Power/knowledge : selected interviews and other writings, 1972-1977 by Foucault, Michel, 1926-1984

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Tokyo story.

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Cloverfield

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Le sang d'un poète The blood of a poet

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Improving the Quality of Child Custody Evaluations A Systematic Model by Tolle, Lauren Woodward

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Take sky : more rhymes of the never was and always is by McCord, David Thompson Watson, 1897-1997

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Psychology in Times of Crisis An economic psychological analysis of the coronavirus pandemic by Kirchler, Erich, Pitters, Julia, Kastlunger, Barbara

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Radiowave Propagation Physics and Applications by Levis, Curt, Johnson, Joel, Teixeira, Fernando

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Alfred Hitchcock : a brief life by Ackroyd, Peter, 1949-

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The Vietnam war. [Episode five], This is what we do (July-December 1967)

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Rising force : the magic of magnetic levitation by Livingston, James D., 1930-

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Statistical disclosure control

Table of Contents: “…Machine generated contents note: Preface vii Acknowledgements ix 1 Introduction 1 1.1 Concepts and Definitions 2 1.1.1 Disclosure 2 1.1.2 Statistical disclosure control 2 1.1.3 Tabular data 3 1.1.4 Microdata 3 1.1.5 Risk and utility 4 1.2 An approach to Statistical Disclosure Control 6 1.3 The chapters of the handbook 8 2 Ethics, Principles, Guidelines and Regulations, a general background 9 2.1 Introduction 9 2.2 Ethical codes and the new ISI code 9 2.2.1 ISI Declaration on Professional Ethics 10 2.2.2 New ISI Declaration on Professional Ethics 10 2.2.3 European Statistics Code of Practice 14 2.3 UNECE Principles and guidelines 14 2.4 Laws 17 2.4.1 Committee on Statistical Confidentiality 18 2.4.2 European Statistical System Committee 18 3 Microdata 21 3.1 Introduction 21 3.2 Microdata Concepts 22 3.2.1 Stage 1: Assess need for confidentiality protection 22 3.2.2 Stage 2: Key characteristics and uses of microdata 24 3.2.3 Stage 3: Disclosure risk 27 3.2.4 Stage 4: Protection methods 29 3.2.5 Stage 5: Implementation 30 3.3 Definitions of disclosure 32 3.3.1 Definitions of disclosure scenarios 33 3.4 Definitions of Disclosure Risk 34 3.4.1 Disclosure risk for categorical quasi-identifiers 35 3.4.2 Disclosure risk for continuous quasi-identifiers 37 3.5 Estimating Re-identification Risk 39 3.5.1 Individual risk based on the sample: threshold rule 39 3.5.2 Estimating individual risk using sampling weights 39 3.5.3 Estimating individual risk by Poisson model 42 3.5.4 Further models that borrow information from other sources 43 3.5.5 Estimating per record risk via heuristics 44 3.5.6 Assessing risk via record linkage 45 3.6 Non-Perturbative Microdata Masking 45 3.6.1 Sampling 46 3.6.2 Global recoding 46 3.6.3 Top and bottom coding 47 3.6.4 Local suppression 47 3.7 Perturbative Microdata Masking 48 3.7.1 Additive noise masking 48 3.7.2 Multiplicative noise masking 52 3.7.3 Microaggregation 54 3.7.4 Data swapping and rank swapping 66 3.7.5 Data shuffling 66 3.7.6 Rounding 67 3.7.7 Resampling 67 3.7.8 PRAM 67 3.7.9 MASSC 71 3.8 Synthetic and Hybrid