Bending invariant correspondence matching on 3D models with feature descriptor.

Li, Sai Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (leaves 91-96).Abstracts in English and Chinese.Abstract --- p.2List of Figures --- p.6Acknowledgement --- p.10Chapter Chapter 1 --- Introduction --- p.11Chapter 1.1 --- Problem definition --- p.11Chapter 1.2. --- Proposed algorithm --- p.12Chapter 1.3. --- Main features --- p.14Chapter Chapter 2 --- Literature Review --- p.16Chapter 2.1 --- Local Feature Matching techniques --- p.16Chapter 2.2. --- Global Iterative alignment techniques --- p.19Chapter 2.3 --- Other Approaches --- p.20Chapter Chapter 3 --- Correspondence Matching --- p.21Chapter 3.1 --- Fundamental Techniques --- p.24Chapter 3.1.1 --- Geodesic Distance Approximation --- p.24Chapter 3.1.1.1 --- Dijkstra ´ةs algorithm --- p.25Chapter 3.1.1.2 --- Wavefront Propagation --- p.26Chapter 3.1.2 --- Farthest Point Sampling --- p.27Chapter 3.1.3 --- Curvature Estimation --- p.29Chapter 3.1.4 --- Radial Basis Function (RBF) --- p.32Chapter 3.1.5 --- Multi-dimensional Scaling (MDS) --- p.35Chapter 3.1.5.1 --- Classical MDS --- p.35Chapter 3.1.5.2 --- Fast MDS --- p.38Chapter 3.2 --- Matching Processes --- p.40Chapter 3.2.1 --- Posture Alignment --- p.42Chapter 3.2.1.1 --- Sign Flip Correction --- p.43Chapter 3.2.1.2 --- Input model Alignment --- p.49Chapter 3.2.2 --- Surface Fitting --- p.52Chapter 3.2.2.1 --- Optimizing Surface Fitness --- p.54Chapter 3.2.2.2 --- Optimizing Surface Smoothness --- p.56Chapter 3.2.3 --- Feature Matching Refinement --- p.59Chapter 3.2.3.1 --- Feature descriptor --- p.61Chapter 3.2.3.3 --- Feature Descriptor matching --- p.63Chapter Chapter 4 --- Experimental Result --- p.66Chapter 4.1 --- Result of the Fundamental Techniques --- p.66Chapter 4.1.1 --- Geodesic Distance Approximation --- p.67Chapter 4.1.2 --- Farthest Point Sampling (FPS) --- p.67Chapter 4.1.3 --- Radial Basis Function (RBF) --- p.69Chapter 4.1.4 --- Curvature Estimation --- p.70Chapter 4.1.5 --- Multi-Dimensional Scaling (MDS) --- p.71Chapter 4.2 --- Result of the Core Matching Processes --- p.73Chapter 4.2.1 --- Posture Alignment Step --- p.73Chapter 4.2.2 --- Surface Fitting Step --- p.78Chapter 4.2.3 --- Feature Matching Refinement --- p.82Chapter 4.2.4 --- Application of the proposed algorithm --- p.84Chapter 4.2.4.1 --- Design Automation in Garment Industry --- p.84Chapter 4.3 --- Analysis --- p.86Chapter 4.3.1 --- Performance --- p.86Chapter 4.3.2 --- Accuracy --- p.87Chapter 4.3.3 --- Approach Comparison --- p.88Chapter Chapter 5 --- Conclusion --- p.89Chapter 5.1 --- Strength and contributions --- p.89Chapter 5.2 --- Limitation and future works --- p.90References --- p.9

Are the waves detected by LIGO the waves according to Einstein, Pirani, Bondi, Trautmann, Kopeikin or what are they?

