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1,913 results on '"Chen, Rongrong"'

1. Understanding the Impact of Referent Design on Scale Perception in Immersive Data Visualization

Author

Hou, Yihan, Cui, Hao, Chen, Rongrong, and Zeng, Wei

Subjects
Computer Science - Human-Computer Interaction
Abstract

Referents are often used to enhance scale perception in immersive visualizations. Common referent designs include the considerations of referent layout (side-by-side vs. in-situ) and referent size (small vs. medium vs. large). This paper introduces a controlled user study to assess how different referent designs affect the efficiency and accuracy of scale perception across different data scales, on the performance of the size-matching task in the virtual environment. Our results reveal that in-situ layouts significantly enhance accuracy and confidence across various data scales, particularly with large referents. Linear regression analyses further confirm that in-situ layouts exhibit greater resilience to changes in data scale. For tasks requiring efficiency, medium-sized referents emerge as the preferred choice. Based on these findings, we offer design guidelines for selecting referent layouts and sizes in immersive visualizations., Comment: 7 pages, 6 figures, Accepted to Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24)

Published
2024
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4. The 1/4 occupied O atoms induced ultraflat band and the one dimensional channels in the Pb$_{10-x}$Cu$_{x}$(PO$_4$)$_{6}$O$_{4}$ (x=0,0.5) crystal

Author

Tao, Kun, Chen, Rongrong, Yang, Lei, Gao, Jin, Xue, Desheng, and Jia, Chenglong

Subjects
Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract

The search for room-temperature superconductors has been a long-standing goal in condensed matter physics. In this study, we investigate the electronic and geometric properties of lead apatite with and without Cu doped within the frame work of the density functional theory. Based on our calculations, we found that without the Cu doped the lead apatite shows an insulator character with flat bands straddle the Fermi level. Once we introduce the O1 vacancies, the flat bands disappear. Furthermore, we analyze the effects of Cu doping on the crystal structure and electronic band structure of the material. Our calculations reveal the presence of one-dimensional channels induced by fully occupied O1 atoms, that are only 1/4 occupied in the literature, which may play a crucial role in the realization of room-temperature superconductivity. Based on our findings, we propose a possible solution to improve the quality of superconductivity by annealing the material in an oxygen atmosphere. These results contribute to a better understanding of the unusual properties of Cu-doped lead apatite and will pave the way for further exploration of its potential as a room-temperature superconductor., Comment: 8 pages, 7 figures

Published
2023

11. Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer

Author

Wang, Yadong, Huang, Zhicheng, Li, Bowen, Xue, Jianchao, Guo, Chao, Bing, Zhongxing, Zheng, Zhibo, Song, Yang, Xu, Yuan, Huang, Guanghua, Li, Haochen, Yu, Xiaoqing, Xia, Yankai, Li, Ruirui, Si, Xiaoyan, Zhang, Li, Li, Ji, Song, Lan, Xiong, Yuanyuan, Gu, Dejian, Song, Mengmeng, Zhou, Zhipeng, Chen, Rongrong, Feng, Zhe, Bie, Zhixin, Li, Xiaoguang, Yang, Huaxia, Li, Shanqing, and Liang, Naixin

Published
2024
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13. Few-Shot Blockchain Domain Named Entity Recognition with Fused Lexical Features

Author

Chen, Hao, Lin, Yang, Chen, Rongrong, Liu, Yuan, Sun, Yanbin, Tian, Zhihong, Qiu, Jing, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, and Li, Shaofan, editor

Published
2024
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26. Separation and Identification of Umami Peptides from the Enzymatic Hydrolysate of Stropharia rugosoannulata and Their Synergistic Umami-Enhancing Effect

Author

CHEN Rongrong, LI Wen, WU Di, ZHANG Zhong, BAO Dapeng, CHEN Wanchao, ZHANG Jingsong, YANG Yan

Subjects
enzymatic hydrolysate of stropharia rugosoannulata, umami peptide, isolation and identification, synergistic umami-enhancing effect, Food processing and manufacture, TP368-456
Abstract

