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Collective dynamics of phase-repulsive oscillators solves graph coloring problem

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arXiv 2019年

作者： Crnkić, Aladin Povh, Janez Jaćimović, Vladimir Levnajić, Zoran Faculty of Technical Engineering University of Bihać Faculty of Mechanical Engineering University of Ljubljana Slovenia Faculty of Natural Sciences and Mathematics University of Montenegro Podgorica Montenegro Complex systems and Data science Lab Faculty of information studies in Novo mesto Slovenia Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

Recent years saw a fast development of quantum algorithms that solve combinatorial optimization problems, such as graph coloring, by finding the ground state of a suitable quantum system. Inspired by this, we show how to couple phase-oscillators on a graph so that collective dynamics ‘searches’ for the coloring of that graph as it relaxes towards the dynamical equilibrium. This translates a combinatorial optimization problem (graph coloring) into a continuous optimization problem (finding and evaluating the global minimum of dynamical non-equilibrium potential, done by the natural system’s evolution). Using a sample of graphs we show that our method performs at least as good as traditional combinatorial algorithms and can serve as a viable alternative to them. Moreover, we show that with the same computational cost our method efficiently solves the harder problem of improper coloring of weighed graphs. Copyright © 2019, The Authors. All rights reserved.

关键词： Graph theory

Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training data

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arXiv 2020年

作者： Chatterjee, Soumick Prabhu, Kartik Pattadkal, Mahantesh Bortsova, Gerda Sarasaen, Chompunuch Dubost, Florian Mattern, Hendrik de Bruijne, Marleen Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg39106 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg39106 Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg39106 Germany Biomedical Imaging Group Rotterdam Department of Radiology & Nuclear Medicine Erasmus MC Rotterdam3015 GD Netherlands Institute for Medical Engineering Otto von Guericke University Magdeburg Magdeburg39106 Germany Department of Computer Science University of Copenhagen CopenhagenDK-2100 Denmark German Center for Neurodegenerative Disease Magdeburg39120 Germany Center for Behavioral Brain Sciences Magdeburg39106 Germany

MSC Codes 68T07 (Primary) 68T45 (Secondary)Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer's disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi's vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects;using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Deformation

Twelve-Dimensional Vectorcardiography - Is More Better?

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Twelve-Dimensional Vectorcardiography - Is More Better?

Computers in Cardiology (CinC)

作者： Alfonso Aranda Joël Karel Pietro Bonizzi Ralf Peeters Medtronic Bakken Research Center Maastricht The Netherlands Department of Data Science and Knowledge Engineering Maastricht University The Netherlands

ISBN: (纸本)9781728109244

Vectorcardiography (VCG) is generated by projecting signals from several leads onto three main orthogonal axes. There is evidence that by doing this projection, some relevant diagnostic information may be lost. We investigated a new way to reduce this information loss. For that, we computed VCG features from the standard 12-lead ECG system and compared its performance in diagnosing myocardial infarction with the standard Frank's VCG, a VCG derived from the 12-lead ECG using the Dower's inverse transform and with a 3-dimensional projection of the 12-lead ECG obtained with principal component analysis. Although the results are not conclusive, they suggest that the proposed 12-dimensional VCG may reduce the information loss by preserving all the ECG leads without the need of any projection.

关键词： Electrocardiography Standards Logistics Feature extraction Principal component analysis Myocardium Training

Performance Analysis of T-Wave-Offset Detection Algorithms on Patients with Cardiac Diseases

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Performance Analysis of T-Wave-Offset Detection Algorithms o...

