CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape

Improving Strength and Microstructure of SiC Reticulated Porous Ceramic through In-Situ Generation of SiC Whiskers within Hollow Voids

SiC Reticulated Porous Ceramic with Excellent Strength and High-Density Ceramic Struts Was Successfully Prepared using the Polymer Replica Method, Followed by Pressureless Sintering under a Buried Charcoal Atmosphere. First, a Polyurethane (PU) Template Was Coated with a Si Slurry and Then a SiC-Containing Slurry, and Subsequently Heated under the Buried Charcoal Atmosphere. to Ensure Excellent Coating Ability of the Slurries, the Viscosity, Thixotropy, and Yield Stresses of the Si Slurry Were Optimized by Adjusting the Content of the Thickening Agent. during Heating, Si in the Coating Layer Reacted with the Residual C and CO Gas from the PU Template and Buried Charcoal, Forming SiC Whiskers that Filled Hollow Voids within the SiC Struts. Additionally, Catalyst Ferric Nitrate Was Added to the Si Slurry to Promote the Generation and Growth of SiC Whiskers. as a Result, When Compared to the Untreated SiC Reticulated Porous Ceramic, the SiC Reticulated Porous Ceramic Pre-Coated with Si Layers Exhibited Significant Improvements in Mechanical Strength and Thermal Shock Resistance, Despite Minor Differences in Porosity. Furthermore, an Industrial Test Conducted in the Copper Smelting Industry Showed that the Structure of SiC Reticulated Porous Ceramic, Prepared in This Study and Used as Filters, Remained Intact Even after 7 Days of Continuous Use. Meanwhile, a Significant Number of Inclusions Was Adhered to the Surfaces of the Filters. Therefore, the Processes Combined with In-Situ Generation of SiC Whiskers is an Ideal and Low-Cost Method for Fabricating SiC Filters with Excellent Properties

SR-OOD: Out-of-Distribution Detection via Sample Repairing

It is widely reported that deep generative models can classify out-of-distribution (OOD) samples as in-distribution with high confidence. In this work, we propose a hypothesis that this phenomenon is due to the reconstruction task, which can cause the generative model to focus too much on low-level features and not enough on semantic information. To address this issue, we introduce SR-OOD, an OOD detection framework that utilizes sample repairing to encourage the generative model to learn more than just an identity map. By focusing on semantics, our framework improves OOD detection performance without external data and label information. Our experimental results demonstrate the competitiveness of our approach in detecting OOD samples

Discovery of Novel Insulin Sensitizers: Promising Approaches and Targets

Insulin resistance is the undisputed root cause of type 2 diabetes mellitus (T2DM). There is currently an unmet demand for safe and effective insulin sensitizers, owing to the restricted prescription or removal from market of certain approved insulin sensitizers, such as thiazolidinediones (TZDs), because of safety concerns. Effective insulin sensitizers without TZD-like side effects will therefore be invaluable to diabetic patients. The specific focus on peroxisome proliferator-activated receptor γ- (PPARγ-) based agents in the past decades may have impeded the search for novel and safer insulin sensitizers. This review discusses possible directions and promising strategies for future research and development of novel insulin sensitizers and describes the potential targets of these agents. Direct PPARγ agonists, selective PPARγ modulators (sPPARγMs), PPARγ-sparing compounds (including ligands of the mitochondrial target of TZDs), agents that target the downstream effectors of PPARγ, along with agents, such as heat shock protein (HSP) inducers, 5′-adenosine monophosphate-activated protein kinase (AMPK) activators, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) selective inhibitors, biguanides, and chloroquines, which may be safer than traditional TZDs, have been described. This minireview thus aims to provide fresh perspectives for the development of a new generation of safe insulin sensitizers

The influence of adatom diffusion on the formation of skyrmion lattice in sub-monolayer Fe on Ir(111)

Room temperature grown Fe monolayer (ML) on the Ir(111) single crystal substrate has attracted great research interests as nano-skyrmion lattice can form under proper growth conditions. The formation of the nanoscale skyrmion, however, appears to be greatly affected by the diffusion length of the Fe adatoms on the Ir(111) surface. We made this observation by employing spin-polarized scanning tunneling microscopy to study skyrmion formation upon systematically changing the impurity density on the substrate surface prior to Fe deposition. Since the substrate surface impurities serve as pinning centers for Fe adatoms, the eventual size and shape of the Fe islands exhibit a direct correlation with the impurity density, which in turn determines whether skyrmion can be formed. Our observation indicates that skyrmion only forms when the impurity density is below 0.006/nm2, i.e., 12 nm averaged spacing between the neighboring defects. We verify the significance of Fe diffusion length by growing Fe on clean Ir(111) substrate at low temperature of 30 K, where no skyrmion was observed to form. Our findings signify the importance of diffusion of Fe atoms on the Ir(111) substrate, which affects the size, shape and lattice perfection of the Fe islands and thus the formation of skyrmion lattice

Creation of nano-skyrmion lattice in Fe/Ir(111) system using voltage pulse

Magnetic ultrathin films grown on heavy metal substrates often exhibit rich spin structures due to the competition between various magnetic interactions such as Heisenberg exchange, Dzyaloshinskii-Moriya interaction and higher-order spin interactions. Here we employ spin-polarized scanning tunneling microscopy to study magnetic nano-skyrmion phase in Fe monolayer grown on Ir(111) substrate. Our observations show that the formation of nano-skyrmion lattice in the Fe/Ir(111) system depends sensitively on the growth conditions and various non-skyrmion spin states can be formed. Remarkably, the application of voltage pulses between the tip and the sample can trigger a non-skyrmion to skyrmion phase transition. The fact that nano-skyrmions can be created using voltage pulse indicates that the balance between the competing magnetic interactions can be affected by an external electric field, which is highly useful to design skyrmion-based spintronic devices with low energy consumption

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%