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Large scale graph learning from smooth signals 7

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Large scale graph learning from smooth signals

7th International Conference on Learning Representations, ICLR 2019

作者： Kalofolias, Vassilis Perraudin, Nathanaël Signal Processing Laboratory 2 EPFL Station 11 Lausanne1015 Switzerland Swiss Data Science Center ETH Zürich Universitätstrasse 25 Zürich8006 Switzerland

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a higher computational cost. In particular, the current state-of-the-art model cost is On2 for n samples. In this paper, we show how to scale it, obtaining an approximation with leading cost of O(nlog(n)), with quality that approaches the exact graph learning model. Our algorithm uses known approximate nearest neighbor techniques to reduce the number of variables, and automatically selects the correct parameters of the model, requiring a single intuitive input: the desired edge density. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

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The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

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The Importance of Gender Specification for Detection of Driv...

Biomedical Engineering International Conference (BMEiCON)

作者： Mohammad Shahbakhti Matin Beiramvand Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology the Human Phenome Institute Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9781665489041

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

关键词： Training Support vector machines Computational modeling Transforms Fatigue Brain modeling Electroencephalography

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

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arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Joint multi-task cascade for instance segmentation

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Joint multi-task cascade for instance segmentation

作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

Null Stream Based Third-generation-ready Glitch Mitigation for Gravitational Wave Measurements

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arXiv 2024年

作者： Narola, Harsh Wouters, Thibeau Negri, Luca Lopez, Melissa Dooney, Tom Cireddu, Francesco Wils, Milan Wong, Isaac C.F. Pang, Peter T.H. Janquart, Justin Samajdar, Anuradha Van Den Broeck, Chris Li, Tjonnie G.F. Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Nikhef National Institute for Subatomic Physics Science Park 105 Amsterdam1098 XG Netherlands Faculty of Science Open Universiteit Valkenburgerweg 177 Heerlen6419 AT Netherlands Centre for Cosmology Particle Physics and Phenomenology - CP3 Université Catholique de Louvain Louvain-La-Neuve B-1348 Belgium Royal Observatory of Belgium Avenue Circulaire 3 Uccle1180 Belgium Leuven Gravity Institute KU Leuven Celestijnenlaan 200D box 2415 Leuven3001 Belgium Department of Physics and Astronomy Laboratory for Semiconductor Physics KU Leuven LeuvenB-3001 Belgium STADIUS Center for Dynamical Systems Signal Processing and Data Analytics LeuvenB-3001 Belgium Dipartimento di Fisica "E. Fermi" Università di Pisa PisaI-56127 Italy

Gravitational Wave (GW) detectors routinely encounter transient noise bursts, known as glitches, which are caused by either instrumental or environmental factors. Due to their high occurrence rate, glitches can overlap with GW signals, as in the notable case of GW170817, the first detection of a binary neutron star merger. Accurate reconstruction and subtraction of these glitches is a challenging problem that must be addressed to ensure that scientific conclusions drawn from the data are reliable. This problem will intensify with third-generation observatories like the Einstein Telescope (ET) due to their higher detection rates of GWs and the longer duration of signals within the sensitivity band of the detectors. Robust glitch mitigation algorithms are, therefore, crucial for maximizing the scientific output of next-generation GW observatories. For the first time, we demonstrate how the null stream inherent in ET’s unique triangular configuration can be leveraged by state-of-the-art glitch characterization methodology to essentially undo the effect of glitches for the purpose of estimating the parameters of the source. The null stream based approach enables characterization and subtraction of glitches that occur arbitrarily close to the peak of the signal without any significant effect on the quality of parameter measurements, and achieves an order of magnitude computational speed-up compared to when the null stream is not available. By contrast, without the null stream, significant biases can occur in the glitch reconstruction, which deteriorate the quality of subsequent measurements of the source parameters. This demonstrates a clear edge which the null stream can offer for precision GW science in the ET era. © 2024, CC BY.

关键词： Gravitational effects

Dense deformation network for high resolution tissue cleared image registration

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arXiv 2019年

作者： Nazib, Abdullah Fookes, Clinton Perrin, Dimitri Data Science Discipline EECS QUT Vision and Signal Processing Discipline EECS QUT

The recent application of Deep Learning in various areas of medical image analysis has brought excellent performance gain. The application of deep learning technologies in medical image registration successfully outperformed traditional optimization based registration algorithms both in registration time and accuracy. In this paper, we present a densely connected convolutional architecture for deformable image registration. The training of the network is unsupervised and does not require ground-truth deformation or any synthetic deformation as a label. The proposed architecture is trained and tested on two different version of tissue cleared data, 10% and 25% resolution of high resolution dataset respectively and demonstrated comparable registration performance with the state-of-the-art ANTS registration method. The proposed method is also compared with the deep-learning based Voxelmorph registration method. Due to the memory limitation, original voxelmorph can work at most 15% resolution of Tissue cleared data. For rigorous experimental comparison we developed a patch-based version of Voxelmorph network, and trained it on 10% and 25% resolution. In both resolution, proposed DenseDeformation network outperformed Voxelmorph in registration accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Deformation

On the Fusion Strategies of Sentinel-1 and Sentinel-2 data for Local Climate Zone Classification

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On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data f...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jakob Gawlikowski Michael Schmitt Anna Kruspe Xiao Xiang Zhu Institute of Data Science German Aerospace Center (DLR) Jena Germany Signal Processing in Earth Observation Technical University of Munich (TUM) Munich Germany Remote Sensing Technology Institute German Aerospace Center (DLR) Germany

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Local Climate Zone (LCZ) classification is the most commonly used scheme to analyze how local urban morphology affects the climate of local areas. Classification methods are often based on remote sensing data or on a fusion of several data sources. In this study, the effects of different fusion strategies of optical and synthetic aperture radar (SAR) data on the accuracy of LCZ classifications are investigated. The data processing is implemented with a convolutional neural network (CNN), where until a fusion layer, separate data sources are processed separately on branches. Strategies of splitting the data into branches and the effects of different fusion stages are compared, together with approaches based on sums of independent classifiers. For our setting, the stage of fusion does not seem to have a big influence on the accuracy. The results of this study contribute to a better understanding of cooperative usage of multispectral and SAR data.

关键词： Training Meteorology Synthetic aperture radar Optical imaging Task analysis Remote sensing Vegetation mapping

Likelihood for a Network of Gravitational-Wave Detectors with Correlated Noise

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arXiv 2023年

作者： Cireddu, Francesco Wils, Milan Wong, Isaac C.F. Pang, Peter T.H. Li, Tjonnie G.F. Del Pozzo, Walter Leuven Gravity Institute KU Leuven Celestijnenlaan 200D box 2415 Leuven3001 Belgium Department of Physics and Astronomy Laboratory for Semiconductor Physics KU Leuven LeuvenB-3001 Belgium Dipartimento di Fisica "E. Fermi" Università di Pisa PisaI-56127 Italy Department of Physics The Chinese University of Hong Kong N.T. Shatin Hong Kong STADIUS Center for Dynamical Systems Signal Processing and Data Analytics LeuvenB-3001 Belgium Nikhef Science Park 105 Amsterdam1098 XG Netherlands Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands INFN Sezione di Pisa PisaI-56127 Italy

关键词： Telescopes

Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & Intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China