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Zero Stability in Hierarchical Co-Simulation

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Zero Stability in Hierarchical Co-Simulation

Simulation Winter Conference

作者： Irene Hafner Martin Bicher Niki Popper dwh GmbH Vienna AUSTRIA Research Unit of Data Science Inst. of Information Systems Eng. Vienna AUSTRIA Research Unit of Computational Statistics Inst. of Statistics and Mathematical Methods in Economics Vienna AUSTRIA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

关键词： Stability analysis Convergence

Event-triggered Learning for Linear Quadratic Control

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arXiv 2019年

作者： Schlüter, Henning Solowjow, Friedrich Trimpe, Sebastian Intelligent Control Systems Group Max Planck Inst. for Intelligent Systems Stuttgart70569 Germany Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany

When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically detects inaccurate models and triggers the learning of a new process model when needed. This is achieved by analyzing the probability distribution of the linear quadratic cost and designing a learning trigger that leverages Chernoff bounds. In particular, whenever empirically observed cost signals are located outside the derived confidence intervals, we can provably guarantee that this is with high probability due to a model mismatch. With the aid of numerical and hardware experiments, we demonstrate that the proposed bounds are tight and that the event-triggered learning algorithm effectively distinguishes between inaccurate models and probabilistic effects such as process noise. Thus, a structured approach is obtained that decides when model learning is beneficial. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

SPECTRAL PARTITIONING OF TIME-VARYING NETWORKS WITH UNOBSERVED EDGES

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arXiv 2019年

作者： Schaub, Michael T. Segarra, Santiago Wai, Hoi-To Inst. for Data Systems and Society MIT & Department of Engineering Science University of Oxford Department of Electrical and Computer Engineering Rice University Department of SEEM Chinese University of Hong Kong NT Hong Kong

We discuss a variant of ‘blind’ community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network. We consider a scenario where our observed graph signals are obtained by filtering white noise input, and the underlying network is different for every observation. In this fashion, the filtered graph signals can be interpreted as defined on a time-varying network. We model each of the underlying network realizations as generated by an independent draw from a latent stochastic blockmodel (SBM). To infer the partition of the latent SBM, we propose a simple spectral algorithm for which we provide a theoretical analysis and establish consistency guarantees for the recovery. We illustrate our results using numerical experiments on synthetic and real data, highlighting the efficacy of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Time varying networks

Review of machine learning algorithms in differential expression analysis

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arXiv 2017年

作者： Kuznetsova, Irina Karpievitch, Yuliya V. Filipovska, Aleksandra Lugmayr, Artur Holzinger, Andreas Graz University of Technology Graz Austria Inst. of Interactive Systems and Data Science Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Plant Energy Biology Centre of Excellence Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Curtin University Perth Australia Inst. of Interactive Systems and Data Science Graz University of Technology Graz Austria

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments. Copyright © 2017, The Authors. All rights reserved.

关键词： data mining

Review of machine learning algorithms in differential expression analysis 9

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Review of machine learning algorithms in differential expres...

9th Workshop on Semantic Ambient Media Experiences: VISualisation, Emerging Media, and User-EXperience (Vis-MX), SAME 2016b

作者： Kuznetsova, Irina Karpievitch, Yuliya V Filipovska, Aleksandra Lugmayr, Artur Holzinger, Andreas Graz University of Technology Graz Austria Inst. of Interactive Systems and Data Science Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Plant Energy Biology Centre of Excellence Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Curtin University Perth Australia Inst. of Interactive Systems and Data Science Graz University of Technology Graz Austria

ISBN: (纸本)9789527023150

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNA-Seq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.

关键词： data mining

The dual graph shift operator: Identifying the support of the frequency domain

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arXiv 2017年

作者： Leus, Geert Segarra, Santiago Ribeiro, Alejandro Marques, Antonio G. Dept. of Electrical Eng. Math. and Comp. Science Delft Univ. of Technology Inst. for Data Systems and Society Massachusetts Inst. of Technology Dept. of Electrical and Systems Eng. Univ. of Pennsylvania Dept. of Signal Theory and Comms. King Juan Carlos Univ.

