Search

Your search keyword '"Liu Qingyuan"' showing total 743 results
Start Over Author "Liu Qingyuan"
743 results on '"Liu Qingyuan"'

1. Status and influencing factors of e-Health literacy among middle-aged and elderly people in rural areas of Suizhou City, Hubei Province, China (农村地区中老年人电子健康素养现状及影响因素研究)

Author

LIU Qingyuan (刘情缘), DU Heng (杜恒), Hu Longyou (胡龙友), Quan Wenjing (全文静), Gao Jin (高进), and XU Jianghua (徐江华)

Subjects
e-health literacy, influencing factors, rural areas, middle-aged and elderly people, 电子健康素养, 影响因素, 农村地区, 中老年人, Nursing, RT1-120
Abstract

Objective To investigate the status of e-Health literacy among middle-aged and elderly people in rural areas, and to analyze its influencing factors, so as to provide a theoretical basis for improving the e-Health literacy among middle-aged and elderly people. Methods From July to August 2022, a questionnaire survey was conducted on 355 middle-aged and elderly people in rural areas of Suizhou City, Hubei Province, using a general information questionnaire and an e-Health literacy scale. Results The total e-Health literacy score of middle-aged and elderly people was (25. 94±6. 01). The results of multiple linear regression analysis showed that trust in online health information (β=0. 294), whether to actively obtain health information through the Internet (β=0. 186), gender (β=-0. 140), education level (β=0. 135), marital status (β=0. 114) were the main factors influencing e-Health literacy among rural middle-aged and older adults. Conclusion The e-Health literacy level of middle-aged and elderly people in rural areas of Suizhou City, Hubei Province was low. Under the situation of rapid development of ageing and health information technology, basic health services need to pay more attention to the e-Health literacy of rural middle-aged and elderly people and to seek a health service model for rural middle-aged and elderly people that is compatible with the development of the digital age. (目的 了解农村地区中老年人电子健康素养现状, 分析其影响因素, 为提高农村地区中老年人电子健康素养提供依据。方法 2022年7月—8月, 采用一般资料问卷、电子健康素养量表对湖北省随州市355名农村中老年人进行问卷调查。结果 湖北省随州市农村地区中老年人电子健康素养总分为(25. 94±6. 01)分。多重线性回归分析结果显示: 对网络健康信息的信任程度(β=0. 294)、是否主动通过网络获取健康信息(β=0. 186)、性别(β=-0. 140)、文化程度(β=0. 135)、婚姻状况(β=0. 114)是影响农村中老年人电子健康素养的主要因素。结论 湖北省随州市农村地区中老年人电子健康素养水平较低, 在人口老龄化与健康信息化快速发展的形势下, 基层卫生服务机构应重视农村中老年人电子健康素养, 积极寻求与数字时代发展相适应的农村中老年人健康服务模式。)

Published
2024
Full Text
View/download PDF

2. CD1d affects the proliferation, migration, and apoptosis of human papillary thyroid carcinoma TPC-1 cells via regulating MAPK/NF-κB signaling pathway

Author

Liu Qingyuan, Zhai Tong, Ma Lei, and Liu Qilun

Subjects
cd1d, papillary thyroid carcinoma, tpc-1 cells, mapk/nf-κb signaling pathway, rna interference, Medicine
Abstract

The study aimed to investigate the effect of CD1d down-regulation on the proliferation, migration, and apoptosis of papillary thyroid carcinoma cells and explore the underlying mechanism. CD1d expression was silenced in TPC-1 cells by transfection of CD1d siRNA lentivirus. The proliferation, apoptosis rate, and migration ability of TPC-1 cells were detected by CCK-8 assay, flow cytometry, and scratch assay, respectively. Western blot and qPCR analyses were performed to detect the expression of related proteins. CD1d was highly expressed in TPC-1 cells. Down-regulation of CD1d significantly decreased ALMS1, CDKN3, CDK6, Ki-67, Bcl2 expression, increased Bax and Caspase 3 expression (all P < 0.05), and decreased the migration ability of TPC-1 cells. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify the relevant signaling pathways. KEGG pathway enrichment analysis showed that the differentially expressed genes were mainly enriched in MAPK and NF-κB signaling pathways. Our findings suggest that CD1d down-regulation inhibited the proliferation and migration abilities of TPC-1 cells, increased cell apoptosis possibly via the MAPK/NF-κB signaling pathway.

