499 research outputs found
She jiao quan li gan zai she jiao he fei she jiao ling yu dui xiao fei zhe xing wei de ying xiang
No full textJia, Yanli.Thesis Ph.D. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 84-100).Abstracts also in Chinese.Title from PDF title page (viewed on 30, September, 2016).Jia, Yanli
Profiling and characterization of deep learning model inference on CPU
Get PDFWith the rapid growth of deep learning models and higher expectations for their accuracy and throughput in real-world applications, the demand for profiling and characterizing model inference on different hardware/software stacks is significantly increased. As the model inference characterization on GPU has already been extensively studied, it is worth exploring how performance-enhancing libraries like Intel MKL-DNN help to boost the performance on Intel CPU. We develop a profiling mechanism to capture the MKL-DNN operation calls and formulate the tracing timeline with spans on the server. Through profiling and characterization that give insights into Intel MKL-DNN, we evaluate and demonstrate that the optimization techniques, including blocked memory layout, layers fusion, and low precision operation used in deep learning model inference, have accelerated the performance on the Intel CPU.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01The student, Yanli Qian, accepted the attached license on 2020-04-28 at 07:31.The student, Yanli Qian, submitted this Thesis for approval on 2020-04-28 at 07:33.This Thesis was approved for publication on 2020-04-28 at 10:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #15088 on 2020-08-25 at 17:41:19Made available in DSpace on 2020-08-27T00:50:04Z (GMT). No. of bitstreams: 2
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Earthworms increase forest litter mass loss irrespective of deposited compounds – A field manipulation experiment in subtropical forests
No full textEarthworms modulate carbon and nitrogen cycling in terrestrial ecosystems, but their effect may be compromised by the deposition of pollutants from industrial emissions. However, studies investigating how deposited compounds affect the role of earthworms in carbon cycling such as litter decomposition are lacking, although the interactions of earthworms and deposited compounds are important for understanding the impact of pollutants on ecosystems and the potential of earthworms in bioremediation. We performed a 365-day in situ litterbag decomposition experiment in a deciduous (Quercus variabilis) and coniferous (Pinus massoniana) forest in southeast China. We manipulated nitrogen (N), sodium (Na), and polycyclic aromatic hydrocarbons (PAHs) as model compounds during litter decomposition with and without earthworms (Eisenia fetida). After one year, N, Na, and PAH all slowed down litter mass loss, with the effects of Na being the strongest. By contrast, E. fetida generally increased litter mass loss, and the positive effects were uniformly maintained irrespective of the type of compounds added. However, the pathways to how earthworms increased litter mass loss varied among the compounds added and the two forests studied. As indicated by structural equation modeling, earthworms mitigated the negative effects of deposited compounds by directly increasing litter mass loss and indirectly increasing soil pH and microbial biomass. Overall, the results indicate that the acceleration of litter mass loss by earthworms is little affected by deposited compounds, and that earthworms have the potential to mitigate negative impacts of pollutants on litter decomposition and ecosystem processes.Jiangsu Forestry Science and technology innovation and promotion projectKey specialized research and development breakthrough program in Henan provinceNational Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Scholarship of China Scholarship CouncilStrategic Priority Research Program of the Chinese Academy of Sciences (A)The Key Program of Scientific Research projects of Hunan Provincial Education DepartmentOpen-Access-Publikationsfonds 202
Correction: Pinin interacts with C-terminal binding proteins for RNA alternative splicing and epithelial cell identity of human ovarian cancer cells
No full textPresent: Due to an omission on the part of the author, the affliations of the first author are incomplete.
Corrected: Additional affliation information for the first author is listed below. The authors sincerely apologize for this error.
Original article: Oncotarget. 2016; 7(10):11397-11411. DOI:10.18632/oncotarget.7242.
