Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are not well understood. We used Gaussian Undirected Graphs with the graphical lasso algorithm on rs-fMRI data from smokers and non-smokers to identify significant changes in brain connections. Our results indicate high stability in the estimated graphs and identify several brain regions significantly affected by smoking, providing valuable insights for future clinical research.
{"title":"Graphical Structural Learning of rs-fMRI data in Heavy Smokers","authors":"Yiru Gong, Qimin Zhang, Huili Zhen, Zheyan Liu, Shaohan Chen","doi":"arxiv-2409.08395","DOIUrl":"https://doi.org/arxiv-2409.08395","url":null,"abstract":"Recent studies revealed structural and functional brain changes in heavy\u0000smokers. However, the specific changes in topological brain connections are not\u0000well understood. We used Gaussian Undirected Graphs with the graphical lasso\u0000algorithm on rs-fMRI data from smokers and non-smokers to identify significant\u0000changes in brain connections. Our results indicate high stability in the\u0000estimated graphs and identify several brain regions significantly affected by\u0000smoking, providing valuable insights for future clinical research.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261152","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}
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth
Considering the growing prominence of production-level AI and the threat of adversarial attacks that can evade a model at run-time, evaluating the robustness of models to these evasion attacks is of critical importance. Additionally, testing model changes likely means deploying the models to (e.g. a car or a medical imaging device), or a drone to see how it affects performance, making un-tested changes a public problem that reduces development speed, increases cost of development, and makes it difficult (if not impossible) to parse cause from effect. In this work, we used survival analysis as a cloud-native, time-efficient and precise method for predicting model performance in the presence of adversarial noise. For neural networks in particular, the relationships between the learning rate, batch size, training time, convergence time, and deployment cost are highly complex, so researchers generally rely on benchmark datasets to assess the ability of a model to generalize beyond the training data. To address this, we propose using accelerated failure time models to measure the effect of hardware choice, batch size, number of epochs, and test-set accuracy by using adversarial attacks to induce failures on a reference model architecture before deploying the model to the real world. We evaluate several GPU types and use the Tree Parzen Estimator to maximize model robustness and minimize model run-time simultaneously. This provides a way to evaluate the model and optimise it in a single step, while simultaneously allowing us to model the effect of model parameters on training time, prediction time, and accuracy. Using this technique, we demonstrate that newer, more-powerful hardware does decrease the training time, but with a monetary and power cost that far outpaces the marginal gains in accuracy.
{"title":"A Cost-Aware Approach to Adversarial Robustness in Neural Networks","authors":"Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth","doi":"arxiv-2409.07609","DOIUrl":"https://doi.org/arxiv-2409.07609","url":null,"abstract":"Considering the growing prominence of production-level AI and the threat of\u0000adversarial attacks that can evade a model at run-time, evaluating the\u0000robustness of models to these evasion attacks is of critical importance.\u0000Additionally, testing model changes likely means deploying the models to (e.g.\u0000a car or a medical imaging device), or a drone to see how it affects\u0000performance, making un-tested changes a public problem that reduces development\u0000speed, increases cost of development, and makes it difficult (if not\u0000impossible) to parse cause from effect. In this work, we used survival analysis\u0000as a cloud-native, time-efficient and precise method for predicting model\u0000performance in the presence of adversarial noise. For neural networks in\u0000particular, the relationships between the learning rate, batch size, training\u0000time, convergence time, and deployment cost are highly complex, so researchers\u0000generally rely on benchmark datasets to assess the ability of a model to\u0000generalize beyond the training data. To address this, we propose using\u0000accelerated failure time models to measure the effect of hardware choice, batch\u0000size, number of epochs, and test-set accuracy by using adversarial attacks to\u0000induce failures on a reference model architecture before deploying the model to\u0000the real world. We evaluate several GPU types and use the Tree Parzen Estimator\u0000to maximize model robustness and minimize model run-time simultaneously. This\u0000provides a way to evaluate the model and optimise it in a single step, while\u0000simultaneously allowing us to model the effect of model parameters on training\u0000time, prediction time, and accuracy. Using this technique, we demonstrate that\u0000newer, more-powerful hardware does decrease the training time, but with a\u0000monetary and power cost that far outpaces the marginal gains in accuracy.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188134","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}
Adversarial actions and a rapid climate change are disrupting operations of infrastructure networks (e.g., energy, water, and transportation systems). Unaddressed disruptions lead to system-wide shutdowns, emphasizing the need for quick and robust identification methods. One significant disruption arises from edge changes (addition or deletion) in networks. We present an $ell_1$-norm regularized least-squares framework to identify multiple but sparse edge changes using noisy data. We focus only on networks that obey equilibrium equations, as commonly observed in the above sectors. The presence or lack of edges in these networks is captured by the sparsity pattern of the weighted, symmetric Laplacian matrix, while noisy data are node injections and potentials. Our proposed framework systematically leverages the inherent structure within the Laplacian matrix, effectively avoiding overparameterization. We demonstrate the robustness and efficacy of the proposed approach through a series of representative examples, with a primary emphasis on power networks.
