Temporal dynamic graphs (TDGs), representing the dynamic evolution of entities and their relationships over time with intricate temporal features, are widely used in various real-world domains. Existing methods typically rely on mainstream techniques such as transformers and graph neural networks (GNNs) to capture the spatiotemporal information of TDGs. However, despite their advanced capabilities, these methods often struggle with significant computational complexity and limited ability to capture temporal dynamic contextual relationships. Recently, a new model architecture called mamba has emerged, noted for its capability to capture complex dependencies in sequences while significantly reducing computational complexity. Building on this, we propose a novel method, TDG-mamba, which integrates mamba for TDG learning. TDG-mamba introduces deep semantic spatiotemporal embeddings into the mamba architecture through a specially designed spatiotemporal prior tokenization module (SPTM). Furthermore, to better leverage temporal information differences and enhance the modeling of dynamic changes in graph structures, we separately design a bidirectional mamba and a directed GNN for improved spatiotemporal embedding learning. Link prediction experiments on multiple public datasets demonstrate that our method delivers superior performance, with an average improvement of 5.11% over baseline methods across various settings.

The analysis of traffic situations under abnormal conditions is one of the bottleneck issues in Intelligent Transportation Systems (ITS). Influenced by the suddenness, randomness, and uncertainty, this issue is challenging to achieve through existing deep learning methods. It needs to be assisted by traffic simulation models for analysis. However, simulation models always require extensive scene modeling and calibration, making it difficult to meet the demands of natural human-machine interaction in the AIGC (Artificial Intelligence Generated Content) era, as well as the need for rapid and flexible implementation of situation analysis. With the accumulation of traffic data, the emergence of diffusion models offers a new entry point for the core method of data-driven analysis, namely Text-to-Traffic Generation (TTG). In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic situation generation, and propose ChatTraffic, the first diffusion model for TTG. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large-scale dataset containing text-traffic pairs for TTG. We benchmarked ChatTraffic qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can rapidly and flexibly generate realistic traffic situations from text, which have practical significance in addressing bottlenecks in ITS. Our code and dataset are available athttps://github.com/ChyaZhang/ChatTraffic.

Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.

Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.

Cell localization still faces two unresolved challenges: 1) the dramatic variations in cell morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) existing cell location maps lack scale information, resulting in insufficient supervision for point maps and inaccurate supervision for density maps. 1) To address the first challenges, we introduce a novel gradient-aware and shape-adaptive Difference-Deformable Convolution (DDConv), which enhances the model's robustness to color by leveraging gradient information while adaptively adjusting the shape of the convolutional kernel to tackle the substantial variability in cell morphology. 2) To overcome the issue of unreasonable location maps, we propose the Pseudo-Scale Instance (PSI) map, which can adaptively provide the corresponding scale information for each cell to realize accurate supervision. We analyze and evaluate DDConv and the PSI map in three challenging cell localization tasks. In comparison to existing methods, our proposed approach significantly enhances localization performance, setting a new benchmark for the cell localization task. Our code is available at https://github.com/ChyaZhang/DDConv-PSI.

Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical data for traffic prediction greatly limits the accuracy of the prediction. In this paper, we introduce Beijing Text-Traffic (BjTT), a large-scale multimodal dataset for traffic prediction. BjTT comprises over 32,000 time-series traffic records, capturing velocity and congestion levels on more than 1,200 roads within the 5th ring area of Beijing. Meanwhile, each piece of traffic data is coupled with a text describing the traffic system (including time, location, and events). We detail the data collection and processing procedures and present a statistical analysis of the BjTT dataset. Furthermore, we conduct comprehensive experiments on the dataset with state-of-the-art traffic prediction methods and text-guided generative models, which reveal the unique characteristics of the BjTT. The dataset is available athttps://github.com/ChyaZhang/BjTT.

Most existing weakly supervised crowd counting methods utilize Convolutional Neural Networks (CNN) or Transformer to estimate the total number of individuals in an image. However, both CNN-based (grid-to-count paradigm) and Transformer-based (sequence-to-count paradigm) methods take images as inputs in a regular form. This approach treats all pixels equally but cannot address the uneven distribution problem within human crowds. This challenge would lead to a decline in the counting performance of the model. Compared with grid and sequence, the graph structure could better explore the relationship among features. In this article, we propose a new graph-based crowd counting method named CrowdGraph, which reinterprets the weakly supervised crowd counting problem from a graph-to-count perspective. In the proposed CrowdGraph, each image is constructed as a graph, and a graph-based network is designed to extract features at the graph level. CrowdGraph comprises three main components: a dynamic graph convolutional backbone, a multi-scale dilated graph convolution module, and a regression head. To the best of our knowledge, CrowdGraph is the first method that is completely formulated based on the Graph Neural Network (GNN) for the crowd counting task. Extensive experiments demonstrate that the proposed CrowdGraph outperforms pure CNN-based and pure Transformer-based weakly supervised methods comprehensively and achieves highly competitive counting performance.

Weakly supervised crowd counting involves the regression of the number of individuals present in an image, using only the total number as the label. However, this task is plagued by two primary challenges: the large variation of head size and uneven distribution of crowd density. To address these issues, we propose a novel Hypergraph Association Crowd Counting (HACC) framework. Our approach consists of a new multi-scale dilated pyramid module that can efficiently handle the large variation of head size. Further, we propose a novel hypergraph association module to solve the problem of uneven distribution of crowd density by encoding higher-order associations among features, which opens a new direction to solve this problem. Experimental results on multiple datasets demonstrate that our HACC model achieves new state-of-the-art results.