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Partial coherence versus entanglement

Physical Review A 2023年第1期108卷 012416-012416页

作者： Sunho Kim Chunhe Xiong Shunlong Luo Asutosh Kumar Junde Wu School of Mathematical Sciences Harbin Engineering University Harbin 150001 China School of Computer and Computing Science Hangzhou City University Hangzhou 310015 China Interdisciplinary Center for Quantum Information Department of Physics Zhejiang University Hangzhou 310027 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China P. G. Department of Physics Gaya College Magadh University Rampur Gaya 823001 India Vaidic and Modern Physics Research Centre Bhagal Bhim Bhinmal Jalore 343029 India School of Mathematical Sciences Zhejiang University Hangzhou 310027 China

We study partial coherence and its connection with entanglement. First, we provide a sufficient and necessary condition for bipartite pure-state transformation under partially incoherent operations: A bipartite pure state can be transformed to another one if and only if a majorization relationship holds between their partial coherence vectors. As a consequence, we introduce the concept of maximal partial coherent states in the sense that they can be used to construct any bipartite state of the same system via partial incoherent operations. Second, we provide a strategy to construct measures of partial coherence by use of symmetric concave functions. Third, we establish the relationship between partial coherence and entanglement. We show that the minimal partial coherence under local unitary transformations is also a measure of entanglement for bipartite pure states, which can be extended to all mixed states by convex-roof extension. Conversely, every entanglement measure can be constructed as a partial coherence measure, and we further show that partial coherence measures are induced through maximal entanglement under partially incoherent operations for bipartite pure states.

关键词： Quantum theory

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2.5 Million-Atom Ab Initio Electronic-Structure Simulation of Complex Metallic Heterostructures with DGDFT

2.5 Million-Atom Ab Initio Electronic-Structure Simulation o...

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Supercomputing Conference

作者： Wei Hu Hong An Zhuoqiang Guo Qingcai Jiang Xinming Qin Junshi Chen Weile Jia Chao Yang Zhaolong Luo Jielan Li Wentiao Wu Guangming Tan Dongning Jia Qinglin Lu Fangfang Liu Min Tian Fang Li Yeqi Huang Liyi Wang Sha Liu Jinlong Yang University of Science and Technology of China Hefei Anhui China Pilot National Laboratory for Marine Science and Technology (Qingdao) China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Mathematical Sciences Peking University Beijing China Institute of Software Chinese Academy of Sciences Beijing China Qilu University of Technology Shandong Computer Science Center Jinan Shangdong China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665454452

Over the past three decades, ab initio electronic structure calculations of large, complex and metallic systems are limited to tens of thousands of atoms in computational accuracy and efficiency on leadership supercomputers. We present a massively parallel discontinuous Galerkin density functional theory (DGDFT) implementation, which adopts adaptive local basis functions to discretize the Kohn-Sham equation, resulting in a block-sparse Hamiltonian matrix. A highly efficient pole expansion and selected inversion (PEXSI) sparse direct solver is implemented in DGDFT to achieve O(N 1.5 ) scaling for quasi two-dimensional systems. DGDFT allows us to compute the electronic structures of complex metallic heterostructures with 2.5 million atoms (17.2 million electrons) using 35.9 million cores on the new Sunway supercomputer. The peak performance of PEXSI can achieve 64 PFLOPS (~5% of theoretical peak), which is un-precedented for sparse direct solvers. This accomplishment paves the way for quantum mechanical simulations into mesoscopic scale for designing next-generation electronic devices.

关键词： Performance evaluation Analytical models Leadership Quantum computing Computational modeling Supercomputers Mathematical models

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A Constrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation

arXiv

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arXiv 2024年

作者： Mauri, Chiara Fritz, Ryan Mora, Jocelyn Billot, Benjamin Iglesias, Juan Eugenio Van Leemput, Koen Augustinack, Jean Greve, Douglas N. Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States MIT Computer Science & Artificial Intelligence Laboratory CambridgeMA United States UCL Centre for Medical Image Computing London United Kingdom Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland

The claustrum is a band-like gray matter structure located between putamen and insula whose exact functions are still actively researched. Its sheet-like structure makes it barely visible in in vivo Magnetic Resonance Imaging (MRI) scans at typical resolutions and neuroimaging tools for its study, including methods for automatic segmentation, are currently very limited. In this paper, we propose a contrast- and resolution-agnostic method for claustrum segmentation at ultra-high resolution (0.35 mm isotropic);the method is based on the SynthSeg segmentation framework (Billot et al., 2023), which leverages the use of synthetic training intensity images to achieve excellent generalization. In particular, SynthSeg requires only label maps to be trained, since corresponding intensity images are synthesized on the fly with random contrast and resolution. We trained a deep learning network for automatic claustrum segmentation, using claustrum manual labels obtained from 18 ultra-high resolution MRI scans (mostly ex vivo). We demonstrated the method to work on these 18 high resolution cases (Dice score = 0.632, mean surface distance = 0.458 mm, and volumetric similarity = 0.867 using 6-fold Cross Validation (CV)), and also on in vivo T1-weighted MRI scans at typical resolutions (≈1 mm isotropic). We also demonstrated that the method is robust in a test-retest setting and when applied to multimodal imaging (T2-weighted, Proton Density and quantitative T1 scans). To the best of our knowledge this is the first accurate method for automatic ultra-high resolution claustrum segmentation, which is robust against changes in contrast and resolution. The method is released at https://***/chiara-mauri/claustrum_segmentation and as part of the neuroimaging package Freesurfer (Fischl, 2012). Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