{"title":"Resilient Infrastructure Network: Sparse Edge Change Identification via L1-Regularized Least Squares","authors":"Rajasekhar Anguluri","doi":"arxiv-2409.08304","DOIUrl":"https://doi.org/arxiv-2409.08304","url":null,"abstract":"Adversarial actions and a rapid climate change are disrupting operations of\u0000infrastructure networks (e.g., energy, water, and transportation systems).\u0000Unaddressed disruptions lead to system-wide shutdowns, emphasizing the need for\u0000quick and robust identification methods. One significant disruption arises from\u0000edge changes (addition or deletion) in networks. We present an $ell_1$-norm\u0000regularized least-squares framework to identify multiple but sparse edge\u0000changes using noisy data. We focus only on networks that obey equilibrium\u0000equations, as commonly observed in the above sectors. The presence or lack of\u0000edges in these networks is captured by the sparsity pattern of the weighted,\u0000symmetric Laplacian matrix, while noisy data are node injections and\u0000potentials. Our proposed framework systematically leverages the inherent\u0000structure within the Laplacian matrix, effectively avoiding\u0000overparameterization. We demonstrate the robustness and efficacy of the\u0000proposed approach through a series of representative examples, with a primary\u0000emphasis on power networks.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"393 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261153","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}
Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Cesar A. Villa, Catherine Leigh, Ryan Turner, Cameron Roberts, Kerrie Mengersen
The use of in-situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real-time data for science, the data are prone to technical anomalies that can undermine the trustworthiness of the data and the accuracy of statistical inferences, particularly in spatial and temporal analyses. Here we propose a framework for detecting anomalies in sensor data recorded in stream networks, which takes advantage of spatial and temporal autocorrelation to improve detection rates. The proposed framework involves the implementation of effective data imputation to handle missing data, alignment of time-series to address temporal disparities, and the identification of water quality events. We explore the effectiveness of a suite of state-of-the-art statistical methods including posterior predictive distributions, finite mixtures, and Hidden Markov Models (HMM). We showcase the practical implementation of automated anomaly detection in near-real time by employing a Bayesian recursive approach. This demonstration is conducted through a comprehensive simulation study and a practical application to a substantive case study situated in the Herbert River, located in Queensland, Australia, which flows into the Great Barrier Reef. We found that methods such as posterior predictive distributions and HMM produce the best performance in detecting multiple types of anomalies. Utilizing data from multiple sensors deployed relatively near one another enhances the ability to distinguish between water quality events and technical anomalies, thereby significantly improving the accuracy of anomaly detection. Thus, uncertainty and biases in water quality reporting, interpretation, and modelling are reduced, and the effectiveness of subsequent management actions improved.
