Adversarial Dropout for Supervised and Semi-supervised Learning

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.Comment: submitted to AAAI-1

Hierarchical Context enabled Recurrent Neural Network for Recommendation

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our \textit{interest drift assumption}. As we suggest a new RNN structure, we support HCRNN with a complementary \textit{bi-channel attention} structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations

Use of National Currencies for Trade Settlement in East Asia: A Proposal

This paper develops a multilateral currency system where national currencies are used for trade settlement in East Asia, comprising the Association of Southeast Asian Nations (ASEAN) member countries, the People's Republic of China, Japan, and the Republic of Korea (ASEAN+3). The currency scheme is expected to mitigate the risks associated with independent attempts at internationalization in non-convertible currency countries. It could also reduce dependence on the US dollar, safeguard against financial spillovers from outside, and deepen trade and financial integration in the region. The patterns and structure of trade and financial openness suggest that East Asia has already established an economic base upon which it could launch such a system. The experience with renminbi internationalization will help the Republic of Korea and ASEAN-5 to emulate this strategy

Adversarial Dropout for Recurrent Neural Networks

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture that the dropout on RNNs could have been improved by adopting the adversarial concept. This paper investigates ways to improve the dropout for RNNs by utilizing intentionally generated dropout masks. Specifically, the guided dropout used in this research is called as adversarial dropout, which adversarially disconnects neurons that are dominantly used to predict correct targets over time. Our analysis showed that our regularizer, which consists of a gap between the original and the reconfigured RNNs, was the upper bound of the gap between the training and the inference phases of the random dropout. We demonstrated that minimizing our regularizer improved the effectiveness of the dropout for RNNs on sequential MNIST tasks, semi-supervised text classification tasks, and language modeling tasks.Comment: published in AAAI1

Fabrication and Evaluation of Mechanical Properties of CF/GNP Composites

AbstractCNT/CFRP (Carbon Nanotube/ Carbon Fiber Reinforced Plastic) composites and GNP/CFRP (Graphene Nano platelet/ Carbon Fiber Reinforced Plastic) have several excellent mechanical properties including, high strength, young's modulus, thermal conductivity, corrosion resistance, electronic shielding and so on. In this study, CNT/CFRP composites were manufactured by varying the CNT weight ratio as 2wt% and 3wt%, While GNP/CFRP composites were manufactured by varying the GNP weight ratio as 0.5wt% and 1wt%. The composites ware manufactured by mechanical method (3-roll-mill). Tensile, impact and wear tests were performed according to ASTM standards D638, D256 and D3181 respectively. It was observed that, increasing the CNT weight ratio improves the mechanical properties, e.g., tensile strength, impact and wear resistance

Physiological responses of two halophytic grass species under drought stress environment

The physiological responses of two halophytic grass species, Halopyrum mucronatum (L.) Staph. and Cenchrus ciliaris (L.), under drought stress were evaluated. Biomass accumulation, relative water content, free proline, H2O2 content, stomatal conductance, photosynthetic performance and quantum yield (Fv/Fm ratio) were studied. Under drought conditions, these halophytic plants expressed differential responses to water defi cit. Stomatal conductance and free proline content were higher in H. mucronatum than in C. ciliaris, while H2O2 content in H. mucronatum was substantially lower than in C. ciliaris. Performance index showed considerable sensitivity to a water deficit condition, more so in C. ciliaris than in H. mucronatum. Results were discussed in relation to comparative physiological performance and antioxidant enzymes activity of both halophytic grasses under drought stress

Korea's developmental program for superconductivity

Superconductivity research in Korea was firstly carried out in the late 70's by a research group in Seoul National University (SNU), who fabricated a small scale superconducting magnetic energy storage system under the financial support from Korea Electric Power Company (KEPCO). But a few researchers were involved in superconductivity research until the oxide high Tc superconductor was discovered by Bednorz and Mueller. After the discovery of YBaCuO superconductor operating above the boiling point of liquid nitrogen (77 K)(exp 2), Korean Ministry of Science and Technology (MOST) sponsored a special fund for the high Tc superconductivity research to universities and national research institutes by recognizing its importance. Scientists engaged in this project organized 'High Temperature Superconductivity Research Association (HITSRA)' for effective conducting of research. Its major functions are to coordinate research activities on high Tc superconductivity and organize the workshop for active exchange of information. During last seven years the major superconductivity research has been carried out through the coordination of HITSRA. The major parts of the Korea's superconductivity research program were related to high temperature superconductor and only a few groups were carrying out research on conventional superconductor technology, and Korea Atomic Energy Research Institute (KAERI) and Korea Electrotechnology Research Institute (KERI) have led this research. In this talk, the current status and future plans of superconductivity research in Korea will be reviewed based on the results presented in interim meeting of HITSRA, April 1-2, 1994. Taejeon, as well as the research activity of KAERI