Published
2024
Full Text
View/download PDF

3. Activating transcription factor 6 promotes proliferation, migration and invasion of PC3 human prostate cancer cells through Hippo-YAP signaling pathway

Author

LIU Hengchuan, YANG Zongke, LIU Qingyuan, ZHANG Jindong, ZHU Huixuan, and WANG Delin

Subjects
prostate cancer, hippo, yap, proliferation, Medicine (General), R5-920
Abstract

Objective To investigate the effects of activating transcription factor 6 (ATF6) on the proliferation, migration and invasion of prostate cancer PC3 cells. Methods Prostate cancer tissue samples and benign prostatic hyperplasia tissue samples were collected, then the mRNA and protein levels of ATF6 were detected by qRT-PCR and Western blotting, respectively. Lentivirus with ATF6 shRNA was transfected into PC3 cells to downregulate ATF6 expression, then CCK8 and EdU assays were adopted to determine the proliferation of PC3 cells, and wound-healing and Transwell invasion tests were performed to evaluate the migration and invasion abilities of PC3 cells. Nude mouse xenograft models were used to assess the influence of ATF6 on tumorigenesis in vivo. Moreover, the association between Yes-associated protein (YAP) and ATF6 was analyzed using online JASPAR database prediction and luciferase reporter. The mRNA and protein levels of YAP were detected with qRT-PCR and immunofluorescence assay, and the mRNA levels of 2 downstream factors of YAP, connective tissue growth factor (CTGF) and cysteine-rich 61 (Cyr61), were also measured by qRT-PCR. Results The mRNA and protein levels of ATF6 in prostate cancer tissues were significantly increased (P < 0.05). Knockdown of ATF6 decreased the proliferation, invasion, and migration abilities of PC3 cells (P < 0.05), and the in vivo study showed that ATF6-knockdown attenuated tumor growth as well (P < 0.05). JASPAR database predicted that there was an initiation binding site between ATF6 and YAP, and the luciferase reporter assay also confirmed the interaction between them. In addition, YAP expression was remarkably downregulated after knockdown of ATF6 (P < 0.05), and the mRNA level of YAP downstream genes CTGF and Cyr61 were also decreased accordingly (P < 0.05). Conclusion ATF6 promotes the proliferation, migration, and invasion of prostate cancer PC3 cells through Hippo-YAP signaling pathway.

Published
2023
Full Text
View/download PDF

4. On Min-Max Robust Data-Driven Predictive Control Considering Non-Unique Solutions to Behavioral Representation

Author

Wang, Yibo, Liu, Qingyuan, and Shang, Chao

Subjects
Mathematics - Optimization and Control
Abstract

Direct data-driven control methods are known to be vulnerable to uncertainty in stochastic systems. In this paper, we propose a new robust data-driven predictive control (DDPC) framework to tackle the uncertainty in dynamic systems. By analyzing non-unique solutions to behavioral representation, we first shed light on the lack of robustness in subspace predictive control (SPC) as well as the projection-based regularized DDPC. This inspires us to construct an uncertainty set that includes admissible output trajectories deviating to some extent from nominal predictions from the subspace predictor and develop a min-max robust formulation of DDPC that endows control sequences with robustness against such unknown deviations. We establish its performance guarantees under bounded additive noise and develop convex reformulations of the min-max problem with moderate complexity. To mitigate the conservatism of robust design, an adaptive robust DDPC scheme is further proposed by incorporating an affine feedback policy, with performance guarantees and tractable reformulations derived. Simulation studies show that the proposed method effectively robustifies SPC and outperforms projection-based regularizer.

Published
2025

5. Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs

Author

Mo, Zhaobin, Liu, Qingyuan, Yan, Baohua, Zhang, Longxiang, and Di, Xuan

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

Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relations among nodes over graphs. However, most studies calculate the adjacency matrix by directly memorizing the data, such as distance- and correlation-based matrices. These adjacency matrices do not consider potential pattern shift for the test data, and may result in suboptimal performance if the test data has a different distribution from the training one. This issue is known as the Out-of-Distribution generalization problem. To address this issue, in this paper we propose a Causal Adjacency Learning (CAL) method to discover causal relations over graphs. The learned causal adjacency matrix is evaluated on a downstream spatiotemporal prediction task using real-world graph data. Results demonstrate that our proposed adjacency matrix can capture the causal relations, and using our learned adjacency matrix can enhance prediction performance on the OOD test data, even though causal learning is not conducted in the downstream task.

