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of 2 422
pro vyhledávání: '"Chi Han"'
Autor:
Chen, Chia-Chia, Peng, Chi-Han
We present a novel discrete optimization-based approach to generate downsampled versions of binary images that are guaranteed to have the same topology as the original, measured by the zeroth and first Betti numbers of the black regions, while having
Externí odkaz:
http://arxiv.org/abs/2407.17786
This work first explores using flexible beam-user mapping to optimize the beam service range and beam position, in order to adapt the non-uniform traffic demand to offer in high-throughput satellite (HTS) systems. Second, on this basis, the joint fle
Externí odkaz:
http://arxiv.org/abs/2407.08981
Social media popularity (SMP) prediction is a complex task involving multi-modal data integration. While pre-trained vision-language models (VLMs) like CLIP have been widely adopted for this task, their effectiveness in capturing the unique character
Externí odkaz:
http://arxiv.org/abs/2407.00556
Autor:
Hsu, Chih-Chung, Lee, Chia-Ming, Chiang, Yang Fan, Chou, Yi-Shiuan, Jiang, Chih-Yu, Tai, Shen-Chieh, Tsai, Chi-Han
Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability
Externí odkaz:
http://arxiv.org/abs/2404.01643
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named en
Externí odkaz:
http://arxiv.org/abs/2403.17645
Autor:
Hsu, Chih-Chung, Lee, Chia-Ming, Chiang, Yang Fan, Chou, Yi-Shiuan, Jiang, Chih-Yu, Tai, Shen-Chieh, Tsai, Chi-Han
This study explores the use of deep learning techniques for analyzing lung Computed Tomography (CT) images. Classic deep learning approaches face challenges with varying slice counts and resolutions in CT images, a diversity arising from the utilizat
Externí odkaz:
http://arxiv.org/abs/2403.11230
With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the codeswitching phenomenon remains a major obstacle that hinders ASR from per
Externí odkaz:
http://arxiv.org/abs/2402.17189
Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language. Prevailing approaches to APA normally leverage neural models trained with a regression loss function, such as
Externí odkaz:
http://arxiv.org/abs/2310.01839
This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis. Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images, whi
Externí odkaz:
http://arxiv.org/abs/2303.08490
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 18910-18928 (2024)
This paper studies a stochastic Leslie-Gower model with a Holling-II functional response that is driven by the Ornstein-Uhlenbeck process. Some asymptotic properties of the solution of the stochastic Leslie-Gower model are introduced: The existence a
Externí odkaz:
https://doaj.org/article/05731aa167424b63b49fb495e9f60adf