From the geometric formulation of gravity, according to the Einstein-Grosmann-Hilbert equations, of November 1915, as the geodesic movement in the semirimennian manifold of positive curvature, spacetime, where due to absence of symmetries, the conservation of energy-impulse is not possible taking together the material processes and that of the gravitational geometric field, however, given those symmetries in the flat Minkowski spacetime, using the De Sitter model, Einstein linearizing gravitation, of course, really in the absence of gravity, in 1916, purged of some mathematical errors in 1918, he introduced "gravitational waves" as disturbances in the curvature of space, and in the absence of knowing physically what spacetime is and philosophically in dispute, that previously in 1936 and definitively in 1937, Einstein showed they did not exist. It was through the works arising from the dynamics of academic discourse, from the perspective not of Einstein but of Weyl, that Bondi, Pirani, Robinson and Trautman, in the 1950s, after Einstein's death, "gravitational waves" were reintroduced and led to experimental search. In 2002, from Sergei Kopeikin's VLBI experiment, its supposed speed was established, without obtaining unanimous recognition from the community of scientists but rather dividing them. And it was in February 2016 that the aLIGO-aVirgo collaboration announced that they had detected them for the first time. In this work, the history that led to this supposed discovery is presented and it is stated that the waves detected are really from the quantum vacuum in which everything that exists is immersed, the author's thesis exposed immediately in response to that 2016 announcement

Are the waves detected by LIGO the waves according to Einstein, Pirani, Bondi, Trautmann, Kopeikin or what are they?

From the geometric formulation of gravity, according to the Einstein-Grosmann-Hilbert equations, of November 1915, as the geodesic movement in the semirimennian manifold of positive curvature, spacetime, where due to absence of symmetries, the conservation of energy-impulse is not possible taking together the material processes and that of the gravitational geometric field, however, given those symmetries in the flat Minkowski spacetime, using the De Sitter model, Einstein linearizing gravitation, of course, really in the absence of gravity, in 1916, purged of some mathematical errors in 1918, he introduced "gravitational waves" as disturbances in the curvature of space, and in the absence of knowing physically what spacetime is and philosophically in dispute, that previously in 1936 and definitively in 1937, Einstein showed they did not exist. It was through the works arising from the dynamics of academic discourse, from the perspective not of Einstein but of Weyl, that Bondi, Pirani, Robinson and Trautman, in the 1950s, after Einstein's death, "gravitational waves" were reintroduced and led to experimental search. In 2002, from Sergei Kopeikin's VLBI experiment, its supposed speed was established, without obtaining unanimous recognition from the community of scientists but rather dividing them. And it was in February 2016 that the aLIGO-aVirgo collaboration announced that they had detected them for the first time. In this work, the history that led to this supposed discovery is presented and it is stated that the waves detected are really from the quantum vacuum in which everything that exists is immersed, the author's thesis exposed immediately in response to that 2016 announcement

Towards the conceptualisation of maritime delimitation: legal and technical aspects of a political process

The United Nations Convention on the Law of the Sea sets a normative framework for an integrated governance of the oceans, with far-reaching implications for states. Its implementation - as to navigation rights, preservation of marine environment, exploitation of resources, economic jurisdiction, or any other marine issues - depends however on one central issue: the spatial allocation of authority. This thesis examines one specific aspect of this international legal problem - maritime boundary delimitation. A major challenge for this thesis lies in the fact that its subject has been extensively and thoroughly reviewed, both in scholarship and in jurisprudence. Notwithstanding this, a closer look reveals a paucity of conceptual analysis. Drawing on historical elements, as well as on state practice and case law, the present thesis endeavours to further the understanding of maritime delimitation from a conceptual standpoint. Focusing on the development of conventional provisions on delimitation, Part I eventually argues that the so-called 'equitable principles doctrine' is not customary law. What is part of customary law is an obligation of result: maritime delimitations must resulting equitable solutions. The distinction between these propositions becomes clear in Part II. By deconstructing the subject into its three core issues - concept, methods and normativity, this thesis submits that the said obligation is to be met through the optimisation of two legal principles: the principle of maritime zoning and the principle of equity. Whilst suggesting that the watchword is reasonableness, it proposes that the reasonableness of the boundary be objectified by reference to a novel concept: the average 'distance ratio' of the line. As a denouement, Part III investigates the 'discovery' of boundary-lines. Recognising that the legal determination of maritime boundaries consists of a multiple-factor analysis, in which the sphere of discretion conferred upon courts is critical, it aims at improving reasoning discourse through 'multicriteria decision-making' and the utilisation of 'yardsticks'. After discussing which elements of the 'factual matrix' are legally relevant, and how they bear on the 'discovery' of the boundary-line, this thesis offers a test study intended to validate the conceptualisation proposed

Discriminant feature pursuit: from statistical learning to informative learning.