{"title":"Unsupervised anomaly detection in spatio-temporal stream network sensor data","authors":"Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Cesar A. Villa, Catherine Leigh, Ryan Turner, Cameron Roberts, Kerrie Mengersen","doi":"arxiv-2409.07667","DOIUrl":"https://doi.org/arxiv-2409.07667","url":null,"abstract":"The use of in-situ digital sensors for water quality monitoring is becoming\u0000increasingly common worldwide. While these sensors provide near real-time data\u0000for science, the data are prone to technical anomalies that can undermine the\u0000trustworthiness of the data and the accuracy of statistical inferences,\u0000particularly in spatial and temporal analyses. Here we propose a framework for\u0000detecting anomalies in sensor data recorded in stream networks, which takes\u0000advantage of spatial and temporal autocorrelation to improve detection rates.\u0000The proposed framework involves the implementation of effective data imputation\u0000to handle missing data, alignment of time-series to address temporal\u0000disparities, and the identification of water quality events. We explore the\u0000effectiveness of a suite of state-of-the-art statistical methods including\u0000posterior predictive distributions, finite mixtures, and Hidden Markov Models\u0000(HMM). We showcase the practical implementation of automated anomaly detection\u0000in near-real time by employing a Bayesian recursive approach. This\u0000demonstration is conducted through a comprehensive simulation study and a\u0000practical application to a substantive case study situated in the Herbert\u0000River, located in Queensland, Australia, which flows into the Great Barrier\u0000Reef. We found that methods such as posterior predictive distributions and HMM\u0000produce the best performance in detecting multiple types of anomalies.\u0000Utilizing data from multiple sensors deployed relatively near one another\u0000enhances the ability to distinguish between water quality events and technical\u0000anomalies, thereby significantly improving the accuracy of anomaly detection.\u0000Thus, uncertainty and biases in water quality reporting, interpretation, and\u0000modelling are reduced, and the effectiveness of subsequent management actions\u0000improved.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187904","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}
J. Hart, I. Manickam, M. Gulian, L. Swiler, D. Bull, T. Ehrmann, H. Brown, B. Wagman, J. Watkins
Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially observed aerosol injections, such as those from volcanic eruptions, is fraught with uncertainties. This article presents a framework for stratospheric aerosol source inversion which accounts for background aerosol noise and earth system internal variability via a Bayesian approximation error approach. We leverage specially designed earth system model simulations using the Energy Exascale Earth System Model (E3SM). A comprehensive framework for data generation, data processing, dimension reduction, operator learning, and Bayesian inversion is presented where each component of the framework is designed to address particular challenges in stratospheric modeling on the global scale. We present numerical results using synthesized observational data to rigorously assess the ability of our approach to estimate aerosol sources and associate uncertainty with those estimates.
{"title":"Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification","authors":"J. Hart, I. Manickam, M. Gulian, L. Swiler, D. Bull, T. Ehrmann, H. Brown, B. Wagman, J. Watkins","doi":"arxiv-2409.06846","DOIUrl":"https://doi.org/arxiv-2409.06846","url":null,"abstract":"Stratospheric aerosols play an important role in the earth system and can\u0000affect the climate on timescales of months to years. However, estimating the\u0000characteristics of partially observed aerosol injections, such as those from\u0000volcanic eruptions, is fraught with uncertainties. This article presents a\u0000framework for stratospheric aerosol source inversion which accounts for\u0000background aerosol noise and earth system internal variability via a Bayesian\u0000approximation error approach. We leverage specially designed earth system model\u0000simulations using the Energy Exascale Earth System Model (E3SM). A\u0000comprehensive framework for data generation, data processing, dimension\u0000reduction, operator learning, and Bayesian inversion is presented where each\u0000component of the framework is designed to address particular challenges in\u0000stratospheric modeling on the global scale. We present numerical results using\u0000synthesized observational data to rigorously assess the ability of our approach\u0000to estimate aerosol sources and associate uncertainty with those estimates.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224681","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}
Coralie FritschIECL, SIMBA, Marie GrosdidierBioSP, Anne Gégout-PetitIECL, SIMBA, Benoit MarçaisIAM
Hymenoscyphus fraxineus is an invasive forest fungal pathogen that induces severe dieback in European ash populations. The spread of the disease has been closely monitored in France by the forest health survey system. We have developed a mechanisticstatistical model that describes the spread of the disease. It takes into account climate (summer temperature and spring rainfall), pathogen population dynamics (foliar infection, Allee effect induced by limited sexual partner encounters) and host density. We fitted this model using available disease reports. We estimated the parameters of our model, first identifying the appropriate ranges for the parameters, which led to a model reduction, and then using an adaptive multiple importance sampling algorithm for fitting. The model reproduces well the propagation observed in France over the last 20 years. In particular, it predicts the absence of disease impact in the south-east of the country and its weak development in the Garonne valley in south-west France. Summer temperature is the factor with the highest overall effect on disease spread, and explains the limited impact in southern France. Among the different temperature indices tested, the number of summer days with temperatures above 28{textdegree}C gave the best qualitative behavior and the best fit. In contrast, the Allee effect and the heterogeneity of spring precipitation did not strongly affect the overall expansion of H. fraxineus in France and could be neglected in the modeling process. The model can be used to infer the average annual dispersal of H. fraxineus in France.
