Urban traffic light control is an important and challenging real-world problem. By regarding intersections as agents, most of the reinforcement learning-based methods generate agents’ actions independently. They can cause action conflict and result in overflow or road resource waste in adjacent intersections. Recently, some collaborative methods have alleviated the above problems by extending the observable surroundings of agents, which can be considered inactive cross-agent communication methods. However, when agents act synchronously in these works, the perceived action value is biased, and the information exchanged is insufficient. In this work, we first propose a novel Multi-agent Communication and Action Rectification (MaCAR) framework. It enables active communication between agents by considering the impact of synchronous actions of agents. Another fundamental problem of traffic light control is the balance between traffic demand and road supply capacity. To fully describe the relation between traffic demand and road supply capacity (Supply-Demand modeling, SD), we further model and forecast the Supply-Demand relation to facilitating the effectiveness of the model’s action. The experiments show that our model outperforms state-of-the-art methods on both synthetic and real-world datasets. Combining the SD with MaCAR, SD-MaCAR can further boost the traffic light control performance even in traffic accident scenarios.

With over 34 billion IoT devices to be installed by 2020, the Internet of Things (IoT) is fundamentally changing our lives. One of the greatest benefits of the IoT is the powerful automations achieved by applying rules to IoT devices. For instance, a rule named "Make me a cup of coffee when I wake up'' automatically turns on the coffee machine when the sensor in the bedroom detects motion in the morning. With large numbers of possible rules out there, a recommendation system is of great necessity to help users find rules they need. However, little effort has been made to design a model tailored for the IoT rule recommendation, which comes with lots of new challenges compared with traditional recommendation tasks. We not only need to re-define "users'' and "items'' in the recommendation task, but also have to consider a new type of entities, devices, and the extra information and constraints brought by them. To handle these challenges, we propose a novel efficient recommendation algorithm, which not only considers the implicit feedback of users on rules, but also takes user-rule-device interactions and the match between rule device requirements and user device possessions into account. In collaboration with Samsung, one of the leading companies in this field, we have designed an IoT rule recommendation framework and evaluated our algorithm on a real-life industry dataset. Experiments show the effectiveness and efficiency of our method.

Low rank matrix factorizations(LRMF) have attracted much attention due to its wide range of applications in computer vision, such as image impainting and video denoising. Most of the existing methods assume that the loss between an observed measurement matrix and its bilinear factorization follows symmetric distribution, like gaussian or gamma families. However, in real-world situations, this assumption is often found too idealized, because pictures under various illumination and angles may suffer from multi-peaks, asymmetric and irregular noises. To address these problems, this paper assumes that the loss follows a mixture of Asymmetric Laplace distributions and proposes robust Asymmetric Laplace Adaptive Matrix Factorization model(ALAMF) under bayesian matrix factorization framework. The assumption of Laplace distribution makes our model more robust and the asymmetric attribute makes our model more flexible and adaptable to real-world noise. A variational method is then devised for model inference. We compare ALAMF with other state-of-the-art matrix factorization methods both on data sets ranging from synthetic and real-world application. The experimental results demonstrate the effectiveness of our proposed approach.