GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving

Autonomous vehicles operating in complex real-world environments require accurate predictions of interactive behaviors between traffic participants. While existing works focus on modeling agent interactions based on their past trajectories, their future interactions are often ignored. This paper addresses the interaction prediction problem by formulating it with hierarchical game theory and proposing the GameFormer framework to implement it. Specifically, we present a novel Transformer decoder structure that uses the prediction results from the previous level together with the common environment background to iteratively refine the interaction process. Moreover, we propose a learning process that regulates an agent's behavior at the current level to respond to other agents' behaviors from the last level. Through experiments on a large-scale real-world driving dataset, we demonstrate that our model can achieve state-of-the-art prediction accuracy on the interaction prediction task. We also validate the model's capability to jointly reason about the ego agent's motion plans and other agents' behaviors in both open-loop and closed-loop planning tests, outperforming a variety of baseline methods

Predicting event attendance exploring social influence

The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-related advertisement. Today social networks (e.g. Twitter) widely reflect large popular events where people discuss their interest with friends. Event participants usually stimulate friends to join the event which propagates a social influence in the network. In this paper, we propose to model the social influence of friends on event attendance. We consider non-geotagged posts besides structures of social groups to infer users' attendance. To leverage the information on network topology we apply some of recent graph embedding techniques such as node2vec, HARP and Poincar`e. We describe the approach followed to design the feature space and feed it to a neural network. The performance evaluation is conducted using two large music festivals datasets, namely the VFestival and Creamfields. The experimental results show that our classifier outperforms the state-of-the-art baseline with 89% accuracy observed for the VFestival dataset

Effectiveness of folic acid fortified flour for prevention of neural tube defects in a high risk region

Despite efforts to tackle folate deficiency and Neural Tube Defects (NTDs) through folic acid fortification, its implementation is still lacking where it is needed most, highlighting the need for studies that evaluate the effectiveness of folic acid fortified wheat flour in a poor, rural, high-risk, NTD region of China. One of the most affected regions, Shanxi Province, was selected as a case study. A community intervention was carried out in which 16,648 women of child-bearing age received fortified flour (eight villages) and a control group received ordinary flour (three villages). NTD birth prevalence and biological indicators were measured two years after program initiation at endline only. The effect on the NTD burden was calculated using the disability-adjusted life years (DALYs) method. In the intervention group, serum folate level was higher than in the control group. NTDs in the intervention group were 68.2% lower than in the control group (OR = 0.313, 95% CI = 0.207-0473, p < 0.001). In terms of DALYs, burden in intervention group was approximately 58.5% lower than in the control group. Flour fortification was associated with lower birth prevalence and burden of NTDs in economically developing regions with a high risk of NTDs. The positive findings confirm the potential of fortification when selecting an appropriate food vehicle and target region. As such, this study provides support for decision makers aiming for the implementation of (mandatory) folic acid fortification in China

Inspection of delamination defect in first wall panel of Tokamak device by using laser infrared thermography technique

First wall panels (FWPs), which adjoin the inner wall of the blanket modules in the vacuum vessel (VV) of a Tokamak device, are in structures of multilayer bounded together with a solid welding technique in order to perform its heat exchange, VV protection, and neutron breeding functions. The quality of the welding joint between layers is the key factor for FWP integrity. In order to conduct online inspection of the delamination defect in the FWPs, a nondestructive testing (NDT) method capable to detect delamination defect without accessing into the VV is required. In this paper, the feasibility of the laser infrared thermography (LIRT) testing NDT method was investigated experimentally for this purpose. To clarify its detectability under practical VV environment, inspections of several inspection modes were conducted based on the practical structure of FWP and VV of the EAST Tokamak device, i.e., modes of different distances and angles of FWPs toward the LIRT transducers. In practice, an LIRT testing system was established and several double-layered plate specimens with different artificial delamination defects were inspected under the selected testing conditions. Through thermography signal reconstruction, an image processing algorithm was proposed and adopted to enhance the defect detectability. From the results of different inspection modes, it was found that the angle factor may worsen the inspection precision and reduce the detectability for delamination defects in case of big defect depth-to-width ratio, even though the LIRT method is still applicable for inspection of relative large defects in FWP. Finally, the detectability in different inspection modes was clarified, which proved the feasibility of LIRT for FWP online inspection