Published
2024

6. Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images

Author

Liu, Qingyuan and Zakhor, Avideh

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Melanoma segmentation in Whole Slide Images (WSIs) is useful for prognosis and the measurement of crucial prognostic factors such as Breslow depth and primary invasive tumor size. In this paper, we present a novel approach that uses the Segment Anything Model (SAM) for automatic melanoma segmentation in microscopy slide images. Our method employs an initial semantic segmentation model to generate preliminary segmentation masks that are then used to prompt SAM. We design a dynamic prompting strategy that uses a combination of centroid and grid prompts to achieve optimal coverage of the super high-resolution slide images while maintaining the quality of generated prompts. To optimize for invasive melanoma segmentation, we further refine the prompt generation process by implementing in-situ melanoma detection and low-confidence region filtering. We select Segformer as the initial segmentation model and EfficientSAM as the segment anything model for parameter-efficient fine-tuning. Our experimental results demonstrate that this approach not only surpasses other state-of-the-art melanoma segmentation methods but also significantly outperforms the baseline Segformer by 9.1% in terms of IoU.

Published
2024

7. Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos

Author

Liu, Qingyuan, Shi, Pengyuan, Tsai, Yun-Yun, Mao, Chengzhi, and Yang, Junfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat Deepfakes videos have developed detectors that are highly accurate at identifying GAN-generated samples. However, the robustness of these detectors on diffusion-generated videos generated from video creation tools (e.g., SORA by OpenAI, Runway Gen-2, and Pika, etc.) is still unexplored. In this paper, we propose a novel framework for detecting videos synthesized from multiple state-of-the-art (SOTA) generative models, such as Stable Video Diffusion. We find that the SOTA methods for detecting diffusion-generated images lack robustness in identifying diffusion-generated videos. Our analysis reveals that the effectiveness of these detectors diminishes when applied to out-of-domain videos, primarily because they struggle to track the temporal features and dynamic variations between frames. To address the above-mentioned challenge, we collect a new benchmark video dataset for diffusion-generated videos using SOTA video creation tools. We extract representation within explicit knowledge from the diffusion model for video frames and train our detector with a CNN + LSTM architecture. The evaluation shows that our framework can well capture the temporal features between frames, achieves 93.7% detection accuracy for in-domain videos, and improves the accuracy of out-domain videos by up to 16 points.

Published
2024

9. Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability

Author

Liu, Qingyuan, Yang, Yanning, Du, Dong, Xia, Yubin, Zhang, Ping, Feng, Jia, Larus, James, and Chen, Haibo

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces Jiagu, which harmonizes efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, dual-staged scaling achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubernetes) while maintaining QoS, and 81.0%--93.7% lower scheduling costs and a 57.4%--69.3% reduction in cold start latency compared to existing QoS-aware schedulers in research work., Comment: 17 pages, 17 figures

Published
2024

16. Untargeted Metabolomics Analysis of Serum Metabolites in Zucker Diabetic Fatty Rats

Author

Liu Qingyuan and Quach Sze Ying

Subjects
Environmental sciences, GE1-350
Abstract

Zucker Diabtic Fatty rats is a common model used for the studies related to type 2 diabetes. Metabolomics, proven by previous studies, can be used for the prediction of phenotype. This investigation held untargeted metabolomics analysis on the serum metabolites of the Zucker Diabetic Fatty rats, intended to gain insights in the study of the pathology of type 2 diabetes. LCMS method (liquid chromatography coupled with mass spectrometry) is an analytical chemistry technique used to separate and purify complex mixtures. After identifying metabolites using MS2 library spectrum matching, this investigation used Metabo Analyst to compare fatty rats and lean rats. Lastly, by applying over representation analysis, we identified seven metabolic pathways that are important for humans, which assists in discovery of therapeutic targets and the developments of diabetes biomarkers.

