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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

arXiv

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arXiv 2020年

作者： Singh, Nalini M. Iglesias, Juan Eugenio Adalsteinsson, Elfar Dalca, Adrian V. Golland, Polina Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Dept. of Health Sciences & Technology MIT CambridgeMA United States A. A. Martinos Center Massachusetts General Hospital BostonMA United States Harvard Medical School CambridgeMA United States Centre for Medical Image Computing UCL London United Kingdom Research Laboratory of Electronics MIT CambridgeMA United States Dept. of Electrical Engineering & Computer Science MIT CambridgeMA United States

We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at https://***/nalinimsingh/interlacer. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

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Information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

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Search for an isotropic gravitational-wave background with the Parkes Pulsar Timing Array

arXiv

引用

arXiv 2023年

作者： Reardon, Daniel J. Zic, Andrew Shannon, Ryan M. Hobbs, George B. Bailes, Matthew Di Marco, Valentina Kapur, Agastya Rogers, Axl F. Thrane, Eric Askew, Jacob Ramesh Bhat, N.D. Cameron, Andrew Curylo, Malgorzata Coles, William A. Dai, Shi Goncharov, Boris Kerr, Matthew Kulkarni, Atharva Levin, Yuri Lower, Marcus E. Manchester, Richard N. Mandow, Rami Miles, Matthew T. Nathan, Rowina S. Oslowski, Stefan Russell, Christopher J. Spiewak, Renée Zhang, Songbo Zhu, Xing-Jiang Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia OzGrav: The Australian Research Council Centre of Excellence for Gravitational Wave Discovery HawthornVIC3122 Australia Australia Telescope National Facility CSIRO Space and Astronomy P.O. Box 76 EppingNSW1710 Australia Department of Physics and Astronomy MQ Research Centre in Astronomy Astrophysics and Astrophotonics Macquarie University NSW2109 Australia School of Physics and Astronomy Monash University VIC3800 Australia OzGrav: The Australian Research Council Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Astronomical Observatory University of Warsaw Aleje Ujazdowskie 4 Warsaw00-478 Poland Electrical and Computer Engineering University of California La Jolla San DiegoCA United States School of Science Western Sydney University Locked Bag 1797 Penrith South DCNSW2751 Australia L'AquilaI-67100 Italy INFN Laboratori Nazionali del Gran Sasso AssergiI-67100 Italy Space Science Division US Naval Research Laboratory 4555 Overlook Ave SW WashingtonDC20375 United States Physics Department Columbia Astrophysics Laboratory Columbia University 538 West 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute 162 5th Ave NY10011 United States Manly Astrophysics 15/41-42 East Esplanade ManlyNSW2095 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester ManchesterM13 9PL United Kingdom Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China Advanced Instit

Pulsar timing arrays aim to detect nanohertz-frequency gravitational waves (GWs). A background of GWs modulates pulsar arrival times and manifests as a stochastic process, common to all pulsars, with a signature spatial correlation. Here we describe a search for an isotropic stochastic gravitational-wave background (GWB) using observations of 30 millisecond pulsars from the third data release of the Parkes Pulsar Timing Array (PPTA), which spans 18 years. Using current Bayesian inference techniques we recover and characterize a common-spectrum noise process. Represented as a strain spectrum hc = A(f/1yr−1)α, we measure (Equation presented) and α = −0.45 ± 0.20 respectively (median and 68% credible interval). For a spectral index of α = −2/3, corresponding to an isotropic background of GWs radiated by inspiraling supermassive black hole binaries, we recover an amplitude of (Equation presented). However, we demonstrate that the apparent signal strength is time-dependent, as the first half of our data set can be used to place an upper limit on A that is in tension with the inferred common-spectrum amplitude using the complete data set. We search for spatial correlations in the observations by hierarchically analyzing individual pulsar pairs, which also allows for significance validation through randomizing pulsar positions on the sky. For a process with α = −2/3, we measure spatial correlations consistent with a GWB, with an estimated false-alarm probability of p. 0.02 (approx. 2σ). The long timing baselines of the PPTA and the access to southern pulsars will continue to play an important role in the International Pulsar Timing Array. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

