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878 results on '"Ma Huifang"'

1. Discovery and Structural Characterization of Impurities in the Synthesis of Darolutamide Intermediate

Author

MA Huifang, TONG Yue, WANG Rongfan, and XIE Jianwei

Subjects
darolutamide intermediate, nmr, drug impurities, Electricity and magnetism, QC501-766
Abstract

Darolutamide is an important drug for the treatment of prostate cancer. When studying its synthesis process, three impurities A, B, and C were discovered and purified in the first step of Suzuki coupling and the second step of hydrolysis deprotection reactions. Impurities A and B came from the first-step reaction, and impurity C came from the second-step reaction. The structures of impurities A and B were further determined through high-resolution mass spectrometry (HRMS), 1H nuclear magnetic resonance (NMR), and 13C NMR methods. It was discovered that impurities A and B were the deborated pinacol ester product of compound 2 and the double coupling product of compound 1, respectively. The accurate structures of compound 3 and impurity C were determined using HRMS, 1H NMR, 13C NMR, 1H-1H COSY (correlation spectroscopy), 1H-13C HSQC (heteronuclear singular quantum correlation), 1H-13C HMBC (heteronuclear multiple bond correlation) and 1H-1H NOESY (nuclear overhauser effect spectroscopy). The formation mechanisms and avoidance methods of these impurities were also discussed.

Published
2024
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2. Adaptive L-statistics for high dimensional test problem

Author

Ma, Huifang, Feng, Long, and Wang, Zhaojun

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

In this study, we focus on applying L-statistics to the high-dimensional one-sample location test problem. Intuitively, an L-statistic with $k$ parameters tends to perform optimally when the sparsity level of the alternative hypothesis matches $k$. We begin by deriving the limiting distributions for both L-statistics with fixed parameters and those with diverging parameters. To ensure robustness across varying sparsity levels of alternative hypotheses, we first establish the asymptotic independence between L-statistics with fixed and diverging parameters. Building on this, we propose a Cauchy combination test that integrates L-statistics with different parameters. Both simulation results and real-data applications highlight the advantages of our proposed methods.

Published
2024

4. Graph Contrastive Learning for Dissolved Gas Analysis

Author

Zhang, Yingyue, Ma, Huifang, Zhang, Di, Shu, Ke, Li, Xiaolong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Quan Z., editor, Dobbie, Gill, editor, Jiang, Jing, editor, Zhang, Xuyun, editor, Zhang, Wei Emma, editor, Manolopoulos, Yannis, editor, Wu, Jia, editor, Mansoor, Wathiq, editor, and Ma, Congbo, editor

Published
2025
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5. GCS: A Graph-Augmented Semi-supervised Contrastive Learning Approach for Imbalanced Dissolved Gas Analysis in Power Transformers

Author

Shu, Ke, Ma, Huifang, Yu, Li, Zhang, Qibin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Quan Z., editor, Dobbie, Gill, editor, Jiang, Jing, editor, Zhang, Xuyun, editor, Zhang, Wei Emma, editor, Manolopoulos, Yannis, editor, Wu, Jia, editor, Mansoor, Wathiq, editor, and Ma, Congbo, editor

Published
2025
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6. HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching

Author

Chen, Ying, Liu, Yong, Wu, Kai, Nie, Qiang, Xu, Shang, Ma, Huifang, Wang, Bing, and Wang, Chengjie

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we present HCPM, an efficient and detector-free local feature-matching method that employs hierarchical pruning to optimize the matching pipeline. In contrast to recent detector-free methods that depend on an exhaustive set of coarse-level candidates for matching, HCPM selectively concentrates on a concise subset of informative candidates, resulting in fewer computational candidates and enhanced matching efficiency. The method comprises a self-pruning stage for selecting reliable candidates and an interactive-pruning stage that identifies correlated patches at the coarse level. Our results reveal that HCPM significantly surpasses existing methods in terms of speed while maintaining high accuracy. The source code will be made available upon publication.

Published
2024

7. Testing Alpha in High Dimensional Linear Factor Pricing Models with Dependent Observations

Author

Ma, Huifang, Feng, Long, Wang, Zhaojun, and Bao, Jigang

Subjects
Statistics - Methodology
Abstract

In this study, we introduce three distinct testing methods for testing alpha in high dimensional linear factor pricing model that deals with dependent data. The first method is a sum-type test procedure, which exhibits high performance when dealing with dense alternatives. The second method is a max-type test procedure, which is particularly effective for sparse alternatives. For a broader range of alternatives, we suggest a Cauchy combination test procedure. This is predicated on the asymptotic independence of the sum-type and max-type test statistics. Both simulation studies and practical data application demonstrate the effectiveness of our proposed methods when handling dependent observations.

Published
2024

9. Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets

Author

MA, Huifang, Feng, Long, and Wang, Zhaojun

Subjects
Statistics - Methodology
Abstract

This paper focuses on testing for the presence of alpha in time-varying factor pricing models, specifically when the number of securities N is larger than the time dimension of the return series T. We introduce a maximum-type test that performs well in scenarios where the alternative hypothesis is sparse. We establish the limit null distribution of the proposed maximum-type test statistic and demonstrate its asymptotic independence from the sum-type test statistics proposed by Ma et al.(2020).Additionally, we propose an adaptive test by combining the maximum-type test and sum-type test, and we show its advantages under various alternative hypotheses through simulation studies and two real data applications.

Published
2023

11. Time-aware Session Modeling for Knowledge Tracing

Author

Wang, Jing, Ma, Huifang, Zhang, Mengyuan, Li, Zhixin, Chang, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Onizuka, Makoto, editor, Lee, Jae-Gil, editor, Tong, Yongxin, editor, Xiao, Chuan, editor, Ishikawa, Yoshiharu, editor, Amer-Yahia, Sihem, editor, Jagadish, H. V., editor, and Lu, Kejing, editor

Published
2024
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12. Key Substructure-Driven Backdoor Attacks on Graph Neural Networks

Author

Tong, Haibin, Ma, Huifang, Shen, Hui, Li, Zhixin, Chang, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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13. Beyond Homophily: Attributed Graph Anomaly Detection via Heterophily-Aware Contrastive Learning Network

Author

Jin, Wangyu, Ma, Huifang, Zhang, Yingyue, Li, Zhixin, Chang, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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14. Weakly-Supervised Semantic Segmentation via Label Re-assignment in Dual-View Framework

Author

Wang, Chen, Zhang, Di, Li, Xiaolong, Ma, Huifang, Li, Zhixin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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15. Dual-Channel Dual-Scale Interactive Learning for the Prediction of Compound-Protein Interaction

Author

Wu, Zheyu, Ma, Huifang, Deng, Bin, Li, Zhixin, Chang, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Onizuka, Makoto, editor, Lee, Jae-Gil, editor, Tong, Yongxin, editor, Xiao, Chuan, editor, Ishikawa, Yoshiharu, editor, Amer-Yahia, Sihem, editor, Jagadish, H. V., editor, and Lu, Kejing, editor

Published
2024
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16. Path-Aware Co-contrastive Learning for Signed Directed Network Embedding

Author

Tang, Yuechen, Ma, Huifang, Shu, Ke, Li, Zhixin, Chang, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Onizuka, Makoto, editor, Lee, Jae-Gil, editor, Tong, Yongxin, editor, Xiao, Chuan, editor, Ishikawa, Yoshiharu, editor, Amer-Yahia, Sihem, editor, Jagadish, H. V., editor, and Lu, Kejing, editor

Published
2024
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46. Band alignment engineering of 2D/3D halide perovskite lateral heterostructures.

Author

Feng, Mengjia, Kong, Lingkun, Chen, Jinlian, Ma, Huifang, Zha, Chenyang, and Zhang, Linghai

Subjects
HETEROSTRUCTURES, PEROVSKITE, DENSITY functional theory, CHARGE transfer, QUANTUM wells, LIGHT absorption, HALIDES
Abstract

Two-dimensional (2D)/three-dimensional (3D) halide perovskite heterostructures have been extensively studied for their ability to combine the outstanding long-term stability of 2D perovskites with the superb optoelectronic properties of 3D perovskites. While current studies mostly focus on vertically stacked 2D/3D perovskite heterostructures, a theoretical understanding regarding the optoelectronic properties of 2D/3D perovskite lateral heterostructures is still lacking. Herein, we construct a series of 2D/3D perovskite lateral heterostructures to study their optoelectronic properties and interfacial charge transfer using density functional theory (DFT) calculations. We find that the band alignments of 2D/3D heterostructures can be regulated by varying the quantum-well thickness of 2D perovskites. Moreover, decreasing the 2D component ratio in 2D/3D heterostructures can be favorable to form type-I band alignment, whereas a large component ratio of 2D perovskites tends to form type-II band alignment. We can improve the amount of charge transfer at the 2D/3D perovskite interfaces and the light absorption of 2D perovskites by increasing quantum-well thickness. These present findings can provide a clear designing principle for achieving 3D/2D perovskite lateral heterostructures with tunable optoelectronic properties. [ABSTRACT FROM AUTHOR]

Published
2024
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