Contemporary data is often supported by an irregular structure, which can be conveniently captured by a graph. Accounting for this graph support is crucial to analyze the data, leading to an area known as graph signal processing (GSP). The two most important tools in GSP are the graph shift operator (GSO), which is a sparse matrix accounting for the topology of the graph, and the graph Fourier transform (GFT), which maps graph signals into a frequency domain spanned by a number of graph-related Fourier-like basis vectors. This alternative representation of a graph signal is denominated the graph frequency signal. Several attempts have been undertaken in order to interpret the support of this graph frequency signal, but they all resulted in a one-dimensional interpretation. However, if the support of the original signal is captured by a graph, why would the graph frequency signal have a simple one-dimensional support? That is why, for the first time, we propose an irregular support for the graph frequency signal, which we coin the dual graph. The dual GSO leads to a better interpretation of the graph frequency signal and its domain, helps to understand how the different graph frequencies are related and clustered, enables the development of better graph filters and filter banks, and facilitates the generalization of classical SP results to the graph domain. Copyright © 2017, The Authors. All rights reserved.

关键词： Topology

The impact of fixed-cost pooling strategies on test collection bias 16

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The impact of fixed-cost pooling strategies on test collecti...

2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016

作者： Lipani, Aldo Zuccon, Guido Lupu, Mihai Koopman, Bevan Hanbury, Allan Inst. of Software Technology and Interactive Systems Vienna University of Technology Vienna Austria Faculty of Science and Technology Queensland University of Technology Brisbane Australia Australian e-Health Research Centre CSIRO Brisbane Australia

ISBN: (纸本)9781450344975

In Information Retrieval, test collections are usually built using the pooling method. Many pooling strategies have been developed for the pooling method. Herein, we address the question of identifying the best pooling strategy when evaluating systems using precision-oriented measures in presence of budget constraints on the number of documents to be evaluated. As a quality measurement we use the bias introduced by the pooling strategy, measured both in terms of Mean Absolute Error of the scores and in terms of ranking errors. Based on experiments on 15 test collections, we conclude that, for precision-oriented measures, the best strategies are based on Rank-Biased Precision (RBP). These results can inform collection builders because they suggest that, under fixed assessment budget constraints, RBP-based sampling produces less biased pools than other alternatives.

关键词： Budget control

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

Lossless data compression for infrared hyperspectral sounders - An overview

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Lossless data compression for infrared hyperspectral sounder...

Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting

作者： Huang, Bormin Huang, Hung-Lung Ahuja, Alok Chen, Hao Schmit, Timothy J. Heymann, Roger W. Coop. Inst. Meteorol. Satellite S. Space Science and Engineering Center University of Wisconsin-Madison Madison WI 53706 United States NOAA Natl. Environ. Satellite Data/I.S. Office of Research and Applications Madison WI 53706 United States Office of Systems Development Suitland MD 20746 United States CIMSS University of Wisconsin Madison

Hyperspectral sounding data requires accuracy for useful retrieval of atmospheric temperature, moisture, trace gases, clouds, aerosols and surface properties. Therefore, compression of hyperspectral sounding data is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral data that will be generated by the hyperspectral sounders such as AIRS, CrIS, IASI, GIFTS and HES inst.uments, the use of robust data compression techniques will be beneficial to data transfer and archive. This paper reviews wavelet-based lossless data compression schemes for the 3D hyperspectral data using 3D integer wavelet transforms followed by 3D EZW and 3D SPIHT zerotree coding schemes. We extend both zerotree coding schemes to take on any size of satellite data, each of whose dimensions need not be divisible by 2N, where N is the layers of the wavelet decomposition being performed. The 2D wavelet-based JPEG-2000 compression scheme and some other prediction-based 2D lossless compression schemes such as CALIC and JPEG-LS are also investigated. Their compression ratios are presented.

关键词： Sounding apparatus