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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Griffin Dube Jiannan Tian Sheng Di Dingwen Tao Jon C. Calhoun Franck Cappello Department of Computer Science Northwestern University Evanston IL Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Lossy compression, helps to mitigate this issue by decreasing the size of data generated by these applications. SZ, a current state-of-the-art lossy compressor, is able to achieve high compression ratios, but its prediction/quantization methods contain RAW dependencies that prevent parallelizing this step of the compression. Recent work proposes a parallel dual prediction/quantization algorithm for GPUs which removes these dependencies. However, some HPC systems and applications do not use GPUs, and could still benefit from the fine-grained parallelism of this method. Using the dual-quantization technique, we implement and optimize a SIMD vectorized CPU version of SZ (vecSZ), and create a heuristic for selecting the optimal block size and vector length. We propose a novel block padding algorithm to decrease the number of unpredictable values along compression block borders and find it reduces the number of prediction outliers by up to 100%. We measure performance of our vecSZ against an CPU version of SZ using dual-quantization, pSZ, as well as SZ-1.4. Using real-world scientific datasets, we evaluate vecSZ on the Intel Skylake and AMD Rome architectures. vecSZ results in up to 32% improvement in rate-distortion and up to 15× speedup over SZ-1.4, achieving a prediction and quantization bandwidth in excess of 3.4 GB/s.

关键词： Analytical models Quantization (signal) Computational modeling Rate-distortion computer architecture Bandwidth Prediction algorithms

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Intelligent Monitoring of Loading and Unloading Process in Enterprise Transport System 6th

Intelligent Monitoring of Loading and Unloading Process in E...

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6th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2022

作者： Knapčíková, Lucia Martiček, Matúš Husár, Jozef Kaščak, Jakub Faculty of Manufacturing Technologies with a Seat in Prešov Department of Industrial Engineering and Informatics The Technical University of Košice Bayerova 1 Prešov080 01 Slovakia Faculty of Manufacturing Technologies with a Seat in Prešov Department of Computer Aided Manufacturing Technologies The Technical University of Košice Štúrova 31 Prešov080 01 Slovakia

ISBN: (纸本)9783031151002

Organizations and businesses need an information infrastructure that will enable them to cope with the enormously rapid development, i.e. the process of globalization and integration of the world economy. It allows you to make accurate decisions in real-time, focus on customer dissatisfaction while maintaining competitiveness and profitability. The result is optimising the supply chain to integrate processes, approaches, systems, databases, strategies between different business partners. The consequence of e-technology is the reorganization of the enterprise's current information base, the calculation of implementation costs, and the consideration of the marketing approach as a source of information advantage. For logistics, it means a significant reduction in delivery times, increasing receipt and order processing automation. High flexibility and reliability are expected, costs do not show an increase, and the importance of planning in the chain is growing, which must be faster and more accurate. The paper aims to monitor the loading and unloading of goods from rail transport using a simulation program regarding protecting employees’ health, minimizing the time required for unloading and loading pallets with goods. © 2022, ICST Institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Supply chains

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Critical Analysis and Evaluation of Current Digital Healthcare Solutions 23

Critical Analysis and Evaluation of Current Digital Healthca...

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23rd International Conference on Control Systems and computer Science Technologies, CSCS 2021

作者： Ianculescu, Marilena Alexandru, Adriana Pop, Florin Systems and Applications for Society Department National Institute for RandD in Informatics Bucharest Romania Doctoral School of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Department of Computer Science and Engineering University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

Digitalization of health, healthcare and healthcare delivery unequivocal have positive consequences contributing to health system goals, but they also might have some negative ones which have to be avoided or mitigated. The scope of this paper is to introduce the necessity of the assessment of Health Technology Assessment (HTA) and to identify the most appropriate and important criteria needed for the evaluation and comparison of functional digital healthcare solutions. It aims to outline consistent criteria against which digital health technologies and services can be evaluated during the prototyping phase, but also during long-term operation. © 2021 IEEE.

关键词： Health care

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Empowering Personalized Health Data Queries with Knowledge Graph and GPT-Enhanced Voice Assistant

Empowering Personalized Health Data Queries with Knowledge G...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xin Wang Wordh Ul Hasan Juan Li Rasha Hendawi Yang Du Bo Xie Cui Tao Department of Computer Science North Dakota State University Fargo USA Department of Computer Systems and Software Engineering Valley City State University Valley City USA School of Nursing The University of Texas at Austin Austin USA School of Biomedical Informatics University of Texas Health Science Center at Houston Houston USA

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing.

