Linguicism? English as the Gatekeeper in South Korea: A Qualitative Study about Mother\u27s Perspectives and Involvement in Their Child\u27s English Education

The rising demand to learn English has become a common phenomenon in many parts of the world. South Korea is no exception. English has become the most important foreign language in a largely monolingual society that rarely uses English in its daily context (J.S.-Y. Park, 2009). English has gained high status since the Korean War (1950-1953) in the Korean context (Grant & Lee, 2010). Education policies in the 1990s further strengthened its stance boosting the ‘English fever’. Because English stands as the gatekeeper to college admission, employment and promotion, Koreans invest heavily into English learning. But the financial expenditure of English education differs along the socioeconomic spectrum, creating inequality. Such inequalities have been referred as the English Divide “where English speakers have more power and access to resources while the non-English speaker are disadvantaged in many ways” (Tsuda, 2008). Learning English begins at a young age in Korea. For young students, mothers are the managers who decide where and how to get more English education. Korean mothers have been known for their fervor in their pursuit of their child’s academic success contributing to the competitive environment that aims for high academic achievement. In this study, I take a look into how mothers are contributing to reinforcing the stature of English. This study investigates 10 Korean mothers on their perspectives of English and how their perspectives influence their involvement in their child’s English education. Findings reveal that social demands, the effect of globalization and competition among students and mothers prompts them to closely monitor their child’s English education. Conversing with other mothers provide insightful feedback about hagwon and where their child is at compared to others but also creates anxiety and further competition

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

Quasi-Normal Modes of a Natural AdS Wormhole in Einstein-Born-Infeld Gravity

We study the matter perturbations of a new AdS wormhole in (3+1)-dimensional Einstein-Born-Infeld gravity, called "natural wormhole", which does not require exotic matters. We discuss the stability of the perturbations by numerically computing the quasi-normal modes (QNMs) of a massive scalar field in the wormhole background. We investigate the dependence of quasi-normal frequencies on the mass of scalar field as well as other parameters of the wormhole. It is found that the perturbations are always stable for the wormhole geometry which has the general relativity (GR) limit when the scalar field mass m satisfies a certain, tachyonic mass bound m^2 > m^2_* with m^2_* < 0, analogous to the Breitenlohner-Freedman (BF) bound in the global-AdS space, m^2_BF = 3 Lambda/4. It is also found that the BF-like bound m^2_* shifts by the changes of the cosmological constant Lambda or angular-momentum number l, with a level crossing between the lowest complex and pure-imaginary modes for zero angular momentum l = 0. Furthermore, it is found that the unstable modes can also have oscillatory parts as well as non-oscillatory parts depending on whether the real and imaginary parts of frequencies are dependent on each other or not, contrary to arguments in the literature. For wormhole geometries which do not have the GR limit, the BF-like bound does not occur and the perturbations are stable for arbitrary tachyonic and non-tachyonic masses, up to a critical mass m^2_c > 0 where the perturbations are completely frozen.Comment: Added comments and references, Accepted in EPJ

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