Author: "Zhang, Zhiqiang" / Publication Type: Electronic Resources - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Zhang, Zhiqiang"' showing total 155 results

Start Over Author "Zhang, Zhiqiang" Publication Type Electronic Resources

155 results on '"Zhang, Zhiqiang"'

1. Real-time auto-segmentation of the ureter in video sequences of gynaecological laparoscopic surgery

Author: Wang, Zhixiang, Liu, Chongdong, Zhang, Zhen, Deng, Yupeng, Xiao, Meizhu, Zhang, Zhiqiang, Dekker, Andre, Wang, Shuzhen, Liu, Yujiang, Qian, Lin Xue, Zhang, Zhenyu, Traverso, Alberto, Feng, Ying, Wang, Zhixiang, Liu, Chongdong, Zhang, Zhen, Deng, Yupeng, Xiao, Meizhu, Zhang, Zhiqiang, Dekker, Andre, Wang, Shuzhen, Liu, Yujiang, Qian, Lin Xue, Zhang, Zhenyu, Traverso, Alberto, and Feng, Ying
Abstract: Background: Ureteral injury is common during gynaecological laparoscopic surgery. Real-time auto-segmentation can assist gynaecologists in identifying the ureter and reduce intraoperative injury risk. Methods: A deep learning segmentation model was crafted for ureter recognition in surgical videos, utilising 3368 frames from 11 laparoscopic surgeries. Class activation maps enhanced the model's interpretability, showing its areas. The model's clinical relevance was validated through an End-User Turing test and verified by three gynaecological surgeons. Results: The model registered a Dice score of 0.86, a Hausdorff 95 distance of 22.60, and processed images in 0.008 s on average. In complex surgeries, it pinpointed the ureter's position in real-time. Fifty five surgeons across eight institutions found the model's accuracy, specificity, and sensitivity comparable to human performance. Yet, artificial intelligence experience influenced some subjective ratings. Conclusions: The model offers precise real-time ureter segmentation in laparoscopic surgery and can be a significant tool for gynaecologists to mitigate ureteral injuries.
Published: 2024

2. Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Author: Vollset, S, Ababneh, H, Abate, Y, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, A, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, R, Abolhassani, H, Abouzid, M, Aboye, G, Abreu, L, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, H, Addolorato, G, Adekanmbi, V, Adetunji, C, Adetunji, J, Adeyeoluwa, T, Adha, R, Adhikary, R, Adnani, Q, Adzigbli, L, Afrashteh, F, Afzal, M, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, B, Ahlstrom, A, Ahmad, A, Ahmad, F, Ahmad, M, Ahmad, S, Ahmed, A, Ahmed, H, Ahmed, S, Akinosoglou, K, Akkaif, M, Akrami, A, Akter, E, Al Awaidy, S, Al Hasan, S, Al Mosa, A, Al Ta'ani, O, Al Zaabi, O, Alahdab, F, Alajlani, M, Al-Ajlouni, Y, Alalalmeh, S, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-amer, R, Alanezi, F, Alanzi, T, Albakri, A, Aldhaleei, W, Aldridge, R, Alemohammad, S, Alemu, Y, Al-Gheethi, A, Al-Hanawi, M, Ali, A, Ali, I, Ali, M, Ali, R, Ali, S, Ali, V, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, S, Aljunid, S, Alla, F, Almazan, J, Al-Mekhlafi, H, Alqutaibi, A, Alrawashdeh, A, Alrousan, S, Al-Sabah, S, Alsabri, M, Altaany, Z, Al-Tammemi, A, Al-Tawfiq, J, Altirkawi, K, Aluh, D, Alvis-Guzman, N, Al-Wardat, M, Al-Worafi, Y, Aly, H, Alyahya, M, Alzoubi, K, Al-Zyoud, W, Amani, R, Ameyaw, E, Amin, T, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, M, Amu, H, Amugsi, D, Ancuceanu, R, Anderlini, D, Anderson, D, Andrade, P, Andrei, C, Andrei, T, Andrews, E, Anil, A, Anil, S, Anoushiravani, A, Antony, C, Antriyandarti, E, Anuoluwa, B, Anvari, S, Anyasodor, A, Appiah, F, Aquilano, M, Arab, J, Arabloo, J, Arafa, E, Arafat, M, Aravkin, A, Ardekani, A, Areda, D, Aregawi, B, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, A, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, S, Atorkey, P, Atout, M, Aujayeb, A, Ausloos, M, Awad, H, Awotidebe, A, Ayatollahi, H, Ayuso-Mateos, J, Azadnajafabad, S, Azeez, F, Azevedo, R, Badar, M, Baghdadi, S, Bagheri, M, Bagheri, N, Bai, R, Baker, J, Bako, A, Balakrishnan, S, Balcha, W, Baltatu, O, Barchitta, M, Bardideh, E, Barker-Collo, S, Bärnighausen, T, Barqawi, H, Barteit, S, Basiru, A, Basso, J, Bastan, M, Basu, S, Bauckneht, M, Baune, B, Bayati, M, Bayileyegn, N, Behnoush, A, Behzadi, P, Beiranvand, M, Bello, O, Belo, L, Beloukas, A, Bemanalizadeh, M, Bensenor, I, Benzian, H, Beran, A, Berezvai, Z, Bernstein, R, Bettencourt, P, Beyene, K, Beyene, M, Bhagat, D, Bhagavathula, A, Bhala, N, Bhandari, D, Bharadwaj, R, Bhardwaj, N, Bhardwaj, P, Bhargava, A, Bhaskar, S, Bhat, V, Bhattacharjee, N, Bhatti, G, Bhatti, J, Bhatti, M, Bhuiyan, M, Bisignano, C, Biswas, B, Bjørge, T, Bodolica, V, Bodunrin, A, Bonakdar Hashemi, M, Bora Basara, B, Borhany, H, Bosoka, S, Botero Carvajal, A, Bouaoud, S, Boufous, S, Boxe, C, Boyko, E, Brady, O, Braithwaite, D, Brauer, M, Brazo-Sayavera, J, Brenner, H, Brown, C, Browne, A, Brugha, T, Bryazka, D, Bulamu, N, Buonsenso, D, Burkart, K, Burns, R, Busse, R, Bustanji, Y, Butt, Z, Caetano dos Santos, F, Çakmak Barsbay, M, Calina, D, Campos, L, Cao, S, Capodici, A, Cárdenas, R, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, J, Castelpietra, G, Cattaruzza, M, Caye, A, Cegolon, L, Cembranel, F, Cenko, E, Cerin, E, Chadban, S, Chadwick, J, Chakraborty, C, Chakraborty, S, Chalek, J, Chan, J, Chandika, R, Chandy, S, Charan, J, Chaudhary, A, Chaurasia, A, Chen, A, Chen, H, Chen, M, Chen, S, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, R, Ching, P, Chirinos-Caceres, J, Chitheer, A, Cho, D, Cho, W, Choi, D, Chong, B, Chong, C, Chopra, H, Chu, D, Chung, E, Chutiyami, M, Clayton, J, Cogen, R, Cohen, A, Columbus, A, Comfort, H, Conde, J, Connolly, J, Cooper, E, Cortese, S, Cruz-Martins, N, da Silva, A, Dadras, O, Dai, X, Dai, Z, Dalton, B, Damiani, G, Dandona, L, Dandona, R, Das, J, Das, S, Dash, N, Davletov, K, De la Hoz, F, De Leo, D, Debopadhaya, S, Delgado-Enciso, I, Denova-Gutiérrez, E, Dervenis, N, Desai, H, Devanbu, V, Dewan, S, Dhama, K, Dhane, A, Dhingra, S, Dias da Silva, D, Diaz, D, Diaz, L, Diaz, M, Dima, A, Ding, D, Do, T, do Prado, C, Dodangeh, M, Doegah, P, Dohare, S, Dong, W, D'Oria, M, Doshi, R, Dowou, R, Dsouza, H, Dsouza, V, Dube, J, Dumith, S, Duncan, B, Duraes, A, Duraisamy, S, Durojaiye, O, Dushpanova, A, Dutta, S, Dzianach, P, Dziedzic, A, Eboreime, E, Ebrahimi, A, Ebrahimi Kalan, M, Edinur, H, Efendi, F, Eikemo, T, Eini, E, Ekundayo, T, El Arab, R, El Sayed, I, Elamin, O, Elemam, N, Elgohary, G, Elhadi, M, Elmeligy, O, Elmoselhi, A, Elshaer, M, Elsohaby, I, Eltahir, M, Emeto, T, Eshrati, B, Eslami, M, Esmaeili, Z, Fabin, N, Fagbamigbe, A, Fagbule, O, Falzone, L, Fareed, M, Farinha, C, Faris, M, Faro, A, Fasihi, K, Fatehizadeh, A, Fauk, N, Fazylov, T, Feigin, V, Fekadu, G, Feng, X, Fereshtehnejad, S, Ferrara, P, Ferreira, N, Firew, B, Fischer, F, Fitriana, I, Flavel, J, Flor, L, Folayan, M, Foley, K, Fonzo, M, Force, L, Foschi, M, Freitas, A, Fridayani, N, Fukutaki, K, Furtado, J, Fux, B, Gaal, P, Gadanya, M, Gallus, S, Ganesan, B, Ganiyani, M, Gautam, R, Gebi, T, Gebregergis, M, Gebrehiwot, M, Getacher, L, Getahun, G, Gething, P, Ghadimi, D, Ghadirian, F, Ghafarian, S, Ghailan, K, Ghasemi, M, Ghasempour Dabaghi, G, Ghazy, R, Ghoba, S, Gholami, E, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Ghorbani Vajargah, P, Ghotbi, E, Gil, A, Gill, T, Girmay, A, Glasbey, J, Glushkova, E, Gnedovskaya, E, Göbölös, L, Goldust, M, Goleij, P, Golinelli, D, Gopalani, S, Goulart, A, Gouravani, M, Goyal, A, Grivna, M, Grosso, G, Guarducci, G, Gubari, M, Guicciardi, S, Guimarães, R, Gulati, S, Gulisashvili, D, Gunawardane, D, Guo, C, Gupta, A, Gupta, R, Gupta, S, Gupta, V, Haakenstad, A, Hadi, N, Haep, N, Hafiz, A, Haghmorad, D, Haile, D, Hajj Ali, A, Haj-Mirzaian, A, Halboub, E, Haller, S, Halwani, R, Hamagharib Abdullah, K, Hamdy, N, Hamoudi, R, Hanifi, N, Hankey, G, Haq, Z, Haque, M, Harapan, H, Hargono, A, Haro, J, Hasaballah, A, Hasan, S, Hasanian, M, Hasnain, M, Hassan, A, Haubold, J, Hay, S, Hebert, J, Hegazi, O, Heidari, M, Hemmati, M, Henson, C, Herrera-Serna, B, Herteliu, C, Heydari, M, Hezam, K, Hidayana, I, Hiraike, Y, Hoan, N, Holla, R, Hoogar, P, Horita, N, Hossain, M, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hu, C, Huang, J, Hultström, M, Hundie, T, Hunt, A, Hushmandi, K, Hussain, J, Hussain, M, Hussein, N, Huynh, H, Hwang, B, Ibitoye, S, Iftikhar, P, Ikiroma, A, Ikwegbue, P, Ilic, I, Ilic, M, Immurana, M, Isa, M, Islam, M, Islam, S, Ismail, F, Ismail, N, Isola, G, Iwagami, M, Iyamu, I, Jacob, L, Jacobsen, K, Jafarinia, M, Jahankhani, K, Jahanmehr, N, Jain, N, Jairoun, A, Jakhmola Mani, D, Jamil, S, Jamora, R, Jatau, A, Javadov, S, Javaheri, T, Jayaram, S, Jee, S, Jeganathan, J, Jiang, H, Jokar, M, Jonas, J, Joseph, N, Joshua, C, Jürisson, M, K, V, Kabir, A, Kabir, Z, Kadashetti, V, Kalankesh, L, Kalra, S, Kamath, A, Kamath, R, Kamireddy, A, Kanaan, M, Kanchan, T, Kanmiki, E, Kanmodi, K, Kansal, S, Karim, A, Karkhah, S, Kashoo, F, Kasraei, H, Kassel, M, Katikireddi, S, Kauppila, J, Kaur, H, Kayode, G, Kazemi, F, Kazemian, S, Kebede, F, Kendal, E, Kesse-Guyot, E, Khademvatan, S, Khajuria, H, Khalaji, A, Khalid, A, Khalid, N, Khalilian, A, Khamesipour, F, Khan, F, Khan, M, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, M, Khatib, M, Khayat Kashani, H, Kheirallah, K, Khokhar, M, Khormali, M, Khorrami, Z, Khosla, A, Khosravi, M, Khosrowjerdi, M, Khubchandani, J, Kifle, Z, Kim, G, Kim, J, Kim, M, Kim, Y, Kimokoti, R, Kisa, A, Kisa, S, Knibbs, L, Knudsen, A, Kochhar, S, Kolahi, A, Kompani, F, Koren, G, Korzh, O, Krishan, K, Krishna, V, Krishnamoorthy, V, Kucuk Bicer, B, Kuddus, M, Kuitunen, I, Kujan, O, Kulimbet, M, Kulkarni, V, Kumar, G, Kumar, H, Kumar, N, Kumar, R, Kumar, V, Kundu, A, Kusuma, D, Kyei-Arthur, F, Kytö, V, Kyu, H, La