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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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An Algorithm for Constructing Monotone Quintic Interpolating Splines

An Algorithm for Constructing Monotone Quintic Interpolating...

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Spring Simulation Conference (SpringSim)

作者： Thomas C. H. Lux Layne T. Watson Tyler H. Chang Li Xu Yueyao Wang Yili Hong Departments of Computer Science Mathematics and Aerospace and Ocean Engineering Virginia Polytechnic Institute & State University Blacksburg VA USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

A novel algorithm for computing monotone order six piecewise polynomial interpolants is proposed. Algebraic constraints for enforcing monotonicity are provided that align with quintic monotonicity theory. The algorithm is implemented, tested, and applied to several sample problems to demonstrate the improved accuracy of monotone quintic spline interpolants compared to the previous state-of-the-art monotone cubic spline interpolants.

关键词： Splines (mathematics) Interpolation Approximation algorithms Distribution functions Software Aerospace engineering Shape

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Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields*

Analytical Estimation of Out-of-plane Strain in Ultrasound E...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Niusha Kheirkhah Ali Sadeghi-Naini Abbas Samani School of Biomedical Engineering Western University London Canada Department of Electrical Engineering and Computer Science York University Toronto Canada Departments of Medical Biophysics and Electrical and Computer Engineering Western University London Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Many types of cancers are associated with changes in tissue mechanical properties. This has led to the development of elastography as a clinically viable method where tissue mechanical properties are mapped and visualized for cancer detection and staging. In quasi-static ultrasound elastography, a mechanical stimulation is applied to the tissue using ultrasound probe. Using ultrasound radiofrequency (RF) data acquired before and after the stimulation, the tissue displacement field can be estimated. Elasticity image reconstruction algorithms use this displacement data to generate images of the tissue elasticity properties. The accuracy of the generated elasticity images depends highly on the accuracy of the tissue displacement estimation. Tissue incompressibility can be used as a constraint to improve the estimation of axial and, more importantly, the lateral displacements in 2D ultrasound elastography. Especially in clinical applications, this requires accurate estimation of the out-of-plane strain. Here, we propose a method for providing an accurate estimate of the out-of-plane strain which is incorporated in the incompressibility equation to improve the axial and lateral displacements estimation before elastography image reconstruction. The method was validated using in silico and tissue mimicking phantom studies, leading to significant improvement in the estimated displacement.

关键词： Mathematical model Strain Breast Estimation Elastography Ultrasonic imaging Probes

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Kernel regression imputation in manifolds via bi-linear modeling: The dynamic-MRI case

TechRxiv

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TechRxiv 2021年

作者： Slavakis, Konstantinos Shetty, Gaurav N. Cannelli, Loris Scutari, Gesualdo Nakarmi, Ukash Ying, Leslie The Department of Information and Communications Engineering of Tokyo Institute of Technology Tokyo Japan PA United States The Dalle Molle Institute for Artificial Intelligence IDSIA/SUPSI Lugano Switzerland The Department of Industrial Engineering of Purdue University West LafayetteIN United States The Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR United States The Departments of Electrical Engineering and Biomedical Engineering of University at Buffalo SUNY BuffaloNY United States

This paper introduces a non-parametric approximation framework for imputation-by-regression on data with missing entries. The proposed framework, coined kernel regression imputation in manifolds (KRIM), is built on the hypothesis that features, generated by the measured data, lie close to an unknown-to-the-user smooth manifold. The feature space, where the smooth manifold is embedded in, takes the form of a reproducing kernel Hilbert space (RKHS). Aiming at concise data descriptions, KRIM identifies a small number of "landmark points" to define approximating "linear patches" in the feature space which mimic tangent spaces to smooth manifolds. This geometric information is infused into the design through a novel bi-linear model that allows for multiple approximating RKHSs. To effect imputation-by-regression, a bi-linear inverse problem is solved by an iterative algorithm with guaranteed convergence to a stationary point of a non-convex loss function. To showcase KRIM’s modularity, the application of KRIM to dynamic magnetic resonance imaging (dMRI) is detailed, where reconstruction of images from severely under-sampled dMRI data is desired. Extensive numerical tests on synthetic and real dMRI data demonstrate the superior performance of KRIM over state-of-the-art approaches under several metrics and with a small computational footprint. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Electromigration-aware architecture for modern microprocessors

arXiv

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arXiv 2020年

作者： Gabbay, Freddy Mendelson, Avi The Electrical Engineering Department Ruppin Academic Center Israel The Computer Science and Electrical Engineering Departments Technion – Israel Institute of Technology of Technology Haifa3200000 Israel

