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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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The Exploration of Neural Collapse under Imbalanced Data

arXiv

引用

arXiv 2024年

作者： Liu, Haixia School of Mathematics and Statistics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan China

Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced data. We consider the L-extended unconstrained feature model with a bias term and provide a theoretical analysis of global minimizer. Our findings include: (1) Features within the same class converge to their class mean, similar to both the balanced case and the imbalanced case without bias. (2) The geometric structure is mainly on the left orthonormal transformation of the product of L linear classifiers and the right transformation of the class-mean matrix. (3) Some rows of the left orthonormal transformation of the product of L linear classifiers collapse to zeros and others are orthogonal, which relies on the singular values of Ŷ = (IK − 1/Nn1TK)D, where K is class size, n is the vector of sample size for each class, D is the diagonal matrix whose diagonal entries are given by √n. Similar results are for the columns of the right orthonormal transformation of the product of class-mean matrix and D. (4) The i-th row of the left orthonormal transformation of the product of L linear classifiers aligns with the i-th column of the right orthonormal transformation of the product of class-mean matrix and D. (5) We provide the estimation of singular values about Ŷ . Our numerical experiments support these theoretical *** Codes ***, *** Copyright © 2024, The Authors. All rights reserved.

关键词： Linear transformations

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Quasiperiodic Symmetric Tilt FCC Grain Boundaries

arXiv

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arXiv 2024年

作者： Zou, Wenwen Zhang, Juan Xu, Jie Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we investigate [110] symmetric tilt FCC grain boundaries (GBs) by a recently developed approach for quasiperiodic interfaces using the Landau-Brazovskii model. On special tilt angles associated with quadratic algebraic numbers, quasiperiodic GBs exhibit generalized Fibonacci substitution rules. The transition mechanism from quasiperiodic GBs to periodic GBs is explored through sphere offsets and spectral coalescence. We also propose an accurate method to calculate the GB energy for arbitrary tilt angle and analyze the factors affecting the GB energy. The revealing GB energy change is continue along with tilt angle except periodic GBs. © 2024, CC BY-NC-SA.

关键词： Grain boundaries

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Testing the unified bounds of the quantum speed limit

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Physical Review A 2024年第4期110卷 042215页

作者： Yaozu Wu Jiale Yuan Chuanyu Zhang Zitian Zhu Jinfeng Deng Xu Zhang Pengfei Zhang Qiujiang Guo Zhen Wang Jiehui Huang Chao Song Hekang Li Da-Wei Wang H. Wang Girish S. Agarwal Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control School of Physics and State Key Laboratory for Extreme Photonics and Instrumentation Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311215 China School of Mathematics Physics and Statistics College of Optical Science and Engineering Institute for Quantum Science and Engineering Departments of Biological and Agricultural Engineering Physics and Astronomy

Quantum speed limits (QSLs) impose fundamental constraints on the evolution speed of quantum systems. For systems with time-independent Hamiltonians, the QSLs are intricately linked to the energy spectra of the initial state, which signify the generalization of the time-energy uncertainty relation. Based on arbitrary-order norms of the Hamiltonian, dual energies from inverted spectra, and the standard deviation of energy, different types of QSLs have been proposed, revealing three dynamical parameter regimes of quantum state evolution. Despite the theoretical advancements, a comprehensive experimental examination remains elusive. In this research, by introducing unified bounds suitable for all evolution times, we present the experimental verification to the QSLs in various scenarios with a superconducting circuit. We explore the dynamical evolution with regard to each parameter regime as well as the critical point by carefully engineering the initial-state population of the superconducting resonator. We test our unified bounds over finite-level states and infinite-level classical and nonclassical Gaussian states, which validates the criteria for the QSLs. Our work substantially deepens our comprehension of unitary quantum evolution.

关键词： Circuit quantum electrodynamics Quantum correlations, foundations & formalism Quantum foundations Quantum optics

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Analysis of Instantaneous Angular Speed Accuracy Considering Encoder Errors

Analysis of Instantaneous Angular Speed Accuracy Considering...

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International Conference on Automation and computing (ICAC)

作者： Yuanhao Liu Fengshou Gu Qiang Zeng State Key Laboratory of Mechanical Transmissions for Advanced Equipment Chongqing University Chongqing China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Instantaneous Angular Speed (IAS) is closely related to the dynamic behavior of rotating machinery and plays a crucial role in diagnosing faults. However, the measurement of the IAS is subject to various errors inevitably, which directly affect the accuracy of IAS estimation and diminish the precision of fault diagnosis in rotating machinery. Currently, the estimation of IAS primarily relies on time counting methods, and there has been limited research conducted on the application of Frequency Domain Demodulation (FDD) as an IAS estimation method, despite its potential for providing lower noise levels. The study of IAS errors based on the FFD method has received even less attention. This study undertakes pertinent research and investigates the issue identified. Firstly, it develops a model for IAS estimation using FDD, considering the specific characteristics of the encoder disk signal. Secondly, it establishes IAS models for eccentric and inclined encoder disks, considering the spatial geometric characteristics associated with different types of encoder disk errors. In conclusion, the experimental results successfully validate the accuracy and validity of the proposed theoretical models. This study lays a theoretical foundation for the precise measurement of IAS, thereby enhancing the reliability of using IAS for condition monitoring in rotating machinery.

