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ZZT is an exploration of a submerged continent, a personal history of the shareware movement, ascii art, messy teen identity struggle, cybersex, transition, outsider art, the thousand deaths of Barney the Dinosaur, and what happens when a ten-year-old gets her hands on a programming language she can understand.
In the theory of minimal submanifolds, Bernstein's problem and Plateau's problem are central topics. This important book presents the Douglas-Rado solution to Plateau's problem, but the main emphasis is on Bernstein's problem and its new developments in various directions: the value distribution of the Gauss image of a minimal surface in Euclidean 3-space, Simons' work for minimal graphic hypersurfaces, and the author's own contributions to Bernstein type theorems for higher codimension. The author also introduces some related topics, such as submanifolds with parallel mean curvature, Weierstrass type representation for surfaces of mean curvature 1 in hyperbolic 3-space, and special Lagrangian submanifolds.This new edition contains the author's recent work on the Lawson-Osserman's problem for higher codimension, and on Chern's problem for minimal hypersurfaces in the sphere. Both Chern's problem and Lawson-Osserman's problem are important problems in minimal surface theory which are still unsolved. In addition, some new techniques were developed to address those problems in detail, which are of interest in the field of geometric analysis.
This study proposes a new spectral representation called the Zeros of Z-Transform (ZZT), which is an all-zero representation of the z-transform of the signal. In addition, new chirp group delay processing techniques are developed for analysis of resonances of a signal. The combination of the ZZT representation with the chirp group delay processing algorithms provides a useful domain to study resonance characteristics of source and filter components of speech. Using the two representations, effective algorithms are developed for: source-tract decomposition of speech, glottal flow parameter estimation, formant tracking and feature extraction for speech recognition. The ZZT representation is mainly important for theoretical studies. Studying the ZZT of a signal is essential to be able to develop effective chirp group delay processing methods. Therefore, first the ZZT representation of the source-filter model of speech is studied for providing a theoretical background. We confirm through ZZT representation that anti-causality of the glottal flow signal introduces mixed-phase characteristics in speech signals. The ZZT of windowed speech signals is also studied since windowing cannot be avoided in practical signal processing algorithms and the effect of windowing on ZZT representation is drastic. We show that separate patterns exist in ZZT representations of windowed speech signals for the glottal flow and the vocal tract contributions. A decomposition method for source-tract separation is developed based on these patterns in ZZT. We define chirp group delay as group delay calculated on a circle other than the unit circle in z-plane. The need to compute group delay on a circle other than the unit circle comes from the fact that group delay spectra are often very noisy and cannot be easily processed for formant tracking purposes (the reasons are explained through ZZT representation). In this thesis, we propose methods to avoid such problems by modifying the ZZT of a signal and further computing the chirp group delay spectrum. New algorithms based on processing of the chirp group delay spectrum are developed for formant tracking and feature estimation for speech recognition. The proposed algorithms are compared to state-of-the-art techniques. Equivalent or higher efficiency is obtained for all proposed algorithms. The theoretical parts of the thesis further discuss a mixed-phase model for speech and phase processing problems in detail. Index Terms—spectral representation, source-filter separation, glottal flow estimation, formant tracking, zeros of z-transform, group delay processing, phase processing.
