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Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

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Infectious Disease Modelling 2024年第3期9卷 816-827页

作者： Xingxing Zhang Liuyang Yang Teng Chen Qing Wang Jin Yang Ting Zhang Jiao Yang Hongqing Zhao Shengjie Lai Luzhao Feng Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100073China State Key Laboratory of Respiratory Health and Multimorbidity China Key Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College) Ministry of EducationChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionChina Department of Management Science and Information System Faculty of Management and EconomicsKunming University of Science and TechnologyKunming650506China Department of Applied Mathematics&Statistics Stony Brook UniversityStony BrookNY11794-3600USA WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonSO171BJUK

Background Influenza is an acute respiratory infectious disease with a significant global disease ***,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of ***,comparative studies on the performance of innovative models and approaches used for influenza prediction are ***,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive *** The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to *** relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three *** Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in ***,the models’predictive performance declined as the weeks ahead ***,blocked cross-validation indicated that all models were robust to changes in data and had low risks of *** Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid ***,in the future,the ARMA—GARCH model may be used

关键词： Influenza-like illness Influenza Sentinel surveillance China Predicting Modeling

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Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion

arXiv

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arXiv 2024年

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio Robotics and Mechatronics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7500AE Netherlands Department of Applied Mathematics University of Twente Enschede7500AE Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. The latent representation is learned using a Cross-Modal Variational Autoencoder, and used to estimate the return for conditioning the trajectory generation of the DDPM. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach is applicable to any velocity-based controlled platform. We test our method with simulated and real-world experiments using generic multi-rotor Uncrewed Aerial Vehicles (UAVs). A video of our experiments can be found at https://***/yu-aTxqceOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Unsupervised Feature Selection via Multi-Structure Learning and Indicator Matrix

Unsupervised Feature Selection via Multi-Structure Learning ...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Zebiao Hu Xiaoyu Gao Ziwei Bian Jian Wang Sergey V. Ablameyko College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Science China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Traditional feature selection methods usually only consider a specific geometric structure of the original data, which cannot reveal the most essential underlying structure of the data. Apart from this, a regularization constraint with sparsity is often imposed on the mapping matrix, which inevitably leads to additional parameter setting issues. To address these challenges, a novel unsupervised feature selection via multi-structure learning and indicator matrix (UFSMI) is proposed in this study. More concretely, we construct a unified framework that incorporates multi-structure learning and feature selection. UFSMI first explores the global and local geometric structure of high dimensional data in subspace to excavate the intrinsic structure of data, effectively reducing the interference of redundant features. Then, an indicator matrix for feature selection that can explicitly indicate which features should be selected is in-troduced without imposing any regularization constraints. Finally, the most useful subset of features can be obtained based on the indices of the non-zero elements in the indicator matrix. A series of experiments are conducted to demonstrate the effectiveness and superiority of the proposed method on nine real-word high dimensional datasets by comparing with existing state-of-the-art feature selection methods.

关键词： Representation learning Interference Feature extraction Excavation Market research Computational intelligence

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A Customized Approach to Anomalies Detection by using Autoencoders

A Customized Approach to Anomalies Detection by using Autoen...

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2021 Scholar's Yearly Symposium of Technology, Engineering and mathematics, SYSTEM 2021

作者： Aureli, Roberto Brandizzi, Nicolo' Magistris, Giorgio De Brociek, Rafal Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00135 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland

When dealing with sensor's data, it's important to keep track of what it's really happening in the tracked environments since failures, interruptions and misreadings must be expected at any time. Especially with logging processes involving extremely voluminous reports, an automatic method to detect entries that are not following the normal distribution of data (i.e. anomalies) should be the ideal solution. In the presented work the task performed by the autoencoder is to generate a reproduction error, used as metric for the classification of a sample in one of two classes: anomalous or non-anomalous. Copyright for this paper by its authors. Use permitted under Creative.

关键词： Field programmable gate arrays (FPGA)

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Constrained Evolutionary Variational-Hemivariational Inequalities with Application to Fluid Flow Model∗

SSRN

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SSRN 2023年

作者： Dudek, Sylwia Migórski, Stanislaw Department of Applied Mathematics Faculty of Computer Science and Telecommunications Krakow University of Technology ul. Warszawska 24 Krakow31155 Poland College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland

We study a new class of abstract evolution variational-hemivariational inequalities with constraints in the framework of evolution triples of spaces. Existence of solution is established based on the so-called mixed equilibrium problem formulation. Then, we apply this result to a mathematical analysis of the unsteady Oseen model for a generalized Newtonian incompressible fluid. A variational-hemivariational inequality for the flow problem is derived and sufficient conditions for existence of weak solutions are obtained. The mixed boundary conditions involve a unilateral boundary condition, the Navier slip condition, a nonmonotone version of the nonlinear Navier-Fujita slip condition, and the threshold slip and leak condition of frictional type. © 2023, The Authors. All rights reserved.

