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

Structures and growth pathways of AunCln+3-(n≤7) cluster anions

Gold chloride clusters play an important role in catalysis and materials chemistry. Due to the diversity of their species and isomers, there is still a dearth of structural studies at the molecular level. In this work, anions of AunCln+3- and AunCln+5- (n = 2–4) clusters were obtained by laser desorption/ionization mass spectrometry (LDI MS), and the most stable isomers of AunCln+3- were determined after a thorough search and optimization at the TPSSh/aug-cc-pVTZ/ECP60MDF level. The results indicate that all isomers with the lowest energy have a planar zigzag skeleton. In each species, there is one Au(III) atom at the edge connected with four Cl atoms, which sets it from the other Au(I) atoms. Four growth pathways for AunCln+3- (n = 2–7) clusters are proposed (labelled R1, R2, R3 and R4). They are all associated with an aurophilic contact and are exothermic. The binding energies tend to stabilize at ~ −41 kcal/mol when the size of the cluster increases in all pathways. The pathway R1, which connects all the most stable isomers of the respective clusters, is characterized by cluster growth due to aurophilic interactions at the terminal atom of Au(I) in the zigzag chains. In the pathway of R4 involving Au-Au bonding in its initial structures (n ≤ 3), the distance between intermediate gold atoms grows with cluster size, ultimately resulting in the transfer of the intermediate Au-Au bonding into aurophilic interaction. The size effect on the structure and aurophilic interactions of these clusters will be better understood based on these discoveries, potentially providing new insights into the active but elusive chemical species involved in the corresponding catalytic reactions or nanoparticle synthesis processes

MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control

Using reinforcement learning for traffic signal control has attracted increasing interests recently. Various value-based reinforcement learning methods have been proposed to deal with this classical transportation problem and achieved better performances compared with traditional transportation methods. However, current reinforcement learning models rely on tremendous training data and computational resources, which may have bad consequences (e.g., traffic jams or accidents) in the real world. In traffic signal control, some algorithms have been proposed to empower quick learning from scratch, but little attention is paid to learning by transferring and reusing learned experience. In this paper, we propose a novel framework, named as MetaLight, to speed up the learning process in new scenarios by leveraging the knowledge learned from existing scenarios. MetaLight is a value-based meta-reinforcement learning workflow based on the representative gradient-based meta-learning algorithm (MAML), which includes periodically alternate individual-level adaptation and global-level adaptation. Moreover, MetaLight improves the-state-of-the-art reinforcement learning model FRAP in traffic signal control by optimizing its model structure and updating paradigm. The experiments on four real-world datasets show that our proposed MetaLight not only adapts more quickly and stably in new traffic scenarios, but also achieves better performance

Interactive effects of warming and increased precipitation on community structure and composition in an annual forb dominated desert steppe.

To better understand how warming, increased precipitation and their interactions influence community structure and composition, a field experiment simulating hydrothermal interactions was conducted at an annual forb dominated desert steppe in northern China over 2 years. Increased precipitation increased species richness while warming significantly decreased species richness, and their effects were additive rather than interactive. Although interannual variations in weather conditions may have a major affect on plant community composition on short term experiments, warming and precipitation treatments affected individual species and functional group composition. Warming caused C4 grasses such as Cleistogenes squarrosa to increase while increased precipitation caused the proportions of non-perennial C3 plants like Artemisia capillaris to decrease and perennial C4 plants to increase

Effects of land use and climate change on ecosystem services in Central Asia's arid regions: A case study in Altay Prefecture, China

The sustainable use of ecosystem services (ES) can contribute to enhancing human well-being. Understanding the effects of land use and climate change on ES can provide scientific and targeted guidance for the sustainable use of ES. The objective of this study was to reveal the way in which land use and climate change influence the spatial and temporal variations of ES in the mountain-oasis-desert system (MODS). In this study, we assessed water yield, soil conservation, crop production, and sand fixation in 1990, 2000, and 2010 in Altay Prefecture, which is representative of the MODS, based on widely used biophysical models. Moreover, we analyzed the effects of different land use and climate change conditions on ES. The results show that the area of forest and bare land decreased in Altay Prefecture. In contrast, the area of grassland with low coverage and cropland increased. The climate of this area presented an overall warming-wetting trend, with warming-drying and cooling-wetting phenomena in some areas. Soil conservation in the mountain zone, water yield in the oasis zone, and sand fixation in the desert zone all decreased under the influence of land use change alone. The warming-drying trend led to decreased water yield in the oasis zone and increased wind erosion in the desert zone. Based on the results, we recommend that local governments achieve sustainable use of ES by planting grasslands with high coverage in the oasis zone, increasing investment in agricultural science and technology, and establishing protected areas in the mountain and desert zones. The methodology in our study can also be applied to other regions with a MODS structure. (C) 2017 Elsevier B.V. All rights reserved

Response of grassland productivity to climate change and anthropogenic activities in arid regions of Central Asia

Background. Quantitative evaluations of the relative impacts of climate change and anthropogenic activity on grasslands are significant for understanding grassland degradation mechanisms and controlling degraded grasslands. However, our knowledge about the effects of anthropogenic activities and climate change on the grassland in a mountain basin system in arid regions of Central Asia is still subject to great uncertainties. Methods. In this research, we have chosen the net primary productivity (NPP) as an index for revealing grassland dynamics processes. Moreover, the human appropriation of net primary production (NPPH), which was calculated as the potential NPP (NPPp) minus the actual NPP (NPPA), was applied to distinguish the relative influences of climate change and human activities on the grassland NPP variations in a mountain basin system of Central Asia from 2001-2015. Results. The results indicated that the grassland NPPA showed an increasing trend (35.88%) that was smaller than the decreasing trend (64.12%). The respective contributions of human activity, climate change and the two together to the increase in the NPPA were 6.19%, 81.30% and 12.51%, respectively. Human activity was largely responsible for the decrease in the grassland NPPA, with the area experiencing human-induced decreases accounting for 98.21% of the total decreased area, which mainly occurred during spring/autumn pasture and winter pasture. Furthermore, the average grazing pressure index (GPI) values of summer pastures, spring/autumn pasture and winter pastures were 1.04, 3.03 and 1.83, respectively, from 2001-2015. In addition, negative correlations between the NPP and GPI occupied most of the research area (92.41%). Discussion. Our results indicate that: (i) anthropogenic activities were the primary cause of the reduction in the grassland NPP, especially grazing activities. (ii) For areas where the grassland NPP has increased, precipitation was the dominant climatic factor over temperature in controlling the grassland NPP changes in the study area. (iii) The findings of the current research indicate that some measures should be taken to reduce livestock pressure, and artificial grasslands can be built along the Irtysh River and the Ulungur River to relieve grazing pressure on spring/autumn pastures and winter pastures. Our results could provide reliable information for grassland management and the prevention of grassland degradation in arid regions of Central Asia