A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules

The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clustering and nonparametric conditional independence tests. However, training the GAN is notoriously difficult due to the issue of mode collapse, which refers to the lack of diversity among generated data. In this paper, we identify the reasons why the GAN suffers from this issue, and to address it, we propose a new formulation for the GAN based on randomized decision rules. In the new formulation, the discriminator converges to a fixed point while the generator converges to a distribution at the Nash equilibrium. We propose to train the GAN by an empirical Bayes-like method by treating the discriminator as a hyper-parameter of the posterior distribution of the generator. Specifically, we simulate generators from its posterior distribution conditioned on the discriminator using a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm, and update the discriminator using stochastic gradient descent along with simulations of the generators. We establish convergence of the proposed method to the Nash equilibrium. Apart from image generation, we apply the proposed method to nonparametric clustering and nonparametric conditional independence tests. A portion of the numerical results is presented in the supplementary material

Honeycomb oxide heterostructure: a new platform for Kitaev quantum spin liquid

Kitaev quantum spin liquid, massively quantum entangled states, is so scarce in nature that searching for new candidate systems remains a great challenge. Honeycomb heterostructure could be a promising route to realize and utilize such an exotic quantum phase by providing additional controllability of Hamiltonian and device compatibility, respectively. Here, we provide epitaxial honeycomb oxide thin film Na3Co2SbO6, a candidate of Kitaev quantum spin liquid proposed recently. We found a spin glass and antiferromagnetic ground states depending on Na stoichiometry, signifying not only the importance of Na vacancy control but also strong frustration in Na3Co2SbO6. Despite its classical ground state, the field-dependent magnetic susceptibility shows remarkable scaling collapse with a single critical exponent, which can be interpreted as evidence of quantum criticality. Its electronic ground state and derived spin Hamiltonian from spectroscopies are consistent with the predicted Kitaev model. Our work provides a unique route to the realization and utilization of Kitaev quantum spin liquid

Propagation Control of Octahedral Tilt in SrRuO(3)via Artificial Heterostructuring

Bonding geometry engineering of metal-oxygen octahedra is a facile way of tailoring various functional properties of transition metal oxides. Several approaches, including epitaxial strain, thickness, and stoichiometry control, have been proposed to efficiently tune the rotation and tilt of the octahedra, but these approaches are inevitably accompanied by unnecessary structural modifications such as changes in thin-film lattice parameters. In this study, a method to selectively engineer the octahedral bonding geometries is proposed, while maintaining other parameters that might implicitly influence the functional properties. A concept of octahedral tilt propagation engineering is developed using atomically designed SrRuO3/SrTiO3(SRO/STO) superlattices. In particular, the propagation of RuO(6)octahedral tilt within the SRO layers having identical thicknesses is systematically controlled by varying the thickness of adjacent STO layers. This leads to a substantial modification in the electromagnetic properties of the SRO layer, significantly enhancing the magnetic moment of Ru. This approach provides a method to selectively manipulate the bonding geometry of strongly correlated oxides, thereby enabling a better understanding and greater controllability of their functional properties

Preparation of large Cu3Sn single crystal by Czochralski method

Cu3Sn was recently predicted to host topological Dirac fermions, but related research is still in its infancy. The growth of large and high-quality Cu3Sn single crystals is, therefore, highly desired to investigate the possible topological properties. In this work, we report the single crystal growth of Cu3Sn by Czochralski (CZ) method. Crystal structure, chemical composition, and transport properties of Cu3Sn single crystals were analyzed to verify the crystal quality. Notably, compared to the mm-sized crystals from a molten Sn flux, the cm-sized crystals obtained by the CZ method are free from contamination from flux materials, paving the way for the follow-up works

경북 통발 조업 특성을 고려한 검은돌장어 생태환경 모니터링 시스템의 개발 및 실험

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Development of Remote Underwater Monitoring System for Image Acquisition and Fish Detection using Neural Network

This paper proposes a system that can remotely acquire and analyze underwater images in real-time. The proposed system utilizes Long Range communication for remote control, Long Term Evolution communication for realtime image transmission, and a neural network (NN) for underwater image analysis. As a result, the proposed system took less cost to install and operated for a long time. Two systems have been installed on an aquafarm and obtained underwater images continuously, and the NN could classify two fish species from acquired images. The proposed system can be applied for broader oceanic monitoring by attaching additional sensors and scaling up to acquire more data.1

Development of a wireless underwater system and Neural Network for real-time monitoring of coastal fish farms

This paper proposes a system to monitor fishery resources remotely in real-time. A wireless monitoring system was designed to capture underwater videos using a fish-eye camera and send them to a server via Long Range and Long Term Evolution communication. A convolutional neural network then detects and classifies fish from the captured videos. The designed system was installed on four coastal fish farms and recognized the two species of fish accurately.2

Zidovudine (AZT) and hepatic lipid accumulation: implication of inflammation, oxidative and endoplasmic reticulum stress mediators.

The clinical effectiveness of Zidovudine (AZT) is constrained due to its side-effects including hepatic steatosis and toxicity. However, the mechanism(s) of hepatic lipid accumulation in AZT-treated individuals is unknown. We hypothesized that AZT-mediated oxidative and endoplasmic reticulum (ER) stress may play a role in the AZT-induced hepatic lipid accumulation. AZT treatment of C57BL/6J female mice (400 mg/day/kg body weight, i.p.) for 10 consecutive days significantly increased hepatic triglyceride levels and inflammation. Markers of oxidative stress such as protein oxidation, nitration, glycation and lipid peroxidation were significantly higher in the AZT-treated mice compared to vehicle controls. Further, the levels of ER stress marker proteins like GRP78, p-PERK, and p-eIF2α were significantly elevated in AZT-treated mice. The level of nuclear SREBP-1c, a transcription factor involved in fat synthesis, was increased while significantly decreased protein levels of phospho-acetyl-CoA carboxylase, phospho-AMP kinase and PPARα as well as inactivation of 3-keto-acyl-CoA thiolase in the mitochondrial fatty acid β-oxidation pathway were observed in AZT-exposed mice compared to those in control animals. Collectively, these data suggest that elevated oxidative and ER stress plays a key role, at least partially, in lipid accumulation, inflammation and hepatotoxicity in AZT-treated mice

Real-time Wireless Underwater Monitoring System and Neural Network Towards Smart Management of Offshore Fish Farms

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