Interactive High‐Quality Visualization of Higher‐Order Finite Elements

Higher‐order finite element methods have emerged as an important discretization scheme for simulation. They are increasingly used in contemporary numerical solvers, generating a new class of data that must be analyzed by scientists and engineers. Currently available visualization tools for this type of data are either batch oriented or limited to certain cell types and polynomial degrees. Other approaches approximate higher‐order data by resampling resulting in trade‐offs in interactivity and quality. To overcome these limitations, we have developed a distributed visualization system which allows for interactive exploration of non‐conforming unstructured grids, resulting from space‐time discontinuous Galerkin simulations, in which each cell has its own higher‐order polynomial solution. Our system employs GPU‐based raycasting for direct volume rendering of complex grids which feature non‐convex, curvilinear cells with varying polynomial degree. Frequency‐based adaptive sampling accounts for the high variations along rays. For distribution across a GPU cluster, the initial object‐space partitioning is determined by cell characteristics like the polynomial degree and is adapted at runtime by a load balancing mechanism. The performance and utility of our system is evaluated for different aeroacoustic simulations involving the propagation of shock fronts.     

Comparing notions of randomness

It is an open problem in the area of effective (algorithmic) randomness whether Kolmogorov-Loveland randomness coincides with Martin-Löf randomness. Joe Miller and André Nies suggested some variations of Kolmogorov-Loveland randomness to approach this problem and to provide a partial solution. We show that their proposed notion of injective randomness is still weaker than Martin-Löf randomness. Since in this proof some of the ideas we use are clearer, we also show the weaker theorem that permutation randomness is weaker than Martin-Löf randomness.     

Active learning for extended finite state machines

We present a black-box active learning algorithm for inferring extended finite state machines (EFSM)s by dynamic black-box analysis. EFSMs can be used to model both data flow and control behavior of software and hardware components. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing. Our algorithm infers a class of EFSMs called register automata. Register automata have a finite control structure, extended with variables (registers), assignments, and guards. Our algorithm is parameterized on a particular theory, i.e., a set of operations and tests on the data domain that can be used in guards.Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We also show that, under these conditions, our framework induces a generalization of the classical Nerode equivalence and canonical automata construction to the symbolic setting. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.     

The Semantic Web: Collective Intelligence on the Web

The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.     

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. In contrast to methods like PCA, SFA is thus well suited for techniques that make direct use of the latent space. Real-world time series can be complex, and current SFA algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to develop a kernelized SFA algorithm which provides a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. We hypothesize that our algorithm generates a feature space that resembles a Fourier basis in the unknown space of latent variables underlying a given real-world time series. We evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel PCA. Our results show excellent classification accuracy and demonstrate the superiority of kernel SFA over kernel PCA in encoding latent variables.     

Print your atomic force microscope

Progress in scanning probe microscopy profited from a flourishing multitude of new instrument designs, which lead to novel imaging modes and as a consequence to innovative microscopes. Often these designs were hampered by the restrictions, which conventional milling techniques impose. Modern rapid prototyping techniques, where layer by layer is added to the growing piece either by light driven polymerization or by three-dimensional printing techniques, overcome this constraint, allowing highly concave or even embedded and entangled structures. We have employed such a technique to manufacture an atomic force microscopy (AFM) head, and we compared its performance with a copy milled from aluminum. We tested both AFM heads for single molecule force spectroscopy applications and found little to no difference in the signal-to-noise ratio as well as in the thermal drift. The lower E modulus seems to be compensated by higher damping making this material well suited for low noise and low drift applications. Printing an AFM thus offers unparalleled freedom in the design and the rapid production of application-tailored custom instruments.     

绿色城市规划法则及中国绿色城市未来展望

当今时代最大的环境挑战是城市和建筑对化石燃料的依赖,以及对能源、土地、水和食品安全日益增长的需求。在这一背景下,城市需向更可持续的城镇化模式转变,建筑和城市规划在这一过程中发挥主要作用。为此提出了15项绿色城市法则,以应对现存街区、区域和城市转型的挑战,并对中国低碳或无碳的未来进行了展望。     

Growth condition dependence of unintentional oxygen incorporation in epitaxial GaN

Growth conditions have a tremendous impact on the unintentional background impurity concentration in gallium nitride (GaN) synthesized by molecular beam epitaxy and its resulting chemical and physical properties. In particular for oxygen identified as the dominant background impurity we demonstrate that under optimized growth stoichiometry the growth temperature is the key parameter to control its incorporation and that an increase by 55?°C leads to an oxygen reduction by one order of magnitude. Quantitatively this reduction and the resulting optical and electrical properties are analyzed by secondary ion mass spectroscopy, photoluminescence, capacitance versus voltage measurements, low temperature magneto-transport and parasitic current paths in lateral transistor test structures based on two-dimensional electron gases. At a growth temperature of 665?°C the residual charge carrier concentration is decreased to below 10¹⁵ cm^?3, resulting in insulating behavior and thus making the material suitable for beyond state-of-the-art device applications.