Data 71 3.8.1 Fully synthetic data 72 3.8.2 Partially synthetic data 77 3.8.3 Hybrid data 79 3.8.4 Pros and cons of synthetic and hybrid data 88 3.9 Information Loss in Microdata 91 3.9.1 Information loss measures for continuous data 92 3.9.2 Information loss measures for categorical data 99 3.10 Release of multiple files from the same microdata set 101 3.11 Software 102 3.11.1 _-ARGUS 102 3.11.2 sdcMicro 103 3.11.3 IVEware 106 3.12 Case Studies 106 3.12.1 Microdata files at Statistics Netherlands 106 3.12.2 The European Labour Force Survey Microdata for Research Purposes 108 3.12.3 The European Structure of Earnings Survey Microdata for Research Purposes 111 3.12.4 NHIS Linked Mortality Data Public Use File, USA 117 3.12.5 Other real case instances 119 4 Magnitude tabular data 121 4.1 Introduction 121 4.1.1 Magnitude Tabular Data: Basic Terminology 121 4.1.2 Complex tabular data structures: hierarchical and linked tables 122 4.1.3 Risk Concepts 124 4.1.4 Protection Concepts 127 4.1.5 Information Loss Concepts 127 4.1.6 Implementation: Software, Guidelines and Case Study 127 4.2 Disclosure Risk Assessment I: Primary Sensitive Cells 128 4.2.1 Intruder Scenarios 128 4.2.2 Sensitivity rules 129 4.3 Disclosure Risk Assessment II: Secondary risk assessment 140 4.3.1 Feasibility Interval 141 4.3.2 Protection Level 142 4.3.3 Singleton and multi cell disclosure 143 4.3.4 Risk models for hierarchical and linked tables 144 4.4 Non-Perturbative Protection Methods 145 4.4.1 Global Recoding 145 4.4.2 The Concept of Cell Suppression 145 4.4.3 Algorithms for Secondary Cell Suppression 146 4.4.4 Secondary Cell Suppression in Hierarchical and Linked Tables 149 4.5 Perturbative Protection Methods 151 4.5.1 A pre-tabular method: Multiplicative Noise 152 4.5.2 A Post-tabular Method: Controlled Tabular Adjustment 153 4.6 Information Loss Measures for Tabular Data 153 4.6.1 Cell Costs for Cell Suppression 153 4.6.2 Cell Costs for CTA 154 4.6.3 Information Loss Measures to Evaluate the Outcome of Table Protection 155 4.7 Software for Tabular Data Protection 155 4.7.1 Empirical comparison of cell suppression algorithms 156 4.8 Guidelines: Setting up an efficient table model systematically 160 4.8.1 Defining Spanning Variables 161 4.8.2 Response Variables and Mapping Rules 162 4.9 Case Studies 164 4.9.1 Response Variables and Mapping Rules of the Case Study 164 4.9.2 Spanning Variables of the Case Study 165 4.9.3 Analysing the Tables of the Case Study 165 4.9.4 Software Issues of the Case Study 167 5 Frequency tables 169 5.1 Introduction 169 5.2 Disclosure risks 169 5.3 Methods 176 5.4 Post-tabular methods 178 5.4.1 Cell Suppression 178 5.4.2 ABS Cell Perturbation 179 5.4.3 Rounding 179 5.5 Information loss 184 5.6 Software 186 5.6.1 Introduction 186 5.7 Case Studies 188 5.7.1 UK Census 188 5.7.2 Australian and New Zealand Censuses 190 6 Data Access Issues 193 6.1 Introduction 193 6.2 Research Data Centres 193 6.3 Remote Execution 194 6.4 Remote Access 195 6.5 Licensing 196 6.6 Guidelines on output checking 196 6.6.1 Introduction 196 6.6.2 General approach 197 6.6.3 Rules for output checking 199 6.6.4 Organizational/procedural aspects of output checking 208 6.6.5 Researcher training 215 6.7 Additional issues concerning data access 218 6.7.1 Examples of disclaimers 218 6.7.2 Output description 218 6.8 Case Studies 219 6.8.1 The U.S. …”
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Proceedings of Fifth International Conference on Soft Computing for Problem Solving SocProS 2015, Volume 2