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Imaging Intracellular Metabolic Activity of Mammalian Oocytes by Dynamic Full-field Optical Coherence Microscopy

Imaging Intracellular Metabolic Activity of Mammalian Oocyte...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Morawiec, Seweryn Stremplewski, Patrycjusz Ajduk, Anna Kennedy, Brendan Szkulmowski, Maciej Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun Grudziadzka 5 Torun87-100 Poland Department of Embryology Faculty of Biology University of Warsaw Miecznikowa 1 Warsaw02-096 Poland BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre NedlandsWA6009 Australia Centre for Medical Research The University of Western Australia CrawleyWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

ISBN: (纸本)9798350345995

Full-field optical coherence microscopy (FF-OCM) is ideally suited to study living cells as it provides high-resolution cross-section images with no need for fluorescent labelling [1,2]. The OCM signal is proportional both to the scattering potential of the sample, providing structural information, and to the motion amplitude of the inter-cellular structures. Thus, the metabolic activity information can be extracted from the spatiotemporal analysis of the OCM images. This technique is particularly appealing for embryology to noninvasively assess the development potential of living cells, e.g., oocytes [3]. © 2023 IEEE.

关键词： Metabolism

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Modeling Path Importance for Effective Alzheimer’s Disease Drug Repurposing

arXiv

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arXiv 2023年

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, ChienWei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network’s rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023, CC BY.

关键词： Embeddings

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Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems 55

Jointly Learned Symbol Detection and Signal Reflection in RI...

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55th Asilomar Conference on Signals, Systems and computers, ACSSC 2021

作者： Wang, Liuhang Shlezinger, Nir Alexandropoulos, George C. Zhang, Haiyang Wang, Baoyun Eldar, Yonina C. Nanjing University of Posts and Telecommunications School of Communication and Information Engineering Nanjing China Ben-Gurion University of the Negev School of Electrical and Computer Engineering Beer-Sheva Israel National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Weizmann Institute of Science Faculty of Math and Computer Science Rehovot Israel

ISBN: (纸本)9781665458283

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments. However, the passive reflecting feature of RISs induces notable challenges on channel estimation, making coherent symbol detection a challenging task. In this paper, we consider the uplink of RIS-aided multi-user Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning (ML) approach to jointly design the multi-antenna receiver and configure the RIS reflection coefficients, which does not require explicit full knowledge of the channel input-output relationship. Our approach devises a ML-based receiver, while the configurations of the RIS reflection patterns affecting the underlying propagation channel are treated as hyperparameters. Based on this system design formulation, we propose a Bayesian ML framework for optimizing the RIS hyperparameters, according to which the transmitted pilots are directly used to jointly tune the RIS and the multi-antenna receiver. Our simulation results demonstrate the capability of the proposed approach to provide reliable communications in non-linear channel conditions corrupted by Gaussian noise. © 2021 IEEE.

关键词： MIMO systems

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Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques 6

Prediction of Heart Disease Based on Robust Artificial Intel...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Ghorbani, Hamzeh Krasnikova, Alla Ghorbani, Parvin Ghorbani, Simin Hovhannisyan, Harutyun S. Minasyan, Arsen Minasian, Natali Alvar, Mehdi Ahmadi Stepanyan, Harutyun Azodinia, Mohammazreza Faculty of General Medicine 38a Marshal Babajanyan St. Yerevan0040 Armenia Yerevan State Medical University Faculty of Medicine Department of Cardiology Yerevan0031 Armenia Ahvaz Jundishapur University of Medical Sciences Faculty of Medicine Department of Cardiology Ahvaz Iran Ahvaz Jundishapur University of Medical Sciences Faculty of Nursing Department of Nursing and Midwifery Ahvaz Iran Yerevan State Medical University Department of Internal Disease Propaedeutics Yerevan Armenia Yaroslavl State Medical University Faculty of General Medicine Yaroslavl Russia Shahid Chamran University Faculty of Engineering Department of Computer Engineering Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

