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643 results on '"Zhang, Zhaofeng"'

1. Quantformer: from attention to profit with a quantitative transformer trading strategy

Author

Zhang, Zhaofeng, Chen, Banghao, Zhu, Shengxin, and Langrené, Nicolas

Subjects
Quantitative Finance - Mathematical Finance, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, G.3, J.2
Abstract

In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architecture based on transformers, to build investment factors. By transfer learning from sentiment analysis, quantformer not only exploits its original inherent advantages in capturing long-range dependencies and modeling complex data relationships, but is also able to solve tasks with numerical inputs and accurately forecast future returns over a given period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies. Notably, the model's innovative use of transformer-liked model to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.

Published
2024

3. Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

Author

Chen, Banghao, Zhang, Zhaofeng, Langrené, Nicolas, and Zhu, Shengxin

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract

This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models. This paper explores both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge, which significantly enhance model performance. Additionally, it examines the prompt method of VLMs through innovative approaches such as Context Optimization (CoOp), Conditional Context Optimization (CoCoOp), and Multimodal Prompt Learning (MaPLe). Critical to this discussion is the aspect of AI security, particularly adversarial attacks that exploit vulnerabilities in prompt engineering. Strategies to mitigate these risks and enhance model robustness are thoroughly reviewed. The evaluation of prompt methods is also addressed, through both subjective and objective metrics, ensuring a robust analysis of their efficacy. This review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.

Published
2023

4. Impact of Sensing Errors on Headway Design: From α-Fair Group Safety to Traffic Throughput

Author

Shao, Wei, Fan, Zejun, Chen, Chia-Ju, Zhang, Zhaofeng, Ma, Jiaqi, PhD, and Zhang, Junshan, PhD

Abstract

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

Published
2024

5. CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

Author

Yue, Sheng, Wang, Guanbo, Shao, Wei, Zhang, Zhaofeng, Lin, Sen, Ren, Ju, and Zhang, Junshan

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

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift. Leveraging both expert data and lower-quality diverse data, we devise a principled algorithm (namely CLARE) that solves offline IRL efficiently via integrating "conservatism" into a learned reward function and utilizing an estimated dynamics model. Our theoretical analysis provides an upper bound on the return gap between the learned policy and the expert policy, based on which we characterize the impact of covariate shift by examining subtle two-tier tradeoffs between the exploitation (on both expert and diverse data) and exploration (on the estimated dynamics model). We show that CLARE can provably alleviate the reward extrapolation error by striking the right exploitation-exploration balance therein. Extensive experiments corroborate the significant performance gains of CLARE over existing state-of-the-art algorithms on MuJoCo continuous control tasks (especially with a small offline dataset), and the learned reward is highly instructive for further learning.

Published
2023

7. Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

Author

Shao, Wei, Jin, Zhiling, Wang, Shuo, Kang, Yufan, Xiao, Xiao, Menouar, Hamid, Zhang, Zhaofeng, Zhang, Junshan, and Salim, Flora

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

Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, and the imbalanced graph fusion approach. To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure. To fuse the information across multiple graphs, we propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks., Comment: Accepted by the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022)

Published
2022
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12. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990–2021: a systematic analysis from the Global Burden of Disease Study 2021

