Shifts in individual movement patterns following disruptive events can reveal changing demands for community resources. However, predicting such shifts before disruptive events remains challenging for several reasons. First, measures are lacking for individuals' heterogeneous social infrastructure resilience (SIR), which directly influences their movement patterns, and commonly used features are often limited or unavailable at scale, e.g., sociodemographic characteristics. Second, the complex interactions between individual movement patterns and spatial contexts have not been sufficiently captured. Third, individual-level movement may be spatially sparse and not well-suited to traditional decision-making methods for movement predictions. This study incorporates individuals' SIR into a conditioned deep learning model to capture the complex relationships between individual movement patterns and local spatial context using large-scale, sparse individual-level data. Our experiments demonstrate that incorporating individuals' SIR and spatial context can enhance the model's ability to predict post-event individual movement patterns. The conditioned model can capture the divergent shifts in movement patterns among individuals who exhibit similar pre-event patterns but differ in SIR.

Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high-quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science. The code and dataset are available at https://github.com/zjuKeLiu/Mat-Instructions

Matrix factorization (MF), a cornerstone of recommender systems, decomposes user-item interaction matrices into latent representations. Traditional MF approaches, however, employ a two-stage, non-end-to-end paradigm, sequentially performing recommendation and clustering, resulting in prohibitive computational costs for large-scale applications like e-commerce and IoT, where billions of users interact with trillions of items. To address this, we propose Matrix Factorization with Dynamic Multi-view Clustering (MFDMC), a unified framework that balances efficient end-to-end training with comprehensive utilization of web-scale data and enhances interpretability. MFDMC leverages dynamic multi-view clustering to learn user and item representations, adaptively pruning poorly formed clusters. Each entity's representation is modeled as a weighted projection of robust clusters, capturing its diverse roles across views. This design maximizes representation space utilization, improves interpretability, and ensures resilience for downstream tasks. Extensive experiments demonstrate MFDMC's superior performance in recommender systems and other representation learning domains, such as computer vision, highlighting its scalability and versatility.

Retinal vessel segmentation is critical for medical diagnosis, yet existing models often struggle to generalize across domains due to appearance variability, limited annotations, and complex vascular morphology. We propose GraphSeg, a variational Bayesian framework that integrates anatomical graph priors with structure-aware image decomposition to enhance cross-domain segmentation. GraphSeg factorizes retinal images into structure-preserved and structure-degraded components, enabling domain-invariant representation. A deformable graph prior, derived from a statistical retinal atlas, is incorporated via a differentiable alignment and guided by an unsupervised energy function. Experiments on three public benchmarks (CHASE, DRIVE, HRF) show that GraphSeg consistently outperforms existing methods under domain shifts. These results highlight the importance of jointly modeling anatomical topology and image structure for robust generalizable vessel segmentation.

The symmetry and geometry of a crystal fundamentally determine its various physical and chemical properties. However, crystal structure prediction, including space group determination and crystal structure optimization, remains an ongoing challenge because traditional DFT-based approaches are time- and computational-intensive even for one specific set of material, not to mention structure prediction of massive materials. This paper determines the geometric structure of massive crystals solely from chemical formulae from scratch. In addition, due to various phases or changing environmental conditions, different pressure and temperature for example, there could be multiple crystal structures corresponding to one chemical formula (MS4OF), which has been overlooked or poorly addressed in previous research. Hereby, we propose a Physics-informed Conditional Variational Auto Encoder (PCVAE) to encode possible symmetry and geometry distribution as well as various phases of a crystal with Gaussian distributions. PCVAE achieves a new state-of-the-art in crystal structure prediction. Extensive experiments demonstrate the strong predictive power of PCVAE. The code and datasets are available at https://github.com/zjuKeLiu/PCVAE.

To assess the authenticity and integrity of digital image history, forensic analysts not only need to identify the types of tampering operations that multimedia has undergone but also distinguish their topological combination order. However, due to the interaction among tampering operations, a forensic method proposed for the operation identification yields unacceptably low performance when the content of a given image was forged by operations in a multi-operator chain. In this paper, we propose an efficient detection framework for determining the type and order of the tampering operations. Specifically, we first divide the operation detection problem into two aspects: operation type identification and their combination order detection. Then, by exploring the feature coupling relationships among different features with Pearson's correlation coefficient, optimal coupled features are designed for tampering operations detection. Finally, the detection of multiple operations applied to the previously JPEG-compression images is examined, and the effectiveness of our proposed framework has been demonstrated by simulation results.