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arXiv 2020年

作者： Nikolaou, Nikolaos Waldmann, Ingo P. Tsiaras, Angelos Morvan, Mario Edwards, Billy Yip, Kai Hou Tinetti, Giovanna Sarkar, Subhajit Dawson, James M. Borisov, Vadim Kasneci, Gjergji Petković, Matej Stepišnik, Tomaž Al-Ubaidi, Tarek Bailey, Rachel Louise Granitzer, Michael Julka, Sahib Kern, Roman Ofner, Patrick Wagner, Stefan Heppe, Lukas Bunse, Mirko Morik, Katharina Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Computer Science University of Tuebingen Tuebingen Germany Jožef Stefan Institute Ljubljana Slovenia DCCS Austria Space Research Institute Austrian Academy of Sciences Austria Chair of Data Science University of Passau Germany Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Austria Commission for Astronomy Austrian Academy of Sciences Graz Austria Artificial Intelligence Group TU Dortmund University Germany

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly nonlinear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrasolar planets

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FAIR Principles:Interpretations and Implementation Considerations

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data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN 10

Towards a Personalized Item Recommendation Approach in Socia...

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10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Guan, Chun Yuen, Kevin Kam Fung Yue, Yong Department of Computer Science University of Liverpool Liverpool United Kingdom Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China School of Business University of Social Sciences Singapore Singapore Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781538658369

In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be applied in recommendation systems. This paper proposes the Intuitionistic Fuzzy DBSCAN (IF-DBSCAN) for the personalized item recommendation in folksonomy. The IF-DBSCAN is used to cluster items with respect to the user-defined tags. The Intuitionistic Fuzzy Set (IFS) is used to represent tag values which are vague and uncertain. DBSCAN can cluster items with the tags represented by using IFSs into different groups. An example of movie recommendation is demonstrated for the applicability of the proposed method. © 2018 IEEE.

关键词： Recommender systems

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Further results on structured regression for multi-scale networks

arXiv

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arXiv 2019年

作者： Bašić, Milan Arsić, Branko Obradović, Zoran Department of Computer Science University of Niš Višegradska 33 Niš18000 Serbia Department of Mathematics and Informatics University of Kragujevac Radoja Domanovića 12 Kragujevac34000 Serbia Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University PhiladelphiaPA United States

Gaussian Conditional Random Fields (GCRF), as a structured regression model, is designed to achieve higher regression accuracy than unstructured predictors at the expense of execution time, taking into account the objects similarities and the outputs of unstructured predictors simultaneously. As most structural models, the GCRF model does not scale well with large networks. One of the approaches consists of performing calculations on factor graphs (if it is possible) rather than on the full graph, which is more computationally efficient. The Kronecker product of the graphs appears to be a natural choice for a graph decomposition. However, this idea is not straightforwardly applicable for GCRF, since characterizing a Laplacian spectrum of the Kronecker product of graphs, which GCRF is based on, from spectra of its factor graphs has remained an open problem. In this paper we apply new estimations for the Laplacian eigenvalues and eigenvectors, and achieve high prediction accuracy of the proposed models, while the computational complexity of the models, compared to the original GCRF model, is improved from O(n31n32) to O(n31+ n32). Furthermore, we study the GCRF model with a non-Kronecker graph, where the model consists of finding the nearest Kronecker product of graph for an initial graph. Although the proposed models are more complex, they achieve high prediction accuracy too, while the execution time is still much better compare to the original GCRF model. The effectiveness of the proposed models is characterized on three types of random networks where the proposed models were consistently away more accurate than the previously presented GCRF model for multiscale networks [Jesse Glass and Zoran Obradovic. Structured regression on multiscale networks. IEEE Intelligent Systems, 32(2):23-30, 2017.]. Moreover, the comparison of the GCRF models which use different approximations for the eigenvalues and eigenvectors is performed. Copyright © 2019, The Authors. All rights

关键词： Graphic methods

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Feature Extraction and Classification from Planetary science datasets enabled by Machine Learning

arXiv

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arXiv 2023年

作者： Nixon, Conor A. Yahn, Zachary Duncan, Ethan Neidel, Ian Mills, Alyssa C. Seignovert, Benoît Larson, Andrew Gansler, Kathryn Liles, Charles Walker, Catherine C. Trent, Douglas M. Santerre, John Planetary Systems Laboratory NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States University of Virginia Computer Science Department 351 McCormick Road CharlottesvilleVA22904 United States School of Information University of California Berkeley 102 South Hall # 4600 BerkeleyCA94720 United States Yale University Timothy Dwight College 345 Temple St New HavenCT06511-8238 United States Baylor University Geosciences Department One Bear Place #97354 WacoTX76798-7354 United States Nantes Université Laboratoire de Planétologie et Géosciences 2 Chem. de la Houssinière Bâtiment 4 Nantes44300 France Business Intelligence Division Inntopia 782 Mountain Rd StoweVT05672 United States University of Maryland Department of Geology 8000 Regents Dr #237 College ParkMD20742 United States NASA Langley Research Center Center Operations Directorate 1 NASA Drive HamptonVA23666 United States Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution 98 Water Street Woods HoleMA02543 United States SAIC | East-2 NASA Langley Research Center OCIO Information Data & Analytics Services 1 NASA Drive HamptonVA23666 United States

