arXiv - STAT - Applications最新文献_第4页

Discovery of Two Ultra-Diffuse Galaxies with Unusually Bright Globular Cluster Luminosity Functions via a Mark-Dependently Thinned Point Process (MATHPOP) 通过标记依赖性稀化点过程（MATHPOP）发现两个具有异常明亮球状星团光度函数的超漫反射星系

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.06040

Dayi Li, Gwendolyn Eadie, Patrick Brown, William Harris, Roberto Abraham, Pieter van Dokkum, Steven Janssens, Samantha Berek, Shany Danieli, Aaron Romanowsky, Joshua Speagle

{"title":"Discovery of Two Ultra-Diffuse Galaxies with Unusually Bright Globular Cluster Luminosity Functions via a Mark-Dependently Thinned Point Process (MATHPOP)","authors":"Dayi Li, Gwendolyn Eadie, Patrick Brown, William Harris, Roberto Abraham, Pieter van Dokkum, Steven Janssens, Samantha Berek, Shany Danieli, Aaron Romanowsky, Joshua Speagle","doi":"arxiv-2409.06040","DOIUrl":"https://doi.org/arxiv-2409.06040","url":null,"abstract":"We present textsc{Mathpop}, a novel method to infer the globular cluster\u0000(GC) counts in ultra-diffuse galaxies (UDGs) and low-surface brightness\u0000galaxies (LSBGs). Many known UDGs have a surprisingly high ratio of GC number\u0000to surface brightness. However, standard methods to infer GC counts in UDGs\u0000face various challenges, such as photometric measurement uncertainties, GC\u0000membership uncertainties, and assumptions about the GC luminosity functions\u0000(GCLFs). textsc{Mathpop} tackles these challenges using the mark-dependent\u0000thinned point process, enabling joint inference of the spatial and magnitude\u0000distributions of GCs. In doing so, textsc{Mathpop} allows us to infer and\u0000quantify the uncertainties in both GC counts and GCLFs with minimal\u0000assumptions. As a precursor to textsc{Mathpop}, we also address the data\u0000uncertainties coming from the selection process of GC candidates: we obtain\u0000probabilistic GC candidates instead of the traditional binary classification\u0000based on the color--magnitude diagram. We apply textsc{Mathpop} to 40 LSBGs in\u0000the Perseus cluster using GC catalogs from a textit{Hubble Space Telescope}\u0000imaging program. We then compare our results to those from an independent study\u0000using the standard method. We further calibrate and validate our approach\u0000through extensive simulations. Our approach reveals two LSBGs having GCLF\u0000turnover points much brighter than the canonical value with Bayes' factor being\u0000$sim4.5$ and $sim2.5$, respectively. An additional crude maximum-likelihood\u0000estimation shows that their GCLF TO points are approximately $0.9$~mag and\u0000$1.1$~mag brighter than the canonical value, with $p$-value $sim 10^{-8}$ and\u0000$sim 10^{-5}$, respectively.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188137","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

Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Misalignment in Emotional Perception 提高移情的准确性：纠正情感感知错位的惩罚性功能对齐法

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.05343

Linh H Nghiem, Jing Cao, Chul Moon

引用次数: 0

Learning about Spatial and Temporal Proximity using Tree-Based Methods 利用基于树的方法学习空间和时间接近性

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.06046

Ines Levin

引用次数: 0

UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection UAVDB：用于无人机探测的轨迹引导可适应边界框

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.06490

Yu-Hsi Chen

{"title":"UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection","authors":"Yu-Hsi Chen","doi":"arxiv-2409.06490","DOIUrl":"https://doi.org/arxiv-2409.06490","url":null,"abstract":"With the rapid development of drone technology, accurate detection of\u0000Unmanned Aerial Vehicles (UAVs) has become essential for applications such as\u0000surveillance, security, and airspace management. In this paper, we propose a\u0000novel trajectory-guided method, the Patch Intensity Convergence (PIC)\u0000technique, which generates high-fidelity bounding boxes for UAV detection tasks\u0000and no need for the effort required for labeling. The PIC technique forms the\u0000foundation for developing UAVDB, a database explicitly created for UAV\u0000detection. Unlike existing datasets, which often use low-resolution footage or\u0000focus on UAVs in simple backgrounds, UAVDB employs high-resolution video to\u0000capture UAVs at various scales, ranging from hundreds of pixels to nearly\u0000single-digit sizes. This broad-scale variation enables comprehensive evaluation\u0000of detection algorithms across different UAV sizes and distances. Applying the\u0000PIC technique, we can also efficiently generate detection datasets from\u0000trajectory or positional data, even without size information. We extensively\u0000benchmark UAVDB using YOLOv8 series detectors, offering a detailed performance\u0000analysis. Our findings highlight UAVDB's potential as a vital database for\u0000advancing UAV detection, particularly in high-resolution and long-distance\u0000tracking scenarios.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188140","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

Analyzing and Forecasting the Success in the Men's Ice Hockey World (Junior) Championships Using a Dynamic Ranking Model 利用动态排名模型分析和预测男子冰球世界（青少年）锦标赛的成功率

