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16,169 results on '"Cheng, Jun"'

206. Dynamic chromatin accessibility licenses STAT5- and STAT6-dependent innate-like function of TH9 cells to promote allergic inflammation

Author

Son, Aran, Meylan, Francoise, Gomez-Rodriguez, Julio, Kaul, Zenia, Sylvester, McKella, Falduto, Guido H., Vazquez, Estefania, Haque, Tamara, Kitakule, Moses M., Wang, Chujun, Manthiram, Kalpana, Qi, Chen-Feng, Cheng, Jun, Gurram, Rama K., Zhu, Jinfang, Schwartzberg, Pamela, Milner, Joshua D., Frischmeyer-Guerrerio, Pamela A., and Schwartz, Daniella M.

Published
2023
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218. Cascade electrocatalysis via AgCu single-atom alloy and Ag nanoparticles in CO2 electroreduction toward multicarbon products

Author

Cheng Du, Joel P. Mills, Asfaw G. Yohannes, Wei Wei, Lei Wang, Siyan Lu, Jian-Xiang Lian, Maoyu Wang, Tao Guo, Xiyang Wang, Hua Zhou, Cheng-Jun Sun, John Z. Wen, Brian Kendall, Martin Couillard, Hongsheng Guo, ZhongChao Tan, Samira Siahrostami, and Yimin A. Wu

Subjects
Science
Abstract

Abstract Electrocatalytic CO2 reduction into value-added multicarbon products offers a means to close the anthropogenic carbon cycle using renewable electricity. However, the unsatisfactory catalytic selectivity for multicarbon products severely hinders the practical application of this technology. In this paper, we report a cascade AgCu single-atom and nanoparticle electrocatalyst, in which Ag nanoparticles produce CO and AgCu single-atom alloys promote C-C coupling kinetics. As a result, a Faradaic efficiency (FE) of 94 ± 4% toward multicarbon products is achieved with the as-prepared AgCu single-atom and nanoparticle catalyst under ~720 mA cm−2 working current density at −0.65 V in a flow cell with alkaline electrolyte. Density functional theory calculations further demonstrate that the high multicarbon product selectivity results from cooperation between AgCu single-atom alloys and Ag nanoparticles, wherein the Ag single-atom doping of Cu nanoparticles increases the adsorption energy of *CO on Cu sites due to the asymmetric bonding of the Cu atom to the adjacent Ag atom with a compressive strain.

Published
2023
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219. Influence of Bypass Location on Two-Dimensional Shock Vectoring Nozzle

Author

Bo-wen SHU, Jiang-tao HUANG, Zheng-hong GAO, Cheng-jun HE, and Lu XIA

Subjects
thrust vectoring, fluidic thrust vectoring, shock vectoring control, two-dimensional nozzle, bypass nozzle, Astrophysics, QB460-466
Abstract

Fluidic thrust vectoring technology can provide good stealth performance and longitudinal maneuvering moment for supersonic tailless configurations, with advantages of fast response and light weight. The bypass shock vectoring nozzle does not need to draw air from the engine, which overcomes the problem of increasing the engine flow to increase the vector angle and reduces the burden on the engine. A study of the effect of bypass position on the performance of two-dimensional shock vectoring nozzle was conducted to lay the foundation for a better understanding of the performance of this nozzle and its practical applications. The results show that the throat bypass jet has the characteristics of both the shock vector and the throat skewing method, and that the inlet bypass jet performs better in the over-expanded condition, while the throat bypass jet has more advantages in the under-expanded condition. The separation mode after the jet significantly affects the nozzle vector performance. Closed separation makes the nozzle vector performance decrease significantly, and the nozzle pressure ratio related to the sudden change in vector performance of the throat bypass nozzle is smaller than that of the inlet bypass nozzle. In practice, it should be avoided to switch the separation mode from open separation to closed separation. Matching different bypass modes according to different expansion states can maximize the bypass shock vectoring nozzle performance.

Published
2023
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220. The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence

Author

In-Hui Hwang, Shelly D. Kelly, Maria K. Y. Chan, Eli Stavitski, Steve M. Heald, Sang-Wook Han, Nicholas Schwarz, and Cheng-Jun Sun

Subjects
axeap, xes, electron interaction, genetic algorithm, spin state, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798, Crystallography, QD901-999
Abstract

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d–3p electron exchange force and Kβ emission core-hole lifetime.

