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694 results on '"Di, Chong‐an"'

1. Multi-heterojunctioned plastics with high thermoelectric figure of merit

Author

Wang, Dongyang, Ding, Jiamin, Ma, Yingqiao, Xu, Chunlin, Li, Zhiyi, Zhang, Xiao, Zhao, Yao, Zhao, Yue, Di, Yuqiu, Liu, Liyao, Dai, Xiaojuan, Zou, Ye, Kim, BongSoo, Zhang, Fengjiao, Liu, Zitong, McCulloch, Iain, Lee, Myeongjae, Chang, Cheng, Yang, Xiao, Wang, Dong, Zhang, Deqing, Zhao, Li-Dong, Di, Chong-an, and Zhu, Daoben

Published
2024
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3. Technology Roadmap for Flexible Sensors

Author

Luo, Yifei, Abidian, Mohammad Reza, Ahn, Jong-Hyun, Akinwande, Deji, Andrews, Anne M, Antonietti, Markus, Bao, Zhenan, Berggren, Magnus, Berkey, Christopher A, Bettinger, Christopher John, Chen, Jun, Chen, Peng, Cheng, Wenlong, Cheng, Xu, Choi, Seon-Jin, Chortos, Alex, Dagdeviren, Canan, Dauskardt, Reinhold H, Di, Chong-an, Dickey, Michael D, Duan, Xiangfeng, Facchetti, Antonio, Fan, Zhiyong, Fang, Yin, Feng, Jianyou, Feng, Xue, Gao, Huajian, Gao, Wei, Gong, Xiwen, Guo, Chuan Fei, Guo, Xiaojun, Hartel, Martin C, He, Zihan, Ho, John S, Hu, Youfan, Huang, Qiyao, Huang, Yu, Huo, Fengwei, Hussain, Muhammad M, Javey, Ali, Jeong, Unyong, Jiang, Chen, Jiang, Xingyu, Kang, Jiheong, Karnaushenko, Daniil, Khademhosseini, Ali, Kim, Dae-Hyeong, Kim, Il-Doo, Kireev, Dmitry, Kong, Lingxuan, Lee, Chengkuo, Lee, Nae-Eung, Lee, Pooi See, Lee, Tae-Woo, Li, Fengyu, Li, Jinxing, Liang, Cuiyuan, Lim, Chwee Teck, Lin, Yuanjing, Lipomi, Darren J, Liu, Jia, Liu, Kai, Liu, Nan, Liu, Ren, Liu, Yuxin, Liu, Yuxuan, Liu, Zhiyuan, Liu, Zhuangjian, Loh, Xian Jun, Lu, Nanshu, Lv, Zhisheng, Magdassi, Shlomo, Malliaras, George G, Matsuhisa, Naoji, Nathan, Arokia, Niu, Simiao, Pan, Jieming, Pang, Changhyun, Pei, Qibing, Peng, Huisheng, Qi, Dianpeng, Ren, Huaying, Rogers, John A, Rowe, Aaron, Schmidt, Oliver G, Sekitani, Tsuyoshi, Seo, Dae-Gyo, Shen, Guozhen, Sheng, Xing, Shi, Qiongfeng, Someya, Takao, Song, Yanlin, Stavrinidou, Eleni, Su, Meng, Sun, Xuemei, Takei, Kuniharu, Tao, Xiao-Ming, Tee, Benjamin CK, Thean, Aaron Voon-Yew, and Trung, Tran Quang

Subjects
Data Management and Data Science, Information and Computing Sciences, Humans, Wearable Electronic Devices, Quality of Life, soft materials, mechanics engineering, flexible electronics, conformable sensors, bioelectronics, human-machine interfaces, body area sensor networks, technology translation, sustainable electronics, Nanoscience & Nanotechnology
Abstract

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Published
2023

9. An Auxiliary Modality Based Text-Image Matching Methodology for Fake News Detection

Author

Guo, Ying, Li, Bingxin, Ge, Hong, Di, Chong, 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, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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10. Influence Maximization on Dynamic Social Networks with Conjugate Learning Automata