Published
2021
Full Text
View/download PDF

19. Alignment for the first precision measurements at Belle II

Author

Bilka Tadeas, Abudinen Jesus, Ackermann Karlheinz Georg, Adamczyk Karol Mateusz, Ahlburg Patrick, Aihara Hiroaki, Alonso Oscar, Albalawi Mohammed, Andricek Ladislav, Ayad Rachid, Aziz Tariq, Babu Varghese, Bacher Szymon Grzegorz, Bahinipati Seema, Batignani Giovanni, Baudot Jerome, Behera Prafulla Kumar, Bettarini Stefano, Boronat Marca, Bozek Andrzej, Braun Nils, Buchsteiner Florian, Caldwell Allen, Camien Christian, Casarosa Giulia, Cervenkov Daniel, Chekelian Vladimir, Chen Yeqi, Chilikin Kirill, Corona Luigi, Czank Thomas Rafael, Das Sanjeeda Bharati, Dash Nibedita, de Marino Gaetano, Deschamps Bruno, Dieguez Angel, Dingfelder Jochen, Doležal Zdeněk, Dujany Giulio, Esperante Daniel, Forti Francesco, Fras Markus, Frey Ariane, Friedl Markus, Fuster Juan, Gabriel Miroslav, Gadow Karsten, Ganiev Eldar, Gebauer Uwe, Gessler Thomas, Giakoustidis Georgios, Gioi Luigi Li, Gobbo Benigno, Gomis López Pablo, Greenwald Daniel, Guan Yinghui, Halder Soumen, Hara Koji, Hartbrich Oskar, Hazra Sagar, Heck Martin, Hemperek Tomasz, Hensel Martin, Higuchi Takeo, Hoek Matthias, Huber Stefan, Itoh Ryosuke, Irmler Christian, Ishikawa Akimasa, Jeon Hyebin, Joo Changwoo, Kaleta Mateusz, Kaliyar Abdul Basith, Kandra Jakub, Kang Kookhyun, Kapusta Piotr Julian, Kiesling Christian, Kisielewski Bartlomiej, Kittlinger David, Kleinwort Claus, Klose Daniel, Kodyš Peter, Koffmane Christian, Kohriky T., Kono Tomoyuki, Konorov Igor, Krivokuca Silvia, Krüger Hans, Kuhr Thomas, Kumar Manish, Kumar Rajeev, Kvasnička Peter, Lacasta Carlos, La Licata Chiara, Lalwani Kavita, Lanceri Livio, Lange Jens Sören, Lautenbach Klemens, Lee Seungcheol, Leis Ulrich, Leitl Philipp, Levit Dmytro, Li Chunhua, Li Y. B., Libby James Frederick, Liemann Gerhard, Liu Qingyuan, Liu Zhen’An, Lück Thomas, Luetticke Florian, Macharski Lydia, Maity Souvik, Mariñas Carlos, Mayekar Sukant Narendra, Mccarney Sara, Mohanty Gagan Bihari, Mora Grimaldo Johnny Alejandro, Morii Tomoko, Moser Hans-Günther, Moya David, Müller Felix Johannes, Müller Felix, Nakamura Katsuro, Nakao Mikihiko, Natkaniec Zbigniew Marian, Niebuhr Carsten, Ninkovic Jelena, Onuki Yoshiyuki, Ostrowicz Waclaw, Paladino Antonio, Paoloni Eugenio, Park Hwanbae, Park SeokHee, Paschen Botho, Paul Stephan Martin, Peric Ivan, Poblotzki Frauke, Rabusov Andrei, Rao K. K., Reiter Simon, Richter Rainer Helmut, Ripp-Baudot Isabelle, Ritter Martin, Ritzert Michael, Rizzo Giuliana, Rout Niharika, Sahoo Debashis, Sanchez Javier Gonzalez, Santelj Luka, Sato Nobuhiko, Scavino Bianca, Schaller Gerhard, Schnecke Martina, Schopper Florian, Schreeck Harrison, Schwanda Christoph, Schwenker Benjamin, Sedlmeyer Reinhard, Sfienti Concettina, Simon Frank, Skambraks Sebastian, Soloviev Yuri, Spruck Björn, Stefková Slavomira, Stever Reimer, Stolzenberg Ulf, Suzuki Soh Yamagata, Takahashi Maiko, Tafelmayer Eva, Tanaka Shuji, Tanigawa Hikaru, Thalmeier Richard, Tsuboyama Toru, Uematsu Yuma, Verbycka O., Vila Ivan, Virto Amparo Lopez, Vitale Lorenzo, Vogt Sven, Vos Marcel, Wan Kun, Wang Boqun, Watanuki Shun, Webb James, Wermes Norbert, Wessel Christian, Wiechczyński Jarosław Paweł, Wieduwilt Philipp, Windel Hendrik, Yamada Satoru, Ye Hua, Yin Hao, Zani Laura, and Zhang Tingyu

Subjects
Physics, QC1-999
Abstract

On March 25th 2019, the Belle II detector recorded the first collisions delivered by the SuperKEKB accelerator. This marked the beginning of the physics run with vertex detector. The vertex detector was aligned initially with cosmic ray tracks without magnetic field simultaneously with the drift chamber. The alignment method is based on Millepede II and the General Broken Lines track model and includes also the muon system or primary vertex position alignment. To control weak modes, we employ sensitive validation tools and various track samples can be used as alignment input, from straight cosmic tracks to mass-constrained decays. With increasing luminosity and experience, the alignment is approaching the target performance, crucial for the first physics analyses in the era of Super-BFactories. We will present the software framework for the detector calibration and alignment, the results from the first physics run and the prospects in view of the experience with the first data.

Published
2020
Full Text
View/download PDF

20. Data quality monitors of vertex detectors at the start of the Belle II experiment

Author

Kodyš Peter, Abudinen Jesus, Ackermann Karlheinz Georg, Adamczyk Karol Mateusz, Ahlburg Patrick, Aihara Hiroaki, Alonso Oscar, Albalawi Mohammed, Andricek Ladislav, Ayad Rachid, Aziz Tariq, Babu Varghese, Bacher Szymon Grzegorz, Bahinipati Seema, Batignani Giovanni, Baudot Jerome, Behera Prafulla Kumar, Bettarini Stefano, Bilka Tadeáš, Boronat Marca, Bozek Andrzej, Braun Nils, Buchsteiner Florian, Caldwell Allen, Camien Christian, Casarosa Giulia, Cervenkov Daniel, Chekelian Vladimir, Chen Yeqi, Corona Luigi, Czank Thomas Rafael, Das Sanjeeda Bharati, Dash Nibedita, de Marino Gaetano, Deschamps Bruno, Dieguez Angel, Dingfelder Jochen, Doležal Zdeněk, Dujany Giulio, Esperante Daniel, Forti Francesco, Fras Markus, Frey Ariane, Friedl Markus, Fuster Juan, Gabriel Miroslav, Gadow Karsten, Ganiev Eldar, Gebauer Uwe, Gessler Thomas, Giakoustidis Georgios, Gioi Luigi Li, Gobbo Benigno, Gomis López Pablo, Greenwald Daniel, Guan Yinghui, Halder Soumen, Hara Koji, Hartbrich Oskar, Hazra Sagar, Heck Martin, Hemperek Tomasz, Hensel Martin, Higuchi Takeo, Hoek Matthias, Huber Stefan, Itoh Ryosuke, Irmler Christian, Ishikawa Akimasa, Jeon Hyebin, Joo Changwoo, Kaleta Mateusz, Kaliyar Abdul Basith, Kandra Jakub, Kang Kookhyun, Kapusta Piotr Julian, Kiesling Christian, Kisielewski Bartlomiej, Kittlinger David, Klose Daniel, Koffmane Christian, Kohriky T., Kono Tomoyuki, Konorov Igor, Krivokuca Silvia, Krüger Hans, Kuhr Thomas, Kumar Manish, Kumar Rajeev, Kvasnicˇka Peter, Lacasta Carlos, La Licata Chiara, Lalwani Kavita, Lanceri Livio, Lange Jens Sören, Lautenbach Klemens, Lee Seungcheol, Leis Ulrich, Leitl Philipp, Levit Dmytro, Li Chunhua, Li Y. B., Libby James Frederick, Liemann Gerhard, Liu Qingyuan, Liu Zhen’An, Lück Thomas, Luetticke Florian, Macharski Lydia, Maity Souvik, Mariñas Carlos, Mayekar Sukant Narendra, Mccarney Sara, Mohanty Gagan Bihari, Mora Grimaldo Johnny Alejandro, Morii Tomoko, Moser Hans-Günther, Moya David, Müller Felix Johannes, Müller Felix, Nakamura Katsuro, Nakao Mikihiko, Natkaniec Zbigniew Marian, Niebuhr Carsten, Ninkovic Jelena, Onuki Yoshiyuki, Ostrowicz Waclaw, Paladino Antonio, Paoloni Eugenio, Park Hwanbae, Park SeokHee, Paschen Botho, Paul Stephan Martin, Peric Ivan, Poblotzki Frauke, Rabusov Andrei, Rao K. K., Reiter Simon, Richter Rainer Helmut, Ripp-Baudot Isabelle, Ritter Martin, Ritzert Michael, Rizzo Giuliana, Rout Niharika, Sahoo Debashis, Sanchez Javier Gonzalez, Santelj Luka, Sato Nobuhiko, Scavino Bianca, Schaller Gerhard, Schnecke Martina, Schopper Florian, Schreeck Harrison, Schwanda Christoph, Schwenker Benjamin, Sedlmeyer Reinhard, Sfienti Concettina, Simon Frank, Skambraks Sebastian, Soloviev Yuri, Spruck Björn, Stefková Slavomira, Stever Reimer, Stolzenberg Ulf, Suzuki Soh Yamagata, Takahashi Maiko, Tafelmayer Eva, Tanaka Shuji, Tanigawa Hikaru, Thalmeier Richard, Tsuboyama Toru, Uematsu Yuma, Verbycka O., Vila Ivan, Virto Amparo Lopez, Vitale Lorenzo, Vogt Sven, Vos Marcel, Wan Kun, Wang Boqun, Watanuki Shun, Webb James, Wermes Norbert, Wessel Christian, Wiechczyński Jarosław Paweł, Wieduwilt Philipp, Windel Hendrik, Yamada Satoru, Ye Hua, Yin Hao, Zani Laura, and Zhang Tingyu

