Lattice-switch Monte Carlo

We present a Monte Carlo method for the direct evaluation of the difference between the free energies of two crystal structures. The method is built on a lattice-switch transformation that maps a configuration of one structure onto a candidate configuration of the other by `switching' one set of lattice vectors for the other, while keeping the displacements with respect to the lattice sites constant. The sampling of the displacement configurations is biased, multicanonically, to favor paths leading to `gateway' arrangements for which the Monte Carlo switch to the candidate configuration will be accepted. The configurations of both structures can then be efficiently sampled in a single process, and the difference between their free energies evaluated from their measured probabilities. We explore and exploit the method in the context of extensive studies of systems of hard spheres. We show that the efficiency of the method is controlled by the extent to which the switch conserves correlated microstructure. We also show how, microscopically, the procedure works: the system finds gateway arrangements which fulfill the sampling bias intelligently. We establish, with high precision, the differences between the free energies of the two close packed structures (fcc and hcp) in both the constant density and the constant pressure ensembles.Comment: 34 pages, 9 figures, RevTeX. To appear in Phys. Rev.

A review of symbolic analysis of experimental data.

This review covers the group of data-analysis techniques collectively referred to as symbolization or symbolic time-series analysis. Symbolization involves transformation of raw time-series measurements (i.e., experimental signals) into a series of discretized symbols that are processed to extract information about the generating process. In many cases, the degree of discretization can be quite severe, even to the point of converting the original data to single-bit values. Current approaches for constructing symbols and detecting the information they contain are summarized. Novel approaches for characterizing and recognizing temporal patterns can be important for many types of experimental systems, but this is especially true for processes that are nonlinear and possibly chaotic. Recent experience indicates that symbolization can increase the efficiency of finding and quantifying information from such systems, reduce sensitivity to measurement noise, and discriminate both specific and general classes of proposed models. Examples of the successful application of symbolization to experimental data are included. Key theoretical issues and limitations of the method are also discussed

Robust Computer Vision Against Adversarial Examples and Domain Shifts

Recent advances in deep learning have achieved remarkable success in various computer vision problems. Driven by progressive computing resources and a vast amount of data, deep learning technology is reshaping human life. However, Deep Neural Networks (DNNs) have been shown vulnerable to adversarial examples, in which carefully crafted perturbations can easily fool DNNs into making wrong predictions. On the other hand, DNNs have poor generalization to domain shifts, as they suffer from performance degradation when encountering data from new visual distributions. We view these issues from the perspective of robustness. More precisely, existing deep learning technology is not reliable enough for many scenarios, where adversarial examples and domain shifts are among the most critical. The lack of reliability inevitably limits DNNs from being deployed in more important computer vision applications, such as self-driving vehicles and medical instruments that have major safety concerns. To overcome these challenges, we focus on investigating and addressing the robustness of deep learning-based computer vision approaches. The first part of this thesis attempts to robustify computer vision models against adversarial examples. We dive into such adversarial robustness from four aspects: novel attacks for strengthening benchmarks, empirical defenses validated by a third-party evaluator, generalizable defenses that can defend against multiple and unforeseen attacks, and defenses specifically designed for less explored tasks. The second part of this thesis improves the robustness against domain shifts via domain adaptation. We dive into two important domain adaptation settings: unsupervised domain adaptation, which is the most common, and source-free domain adaptation, which is more practical in real-world scenarios. The last part explores the intersection of adversarial robustness and domain adaptation fields to provide new insights for robust DNNs. We study two directions: adversarial defense for domain adaptation and adversarial defense via domain adaptations. This dissertation aims at more robust, reliable, and trustworthy computer vision

A Framework of Multi-Dimensional and Multi-Scale Modeling with Applications

In this dissertation, a framework for multi-dimensional and multi-scale modeling is proposed. The essential idea is based on oriented space curves, which can be represented as a 3D slender object or 1D step parameters. SMILES and Masks provide functionalities that extend slender objects into branched and other objects. We treat the conversion between 1D, 2D, 3D, and 4D representations as data unification. A mathematical analysis of different methods applied to helices (a special type of space curves) is also provided. Computational implementation utilizes Model-ViewController design principles to integrate data unification with graphical visualizations to create a dashboard. Applications of multi-dimensional and multi-scale modeling are provided to study “Magic Snake”, “Nanocar” and “Genome Dashboard”

Festschrift zum 60. Geburtstag von Wolfgang Strasser

Die vorliegende Festschrift ist Prof. Dr.-Ing. Dr.-Ing. E.h. Wolfgang Straßer zu seinem 60. Geburtstag gewidmet. Eine Reihe von Wissenschaftlern auf dem Gebiet der Computergraphik, die alle aus der "Tübinger Schule" stammen, haben - zum Teil zusammen mit ihren Schülern - Aufsätze zu dieser Schrift beigetragen. Die Beiträge reichen von der Objektrekonstruktion aus Bildmerkmalen über die physikalische Simulation bis hin zum Rendering und der Visualisierung, vom theoretisch ausgerichteten Aufsatz bis zur praktischen gegenwärtigen und zukünftigen Anwendung. Diese thematische Buntheit verdeutlicht auf anschauliche Weise die Breite und Vielfalt der Wissenschaft von der Computergraphik, wie sie am Lehrstuhl Straßer in Tübingen betrieben wird. Schon allein an der Tatsache, daß im Bereich der Computergraphik zehn Professoren an Universitäten und Fachhochschulen aus Tübingen kommen, zeigt sich der prägende Einfluß Professor Straßers auf die Computergraphiklandschaft in Deutschland. Daß sich darunter mehrere Physiker und Mathematiker befinden, die in Tübingen für dieses Fach gewonnen werden konnten, ist vor allem seinem Engagement und seiner Ausstrahlung zu verdanken. Neben der Hochachtung vor den wissenschaftlichen Leistungen von Professor Straßer hat sicherlich seine Persönlichkeit einen entscheidenden Anteil an der spontanten Bereischaft der Autoren, zu dieser Festschrift beizutragen. Mit außergewöhnlich großem persönlichen Einsatz fördert er Studenten, Doktoranden und Habilitanden, vermittelt aus seinen reichen internationalen Beziehungen Forschungskontakte und schafft so außerordentlich gute Voraussetzungen für selbständige wissenschafliche Arbeit. Die Autoren wollen mit ihrem Beitrag Wolfgang Straßer eine Freude bereiten und verbinden mit ihrem Dank den Wunsch, auch weiterhin an seinem fachlich wie menschlich reichen und bereichernden Wirken teilhaben zu dürfen