Zobrazeno 1 - 10
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pro vyhledávání: '"Hsin Yu"'
Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this
Externí odkaz:
http://arxiv.org/abs/2411.08698
We consider reinforcement learning (RL) for a class of problems with bagged decision times. A bag contains a finite sequence of consecutive decision times. The transition dynamics are non-Markovian and non-stationary within a bag. Further, all action
Externí odkaz:
http://arxiv.org/abs/2410.14659
The Keldysh theory of photoionization for solids is generalized to atomically thin two-dimensional semiconductors. We derive a closed-form formula and its asymptotic forms for a two-band model with a Kane dispersion. These formulas exhibit characteri
Externí odkaz:
http://arxiv.org/abs/2408.02569
Autor:
Cheng, Yang, Shu, Qingyuan, Lee, Albert, He, Haoran, Zhu, Ivy, Suhail, Haris, Chen, Minzhang, Chen, Renhe, Wang, Zirui, Zhang, Hantao, Wang, Chih-Yao, Yang, Shan-Yi, Hsin, Yu-Chen, Shih, Cheng-Yi, Lee, Hsin-Han, Cheng, Ran, Pamarti, Sudhakar, Kou, Xufeng, Wang, Kang L.
Stochastic diffusion processes are pervasive in nature, from the seemingly erratic Brownian motion to the complex interactions of synaptically-coupled spiking neurons. Recently, drawing inspiration from Langevin dynamics, neuromorphic diffusion model
Externí odkaz:
http://arxiv.org/abs/2407.12261
Autor:
Chang, Hsin-Yu, Chen, Pei-Yu, Chou, Tun-Hsiang, Kao, Chang-Sheng, Yu, Hsuan-Yun, Lin, Yen-Ting, Chen, Yun-Nung
This paper provides a detailed survey of synthetic data techniques. We first discuss the expected goals of using synthetic data in data augmentation, which can be divided into four parts: 1) Improving Diversity, 2) Data Balancing, 3) Addressing Domai
Externí odkaz:
http://arxiv.org/abs/2407.03672
Autor:
Li, Cheng-Yi, Chang, Kao-Jung, Yang, Cheng-Fu, Wu, Hsin-Yu, Chen, Wenting, Bansal, Hritik, Chen, Ling, Yang, Yi-Ping, Chen, Yu-Chun, Chen, Shih-Pin, Lirng, Jiing-Feng, Chang, Kai-Wei, Chiou, Shih-Hwa
Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary success in 2D radiology captioning is incompetent to refle
Externí odkaz:
http://arxiv.org/abs/2407.02235
Autor:
Salvarese, Alberto, Chen, Hsin-Yu
Publikováno v:
The Astrophysical Journal Letters, 974, 1, L16, 2024
The inconsistency between experiments in the measurements of the local Universe expansion rate, the Hubble constant, suggests unknown systematics in the existing experiments or new physics. Gravitational-wave standard sirens, a method to independentl
Externí odkaz:
http://arxiv.org/abs/2406.11126
We develop an approach for building quantum models based on the exponentially growing orthonormal basis of Hartley kernel functions. First, we design a differentiable Hartley feature map parametrized by real-valued argument that enables quantum model
Externí odkaz:
http://arxiv.org/abs/2406.03856
Autor:
Wu, Chun-Hung, Chen, Shih-Hong, Hu, Chih-Yao, Wu, Hsin-Yu, Chen, Kai-Hsin, Chen, Yu-You, Su, Chih-Hai, Lee, Chih-Kuo, Liu, Yu-Lun
This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth. DeNVeR utilizes optical flow and layer separation techniques, enhancin
Externí odkaz:
http://arxiv.org/abs/2406.01591
Autor:
Lau, Mu-Te, Cheng, Chin-Yi, Lu, Cheng-Hua, Chuang, Chia-Hsu, Kuo, Yi-Hsiang, Yang, Hsiang-Chun, Kuo, Chien-Tung, Chen, Hsin-Yu, Tung, Chen-Ying, Tsai, Cheng-En, Chen, Guan-Hao, Lin, Leng-Kai, Wang, Ching-Huan, Wang, Tzu-Hsu, Huang, Chung-Yang Ric
In this paper, we introduce a new quantum circuit synthesis (QCS) framework, Qsyn, for developers to research, develop, test, experiment, and then contribute their QCS algorithms and tools to the framework. Our framework is more developer-friendly th
Externí odkaz:
http://arxiv.org/abs/2405.07197