Author

Li, Fangqi, Di, Chong, and Li, Shenghong

Subjects
Computer Science - Social and Information Networks
Abstract

Selecting the optimal subset from all vertices as seeds to maximize the influence in a social network has been a task of interest. Various methods have been proposed to select the optimal vertices in a static network, however, they are challenged by the dynamics, i.e. the time-dependent variation of the social network structure. Such dynamics hinder the paradigm for static networks and leaves a seemingly unbridgeable gap between algorithms of influence maximization on static networks and those on dynamic ones. In this paper, we extend our previous work and demonstrate that conjugate learning automata (an elementary variant of reinforcement learning) that have been successfully applied to maximize influence on static networks can be applied to dynamic networks as well. The network dynamics is measured by the variation of the influence range and absorbed into the learning procedure. Our proposal delicately formulates the effect of network dynamics: the more the influence range varies, the more likely the seeds are to be learned from scratch. Under this assumption, the continuity of the network variation is fully taken advantage of. Experimental results on both synthetic and real-world networks verify the privileges of our proposal against alternative methods., Comment: 6 pages

Published
2020

11. On the Submodularity of Diffusion Models: Equivalent Conditions and Applications

Author

Li, Fangqi, Di, Chong, and Xia, Wenwen

Subjects
Computer Science - Social and Information Networks, Mathematics - Combinatorics
Abstract

The diffusion model has been a crucial component in studies about social networks. Many studies, especially these about influence maximization concern the proof of the submodularity of particular diffusion models. Such proofs have been model-dependent and are somewhat ad hoc. In this paper, we prove a theorem that provides a necessary and sufficient condition for a diffusion model to be submodular. This theorem can be used to justify the submodularity of an arbitrary diffusion model. We also apply this theorem to build a projection operator that maps an arbitrary diffusion model into a submodular one. Moreover, we use the established theorem to propose a diffusion model of multiple heterogeneous pieces of information that partially features submodularity., Comment: Conference pre-print

Published
2019

15. An Efficient Learning Automaton Scheme for Massive-Action Environments

Author

Zhu, Hongyu, Tian, Jianwei, Di, Chong, Tian, Zheng, Sun, Yizhen, Li, Qiyao, Li, Shenghong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2022
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16. Learning automata based SVM for intrusion detection

Author

Di, Chong

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of training the classifier for intrusion detection and increase the time of making predictions. To simplify the training process and improve the efficiency of the classifier, it is necessary to remove these dispensable features. in this paper, we propose a novel LA-SVM scheme to automatically remove redundant features focusing on intrusion detection. This is the first application of learning automata for solving dimension reduction problems. The simulation results indicate that the LA-SVM scheme achieves a higher accuracy and is more efficient in making predictions compared with traditional SVM.

Published
2018
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17. A double competitive strategy based learning automata algorithm

Author

Di, Chong

Subjects
Computer Science - Artificial Intelligence
Abstract

Learning Automata (LA) are considered as one of the most powerful tools in the field of reinforcement learning. The family of estimator algorithms is proposed to improve the convergence rate of LA and has made great achievements. However, the estimators perform poorly on estimating the reward probabilities of actions in the initial stage of the learning process of LA. In this situation, a lot of rewards would be added to the probabilities of non-optimal actions. Thus, a large number of extra iterations are needed to compensate for these wrong rewards. In order to improve the speed of convergence, we propose a new P-model absorbing learning automaton by utilizing a double competitive strategy which is designed for updating the action probability vector. In this way, the wrong rewards can be corrected instantly. Hence, the proposed Double Competitive Algorithm overcomes the drawbacks of existing estimator algorithms. A refined analysis is presented to show the $\epsilon-optimality$ of the proposed scheme. The extensive experimental results in benchmark environments demonstrate that our proposed learning automata perform more efficiently than the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.

Published
2017

20. Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning.

