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967 results on '"Liu Jianquan"'

1. Analysis on heat transfer performance sub-channels of sodium-cooled fast reactor fuel assemblies based on entransy

Author

Dong Zenghao, Liu Jianquan, Huang Chao, Niu Xinyi, and Hai Lihan

Subjects
sodium-cooled fast reactor, fuel assembly, sub-channel method, entransy dissipation, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

In this paper, the symmetric heat transfer performance of sodium-cooled fast reactor fuel assemblies was analyzed and studied. The model is analytically optimized based on sub-channel calculations. The deviations of the numerical simulation results from the pre-existing experimental data in the literature are within 10 %, with an average deviation of 2.5 %, which tested the reliability of the model. The calculated results demonstrated that the distribution of the axial power, temperature, and coolant of the reactor core is approximately symmetric M-shape. The reactor core coolant has a monotonic increase in axial distribution with the cladding temperature and the temperature peaks all appear at the reactor core outlet. The individual fuel assemblies' internal temperature is relatively sensitive to the axial power distribution, and there are troughs around the imports and exports. The simulated results showed that the center temperature of the hottest rod reactor core block reached 965.65 K. This pa- per provides a better guide to understanding the overall heat transfer effect by optimizing the heat transfer model.

Published
2023
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2. Transductive Cross-Lingual Scene-Text Visual Question Answering

Author

Li, Lin, Zhang, Haohan, Fang, Zeqin, Xie, Zhongwei, Liu, Jianquan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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4. Overview of nuclear waste treatment and management

Author

Liu Jianquan and Dai Wentai

Subjects
Environmental sciences, GE1-350
Abstract

Nuclear energy is an efficient energy source. Nuclear fuel has the advantages of high energy density and convenient transportation and storage. After decades of tortuous development, nuclear energy has been well utilized in many ways, especially in the field of nuclear power generation. However, as the number of nuclear power plants continues to increase, the problem of nuclear waste disposal is becoming more and more serious. Nuclear waste disposal is a complex process. For nuclear waste treatment, people initially only temporarily deposit these nuclear wastes or dump them directly. However, as people’s awareness of nuclear waste increases, and the huge potential threat of nuclear waste is known, it is necessary to analyze the current characteristics of nuclear waste and its pollution status in order to find a better nuclear waste treatment and management method.

Published
2019
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6. What is Next when Sequential Prediction Meets Implicitly Hard Interaction?

Author

Hu, Kaixi, Li, Lin, Xie, Qing, Liu, Jianquan, and Tao, Xiaohui

Subjects
Computer Science - Machine Learning
Abstract

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics., Comment: 10 pages; 4 figures; Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM '21), November 1--5, 2021, Virtual Event, QLD, Australia

Published
2022
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13. Diversification of flowering plants in space and time

Author

Dimitrov, Dimitar, Xu, Xiaoting, Su, Xiangyan, Shrestha, Nawal, Liu, Yunpeng, Kennedy, Jonathan D., Lyu, Lisha, Nogués-Bravo, David, Rosindell, James, Yang, Yong, Fjeldså, Jon, Liu, Jianquan, Schmid, Bernhard, Fang, Jingyun, Rahbek, Carsten, and Wang, Zhiheng

Published
2023
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20. Basketball Player's Value Evaluation by a Networks-based Variant Parameter Hidden Markov Model

Author

Du, Xin, Cai, Weihong, Liu, Jianquan, Yu, Ding, Xu, Kai, and Li, Wei

Subjects
Computer Science - Social and Information Networks
Abstract

Determining the value of basketball players through analyzing the players' behavior is important for the managers of modern basketball teams. However, conventional methods always utilize isolated statistical data, leading to ineffective and inaccurate evaluations. Existing models based on dynamic network theory offer major improvements to the results of such evaluations, but said models remain imprecise because they focus merely on evaluating the values of individual players rather than considering them within their current teams. To solve this problem, we propose an analysis and evaluation model based on networks and a hidden Markov model. To the best of our knowledge, we are the first to combine a network form representing the players who are playing with the use of a hidden Markov model to mine the network and generate the desired results. Applying our approach to SportVU data collected from the National Basketball Association shows that this analysis and evaluation model can effectively analyze the performance of each player in a game and provides an assistive tool for team managers.

