Alcohol Use Disorder (AUD), encompassing Alcohol Abuse (AA) and Alcohol Dependence (AD), is a chronic, relapsing brain condition that can cause mental and physical issues. Confusion between AA and AD can lead to ineffective or even excessively aggressive treatments, but distinguishing them is difficult due to similar symptoms and physiological indicators. Fortunately, from a psychological perspective, AA is closely linked to poor behavioral control, while AD is associated with affective lability. These psychological mechanisms are differently featured in the activity of patients' brain regions. Inspired by this, we propose a model called Multiscale and Hybrid Spatial Temporal Graph Convolutional Network (MuST-GCN) that enables to extract the features of electroencephalogram (EEG) signals for accurate identification of AA and AD within AUD. It includes two modules: Multiscale Feature Extraction (MSFE) module with GCN concurrently analyzes inter-regional and intra-regional connectivity of brain regions to extract EEG features, exploring functional connectivity differences in AA and AD patients; Hybrid Spatial Temporal Memory (HSTM) module integrates spatial and temporal attention mechanisms to refine the significant features extracted from MSFE, targeting the key brain regions and temporal dynamics. The HSTM module yields a refined feature representation reducing overfitting and improving multiclass classification accuracy. MuST-GCN is evaluated using five-fold cross-validation on two datasets, achieving classification accuracies of 90.74% and 99.99%, respectively, and demonstrating superior performance in identifying AA, AD, and AUD compared to existing methods.

Multi-person motion prediction is an emerging and intricate task with broad real-world applications. Unlike single person motion prediction, it considers not just the skeleton structures or human trajectories but also the interactions between others. Previous methods achieve impressive predictions using various networks but often overlook the distinct representations of joint relations within individuals (intra-relations) and interactions among groups (inter-relations), inevitably leading to undesired dependencies. To address this issue, we introduce a new collaborative framework for multi-person motion prediction that explicitly modeling these relations: a GCN-based network for intra-relations and a novel reasoning network for inter-relations. Specifically, we propose a distance-aware cross-attention that incorporates physical distance constraints into inter-relation learning through a learnable distance weighting coefficient. Moreover, we propose a novel plug-and-play aggregation module called the Interaction Aggregation Module (IAM), which employs an aggregate-attention mechanism to seamlessly integrate these relations. Experiments indicate that the module can also be applied to other dual-path models. Extensive experiments on the 3DPW, 3DPW-RC, CMU-Mocap, MuPoTS-3D, as well as synthesized datasets Mix1 & Mix2 (9$\sim$15 persons), demonstrate that our method achieves state-of-the-art performance.

The dynamic information of the joints, such as the movement amplitude, is critical for forecasting precise human joint trajectories. Existing methods adopt global modeling in which all joints are treated as a whole to extract features for movement coordination. Though global modeling can exploit hidden relationships between joints, it also inevitably introduces undesired trajectory dependencies, which weakens the dynamic information effects and simplifies the constraints and kinetics model of joints. Therefore, we propose a dynamic pattern-based collaborative modeling framework (DPnet) that contains a keyframe enhanced module (KEM) and multi-channel feature extractor blocks (MFE-block). The KEM tackles the discontinuity between the last frame of observation and the first predicted one by duplicating the decisive frame. The MFE-block utilizes a multi-channel graph structure to enrich the dynamic information effects and recessive constraints of joints. To distinguish the dynamic information of each joint, we calculate the movement amplitude of the joints and propose three dynamic patterns, including active, inactive, and static patterns. We also propose a dynamic pattern-guided feature extractor (DP-FE) to alleviate the trajectory dependencies between joints with different dynamic patterns. We evaluate our approach on three standard benchmark datasets, including H3.6M [8], CMU-Mocap [44], and 3DPW [45]. Our approach achieves impressive results in both short-term and long-term predictions, confirming its effectiveness and efficiency.