Fine-grained action evaluation in medical vision faces unique challenges due to the unavailability of comprehensive datasets, stringent precision requirements, and insufficient spatiotemporal dynamic modeling of very rapid actions. To support development and evaluation, we introduce CPREval-6k, a multi-view, multi-label medical action benchmark containing 6,372 expert-annotated videos with 22 clinical labels. Using this dataset, we present GaussMedAct, a multivariate Gaussian encoding framework, to advance medical motion analysis through adaptive spatiotemporal representation learning. Multivariate Gaussian Representation projects the joint motions to a temporally scaled multi-dimensional space, and decomposes actions into adaptive 3D Gaussians that serve as tokens. These tokens preserve motion semantics through anisotropic covariance modeling while maintaining robustness to spatiotemporal noise. Hybrid Spatial Encoding, employing a Cartesian and Vector dual-stream strategy, effectively utilizes skeletal information in the form of joint and bone features. The proposed method achieves 92.1% Top-1 accuracy with real-time inference on the benchmark, outperforming the baseline by +5.9% accuracy with only 10% FLOPs. Cross-dataset experiments confirm the superiority of our method in robustness.

Brain-computer interfaces (BCIs) have opened new platforms for human-computer interaction, medical diagnostics, and neurorehabilitation. Wearable BCI systems, which typically employ non-invasive electrodes for portable monitoring, hold great promise for real-world applications, but also face significant challenges of signal quality degradation caused by motion artifacts and environmental interferences. Most existing wearable BCI datasets are collected under stationary or controlled lab settings, limiting their utility for evaluating performance under body movement. To bridge this gap, we introduce WearBCI, the first dataset that comprehensively evaluates wearable BCI signals under different motion dynamics with synchronized multimodal recordings (EEG, IMU, and egocentric video), and systematic benchmark evaluations for studying impacts of motion artifact. Specifically, we collect data from 36 participants across different motion dynamics, including body movements, walking, and navigation. This dataset includes synchronized electroencephalography (EEG), inertial measurement unit (IMU) data, and egocentric video recordings. We analyze the collected wearable EEG signals to understand the impact of motion artifacts across different conditions, and benchmark representative EEG signal enhancement techniques on our dataset. Furthermore, we explore two new case studies: cross-modal EEG signal enhancement and multi-dimension human behavior understanding. These findings offer valuable insights into real-world wearable BCI deployment and new applications.

3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require large amounts of training data, and suffer from high inference latency. We propose MoViD, a viewpoint-invariant 3D human pose estimation framework that disentangles viewpoint information from motion features. The key idea is to extract viewpoint information from intermediate pose features and leverage it to enhance both the robustness and efficiency of pose estimation. MoViD introduces a view estimator that models key joint relationships to predict viewpoint information, and an orthogonal projection module to disentangle motion and view features, further enhanced through physics-grounded contrastive alignment across views. For real-time edge deployment, MoViD employs a frame-by-frame inference pipeline with a view-aware strategy that adaptively activates flip refinement based on the estimated viewpoint. Evaluations on nine public datasets and newly collected multiview UAV and gait analysis datasets show that MoViD reduces pose estimation error by over 24.2% compared to state-of-the-art methods, maintains robust performance under severe occlusions with 60% less training data, and achieves real-time inference at 15 FPS on NVIDIA edge devices.

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present MobileAF , a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What’s more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 98.4% accuracy, 97.6% precision, 95.8% recall, 99.2% specificity, and 96.7% F1 score.

A Radio Environment Map (REM) is pivotal for optimizing wireless communication systems, yet its accuracy is inherently tied to the complex interplay of electromagnetic propagation and landform heterogeneity. Existing REM construction methods often implicitly ignore land features, leading to inaccuracies in REM estimation. In this paper, we introduce a novel Radio Oriented Heterogeneous Multitask Gaussian Process (RO-HMTGP) to address this gap by jointly integrating heterogeneous inputs, including land features and Reference Signal Receiving Power (RSRP). The proposed RO-HMTGP model treats land features as auxiliary knowledge and captures spatially correlated propagation effects across varying landforms while preserving feature-specific attenuation characteristics. RO-HMTGP enhances the construction accuracy of REM while quantifying uncertainty. Empirical evaluations using real-world datasets demonstrate that the land feature-aware RO-HMTGP achieves superior predictive performance compared to existing methods without land feature awareness. RO-HMTGP advances the integration of geospatial analytics into wireless communications, providing a pathway toward land feature-aware cognitive radio systems.

Volumetric videos are essential for immersive applications due to their engaging and realistic experiences. However, streaming them in real time over constrained, fluctuating networks remains challenging. Progressive streaming is an effective method to mitigate this issue by gradually enhancing video quality through incremental data transmission. However, existing progressive volumetric streaming solutions often rely on specific compression algorithms or require codec modifications, leading to poor compatibility with standard codecs. In this paper, we propose P2VS, a progressive partition-based volumetric video streaming framework, to achieve codec-independent progressive streaming. Specifically, P2VS leverages the unique structure of point cloud-based volumetric video to incrementally enhance video quality without being constrained by specific compression algorithms. Moreover, we propose adaptive streaming algorithms under this framework to enhance the quality of experience (QoE). Extensive simulations demonstrate that P2VS improves QoE by 21% on average compared to non-progressive streaming schemes. It also achieves better bandwidth efficiency and full compatibility with standard codecs. A prototype is built to verify the feasibility of P2VS.

Recent developments in the applications of deep reinforcement learning methods to portfolio selection have achieved superior performance to conventional methods. However, two major challenges remain unaddressed in these models and inevitably lead to the deterioration of model performance. First, asset characteristics often suffer from low and unstable signal-to-noise ratios, leading to poor learning robustness of the predictive feature representations. Second, existing literature fails to consider the complexity and diversity in long-term and short-term spatio-temporal predictive relations between the feature sequences and portfolio objectives. To tackle these problems, we propose a novel Context-Aware Frequency-Embedding Graph Convolution Network (Cafe-GCN) for spatio-temporal portfolio selection. It contains three important modules: (1) frequency-embedding block that explicitly captures the short-term and long-term predictive information embedded in asset characteristics meanwhile filtering out noise; (2) context-aware block that learns multiscale temporal dependencies in the feature space; and (3) multi-relation graph convolutional block that exploits both static and dynamic spatial relations among assets. Extensive experiments on two real-world datasets demonstrate that Cafe-GCN consistently outperforms proposed techniques in the literature.

The human immune response relies on the unique ability of T-cell receptors (TCRs) to specifically bind to peptides, a process essential for immune surveillance and response. Although deep learning methods for prediction of TCR-peptide binding have proliferated, many encoder-based approaches learn dataset biases, greatly overestimating the model results, and ignoring the biochemical mechanisms and spatial properties affecting binding. Through our analysis, we found that interaction pairs generated by cross-mapping the amino acid properties between TCR and peptide implicitly simulate spatial structure, enabling machine learning models to capture information more effectively. Based on this insight, we developed T-cell receptor cross (TCRoss), a transformer-based model for large-scale learning. In addition, we observed that incorporating environmental information into the dataset not only mitigates learning biases but also improves performance. Experiments show that TCRoss consistently outperforms existing models in both observed contexts and de novo peptide scenarios. Wet-lab validation using T-cell activation assays confirmed the model's predictions for nonbinding peptides and provided critical experimental evidence for model assessment. Biophysical validation confirms that high-attention residue pairs correspond to crystallographically observed binding interfaces.