arXiv - PHYS - Data Analysis, Statistics and Probability最新文献_第7页

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-26 DOI: arxiv-2406.18171

Hilda Sandström, Patrick Rinke

{"title":"Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications","authors":"Hilda Sandström, Patrick Rinke","doi":"arxiv-2406.18171","DOIUrl":"https://doi.org/arxiv-2406.18171","url":null,"abstract":"The formation of aerosol particles in the atmosphere impacts air quality and\u0000climate change, but many of the organic molecules involved remain unknown.\u0000Machine learning could aid in identifying these compounds through accelerated\u0000analysis of molecular properties and detection characteristics. However, such\u0000progress is hindered by the current lack of curated datasets for atmospheric\u0000molecules and their associated properties. To tackle this challenge, we propose\u0000a similarity analysis that connects atmospheric compounds to existing large\u0000molecular datasets used for machine learning development. We find a small\u0000overlap between atmospheric and non-atmospheric molecules using standard\u0000molecular representations in machine learning applications. The identified\u0000out-of-domain character of atmospheric compounds is related to their distinct\u0000functional groups and atomic composition. Our investigation underscores the\u0000need for collaborative efforts to gather and share more molecular-level\u0000atmospheric chemistry data. The presented similarity based analysis can be used\u0000for future dataset curation for machine learning development in the atmospheric\u0000sciences.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500971","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

Brownian friction dynamics: fluctuations in sliding distance 布朗摩擦动力学：滑动距离的波动

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-23 DOI: arxiv-2406.16139

Ruibin Xu, Feng Zhou, B. N. J. Persson

引用次数: 0

Gaussian approximation of dynamic cavity equations for linearly-coupled stochastic dynamics 线性耦合随机动力学的动态空腔方程的高斯近似值

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-20 DOI: arxiv-2406.14200

Mattia Tarabolo, Luca Dall'Asta

{"title":"Gaussian approximation of dynamic cavity equations for linearly-coupled stochastic dynamics","authors":"Mattia Tarabolo, Luca Dall'Asta","doi":"arxiv-2406.14200","DOIUrl":"https://doi.org/arxiv-2406.14200","url":null,"abstract":"Stochastic dynamics on sparse graphs and disordered systems often lead to\u0000complex behaviors characterized by heterogeneity in time and spatial scales,\u0000slow relaxation, localization, and aging phenomena. The mathematical tools and\u0000approximation techniques required to analyze these complex systems are still\u0000under development, posing significant technical challenges and resulting in a\u0000reliance on numerical simulations. We introduce a novel computational framework\u0000for investigating the dynamics of sparse disordered systems with continuous\u0000degrees of freedom. Starting with a graphical model representation of the\u0000dynamic partition function for a system of linearly-coupled stochastic\u0000differential equations, we use dynamic cavity equations on locally tree-like\u0000factor graphs to approximate the stochastic measure. Here, cavity marginals are\u0000identified with local functionals of single-site trajectories. Our primary\u0000approximation involves a second-order truncation of a small-coupling expansion,\u0000leading to a Gaussian form for the cavity marginals. For linear dynamics with\u0000additive noise, this method yields a closed set of causal integro-differential\u0000equations for cavity versions of one-time and two-time averages. These\u0000equations provide an exact dynamical description within the local tree-like\u0000approximation, retrieving classical results for the spectral density of sparse\u0000random matrices. Global constraints, non-linear forces, and state-dependent\u0000noise terms can be addressed using a self-consistent perturbative closure\u0000technique. The resulting equations resemble those of dynamical mean-field\u0000theory in the mode-coupling approximation used for fully-connected models.\u0000However, due to their cavity formulation, the present method can also be\u0000applied to ensembles of sparse random graphs and employed as a message-passing\u0000algorithm on specific graph instances.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500976","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

Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots 用于准确预测 CsPbCl3 Perovskite 量子点特性的机器学习模型

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-20 DOI: arxiv-2406.15515

Mehmet Sıddık Çadırcı, Musa Çadırcı

引用次数: 0

Encoder-Decoder Neural Networks in Interpretation of X-ray Spectra 解读 X 射线光谱的编码器-解码器神经网络

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-20 DOI: arxiv-2406.14044

Jalmari Passilahti, Anton Vladyka, Johannes Niskanen

引用次数: 0

On countering adversarial perturbations in graphs using error correcting codes 利用纠错码对抗图中的对抗性扰动

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-20 DOI: arxiv-2406.14245

Saif Eddin Jabari

引用次数: 0

On the analysis of two-time correlation functions: equilibrium vs non-equilibrium systems 双时相关函数分析：平衡与非平衡系统

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-18 DOI: arxiv-2406.12520

Anastasia Ragulskaya, Vladimir Starostin, Fajun Zhang, Christian Gutt, Frank Schreiber

引用次数: 0

Uncertainties in ROC (Receiver Operating Characteristic) Curves Derived from Counting Data 从计数数据得出的 ROC（接收者工作特征）曲线的不确定性

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-17 DOI: arxiv-2406.11396

M. P. Fewell

引用次数: 0

Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions 用均匀分布和高斯分布的残差模型预测系外行星特征

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-16 DOI: arxiv-2406.10771

Andrew Sweet

{"title":"Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions","authors":"Andrew Sweet","doi":"arxiv-2406.10771","DOIUrl":"https://doi.org/arxiv-2406.10771","url":null,"abstract":"The advancement of technology has led to rampant growth in data collection\u0000across almost every field, including astrophysics, with researchers turning to\u0000machine learning to process and analyze this data. One prominent example of\u0000this data in astrophysics is the atmospheric retrievals of exoplanets. In order\u0000to help bridge the gap between machine learning and astrophysics domain\u0000experts, the 2023 Ariel Data Challenge was hosted to predict posterior\u0000distributions of 7 exoplanetary features. The procedure outlined in this paper\u0000leveraged a combination of two deep learning models to address this challenge:\u0000a Multivariate Gaussian model that generates the mean and covariance matrix of\u0000a multivariate Gaussian distribution, and a Uniform Quantile model that\u0000predicts quantiles for use as the upper and lower bounds of a uniform\u0000distribution. Training of the Multivariate Gaussian model was found to be\u0000unstable, while training of the Uniform Quantile model was stable. An ensemble\u0000of uniform distributions was found to have competitive results during testing\u0000(posterior score of 696.43), and when combined with a multivariate Gaussian\u0000distribution achieved a final rank of third in the 2023 Ariel Data Challenge\u0000(final score of 681.57).","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500980","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

Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning 暗物质直接探测实验的启示：决策树与深度学习

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-06-14 DOI: arxiv-2406.10372

Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri

{"title":"Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning","authors":"Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri","doi":"arxiv-2406.10372","DOIUrl":"https://doi.org/arxiv-2406.10372","url":null,"abstract":"The detection of Dark Matter (DM) remains a significant challenge in particle\u0000physics. This study exploits advanced machine learning models to improve\u0000detection capabilities of liquid xenon time projection chamber experiments,\u0000utilizing state-of-the-art transformers alongside traditional methods like\u0000Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various\u0000data representations and find that simplified feature representations,\u0000particularly corrected S1 and S2 signals, retain critical information for\u0000classification. Our results show that while transformers offer promising\u0000performance, simpler models like XGBoost can achieve comparable results with\u0000optimal data representations. We also derive exclusion limits in the\u0000cross-section versus DM mass parameter space, showing minimal differences\u0000between XGBoost and the best performing deep learning models. The comparative\u0000analysis of different machine learning approaches provides a valuable reference\u0000for future experiments by guiding the choice of models and data representations\u0000to maximize detection capabilities.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500981","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