Intensive care unit-acquired weakness (ICUAW) is frequent in critically ill adults and is associated with adverse outcomes, but early recognition is difficult because standard diagnosis relies on volitional strength testing. In this prospective multicentre cohort study across 16…
Machine learning has become an emerging paradigm for microrobotics, enabling autonomous micro-/nanorobot navigation in complex and highly disturbed environments without requirements of precise models. However, the state-of-the-art learning-based methods adopt “black-box” neural n…
Gastrointestinal involvement is common in systemic sclerosis (SSc), but severe abdominal pain should not be attributed automatically to dysmotility or malabsorption. We report a 72-year-old woman with long-standing limited cutaneous SSc who developed recurrent severe abdominal pa…
Background and aim Liver transplantation (LT) is considered the optimal treatment for hepatocellular carcinoma (HCC) within extended transplant criteria, but it is available to only a minority of patients. The optimal alternative treatment strategy for patients with HCC beyond th…
Background The optimal vasopressor-escalation strategy after norepinephrine in septic shock remains uncertain, with randomized trials showing no clear winner and observational data suggesting severity-dependent effects. We characterized severity-stratified heterogeneity for addin…
Background Medication non-adherence is widely occurring in children and adolescents with ADHD, which may lead to adverse consequences. However, the pooled adherence rate and influencing factors are inconsistent in different studies. Methods We searched PubMed, Embase (Ovid), Coch…
BACKGROUND: Early-life growth trajectories, especially during the first two years, are important for future health. However, in the context of rapidly rising childhood obesity, it is essential to characterize BMI-for-age Z-score (BAZ) trajectories in early childhood, compare opti…
Effective molecular representation learning is crucial for accurate molecular property prediction. Recently, numerous self-supervised learning (SSL) approaches leveraging 3D GNNs have been developed to capture comprehensive 3D structural information for drug discovery. However, e…
The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we pr…
Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-ortho…
4D scene synthesis from monocular videos has made significant progress in recent years. However, existing methods are typically constrained by view interpolation. As a result, they struggle to infer unseen regions beyond the observed views. In this paper, we reformulate the task…
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parame…
This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies. We propose the Soft-Threshold NMI-prior Transformer Graph Attention Network (STN-TGAT), which int…
Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis. However, real clinical data often exhibit extreme long-tailed distributions, leading to degraded performance on rare diseases in tail classes. This issue is not only driven by data sca…
Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments. Network…
A pivotal step in autonomous driving simulation involves inserting foreground vehicles with predefined trajectories into simulated scenes. This process enhances scene diversity and facilitates the creation of various corner cases for testing and improving autonomous driving model…
Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities. However, existing GS-based methods for deformable objects face two key limitations: (i) illumination is erro…
We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict how actions drive visual scene evolution, the resulting world model captures reusable interaction dynamics beyond appearance-le…
With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and transmission bandwidth often lead to low-resolution ODIs, hindering the rendering of fine-grained 3…
Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often faili…
Cognitive load is a critical factor that influences learning and performance. In recent years, eye-tracking technologies have emerged as a promising method for detecting and measuring cognitive load in real-time during learning activities. This paper presents a comprehensive revi…