High-Dimensional Dependency Structure Learning for Physical Processes

In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Such processes are often synthetically characterized using PDEs (partial differential equations) and are observed in a variety of natural phenomena, including geoscience data capturing atmospheric and hydrological phenomena. Classical structure learning approaches such as the PC algorithm and variants are challenging to apply due to their high computational and sample requirements. Modern approaches, often based on sparse regression and variants, do come with finite sample guarantees, but are usually highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$ for sparsity inducing constraint or regularization. In this paper, we present ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning, which estimates an edge specific parameter $\lambda_{ij}$ in the first step, and uses these parameters to learn the structure in the second step. Both steps of our algorithm use (inexact) ADMM to solve suitable linear programs, and all iterations can be done in closed form in an efficient block parallel manner. We compare ACLIME-ADMM with baselines on both synthetic data simulated by partial differential equations (PDEs) that model advection-diffusion processes, and real data (50 years) of daily global geopotential heights to study information flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and competitive, usually better than the baselines especially on difficult problems. On real data, ACLIME-ADMM recovers the underlying structure of global atmospheric circulation, including switches in wind directions at the equator and tropics entirely from the data.Comment: 21 pages, 8 figures, International Conference on Data Mining 201

An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial datasets. Spatial data mining is important for societal applications in public health, public safety, agriculture, environmental science, climate etc. For example,in epidemiology, spatial data mining helps to find areas with a high concentrations of disease incidents to manage disease outbreaks. Computerized methods are needed to discover spatial patterns since the volume and velocity of spatial data exceeds the number of human experts available to analyze it. In addition, spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed data samples) assumption of traditional statistics and data mining methods. So, using traditional methods may miss patterns or may yield spurious patterns which are costly (e.g., stigmatization) in spatial applications. Also, there are other intrinsic challenges such as MAUP (Modifiable Areal Unit Problem) as illustrated by a current court case debating gerrymandering in elections. Spatial data mining considers the unique characteristics, and challenges of spatial data and domain knowledge of the target application to discover more accurate and interesting patterns.In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, colocation detection, spatial prediction and spatial outlier detection. Hotspot detection methods use domain information to model accurately more active and high density areas. Colocation detection methods find objects whose instances are in proximity of each other in a location. Spatial prediction approaches explicitly model neighborhood relationship of locations to predict target variables from input features. The goal of spatial outlier detection methods is to find data that are different from their neighbors

An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial data-sets (e.g., GPS trajectory of smartphones). Spatial data mining is societally important having applications in public health, public safety, climate science, etc. For example, in epidemiology, spatial data mining helps to find areas with a high concentration of disease incidents to manage disease outbreaks. Computational methods are needed to discover spatial patterns since the volume and velocity of spatial data exceed the ability of human experts to analyze it. Spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed) assumption of traditional statistic and data mining methods. Therefore, using traditional methods may miss patterns or may yield spurious patterns, which are costly in societal applications. Further, there are additional challenges such as MAUP (Modifiable Areal Unit Problem) as illustrated by a recent court case debating gerrymandering in elections. In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, colocation detection, spatial prediction, and spatial outlier detection. Hotspot detection methods use domain information to accurately model more active and high-density areas. Colocation detection methods find objects whose instances are in proximity to each other in a location. Spatial prediction approaches explicitly model the neighborhood relationship of locations to predict target variables from input features. Finally, spatial outlier detection methods find data that differ from their neighbors. Lastly, we describe future research and trends in spatial data mining

The Epidemiology of Aggression and Associated Factors among Iranian Adult Population: A National survey

Background: This survey was conducted to determine the level of aggression among the Iranian adult population and underlying predisposing factors. Study design: A cross-sectional study. Methods: This cross-sectional study included 10,957 participants, involving 23 out of the 31 provinces of Iran in 2019. The outcome of interest was aggression, evaluated by the Buss & Perry aggression questionnaire. The association between aggression and 34 demographic, behavioral, social, and cultural characteristics was assessed using simple and multiple linear regression. Results: The overall mean (SD) score of aggression was 77.10 (22.53). Based on the severity of aggression, the participants were categorized into four groups as follows: 2,464 (23.1%) nonaggressive, 4,692 (43.9%) mild, 3,071 (28.8%) moderate, and 454 (4.2%) severe aggressive. Aggression was more likely to occur in people with the following characteristics: younger ages, having several siblings, lower ranks of birth, having an intimate friend of the opposite sex, having an aggressive father/mother, history of parental divorce, interest in watching action/porn movies, listening to music, history of escape from home/school, using neuropsychiatric drugs, using illicit drugs, history of suicidal thoughts/attempt, and family conflict and hostility. Aggression was less likely to occur with the following characteristics: reading, regular physical exercise, the ability to control anger, regular prayer, adherence to avoid lying, respect to other people's rights, sexual satisfaction, and attachment to parents. Conclusions: A majority of the population has some degree of aggression. Aggression is a multifactorial behavior corresponding with several demographical, social, cultural, and religious factors, some of which back to early childhood events