Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the adaptive behavior controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains.

Session-based recommender systems face significant challenges in accurately predicting user preferences due to the limited availability of long-term historical interactions. While recent advances in deep learning and graph-based approaches have improved recommendation performance, the temporal aspects of user interactions remain underutilized. This paper identifies three critical temporal challenges in session-based recommendations: interest shifts indicated by long intervals between interactions, interaction noise from brief engagements, and system popularity effects during high-traffic periods. To address these challenges, we propose a novel Dual-channel Time-aware Graph Attention Network (DT-GAT) to incorporate temporal signal, i.e., time intervals between interactions and time differences between sessions, into session representations from both item and session perspectives. The item-wise learning channel employs a temporal graph attention network to capture interest shifts and filter interaction noise, while the session-wise learning channel utilizes a temporal graph attention network to handle inconsistent popularity trends. Additionally, we introduce a multi-temporal window processing mechanism to construct robust session representations that effectively capture short-term interests while filtering noise. Extensive experiments conducted on three real-world datasets demonstrate that DT-GAT consistently outperforms state-of-the-art baseline models. Our code is available at: https://github.com/downw/DT-GAT • We propose DT-GAT to integrate item- and session-level temporal signals. • Dual temporal GATs capture dependencies via temporal intra- and inter-session graphs. • Contrastive learning aligns dual channels to enhance session representations. • Experiments on three datasets validate the effectiveness of DT-GAT.

Breast cancer is the leading cause of death among women worldwide. Ultrasound imaging is an important means for the early detection of breast cancer to improve the survival rate. Due to the shortage of experienced sonographers, computer-aided systems for breast cancer recognition become particularly important. Some recent studies analyze tumor types in lesion regions but rely on predefined ROIs. Some other studies recognize cancer in the whole ultrasound image, but always suffer from the extremely variable proportion, location and quantity of the tumor lesions. In this paper, we propose a Regional Prior Distillation (RPD) framework for breast cancer diagnosis in ultrasound images. To enhance the analysis of the tumor region, we propose an Image-Cross Attention (ICA) to fuse the predefined ROI prior information with ultrasound images by training a prior-fused model. To remove the constraint of predefined ROIs, we propose a Distribution Distillation Learning (DDL) to distill the prior-fused sample distribution from the prior-fused model into a diagnostic model, which analyzes the disease from only ultrasound images, based on the knowledge distillation paradigm of the teacher-student framework. Comprehensive experiments are conducted on multi-institutional datasets to validate the proposed RPD framework. The results demonstrate the following points. The ICA fuses regional prior information adequately, leading to a high-performance prior-fused model. The DDL distills the prior information effectively, enhancing the diagnostic model to focus on the tumor lesions. The performance of the diagnostic model surpasses that of current SOTA methods. In addition, the diagnostic model is robust to slight perturbations and achieves good generalization performance.

Ultrasound imaging has become an important modality for early breast cancer diagnosis. However, breast tumors in ultrasound images often exhibit location uncertainty, scale variations, and boundary ambiguity, which severely hinder accurate automatic classification. This paper proposes a Multiscale Convolutional and Transformer Fusion Network (MSCTFN). To address location uncertainty and boundary ambiguity, a Convolutional Block Attention Module (CBAM) is employed to process the extracted features, highlighting key tumor regions and enhancing boundary representations. To handle scale variations, a Multi-Scale Convolution (MSC) structure is designed to process features with varying receptive fields in parallel, which effectively models the multi-scale features. Additionally, a Dynamic Weighted FPN module is introduced to adaptively fuse multiscale features, significantly enhancing the model's discriminative ability and generalization performance. Experimental results on the BUSI and UDIAT breast ultrasound datasets validate the effectiveness of the proposed method, demonstrating strong generalization capability and promising clinical potential.

Accurate spatiotemporal prediction is crucial for improving public health and enhancing travel efficiency. However, this task is challenging due to the complex spatiotemporal dependencies between regions. Most methods learn spatial representations without considering spatially skewed distributions, where data from the different regions often exhibit long-tailed distributions, limiting the spatial representation capabilities of the tail distribution regions. To address the above challenges, we propose a novel Multi-level Cross-view Graph Convolution Network (MCG-STNet) for spatiotemporal prediction. The core advantage of MCG-STNet is its ability to jointly capture non-Euclidean dependencies from global and local views while enhancing the spatial feature representation of tail distribution regions through an adaptive cross-view information transfer network. Specifically, we design a fuzzy neural network-based soft clustering approach to capture the functional relationships of regions with long-tail distributed data in the global view, integrating domain expertise to capture spatial dependencies among regional functionalities representations adaptively. We propose an adaptive cross-view information transfer network, allowing for the adaptive extraction of supplementary spatial feature representations from global to local view. Experimental results on five real-world datasets demonstrate that our proposed MCG-STNet outperforms other baselines. Further analysis validates the insights into the effectiveness of MCG-STNet.

Numerous pervasive applications, such as guiding outdoor health activities and allocating public transportation resources, rely on accurate predictions. Intuitively, the readings of a region may be influenced by various relational patterns with other regions, which can be revealed by exploring the complex spatial correlations between them. Recent studies have demonstrated the potential for improving spatiotemporal prediction performance by modeling various spatial correlations as graphs and capturing spatial dependencies using parallel multi-graph convolution network methods. However, these methods cannot directly learn dynamic spatial dependencies across graphs, leading to insufficient utilization of spatial contextual information. To address the above issues, we propose a novel dynamic multi-graph convolution networks, named DyMGCN, for spatiotemporal forecasting. Specifically, we introduce fuzzy theory, which combines fuzzy systems and neural networks, to learn uncertain dynamic dependencies across graphs and represent them as weight matrices. We further improve the understanding and utilization of spatial correlations by capturing stable long-term autocorrelations between regions. Many experiments on multiple real-world spatiotemporal datasets demonstrate that our proposed DyMGCN outperforms other baselines in spatiotemporal prediction methods.

Accurate air quality forecasting is essential in managing outdoor activity risk and responding to pollution emergencies. However, effectively modeling complex underlying spatiotemporal dependencies among monitoring stations remains a challenging task. Most existing methods deeply rely on local features to model dynamic spatial correlations and on RNNs to model temporal evolution. In this paper, we propose a novel multi-task deep spatiotemporal graph neural network, named MTGnet, for air quality prediction. MTGnet's main advantage is its ability to adaptively capture complex correlations among different stations, represented as an adjacency matrix, using both local features and global patterns. MTGnet consists of multiple convolutional layers for aggregating information about nearby stations and extracting essential spatial and temporal features for future air quality prediction. Using this architecture, we implement a multi-task learning scheme that trains the model to predict air quality both finely, at the station level, and coarsely, at the city level. Experiments on multiple real datasets demonstrate that MTGnet outperforms state-of-the-art methods.

In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss Lmax , which is the user’s tolerance. Lmax can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.