欧洲白蜡疫霉菌（Hymenoscyphus fraxineus）是一种入侵性森林真菌病原体，会导致欧洲白蜡种群严重衰退。在法国，森林健康调查系统对该疾病的传播进行了密切监测。我们开发了一个描述该疾病传播的机理统计模型。该模型考虑了气候（夏季温度和春季降雨量）、病原体种群动态（叶面感染、有限的性伴侣接触引起的阿利效应）和宿主密度。我们利用现有的疾病报告对该模型进行了拟合。我们对模型的参数进行了估计，首先确定了参数的适当范围，从而缩小了模型，然后使用自适应多重重要性采样算法进行拟合。该模型很好地再现了过去 20 年在法国观察到的传播情况。特别是，该模型预测法国东南部没有疾病影响，而法国西南部的加龙河谷则发展较弱。夏季气温是对疾病传播总体影响最大的因素，也是法国南部影响有限的原因。在测试的不同温度指数中，夏季温度超过 28{textdegree}C 的天数的定性和拟合效果最好。相比之下，阿利效应和春季降水的异质性并没有对H.fraxineus在法国的总体扩展产生很大影响，因此在建模过程中可以忽略。该模型可用于推断 H. fraxineus 在法国的年平均扩散量。
{"title":"Mechanistic-statistical model for the expansion of ash dieback","authors":"Coralie FritschIECL, SIMBA, Marie GrosdidierBioSP, Anne Gégout-PetitIECL, SIMBA, Benoit MarçaisIAM","doi":"arxiv-2409.06273","DOIUrl":"https://doi.org/arxiv-2409.06273","url":null,"abstract":"Hymenoscyphus fraxineus is an invasive forest fungal pathogen that induces\u0000severe dieback in European ash populations. The spread of the disease has been\u0000closely monitored in France by the forest health survey system. We have\u0000developed a mechanisticstatistical model that describes the spread of the\u0000disease. It takes into account climate (summer temperature and spring\u0000rainfall), pathogen population dynamics (foliar infection, Allee effect induced\u0000by limited sexual partner encounters) and host density. We fitted this model\u0000using available disease reports. We estimated the parameters of our model,\u0000first identifying the appropriate ranges for the parameters, which led to a\u0000model reduction, and then using an adaptive multiple importance sampling\u0000algorithm for fitting. The model reproduces well the propagation observed in\u0000France over the last 20 years. In particular, it predicts the absence of\u0000disease impact in the south-east of the country and its weak development in the\u0000Garonne valley in south-west France. Summer temperature is the factor with the\u0000highest overall effect on disease spread, and explains the limited impact in\u0000southern France. Among the different temperature indices tested, the number of\u0000summer days with temperatures above 28{textdegree}C gave the best qualitative\u0000behavior and the best fit. In contrast, the Allee effect and the heterogeneity\u0000of spring precipitation did not strongly affect the overall expansion of H.\u0000fraxineus in France and could be neglected in the modeling process. The model\u0000can be used to infer the average annual dispersal of H. fraxineus in France.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188136","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}
Purpose: The teacher role in the classroom can explain important aspects of the student's school experience. The teacher-student relationship, a central dimension of social capital, influences students' engagement, and the teaching style plays an important role in student outcomes. But there is scarce literature that links teaching styles to teacher-student relationship. This article aims to: 1) analyze whether there is a relationship between teaching styles and the type of relationship perceived by students; 2) test whether this relationship is equally strong for any teaching style; and 3) determine the extent to which students' perceptions vary according to their profile. Design/methodology/approach: A structural equation model with four latent variables is estimated: two for the teacher-student relationship (emotional vs. educational) and two for the teaching styles (directive vs. participative), with information for 21126 sixth-grade primary-students in 2019 in Spain. Findings: Teacher-student relationships and teaching styles are interconnected. The participative style implies a better relationship. The perceptions of the teacher are heterogeneous, depending on gender (girls perceive clearer than boys) and with the educational background (children from lower educational background perceive both types of teaching styles more clearly). Originality/value: The analysis is based on the point of view of the addressee of the teacher's work, i.e. the student. It provides a model that can be replicated in any other education system. The latent variables, based on a periodically administered questionnaire, could be estimated with data from diagnostic assessments in other countries, which in turn would allow the formulation of context-specific educational policy proposals that take into account student feedback.