Published
2023
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221. MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation

Author

Liu, Yepeng, Gu, Zaiwang, Gao, Shenghua, Wang, Dong, Zeng, Yusheng, and Cheng, Jun

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

With the emergence of service robots and surveillance cameras, dynamic face recognition (DFR) in wild has received much attention in recent years. Face detection and head pose estimation are two important steps for DFR. Very often, the pose is estimated after the face detection. However, such sequential computations lead to higher latency. In this paper, we propose a low latency and lightweight network for simultaneous face detection, landmark localization and head pose estimation. Inspired by the observation that it is more challenging to locate the facial landmarks for faces with large angles, a pose loss is proposed to constrain the learning. Moreover, we also propose an uncertainty multi-task loss to learn the weights of individual tasks automatically. Another challenge is that robots often use low computational units like ARM based computing core and we often need to use lightweight networks instead of the heavy ones, which lead to performance drop especially for small and hard faces. In this paper, we propose online feedback sampling to augment the training samples across different scales, which increases the diversity of training data automatically. Through validation in commonly used WIDER FACE, AFLW and AFLW2000 datasets, the results show that the proposed method achieves the state-of-the-art performance in low computational resources. The code and data will be available at https://github.com/lyp-deeplearning/MOS-Multi-Task-Face-Detect., Comment: Accepted at BMVC 2021

Published
2021

222. Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

Author

Zhou, Kang, Li, Jing, Luo, Weixin, Li, Zhengxin, Yang, Jianlong, Fu, Huazhu, Cheng, Jun, Liu, Jiang, and Gao, Shenghua

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels' intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection., Comment: This paper is accepted to IEEE TMI

Published
2021

223. Quark condensate and chiral symmetry restoration in neutron stars

Author

Jin, Hao-Miao, Xia, Cheng-Jun, Sun, Ting-Ting, and Peng, Guang-Xiong

Subjects
Nuclear Theory, High Energy Physics - Phenomenology
Abstract

Based on an equivparticle model, we investigate the in-medium quark condensate in neutron stars. Carrying out a Taylor expansion of the nuclear binding energy to the order of $\rho^3$, we obtain a series of EOSs for neutron star matter, which are confronted with the latest nuclear and astrophysical constraints. The in-medium quark condensate is then extracted from the constrained properties of neutron star matter, which decreases non-linearly with density. However, the chiral symmetry is only partially restored with non-vanishing quark condensates, which may vanish at a density that is out of reach for neutron stars.

Published
2021
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224. Fibroblasts in Pulmonary Hypertension: Roles and Molecular Mechanisms

Author

Hui Zhang, Min Li, Cheng-Jun Hu, and Kurt R. Stenmark

Subjects
adventitial fibroblast, vascular remodeling, hypoxia, inflammation, metabolism, mitochondrial, Cytology, QH573-671
Abstract

Fibroblasts, among the most prevalent and widely distributed cell types in the human body, play a crucial role in defining tissue structure. They do this by depositing and remodeling extracellular matrixes and organizing functional tissue networks, which are essential for tissue homeostasis and various human diseases. Pulmonary hypertension (PH) is a devastating syndrome with high mortality, characterized by remodeling of the pulmonary vasculature and significant cellular and structural changes within the intima, media, and adventitia layers. Most research on PH has focused on alterations in the intima (endothelial cells) and media (smooth muscle cells). However, research over the past decade has provided strong evidence of the critical role played by pulmonary artery adventitial fibroblasts in PH. These fibroblasts exhibit the earliest, most dramatic, and most sustained proliferative, apoptosis-resistant, and inflammatory responses to vascular stress. This review examines the aberrant phenotypes of PH fibroblasts and their role in the pathogenesis of PH, discusses potential molecular signaling pathways underlying these activated phenotypes, and highlights areas of research that merit further study to identify promising targets for the prevention and treatment of PH.

Published
2024
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232. A preliminary integrated analysis of regional paleoclimate variations in China over the past ∼ 21 ka

Author

Lu, Huayu, Zhao, Yan, Yang, Xiangdong, Wu, Haibing, Zhao, Cheng, Wang, Jingjing, Wang, Xiaoyong, Kuang, Xueyuan, Zhang, Xiaojian, Ma, Chunmei, Lu, Fuzhi, Xiao, Xiayun, Zhang, Wenchao, Wang, Hanlin, Xu, Zhiwei, Cheng, Jun, Zheng, Zhuo, Shi, Feng, Zhang, Enlou, Liang, Chen, Huang, Zhenghui, Liang, Chenghong, Yi, Shuangwen, Wu, Jiang, Shao, Kehan, Gu, Yao, Zhang, Hongyan, Li, Xusheng, Han, Zhiyong, Wang, Xianyan, Wang, Sumin, and Guo, Zhengtang

Published
2024
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