Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
For decades, the photosynthetic bacteria (PSB)-based nitrogen treatment and valorization from wastewater have been explored. However, balancing nitrogen removal performance and resource recovery potential in PSB has remained a key unresolved issue for a long time. This study employed generative deep learning algorithms to achieve high-quality data generation, supporting multi-objective optimizatio...
Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source identification, and public health early warning systems. Prolonged VOC monitoring is routinely implemented by gas chromatographs. However, accurate identification of target contaminants heavily relies on time-consuming and error-prone manual processe...
The accurate characterization of the potential energy surface (PES) is fundamental to understanding molecular structures and chemical reaction mechani...
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable ...
We conduct an extensive study on the state of calibration under real-world dataset shift for image classification. Our work provides important insig...
Advances in low-communication training algorithms are enabling a shift from centralised model training to compute setups that are either distributed...
Decoding speech from brain signals is a challenging research problem. Although existing technologies have made progress in reconstructing the mel sp...
Precise Event Spotting (PES) in sports videos requires frame-level recognition of fine-grained actions from single-camera footage. Existing PES mode...
Image fusion aims to integrate complementary information across modalities to generate high-quality fused images, thereby enhancing the performance ...
Text-to-image diffusion models (T2I DMs), represented by Stable Diffusion, which generate highly realistic images based on textual input, have been ...
Text-to-image diffusion models (T2I DMs), represented by Stable Diffusion, which generate highly realistic images based on textual input, have been ...
Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpret...
Despite the success of deep learning across various domains, it remains vulnerable to adversarial attacks. Although many existing adversarial attack...
Composed Image Retrieval (CIR) represents a novel retrieval paradigm that is capable of expressing users' intricate retrieval requirements flexibly....
PURPOSE: Development of aphasia therapies is limited by clinician shortages, patient recruitment challenges, and funding constraints. To address these...
We consider the problem of learning robust discriminative representations of causally-related latent variables. In addition to observational data, t...
Ethereum smart contracts operate in a concurrent environment where multiple transactions can be submitted simultaneously. However, the Ethereum Virt...
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memor...
Continual learning in medical image segmentation involves sequential data acquisition across diverse domains (e.g., clinical sites), where task inte...
As digital emotional support needs grow, Large Language Model companions offer promising authentic, always-available empathy, though rigorous evalua...