Reliability is a fundamental requirement in microprocessors to guarantee correct execution over their lifetime. The reliability-related design rules depend on the process technology and the device operating conditions. To meet reliability requirements, advanced technologies impose challenging design rules, which have become a major burden on the VLSI implementation flow because of the severe physical constraints they impose. This paper focuses on electromigration (EM), which is one of the critical factors affecting semiconductor reliability. EM is the aging process of on-die wires and is induced by excessive current flow that can damage wires in integrated circuits. Traditionally, reliability and EM issues have been handled at the physical-design level that enforces reliability rules using worst-case scenario analysis to detect and solve violations. In this paper we offer architectural solutions that exploit architectural characteristics to reduce EM impact. The use of architectural methods can simplify EM solutions and can also be incorporated in conjunction with standard physical-design-based solutions where they offer a complementary enhancement to the current methods. Our comprehensive physical simulation results show that, with minimal area, power, and performance overhead, the proposed solution can relax EM design efforts and significantly extend microprocessor lifetime. Copyright © 2020, The Authors. All rights reserved.

关键词： Electromigration

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On population-based algorithms for distributed constraint optimization problems

arXiv

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arXiv 2020年

作者： Mahmud, Saaduddin Khan, Md. Mosaddek Jennings, Nicholas R. Department of Computer Science and Engineering University of Dhaka Bangladesh Departments of Computing and Electrical and Electronic Engineering Imperial College London United Kingdom

Distributed Constraint Optimization Problems (DCOPs) are a widely studied class of optimization problems in which interaction between a set of cooperative agents are modeled as a set of constraints. DCOPs are NP-hard and significant effort has been devoted to developing methods for finding incomplete solutions. In this paper, we study an emerging class of such incomplete algorithms that are broadly termed as population-based algorithms. The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima. In recent years, this class of algorithms has gained significant attention due to their ability to produce high-quality incomplete solutions. With the primary goal of further improving the quality of solutions compared to the state-of-the-art incomplete DCOP algorithms, we present two new population-based algorithms in this paper. Our first approach, Anytime Evolutionary DCOP or AED, exploits evolutionary optimization meta-heuristics to solve DCOPs. We also present a novel anytime update mechanism that gives AED its anytime property. While in our second contribution, we show that population-based approaches can be combined with local search approaches. Specifically, we develop an algorithm called DPSA based on the Simulated Annealing meta-heuristic. We empirically evaluate these two algorithms to illustrate their respective effectiveness in different settings against the state-of-the-art incomplete DCOP algorithms including all existing population-based algorithms in a wide variety of benchmarks. Our evaluation shows AED and DPSA markedly outperform the state-of-the-art and produce up to 75% improved solutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Simulated annealing

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Adversarial Score-Based Generative Models for MMSE-achieving AmBC Channel Estimation

arXiv

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arXiv 2023年

作者： Rezaei, Fatemeh Marvasti-Zadeh, S. Mojtaba Tellambura, Chintha Maaref, Amine Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada Departments of Computing Science and Renewable Resources University of Alberta EdmontonABT6G 1H9 Canada Huawei Canada 303 Terry Fox Drive Suite 400 OttawaONK2K 3J1 Canada

This letter presents a pioneering method that employs deep learning within a probabilistic framework for the joint estimation of both direct and cascaded channels in an ambient backscatter (AmBC) network comprising multiple tags. In essence, we leverage an adversarial score-based generative model for training, enabling the acquisition of channel distributions. Subsequently, our channel estimation process involves sampling from the posterior distribution, facilitated by the annealed Langevin sampling technique. Notably, our method demonstrates substantial advancements over standard least square (LS) estimation techniques, achieving performance akin to that of the minimum mean square error (MMSE) estimator for the direct channel, and outperforming it for the cascaded channels. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Lifted multiplicity codes and the disjoint repair group property 22

Lifted multiplicity codes and the disjoint repair group prop...