Author

Bender, Rose Grace, Sirota, Sarah Brooke, Swetschinski, Lucien R, Dominguez, Regina-Mae Villanueva, Novotney, Amanda, Wool, Eve E, Ikuta, Kevin S, Vongpradith, Avina, Rogowski, Emma Lynn Best, Doxey, Matthew, Troeger, Christopher E, Albertson, Samuel B, Ma, Jianing, He, Jiawei, Maass, Kelsey Lynn, A.F.Simões, Eric, Abdoun, Meriem, Abdul Aziz, Jeza Muhamad, Abdulah, Deldar Morad, Abu Rumeileh, Samir, Abualruz, Hasan, Aburuz, Salahdein, Adepoju, Abiola Victor, Adha, Rishan, Adikusuma, Wirawan, Adra, Saryia, Afraz, Ali, Aghamiri, Shahin, Agodi, Antonella, Ahmadzade, Amir Mahmoud, Ahmed, Haroon, Ahmed, Ayman, Akinosoglou, Karolina, AL-Ahdal, Tareq Mohammed Ali, Al-amer, Rasmieh Mustafa, Albashtawy, Mohammed, AlBataineh, Mohammad T., Alemi, Hediyeh, Al-Gheethi, Adel Ali Saeed, Ali, Abid, Ali, Syed Shujait Shujait, Alqahtani, Jaber S, AlQudah, Mohammad, Al-Tawfiq, Jaffar A., Al-Worafi, Yaser Mohammed, Alzoubi, Karem H, Amani, Reza, Amegbor, Prince M, Ameyaw, Edward Kwabena, Amuasi, John H, Anil, Abhishek, Anyanwu, Philip Emeka, Arafat, Mosab, Areda, Damelash, Arefnezhad, Reza, Atalell, Kendalem Asmare, Ayele, Firayad, Azzam, Ahmed Y, Babamohamadi, Hassan, Babin, François-Xavier, Bahurupi, Yogesh, Baker, Stephen, Banik, Biswajit, Barchitta, Martina, Barqawi, Hiba Jawdat, Basharat, Zarrin, Baskaran, Pritish, Batra, Kavita, Batra, Ravi, Bayileyegn, Nebiyou Simegnew, Beloukas, Apostolos, Berkley, James A, Beyene, Kebede A, Bhargava, Ashish, Bhattacharjee, Priyadarshini, Bielicki, Julia A, Bilalaga, Mariah Malak, Bitra, Veera R, Brown, Colin Stewart, Burkart, Katrin, Bustanji, Yasser, Carr, Sinclair, Chahine, Yaacoub, Chattu, Vijay Kumar, Chichagi, Fatemeh, Chopra, Hitesh, Chukwu, Isaac Sunday, Chung, Eunice, Dadana, Sriharsha, Dai, Xiaochen, Dandona, Lalit, Dandona, Rakhi, Darban, Isaac, Dash, Nihar Ranjan, Dashti, Mohsen, Dashtkoohi, Mohadese, Dekker, Denise Myriam, Delgado-Enciso, Ivan, Devanbu, Vinoth Gnana Chellaiyan, Dhama, Kuldeep, Diao, Nancy, Do, Thao Huynh Phuong, Dokova, Klara Georgieva, Dolecek, Christiane, Dziedzic, Arkadiusz Marian, Eckmanns, Tim, Ed-Dra, Abdelaziz, Efendi, Ferry, Eftekharimehrabad, Aziz, Eyre, David William, Fahim, Ayesha, Feizkhah, Alireza, Felton, Timothy William, Ferreira, Nuno, Flor, Luisa S, Gaihre, Santosh, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Geffers, Christine, Gerema, Urge, Ghaffari, Kazem, Goldust, Mohamad, Goleij, Pouya, Guan, Shi-Yang, Gudeta, Mesay Dechasa, Guo, Cui, Gupta, Veer Bala, Gupta, Ishita, Habibzadeh, Farrokh, Hadi, Najah R, Haeuser, Emily, Hailu, Wase Benti, Hajibeygi, Ramtin, Haj-Mirzaian, Arvin, Haller, Sebastian, Hamiduzzaman, Mohammad, Hanifi, Nasrin, Hansel, Jan, Hasnain, Md Saquib, Haubold, Johannes, Hoan, Nguyen Quoc, Huynh, Hong-Han, Iregbu, Kenneth Chukwuemeka, Islam, Md. Rabiul, Jafarzadeh, Abdollah, Jairoun, Ammar Abdulrahman, Jalili, Mahsa, Jomehzadeh, Nabi, Joshua, Charity Ehimwenma, Kabir, Md. Awal, Kamal, Zul, Kanmodi, Kehinde Kazeem, Kantar, Rami S., Karimi Behnagh, Arman, Kaur, Navjot, Kaur, Harkiran, Khamesipour, Faham, Khan, M Nuruzzaman, Khan suheb, Mahammed Ziauddin, Khanal, Vishnu, Khatab, Khaled, Khatib, Mahalaqua Nazli, Kim, Grace, Kim, Kwanghyun, Kitila, Aiggan Tamene Tamene, Komaki, Somayeh, Krishan, Kewal, Krumkamp, Ralf, Kuddus, Md Abdul, Kurniasari, Maria Dyah, Lahariya, Chandrakant, Latifinaibin, Kaveh, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Le, Trang Diep Thanh, Lee, Seung Won, LEPAPE, Alain, Lerango, Temesgen L., Li, Ming-Chieh, Mahboobipour, Amir Ali, Malhotra, Kashish, Mallhi, Tauqeer Hussain, Manoharan, Anand, Martinez-Guerra, Bernardo Alfonso, Mathioudakis, Alexander G., Mattiello, Rita, May, Jürgen, McManigal, Barney, McPhail, Steven M, Mekene Meto, Tesfahun, Mendez-Lopez, Max Alberto Mendez, Meo, Sultan Ayoub, Merati, Mohsen, Mestrovic, Tomislav, Mhlanga, Laurette, Minh, Le Huu Nhat, Misganaw, Awoke, Mishra, Vinaytosh, Misra, Arup Kumar, Mohamed, Nouh Saad, Mohammadi, Esmaeil, Mohammed, Mesud, Mohammed, Mustapha, Mokdad, Ali H, Monasta, Lorenzo, Moore, Catrin E, Motappa, Rohith, Mougin, Vincent, Mousavi, Parsa, Mulita, Francesk, Mulu, Atsedemariam Andualem, Naghavi, Pirouz, Naik, Ganesh R, Nainu, Firzan, Nair, Tapas Sadasivan, Nargus, Shumaila, Negaresh, Mohammad, Nguyen, Hau Thi Hien, Nguyen, Dang H, Nguyen, Van Thanh, Nikolouzakis, Taxiarchis Konstantinos, Noman, Efaq Ali, Nri-Ezedi, Chisom Adaobi, Odetokun, Ismail A., Okwute, Patrick Godwin, Olana, Matifan Dereje, Olanipekun, Titilope O, Olasupo, Omotola O., Olivas-Martinez, Antonio, Ordak, Michal, Ortiz-Brizuela, Edgar, Ouyahia, Amel, Padubidri, Jagadish Rao, Pak, Anton, Pandey, Anamika, Pantazopoulos, Ioannis, Parija, Pragyan Paramita, Parikh, Romil R, Park, Seoyeon, Parthasarathi, Ashwaghosha, Pashaei, Ava, Peprah, Prince, Pham, Hoang Tran, Poddighe, Dimitri, Pollard, Andrew, Ponce-De-Leon, Alfredo, Prakash, Peralam Yegneswaran, Prates, Elton Junio Sady, Quan, Nguyen Khoi, Raee, Pourya, Rahim, Fakher, Rahman, Mosiur, Rahmati, Masoud, Ramasamy, Shakthi Kumaran, Ranjan, Shubham, Rao, Indu Ramachandra, Rashid, Ahmed Mustafa, Rattanavong, Sayaphet, Ravikumar, Nakul, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Reiner, Robert C, Jr., Reyes, Luis Felipe, Roberts, Tamalee, Rodrigues, Mónica, Rosenthal, Victor Daniel, Roy, Priyanka, Runghien, Tilleye, Saeed, Umar, Saghazadeh, Amene, Saheb Sharif-Askari, Narjes, Saheb Sharif-Askari, Fatemeh, Sahoo, Soumya Swaroop, Sahu, Monalisha, Sakshaug, Joseph W, Salami, Afeez Abolarinwa, Saleh, Mohamed A., Salehi omran, Hossein, Sallam, Malik, Samadzadeh, Sara, Samodra, Yoseph Leonardo, Sanjeev, Rama Krishna, Sarasmita, Made Ary, Saravanan, Aswini, Sartorius, Benn, Saulam, Jennifer, Schumacher, Austin E, Seyedi, Seyed Arsalan, Shafie, Mahan, Shahid, Samiah, Sham, Sunder, Shamim, Muhammad Aaqib, Shamshirgaran, Mohammad Ali, Shastry, Rajesh P., Sherchan, Samendra P, Shiferaw, Desalegn, Shittu, Aminu, Siddig, Emmanuel Edwar, Sinto, Robert, Sood, Aayushi, Sorensen, Reed J D, Stergachis, Andy, Stoeva, Temenuga Zhekova, Swain, Chandan Kumar, Szarpak, Lukasz, Tamuzi, Jacques Lukenze, Temsah, Mohamad-Hani, Tessema, Melkamu B Tessema, Thangaraju, Pugazhenthan, Tran, Nghia Minh, Tran, Ngoc-Ha, Tumurkhuu, Munkhtuya, Ty, Sree Sudha, Udoakang, Aniefiok John, Ulhaq, Inam, Umar, Tungki Pratama, Umer, Abdurezak Adem, Vahabi, Seyed Mohammad, Vaithinathan, Asokan Govindaraj, Van den Eynde, Jef, Walson, Judd L, Waqas, Muhammad, Xing, Yuhan, Yadav, Mukesh Kumar, Yahya, Galal, Yon, Dong Keon, Zahedi Bialvaei, Abed, Zakham, Fathiah, Zeleke, Abyalew Mamuye, Zhai, Chunxia, Zhang, Zhaofeng, Zhang, Haijun, Zielińska, Magdalena, Zheng, Peng, Aravkin, Aleksandr Y, Vos, Theo, Hay, Simon I, Mosser, Jonathan F., Lim, Stephen S, Naghavi, Mohsen, Murray, Christopher J L, and Kyu, Hmwe Hmwe

