In Search of a Breakthrough for the Korea-EU FTA Negotiations

The purpose of this paper is to seek a breakthrough for the Korea-EU FTA negotiation in agriculture which started last year, but has not made much progress yet. The EU has been asking Korea to open its agricultural market to the same extent that was agreed upon in Korea’s free trade agreement with the U.S. while the Korean government firmly opposes the EU’s request, saying that FTA negotiations differ from multilateral trade talks as in the WTO. An examination of agricultural trade among Korea, the EU, and the U.S. shows that there may be many niche markets of which the EU can take advantage over other agricultural exporting countries including the U.S. Also, it is expected that the EU could gain much from its agricultural trade with Korea through an FTA, even without a market access level equal to one stipulated in the Korea-U.S. FTA accord. As far as agriculture is concerned, the slower does the talk proceed, the better to Korea, with the EU losing huge expected trade gains. It is suggested that the EU needs to be more flexible in the talks by focusing on a new strategy that can maximize trade diversion effects stemming not only from price but from non-price factors, rather than simply asking for the same level of market opening as in Korea-U.S. FTA. This would be the better way for the EU to become a winner in the battle for Korean agricultural market.Korea-EU FTA, Agricultural Trade, Trade Diversion Effects, International Relations/Trade,

Progressive evolution of tunneling characteristics of in-situ fabricated intrinsic Josephson junctions in Bi_2Sr_2CaCu_2O_{8+delta} single crystals

Stacks of a few intrinsic tunnel junctions were micro-fabricated on the surface of Bi-2212 single crystals. The number of junctions in a stack was tailored by progressively increasing the height of the stack by ion-beam etching, while its tunneling characteristics were measured in-situ in a vacuum chamber for temperatures down to ~13 K. Using this in-situ etching/measurements technique in a single piece of crystal, we systematically excluded any spurious effects arising from variations in the junction parameters and made clear analysis on the following properties of the surface and inner conducting planes. First, the tunneling resistance and the current-voltage curves are scaled by the surface junction resistance. Second, we confirm that the reduction in both the gap and the superconducting transition temperature of the surface conducting plane in contact with a normal metal is not caused by the variation in the doping level, but is caused by the proximity contact. Finally, the main feature of a junction is not affected by the presence of other junctions in a stack in a low bias region.Comment: 25 pages, 7 figures, submitted to Phys. Rev.

Suppressed Superconductivity of the Surface Conduction Layer in Bi2_2Sr2_2CaCu2_2O8+x_{8+x} Single Crystals Probed by {\it c}-Axis Tunneling Measurements

We fabricated small-size stacks on the surface of Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (BSCCO-2212) single crystals with the bulk transition temperature $T_c$$\simeq$90 K, each containing a few intrinsic Josephson junctions. Below a critical temperature $T_c'$ ($\ll$ $T_c$), we have observed a weakened Josephson coupling between the CuO$_2$ superconducting double layer at the crystal surface and the adjacent one located deeper inside a stack. The quasiparticle branch in the $IV$ data of the weakened Josephson junction (WJJ) fits well to the tunneling characteristics of a d-wave superconductor($'$)/insulator/d-wave superconductor (D$'$ID) junction. Also, the tunneling resistance in the range $T_c'$$<$$T$$<$$T_c$ agrees well with the tunneling in a normal metal/insulator/d-wave superconductor (NID) junction. In spite of the suppressed superconductivity at the surface layer the symmetry of the order parameter appears to remain unaffected.Comment: 13 pages, 6 figure

The Efficiency and Productivity Analysis of Large Logistics Providers Services in Korea

In the fierce competition at the global logistics markets, Korean logistics providers were deemed more vulnerable than global logistics providers in terms of the quality and price competitiveness. To strengthen their competitiveness, logistics providers in Korea have focused on delivering integrated logistics services. In this regard, the Korean government has enacted the “Integrated Logistics Industry Certification Act” in 2006 to assist integrated logistics providers to offer logistics services based on their specialization and differentiation. It has been several years since the system was implemented, and the evaluation of the system implementation was necessary. Hence, in our study, we attempt to examine the efficiency and productivity of fourteen certified Korean logistics providers employing the DEA (Data Envelopment Analysis) method with a five-year panel data since the inception of the Act. Through our static and dynamic analyses, We found that Pantos Logistics and HYUNDAI Glovis are running their businesses at the highest level of efficiency and Hanjin Transportation was the most stable company in their logistics operation

Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning

Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing more direct supervision on the discriminative feature. However, existing approaches lack an understanding of learning adversarially robust feature representation. In this paper, we propose a novel training framework called Robust Proxy Learning. In the proposed method, the model explicitly learns robust feature representations with robust proxies. To this end, firstly, we demonstrate that we can generate class-representative robust features by adding class-wise robust perturbations. Then, we use the class representative features as robust proxies. With the class-wise robust features, the model explicitly learns adversarially robust features through the proposed robust proxy learning framework. Through extensive experiments, we verify that we can manually generate robust features, and our proposed learning framework could increase the robustness of the DNNs.Comment: Accepted at IEEE Transactions on Information Forensics and Security (TIFS

Concomitant Laparoendoscopic Single-Site Surgery for Ureterolithotomy and Contralateral Renal Cyst Marsupialization

A 63-year-old woman presented with acute right-flank pain and left-flank pain. Computed tomography identified a right ureter stone and a left renal cyst. The patient underwent concomitant laparoendoscopic single-site surgery (LESS) for ureterolithotomy and renal cyst marsupialization with the use of an Alexis® wound retractor, which was inserted through the umbilical incision. Flexible laparoscopic instruments and conventional rigid instruments were used during LESS following a procedure similar to that used with conventional laparoscopic surgery without additional transcutaneous ports. LESS may be more efficient at treating bilateral diseases than is conventional laparoscopic surgery

A photonic-crystal optical antenna for extremely large local-field enhancement

We propose a novel design of an all-dielectric optical antenna based on photonic-band-gap confinement. Specifically, we have engineered the photonic-crystal dipole mode to have broad spectral response (Q ~70) and well-directed vertical-radiation by introducing a plane mirror below the cavity. Considerably large local electric-field intensity enhancement ~4,500 is expected from the proposed design for a normally incident planewave. Furthermore, an analytic model developed based on coupled-mode theory predicts that the electric-field intensity enhancement can easily be over 100,000 by employing reasonably high-Q (~10,000) resonators

Advancing Adversarial Training by Injecting Booster Signal

Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have been proposed, among which adversarial training has been demonstrated to be the most effective strategy. However, it has been known that adversarial training sometimes hurts natural accuracy. Then, many works focus on optimizing model parameters to handle the problem. Different from the previous approaches, in this paper, we propose a new approach to improve the adversarial robustness by using an external signal rather than model parameters. In the proposed method, a well-optimized universal external signal called a booster signal is injected into the outside of the image which does not overlap with the original content. Then, it boosts both adversarial robustness and natural accuracy. The booster signal is optimized in parallel to model parameters step by step collaboratively. Experimental results show that the booster signal can improve both the natural and robust accuracies over the recent state-of-the-art adversarial training methods. Also, optimizing the booster signal is general and flexible enough to be adopted on any existing adversarial training methods.Comment: Accepted at IEEE Transactions on Neural Networks and Learning System