Abstract: With the advent of advanced genomic data extraction methods, numerous studies have been utilized these data to identify cancer subtypes. Given the complexity of cancer subtyping and the ...
Abstract: Transformers are widely used in natural language processing and computer vision, and Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pre-trained ...
Abstract: Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate ...
Abstract: The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a ...
Abstract: Light detection and ranging (LiDAR) point cloud denoising is critical for reliable environmental perception in autonomous driving and robotics. To overcome the lack of real-noise datasets ...
Abstract: Normally, three-phase linear Hall sensor-based embedded magnetic encoder (EME) are used in permanent magnet synchronous motors to detect the rotor angle, in which prefilters are used to ...
Abstract: Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal ...
Abstract: Accurate acquisition of 3-D human joint poses holds significant implications for tasks such as human action recognition. Monocular single-frame 2-D -to-3-D pose estimation focuses on ...
Abstract: Spiking neural networks (SNNs), known for their low-power, event-driven computation, and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous ...
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