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1,455 results on '"Lin, Xiaofeng"'

1. Efficient Reinforcement Learning in Probabilistic Reward Machines

Author

Lin, Xiaofeng and Zhang, Xuezhou

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In this paper, we study reinforcement learning in Markov Decision Processes with Probabilistic Reward Machines (PRMs), a form of non-Markovian reward commonly found in robotics tasks. We design an algorithm for PRMs that achieves a regret bound of $\widetilde{O}(\sqrt{HOAT} + H^2O^2A^{3/2} + H\sqrt{T})$, where $H$ is the time horizon, $O$ is the number of observations, $A$ is the number of actions, and $T$ is the number of time-steps. This result improves over the best-known bound, $\widetilde{O}(H\sqrt{OAT})$ of \citet{pmlr-v206-bourel23a} for MDPs with Deterministic Reward Machines (DRMs), a special case of PRMs. When $T \geq H^3O^3A^2$ and $OA \geq H$, our regret bound leads to a regret of $\widetilde{O}(\sqrt{HOAT})$, which matches the established lower bound of $\Omega(\sqrt{HOAT})$ for MDPs with DRMs up to a logarithmic factor. To the best of our knowledge, this is the first efficient algorithm for PRMs. Additionally, we present a new simulation lemma for non-Markovian rewards, which enables reward-free exploration for any non-Markovian reward given access to an approximate planner. Complementing our theoretical findings, we show through extensive experiment evaluations that our algorithm indeed outperforms prior methods in various PRM environments., Comment: 33 pages, 4 figures

Published
2024

2. CTSyn: A Foundational Model for Cross Tabular Data Generation

Author

Lin, Xiaofeng, Xu, Chenheng, Yang, Matthew, and Cheng, Guang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Generative Foundation Models (GFMs) have produced synthetic data with remarkable quality in modalities such as images and text. However, applying GFMs to tabular data poses significant challenges due to the inherent heterogeneity of table features. Existing cross-table learning frameworks are hindered by the absence of both a generative model backbone and a decoding mechanism for heterogeneous feature values. To overcome these limitations, we introduce the Cross-Table Synthesizer (CTSyn), a diffusion-based foundational model tailored for tabular data generation. CTSyn introduces three major components: an aggregator that consolidates heterogeneous tables into a unified latent space; a conditional latent diffusion model for sampling from this space; and type-specific decoders that reconstruct values of varied data types from sampled latent vectors. Extensive testing on real-world datasets reveals that CTSyn not only significantly outperforms existing table synthesizers in utility and diversity, but also uniquely enhances performances of downstream machine learning beyond what is achievable with real data, thus establishing a new paradigm for synthetic data generation.

Published
2024

3. Evaluating Saliency Explanations in NLP by Crowdsourcing

Author

Lu, Xiaotian, Li, Jiyi, Wan, Zhen, Lin, Xiaofeng, Takeuchi, Koh, and Kashima, Hisashi

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a score proportional to the contribution of output, have been proposed to determine the part of the input which a model values most. Despite a considerable body of work on the evaluation of saliency methods, whether the results of various evaluation metrics agree with human cognition remains an open question. In this study, we propose a new human-based method to evaluate saliency methods in NLP by crowdsourcing. We recruited 800 crowd workers and empirically evaluated seven saliency methods on two datasets with the proposed method. We analyzed the performance of saliency methods, compared our results with existing automated evaluation methods, and identified notable differences between NLP and computer vision (CV) fields when using saliency methods. The instance-level data of our crowdsourced experiments and the code to reproduce the explanations are available at https://github.com/xtlu/lreccoling_evaluation., Comment: 13 pages, 4 figures, Accepted for LREC-Coling 2024 (Oral)

Published
2024

4. Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data

Author

Xing, Yue, Lin, Xiaofeng, Xu, Chenheng, Suh, Namjoon, Song, Qifan, and Cheng, Guang

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning
Abstract

Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023trained}, attempt to explain the mechanism of ICL, they assume the input $x_i$ and the output $y_i$ of each demonstration example are in the same token (i.e., structured data). However, in real practice, the examples are usually text input, and all words, regardless of their logic relationship, are stored in different tokens (i.e., unstructured data \cite{wibisono2023role}). To understand how LLMs learn from the unstructured data in ICL, this paper studies the role of each component in the transformer architecture and provides a theoretical understanding to explain the success of the architecture. In particular, we consider a simple transformer with one/two attention layers and linear regression tasks for the ICL prediction. We observe that (1) a transformer with two layers of (self-)attentions with a look-ahead attention mask can learn from the prompt in the unstructured data, and (2) positional encoding can match the $x_i$ and $y_i$ tokens to achieve a better ICL performance.

