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• Quality Management of Deep Learning Systems.- Can Attention Masks Improve Adversarial Robustness?.- Learner-Independent Data Omission Attacks.- Extraction of Complex DNN Models: Real Threat or Boogeyman?.- Principal Component Properties of Adversarial Samples.- FreaAI: Automated extraction of data slices to test machine learning models.- Density estimation in representation space to predict model uncertainty.- Automated detection of drift in deep learning based classifiers using network embedding.- Quality of syntactic implication of RL-based sentence summarization.- Dependable Neural Networks for Safety Critical Tasks.
• (source: Nielsen Book Data)

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc. .
(source: Nielsen Book Data)

• Regular Dynamics: Newton to Poincare
• KAM Theorem
• (source: Nielsen Book Data)

This text examines methods for separating the signal of physical interest from the contamination by chaotic noise, for investigating the phase space of the chaotic noise and its properties, and for modelling the behaviour of the chaotic noise. The author considers the means of controlling chaotic behaviour and using this control to communicate between source and receiver. The emphasis throughout is on the use of the modern mathematical tools for investigating chaotic behaviour to uncover properties of physical systems.
(source: Nielsen Book Data)
A clear and systematic treatment of time series of data, regular and chaotic, found in nonlinear systems. The text leads readers from measurements of one or more variables through the steps of building models of the source as a dynamical system, classifying the source by its dynamical characteristics, and finally predicting and controlling the dynamical system. It examines methods for separating the signal of physical interest from contamination by unwanted noise, and for investigating the phase space of the chaotic signal and its properties. The emphasis throughout is on the use of modern mathematical tools for investigating chaotic behaviour to uncover properties of physical systems, requiring knowledge of dynamical systems at the advanced undergraduate level and some knowledge of Fourier transforms and other signal processing methods.
(source: Nielsen Book Data)

• Bd. 1. Abhandlungen über die Theorie des Mikroskops
• Bd. 2. Wissenschaftliche Abhandlungen aus verschiedenen Gebieten. Patentschriften. Gedächtnisreden
• Bd. 3. Vorträge, Reden und Schriften sozialpolitischen und verwandten Inhalts
• Bd. 4. Unveröffentlichte Schriften wissenschaftlich-technischen Inhalts
• Bd. 5. Werden und Wesen der Carl-Zeiss-Stiftung.

• v. 1. Water.--
• v. 2. Air.--
• v. 3. Fire.--
• v. 4. Sky.

• Preface xvii Acknowledgments xix PART I WHEEL OF SCIENCE: PREMISES OF RESEARCH 1
• 1 "DUH" SCIENCE VERSUS "HUH" SCIENCE 3
• 2 THEORIES AND HYPOTHESES 21
• 3 OBSERVATION AND EMPIRICAL GENERALIZATION 35
• 4 ETHICS 52 PART II WHEEL OF SCIENCE: PROCEDURES OF RESEARCH 63
• 5 MEASUREMENT 65
• 6 USING SPSS IN RESEARCH 83
• 7 CHI-SQUARE AND CONTINGENCY TABLE ANALYSIS 90
• 8 LEARNING FROM POPULATIONS: CENSUSES AND SAMPLES 102
• 9 CORRELATION 127
• 10 REGRESSION 146
• 11 CAUSATION 162 PART III WHEEL OF SCIENCE: DESIGNS OF RESEARCH 203
• 12 SURVEY RESEARCH 205
• 13 AGGREGATE RESEARCH 234
• 14 EXPERIMENTS 251
• 15 STATISTICAL METHODS OF DIFFERENCE: T TEST 270
• 16 ANALYSIS OF VARIANCE 280
• 17 FIELD RESEARCH 301
• 18 CONTENT ANALYSIS 316 PART IV STATISTICS AND DATA MANAGEMENT 327 STATISTICAL PROCEDURES UNIT A: WRITING THE STATISTICAL RESEARCH SUMMARY 329 STATISTICAL PROCEDURES UNIT B: THE NATURE OF INFERENTIAL STATISTICS 333 DATA MANAGEMENT UNIT A: USE AND FUNCTIONS OF SPSS 343 DATA MANAGEMENT UNIT B: USING SPSS TO RECODE FOR T TEST 357 DATA MANAGEMENT UNIT C: DESCRIPTIVE STATISTICS 364 STATISTICAL PROCEDURES UNIT C: Z SCORES 389 Glossary 397 Bibliography 411 Index 416.
• (source: Nielsen Book Data)

This introductory guide provides a fresh approach to integrating design and statistics in a hands-on fashion that incorporates the power of SPSS (r) software to solve real-world problems. It explores the areas of design and causation--allowing readers to pose, test, and interpret research questions with live data. By integrating exercises and worksheets, the book allows readers to examine real world data from various fields of research--making it an ideal text for professionals and upper-level students involved in the social sciences.
(source: Nielsen Book Data)

• Preface xvii Acknowledgments xix PART I WHEEL OF SCIENCE: PREMISES OF RESEARCH 1
• 1 "DUH" SCIENCE VERSUS "HUH" SCIENCE 3
• 2 THEORIES AND HYPOTHESES 21
• 3 OBSERVATION AND EMPIRICAL GENERALIZATION 35
• 4 ETHICS 52 PART II WHEEL OF SCIENCE: PROCEDURES OF RESEARCH 63
• 5 MEASUREMENT 65
• 6 USING SPSS IN RESEARCH 83
• 7 CHI-SQUARE AND CONTINGENCY TABLE ANALYSIS 90
• 8 LEARNING FROM POPULATIONS: CENSUSES AND SAMPLES 102
• 9 CORRELATION 127
• 10 REGRESSION 146
• 11 CAUSATION 162 PART III WHEEL OF SCIENCE: DESIGNS OF RESEARCH 203
• 12 SURVEY RESEARCH 205
• 13 AGGREGATE RESEARCH 234
• 14 EXPERIMENTS 251
• 15 STATISTICAL METHODS OF DIFFERENCE: T TEST 270
• 16 ANALYSIS OF VARIANCE 280
• 17 FIELD RESEARCH 301
• 18 CONTENT ANALYSIS 316 PART IV STATISTICS AND DATA MANAGEMENT 327 STATISTICAL PROCEDURES UNIT A: WRITING THE STATISTICAL RESEARCH SUMMARY 329 STATISTICAL PROCEDURES UNIT B: THE NATURE OF INFERENTIAL STATISTICS 333 DATA MANAGEMENT UNIT A: USE AND FUNCTIONS OF SPSS 343 DATA MANAGEMENT UNIT B: USING SPSS TO RECODE FOR T TEST 357 DATA MANAGEMENT UNIT C: DESCRIPTIVE STATISTICS 364 STATISTICAL PROCEDURES UNIT C: Z SCORES 389 Glossary 397 Bibliography 411 Index 416.
• (source: Nielsen Book Data)

This introductory guide provides a fresh approach to integrating design and statistics in a hands-on fashion that incorporates the power of SPSS (r) software to solve real-world problems. It explores the areas of design and causation--allowing readers to pose, test, and interpret research questions with live data. By integrating exercises and worksheets, the book allows readers to examine real world data from various fields of research--making it an ideal text for professionals and upper-level students involved in the social sciences.
(source: Nielsen Book Data)