Vecchia, C, Lacey, B, Ladan, M, Laflamme, L, Lahariya, C, Lai, D, Lalloo, R, Lallukka, T, Lám, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, V, Laplante-Lévesque, A, Larijani, B, Larsson, A, Lasrado, S, Lauriola, P, Lawlor, H, Le, H, Le, L, Le, N, Le, T, Leasher, J, Lee, D, Lee, M, Lee, P, Lee, S, Lee, Y, Leigh, J, Leong, E, Li, M, Libra, M, Ligade, V, Lim, L, Lim, S, Limenh, L, Lindholm, D, Lindstedt, P, Listl, S, Liu, G, Liu, S, Liu, X, Llanaj, E, López-Bueno, R, López-Gil, J, Loreche, A, Lotufo, P, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, Lusk, J, Lv, L, M Amin, H, Ma, Z, Maass, K, Machairas, N, Machoy, M, Madureira-Carvalho, Á, Magdy Abd El Razek, H, Maghazachi, A, Mahadeshwara Prasad, D, Mahalleh, M, Mahasha, P, Mahmoud, M, Mahmoudi, E, Mahmoudvand, G, Makama, M, Malakan Rad, E, Malhotra, K, Malik, A, Malta, D, Manla, Y, Mansour, A, Mansouri, M, Mansouri, P, Mansouri, V, Mansourian, M, Mansournia, M, Marasini, B, Marateb, H, Maravilla, J, Mardi, P, Marjani, A, Markazi Moghadam, H, Marrugo Arnedo, C, Martinez, G, Martinez-Piedra, R, Martins-Melo, F, Martorell, M, Marx, W, Marzo, R, Masoudi, S, Mathangasinghe, Y, Mathioudakis, A, Mathur, M, Mathur, N, Matozinhos, F, Mattumpuram, J, Maude, R, Maugeri, A, Mayeli, M, Mazidi, M, Mazzotti, A, Mcgrath, J, Mckee, M, Mckowen, A, Mcphail, M, Mcphail, S, Mehmood, A, Mehrabani-Zeinabad, K, Mehravar, S, Mekene Meto, T, Melese, E, Mendez-Lopez, M, Mendoza, W, Menezes, R, Mensah, G, Mensah, L, Mentis, A, Meo, S, Meretoja, A, Meretoja, T, Mersha, A, Mestrovic, T, Mettananda, K, Mettananda, S, Mhlanga, A, Mhlanga, L, Miazgowski, T, Michalek, I, Micheletti Gomide Nogueira de Sá, A, Miller, T, Minh, L, Mirahmadi, A, Mirijello, A, Mirrakhimov, E, Mirzaei, R, Mitchell, P, Mittal, C, Moberg, M, Moghadam Fard, A, Mohajelin, S, Mohamadkhani, A, Mohamed, A, Mohamed, J, Mohamed, M, Mohamed, N, Mohammad, A, Mohammadi, S, Mohammed, H, Mohammed, M, Mohammed, S, Mokdad, A, Molokhia, M, Momani, S, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, M, Montazeri, F, Moodi Ghalibaf, A, Moradi, M, Moradi, Y, Moraga, P, Morawska, L, Moreira, R, Morovatdar, N, Morrison, S, Mosapour, A, Mosser, J, Mossialos, E, Motappa, R, Mougin, V, Mousavi, P, Mrejen, M, Mubarik, S, Mueller, U, Mulita, F, Munjal, K, Murillo-Zamora, E, Musallam, K, Musina, A, Mustafa, G, Myung, W, Nafei, A, Nagarajan, A, Naghavi, P, Naik, G, Nainu, F, Najdaghi, S, Nakhostin Ansari, N, Nangia, V, Narasimha Swamy, S, Nargus, S, Narimani Davani, D, Nascimento, B, Nascimento, G, Naser, A, Nashwan, A, Natto, Z, Nauman, J, Navaratna, S, Naveed, M, Nawsherwan, N, Nayak, B, Nayak, V, Negash, H, Negoi, I, Negoi, R, Nejadghaderi, S, Nejjari, C, Nematollahi, S, Netsere, H, Ng, M, Nguefack-Tsague, G, Ngunjiri, J, Nguyen, A, Nguyen, D, Nguyen, H, Nguyen, N, Nguyen, P, Nguyen, Q, Nguyen, V, Nguyen Tran Minh, D, Niazi, R, Nigatu, Y, Niknam, M, Nikoobar, A, Nikpoor, A, Nikravangolsefid, N, Noman, E, Nomura, S, Noor, S, Noroozi, N, Nouri, M, Nozari, M, Nri-Ezedi, C, Ntaios, G, Nunemo, M, Nurrika, D, Nutor, J, Nzoputam, C, Nzoputam, O, Oancea, B, Obamiro, K, Odetokun, I, Oduro, M, Ogundijo, O, Ogunfowokan, A, Ogunkoya, A, Oguntade, A, Oh, I, Ojo-Akosile, T, Okati-Aliabad, H, Okekunle, A, Okonji, O, Olagunju, A, Olatubi, M, Oliveira, G, Olusanya, B, Olusanya, J, Oluwafemi, Y, Omar, H, Omer, G, Ong, S, Onie, S, Onwujekwe, O, Opejin, A, Ordak, M, Orish, V, Ortiz, A, Ortiz-Prado, E, Osman, W, Ostojic, S, Ostroff, S, Osuagwu, U, Otoiu, A, Otstavnov, S, Ouyahia, A, Owolabi, M, Oyeyemi, O, Ozair, A, P A, M, Padron-Monedero, A, Padubidri, J, Pal, P, Palicz, T, Pan, F, Pan, H, Panda-Jonas, S, Pandey, A, Pando-Robles, V, Pangaribuan, H, Panos, G, Panos, L, Pantazopoulos, I, Pantea Stoian, A, Parikh, R, Park, E, Park, S, Parsons, N, Parthasarathi, A, Pasovic, M, Passera, R, Patel, J, Pathan, A, Patil, S, Patoulias, D, Pawar, S, Pazoki Toroudi, H, Pease, S, Peden, A, Pedersini, P, Pensato, U, Pepito, V, Peprah, P, Pereira, M, Perianayagam, A, Perico, N, Perna, S, Pesudovs, K, Petermann-Rocha, F, Pham, H, Philip, A, Phillips, M, Pigeolet, M, Piradov, M, Pisoni, E, Plotnikov, E, Poddighe, D, Polibin, R, Poluru, R, Ponkilainen, V, Popovic, D, Postma, M, Pour-Rashidi, A, Prabhu, D, Prada, S, Pradhan, J, Pradhan, P, Prashant, A, Prates, E, Priscilla, T, Purnobasuki, H, Purohit, B, Puvvula, J, Qasim, N, Qattea, I, Qazi, A, Qian, G, Rabiee Rad, M, Radhakrishnan, V, Raeisi Shahraki, H, Rafferty, Q, Raggi, A, Raggi, C, Raheem, N, Rahim, F, Rahim, M, Rahimibarghani, S, Rahman, M, Rahman, T, Rahmani, A, Rahmanian, M, Rahmanian, N, Rahmati, R, Rahmawaty, S, Raimondo, D, Raja, A, Rajput, P, Ramadan, M, Ramasamy, S, Ramazanu, S, Ramteke, P, Rana, K, Rana, R, Ranabhat, C, Rane, A, Rao, C, Rao, M, Rasella, D, Rashedi, V, Rashid, A, Rasouli-Saravani, A, Rastogi, P, Rasul, A, Rathish, D, Rathnaiah Babu, G, Rauniyar, S, Ravangard, R, Rawaf, D, Rawaf, S, Raza, R, Redwan, E, Reifels, L, Reitsma, M, Remuzzi, G, Rengasamy, K, Reshmi, B, Resnikoff, S, Restaino, S, Reyes, L, Rezaei, N, Rezaei, Z, Rezaeian, M, Rhee, T, Rickard, J, Robalik, T, Robinson-Oden, H, Rocha, H, Rodrigues, M, Rodriguez, J, Roever, L, Romadlon, D, Ronfani, L, Rony, M, Roshandel, G, Rotimi, K, Rout, H, Roy, B, Rubagotti, E, Ruela, G, Rumisha, S, Runghien, T, Russo, M, Saad, A, Saber, K, Saber-Ayad, M, Sabet, C, Sabour, S, Sachdev, P, Saddler, A, Sadee, B, Sadeghi, M, Saeb, M, Saeed, U, Safi, S, Sagar, R, Saghafi, A, Sagoe, D, Sahebkar, A, Sahoo, P, Sajid, M, Salam, N, Salamati, P, Salami, A, Saleh, M, Salehi, L, Salem, M, Salemcity, A, Salimi, S, Samadi Kafil, H, Samargandy, S, Samodra, Y, Samy, A, Sanabria, J, Sanna, F, Santric-Milicevic, M, Sao Jose, B, Saraswathy, S, Saravanan, A, Sarmiento-Suárez, R, Sarode, G, Sarode, S, Sartorius, B, Satpathy, M, Sayeed, A, Scarmeas, N, Schaarschmidt, B, Schinckus, C, Schuermans, A, Schumacher, A, Schutte, A, Schwebel, D, Schwendicke, F, Selvaraj, S, Semreen, M, Senapati, S, Sengupta, P, Senthilkumaran, S, Serban, D, Sethi, Y, Seylani, A, Shafie, M, Shah, P, Shahbandi, A, Shahid, S, Shahid, W, Shahsavari, H, Shahwan, M, Shaikh, M, Shalash, A, Shamekh, A, Shamim, M, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharfaei, S, Sharifan, A, Sharifi-Rad, J, Sharma, A, Sharma, M, Sharma, S, Sharma, V, Shastry, R, Shayan, M, Shekhar, S, Shenoy, R, Shetty, M, Shetty, P, Shi, P, Shiani, A, Shigematsu, M, Shimels, T, Shiri, R, Shittu, A, Shiue, I, Shivakumar, K, Shool, S, Shorofi, S, Shrestha, S, Shuval, K, Si, Y, Siddig, E, Sidhu, J, Silva, J, Silva, L, Silva, S, Silva, T, Simpson, C, Simpson, K, Singh, A, Singh, B, Singh, H, Singh, J, Singh, P, Skou, S, Smith, G, Sobia, F, Socea, B, Solanki, S, Soleimani, H, Soliman, S, Song, Y, Soyiri, I, Spartalis, M, Spearman, S, Sreeramareddy, C, Stanaway, J, Stanikzai, M, Starodubova, A, Stein, D, Steiner, C, Steiropoulos, P, Stockfelt, L, Stokes, M, Straif, K, Subedi, N, Suliankatchi Abdulkader, R, Sultana, A, Sun, J, Sundström, J, Swain, C, Szarpak, L, Szeto, M, Tabaee Damavandi, P, Tabarés-Seisdedos, R, Tabatabaei Malazy, O, Tabatabaeizadeh, S, Tabatabai, S, Tabb, K, Tabche, C, Tabish, M, Taheri Abkenar, Y, Taheri Soodejani, M, Taiba, J, Talaat, I, Tamuzi, J, Tan, K, Tang, H, Tat, N, Tavakoli Oliaee, R, Tavangar, S, Taveira, N, Tbakhi, A, Tehrani, H, Temsah, M, Teramoto, M, Tesfaye, B, Teye-Kwadjo, E, Thangaraju, P, Thankappan, K, Thapar, R, Thayakaran, R, Thirunavukkarasu, S, Thomas, N, Thygesen, L, Ticoalu, J, Timalsena, D, Tiruye, T, Tiwari, K, Tomo, S, Tonelli, M, Topor-Madry, R, Touvier, M, Tovani-Palone, M, Tran, A, Tran, J, Tran, N, Tran, T, Trico, D, Tromans, S, Truyen, T, Tsatsakis, A, Tsermpini, E, Tumurkhuu, M, Turnock, S, Udoh, A, Ullah, A, Ullah, S, Umakanthan, S, Umar, M, Umar, S, Unim, B, Unnikrishnan, B, Upadhyay, E, Usman, J, Vahdati, S, Vaithinathan, A, Vakili, O, Valizadeh, R, Van den Eynde, J, Vart, P, Varthya, S, Vasankari, T, Vasic, M, Venketasubramanian, N, Veroux, M, Verras, G, Vervoort, D, Vijayageetha, M, Villafañe, J, Vinayak, M, Violante, F, Vladimirov, S, Vlassov, V, Vo, B, Vohra, K, Vos, T, Wadood, A, Waheed, Y, Wang, F, Wang, S, Wang, Y, Wanjau, M, Waqas, M, Ward, P, Waris, A, Wassie, E, Watson, S, Weaver, M, Weerakoon, K, Weintraub, R, Weldetinsaa, H, Wells, K, Wen, Y, Westerman, R, Wiangkham, T, Wickramasinghe, D, Widowati, E, Wojewodzic, M, Woldeyes, D, Wolf, A, Wolfe, C, Wu, C, Wu, D, Wu, F, Wu, J, Wu, Z, Wulf Hanson, S, Xiao, H, Xu, S, Yadav, R, Yamagishi, K, Yang, D, Yano, Y, Yarahmadi, A, Yazdani Nia, I, Ye, P, Yesodharan, R, Yesuf, S, Yezli, S, Yiğit, A, Yiğit, V, Yigzaw, Z, Yin, D, Yip, P, Yonemoto, N, You, Y, Younis, M, Yu, C, Yu, E, Yu, Y, Yuan, C, Yusuf, H, Zafar, U, Zafari, N, Zahid, M, Zakham, F, Zaki, N, Zerfu, T, Zhang, H, Zhang, J, Zhang, L, Zhang, Y, Zhang, Z, Zhao, X, Zhao, Y, Zhao, Z, Zhong, C, Zhou, B, Zhou, J, Zhou, S, Zhu, B, Zhumagaliuly, A, Zielińska, M, Zoghi, G, Zumla, A, Zyoud, S, Smith, A, Murray, C, Vollset, Stein Emil, Ababneh, Hazim S, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbastabar, Hedayat, Abd Al Magied, Abdallah H A, Abd ElHafeez, Samar, Abdelkader, Atef, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdi, Parsa, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abebe, Mesfin, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Absalan, Abdorrahim, Abualruz, Hasan, Abubakar, Bilyaminu, Abukhadijah, Hana Jihad Jihad, Addolorato, Giovanni, Adekanmbi, Victor, Adetunji, Charles Oluwaseun, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adha, Rishan, Adhikary, Ripon Kumar, Adnani, Qorinah Estiningtyas Sakilah, Adzigbli, Leticia Akua, Afrashteh, Fatemeh, Afzal, Muhammad Sohail, Afzal, Saira, Agbozo, Faith, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Shahzaib, Ahmed, Anisuddin, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Safoora, Ahmed, Syed Anees, Akinosoglou, Karolina, Akkaif, Mohammed Ahmed, Akrami, Ashley E, Akter, Ema, Al Awaidy, Salah, Al Hasan, Syed Mahfuz, Al Mosa, Amjad S, Al Ta'ani, Omar, Al Zaabi, Omar Ali Mohammed, Alahdab, Fares, Alajlani, Muaaz M, Al-Ajlouni, Yazan, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore, Alam, Tahiya, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, Aldhaleei, Wafa A, Aldridge, Robert W, Alemohammad, Seyedeh Yasaman, Alemu, Yihun Mulugeta, Al-Gheethi, Adel Ali Saeed, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Amjad, Ali, Iman, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Victor Ekoche, Ali, Waad, Al-Ibraheem, Akram, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Alla, François, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Alqutaibi, Ahmed Yaseen, Alrawashdeh, Ahmad, Alrousan, Sahel Majed, Al-Sabah, Salman Khalifah, Alsabri, Mohammed A, Altaany, Zaid, Al-Tammemi, Ala'a B., Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvis-Guzman, Nelson, Al-Wardat, Mohammad Sami, Al-Worafi, Yaser Mohammed, Aly, Hany, Alyahya, Mohammad Sharif, Alzoubi, Karem H, Al-Zyoud, Walid, Amani, Reza, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amiri, Sohrab, Amirzade-Iranaq, Mohammad Hosein, Amu, Hubert, Amugsi, Dickson A, Ancuceanu, Robert, Anderlini, Deanna, Anderson, David B, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Andrews, Erick Adrian, Anil, Abhishek, Anil, Sneha, Anoushiravani, Amir, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Boluwatife Stephen, Anvari, Saeid, Anyasodor, Anayochukwu Edward, Appiah, Francis, Aquilano, Michele, Arab, Juan Pablo, Arabloo, Jalal, Arafa, Elshaimaa A, Arafat, Mosab, Aravkin, Aleksandr Y, Ardekani, Ali, Areda, Demelash, Aregawi, Brhane Berhe, Aremu, Abdulfatai, Ariffin, Hany, Arkew, Mesay, Armani, Keivan, Artamonov, Anton A, Arumugam, Ashokan, Asghari-Jafarabadi, Mohammad, Ashbaugh, Charlie, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atorkey, Prince, Atout, Maha Moh'd Wahbi, Aujayeb, Avinash, Ausloos, Marcel, Awad, Hamzeh, Awotidebe, Adedapo Wasiu, Ayatollahi, Haleh, Ayuso-Mateos, Jose L, Azadnajafabad, Sina, Azeez, Fahad Khan, Azevedo, Rui M S, Badar, Muhammad, Baghdadi, Soroush, Bagheri, Mahboube, Bagheri, Nasser, Bai, Ruhai, Baker, Jennifer L, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balcha, Wondu Feyisa, Baltatu, Ovidiu Constantin, Barchitta, Martina, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barqawi, Hiba Jawdat, Barteit, Sandra, Basiru, Afisu, Basso, João Diogo, Bastan, Mohammad-Mahdi, Basu, Sanjay, Bauckneht, Matteo, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Behnoush, Amir Hossein, Behzadi, Payam, Beiranvand, Maryam, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bemanalizadeh, Maryam, Bensenor, Isabela M, Benzian, Habib, Beran, Azizullah, Berezvai, Zombor, Bernstein, Robert S, Bettencourt, Paulo J G, Beyene, Kebede A, Beyene, Melak Gedamu, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhandari, Dinesh, Bharadwaj, Ravi, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhargava, Ashish, Bhaskar, Sonu, Bhat, Vivek, Bhattacharjee, Natalia V, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhuiyan, Mohiuddin Ahmed, Bisignano, Catherine, Biswas, Bijit, Bjørge, Tone, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bonakdar Hashemi, Milad, Bora Basara, Berrak, Borhany, Hamed, Bosoka, Samuel Adolf, Botero Carvajal, Alejandro, Bouaoud, Souad, Boufous, Soufiane, Boxe, Christopher, Boyko, Edward J, Brady, Oliver J, Braithwaite, Dejana, Brauer, Michael, Brazo-Sayavera, Javier, Brenner, Hermann, Brown, Colin Stewart, Browne, Annie J, Brugha, Traolach, Bryazka, Dana, Bulamu, Norma B, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Busse, Reinhard, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Çakmak Barsbay, Mehtap, Calina, Daniela, Campos, Luciana Aparecida, Cao, Shujin, Capodici, Angelo, Cárdenas, Rosario, Carreras, Giulia, Carugno, Andrea, Carvalho, Márcia, Castaldelli-Maia, Joao Mauricio, Castelpietra, Giulio, Cattaruzza, Maria Sofia, Caye, Arthur, Cegolon, Luca, Cembranel, Francieli, Cenko, Edina, Cerin, Ester, Chadban, Steven J, Chadwick, Joshua, Chakraborty, Chiranjib, Chakraborty, Sandip, Chalek, Julian, Chan, Jeffrey Shi Kai, Chandika, Rama Mohan, Chandy, Sara, Charan, Jaykaran, Chaudhary, Anis Ahmad, Chaurasia, Akhilanand, Chen, An-Tian, Chen, Haowei, Chen, Meng Xuan, Chen, Simiao, Cherbuin, Nicolas, Chi, Gerald, Chichagi, Fatemeh, Chimed-Ochir, Odgerel, Chimoriya, Ritesh, Ching, Patrick R, Chirinos-Caceres, Jesus Lorenzo, Chitheer, Abdulaal, Cho, Daniel Youngwhan, Cho, William C S, Choi, Dong-Woo, Chong, Bryan, Chong, Chean Lin, Chopra, Hitesh, Chu, Dinh-Toi, Chung, Eric, Chutiyami, Muhammad, Clayton, Justin T, Cogen, Rebecca M, Cohen, Aaron J, Columbus, Alyssa, Comfort, Haley, Conde, Joao, Connolly, Jon T, Cooper, Ezra E K, Cortese, Samuele, Cruz-Martins, Natália, da Silva, Alanna Gomes, Dadras, Omid, Dai, Xiaochen, Dai, Zhaoli, Dalton, Bronte E, Damiani, Giovanni, Dandona, Lalit, Dandona, Rakhi, Das, Jai K, Das, Saswati, Das, Subasish, Dash, Nihar Ranjan, Davletov, Kairat, De la Hoz, Fernando Pio, De Leo, Diego, Debopadhaya, Shayom, Delgado-Enciso, Ivan, Denova-Gutiérrez, Edgar, Dervenis, Nikolaos, Desai, Hardik Dineshbhai, Devanbu, Vinoth Gnana Chellaiyan, Dewan, Syed Masudur Rahman, Dhama, Kuldeep, Dhane, Amol S, Dhingra, Sameer, Dias da Silva, Diana, Diaz, Daniel, Diaz, Luis Antonio, Diaz, Michael J, Dima, Adriana, Ding, Delaney D, Do, Thao Huynh Phuong, do Prado, Camila Bruneli, Dodangeh, Masoud, Dodangeh, Milad, Doegah, Phidelia Theresa, Dohare, Sushil, Dong, Wanyue, D'Oria, Mario, Doshi, Rajkumar, Dowou, Robert Kokou, Dsouza, Haneil Larson, Dsouza, Viola, Dube, John, Dumith, Samuel C, Duncan, Bruce B, Duraes, Andre Rodrigues, Duraisamy, Senbagam, Durojaiye, Oyewole Christopher, Dushpanova, Anar, Dutta, Sulagna, Dzianach, Paulina Agnieszka, Dziedzic, Arkadiusz Marian, Eboreime, Ejemai, Ebrahimi, Alireza, Ebrahimi Kalan, Mohammad, Edinur, Hisham Atan, Efendi, Ferry, Eikemo, Terje Andreas, Eini, Ebrahim, Ekundayo, Temitope Cyrus, El Arab, Rabie Adel, El Sayed, Iman, Elamin, Osman, Elemam, Noha Mousaad, ElGohary, Ghada Metwally Tawfik, Elhadi, Muhammed, Elmeligy, Omar Abdelsadek Abdou, Elmoselhi, Adel B, Elshaer, Mohammed, Elsohaby, Ibrahim, Eltahir, Mohd. Elmagzoub, Emeto, Theophilus I, Eshrati, Babak, Eslami, Majid, Esmaeili, Zahra, Fabin, Natalia, Fagbamigbe, Adeniyi Francis, Fagbule, Omotayo Francis, Falzone, Luca, Fareed, Mohammad, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Faro, Andre, Fasihi, Kiana, Fatehizadeh, Ali, Fauk, Nelsensius Klau, Fazylov, Timur, Feigin, Valery L, Fekadu, Ginenus, Feng, Xiaoqi, Fereshtehnejad, Seyed-Mohammad, Ferrara, Pietro, Ferreira, Nuno, Firew, Belete Sewasew, Fischer, Florian, Fitriana, Ida, Flavel, Joanne, Flor, Luisa S, Folayan, Morenike Oluwatoyin, Foley, Kristen Marie, Fonzo, Marco, Force, Lisa M, Foschi, Matteo, Freitas, Alberto, Fridayani, Ni Kadek Yuni, Fukutaki, Kai Glenn, Furtado, João M, Fux, Blima, Gaal, Peter Andras, Gadanya, Muktar A, Gallus, Silvano, Ganesan, Balasankar, Ganiyani, Mohammad Arfat, Gautam, Rupesh K, Gebi, Tilaye Gebru, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Getacher, Lemma, Getahun, Genanew K A, Gething, Peter W, Ghadimi, Delaram J, Ghadirian, Fataneh, Ghafarian, Sadegh, Ghailan, Khalid Yaser, Ghasemi, MohammadReza, Ghasempour Dabaghi, Ghazal, Ghazy, Ramy Mohamed, Ghoba, Sama, Gholami, Ehsan, Gholamrezanezhad, Ali, Gholizadeh, Nasim, Ghorbani, Mahsa, Ghorbani Vajargah, Pooyan, Ghotbi, Elena, Gil, Artyom Urievich, Gill, Tiffany K, Girmay, Alem, Glasbey, James C, Glushkova, Ekaterina Vladimirovna, Gnedovskaya, Elena V, Göbölös, Laszlo, Goldust, Mohamad, Goleij, Pouya, Golinelli, Davide, Gopalani, Sameer Vali, Goulart, Alessandra C, Gouravani, Mahdi, Goyal, Anmol, Grivna, Michal, Grosso, Giuseppe, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Guicciardi, Stefano, Guimarães, Rafael Alves, Gulati, Snigdha, Gulisashvili, David, Gunawardane, Damitha Asanga, Guo, Cui, Gupta, Anish Kumar, Gupta, Rahul, Gupta, Rajeev, Gupta, Renu, Gupta, Sapna, Gupta, Vijai Kumar, Haakenstad, Annie, Hadi, Najah R, Haep, Nils, Hafiz, Abdul, Haghmorad, Dariush, Haile, Demewoz, Hajj Ali, Adel, Hajj Ali, Ali, Haj-Mirzaian, Arvin, Halboub, Esam S, Haller, Sebastian, Halwani, Rabih, Hamagharib Abdullah, Kanaan, Hamdy, Nadia M, Hamoudi, Rifat, Hanifi, Nasrin, Hankey, Graeme J, Haq, Zaim Anan, Haque, Md Rabiul, Harapan, Harapan, Hargono, Arief, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, S. M. Mahmudul, Hasanian, Mohammad, Hasnain, Md Saquib, Hassan, Amr, Haubold, Johannes, Hay, Simon I, Hebert, Jeffrey J, Hegazi, Omar E, Heidari, Mohammad, Hemmati, Mehdi, Henson, Claire A, Herrera-Serna, Brenda Yuliana, Herteliu, Claudiu, Heydari, Majid, Hezam, Kamal, Hidayana, Irma, Hiraike, Yuta, Hoan, Nguyen Quoc, Holla, Ramesh, Hoogar, Praveen, Horita, Nobuyuki, Hossain, Md Mahbub, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hu, Chengxi, Huang, Junjie, Hultström, Michael, Hundie, Tsegaye Gebreyes, Hunt, Aliza J, Hushmandi, Kiavash, Hussain, Javid, Hussain, M. Azhar, Hussein, Nawfal R, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Iftikhar, Pulwasha Maria, Ikiroma, Adalia I, Ikwegbue, Paul Chukwudi, Ilic, Irena M, Ilic, Milena D, Immurana, Mustapha, Isa, Mustafa Alhaji, Islam, Md. Rabiul, Islam, Sheikh Mohammed Shariful, Ismail, Faisal, Ismail, Nahlah Elkudssiah, Isola, Gaetano, Iwagami, Masao, Iyamu, Ihoghosa Osamuyi, Jacob, Louis, Jacobsen, Kathryn H, Jafarinia, Morteza, Jahankhani, Kasra, Jahanmehr, Nader, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jakhmola