Published
2024

5. Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective

Author

Xing, Yue, Lin, Xiaofeng, Song, Qifan, Xu, Yi, Zeng, Belinda, and Cheng, Guang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models. Existing literature, e.g., \cite{kim2020adversarial}, empirically observe that the downstream tasks can inherit the adversarial robustness of the pre-trained model. We provide theoretical justifications for this robustness inheritance phenomenon. Our theoretical results reveal that feature purification plays an important role in connecting the adversarial robustness of the pre-trained model and the downstream tasks in two-layer neural networks. Specifically, we show that (i) with adversarial training, each hidden node tends to pick only one (or a few) feature; (ii) without adversarial training, the hidden nodes can be vulnerable to attacks. This observation is valid for both supervised pre-training and contrastive learning. With purified nodes, it turns out that clean training is enough to achieve adversarial robustness in downstream tasks., Comment: To appear in AISTATS2024

Published
2024

6. AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

Author

Suh, Namjoon, Lin, Xiaofeng, Hsieh, Din-Yin, Honarkhah, Merhdad, and Cheng, Guang

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis. In this paper, we leverage the power of diffusion model for generating synthetic tabular data. The heterogeneous features in tabular data have been main obstacles in tabular data synthesis, and we tackle this problem by employing the auto-encoder architecture. When compared with the state-of-the-art tabular synthesizers, the resulting synthetic tables from our model show nice statistical fidelities to the real data, and perform well in downstream tasks for machine learning utilities. We conducted the experiments over $15$ publicly available datasets. Notably, our model adeptly captures the correlations among features, which has been a long-standing challenge in tabular data synthesis. Our code is available at https://github.com/UCLA-Trustworthy-AI-Lab/AutoDiffusion.

Published
2023

8. Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach

Author

Xu, Zirui, Lin, Xiaofeng, and Tzoumas, Vasileios

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Computer Science - Robotics, Mathematics - Optimization and Control
Abstract

We study the problem of multi-agent coordination in unpredictable and partially-observable environments with untrustworthy external commands. The commands are actions suggested to the robots, and are untrustworthy in that their performance guarantees, if any, are unknown. Such commands may be generated by human operators or machine learning algorithms and, although untrustworthy, can often increase the robots' performance in complex multi-robot tasks. We are motivated by complex multi-robot tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization problems due to the information overlap among the robots. We provide an algorithm, Meta Bandit Sequential Greedy (MetaBSG), which enjoys performance guarantees even when the external commands are arbitrarily bad. MetaBSG leverages a meta-algorithm to learn whether the robots should follow the commands or a recently developed submodular coordination algorithm, Bandit Sequential Greedy (BSG) [1], which has performance guarantees even in unpredictable and partially-observable environments. Particularly, MetaBSG asymptotically can achieve the better performance out of the commands and the BSG algorithm, quantifying its suboptimality against the optimal time-varying multi-robot actions in hindsight. Thus, MetaBSG can be interpreted as robustifying the untrustworthy commands. We validate our algorithm in simulated scenarios of multi-target tracking.

Published
2023

9. Estimating Treatment Effects Under Heterogeneous Interference

Author

Lin, Xiaofeng, Zhang, Guoxi, Lu, Xiaotian, Bao, Han, Takeuchi, Koh, and Kashima, Hisashi

Subjects
Computer Science - Machine Learning, Statistics - Methodology
Abstract

Treatment effect estimation can assist in effective decision-making in e-commerce, medicine, and education. One popular application of this estimation lies in the prediction of the impact of a treatment (e.g., a promotion) on an outcome (e.g., sales) of a particular unit (e.g., an item), known as the individual treatment effect (ITE). In many online applications, the outcome of a unit can be affected by the treatments of other units, as units are often associated, which is referred to as interference. For example, on an online shopping website, sales of an item will be influenced by an advertisement of its co-purchased item. Prior studies have attempted to model interference to estimate the ITE accurately, but they often assume a homogeneous interference, i.e., relationships between units only have a single view. However, in real-world applications, interference may be heterogeneous, with multi-view relationships. For instance, the sale of an item is usually affected by the treatment of its co-purchased and co-viewed items. We hypothesize that ITE estimation will be inaccurate if this heterogeneous interference is not properly modeled. Therefore, we propose a novel approach to model heterogeneous interference by developing a new architecture to aggregate information from diverse neighbors. Our proposed method contains graph neural networks that aggregate same-view information, a mechanism that aggregates information from different views, and attention mechanisms. In our experiments on multiple datasets with heterogeneous interference, the proposed method significantly outperforms existing methods for ITE estimation, confirming the importance of modeling heterogeneous interference.

Published
2023

10. Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments

Author

Xu, Zirui, Lin, Xiaofeng, and Tzoumas, Vasileios

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Computer Science - Robotics, Mathematics - Optimization and Control
Abstract

We study the problem of multi-agent coordination in unpredictable and partially observable environments, that is, environments whose future evolution is unknown a priori and that can only be partially observed. We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems due to the information overlap among the robots. We introduce the first submodular coordination algorithm with bandit feedback and bounded tracking regret -- bandit feedback is the robots' ability to compute in hindsight only the effect of their chosen actions, instead of all the alternative actions that they could have chosen instead, due to the partial observability; and tracking regret is the algorithm's suboptimality with respect to the optimal time-varying actions that fully know the future a priori. The bound gracefully degrades with the environments' capacity to change adversarially, quantifying how often the robots should re-select actions to learn to coordinate as if they fully knew the future a priori. The algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to the bandit setting, by leveraging submodularity and algorithms for the problem of tracking the best action. We validate our algorithm in simulated scenarios of multi-target tracking., Comment: Accepted to RSS 2023

Published
2023

11. Treatment Effect Estimation Under Unknown Interference

Author

Lin, Xiaofeng, Zhang, Guoxi, Lu, Xiaotian, Kashima, Hisashi, 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, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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15. Author Correction: Identification of an intraocular microbiota

Author

Deng, Yuhua, Ge, Xiaofei, Li, Yan, Zou, Bin, Wen, Xiaofeng, Chen, Weirong, Lu, Lin, Zhang, Meifen, Zhang, Xiaomin, Li, Chunmei, Zhao, Chan, Lin, Xiaofeng, Zhang, Xiulan, Huang, Xinhua, Li, Xiaorong, Jin, Ming, Peng, Guang-Hua, Wang, Dongni, Wang, Xun, Lai, Weiyi, Liang, Juanran, Li, Jing Jing, Liang, Qiaoxing, Yang, Liu, Zhang, Qinfen, Li, Yinyin, Lu, Ping, Hu, Xiao, Li, Xifang, Deng, Xiuli, Liu, Yu, Zou, Yanli, Guo, Shixin, Chen, Tingting, Qin, Yali, Yang, Fuhua, Miao, Li, Chen, Wei, Chan, Chi-Chao, Lin, Haotian, Liu, Yizhi, Lee, Richard W. J., and Wei, Lai

Published
2024
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17. FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification

Author

Lin, Xiaofeng, Kim, Seungbae, and Joo, Jungseock

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

Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process. We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of pruning on different sub-groups. Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. The proposed method then prunes network edges with small importance values and repeats the procedure by updating importance values. We demonstrate the effectiveness of our method on four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks of face attribute classification where our method reduces the disparity in performance degradation by up to 90% compared to the state-of-the-art pruning algorithms. Our method is substantially more effective in a setting with a high pruning rate (99%). The code and dataset used in the experiments are available at https://github.com/Bernardo1998/FairGRAPE, Comment: To appear in ECCV 2022

Published
2022

31. Mask images on Twitter increase during COVID-19 mandates, especially in Republican counties

Author

Lin, Xiaofeng, Kernell, Georgia, Groeling, Tim, Joo, Jungseock, Luo, Jun, and Steinert-Threlkeld, Zachary C

Subjects
Public Health, Health Sciences, Human Society, Humans, United States, COVID-19, Government, Public Opinion, Public Policy
Abstract

Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing-an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises.

