Challenges Facing East Asian Immigrant Children in Sexual Abuse Cases

Immigrants from East Asia make up 14.21% of the total number of immigrants in Canada. These families face many challenges as they acculturate to North America but, sadly, some of these children may be at risk for sexual abuse. In this position paper, we outline the ways in which East Asian children are at a particular disadvantage when considering prosecution of those who perpetrate abuse compared to Western non-immigrant children. We focus specifically on two areas of concern: 1) Cultural differences that can impact the disclosure of sexual abuse; and, 2) Language differences which reduce the chances that perpetrators will be prosecuted for sexual abuse. The consequences for East Asian immigrant youth who allege (or are suspected) that they are victims of abuse are serious. East Asian children face an uphill battle to see justice in sexual abuse cases. Thus, a significant portion of immigrant children will not see their abusers punished and, worse, the knowledge that prosecution is unlikely makes East Asian immigrant children a targeted population for those who abuse

Deep Extreme Multi-label Learning

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result

Thermal strength and transient dynamics analysis of a diesel engine piston

As the research object is to set up four-stroke direct injection diesel engine pistons, with the research method of thermo-mechanical coupling, a three-dimensional finite element analysis model is established. Calculate transient heat transfer coefficient and the transient gas temperature. Piston stress is calculated under the conditions of thermal load, mechanical load and the thermal-mechanical coupling load. Results show that, the piston safety, the main cause of the piston deformation and the great stress is the temperature so it is feasible to further decrease the piston temperature with structure optimization

Thermo-Mechanical Coupling Analysis of a Diesel Engine Piston

As the research object to a certain type of diesel engine pistons, a three-dimensional finite element analysis model is established. Piston stress is calculated under the conditions of thermal load, mechanical load and coupled load. Results show that, the main cause of the piston safety, the piston deformation and the great stress is the temperature, so it is feasible to further decrease the piston temperature with structure optimization

Discretize Relaxed Solution of Spectral Clustering via a Non-Heuristic Algorithm

Spectral clustering and its extensions usually consist of two steps: (1) constructing a graph and computing the relaxed solution; (2) discretizing relaxed solutions. Although the former has been extensively investigated, the discretization techniques are mainly heuristic methods, e.g., k-means, spectral rotation. Unfortunately, the goal of the existing methods is not to find a discrete solution that minimizes the original objective. In other words, the primary drawback is the neglect of the original objective when computing the discrete solution. Inspired by the first-order optimization algorithms, we propose to develop a first-order term to bridge the original problem and discretization algorithm, which is the first non-heuristic to the best of our knowledge. Since the non-heuristic method is aware of the original graph cut problem, the final discrete solution is more reliable and achieves the preferable loss value. We also theoretically show that the continuous optimum is beneficial to discretization algorithms though simply finding its closest discrete solution is an existing heuristic algorithm which is also unreliable. Sufficient experiments significantly show the superiority of our method