Published
2024
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13. A market power assessment method for load aggregators considering multiple uncertainties under ice disasters

Author

ZHU Yongqing, LIN Jianing, LI Qingsheng, LI Zhen, ZHANG Yan, XU Tao, and ZHANG Zhaofeng

Subjects
load aggregator, market power assessment, multiple uncertainties, ugf, extreme weather, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In extreme scenarios， load aggregators may control the market to rake in excess profits. To address this， this paper proposes a market power assessment method for load aggregators considering multiple uncertainties under ice disasters. Firstly， the universal generating function （UGF） is used to describe uncertainties of faults on overhead lines and wind turbines under ice disasters. Secondly， by aggregating standard probabilistic functions of various uncertainties， the paper proposes a market power assessment operator to construct a market power assessment model for load aggregator nodes. Finally， based on the results of the clearance model， a spatiotemporal market power index system is constructed to identify key load aggregators with high market power levels and the corresponding critical periods. Case study results demonstrate that this method effectively quantifies the market power levels of load aggregators under ice disasters， and that load aggregators in regions with higher loads are more likely to exercise market power during severe icing periods.

Published
2024
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14. Research progress on impact resistance wing fuel tank cover

Author

CAI Xuanlong, PENG Guohui, ZHANG Zhaofeng, LI Xiaohui, and KANG Yijie

Subjects
wing fuel tank cover, airworthiness regulations, impact resistance, research progress, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

The wing fuel tank cover and its surrounding wing outer plate together form a part of the aircraft's aerodynamic shape. During the service process of an aircraft,the fuel tank cover is inevitably hit by foreign objects,ensuring the impact resistance of the fuel tank cover is of great significance for ensuring the safety of the aircraft. In this paper the research progress of impact resistance performance of wing fuel tank cover is systematically introduced from three aspects: airworthiness regulations requirements,common materials and structural forms,assessment methods and evaluation,the current research conclusions are summarized,and the future development of wing fuel tank cover is prospected. The research results can provide theoretical and technical references for the design and manufacturing of new types of masks in the future.

Published
2023
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16. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author