Published
2022

37. Modified ethylene/(alpha)-octene co-polymer elastomer composites with sacrificial bonds crosslinking networks and their reinforced mechanical performance

Author

Liu, Huameng, Lin, Ruijun, Huang, Zhiyi, Yin, Xingshan, Lin, Xiaofeng, Lin, Wenjing, Li, Yong, Gu, Yuxin, and Yi, Guobin

Subjects
Crosslinked polymers -- Mechanical properties, Hydrogen -- Mechanical properties, Polymer industry -- Mechanical properties, Infrared spectroscopy -- Mechanical properties, Thermoplastics -- Mechanical properties, Elastomers -- Mechanical properties, X-ray spectroscopy -- Mechanical properties, Maleic anhydride -- Mechanical properties, Hydrogen bonding -- Mechanical properties, Engineering and manufacturing industries, Science and technology
Abstract

The low tensile strength of ethylene/[alpha]-octene co-polymer (POE) limits its application as high-strength materials. In this study, 3-amino-l,2,4-triazole (ATA) was grafted onto maleic anhydride functionalized POE (PM) by melt reaction to obtain side chains capable of forming hydrogen bonding and metal coordination bonding, and then ferric chloride hexahydrate and POE are blended with them to obtain composite materials with high strength and fracture energy. The introduction of iron-based coordination bonding and hydrogen bonding double dynamic crosslinking network endow thermoplastic elastomers with excellent mechanical strength and high toughness. Fourier transform infrared spectroscopy, rheological tests, and X-ray photoelectron spectroscopy reveal the existence of non-covalent crosslinking networks. Based on the strengthening and toughening of non-covalent dynamic crosslinking network, the tensile strength of the modified POE elastomer composites achieves 12.5 MPa along with the elongation at break of 3540%. In addition, the modified POE elastomer composites exhibit improved melt elasticity and thermal stability. KEYWORDS ethylene/[alpha]-octene co-polymer, modification, strengthening, thermoplastic elastomer, 1 | INTRODUCTION Thermoplastic elastomer is a new material that can be plasticized at high temperature and show rubber elasticity at room temperature. It has been widely used in medical [...]

Published
2023
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43. The third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3): an international, stepped wedge cluster randomised controlled trial

