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Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

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Reviewing two decades of Energy system analysis with bibliometrics

arXiv

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arXiv 2021年

作者： Dominković, Dominik Franjo Weinand, Jann Michael Scheller, Fabian D'Andrea, Matteo McKenna, Russell Department of Applied Mathematics and Computer Science Technical University of Denmark Matematiktorvet Kgs. Lyngby2800 Denmark Karlsruhe Institute of Technology Karlsruhe Germany Energy Systems Analysis Division of Sustainability Department of Technology Management and Economics Technical University of Denmark Matematiktorvet Kgs. Lyngby2800 Denmark Chair of Energy Transition School of Engineering University of Aberdeen King's College AberdeenAB24 3FX United Kingdom

The field of Energy System Analysis (ESA) has experienced exponential growth in the number of publications since at least the year 2000. This paper presents a comprehensive bibliometric analysis on ESA by employing different algorithms in Matlab and R. The focus of results is on quantitative indicators relating to number and type of publication outputs, collaboration links between institutions, authors and countries, and dynamic trends within the field. The five and twelve most productive countries have 50% and 80% of ESA publications respectively. The dominant institutions are even more concentrated within a small number of countries. A significant concentration of published papers within countries and institutions was also confirmed by analysing collaboration networks. These show dominant collaboration within the same university or at least the same country. There is also is a strong link among the most successful journals, authors and institutions. The Energy journal has had the most publications in the field, and its editor-in-chief Lund H is the author with most of the publications in the field, as well as the author with most of the highly cited publications in the field. In terms of the dynamics within the field in the past decade, recent years have seen a higher impact of topics related to flexibility and hybrid/integrated energy systems alongside a decline in individual technologies. This paper provides a holistic overview of two decades' research output and enables interested readers to obtain a comprehensive overview of the key trends in this active field. © 2021, CC BY-NC-ND.

关键词： Systems analysis

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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

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Journal of computer science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ji, Chen Xu, Wenyang Zheng, Xiangtian Ahmed, Yasmeen Jamal, Saad Ahmed Imam, Fakhar Ali, Mohammed Saleh Muthanna, Maha Ibrahim Ullah, Sajid Kucher, Dmitry E. Nanjing University School of Geography and Ocean Science Nanjing210046 China Nanjing Institute of Technology Nanjing211167 China Mississippi State University Department of Building Construction Science College of Architecture Art & Design MS39762 United States Paris Lodron Universitat Salzburg Institute for Research and Advanced Studies Universidade de Evora Department of Computer Science University of South Brittany Department of Geoinformatics PortugalZ_GIS France Austria University of the Punjab Lahore Pakistan Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan Nangarhar University Department of Water Resources and Environmental Engineering Nangarhar 2600 Afghanistan RUDN University Department of Environmental Management Institute of Environmental Engineering Moscow117198 Russia

Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems' energy transfer, nutrients, and biogeochemical cycles. These eddies' precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely Swin Transformer U-Net (Swin-Unet) and SegFormer, to categorize ocean eddies from Sea Surface Temperature (SST) maps sourced from the Copernicus Marine Environment Monitoring Service (CMEMS). In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using Intersection over Union (IoU), Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention based segmentation algorithms for resilient oceanographic applications. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Calculation of descriptive statistics by devices with low computational resources for use in calibration of V2I system

Calculation of descriptive statistics by devices with low co...

引用

International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Katarzyna Kubiak Marzena Banach Rafał Długosz Faculty of Mathematics and Computer Science Adam Mickiewicz University Poznan Poland Poznan University of Technology Institute of Architecture and Spatial Planning Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering UTP University of Science and Technology Bydgoszcz Poland

In this work we present modified iterative methods for computing basic statistical quantities (mean, variance) for use in the calibration process of a system based on V2I (vehicle-to-infrastructure) communication devices. Such devices, mounted in the road and urban infrastructure (RSU - road side equipment) may be used as support for autonomous vehicles moving in urban environment. Calibration is necessary to determine the positions of the RSUs in global coordinate system (GCS) and to record this information in their internal memory. The proposed modifications to conventional iterative algorithms aim at adapting these methods to the application in devices with low computational abilities or directly at the transistor level in specialized integrated circuits.

关键词： Iterative methods Calibration Roads Global Positioning System Autonomous vehicles Urban areas Hardware

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A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

arXiv

引用

arXiv 2019年

作者： Bengio, Yoshua Frejinger, Emma Lodi, Andrea Patel, Rahul Sankaranarayanan, Sriram Canada Excellence Research Chair Polytechnique Montreal Department of Computer Science and Operations Research University of Montreal Mila - Quebec Artificial Intelligence Institute

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a representative scenario (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality. Copyright © 2019, The Authors. All rights reserved.

