Decoding of continuous speech from electroencephalography (EEG) presents a promising avenue for understanding neural mechanisms of auditory processing and developing applications in hearing diagnostics. Recent advances in deep learning have improved ... read more
As the leading cause of death worldwide, cardiovascular diseases motivate the
development of more sophisticated methods to analyze the heart and its
substructures from medical images like Computed Tomography (CT) and Magnetic
Resonance (MR). Semant... read more
Reference Expression Segmentation (RES) aims to segment image regions
specified by referring expressions and has become popular with the rise of
multimodal large models (MLLMs). While MLLMs excel in semantic understanding,
their token-generation pa... read more
BACKGROUND: The assessment of osteonecrosis of the femoral head (ONFH) often presents challenges in accuracy and efficiency. Traditional methods rely on imaging studies and clinical judgment, prompting the need for advanced approaches. This study aim... read more
Deep learning for microbiome analysis has shown potential for understanding microbial communities and human phenotypes. Here, we propose an approach, Transformer-based Robust Principal Component Analysis(TRPCA), which leverages the strengths of trans... read more
Accurate system identification is crucial for reducing trajectory drift in
bipedal locomotion, particularly in reinforcement learning and model-based
control. In this paper, we present a novel control framework that integrates
system identification... read more
Our purpose is to improve performance-based animation which can drive
believable 3D stylized characters that are truly perceptual. By combining
traditional blendshape animation techniques with multiple machine learning
models, we present both non-r... read more
Federated learning (FL) enables collaborative model training without sharing
raw data, but individual model updates may still leak sensitive information.
Secure aggregation (SecAgg) mitigates this risk by allowing the server to
access only the sum ... read more
This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By out... read more
Single-mode biophysical fingerprints have specific overlap when cell heterogeneity is analyzed. The cross-sensitivity between these parameters results in significant fuzzy intervals in cell phenotype classification based on one-dimensional features, ... read more
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