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2. LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering

Author

Chen, Yuhan, Su, Lumei, Chen, Lihua, and Lin, Zhiwei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In this paper, the LCV2 modular method is proposed for the Grounded Visual Question Answering task in the vision-language multimodal domain. This approach relies on a frozen large language model (LLM) as intermediate mediator between the off-the-shelf VQA model and the off-the-shelf visual grounding (VG) model, where the LLM transforms and conveys textual information between the two modules based on a designed prompt. LCV2 establish an integrated plug-and-play framework without the need for any pre-training process. This framework can be deployed for VQA Grounding tasks under low computational resources. The modularized model within the framework allows application with various state-of-the-art pre-trained models, exhibiting significant potential to be advance with the times. Experimental implementations were conducted under constrained computational and memory resources, evaluating the proposed method's performance on benchmark datasets including GQA, CLEVR, and VizWiz-VQA-Grounding. Comparative analyses with baseline methods demonstrate the robust competitiveness of LCV2., Comment: 21 pages,9 figures

Published
2024

21. A spatial transcriptome map of the developing maize ear

Author

Wang, Yuebin, Luo, Yun, Guo, Xing, Li, Yunfu, Yan, Jiali, Shao, Wenwen, Wei, Wenjie, Wei, Xiaofeng, Yang, Tao, Chen, Jing, Chen, Lihua, Ding, Qian, Bai, Minji, Zhuo, Lin, Li, Li, Jackson, David, Zhang, Zuxin, Xu, Xun, Yan, Jianbing, Liu, Huan, Liu, Lei, and Yang, Ning

Published
2024
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25. Measurement of $^{19}$F($p$,$\gamma$)$^{20}$Ne reaction suggests CNO break-out in first stars

Author

Zhang, Liyong, He, Jianjun, deBoer, Richard J., Wiescher, Michael, Heger, Alexander, Kahl, Daid, Su, Jun, Odell, Daniel, Chen, Yinji, Li, Xinyue, Wang, Jianguo, Zhang, Long, Cao, Fuqiang, Zhang, Hao, Zhang, Zhicheng, Jiang, Xinzhi, Wang, Luohuan, Li, Ziming, Song, Luyang, Zhao, Hongwei, Sun, Liangting, Wu, Qi, Li, Jiaqing, Cui, Baoqun, Chen, Lihua, Ma, Ruigang, Li, Ertao, Lian, Gang, Sheng, Yaode, Li, Zhihong, Guo, Bing, Zhou, Xiaohong, Zhang, Yuhu, Xu, Hushan, Cheng, Jianping, and Liu, Weiping

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Nuclear Experiment
Abstract

The origin of calcium production in the first stars (Pop III stars), which formed out of the primordial matter of the Big Bang, and their fates, remain most fascinating mysteries in astrophysics. Advanced nuclear burning and supernovae were thought to be the dominant source of the Ca production seen in all stars. Here we report on a qualitatively different path to Ca production through break-out from the "warm" carbon-nitrogen-oxygen (CNO) cycle. We extend direct measurement of the $^{19}$F($p$, $\gamma$)$^{20}$Ne break-out reaction down to an unprecedentedly low energy point of 186 keV and discover a key resonance at 225 keV. In the domain of astrophysical interest, at around 0.1 giga kelvin, this thermonuclear $^{19}$F($p$,$\gamma$)$^{20}$Ne rate is up to a factor of 7.4 larger than the previous recommended rate. Our stellar models show a stronger break-out during stellar hydrogen burning than thought before, and may reveal the nature of Ca production in Pop III stars imprinted on the oldest known ultra-iron poor star, SMSS0313-6708. This result from the China Jinping Underground Laboratory, the deepest laboratory in the world, offering an environment with extremely low cosmic-ray induced background, has far-reaching implications on our understanding of how the first stars evolve and die. Our rate showcases the impact that faint Pop III star supernovae can have on the nucleosynthesis observed in the oldest known stars and first galaxies, key mission targets of the James Webb Space Telescope., Comment: 32 pages, 8 figures, 6 tables

Published
2023
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26. Expression and clinical significance of lncRNA PART1 in patients with unexplained recurrent pregnancy loss

