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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Federica D’Annunzio Giulia Fiscon Gaia Bertolotti Raffaele Dello Ioio Paola Paci Department of Translational and Precision Medicine Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" National Research Council of Rome Sapienza University of Rome Institute for Systems Analysis and Computer Science "A. Ruberti" Rome Italy Department of Biology and Biotechnology "Charles Darwin" Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" Sapienza University of Rome Karolinska Institutet Stockholm Sweden Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes’ relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots’ orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems.

关键词： Correlation Buildings Biological systems Predictive models Phylogeny Complexity theory Bioinformatics

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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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Parental lifestyle patterns around pregnancy and risk of childhood obesity in four European birth cohort studies

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The Lancet. Global health 2023年 11卷 S5页

作者： Lecorguillé, Marion Schipper, Mireille Aubert, Adrien M. Tafflet, Muriel Gassama, Malamine Douglass, Alexander Hébert, James R. Kelleher, Cecily Charles, Marie-Aline Phillips, Catherine M. Gaillard, Romy Lioret, Sandrine Heude, Barbara Center for Research in Epidemiology and Statistics National Institute of Health and Medical Research and National Research Institute for Agriculture Food and the Environment Paris France Generation R Study Group Erasmus University Medical Center Rotterdam Netherlands School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland UMS Elfe Team French Institute for Demographic Studies National Institute of Health and Medical Research French Blood Agency Aubervilliers France Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina Columbia SC USA Connecting Health Innovations Columbia SC USA Center for Research in Epidemiology and Statistics National Institute of Health and Medical Research and National Research Institute for Agriculture Food and the Environment Université Paris Cité and Université Sorbonne Paris Nord Paris France UMS Elfe Team French Institute for Demographic Studies National Institute of Health and Medical Research French Blood Agency Aubervilliers France

BACKGROUND: A high prevalence of excess weight in children younger than 5 years suggests the involvement of early-life risk factors. The preconception and pregnancy periods are crucial stages for the implementation of interventions to prevent childhood obesity. Most studies so far have evaluated the effects of early-life factors separately, with only a few investigating the combined effect of parental lifestyle factors. Our objective was to fill the literature gap regarding parental lifestyle factors in the preconception and pregnancy periods and to study their association with the risk of overweight in children after the age of 5 years. METHODS: We harmonised and interpreted data from four European mother-offspring cohorts (EDEN [comprising 1900 families], Elfe [comprising 18 000 families], Lifeways [comprising 1100 families], and Generation R [comprising 9500 families]). Written informed consent was obtained from parents of all involved children. Lifestyle factor data collected through questionnaires comprised parental smoking, BMI, gestational weight gain, diet, physical activity, and sedentary behaviour. We applied principal component analyses to identify multiple lifestyle patterns in preconception and pregnancy. Their association with child BMI z-score and risk of overweight (including obesity, overweight and obesity, as defined by the International Task Force reference) between the ages of 5 and 12 years were assessed using cohort-specific multivariable linear and logistic regression models (adjusted for confounders including parental age, education level, employment status, geographic origin, parity, and household income). FINDINGS: Among the various lifestyle patterns identified in all cohorts, the two that better explained variance were high parental smoking plus low maternal diet quality or high maternal sedentary behaviour, and high parental BMI plus low gestational weight gain. Overall, we observed that patterns characterised by high parental BMI, smoki

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Study and prediction of the 2022 global monkeypox epidemic

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Journal of Biosafety and Biosecurity 2022年第2期4卷 158-162页

作者： Fengying Wei Zhihang Peng Zhen Jin Jinjie Wang Ximing Xu Xinyan Zhang Jun Xu Zixiong Ren Yao Bai Xiaoli Wang Bulai Lu Zhaojun Wang Jianguo Xu Senzhong Huang School of Mathematics and Statistics Fuzhou UniversityFuzhou 350116FujianChina Department of Biostatistics School of Public HealthNanjing Medical UniversityNanjing 211166JiangsuChina Complex Systems Research Center Key Discipline of Computer Science and Technology of“Double-First-Class”Project of Shanxi ProvinceShanxi UniversityTaiyuan 030006ShanxiChina Nankai Institute of Economics Binhai Development InstituteNankai UniversityTianjin 300071China Children’s Hospital of Chongqing Medical University Chongqing 400010China Jinzhou Center for Disease Control and Prevention Jinzhou 121000LiaoningChina School of Statistics and Data Science Nankai UniversityTianjin 300071China Xi’an Center for Disease Control and Prevention Xi’an 710061ShaanxiChina Beijing Center for Disease Control and Prevention Beijing 100013BeijingChina Suzhou Center for Disease Control and Prevention Suzhou 215004JiangsuChina State Key Laboratory of Communicable Disease Prevention and Control National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China School of Statistics and Data Science ZhiYing Research Center for Health DataNankai UniversityTianjin 300071China Institute of Public Health Nankai UniversityTianjin 300071China