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Converting coherence based on positive-operator-valued measures into entanglement

arXiv

引用

arXiv 2020年

作者： Kim, Sunho Xiong, Chunhe Kumar, Asutosh Wu, Junde School of Mathematical Sciences Harbin Engineering University Harbin150001 China School of Computer and Computing Science Zhejiang University City College Hangzhou310015 China P.G. Department of Physics Gaya College Magadh University Rampur Gaya823001 India Harish-Chandra Research Institute HBNI Chhatnag Road Jhunsi Allahabad211019 India Vaidic and Modern Physics Research Centre Bhagal Bhim Bhinmal Jalore343029 India School of Mathematical Sciences Zhejiang University Hangzhou310027 China

Quantum resource theories provide a diverse and powerful framework for extensively studying the phenomena in quantum physics. Quantum coherence, a quantum resource, is the basic ingredient in many quantum information tasks. It is a subject of broad and current interest in quantum information, and many new concepts have been introduced and generalized since its establishment. Here we show that the block coherence can be transformed into entanglement via a block incoherent operation. Moreover, we find that the coherence based on positive-operator-valued measures (POVMs) associated with block coherence through the Naimark extension acts as a potential resource from the perspective of generating entanglement. Finally, we discuss avenues of creating entanglement from POVM-based coherence, present strategies that require embedding channels and auxiliary systems, give some examples, and generalize them. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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SMK-means:An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

引用

computers, Materials & Continua 2018年第9期56卷 365-379页

作者： Bo Xiao Zhen Wang Qi Liu Xiaodong Liu Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computer and Software Nanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computing Edinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

In recent years,the rapid development of big data technology has also been favored by more and more *** data storage and calculation problems have also been *** the same time,outlier detection problems in mass data have also come along with ***,more research work has been devoted to the problem of outlier detection in big ***,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect *** this paper,an improved algorithm is *** SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of *** this paper,several experiments are performed to compare and analyze multiple performances of the *** analysis,we know that the proposed algorithm is superior to the existing algorithms.

关键词： Big data outlier detection SMK-means Mini Batch K-means simulated annealing

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Multiple transmit power levels based NOMA for massive machine-type communications

arXiv

引用

arXiv 2020年

作者： Yi, Wenqiang Yu, Wenjuan Liu, Yuanwei Foh, Chuan Heng Ding, Zhiguo Nallanathan, Arumugam School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computing and Communications InfoLab21 Lancaster University LancasterLA1 4WA United Kingdom 5G Innovation Centre Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom School of Electrical and Electronic Engineering University of Manchester ManchesterM13 9PL United Kingdom

This paper proposes a tractable solution for integrating non-orthogonal multiple access (NOMA) into massive machine-type communications (mMTC) to increase the uplink connectivity. Multiple transmit power levels are provided at the user end to enable open-loop power control, which is absent from the traditional uplink NOMA with the fixed transmit power. The basics of this solution are firstly presented to analytically show the inherent performance gain in terms of the average arrival rate (AAR). Then, a practical framework based on a novel power map is proposed to associate a set of well-designed transmit power levels with each geographical region for handling the no instantaneous channel state information problem. Based on this framework, the semi-grant-free (semi-GF) transmission with two practical protocols is introduced to enhance the connectivity, which has higher AAR than both the conventional grand-based and GF transmissions. When the number of active GF devices in mMTC far exceeds the available resource blocks, the corresponding AAR tends to zero. To solve this problem, user barring techniques are employed into the semi-GF transmission to stable the traffic flow and thus increase the AAR. Lastly, promising research directions are discussed for improving the proposed networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel state information