Mani, Dr Ruchi, Jamil, Safayet, Jamora, Roland Dominic G, Jatau, Abubakar Ibrahim, Javadov, Sabzali, Javaheri, Tahereh, Jayaram, Shubha, Jee, Sun Ha, Jeganathan, Jayakumar, Jiang, Heng, Jokar, Mohammad, Jonas, Jost B, Joseph, Nitin, Joshua, Charity Ehimwenma, Jürisson, Mikk, K, Vaishali, Kabir, Ali, Kabir, Zubair, Kadashetti, Vidya, Kalankesh, Laleh R, Kalra, Sanjay, Kamath, Ashwin, Kamath, Rajesh, Kamireddy, Arun, Kanaan, Mona, Kanchan, Tanuj, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kansal, Sushil Kumar, Karim, Asima, Karkhah, Samad, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassel, Molly B, Katikireddi, Srinivasa Vittal, Kauppila, Joonas H, Kaur, Harkiran, Kayode, Gbenga A, Kazemi, Foad, Kazemian, Sina, Kebede, Fassikaw, Kendal, Evie Shoshannah, Kesse-Guyot, Emmanuelle, Khademvatan, Shahram, Khajuria, Himanshu, Khalaji, Amirmohammad, Khalid, Asaad, Khalid, Nauman, Khalilian, Alireza, Khamesipour, Faham, Khan, Fayaz, Khan, Mohammad Jobair, Khan, Moien AB, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khatib, Mahalaqua Nazli, Khayat Kashani, Hamid Reza, Kheirallah, Khalid A, Khokhar, Manoj, Khormali, Moein, Khorrami, Zahra, Khosla, Atulya Aman, Khosravi, Majid, Khosrowjerdi, Mahmood, Khubchandani, Jagdish, Kifle, Zemene Demelash, Kim, Grace, Kim, Julie Sojin, Kim, Min Seo, Kim, Yun Jin, Kimokoti, Ruth W, Kisa, Adnan, Kisa, Sezer, Knibbs, Luke D, Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kolahi, Ali-Asghar, Kompani, Farzad, Koren, Gerbrand, Korzh, Oleksii, Krishan, Kewal, Krishna, Varun, Krishnamoorthy, Vijay, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kuitunen, Ilari, Kujan, Omar, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, G Anil, Kumar, Harish, Kumar, Nithin, Kumar, Rakesh, Kumar, Vijay, Kundu, Amartya, Kusuma, Dian, Kyei-Arthur, Frank, Kytö, Ville, Kyu, Hmwe Hmwe, La Vecchia, Carlo, Lacey, Ben, Ladan, Muhammad Awwal, Laflamme, Lucie, Lahariya, Chandrakant, Lai, Daphne Teck Ching, Lalloo, Ratilal, Lallukka, Tea, Lám, Judit, Lan, Qing, Lan, Tuo, Landires, Iván, Lanfranchi, Francesco, Langguth, Berthold, Lansingh, Van Charles, Laplante-Lévesque, Ariane, Larijani, Bagher, Larsson, Anders O, Lasrado, Savita, Lauriola, Paolo, Lawlor, Hilary R, Le, Huu-Hoai, Le, Long Khanh Dao, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Le, Trang Diep Thanh, Leasher, Janet L, Lee, Doo Woong, Lee, Munjae, Lee, Paul H, Lee, Sang-woong, Lee, Seung Won, Lee, Shaun Wen Huey, Lee, Yo Han, Leigh, James, Leong, Elvynna, Li, Ming-Chieh, Libra, Massimo, Ligade, Virendra S, Lim, Lee-Ling, Lim, Stephen S, Limenh, Liknaw Workie, Lindholm, Daniel, Lindstedt, Paulina A, Listl, Stefan, Liu, Gang, Liu, Shiwei, Liu, Shuke, Liu, Xiaofeng, Liu, Xuefeng, Llanaj, Erand, López-Bueno, Rubén, López-Gil, José Francisco, Loreche, Arianna Maever, Lotufo, Paulo A, Lozano, Rafael, Lubinda, Jailos, Lucchetti, Giancarlo, Luo, Lisha, Lusk, Jay B, Lv, Lei, M Amin, Hawraz Ibrahim, Ma, Zheng Feei, Maass, Kelsey Lynn, Machairas, Nikolaos, Machoy, Monika, Madureira-Carvalho, Áurea M, Magdy Abd El Razek, Hassan, Maghazachi, Azzam A, Mahadeshwara Prasad, D. R., Mahalleh, Mehrdad, Mahasha, Phetole Walter, Mahmoud, Mansour Adam, Mahmoudi, Elham, Mahmoudvand, Golnaz, Makama, Maureen, Malakan Rad, Elaheh, Malhotra, Kashish, Malik, Ahmad Azam, Malta, Deborah Carvalho, Manla, Yosef, Mansour, Ali, Mansouri, Mohammad Hadi, Mansouri, Pejman, Mansouri, Vahid, Mansourian, Marjan, Mansournia, Mohammad Ali, Marasini, Bishnu P, Marateb, Hamid Reza, Maravilla, Joemer C, Mardi, Parham, Marjani, Abdoljalal, Markazi Moghadam, Hamed, Marrugo Arnedo, Carlos Alberto, Martinez, Gabriel, Martinez-Piedra, Ramon, Martins-Melo, Francisco Rogerlândio, Martorell, Miquel, Marx, Wolfgang, Marzo, Roy Rillera, Masoudi, Sahar, Mathangasinghe, Yasith, Mathioudakis, Alexander G, Mathur, Medha, Mathur, Navgeet, Mathur, Neeta, Matozinhos, Fernanda Penido, Mattumpuram, Jishanth, Maude, Richard James, Maugeri, Andrea, Mayeli, Mahsa, Mazidi, Mohsen, Mazzotti, Antonio, McGrath, John J, McKee, Martin, McKowen, Anna Laura W, McPhail, Michael A, McPhail, Steven M, Mehmood, Asim, Mehrabani-Zeinabad, Kamran, Mehravar, Sepideh, Mekene Meto, Tesfahun, Melese, Endalkachew Belayneh, Mendez-Lopez, Max Alberto Mendez, Mendoza, Walter, Menezes, Ritesh G, Mensah, George A, Mensah, Laverne G, Mentis, Alexios-Fotios A, Meo, Sultan Ayoub, Meretoja, Atte, Meretoja, Tuomo J, Mersha, Abera M, Mestrovic, Tomislav, Mettananda, Kukulege Chamila Dinushi, Mettananda, Sachith, Mhlanga, Adquate, Mhlanga, Laurette, Miazgowski, Tomasz, Michalek, Irmina Maria, Micheletti Gomide Nogueira de Sá, Ana Carolina, Miller, Ted R, Minh, Le Huu Nhat, Mirahmadi, Alireza, Mirijello, Antonio, Mirrakhimov, Erkin M, Mirzaei, Roya, Mitchell, Philip B, Mittal, Chaitanya, Moberg, Madeline E, Moghadam Fard, Atousa, Mohajelin, Seyedehfatemeh, Mohamadkhani, Ashraf, Mohamed, Ahmed Ismail, Mohamed, Jama, Mohamed, Mouhand F H, Mohamed, Nouh Saad, Mohammad, Ameen Mosa, Mohammadi, Soheil, Mohammed, Hussen, Mohammed, Mustapha, Mohammed, Shafiu, Mokdad, Ali H, Molokhia, Mariam, Momani, Shaher Mohammad, Momtazmanesh, Sara, Monasta, Lorenzo, Mondello, Stefania, Moni, Mohammad Ali, Montazeri, Fateme, Moodi Ghalibaf, AmirAli, Moradi, Maryam, Moradi, Yousef, Moraga, Paula, Morawska, Lidia, Moreira, Rafael Silveira, Morovatdar, Negar, Morrison, Shane Douglas, Mosapour, Abbas, Mosser, Jonathan F, Mossialos, Elias, Motappa, Rohith, Mougin, Vincent, Mousavi, Parsa, Mrejen, Matías, Mubarik, Sumaira, Mueller, Ulrich Otto, Mulita, Francesk, Munjal, Kavita, Murillo-Zamora, Efrén, Musallam, Khaled M, Musina, Ana-Maria, Mustafa, Ghulam, Myung, Woojae, Nafei, Ayoub, Nagarajan, Ahamarshan Jayaraman, Naghavi, Pirouz, Naik, Ganesh R, Naik, Gurudatta, Nainu, Firzan, Najdaghi, Soroush, Nakhostin Ansari, Noureddin, Nangia, Vinay, Narasimha Swamy, Sreenivas, Nargus, Shumaila, Narimani Davani, Delaram, Nascimento, Bruno Ramos, Nascimento, Gustavo G, Naser, Abdallah Y, Nashwan, Abdulqadir J, Natto, Zuhair S, Nauman, Javaid, Navaratna, Samidi N K, Naveed, Muhammad, Nawsherwan, null, Nayak, Biswa Prakash, Nayak, Vinod C, Negash, Hadush, Negoi, Ionut, Negoi, Ruxandra Irina, Nejadghaderi, Seyed Aria, Nejjari, Chakib, Nematollahi, Soroush, Netsere, Henok Biresaw, Ng, Marie, Nguefack-Tsague, Georges, Ngunjiri, Josephine W, Nguyen, Anh Hoang, Nguyen, Dang H, Nguyen, Duc Hoang, Nguyen, Hau Thi Hien, Nguyen, Nhan, Nguyen, Nhien Ngoc Y, Nguyen, Phat Tuan, Nguyen, QuynhAnh P, Nguyen, Van Thanh, Nguyen Tran Minh, Duc, Niazi, Robina Khan, Nigatu, Yeshambel T, Niknam, Mahdieh, Nikoobar, Ali, Nikpoor, Amin Reza, Nikravangolsefid, Nasrin, Noman, Efaq Ali, Nomura, Shuhei, Noor, Syed Toukir Ahmed, Noroozi, Nafise, Nouri, Mehran, Nozari, Majid, Nri-Ezedi, Chisom Adaobi, Ntaios, George, Nunemo, Mengistu H, Nurrika, Dieta, Nutor, Jerry John, Nzoputam, Chimezie Igwegbe, Nzoputam, Ogochukwu Janet, Oancea, Bogdan, Obamiro, Kehinde O, Odetokun, Ismail A, Oduro, Michael Safo, Ogundijo, Oluwaseun Adeolu, Ogunfowokan, Adesola Adenike, Ogunkoya, Abiola, Oguntade, Ayodipupo Sikiru, Oh, In-Hwan, Ojo-Akosile, Tolulope R, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okonji, Osaretin Christabel, Olagunju, Andrew T, Olatubi, Matthew Idowu, Oliveira, Gláucia Maria Moraes, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Oluwafemi, Yinka Doris, Omar, Hany A, Omer, Goran Latif, Ong, Sokking, Onie, Sandersan, Onwujekwe, Obinna E, Opejin, Abdulahi Opejin, Ordak, Michal, Orish, Verner N, Ortiz, Alberto, Ortiz-Prado, Esteban, Osman, Wael M S, Ostojic, Sergej M, Ostroff, Samuel M, Osuagwu, Uchechukwu Levi, Otoiu, Adrian, Otstavnov, Stanislav S, Ouyahia, Amel, Owolabi, Mayowa O, Oyeyemi, Oyetunde T, Ozair, Ahmad, P A, Mahesh Padukudru, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pal, Pramod Kumar, Palicz, Tamás, Pan, Feng, Pan, Hai-Feng, Panda-Jonas, Songhomitra, Pandey, Anamika, Pando-Robles, Victoria, Pangaribuan, Helena Ullyartha, Panos, Georgios D, Panos, Leonidas D, Pantazopoulos, Ioannis, Pantea Stoian, Anca Mihaela, Parikh, Romil R, Park, Eun-Kee, Park, Seoyeon, Park, Sungchul, Parsons, Nicholas, Parthasarathi, Ashwaghosha, Pasovic, Maja, Passera, Roberto, Patel, Jay, Pathan, Aslam Ramjan, Patil, Shankargouda, Patoulias, Dimitrios, Pawar, Shrikant, Pazoki Toroudi, Hamidreza, Pease, Spencer A, Peden, Amy E, Pedersini, Paolo, Pensato, Umberto, Pepito, Veincent Christian Filipino, Peprah, Prince, Pereira, Marcos, Pereira, Maria Odete, Perianayagam, Arokiasamy, Perico, Norberto, Perna, Simone, Pesudovs, Konrad, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Philip, Anil K, Phillips, Michael R, Pigeolet, Manon, Piradov, Michael A, Pisoni, Enrico, Plotnikov, Evgenii, Poddighe, Dimitri, Polibin, Roman V, Poluru, Ramesh, Ponkilainen, Ville T, Popovic, Djordje S, Postma, Maarten J, Pour-Rashidi, Ahmad, Prabhu, Disha, Prada, Sergio I, Pradhan, Jalandhar, Pradhan, Pranil Man Singh, Prashant, Akila, Prates, Elton Junio Sady, Priscilla, Tina, Purnobasuki, Hery, Purohit, Bharathi M, Puvvula, Jagadeesh, Qasim, Nameer Hashim, Qattea, Ibrahim, Qazi, Asma Saleem, Qian, Gangzhen, Rabiee Rad, Mehrdad, Radhakrishnan, Venkatraman, Raeisi Shahraki, Hadi, Rafferty, Quinn, Raggi, Alberto, Raggi, Cat, Raheem, Nasiru, Rahim, Fakher, Rahim, Md Jillur, Rahimibarghani, Sarvenaz, Rahman, Md Mijanur Mijanur Rahman, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahman, Tafhimur, Rahmani, Amir Masoud, Rahmanian, Mohammad, Rahmanian, Nazanin, Rahmati, Rahem, Rahmawaty, Setyaningrum, Raimondo, Diego, Raja, Adarsh, Rajput, Prashant, Ramadan, Majed, Ramasamy, Shakthi Kumaran, Ramazanu, Sheena, Ramteke, Pramod W, Rana, Kritika, Rana, Rishabh Kumar, Ranabhat, Chhabi Lal, Rane, Amey, Rao, Chythra R, Rao, Mithun, Rasella, Davide, Rashedi, Vahid, Rashid, Ahmed Mustafa, Rasouli-Saravani, Ashkan, Rastogi, Prateek, Rasul, Azad, Rathish, Devarajan, Rathnaiah Babu, Giridhara, Rauniyar, Santosh Kumar, Ravangard, Ramin, Rawaf, David Laith, Rawaf, Salman, Raza, Rabail Zehra, Redwan, Elrashdy Moustafa Mohamed, Reifels, Lennart, Reitsma, Marissa B, Remuzzi, Giuseppe, Rengasamy, Kannan RR, Reshmi, Bhageerathy, Resnikoff, Serge, Restaino, Stefano, Reyes, Luis Felipe, Rezaei, Nazila, Rezaei, Negar, Rezaei, Zahra Sadat, Rezaeian, Mohsen, Rhee, Taeho Gregory, Rickard, Jennifer, Robalik, Toshana, Robinson-Oden, Hannah Elizabeth, Rocha, Hermano Alexandre Lima, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Romadlon, Debby Syahru, Ronfani, Luca, Rony, Moustaq karim khan, Roshandel, Gholamreza, Rotimi, Kunle, Rout, Himanshu Sekhar, Roy, Bedanta, Rubagotti, Enrico, Ruela, Guilherme de Andrade, Rumisha, Susan Fred, Runghien, Tilleye, Russo, Michele, Saad, Aly M A, Saber, Korosh, Saber-Ayad, Maha Mohamed, Sabet, Cameron John, Sabour, Siamak, Sachdev, Perminder S, Saddler, Adam, Sadee, Bashdar Abuzed, Sadeghi, Masoumeh, Saeb, Mohammad Reza, Saeed, Umar, Safi, Sher Zaman, Sagar, Rajesh, Saghafi, Alireza, Sagoe, Dominic, Sahebkar, Amirhossein, Sahoo, Pragyan Monalisa, Sajid, Mirza Rizwan, Salam, Nasir, Salamati, Payman, Salami, Afeez Abolarinwa, Saleh, Mohamed A, Salehi, Leili, Salem, Marwa Rashad, Salemcity, Aanuoluwa James, Salimi, Sohrab, Samadi Kafil, Hossein, Samargandy, Saad, Samodra, Yoseph Leonardo, Samy, Abdallah M, Sanabria, Juan, Sanna, Francesca, Santric-Milicevic, Milena M, Sao Jose, Bruno Piassi, Saraswathy, Sivan Yegnanarayana Iyer, Saravanan, Aswini, Sarmiento-Suárez, Rodrigo, Sarode, Gargi Sachin, Sarode, Sachin C, Sartorius, Benn, Satpathy, Maheswar, Sayeed, Abu, Scarmeas, Nikolaos, Schaarschmidt, Benedikt Michael, Schinckus, Christophe, Schuermans, Art, Schumacher, Austin E, Schutte, Aletta Elisabeth, Schwebel, David C, Schwendicke, Falk, Selvaraj, Siddharthan, Semreen, Mohammad H, Senapati, Sabyasachi, Sengupta, Pallav, Senthilkumaran, Subramanian, Serban, Dragos, Sethi, Yashendra, Seylani, Allen, Shafie, Mahan, Shah, Pritik A, Shahbandi, Ataollah, Shahid, Samiah, Shahid, Wajeehah, Shahsavari, Hamid R, Shahwan, Moyad Jamal, Shaikh, Masood Ali, Shalash, Ali S, Shamekh, Ali, Shamim, Muhammad Aaqib, Shanawaz, Mohd, Shankar, Abhishek, Shannawaz, Mohammed, Sharath, Medha, Sharfaei, Sadaf, Sharifan, Amin, Sharifi-Rad, Javad, Sharma, Anupam, Sharma, Manoj, Sharma, Saurab, Sharma, Vishal, Shastry, Rajesh P, Shayan, Maryam, Shekhar, Shashank, Shenoy, Rekha R, Shetty, Mahabalesh, Shetty, Pavanchand H, Shetty, Premalatha K, Shi, Peilin, Shiani, Amir, Shigematsu, Mika, Shimels, Tariku, Shiri, Rahman, Shittu, Aminu, Shiue, Ivy, Shivakumar, K M, Shool, Sina, Shorofi, Seyed Afshin, Shrestha, Sunil, Shuval, Kerem, Si, Yafei, Siddig, Emmanuel Edwar, Sidhu, Jaspreet Kaur, Silva, João Pedro, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Silva, Thales Philipe R, Simpson, Colin R, Simpson, Kyle E, Singh, Abhinav, Singh, Balbir Bagicha, Singh, Baljinder, Singh, Harmanjit, Singh, Jasbir, Singh, Paramdeep, Singh, Puneetpal, Skou, Søren T, Smith, Georgia, Sobia, Farrukh, Socea, Bogdan, Solanki, Shipra, Soleimani, Hamidreza, Soliman, Sameh S M, Song, Yi, Soyiri, Ireneous N, Spartalis, Michael, Spearman, Sandra, Sreeramareddy, Chandrashekhar T, Stanaway, Jeffrey D, Stanikzai, Muhammad Haroon, Starodubova, Antonina V, Stein, Dan J, Steiner, Caitlyn, Steiropoulos, Paschalis, Stockfelt, Leo, Stokes, Mark A, Straif, Kurt, Subedi, Narayan, Suliankatchi Abdulkader, Rizwan, Sultana, Abida, Sun, Jing, Sundström, Johan, Swain, Chandan Kumar, Szarpak, Lukasz, Szeto, Mindy D, Tabaee Damavandi, Payam, Tabarés-Seisdedos, Rafael, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabb, Karen M, Tabche, Celine, Tabish, Mohammad, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Taiba, Jabeen, Talaat, Iman M, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tat, Nathan Y, Tavakoli Oliaee, Razieh, Tavangar, Seyed Mohammad, Taveira, Nuno, Tbakhi, Abdelghani, Tehrani, Hadi, Temsah, Mohamad-Hani, Teramoto, Masayuki, Tesfaye, Behailu Terefe, Teye-Kwadjo, Enoch, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapar, Rekha, Thayakaran, Rasiah, Thirunavukkarasu, Sathish, Thomas, Nihal, Thygesen, Lau Caspar, Ticoalu, Jansje Henny Vera, Timalsena, Dinesh, Tiruye, Tenaw Yimer, Tiwari, Krishna, Tomo, Sojit, Tonelli, Marcello, Topor-Madry, Roman, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Tran, An Thien, Tran, Jasmine T, Tran, Nghia Minh, Tran, Thang Huu, Trico, Domenico, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Turnock, Steven T, Udoh, Arit, Ullah, Atta, Ullah, Saeed, Ullah, Sana, Umakanthan, Srikanth, Umar, Muhammad, Umar, Shehu Salihu, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Usman, Jibrin Sammani, Vahdati, Sanaz, Vaithinathan, Asokan Govindaraj, Vakili, Omid, Valizadeh, Rohollah, Van den Eynde, Jef, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vasic, Milena, Venketasubramanian, Narayanaswamy, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Vijayageetha, Mathavaswami, Villafañe, Jorge Hugo, Vinayak, Manish, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Vohra, Karn, Vos, Theo, Wadood, Abdul Wadood, Waheed, Yasir, Wang, Fang, Wang, Shaopan, Wang, Shu, Wang, Yanqing, Wang, Yanzhong, Wang, Yuan-Pang, Wanjau, Mary Njeri, Waqas, Muhammad, Ward, Paul, Waris, Abdul, Wassie, Emebet Gashaw, Watson, Stefanie, Weaver, Marcia R, Weerakoon, Kosala Gayan, Weintraub, Robert G, Weldetinsaa, Haftom Legese Legese, Wells, Katherine M, Wen, Yi Feng, Westerman, Ronny, Wiangkham, Taweewat, Wickramasinghe, Dakshitha Praneeth, Widowati, Evi, Wojewodzic, Marcin W, Woldeyes, Dawit Habte, Wolf, Axel Walter, Wolfe, Charles D A, Wu, Chenkai, Wu, Dongze, Wu, Felicia, Wu, Jiayuan, Wu, Zenghong, Wulf Hanson, Sarah, Xiao, Hong, Xu, Suowen, Yadav, Rakesh, Yamagishi, Kazumasa, Yang, Danting, Yano, Yuichiro, Yarahmadi, Amir, Yazdani Nia, Iman, Ye, Pengpeng, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yezli, Saber, Yiğit, Arzu, Yiğit, Vahit, Yigzaw, Zeamanuel Anteneh, Yin, Dehui, Yip, Paul, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yu, Chuanhua, Yu, Elaine A, Yu, Yong, Yuan, Chun-Wei, Yusuf, Hadiza, Zafar, Uzma, Zafari, Nima, Zahid, Mondal Hasan, Zakham, Fathiah, Zaki, Nazar, Zerfu, Taddese Alemu, Zhang, Haijun, Zhang, Jingya, Zhang, Liqun, Zhang, Yunquan, Zhang, Zhiqiang, Zhao, Xiu-Ju George, Zhao, Yang, Zhao, Zhongyi, Zhong, Chenwen, Zhou, Bolun, Zhou, Juexiao, Zhou, Shangcheng, Zhu, Bin, Zhumagaliuly, Abzal, Zielińska, Magdalena, Zoghi, Ghazal, Zumla, Alimuddin, Zyoud, Sa'ed H, Zyoud, Samer H, Smith, Amanda E, Murray, Christopher J L, Vollset, S, Ababneh, H, Abate, Y, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, A, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, R, Abolhassani, H, Abouzid, M, Aboye, G, Abreu, L, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, H, Addolorato, G, Adekanmbi, V, Adetunji, C, Adetunji, J, Adeyeoluwa, T, Adha, R, Adhikary, R, Adnani, Q, Adzigbli, L, Afrashteh, F, Afzal, M, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, B, Ahlstrom, A, Ahmad, A, Ahmad, F, Ahmad, M, Ahmad, S, Ahmed, A, Ahmed, H, Ahmed, S, Akinosoglou, K, Akkaif, M, Akrami, A, Akter, E, Al Awaidy, S, Al Hasan, S, Al Mosa, A, Al Ta'ani, O, Al Zaabi, O, Alahdab, F, Alajlani, M, Al-Ajlouni, Y, Alalalmeh, S, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-amer, R, Alanezi, F, Alanzi, T, Albakri, A, Aldhaleei, W, Aldridge, R, Alemohammad, S, Alemu, Y, Al-Gheethi, A, Al-Hanawi, M, Ali, A, Ali, I, Ali, M, Ali, R, Ali, S, Ali, V, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, S, Aljunid, S, Alla, F, Almazan, J, Al-Mekhlafi, H, Alqutaibi, A, Alrawashdeh, A, Alrousan, S, Al-Sabah, S, Alsabri, M, Altaany, Z, Al-Tammemi, A, Al-Tawfiq, J, Altirkawi, K, Aluh, D, Alvis-Guzman, N, Al-Wardat, M, Al-Worafi, Y, Aly, H, Alyahya, M, Alzoubi, K, Al-Zyoud, W, Amani, R, Ameyaw, E, Amin, T, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, M, Amu, H, Amugsi, D, Ancuceanu, R, Anderlini, D, Anderson, D, Andrade, P, Andrei, C, Andrei, T, Andrews, E, Anil, A, Anil, S, Anoushiravani, A, Antony, C, Antriyandarti, E, Anuoluwa, B, Anvari, S, Anyasodor, A, Appiah, F, Aquilano, M, Arab, J, Arabloo, J, Arafa, E, Arafat, M, Aravkin, A, Ardekani, A, Areda, D, Aregawi, B, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, A, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, S, Atorkey, P, Atout, M, Aujayeb, A, Ausloos, M, Awad, H, Awotidebe, A, Ayatollahi, H, Ayuso-Mateos, J, Azadnajafabad, S, Azeez, F, Azevedo, R, Badar, M, Baghdadi, S, Bagheri, M, Bagheri, N, Bai, R, Baker, J, Bako, A, Balakrishnan, S, Balcha, W, Baltatu, O, Barchitta, M, Bardideh, E, Barker-Collo, S, Bärnighausen, T, Barqawi, H, Barteit, S, Basiru, A, Basso, J, Bastan, M, Basu, S, Bauckneht, M, Baune, B, Bayati, M, Bayileyegn, N, Behnoush, A, Behzadi, P, Beiranvand, M, Bello, O, Belo, L, Beloukas, A, Bemanalizadeh, M, Bensenor, I, Benzian, H, Beran, A, Berezvai, Z, Bernstein, R, Bettencourt, P, Beyene, K, Beyene, M, Bhagat, D, Bhagavathula, A, Bhala, N, Bhandari, D, Bharadwaj, R, Bhardwaj, N, Bhardwaj, P, Bhargava, A, Bhaskar, S, Bhat, V, Bhattacharjee, N, Bhatti, G, Bhatti, J, Bhatti, M, Bhuiyan, M, Bisignano, C, Biswas, B, Bjørge, T, Bodolica, V, Bodunrin, A, Bonakdar Hashemi, M, Bora Basara, B, Borhany, H, Bosoka, S, Botero Carvajal, A, Bouaoud, S, Boufous, S, Boxe, C, Boyko, E, Brady, O, Braithwaite, D, Brauer, M, Brazo-Sayavera, J, Brenner, H, Brown, C, Browne, A, Brugha, T, Bryazka, D, Bulamu, N, Buonsenso, D, Burkart, K, Burns, R, Busse, R, Bustanji, Y, Butt, Z, Caetano dos Santos, F, Çakmak Barsbay, M, Calina, D, Campos, L, Cao, S, Capodici, A, Cárdenas, R, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, J, Castelpietra, G, Cattaruzza, M, Caye, A, Cegolon, L, Cembranel, F, Cenko, E, Cerin, E, Chadban, S, Chadwick, J, Chakraborty, C, Chakraborty, S, Chalek, J, Chan, J, Chandika, R, Chandy, S, Charan, J, Chaudhary, A, Chaurasia, A, Chen, A, Chen, H, Chen, M, Chen, S, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, R, Ching, P, Chirinos-Caceres, J, Chitheer, A, Cho, D, Cho, W, Choi, D, Chong, B, Chong, C, Chopra, H, Chu, D, Chung, E, Chutiyami, M, Clayton, J, Cogen, R, Cohen, A, Columbus, A, Comfort, H, Conde, J, Connolly, J, Cooper, E, Cortese, S, Cruz-Martins, N, da Silva, A, Dadras, O, Dai, X, Dai, Z, Dalton, B, Damiani, G, Dandona, L, Dandona, R, Das, J, Das, S, Dash, N, Davletov, K, De la Hoz, F, De Leo, D, Debopadhaya, S, Delgado-Enciso, I, Denova-Gutiérrez, E, Dervenis, N, Desai, H, Devanbu, V, Dewan, S, Dhama, K, Dhane, A, Dhingra, S, Dias da Silva, D, Diaz, D, Diaz, L, Diaz, M, Dima, A, Ding, D, Do, T, do Prado, C, Dodangeh, M, Doegah, P, Dohare, S, Dong, W, D'Oria, M, Doshi, R, Dowou, R, Dsouza, H, Dsouza, V, Dube, J, Dumith, S, Duncan, B, Duraes, A, Duraisamy, S, Durojaiye, O, Dushpanova, A, Dutta, S, Dzianach, P, Dziedzic, A, Eboreime, E, Ebrahimi, A, Ebrahimi Kalan, M, Edinur, H, Efendi, F, Eikemo, T, Eini, E, Ekundayo, T, El Arab, R, El Sayed, I, Elamin, O, Elemam, N, Elgohary, G, Elhadi, M, Elmeligy, O, Elmoselhi, A, Elshaer, M, Elsohaby, I, Eltahir, M, Emeto, T, Eshrati, B, Eslami, M, Esmaeili, Z, Fabin, N, Fagbamigbe, A, Fagbule, O, Falzone, L, Fareed, M, Farinha, C, Faris, M, Faro, A, Fasihi, K, Fatehizadeh, A, Fauk, N, Fazylov, T, Feigin, V, Fekadu, G, Feng, X, Fereshtehnejad, S, Ferrara, P, Ferreira, N, Firew, B, Fischer, F, Fitriana, I, Flavel, J, Flor, L, Folayan, M, Foley, K, Fonzo, M, Force, L, Foschi, M, Freitas, A, Fridayani, N, Fukutaki, K, Furtado, J, Fux, B, Gaal, P, Gadanya, M, Gallus, S, Ganesan, B, Ganiyani, M, Gautam, R, Gebi, T, Gebregergis, M, Gebrehiwot, M, Getacher, L, Getahun, G, Gething, P, Ghadimi, D, Ghadirian, F, Ghafarian, S, Ghailan, K, Ghasemi, M, Ghasempour Dabaghi, G, Ghazy, R, Ghoba, S, Gholami, E, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Ghorbani Vajargah, P, Ghotbi, E, Gil, A, Gill, T, Girmay, A, Glasbey, J, Glushkova, E, Gnedovskaya, E, Göbölös, L, Goldust, M, Goleij, P, Golinelli, D, Gopalani, S, Goulart, A, Gouravani, M, Goyal, A, Grivna, M, Grosso, G, Guarducci, G, Gubari, M, Guicciardi, S, Guimarães, R, Gulati, S, Gulisashvili, D, Gunawardane, D, Guo, C, Gupta, A, Gupta, R, Gupta, S, Gupta, V, Haakenstad, A, Hadi, N, Haep, N, Hafiz, A, Haghmorad, D, Haile, D, Hajj Ali, A, Haj-Mirzaian, A, Halboub, E, Haller, S, Halwani, R, Hamagharib Abdullah, K, Hamdy, N, Hamoudi, R, Hanifi, N, Hankey, G, Haq, Z, Haque, M, Harapan, H, Hargono, A, Haro, J, Hasaballah, A, Hasan, S, Hasanian, M, Hasnain, M, Hassan, A, Haubold, J, Hay, S, Hebert, J, Hegazi, O, Heidari, M, Hemmati, M, Henson, C, Herrera-Serna, B, Herteliu, C, Heydari, M, Hezam, K, Hidayana, I, Hiraike, Y, Hoan, N, Holla, R, Hoogar, P, Horita, N, Hossain, M, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hu, C, Huang, J, Hultström, M, Hundie, T, Hunt, A, Hushmandi, K, Hussain, J, Hussain, M, Hussein, N, Huynh, H, Hwang, B, Ibitoye, S, Iftikhar, P, Ikiroma, A, Ikwegbue, P, Ilic, I, Ilic, M, Immurana, M, Isa, M, Islam, M, Islam, S, Ismail, F, Ismail, N, Isola, G, Iwagami, M, Iyamu, I, Jacob, L, Jacobsen, K, Jafarinia, M, Jahankhani, K, Jahanmehr, N, Jain, N, Jairoun, A, Jakhmola Mani, D, Jamil, S, Jamora, R, Jatau, A, Javadov, S, Javaheri, T, Jayaram, S, Jee, S, Jeganathan, J, Jiang, H, Jokar, M, Jonas, J, Joseph, N, Joshua, C, Jürisson, M, K, V, Kabir, A, Kabir, Z, Kadashetti, V, Kalankesh, L, Kalra, S, Kamath, A, Kamath, R, Kamireddy, A, Kanaan, M, Kanchan, T, Kanmiki, E, Kanmodi, K, Kansal, S, Karim, A, Karkhah, S, Kashoo, F, Kasraei, H, Kassel, M, Katikireddi, S, Kauppila, J, Kaur, H, Kayode, G, Kazemi, F, Kazemian, S, Kebede, F, Kendal, E, Kesse-Guyot, E, Khademvatan, S, Khajuria, H, Khalaji, A, Khalid, A, Khalid, N, Khalilian, A, Khamesipour, F, Khan, F, Khan, M, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, M, Khatib, M, Khayat Kashani, H, Kheirallah, K, Khokhar, M, Khormali, M, Khorrami, Z, Khosla, A, Khosravi, M, Khosrowjerdi, M, Khubchandani, J, Kifle, Z, Kim, G, Kim, J, Kim, M, Kim, Y, Kimokoti, R, Kisa, A, Kisa, S, Knibbs, L, Knudsen, A, Kochhar, S, Kolahi, A, Kompani, F, Koren, G, Korzh, O, Krishan, K, Krishna, V, Krishnamoorthy, V, Kucuk Bicer, B, Kuddus, M, Kuitunen, I, Kujan, O, Kulimbet, M, Kulkarni, V, Kumar, G, Kumar, H, Kumar, N, Kumar, R, Kumar, V, Kundu, A, Kusuma, D, Kyei-Arthur, F, Kytö, V, Kyu, H, La Vecchia, C, Lacey, B, Ladan, M, Laflamme, L, Lahariya, C, Lai, D, Lalloo, R, Lallukka, T, Lám, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, V, Laplante-Lévesque, A, Larijani, B, Larsson, A, Lasrado, S, Lauriola, P, Lawlor, H, Le, H, Le, L, Le, N, Le, T, Leasher, J, Lee, D, Lee, M, Lee, P, Lee, S, Lee, Y, Leigh, J, Leong, E, Li, M, Libra, M, Ligade, V, Lim, L, Lim, S, Limenh, L, Lindholm, D, Lindstedt, P, Listl, S, Liu, G, Liu, S, Liu, X, Llanaj, E, López-Bueno, R, López-Gil, J, Loreche, A, Lotufo, P, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, Lusk, J, Lv, L, M Amin, H, Ma, Z, Maass, K, Machairas, N, Machoy, M, Madureira-Carvalho, Á, Magdy Abd El Razek, H, Maghazachi, A, Mahadeshwara Prasad, D, Mahalleh, M, Mahasha, P, Mahmoud, M, Mahmoudi, E, Mahmoudvand, G, Makama, M, Malakan Rad, E, Malhotra, K, Malik, A, Malta, D, Manla, Y, Mansour, A, Mansouri, M, Mansouri, P, Mansouri, V, Mansourian, M, Mansournia, M, Marasini, B, Marateb, H, Maravilla, J, Mardi, P, Marjani, A, Markazi Moghadam, H, Marrugo Arnedo, C, Martinez, G, Martinez-Piedra, R, Martins-Melo, F, Martorell, M, Marx, W, Marzo, R, Masoudi, S, Mathangasinghe, Y, Mathioudakis, A, Mathur, M, Mathur, N, Matozinhos, F, Mattumpuram, J, Maude, R, Maugeri, A, Mayeli, M, Mazidi, M, Mazzotti, A, Mcgrath, J, Mckee, M, Mckowen, A, Mcphail, M, Mcphail, S, Mehmood, A, Mehrabani-Zeinabad, K, Mehravar, S, Mekene Meto, T, Melese, E, Mendez-Lopez, M, Mendoza, W, Menezes, R, Mensah, G, Mensah, L, Mentis, A, Meo, S, Meretoja, A, Meretoja, T, Mersha, A, Mestrovic, T, Mettananda, K, Mettananda, S, Mhlanga, A, Mhlanga, L, Miazgowski, T, Michalek, I, Micheletti Gomide Nogueira de Sá, A, Miller, T, Minh, L, Mirahmadi, A, Mirijello, A, Mirrakhimov, E, Mirzaei, R, Mitchell, P, Mittal, C, Moberg, M, Moghadam Fard, A, Mohajelin, S, Mohamadkhani, A, Mohamed, A, Mohamed, J, Mohamed, M, Mohamed, N, Mohammad, A, Mohammadi, S, Mohammed, H, Mohammed, M, Mohammed, S, Mokdad, A, Molokhia, M, Momani, S, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, M, Montazeri, F, Moodi Ghalibaf, A, Moradi, M, Moradi, Y, Moraga, P, Morawska, L, Moreira, R, Morovatdar, N, Morrison, S, Mosapour, A, Mosser, J, Mossialos, E, Motappa, R, Mougin, V, Mousavi, P, Mrejen, M, Mubarik, S, Mueller, U, Mulita, F, Munjal, K, Murillo-Zamora, E, Musallam, K, Musina, A, Mustafa, G, Myung, W, Nafei, A, Nagarajan, A, Naghavi, P, Naik, G, Nainu, F, Najdaghi, S, Nakhostin Ansari, N, Nangia, V, Narasimha Swamy, S, Nargus, S, Narimani Davani, D, Nascimento, B, Nascimento, G, Naser, A, Nashwan, A, Natto, Z, Nauman, J, Navaratna, S, Naveed, M, Nawsherwan, N, Nayak, B, Nayak, V, Negash, H, Negoi, I, Negoi, R, Nejadghaderi, S, Nejjari, C, Nematollahi, S, Netsere, H, Ng, M, Nguefack-Tsague, G, Ngunjiri, J, Nguyen, A, Nguyen, D, Nguyen, H, Nguyen, N, Nguyen, P, Nguyen, Q, Nguyen, V, Nguyen Tran Minh, D, Niazi, R, Nigatu, Y, Niknam, M, Nikoobar, A, Nikpoor, A, Nikravangolsefid, N, Noman, E, Nomura, S, Noor, S, Noroozi, N, Nouri, M, Nozari, M, Nri-Ezedi, C, Ntaios, G, Nunemo, M, Nurrika, D, Nutor, J, Nzoputam, C, Nzoputam, O, Oancea, B, Obamiro, K, Odetokun, I, Oduro, M, Ogundijo, O, Ogunfowokan, A, Ogunkoya, A, Oguntade, A, Oh, I, Ojo-Akosile, T, Okati-Aliabad, H, Okekunle, A, Okonji, O, Olagunju, A, Olatubi, M, Oliveira, G, Olusanya, B, Olusanya, J, Oluwafemi, Y, Omar, H, Omer, G, Ong, S, Onie, S, Onwujekwe, O, Opejin, A, Ordak, M, Orish, V, Ortiz, A, Ortiz-Prado, E, Osman, W, Ostojic, S, Ostroff, S, Osuagwu, U, Otoiu, A, Otstavnov, S, Ouyahia, A, Owolabi, M, Oyeyemi, O, Ozair, A, P A, M, Padron-Monedero, A, Padubidri, J, Pal, P, Palicz, T, Pan, F, Pan, H, Panda-Jonas, S, Pandey, A, Pando-Robles, V, Pangaribuan, H, Panos, G, Panos, L, Pantazopoulos, I, Pantea Stoian, A, Parikh, R, Park, E, Park, S, Parsons, N, Parthasarathi, A, Pasovic, M, Passera, R, Patel, J, Pathan, A, Patil, S, Patoulias, D, Pawar, S, Pazoki Toroudi, H, Pease, S, Peden, A, Pedersini, P, Pensato, U, Pepito, V, Peprah, P, Pereira, M, Perianayagam, A, Perico, N, Perna, S, Pesudovs, K, Petermann-Rocha, F, Pham, H, Philip, A, Phillips, M, Pigeolet, M, Piradov, M, Pisoni, E, Plotnikov, E, Poddighe, D, Polibin, R, Poluru, R, Ponkilainen, V, Popovic, D, Postma, M, Pour-Rashidi, A, Prabhu, D, Prada, S, Pradhan, J, Pradhan, P, Prashant, A, Prates, E, Priscilla, T, Purnobasuki, H, Purohit, B, Puvvula, J, Qasim, N, Qattea, I, Qazi, A, Qian, G, Rabiee Rad, M, Radhakrishnan, V, Raeisi Shahraki, H, Rafferty, Q, Raggi, A, Raggi, C, Raheem, N, Rahim, F, Rahim, M, Rahimibarghani, S, Rahman, M, Rahman, T, Rahmani, A, Rahmanian, M, Rahmanian, N, Rahmati, R, Rahmawaty, S, Raimondo, D, Raja, A, Rajput, P, Ramadan, M, Ramasamy, S, Ramazanu, S, Ramteke, P, Rana, K, Rana, R, Ranabhat, C, Rane, A, Rao, C, Rao, M, Rasella, D, Rashedi, V, Rashid, A, Rasouli-Saravani, A, Rastogi, P, Rasul, A, Rathish, D, Rathnaiah