Author

Robinson, Thompson, Miranda, J. Jaime, Anderson, Craig S., You, Chao, Song, Lili, Parry-Jones, Adrian, Sprigg, Nikola, Durrans, Sophie, Harris, Caroline, Bamford, Ann, Smith, Olivia, Herbert, Robert, Chen, Christopher, Whiteley, William, Hu, Rong, Billot, Laurent, Li, Qiang, Mysore, Jayanthi, Hu, Xin, Zhang, Yao, Liu, Feifeng, Sakamoto, Yuki, You, Shoujiang, Han, Qiao, Crutzen, Bernard, Li, Yunke, Cheung, Emily, Jan, Stephen, Liu, Hueiming, Ouyang, Menglu, Sun, Lingli, Chu, Honglin, Anjum, Anila, Gonzalez Mc Cawley, Francisca, Del Rio, Alejandra, Rimoli, Bruna, Cerantola, Rodrigo, Jeevarajah, Thanushanthan, Kannangara, Madhushani, Joseph, Andrene, Nanayakkara, Chamath, Chen, Xiaoying, Malavera, Alejandra, Zhang, Chunmiao, Yang, Zhao, Li, Brook, Meng, Zhuo, Liu, Leibo, Ning, Yi, Dong, Le, Armenis, Manuela, Lim, Joyce, Monaghan, Helen, Ma, Lu, Li, Xi, Luo, Rui, Cheng, Guojuan, Dong, Yilin, Liu, Ziqin, Wang, Shuihong, Zhang, Ying, Cheng, Jipeng, Shi, Hui, Li, Wenjing, Mou, Langming, Yi, Ping, Chen, Chen, Chen, Xue, Weerawardena, Shalomi, Ellawala, Poornima, Ranasinghe, Enalee, Rodrigo, Chrishmi, Wahab, Kolawala, Adeniyi, Sunday, Pandian, Jeyaraj, Khanna, Megha, Muñoz Venturelli, Paula, González, Francisca, Urrutia Goldsack, Francisca, Wasay, Mohammad, Begum, Dilshad, Pontes-Neto, Octavio, Camilo, Millene, Dias, Francisco, Vincenzi, Octavio, Moro, Carla, Santos, Renata, Texeira, Nara, Longo, Alexandre, Liberato, Rafaela, Martins, Sheila, Pille, Arthur, Chwal, Bruna, Silva, Isabel, Titton, Natacha, Weiss, Gustavo, Mora, Daissy, Ouriques, Magda, Carbonera, Leonardo, Bazan, Rodrigo, Modolo, Gabriel, Winckler, Fernanda, Miranda, Luana, Souza, Juli, Rojo, Alexis, Uslar, Wilhelm, Medel, Lorena, Lopez, Javiera, Herrero, Diego, Lavados, Pablo, Vargas Latorre, Barbara, Conejan, Nathalie, Esparza, Tomas, Sotomayor, Patricio, Wenger, Denisse, Gigoux, Juan Pablo, Letelier, Aldo, Acevedo, Lilian, Moya, Vivianne, Figueroa, Cristian, Vallejos, Nicol, Guerrero, Rodrigo, Velasquez, Mauricio, Vallejos, Jose, Pallauta, Kimerly, Santibanez, Tamara, Queirolo, Angelo, Lobos, Andrea, Jiang, Yongming, Li, Weimin, Huang, Wei, Luo, Ke, Liu, Gangying, Tang, Guanghai, Yang, Guang, Jiang, Hongtao, Zhang, Xu, Jing, Hongyan, Zhu, Sheng, Pu, Bo, Lv, Dong, Kang, Hui, Hu, Qiuping, She, Xiaochun, Jiang, Xiaoming, Chen, Yanli, Yang, Shenghua, He, Jianjun, Li, Zongping, Cheng, Gang, Huang, Hailin, Wang, Xiaoyi, Lin, Jianqiong, Chen, Minhui, Yang, Chenghao, Ding, Hao, Deng, Yunliang, Luo, Fei, Zhang, Rongjun, Wang, Xiaofeng, Zhang, Hongbing, Yang, Xiaoliang, Zhang, Yang, Yang, Chengyi, He, Yu, Liu, Feng, Wang, Rongjie, Zhang, Yuhui, Xin, Xiaodong, Feng, Bin, Hao, Wanru, Song, Chang, Guo, Yun