arXiv - PHYS - Data Analysis, Statistics and Probability最新文献_第2页

Burst-tree structure and higher-order temporal correlations 突发树结构和高阶时间相关性

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-09-03 DOI: arxiv-2409.01674

Tibebe Birhanu, Hang-Hyun Jo

{"title":"Burst-tree structure and higher-order temporal correlations","authors":"Tibebe Birhanu, Hang-Hyun Jo","doi":"arxiv-2409.01674","DOIUrl":"https://doi.org/arxiv-2409.01674","url":null,"abstract":"Understanding characteristics of temporal correlations in time series is\u0000crucial for developing accurate models in natural and social sciences. The\u0000burst-tree decomposition method was recently introduced to reveal higher-order\u0000temporal correlations in time series in a form of an event sequence, in\u0000particular, the hierarchical structure of bursty trains of events for the\u0000entire range of timescales [Jo et al., Sci.~Rep.~textbf{10}, 12202 (2020)].\u0000Such structure has been found to be simply characterized by the burst-merging\u0000kernel governing which bursts are merged together as the timescale for\u0000detecting bursts increases. In this work, we study the effects of kernels on\u0000the higher-order temporal correlations in terms of burst size distributions,\u0000memory coefficients for bursts, and the autocorrelation function. We employ\u0000several kernels, including the constant, additive, and product kernels as well\u0000as those inspired by the empirical results. We find that kernels with\u0000preferential mixing lead to the heavy-tailed burst size distributions, while\u0000kernels with assortative mixing lead to positive correlations between burst\u0000sizes. The decaying exponent of the autocorrelation function depends not only\u0000on the kernel but also on the power-law exponent of the interevent time\u0000distribution. In addition, thanks to the analogy to the coagulation process,\u0000analytical solutions of burst size distributions for some kernels could be\u0000obtained.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179400","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

Categorising current-voltage curves in single-molecule junctions and their comparison to Single-Level Model 单分子结的电流-电压曲线分类及其与单级模型的比较

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-30 DOI: arxiv-2409.09051

Giovanna Angelis Schmidt

{"title":"Categorising current-voltage curves in single-molecule junctions and their comparison to Single-Level Model","authors":"Giovanna Angelis Schmidt","doi":"arxiv-2409.09051","DOIUrl":"https://doi.org/arxiv-2409.09051","url":null,"abstract":"This thesis investigates the mechanically controlled break junctions, with a\u0000particular emphasis on elucidating the behaviour of molecular currents at room\u0000temperature. The core of this experimental investigation involves a detailed\u0000analysis of conductance, examining how it varies over time and with changes in\u0000the gap between electrodes. Additionally, this study thoroughly evaluates\u0000transmission properties, coupling effects, and current characteristics. A\u0000pivotal aspect of the research was the meticulous current measurement, followed\u0000by carefully selecting optimal data sets. This process set the stage for an\u0000in-depth analysis of resonant tunnelling phenomena observed through a single\u0000channel. Notably, these experiments were conducted under open atmospheric\u0000conditions at room temperature. A significant finding from this study is the\u0000recognition that our current model requires refinement. This adjustment is\u0000necessary to encapsulate a broader spectrum of molecular transport mechanisms\u0000more accurately. Furthermore, this work significantly advances our\u0000comprehension of quantum effects in single-molecule junctions, particularly\u0000concerning similar molecules to Corannulene extending to some organometallics.\u0000One of the essential disclosures is the identification of deviations in the\u0000transport model, primarily attributable to electron-electron interactions. This\u0000insight is crucial as it paves the way for developing a more comprehensive and\u0000precise model, enhancing our understanding of molecular-scale electronic\u0000transport.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253356","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

Preservation of the Direct Photon and Neutral Meson Analysis in the PHENIX Experiment at RHIC 在 RHIC 的 PHENIX 实验中保留直接光子和中性介子分析