Babu, G, Rauniyar, S, Ravangard, R, Rawaf, D, Rawaf, S, Raza, R, Redwan, E, Reifels, L, Reitsma, M, Remuzzi, G, Rengasamy, K, Reshmi, B, Resnikoff, S, Restaino, S, Reyes, L, Rezaei, N, Rezaei, Z, Rezaeian, M, Rhee, T, Rickard, J, Robalik, T, Robinson-Oden, H, Rocha, H, Rodrigues, M, Rodriguez, J, Roever, L, Romadlon, D, Ronfani, L, Rony, M, Roshandel, G, Rotimi, K, Rout, H, Roy, B, Rubagotti, E, Ruela, G, Rumisha, S, Runghien, T, Russo, M, Saad, A, Saber, K, Saber-Ayad, M, Sabet, C, Sabour, S, Sachdev, P, Saddler, A, Sadee, B, Sadeghi, M, Saeb, M, Saeed, U, Safi, S, Sagar, R, Saghafi, A, Sagoe, D, Sahebkar, A, Sahoo, P, Sajid, M, Salam, N, Salamati, P, Salami, A, Saleh, M, Salehi, L, Salem, M, Salemcity, A, Salimi, S, Samadi Kafil, H, Samargandy, S, Samodra, Y, Samy, A, Sanabria, J, Sanna, F, Santric-Milicevic, M, Sao Jose, B, Saraswathy, S, Saravanan, A, Sarmiento-Suárez, R, Sarode, G, Sarode, S, Sartorius, B, Satpathy, M, Sayeed, A, Scarmeas, N, Schaarschmidt, B, Schinckus, C, Schuermans, A, Schumacher, A, Schutte, A, Schwebel, D, Schwendicke, F, Selvaraj, S, Semreen, M, Senapati, S, Sengupta, P, Senthilkumaran, S, Serban, D, Sethi, Y, Seylani, A, Shafie, M, Shah, P, Shahbandi, A, Shahid, S, Shahid, W, Shahsavari, H, Shahwan, M, Shaikh, M, Shalash, A, Shamekh, A, Shamim, M, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharfaei, S, Sharifan, A, Sharifi-Rad, J, Sharma, A, Sharma, M, Sharma, S, Sharma, V, Shastry, R, Shayan, M, Shekhar, S, Shenoy, R, Shetty, M, Shetty, P, Shi, P, Shiani, A, Shigematsu, M, Shimels, T, Shiri, R, Shittu, A, Shiue, I, Shivakumar, K, Shool, S, Shorofi, S, Shrestha, S, Shuval, K, Si, Y, Siddig, E, Sidhu, J, Silva, J, Silva, L, Silva, S, Silva, T, Simpson, C, Simpson, K, Singh, A, Singh, B, Singh, H, Singh, J, Singh, P, Skou, S, Smith, G, Sobia, F, Socea, B, Solanki, S, Soleimani, H, Soliman, S, Song, Y, Soyiri, I, Spartalis, M, Spearman, S, Sreeramareddy, C, Stanaway, J, Stanikzai, M, Starodubova, A, Stein, D, Steiner, C, Steiropoulos, P, Stockfelt, L, Stokes, M, Straif, K, Subedi, N, Suliankatchi Abdulkader, R, Sultana, A, Sun, J, Sundström, J, Swain, C, Szarpak, L, Szeto, M, Tabaee Damavandi, P, Tabarés-Seisdedos, R, Tabatabaei Malazy, O, Tabatabaeizadeh, S, Tabatabai, S, Tabb, K, Tabche, C, Tabish, M, Taheri Abkenar, Y, Taheri Soodejani, M, Taiba, J, Talaat, I, Tamuzi, J, Tan, K, Tang, H, Tat, N, Tavakoli Oliaee, R, Tavangar, S, Taveira, N, Tbakhi, A, Tehrani, H, Temsah, M, Teramoto, M, Tesfaye, B, Teye-Kwadjo, E, Thangaraju, P, Thankappan, K, Thapar, R, Thayakaran, R, Thirunavukkarasu, S, Thomas, N, Thygesen, L, Ticoalu, J, Timalsena, D, Tiruye, T, Tiwari, K, Tomo, S, Tonelli, M, Topor-Madry, R, Touvier, M, Tovani-Palone, M, Tran, A, Tran, J, Tran, N, Tran, T, Trico, D, Tromans, S, Truyen, T, Tsatsakis, A, Tsermpini, E, Tumurkhuu, M, Turnock, S, Udoh, A, Ullah, A, Ullah, S, Umakanthan, S, Umar, M, Umar, S, Unim, B, Unnikrishnan, B, Upadhyay, E, Usman, J, Vahdati, S, Vaithinathan, A, Vakili, O, Valizadeh, R, Van den Eynde, J, Vart, P, Varthya, S, Vasankari, T, Vasic, M, Venketasubramanian, N, Veroux, M, Verras, G, Vervoort, D, Vijayageetha, M, Villafañe, J, Vinayak, M, Violante, F, Vladimirov, S, Vlassov, V, Vo, B, Vohra, K, Vos, T, Wadood, A, Waheed, Y, Wang, F, Wang, S, Wang, Y, Wanjau, M, Waqas, M, Ward, P, Waris, A, Wassie, E, Watson, S, Weaver, M, Weerakoon, K, Weintraub, R, Weldetinsaa, H, Wells, K, Wen, Y, Westerman, R, Wiangkham, T, Wickramasinghe, D, Widowati, E, Wojewodzic, M, Woldeyes, D, Wolf, A, Wolfe, C, Wu, C, Wu, D, Wu, F, Wu, J, Wu, Z, Wulf Hanson, S, Xiao, H, Xu, S, Yadav, R, Yamagishi, K, Yang, D, Yano, Y, Yarahmadi, A, Yazdani Nia, I, Ye, P, Yesodharan, R, Yesuf, S, Yezli, S, Yiğit, A, Yiğit, V, Yigzaw, Z, Yin, D, Yip, P, Yonemoto, N, You, Y, Younis, M, Yu, C, Yu, E, Yu, Y, Yuan, C, Yusuf, H, Zafar, U, Zafari, N, Zahid, M, Zakham, F, Zaki, N, Zerfu, T, Zhang, H, Zhang, J, Zhang, L, Zhang, Y, Zhang, Z, Zhao, X, Zhao, Y, Zhao, Z, Zhong, C, Zhou, B, Zhou, J, Zhou, S, Zhu, B, Zhumagaliuly, A, Zielińska, M, Zoghi, G, Zumla, A, Zyoud, S, Smith, A, Murray, C, Vollset, Stein Emil, Ababneh, Hazim S, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbastabar, Hedayat, Abd Al Magied, Abdallah H A, Abd ElHafeez, Samar, Abdelkader, Atef, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdi, Parsa, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abebe, Mesfin, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Absalan, Abdorrahim, Abualruz, Hasan, Abubakar, Bilyaminu, Abukhadijah, Hana Jihad Jihad, Addolorato, Giovanni, Adekanmbi, Victor, Adetunji, Charles Oluwaseun, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adha, Rishan, Adhikary, Ripon Kumar, Adnani, Qorinah Estiningtyas Sakilah, Adzigbli, Leticia Akua, Afrashteh, Fatemeh, Afzal, Muhammad Sohail, Afzal, Saira, Agbozo, Faith, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Shahzaib, Ahmed, Anisuddin, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Safoora, Ahmed, Syed Anees, Akinosoglou, Karolina, Akkaif, Mohammed Ahmed, Akrami, Ashley E, Akter, Ema, Al Awaidy, Salah, Al Hasan, Syed Mahfuz, Al Mosa, Amjad S, Al Ta'ani, Omar, Al Zaabi, Omar Ali Mohammed, Alahdab, Fares, Alajlani, Muaaz M, Al-Ajlouni, Yazan, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore, Alam, Tahiya, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, Aldhaleei, Wafa A, Aldridge, Robert W, Alemohammad, Seyedeh Yasaman, Alemu, Yihun Mulugeta, Al-Gheethi, Adel Ali Saeed, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Amjad, Ali, Iman, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Victor Ekoche, Ali, Waad, Al-Ibraheem, Akram, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Alla, François, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Alqutaibi, Ahmed Yaseen, Alrawashdeh, Ahmad, Alrousan, Sahel Majed, Al-Sabah, Salman Khalifah, Alsabri, Mohammed A, Altaany, Zaid, Al-Tammemi, Ala'a B., Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvis-Guzman, Nelson, Al-Wardat, Mohammad Sami, Al-Worafi, Yaser Mohammed, Aly, Hany, Alyahya, Mohammad Sharif, Alzoubi, Karem H, Al-Zyoud, Walid, Amani, Reza, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amiri, Sohrab, Amirzade-Iranaq, Mohammad Hosein, Amu, Hubert, Amugsi, Dickson A, Ancuceanu, Robert, Anderlini, Deanna, Anderson, David B, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Andrews, Erick Adrian, Anil, Abhishek, Anil, Sneha, Anoushiravani, Amir, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Boluwatife Stephen, Anvari, Saeid, Anyasodor, Anayochukwu Edward, Appiah, Francis, Aquilano, Michele, Arab, Juan Pablo, Arabloo, Jalal, Arafa, Elshaimaa A, Arafat, Mosab, Aravkin, Aleksandr Y, Ardekani, Ali, Areda, Demelash, Aregawi, Brhane Berhe, Aremu, Abdulfatai, Ariffin, Hany, Arkew, Mesay, Armani, Keivan, Artamonov, Anton A, Arumugam, Ashokan, Asghari-Jafarabadi, Mohammad, Ashbaugh, Charlie, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atorkey, Prince, Atout, Maha Moh'd Wahbi, Aujayeb, Avinash, Ausloos, Marcel, Awad, Hamzeh, Awotidebe, Adedapo Wasiu, Ayatollahi, Haleh, Ayuso-Mateos, Jose L, Azadnajafabad, Sina, Azeez, Fahad Khan, Azevedo, Rui M S, Badar, Muhammad, Baghdadi, Soroush, Bagheri, Mahboube, Bagheri, Nasser, Bai, Ruhai, Baker, Jennifer L, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balcha, Wondu Feyisa, Baltatu, Ovidiu Constantin, Barchitta, Martina, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barqawi, Hiba Jawdat, Barteit, Sandra, Basiru, Afisu, Basso, João Diogo, Bastan, Mohammad-Mahdi, Basu, Sanjay, Bauckneht, Matteo, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Behnoush, Amir Hossein, Behzadi, Payam, Beiranvand, Maryam, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bemanalizadeh, Maryam, Bensenor, Isabela M, Benzian, Habib, Beran, Azizullah, Berezvai, Zombor, Bernstein, Robert S, Bettencourt, Paulo J G, Beyene, Kebede A, Beyene, Melak Gedamu, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhandari, Dinesh, Bharadwaj, Ravi, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhargava, Ashish, Bhaskar, Sonu, Bhat, Vivek, Bhattacharjee, Natalia V, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhuiyan, Mohiuddin Ahmed, Bisignano, Catherine, Biswas, Bijit, Bjørge, Tone, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bonakdar Hashemi, Milad, Bora Basara, Berrak, Borhany, Hamed, Bosoka, Samuel Adolf, Botero Carvajal, Alejandro, Bouaoud, Souad, Boufous, Soufiane, Boxe, Christopher, Boyko, Edward J, Brady, Oliver J, Braithwaite, Dejana, Brauer, Michael, Brazo-Sayavera, Javier, Brenner, Hermann, Brown, Colin Stewart, Browne, Annie J, Brugha, Traolach, Bryazka, Dana, Bulamu, Norma B, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Busse, Reinhard, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Çakmak Barsbay, Mehtap, Calina, Daniela, Campos, Luciana Aparecida, Cao, Shujin, Capodici, Angelo, Cárdenas, Rosario, Carreras, Giulia, Carugno, Andrea, Carvalho, Márcia, Castaldelli-Maia, Joao Mauricio, Castelpietra, Giulio, Cattaruzza, Maria Sofia, Caye, Arthur, Cegolon, Luca, Cembranel, Francieli, Cenko, Edina, Cerin, Ester, Chadban, Steven J, Chadwick, Joshua, Chakraborty, Chiranjib, Chakraborty, Sandip, Chalek, Julian, Chan, Jeffrey Shi Kai, Chandika, Rama Mohan, Chandy, Sara, Charan, Jaykaran, Chaudhary, Anis Ahmad, Chaurasia, Akhilanand, Chen, An-Tian, Chen, Haowei, Chen, Meng Xuan, Chen, Simiao, Cherbuin, Nicolas, Chi, Gerald, Chichagi, Fatemeh, Chimed-Ochir, Odgerel, Chimoriya, Ritesh, Ching, Patrick R, Chirinos-Caceres, Jesus Lorenzo, Chitheer, Abdulaal, Cho, Daniel Youngwhan, Cho, William C S, Choi, Dong-Woo, Chong, Bryan, Chong, Chean Lin, Chopra, Hitesh, Chu, Dinh-Toi, Chung, Eric, Chutiyami, Muhammad, Clayton, Justin T, Cogen, Rebecca M, Cohen, Aaron J, Columbus, Alyssa, Comfort, Haley, Conde, Joao, Connolly, Jon T, Cooper, Ezra E K, Cortese, Samuele, Cruz-Martins, Natália, da Silva, Alanna