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1,412 results on '"Liu, Xiao-Ming"'

4. Quantum coherence can be transformed into heat

Author

Yan, Xue-Qun, Du, Yan-Jiao, Hou, Wen-Tao, and Liu, Xiao-Ming

Subjects
Quantum Physics
Abstract

The first law of thermodynamics restates the law of conservation of energy. It partitions the change in energy of a system into two pieces, heat and work. While there is no ambiguity to define heat and work in classical thermodynamics, their classification in the quantum regime is not that obvious. Thus, the first law of thermodynamics becomes problematic in the quantum regime. However, recent studies have shown if contribution of quantum coherence is considered to the change of internal energy of the system, the first law of thermodynamics can be extended to the quantum domain. Here we investigate the new version of first law of thermodynamics for some quantum transformations by using two-level atomic system under non-dissipative channel. In our work we achieve a novel result that quantum coherence can be transformed into heat, and the heat can dissipate into the environments.

Published
2022

17. A persistently low level of atmospheric oxygen in Earth’s middle age

Author

Liu, Xiao-Ming, Kah, Linda C, Knoll, Andrew H, Cui, Huan, Wang, Chao, Bekker, Andrey, and Hazen, Robert M

Subjects
Earth Sciences, Geochemistry, Geology, Life Below Water
Abstract

Resolving how Earth surface redox conditions evolved through the Proterozoic Eon is fundamental to understanding how biogeochemical cycles have changed through time. The redox sensitivity of cerium relative to other rare earth elements and its uptake in carbonate minerals make the Ce anomaly (Ce/Ce*) a particularly useful proxy for capturing redox conditions in the local marine environment. Here, we report Ce/Ce* data in marine carbonate rocks through 3.5 billion years of Earth's history, focusing in particular on the mid-Proterozoic Eon (i.e., 1.8 - 0.8 Ga). To better understand the role of atmospheric oxygenation, we use Ce/Ce* data to estimate the partial pressure of atmospheric oxygen (pO2) through this time. Our thermodynamics-based modeling supports a major rise in atmospheric oxygen level in the aftermath of the Great Oxidation Event (~ 2.4 Ga), followed by invariant pO2 of about 1% of present atmospheric level through most of the Proterozoic Eon (2.4 to 0.65 Ga).

Published
2021

36. Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion

Author

ZUO Jie-ge, LIU Xiao-ming, CAI Bing

Subjects
image blocks, weather recognition, convolutional neural network, transfer learning, feature extraction, feature fusion, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

In video surveillance and intelligent traffic,bad weather such as foggy,rainy and snowy can seriously affect the visibility of video images.Therefore,it is very important to quickly identify the current weather conditions and make adaptive clearness processing of surveillance videos.Aiming at the problems of poor effect of traditional weather recognition methods and lack of weather image data sets,a multi-class weather image blocks data set is constructed,and a weather recognition algorithm based on image blocks and feature fusion is proposed.The algorithm uses traditional methods to extract four features,namely average gradient,contrast,saturation and dark channel,which are taken as the shallow features of weather images.The algorithm uses transfer learning to fine -tune the VGG16 pre-training model,and extracts the full-connection layer features of the fine-tuning model,which are taken as the deep features of the weather image.The shallow and deep features of weather images are fused and used as the final features to train the Softmax classifier.The classifier can realize the recognition of foggy,rainy,snowy and sunny wea-ther images. Experimental results show that the recognition accuracy of the proposed algorithm can reach 99.26%,and the algorithm can be used as a weather recognition module in the adaptive video image sharpening system.

Published
2022
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38. Laboratory Safety of Dupilumab, and Its Effect on Inflammatory Biomarkers, in Chinese Adults With Moderate-to-Severe Atopic Dermatitis: An Analysis of a Randomized, Double-Blind Phase III Study

Author

Zhao, Yan, Wu, Li-Ming, Lu, Qian-Jin, Gao, Xing-Hua, Zhu, Xiao-Hong, Yao, Xu, Li, Lin-Feng, Li, Wei, Ding, Yang-Feng, Song, Zhi-Qiang, Liu, Ling-Ling, Dang, Ning-Ning, Zhang, Chun-Lei, Liu, Xiao-Ming, Gu, Jun, Wang, Jin-Yan, Geng, Song-Mei, Liu, Quan-Zhong, Guo, Yi-Feng, Dong, Li, Li, Shelley, Xu, Christine, O’Malley, John T., Laws, Elizabeth, Amin, Nikhil, Bansal, Ashish, Wang, Min, and Zhang, Jian-Zhong

Published
2022
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39. A bioavailable strontium isoscape for Western Europe: A machine learning approach.

Author

Bataille, Clement, von Holstein, Isabella, Laffoon, Jason, Willmes, Malte, Liu, Xiao-Ming, and Davies, Gareth

Subjects
Algorithms, Atmosphere, Climate, Environmental Monitoring, Europe, Geography, Geology, Linear Models, Machine Learning, Regression Analysis, Strontium Isotopes
Abstract

Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline models predicting surficial 87Sr/86Sr variations (isoscapes). A variety of empirically-based and process-based models have been proposed to build terrestrial 87Sr/86Sr isoscapes but, in their current forms, those models are not mature enough to be integrated with continuous-probability surface models used in geographic assignment. In this study, we aim to overcome those limitations and to predict 87Sr/86Sr variations across Western Europe by combining process-based models and a series of remote-sensing geospatial products into a regression framework. We find that random forest regression significantly outperforms other commonly used regression and interpolation methods, and efficiently predicts the multi-scale patterning of 87Sr/86Sr variations by accounting for geological, geomorphological and atmospheric controls. Random forest regression also provides an easily interpretable and flexible framework to integrate different types of environmental auxiliary variables required to model the multi-scale patterning of 87Sr/86Sr variability. The method is transferable to different scales and resolutions and can be applied to the large collection of geospatial data available at local and global levels. The isoscape generated in this study provides the most accurate 87Sr/86Sr predictions in bioavailable strontium for Western Europe (R2 = 0.58 and RMSE = 0.0023) to date, as well as a conservative estimate of spatial uncertainty by applying quantile regression forest. We anticipate that the method presented in this study combined with the growing numbers of bioavailable 87Sr/86Sr data and satellite geospatial products will extend the applicability of the 87Sr/86Sr geo-profiling tool in provenance applications.

Published
2018

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