ISBN: (纸本)9798350328752

Heart disease (HD) is a concerning health condition that demands immediate attention. This article employs three artificial intelligence classification algorithms: DT, NB, and SGD to address this issue. The primary goal is to determine the most effective algorithm for early-stage prediction of heart disease, using easily accessible patient information. A dataset of 290 simple input variables, including sex, age, blood sugar, fasting blood sugar levels, and maximal heart rate, was collected from a hospital in Armenia. The results reveal that the DT algorithm outperforms NB and SGD in accuracy, with CAtrain of 0.96 and CAtest of 0.93. Accurate early prediction enables timely interventions and support for patients, improving healthcare management. Leveraging artificial intelligence, particularly DT, enables swift identification of potential heart disease cases, aiding healthcare professionals in providing essential care and preventive measures. Furthermore, the pragmatic approach of employing easily accessible patient data for prediction underscores the feasibility and applicability of the proposed methodology. By relying on readily available and cost-effective information, medical practitioners can efficiently screen and diagnose individuals at risk of heart disease, leading to improved healthcare management and better patient outcomes. © 2023 IEEE.

关键词： Decision trees

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Personalized web search using key phrase-based user profiles 19

Personalized web search using key phrase-based user profiles

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19th Ibero-American International Conference on WWW/Internet 2020, ICWI 2020

作者： Abri, Sara Abri, Rayan Cetin, Salih Department of Computer Engineering Hacettepe University Ankara Turkey Mavinci Informatics Inc. Ankara Turkey

ISBN: (纸本)9789898704238

In the context of the personalized web, the structure of user profiles remains a challenge due to its effect on information retrieval efficiency. In creating a user profile, how to extract collections of metadata of documents is a significant point. Although the efficiency of the structures such as keyword-based has proven, there is not research on key phrase based user profiles in the process of personalization. Previous methods to create user profiles emphasize keyword extraction while ignoring the existence of key phrases in the documents. This article proposes the use of keyphrase-based user profiles in personalized web search. We investigate the state of the art keyphrase extraction algorithms by considering different models of supervised and unsupervised methods. This can overcome the problems of missing keyphrases in user profiles and increase the accuracy of information retrieval. Finally, we evaluate keyphrase-based user profiles using a re-ranking algorithm to complete the process of personalization using different datasets. Personalized models based on supervised keyphrase extraction approaches obtained more accuracy by 7% than unsupervised approaches. © Ibero-American WWW / Internet Conference 2020.

关键词： User profile

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Analysis of the Contribution of Cardiovascular Compartments to the Ballistocardiogram Signal Using Mathematical Modeling 50

Analysis of the Contribution of Cardiovascular Compartments ...

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50th Computing in Cardiology, CinC 2023

作者： Zaid, Mohamed Invernizzi, Raul Sala, Lorenzo Despins, Laurel Popescu, Mihail Keller, James Skubic, Marjorie Sacco, Riccardo Szopos, Marcela Huxley, Virginia H. Guidoboni, Giovanna Maine College of Engineering and Computing University of Maine OronoME United States Politecnico di Milano Department of Mathematics Milan Italy Inrae MaIAGE Universite Paris-Saclay Paris France Sinclair School of Nursing University of Missouri ColumbiaMO United States Health Management and Informatics University of Missouri ColumbiaMO United States Electrical and Computer Engineering University of Missouri ColumbiaMO United States Université Paris Cité Cnrs ParisF-75006 France University of Missouri Department of Medical Pharmacology and Physiology ColumbiaMO United States

ISBN: (纸本)9798350382525

The ballistocardiogram (BCG) captures the mechanics and fluid-dynamic properties of the cardiovascular system and provides useful insights into cardiovascular function. However, the cardiovascular mechanisms generating the BCG are still poorly understood and this limits its clinical applications. The present work examines computer-aided methods for understanding the cardiovascular foundations of the BCG signal. The main goal is to investigate how different cardiovascular compartments contribute to the BCG waveform. This is achieved by comparing BCG signals generated by cardiovascular models of increased complexity, while assessing the impact on pressure-volume loops in the left ventricle. All models describe the circulation as a closed-loop system, which is crucial to capture the dynamic interplay among all cardiovascular compartments, with increasing anatomical and physiological detail. The results highlight the importance of including the ventricles, the main arteries, and the cerebral vasculature in a constructive model for the BCG. The findings emphasize the potential of mathematical modeling to significantly impact cardiovascular diagnostics and healthcare. © 2023 CinC.

关键词： Cardiovascular system

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