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Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

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Infectious Disease Modelling 2024年第1期9卷 234-244页

作者： Ashley N.Micuda Mark R.Anderson Irina Babayan Erin Bolger Logan Cantin Gillian Groth Ry Pressman-Cyna Charlotte Z.Reed Noah J.Rowe Mehdi Shafiee Benjamin Tam Marie C.Vidal Tianai Ye Ryan D.Martin Department of Physics Engineering Physics&AstronomyQueen's UniversityKingstonONCanada Department of Medical Biophysics Western UniversityLondonONCanada Department of Mathematics and Statistics Queen's UniversityKingstonONCanada Department of Biology Queen's UniversityKingstonONCanada Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan Energetic Cosmos Laboratory Nazarbayev UniversityNur-SultanKazakhstan School of Computing Queen's UniversityKingstonONCanada Department of Biomedical and Molecular Sciences Queen's UniversityKingstonONCanada Department of Physics University of OxfordOxfordUnited Kingdom Department of Physics Stanford UniversityStanfordCAUnited States

This work introduces the Queen's University Agent-Based Outbreak Outcome Model(QUABOOM).This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health *** illustrate the use of the model by examining capacity restrictions during a *** find that public health measures should focus on the few locations where many people interact,such as grocery stores,rather than the many locations where few people interact,such as small *** also discuss a case where the results of the simulation can be scaled to larger population sizes,thereby improving computational efficiency.

关键词： Monte-carlo Agent-based epidemic modelling COVID-19 Small business capacity restrictions Public health Basic reproductive number

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On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

arXiv

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

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Track Initialization and Re-Identification for 3D Multi-View Multi-Object Tracking

arXiv

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arXiv 2024年

作者： Van Ma, Linh Nguyen, Tran Thien Dat Vo, Ba-Ngu Jang, Hyunsung Jeon, Moongu The School of Electrical Engineering and Computer Science at GIST Gwangju Korea Republic of The School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia The Department of EO/IR Systems Research and Development LIG Nex1 Korea Republic of

We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance-reappearance and occlusions. Moreover, this approach does not require detector retraining when cameras are reconfigured but only the camera matrices of reconfigured cameras need to be updated. Our approach is based on a Bayesian multi-object formulation that integrates track initiation/termination, re-identification, occlusion handling, and data association into a single Bayes filtering recursion. However, the exact filter that utilizes all these functionalities is numerically intractable due to the exponentially growing number of terms in the (multi-object) filtering density, while existing approximations trade-off some of these functionalities for speed. To this end, we develop a more efficient approximation suitable for online MOT by incorporating object features and kinematics into the measurement model, which improves data association and subsequently reduces the number of terms. Specifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and re-identification functionalities. Further, incorporating a tractable geometric occlusion model based on 2D projections of 3D objects on the camera planes realizes the occlusion handling functionality of the filter. Evaluation of the proposed solution on challenging datasets demonstrates significant improvements and robustness when camera configurations change on-the-fly, compared to existing multi-view MOT solutions. The source code is publicly available at https://***/linh-gist/mv-glmb-ab. © 2024, CC BY.

关键词： Cameras

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Exploring transfer learning for Deep NLP systems on rarely annotated languages

arXiv

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arXiv 2024年

作者： Yadav, Dipendra Strauß, Tobias Yordanova, Kristina University of Rostock Department of Computer Science and Electrical Engineering Institute of Visual & Analytic Computing Mobile Multimedia Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany

Natural language processing has seen exponential growth since deep learning has been used for various tasks. Deep learning has replaced the old rule-based methods by providing better performance on almost all the tasks in NLP, as deep learning basically looks into the hidden patterns and structures in the data which were sometimes not detected and at other-times overlooked by the rule-based systems. The research and development in NLP have been however focused on some handful languages of the world given their financial importance and a huge number of speakers. This has left out most of the other languages unexplored. The other reason for the lack of research in these languages is the unavailability of properly annotated and the required amount of datasets which is very important for training deep learning neural networks. The situation looks grim. When we look at the world language tree, thankfully we see that most of the unexplored languages have other well-explored languages nearby which basically means that both the languages are kind of similar. So if we could somehow adapt research and development from one language to another, it could be really helpful for both the languages. The goal of this thesis work is to explore transfer learning for the task of Part-of-speech(POS) tagging between Hindi and Nepali languages which are part of the Indo-Aryan language family with very high similarity. We have tried to explore the possibility of jointly training a model for the task of POS tagging in both Hindi and Nepali language and see if it helps in improving the performance of the model. We also try to explore if multitask learning in the Hindi language can be helpful for the task of POS tagging with auxiliary tasks of the gender tagging and singularity/plurality tagging. The deep learning architecture used for this work is BLSTM-CNN-CRF. The model is trained with monolingual word embeddings, vector mapped embeddings, and also with jointly trained Hindi-Nepali word emb

