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258 results on '"Wang, Xinkai"'

1. Diffusion Conditional Expectation Model for Efficient and Robust Target Speech Extraction

Author

Zhang, Leying, Qian, Yao, Yu, Linfeng, Wang, Heming, Wang, Xinkai, Yang, Hemin, Zhou, Long, Liu, Shujie, Qian, Yanmin, and Zeng, Michael

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound
Abstract

Target Speech Extraction (TSE) is a crucial task in speech processing that focuses on isolating the clean speech of a specific speaker from complex mixtures. While discriminative methods are commonly used for TSE, they can introduce distortion in terms of speech perception quality. On the other hand, generative approaches, particularly diffusion-based methods, can enhance speech quality perceptually but suffer from slower inference speed. We propose an efficient generative approach named Diffusion Conditional Expectation Model (DCEM) for TSE. It can handle multi- and single-speaker scenarios in both noisy and clean conditions. Additionally, we introduce Regenerate-DCEM (R-DCEM) that can regenerate and optimize speech quality based on pre-processed speech from a discriminative model. Our method outperforms conventional methods in terms of both intrusive and non-intrusive metrics and demonstrates notable strengths in inference efficiency and robustness to unseen tasks. Audio examples are available online (https://vivian556123.github.io/dcem)., Comment: Submitted to ICASSP 2024

Published
2023

19. Novel Reassortant Avian Influenza A(H5N6) Virus, China, 2021

Author

Chen, Junhong, Xu, Lingyu, Liu, Tengfei, Xie, Shumin, Li, Ke, Li, Xiao, Zhang, Mengmeng, Wu, Yifan, Wang, Xinkai, Wang, Jinfeng, Shi, Keyi, Niu, Beibei, Liao, Ming, and Jia, Weixin

Subjects
Avian influenza viruses -- Distribution -- Testing -- Genetic aspects, Poultry -- Diseases, Company distribution practices, Health
Abstract

Severe human infection with influenza A(H5N6) virus was identified in China in 2014. During 2014-2020, a total of 26 cases of human infection were laboratory confirmed (1,2). Sporadic cases did [...]

Published
2022
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42. The child-friendly cities concept in China: A prototype case study of a migrant workers' community.

Author

Wang, Xinkai, Elkhouly, Aya AN, Shukla, Prasenjit, Jiang, Wenwen, Zhang, Xiupeng, Zhang, Qianxi, Wu, Shanshan, Ni, Mindong, Fan, Shuying, Günay, Zeynep, and Aerts, Jens

Subjects
*SOCIAL participation, *NOMADS, *VOCATIONAL guidance, *MATHEMATICAL models, *SOCIAL workers, *SOCIAL change, *RURAL conditions, *COMMUNITIES, *SOCIAL cohesion, *COMPARATIVE studies, *THEORY, *INTERPROFESSIONAL relations, *RESEARCH funding, *METROPOLITAN areas, *ADULT education workshops
Abstract

The United Nations International Children's Emergency Fund (UNICEF) initiatives of Child-Friendly Cities have operated in more than 3000 cities around the world. However, the concept of making the city child-friendly has not been extensively recognized in China. This article aims to report a regeneration project in a migrant workers' community, the Mingdong, and is used to illustrate the reactions of residents to youth participation (youth civic engagement) and the project through an international workshop. The article concludes with a discussion of how social workers help in the social development of migrant communities by promoting youth participation, community social awareness and social cohesiveness. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Understanding the importance of atmospheric transformation in assessing the hazards of liquid crystal monomers.

Author

Wang, Jinlong, Wang, Shenghong, Zhang, Zhizhen, Wang, Xinkai, Xia, Kaihui, Li, Li, and Liu, Qifan