{"title":"Teacher-student relationship and teaching styles in primary education. A model of analysis","authors":"Maria-Eugenia Cardenal, Octavio-David Diaz-Santana, Sara-Maria Gonzalez-Betancor","doi":"arxiv-2409.06562","DOIUrl":"https://doi.org/arxiv-2409.06562","url":null,"abstract":"Purpose: The teacher role in the classroom can explain important aspects of\u0000the student's school experience. The teacher-student relationship, a central\u0000dimension of social capital, influences students' engagement, and the teaching\u0000style plays an important role in student outcomes. But there is scarce\u0000literature that links teaching styles to teacher-student relationship. This\u0000article aims to: 1) analyze whether there is a relationship between teaching\u0000styles and the type of relationship perceived by students; 2) test whether this\u0000relationship is equally strong for any teaching style; and 3) determine the\u0000extent to which students' perceptions vary according to their profile.\u0000Design/methodology/approach: A structural equation model with four latent\u0000variables is estimated: two for the teacher-student relationship (emotional vs.\u0000educational) and two for the teaching styles (directive vs. participative),\u0000with information for 21126 sixth-grade primary-students in 2019 in Spain.\u0000Findings: Teacher-student relationships and teaching styles are interconnected.\u0000The participative style implies a better relationship. The perceptions of the\u0000teacher are heterogeneous, depending on gender (girls perceive clearer than\u0000boys) and with the educational background (children from lower educational\u0000background perceive both types of teaching styles more clearly).\u0000Originality/value: The analysis is based on the point of view of the addressee\u0000of the teacher's work, i.e. the student. It provides a model that can be\u0000replicated in any other education system. The latent variables, based on a\u0000periodically administered questionnaire, could be estimated with data from\u0000diagnostic assessments in other countries, which in turn would allow the\u0000formulation of context-specific educational policy proposals that take into\u0000account student feedback.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188114","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}
This paper introduces a dependent toroidal distribution, to analyze astigmatism data following cataract surgery. Rather than utilizing the flat torus, we opt to represent the bivariate angular data on the surface of a curved torus, which naturally offers smooth edge identifiability and accommodates a variety of curvatures: positive, negative, and zero. Beginning with the area-uniform toroidal distribution on this curved surface, we develop a five-parameter-dependent toroidal distribution that harnesses its intrinsic geometry via the area element to model the distribution of two dependent circular random variables. We show that both marginal distributions are Cardioid, with one of the conditional variables also following a Cardioid distribution. This key feature enables us to propose a circular-circular regression model based on conditional expectations derived from circular moments. To address the high rejection rate (approximately 50%) in existing acceptance-rejection sampling methods for Cardioid distributions, we introduce an exact sampling method based on a probabilistic transformation. Additionally, we generate random samples from the proposed dependent toroidal distribution through suitable conditioning. This bivariate distribution and the regression model are applied to analyze astigmatism data arising in the follow-up of one and three months due to cataract surgery.