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.05714

Vladimír Holý

引用次数: 0

Modeling the Spatial Distributions of Macro Base Stations with Homogeneous Density: Theory and Application to Real Networks 同密度宏基站空间分布建模：真实网络的理论与应用

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.05468

Q. Gontier, C. Tsigros, F. Horlin, J. Wiart, C. Oestges, P. De Doncker

{"title":"Modeling the Spatial Distributions of Macro Base Stations with Homogeneous Density: Theory and Application to Real Networks","authors":"Q. Gontier, C. Tsigros, F. Horlin, J. Wiart, C. Oestges, P. De Doncker","doi":"arxiv-2409.05468","DOIUrl":"https://doi.org/arxiv-2409.05468","url":null,"abstract":"Stochastic geometry is a highly studied field in telecommunications as in\u0000many other scientific fields. In the last ten years in particular, theoretical\u0000knowledge has evolved a lot, whether for the calculation of metrics to\u0000characterize interference, coverage, energy or spectral efficiency, or exposure\u0000to electromagnetic fields. Many spatial point process models have been\u0000developed but are often left aside because of their unfamiliarity, their lack\u0000of tractability in favor of the Poisson point process or the regular lattice,\u0000easier to use. This article is intended to be a short guide presenting a\u0000complete and simple methodology to follow to infer a real stationary macro\u0000antenna network using tractable spatial models. The focus is mainly on\u0000repulsive point processes and in particular on determinantal point processes\u0000which are among the most tractable repulsive point processes. This methodology\u0000is applied on Belgian and French cell towers. The results show that for all\u0000stationary distributions in France and Belgium, the best inference model is the\u0000$beta$-Ginibre point process.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224682","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

Kramnik vs Nakamura: A Chess Scandal 克拉姆尼克对中村国际象棋丑闻

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.06739

Shiva Maharaj, Nick Polson, Vadim Sokolov

引用次数: 0

A Comprehensive Framework for Estimating Aircraft Fuel Consumption Based on Flight Trajectories 基于飞行轨迹估算飞机耗油量的综合框架

arXiv - STAT - Applications Pub Date : 2024-09-09 DOI: arxiv-2409.05429

Linfeng Zhang, Alex Bian, Changmin Jiang, Lingxiao Wu

{"title":"A Comprehensive Framework for Estimating Aircraft Fuel Consumption Based on Flight Trajectories","authors":"Linfeng Zhang, Alex Bian, Changmin Jiang, Lingxiao Wu","doi":"arxiv-2409.05429","DOIUrl":"https://doi.org/arxiv-2409.05429","url":null,"abstract":"Accurate calculation of aircraft fuel consumption plays an irreplaceable role\u0000in flight operations, optimization, and pollutant accounting. Calculating\u0000aircraft fuel consumption accurately is tricky because it changes based on\u0000different flying conditions and physical factors. Utilizing flight surveillance\u0000data, this study developed a comprehensive mathematical framework and\u0000established a link between flight dynamics and fuel consumption, providing a\u0000set of high-precision, high-resolution fuel calculation methods. It also allows\u0000other practitioners to select data sources according to specific needs through\u0000this framework. The methodology begins by addressing the functional aspects of\u0000interval fuel consumption. We apply spectral transformation techniques to mine\u0000Automatic Dependent Surveillance-Broadcast (ADS-B) data, identifying key\u0000aspects of the flight profile and establishing their theoretical relationships\u0000with fuel consumption. Subsequently, a deep neural network with tunable\u0000parameters is used to fit this multivariate function, facilitating\u0000high-precision calculations of interval fuel consumption. Furthermore, a\u0000second-order smooth monotonic interpolation method was constructed along with a\u0000novel estimation method for instantaneous fuel consumption. Numerical results\u0000have validated the effectiveness of the model. Using ADS-B and Aircraft\u0000Communications Addressing and Reporting System (ACARS) data from 2023 for\u0000testing, the average error of interval fuel consumption can be reduced to as\u0000low as $3.31%$, and the error in the integral sense of instantaneous fuel\u0000consumption is $8.86%$. These results establish this model as the state of the\u0000art, achieving the lowest estimation errors in aircraft fuel consumption\u0000calculations to date.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187909","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

Rating Players of Counter-Strike: Global Offensive Based on Plus/Minus value 为《反恐精英：全球攻势》玩家评分根据正/负值为《反恐精英：全球攻势

arXiv - STAT - Applications Pub Date : 2024-09-08 DOI: arxiv-2409.05052

Hongyu Xu, Sarat Moka

引用次数: 0

Moving from Machine Learning to Statistics: the case of Expected Points in American football 从机器学习到统计学：美式橄榄球的预期得分案例

arXiv - STAT - Applications Pub Date : 2024-09-07 DOI: arxiv-2409.04889

Ryan S. Brill, Ryan Yee, Sameer K. Deshpande, Abraham J. Wyner

引用次数: 0