, Jiang, Dehua, Chen, Jie, Tang, Changtong, Zhu, Hongliang, Li, Xin, Cui, Jin, Xu, Haidong, Li, Boyang, Tang, Fusheng, Li, Yuanbin, Gao, Min, Yang, Bo, Xu, Xuejun, Deng, Bing, Zheng, Yi, Ge, Yuanhong, Chen, Keyu, Liu, Yang, Li, Xinshen, Zhong, Tingting, Xu, Jianfeng, Zhang, Hai, Wang, Jiyue, Zhu, Jianxin, Sun, Hanyu, Yu, Fuhua, Zhang, Xueguang, Xu, Jianguo, Zhang, Mingsen, Wang, Bin, Ma, Yiming, Jiang, Donglin, Zhou, Jun, Liu, Cong, Nie, Wenhong, Li, Mingguo, Tian, Tao, Li, Yong, He, Mingfang, Tu, Xiaolong, Wu, Zhengjun, Liu, Hong, Zhong, Dongsheng, Jiang, Rongcai, Sun, Jian, Tian, Ye, Wei, Yingsheng, An, Shuo, Wei, Pingbo, Luo, Le, Lin, Bin, Liu, Gang, Wen, Yan, Cai, Qiang, Chen, Qianxue, Lei, Pan, Li, Zhiyang, Zhang, Meifang, He, Jiaquan, Chen, Yan, Liu, Jun, Liu, Xinghai, Li, Junyan, Chen, Min, Wang, Jing, Zhou, Bingzhi, Ye, Baichun, Zhang, Jiancheng, Zhang, Manyuan, Pan, Xuming, Yu, Xiaoxiang, Xu, Jian, Xiao, Qingbao, Wang, Yuefei, Tao, Liang, Shi, Lin, Zheng, Niandong, You, Guoliang, Lei, Bo, Chen, Shu, Wu, Honggang, Hu, Jin, Zhao, Jianlan, Yu, Jian, Yuan, Qiang, Du, Zhuoying, Tang, Xielin, Li, Qianke, Liu, Shenghua, Yang, Feilong, Xiao, Kui, Luo, Chao, Wang, Guang, Che, Xudong, Teng, Zhipeng, Wan, Wenwu, Li, Jun, Liu, Yu, Fan, Mingbo, Zhang, Tao, Cai, Lun, Ma, Yuan, Ma, Zhifeng, Li, Bin, He, Linlin, Li, Jinghui, Zhang, Weibing, Zhang, Shuxin, Zhang, Hongzhen, Dai, Yingguang, Lei, Jun, Mao, Lei, Huang, Yiyang, Zhou, Zhi, Chen, Ping, Chen, Fang, Wei, Pan, Li, Tiangui, Chen, Honglin, Zeng, Mengfei, Mou, Kejie, Xue, Jun, Jiang, Yong, Tang, Xiaoping, Chen, Tao, Zhang, Yalan, Xu, Yanbing, Gu, Yuchen, Chen, Lei, Zhao, Yujun, Yang, Bin, Kuai, Peng, Wang, Xi, Yang, Yuwang, Hu, Xueling, Zhang, Huitian, Yang, Yintao, Wang, Weifeng, Zhang, Junyi, Cheng, Wei, Zhang, Xiaoxue, Ma, Xiaowen, He, Qin, Zhang, Li, Gao, Rong, Liu, Huixiang, Ye, Jingwei, Xu, Ping, Wu, Xin, Yuan, Yuan, Zou, Peng, Zhang, Zhen, Cheng, Jiyong, Zhou, Zhangming, Zeng, Yijun, Liang, Zhang, Du, Deming, Yu, Shui, Cao, Yongjun, Xu, Jiaping, Huang, Zhichao, Chen, Dongqin, Xiao, Wenfeng, Zhu, Li, Yuan, Miao, Wang, Yuhai, Shi, Dongliang, Hu, Xu, Xiang, Dingchao, Shi, Like, Wang, Hongqin, Yang, Liu, Miao, Wang, Hu, Yiyi, Zhao, Yuchun, Hu, Xi, Zhou, Weiduo, Sun, Chao, Tang, Dong, Yao, Kun, You, Jin, Chen, Shishi, Yao, Jianmin, Li, Huanmei, Liu, Jinmei, Bai, Ailin, Yi, Yong, Deng, Qingshan, Luo, Peng, Wang, Han, Jiang, Jingcheng, Yang, Qingwei, He, Shunpo, Wang, Jun, Chen, Yu, He, Hua, Deng, Yuyang, Cao, Zhikai, Yi, Xuxia, Luo, Jinbiao, Luo, Shuang, Gong, Min, Liu, Li, Gao, Xuejun, Liu, Jia, Wu, Li'e, Zhang, Jia, Sun, Hongying, Li, Xinhui, Jia, Lu, Wu, Jianbing, Zhang, Jie, Zhang, Huajun, Du, Chunfu, Li, Shun, Yang, Xiaobin, He, Jie, Liao, Lei, Zhou, Gezhi, Dong, Wentao, Chen, Yunxiang, Lin, Xiaofeng, Shui, Xujian, Zhang, Peng, Zhao, Yuan, Yang, Hongli, Zhao, Wenbin, Zhang, Xiaoyi, Chen, Jincao, Wu, Qian, Dai, Xuan, Tang, Baogui, Wang, Yinjuan, Liu, Tao, Zhang, Haixia, Duan, Faliang, Luo, Ming, Jiao, Qingfang, Lei, Guoliang, Wang, Dong, Song, Chunwang, Tan, Haopeng, Ye, Feng, Qin, Xinghu, Liang, Xiaolong, Liu, Junling, Yang, Lang, Yang, Jie, Lin, Yapeng, Yang, Qian, Ma, Xuntai, Qi, Yinkuang, Pan, Baogen, Jiang, Caixia, Ye, Zhanying, Dong, Ce, Yue, Xiongfei, Yang, Xiaopeng, Maimaitiyiming, Tuoheti, Dong, Jun, Wu, Yonggang, Gao, Feng, Zhao, Deqiang, Zhang, Xinghai, Wang, PengJun, Jiang, Hongbo, Li, Jianping, Zhang, Wei, Chen, Jing, Tong, Haibo, Wang, Yonghong, Qiao, Kaipeng, Guo, Fuyou, Zhang, Mingchu, Hu, Yan, Feng, Mengzhao, Song, Dengpan, Zuo, Yi, Chen, Shangjun, Qian, Chao, Li, Baoming, Ma, Jingku, Zhang, Sunfu, Kong, Bin, Dong, Xingyu, Fang, Sheng, Lu, Bin, Li, Yang, Yang, Yongling, Yu, Hong, Sun, Huaiyu, Wang, Yue, Wang, Weimin, Li, Tong, Li, Shengli, Xu, Zhiming, Wang, Yongyi, Dong, Qiang, Tang, Yuping, Chu, Heling, Lu, Ying, Wang, Zhong, Sun, Xiaoou, Zhao, Jianhua, Yang, Shuaifeng, Qian, Xiying, Saroja, Aralikatte Onkarappa, Naik k, Ravishankar, Chindhi, Sandip, Pampaniya, Nakul, Amaresh, Kurubara, Iype, Thomas, R, Dileep, Rajan, Reeja, Panicker, Praveen, Das, Rupjyoti, Choudhury, Nupur, Gohain, Pankaja, Webster, Jemin, Pakma, Biyol, Sangi, Lalbiak, Sebastian, Ivy, Aggrawal, Gaurav, Raj, Komal, Rajoura, Deepankshi, Singh, Sulena, Aggrawal, Varun, Narang, Amit, Arauz, Antonio, Cano-Nigenda, Vanesa, López-Mena, Diego, Valdez-Ruvalcaba, Héctor, Toledo-Treviño, Roberto, Obiako, Reginald, Abubakar, Sani, Emeka, Oguike, Olayemi, Balogun, Lois, Melika, Philip, Ibinaiye, Comfort O, Olurishe, Okubadejo, Njideka, Agabi, Osigwe, Ojo, Oluwadamilola, Wahab, Kolawole, Bello, Abiodun, Ibukun, Oyinloye, Sanayaolu, Olufemi, Jimoh, Abdulraheem, Waheed, Shahid, Kamal, Dr.Ayeesha, Shoaib, Raja Farhat, Orooj, Fizza, Majid, Sadaf, Zehra, Taskeen, Khan, Abdus Salam, Shanker, Ravi, Syed, Nadir Ali, Ahmad, Nashwa, Abanto, Carlos, Valencia, Ana, Barrientos, Danny, Ramirez, Jorge, Calle, Pilar, Palliyeguruge, Dilum, Muthucumarana, Sumudu, Ratnayaka, Shiroma, Ganihiarachchi, Dilhara, Bandaranayake, Arundathi, Somaratne, S.D.B, Narayana, Saumya, Gallage, Sithara, Senanayake, Bimsara, Samarasiri, Udari, Luke, Dunya, Sivapathasundaram, Mythily, Sahadevan, Vithoosan, Rasmi, Amani, Deshaka, Yuran, Fernando, Nilukshi, Munasinghe, Aruna, Rathnapriya, Kapilanga, Nissanka, A.S, 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2023
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