{"title":"Intrinsic geometry-inspired dependent toroidal distribution: Application to regression model for astigmatism data","authors":"Buddhananda Banerjee, Surojit Biswas","doi":"arxiv-2409.06229","DOIUrl":"https://doi.org/arxiv-2409.06229","url":null,"abstract":"This paper introduces a dependent toroidal distribution, to analyze\u0000astigmatism data following cataract surgery. Rather than utilizing the flat\u0000torus, we opt to represent the bivariate angular data on the surface of a\u0000curved torus, which naturally offers smooth edge identifiability and\u0000accommodates a variety of curvatures: positive, negative, and zero. Beginning\u0000with the area-uniform toroidal distribution on this curved surface, we develop\u0000a five-parameter-dependent toroidal distribution that harnesses its intrinsic\u0000geometry via the area element to model the distribution of two dependent\u0000circular random variables. We show that both marginal distributions are\u0000Cardioid, with one of the conditional variables also following a Cardioid\u0000distribution. This key feature enables us to propose a circular-circular\u0000regression model based on conditional expectations derived from circular\u0000moments. To address the high rejection rate (approximately 50%) in existing\u0000acceptance-rejection sampling methods for Cardioid distributions, we introduce\u0000an exact sampling method based on a probabilistic transformation. Additionally,\u0000we generate random samples from the proposed dependent toroidal distribution\u0000through suitable conditioning. This bivariate distribution and the regression\u0000model are applied to analyze astigmatism data arising in the follow-up of one\u0000and three months due to cataract surgery.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188135","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}
The information about pavement surface type is rarely available in road network databases of developing countries although it represents a cornerstone of the design of efficient mobility systems. This research develops an automatic classification pipeline for road pavement which makes use of satellite images to recognize road segments as paved or unpaved. The proposed methodology is based on an object-oriented approach, so that each road is classified by looking at the distribution of its pixels in the RGB space. The proposed approach is proven to be accurate, inexpensive, and readily replicable in other cities.
{"title":"Monitoring road infrastructures from satellite images in Greater Maputo: an object-oriented classification approach","authors":"Arianna Burzacchi, Matteo Landrò, Simone Vantini","doi":"arxiv-2409.06406","DOIUrl":"https://doi.org/arxiv-2409.06406","url":null,"abstract":"The information about pavement surface type is rarely available in road\u0000network databases of developing countries although it represents a cornerstone\u0000of the design of efficient mobility systems. This research develops an\u0000automatic classification pipeline for road pavement which makes use of\u0000satellite images to recognize road segments as paved or unpaved. The proposed\u0000methodology is based on an object-oriented approach, so that each road is\u0000classified by looking at the distribution of its pixels in the RGB space. The\u0000proposed approach is proven to be accurate, inexpensive, and readily replicable\u0000in other cities.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188115","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}
Simon N. Wood, Ernst C. Wit, Paul M. McKeigue, Danshu Hu, Beth Flood, Lauren Corcoran, Thea Abou Jawad
This paper discusses some statistical aspects of the U.K. Covid-19 pandemic response, focussing particularly on cases where we believe that a statistically questionable approach or presentation has had a substantial impact on public perception, or government policy, or both. We discuss the presentation of statistics relating to Covid risk, and the risk of the response measures, arguing that biases tended to operate in opposite directions, overplaying Covid risk and underplaying the response risks. We also discuss some issues around presentation of life loss data, excess deaths and the use of case data. The consequences of neglect of most individual variability from epidemic models, alongside the consequences of some other statistically important omissions are also covered. Finally the evidence for full stay at home lockdowns having been necessary to reverse waves of infection is examined, with new analyses provided for a number of European countries.
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