关键词： Fault diagnosis Accuracy Frequency-domain analysis Measurement uncertainty Estimation Machining Reliability theory Machinery Rotation measurement Noise level

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TIME-INDEPENDENT SPIKING NEURON VIA MEMBRANE POTENTIAL ESTIMATION FOR EFFICIENT SPIKING NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Chen, Hanqi Yu, Lixing Zhan, Shaojie Yao, Penghui Shao, Jiankun School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN. © 2024, CC BY.

关键词： Neurons

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ASBase:The universal database for aggregate science

Aggregate

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Aggregate 2023年第1期4卷 148-157页

作者： Junyi Gong Wanjun Gong BoWu Haoran Wang Wei He Zhongyang Dai Yangzhong Li Yong Liu Zhiming Wang Xiaojun Tuo Jacky W.Y.Lam Zijie Qiu Zheng Zhao Ben Zhong Tang Shenzhen Institute of Aggregate Science and Technology School of Science and EngineeringThe Chinese University of Hong KongShenzhenGuangdongChina AIE-Institute State Key Laboratory of Luminescence Materials and DevicesCenter of Aggregation-induced EmissionKey Laboratory of Luminescence from Molecular Aggregates of Guangdong ProvinceSouth China University of Technology(SCUT)GuangzhouChina High Performance Computing Department National Supercomputing Center in ShenzhenShenzhenChina Department of Chemistry Hong Kong University of Science and TechnologyKowloonHong KongChina

This paper reports the first universal and versatile database on aggregate materials for the field of aggregate science *** the current stage,the database(http://119.91.135.188:8080/)contains over 1000 entries of organic aggregate material systems(mainly luminescent systems at the current stage)with a unique data structure which is designed particularly for aggregate materials and containing the photophysics and physicochemical properties of the compounds in different statuses of aggregation,including dilute solution form,pristine solid-state,stable crystalline,and nanoaggregates formed in *** web-based interface of the database provided functions to index,search,manipulate,fetch and deposit data *** addition,a background calculation service optimizes the chemical structure of new entries on different levels of *** database also provided background API for interactive developments of prediction or regression models based on machine-learning algorithms.

关键词： aggregate science aggregation-induced emission data-driven model database

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Rapid Screening and Optimization of CO2 Enhanced Oil Recovery Process in Unconventional Reservoirs Using Interpretable Random Forests

Rapid Screening and Optimization of CO2 Enhanced Oil Recover...

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2024 SPE Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2024

作者： Wei, Bing Wen, Shuqin You, Junyu He, Yujiao Wu, Runnan Chen, Yongqiang Lu, Jun State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Southwest Petroleum University Sichuan Chengdu China School of Petroleum and Natural Gas Engineering Chongqing University of Science and Technology Chongqing China Commonwealth Scientific and Industrial Research Organisation Canberra Australia McDougall School of Petroleum Engineering The University of Tulsa TulsaOK United States

ISBN: (纸本)9781959025269

Rapid screening and optimization of CO2 enhanced oil recovery (EOR) process in unconventional reservoirs is urgently needed but challenging due to the complexity of this process and poor understanding of seepage in multiscale porous media. To address this issue, an interpretable model with high interpretability was developed to evaluate the response of CO2-EOR processes using random forest (RF) to maximize performance. The feature importance and interaction analyses were conducted to enhance the interpretability of RF model from global to local levels. The genetic algorithm (GA) was used to optimize the CO2-EOR response, and select operational parameters with a minimum CO2 injection and maximum incremental oil recovery. To validate the reliability of the method, the results of the GA-RF model were compared with numerical simulation results under the same conditions, yielding an average relative error of 5.3%. The feature importance analyses show that geological parameters such as Φ and SO have a greater impact on the oil recovery compared to operational parameters. This work represents a pioneering advancement in the rapid optimization of the CO2-EOR process in unconventional reservoirs. The developed workflow offers an efficient process for optimizing the most suitable CO2-EOR process for various types of reservoirs and provides valuable time and resource savings in the optimization. Copyright 2024, Society of Petroleum Engineers.

关键词： Gas injection (Enhanced recovery)

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Line positions,intensities,and Einstein A coefficients for 3-0 band of^(12)C^(16)O:A spectroscopy learning method

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Chinese Physics B 2021年第12期30卷 370-377页

作者： Zhi-Xiang Fan Zhi-Zhang Ni Jie-Jie He Yi-Fan Wang Qun-Chao Fan Jia Fu Yong-Gen Xu Hui-Dong Li Jie Ma Feng Xie School of Science Key Laboratory of High Performance Scientific ComputationXihua UniversityChengdu 610039China State Key Laboratory of Quantum Optics and Quantum Optics Devices Laser Spectroscopy LaboratoryCollege of Physics and Electronics EngineeringShanxi UniversityTaiyuan 030006China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and Safety of Ministry of EducationTsinghua UniversityBeijing 100084China

Based on the model-and data-driven strategy,a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of^(12)C^(16)*** labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the *** quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the *** comparisons are made for line positions,line intensities,Einstein A coefficients,which demonstrate that the model-and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.

关键词： carbon monoxide line lists a model-and data-driven strategy spectroscopy learning

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