Schumacher, Austin E, Kyu, Hmwe Hmwe, Aali, Amirali, Abbafati, Cristiana, Abbas, Jaffar, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abdurehman, Ame Mehadi, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abhilash, E S, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abu Rumeileh, Samir, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Acuna, Juan Manuel, Adair, Tim, Addo, Isaac Yeboah, Adebayo, Oladimeji M, Adegboye, Oyelola A, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adeyeoluwa, Temitayo Esther, Adeyomoye, Olorunsola Israel, Adha, Rishan, Adibi, Amin, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afework, Abel, Afolabi, Aanuoluwapo Adeyimika, Afraz, Ali, Afyouni, Shadi, Afzal, Saira, Agasthi, Pradyumna, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmadzade, Mohadese, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Ajumobi, Olufemi, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akinosoglou, Karolina, Akkala, Sreelatha, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Homsi, Ammar, Al Qadire, Mohammad, Ala, Moein, Aladelusi, Timothy Olukunle, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albashtawy, Mohammed, AlBataineh, Mohammad T, Aldridge, Robert W, Alemi, Sharifullah, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Akhtar, Ali, Beriwan Abdulqadir, Ali, Hassam, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Zahid, Alian Samakkhah, Shohreh, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aligol, Mohammad, Alimi, Rasoul, Aliyi, Ahmednur Adem, Al-Jumaily, Adel, Aljunid, Syed Mohamed, Almahmeed, Wael, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Alonso, Nivaldo, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Alvi, Farrukh Jawad, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Alzoubi, Karem H, Amare, Azmeraw T, Ameyaw, Edward Kwabena, Amhare, Abebe Feyissa, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Aminian Dehkordi, Javad, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amzat, Jimoh, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Angappan, Dhanalakshmi, Anil, Abhishek, Anjum, Afifa, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Iyadunni Adesola, Anwar, Sumadi Lukman, Anyasodor, Anayochukwu Edward, Appiah, Seth Christopher Yaw, Aqeel, Muhammad, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arab-Zozani, Morteza, Arafat, Mosab, Araújo, Ana Margarida, Aravkin, Aleksandr Y, Aremu, Abdulfatai, Ariffin, Hany, Aripov, Timur, Armocida, Benedetta, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Arulappan, Judie, Aruleba, Idowu Thomas, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Asaad, Malke, Asgary, Saeed, Ashemo, Mubarek Yesse, Ashraf, Muhammad, Asika, Marvellous O, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Atreya, Alok, Attia, Sameh, Aujayeb, Avinash, Avan, Abolfazl, Awotidebe, Adedapo Wasiu, Ayala Quintanilla, Beatriz Paulina, Ayanore, Martin Amogre, Ayele, Getnet Melaku, Ayuso-Mateos, Jose L, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azhar, Gulrez Shah, Aziz, Shahkaar, Azzam, Ahmed Y, Babashahi, Mina, Babu, Abraham Samuel, Badar, Muhammad, Badawi, Alaa, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadorikhalili, Saeed, Bai, Jianjun, Bai, Ruhai, Baker, Jennifer L, Bakkannavar, Shankar M, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balogun, Saliu A, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Bansal, Hansi, Barati, Shirin, Barchitta, Martina, Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barr, Ronald D, Barrero, Lope H, Basharat, Zarrin, Bashir, Asma'u I J, Bashiru, Hameed Akande, Baskaran, Pritish, Basnyat, Buddha, Bassat, Quique, Basso, João Diogo, Basu, Saurav, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Bedi, Neeraj, Begum, Tahmina, Behboudi, Emad, Behnoush, Amir Hossein, Beiranvand, Maryam, Bejarano Ramirez, Diana Fernanda, Belgaumi, Uzma Iqbal, Bell, Michelle L, Bello, Aminu K, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bendak, Salaheddine, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berezvai, Zombor, Berman, Adam E, Bermudez, Amiel Nazer C, Bettencourt, Paulo J G, Beyene, Habtamu B, Beyene, Kebede A, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Ashish, Bhandari, Dinesh, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhardwaj, Prarthna V, Bhargava, Ashish, Bhaskar, Sonu, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Bhutta, Zulfiqar A, Bikbov, Boris, Binmadi, Nada, Bintoro, Bagas Suryo, Biondi, Antonio, Bisignano, Catherine, Bisulli, Francesca, Biswas, Atanu, Biswas, Raaj Kishore, Bitaraf, Saeid, Bjørge, Tone, Bleyer, Archie, Boampong, Mary Sefa, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bolarinwa, Obasanjo Afolabi, Bonakdar Hashemi, Milad, Bonny, Aime, Bora, Kaustubh, Bora Basara, Berrak, Borodo, Safiya Bala, Borschmann, Rohan, Botero Carvajal, Alejandro, Bouaoud, Souad, Boudalia, Sofiane, Boyko, Edward J, Bragazzi, Nicola Luigi, Braithwaite, Dejana, Brenner, Hermann, Britton, Gabrielle, Browne, Annie J, Brunoni, Andre R, Bulamu, Norma B, Bulto, Lemma N, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Burugina Nagaraja, Sharath, Busse, Reinhard, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cai, Tianji, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Chao, Cardenas, Carlos Alberto, Cárdenas, Rosario, Carr, Sinclair, Carreras, Giulia, Carrero, Juan J, Carugno, Andrea, Carvalho, Felix, Carvalho, Márcia, Castaldelli-Maia, Joao Mauricio, Castañeda-Orjuela, Carlos A, Castelpietra, Giulio, Catalá-López, Ferrán, Catapano, Alberico L, Cattaruzza, Maria Sofia, Caye, Arthur, Cederroth, Christopher 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Kumar, Nazri-Panjaki, Athare, Negash, Hadush, Negero, Amayu Kumesa, Negoi, Ionut, Negoi, Ruxandra Irina, Negru, Serban Mircea, Nejadghaderi, Seyed Aria, Nejjari, Chakib, Nematollahi, Mohammad Hadi, Nena, Evangelia, Nepal, Samata, Nesbit, Olivia D, Newton, Charles Richard James, Ngunjiri, Josephine W, Nguyen, Dang H, Nguyen, Phat Tuan, Nguyen, Phuong The, Nguyen, Tuan Thanh, Nguyen, Van Thanh, Nigatu, Yeshambel T, Nikolouzakis, Taxiarchis Konstantinos, Nikoobar, Ali, Nikpoor, Amin