The World Health Organization(WHO)declared monkeypox as a public health emergency of international concern(PHEIC)on July 23,2022,their highest level of *** raised concerns about the management of the global monkeypox outbreak,as well as the scientific analysis and accurate prediction of the future course of the *** study used EpiSIX(an analysis and prediction system for epidemics based on a general SEIR model)to analyze the monkeypox epidemic and to forecast the major tendencies based on data from the USA CDC(https://***)and the WHO(https://***/health-topics/monkeypox).The global outbreak of monkeypox started in the UK on May 2,2022,which marked the beginning of an epidemic *** of October 28,2022,the cumulative number of reported cases worldwide was 77,115,with 36 *** simulations predict that the global monkeypox epidemic will enter a low epidemic status on March 1,2023 with the cumulative number of confirmed cases ranging from 85,000 to 124,000,and the total number of deaths ranging from 60 to *** analysis revealed that the basic reproduction number(R0)of monkeypox virus(MPXV)is near to 3.1 and the percentage of asymptomatic individuals is 13.1%–14.5%,both of which are similar to the data for *** vaccination efficiency against susceptibility(VEs)of individuals who have had monkeypox is~79%,and the vaccination efficiency against infectiousness(VEi)of individuals who have had monkeypox is~76%–82%.The mean incubation period for monkeypox is 8 *** total,94.7%of infected individuals develop symptoms within 20 days and recover within 2 weeks after the confirmation of *** results using EpiSIX showed that ring vaccination was remarkably effective against *** findings confirmed that a 20-day isolation for close contacts is necessary.

关键词： Monkeypox Epidemic Prediction Tendency

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Prioritized SIPP for multi-agent path finding with kinematic constraints

arXiv

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arXiv 2021年

作者： Ali, Zain Alabedeen Yakovlev, Konstantin Moscow Institute of Physics and Technology Federal Research Center Computer Science and Control RAS

Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its importance, the problem is wellstudied and multiple optimal and approximate algorithms are known. However, many of them abstract away from the kinematic constraints and assume that the agents can accelerate/decelerate instantaneously. This complicates the application of the algorithms on the real robots. In this paper, we present a method that mitigates this issue to a certain extent. The suggested solver is essentially, a prioritized planner based on the well-known Safe Interval Path Planning (SIPP) algorithm. Within SIPP we explicitly reason about the speed and the acceleration thus the constructed plans directly take kinematic constraints of agents into account. We suggest a range of heuristic functions for that setting and conduct a thorough empirical evaluation of the suggested algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Zero-Order Stochastic Conditional Gradient Sliding Method for Non-smooth Convex Optimization

arXiv

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arXiv 2023年

作者： Lobanov, Aleksandr Anikin, Anton Gasnikov, Alexander Gornov, Alexander Chukanov, Sergey Moscow Institute of Physics and Technology Dolgoprudny Russia ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia Moscow Aviation Institute Moscow Russia Matrosov Institute for System Dynamics and Control Theory Irkutsk Russia Institute for Information Transmission Problems RAS Moscow Russia Caucasus Mathematical Center Adyghe State University Maikop Russia Federal Research Center"Computer Science and Control" RAS Moscow Russia

The conditional gradient idea proposed by Marguerite Frank and Philip Wolfe in 1956 was so well received by the community that new algorithms (also called Frank-Wolfe type algorithms) are still being actively created. In this paper, we study a non-smooth stochastic convex optimization problem with constraints. Using a smoothing technique and based on an accelerated batched first-order Stochastic Conditional Gradient Sliding method, we propose a novel gradient-free Frank-Wolfe type algorithm called Zero-Order Stochastic Conditional Gradient Sliding (ZO-SCGS). This algorithm is robust not only for the class of non-smooth problems, but surprisingly also for the class of smooth black box problems, outperforming the SOTA algorithms in the smooth case in term oracle calls. In practical experiments we confirm our theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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4.6-Bit Quantization for Fast and Accurate Neural Network Inference on Cpus

