Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
We present MatDecompSDF, a novel framework for recovering high-fidelity 3D shapes and decomposing their physically-based material properties from multi-view images. The core challenge of inverse rendering lies in the ill-posed disentanglement of geometry, materials, and illumination from 2D observations. Our method addresses this by jointly optimizing three neural components: a neural Signed Dis...
Base station (BS) association and beam selection in multi-cell drone corridor networks present unique challenges due to the high altitude, mobility and three-dimensional movement of drones. These factors lead to frequent handovers and complex beam alignment issues, especially in environments with dense BS deployments and varying signal conditions. To address these challenges, this paper proposes...
Camera relocalization, a cornerstone capability of modern computer vision, accurately determines a camera's position and orientation (6-DoF) from im...
This paper presents Edge-based Mixture of Experts (MoE) Collaborative Computing (EMC2), an optimal computing system designed for autonomous vehicles...
The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing...
Academic well-being is deeply influenced by peer-support networks, yet they remain informal, inequitable, and unsustainable, often relying on person...
As modern computing advances, new interaction paradigms have emerged, particularly in Augmented Reality (AR), which overlays virtual interfaces onto...
Unmanned Aerial Vehicle-based Object Detection (UAV-OD) faces substantial challenges, including small target sizes, high-density distributions, and ...
6D pose estimation of rigid objects is a long-standing and challenging task in computer vision. Recently, the emergence of deep learning reveals the p...
OBJECTIVE: Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed...
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorpora...
Simulation-based design, optimization, and validation of autonomous driving algorithms have proven to be crucial for their iterative improvement ove...
While personalization has been explored in traditional autonomous driving systems, it remains largely overlooked in end-to-end autonomous driving (E...
Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused...
Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these e...
Solving Electromagnetic Inverse Scattering Problems (EISP) is fundamental in applications such as medical imaging, where the goal is to reconstruct ...
3D characters play a crucial role in our daily entertainment. To improve the efficiency of 3D character modeling, recent image-based methods use two...
Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in ado...
The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammar...
Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic...