光在体积中的散射 作者：H.C. Van De Hulst 卷1 英文版，最早出版于1965年，PBRT中关于体积散射的推荐阅读资料

Alogrithm Design Manual一书中作者强烈推荐的数据结构方面的参考书，可以当做数据结构的工具书来用，可以查到各种基本数据结构及其用法。在网上找了很久才下到的，拿来和大家分享！

POD原理解析与详细证明，SVD/KLD/PCA三者的等价性证明。

离散数学初学教材，编程基础

pylbm pylbm是使用Lattice Boltzmann求解器进行数值模拟的多合一软件包。 该软件包提供了用于描述1D，2D和3D问题中的格子Boltzmann方案的所有工具。 我们选择D'Humières形式主义来描述问题。 您可以使用一组简单的形状（例如圆形，球形，...）来制作复杂的几何图形。 pylbm使用Cython，NumPy或Loo.py根据用户指定的方案和域执行数值方案。 Pythran和Numba即将面市。 pylbm具有mpi4py的MPI支持。 安装 您可以通过多种方式安装pylbm 与曼巴或conda mamba install pylbm -c conda-forge conda install pylbm -c conda-forge 与Pypi pip install pylbm 或者 pip install pylbm --user 从来源

英文原名：Programming Applications for Microsoft Windows 中文：Windows核心编程

Springer-Verlag Berlin Heidelberg 2013 VCSEL 垂直腔面发射激光器 Vertical Cavity Surface Emitting Laser Vertical-cavity surface-emitting lasers (VCSELs) are firmly established in the laser diode market for about 15 years meanwhile. It took about 17 years before these devices were commercialized. Multimode fiber optical networks with data rates in the 1 Gbit/s range were the initial market. In 2004, VCSELs were first incorporated in optical computer mice. Today, these two application areas approximately equally share a production volume of almost 100 million units per year. In future, high volumes can be expected also, e.g., for diverse sensing and illumination purposes, the latter using high-power laser arrays.