Abstract

Liquid crystal monomers (LCMs), a group of synthetic chemicals released from liquid crystal devices such as televisions and smartphones, have recently been recognized as emerging contaminants due to their widespread occurrence in the environment and potential negative impacts on human health. Airborne LCMs can undergo atmospheric oxidation reactions to form various transformation products. Despite the certainty of atmospheric transformation chemistry, the knowledge about the hazard properties of transformation products remains largely unknown. Here, we perform an in silico model-based evaluation of the persistence, bioaccumulation potential, mobility, and toxicity of two representative LCMs, namely, 1-ethyl-4-(4-(4-propylcyclohexyl)phenyl)benzene and 4′′-ethyl-2′-fluoro-4-propyl-1,1′:4′,1′′-terphenyl, and their transformation products. We found that, among the investigated transformation products, 38% have overall persistence greater than the minimum of 331 days among the persistent organic pollutants regulated by the Stockholm Convention, 62% meet the bioaccumulation threshold of 1000 L kg−1 used by the United States Environmental Protection Agency, 44% are classified "mobile" according to the criterion used by the German Environmental Agency, and 58% have the potential to induce unacceptable toxic effects in aquatic organisms. Furthermore, we identified several transformation products with increased persistence, bioaccumulation potential, and mobility compared to their parent compounds. These findings not only offer insights for prioritizing LCM transformation products for future risk assessment, but also underscore the significance of considering atmospheric transformation in the evaluation of environmental risks posed by emerging contaminants, including LCMs. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Research on decision making of intelligent vehicle based on composite priority experience replay.

Author

Wang, Shufeng, Zhang, Baokang, Liang, Qingwei, and Wang, Xinkai

Subjects
REINFORCEMENT learning, DECISION making, REWARD (Psychology), DEEP reinforcement learning, LANE changing
Abstract

To address the problems of underutilization of samples and unstable training for intelligent vehicle training in the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, a TD3 algorithm based on the Composite Prioritized Experience Replay (CPR-TD3) mechanism is proposed. It considers experience immediate reward value and Temporal Difference error (TD-error) based and respectively to construct priorities to rank the samples. Subsequently composite average ranking of the samples to recalculate the priorities for sampling, uses the collected samples to train the target network. Then introduces the minimum lane change distance and the variable headway time distance to improve the reward function. Finally, the improved algorithm is proved to be effective by comparing it with the traditional TD3 on the highway scenario, and the CPR-TD3 algorithm improves the training efficiency of intelligent vehicles. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Chinese-English cross-lingual information retrieval in biomedicine using ontology-based query expansion

Author

Wang, Xinkai and Ananiadou, Sophia

Subjects
026.61, Cross-lingual information retrieval, Biomedical information retrieval, CMeSH, Ontology-based query expansion
Abstract

In this thesis, we propose a new approach to Chinese-English Biomedical cross-lingual information retrieval (CLIR) using query expansion based on the eCMeSH Tree, a Chinese-English ontology extended from the Chinese Medical Subject Headings (CMeSH) Tree. The CMeSH Tree is not designed for information retrieval (IR), since it only includes heading terms and has no term weighting scheme for these terms. Therefore, we design an algorithm, which employs a rule-based parsing technique combined with the C-value term extraction algorithm and a filtering technique based on mutual information, to extract Chinese synonyms for the corresponding heading terms. We also develop a term-weighting mechanism. Following the hierarchical structure of CMeSH, we extend the CMeSH Tree to the eCMeSH Tree with synonymous terms and their weights. We propose an algorithm to implement CLIR using the eCMeSH Tree terms to expand queries. In order to evaluate the retrieval improvements obtained from our approach, the results of the query expansion based on the eCMeSH Tree are individually compared with the results of the experiments of query expansion using the CMeSH Tree terms, query expansion using pseudo-relevance feedback, and document translation. We also evaluate the combinations of these three approaches. This study also investigates the factors which affect the CLIR performance, including a stemming algorithm, retrieval models, and word segmentation.

Published
2011

48. Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE

Author

Wang, Xinkai, Feng, Yanbo, Tong, Boning, Bao, Jingxuan, Ritchie, Marylyn D., Saykin, Andrew J., Moore, Jason H., Urbanowicz, Ryan, and Shen, Li

Subjects
Articles
Abstract

STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer’s disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.

Published
2023

49. Comparing Amyloid Imaging Normalization Strategies for Alzheimer’s Disease Classification using an Automated Machine Learning Pipeline

Author

Tong, Boning, Risacher, Shannon L., Bao, Jingxuan, Feng, Yanbo, Wang, Xinkai, Ritchie, Marylyn D., Moore, Jason H., Urbanowicz, Ryan, Saykin, Andrew J., and Shen, Li

Subjects
Articles
Abstract

Amyloid imaging has been widely used in Alzheimer’s disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.

Published
2023

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