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14,867 results on '"Dong, Yan"'

1. Hierarchical Structured Neural Network for Retrieval

Author

Rangadurai, Kaushik, Yuan, Siyang, Huang, Minhui, Liu, Yiqun, Ghasemiesfeh, Golnaz, Pu, Yunchen, Xie, Xinfeng, He, Xingfeng, Xu, Fangzhou, Cui, Andrew, Viswanathan, Vidhoon, Dong, Yan, Xiong, Liang, Yang, Lin, Wang, Liang, Yang, Jiyan, and Sun, Chonglin

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Embedding Based Retrieval (EBR) is a crucial component of the retrieval stage in (Ads) Recommendation System that utilizes Two Tower or Siamese Networks to learn embeddings for both users and items (ads). It then employs an Approximate Nearest Neighbor Search (ANN) to efficiently retrieve the most relevant ads for a specific user. Despite the recent rise to popularity in the industry, they have a couple of limitations. Firstly, Two Tower model architecture uses a single dot product interaction which despite their efficiency fail to capture the data distribution in practice. Secondly, the centroid representation and cluster assignment, which are components of ANN, occur after the training process has been completed. As a result, they do not take into account the optimization criteria used for retrieval model. In this paper, we present Hierarchical Structured Neural Network (HSNN), a deployed jointly optimized hierarchical clustering and neural network model that can take advantage of sophisticated interactions and model architectures that are more common in the ranking stages while maintaining a sub-linear inference cost. We achieve 6.5% improvement in offline evaluation and also demonstrate 1.22% online gains through A/B experiments. HSNN has been successfully deployed into the Ads Recommendation system and is currently handling major portion of the traffic. The paper shares our experience in developing this system, dealing with challenges like freshness, volatility, cold start recommendations, cluster collapse and lessons deploying the model in a large scale retrieval production system., Comment: 9 pages

Published
2024

10. High-Performance and Low-Power Sub-5 nm Field-Effect Transistors Based on 7-9-7-AGNR

Author

Guo, Hang, Zhang, Xian, Chen, Shuai, Huang, Li, Dong, Yan, and Guo, Zhi-Xin

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Recently, an extremely-air-stable one-dimensional 7-9-7-AGNR was successfully fabricated. To further reveal its potential application in sub-5-nm field-effect transistors (FETs), there is an urgent need to develop integrated circuits. Here, we report first-principles quantum-transport simulations on the performance limits of n- and p-type sub-5-nm one-dimensional 7-9-7-AGNR FET. We find that the on-state current (Ion) in 7-9-7-AGNR FET can be effectively manipulated by the length of the gate and underlap. Particularly, the optimized Ion in n-type (p-type) device can reach up to 2423 (4277) and 1988 (920) {\mu}A/{\mu}m for high-performance and low-power applications, respectively. The large Ion values are in the first class among the LD FETs, which can well satisfy the ITRS requirements. We also find that the 7-9-7-AGNR FET can have ultralow subthreshold swing below 60mV/dev, ultrashort delay time (<0.01 ps), and very small power-delay product (<0.01 fJ/{\mu}m). Our results show that the 7-9-7-AGNR based FETs have great potential applications in the high-speed and low-power consumption chips., Comment: 7 figures, 2 tables

Published
2023

19. User-centric AIGC products: Explainable Artificial Intelligence and AIGC products

Author

Yu, Hanjie, Dong, Yan, and Wu, Qiong

Subjects
Computer Science - Human-Computer Interaction
Abstract

Generative AI tools, such as ChatGPT and Midjourney, are transforming artistic creation as AI-art integration advances. However, Artificial Intelligence Generated Content (AIGC) tools face user experience challenges, necessitating a human-centric design approach. This paper offers a brief overview of research on explainable AI (XAI) and user experience, examining factors leading to suboptimal experiences with AIGC tools. Our proposed solution integrates interpretable AI methodologies into the input and adjustment feedback stages of AIGC products. We underscore XAI's potential to enhance the user experience for ordinary users and present a conceptual framework for improving AIGC user experience.

