arXiv - CS - Computational Engineering, Finance, and Science最新文献_第5页

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data 利用真实的跨省初级保健数据对加拿大成年人进行联合糖尿病预测

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-21 DOI: arxiv-2408.12029

Guojun Tang, Jason E. Black, Tyler S. Williamson, Steve H. Drew

{"title":"Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data","authors":"Guojun Tang, Jason E. Black, Tyler S. Williamson, Steve H. Drew","doi":"arxiv-2408.12029","DOIUrl":"https://doi.org/arxiv-2408.12029","url":null,"abstract":"Integrating Electronic Health Records (EHR) and the application of machine\u0000learning present opportunities for enhancing the accuracy and accessibility of\u0000data-driven diabetes prediction. In particular, developing data-driven machine\u0000learning models can provide early identification of patients with high risk for\u0000diabetes, potentially leading to more effective therapeutic strategies and\u0000reduced healthcare costs. However, regulation restrictions create barriers to\u0000developing centralized predictive models. This paper addresses the challenges\u0000by introducing a federated learning approach, which amalgamates predictive\u0000models without centralized data storage and processing, thus avoiding privacy\u0000issues. This marks the first application of federated learning to predict\u0000diabetes using real clinical datasets in Canada extracted from the Canadian\u0000Primary Care Sentinel Surveillance Network (CPCSSN) without crossprovince\u0000patient data sharing. We address class-imbalance issues through downsampling\u0000techniques and compare federated learning performance against province-based\u0000and centralized models. Experimental results show that the federated MLP model\u0000presents a similar or higher performance compared to the model trained with the\u0000centralized approach. However, the federated logistic regression model showed\u0000inferior performance compared to its centralized peer.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chemical Reaction Neural Networks for Fitting Accelerated Rate Calorimetry Data 拟合加速速率量热数据的化学反应神经网络

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-21 DOI: arxiv-2408.11984

Saakaar Bhatnagar, Andrew Comerford, Zelu Xu, Davide Berti Polato, Araz Banaeizadeh, Alessandro Ferraris

{"title":"Chemical Reaction Neural Networks for Fitting Accelerated Rate Calorimetry Data","authors":"Saakaar Bhatnagar, Andrew Comerford, Zelu Xu, Davide Berti Polato, Araz Banaeizadeh, Alessandro Ferraris","doi":"arxiv-2408.11984","DOIUrl":"https://doi.org/arxiv-2408.11984","url":null,"abstract":"As the demand for lithium-ion batteries rapidly increases there is a need to\u0000design these cells in a safe manner to mitigate thermal runaway. Thermal\u0000runaway in batteries leads to an uncontrollable temperature rise and\u0000potentially fires, which is a major safety concern. Typically, when modelling\u0000the chemical kinetics of thermal runaway calorimetry data ( e.g. Accelerated\u0000Rate Calorimetry (ARC)) is needed to determine the temperature-driven\u0000decomposition kinetics. Conventional methods of fitting Arrhenius Ordinary\u0000Differential Equation (ODE) thermal runaway models to Accelerated Rate\u0000Calorimetry (ARC) data make several assumptions that reduce the fidelity and\u0000generalizability of the obtained model. In this paper, Chemical Reaction Neural\u0000Networks (CRNNs) are trained to fit the kinetic parameters of N-equation\u0000Arrhenius ODEs to ARC data obtained from a Molicel 21700 P45B. The models are\u0000found to be better approximations of the experimental data. The flexibility of\u0000the method is demonstrated by experimenting with two-equation and four-equation\u0000models. Thermal runaway simulations are conducted in 3D using the obtained\u0000kinetic parameters, showing the applicability of the obtained thermal runaway\u0000models to large-scale simulations.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing skin thermal injury risk in exposure tests of heating until flight 评估飞行前加热暴露试验中的皮肤热损伤风险