Table of Contents: “…An Aggregation Based Approach with Pareto Ranking in Multi-objective Genetic Algorithm -- Chapter 26. Weighted CoHOG (W-CoHOG) Feature Extraction for Human Detection -- Chapter 27. …”
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Machine learning for protein subcellular localization prediction by Wan, Shibiao, Mak, M. W.

Table of Contents: “…9 Results and analysis -- 9.1 Performance of GOASVM -- 9.1.1 Comparing GO vector construction methods -- 9.1.2 Performance of successive-search strategy -- 9.1.3 Comparing with methods based on other features -- 9.1.4 Comparing with state-of-the-art GO methods -- 9.1.5 GOASVM using old GOA databases -- 9.2 Performance of FusionSVM -- 9.2.1 Comparing GO vector construction and normalization methods -- 9.2.2 Performance of PairProSVM -- 9.2.3 Performance of FusionSVM -- 9.2.4 Effect of the fusion weights on the performance of FusionSVM -- 9.3 Performance of mGOASVM -- 9.3.1 Kernel selection and optimization -- 9.3.2 Term-frequency for mGOASVM -- 9.3.3 Multi-label properties for mGOASVM -- 9.3.4 Further analysis of mGOASVM -- 9.3.5 Comparing prediction results of novel proteins -- 9.4 Performance of AD-SVM -- 9.5 Performance of mPLR-Loc -- 9.5.1 Effect of adaptive decisions on mPLR-Loc -- 9.5.2 Effect of regularization on mPLR-Loc -- 9.6 Performance of HybridGO-Loc -- 9.6.1 Comparing different features -- 9.7 Performance of RP-SVM -- 9.7.1 Performance of ensemble random projection -- 9.7.2 Comparison with other dimension-reduction methods -- 9.7.3 Performance of single random-projection -- 9.7.4 Effect of dimensions and ensemble size -- 9.8 Performance of R3P-Loc -- 9.8.1 Performance on the compact databases -- 9.8.2 Effect of dimensions and ensemble size -- 9.8.3 Performance of ensemble random projection -- 9.9 Comprehensive comparison of proposed predictors -- 9.9.1 Comparison of benchmark datasets -- 9.9.2 Comparison of novel datasets -- 9.10 Summary -- 10 Properties of the proposed predictors -- 10.1 Noise data in the GOA Database -- 10.2 Analysis of single-label predictors -- 10.2.1 GOASVM vs FusionSVM -- 10.2.2 Can GOASVM be combined with PairProSVM? …”
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Machine learning with Swift : artificial intelligence for iOS by Sosnovshchenko, Alexander

Table of Contents: “…Torch -- YCML -- Inference-only libraries -- Keras -- LibSVM -- Scikit-learn -- XGBoost -- NLP libraries -- Word2Vec -- Twitter text -- Speech recognition -- TLSphinx -- OpenEars -- Computer vision -- OpenCV -- ccv -- OpenFace -- Tesseract -- Low-level subroutine libraries -- Eigen -- fmincg-c -- IntuneFeatures -- SigmaSwiftStatistics -- STEM -- Swix -- LibXtract -- libLBFGS -- NNPACK -- Upsurge -- YCMatrix -- Choosing a deep learning framework -- Summary -- Chapter 12: Optimizing Neural Networks for Mobile Devices -- Delivering perfect user experience -- Calculating the size of a convolutional neural network -- Lossless compression -- Compact CNN architectures -- SqueezeNet -- MobileNets -- ShuffleNet -- CondenseNet -- Preventing a neural network from growing big -- Lossy compression -- Optimizing for inference -- Network pruning -- Weights quantization -- Reducing precision -- Other approaches -- Facebook's approach in Caffe2 -- Knowledge distillation -- Tools -- An example of the network compression -- Summary -- Bibliography -- Chapter 13: Best Practices -- Mobile machine learning project life cycle -- Preparatory stage -- Formulate the problem -- Define the constraints -- Research the existing approaches -- Research the data -- Make design choices -- Prototype creation -- Data preprocessing -- Model training, evaluation, and selection -- Field testing -- Porting or deployment for a mobile platform -- Production -- Best practices -- Benchmarking -- Privacy and differential privacy -- Debugging and visualization -- Documentation -- Machine learning gremlins -- Data kobolds -- Tough data -- Biased data -- Batch effects -- Goblins of training -- Product design ogres -- Magical thinking -- Cargo cult -- Feedback loops -- Uncanny valley effect -- Recommended learning resources -- Mathematical background -- Machine learning -- Computer vision -- NLP.…”
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Numerical Mathematics and Advanced Applications ENUMATH 2017

