Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

This collection contains invited papers by distinguished statisticians to honour and acknowledge the contributions of Professor Dr. Dr. Helge Toutenburg to Statistics on the occasion of his sixty-?fth birthday. These papers present the most recent developments in the area of the linear model and its related topics. Helge Toutenburg is an established statistician and currently a Professor in the Department of Statistics at the University of Munich (Germany) and Guest Professor at the University of Basel (Switzerland). He studied Mathematics in his early years at Berlin and specialized in Statistics. Later he completed his dissertation (Dr. rer. nat. ) in 1969 on optimal prediction procedures at the University of Berlin and completed the post-doctoral thesis in 1989 at the University of Dortmund on the topic of mean squared error superiority. He taught at the Universities of Berlin, Dortmund and Regensburg before joining the University of Munich in 1991. He has various areas of interest in which he has authored and co-authored over 130 research articles and 17 books. He has made pioneering contributions in several areas of statistics, including linear inference, linear models, regression analysis, quality engineering, Taguchi methods, analysis of variance, design of experiments, and statistics in medicine and dentistry.

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

The goal of this work is the development of a novel computational formalization of whole-body affordances which is suitable for the multimodal detection and validation of interaction possibilities in unknown environments. The hierarchical framework allows the consistent fusion of affordance-related evidence and can be utilized for realizing shared autonomous control of humanoid robots. The affordance formalization is evaluated in several experiments in simulation and on real humanoid robots.

In Augustine’s Cyprian Matthew Gaumer retraces how Augustine of Hippo devised the ultimate strategy to suppress Donatist Christianity, an indigenous form of the religion in ancient North Africa. Spanning nearly forty years, Augustine’s entire clerical career was spent combating the Donatists and seeking the dominance of the Catholic Church in North Africa. Through a variety of approaches Augustine evolved a method to successfully outlaw and deconstruct the Donatist Church’s organisation. This hinged on concerted preaching, tract writing, integrating Roman imperial authorities, and critically: by denying the Donatists’ exclusive claim to Cyprian of Carthage. Re-appropriation of Cyprian’s authority required Augustine and his allies to re-write history and pose positions contrary to Cyprian’s. In the end, Cyprian was the Donatists’ no longer.

This book contains high-quality refereed research papers presented at the 6th International Conference on Computer Science, Engineering, and Education Applications (ICCSEEA2023), which took place in Warsaw, Poland, on March 17–19, 2023, and was organized by the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute”, the National Aviation University, Lviv Polytechnic National University, the Polish Operational and Systems Society, Warsaw University of Technology, and the International Research Association of Modern Education and Computer Science. The book covers a variety of topics, including cutting-edge research in computer science, artificial intelligence, engineering techniques, smart logistics, and knowledge representation with educational applications. The book is an invaluable resource for academics, graduate students, engineers, management professionals, and undergraduate students who are interested in computer science and its applications in engineering and education.