Lin Dahua.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 233-250).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Problem We are Facing --- p.1Chapter 1.2 --- Generative vs. Discriminative Models --- p.2Chapter 1.3 --- Statistical Feature Extraction: Success and Challenge --- p.3Chapter 1.4 --- Overview of Our Works --- p.5Chapter 1.4.1 --- New Linear Discriminant Methods: Generalized LDA Formulation and Performance-Driven Sub space Learning --- p.5Chapter 1.4.2 --- Coupled Learning Models: Coupled Space Learning and Inter Modality Recognition --- p.6Chapter 1.4.3 --- Informative Learning Approaches: Conditional Infomax Learning and Information Chan- nel Model --- p.6Chapter 1.5 --- Organization of the Thesis --- p.8Chapter I --- History and Background --- p.10Chapter 2 --- Statistical Pattern Recognition --- p.11Chapter 2.1 --- Patterns and Classifiers --- p.11Chapter 2.2 --- Bayes Theory --- p.12Chapter 2.3 --- Statistical Modeling --- p.14Chapter 2.3.1 --- Maximum Likelihood Estimation --- p.14Chapter 2.3.2 --- Gaussian Model --- p.15Chapter 2.3.3 --- Expectation-Maximization --- p.17Chapter 2.3.4 --- Finite Mixture Model --- p.18Chapter 2.3.5 --- A Nonparametric Technique: Parzen Windows --- p.21Chapter 3 --- Statistical Learning Theory --- p.24Chapter 3.1 --- Formulation of Learning Model --- p.24Chapter 3.1.1 --- Learning: Functional Estimation Model --- p.24Chapter 3.1.2 --- Representative Learning Problems --- p.25Chapter 3.1.3 --- Empirical Risk Minimization --- p.26Chapter 3.2 --- Consistency and Convergence of Learning --- p.27Chapter 3.2.1 --- Concept of Consistency --- p.27Chapter 3.2.2 --- The Key Theorem of Learning Theory --- p.28Chapter 3.2.3 --- VC Entropy --- p.29Chapter 3.2.4 --- Bounds on Convergence --- p.30Chapter 3.2.5 --- VC Dimension --- p.35Chapter 4 --- History of Statistical Feature Extraction --- p.38Chapter 4.1 --- Linear Feature Extraction --- p.38Chapter 4.1.1 --- Principal Component Analysis (PCA) --- p.38Chapter 4.1.2 --- Linear Discriminant Analysis (LDA) --- p.41Chapter 4.1.3 --- Other Linear Feature Extraction Methods --- p.46Chapter 4.1.4 --- Comparison of Different Methods --- p.48Chapter 4.2 --- Enhanced Models --- p.49Chapter 4.2.1 --- Stochastic Discrimination and Random Subspace --- p.49Chapter 4.2.2 --- Hierarchical Feature Extraction --- p.51Chapter 4.2.3 --- Multilinear Analysis and Tensor-based Representation --- p.52Chapter 4.3 --- Nonlinear Feature Extraction --- p.54Chapter 4.3.1 --- Kernelization --- p.54Chapter 4.3.2 --- Dimension reduction by Manifold Embedding --- p.56Chapter 5 --- Related Works in Feature Extraction --- p.59Chapter 5.1 --- Dimension Reduction --- p.59Chapter 5.1.1 --- Feature Selection --- p.60Chapter 5.1.2 --- Feature Extraction --- p.60Chapter 5.2 --- Kernel Learning --- p.61Chapter 5.2.1 --- Basic Concepts of Kernel --- p.61Chapter 5.2.2 --- The Reproducing Kernel Map --- p.62Chapter 5.2.3 --- The Mercer Kernel Map --- p.64Chapter 5.2.4 --- The Empirical Kernel Map --- p.65Chapter 5.2.5 --- Kernel Trick and Kernelized Feature Extraction --- p.66Chapter 5.3 --- Subspace Analysis --- p.68Chapter 5.3.1 --- Basis and Subspace --- p.68Chapter 5.3.2 --- Orthogonal Projection --- p.69Chapter 5.3.3 --- Orthonormal Basis --- p.70Chapter 5.3.4 --- Subspace Decomposition --- p.70Chapter 5.4 --- Principal Component Analysis --- p.73Chapter 5.4.1 --- PCA Formulation --- p.73Chapter 5.4.2 --- Solution to PCA --- p.75Chapter 5.4.3 --- Energy Structure of PCA --- p.76Chapter 5.4.4 --- Probabilistic Principal Component Analysis --- p.78Chapter 5.4.5 --- Kernel Principal Component Analysis --- p.81Chapter 5.5 --- Independent Component Analysis --- p.83Chapter 5.5.1 --- ICA Formulation --- p.83Chapter 5.5.2 --- Measurement of Statistical Independence --- p.84Chapter 5.6 --- Linear Discriminant Analysis --- p.85Chapter 5.6.1 --- Fisher's Linear Discriminant Analysis --- p.85Chapter 5.6.2 --- Improved Algorithms for Small Sample Size Problem . --- p.89Chapter 5.6.3 --- Kernel Discriminant Analysis --- p.92Chapter II --- Improvement in Linear Discriminant Analysis --- p.100Chapter 6 --- Generalized LDA --- p.101Chapter 6.1 --- Regularized LDA --- p.101Chapter 6.1.1 --- Generalized LDA Implementation Procedure --- p.101Chapter 6.1.2 --- Optimal Nonsingular Approximation --- p.103Chapter 6.1.3 --- Regularized LDA algorithm --- p.104Chapter 6.2 --- A Statistical View: When is LDA optimal? --- p.105Chapter 6.2.1 --- Two-class Gaussian Case --- p.106Chapter 6.2.2 --- Multi-class Cases --- p.107Chapter 6.3 --- Generalized LDA Formulation --- p.108Chapter 6.3.1 --- Mathematical Preparation --- p.108Chapter 6.3.2 --- Generalized Formulation --- p.110Chapter 7 --- Dynamic Feedback Generalized LDA --- p.112Chapter 7.1 --- Basic Principle --- p.112Chapter 7.2 --- Dynamic Feedback Framework --- p.113Chapter 7.2.1 --- Initialization: K-Nearest Construction --- p.113Chapter 7.2.2 --- Dynamic Procedure --- p.115Chapter 7.3 --- Experiments --- p.115Chapter 7.3.1 --- Performance in Training Stage --- p.116Chapter 7.3.2 --- Performance on Testing set --- p.118Chapter 8 --- Performance-Driven Subspace Learning --- p.119Chapter 8.1 --- Motivation and Principle --- p.119Chapter 8.2 --- Performance-Based Criteria --- p.121Chapter 8.2.1 --- The Verification Problem and Generalized Average Margin --- p.122Chapter 8.2.2 --- Performance Driven Criteria based on Generalized Average Margin --- p.123Chapter 8.3 --- Optimal Subspace Pursuit --- p.125Chapter 8.3.1 --- Optimal threshold --- p.125Chapter 8.3.2 --- Optimal projection matrix --- p.125Chapter 8.3.3 --- Overall procedure --- p.129Chapter 8.3.4 --- Discussion of the Algorithm --- p.129Chapter 8.4 --- Optimal Classifier Fusion --- p.130Chapter 8.5 --- Experiments --- p.131Chapter 8.5.1 --- Performance Measurement --- p.131Chapter 8.5.2 --- Experiment Setting --- p.131Chapter 8.5.3 --- Experiment Results --- p.133Chapter 8.5.4 --- Discussion --- p.139Chapter III --- Coupled Learning of Feature Transforms --- p.140Chapter 9 --- Coupled Space Learning --- p.141Chapter 9.1 --- Introduction --- p.142Chapter 9.1.1 --- What is Image Style Transform --- p.142Chapter 9.1.2 --- Overview of our Framework --- p.143Chapter 9.2 --- Coupled Space Learning --- p.143Chapter 9.2.1 --- Framework of Coupled Modelling --- p.143Chapter 9.2.2 --- Correlative Component Analysis --- p.145Chapter 9.2.3 --- Coupled Bidirectional Transform --- p.148Chapter 9.2.4 --- Procedure of Coupled Space Learning --- p.151Chapter 9.3 --- Generalization to Mixture Model --- p.152Chapter 9.3.1 --- Coupled Gaussian Mixture Model --- p.152Chapter 9.3.2 --- Optimization by EM Algorithm --- p.152Chapter 9.4 --- Integrated Framework for Image Style Transform --- p.154Chapter 9.5 --- Experiments --- p.156Chapter 9.5.1 --- Face Super-resolution --- p.156Chapter 9.5.2 --- Portrait Style Transforms --- p.157Chapter 10 --- Inter-Modality Recognition --- p.162Chapter 10.1 --- Introduction to the Inter-Modality Recognition Problem . . . --- p.163Chapter 10.1.1 --- What is Inter-Modality Recognition --- p.163Chapter 10.1.2 --- Overview of Our Feature Extraction Framework . . . . --- p.163Chapter 10.2 --- Common Discriminant Feature Extraction --- p.165Chapter 10.2.1 --- Formulation of the Learning Problem --- p.165Chapter 10.2.2 --- Matrix-Form of the Objective --- p.168Chapter 10.2.3 --- Solving the Linear Transforms --- p.169Chapter 10.3 --- Kernelized Common Discriminant Feature Extraction --- p.170Chapter 10.4 --- Multi-Mode Framework --- p.172Chapter 10.4.1 --- Multi-Mode Formulation --- p.172Chapter 10.4.2 --- Optimization Scheme --- p.174Chapter 10.5 --- Experiments --- p.176Chapter 10.5.1 --- Experiment Settings --- p.176Chapter 10.5.2 --- Experiment Results --- p.177Chapter IV --- A New Perspective: Informative Learning --- p.180Chapter 11 --- Toward Information Theory --- p.181Chapter 11.1 --- Entropy and Mutual Information --- p.181Chapter 11.1.1 --- Entropy --- p.182Chapter 11.1.2 --- Relative Entropy (Kullback Leibler Divergence) --- p.184Chapter 11.2 --- Mutual Information --- p.184Chapter 11.2.1 --- Definition of Mutual Information --- p.184Chapter 11.2.2 --- Chain rules --- p.186Chapter 11.2.3 --- Information in Data Processing --- p.188Chapter 11.3 --- Differential Entropy --- p.189Chapter 11.3.1 --- Differential Entropy of Continuous Random Variable . --- p.189Chapter 11.3.2 --- Mutual Information of Continuous Random Variable . --- p.190Chapter 12 --- Conditional Infomax Learning --- p.191Chapter 12.1 --- An Overview --- p.192Chapter 12.2 --- Conditional Informative Feature Extraction --- p.193Chapter 12.2.1 --- Problem Formulation and Features --- p.193Chapter 12.2.2 --- The Information Maximization Principle --- p.194Chapter 12.2.3 --- The Information Decomposition and the Conditional Objective --- p.195Chapter 12.3 --- The Efficient Optimization --- p.197Chapter 12.3.1 --- Discrete Approximation Based on AEP --- p.197Chapter 12.3.2 --- Analysis of Terms and Their Derivatives --- p.198Chapter 12.3.3 --- Local Active Region Method --- p.200Chapter 12.4 --- Bayesian Feature Fusion with Sparse Prior --- p.201Chapter 12.5 --- The Integrated Framework for Feature Learning --- p.202Chapter 12.6 --- Experiments --- p.203Chapter 12.6.1 --- A Toy Problem --- p.203Chapter 12.6.2 --- Face Recognition --- p.204Chapter 13 --- Channel-based Maximum Effective Information --- p.209Chapter 13.1 --- Motivation and Overview --- p.209Chapter 13.2 --- Maximizing Effective Information --- p.211Chapter 13.2.1 --- Relation between Mutual Information and Classification --- p.211Chapter 13.2.2 --- Linear Projection and Metric --- p.212Chapter 13.2.3 --- Channel Model and Effective Information --- p.213Chapter 13.2.4 --- Parzen Window Approximation --- p.216Chapter 13.3 --- Parameter Optimization on Grassmann Manifold --- p.217Chapter 13.3.1 --- Grassmann Manifold --- p.217Chapter 13.3.2 --- Conjugate Gradient Optimization on Grassmann Manifold --- p.219Chapter 13.3.3 --- Computation of Gradient --- p.221Chapter 13.4 --- Experiments --- p.222Chapter 13.4.1 --- A Toy Problem --- p.222Chapter 13.4.2 --- Face Recognition --- p.223Chapter 14 --- Conclusion --- p.23

