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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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Incoherent Digital Holography using Spiral Rotating Point Spread Functions Created by Double-helix Beams

Incoherent Digital Holography using Spiral Rotating Point Sp...

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Digital Holography and Three-Dimensional Imaging, DH 2022

作者： Dubey, Nitin Anand, Vijayakumar Khonina, Svetlana Kumar, Ravi Reddy, Andra Naresh Kumar Rosen, Joseph School of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Institute of Physics University of Tartu Tartu50411 Estonia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia Samara National Research University Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences Samara443001 Russia Hee Photonic Labs RigaLV-1002 Latvia Department of Physics of Complex Systems Weizmann Institute of Science Rehovot7610001 Israel

A new incoherent 3D imaging system with a rotating point spread function has been developed. Different computational reconstruction methods such as non-linear reconstruction and the Lucy-Richardson-Rosen algorithm wer... 详细信息

ISBN: (纸本)9781957171128

关键词： Optical transfer function

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Pioneering the Use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis

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Microscopy and Microanalysis 2018年第S1期24卷 514-515页

作者： Matthew L. Gong Su Jong Yoon Raymond R. Unocic Hope Ishii John P. Bradley Brandon D. Miller Daniel Masiel Bryan Reed Tolga Tasdizen Jeffery A. Aguiar University of Utah Scientific Computing Imaging Institute Department of Electrical and Computer Engineering Salt Lake City UT Idaho National Laboratory Nuclear Science and Technology Division Idaho Falls ID Oak Ridge National Laboratory Center for Nanophase Materials Science Oak Ridge TN University of Hawai’i at Manoa School of Ocean and Earth Science and Technology Honolulu HI Integrated Dynamic Electron Solutions Pleasanton CA University of Utah Department of Materials Science and Engineering Salt Lake City UT

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Linear Algebra and Its Applications: Preface

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Linear Algebra and Its Applications 2005年第1-3期411卷 1-2页

作者： Barrett, Wayne Hedayat, Samad Krattenthaler, Christian Loewy, Raphael Dept. of Math. and Computer Science University of Paderborn D-33095 Paderborn Germany Dept. of Mathematics and Statistics University of Regina Regina Sask. S4S 0A2 Canada Department of Mathematics University of Connecticut Storrs CT 06269-3009 United States Dept. of Math. and Computer Science Numer. Math./Scientific Computing Free University Berlin D-14195 Berlin Arnimallee 2-6 Germany

No abstract available

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ComPile: A Large IR Dataset from Production Sources

arXiv

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arXiv 2023年

作者： Grossman, Aiden Paehler, Ludger Parasyris, Konstantinos Ben-Nun, Tal Hegna, Jacob Moses, William S. Monsalve Diaz, Jose M. Trofin, Mircea Doerfert, Johannes University of California Davis United States School of Computation Information and Technology Technical University of Munich Germany Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States University of Minnesota Twin Cities United States Department of Computer Science University of Illinois Urbana-Champaign United States Division of Mathematics and Computer Science Argonne National Laboratory United States Google Inc. United States

Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI’s ChatGPT, Google’s Bard, or Anthropic’s Claude, the way we translate code from one language into another, but also the compiler infrastructure underlying the language. While modeling approaches may vary and representations differ, the targeted tasks often remain the same within the individual classes of models. Yet, relying solely on the ability of modern models to extract information from unstructured code does not take advantage of 70 years of programming language and compiler development by not utilizing the structure inherent to programs in the data collection. This detracts from the performance of models working over a tokenized representation of input code and precludes the use of these models in the compiler itself. To work towards the first intermediate representation (IR) based models, we fully utilize the LLVM compiler infrastructure, shared by a number of languages, to generate a 1.4T Llama 2 token dataset of LLVM IR. We generated this dataset from programming languages built on the shared LLVM infrastructure, including Rust, Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language’s package manager or the compiler directly to extract the dataset of intermediate representations from production grade programs. Statistical analysis proves the utility of our dataset not only for large language model training, but also for the introspection into the code generation process itself as well as for training of machine-learned compiler components. Copyright © 2023, The Authors. All rights reserved.

关键词： Large datasets

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CNN-based Classification Framework for Tissues of Lung with Additional Information

arXiv

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arXiv 2022年

作者： Hu, Huafeng Ye, Ruijie Thiyagalingam, Jeyarajan Coenen, Frans Su, Jionglong Department of Electrical and Electronic Engineering University of Liverpool Based at Xi'an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Scientific Computing Department Science and Technologies Facilities Council Harwell Campus OxfordOX11 0GD United Kingdom School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Jiangsu Suzhou215123 China

Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formulating treatment plans. Although previous work has produced impressive results in classifying interstitial lung diseases, there is still room for improving the accuracy of these techniques, mainly to enhance automated decision-making. In order to improve the classification precision, our study proposes a convolutional neural networks-based framework with additional information. Firstly, ILD images are added with their medical information by re-scaling the original image in Hounsfield Units. Secondly, a modified CNN model is used to produce a vector of classification probability for each tissue. Thirdly, location information of the input image, consisting of the occurrence frequencies of different diseases in the CT scans on certain locations, is used to calculate a location weight vector. Finally, the Hadamard product between two vectors is used to produce a decision vector for the prediction. Compared to the state-of-the-art methods, the results using a publicly available ILD database show the potential of predicting these using different additional information. Copyright © 2022, The Authors. All rights reserved.

