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13,598 results on '"Yang, Jia"'

175. A potential role for inflammatory cytokines in a rare late-onset capsular block syndrome: a case report

Author

Ying-Hua Du, Xiao-Fang Liang, Kazuyuki Hirooka, Hui-Ka Xia, and Zhi-Yang Jia

Subjects
Case report, Capsular block syndrome, Late-onset, Cataract surgery, Inflammatory cytokines, Ophthalmology, RE1-994
Abstract

Abstract Background Late-onset capsule block syndrome (CBS) is a rare complication of cataract phacoemulsification and the implantation of a posterior chamber intraocular lens (PCIOL), which manifests six months to years after surgery. The hallmark of CBS is the formation of an opaque liquid substance between the implanted intraocular lens (IOL) and the posterior capsule. However, its pathogenesis remains unclear. Case presentation A 64-year-old female patient with chronic angle-closure glaucoma (axis length

Published
2024
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176. MAST: An Earthquake-Triggered Landslides Extraction Method Combining Morphological Analysis Edge Recognition With Swin-Transformer Deep Learning Model

Author

Yu Huang, Jianqiang Zhang, Haiqing He, Yang Jia, Rong Chen, Yonggang Ge, Zaiyang Ming, Lili Zhang, and Haoyu Li

Subjects
Deep learning, earthquake-triggered landslides (ETLs), edge recognition, morphological analysis, transformer, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Earthquake-triggered landslides (ETLs) are characterized by their extensive occurrences, having wide distributions. The conventional human–computer interaction extraction method is often time-consuming and labor-intensive, failing to meet the demands of disaster emergency response. There is a pressing need for a swift detection of ETLs. In this study, we introduce an ETLs extraction method (MAST) combining morphological analysis edge recognition with a Swin-Transformer (SWT) deep learning model, which is specifically designed for landslide extraction. The MAST model adopts a hierarchical construction approach akin to convolution neural networks, aiding in tasks such as target detection and semantic segmentation. To enhance the accuracy of landslide edge extraction, we incorporate an edge recognition algorithm based on the morphological analysis into the MAST model. This algorithm leverages morphological operations to extract the features of landslide boundaries. It effectively addresses issues such as discretization and irregularization of the extracted landslide boundaries, leading to more precise delineation of landslide boundaries. Drawing on UAV data collected from Wan Dong Village, De Tou Town, Sichuan Luding, China, during the 2022 Ms 6.8 Luding Earthquake, we conducted automated extraction of ETLs utilizing the MAST model. Experimental results demonstrate the superior performance of the MAST model compared to the traditional full convolution neural network (FCN) model and normal SWT model. The MAST model exhibits enhanced value in landslide extraction. Notably, it demonstrates a significant advantage in boundary extraction. Employing the Boundary IoU metric to evaluate the accuracy of ETLs extraction, the MAST model outperforms the SWT and FCN models at various distances.

Published
2024
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181. The In-Flight Realtime Trigger and Localization Software of GECAM

Author

Zhao, Xiao-Yun, Xiong, Shao-Lin, Wen, Xiang-Yang, Li, Xin-Qiao, Cai, Ce, Xiao, Shuo, Luo, Qi, Peng, Wen-Xi, Guo, Dong-Ya, An, Zheng-Hua, Gong, Ke, Liao, Jin-Yuan, Zhang, Yan-Qiu, Huang, Yue, Li, Lu, Wen, Xing, Zhang, Fei, Duan, Jing, Wang, Chen-Wei, Shi, Dong-Li, Zhang, Peng, Yi, Qi-Bin, Li, Chao-Yang, Xu, Yan-Bing, Liang, Xiao-Hua, Liu, Ya-Qing, Zhang, Da-Li, Sun, Xi-Lei, Zhang, Fan, Chen, Gang, Wang, Huan-Yu, Yang, Sheng, Liu, Xiao-Jing, Gao, Min, Li, Mao-Shun, Wang, Jin-Zhou, Zhou, Xing, Zhao, Yi, Xue, Wang-Chen, Zheng, Chao, Liu, Jia-Cong, Han, Xing-Bo, Qi, Jin-Ling, Huang, Jia, Zhang, Ke-Ke, Chen, Can, Yang, Xiong-Tao, Hou, Dong-Jie, Wang, Yu-Sa, Qiao, Rui, Ma, Xiang, Li, Xiao-Bo, Wang, Ping, Song, Xin-Ying, Song, Li-Ming, Zheng, Shi-Jie, Li, Bing, Zhang, Hong-Mei, Zhu, Yue, Chen, Wei, He, Jian-Jian, Zhang, Zhen, Hou, Jin, Wang, Hong-Jun, Hao, Yan-Chao, Wang, Xiang-Yu, Yang, Zong-Yuan, Wen, Zhi-Long, Chang, Zhi, Du, Yuan-Yuan, Gao, Rui, Lan, Xiao-Fei, Li, Yan-Guo, Li, Gang, Li, Xu-Fang, Lu, Fang-Jun, Lu, Hong, Meng, Bin, Shi, Feng, Wang, Hui, Wang, Hui-Zhen, Xu, Yu-Peng, Yang, Jia-Wei, Yang, Xue-Juan, Zhang, Shuang-Nan, Zhang, Chao-Yue, Zhang, Cheng-Mo, Tang, Zhi-Cheng, and Cheng, Cheng

