Latest AI and machine learning research in medical education for healthcare professionals.
Artists and especially new media artists contribute to public perceptions and adoption of new technologies through their own use of emerging media technologies such as augmented and virtual reality, generative image systems, and high-resolution displays in the production of their work. In this way, art and media production can be understood as part of the larger issue of unsustainable computatio...
We present COMPASS, a novel simulation-based inference framework that combines score-based diffusion models with transformer architectures to jointly perform parameter estimation and Bayesian model comparison across competing Galactic Chemical Evolution (GCE) models. COMPASS handles high-dimensional, incomplete, and variable-size stellar abundance datasets. Applied to high-precision elemental ab...
Understanding surgical scenes can provide better healthcare quality for patients, especially with the vast amount of video data that is generated du...
Differentiable programming that enables automatic differentiation through simulation pipelines has emerged as a powerful paradigm in scientific comp...
The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking...
Assisting medical students with clinical reasoning (CR) during clinical scenario training remains a persistent challenge in medical education. This ...
Despite years of research and the dramatic scaling of artificial intelligence (AI) systems, a striking misalignment between artificial and human vis...
Single-cell data reveal the presence of biological stochasticity between cells of identical genome and environment, in particular highlighting the t...
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple...
Deep learning models have proven to be effective on medical datasets for accurate diagnostic predictions from images. However, medical datasets ofte...
Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic ul...
Existing open-vocabulary 3D semantic segmentation methods typically supervise 3D segmentation models by merging text-aligned features (e.g., CLIP) e...
Quantum Neural Networks (QNNs), a prominent approach in Quantum Machine Learning (QML), are emerging as a powerful alternative to classical machine ...
Fault diagnosis in Cyber-Physical Systems (CPSs) is essential for ensuring system dependability and operational efficiency by accurately detecting a...
Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn ...
As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be pai...
In order to address the scalability challenge within Neural Architecture Search (NAS), we speed up NAS training via dynamic hard example mining with...
Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video g...
To address the challenges of 3D modeling and structural simulation in industrial environment, such as the difficulty of equipment deployment, and th...
Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressi...