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Your search keyword '"Qinbin Li"' showing total 159 results
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159 results on '"Qinbin Li"'

1. A Conical-Scanning Microwave Limb Sounder for Atmospheric Measurements

Author

Joe W. Waters, Nathaniel J. Livesey, Michelle L. Santee, Paul C. Stek, Richard E. Cofield, Peter H. Siegel, Jonathan H. Jiang, Qinbin Li, Gloria L. Manney, William G. Read, Duane E. Waliser, and Dong L. Wu

Subjects
Microwave limb sounding, microwave remote sensing, ozone monitoring, temperature profiling, cloud ice measurements, pollution measurements, Telecommunication, TK5101-6720, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

We describe a novel scanning microwave limb sounder (SMLS) instrument that performs rapid and broad azimuth conical scans of Earth's limb while simultaneously scanning the limb in the vertical. This azimuthal scanning capability gives dramatic improvement in temporal and spatial coverage over that of previous limb sounding instruments. In a 1500-kilometer altitude, 52°-inclination Earth orbit, SMLS provides 6–8 vertical profile measurements separated by 1.9 hours every 24 hours everywhere between ±65° latitude, and 2–4 such measurements everywhere between ±(65–82°). Horizontal resolution is ∼50×50 km. Vertical resolution is ∼2 km for water vapor and cloud ice and ∼1–3 km for chemical species. In an equatorial orbit, emphasizing the tropics and subtropics, SMLS produces profile measurements every 1.9 hours everywhere between ±35° latitude. SMLS measurements address scientific issues of relevance to the upper troposphere, stratosphere, mesosphere, and lower thermosphere regions of the atmosphere (heights from ∼10 km to ∼100 km).

Published
2024
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3. Enhanced PM2.5 pollution in China due to aerosol-cloud interactions

Author

Bin Zhao, Kuo-Nan Liou, Yu Gu, Qinbin Li, Jonathan H. Jiang, Hui Su, Cenlin He, Hsien-Liang R. Tseng, Shuxiao Wang, Run Liu, Ling Qi, Wei-Liang Lee, and Jiming Hao

Subjects
Medicine, Science
Abstract

Abstract Aerosol-cloud interactions (aerosol indirect effects) play an important role in regional meteorological variations, which could further induce feedback on regional air quality. While the impact of aerosol-cloud interactions on meteorology and climate has been extensively studied, their feedback on air quality remains unclear. Using a fully coupled meteorology-chemistry model, we find that increased aerosol loading due to anthropogenic activities in China substantially increases column cloud droplet number concentration and liquid water path (LWP), which further leads to a reduction in the downward shortwave radiation at surface, surface air temperature and planetary boundary layer (PBL) height. The shallower PBL and accelerated cloud chemistry due to larger LWP in turn enhance the concentrations of particulate matter with diameter less than 2.5 μm (PM2.5) by up to 33.2 μg m−3 (25.1%) and 11.0 μg m−3 (12.5%) in January and July, respectively. Such a positive feedback amplifies the changes in PM2.5 concentrations, indicating an additional air quality benefit under effective pollution control policies but a penalty for a region with a deterioration in PM2.5 pollution. Additionally, we show that the cloud processing of aerosols, including wet scavenging and cloud chemistry, could also have substantial effects on PM2.5 concentrations.

Published
2017
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50. Practical Federated Gradient Boosting Decision Trees

Author

Bingsheng He, Zeyi Wen, and Qinbin Li

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Hash function, Decision tree, Machine Learning (stat.ML), 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Machine Learning (cs.LG), Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), Differential privacy, business.industry, 010401 analytical chemistry, Homomorphic encryption, 020206 networking & telecommunications, General Medicine, 0104 chemical sciences, Gradient boosting, Artificial intelligence, Raw data, business, computer
Abstract

Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. However, existing studies are not efficient or effective enough for practical use. They suffer either from the inefficiency due to the usage of costly data transformations such as secret sharing and homomorphic encryption, or from the low model accuracy due to differential privacy designs. In this paper, we study a practical federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. We prove that our framework is secure without exposing the original record to other parties, while the computation overhead in the training process is kept low. Our experimental studies show that, compared with normal training with the local data of each party, our approach can significantly improve the predictive accuracy, and achieve comparable accuracy to the original GBDT with the data from all parties., Comment: Accepted to AAAI-20

Published
2020
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