Reza, Nizam, Muhammad A, Nomura, Shuhei, Noreen, Mamoona, Noroozi, Nafise, Norouzian Baghani, Abbas, Norrving, Bo, Noubiap, Jean Jacques, Novotney, Amanda, Nri-Ezedi, Chisom Adaobi, Ntaios, George, Ntsekhe, Mpiko, Nuñez-Samudio, Virginia, Nurrika, Dieta, Oancea, Bogdan, Obamiro, Kehinde O, Odetokun, Ismail A, Ofakunrin, Akinyemi O D, Ogunsakin, Ropo Ebenezer, Oguta, James Odhiambo, Oh, In-Hwan, Okati-Aliabad, Hassan, Okeke, Sylvester Reuben, Okekunle, Akinkunmi Paul, Okidi, Lawrence, Okonji, Osaretin Christabel, Okwute, Patrick Godwin, Olagunju, Andrew T, Olaiya, Muideen Tunbosun, Olanipekun, Titilope O, Olatubi, Matthew Idowu, Olivas-Martinez, Antonio, Oliveira, Gláucia Maria Moraes, Oliver, Susan, Olorukooba, Abdulhakeem Abayomi, Olufadewa, Isaac Iyinoluwa, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Oluwafemi, Yinka Doris, Oluwatunase, Gideon Olamilekan, Omar, Hany A, Omer, Goran Latif, Ong, Sokking, Onwujekwe, Obinna E, Onyedibe, Kenneth Ikenna, Opio, John Nelson, Ordak, Michal, Orellana, E Roberto, Orisakwe, Orish Ebere, Orish, Verner N, Orru, Hans, Ortega-Altamirano, Doris V, Ortiz, Alberto, Ortiz-Brizuela, Edgar, Ortiz-Prado, Esteban, Osuagwu, Uchechukwu Levi, Otoiu, Adrian, Otstavnov, Nikita, Ouyahia, Amel, Ouyang, Guoqing, Owolabi, Mayowa O, Oyeyemi, Ifeoluwa Temitayo, Oyeyemi, Oyetunde T, Ozten, Yaz, P A, Mahesh Padukudru, Padubidri, Jagadish Rao, Pahlavikhah Varnosfaderani, Mahsa, Pal, Pramod Kumar, Palicz, Tamás, Palladino, Claudia, Palladino, Raffaele, Palma-Alvarez, Raul Felipe, Pana, Adrian, Panahi, Parsa, Pandey, Ashok, Pandi-Perumal, Seithikurippu R, Pando-Robles, Victoria, Pangaribuan, Helena Ullyartha, Panos, Georgios D, Pantazopoulos, Ioannis, Papadopoulou, Paraskevi, Pardhan, Shahina, Parikh, Romil R, Park, Seoyeon, Parthasarathi, Ashwaghosha, Pashaei, Ava, Pasupula, Deepak Kumar, Patel, Jenil R, Patel, Sangram Kishor, Pathan, Aslam Ramjan, Patil, Ashlesh, Patil, Shankargouda, Patoulias, Dimitrios, Patthipati, Venkata Suresh, Paudel, Uttam, Pawar, Shrikant, Pazoki Toroudi, Hamidreza, Pease, Spencer A, Peden, Amy E, Pedersini, Paolo, Peng, Minjin, Pensato, Umberto, Pepito, Veincent Christian Filipino, Peprah, Emmanuel K, Pereira, Gavin, Pereira, Jeevan, Pereira, Marcos, Peres, Mario F P, Perianayagam, Arokiasamy, Perico, Norberto, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pezzani, Raffaele, Pham, Hoang Tran, Phillips, Michael R, Pierannunzio, Daniela, Pigeolet, Manon, Pigott, David M, Pilgrim, Thomas, Pinheiro, Marina, Piradov, Michael A, Plakkal, Nishad, Plotnikov, Evgenii, Poddighe, Dimitri, Pollner, Peter, Poluru, Ramesh, Pond, Constance Dimity, Postma, Maarten J, Poudel, Govinda Raj, Poudel, Lisasha, Pourali, Ghazaleh, Pourtaheri, Naeimeh, Prada, Sergio I, Pradhan, Pranil Man Singh, Prajapati, Vijay Kumar, Prakash, V, Prasad, Chandra P, Prasad, Manya, Prashant, Akila, Prates, Elton Junio Sady, Purnobasuki, Hery, Purohit, Bharathi M, Puvvula, Jagadeesh, Qaisar, Rizwan, Qasim, Nameer Hashim, Qattea, Ibrahim, Qian, Gangzhen, Quan, Nguyen Khoi, Radfar, Amir, Radhakrishnan, Venkatraman, Raee, Pourya, Raeisi Shahraki, Hadi, Rafiei Alavi, Seyedeh Niloufar, Rafique, Ibrar, Raggi, Alberto, Rahim, Fakher, Rahman, Md Mosfequr, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahman, Tafhimur, Rahmani, Amir Masoud, Rahmani, Shayan, Rahnavard, Niloufar, Rai, Pramila, Rajaa, Sathish, Rajabpour-Sanati, Ali, Rajput, Prashant, Ram, Prasanna, Ramadan, Hazem, Ramasamy, Shakthi Kumaran, Ramazanu, Sheena, Rana, Juwel, Rana, Kritika, Ranabhat, Chhabi Lal, Rancic, Nemanja, Rani, Smitha, Ranjan, Shubham, Rao, Chythra R, Rao, Indu Ramachandra, Rao, Mithun, Rao, Sowmya J, Rasali, Drona Prakash, Rasella, Davide, Rashedi, Sina, Rashedi, Vahid, Rashid, Ahmed Mustafa, Rasouli-Saravani, Ashkan, Rastogi, Prateek, Rasul, Azad, Ravangard, Ramin, Ravikumar, Nakul, Rawaf, David Laith, Rawaf, Salman, Rawassizadeh, Reza, Razeghian-Jahromi, Iman, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Rehman, Faizan Ur, Reiner Jr, Robert C, Remuzzi, Giuseppe, Reshmi, Bhageerathy, Resnikoff, Serge, Reyes, Luis Felipe, Rezaee, Malihe, Rezaei, Negar, Rezaei, Nima, Rezaeian, Mohsen, Riaz, Mavra A, Ribeiro, Ana Isabel, Ribeiro, Daniel Cury, Rickard, Jennifer, Rios-Blancas, Maria Jesus, Robinson-Oden, Hannah Elizabeth, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Rohilla, Ravi, Rohloff, Peter, Romadlon, Debby Syahru, Ronfani, Luca, Roshandel, Gholamreza, Roshanzamir, Sharareh, Rostamian, Morteza, Roy, Bedanta, Roy, Priyanka, Rubagotti, Enrico, Rumisha, Susan Fred, Rwegerera, Godfrey M, Rynkiewicz, Andrzej, S, Manjula, S N, Chandan, S Sunnerhagen, Katharina, Saad, Aly M A, Sabbatucci, Michela, Saber, Korosh, Saber-Ayad, Maha Mohamed, Sacco, Simona, Saddik, Basema, Saddler, Adam, Sadee, Bashdar Abuzed, Sadeghi, Ehsan, Sadeghi, Masoumeh, Sadeghian, Saeid, Saeed, Umar, Saeedi, Maryam, Safi, Sare, Sagar, Rajesh, Saghazadeh, Amene, Saheb Sharif-Askari, Narjes, Sahoo, Soumya Swaroop, Sahraian, Mohammad Ali, Sajedi, Seyed Aidin, Sajid, Mirza Rizwan, Sakshaug, Joseph W, Salahi, Saina, Salahi, Sarvenaz, Salamati, Payman, Salami, Afeez Abolarinwa, Salaroli, Luciane B, Saleh, Mohamed A, Salehi, Sana, Salem, Marwa Rashad, Salem, Mohammed Z Y, Salimi, Sohrab, Samadi Kafil, Hossein, Samadzadeh, Sara, Samara, Kamel A, Samargandy, Saad, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Samy, Abdallah M, Sanabria, Juan, Sanadgol, Nima, Sanganyado, Edmond, Sanjeev, Rama Krishna, Sanmarchi, Francesco, Sanna, Francesca, Santri, Ichtiarini Nurullita, Santric-Milicevic, Milena M, Sarasmita, Made Ary, Saravanan, Aswini, Saravi, Babak, Sarikhani, Yaser, Sarkar, Chinmoy, Sarmiento-Suárez, Rodrigo, Sarode, Gargi Sachin, Sarode, Sachin C, Sarveazad, Arash, Sathian, Brijesh, Sathish, Thirunavukkarasu, Sattin, Davide, Saulam, Jennifer, Sawyer, Susan M, Saxena, Sonia, Saya, Ganesh Kumar, Sayadi, Yaser, Sayeed, Abu, Sayeed, Md Abu, Saylan, Mete, Scarmeas, Nikolaos, Schaarschmidt, Benedikt Michael, Schlee, Winfried, Schmidt, Maria Inês, Schuermans, Art, Schwebel, David C, Schwendicke, Falk, Šekerija, Mario, Selvaraj, Siddharthan, Semreen, Mohammad