Published
2020

21. Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos

Author

Li, Junnan, Liu, Jianquan, Wong, Yongkang, Nishimura, Shoji, and Kankanhalli, Mohan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The recent development of commodity 360$^{\circ}$ cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. However, research in omnidirectional video analysis has lagged behind the hardware advances. In this work, we address the important problem of action recognition in top-view 360$^{\circ}$ videos. Due to the wide filed-of-view, 360$^{\circ}$ videos usually capture multiple people performing actions at the same time. Furthermore, the appearance of people are deformed. The proposed framework first transforms omnidirectional videos into panoramic videos, then it extracts spatial-temporal features using region-based 3D CNNs for action recognition. We propose a weakly-supervised method based on multi-instance multi-label learning, which trains the model to recognize and localize multiple actions in a video using only video-level action labels as supervision. We perform experiments to quantitatively validate the efficacy of the proposed method and qualitatively demonstrate action localization results. To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.

Published
2020

22. Statistical Detection of Collective Data Fraud

Author

Wang, Ruoyu, Hu, Xiaobo, Sun, Daniel, Li, Guoqiang, Wong, Raymond, Chen, Shiping, and Liu, Jianquan

Subjects
Computer Science - Databases, E.0, H.2
Abstract

Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world., Comment: 6 pages, 6 figures and tables, submitted to ICME 2020

Published
2020
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27. Noise-Robust Semi-supervised Multi-modal Machine Translation

Author

Li, Lin, Hu, Kaixi, Tayir, Turghun, Liu, Jianquan, Lee, Kong Aik, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Khanna, Sankalp, editor, Cao, Jian, editor, Bai, Quan, editor, and Xu, Guandong, editor

Published
2022
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36. The Cycas genome and the early evolution of seed plants

Author

Liu, Yang, Wang, Sibo, Li, Linzhou, Yang, Ting, Dong, Shanshan, Wei, Tong, Wu, Shengdan, Liu, Yongbo, Gong, Yiqing, Feng, Xiuyan, Ma, Jianchao, Chang, Guanxiao, Huang, Jinling, Yang, Yong, Wang, Hongli, Liu, Min, Xu, Yan, Liang, Hongping, Yu, Jin, Cai, Yuqing, Zhang, Zhaowu, Fan, Yannan, Mu, Weixue, Sahu, Sunil Kumar, Liu, Shuchun, Lang, Xiaoan, Yang, Leilei, Li, Na, Habib, Sadaf, Yang, Yongqiong, Lindstrom, Anders J., Liang, Pei, Goffinet, Bernard, Zaman, Sumaira, Wegrzyn, Jill L., Li, Dexiang, Liu, Jian, Cui, Jie, Sonnenschein, Eva C., Wang, Xiaobo, Ruan, Jue, Xue, Jia-Yu, Shao, Zhu-Qing, Song, Chi, Fan, Guangyi, Li, Zhen, Zhang, Liangsheng, Liu, Jianquan, Liu, Zhong-Jian, Jiao, Yuannian, Wang, Xiao-Quan, Wu, Hong, Wang, Ertao, Lisby, Michael, Yang, Huanming, Wang, Jian, Liu, Xin, Xu, Xun, Li, Nan, Soltis, Pamela S., Van de Peer, Yves, Soltis, Douglas E., Gong, Xun, Liu, Huan, and Zhang, Shouzhou

Published
2022
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37. Repeated turnovers keep sex chromosomes young in willows

Author

Wang, Deyan, Li, Yiling, Li, Mengmeng, Yang, Wenlu, Ma, Xinzhi, Zhang, Lei, Wang, Yubo, Feng, Yanlin, Zhang, Yuanyuan, Zhou, Ran, Sanderson, Brian J., Keefover-Ring, Ken, Yin, Tongming, Smart, Lawrence B., DiFazio, Stephen P., Liu, Jianquan, Olson, Matthew, and Ma, Tao

Published
2022
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47. Reflections on the life and work of Richard Abbott (1945–2024).