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-21 DOI: arxiv-2408.11947

Hongyun Wang, Shannon E. Foley, Hong Zhou

{"title":"Assessing skin thermal injury risk in exposure tests of heating until flight","authors":"Hongyun Wang, Shannon E. Foley, Hong Zhou","doi":"arxiv-2408.11947","DOIUrl":"https://doi.org/arxiv-2408.11947","url":null,"abstract":"We assess the skin thermal injury risk in the situation where a test subject\u0000is exposed to an electromagnetic beam until the occurrence of flight action.\u0000The physical process is modeled as follows. The absorbed electromagnetic power\u0000increases the skin temperature. Wherever it is above a temperature threshold,\u0000thermal nociceptors are activated and transduce an electrical signal. When the\u0000activated skin volume reaches a threshold, the flight signal is initiated.\u0000After the delay of human reaction time, the flight action is materialized when\u0000the subject moves away or the beam power is turned off. The injury risk is\u0000quantified by the thermal damage parameter calculated in the Arrhenius\u0000equation. It depends on the beam power density absorbed into the skin, which is\u0000not measurable. In addition, the volume threshold for flight initiation is\u0000unknown. To circumference these difficulties, we normalize the formulation and\u0000write the thermal damage parameter in terms of the occurrence time of flight\u0000action, which is reliably observed in exposure tests. This thermal injury\u0000formulation provides a viable framework for investigating the effects of model\u0000parameters.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Evolution of Reinforcement Learning in Quantitative Finance 强化学习在量化金融领域的演变

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-20 DOI: arxiv-2408.10932

Nikolaos Pippas, Cagatay Turkay, Elliot A. Ludvig

引用次数: 0

Effects of the Plan Vélo I and II on vehicular flow in Paris -- An Empirical Analysis 一期和二期 Vélo 计划对巴黎车流量的影响--实证分析

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-19 DOI: arxiv-2408.09836

Elena Natterer, Allister Loder, Klaus Bogenberger

{"title":"Effects of the Plan Vélo I and II on vehicular flow in Paris -- An Empirical Analysis","authors":"Elena Natterer, Allister Loder, Klaus Bogenberger","doi":"arxiv-2408.09836","DOIUrl":"https://doi.org/arxiv-2408.09836","url":null,"abstract":"In recent years, Paris, France, transformed its transportation\u0000infrastructure, marked by a notable reallocation of space away from cars to\u0000active modes of transportation. Key initiatives driving this transformation\u0000included Plan V'elo I and II, during which the city created over 1,000\u0000kilometres of new bike paths to encourage cycling. For this, substantial road\u0000capacity has been removed from the system. This transformation provides a\u0000unique opportunity to investigate the impact of the large-scale network\u0000re-configuration on the network-wide traffic flow. Using the Network\u0000Fundamental Diagram (NFD) and a re-sampling methodology for its estimation, we\u0000investigate with empirical loop detector data from 2010 and 2023 the impact on\u0000the network's capacity, critical density, and free-flow speed resulting from\u0000these policy interventions. We find that in the urban core with the most policy\u0000interventions, per lane capacity decreased by over 50%, accompanied by a 60%\u0000drop in free-flow speed. Similarly, in the zone with fewer interventions,\u0000capacity declined by 34%, with a 40% reduction in free-flow speed. While these\u0000changes seem substantial, the NFDs show that overall congestion did not\u0000increase, indicating a modal shift to other modes of transport and hence\u0000presumably more sustainable urban mobility.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction BatGPT-Chem：用于逆合成预测的大型基础模型

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-19 DOI: arxiv-2408.10285

Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Yang Yang, Hai Zhao

{"title":"BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction","authors":"Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Yang Yang, Hai Zhao","doi":"arxiv-2408.10285","DOIUrl":"https://doi.org/arxiv-2408.10285","url":null,"abstract":"Retrosynthesis analysis is pivotal yet challenging in drug discovery and\u0000organic chemistry. Despite the proliferation of computational tools over the\u0000past decade, AI-based systems often fall short in generalizing across diverse\u0000reaction types and exploring alternative synthetic pathways. This paper\u0000presents BatGPT-Chem, a large language model with 15 billion parameters,\u0000tailored for enhanced retrosynthesis prediction. Integrating chemical tasks via\u0000a unified framework of natural language and SMILES notation, this approach\u0000synthesizes extensive instructional data from an expansive chemical database.\u0000Employing both autoregressive and bidirectional training techniques across over\u0000one hundred million instances, BatGPT-Chem captures a broad spectrum of\u0000chemical knowledge, enabling precise prediction of reaction conditions and\u0000exhibiting strong zero-shot capabilities. Superior to existing AI methods, our\u0000model demonstrates significant advancements in generating effective strategies\u0000for complex molecules, as validated by stringent benchmark tests. BatGPT-Chem\u0000not only boosts the efficiency and creativity of retrosynthetic analysis but\u0000also establishes a new standard for computational tools in synthetic design.\u0000This development empowers chemists to adeptly address the synthesis of novel\u0000compounds, potentially expediting the innovation cycle in drug manufacturing\u0000and materials science. We release our trial platform at\u0000url{https://www.batgpt.net/dapp/chem}.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-layer diffusion model of photovoltaic installations 光伏装置的多层扩散模型