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In utero diffusion MRI: Challenges, advances, and applications

arXiv

引用

arXiv 2019年

作者： Christiaens, Daan Slator, Paddy J. Cordero-Grande, Lucilio Price, Anthony N. Deprez, Maria Alexander, Daniel C. Rutherford, Mary Hajnal, Joseph V. Hutter, Jana Centre for Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom Centre for Medical Image Computing and Department of Computer Science University College London London United Kingdom

In utero diffusion MRI provides unique opportunities to non-invasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental villous trees, or the fibres in the fetal heart remain to be studied and understood in detail. Advances in fetal interventions and surgery furthermore increase the need for ever more precise antenatal diagnosis from fetal MRI. However, the specific properties of the in utero environment, such as fetal and maternal motion, increased field-of-view, tissue interfaces and safety considerations, are significant challenges for most MRI techniques, and particularly for diffusion. Recent years have seen major improvements, driven by the development of bespoke techniques adapted to these specific challenges in both acquisition and processing. Fetal diffusion MRI, an emerging research tool, is now adding valuable novel information for both research and clinical questions. This paper will highlight specific challenges, outline strategies to target them, and discuss two main applications: Fetal brain connectomics and placental maturation. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Investigation into Sub-Receptive Fields of Retinal Ganglion Cells with Natural Images

Investigation into Sub-Receptive Fields of Retinal Ganglion ...

引用

2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Philip Vance, J. Gautham Das, P. Sonya Coleman, A. Kerr, Dermot Emmett Kerr, P. Thomas McGinnity, M. School of Computing Engineering and Intelligent Systems University of Ulster Magee Campus I-Londonderry-N. United Kingdom Lincoln Centre for Autonomous Systems School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9781509060146

Determining the receptive field of a retinal ganglion cell is critically important when formulating a computational model that maps the relationship between the stimulus and response. This process is traditionally undertaken using reverse correlation to estimate the receptive field. By stimulating the retina with artificial stimuli, such as alternating checkerboards, bars or gratings and recording the neural response it is possible to estimate the cell's receptive field by analysing the stimuli that produced the response. Artificial stimuli such as white noise is known to not stimulate the full range of the cell's responses. By using natural image stimuli, it is possible to estimate the receptive field and obtain a resulting model that more accurately mimics the cells' responses to natural stimuli. This paper extends on previous work to seek further improvements in estimating a ganglion cell's receptive field by considering that the receptive field can be divided into subunits. It is thought that these subunits may relate to receptive fields which are associated with bipolar retinal cells. The findings of this preliminary study show that by using subunits to define the receptive field we achieve a significant improvement over existing approaches when deriving computational models of the cell's response. © 2018 IEEE.

关键词： Computation theory

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Quantum engineering with hybrid magnonics systems and materials

arXiv

引用

arXiv 2021年

作者： Awschalom, D.D. Du, C.H.R. He, R. Heremans, F.J. Hoffmann, A. Hou, J.T. Kurebayashi, H. Li, Y. Liu, L. Novosad, V. Sklenar, J. Sullivan, S.E. Sun, D. Tang, H. Tiberkevich, V. Trevillian, C. Tsen, A.W. Weiss, L.R. Zhang, W. Zhang, X. Zhao, L. Zollitsch, C.W. Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Materials Science Division Center for Molecular Engineering Argonne National Laboratory LemontIL United States Department of Physics University of California San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Materials Research Laboratory Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States London Centre for Nanotechnology University College London 17-19 Gordon Street LondonWCH1 0AH United Kingdom Materials Science Division Argonne National Laboratory LemontIL United States Department of Physics and Astronomy Wayne State University DetroitMI48201 United States Department of Physics North Carolina State University RaleighNC27695 United States Department of Electrical Engineering Yale University New HavenCT United States Department of Physics Oakland University MI48309 United States Institute for Quantum Computing Department of Chemistry University of Waterloo WaterlooON Canada Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Department of Physics University of Michigan Ann ArborMI48109 United States

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitatons or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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