Abstract: Rainy days with ponding water on the road surface can cause vehicle crashes, injuries, and deaths. Surveillance video-based splashed water detection is a promising way to reduce traffic accidents. Surveillance videos have many lighting…
Abstract: Protein sequence data from computational biology and bioinformatics is ideal for NLP Language Models (LMs). Low-inference LMs seek new prediction frontiers. We trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert,…
Abstract: Automatic vessel segmentation in fundus images helps screen, diagnose, treat, and evaluate cardiovascular and ophthalmologic diseases. Retinal vessel segmentation has been difficult due to limited well-annotated data, varying vessel sizes, and intricate vessel structures….
Abstract: Deep neural networks succeed because of good architectures. Some Neural Architecture Search (NAS) methods search or manually design deep architectures. Even a well-designed/searched architecture may contain many nonsignificant or redundant modules/operations (e.g., intermediate convolution…
Abstract: With the development of sensing technologies and machine learning, electroencephalography (EEG) techniques that can identify human emotions and inner states through physiological signals have been actively developed and applied to automobiles, robotics, healthcare, and…
Abstract: Traffic state estimation (TSE) is complicated by sensor noise and data sparsity. This paper introduces physics-informed deep learning (PIDL) to solve this problem. PIDL strengthens a deep learning neural network to better estimate traffic…
Abstract: We test a Deep Learning model for 3D medical image segmentation. Our model addresses manual segmentation’s high inter-rater contouring variability and contouring time. The careful analysis can be applied to other medical image segmentation…
Abstract: Deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique that can reduce radiographer workload, optimize imaging workflow, and improve image quality. X-ray imaging research…
Abstract: Federated learning allows deep learning model training without sharing patient data. Federated learning models may perform poorly due to heterogeneous data across institutions. We propose SplitAVG, a heterogeneity-aware federated learning method, to overcome performance…
Abstract: Due to deployment, coverage, and capacity issues, ground wireless communication networks cannot support AI applications like intelligent transportation systems (ITS). The industry is researching the space-air-ground integrated network (SAGIN). SAGIN is flexible, reliable, and…
Abstract: Transportation services are increasingly important in people’s daily lives and benefit individuals and economic development. Traffic flows are unpredictable and volatile, limiting transportation services. To stabilize intelligent transport systems and optimize traffic scheduling, precise…
Abstract: Autonomous driving and ADAS tasks are growing. However, porting multiple functions into a power-constrained computing device is difficult. This work aims to simplify the pixel-wise driving scene understanding learning process. This paper uses semantic…
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