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Machine Learning in High Energy Physics Community White Paper

Journal of Physics: Conference Series 2018年第2期1085卷

作者： Kim Albertsson Piero Altoe Dustin Anderson Michael Andrews Juan Pedro Araque Espinosa Adam Aurisano Laurent Basara Adrian Bevan Wahid Bhimji Daniele Bonacorsi Paolo Calafiura Mario Campanelli Louis Capps Federico Carminati Stefano Carrazza Taylor Childers Elias Coniavitis Kyle Cranmer Claire David Douglas Davis Javier Duarte Martin Erdmann Jonas Eschle Amir Farbin Matthew Feickert Nuno Filipe Castro Conor Fitzpatrick Michele Floris Alessandra Forti Jordi Garra-Tico Jochen Gemmler Maria Girone Paul Glaysher Sergei Gleyzer Vladimir Gligorov Tobias Golling Jonas Graw Lindsey Gray Dick Greenwood Thomas Hacker John Harvey Benedikt Hegner Lukas Heinrich Ben Hooberman Johannes Junggeburth Michael Kagan Meghan Kane Konstantin Kanishchev Przemysław Karpiński Zahari Kassabov Gaurav Kaul Dorian Kcira Thomas Keck Alexei Klimentov Jim Kowalkowski Luke Kreczko Alexander Kurepin Rob Kutschke Valentin Kuznetsov Nicolas Köhler Igor Lakomov Kevin Lannon Mario Lassnig Antonio Limosani Gilles Louppe Aashrita Mangu Pere Mato Helge Meinhard Dario Menasce Lorenzo Moneta Seth Moortgat Meenakshi Narain Mark Neubauer Harvey Newman Hans Pabst Michela Paganini Manfred Paulini Gabriel Perdue Uzziel Perez Attilio Picazio Jim Pivarski Harrison Prosper Fernanda Psihas Alexander Radovic Ryan Reece Aurelius Rinkevicius Eduardo Rodrigues Jamal Rorie David Rousseau Aaron Sauers Steven Schramm Ariel Schwartzman Horst Severini Paul Seyfert Filip Siroky Konstantin Skazytkin Mike Sokoloff Graeme Stewart Bob Stienen Ian Stockdale Giles Strong Savannah Thais Karen Tomko Eli Upfal Emanuele Usai Andrey Ustyuzhanin Martin Vala Sofia Vallecorsa Justin Vasel Mauro Verzetti Xavier Vilasís-Cardona Jean-Roch Vlimant Ilija Vukotic Sean-Jiun Wang Gordon Watts Michael Williams Wenjing Wu Stefan Wunsch Omar Zapata Lulea University of Technology NVidia California Institute of Technology Carnegie Mellon University LIP Lisboa University of Cincinnati Universita e INFN Padova University of London Lawrence Berkeley National Laboratory Universita e INFN Bologna CERN Argonne National Laboratory Universitaet Freiburg New York University Deutsches Elektronen-Synchrotron Duke University Fermi National Accelerator Laboratory RWTH Aachen University Universität Zürich University of Texas at Arlington Southern Methodist University Ecole Polytechnique Federale de Lausanne University of Manchester University of Cambridge Karlsruher Institut für Technologie University of Florida Centre National de la Recherche Scientifique Université de Genève Louisiana Tech University Purdue University University of Illinois at Urbana-Champaign Max Planck Institut für Physik SLAC National Accelerator Laboratory Sound Cloud University of Milan Intel Brookhaven National Laboratory University of Bristol Russian Academy of Sciences Cornell University University of Notre Dame University of Melbourne University of California Berkeley Universita & INFN Milano Bicocca Vrije Universiteit Brussel Brown University Yale University University of Alabama University of Massachusetts Princeton University Florida State University Indiana University College of William and Mary University of California Santa Cruz Rice University Universite de Paris Sud 11 University of Oklahoma Masaryk University University of Glasgow Radboud Universiteit Nijmegen Altair Engineering Ohio Supercomputer Center Yandex School of Data Analysis Technical University of Kosice Gangneung-Wonju National University University of Rochester University of Barcelona University of Chicago University of Washington Massachusetts Institute of Technology Chinese Academy of Sciences OProject and University of Antioquia