Collective consciousness and its pathologies: Understanding the failure of AIDS control and treatment in the United States

We address themes of distributed cognition by extending recent formal developments in the theory of individual consciousness. While single minds appear biologically limited to one dynamic structure of linked cognitive submodules instantiating consciousness, organizations, by contrast, can support several, sometimes many, such constructs simultaneously, although these usually operate relatively slowly. System behavior remains, however, constrained not only by culture, but by a developmental path dependence generated by organizational history, in the context of market selection pressures. Such highly parallel multitasking – essentially an institutional collective consciousness – while capable of reducing inattentional blindness and the consequences of failures within individual workspaces, does not eliminate them, and introduces new characteristic malfunctions involving the distortion of information sent between workspaces and the possibility of pathological resilience – dysfunctional institutional lock-in. Consequently, organizations remain subject to canonical and idiosyncratic failures analogous to, but more complicated than, those afflicting individuals. Remediation is made difficult by the manner in which pathological externalities can write images of themselves onto both institutional function and corrective intervention. The perspective is applied to the failure of AIDS control and treatment in the United States

Process and Outcome Evaluation of a Social-Networking Website for Health Promotion

Overweight and obesity pose a significant threat to the health and wellbeing of college students. However, studies of interventions to improve the health behaviors of college students are few in number, largely atheoretical, and have limited potential for widespread dissemination. The goal of this study was to evaluate a pilot of an internet based social-networking intervention to promote health behavior change. Specific aims were to assess the role of behavioral engagement as a mechanism of change over time, review qualitative feedback regarding participants\u27 likes and dislikes of the website, and use social networking analysis (SNA) to analyze structural support and its effects on behavior change. The sample consisted of 39 students from the Loma Linda University School of Public Health. Participants each selected a specific health behavior goal that they wished to achieve in the 10-week period of the study and completed the web-based individual health behavior change project as part of the coursework. Results showed a significant improvement in participant health behavior across the course of the study period. Results also indicated that level of peer feedback and support received significantly moderated change in health behavior across time such that greater improvement in health behavior was observed in those who received a greater amount of peer feedback. Qualitative analysis revealed participants reported the features of peer feedback, personal blog, and line graph of heath behavior change to be the most helpful. The most commonly reported frustrations were website technical difficulties, particularly at the start of the study. The SNA showed that indegree (number of ties received) and, to a lesser extent, outdegree (number of ties originated with another) predicted attainment of clinically significant change. Furthermore, examination of the structural network diagram revealed that more concentrated sets of reciprocal ties existed among participants who attained clinically significant change. Although further research is needed, these findings suggest that web-based social support interventions may be effective in promoting change in variety of health behaviors and that SNA is a useful technique for investigating the influence of aspects of structural support on health behavior change

Aeronautical engineering: A continuing bibliography with indexes (supplement 304)

This bibliography lists 453 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics