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arXiv 2024年

作者： Che, Maolin Wei, Yimin Yan, Hong School of Mathematics and Statistics Guizhou University Guizhou Guiyang550025 China Center for Intelligent Multidimensional Data Analysis Hong Science Park Shatin Hong Kong School of Mathematical Sciences Key Laboratory of Mathematics for Nonlinear Sciences Fudan University Shanghai200433 China Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong

In this paper, we focus on the fixed TT-rank and precision problems of finding an approximation of the tensor train (TT) decomposition of a tensor. Note that the TT-SVD and TT-cross are two well-known algorithms for these two problems. Firstly, by combining the random projection technique with the power scheme, we obtain two types of randomized algorithms for the fixed TT-rank problem. Secondly, by using the non-asymptotic theory of sub-random Gaussian matrices, we derive the upper bounds of the proposed randomized algorithms. Thirdly, we deduce a new deterministic strategy to estimate the desired TT-rank with a given tolerance and another adaptive randomized algorithm that finds a low TT-rank representation satisfying a given tolerance, and is beneficial when the target TT-rank is not known in advance. We finally illustrate the accuracy of the proposed algorithms via some test tensors from synthetic and real databases. In particular, for the fixed TT-rank problem, the proposed algorithms can be several times faster than the TT-SVD, and the accuracy of the proposed algorithms and the TT-SVD are comparable for several test *** Codes 15A18, 15A69, 65F55, 68W20 Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation

arXiv

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arXiv 2024年

作者： Liu, Kang Ma, Zhuoqi Kang, Xiaolu Zhong, Zhusi Jiao, Zhicheng Baird, Grayson Bai, Harrison Miao, Qiguang School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China Warren Alpert Medical School Brown University Providence United States School of Electronic Engineering Xidian University Xi’an China Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine Baltimore United States

The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, Structural Entities extraction and patient indications Incorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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Quaternary Neural Network Equalization for Three-Dimentional Magnetic Recording

Quaternary Neural Network Equalization for Three-Dimentional...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Ke Luo Yufei Wu Yanzhe Liao Shaobing Wang Yugen Jian Jincai Chen Ping Lu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

One approach considered promising for advancing the development of magnetic data storage systems is three-dimensional magnetic recording. But when the recording density rises, media noise becomes a bigger problem for magnetic recording technology. Machine learning algorithms have been proven to outperform traditional numerical techniques in solving the magnetic recording signal quality issue. We compared the detection bit error rate (BER) performance of a per-layer shallow neural network equalizer and a quaternary neural network equalizer for two recording layers. For a two-layer recording density of 4 Tb/in 2 , the results show that the combined quaternary neural network equalization and detection scheme outperforms the per-layer shallow neural network equalization and one-dimensional Viterbi detection and outperforms the conventional two-dimensional generalized partial response equalizer combined with a quaternary Viterbi detector, especially for the bottom layer.

关键词： Machine learning algorithms Equalizers Viterbi algorithm Buffer storage Neural networks Bit error rate Noise

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Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence

Robust Low-rank Deep Feature Recovery in CNNs: Toward Low In...

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IEEE International Conference on data Mining (ICDM)

作者： Jiahuan Ren Zhao Zhang Jicong Fan Haijun Zhang Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong (Shenzhen) & Shenzhen Research Institute of Big Data Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.

关键词： Training Image recognition Convolution Conferences Image representation data mining Convolutional neural networks

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Spin-Phonon Interactions Induced Anomalous Thermal Conductivity in Nickel (Ii) Oxide

SSRN

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SSRN 2023年

作者： Sun, Qiyang Hou, Songrui Wei, Bin Su, Yaokun Ortiz, Victor Sun, Bo Lin, Jiao Y.Y. Smith, Hillary Danilkin, Sergey Abernathy, Douglas L. Wilson, Richard Li, Chen Mechanical Engineering University of California Riverside RiversideCA92521 United States Materials Science and Engineering University of California Riverside RiversideCA92521 United States Henan Key Laboratory of Materials on Deep-Earth Engineering School of Materials Science and Engineering Henan Polytechnic University Jiaozuo454000 China Tsinghua-Berkeley Shenzhen Institute Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials TSIGS Tsinghua University Shenzhen518055 China Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Physics and Astronomy Swarthmore College SwarthmorePA19081 United States Australian Centre for Neutron Scattering Australian Nuclear Science and Technology Organization Lucas HeightsNSW2234 Australia Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

Nickel (II) oxide is a prominent candidate for spintronic and spin-caloritronic applications operating at room temperature. Although there are extensive studies on nickel oxide, the roles of magnon- and spin-phonon interactions on thermal transport are not well understood. In the present work, the relationship between spin-phonon interactions and thermal transport is investigated by performing inelastic neutron scattering, time-domain thermoreflectance thermal conductivity measurements, and atomistic thermal transport calculations. Inelastic neutron scattering measurements of the magnon lifetime imply that magnon thermal conductivity is trivial, and so heat is conducted only by phonons. Time-domain thermoreflectance measurements of the thermal conductivity vs. temperature follow T-1.47 in the antiferromagnetic phase. This temperature dependence cannot be explained by phonon-isotope and phonon-defect scattering or phonon softening. Instead, we attribute this to magnon-phonon scattering and spin-induced dynamic symmetry breaking. The spin-phonon interactions are saturated in the paramagnetic phase and lead to a weaker temperature dependence of T-1.00 at 550-700 K. These results reveal the importance of spin-phonon interactions on lattice thermal transport, shedding light on the engineering of functional antiferromagnetic spintronic and spin-caloritronic materials through these interactions. © 2023, The Authors. All rights reserved.

