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arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

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Line drawing technique for building images

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Ruan Jian Xue Bao/Journal of software 2012年第SUPPL.2期23卷 34-41页

作者： Liu, You-Quan Wu, Zong-Sheng Wang, Shan-Dong Xue, Ru Wu, En-Hua School of Information Engineering Chang'an University Xi'an 710064 China Faculty of Science and Technology University of Macau Macao China State Key Laboratory of Computer Science Institute of Software The Chinese Academy of Sciences Beijing 100190 China Tibet University for Nationalities Xianyang 712082 China

This paper presents an automatic line-drawing method designed for building images. The fast line segment detector with a false detection control and detects the outline and the door, or window edge features detected, which are used in the construction of edge tangent flow. Based on the modified ETF, the Difference-of-Gaussians is used to render the final line drawing styles. This scheme gives clear line features for building images without much noise, which is also simple and easy to implement. The test results verify the effectiveness of the presented algorithm. © Copyright 2012, Editorial Department of Journal of software.

关键词： Rendering (computer graphics)

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A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Some Generalized Normal Subgroups and p-nilpotency of Finite Groups

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Chinese Quarterly Journal of Mathematics 2018年第1期33卷 17-24页

作者： ZHONG Guo LIN Shi-Xun CHEN Qi-le QIN Li-hua Faculty of Science and Technology University of Macao Macao SAR China School of Mathematics and Statistics Zhaotong University Zhaotong 657000 China School of Software Engineering South China University of Technology Guangzhou Guangdong China College of Mathematics and Computer Science Guangxi Normal University for Nationlities Chongzuo Guangxi China

In this paper, the influence of s-semipermutable, c~#-normal, subnormally embedded and ss-quasinormal subgroups on the p-nilpotency of finite groups is investigated and some recent results are generalized.

关键词： s-semipermutable subgroups c#-normal subgroups subnormally embedded subgroups ss-quasinormal subgroups p-nilpotency

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Exact quantum sensing limits for bosonic dephasing channels

arXiv

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arXiv 2024年

作者： Huang, Zixin Lami, Ludovico Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges towards realizing useful quantum computation, communication, and sensing. Here we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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C²-CapsViT: Cross-Context and Cross-Scale Capsule Vision Transformers for Remote Sensing Image Scene Classification

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IEEE Geoscience and Remote Sensing Letters 2022年 19卷

作者： Yongtao Yu Yinyin Li Jun Wang Haiyan Guan Fenfen Li Shaozhang Xiao E. Tang Xiwang Ding Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing China

Accurately interpreting image contents plays a vital role in many Earth observation tasks. This letter constructs a novel cross-context and cross-scale capsule vision transformer (C 2 -CapsViT) architecture to serve for remote sensing image scene classification. First, employed with a multicontext patch embedding strategy, the token representation quality is greatly boosted to encode different-context feature semantics. Second, designed with a multiscale transformer block, different-grained long-range global feature interactions and different-type feature self-attentions are concurrently exploited to promote the feature encoding quality. Moreover, by combining the convolution and transformer structures, local and global feature semantics are effectively fused to direct accurate predictions. The C 2 -CapsViT is elaborately verified on three scene classification datasets. Both quantitative evaluations and comparative analyses prove its competitive capability and advanced performance.

关键词： Semantics Transformers Image analysis Remote sensing Encoding Convolution Task analysis

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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

arXiv

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arXiv 2024年

作者： Stewart, Kenneth Neumeier, Michael Shrestha, Sumit Bam Orchard, Garrick Neftci, Emre Department of Computer Science UC Irvine IrvineCA United States Department of Neuromorphic Computing Fortiss-Research Institute of the Free State of Bavaria Munich Germany Intel Labs Intel Corporation Santa ClaraCA United States Peter Grünberg Institute - Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. © 2024, CC BY.

关键词： Zero-shot learning

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Efficient Algorithms for Approximating Quantum Partition Functions at Low Temperature

arXiv

引用

arXiv 2022年

作者： Helmuth, Tyler Mann, Ryan L. Department of Mathematical Sciences Durham University DurhamDH1 3LE United Kingdom Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We establish an efficient approximation algorithm for the partition functions of a class of quantum spin systems at low temperature, which can be viewed as stable quantum perturbations of classical spin systems. Our algorithm is based on combining the contour representation of quantum spin systems of this type due to Borgs, Kotecký, and Ueltschi with the algorithmic framework developed by Helmuth, Perkins, and Regts, and Borgs et al. © 2022, CC BY.

关键词： Temperature

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Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications

arXiv

引用

arXiv 2018年

作者： Zhang, Chao Gao, Xianjie Hsieh, Min-Hsiu Hang, Hanyuan Tao, Dacheng School of Mathematical Sciences Dalian University of Technology Liaoning Dalian116024 China Centre for Quantum Software and Information University of Technology Sydney SydneyNSW2007 Australia AI Lab Samsung Research China - Beijing Beijing100028 China UBTECH Sydney Artificial Intelligence Centre The School of Computer Science Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random variables. We obtain several types of tail inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound the expectation of the spectral norm of a matrix i.d. series. Moreover, by developing a new lower-bound function for Q(s) = (s + 1) log(s + 1) − s that appears in the Bennett-type inequality, we derive a tighter tail inequality of the largest eigenvalue of the matrix i.d. series than the Bernstein-type inequality when the matrix dimension is high. The resulting lower-bound function is of independent interest and can improve any Bennett-type concentration inequality that involves the function Q(s). The class of i.d. probability distributions is large and includes Gaussian and Poisson distributions, among many others. Therefore, our results encompass the existing work [1] on matrix Gaussian series as a special case. Lastly, we show that the tail inequalities of a matrix i.d. series have applications in several optimization problems including the chance constrained optimization problem and the quadratic optimization problem with orthogonality constraints. In addition, we also use the resulting tail bounds to show that random matrices constructed from i.d. random variables satisfy the restricted isometry property (RIP) when it acts as a measurement matrix in compressed sensing. © 2018, CC BY-NC-ND.

关键词： Compressed sensing

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Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

arXiv

引用

arXiv 2022年

作者： Trajanov, Risto Nikolikj, Ana Cenikj, Gjorgjina Teytaud, Fabien Videau, Mathurin Teytaud, Olivier Eftimov, Tome López-Ibáñez, Manuel Doerr, Carola Faculty of Computer Science and Engineering Ss. Cyril and Methodius Skopje Macedonia Jožef Stefan Institute Ljubljana Slovenia Jozef Stefan International Postgraduate School Ljubljana Slovenia Universitś Littoral Côte d'Opale France Facebook AI Research ITIS Software Universidad de Málaga Spain Sorbonne Universitś CNRS LIP6 Paris France

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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