Author

Chen Lihua, Su Hua, Wang Wenzhan, Jeson Standard, and Liang Denghui

Subjects
Unexplained recurrent pregnancy loss, PART1, pregnancy outcome, androgen, lncRNA, Gynecology and obstetrics, RG1-991, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Purpose Previous studies have reported the involvement of long noncoding RNAs (lncRNAs) in reproductive diseases via the regulation of target genes. This study aimed to determine whether lnc-prostate androgen-regulated transcript 1 (lnc-PART1)could be used as a biomarker of unexplained recurrent pregnancy loss (URPL) and a possible predictor of poor pregnancy outcomes in women with URPL.Materials and methods Sixty patients with URPL and 15 healthy women were included in this study. PART1 expression was detected in plasma and endometrial tissues using a quantitative reverse transcription polymerase chain reaction. Logistic regression and receiver operating characteristic curve analyses were performed to analyze the association between PART1 expression and pregnancy outcomes in women with URPL.Results The expression of PART1transcript variant 2 was significantly up-regulated in the endometrial specimens from patients with URPL compared to control tissues. High tissue expression levels of PART1transcript variant 2 were associated with poor pregnancy outcomes in women with URPL, indicating that it could serve as a potential risk factor. Additionally, PART1 could serve as a potential risk factor for adverse pregnancy outcomes in patients with URPL (OR = 4.374; 95% CI = 1.052–18.189; p = .042).Conclusion lncRNA PART1 transcript variant 2 was highly expressed in patients with URPL. Therefore, it is important to conduct in-depth studies on the relationship between PART1 expression and URPL.

Published
2024
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31. Time Lag Aware Sequential Recommendation

Author

Chen, Lihua, Yang, Ning, and Yu, Philip S

Subjects
Computer Science - Information Retrieval
Abstract

Although a variety of methods have been proposed for sequential recommendation, it is still far from being well solved partly due to two challenges. First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference. Second, the existing methods often use a scalar based weighting schema to fuse the long-term and short-term preferences, which is too coarse to learn an expressive embedding of current preference. To address the two challenges, we propose a novel model called Time Lag aware Sequential Recommendation (TLSRec), which integrates a hierarchical modeling of user preference and a time lag sensitive fine-grained fusion of the long-term and short-term preferences. TLSRec employs a hierarchical self-attention network to learn users' preference at both global and local time scales, and a neural time gate to adaptively regulate the contributions of the long-term and short-term preferences for the learning of a user's current preference at the aspect level and based on the lag between the current time and the time of the last behavior of a user. The extensive experiments conducted on real datasets verify the effectiveness of TLSRec., Comment: This paper has been accepted by CIKM 2022

Published
2022

32. Adaptation and Validation of BullyHARM-China--A Chinese Version of the Bullying, Harassment, and Aggression Receipt Measure

Author

Yang, Jingyi, Ferraz, Raul, Shi, Dexin, Harrison, Sayward E., Ye, Zhi, Chen, Lihua, and Lin, Danhua

Abstract

Bullying is a growing concern in China, yet there are few validated scales designed to measure different types of bullying among Chinese children. In this present study, a bilingual team of researchers use a forward-backward translation process to adapt the Bullying, Harassment, and Aggression Receipt Measure (BullyHARM) for Chinese youth. BullyHARM has previously been shown to be a reliable scale for measuring six bullying domains (i.e., physical, verbal, social/relational, cyber, property, sexual) among children in the United States (US). After cultural and linguistic adaptation, we enrolled 397 middle school students from Beijing, China in a validation study to assess the psychometric properties of the new BullyHARM-China scale. Results of confirmatory factor analysis suggest the final 21-item scale displays strong internal consistency. Consistent with findings from the US, the first-order model of six factors (i.e., six bullying subscales) displays the best fit to the data. Our findings suggest that BullyHARM-China is a reliable tool for measuring bullying victimization among Chinese students.

Published
2023
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33. Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education

Author

Fan, Si, Chen, Lihua, Nair, Manoj, Garg, Saurabh, Yeom, Soonja, Kregor, Gerry, Yang, Yu, and Wang, Yanjun

Abstract

This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.

Published
2021

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