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

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Matrix-Product Operators and States: NP-Hardness and Undecidability

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Physical Review Letters 2014年第16期113卷 160503-160503页

作者： M. Kliesch D. Gross J. Eisert QMIO Group Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Physikalisches Institut and Freiburg Center for Data Analysis and Modeling Universität Freiburg 79104 Freiburg Germany

Tensor network states constitute an important variational set of quantum states for numerical studies of strongly correlated systems in condensed-matter physics, as well as in mathematical physics. This is specifically true for finitely correlated states or matrix-product operators, designed to capture mixed states of one-dimensional quantum systems. It is a well-known open problem to find an efficient algorithm that decides whether a given matrix-product operator actually represents a physical state that in particular has no negative eigenvalues. We address and answer this question by showing that the problem is provably undecidable in the thermodynamic limit and that the bounded version of the problem is NP-hard (nondeterministic-polynomial-time hard) in the system size. Furthermore, we discuss numerous connections between tensor network methods and (seemingly) different concepts treated before in the literature, such as hidden Markov models and tensor trains.

关键词： Tensors

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Optimized spectral estimation for nonlinear synchronizing systems

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Physical Review E 2014年第3期89卷 032912-032912页

作者： Linda Sommerlade Malenka Mader Wolfgang Mader Jens Timmer Marco Thiel Celso Grebogi Björn Schelter Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics University of Freiburg Hermann-Herder-Strasse 3 79104 Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg Eckerstrasse 1 79104 Freiburg Germany Freiburg Institute for Advanced Studies (FRIAS) University of Freiburg Albertstrasse 19 79104 Freiburg Germany Hospital for Neuropaediatrics and Muscular Diseases University Medical Center of Freiburg Germany

In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.

关键词： Nonlinear dynamical systems

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Statistical evaluation of forecasts

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Physical Review E 2014年第2期90卷 022133-022133页

作者： Malenka Mader Wolfgang Mader Bruce J. Gluckman Jens Timmer Björn Schelter Department of Neuropediatrics and Muscular Disease University Medical Center of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg Freiburg Germany Institute for Physics University of Freiburg Freiburg Germany Center for Neural Engineering Pennsylvania State University State College Pennsylvania 16801 USA Department of Engineering Science and Mechanics Pennsylvania State University State College Pennsylvania 16801 USA Department of Neurosurgery Pennsylvania State University Hershey Pennsylvania 17033 USA Department of Biomedical Engineering Pennsylvania State University State College Pennsylvania 16801 USA BIOSS Center for Biological Signalling Studies University of Freiburg Freiburg Germany Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen UK

Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

关键词： Forecasting

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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Statistical validation of event predictors: A comparative study based on the field of seizure prediction

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Physical Review E 2011年第6期83卷 066704-066704页

作者： Hinnerk Feldwisch-Drentrup Andreas Schulze-Bonhage Jens Timmer Björn Schelter Bernstein Center Freiburg (BCF) University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg Freiburg Germany Department of Neurobiology and Biophysics Faculty of Biology University of Freiburg Freiburg Germany Freiburg Institute for Advanced Studies University of Freiburg Freiburg Germany Department of Physics University of Freiburg Freiburg Germany Epilepsy Center University Hospital of Freiburg Freiburg Germany Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Institute for Complex Systems and Mathematical Biology SUPA University of Aberdeen Aberdeen United Kingdom

The prediction of events is of substantial interest in many research areas. To evaluate the performance of prediction methods, the statistical validation of these methods is of utmost importance. Here, we compare an analytical validation method to numerical approaches that are based on Monte Carlo simulations. The comparison is performed in the field of the prediction of epileptic seizures. In contrast to the analytical validation method, we found that for numerical validation methods insufficient but realistic sample sizes can lead to invalid high rates of false positive conclusions. Hence we outline necessary preconditions for sound statistical tests on above chance predictions.

关键词： Forecasting

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Feature selection in high dimensional EEG features spaces for epileptic seizure prediction

Feature selection in high dimensional EEG features spaces fo...