关键词： Integer programming

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Advancing IoT-Based Smart Irrigation

引用

IEEE Internet of Things Magazine 2019年第4期2卷 20-25页

作者： Togneri, Rodrigo Kamienski, Carlos Dantas, Ramide Prati, Ronaldo Toscano, Attilio Soininen, Juha-Pekka Conic, Tullio Salmon Getúlio Vargas Foundation (FGV) São Paulo School of Business Administration (EAESP) Federal University of ABC (UFABC) UFABC NUVEM Strategic Research Group Federal Institute of Technology of Pernambuco (IFPE) Center of Computer Science Mathematics and Cognition UFABC University of Bologna VTT Technical Research Centre of Finland School of Engineering and Architecture University of Bologna

Integrated Internet of Things (IoT) platforms are needed for realizing IoT potential in commercial-scale applications. The main challenge is to provide solution flexibility to meet custom application needs. We developed an IoT-based platform for smart irrigation, with a flexible architecture to easily connect IoT and machine learning (ML) components to build application solutions. Our architecture enables multiple and customizable analytical approaches to precision irrigation, making room for the improvement of ML approaches. Impacts on different stakeholders can be anticipated, including IoT professionals, by facilitating system deployment, and farmers, by providing cost reduction and safer crop yields. Examples are given based on pilots in Europe and Brazil. © 2018 IEEE.

关键词： Internet of things

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Message from the SAIP, NEMI, ECRF, Journal First, and Workshops Track chairs

Message from the SAIP, NEMI, ECRF, Journal First, and Worksh...

引用

IEEE International Conference on Software architecture Workshops (ICSAW)

作者： Steffen Becker Javier Cámara Stéphanie Challita Christoph Fehling Anton Jansen Oliver Kopp Heiko Koziolek Philippe Kruchten Grace A. Lewis Carola Lilienthal Romina Spalazzese Catia Trubiani Software Quality and Architecture University of Stuttgart Stuttgart Germany University of York York UK University of Rennes 1 Rennes France Mercedes-Benz AG Sindelfingen Germany Philips Innovation Services Philips Eindhoven The Netherlands ABB Research Ladenburg Germany Electrical and Computer Engineering The University of British Columbia Canada Software Engineering Institute Carnegie Mellon University Pittsburgh PA USA WPS – Workplace Solutions GmbH Hamburg Germany Malmö University Malmö Sweden Gran Sasso Science Institute L’Aquila Italy

The 18th IEEE International Conference on Software architecture (ICSA 2021) solicited different types of submissions structured into the following tracks: the main Technical Track (included in the ICSA main proceedings), the Software architecture in Practice (SAIP) track, the New and Emerging Ideas (NEMI) track, the Early Career Researchers Forum (ECRF), the Journal First track, and the Workshop track (included in the companion volume of ICSA 2021 proceedings). Each of these tracks, except for the Technical Track, is presented in the following sections.

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Localized resolution of identity approach to the analytical gradients of random-phase approximation ground-state energy: Algorithm and benchmarks

arXiv

引用

arXiv 2021年

作者： Tahir, Muhammad N. Li, Jia Zhu, Tong Shang, Honghui Blum, Volker Ren, Xinguo Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Chemistry Duke University DurhamNC27708 United States State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

We develop and implement a formalism which enables calculating the analytical gradients of particle-hole random-phase approximation (RPA) ground-state energy with respect to the atomic positions within the atomic orbital basis set framework. Our approach is based on a localized resolution of identity (LRI) approximation for evaluating the two-electron Coulomb integrals and their derivatives, and the density functional perturbation theory for computing the first-order derivatives of the Kohn-Sham (KS) orbitals and orbital energies. Our implementation allows one to relax molecular structures at the RPA level using both Gaussian-type orbitals (GTOs) and numerical atomic orbitals (NAOs). Benchmark calculations show that our approach delivers high numerical precision compared to previous implementations. A careful assessment of the quality of RPA geometries for small molecules reveals that post-KS RPA systematically overestimates the bond lengths. We furthermore optimized the geometries of the four low-lying water hexamers – cage, prism, cyclic and book isomers, and determined the energy hierarchy of these four isomers using RPA. The obtained RPA energy ordering is in good agreement with that yielded by the coupled cluster method with single, double and perturbative triple excitations, despite that the dissociation energies themselves are appreciably underestimated. The underestimation of the dissociation energies by RPA is well corrected by the renormalized single excitation correction. © 2021, CC BY.

关键词： Approximation algorithms

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AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence 1st

AIoT Bench: Towards Comprehensive Benchmarking Mobile and Em...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Luo, Chunjie Zhang, Fan Huang, Cheng Xiong, Xingwang Chen, Jianan Wang, Lei Gao, Wanling Ye, Hainan Wu, Tong Zhou, Runsong Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Frontier Science and Technology Beijing China Dover United Kingdom China National Institute of Metrology Beijing China China Software Testing Center Beijing China

ISBN: (纸本)9783030328122

Due to increasing amounts of data and compute resources, the deep learning achieves many successes in various domains. Recently, researchers and engineers make effort to apply the intelligent algorithms to the mobile or embedded devices. In this paper, we propose a benchmark suite, AIoT Bench, to evaluate the AI ability of mobile and embedded devices. Our benchmark (1) covers different application domains, e.g. image recognition, speech recognition and natural language processing;(2) covers different platforms, including Android and Raspberry Pi;(3) covers different development frameworks, including TensorFlow and Caffe2;(4) offers both end-to-end application workloads and micro workloads. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

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