World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data scarcity challenges. However, current embodied world models exhibit limited physical awareness, particularly in modeling 3D geometry and motion dynamics, resulting in unrealistic video generation for contact-rich robotic scenarios. In this paper, we present RoboScape, a unified physics-informed world model that jointly learns RGB video generation and physics knowledge within an integrated framework. We introduce two key physics-informed joint training tasks: temporal depth prediction that enhances 3D geometric consistency in video rendering, and keypoint dynamics learning that implicitly encodes physical properties (e.g., object shape and material characteristics) while improving complex motion modeling. Extensive experiments demonstrate that RoboScape generates videos with superior visual fidelity and physical plausibility across diverse robotic scenarios. We further validate its practical utility through downstream applications including robotic policy training with generated data and policy evaluation. Our work provides new insights for building efficient physics-informed world models to advance embodied intelligence research. Our code and demos are available at: https://github.com/tsinghua-fib-lab/RoboScape.

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this article proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.

With the increasing prevalence of infectious diseases like COVID-19, there is a growing interest in modeling and predicting their transmission. Leveraging the wealth of mobile trajectory data collected through advanced localization and mobile communication techniques, numerous approaches have been proposed to predict the spatial spread of infectious diseases based on human mobility dynamics characterized by microscopic user contact graphs or macroscopic population flow graphs. However, existing pure macroscopic and microscopic models have limitations in terms of modeling capabilities or in protecting user privacy. Thus, in this study, we present a Multi-scale Spatial Disease prediction Network (MSDNet) for predicting the spatial spread of infectious diseases. The model predicts the spread of infectious diseases using a macromicro collaborative approach by combining the temporal and spatial characteristics of the macroscopic information in the population flow graph and the microscopic information in the user contact graph. To understand the coupling between human mobility and infectious disease transmission, we propose a loss term that combines infectious disease spread dynamics and modeling of infectious disease parameters that can achieve stable adaptation to key characteristics of infectious diseases even when human mobility is affected by policy measures such as travel restrictions. Extensive experimental results show the MSDNet model's superiority for epidemic prediction on graph networks using macro-micro collaboration, achieving a 15%-20% improvement in terms of RMSE and a 15%-30% improvement in terms of SMAPE compared to existing baseline models. In addition, we predict infectious disease parameters under changes in human mobility, and the results show that MSDNet could effectively distinguish between human mobility and infectious disease characteristics, achieving a relative improvement of 76% in terms of RMSE and 80% in terms of SMAPE in predicting infectious disease parameters under changes in human mobility.