Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and first-principles methods such as density functional theory (DFT). Here we propose a machine learning (ML) model for predicting TS energies based on Gaussian process regression with the Wasserstein Weisfeiler-Lehman g...
We present JointDiT, a diffusion transformer that models the joint distribution of RGB and depth. By leveraging the architectural benefit and outstanding image prior of the state-of-the-art diffusion transformer, JointDiT not only generates high-fidelity images but also produces geometrically plausible and accurate depth maps. This solid joint distribution modeling is achieved through two simple...
Computing has a huge memory problem. The memory system, consisting of multiple technologies at different levels, is responsible for most of the ener...
The proliferation of end devices has led to a distributed computing paradigm, wherein on-device machine learning models continuously process diverse...
Large Language Models (LLMs) have brought about revolutionary changes in diverse fields, rendering LLM training of utmost importance for modern ente...
The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resou...
A sensitive and efficient method for simultaneous quantifying molnupiravir and its active metabolite β-d-N-hydroxycytidine in human plasma was develop...
We present a novel hybrid quantum-classical neural network architecture for fraud detection that integrates a classical Long Short-Term Memory (LSTM...
Artificial Intelligence (AI) models deployed in production frequently face challenges in maintaining their performance in non-stationary environment...
Generative artificial intelligence (AI), particularly large language models (LLMs), is being rapidly deployed in recruitment and for candidate short...
Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industri...
Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization alg...
Traffic flow prediction is a critical component of intelligent transportation systems, yet accurately forecasting traffic remains challenging due to...
Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perfor...
Modern LLM serving systems confront inefficient GPU utilization due to the fundamental mismatch between compute-intensive prefill and memory-bound d...
Large Vision Language Models have demonstrated impressive versatile capabilities through extensive multimodal pre-training, but face significant lim...
Knowledge distillation (KD) compresses the network capacity by transferring knowledge from a large (teacher) network to a smaller one (student). It ...
Hyperspectral image (HSI) classification presents significant challenges due to the high dimensionality, spectral redundancy, and limited labeled da...
Explainable artificial intelligence (XAI) has become increasingly important in decision-critical domains such as healthcare, finance, and law. Count...
For deep learning-based image steganography frameworks, in order to ensure the invisibility and recoverability of the information embedding, the los...