2009年新书，非扫描 Contents List of Figures xiii List of Tables xix Introduction xxi About the Editors xxvii Contributor List xxix 1 Analysis of Text Patterns Using Kernel Methods 1 Marco Turchi, Alessia Mammone, and Nello Cristianini 1.1 Introduction . . . . . . . . . . . . . . . 1 1.2 General Overview on Kernel Methods . . . . . . . 1 1.2.1 Finding Patterns in Feature Space . . . . . . . . . . . 5 1.2.2 Formal Properties of Kernel Functions . . . . . . . . . 8 1.2.3 Operations on Kernel Functions . . . . . . . . . . . . 10 1.3 Kernels for Text . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1 Vector SpaceModel . . . . . . . . . . . . . . . . . . . 11 1.3.2 Semantic Kernels . . . . . . . . . . . . . . . . . . . . . 13 1.3.3 String Kernels . . . . . . . . . . . . . . . . . . . . . . 17 1.4 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.5 Conclusion and Further Reading . . . . . . . . . . . . . . . . 22 2 Detection of Bias in Media Outlets with Statistical Learning Methods 27 Blaz Fortuna, Carolina Galleguillos, and Nello Cristianini 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2 Overview of the Experiments . . . . . . . . . . . . . . . . . . 29 2.3 Data Collection and Preparation . . . . . . . . . . . . . . . . 30 2.3.1 Article Extraction from HTML Pages . . . . . . . . . 31 2.3.2 Data Preparation . . . . . . . . . . . . . . . . . . . . . 31 2.3.3 Detection of Matching News Items . . . . . . . . . . . 32 2.4 News Outlet Identification . . . . . . . . . . . . . . . . . . . . 35 2.5 Topic-Wise Comparison of Term Bias . . . . . . . . . . . . . 38 2.6 News OutletsMap . . . . . . . . . . . . . . . . . . . . . . . . 40 2.6.1 Distance Based on Lexical Choices . . . . . . . . . . . 42 vii © 2009 by Taylor and Francis Group, LLC viii 2.6.2 Distance Based on Choice of Topics . . . . . . . . . . 43 2.7 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.9 Appendix A: Support Vector Machines . . . . . . . . . . . . . 48 2.10 Appendix B: Bag of Words and Vector Space Models . . . . . 48 2.11 Appendix C: Kernel Canonical Correlation Analysis . . . . . 49 2.12 Appendix D: Multidimensional Scaling . . . . . . . . . . . . . 50 3 Collective Classification for Text Classification 51 Galileo Namata, Prithviraj Sen, Mustafa Bilgic, and Lise Getoor 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2 Collective Classification: Notation and Problem Definition . . 53 3.3 Approximate Inference Algorithms for Approaches Based on Local Conditional Classifiers . . . . . . . . . . . . . . . . . . . 53 3.3.1 Iterative Classification . . . . . . . . . . . . . . . . . . 54 3.3.2 Gibbs Sampling . . . . . . . . . . . . . . . . . . . . . . 55 3.3.3 Local Classifiers and Further Optimizations . . . . . . 55 3.4 Approximate Inference Algorithms for Approaches Based on Global Formulations . . . . . . . . . . . . . . . . . . . . . . . 56 3.4.1 Loopy Belief Propagation . . . . . . . . . . . . . . . . 58 3.4.2 Relaxation Labeling via Mean-Field Approach . . . . 59 3.5 Learning the Classifiers . . . . . . . . . . . . . . . . . . . . . 60 3.6 Experimental Comparison . . . . . . . . . . . . . . . . . . . . 60 3.6.1 Features Used . . . . . . . . . . . . . . . . . . . . . . . 60 3.6.2 Real-World Datasets . . . . . . . . . . . . . . . . . . . 60 3.6.3 Practical Issues . . . . . . . . . . . . . . . . . . . . . . 63 3.7 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Topic Models 71 David M. Blei and John D. Lafferty 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2 Latent Dirichlet Allocation . . . . . . . . . . . . . . . . . . . 72 4.2.1 Statistical Assumptions . . . . . . . . . . . . . . . . . 73 4.2.2 Exploring a Corpus with the Posterior Distribution . . 75 4.3 Posterior Inference for LDA . . . . . . . . . . . . . . . . . . . 76 4.3.1 Mean Field Variational Inference . . . . . . . . . . . . 78 4.3.2 Practical Considerations . . . . . . . . . . . . . . . . . 81 4.4 Dynamic Topic Models and Correlated Topic Models . . . . . 82 4.4.1 The Correlated Topic Model . . . . . . . . . . . . . . 82 4.4.2 The Dynamic Topic Model . . . . . . . . . . . . . . . 84 4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 © 2009 by Taylor and Francis Group, LLC ix 5 Nonnegative Matrix and Tensor Factorization for Discussion Tracking 95 Brett W. Bader, Michael W. Berry, and Amy N. Langville 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.1.1 Extracting Discussions . . . . . . . . . . . . . . . . . . 