关键词： Tissue

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Laplacian preconditioning of elliptic pdes: Localization of the eigenvalues of the discretized operator

arXiv

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arXiv 2018年

作者： Gergelits, Tomáš Mardal, Kent-André Nielsen, Bjørn Fredrik Strakoš, Zdeněk Institute of Computer Sciences Czech Academy of Science Prague Czech Republic Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Mathematics University of Oslo Oslo0316 Norway Department of Numerical Analysis and Scientific Computing Simula Research Laboratory Lysaker1325 Norway Faculty of Science and Technology Norwegian University of Life Sciences ÅsNO-1432 Norway

In the paper Preconditioning by inverting the Laplacian;an analysis of the eigenvalues. IMA Journal of Numerical Analysis 29, 1 (2009), 24-42, Nielsen, Hackbusch and Tveito study the operator generated by using the inverse of the Laplacian as preconditioner for second order elliptic PDEs ∇ · (k(x)∇u) = f. They prove that the range of k(x) is contained in the spectrum of the preconditioned operator, provided that k is continuous. Their rigorous analysis only addresses mappings defined on infinite dimensional spaces, but the numerical experiments in the paper suggest that a similar property holds in the discrete case. Motivated by this investigation, we analyze the eigenvalues of the matrix L−1A, where L and A are the stiffness matrices associated with the Laplace operator and general second order elliptic operators, respectively. Without any assumption about the continuity of k(x), we prove the existence of a one-to-one pairing between the eigenvalues of L−1A and the intervals determined by the images under k(x) of the supports of the FE nodal basis functions. As a consequence, we can show that the nodal values of k(x) yield accurate approximations of the eigenvalues of L−1A. Our theoretical results are illuminated by several numerical experiments.65F08, 65F15, 65N12, 35J99 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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AClog: Attack Chain Construction Based on Log Correlation

AClog: Attack Chain Construction Based on Log Correlation

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IEEE Global Communications Conference

作者： Teng Li Jianfeng Ma Qingqi Pei Yulong Shen Chi Lin Siqi Ma Mohammad S. Obaidat School of Cyber Engineering Xidian University Shaanxi China Shaanxi Key Laboratory of BlockChain and Security Computing Xidian University Shaanxi China School of Computer Science Xidian University Shaanxi China School of Software Technology Dalian University of Technology Dalian China Commonwealth Scientific and Industrial Research Organisation Sydney Australia College of Computing and Informatics University of Sharjah UAE University of Jordan Jordan Nazarbayev University Kazakhstan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Before the final attack happens, clandestine attackers conduct sequenced stages for being stealthy and elusive. These attacks can leave clues in several different log files. However existing approaches can only detect the anomalies using single type of log and fail to reveal all of the attack steps through log integration and correlation. Such methods can hardly detect the relationships among events and prevent the attack in advance. Additionally, traditional machine learning or data mining in log analysis has a high overhead in computing which is impractically applied in a real product or system. To address these problems, we present AClog, a multiple log correlated analysis system to construct the attack chain. Inspired by penetration testing and social network analysis, we transfer the attack provenance as an event relationship discover problem. We use different logs to form the steps of the system and regard them as the event sequences before the attack. Then, we leverage Fast Linear SVM and Longest Common Subsequences to find out the regular steps before the attack. Finally, we spot the corresponding log sequences to identify the pre-attack steps proactively. We apply our approach in the attack prediction of a cloud computing platform and a university network. The results show that the proposed method can effectively and precisely construct the attack steps and identify the corresponding syslogs.

关键词： Correlation Clustering algorithms Support vector machines Windows

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Diffusion-based Graph Generative Methods

arXiv

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arXiv 2024年

作者： Chen, Hongyang Xu, Can Zhen, Lingyu Zhang, Qiang Lin, Xuemin The Research Center for Graph Computing Hangzhou China School of Data Science and Engineering East China Normal University Shanghai China The Research Center for Graph Computing Hangzhou China Hangzhou Institue for Advanced Study UCAS Hangzhou China The College of Computer Science and Technology Zhejiang University Hangzhou China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China

Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our survey, we systematically and comprehensively review on diffusion-based graph generative methods. We first make a review on three mainstream paradigms of diffusion methods, which are denoising diffusion probabilistic models, score-based genrative models, and stochastic differential equations. Then we further categorize and introduce the latest applications of diffusion models on graphs. In the end, we point out some limitations of current studies and future directions of future explorations. The summary of existing methods metioned in this survey is in our Github: https://***/zhejiangzhuque/Diffusion-based-Graph-Generative-Methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Quadratic pseudospectrum for identifying localized states

arXiv

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arXiv 2022年

作者： Cerjan, Alexander Loring, Terry A. Vides, Fredy Center for Integrated Nanotechnologies Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mathematics and Statistics University of New Mexico AlbuquerqueNM87123 United States Scientific Computing Innovation Center School of Mathematics and Computer Science Universidad Nacional Autónoma de Honduras Tegucigalpa Honduras

We examine the utility of the quadratic pseudospectrum in photonics and condensed matter. Specifically, the quadratic pseudospectrum represents a method for approaching systems with incompatible observables, as it both minimizes the "eigen-error" in the joint approximate spectrum of the incompatible observables and does not increase the system's computational complexity. Moreover, we derive an important estimate relating the Clifford and quadratic pseudospectra. Finally, we prove that the quadratic pseudospectrum is local, and derive the bounds on the errors that are incurred by truncating the system in the vicinity of where the pseudospectrum is being calculated. Copyright © 2022, The Authors. All rights reserved.

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