Author

Chapman, Mark A., Hiscock, Simon J., Antonovics, Janis, Barrett, Spencer C. H., Brennan, Adrian C., and Liu, Jianquan

Subjects
SPECIES hybridization, NUCLEOTIDE sequencing, GIFTED children, PLANT hybridization, PLANT populations, WHOLE genome sequencing
Abstract

Richard J. Abbott, a prominent researcher in plant evolutionary biology, passed away in 2024. He was known for his work on plant hybridization, mating system evolution, speciation, and phylogeography, with a particular focus on British and European Senecio. Abbott had a long and successful career, publishing over 200 articles and supervising 20 PhD students. His research on speciation in Senecio was particularly notable, as it demonstrated that new species often go extinct before they can be formally recognized. Abbott was highly respected by his colleagues and will be greatly missed in the scientific community. This text is a collection of personal reflections and tributes from colleagues and collaborators of Richard Abbott, a respected scientist in the field of plant evolution and systematics. The authors discuss their experiences working with Richard, highlighting his mentorship, collaborative nature, and contributions to the field. They also mention specific research projects and collaborations that Richard was involved in, including joint projects with researchers in China. The text concludes with a recognition of Richard's scientific achievements and condolences to his family. [Extracted from the article]

Published
2024
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48. Chromosome-level genome assembly of Helwingia omeiensis: the first genome in the family Helwingiaceae.

Author

Chen, Yanyu, Feng, Landi, Lin, Hao, Liu, Jianquan, and Hu, Quanjun

Subjects
GERMPLASM, DATABASES, CHROMOSOMES, COMPARATIVE method, FAMILIES, HORDEUM
Abstract

Helwingia, a shrub of the monotypic cosmopolitan family Helwingiaceae, is distinguished by its inflorescence, in which flowers are borne on the midrib of the leaf—a trait not commonly observed in related plant families. Previous studies have investigated the development of this unusual structure using comparative anatomical methods. However, the scarcity of genomic data has hindered our understanding of the origins and evolutionary history of this uncommon trait at the molecular level. Here, we report the first high-quality genome of the family Helwingiaceae. Assembled using HiFi sequencing and Hi-C technologies, the genome of H. omeiensis is anchored to 19 chromosomes, with a total length of 2.75 Gb and a contig N50 length of 6.78 Mb. The BUSCO completeness score of the assembled genome was 98.2%. 53,951 genes were identified, of which 99.7% were annotated in at least one protein database. The high-quality reference genome of H. omeiensis provides an essential genetic resource and sheds light on the phylogeny and evolution of specific traits in the family Helwingiaceae. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Chromosome‐level genome assembly of Pedicularis kansuensis illuminates genome evolution of facultative parasitic plant.

Author

Fang, Longfa, Li, Mingyu, Zhang, Jia, Jia, Chenglin, Qiang, Yuqing, He, Xiaojuan, Liu, Tao, Zhou, Qiang, Luo, Dong, Han, Yuling, Li, Zhen, Liu, Wenxian, Yang, Yongzhi, Liu, Jianquan, and Liu, Zhipeng

Subjects
HORIZONTAL gene transfer, PARASITIC plants, GENOME size, GENOMES, PLANT genomes
Abstract

Parasitic plants have a heterotrophic lifestyle, in which they withdraw all or part of their nutrients from their host through the haustorium. Despite the release of many draft genomes of parasitic plants, the genome evolution related to the parasitism feature of facultative parasites remains largely unknown. In this study, we present a high‐quality chromosomal‐level genome assembly for the facultative parasite Pedicularis kansuensis (Orobanchaceae), which invades both legume and grass host species in degraded grasslands on the Qinghai‐Tibet Plateau. This species has the largest genome size compared with other parasitic species, and expansions of long terminal repeat retrotransposons accounting for 62.37% of the assembly greatly contributed to the genome size expansion of this species. A total of 42,782 genes were annotated, and the patterns of gene loss in P. kansuensis differed from other parasitic species. We also found many mobile mRNAs between P. kansuensis and one of its host species, but these mobile mRNAs could not compensate for the functional losses of missing genes in P. kansuensis. In addition, we identified nine horizontal gene transfer (HGT) events from rosids and monocots, as well as one single‐gene duplication events from HGT genes, which differ distinctly from that of other parasitic species. Furthermore, we found evidence for HGT through transferring genomic fragments from phylogenetically remote host species. Taken together, these findings provide genomic insights into the evolution of facultative parasites and broaden our understanding of the diversified genome evolution in parasitic plants and the molecular mechanisms of plant parasitism. [ABSTRACT FROM AUTHOR]

Published
2024
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