Umami peptides from the enzymatic hydrolysate of Stropharia rugosoannulata were separated by ultrafiltration (UF) and gel filtration chromatography (GFC) combined with sensory evaluation and electronic tongue analysis. Fraction F3, with the strongest umami intensity, were identified by reversed-phase high performance liquid chromatography-tandem mass spectrometry (RP-HPLC-MS/MS). The identified peptides were synthesized and evaluated for their taste characteristics. The results showed that eight umami peptides were identified in fraction F3, namely ELWR, RLVDR, KPDNR, AHLRF, LDWDR, LAEWR, DDWWR and EGHKGW, and all of them possessed umami characteristics and umami-enhancing effects with thresholds ranging from 0.30 to 1.33 mmol/L and from 0.53 to 2.43 mmol/L, respectively. All peptides except LDWDR showed different degrees of umami improvement in monosodium glutamate (MSG) solution, ranging from 0.18% to 61.12%. ELWR, RLVDR, KPDNR, AHLRF and EGHKGW showed good umami-enhancing effects at low concentrations. The peak concentration of RLVDR and KPDNR for synergistic umami-enhancing effect was 5 mg/mL. LDWDR, LAEWR, DDWWR and EGHKGW reached the umami peak at 4 mg/mL. The amino acid composition and the spatial structure of umami peptides could affect their taste properties. The results of this study provide a theoretical basis for the development and utilization of umami peptides from S. rugosoannulata.

Published
2024
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30. Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

Author

Chen, Ziyi, Zhou, Yi, Chen, Rongrong, and Zou, Shaofeng

Subjects
Computer Science - Machine Learning
Abstract

Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy. However, existing decentralized AC algorithms either do not preserve the privacy of agents or are not sample and communication-efficient. In this work, we develop two decentralized AC and natural AC (NAC) algorithms that are private, and sample and communication-efficient. In both algorithms, agents share noisy information to preserve privacy and adopt mini-batch updates to improve sample and communication efficiency. Particularly for decentralized NAC, we develop a decentralized Markovian SGD algorithm with an adaptive mini-batch size to efficiently compute the natural policy gradient. Under Markovian sampling and linear function approximation, we prove the proposed decentralized AC and NAC algorithms achieve the state-of-the-art sample complexities $\mathcal{O}\big(\epsilon^{-2}\ln(\epsilon^{-1})\big)$ and $\mathcal{O}\big(\epsilon^{-3}\ln(\epsilon^{-1})\big)$, respectively, and the same small communication complexity $\mathcal{O}\big(\epsilon^{-1}\ln(\epsilon^{-1})\big)$. Numerical experiments demonstrate that the proposed algorithms achieve lower sample and communication complexities than the existing decentralized AC algorithm., Comment: 40 pages, 2 figures

Published
2021

38. Polo-like kinase 1 promotes pulmonary hypertension

Author

Chen, Rongrong, Wang, Hongfei, Zheng, Cuiting, Zhang, Xiyu, Li, Li, Wang, Shengwei, Chen, Hongyu, Duan, Jing, Zhou, Xian, Peng, Haiyong, Guo, Jing, Zhang, Anchen, Li, Feifei, Wang, Wang, Zhang, Yu, Wang, Jun, Wang, Chen, Meng, Yan, Du, Xinling, and Zhang, Hongbing

Published
2023
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49. Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity

Author

Chen, Ziyi, Zhou, Yi, and Chen, Rongrong

Subjects
Computer Science - Machine Learning, Computer Science - Multiagent Systems, Mathematics - Optimization and Control
Abstract

The finite-time convergence of off-policy TD learning has been comprehensively studied recently. However, such a type of convergence has not been well established for off-policy TD learning in the multi-agent setting, which covers broader applications and is fundamentally more challenging. This work develops two decentralized TD with correction (TDC) algorithms for multi-agent off-policy TD learning under Markovian sampling. In particular, our algorithms preserve full privacy of the actions, policies and rewards of the agents, and adopt mini-batch sampling to reduce the sampling variance and communication frequency. Under Markovian sampling and linear function approximation, we proved that the finite-time sample complexity of both algorithms for achieving an $\epsilon$-accurate solution is in the order of $\mathcal{O}(\epsilon^{-1}\ln \epsilon^{-1})$, matching the near-optimal sample complexity of centralized TD(0) and TDC. Importantly, the communication complexity of our algorithms is in the order of $\mathcal{O}(\ln \epsilon^{-1})$, which is significantly lower than the communication complexity $\mathcal{O}(\epsilon^{-1}\ln \epsilon^{-1})$ of the existing decentralized TD(0). Experiments corroborate our theoretical findings., Comment: 34 pages, 3 figures

Published
2021

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