This textbook offers an introduction to differential geometry designed for readers interested in modern geometry processing. Working from basic undergraduate prerequisites, the authors develop manifold theory and Lie groups from scratch; fundamental topics in Riemannian geometry follow, culminating in the theory that underpins manifold optimization techniques. Students and professionals working in computer vision, robotics, and machine learning will appreciate this pathway into the mathematical concepts behind many modern applications. Starting with the matrix exponential, the text begins with an introduction to Lie groups and group actions. Manifolds, tangent spaces, and cotangent spaces follow; a chapter on the construction of manifolds from gluing data is particularly relevant to the reconstruction of surfaces from 3D meshes. Vector fields and basic point-set topology bridge into the second part of the book, which focuses on Riemannian geometry. Chapters on Riemannian manifolds encompass Riemannian metrics, geodesics, and curvature. Topics that follow include submersions, curvature on Lie groups, and the Log-Euclidean framework. The final chapter highlights naturally reductive homogeneous manifolds and symmetric spaces, revealing the machinery needed to generalize important optimization techniques to Riemannian manifolds. Exercises are included throughout, along with optional sections that delve into more theoretical topics. Differential Geometry and Lie Groups: A Computational Perspective offers a uniquely accessible perspective on differential geometry for those interested in the theory behind modern computing applications. Equally suited to classroom use or independent study, the text will appeal to students and professionals alike; only a background in calculus and linear algebra is assumed. Readers looking to continue on to more advanced topics will appreciate the authors’ companion volume Differential Geometry and Lie Groups: A Second Course.
Jonnie Goodboy Tyler ventures out of the tiny community of humans barely surviving in the Rocky Mountain refuge and finds himself challenging the Psychlos, the malignant and oppressive alien conquerors of Earth.
The Ichiakukai association comes to Tomikyu. Can Sota meet their expectations? Meanwhile the connoisseur Jitsunichido visits Tomikyu with an elderly customer… who is he? Finally the story of how Tomikyu opened is revealed! Tomikyu is now facing a serious problem! Satsuki and Sota’s fate is about to be shaken up!
Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.
“Ball’s lucid and timely book offers a portal into a new realm.”—The Economist "The term “Metaverse” is thirty years old, yet only recently entered mainstream conversation, attracting both fascinating and skepticism. While some have promised its imminent arrival, in fact it will take a series of technological and societal leaps to realize its full potential. In The Metaverse, pioneering theorist, former tech executive, and acclaimed entrepreneur Matthew Ball offers an expansive tour of the “next internet”: he presents a comprehensive definition of the Metaverse (going far beyond mere virtual reality headsets), explains the technologies that will power it, addresses governance challenges, and predicts Metaverse winners and losers. Bringing clarity and authority to a frequently misunderstood concept, this revised and updated edition of Ball’s authoritative work demonstrates how the Metaverse will radically reshape society. “A comprehensive guide to every aspect of the metaverse.”—John Thornhill, Financial Times “Offers a better understanding of the metaverse than the novel that coined the term—1992’s Snow Crash.”—Cecilia D’Anastasio, Bloomberg
An analysis of the game engine Unreal through feminist, race, and queer theories of technology and media, as well as a critique of the platform studies framework itself. In this first scholarly book on the Unreal game engine, James Malazita explores one of the major contemporary game development platforms through feminist, race, and queer theories of technology and media, revealing how Unreal produces, and is produced by, broader intersections of power. Enacting Platforms takes a novel critical platform studies approach, raising deeper questions: what are the material and cultural limits of platforms themselves? What is the relationship between the analyst and the platform of study, and how does that relationship in part determine what “counts” as the platform itself? Malazita also offers a forward-looking critique of the platform studies framework itself. The Unreal platform serves as a kind of technical and political archive of the games industry, highlighting how the techniques and concerns of games have shifted and accreted over the past 30 years. Today, Unreal is also used in contexts far beyond games, including in public communication, biomedical research, civil engineering, and military simulation and training. The author’s depth of technical analysis, combined with new archival findings, contributes to discussions of topics rarely covered in games studies (such as the politics of graphical rendering algorithms), as well as new readings of previously “closed” case studies (such as the engine’s entanglement with the US military and American masculinity in America’s Army). Culture, Malazita writes, is not “built into” software but emerges through human practices with code.
This intriguing book constitutes the thoroughly refereed postproceedings of the International Conference on Non-Linear Speech Processing, NOLISP 2007, held in Paris, France, in May 2007. The 24 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on nonlinear and non-conventional techniques, speech synthesis, speaker recognition, speech recognition, and many other subjects.