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22nd International Conference on Approximation Algorithms for Combinatorial Optimization Problems and 23rd International Conference on Randomization and Computation, APPROX/RANDOM 2019

作者： Li, Ray Wootters, Mary Department of Computer Science Stanford University CA United States Departments of Computer Science and Electrical Engineering Stanford University CA United States

ISBN: (纸本)9783959771252

Lifted Reed Solomon Codes (Guo, Kopparty, Sudan 2013) were introduced in the context of locally correctable and testable codes. They are multivariate polynomials whose restriction to any line is a codeword of a Reed-Solomon code. We consider a generalization of their construction, which we call lifted multiplicity codes. These are multivariate polynomial codes whose restriction to any line is a codeword of a multiplicity code (Kopparty, Saraf, Yekhanin 2014). We show that lifted multiplicity codes have a better trade-off between redundancy and a notion of locality called the t-disjoint-repair-group property than previously known constructions. More precisely, we show that, for t ≤ √ N, lifted multiplicity codes with length N and redundancy O(t0.585 √ N) have the property that any symbol of a codeword can be reconstructed in t different ways, each using a disjoint subset of the other coordinates. This gives the best known trade-off for this problem for any super-constant t © Ray Li and Mary Wootters.

关键词： Repair

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Simulation of the Flux-to-voltage Characteristics of DC SQUID with Small Screening Parameter and a Large Stewart-McCumber Parameter

Simulation of the Flux-to-voltage Characteristics of DC SQUI...

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International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD

作者： Bao Wu Hai Wang Zhidan Zhang Xiangyan Kong Meiling Wang Faculty of Electrical Engineering anc Computer Science Ningbo University Ningbo China College of Engineering Science and Technology Shanghai Ocean University Shanghai China

ISBN: (数字)9781728152158

ISBN: (纸本)9781728152165

There are three important parameters in the RCSJ Model of direct current superconducting quantum interference devices (DC SQUIDs), namely screening parameter βL, Stewart- McCumber parameter βC, and thermal noise parameter Γ. The three parameters will dominate the SQUID sensor intrinsic noise, but not the application system noise, because the electric noise of readout in room temperature always cannot be neglected. In order to suppress the readout electric noise, a large flux to voltage (∂V/∂Φ) character of SQUID sensor is essential in direct readout scheme. In the real application system, SQUID sensor temperature fluctuation noise is negligible, so we simulated influence of the parameter βL and βC to SQUID's ∂V/∂Φ. In the simulation we used three combinations based on βL and βC under the condition of biased current i=1.65 μA and noise parameter Γ = 0.05. We found that the weakly damped SQUID with small βL have a high flux response ∂V/∂Φ. At last some experiment have been done, and the results fit well to our simulation.

关键词： SQUIDs Harmonic analysis Preamplifiers Mathematical model Temperature Predictive models Physics

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Searching for consistent associations with a multi-environment knockoff filter

arXiv

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arXiv 2021年

作者： Li, Shuangning Sesia, Matteo Romano, Yaniv Candès, Emmanuel Sabatti, Chiara Department of Statistics Stanford University United States Department of Data Sciences and Operations University of Southern California United States Departments of Electrical Engineering and of Computer Science Technion Israel Departments of Statistics and of Mathematics Stanford University United States Departments of Statistics and of Biomedical Data Sciences Stanford University United States

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The motivation for this problem is that large data sets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations consistently replicated under different conditions may be more interesting. In fact, consistency sometimes provably leads to valid causal inferences even if conditional associations do not. While the proposed method is flexible and can be deployed in a wide range of applications, this paper highlights its relevance to genome-wide association studies, in which consistency across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to the UK Biobank data. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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