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3,069 results on '"Xiong, Chao"'

3. Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18

Author

Xiong, Chao, Pei, Cuiying, Wang, Qi, Zhao, Yi, Jiang, Yuyang, Li, Changhua, Cao, Weizheng, Yu, Na, Zhang, Lili, and Qi, Yanpeng

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

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance both the metallicity and the superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure synchrotron XRD measurements demonstrate the stability of the pristine crystal structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution of La ions in Lu sites, inducing negative chemical pressure. Our study provides valuable insights into the improvement of superconductivity in caged compounds., Comment: 9 pages, 8 figures

Published
2023

5. GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning

Author

Dong, Xin, Wu, Ruize, Xiong, Chao, Li, Hai, Cheng, Lei, He, Yong, Qian, Shiyou, Cao, Jian, and Mo, Linjian

Subjects
Computer Science - Machine Learning
Abstract

Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance degeneration with negative transfer due to learning several tasks simultaneously. Some related work attributed the source of the problem is the conflicting gradients. In this case, it is needed to select useful gradient updates for all tasks carefully. To this end, we propose a novel optimization approach for MTL, named GDOD, which manipulates gradients of each task using an orthogonal basis decomposed from the span of all task gradients. GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients. This allows guiding the update directions depending on the task-shared components. Moreover, we prove the convergence of GDOD theoretically under both convex and non-convex assumptions. Experiment results on several multi-task datasets not only demonstrate the significant improvement of GDOD performed to existing MTL models but also prove that our algorithm outperforms state-of-the-art optimization methods in terms of AUC and Logloss metrics.

Published
2023
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8. The cavitation characteristics of aerospace high-speed centrifugal pumps with different tip clearance

Author

Wen-xiong Chao, Wang-cheng Wang, Wei Zhang, Gao-yang Bai, and Wei Dong

Subjects
Gap cavitation, Different tip clearance, Cavitation characteristic, Numerical analysis, Glycol aqueous solution, Experiment, Medicine, Science
Abstract

Abstract This study investigates the Tip Clearance Cavitation (TCC) characteristics of three different Tip Clearances (TC) (0.4, 0.6, 0.8) and five inlet negative pressure conditions Pj = (− 20–60)kPa to improve the reliability of the aerospace high-speed centrifugal pump during in-orbit operation, based on the premise of good agreement between the TC 0.6 test curve and the simulation performance curve. Under negative pressure and high-speed conditions, the variation gradient of cavitation characteristics with various inlet negative pressures is non-linear and has a sudden change, but the trend becomes stable after the inlet negative pressure drops to a certain stage. The tip clearance cavitation characteristics vary from the blade surface cavitation characteristics due to the difference in forces on both sides. This study is a proper starting point for the design of aerospace power pumps.

Published
2024
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9. A novel neural network model of Earth’s topside ionosphere

Author

Smirnov, Artem, Shprits, Yuri, Prol, Fabricio, Lühr, Hermann, Berrendorf, Max, Zhelavskaya, Irina, and Xiong, Chao

Subjects
Engineering, Aerospace Engineering
Abstract

The Earth's ionosphere affects the propagation of signals from the Global Navigation Satellite Systems (GNSS). Due to the non-uniform coverage of available observations and complicated dynamics of the region, developing accurate models of the ionosphere has been a long-standing challenge. Here, we present a Neural network-based model of Electron density in the Topside ionosphere (NET), which is constructed using 19 years of GNSS radio occultation data. The NET model is tested against in situ measurements from several missions and shows excellent agreement with the observations, outperforming the state-of-the-art International Reference Ionosphere (IRI) model by up to an order of magnitude, especially at 100-200 km above the F2-layer peak. This study provides a paradigm shift in ionospheric research, by demonstrating that ionospheric densities can be reconstructed with very high fidelity. The NET model depicts the effects of numerous physical processes governing the topside dynamics and can have wide applications in ionospheric research.

Published
2023

48. Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots

Author

Xiong, Chao, Liu, Che, Xu, Zijun, Jiang, Junfeng, and Ye, Jieping

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Recently, open domain multi-turn chatbots have attracted much interest from lots of researchers in both academia and industry. The dominant retrieval-based methods use context-response matching mechanisms for multi-turn response selection. Specifically, the state-of-the-art methods perform the context-response matching by word or segment similarity. However, these models lack a full exploitation of the sentence-level semantic information, and make simple mistakes that humans can easily avoid. In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem. Firstly and most importantly, we find that by using the sentence-level semantic information, the network successfully addresses the problem and gets a significant improvement on matching, resulting in a state-of-the-art performance. Furthermore, we integrate the sentence matching we introduced here and the usual word similarity matching reported in the current literature, to match at different semantic levels. Experiments on three public data sets show that such integration further improves the model performance., Comment: 10 pages, 4 figures

Published
2020

49. A Lattice Study of the Two-photon Decay Widths for Scalar and Pseudo-scalar Charmonium

Author

Chen, Ying, Gong, Ming, Li, Ning, Liu, Chuan, Liu, Yu-Bin, Liu, Zhaofeng, Ma, Jian-Ping, Meng, Yu, Xiong, Chao, and Zhang, Ke-Long

Subjects
High Energy Physics - Lattice
Abstract

In this exploratory study, two photon decay widths of pseudo-scalar ($\eta_c$) and scalar ($\chi_{c0}$) charmonium are computed using two ensembles of $N_f=2$ twisted mass lattice QCD gauge configurations. The simulation is performed two lattice ensembles with lattice spacings $a=0.067$ fm with size $32^3\times{64}$ and $a=0.085$ fm with size $24^3\times{48}$, respectively. The results for the decay widths for the two charmonia are obtained which are in the right ballpark however smaller than the experimental ones. Possible reasons for these discrepancies are discussed., Comment: version accepted by Chinese Physics C

Published
2020
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