A Bayesian marked spatial point processes model for basketball shot chart

The success rate of a basketball shot may be higher at locations where a player makes more shots. For a marked spatial point process, this means that the mark and the intensity are associated. We propose a Bayesian joint model for the mark and the intensity of marked point processes, where the intensity is incorporated in the mark model as a covariate. Inferences are done with a Markov chain Monte Carlo algorithm. Two Bayesian model comparison criteria, the Deviance Information Criterion and the Logarithm of the Pseudo-Marginal Likelihood, were used to assess the model. The performances of the proposed methods were examined in extensive simulation studies. The proposed methods were applied to the shot charts of four players (Curry, Harden, Durant, and James) in the 2017--2018 regular season of the National Basketball Association to analyze their shot intensity in the field and the field goal percentage in detail. Application to the top 50 most frequent shooters in the season suggests that the field goal percentage and the shot intensity are positively associated for a majority of the players. The fitted parameters were used as inputs in a secondary analysis to cluster the players into different groups

Why do Victims Keep Returning to Abusive Intimate Partners

Purpose: The National Intimate Partner and Sexual Violence Survey (2010) states that more than 1 in 3 women (35.6%) and more than 1 in 4 men (28.5%) in the United States have experienced rape, physical violence, and/or stalking by an intimate partner in their lifetime. Most of the victims keep returning back to the abusive partners. The purpose of this study is to examine the following three questions: a) why would an individual keep on returning to an abusive relationship, b) how do attachment patterns enter into abusive relationships, and c) what individual characteristics make some more likely than others to be targets of abuse. Methods: The 480 participants filled out questionnaires measuring degrees of partner abuse, as well as measures of attachments and clinical issues, adverse childhood events, and measures tapping their own histories of abusive behaviors. Results: It was found that there are significant correlations between the scales measuring degrees of being a victim of partner abuse and scales of insecure attachments to the partner, partner addiction, their own partner abusive behavior, and several scales of the ACIQ. Conclusions The causes of why some are more likely to be victims of partner abuse are several fold and complex. These relations must be kept in mind when dealing with victims of partner abuse

Dense Medial Descriptors for Image Compression and Manipulation

With the unprecedented growth in resolution, diversity, and applications of image data, the demand for efficient and effective image compression and manipulation is ever increasing. In this thesis, we first explore the possibilities of dense skeletons, represented suitably by raster or vector models, using saliency maps or not, for lossy image compression. Qualitative and quantitative evaluations show that our skeleton-based image compression methods achieve better trade-offs of image quality vs compression than JPEG on a wide variety of image types, and produce comparable results to JPEG 2000 and BPG on certain image types. In the second part, we show how spline-based dense skeletons, when combined with morphological trees, can be effective and efficient tools for manipulating grayscale or color images by showing various applications, including super-resolution image generation, watermark removal, and image deformation. We finally conclude that dense skeletons are a valuable addition to the toolkit of image processing researchers and practitioners

Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services

It is universal to see people obtain knowledge on micro-blog services by asking others decision making questions. In this paper, we study the Jury Selection Problem(JSP) by utilizing crowdsourcing for decision making tasks on micro-blog services. Specifically, the problem is to enroll a subset of crowd under a limited budget, whose aggregated wisdom via Majority Voting scheme has the lowest probability of drawing a wrong answer(Jury Error Rate-JER). Due to various individual error-rates of the crowd, the calculation of JER is non-trivial. Firstly, we explicitly state that JER is the probability when the number of wrong jurors is larger than half of the size of a jury. To avoid the exponentially increasing calculation of JER, we propose two efficient algorithms and an effective bounding technique. Furthermore, we study the Jury Selection Problem on two crowdsourcing models, one is for altruistic users(AltrM) and the other is for incentive-requiring users(PayM) who require extra payment when enrolled into a task. For the AltrM model, we prove the monotonicity of JER on individual error rate and propose an efficient exact algorithm for JSP. For the PayM model, we prove the NP-hardness of JSP on PayM and propose an efficient greedy-based heuristic algorithm. Finally, we conduct a series of experiments to investigate the traits of JSP, and validate the efficiency and effectiveness of our proposed algorithms on both synthetic and real micro-blog data.Comment: VLDB201

How Yutori Education System Affected Japanese Society

The purpose of this paper is to analyze how yutori education system affected Japanese society in different fields. Yutori kyoiku (relaxed education), which was announced in 1998, is a change that the government decided to give younger Japanese:an education system with lower pressure. It was used in all Japanese schools for about twenty years. Through the growth of people who had yutori education, more Japanese people started to realize there are several negative effects caused by this policy in Japanese society. This paper will focus on the failure of yutori education and why it brought a different result from people’s expectations