关键词： Boundary conditions

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, mathematics, and Engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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A Scalable Simulation of the BB84 Protocol Involving Eavesdropping 1

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13th International Conference on Security for Information Technology and Communications, SecITC 2020

作者： Mina, Mihai-Zicu Simion, Emil Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Center for Research and Training in Innovative Techniques of Applied Mathematics in Engineering – "Traian Lalescu" University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9783030692551

ISBN: (纸本)9783030692544

In this article we present the BB84 quantum key distribution scheme from two perspectives. First, we provide a theoretical discussion of the steps Alice and Bob take to reach a shared secret using this protocol, while an eavesdropper Eve is either involved or not. Then, we offer and discuss two distinct implementations that simulate BB84 using IBM’s Qiskit framework, the first being an exercise solved during the "IBM Quantum Challenge" event in early May 2020, while the other was developed independently to showcase the intercept-resend attack strategy in detail. We note the latter’s scalability and increased output verbosity, which allow for a statistical analysis to determine the probability of detecting the act of eavesdropping. © 2021, Springer Nature Switzerland AG.

关键词： Quantum cryptography

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Explainable AI: Using shapley value to explain complex anomaly detection ML-based systems

Explainable AI: Using shapley value to explain complex anoma...

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2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020

作者： Zou, Jinying Petrosian, Ovanes Faculty of Applied Mathematics and Control Processes Saint-Petersburg State University Saint-Petersburg Russia

ISBN: (纸本)9781643681368

Generally, Artificial Intelligence (AI) algorithms are unable to account for the logic of each decision they take during the course of arriving at a solution. This 'black box' problem limits the usefulness of AI in military, medical, and financial security applications, among others, where the price for a mistake is great and the decision-maker must be able to monitor and understand each step along the process. In our research, we focus on the application of Explainable AI for log anomaly detection systems of a different kind. In particular, we use the Shapley value approach from cooperative game theory to explain the outcome or solution of two anomaly-detection algorithms: Decision tree and DeepLog. Both algorithms come from the machine learning-based log analysis toolkit for the automated anomaly detection 'Loglizer'. The novelty of our research is that by using the Shapley value and special coding techniques we managed to evaluate or explain the contribution of both a single event and a grouped sequence of events of the Log for the purposes of anomaly detection. We explain how each event and sequence of events influences the solution, or the result, of an anomaly detection system. © 2020 The authors and IOS Press.

关键词： Decision trees

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Special Spectral Approach to Solutions of SISO LTI H-Optimization Problems

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International Journal of Automation and computing 2019年第1期16卷 112-128页

作者： Evgeny I.Veremey Faculty of Applied Mathematics and Control Processes Saint-Petersburg State University

The paper is devoted to H-optimization problems for linear time invariant(LTI) systems with scalar control, external disturbance and measurement noise. All these problems can be numerically solved with the help of the well-known universal approaches based on Riccati equations, linear matrix inequalities(LMI) or maximum entropy technique. Nevertheless, in our opinion there exists a possibility to increase the computational efficiency of synthesis using a special spectral approach to the above mentioned problems in frequency domain. Some relevant details are discussed and efficient numerical algorithms are proposed for the practical implementation of spectral approach. One of its virtues is a possibility to present optimal solutions in a specific form, which is convenient for investigation.

关键词： Stabilizing controller performance index synthesis H-optimization spectral approach

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Inter-Stream Dependencies in Time-Sensitive Networking

Inter-Stream Dependencies in Time-Sensitive Networking

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Hamza Chahed Mark Yarvis Florian Frick Andreas Kassler Intel Deutschland GmbH Germany Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Intel Corporation USA Institute for Control Technology of Machine Tools and Manufacturing Equipment University of Stuttgart Germany Faculty of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Effective configuration of Time-Sensitive Networks is crucial for providing timeliness and reliability guarantees for real-time industrial applications, where many inter-dependent streams may co-exist. However, existing methods for specifying the characteristics of real-time streams to the Centralized Network Configuration element (CNC) do not allow the specification of stream dependencies. Consequently, the CNC may schedule the real-time streams in a sub-optimal way, leading to low number of real-time applications supported. To address this pressing issue, we present methods that allow to model and express such inter-stream dependencies and propose extensions to the user network interface (UNI) to convey these dependencies to the CNC, transforming it into an intent-based UNI. We define a novel metric to evaluate a specific network configuration regarding the latency budget available for scheduling the transmission of a stream. Using a set of numerical simulations, we show that when exploiting the knowledge of inter-stream dependencies, we can derive better network schedules, which results in significant increase in applications onboarding up to 2.2x and increased network utilization for real-time streams up to 3x.

关键词： Schedules Technological innovation Sequential analysis Pressing Reliability engineering Real-time systems Network interfaces Synchronization Streams Optimization

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