Latest AI and machine learning research in medicare for healthcare professionals.
We explore Large Language Models (LLMs)' human motion knowledge through 3D avatar control. Given a motion instruction, we prompt LLMs to first generate a high-level movement plan with consecutive steps (High-level Planning), then specify body part positions in each step (Low-level Planning), which we linearly interpolate into avatar animations as a clear verification lens for human evaluators. T...
Peptide compounds demonstrate considerable potential as therapeutic agents due to their high target affinity and low toxicity, yet their drug development is constrained by their low membrane permeability. Molecular weight and peptide length have significant effects on the logD of peptides, which in turn influences their ability to cross biological membranes. However, accurate prediction of pepti...
While recent text-to-image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, their performance significantly...
Multi-view Synthetic Aperture Radar (SAR) imaging can effectively enhance the performance of tasks such as automatic target recognition and image in...
Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, exist...
Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and phys...
Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relie...
Conformal prediction enables the construction of high-coverage prediction sets for any pre-trained model, guaranteeing that the true label lies with...
We introduce $\textit{Backward Conformal Prediction}$, a method that guarantees conformal coverage while providing flexible control over the size of...
Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans compared to planning i...
Knowledge distillation (KD) is a core component in the training and deployment of modern generative models, particularly large language models (LLMs...
Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets, assuming exch...
This paper investigates advertising practices in print newspapers across India using a novel data-driven approach. We develop a pipeline employing i...
Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalab...
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are cap...
The demand for quality in mobile applications has increased greatly given users' high reliance on them for daily tasks. Developers work tirelessly t...
Drones are promising for data collection in precision agriculture, however, they are limited by their battery capacity. Efficient path planners are ...
Despite recent advances in facial recognition, there remains a fundamental issue concerning degradations in performance due to substantial perspecti...
In Wireless Sensor Networks (WSNs), achieving optimal coverage in dynamic environments remains a significant challenge. Traditional optimization techn...
As machine learning (ML) models are increasingly deployed in high-stakes domains, trustworthy uncertainty quantification (UQ) is critical for ensuri...