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-19 DOI: arxiv-2408.09904

Tomasz Weron, Janusz Szwabinski

引用次数: 0

Provide Proactive Reproducible Analysis Transparency with Every Publication 在每份出版物中主动提供可重复的分析透明度

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-17 DOI: arxiv-2408.09103

Paul Meijer, Nicole Howard, Jessica Liang, Autumn Kelsey, Sathya Subramanian, Ed Johnson, Paul Mariz, James Harvey, Madeline Ambrose, Vitalii Tereshchenko, Aldan Beaubien, Neelima Inala, Yousef Aggoune, Stark Pister, Anne Vetto, Melissa Kinsey, Tom Bumol, Ananda Goldrath, Xiaojun Li, Troy Torgerson, Peter Skene, Lauren Okada, Christian La France, Zach Thomson, Lucas Graybuck

引用次数: 0

ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment ADformer：基于脑电图的阿尔茨海默病评估多粒度变换器

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-17 DOI: arxiv-2409.00032

Yihe Wang, Nadia Mammone, Darina Petrovsky, Alexandros T. Tzallas, Francesco C. Morabito, Xiang Zhang

{"title":"ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment","authors":"Yihe Wang, Nadia Mammone, Darina Petrovsky, Alexandros T. Tzallas, Francesco C. Morabito, Xiang Zhang","doi":"arxiv-2409.00032","DOIUrl":"https://doi.org/arxiv-2409.00032","url":null,"abstract":"Electroencephalogram (EEG) has emerged as a cost-effective and efficient\u0000method for supporting neurologists in assessing Alzheimer's disease (AD).\u0000Existing approaches predominantly utilize handcrafted features or Convolutional\u0000Neural Network (CNN)-based methods. However, the potential of the transformer\u0000architecture, which has shown promising results in various time series analysis\u0000tasks, remains underexplored in interpreting EEG for AD assessment.\u0000Furthermore, most studies are evaluated on the subject-dependent setup but\u0000often overlook the significance of the subject-independent setup. To address\u0000these gaps, we present ADformer, a novel multi-granularity transformer designed\u0000to capture temporal and spatial features to learn effective EEG\u0000representations. We employ multi-granularity data embedding across both\u0000dimensions and utilize self-attention to learn local features within each\u0000granularity and global features among different granularities. We conduct\u0000experiments across 5 datasets with a total of 525 subjects in setups including\u0000subject-dependent, subject-independent, and leave-subjects-out. Our results\u0000show that ADformer outperforms existing methods in most evaluations, achieving\u0000F1 scores of 75.19% and 93.58% on two large datasets with 65 subjects and 126\u0000subjects, respectively, in distinguishing AD and healthy control (HC) subjects\u0000under the challenging subject-independent setup.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Partitioned Surrogates and Thompson Sampling for Multidisciplinary Bayesian Optimization 多学科贝叶斯优化的分区代理和汤普森取样

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-16 DOI: arxiv-2408.08691

Susanna Baars, Jigar Parekh, Ihar Antonau, Philipp Bekemeyer, Ulrich Römer

{"title":"Partitioned Surrogates and Thompson Sampling for Multidisciplinary Bayesian Optimization","authors":"Susanna Baars, Jigar Parekh, Ihar Antonau, Philipp Bekemeyer, Ulrich Römer","doi":"arxiv-2408.08691","DOIUrl":"https://doi.org/arxiv-2408.08691","url":null,"abstract":"The long runtime associated with simulating multidisciplinary systems\u0000challenges the use of Bayesian optimization for multidisciplinary design\u0000optimization (MDO). This is particularly the case if the coupled system is\u0000modeled in a partitioned manner and feedback loops, known as strong coupling,\u0000are present. This work introduces a method for Bayesian optimization in MDO\u0000called \"Multidisciplinary Design Optimization using Thompson Sampling\",\u0000abbreviated as MDO-TS. Instead of replacing the whole system with a surrogate,\u0000we substitute each discipline with such a Gaussian process. Since an entire\u0000multidisciplinary analysis is no longer required for enrichment, evaluations\u0000can potentially be saved. However, the objective and associated uncertainty are\u0000no longer analytically estimated. Since most adaptive sampling strategies\u0000assume the availability of these estimates, they cannot be applied without\u0000modification. Thompson sampling does not require this explicit availability.\u0000Instead, Thompson sampling balances exploration and exploitation by selecting\u0000actions based on optimizing random samples from the objective. We combine\u0000Thompson sampling with an approximate sampling strategy that uses random\u0000Fourier features. This approach produces continuous functions that can be\u0000evaluated iteratively. We study the application of this infill criterion to\u0000both an analytical problem and the shape optimization of a simple\u0000fluid-structure interaction example.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0