H, Senapati, Sabyasachi, Sengupta, Pallav, Senthilkumaran, Subramanian, Sepanlou, Sadaf G, Serban, Dragos, Sertsu, Addisu, Sethi, Yashendra, SeyedAlinaghi, SeyedAhmad, Seyedi, Seyed Arsalan, Shafaat, Amir, Shafaat, Omid, Shafie, Mahan, Shafiee, Arman, Shah, Nilay S, Shah, Pritik A, Shahabi, Saeed, Shahbandi, Ataollah, Shahid, Izza, Shahid, Samiah, Shahid, Wajeehah, Shahwan, Moyad Jamal, Shaikh, Masood Ali, Shakeri, Alireza, Shakil, Husain, Sham, Sunder, Shamim, Muhammad Aaqib, Shams-Beyranvand, Mehran, Shamshad, Hina, Shamshirgaran, Mohammad Ali, Shamsi, Mohammad Anas, Shanawaz, Mohd, Shankar, Abhishek, Sharfaei, Sadaf, Sharifan, Amin, Shariff, Mariam, Sharifi-Rad, Javad, Sharma, Manoj, Sharma, Rajesh, Sharma, Saurab, Sharma, Vishal, Shastry, Rajesh P, Shavandi, Amin, Shaw, David H, Shayan, Amir Mehdi, Shehabeldine, Amr Mohamed Elsayed, Sheikh, Aziz, Sheikhi, Rahim Ali, Shen, Jiabin, Shenoy, Manjunath Mala, Shetty, B Suresh Kumar, Shetty, Ranjitha S, Shey, Robert Adamu, Shiani, Amir, Shibuya, Kenji, Shiferaw, Desalegn, Shigematsu, Mika, Shin, Jae Il, Shin, Min-Jeong, Shiri, Rahman, Shirkoohi, Reza, Shittu, Aminu, Shiue, Ivy, Shivakumar, K M, Shivarov, Velizar, Shool, Sina, Shrestha, Sunil, Shuja, Kanwar Hamza, Shuval, Kerem, Si, Yafei, Sibhat, Migbar Mekonnen, Siddig, Emmanuel Edwar, Sigfusdottir, Inga Dora, Silva, João Pedro, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Simões, Jorge Piano, Simpson, Colin R, Singal, Anjali, Singh, Abhinav, Singh, Aditya, Singh, Ambrish, Singh, Balbir Bagicha, Singh, Baljinder, Singh, Mahendra, Singh, Mayank, Singh, Narinder Pal, Singh, Paramdeep, Singh, Surjit, Siraj, Md Shahjahan, Sitas, Freddy, Sivakumar, Shravan, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Sleet, David A, Slepak, Erica Leigh N, Sohrabi, Hanye, Soleimani, Hamidreza, Soliman, Sameh S M, Solmi, Marco, Solomon, Yonatan, Song, Yimeng, Sorensen, Reed J D, Soriano, Joan B, Soyiri, Ireneous N, Spartalis, Michael, Sreeramareddy, Chandrashekhar T, Starnes, Joseph R, Starodubov, Vladimir I, Starodubova, Antonina V, Stefan, Simona Cătălina, Stein, Dan J, Steinbeis, Fridolin, Steiropoulos, Paschalis, Stockfelt, Leo, Stokes, Mark A, Stortecky, Stefan, Stranges, Saverio, Stroumpoulis, Konstantinos, Suleman, Muhammad, Suliankatchi Abdulkader, Rizwan, Sultana, Abida, Sun, Jing, Sunkersing, David, Susanty, Sri, Swain, Chandan Kumar, Sykes, Bryan L, Szarpak, Lukasz, Szeto, Mindy D, Szócska, Miklós, Tabaee Damavandi, Payam, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabb, Karen M, Tabish, Mohammad, Taborda-Barata, Luis M, Tabuchi, Takahiro, Tadesse, Birkneh Tilahun, Taheri, Amirmasoud, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Taherkhani, Amir, Taiba, Jabeen, Tajbakhsh, Ardeshir, Talaat, Iman M, Talukder, Ashis, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tang, Hong K, Tat, Nathan Y, Tat, Vivian Y, Tavakoli Oliaee, Razieh, Tavangar, Seyed Mohammad, Taveira, Nuno, Tebeje, Tsion Mulat, Tefera, Yibekal Manaye, Teimoori, Mojtaba, Temsah, Mohamad-Hani, Temsah, Reem Mohamad Hani, Teramoto, Masayuki, Tesfaye, Solomon Hailemariam, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapa, Rajshree, Thapar, Rekha, Thomas, Nihal, Thrift, Amanda G, Thum, Chern Choong Chern, Tian, Jing, Tichopad, Ales, Ticoalu, Jansje Henny Vera, Tiruye, Tenaw Yimer, Tohidast, Seyed Abolfazl, Tonelli, Marcello, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Tram, Khai Hoan, Tran, Nghia Minh, Trico, Domenico, Trihandini, Indang, Tromans, Samuel Joseph, Truong, Vien T, Truyen, Thien Tan Tri Tai, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Tung, Kang, Tyrovolas, Stefanos, Ubah, Chukwudi S, Udoakang, Aniefiok John, Udoh, Arit, Ulhaq, Inam, Ullah, Saeed, Ullah, Sana, Umair, Muhammad, Umar, Tungki Pratama, Umeokonkwo, Chukwuma David, Umesh, Anushri, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Urso, Daniele, Vacante, Marco, Vahdani, Amir Mohammad, Vaithinathan, Asokan Govindaraj, Valadan Tahbaz, Sahel, Valizadeh, Rohollah, Van den Eynde, Jef, Varavikova, Elena, Varga, Orsolya, Varma, Siddhartha Alluri, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Veerman, Lennert J, Venketasubramanian, Narayanaswamy, Venugopal, Deneshkumar, Verghese, Nicholas Alexander, Verma, Madhur, Verma, Pratibha, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Vieira, Rafael José, Villafañe, Jorge Hugo, Villani, Leonardo, Villanueva, Gabriela Ines, Villeneuve, Paul J, Violante, Francesco S, Visontay, Rachel, Vlassov, Vasily, Vo, Bay, Vollset, Stein Emil, Volovat, Simona Ruxandra, Volovici, Victor, Vongpradith, Avina, Vos, Theo, Vujcic, Isidora S, Vukovic, Rade, Wado, Yohannes Dibaba, Wafa, Hatem A, Waheed, Yasir, Wamai, Richard G, Wang, Cong, Wang, Denny, Wang, Fang, Wang, Shu, Wang, Song, Wang, Yanzhong, Wang, Yuan-Pang, Ward, Paul, Watson, Stefanie, Weaver, Marcia R, Weerakoon, Kosala Gayan, Weiss, Daniel J, Weldemariam, Abrha Hailay, Wells, Katherine M, Wen, Yi Feng, Werdecker, Andrea, Westerman, Ronny, Wickramasinghe, Dakshitha Praneeth, Wickramasinghe, Nuwan Darshana, Wijeratne, Tissa, Wilson, Shadrach, Wojewodzic, Marcin W, Wool, Eve E, Woolf, Anthony D, Wu, Dongze, Wulandari, Ratna Dwi, Xiao, Hong, Xu, Bin, Xu, Xiaoyue, Yadav, Lalit, Yaghoubi, Sajad, Yang, Lin, Yano, Yuichiro, Yao, Yao, Ye, Pengpeng, Yesera, Gesila Endashaw, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yiğit, Arzu, Yiğit, Vahit, Yip, Paul, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yu, Chuanhua, Zadey, Siddhesh, Zadnik, Vesna, Zafari, Nima, Zahedi, Mohammad, Zahid, Muhammad Nauman, Zahir, Mazyar, Zakham, Fathiah, Zaki, Nazar, Zakzuk, Josefina, Zamagni, Giulia, Zaman, Burhan Abdullah, Zaman, Sojib Bin, Zamora, Nelson, Zand, Ramin, Zandi, Milad, Zandieh, Ghazal G Z, Zanghì, Aurora, Zare, Iman, Zastrozhin, Mikhail Sergeevich, Zeariya, Mohammed G M, Zeng, Youjie, Zhai, Chunxia, Zhang, Chen, Zhang, Haijun, Zhang, Hongwei, Zhang, Yunquan, Zhang, Zhaofeng, Zhang, Zhenyu, Zhao, Hanqing, Zhao, Yang, Zhao, Yong, Zheng, Peng, Zhong, Chenwen, Zhou, Juexiao, Zhu, Bin, Zhu, Zhaohua, Ziaeefar, Pardis, Zielińska, Magdalena, Zou, Zhiyong, Zumla, Alimuddin, Zweck, Elric, Zyoud, Samer H, Lim, Stephen S, and Murray, Christopher J L

