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The 26th Report on migrations by ISMU Foundation situates migration phenomena in Italy within the broader framework of the sudden outbreak and spread of COVID-19. The report analyzes the enduring impact of the pandemic on migration flows as well as on foreign residents in Italy. Statistical aspects and analyses of health, labour, and education are complemented with an in-depth study of the Italian legal framework, with particular regard to the most important legislative innovation on migration introduced in 2020: the regularization of migrant workers. The report is further complemented with detailed analyses of the link between immigration, politics, and the media, of racism, and of discrimination during the pandemic. Finally, the report devotes particular attention to the European arena, focusing on the new perspectives for European migration policy.

Migration --- Immigration --- Pandemic --- Regularization --- Migration policy --- Discrimination

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The 26th Report on migrations by ISMU Foundation situates migration phenomena in Italy within the broader framework of the sudden outbreak and spread of COVID-19. The report analyzes the enduring impact of the pandemic on migration flows as well as on foreign residents in Italy. Statistical aspects and analyses of health, labour, and education are complemented with an in-depth study of the Italian legal framework, with particular regard to the most important legislative innovation on migration introduced in 2020: the regularization of migrant workers. The report is further complemented with detailed analyses of the link between immigration, politics, and the media, of racism, and of discrimination during the pandemic. Finally, the report devotes particular attention to the European arena, focusing on the new perspectives for European migration policy.

Migration, immigration & emigration --- Migration --- Immigration --- Pandemic --- Regularization --- Migration policy --- Discrimination

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This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.

Intention Recognition --- Dynamic Systems --- (Conditional) Density Estimation --- Regularization --- Human-Robot-Cooperation

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This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.

Machine learning --- Automatic control engineering --- Statistical physics --- Bayesian inference --- Probability & statistics --- Cybernetics & systems theory --- System Identification --- Machine Learning --- Linear Dynamical Systems --- Nonlinear Dynamical Systems --- Kernel-based Regularization --- Bayesian Interpretation of Regularization --- Gaussian Processes --- Reproducing Kernel Hilbert Spaces --- Estimation Theory --- Support Vector Machines --- Regularization Networks

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Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Inverse problems arise in a large variety of applications ranging from medical imaging and non-destructive testing via finance to systems biology. Many of these problems belong to the class of parameter identification problems in partial differential equations (PDEs) and thus are computationally demanding and mathematically challenging. Hence there is a substantial need for stable and efficient solvers for this kind of problems as well as for a rigorous convergence analysis of these methods. This monograph consists of five parts. Part I motivates the importance of developing and analyzing regularization methods in Banach spaces by presenting four applications which intrinsically demand for a Banach space setting and giving a brief glimpse of sparsity constraints. Part II summarizes all mathematical tools that are necessary to carry out an analysis in Banach spaces. Part III represents the current state-of-the-art concerning Tikhonov regularization in Banach spaces. Part IV about iterative regularization methods is concerned with linear operator equations and the iterative solution of nonlinear operator equations by gradient type methods and the iteratively regularized Gauß-Newton method. Part V finally outlines the method of approximate inverse which is based on the efficient evaluation of the measured data with reconstruction kernels.

Banach spaces --- Parameter estimation --- Differential equations, Partial --- Banach, Espaces de --- Estimation d'un paramètre --- Equations aux dérivées partielles --- Banach spaces. --- Parameter estimation. --- Differential equations, Partial. --- Partial differential equations --- Estimation theory --- Stochastic systems --- Functions of complex variables --- Generalized spaces --- Topology --- Banach Space. --- Iterative Method. --- Regularization Theory. --- Tikhonov Regularization.