrays -- 8.17.4 References -- 8.18 User-defined Data Type -- 8.18.1 Structure and Classes -- 8.18.2 Union -- 8.18.3 Enumerated Data Type -- 8.19 The void Data Type -- 8.20 Type Modifiers -- 8.21 Wrapping Around -- 8.22 Constants -- 8.22.1 Literal Constant -- 8.22.2 Symbolic Constant -- 8.23 Constant Pointers -- 8.23.1 Constant Pointer -- 8.23.2 Pointer to Constant -- 8.23.3 Pointer and Variable both Constants -- Summary -- Exercises -- Chapter 9: Operators and Expressions -- 9.1 Introduction -- 9.2 Precedence of Operators in C++ -- 9.3 Expressions and their Evaluation -- 9.4 Referencing (&amp -- ) and Dereferencing (*) Operators -- 9.5 Scope Access Operator -- 9.6 Memory Management Operators -- 9.7 Comma Operator -- 9.8 Comma in Place of Curly Braces -- 9.9 Type Conversion -- 9.9.1 Implicit Type Conversion -- 9.9.2 Explicit Type Conversion -- Summary -- Exercises -- Chapter 10: Input and Output in C++ -- 10.1 Introduction -- 10.2 Streams in C++…”

“…Theoretical, psycholinguistic as well as computational linguistic research remain puzzled by when and how MWEs receive literal vs. meaning-shifted interpretations, what the contributions of the MWE constituents are to the degree of semantic transparency (i.e., meaning compositionality) of the MWE, and how literal vs. meaning-shifted MWEs are processed and computed. …”

“…Se abarca un ámbito tan heterogéneo como es el litoral que recorre Cataluña, País Valenciano, Murcia y Andalucía@Estudi que analitza quines respostes s'estan donant des de les diferents administracions del territori per afrontar velles dinàmiques territorials crítiques com el despoblament rural, la dispersió i explosió urbana, la crisi industrial i del sector primari, l'esgotament del recurs aigua, la depredació del litoral i la pèrdua de paisatges, entre d'altres a unes àrees d'estudi que es poden considerar com a representatives de tots els tipus de territoris que podem trobar hui en dia l'Arc Mediterrani. s'abasta un àmbit tan heterogeni com és el litoral que recorre Catalunya, País Valencià, Múrcia i Andalusia…”
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“…A little girl falls down a rabbit hole and discovers a world of nonsensical and amusing characters…”
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