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

How Do We Move: Modeling Human Movement with System Dynamics

Modeling how human moves in the space is useful for policy-making in transportation, public safety, and public health. Human movements can be viewed as a dynamic process that human transits between states (\eg, locations) over time. In the human world where intelligent agents like humans or vehicles with human drivers play an important role, the states of agents mostly describe human activities, and the state transition is influenced by both the human decisions and physical constraints from the real-world system (\eg, agents need to spend time to move over a certain distance). Therefore, the modeling of state transition should include the modeling of the agent's decision process and the physical system dynamics. In this paper, we propose \ours to model state transition in human movement from a novel perspective, by learning the decision model and integrating the system dynamics. \ours learns the human movement with Generative Adversarial Imitation Learning and integrates the stochastic constraints from system dynamics in the learning process. To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics. In extensive experiments on real-world datasets, we demonstrate that the proposed method can generate trajectories similar to real-world ones, and outperform the state-of-the-art methods in predicting the next location and generating long-term future trajectories.Comment: Accepted by AAAI 2021, Appendices included. 12 pages, 8 figures. in Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI'21), Feb 202

Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling

In many industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. For building user profiles, deep learning is widely used to mine expressive tags to describe users' preferences from their historical actions. For example, tags mined from users' click-action history can represent the categories of ads that users are interested in, and they are likely to continue being clicked in the future. Traditional solutions usually introduce multiple independent Two-Tower models to mine tags from different actions, e.g., click, conversion. However, the models cannot learn complementarily and support effective training for data-sparse actions. Besides, limited by the lack of information fusion between the two towers, the model learning is insufficient to represent users' preferences on various topics well. This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn multiple topic-related user preferences based on different actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which focuses on modeling one particular facet of the user's preference, and all of them learn coordinately. Besides, the gate-based structure used in MVKE builds an information fusion bridge between two towers, improving the model's capability much and maintaining high efficiency. We apply the model in Tencent Advertising System, where both online and offline evaluations show that our method has a significant improvement compared with the existing ones and brings about an obvious lift to actual advertising revenue.Comment: 10 pages, under revie

Weakly Supervised Point Clouds Transformer for 3D Object Detection

The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of a point clouds transformer that is used for 3D object detection. The aim is to decrease the required amount of supervision needed for training, as a result of the high cost of annotating a 3D datasets. We propose an Unsupervised Voting Proposal Module, which learns randomly preset anchor points and uses voting network to select prepared anchor points of high quality. Then it distills information into student and teacher network. In terms of student network, we apply ResNet network to efficiently extract local characteristics. However, it also can lose much global information. To provide the input which incorporates the global and local information as the input of student networks, we adopt the self-attention mechanism of transformer to extract global features, and the ResNet layers to extract region proposals. The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet. On the challenging KITTI datasets, the experimental results have achieved the highest level of average precision compared with the most recent weakly supervised 3D object detectors.Comment: International Conference on Intelligent Transportation Systems (ITSC), 202

Light Multi-segment Activation for Model Compression

Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a compact student model, has been widely used for model compression. However, under the strict requirement on the resource cost, it is quite challenging to achieve comparable performance with the teacher model, essentially due to the drastically-reduced expressiveness ability of the compact student model. Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. Specifically, we propose a highly efficient multi-segment activation, called Light Multi-segment Activation (LMA), which can rapidly produce multiple linear regions with very few parameters by leveraging the statistical information. With using LMA, the compact student model is capable of achieving much better performance effectively and efficiently, than the ReLU-equipped one with same model scale. Furthermore, the proposed method is compatible with other model compression techniques, such as quantization, which means they can be used jointly for better compression performance. Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA

Arabidopsis SWR1-associated protein methyl-CpG-binding domain 9 is required for histone H2A.Z deposition.

Deposition of the histone variant H2A.Z by the SWI2/SNF2-Related 1 chromatin remodeling complex (SWR1-C) is important for gene regulation in eukaryotes, but the composition of the Arabidopsis SWR1-C has not been thoroughly characterized. Here, we aim to identify interacting partners of a conserved Arabidopsis SWR1 subunit ACTIN-RELATED PROTEIN 6 (ARP6). We isolate nine predicted components and identify additional interactors implicated in histone acetylation and chromatin biology. One of the interacting partners, methyl-CpG-binding domain 9 (MBD9), also strongly interacts with the Imitation SWItch (ISWI) chromatin remodeling complex. MBD9 is required for deposition of H2A.Z at a distinct subset of ARP6-dependent loci. MBD9 is preferentially bound to nucleosome-depleted regions at the 5' ends of genes containing high levels of activating histone marks. These data suggest that MBD9 is a SWR1-C interacting protein required for H2A.Z deposition at a subset of actively transcribing genes

Sex‐specific activation of SK current by isoproterenol facilitates action potential triangulation and arrhythmogenesis in rabbit ventricles

Sex has a large influence on cardiac electrophysiological properties. Whether sex differences exist in apamin‐sensitive small conductance Ca2+‐activated K+ (SK) current (IKAS) remains unknown. We performed optical mapping, transmembrane potential, patch clamp, western blot and immunostaining in 62 normal rabbit ventricles, including 32 females and 30 males. IKAS blockade by apamin only minimally prolonged action potential (AP) duration (APD) in the basal condition for both sexes, but significantly prolonged APD in the presence of isoproterenol in females. Apamin prolonged APD at the level of 25% repolarization (APD25) more prominently than APD at the level of 80% repolarization (APD80), consequently reversing isoproterenol‐induced AP triangulation in females. In comparison, apamin prolonged APD to a significantly lesser extent in males and failed to restore the AP plateau during isoproterenol infusion. IKAS in males did not respond to the L‐type calcium current agonist BayK8644, but was amplified by the casein kinase 2 (CK2) inhibitor 4,5,6,7‐tetrabromobenzotriazole. In addition, whole‐cell outward IKAS densities in ventricular cardiomyocytes were significantly larger in females than in males. SK channel subtype 2 (SK2) protein expression was higher and the CK2/SK2 ratio was lower in females than in males. IKAS activation in females induced negative intracellular Ca2+–voltage coupling, promoted electromechanically discordant phase 2 repolarization alternans and facilitated ventricular fibrillation (VF). Apamin eliminated the negative Ca2+–voltage coupling, attenuated alternans and reduced VF inducibility, phase singularities and dominant frequencies in females, but not in males. We conclude that β‐adrenergic stimulation activates ventricular IKAS in females to a much greater extent than in males. IKAS activation plays an important role in ventricular arrhythmogenesis in females during sympathetic stimulation