Transgender Erasure: Barriers facing transgender refugees in Canada

This paper explores the experiences of transgender refugee claimants in Canada’s refugee status determination system, using mixed methods: quantitative analysis of data obtained from the Immigration and Refugee Board (IRB), reviews of published and unpublished decisions, country condition documentation packages and IRB guidelines, as well as interviews with refugee lawyers. Using these methods, we explore how credibility arises in transgender refugee claims, noting the impact of medicalization and country conditions materials on transgender claims, and drawing parallels between medical gatekeeping and credibility assessments in refugee claims. We identify potential explanations for low recorded numbers of transgender claims as rooted in data-gathering and decision-making practices that are misaligned with transgender experiences, and we offer policy recommendations to overcome this mismatch. Though transgender refugee claims appear to be largely successful in recent years, longstanding patterns of exclusion and erasure as policy nevertheless lead many transgender claimants to experience the refugee determination process as traumatic and transphobic, resulting in unaccounted for complications and challenges to practice

Cavern Wall Support Requirements in a Hydro-Electric Project

Construction of a 23m wide, 57m high, and 210m long underground power house cavern is in progress as a part of the multi-purpose Sardar Sarovar Project in India. The rock mass around the cavern is basalt which is intruded by a number of dolerite dykes. In view of the high side walls of the cavern, and the presence of a 1 to 2m thick shear zone running across the cavern width, a comprehensive approach was worked out for estimation of the wall support requirements. The approach included estimation of the roof support requirements using the four available approaches, and comparison of these requirements with the roof support system actually provided, and established as safe and adequate by the instrumentation data of six years. A favourable comparison established the reliability of the approaches used, and the most reliable of these approaches, i.e., the Barton\u27s approach was then used with confidence for estimation of the wall support requirements

Standardization as emerging content in technology education at all levels of education

Integration of standardization into different levels of technology education has surfaced as a critical issue for educational practitioners and policy makers at national and regional (APEC, EU) level. In this paper, we describe and analyze empirical data collected from 118 educational experiences and practices about technology standards and standardization in 21 countries of a regional variety. Specifically, this research examines standardization education programs these countries have implemented, and explores suggestive indications for the design and development of an educational policy for standardization. Online surveys, offline interviews, face-to-face meetings and case studies have been used to determine the way these standardization education programs are segmented and implemented in different contexts. The findings are consolidated into a framework for standardization education. The framework presents an applicable combination of target groups (who), appropriate learning objectives (why), probable program operators (where), prospective contents modules (what), and preferred teaching methods (how). This framework may contribute to planning and implementing more inclusive standardization education programs

Cuilt: a Scalable, Mix-and-Match Framework for Local Iterative Approximate Best-Response Algorithms

Many real-world tasks can be modeled as constraint optimization problems. To ensure scalability and mapping to distributed scenarios, distributed constraint optimization problems (DCOPs) have been proposed, where each variable is locally controlled by its own agent. Most practical applications prefer approximate local iterative algorithms to reach a locally optimal and sufficiently good solution fast. Most implementations presented in the literature, however, only explored small-sized problems, typically up to 100 agents/variables. We implement CUILT, a scalable mix-and-match framework for Local Iterative Approximate Best-Response Algorithms for DCOPs, using the graph processing framework SIGNAL/COLLECT, where each agent is modeled as a vertex and communication pathways are represented as edges. Choosing this abstraction allows us to exploit the generic graph-oriented distribution/optimization heuristics and makes our proposed framework scalable, configurable, as well as extensible. We found that this approach allows us to scale to problems more than 3 orders of magnitude larger than results commonly published so far, to easily combine algorithms by mixing and matching, and to run the algorithms fast, in a parallel fashion

Exploring Hybrid Iterative Approximate Best-Response Algorithms for Solving DCOPs

Many real-world tasks can be modeled as constraint optimization problems. To ensure scalability and mapping to distributed scenarios, distributed constraint optimization problems (DCOPs) have been proposed, where each variable is locally controlled by its own agent. Most practical applications prefer approximate local iterative algorithms to reach a locally optimal and sufficiently good solution fast. The Iterative Approximate Best-Response Algorithms can be decomposed in three types of components and mixing different components allows to create hybrid algorithms. We implement a mix-and-match framework for these algorithms, using the graph processing framework SIGNAL/COLLECT, where each agent is modeled as a vertex and communication pathways are represented as edges. Choosing this abstraction allows us to exploit the generic graph-oriented distribution/optimization heuristics and makes our proposed framework configurable as well as extensible. It allows us to easily recombine the components, create and exhaustively evaluate possible hybrid algorithms

Improving Approximate Algorithms for DCOPs Using Ranks

Distributed Constraint Optimization Problems (DCOPs) have long been studied for problems that need scaling and are inherently distributed. As complete algorithms are exponential, approximate algorithms such as the Distributed Stochastic Algorithm (DSA) and Distributed Simulated Annealing (DSAN) have been proposed to reach solutions fast. Combining DSA with the PageRank algorithm has been studied before as a method to increase convergence speed, but without significant improvements in terms of solution quality when comparing with DSA. We propose a modification in terms of the rank calculation and we introduce three new algorithms, based on DSA and DSAN, to find approximate solutions to DCOPs. Our experiments with graph coloring problems and randomized DCOPs show good results in terms of solution quality in particular for the new DSAN based algorithms. They surpass the classical DSA and DSAN in the longer term, and are only outperformed in a few cases, by the new DSA based algorithm

Kristallnacht Presentation - Samuel Heider

Samuel Heider tells his experiences as a Holocaust survivor. In 1941, Heider and his family were deported to a small camp outside of Warsaw, Poland. Heider was soon separated from his family and was the only person in his family who was not killed by the Nazis. This lecture was hosted by Mark Verman and co-sponsored by the Zusman Chair in Judaic Studies, the Department of Religion, Wright State’s Honors Program, the Frydman Educational Resource Center and the Dayton Holocaust Resource Center