关键词： Embeddings

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Direct Truncation and Routh Approximation Assisted Reduced Order Modelling of Islanded Microgrid

Direct Truncation and Routh Approximation Assisted Reduced O...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Richa Chaudhary V. P. Singh Akhilesh Mathur U. K. Yadav Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Microgrid (MG) networks, with their diverse energy sources and storage systems, represent complex higher-order systems (HOS). Analyzing such networks is challenging due to the uncertainty associated with energy sources like solar and wind power. To address this complexity, reduced-order models (ROMs) are essential, achieved through order reduction techniques. This article presents a method to reduce the eighth-order transfer function of an islanded microgrid to a second-order model. The reduction involves truncation reduction technique (TRT) for the numerator and Routh approximation reduction technique (RART) for the denominator. A comparative analysis is conducted between the original higher-order islanded microgrid system and its ROM. The comparison includes various error indices to determine the effectiveness of the ROM. Graphical representations of impulse, Nichols, step, Nyquist, and Bode responses are provided for both the original system and the ROM to visually compare their performances. This study demonstrates the practical application of mixed reduction techniques to simplify complex microgrid models, making them more manageable for analysis and design purposes.

关键词： Uncertainty Industry applications Transfer functions Microgrids Wind power generation Reduced order systems Complexity theory Time-domain analysis Read only memory Energy storage

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Millimeter-Wave Agricultural Channel Measurements in Corn and Soybean Fields at Different Growth Stages

Millimeter-Wave Agricultural Channel Measurements in Corn an...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Vuran, Mehmet C. Lunar, Mohammad Mosiur Nie, Shuai Ge, Yufeng Pitla, Santosh Bai, Geng Koksal, Can Emre University of Nebraska-Lincoln School of Computing LincolnNE68588 United States University of Nebraska-Lincoln Department of Biological Systems Engineering LincolnNE68583 United States Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

Extensive agricultural field experiments in the millimeter-wave spectrum have been conducted for the first time in corn and soybean fields to study the impacts of crop type, crop growth stage, and antenna height on the path loss. Path loss analysis is conducted based on the field measurements and control experiment data. Based on experiments with over 6,860 data points collected over a five-month period in three crop fields, our results suggest that common crops, such as corn and soybean, impact the mmWave channel. The observations provide essential guidelines for the deployment of agricultural millimeter-wave wireless networks. © 2022 IEEE.

关键词： Millimeter waves

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Automatic Diagnosis of Pectus Excavatum from CT Images Using a Joint CNN-LSTM Model 9

Automatic Diagnosis of Pectus Excavatum from CT Images Using...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Liao, Yizhi Zhou, Haiyu Xie, Longhan Cai, Siqi School of Computing and Information Systems University of Melbourne Melbourne Australia Guangdong Lung Cancer Institute Guangdong Provincial People's Hospital Division of Thoracic Surgery Guangzhou China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350311402

Pectus excavatum (PE) is one of the most common congenital sternal deformities. Accurate preoperative diagnosis of PE is of great significance for subsequent correction and improvement of the patient's quality of life. However, current diagnostic methods rely on the calculation of some PE indices, which is a heavy workload for physical therapists and suffers from measurement errors. To address this issue, we propose an end-to-end automatic assessment of PE, which features a cascaded structure of CNN and LSTM. Specifically, the high-level feature representations of CT images are extracted by the pretrained CNN and then processed through the LSTM layers for classification. In addition, we build up a medical image dataset for PE diagnosis by collecting chest CT images of 42 subjects. Results on this dataset show that the proposed CNN-LSTM framework achieves a relatively high accuracy of 90.20%, which provides a new perspective for the automatic diagnosis of PE in clinics. © 2023 IEEE.

关键词： computerized tomography

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Facial Emotion Recognition using YOLO based Deep Learning Classifier 1

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Vanamoju, Sri Vaibhav Mohan Dev Vineetha, Moturi Veda Tekchandani, Hitesh Joshi, Pallavi Shukla, Praveen Kumar Khanna, Anita Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Amrita Vishwa Vidyapeetham Mysuru Department of Computer Science Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (纸本)9798350364590

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task. © 2024 IEEE.

关键词： Adversarial machine learning

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AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.

关键词： Renewable energy sources Microgrids Drives Electric vehicles Software Power electronics Distributed power generation Planning MATLAB Load flow analysis

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