Published
2024
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19. Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence

Author

Dedeoglu, Mehmet, Lin, Sen, Zhang, Zhaofeng, and Zhang, Junshan

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share model similarity, it is plausible to leverage pre-trained generative models from the cloud or other edge nodes. Appealing to optimal transport theory tailored towards Wasserstein-1 generative adversarial networks (WGAN), this study aims to develop a framework which systematically optimizes continual learning of generative models using local data at the edge node while exploiting adaptive coalescence of pre-trained generative models. Specifically, by treating the knowledge transfer from other nodes as Wasserstein balls centered around their pre-trained models, continual learning of generative models is cast as a constrained optimization problem, which is further reduced to a Wasserstein-1 barycenter problem. A two-stage approach is devised accordingly: 1) The barycenters among the pre-trained models are computed offline, where displacement interpolation is used as the theoretic foundation for finding adaptive barycenters via a "recursive" WGAN configuration; 2) the barycenter computed offline is used as meta-model initialization for continual learning and then fast adaptation is carried out to find the generative model using the local samples at the target edge node. Finally, a weight ternarization method, based on joint optimization of weights and threshold for quantization, is developed to compress the generative model further.

Published
2021

20. GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

Author

Huang, Yu, Li, Yan, Zhang, Zhaofeng, and Liu, Ryan Wen

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The automatic identification system (AIS), an automatic vessel-tracking system, has been widely adopted to perform intelligent traffic management and collision avoidance services in maritime Internet of Things (IoT) industries. With the rapid development of maritime transportation, tremendous numbers of AIS-based vessel trajectory data have been collected, which make trajectory data compression imperative and challenging. This paper mainly focuses on the compression and visualization of large-scale vessel trajectories and their Graphics Processing Unit (GPU)-accelerated implementations. The visualization was implemented to investigate the influence of compression on vessel trajectory data quality. In particular, the Douglas-Peucker (DP) and Kernel Density Estimation (KDE) algorithms, respectively utilized for trajectory compression and visualization, were significantly accelerated through the massively parallel computation capabilities of GPU architecture. Comprehensive experiments on trajectory compression and visualization have been conducted on large-scale AIS data of recording ship movements collected from 3 different water areas, i.e., the South Channel of Yangtze River Estuary, the Chengshan Jiao Promontory, and the Zhoushan Islands. Experimental results illustrated that (1) the proposed GPU-based parallel implementation frameworks could significantly reduce the computational time for both trajectory compression and visualization; (2) the influence of compressed vessel trajectories on trajectory visualization could be negligible if the compression threshold was selected suitably; (3) the Gaussian kernel was capable of generating more appropriate KDE-based visualization performance by comparing with other seven kernel functions., Comment: 19 pages, 21 figures

Published
2020

29. METTL3/LINC00662/miR-186-5p feedback loop regulates docetaxel resistance in triple negative breast cancer

Author

Lei Jing, Liu Lan, Zhang Mingxin, and Zhang Zhaofeng

Subjects
Medicine, Science
Abstract

Abstract Insight into the mechanism of docetaxel resistance in breast cancer may help to improve prognosis. We aimed to investigate the role of N6-methyladenosine (m6A) and the METTL3/LINC00662/miR-186-5p pathway in regulating docetaxel resistance in triple negative breast cancer (TNBC). We have recruited 193 pathologically diagnosed TNBC patients from 2016 to 2017 in our hospital. Quantitative real-time PCR was used to evaluate the expression of LINC00662 and miR-186-5p both in vivo and in vitro. CCK8 tests were used to assess cell viability. ELISA was used for protein expression evaluation. Dual luciferase reporter gene assay and RNA pull-down were used to evaluate the interaction between LINC00662 and miR-186-5p. m6A levels were enhanced in breast cancer tissues and cells. LINC00662, miR-186-5p and METTL3 were differentially expressed in vivo, and METTL3 expression was associated with LINC00662 and miR-186-5p expression. LINC00662 and miR-186-5p were differentially expressed in vitro; LINC00662 promoted cell viability and decreased the apoptosis rate, whereas miR-186-5p inhibited cell viability and increased the apoptosis rate. Furthermore, we found that METTL3 regulated m6A levels in docetaxel-resistant breast cancer cells by regulating the expression of LINC00662. Moreover, LINC00662 and miR-186-5p regulated the cell viability rate of docetaxel-resistant breast cancer cells. Further experiments showed that LINC00662 directly interacted with miR-186-5p to exert biological functions; besides miR-186-5p could regulate the expression of METTL3. METTL3 promotes m6A levels and docetaxel resistance in breast cancer by regulating the expression of LINC00662 and miR-186-5p; more experiments are needed to clarify the role of m6A regulation in drug resistance.

