Convergence criteria for waveform iteration methods applied to partitioned DAE systems in chemical process simulation

The application of block waveform iteration methods to initial value problems for implicit DAE systems of index 1 arising in chemical process simulation is considered. These methods permit the concurrent treatment of blocks of subsystems of the entire system gaining a coarse granular parallelism. Their convergence properties strongly depend on the assignment of variables to equations and the partitioning of the system into subsystem blocks. The convergence of block waveform iteration methods applied to semiexplicit DAE sytems of index 1 is proved. The convergence conditions are given in such a way that only the single blocksystems have to satisfy some corresponding conditions. It is shown that these conditions are fulfilled for a simplified modeling of distillation columns. Resulting from the convergence considerations an assignment and partitioning algorithm is given. A prototype of a waveform-iteration code has been implemented and tested by means of examples included in the user package of the chemical process simulator SPEEDUP

A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal Processing Conference (EUSIPCO), 201

How Leaders Invest Staffing Resources for Learning Improvement

Analyzes staffing challenges that guide school leaders' resource decisions in the context of a learning improvement agenda, staff resource investment strategies that improve learning outcomes equitably, and ways to win support for differential investment

Analyzing the Fine Structure of Distributions

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.Comment: 66 pages, 81 figures, accepted in PLOS ON

Kritische studentische Initiativen an der Bologna-reformierten Universität – Möglichkeiten und Grenzen

Einleitung: Wahrscheinlich hat es seit den 1970er Jahren nicht mehr so viele kritische Initiativen von Geographiestudierenden an Hochschulen in deutschsprachigen Ländern gegeben wie heute. Ihre Aktivitäten reichen von Lesekreisen, Tutorien, Exkursionen, Film- und Vortragsreihen bis zu politischen Aktionen, wobei viele Initiativen über den Arbeitskreis (AK) Kritische Geographie vernetzt sind. Parallel zu dieser erfreulichen Beobachtung findet ein fundamentaler Umbau der Hochschule statt. Die zunehmende Ökonomisierung des Studiums durch den Bologna-Prozess birgt die Gefahr, Gesellschaftskritik auf dem Altar der Verwertbarkeit und der "Praxisrelevanz" zu opfern. Diese Entwicklungen geben Anlass, das Verhältnis zwischen der Arbeit kritischer Initiativen und den reformierten Universitäten kritisch zu hinterfragen und damit den Zusammenhang zwischen Bologna-Prozess, Neoliberalisierung und kritischer Wissenschaft für die geographische Hochschullehre aus einer studentischen Perspektive zu beleuchten, wird doch ein nicht unbedeutender Teil der kritischen Geographie von Studierenden getragen. Im Folgenden werden wir kurz auf den Bologna-Prozess eingehen, anschließend die Fragestellung und das methodische Vorgehen erläutern, um darauf aufbauend die empirischen Ergebnisse vorzustellen und zu diskutieren