96 5.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . 96 5.2 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.3 Tensor Decompositions and Algorithms . . . . . . . . . . . . 98 5.3.1 PARAFAC-ALS . . . . . . . . . . . . . . . . . . . . . 100 5.3.2 Nonnegative Tensor Factorization . . . . . . . . . . . . 100 5.4 Enron Subset . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.4.1 TermWeighting Techniques . . . . . . . . . . . . . . . 103 5.5 Observations and Results . . . . . . . . . . . . . . . . . . . . 105 5.5.1 Nonnegative Tensor Decomposition . . . . . . . . . . . 105 5.5.2 Analysis of Three-Way Tensor . . . . . . . . . . . . . 106 5.5.3 Analysis of Four-Way Tensor . . . . . . . . . . . . . . 108 5.6 Visualizing Results of the NMF Clustering . . . . . . . . . . . 111 5.7 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6 Text Clustering with Mixture of von Mises-Fisher Distributions 121 Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, and Suvrit Sra 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.3 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6.3.1 The von Mises-Fisher (vMF) Distribution . . . . . . . 124 6.3.2 Maximum Likelihood Estimates . . . . . . . . . . . . . 125 6.4 EMon aMixture of vMFs (moVMF) . . . . . . . . . . . . . . 126 6.5 Handling High-Dimensional Text Datasets . . . . . . . . . . . 127 6.5.1 Approximating κ . . . . . . . . . . . . . . . . . . . . . 128 6.5.2 Experimental Study of the Approximation . . . . . . . 130 6.6 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 6.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 134 6.7.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.7.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . 138 6.7.3 Simulated Datasets . . . . . . . . . . . . . . . . . . . . 138 6.7.4 Classic3 Family of Datasets . . . . . . . . . . . . . . . 140 6.7.5 Yahoo News Dataset . . . . . . . . . . . . . . . . . . . 143 6.7.6 20 Newsgroup Family of Datasets . . . . . . . . . . . . 143 6.7.7 Slashdot Datasets . . . . . . . . . . . . . . . . . . . . 145 6.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 6.9 Conclusions and Future Work . . . . . . . . . . . . . . . . . . 148 © 2009 by Taylor and Francis Group, LLC x 7 Constrained Partitional Clustering of Text Data: An Overview 155 Sugato Basu and Ian Davidson 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 7.2 Uses of Constraints . . . . . . . . . . . . . . . . . . . . . . . . 157 7.2.1 Constraint-Based Methods . . . . . . . . . . . . . . . 157 7.2.2 Distance-BasedMethods . . . . . . . . . . . . . . . . . 158 7.3 Text Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 159 7.3.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . 161 7.3.2 DistanceMeasures . . . . . . . . . . . . . . . . . . . . 162 7.4 Partitional Clustering with Constraints . . . . . . . . . . . . 163 7.4.1 COP-KMeans . . . . . . . . . . . . . . . . . . . . . . . 163 7.4.2 Algorithms with Penalties – PKM, CVQE . . . . . . . 164 7.4.3 LCVQE: An Extension to CVQE . . . . . . . . . . . . 167 7.4.4 Probabilistic Penalty – PKM . . . . . . . . . . . . . . 167 7.5 Learning Distance Function with Constraints . . . . . . . . . 168 7.5.1 Generalized Mahalanobis Distance Learning . . . . . . 168 7.5.2 Kernel Distance Functions Using AdaBoost . . . . . . 169 7.6 Satisfying Constraints and Learning Distance Functions . . . 170 7.6.1 Hidden Markov Random Field (HMRF) Model . . . . 170 7.6.2 EMAlgorithm . . . . . . . . . . . . . . . . . . . . . . 173 7.6.3 Improvements to HMRF-KMeans . . . . . . . . . . . 173 7.7 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 7.7.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 174 7.7.2 Clustering Evaluation . . . . . . . . . . . . . . . . . . 175 7.7.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . 176 7.7.4 Comparison of Distance Functions . . . . . . . . . . . 176 7.7.5 Experimental Results . . . . . . . . . . . . . . . . . . 177 7.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 8 Adaptive Information Filtering 185 Yi Zhang 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 8.2 Standard EvaluationMeasures . . . . . . . . . . . . . . . . . 188 8.3 Standard Retrieval Models and Filtering Approaches . . . . . 