Subjects
Physics, QC1-999
Abstract

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the final arrangement of the silicon vertex detector part of Belle II with a focus on online monitoring of detector conditions and data quality, on the design and use of diagnostic and reference plots, and on integration with the software framework of Belle II. Data quality monitoring plots will be discussed with a focus on simulation and acquired cosmic and collision data.

Published
2020
Full Text
View/download PDF

21. Accelerated Nonconvex ADMM with Self-Adaptive Penalty for Rank-Constrained Model Identification

Author

Liu, Qingyuan, Huang, Zhengchao, Ye, Hao, Huang, Dexian, and Shang, Chao

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

The alternating direction method of multipliers (ADMM) has been widely adopted in low-rank approximation and low-order model identification tasks; however, the performance of nonconvex ADMM is highly reliant on the choice of penalty parameter. To accelerate ADMM for solving rank-constrained identification problems, this paper proposes a new self-adaptive strategy for automatic penalty update. Guided by first-order analysis of the increment of the augmented Lagrangian, the self-adaptive penalty updating enables effective and balanced minimization of both primal and dual residuals and thus ensures a stable convergence. Moreover, improved efficiency can be obtained within the Anderson acceleration scheme. Numerical examples show that the proposed strategy significantly accelerates the convergence of nonconvex ADMM while alleviating the critical reliance on tedious tuning of penalty parameters., Comment: 7 pages, 5 figures. Accepted by 62nd IEEE Conference on Decision and Control (CDC 2023)

Published
2023

32. Safety and efficacy of early versus delayed acetylsalicylic acid after surgery for spontaneous intracerebral haemorrhage in China (E-start): a prospective, multicentre, open-label, blinded-endpoint, randomised trial

Author

Ren, Jinrui, Jiang, Pengjun, Liu, Qingyuan, Mo, Shaohua, Wu, Jun, Tong, Xianzeng, Wang, Kaiwen, Chen, Xu, Chen, Shanwen, Guo, Shuaiwei, Li, Xiong, Li, Mingde, Peng, Lei, Sun, Xinguo, Wang, Yang, Sun, Jianjun, Pu, Jun, Zheng, Kaige, Zhang, Jiaming, Liu, Yang, Yang, Yi, Wen, Zheng, Nie, Xin, Feng, Yinghe, Lan, Chuanjin, Tang, Haishuang, Wang, Nuochuan, Li, Jiangan, Miao, Zengli, Lu, Xiaojie, Ning, Bo, Zhao, Bing, Kang, Dezhi, Chen, Xiaolin, Zhang, Yanan, Zhang, Yan, Wang, Anxin, Zhu, Chengcheng, Araki, Yoshio, Uda, Kenji, Yoshimura, Shinichi, Uchida, Kazutaka, Morimoto, Takeshi, Yoshioka, Hideyuki, Hasan, David, Du, Rose, Levitt, Michael R, Cao, Yong, Wang, Shuo, and Zhao, Jizong

Published
2024
Full Text
View/download PDF

38. Carnitine is a friend in HFpEF and foe in HFrEF

Author

Wang, Huiqing, Wei, Haoran, Zhao, Mingming, Wu, Junfang, Fei, Min, Lin, Nan, Zhan, Rui, Liu, Qingyuan, Zhang, Qi, Yao, Xiaodong, Wu, Yufei, Shan, Wenxin, Cui, Hongtu, Ji, Liang, Pan, Bing, Fang, Lu, Zhu, Yujie, Li, Xin, Guo, Yansong, Wang, Dao Wen, and Zheng, Lemin

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results