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts., Comment: Draft, comments welcome

Published
2021

182. RID-Noise: Towards Robust Inverse Design under Noisy Environments

Author

Yang, Jia-Qi, Fan, Ke-Bin, Ma, Hao, and Zhan, De-Chuan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single design target, while the results of these evaluations could not be reused for a new target. To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN). Specifically, we estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods. Code and supplementary is publicly available at https://github.com/ThyrixYang/rid-noise-aaai22, Comment: AAAI'22

Published
2021

183. Unraveling intrinsic mobility limits in two-dimensional (AlxGa1−x)2O3 alloys.

Author

Duan, Xinlei, lqbal, Safdar, Shi, Min, Wang, Bao, Liu, Linhua, and Yang, Jia-Yue

Subjects
BOLTZMANN'S equation, ELECTRON mobility, TWO-dimensional electron gas, ALLOYS, ELECTRON gas, CHARGE carrier mobility, GALLIUM alloys, GALLIUM
Abstract

β-(AlxGa1−x)2O3 presents a diverse material characterization exhibiting exceptional electrical and optical properties. Considering the miniaturization of gallium oxide devices, two-dimensional (AlxGa1−x)2O3 alloys, as a critical component in the formation of two-dimensional electron gases, demand an in-depth examination of their carrier transport properties. Herein, we investigate the temperature-dependent carrier mobility and scattering mechanisms of quasi-two-dimensional (2D) (AlxGa1−x)2O3 (x ≤ 5) by solving the Boltzmann transport equation from first-principles. Anisotropic electron mobility of 2D (AlxGa1−x)2O3 is limited to 30−80 cm2/Vs at room temperature, and it finds that the relatively large ion-clamped dielectric tensors (Δɛ) suggest a major scattering role for polar optical phonons. The mobility of 2D (AlxGa1−x)2 is less than that of bulk β-(AlxGa1−x)2O3 and shows no quantum effects attributed to the dangling bonds on the surface. We further demonstrate that the bandgap of 2D (AlxGa1−x)2O3 decreases with the number of layers, and the electron localization function also shows an anisotropy. This work comprehensively interprets the scattering mechanism and unintentional doping intrinsic electron mobility of (AlxGa1−x)2O3 alloys, providing physical elaboration and alternative horizons for experimental synthesis, crystallographic investigations, and power device fabrication of 2D (AlxGa1−x)2O3 atomically thin layered systems. [ABSTRACT FROM AUTHOR]

Published
2024
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194. American Students' Cultural Adjustment in China: Experiences and Coping Strategies

Author

Yang, Jia

Abstract

Employing critical incident technique and the constructivist grounded approach, this study conducted individual in-depth interviews with 13 undergraduate students who had experiences studying abroad in China to find out how American college students perceived their new learning environment in China, and to examine the strategies students employed to adjust to the new culture. The results provided practical suggestions for the design and implementation of study abroad programs in China, including the compatibility between the program structure and individual student's learning needs and aspirations, the use of service-based community-building activities to foster the mutual engagement in interactions between American students and host nationals, and the inclusiveness of local perspectives in American students' reflection of their cultural immersion experiences.

Published
2020

195. Why Existing Machine Learning Methods Fails At Extracting the Information of Future Returns Out of Historical Sctock Prices : the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes

Author

Yang, Jia-Yao, Zhu, Hao, Hou, Yue-Jie, Zhang, Ping, and Zhou, Chi-Chun

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

The financial time series analysis is important access to touch the complex laws of financial markets. Among many goals of the financial time series analysis, one is to construct a model that can extract the information of the future return out of the known historical stock data, such as stock price, financial news, and e.t.c. To design such a model, prior knowledge on how the future return is correlated with the historical stock prices is needed. In this work, we focus on the issue: in what mode the future return is correlated with the historical stock prices. We manually design several financial time series where the future return is correlated with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future return can be extracted from the curve shapes of the historical stock prices. By applying various kinds of existing algorithms on those pre-designed time series and real financial time series, we show that: (1) the major information of the future return is not contained in the curve-shape features of historical stock prices. That is, the future return is not mainly correlated with the historical stock prices in the CSF mode. (2) Various kinds of existing machine learning algorithms are good at extracting the curveshape features in the historical stock prices and thus are inappropriate for financial time series analysis although they are successful in the image recognition and natural language processing. That is, new models handling the NCSF series are needed in the financial time series analysis.

Published
2021

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