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-22 DOI: arxiv-2408.12072

Gabor David, Maxim Potekhin, Dmitri Smirnov

引用次数: 0

Active Learning of Molecular Data for Task-Specific Objectives 针对特定任务目标主动学习分子数据

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-20 DOI: arxiv-2408.11191

Kunal Ghosh, Milica Todorović, Aki Vehtari, Patrick Rinke

{"title":"Active Learning of Molecular Data for Task-Specific Objectives","authors":"Kunal Ghosh, Milica Todorović, Aki Vehtari, Patrick Rinke","doi":"arxiv-2408.11191","DOIUrl":"https://doi.org/arxiv-2408.11191","url":null,"abstract":"Active learning (AL) has shown promise for being a particularly\u0000data-efficient machine learning approach. Yet, its performance depends on the\u0000application and it is not clear when AL practitioners can expect computational\u0000savings. Here, we carry out a systematic AL performance assessment for three\u0000diverse molecular datasets and two common scientific tasks: compiling compact,\u0000informative datasets and targeted molecular searches. We implemented AL with\u0000Gaussian processes (GP) and used the many-body tensor as molecular\u0000representation. For the first task, we tested different data acquisition\u0000strategies, batch sizes and GP noise settings. AL was insensitive to the\u0000acquisition batch size and we observed the best AL performance for the\u0000acquisition strategy that combines uncertainty reduction with clustering to\u0000promote diversity. However, for optimal GP noise settings, AL did not\u0000outperform randomized selection of data points. Conversely, for targeted\u0000searches, AL outperformed random sampling and achieved data savings up to 64%.\u0000Our analysis provides insight into this task-specific performance difference in\u0000terms of target distributions and data collection strategies. We established\u0000that the performance of AL depends on the relative distribution of the target\u0000molecules in comparison to the total dataset distribution, with the largest\u0000computational savings achieved when their overlap is minimal.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179256","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

Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization 通过约束优化精细结构拟合提高电子能量损失光谱量化的精度和准确性

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-19 DOI: arxiv-2408.11870

Daen Jannis, Wouter Van den Broek, Zezhong Zhang, Sandra Van Aert, Jo Verbeeck

引用次数: 0

KAN 2.0: Kolmogorov-Arnold Networks Meet Science KAN 2.0：柯尔莫哥洛夫-阿诺德网络与科学相遇

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-19 DOI: arxiv-2408.10205

Ziming Liu, Pingchuan Ma, Yixuan Wang, Wojciech Matusik, Max Tegmark

引用次数: 0

Two points are enough 两点就够了

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-19 DOI: arxiv-2408.11872

Hao Liu, Yanbin Zhao, Huarong Zheng, Xiulin Fan, Zhihua Deng, Mengchi Chen, Xingkai Wang, Zhiyang Liu, Jianguo Lu, Jian Chen

{"title":"Two points are enough","authors":"Hao Liu, Yanbin Zhao, Huarong Zheng, Xiulin Fan, Zhihua Deng, Mengchi Chen, Xingkai Wang, Zhiyang Liu, Jianguo Lu, Jian Chen","doi":"arxiv-2408.11872","DOIUrl":"https://doi.org/arxiv-2408.11872","url":null,"abstract":"Prognosis and diagnosis play an important role in accelerating the\u0000development of lithium-ion batteries, as well as reliable and long-life\u0000operation. In this work, we answer an important question: What is the minimum\u0000amount of data required to extract features for accurate battery prognosis and\u0000diagnosis? Based on the first principle, we successfully extracted the best\u0000two-point feature (BTPF) for accurate battery prognosis and diagnosis using the\u0000fewest data points (only two) and the simplest feature selection method\u0000(Pearson correlation coefficient). The BTPF extraction method is tested on 820\u0000cells from 6 open-source datasets (covering five different chemistry types,\u0000seven manufacturers, and three data types). It achieves comparable accuracy to\u0000state-of-the-art features in both prognosis and diagnosis tasks. This work\u0000challenges the cognition of existing studies on the difficulty of battery\u0000prognosis and diagnosis tasks, subverts the fixed pattern of establishing\u0000prognosis and diagnosis methods for complex dynamic systems through deliberate\u0000feature engineering, highlights the promise of data-driven methods for field\u0000battery prognosis and diagnosis applications, and provides a new benchmark for\u0000future studies.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179254","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

Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics 大规模预训练和微调，实现粒子物理中的高效射流分类

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-18 DOI: arxiv-2408.09343

Zihan Zhao, Farouk Mokhtar, Raghav Kansal, Haoyang Li, Javier Duarte

{"title":"Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics","authors":"Zihan Zhao, Farouk Mokhtar, Raghav Kansal, Haoyang Li, Javier Duarte","doi":"arxiv-2408.09343","DOIUrl":"https://doi.org/arxiv-2408.09343","url":null,"abstract":"This study introduces an innovative approach to analyzing unlabeled data in\u0000high-energy physics (HEP) through the application of self-supervised learning\u0000(SSL). Faced with the increasing computational cost of producing high-quality\u0000labeled simulation samples at the CERN LHC, we propose leveraging large volumes\u0000of unlabeled data to overcome the limitations of supervised learning methods,\u0000which heavily rely on detailed labeled simulations. By pretraining models on\u0000these vast, mostly untapped datasets, we aim to learn generic representations\u0000that can be finetuned with smaller quantities of labeled data. Our methodology\u0000employs contrastive learning with augmentations on jet datasets to teach the\u0000model to recognize common representations of jets, addressing the unique\u0000challenges of LHC physics. Building on the groundwork laid by previous studies,\u0000our work demonstrates the critical ability of SSL to utilize large-scale\u0000unlabeled data effectively. We showcase the scalability and effectiveness of\u0000our models by gradually increasing the size of the pretraining dataset and\u0000assessing the resultant performance enhancements. Our results, obtained from\u0000experiments on two datasets -- JetClass, representing unlabeled data, and Top\u0000Tagging, serving as labeled simulation data -- show significant improvements in\u0000data efficiency, computational efficiency, and overall performance. These\u0000findings suggest that SSL can greatly enhance the adaptability of ML models to\u0000the HEP domain. This work opens new avenues for the use of unlabeled data in\u0000HEP and contributes to a better understanding the potential of SSL for\u0000scientific discovery.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179259","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

Euler Characteristic Surfaces: A Stable Multiscale Topological Summary of Time Series Data 欧拉特征曲面：时间序列数据的稳定多尺度拓扑总结

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-18 DOI: arxiv-2408.09400

Anamika Roy, Atish J. Mitra, Tapati Dutta

引用次数: 0

NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance NEAR：机器学习模型性能的免训练预估器

arXiv - PHYS - Data Analysis, Statistics and Probability Pub Date : 2024-08-16 DOI: arxiv-2408.08776

Raphael T. Husistein, Markus Reiher, Marco Eckhoff

{"title":"NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance","authors":"Raphael T. Husistein, Markus Reiher, Marco Eckhoff","doi":"arxiv-2408.08776","DOIUrl":"https://doi.org/arxiv-2408.08776","url":null,"abstract":"Artificial neural networks have been shown to be state-of-the-art machine\u0000learning models in a wide variety of applications, including natural language\u0000processing and image recognition. However, building a performant neural network\u0000is a laborious task and requires substantial computing power. Neural\u0000Architecture Search (NAS) addresses this issue by an automatic selection of the\u0000optimal network from a set of potential candidates. While many NAS methods\u0000still require training of (some) neural networks, zero-cost proxies promise to\u0000identify the optimal network without training. In this work, we propose the\u0000zero-cost proxy Network Expressivity by Activation Rank (NEAR). It is based on\u0000the effective rank of the pre- and post-activation matrix, i.e., the values of\u0000a neural network layer before and after applying its activation function. We\u0000demonstrate the cutting-edge correlation between this network score and the\u0000model accuracy on NAS-Bench-101 and NATS-Bench-SSS/TSS. In addition, we present\u0000a simple approach to estimate the optimal layer sizes in multi-layer\u0000perceptrons. Furthermore, we show that this score can be utilized to select\u0000hyperparameters such as the activation function and the neural network weight\u0000initialization scheme.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179260","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