关键词： Nickel oxide

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Spatiotemporal Characteristics of Urbanization in the Taiwan Strait Based on Nighttime Light data from 1992 to 2020

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Remote Sensing 2023年第13期15卷 3226页

作者： Ye, Yuqing Yun, Guoliang He, Yuanrong Lin, Ruijin He, Tingting Qian, Zhiheng College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China College of Urban and Environmental Sciences Key Laboratory for Earth Surface Processes of the Ministry of Education Peking University Beijing100871 China

Urbanization is a crucial indicator which reflects the socio-economic development of a country or region. The regions across the Taiwan Strait (TSR) have garnered attention worldwide as being representative of typical urbanization development along the southeastern coast. Currently, research in the TSR predominantly focuses on individual regions, with limited academic achievements comparing urbanization paths across the strait. In particular, the domain of comparative analysis of the spatiotemporal characteristics of urbanization dynamics in TSR by using long time series of nighttime light data remains largely underexplored. Therefore, this study focused on comparing the urbanization paths in the TSR and analyzing the spatiotemporal characteristics of urbanization by using the long-term nighttime light data from 1992 to 2020. Additionally, some methods such as Theil–Sen median trend analysis, Mann–Kendall significance test, Hurst exponent, spatial statistics, and time series were used to quantitatively analyze the spatial distribution patterns and temporal trends of nighttime lights in the TSR since 1992. The results were as follows: (1) From 1992 to 2020, the spatial distribution of nighttime light data in TSR exhibited significant spatial heterogeneity, with high-value areas mainly located in southeastern Fujian and northwestern Taiwan, while low-value areas were concentrated in Fujian’s inland regions;(2) During this period, nighttime lighting data increased from 729,863 in 1992 to 2,729,052 in 2020, and the percentage of its high-value (40–063) increased from 2.59% in 1992 to 12.22% in 2020;(3) Comparison of nighttime light data across representative cities from Taiwan (Taipei, Hsinchu) and Fujian (Xiamen, Fuzhou) uncovered distinct growth patterns—while Taiwanese cities had a high initial brightness value (the lowest value in the last 30 years was 518,379.4), their growth was relatively slow (average growth rate of 17%);Fujian cities, on the other hand, started

关键词： Time series

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First-Principles Calculation of Ferromagnetic Elements (Fe, Co)-Doped Znv2o4

SSRN

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SSRN 2022年

作者： Luo, Jiaolian Liu, Xingwang Yang, Anqi Xie, Zhenyu School of materials science and engineering Guizhou Minzu University Guiyang550025 China Special and Key Laboratory of Guizhou Provincial Higher Education for GreenEnergySaving Materials Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China Architectural Engineering College Guizhou Minzu University Guiyang50025 China

ZnV 2 O 4 with spinel structure has been proved to emit light mainly in the ultraviolet region, expanding the response range of ZnV 2 O 4 in the visible region is of great significance to explore its wider application. Therefore, the electronic structure, optical properties and magnetic changes of ferromagnetic elements Fe, Co doped ZnV 2 O 4 are calculated by using the first-principles under density functional theory. The results show that the doping of Fe and Co leads to the increase of energy band gap, the energy band changes from indirect band gap to direct band gap after doping Co, which is closely related to the direct electron transition of Co itself. The introduced orbital electrons of Fe-3d and Co-3d contribute greatly to the composition of valence band and conduction band near Fermi level. In addition, in the visible region, profit from the doping of Co element, the photoelectric characteristics of ZnV 2 O 4 have been greatly improved and there is a red shift phenomenon, which provides important guiding significance for the potential application of ZnV 2 O 4 in photoelectric devices. © 2022, The Authors. All rights reserved.

关键词： Binary alloys

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Anomaly detection mechanism based on hierarchical weights through large-scale log data

Anomaly detection mechanism based on hierarchical weights th...

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2021 International Conference on Computer Communication and Artificial Intelligence, CCAI 2021

作者： Xi, Lanlan Xin, Yang Luo, Shoushan Shang, Yanlei Tang, Qifeng University of Posts and Telecommunications School of Beijing Institute of Cyberspace Security Beijing China University of Postsand Telecommunications School of Beijing Institute of Cyberspace Security Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Beijing China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China China-Europe Digital Economy Expert Group Shanghai China

ISBN: (纸本)9781728194011

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery. © 2021 IEEE.

关键词： Anomaly detection

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An Efficient Implementation for Bayesian Manifold Regularization Method

An Efficient Implementation for Bayesian Manifold Regulariza...

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IEEE Conference on Decision and Control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

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A Recommendation Method for Highly Sparse dataset Based on Teaching Recommendation Factorization Machines

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Computers, Materials & Continua 2020年第9期64卷 1959-1975页

作者： Dunhong Yao Shijun Li Ang Li Yu Chen School of Computer Science and Engineering Huaihua UniversityHuaihua418000China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China School of Computer Wuhan UniversityWuhan430072China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA School of Computer Science and Engineering Yulin Normal UniversityYulin537000China

There is no reasonable scientific basis for selecting the excellent teachers of the school’s *** solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of ***,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation ***,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation ***,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse *** results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching *** recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse *** proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.

关键词： Highly sparse dataset normalized models teaching recommendation factorization machines excellent teacher recommendation

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