Author

Huang, Jinchao, Xie, Zhipu, Zhang, Han, Yang, Bin, Di, Chong, and Huang, Runhe

Subjects
KNOWLEDGE graphs, INTERACTIVE learning, RECOMMENDER systems
Abstract

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user–item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user–item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: (1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and (2) how to merge interaction information from the two graphs while ensuring that user–item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Weight-Assignment Last-Position Elimination-Based Learning Automata

Author

An, Haiwei, Di, Chong, Li, Shenghong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
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27. Bayesian Method-Based Learning Automata for Two-Player Stochastic Games with Incomplete Information

Author

Ding, Hua, Di, Chong, Shenghong, Li, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
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28. A Double Competitive Strategy-Based Learning Automata Algorithm

Author

Di, Chong, Guo, Mingda, Huang, Jinchao, Li, Shenghong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
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31. A drug-mediated organic electrochemical transistor for robustly reusable biosensors

Author

Jiang, Ziling, Ye, Dekai, Xiang, Lanyi, He, Zihan, Dai, Xiaojuan, Yang, Junfang, Xiong, Qi, Ma, Yingqiao, Zhi, Danfeng, Zou, Ye, Peng, Qian, Wang, Shu, Li, Jia, Zhang, Fengjiao, and Di, Chong-an

Abstract

Reusable point-of-care biosensors offer a cost-effective solution for serial biomarker monitoring, addressing the critical demand for tumour treatments and recurrence diagnosis. However, their realization has been limited by the contradictory requirements of robust reusability and high sensing capability to multiple interactions among transducer surface, sensing probes and target analytes. Here we propose a drug-mediated organic electrochemical transistor as a robust, reusable epidermal growth factor receptor sensor with striking sensitivity and selectivity. By electrostatically adsorbing protonated gefitinib onto poly(3,4-ethylenedioxythiophene):polystyrene sulfonate and leveraging its strong binding to the epidermal growth factor receptor target, the device operates with a unique refresh-in-sensing mechanism. It not only yields an ultralow limit-of-detection concentration down to 5.74 fg ml−1for epidermal growth factor receptor but, more importantly, also produces an unprecedented regeneration cycle exceeding 200. We further validate the potential of our devices for easy-to-use biomedical applications by creating an 8 × 12 diagnostic drug-mediated organic electrochemical transistor array with excellent uniformity to clinical blood samples.

Published
2024
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32. Photoexcitation-Assisted Molecular Doping for High-Performance Polymeric Thermoelectric Materials

Author

Ji, Zhen, Li, Zhiyi, Dai, Xiaojuan, Xiang, Lanyi, Zhao, Yue, Wang, Dongyang, Zhang, Xiao, Liu, Liyao, Han, Zhiyuan, Niu, Lixin, Di, Yuqiu, Zou, Ye, Di, Chong-an, and Zhu, Daoben

Abstract

Molecular doping plays a crucial role in modulating the performance of polymeric semiconductor (PSC) materials and devices. Despite the development of numerous molecular dopants and doping methods over the past few decades, achieving highly efficient doping of PSCs remains challenging, primarily because of the inadequate matching of frontier energy levels between the host polymers and the dopants, which is critical for facilitating charge transfer. In this work, we introduce a novel doping method termed photoexcitation-assisted molecular doping (PE-MD), capable of transcending limitations imposed by energy level disparities through the mediation of efficient photoinduced electron transfer between polymers and dopants. This approach significantly amplifies the electrical conductivity of the PDPP4T polymer, increasing it by more than 4 orders of magnitude to a maximum value of 349.67 S cm–1. Given that only the irradiated region experiences a substantial increase in doping level, the PE-MD process facilitates the photoresist-free and precise patterning of doped polymers at a resolution down to 1 μm. Furthermore, the enhanced electrical conductivity of the photoexcitation-assisted molecularly doped PDPP4T film promotes efficient thermoelectric conversion, yielding an impressive initial power factor of 226.1 μW m–1K–2and a figure-of-merit (ZT) of 0.18, accompanied by improved thermal and ambient stability. The PE-MD strategy not only remarkably elevates the doping level of PSCs toward efficient thermoelectric conversion but also preserves the easy processability of flexible and integrated devices.

Published
2024
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36. Learning Automata Based SVM for Intrusion Detection

Author

Di, Chong, Su, Yu, Han, Zhuoran, Li, Shenghong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mu, Jiasong, editor, Jia, Min, editor, Wang, Wei, editor, Feng, Xuhong, editor, and Zhang, Baoju, editor

Published
2019
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43. Multilevel functional principal component analysis

Author

Di, Chong-Zhi, Crainiceanu, Ciprian M., Caffo, Brian S., and Punjabi, Naresh M.

Subjects
Statistics - Applications
Abstract

The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes., Comment: Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2009
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