Latest AI and machine learning research in heart transplantation for healthcare professionals.
Diffusion models often exhibit inconsistent sample quality due to stochastic variations inherent in their sampling trajectories. Although training-based fine-tuning (e.g. DDPO [1]) and inference-time alignment techniques[2] aim to improve sample fidelity, they typically necessitate full denoising processes and external reward signals. This incurs substantial computational costs, hindering their ...
While pre-trained multimodal representations (e.g., CLIP) have shown impressive capabilities, they exhibit significant compositional vulnerabilities leading to counterintuitive judgments. We introduce Multimodal Adversarial Compositionality (MAC), a benchmark that leverages large language models (LLMs) to generate deceptive text samples to exploit these vulnerabilities across different modalitie...
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patie...
In Human-Robot Interaction, speech is one of the most intuitive and effective communication channel. In Industry 4.0, speech-based communication can s...
A significant risk following a kidney transplantation is graft loss. The Screen Reject Project has developed a Clinical Data Warehouse (CDWH) as a fou...
BACKGROUND: As the optimal treatment for end-stage renal disease, kidney transplantation has proven instrumental in enhancing patient survival and qua...
Severe community-acquired pneumonia (sCAP) is a major global health challenge, with high morbidity and mortality, especially among patients requiring ...
Although immunotherapy demonstrates considerable prospect in overcoming solid tumors, its clinical efficacy is limited by several factors, such as poo...
Current immunotherapeutic approaches for autoimmune disorders primarily rely on the use of generalized immunosuppressive medications. However, most im...
Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements...
Recent advances in multimodal Reward Models (RMs) have shown significant promise in delivering reward signals to align vision models with human pref...
Population pharmacokinetic (PopPK) modelling is a fundamental tool for understanding drug behaviour across diverse patient populations and enabling ...
Many soft robots struggle to produce dynamic motions with fast, large displacements. We develop a parallel 6 degree-of-freedom (DoF) Stewart-Gough m...
Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source...
Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization m...
Recent advances in general medical AI have made significant strides, but existing models often lack the reasoning capabilities needed for complex me...
Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their relia...
Recent advances in deep thinking models have demonstrated remarkable reasoning capabilities on mathematical and coding tasks. However, their effecti...
The question of how to make decisions that maximise the well-being of all persons is very relevant to design language models that are beneficial to ...
Socio-psychological studies have identified a common phenomenon where an individual's public actions do not necessarily coincide with their private ...