arXiv - PHYS - Data Analysis, Statistics and Probability最新文献_第3页

Machine learning from limited data: Predicting biological dynamics under a time-varying external input 从有限数据中进行机器学习：预测时变外部输入下的生物动态

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-15 DOI: arxiv-2408.07998

Hoony Kang, Keshav Srinivasan, Wolfgang Losert

引用次数: 0

Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services 自适应行为人工智能：强化学习提升药学服务

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-14 DOI: arxiv-2408.07647

Ana Fernández del Río, Michael Brennan Leong, Paulo Saraiva, Ivan Nazarov, Aditya Rastogi, Moiz Hassan, Dexian Tang, África Periáñez

引用次数: 0

Bayesian analysis of nucleon-nucleon scattering data in pionless effective field theory 无先锋有效场理论中核子-核子散射数据的贝叶斯分析

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-05 DOI: arxiv-2408.02480

J. M. Bub, M. Piarulli, R. J. Furnstahl, S. Pastore, D. R. Phillips

{"title":"Bayesian analysis of nucleon-nucleon scattering data in pionless effective field theory","authors":"J. M. Bub, M. Piarulli, R. J. Furnstahl, S. Pastore, D. R. Phillips","doi":"arxiv-2408.02480","DOIUrl":"https://doi.org/arxiv-2408.02480","url":null,"abstract":"We perform Bayesian model calibration of two-nucleon ($NN$) low-energy\u0000constants (LECs) appearing in an $NN$ interaction based on pionless effective\u0000field theory (EFT). The calibration is carried out for potentials constructed\u0000using naive dimensional analysis in $NN$ relative momenta ($p$) up to\u0000next-to-leading order [NLO, $O(p^2)$] and next-to-next-to-next-to-leading order\u0000[N3LO, $O(p^4)$]. We consider two classes of pionless EFT potential: one that\u0000acts in all partial waves and another that is dominated by $s$-wave physics.\u0000The two classes produce broadly similar results for calibrations to $NN$ data\u0000up to $E_{rm lab}=5$ MeV. Our analysis accounts for the correlated\u0000uncertainties that arise from the truncation of the pionless EFT. We\u0000simultaneously estimate both the EFT LECs and the parameters that quantify the\u0000truncation error. This permits the first quantitative estimates of the pionless\u0000EFT breakdown scale, $Lambda_b$: the 95% intervals are $Lambda_b in\u0000[50.11,63.03]$ MeV at NLO and $Lambda_b in [72.27, 88.54]$ MeV at N3LO.\u0000Invoking naive dimensional analysis for the $NN$ potential, therefore, does not\u0000lead to consistent results across orders in pionless EFT. This exemplifies the\u0000possible use of Bayesian tools to identify inconsistencies in a proposed EFT\u0000power counting.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934987","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

KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example 我们能否改进 HEP 分类任务？柯尔莫哥洛夫-阿诺德网络在大型强子对撞机物理实例中的应用

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-05 DOI: arxiv-2408.02743

Johannes Erdmann, Florian Mausolf, Jan Lukas Späh

引用次数: 0

On marginals and profiled posteriors for cosmological parameter estimation 关于宇宙学参数估计的边际和剖面后验

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-04 DOI: arxiv-2408.02063

Martin Kerscher, Jochen Weller

引用次数: 0

TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics TrackSorter：基于变压器的排序算法，用于高能物理中的轨迹查找

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-07-31 DOI: arxiv-2407.21290

Yash Melkani, Xiangyang Ju

{"title":"TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics","authors":"Yash Melkani, Xiangyang Ju","doi":"arxiv-2407.21290","DOIUrl":"https://doi.org/arxiv-2407.21290","url":null,"abstract":"Track finding in particle data is a challenging pattern recognition problem\u0000in High Energy Physics. It takes as inputs a point cloud of space points and\u0000labels them so that space points created by the same particle have the same\u0000label. The list of space points with the same label is a track candidate. We\u0000argue that this pattern recognition problem can be formulated as a sorting\u0000problem, of which the inputs are a list of space points sorted by their\u0000distances away from the collision points and the outputs are the space points\u0000sorted by their labels. In this paper, we propose the TrackSorter algorithm: a\u0000Transformer-based algorithm for pattern recognition in particle data.\u0000TrackSorter uses a simple tokenization scheme to convert space points into\u0000discrete tokens. It then uses the tokenized space points as inputs and sorts\u0000the input tokens into track candidates. TrackSorter is a novel end-to-end track\u0000finding algorithm that leverages Transformer-based models to solve pattern\u0000recognition problems. It is evaluated on the TrackML dataset and has good track\u0000finding performance.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"413 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865163","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

Low dimensional fragment-based descriptors for property predictions in inorganic materials with machine learning 基于片段的低维描述符，利用机器学习预测无机材料的性能

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-07-30 DOI: arxiv-2407.21146