190 8.3.1 Existing Retrieval Models . . . . . . . . . . . . . . . . 190 8.3.2 Existing Adaptive Filtering Approaches . . . . . . . . 192 8.4 CollaborativeAdaptive Filtering . . . . . . . . . . . . . . . . 194 8.5 Novelty and Redundancy Detection . . . . . . . . . . . . . . . 196 8.5.1 Set Difference . . . . . . . . . . . . . . . . . . . . . . . 199 8.5.2 Geometric Distance . . . . . . . . . . . . . . . . . . . 199 8.5.3 Distributional Similarity . . . . . . . . . . . . . . . . . 200 8.5.4 Summary of Novelty Detection . . . . . . . . . . . . . 201 8.6 Other Adaptive Filtering Topics . . . . . . . . . . . . . . . . 201 8.6.1 Beyond Bag ofWords . . . . . . . . . . . . . . . . . . 202 © 2009 by Taylor and Francis Group, LLC xi 8.6.2 Using Implicit Feedback . . . . . . . . . . . . . . . . . 202 8.6.3 Exploration and Exploitation Trade Off . . . . . . . . 203 8.6.4 Evaluation beyond Topical Relevance . . . . . . . . . 203 8.7 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 204 9 Utility-Based Information Distillation 213 Yiming Yang and Abhimanyu Lad 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 9.1.1 Related Work in Adaptive Filtering (AF) . . . . . . . 213 9.1.2 Related Work in Topic Detection and Tracking (TDT) 214 9.1.3 Limitations of Current Solutions . . . . . . . . . . . . 215 9.2 A Sample Task . . . . . . . . . . . . . . . . . . . . . . . . . . 216 9.3 Technical Cores . . . . . . . . . . . . . . . . . . . . . . . . . . 218 9.3.1 Adaptive Filtering Component . . . . . . . . . . . . . 218 9.3.2 Passage Retrieval Component . . . . . . . . . . . . . . 219 9.3.3 Novelty Detection Component . . . . . . . . . . . . . 220 9.3.4 Anti-Redundant Ranking Component . . . . . . . . . 220 9.4 EvaluationMethodology . . . . . . . . . . . . . . . . . . . . . 221 9.4.1 Answer Keys . . . . . . . . . . . . . . . . . . . . . . . 221 9.4.2 Evaluating the Utility of a Sequence of Ranked Lists . 223 9.5 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 9.6 Experiments and Results . . . . . . . . . . . . . . . . . . . . . 226 9.6.1 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . 226 9.6.2 Experimental Setup . . . . . . . . . . . . . . . . . . . 226 9.6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 227 9.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 229 9.8 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 229 10 Text Search-Enhanced with Types and Entities 233 Soumen Chakrabarti, Sujatha Das, Vijay Krishnan, and Kriti Puniyani 10.1 Entity-Aware Search Architecture . . . . . . . . . . . . . . . . 233 10.1.1 Guessing Answer Types . . . . . . . . . . . . . . . . . 234 10.1.2 Scoring Snippets . . . . . . . . . . . . . . . . . . . . . 235 10.1.3 Efficient Indexing and Query Processing . . . . . . . . 236 10.1.4 Comparison with Prior Work . . . . . . . . . . . . . . 236 10.2 Understanding the Question . . . . . . . . . . . . . . . . . . . 236 10.2.1 Answer Type Clues in Questions . . . . . . . . . . . . 239 10.2.2 Sequential Labeling of Type Clue Spans . . . . . . . . 240 10.2.3 From Type Clue Spans to Answer Types . . . . . . . . 245 10.2.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . 247 10.3 Scoring Potential Answer Snippets . . . . . . . . . . . . . . . 251 10.3.1 A ProximityModel . . . . . . . . . . . . . . . . . . . . 253 10.3.2 Learning the Proximity Scoring Function . . . . . . . 255 10.3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . 257 10.4 Indexing and Query Processing . . . . . . . . . . . . . . . . . 260 © 2009 by Taylor and Francis Group, LLC xii 10.4.1 Probability of a Query Atype . . . . . . . . . . . . . . 262 10.4.2 Pre-Generalize and Post-Filter . . . . . . . . . . . . . 262 10.4.3 Atype Subset Index Space Model . . . . . . . . . . . . 265 10.4.4 Query Time BloatModel . . . . . . . . . . . . . . . . 266 10.4.5 Choosing an Atype Subset . . . . . . . . . . . . . . . . 269 10.4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . 271 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 10.5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 272 10.5.2 Ongoing and Future Work . . . . . . . . . . . . . . . . 273 © 2009

Web Applications with Javascript or Java, Volume 1_Constraint Validation, Enumerations, Special Datatypes-De Gruyter(2018).pdf

An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics. Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics