CoLight: Learning Network-level Cooperation for Traffic Signal Control

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on Information and Knowledge Management. ACM, 201

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

Low temperature and temperature decline increase acute aortic dissection risk and burden: A nationwide case crossover analysis at hourly level among 40,270 patients.

Background: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency with high mortality, so identifying modifiable risk factors of AAD is of great public health significance. The associations of non-optimal temperature and temperature variability with AAD onset and the disease burden have not been fully understood. Methods: We conducted a time-stratified case-crossover study using a nationwide registry dataset from 1,868 hospitals in 313 Chinese cities. Conditional logistic regression and distributed lag models were used to investigate associations of temperature and temperature changes between neighboring days (TCN) with the hourly AAD onset and calculate the attributable fractions. We also evaluated the heterogeneity of the associations. Findings: A total of 40,270 eligible AAD cases were included. The exposure-response curves for temperature and TCN with AAD onset risk were both inverse and approximately linear. The risks were present on the concurrent hour (for temperature) or day (for TCN) and lasted for almost 1 day. The cumulative relative risks of AAD were 1.027 and 1.026 per 1°C lower temperature and temperature decline between neighboring days, respectively. The associations were significant during the non-heating period, but were not present during the heating period in cities with central heating. 23.13% of AAD cases nationwide were attributable to low temperature and 1.58% were attributable to temperature decline from the previous day. Interpretation: This is the largest nationwide study demonstrating robust associations of low temperature and temperature decline with AAD onset. We, for the first time, calculated the corresponding disease burden and further showed that central heating may be a modifier for temperature-related AAD risk and burden. Funding: This work was supported by the National Natural Science Foundation of China (92043301 and 92143301), Shanghai International Science and Technology Partnership Project (No. 21230780200), the Medical Research Council-UK (MR/R013349/1), and the Natural Environment Research Council UK (NE/R009384/1)

Effect of Grape Marc Added Diet on Live Weight Gain, Blood Parameters, Nitrogen Excretion, and Behaviour of Sheep

A 39-day field experiment was conducted to assess the effect of grape marc (GM) feeding on sheep productivity, health, and environmental sustainability. Forty merino sheep were divided into two dietary groups, each having five replications (n = 4 sheep/replication). Experimental diet consisted of: (i) control: 55% lucerne hay + 40% wheat grain + 5% faba bean; (ii) GM treatment: control diet with 20% replaced by GM on a dry matter (DM) basis. The GM treatment contained 2–10% higher phytochemical contents than the control. The DMI from the GM treatment was 15% higher than the control (p < 0.001). No difference was found in sheep live weight gain, behaviour, and quality between groups (p > 0.05). No difference was found in total faecal production, faecal organic matter, and nitrogen contents (p > 0.05) and parasitic egg count. The GM treatment led to higher nitrogen intake (23.1 vs. 27.2 g/d) and faecal nitrogen excretion (6.3 vs. 8.7 g/d) compared to the control. Urinary creatinine, allantoin, and purine derivatives were lower in the GM treatment than control (p < 0.05). However, both groups had similar purine derivatives/DMI (i.e., indicator of rumen microbial protein synthesis efficiency; p > 0.05). Overall, the results showed that GM can replace 20% of the control ration to maintain sheep productivity, health, and environmental sustainability

Fine Particulate Matter Constituents, Nitric Oxide Synthase DNA Methylation and Exhaled Nitric Oxide

It remains unknown how fine particulate matter (PM_2.5) constituents affect differently the fractional concentration of exhaled nitric oxide (FeNO, a biomarker of airway inflammation) and the DNA methylation of its encoding gene (<i>NOS2A</i>). We aimed to investigate the short-term effects of PM_2.5 constituents on <i>NOS2A</i> methylation and FeNO. We designed a longitudinal study among chronic obstructive pulmonary disease (COPD) patients with six repeated health measurements in Shanghai, China. We applied linear mixed-effect models to evaluate the associations. We observed that the inverse association between PM_2.5 and methylation at position 1 was limited within 24 h, and the positive association between PM_2.5 and FeNO was the strongest at lag 1 day. Organic carbon, element carbon, NO₃^– and NH₄⁺ were robustly and significantly associated with decreased methylation and elevated FeNO. An interquartile range increase in total PM_2.5 and the four constituents was associated with decreases of 1.19, 1.63, 1.62, 1.17, and 1.14 in percent methylation of <i>NOS2A</i>, respectively, and increases of 13.30%,16.93%, 8.97%, 18.26%, and 11.42% in FeNO, respectively. Our results indicated that organic carbon, element carbon, NO₃^– and NH₄⁺ might be mainly responsible for the effects of PM_2.5 on the decreased <i>NOS2A</i> DNA methylation and elevated FeNO in COPD patients