A Portrait of Rural Health in America

introduction to special issu

Migration, Marginalization, and Institutional Injustice in the Rural South

At the new beginning of the next 50 years of the Southern Rural Sociological Association (SRSA), the SRSA Presidential Address calls for attention to the issues that rural immigrants have faced – the everyday experiences of international migrants, their marginalization, and institutional injustice in rural America, particularly in the rural South. These issues have often been ignored or downplayed in the larger dialogue on rural issues and in the public debates about immigration policy, even though these social problems have been a perennial issue. Rural social scientists are challenged to be organic intellectuals who do not hide in the ivory tower of the academy, but rather use our intellect to diagnose the ills of society and help exploited rural migrants better understand their situation and the most fruitful strategies available to them to improve their lives and achieve a more just and humane society

Knowledge Distillation with Adversarial Samples Supporting Decision Boundary

Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary. Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation. To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary. Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary. Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance.Comment: Accepted to AAAI 201

Jenseits des culturul turn

Auslands-DaF leidet stets unter der Diskrepanz zwischen Theorie und Praxis. ‚Exportierte‘ Konzepte gelten in der Regel als Diktat und sind schwer zu integrieren. Mit der Tendenz der Kulturkundisierung der Germanistik wird in Korea Schritt gehalten. Der DaF-Unterricht wird aber nach wie vor dem Zufall überlassen. Davor warnt die Autorin und appelliert an die DaFler in Deutschland, die Bedürfnisse des Auslands wahrzunehmen

Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons

An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks that the activations of neurons, rather than their exact output values, play the most important role in forming classification friendly partitions of the hidden feature space. However, as far as we know, this aspect of neural networks has not been considered in the literature of knowledge transfer. In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons. For the distillation, we propose an activation transfer loss that has the minimum value when the boundaries generated by the student coincide with those by the teacher. Since the activation transfer loss is not differentiable, we design a piecewise differentiable loss approximating the activation transfer loss. By the proposed method, the student learns a separating boundary between activation region and deactivation region formed by each neuron in the teacher. Through the experiments in various aspects of knowledge transfer, it is verified that the proposed method outperforms the current state-of-the-art.Comment: Accepted to AAAI 201

Skeleton-based Action Recognition of People Handling Objects

In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses. In this framework, we construct skeletal graphs of reliable human poses by selectively sampling the informative frames in a video, which include human joints with high confidence scores obtained in pose estimation. The skeletal graphs generated from the sampled frames represent human poses related to the object position in both the spatial and temporal domains, and these graphs are used as inputs to the graph convolutional networks. Through experiments over an open benchmark and our own data sets, we verify the validity of our framework in that our method outperforms the state-of-the-art method for skeleton-based action recognition.Comment: Accepted in WACV 201