SSRN

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SSRN 2023年

作者： Trusov, Anton Limonova, Elena Nikolaev, Dmitry Arlazarov, Vladimir V. Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow Russia Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia

Quantization is a widespread method for reducing the inference time of neural networks on mobile Central Processing Units (CPUs).8-bit quantized networks demonstrate almost as high quality as full precision models and perfectly fit the hardware architecture with one-byte coefficients and 32-bit dot product *** precision quantizations usually suffer from noticeable quality loss and require specific computational algorithms to outperform 8-bit quantization. In this paper, we propose a novel 4.6-bit quantization scheme that allows for more efficient use of CPU *** scheme has more quantization bins than 4-bit quantization and is more accurate while preserving the computational efficiency of the later(it runs only 4% slower). Our multiplication uses a combination of 16- and 32-bit accumulators and avoids multiplication depth limitation, which the previous 4-bit multiplication algorithm *** experiments with different convolutional neural networks on CIFAR-10 and ImageNet datasets show, that 4.6-bit quantized networks are 1.5-1.6 times faster than 8-bit networks on the ARMv8 CPU. The quality loss in comparison to 8-bit and full precision models is also not high. Therefore, 4.6-bit quantization may serve as an intermediate solution between fast and inaccurate low-bit network quantizations and accurate but relatively slow 8-bit ones. © 2023, The Authors. All rights reserved.

关键词： Program processors

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Impulsive Model Predictive control in Type 1 Diabetes Mellitus Applications

Impulsive Model Predictive Control in Type 1 Diabetes Mellit...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kamilla Novák Máté Siket Levente Kovács Dániel András Drexler György Eigner Physiological Controls Research Center Óbuda University Applied Informatics and Applied Mathematics Doctoral School Óbuda University Institute for Computer Science and Control Budapest Hungary John von Neumann Faculty of Informatics Biomatics and Applied Artificial Intelligence Institute Obuda University

Despite the increasing availability and reliability of artificial pancreas devices, many people with type 1 diabetes mellitus are still on multiple daily injections therapy consisting of a daily basal insulin injection and mealtime boluses. The use of an insulin bolus advisor may improve glycaemic control as well as reduce the burden of the disease on these patients. This paper investigates the application of an impulsive model predictive controller in a bolus advisor system. The bolus calculator is assessed in closed-loop simulations using different basal insulin scenarios. The simulations show that a model predictive controller can achieve good glycaemic control and may be suitable to give bolus recommendations to people with diabetes. Higher basal insulin levels can result in better times in the target range but also carry a higher risk of hypoglycemia.

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements 36

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Somogyi, Árpád Tettamanti, Tamás Varga, Pál Szalay, Zsolt Baranyai, Dániel Lovas, Tamás Budapest University of Technology and Economics Fac. of Civil Engineering Dept. of Photogrammetry and Geoinformatics Budapest Hungary Institute for Computer Science and Control The Eötvös Loránd Research Network Systems and Control Laboratory Budapest Hungary Budapest University of Technology and Economics Fac. of Electrical Engineering and Informatics Dept. of Telecommunications and Media Informatics Budapest Hungary Budapest University of Technology and Economics Fac. of Transportation Eng. and Vehicle Eng. Dept. of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665477161

One major goal of digital twin technology applied in the Architecture, Engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Model Predictive control with Dynamic Positive Input Extension for Artificial Pancreas Applications

Model Predictive Control with Dynamic Positive Input Extensi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kamilla Novák Máté Siket Levente Kovács Dániel András Drexler Imre Rudas György Eigner Physiological Controls Research Center Óbuda University Applied Informatics and Applied Mathematics Doctoral School Obuda University Institute for Computer Science and Control Budapest Hungary Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Obuda University

Like many physiological systems, the various mathematical models describing the glucose-insulin system can only have non-negative inputs. Thus, the control method — often model predictive control in artificial pancreas systems—must provide a non-negative control signal. Existing solutions include saturation and constrained optimization. In this paper, we propose a dynamic extension of the patient model as a way of ensuring the positivity of the control signal, a method previously applied in tumor growth control. We evaluate the controller in a closed-loop simulation and compare the results with a controller using saturation. Our simulations show that control performance with the extended model can reach or exceed the performance achieved with saturation.

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