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Algorithms for low-distortion embeddings into arbitrary 1-dimensional spaces

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arXiv 2017年

作者： Carpenter, Timothy Fomin, Fedor V. Lokshtanov, Daniel Saurabh, Saket Sidiropoulos, Anastasios Dept. of Computer Science & Engineering Ohio State University Department of Informatics University of Bergen Norway Department of Informatics University of Bergen Norway Institute of Mathematical Sciences India Dept. of Mathematics Dept. of Computer Science & Engineering Ohio State University

We study the problem of finding a minimum-distortion embedding of the shortest path metric of an unweighted graph into a "simpler" metric X. Computing such an embedding (exactly or approximately) is a non-trivial task even when X is the metric induced by a path, or, equivalently, into the real line. In this paper we give approximation and fixed-parameter tractable (FPT) algorithms for minimum-distortion embeddings into the metric of a subdivision of some fixed graph H, or, equivalently, into any fixed 1-dimensional simplicial complex. More precisely, we study the following problem: For given graphs G, H and integer c, is it possible to embed G with distortion c into a graph homeomorphic to H? Then embedding into the line is the special case H = K2, and embedding into the cycle is the case H = K3, where Kk denotes the complete graph on k vertices. For this problem we give • an approximation algorithm, which in time f(H) · poly(n), for some function f, either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion poly(c);• an exact algorithm, which in time f0(H, c) · poly(n), for some function f0, either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion c. Prior to our work, poly(OPT)-approximation or FPT algorithms were known only for embedding into paths and trees of bounded degrees. Copyright © 2017, The Authors. All rights reserved.

关键词： Embeddings

Corrigendum to "Design and implementation of a low-cost, tabletop MRI scanner for education and research prototyping" [J. Magn. Reson. 310 (2020) 106625]

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Journal of magnetic resonance (San Diego, Calif. : 1997) 2020年 317卷 106764页

作者： Clarissa Zimmerman Cooley Jason P Stockmann Thomas Witzel Cris LaPierre Azma Mareyam Feng Jia Maxim Zaitsev Wenhui Yang Wang Zheng Pascal Stang Greig Scott Elfar Adalsteinsson Jacob K White Lawrence L Wald A. A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA. Electronic address: clarissa@nmr.mgh.harvard.edu. Harvard Medical School Boston MA USA. A. A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Dept. of Radiology Medical Physics Medical Center University of Freiburg Faculty of Medicine University of Freiburg Freiburg Germany. Institute of Electrical Engineering Chinese Academy of Sciences Beijing China. Procyon Engineering San Jose CA USA Department of Electrical Engineering Stanford University Stanford CA USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Division of Health Sciences Technology Cambridge MA USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA. Harvard Medical School Boston MA USA

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

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arXiv 2018年

作者： Grattarola, Daniele Zambon, Daniele Alippi, Cesare Livi, Lorenzo The Faculty of Informatics Università della Svizzera italiana Lugano Switzerland The Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Faculty of Informatics Università della Svizzera italiana Lugano Switzerland The Department of Computer Science and Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada The Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom

The space of graphs is often characterised by a nontrivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs. Among these, constant-curvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric geodesic distances. In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs. To this end, we introduce a novel change detection framework based on neural networks and CCMs, that takes into account the non-Euclidean nature of graphs. Our contribution in this work is twofold. First, via a novel approach based on adversarial learning, we compute graph embeddings by training an autoencoder to represent graphs on CCMs. Second, we introduce two novel change detection tests operating on CCMs. We perform experiments on synthetic data, as well as two real-world application scenarios: the detection of epileptic seizures using functional connectivity brain networks, and the detection of hostility between two subjects, using human skeletal graphs. Results show that the proposed methods are able to detect even small changes in a graph-generating process, consistently outperforming approaches based on Euclidean embeddings. Copyright © 2018, The Authors. All rights reserved.

关键词： Change detection

EM-driven multi-objective design of impedance transformers by pareto ranking bisection algorithm

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EM-driven multi-objective design of impedance transformers b...

2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

作者： Bekasiewicz, Adrian Koziel, Slawomir Cheng, Qingsha S. Bandler, J.W. School of Science and Engineering Reykjavik University Reykjavik Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Dept. Electrical and Electronic Eng. Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9780996007856

In the paper, the problem of fast multi-objective optimization of compact impedance matching transformers is addressed by utilizing a novel Pareto ranking bisection algorithm. It approximates the Pareto front by dividing line segments connecting the designs found in the previous iterations, and refining the obtained candidate solutions by means of poll-type search involving Pareto ranking. The final Pareto set is obtained using surrogate-based optimization techniques. Our approach is validated using a compact impedance matching transformer and compared to state-of-the-art surrogate-assisted techniques. © 2017 Applied Computational Electromagnetics Society.

关键词： Multiobjective optimization

Change point methods on a sequence of graphs

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arXiv 2018年

作者： Zambon, Daniele Alippi, Cesare Livi, Lorenzo Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom

Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs. In order to cover a large class of applications, we consider the general family of attributed graphs where both topology (number of vertexes and edge configuration) and related attributes are allowed to change also in the stationary case. Novel Change Point Methods (CPMs) are proposed, that (i) map graphs into a vector domain;(ii) apply a suitable statistical test in the vector space;(iii) detect the change –if any– according to a confidence level and provide an estimate for its time occurrence. Two specific multivariate CPMs have been designed: one that detects shifts in the distribution mean, the other addressing generic changes affecting the distribution. We ground our proposal with theoretical results showing how to relate the inference attained in the numerical vector space to the graph domain, and vice versa. We also show how to extend the methodology for handling multiple change points in the same sequence. Finally, the proposed CPMs have been validated on real data sets coming from epileptic-seizure detection problems and on labeled data sets for graph classification. Results show the effectiveness of what proposed in relevant application scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Vector spaces

Towards Enabling Deep Learning Techniques for Adaptive Dynamic Programming

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Towards Enabling Deep Learning Techniques for Adaptive Dynam...

International Joint Conference on Neural Networks

作者： Zhen Ni Naresh Malla Xiangnan Zhong Electrical Engineering and Computer Science Department South Dakota State University Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island

ISBN: (纸本)9781509061839

Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial intelligence, computational intelligence and so on. Adaptive dynamic programming (ADP) methods have the similar fundamental principle with reinforcement learning, and show strong performance for continuous time and continuous state systems. Deep learning techniques are also possible to be integrated for ADP designs. In this paper, we discuss the key techniques and components in deep reinforcement learning and then present the successful applications for computer games and maze navigation. Future opportunities for deep learning enabled ADP will be discussed at the end.

关键词： Deep Learning Deep reinforcement learning (DRL) Adaptive dynamic programming (ADP) Experience replay Computational intelligence Markov decision process Artificial Intelligence Computational intelligence Markov chain learning (artificial intelligence) dynamic programming Automatic data processing Adenosine Diphosphate Machine Learning game player Learning Technological Advancement computer Games

Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

Neuromatch academy: Teaching computational neuroscience with global accessibility

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arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

Proceedings #24: Transcranial Magnetic Stimulation Pattern Mimicking Thalamic Burst Reduces Nociceptive Responses in Mice

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Brain Stimulation 2019年第2期12卷 e89-e89页

作者： Yeowool Huh Taeyoon Seo Youngwoo Kwon Jeiwon Cho Dept. of Medical Science Catholic Kwandong University South Korea Biomedical Research Institute International St. Mary's Hospital South Korea Institute for Bio-Medical Convergence Incheon St. Mary's Hospital South Korea Department of Electrical and Computer Engineering Seoul National University South Korea