arXiv

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arXiv 2024年

作者： Xia, Yizhang Song, Shihao Hou, Zhanglu Xu, Junwen Zou, Juan Liu, Yuan Yang, Shengxiang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The Centre for Computational Intelligence School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention owing to its great potential in applications. Various manually designed multimodal deep networks have performed well in multimodal HGR (MHGR), but most of existing algorithms require a lot of expert experience and time-consuming manual trials. To address these issues, we propose an evolutionary network architecture search framework with the adaptive multimodel fusion (AMF-ENAS). Specifically, we design an encoding space that simultaneously considers fusion positions and ratios of the multimodal data, allowing for the automatic construction of multimodal networks with different architectures through decoding. Additionally, we consider three input streams corresponding to intra-modal surface electromyography (sEMG), intra-modal accelerometer (ACC), and inter-modal sEMG-ACC. To automatically adapt to various datasets, the ENAS framework is designed to automatically search a MHGR network with appropriate fusion positions and ratios. To the best of our knowledge, this is the first time that ENAS has been utilized in MHGR to tackle issues related to the fusion position and ratio of multimodal data. Experimental results demonstrate that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Design of Turing Systems with Physics-Informed Neural Networks

arXiv

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arXiv 2022年

作者： Kho, Jordon Koh, Winston Wong, Jian Cheng Chiu, Pao-Hsiung Ooi, Chin Chun School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Bioengineering and Bioimaging A*STAR Bioinformatics Institute A*STAR Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Centre for Frontier AI Research Singapore Singapore

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design. © 2022, CC BY-NC-ND.

关键词： Stochastic systems

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Nonlinear Feedforward enabling Nonlinear Quadrature Measurement toward Fault-tolerant Universal Quantum Computation

Nonlinear Feedforward enabling Nonlinear Quadrature Measurem...

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Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Sakguchi, Atsushi Konno, Shunya Hanamura, Fumiya Ogawa, Hisashi Takase, Kan Asavanant, Warit Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Yonezawa, Hidehiro Huntington, Elanor Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology Research School of Electrical Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia

We demonstrate nonlinear feedforward combined with non-Gaussian ancillae localized in time, which realizes nonlinear quadrature measurement. Our technology in principle enables fault-tolerant universal quantum computa... 详细信息

ISBN: (纸本)9781557528209

关键词： Quantum computers

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Design of Turing Systems with Physics-Informed Neural Networks

Design of Turing Systems with Physics-Informed Neural Networ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jordon Kho Winston Koh Jian Cheng Wong Pao-Hsiung Chiu Chin Chun Ooi School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Bioengineering and Bioimaging A*STAR Bioinformatics Institute A*STAR Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Centre for Frontier AI Research Singapore Singapore

ISBN: (纸本)9781665487696

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction─diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design.

关键词： Self-assembly Computational modeling Biological system modeling Partial differential equations Neural networks Stochastic processes Mathematical models

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

arXiv

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arXiv 2022年

作者： Zhang, Fangyu Wei, Yanjie Liu, Jin Wang, Yanlin Xi, Wenhui Pan, Yi College of Engineering Southern University of Science and Technology Shenzhen518055 China Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Information Science and Engineering Central South University Changsha410083 China

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the original tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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nBLAST-JC: An accelerated deployment of nucleotide BLAST on high-performance cluster

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Heliyon 2025年第10期11卷

作者： Rani, Sita Kataria, Aman Gupta, O.P. Bhambri, Pankaj Min, Hong Department of Computer Science & Engineering Guru Nanak Dev Engineering College Punjab Ludhiana 141006 India University Centre for Research and Development Chandigarh University Gharuan Mohali 140413 India Associate Director IT Punjab Agricultural University Punjab Ludhiana 141004 India Department of Information Technology Guru Nanak Dev Engineering College Punjab Ludhiana 141006 India School of Computing Gachon University 1342 Seongnamdaero Sujeong-gu Gyeonggi-do Seongnam-si 13120 South Korea

BLAST is the most practiced algorithm to perform the task of sequence alignment. To cope with the voluminous growth in molecular data and reduce processing time, several accelerated versions of BLAST have been proposed by researchers. In this paper, an expeditious design of nucleotide BLAST named nBLAST-JC for a high-performance cluster (HPC) is proposed. The proposed algorithm is implemented on the Hadoop framework using JCuda. It can run multiple query sequences in parallel. The proposed nBLAST-JC gave a speed-up ranging from 7.1 × to 9 × in comparison to HS-BLASN and 1.8 × to 2.3 × in comparison to G-BLASTN (‘blastn’ mode). This algorithm is highly scalable for huge-sized databases due to the fundamental characteristics of the Hadoop framework. The performance of nBLAST-JC can be further enhanced using available CPU cores along with the GPU of each client node. The reliability of the proposed model can further be improved using a two-server approach. © 2025 The Authors

关键词： Basic local alignment search tool (BLAST) Compute unified device architecture (CUDA) Hadoop High-performance cluster (HPC) JCuda Sequence alignment

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