分类： 天文学 >> 天文学 提交时间： 2023-02-19
Ying Mei
Shoulin Wei
Feng Wang
Chen Wu
Rodrigo Tobar
Mohsim Shaikh
Hui Deng
Wei Dai
Bo Liang
Andreas Wicenec
摘要： The Square Kilometre Array (SKA) project is an international cooperation project to build the largest radio telescope worldwide. Data processing is one of the biggest challenges of building the SKA telescope. As a distributed execution framework, the Data Activated Liu Graph Engine (DALiuGE) was proposed to be one of the candidates for addressing the massive data of the SKA. DALiuGE has many distinctive features, but its actual ability to handle scientific data is still not evident. In this paper, we perform an objective evaluation of the usability of DALiuGE concerning the execution performance, developer workload, and implementation difficulty of porting the SAGECal to DALiuGE. The evaluation results showed that the DALiuGE enables fast integration of astronomical software, but there are significant differences in the efficiency of different parallel granularities. Even with the deep optimization of the program, there is still a gap between the current DALiuGE and the traditional MPI in execution performance. Therefore, we come to a preliminary conclusion that the DALiuGE has no performance advantage in batch processing of massive data, while it may be more suitable for application scenarios with more customized computational tasks, such as SKA science regional centers.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.
分类： 医学、药学 >> 临床医学 分类： 生物学 >> 遗传学 提交时间： 2020-03-06
Yun-Miao Guo
Jie-Rong Chen
Yan-Chun Feng
Chua, Melvin L. K.
Zeng, Yanni
Edwin Pun Hui
Allen K. C. Chan
Lin-Quan Tang
Lin Wang
Qian Cui
Hui-Qiong Han
Chun-Ling Luo
Guo-Wang Lin
Yan Liang
Yang Liu
Zhong-Lian He
Yu-Xiang Liu
Pan-Pan Wei
Chu-Jun Liu
Wan Peng
摘要： Germline polymorphisms have been linked with differential survival outcomes in cancers but have not been well studied in nasopharyngeal carcinoma (NPC). Here, two-phase association study is conducted to discover germline polymorphisms that are associated with the prognosis of NPC. The discovery phase includes two consecutive hospital cohorts of patients with NPC from Southern China. Exome-wide genotypes at 246,173 single nucleotide polymorphisms (SNPs) are determined, followed by survival analysis for each SNP under Cox proportional hazards regression model. Candidate SNP is replicated in another two independent cohorts from Southern China and Singapore. Meta-analysis of all samples (n = 5,553) confirm that the presence of rs1131636-T, located in the 3′-UTR of RPA1, confers an inferior overall survival (HR = 1.33, 95% CI = 1.20-1.47, P = 6.31 × 10-8). Bioinformatics and biological assays show that rs1131636 has regulatory effects on upstream RPA1. Functional studies further demonstrate that RPA1 promoted the growth, invasion, migration, and radioresistance of NPC cells. Additionally, miR-1253 has been identified as a suppressor for RPA1 expression, likely through regulation of its binding affinity to rs1131636 locus. Collectively, these findings provide a promising biomarker aiding in stratifying patients with poor survival, as well as a potential drug target for NPC.
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