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice floes (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled floes in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 30% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Simultaneous dimensionality and complexity model selection for spectral graph clustering

arXiv

引用

arXiv 2019年

作者： Yang, Congyuan Priebe, Carey E. Park, Youngser Marchette, David J. Department of Electrical and Computer Engineering Johns Hopkins University Department of Applied Math and Statistics Johns Hopkins University Center for Imaging Science Johns Hopkins University Applied Mathematics and Data Analytics Group Naval Surface Warfare Center Dahlgren Division

Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a graph. The model is constructed based on a theoretical result of asymptotic normality of the informative part of the embedding, and on a simulation result providing a conjecture for the limiting behavior of the redundant part of the embedding. The second contribution is a simultaneous model selection framework. In contrast with the traditional approaches, our model selection procedure estimates embedding dimension and number of clusters simultaneously. Based on our conjectured distributional model, a theorem on the consistency of the estimates of model parameters is stated and proven. The theorem provides statistical support for the validity of our method. Heuristic algorithms via the simultaneous model selection framework for vertex clustering are proposed, with good performance shown in the experiment on synthetic data and on the real application of connectome analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Operational Research: Methods and Applications

arXiv

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

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OpEn: Code Generation for Embedded Nonconvex Optimization *

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IFAC-PapersOnLine 2020年第2期53卷 6548-6554页

作者： Pantelis Sopasakis Emil Fresk Panagiotis Patrinos Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science (EEECS) and Centre for Intelligent and Autonomous Manufacturing Systems (i-AMS) Ashby Building Stranmillis Road BT9 5AG Belfast Northern Ireland WideFind AB Aurorum 1C 977 75 Luleå Sweden KU Leuven Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics & Optimization in Engineering (OPTEC) Kasteelpark Arenberg 10 3001 Leuven Belgium

We present Optimization Engine (OpEn): an open-source code generation framework for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of nonconvex problems. The proposed method involves very simple algebraic operations such as vector products, has a low memory footprint and exhibits very good convergence properties that allow the solution of nonconvex problems on embedded devices. OpEn’s core solver is written is Rust — a modern, high-performance, memory-safe and thread-safe systems programming language — while users can call it from Python, MATLAB, C, C++, ROS or over a TCP socket.

关键词： Embedded numerical optimization nonconvex optimization problems code generation model predictive control moving horizon estimation Rust Robot Operating System

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Dynamic Collaborative Visualization Ecosystem to Support the Analysis of Large-Scale Disparate data

Dynamic Collaborative Visualization Ecosystem to Support the...

引用

IEEE International Conference on Big data

作者： Christopher Koehler Andrew Berger Raksha Rajashekar Thomas Wischgoll Simon Su Data Analytics Division Universal Technology Corporation Dayton United States Computer Science Department Wright State University Dayton United States DoD Supercomputing Resource Center US Army Research Laboratory Maryland United States

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

There is no one display device or software package that is ideally suited for interactively visualizing related nonspatial, 1D, 2D, 3D, and 4D datasets. This is a major drawback, as the benefits of interactive visualization and advanced display technologies cannot be brought to bear. The Dynamic Collaborative Visualization Ecosystem (DynaCoVE) framework addresses this limitation by unifying SciVis, InfoVis, and display technology tools. Pre-existing packages are wrapped as DynaCoVE clients. This entails tracking user interactions with their GUI's and also automatically updating them in response to cues from the DynaCoVE server. The DynaCoVE server then acts as a message broker between many collaborating clients. A messaging protocol was defined based on ZeroMQ's majordomo protocol to enable efficient communication between clients and the server. Thus, visualization software packages that were never intended to be used together can be linked to perform cross display visual analytics. The system is intended to be data and user centric while remaining software, algorithm and display agnostic. This is accomplished in part by providing a common metavisualization graph interface to setup cross-display visualizations. To date DynaCoVE clients for Looker, VisIt, and VTK have been tested together with traditional monitors, tiled displays, and an immersive CAVE-type system.

关键词： data visualization Tools Servers Visual analytics Ecosystems Collaboration Three-dimensional displays

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Denoising dictionary learning against adversarial perturbations

arXiv

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arXiv 2018年

作者： Mitro, John Bridge, Derek Prestwich, Steven Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

We propose denoising dictionary learning (DDL), a simple yet effective technique as a protection measure against adversarial *** examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA). We evaluated it against five different deep neural networks (DNN) representing the building blocks of most recent architectures indicating a successive progression of model complexity of each *** show that each model tends to capture different representations based on their architecture. For each model we recorded its accuracy both on the perturbed test data previously misclassified with high confidence and on the denoised one after the reconstruction using dictionary learning. The reconstruction quality of each data point is assessed by means of PSNR (Peak Signal to Noise Ratio) and Structure Similarity Index (SSI). We show that after applying (DDL) the reconstruction of the original data point from a noisy sample results in a correct prediction with high confidence. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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