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作者： Direito, Bruno Duarte, João Teixeira, César Schelter, Björn Le Van Quyen, Michel Schulze-Bonhage, Andreas Sales, Francisco Dourado, António Center for Informatics and Systems of the University of Coimbra University of Coimbra Portugal IBILI - Institute of Biomedical Research in Light and Image Faculty of Medicine University of Coimbra Portugal Centre de Recherche Institut du Cerveau et de la Moelle Epinière-INSERM Hôpital de la Pitié-Salpêtrière Germany Freiburg Center for Data Analysis and Modelling Albert-Ludwigs-Universität Freiburg Germany Epilepsy Center University Hospital of Freiburg Germany Hospitals of the University of Coimbra Portugal

ISBN: (纸本)9783902661937

Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy;seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods. © 2011 IFAC.

关键词： Classification (of information)

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Feature selection in high dimensional EEG features spaces for epileptic seizure prediction

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IFAC Proceedings Volumes 2011年第1期44卷 6206-6211页

作者： Bruno Direito João Duarte César Teixeira Björn Schelter Michel Le Van Quyen Andreas Schulze-Bonhage Francisco Sales António Dourado Center for Informatics and Systems of the University of Coimbra University of Coimbra Portugal IBILI – Institute of Biomedical Research in Light and Image Faculty of Medicine of the University of Coimbra Portugal Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière-INSERM Unité Mixte de Recherche en Santé 975-Centre National de la Recherche Scientifique Unité Mixte de Recherche 7225 Hôpital de la Pitié-Salpětrière Freiburg Center for Data Analysis and Modelling Albert-Ludwigs-Universität Freiburg Germany Epilepsy Center University Hospital of Freiburg Germany Hospitals of the University of Coimbra Portugal

Abstract Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods.

关键词： Classification Methods Classification Feature Selection Seizure Prediction Epilepsy

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Observation of oscillation coupling ratios and the meridional flow

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Journal of Physics: Conference Series 2011年第1期271卷

作者： A Schad M Roth J Timmer Freiburg Center for Data Analysis and Modeling University of Freiburg 79104 Freiburg Germany Kiepenheuer-Insitut für Sonnenphysik 79104 Freiburg Germany Freiburg Institute for Advanced Studies University of Freiburg 79104 Freiburg Germany

Measurements from local helioseismology indicate the existence of a meridional flow in the Sun with a strength up to 15 m/s near the solar surface. The flow profile at depths below ≈ 15 Mm are not accessible. We propose a method using global helioseismic measurements with the prospect to infer the meridional flow profile throughout the solar convection zone and show its performance on simulated data.

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Distinguishing Direct from Indirect Interactions in Oscillatory Networks with Multiple Time Scales

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Physical Review Letters 2010年第3期104卷 038701-038701页

作者： Jakob Nawrath M. Carmen Romano Marco Thiel István Z. Kiss Mahesh Wickramasinghe Jens Timmer Jürgen Kurths Björn Schelter Physics Department University of Freiburg Hermann-Herder Str. 3 79104 Freiburg Germany Center for Data Analysis and Modeling University of Freiburg Eckertstr. 1 79104 Freiburg Germany Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen AB24 3UE United Kingdom Institute of Medical Sciences Foresterhill University of Aberdeen Aberdeen AB25 2ZD United Kingdom Department of Chemistry Saint Louis University St. Louis Missouri 63103 USA Freiburg Institute for Advanced Studies Albertstr. 19 79104 Freiburg Germany Institute of Physics Humboldt University Berlin 12489 Berlin Germany Potsdam Institute for Climate Impact Research 14412 Potsdam Germany

We propose a method to infer the coupling structure in networks of nonlinear oscillatory systems with multiple time scales. The method of partial phase synchronization allows us to infer the coupling structure for coupled nonlinear oscillators with one well-defined time scale. The case of oscillators with multiple time scales has remained a challenge until now. Here, we introduce partial recurrence based synchronization analysis to tackle this challenge. We successfully apply the proposed method to model systems and experimental data from coupled electrochemical oscillators. The statistical significance of the results is evaluated based on a surrogate hypothesis test.

关键词： Time measurement

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