Table of Contents: “…Plenary lectures -- PDE Apps for Acoustic Ducts: A Parametrized Component-to-System Model-Order-Reduction Approach -- Sub-voxel perfusion modeling in terms of coupled 3d-1d problem -- Iterative linearisation schemes for doubly degenerate parabolic equations -- Mathematics and Medicine: How mathematics, modelling and simulations can lead to better diagnosis and treatments -- Kernel methods for large scale problems: algorithms and applications -- Convergence of Multilevel Stationary Gaussian Convolution -- Anisotropic weights for RBF-PU interpolation with subdomains of variable shapes -- Radial basis function approximation method for pricing of basket options under jump diffusion model -- Greedy Algorithms for Matrix-Valued Kernels -- GPU Optimization of Large-Scale Eigenvalue Solver -- Advanced discretization methods for computational wave propagation -- On the efficiency of the Peaceman–Rachford ADI-dG method for wave-type problems -- Trefftz - Discontinuous Galerkin Approach for Solving Elastodynamic Problem -- Unfitted finite element methods: analysis and applications -- FETI-DP preconditioners for the Virtual Element Method on general 2D -- Modeling flow and transport in fractured media by a hybrid finite volume-finite element method -- A cut-cell Hybrid High-Order method for elliptic problems with curved boundaries -- A cut finite element method with boundary value correction for the incompressible Stokes equations -- Numerical Integration on Hyperrectangles in Isoparametric Unfitted Finite Elements -- Advances in numerical linear algebra methods and applications to partial differential equations -- On a generalization of Neumann series of Bessel functions using Hessenberg matrices and matrix exponentials -- Influence of the SIPG penalisation on the numerical properties of linear systems for elastic wave propagation -- Function-based Algebraic Multigrid method for the 3D Poisson problem on structured meshes -- Numerical methods in biophysics -- Mathematical modelling of phenotypic selection within solid tumours -- Uncertainty assessment of a hybrid cell-continuum based model for wound contraction -- Structure preserving discretizations and high order finite elements for differential forms -- The discrete relations between fields and potentials with high order Whitney forms -- Model Order Reduction of an Elastic Body under Large Rigid Motion -- On surface area and length preserving flows of closed curves on a given surface -- Derivation of higher-order terms in FFT-based numerical homogenization -- Monge-Ampère solvers with applications to illumination optics -- A Least-Squares Method for a Monge-Ampère Equation with Non-Quadratic Cost Function Applied to Optical Design -- Solving inverse illumination problems with Liouville’s equation -- Mixed and nonsmooth methods in numerical solid mechanics -- Strong vs. …”
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Mergers, acquisitions, and other restructuring activities : an integrated approach to process, tools, cases, and solutions by DePamphilis, Donald M.

Table of Contents: “…Relative, Asset-Oriented, and Real-Option Valuation Basics -- Inside M & A: Bristol-Myers Squibb Places a Big Bet on Inhibitex -- Chapter Overview -- Relative-Valuation Methods -- Asset-Oriented Methods -- The Weighted-Average Valuation Method -- Adjusting Valuation Estimates for Purchase Price Premiums -- Real-Options Analysis -- Determining When to Use the Different Approaches to Valuation -- Some Things to Remember -- Discussion Questions -- Questions from the CFA Curriculum -- Practice Problems and Answers -- 9. …”
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Probability and conditional expectation : fundamentals for the empirical sciences by Steyer, Rolf, 1950-

Table of Contents: “…3.2.2 Integral with respect to a weighted sum of measures -- 3.2.3 Integral with respect to an image measure -- 3.2.4 Convergence theorems -- 3.3 Lebesgue and Riemann integral -- 3.4 Density -- 3.5 Absolute continuity and the Radon-Nikodym theorem -- 3.6 Integral with respect to a product measure -- 3.7 Proofs -- Part II Probability, Random Variable, and Its Distribution -- 4 Probability measure -- 4.1 Probability measure and probability space -- 4.1.1 Definition -- 4.1.2 Formal and substantive meaning of probabilistic terms -- 4.1.3 Properties of a probability measure -- 4.1.4 Examples -- 4.2 Conditional probability -- 4.2.1 Definition -- 4.2.2 Filtration and time order between events and sets of events -- 4.2.3 Multiplication rule -- 4.2.4 Examples -- 4.2.5 Theorem of total probability -- 4.2.6 Bayes' theorem -- 4.2.7 Conditional-probability measure -- 4.3 Independence -- 4.3.1 Independence of events -- 4.3.2 Independence of set systems -- 4.4 Conditional independence given an event -- 4.4.1 Conditional independence of events given an event -- 4.4.2 Conditional independence of set systems given an event -- 4.5 Proofs -- 5 Random variable, distribution, density, and distribution function -- 5.1 Random variable and its distribution -- 5.2 Equivalence of two random variables with respect to a probability measure -- 5.2.1 Identical and P-equivalent random variables -- 5.2.2 P-equivalence, PB-equivalence, and absolute continuity -- 5.3 Multivariate random variable -- 5.4 Independence of random variables -- 5.5 Probability function of a discrete random variable -- 5.6 Probability density with respect to a measure -- 5.6.1 General concepts and properties -- 5.6.2 Density of a discrete random variable -- 5.6.3 Density of a bivariate random variable -- 5.7 Uni- or multivariate real-valued random variable.…”
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The ruling class by Barnes, Peter, 1931-2004

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Bending steel : modernity and the American superhero by Regalado, Aldo J.

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