This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across two points (...) in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession. We hypothesize specific socioeconomic processes can be substantially reflected in the built environment of cities and, thus, render themselves visible at the street level. This paper describes the process of incorporating subjective visual ratings across two datasets of temporally separated street-view images, an algorithm, and a visualization tool. This work serves as a case study for utilizing AI and visualization techniques in a richer characterization of urban spaces that includes both objective metrics such as income (that operates at a broader scale) and subjective metrics such as perception of individuals (that operates at a narrower scale at specific locations). We outline an interdisciplinary methodology to test this hypothesis in streetscape data from Raleigh, NC, from 2008 to 2020. We describe the results of training algorithms that utilized image features with crowdsourced human perception ratings. We provide a comparison of the results with income data. The analysis and interpretation of this comparison provide insight into the challenges and opportunities for using AI technology in characterizing changes in urban environments. One challenge is the ability of human domain experts to interpret the output of algorithms through manipulation and to integrate these results into their workflow. This is addressed with a novel interface designed for interactive analysis and visualization. We conclude with a discussion of some of the benefits and limitations of integrating AI models in the human expert’s decision-making process in the presence of both subjective and objective metrics. (shrink)

We propose an approach for texture segmentation based on weak supervised learning. The weak supervision implies that the user marks only a single small patch for each class in the input image. These patches are used for training. We employ the method of graph cuts for the segmentation task. Our work demonstrates that even under such weak training, texture segmentation can be achieved efficiently and with good accuracy via graph cuts. Moreover, our approach uses a simpler feature representation than that (...) in similar contemporary segmentation approaches. We also provide a brief discussion indicating the good performance of our approach. We validate our method on various standard texture mosaics and also on segmentation of natural images with large texture variations. (shrink)

We conduct an experiment to investigate the degree to which deviations from exponential discounting can be accounted for by the hypothesis of hyperbolic discounting. Subjects are asked to choose between an earlier or later payoff in a series of 40 choice questions. Each question consists of a pair of monetary amounts determined by compounding a given base amount at a constant rate per period. Two bases (8 and 20 dollars), three compounding rates (low, medium and high) and three delays (2, (...) 4, and 6 weeks) are each used. There are also 2 initial periods (Today and 2 weeks) and there are two separate questionnaires, one with lower “realistic” compounding rates and the other with higher compounding rates, typical of those used in previous studies. We analyze the detailed patterns of choice in 6 groups of 6 related questions each (in which the base and rate is fixed but the initial period and delay varies), documenting the frequency of patterns consistent with exponential discounting and with hyperbolic discounting. We find that exponential discounting is the clear modal choice pattern in virtually all cases. Hyperbolic discounting is never the modal pattern (except in the sense that constant discounting is a special case of hyperbolic discounting). We also estimate a linear probability model that takes account of individual heterogeneity. The estimates show substantial increases in the probability of choosing the later option when the compounding rate increases, as one would expect. There are small, sometimes significant, increases in this probability when the delay is increased or the initial period is in the future. Such behavior is consistent with hyperbolic discounting, but can account for only a small proportion of choices. Overall, deviations from exponential discounting appear to be due to error, or to other effects not accounted for by hyperbolic discounting. Principal among these is an increase in later choices when the base is larger. (shrink)

The American Urological Association (AUA) and American College of Surgeons (ACS) codes of professionalism require surgeons to disclose the specific roles and responsibilities of trainees to patients during the informed consent process. The objective of this study is to analyze how these requirements are met by urology training programs. An anonymous electronic survey was distributed to the program directors (PDs) of the 143 Accreditation Council for Graduate Medical Education urology residency programs in the United States in 2021. Information was collected (...) regarding program demographics, aspects of the program’s consent process, and the disclosure to patients of the role and participation of residents in their surgery. There were 49 responses to the survey (34.3% response rate). Nearly 70 percent of PDs reported that attending physicians lead the consent process. The topics covered during consent discussion include possible complications (25%), expected recovery time (23%), length of the surgery (22%), the people involved (18%), and their specific roles (7%). Many PDs do not explicitly discuss trainee involvement (48.8%) or when a resident is to perform the majority of the case (87.8%). The majority of PDs (78.8%) communicate medical student involvement, but 73.2 percent reported having a patient decline participation of a trainee after describing their role. Despite the AUA and ACS codes of professionalism, many urologists do not disclose resident involvement in surgery to patients. Further discussions are needed to explore how to better balance resident education and patient autonomy. (shrink)

Twaalf inheemse en religieuze leiders uit verschillende werelddelen spreken zich in dit boek uit over de heiligheid van de natuur. Hun eeuwenoude wijsheden inspireren ons om anders te gaan denken over onze omgang met alles wat leeft. Aan het woord komen de Groenlandse sjamaan Angakkorsuaq, de Amerikaanse indianenleider Chief Lane Jr., masaileider Mwarabu, de boeddhistische geestelijke Shih, de dichtende islamgeleerde Ur Rehman Chishti, orthodox rabbijn Slifkin, Maya-priester Sac Coyoy, hindoe-prins Jhala, Aztekenleider Sanchez, leider van de Canadese Bear Clan Wawatie, (...) bisschop Smeets van Roermond en theoloog Valerio van Rochester Cathedral. 0Oeroude natuurwijsheden uit alle streken! (shrink)