Computers in Cardiology (CinC)

作者： Deborah Nairn Pietro Bonizzi Joël Karel Alfonso Aranda Medtronic Bakken Research Center Maastricht The Netherlands Department of Data Science and Knowledge Engineering Maastricht University The Netherlands

ISBN: (纸本)9781728109244

Cardiovascular diseases are the number one cause of death in the world. The application of automatic processing algorithms can provide important information about these heart diseases. However, the design of these algorithms can be challenging due to the morphological variations in ECG signals, specifically in the T-wave-offset. This study proposes a comparison of several T-offset detection algorithms on healthy subjects and patients suffering from cardiac diseases. Seven state of the art algorithms were selected for implementation and were evaluated using the same dataset and benchmark to provide a fair comparison. Although no algorithm performs with 100% accuracy for all patients, most can perform well with regards to the healthy patients, with two algorithms having a high performance, above 70% accuracy, on all patients.

关键词： databases Electrocardiography Morphology Cardiac disease Heart Standards Detection algorithms

Foundations of digital archæoludology

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arXiv 2019年

作者： Browne, Cameron Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Conrad, Michael Crist, Walter Depaulis, Thierry Duggan, Eddie Horn, Fred Kelk, Steven Lucas, Simon M. Neto, João Pedro Parlett, David Saffidine, Abdallah Schädler, Ulrich Silva, Jorge Nuno de Voogt, Alex Winands, Mark H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Kunsthistorisches Institut Universität Zürich Switzerland Department of Anthropology American Museum of Natural History United States Department of Science Technology and Engineering University of Suffolk United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom University of Lisbon Portugal School of Computer Science and Engineering University of New South Wales Australia Swiss Museum of Games Switzerland Economics & Business Department Drew University United States

Digital Archæoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project. Copyright © 2019, The Authors. All rights reserved.

关键词： History

Uncertainty quantification in multivariate mixed models for mass cytometry data

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arXiv 2019年

作者： Seiler, Christof Kronstad, Lisa M. Simpson, Laura J. Le Gars, Mathieu Vendrame, Elena Blish, Catherine A. Holmes, Susan Department of Statistics Stanford University Department of Data Science and Knowledge Engineering Maastricht University Netherlands Immunology Program Department of Medicine Stanford University School of Medicine Immunology Program Department of Medicine Stanford University School of Medicine Chan Zuckerberg Biohub Department of Statistics Stanford University

Mass cytometry technology enables the simultaneous measurement of over 40 proteins on single cells. This has helped immunologists to increase their understanding of heterogeneity, complexity, and lineage relationships of white blood cells. Current statistical methods often collapse the rich single-cell data into summary statistics before proceeding with downstream analysis, discarding the information in these multivariate datasets. In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles. We show that multivariate generative models are a valid alternative to univariate hypothesis testing. We propose two models: a multivariate Poisson log-normal mixed model and a logistic linear mixed model. We show that these models are complementary and that either model can account for different confounders. We use Hamiltonian Monte Carlo to provide Bayesian uncertainty quantification. Our models applied to a recent pregnancy study successfully reproduce key findings while quantifying increased overall protein-to-protein correlations between first and third trimester. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

Hierarchical Information Quadtree: Efficient Spatial Temporal Image Search for Multimedia Stream

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arXiv 2018年

作者： Zhang, Chengyuan Chen, Ruipeng Zhu, Lei Liu, Anfeng Lin, Yunwu Huang, Fang School of Information Science and Engineering Central South University China Big Data and Knowledge Engineering Institute Central South University China

Massive amount of multimedia data that contain times- tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(HI-Quadtree), to efficiently insert/delete spatial temporal images with high arrive rates. Base on HI-Quadtree an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods. Copyright © 2018, The Authors. All rights reserved.

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Automatic knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

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Automatic Knowledge Graph Construction: A Report on the 2019...

IEEE International Conference on data Mining (ICDM)

作者： Xindong Wu Jia Wu Xiaoyi Fu Jiachen Li Peng Zhou Xu Jiang Mininglamp Academy of Sciences Mininglamp Technology Beijing China Key Laboratory of Knowledge Engineering with Big Data (Hefei Univ. of Technology) Ministry of Education Hefei China Department of Computing Macquarie University Sydney Australia School of Computer Science and Technology Anhui University Hefei China

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

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