Published
2022
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46. A heterozygous SPRY4 variant identified in female infertility characterized by reduced oocyte potential and early embryonic arrest.

Author

Xia, Lingjin, Huang, Jiami, Che, Qi, Zhang, Jian, Zhang, Zhaofeng, Shen, Yupei, Wang, Difei, Zhong, Yushun, Liu, Suying, and Du, Jing

Subjects
GENETIC variation, EMBRYOLOGY, FEMALE infertility, GONADOTROPIN releasing hormone, RECURRENT miscarriage, INFERTILITY
Abstract

STUDY QUESTION Can novel genetic factors contributing to early embryonic arrest in infertile patients be identified, along with the underlying mechanisms of the pathogenic variant? SUMMARY ANSWER We identified a heterozygous variant in the SPRY4 (sprouty RTK signaling antagonist 4) in infertile patients and conducted in vitro and in vivo studies to investigate the effects of the variant/deletion, highlighting its critical role in female reproductive health. WHAT IS KNOWN ALREADY SPRY4 acts as a negative regulator of receptor tyrosine kinases (RTKs) and functions as a tumor suppressor. Its abnormal expression can lead to recurrent miscarriage by affecting trophoblast function. In mice, Spry4 knockout (KO) leads to craniofacial anomalies and growth defects. A human study links the SPRY4 variant to a male patient with isolated hypogonadotropic hypogonadism (IHH), hypothetically impacting gonadotropin-releasing hormone (GnRH) neurons, and causing reproductive dysfunctions. SPRY4 is thus potentially integral in regulating endocrine homeostasis and reproductive function. To date, no study has reported SPRY4 variants associated with female fertility, and a causal relationship has not been established with functional evidence. STUDY DESIGN, SIZE, DURATION Whole-exome sequencing (WES) was performed in 392 infertile women who suffered from primary infertility of unknown reason, and the heterozygous SPRY4 variant were identified in one independent family. The infertile patients presenting were recruited from July 2017 to November 2023. PARTICIPANTS/MATERIALS, SETTING, METHODS Women diagnosed with primary infertility were recruited from the Reproduction Center of Zhongshan Hospital, Fudan University. Genomic DNA was extracted from peripheral blood for WES analysis. The SPRY4 variant were identified through WES, in silico analysis, and variant screening. All variants were confirmed by Sanger sequencing. The effects of the variants were investigated in human embryonic kidney (HEK) 293T (HEK293T) cells via western blotting, and in mouse oocytes and embryos through complementary RNA (cRNA) injection, RNA sequencing, fluorescence, absorbance, and RT-qPCR assays. Gene function was further examined in Spry4 KO mice via histology, western blotting, ELISA, and RT-qPCR assays. MAIN RESULTS AND THE ROLE OF CHANCE We identified a missense heterozygous pathogenic variant in SPRY4 (GRCh38, GenBank: NM_030964.5 , c.157C>T p.(Arg53Trp), rs200531302) that reduces SPRY4 protein levels in HEK293T cells and disrupts the redox system and mitochondrial function in mouse oocyte, and perturbs developmental potential in mouse embryos. These phenotypes could be partially reversed by the exogenous addition of Nrf1 cRNA. Additionally, Spry4−/ − mice exhibit ovarian oxidative stress and decreased ovarian function. LIMITATIONS, REASONS FOR CAUTION Due to the limited WES data and population, we identified only one family with a SPRY4 mutation. The deeper mechanism and therapeutic strategy should be further investigated through mutant mice and recovery experiment. WIDER IMPLICATIONS OF THE FINDINGS Our study has identified a pathogenic variant in SPRY4 associated with early embryonic arrest in humans. These findings enhance our understanding of the role of SPRY4 in early embryonic development and present a new genetic marker for female infertility. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Natural Science Foundation of China (82071643 and 82171655) and Natural Science Foundation of Shanghai (22ZR1456200). None of the authors have any competing interests. TRIAL REGISTRATION NUMBER N/A [ABSTRACT FROM AUTHOR]

Published
2024
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47. Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs.

Author

Li, Qingsheng, Lu, Kaicheng, Zhu, Yongqing, Zhang, Zhaofeng, and Li, Zhen

Subjects
MULTIAGENT systems, MICROGRIDS, ENERGY storage, RESEARCH personnel, COMPUTER simulation
Abstract

Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Identification of potential therapeutic targets from bioinformatics analysis of necroptosis and immune infiltration in acute myocardial infarction.

Author

Ma, Likang, Chen, Keyuan, Li, Jiakang, Xie, Linfeng, Zhang, Zhaofeng, Zarif, Mohammad, Chai, Tianci, Wu, Qingsong, Chen, Liangwan, and Qiu, Zhihuang

Subjects
TRANSCRIPTION factors, MYOCARDIAL infarction, NF-kappa B, GENE expression, RECEIVER operating characteristic curves
Abstract

Introduction: Acute myocardial infarction (AMI) is a serious, deadly disease with a high incidence. However, it remains unclear how necroptosis affects the pathophysiology of AMI. Using bioinformatic analyses, this study investigated necroptosis in AMI. Methods: We obtained the GSE66360 dataset related to AMI by the GEO database. Venn diagrams were used to identify necroptosis-related differential genes (NRDEGs). The genes with differential expression in AMI were analyzed using gene set enrichment analysis, and a PPI network was established. A transcription factor prediction and enrichment analysis were conducted for the NRDEGs, and the relationships between AMI, NRDEGs, and immune cells were determined. Finally, in the additional dataset, NRDEG expression levels, immune infiltration, and ROC curve analysis were confirmed, and gene expression levels were further verified experimentally. Results: GSEA revealed that necroptosis pathways were significantly enriched in AMI. We identified 10 NRDEGs, including TNF, TLR4, FTH1 and so on. Enrichment analysis indicated that the NOD-like receptor and NF-kappa B signaling pathways were significantly enriched. Four NRDEGs, FTH1, IFNGR1, STAT3, and TLR4, were identified; however, additional datasets and further experimental validation are required to confirm their roles. In addition, we determined that a high abundance of macrophages and neutrophils prompted AMI development. Conclusions: In this study, four potential genes that affect the development of AMI through necroptosis (FTH1, IFNGR1, STAT3, and TLR4) were identified. In addition, we found that a high abundance of macrophages and neutrophils affected AMI. This helps determine the pathological mechanism of necroptosis and immune cells that influence AMI and provides a novel strategy for targeted therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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