Md Mohaiminul Islam

{"title":"Low dimensional fragment-based descriptors for property predictions in inorganic materials with machine learning","authors":"Md Mohaiminul Islam","doi":"arxiv-2407.21146","DOIUrl":"https://doi.org/arxiv-2407.21146","url":null,"abstract":"In recent times, the use of machine learning in materials design and\u0000discovery has aided to accelerate the discovery of innovative materials with\u0000extraordinary properties, which otherwise would have been driven by a laborious\u0000and time-consuming trial-and-error process. In this study, a simple yet\u0000powerful fragment-based descriptor, Low Dimensional Fragment Descriptors\u0000(LDFD), is proposed to work in conjunction with machine learning models to\u0000predict important properties of a wide range of inorganic materials such as\u0000perovskite oxides, metal halide perovskites, alloys, semiconductor, and other\u0000materials system and can also be extended to work with interfaces. To predict\u0000properties, the generation of descriptors requires only the structural formula\u0000of the materials and, in presence of identical structure in the dataset,\u0000additional system properties as input. And the generation of descriptors\u0000involves few steps, encoding the formula in binary space and reduction of\u0000dimensionality, allowing easy implementation and prediction. To evaluate\u0000descriptor performance, six known datasets with up to eight components were\u0000compared. The method was applied to properties such as band gaps of perovskites\u0000and semiconductors, lattice constant of magnetic alloys, bulk/shear modulus of\u0000superhard alloys, critical temperature of superconductors, formation enthalpy\u0000and energy above hull convex of perovskite oxides. An advanced python-based\u0000data mining tool matminer was utilized for the collection of data. The\u0000prediction accuracies are equivalent to the quality of the training data and\u0000show comparable effectiveness as previous studies. This method should be\u0000extendable to any inorganic material systems which can be subdivided into\u0000layers or crystal structures with more than one atom site, and with the\u0000progress of data mining the performance should get better with larger and\u0000unbiased datasets.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"263 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865095","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

Automated Review Generation Method Based on Large Language Models 基于大型语言模型的自动评论生成方法

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-07-30 DOI: arxiv-2407.20906

Shican Wu, Xiao Ma, Dehui Luo, Lulu Li, Xiangcheng Shi, Xin Chang, Xiaoyun Lin, Ran Luo, Chunlei Pei, Zhi-Jian Zhao, Jinlong Gong

{"title":"Automated Review Generation Method Based on Large Language Models","authors":"Shican Wu, Xiao Ma, Dehui Luo, Lulu Li, Xiangcheng Shi, Xin Chang, Xiaoyun Lin, Ran Luo, Chunlei Pei, Zhi-Jian Zhao, Jinlong Gong","doi":"arxiv-2407.20906","DOIUrl":"https://doi.org/arxiv-2407.20906","url":null,"abstract":"Literature research, vital for scientific advancement, is overwhelmed by the\u0000vast ocean of available information. Addressing this, we propose an automated\u0000review generation method based on Large Language Models (LLMs) to streamline\u0000literature processing and reduce cognitive load. In case study on propane\u0000dehydrogenation (PDH) catalysts, our method swiftly generated comprehensive\u0000reviews from 343 articles, averaging seconds per article per LLM account.\u0000Extended analysis of 1041 articles provided deep insights into catalysts'\u0000composition, structure, and performance. Recognizing LLMs' hallucinations, we\u0000employed a multi-layered quality control strategy, ensuring our method's\u0000reliability and effective hallucination mitigation. Expert verification\u0000confirms the accuracy and citation integrity of generated reviews,\u0000demonstrating LLM hallucination risks reduced to below 0.5% with over 95%\u0000confidence. Released Windows application enables one-click review generation,\u0000aiding researchers in tracking advancements and recommending literature. This\u0000approach showcases LLMs' role in enhancing scientific research productivity and\u0000sets the stage for further exploration.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865168","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

Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection 将独立训练的机器学习模型应用于暗物质直接探测的贝叶斯技术

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-07-30 DOI: arxiv-2407.21008

David Cerdeno, Martin de los Rios, Andres D. Perez

{"title":"Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection","authors":"David Cerdeno, Martin de los Rios, Andres D. Perez","doi":"arxiv-2407.21008","DOIUrl":"https://doi.org/arxiv-2407.21008","url":null,"abstract":"We carry out a Bayesian analysis of dark matter (DM) direct detection data to\u0000determine particle model parameters using the Truncated Marginal Neural Ratio\u0000Estimation (TMNRE) machine learning technique. TMNRE avoids an explicit\u0000calculation of the likelihood, which instead is estimated from simulated data,\u0000unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This\u0000considerably speeds up, by several orders of magnitude, the computation of the\u0000posterior distributions, which allows to perform the Bayesian analysis of an\u0000otherwise computationally prohibitive number of benchmark points. In this\u0000article we demonstrate that, in the TMNRE framework, it is possible to include,\u0000combine, and remove different datasets in a modular fashion, which is fast and\u0000simple as there is no need to re-train the machine learning algorithm or to\u0000define a combined likelihood. In order to assess the performance of this\u0000method, we consider the case of WIMP DM with spin-dependent and independent\u0000interactions with protons and neutrons in a xenon experiment. After validating\u0000our results with MCMC, we employ the TMNRE procedure to determine the regions\u0000where the DM parameters can be reconstructed. Finally, we present CADDENA, a\u0000Python package that implements the modular Bayesian analysis of direct\u0000detection experiments described in this work.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865166","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

Universal New Physics Latent Space 通用新物理学潜空间

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-07-29 DOI: arxiv-2407.20315

Anna Hallin, Gregor Kasieczka, Sabine Kraml, André Lessa, Louis Moureaux, Tore von Schwartz, David Shih

引用次数: 0