PRESENT LIST:
Yanli Zhang1, Jamie Sui-Lam Kwok2, Pui-Wah Choi1, Minghua Liu2, Junzheng Yang1, Margit Singh1, Shu-Kay Ng4, William R. Welch5, Michael G. Muto1, Stephen KW Tsui2, Stephen P. Sugrue3, Ross S. Berkowitz1, Shu-Wing Ng1
1 Laboratory of Gynecologic Oncology, Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, MA, USA
2 School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong
3 Department of Anatomy and Cell Biology, University of Florida College of Medicine, Gainesville, FL, USA
4 School of Medicine and Menzies Health Institute Queensland, Griffith University, Meadowbrook, Australia
5 Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
UPDATED LIST:
Yanli Zhang1,6, Jamie Sui-Lam Kwok2, Pui-Wah Choi1, Minghua Liu2, Junzheng Yang1, Margit Singh1, Shu-Kay Ng4, William R. Welch5, Michael G. Muto1, Stephen KW Tsui2, Stephen P. Sugrue3, Ross S. Berkowitz1, Shu-Wing Ng1
1 Laboratory of Gynecologic Oncology, Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, MA, USA
2 School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong
3 Department of Anatomy and Cell Biology, University of Florida College of Medicine, Gainesville, FL, USA
4 School of Medicine and Menzies Health Institute Queensland, Griffith University, Meadowbrook, Australia
5 Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
6 Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaFull Tex
On the Path of Teaching Reform and Innovation in Ideological and Political Courses in Colleges and Universities
No full textResearch on the tourism resource development from the perspective of network capability--Taking Wuxi Huishan Ancient Town as an example
No full textAttributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China's Jing-Jin-Ji area
No full textAbstract. In the 2015 winter month of December, northern China witnessed the most severe air pollution phenomena since the 2013 winter haze events occurred. This triggered the first-ever red alert in the air pollution control history of Beijing, with an instantaneous fine particulate matter (PM2. 5) concentration over 1 mg m−3. Air quality observations reveal large temporal–spatial variations in PM2. 5 concentrations over the Beijing–Tianjin–Hebei (Jing-Jin-Ji) area between 2014 and 2015. Compared to 2014, the PM2. 5 concentrations over the area decreased significantly in all months except November and December of 2015, with an increase of 36 % in December. Analysis shows that the PM2. 5 concentrations are significantly correlated with the local meteorological parameters in the Jing-Jin-Ji area such as the stable conditions, relative humidity (RH), and wind field. A comparison of two month simulations (December 2014 and 2015) with the same emission data was performed to explore and quantify the meteorological impacts on the PM2. 5 over the Jing-Jin-Ji area. Observation and modeling results show that the worsening meteorological conditions are the main reasons behind this unusual increase of air pollutant concentrations and that the emission control measures taken during this period of time have contributed to mitigate the air pollution ( ∼ 9 %) in the region. This work provides a scientific insight into the emission control measures vs. the meteorology impacts for the period.
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Decoupling Effect of County Carbon Emissions and Economic Growth in China: Empirical Evidence from Jiangsu Province
No full textUnder the pressure of low-carbon development at county level in China, this paper takes Jiangsu province as an example to analyze the relationship between economic growth and carbon emissions, aiming to provide a reference for the low-carbon development in Jiangsu and other regions in China. Based on the county-level panel data from 2000 to 2017, this paper uses the Tapio elasticity model and environmental Kuznets curve model, and focuses on the differences in regional economic development and the impacts of the 2008 global economic crisis. The results show that, in general, the decoupling effect of carbon emissions in Jiangsu counties has gradually increased during the study period. Since 2011, all counties achieved the speed decoupling, with more than half of them showing strong decoupling. The environmental Kuznets curves of carbon emissions in different income groups are established, and changed before and after the 2008 global economic crisis. In 2017, only 10 of the 53 counties were on the right side of the curve, realizing the quantity decoupling between the two. Therefore, to achieve a win–win situation between carbon emission reduction and economic growth, efforts should be made from the aspects of industrial structure and energy efficiency, and measures should be taken according to local conditions
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