Published
2023

24. Effect of high-fat diet on adipose tissues structure and inflammatory characteristics during pregnancy in mice

Author

LU Xingyu, XU Yidan, LIU Yiqin, ZHANG Qianren, and DONG Yan

Subjects
high-fat diet, adipose tissue, pregnancy, intergenerational transmission, Medicine
Abstract

Objective·To investigate the changes of adipose tissues in mice after high-fat diet before and during pregnancy and the potential effects on adipose tissues in their offspring.Methods·C57BL/6J female mice were randomly assigned to the normal diet (CON group, n=12) or the high-fat diet (HFD group, n=12) for 5 weeks. The two groups were further subdivided according to pregnancy: a normal diet non-pregnancy group (CON-UN group), a normal diet pregnancy group (CON-P group), a high-fat diet non-pregnancy group (HFD-UN group), and a high-fat diet pregnancy group (HFD-P group). The original diet was maintained during pregnancy and lactation. White adipose tissues (WAT) and brown adipose tissues (BAT) were collected from visceral and scapula of mice after 5 weeks of feeding or E18.5d. Offspring from both dietary groups were placed on a normal diet after weaning, and their adipose tissues were collected at the 11th week. H-E staining was used to observe the changes of adipocytes. Flow cytometry was employed to detect the proportions of CD4+T cells, CD8+T cells, total T cells, CD4+T/CD8+T and NK cells in WAT. RT-PCR was used to assess the expression of IL-6 and IL-1β mRNA in WAT.Results·After 5 weeks on a high-fat diet, the body weight of female mice in the HFD group was higher than that in the CON group (P

Published
2024
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26. Requesting a Commitment in School Teachers to Teach in Unprecedented Ways: The Mediating Role of Teacher Agency

Author

Yang, Yunying, Dong, Yan, Jiang, Liming, Xu, Chang, Luo, Fang, Zhao, Guoqing, and Kurup, Premnadh M.

Abstract

The necessity for the development and enhancement of teacher commitment to satisfying students' learning needs in response to the COVID crisis is increasingly highlighted. It is not known, however, how to increase commitment in schoolteachers to boost online teaching in light of the fact that they, too, are struggling to cope with the rapid, unexpected change. A total of 601 teachers from primary and secondary schools across China participated in this study, with an average teaching experience of 15.9 years. Structural equation modelling was used to verify the significance of contextual, cognitive, affective and behavioural factors in boosting teachers' commitment to online teaching. The findings demonstrated that teacher agency played a complete mediating role in the predicting power of other factors to teacher commitment. Therefore, it was recommended that attention be paid to the practice and opportunities for teacher agentic actions, which necessitates real encounters with online teaching, allowing teachers to act meaningfully and initiate a new set of teaching strategies.

Published
2023
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27. Knowledge-Network-Based Translation Learning and Learners’ Perception

Author

Zheng, Shuangshuang, Dong, Yan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Kubincová, Zuzana, editor, Hao, Tianyong, editor, Capuano, Nicola, editor, Temperini, Marco, editor, Ge, Shili, editor, Mu, Yuanyuan, editor, Fantozzi, Paolo, editor, and Yang, Jing, editor

Published
2024
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28. SE-UNet: Channel Attention Based UNet for Water Body Segmentation from SAR Image

Author

Li, Wenshuo, Dong, Yan, Wang, Yulin, Xu, Tao, Liu, Zhen, Yu, Kunfeng, Xiao, Chenxin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, You, Peng, editor, Liu, Shuaiqi, editor, and Wang, Jun, editor

Published
2024
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29. Deep Neural Network Model over Encrypted Data

Author

Li, Weixun, Sun, Guanghui, Wang, Yajun, Yuan, Long, Gao, Minghui, Dong, Yan, Wang, Chen, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Shao, Jun, editor, Katsikas, Sokratis K., editor, and Meng, Weizhi, editor

Published
2024
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30. AI-Based Intelligent-Annotation Algorithm for Medical Segmentation from Ultrasound Data

Author

Peng, Tao, Zhang, Yaogong, Dong, Yan, Ruan, Yiwen, Jin, Rui, Liu, Zhaorui, Wu, Hongzhuang, Shen, Yuling, Zhang, Lei, Goos, Gerhard, Founding 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, Liu, Fenrong, editor, Sadanandan, Arun Anand, editor, Pham, Duc Nghia, editor, Mursanto, Petrus, editor, and Lukose, Dickson, editor

Published
2024
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