Causality-based Cost Allocation for Peer-to-Peer Energy Trading in Distribution System

While peer-to-peer energy trading has the potential to harness the capabilities of small-scale energy resources, a peer-matching process often overlooks power grid conditions, yielding increased losses, line congestion, and voltage problems. This imposes a great challenge on the distribution system operator (DSO), which can eventually limit peer-to-peer energy trading. To align the peer-matching process with the physical grid conditions, this paper proposes a cost causality-based network cost allocation method and the grid-aware peer-matching process. Building on the cost causality principle, the proposed model utilizes the network cost (loss, congestion, and voltage) as a signal to encourage peers to adjust their preferences ensuring that matches are more in line with grid conditions, leading to enhanced social welfare. Additionally, this paper presents mathematical proof showing the superiority of the causality-based cost allocation over existing methods.Comment: 7 pages, 7 figure

EMI: Exploration with Mutual Information

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at https://github.com/snu-mllab/EMI .Comment: Accepted and to appear at ICML 201

Cell Therapy in Huntington’s Disease

Huntington’s disease (HD) is a rare neurodegenerative disease inherited in an autosomal dominant pattern. Expanded cytosine-adenine-guanine (CAG) repeats (polyQ) in the huntingtin gene cause the aggregates of abnormally expanded polyQ-containing huntingtin protein, and striatal medium spiny neurons are shown to be the most vulnerable. Affected patients develop cognitive, motor, and psychiatric symptoms typically in middle age, and several pharmacological drugs are currently used for symptomatic relief. Since the effect of current therapies is very limited and there is no way to modify disease progression, there is an unmet need for developing new therapies for HD. Toxin or genetic rodent models are widely used for drug development, and large animal models are also available. Previous studies transplanting cells originating from embryonic or fetal striatal tissues, neural stem cells, mesenchymal stem cells, and induced pluripotent stem cells (iPSCs) in HD animal models have shown the possibilities of clinical trials. Because clinical trials performed using human fetal striatal cells have shown variable outcomes, future directions of cell therapy in HD should consider the reconstitution of a functional dynamic information-processing circuit without ectopic connections. Another major challenge is to achieve controlled differentiation of embryonic stem cells or iPSCs into specific neuronal phenotypes

Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data

Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is challenging due to the presence of complex issues such as label errors, under-representation, and outliers. In this paper, we propose a unified approach for identifying the problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data. We further introduce a visualization tool that provides contextual information of a data point in the feature-embedded space, serving as an effective tool for interactively diagnosing data. We evaluate the label error and outlier/out-of-distribution (OOD) detection performances of our approach on the large-scale image, speech, and language domain tasks, including ImageNet, ESC-50, and MNLI. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in debugging large-scale real-world datasets across various domains.Comment: preprin

Conducting Polymers with Functional Dopants and their Applications in Energy, Environmental Technology, and Nanotechnology

Development of novel conducting polymers (CPs) is expected to facilitate the advancement of functional materials used for energy, environmental, and nanotechnology. Recent research efforts are focused on doping CPs with functional dopants to enhance their performance or add additional functions that are not inherent in CPs. This review surveys literatures about the doped CPs focusing on the roles of functional dopants, unlike other reviews focusing on the development of new conducting polymer backbones. The functional dopants presented in this review include redox active molecules, carbon nanomaterials, biopolymers, and chelating molecules. Depending on the dopants and their physicochemical properties, the doped CPs can be used for a variety of applications such as polymer batteries, membranes for waste water treatment, and chemical sensors. A major challenge of the CPs is presented and the ways to overcome the challenge is also suggested for the future development of stable, high performance CPs.ope

Transit network expansion and accessibility implications: a case study of Gwangju metropolitan area, South Korea

Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Observation of a linear temperature dependence of the critical current density in a Ba_{0.63}K_{0.37}BiO_3 single crystal

For a Ba_{0.63}K_{0.37}BiO_3 single crystal with T_c=31 K, H_{c1}=750 Oe at 5 K, and dimensions 3x3x1 mm^3, the temperature and field dependences of magnetic hysteresis loops have been measured within 5-25 K in magnetic fields up to 6 Tesla. The critical current density is J_c(0)=1.5 x 10^5 A/cm^2 at zero field and 1 x 10^5 A/cm^2 at 1 kOe at 5 K. J_c decreases exponentially with increasing field up to 10 kOe. A linear temperature dependence of J_c is observed below 25 K, which differs from the exponential and the power-law temperature dependences in high-Tc superconductors including the BKBO. The linear temperature dependence can be regarded as an intrinsic effect in superconductors.Comment: RevTex, Physica C Vol. 341-348, 729 (2000

Synergistic effect of a defect-free graphene nanostructure as an anode material for lithium ion batteries

© 2019 by the authors. Licensee MDPI, Basel, Switzerland.Graphene nanosheets have been among the most promising candidates for a highperformance anode material to replace graphite in lithium ion batteries (LIBs). Studies in this area have mainly focused on nanostructured electrodes synthesized by graphene oxide (GO) or reduced graphene oxide (rGO) and surface modifications by a chemical treatment. Herein, we propose a cost-effective and reliable route for generating a defect-free, nanoporous graphene nanostructure (df-GNS) through the sequential insertion of pyridine into a potassium graphite intercalation compound (K-GIC). The as-prepared df-GNS preserves the intrinsic property of graphene without any crystal damage, leading to micro-/nano-porosity (microporosity: ~10–50 µm, nanoporosity: ~2– 20 nm) with a significantly large specific surface area. The electrochemical performance of the dfGNS as an anode electrode was assessed and showed a notably enhanced capacity, rate capability, and cycle stability, without fading in capacity or decaying. This is because of the optimal porosity, with perfect preservation of the graphene crystal, allowing faster ion access and a high amount of electron pathways onto the electrode. Therefore, our work will be very helpful for the development of anode and cathode electrodes with higher energy and power performance requirement