Gomes, Dadras, Omid, Dai, Xiaochen, Dai, Zhaoli, Dalton, Bronte E, Damiani, Giovanni, Dandona, Lalit, Dandona, Rakhi, Das, Jai K, Das, Saswati, Das, Subasish, Dash, Nihar Ranjan, Davletov, Kairat, De la Hoz, Fernando Pio, De Leo, Diego, Debopadhaya, Shayom, Delgado-Enciso, Ivan, Denova-Gutiérrez, Edgar, Dervenis, Nikolaos, Desai, Hardik Dineshbhai, Devanbu, Vinoth Gnana Chellaiyan, Dewan, Syed Masudur Rahman, Dhama, Kuldeep, Dhane, Amol S, Dhingra, Sameer, Dias da Silva, Diana, Diaz, Daniel, Diaz, Luis Antonio, Diaz, Michael J, Dima, Adriana, Ding, Delaney D, Do, Thao Huynh Phuong, do Prado, Camila Bruneli, Dodangeh, Masoud, Dodangeh, Milad, Doegah, Phidelia Theresa, Dohare, Sushil, Dong, Wanyue, D'Oria, Mario, Doshi, Rajkumar, Dowou, Robert Kokou, Dsouza, Haneil Larson, Dsouza, Viola, Dube, John, Dumith, Samuel C, Duncan, Bruce B, Duraes, Andre Rodrigues, Duraisamy, Senbagam, Durojaiye, Oyewole Christopher, Dushpanova, Anar, Dutta, Sulagna, Dzianach, Paulina Agnieszka, Dziedzic, Arkadiusz Marian, Eboreime, Ejemai, Ebrahimi, Alireza, Ebrahimi Kalan, Mohammad, Edinur, Hisham Atan, Efendi, Ferry, Eikemo, Terje Andreas, Eini, Ebrahim, Ekundayo, Temitope Cyrus, El Arab, Rabie Adel, El Sayed, Iman, Elamin, Osman, Elemam, Noha Mousaad, ElGohary, Ghada Metwally Tawfik, Elhadi, Muhammed, Elmeligy, Omar Abdelsadek Abdou, Elmoselhi, Adel B, Elshaer, Mohammed, Elsohaby, Ibrahim, Eltahir, Mohd. Elmagzoub, Emeto, Theophilus I, Eshrati, Babak, Eslami, Majid, Esmaeili, Zahra, Fabin, Natalia, Fagbamigbe, Adeniyi Francis, Fagbule, Omotayo Francis, Falzone, Luca, Fareed, Mohammad, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Faro, Andre, Fasihi, Kiana, Fatehizadeh, Ali, Fauk, Nelsensius Klau, Fazylov, Timur, Feigin, Valery L, Fekadu, Ginenus, Feng, Xiaoqi, Fereshtehnejad, Seyed-Mohammad, Ferrara, Pietro, Ferreira, Nuno, Firew, Belete Sewasew, Fischer, Florian, Fitriana, Ida, Flavel, Joanne, Flor, Luisa S, Folayan, Morenike Oluwatoyin, Foley, Kristen Marie, Fonzo, Marco, Force, Lisa M, Foschi, Matteo, Freitas, Alberto, Fridayani, Ni Kadek Yuni, Fukutaki, Kai Glenn, Furtado, João M, Fux, Blima, Gaal, Peter Andras, Gadanya, Muktar A, Gallus, Silvano, Ganesan, Balasankar, Ganiyani, Mohammad Arfat, Gautam, Rupesh K, Gebi, Tilaye Gebru, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Getacher, Lemma, Getahun, Genanew K A, Gething, Peter W, Ghadimi, Delaram J, Ghadirian, Fataneh, Ghafarian, Sadegh, Ghailan, Khalid Yaser, Ghasemi, MohammadReza, Ghasempour Dabaghi, Ghazal, Ghazy, Ramy Mohamed, Ghoba, Sama, Gholami, Ehsan, Gholamrezanezhad, Ali, Gholizadeh, Nasim, Ghorbani, Mahsa, Ghorbani Vajargah, Pooyan, Ghotbi, Elena, Gil, Artyom Urievich, Gill, Tiffany K, Girmay, Alem, Glasbey, James C, Glushkova, Ekaterina Vladimirovna, Gnedovskaya, Elena V, Göbölös, Laszlo, Goldust, Mohamad, Goleij, Pouya, Golinelli, Davide, Gopalani, Sameer Vali, Goulart, Alessandra C, Gouravani, Mahdi, Goyal, Anmol, Grivna, Michal, Grosso, Giuseppe, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Guicciardi, Stefano, Guimarães, Rafael Alves, Gulati, Snigdha, Gulisashvili, David, Gunawardane, Damitha Asanga, Guo, Cui, Gupta, Anish Kumar, Gupta, Rahul, Gupta, Rajeev, Gupta, Renu, Gupta, Sapna, Gupta, Vijai Kumar, Haakenstad, Annie, Hadi, Najah R, Haep, Nils, Hafiz, Abdul, Haghmorad, Dariush, Haile, Demewoz, Hajj Ali, Adel, Hajj Ali, Ali, Haj-Mirzaian, Arvin, Halboub, Esam S, Haller, Sebastian, Halwani, Rabih, Hamagharib Abdullah, Kanaan, Hamdy, Nadia M, Hamoudi, Rifat, Hanifi, Nasrin, Hankey, Graeme J, Haq, Zaim Anan, Haque, Md Rabiul, Harapan, Harapan, Hargono, Arief, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, S. M. Mahmudul, Hasanian, Mohammad, Hasnain, Md Saquib, Hassan, Amr, Haubold, Johannes, Hay, Simon I, Hebert, Jeffrey J, Hegazi, Omar E, Heidari, Mohammad, Hemmati, Mehdi, Henson, Claire A, Herrera-Serna, Brenda Yuliana, Herteliu, Claudiu, Heydari, Majid, Hezam, Kamal, Hidayana, Irma, Hiraike, Yuta, Hoan, Nguyen Quoc, Holla, Ramesh, Hoogar, Praveen, Horita, Nobuyuki, Hossain, Md Mahbub, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hu, Chengxi, Huang, Junjie, Hultström, Michael, Hundie, Tsegaye Gebreyes, Hunt, Aliza J, Hushmandi, Kiavash, Hussain, Javid, Hussain, M. Azhar, Hussein, Nawfal R, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Iftikhar, Pulwasha Maria, Ikiroma, Adalia I, Ikwegbue, Paul Chukwudi, Ilic, Irena M, Ilic, Milena D, Immurana, Mustapha, Isa, Mustafa Alhaji, Islam, Md. Rabiul, Islam, Sheikh Mohammed Shariful, Ismail, Faisal, Ismail, Nahlah Elkudssiah, Isola, Gaetano, Iwagami, Masao, Iyamu, Ihoghosa Osamuyi, Jacob, Louis, Jacobsen, Kathryn H, Jafarinia, Morteza, Jahankhani, Kasra, Jahanmehr, Nader, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jakhmola Mani, Dr Ruchi, Jamil, Safayet, Jamora, Roland Dominic G, Jatau, Abubakar Ibrahim, Javadov, Sabzali, Javaheri, Tahereh, Jayaram, Shubha, Jee, Sun Ha, Jeganathan, Jayakumar, Jiang, Heng, Jokar, Mohammad, Jonas, Jost B, Joseph, Nitin, Joshua, Charity Ehimwenma, Jürisson, Mikk, K, Vaishali, Kabir, Ali, Kabir, Zubair, Kadashetti, Vidya, Kalankesh, Laleh R, Kalra, Sanjay, Kamath, Ashwin, Kamath, Rajesh, Kamireddy, Arun, Kanaan, Mona, Kanchan, Tanuj, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kansal, Sushil Kumar, Karim, Asima, Karkhah, Samad, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassel, Molly B, Katikireddi, Srinivasa Vittal, Kauppila, Joonas H, Kaur, Harkiran, Kayode, Gbenga A, Kazemi, Foad, Kazemian, Sina, Kebede, Fassikaw, Kendal, Evie Shoshannah, Kesse-Guyot, Emmanuelle, Khademvatan, Shahram, Khajuria, Himanshu, Khalaji, Amirmohammad, Khalid, Asaad, Khalid, Nauman, Khalilian, Alireza, Khamesipour, Faham, Khan, Fayaz, Khan, Mohammad Jobair, Khan, Moien AB, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khatib, Mahalaqua Nazli, Khayat Kashani, Hamid Reza, Kheirallah, Khalid A, Khokhar, Manoj, Khormali, Moein, Khorrami, Zahra, Khosla, Atulya Aman, Khosravi, Majid, Khosrowjerdi, Mahmood, Khubchandani, Jagdish, Kifle, Zemene Demelash, Kim, Grace, Kim, Julie Sojin, Kim, Min Seo, Kim, Yun Jin, Kimokoti, Ruth W, Kisa, Adnan, Kisa, Sezer, Knibbs, Luke D, Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kolahi, Ali-Asghar, Kompani, Farzad, Koren, Gerbrand, Korzh, Oleksii, Krishan, Kewal, Krishna, Varun, Krishnamoorthy, Vijay, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kuitunen, Ilari, Kujan, Omar, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, G Anil, Kumar, Harish, Kumar, Nithin, Kumar, Rakesh, Kumar, Vijay, Kundu, Amartya, Kusuma, Dian, Kyei-Arthur, Frank, Kytö, Ville, Kyu, Hmwe Hmwe, La Vecchia, Carlo, Lacey, Ben, Ladan, Muhammad Awwal, Laflamme, Lucie, Lahariya, Chandrakant, Lai, Daphne Teck Ching, Lalloo, Ratilal, Lallukka, Tea, Lám, Judit, Lan, Qing, Lan, Tuo, Landires, Iván, Lanfranchi, Francesco, Langguth, Berthold, Lansingh, Van Charles, Laplante-Lévesque, Ariane, Larijani, Bagher, Larsson, Anders O, Lasrado, Savita, Lauriola, Paolo, Lawlor, Hilary R, Le, Huu-Hoai, Le, Long Khanh Dao, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Le, Trang Diep Thanh, Leasher, Janet L, Lee, Doo Woong, Lee, Munjae, Lee, Paul H, Lee, Sang-woong, Lee, Seung Won, Lee, Shaun Wen Huey, Lee, Yo Han, Leigh, James, Leong, Elvynna, Li, Ming-Chieh, Libra, Massimo, Ligade, Virendra S, Lim, Lee-Ling, Lim, Stephen S, Limenh, Liknaw Workie, Lindholm, Daniel, Lindstedt, Paulina A, Listl, Stefan, Liu, Gang, Liu, Shiwei, Liu, Shuke, Liu, Xiaofeng, Liu, Xuefeng, Llanaj, Erand, López-Bueno, Rubén, López-Gil, José Francisco, Loreche, Arianna Maever, Lotufo, Paulo A, Lozano, Rafael, Lubinda, Jailos, Lucchetti, Giancarlo, Luo, Lisha, Lusk, Jay B, Lv, Lei, M Amin, Hawraz Ibrahim, Ma, Zheng Feei, Maass, Kelsey Lynn, Machairas, Nikolaos, Machoy, Monika, Madureira-Carvalho, Áurea M, Magdy Abd El Razek, Hassan, Maghazachi, Azzam A, Mahadeshwara Prasad, D. R., Mahalleh, Mehrdad, Mahasha, Phetole Walter, Mahmoud, Mansour Adam, Mahmoudi, Elham, Mahmoudvand, Golnaz, Makama, Maureen, Malakan Rad, Elaheh, Malhotra, Kashish, Malik, Ahmad Azam, Malta, Deborah Carvalho, Manla, Yosef, Mansour, Ali, Mansouri, Mohammad Hadi, Mansouri, Pejman, Mansouri, Vahid, Mansourian, Marjan, Mansournia, Mohammad Ali, Marasini, Bishnu P, Marateb, Hamid Reza, Maravilla, Joemer C, Mardi, Parham, Marjani, Abdoljalal, Markazi Moghadam, Hamed, Marrugo Arnedo, Carlos Alberto, Martinez, Gabriel, Martinez-Piedra, Ramon, Martins-Melo, Francisco Rogerlândio, Martorell, Miquel, Marx, Wolfgang, Marzo, Roy Rillera, Masoudi, Sahar, Mathangasinghe, Yasith, Mathioudakis, Alexander G, Mathur, Medha, Mathur, Navgeet, Mathur, Neeta, Matozinhos, Fernanda Penido, Mattumpuram, Jishanth, Maude, Richard James, Maugeri, Andrea, Mayeli, Mahsa, Mazidi, Mohsen, Mazzotti, Antonio, McGrath, John J, McKee, Martin, McKowen, Anna Laura W, McPhail, Michael A, McPhail, Steven M, Mehmood, Asim, Mehrabani-Zeinabad, Kamran, Mehravar, Sepideh, Mekene Meto, Tesfahun, Melese, Endalkachew Belayneh, Mendez-Lopez, Max Alberto Mendez, Mendoza, Walter, Menezes, Ritesh G, Mensah, George A, Mensah, Laverne G, Mentis, Alexios-Fotios A, Meo, Sultan Ayoub, Meretoja, Atte, Meretoja, Tuomo J, Mersha, Abera M, Mestrovic, Tomislav, Mettananda, Kukulege Chamila Dinushi, Mettananda, Sachith, Mhlanga, Adquate, Mhlanga, Laurette, Miazgowski, Tomasz, Michalek, Irmina Maria, Micheletti Gomide Nogueira de Sá, Ana Carolina, Miller, Ted R, Minh, Le Huu Nhat, Mirahmadi, Alireza, Mirijello, Antonio, Mirrakhimov, Erkin M, Mirzaei, Roya, Mitchell, Philip B, Mittal, Chaitanya, Moberg, Madeline E, Moghadam Fard, Atousa, Mohajelin, Seyedehfatemeh, Mohamadkhani, Ashraf, Mohamed, Ahmed Ismail, Mohamed, Jama, Mohamed, Mouhand F H, Mohamed, Nouh Saad, Mohammad, Ameen Mosa, Mohammadi, Soheil, Mohammed, Hussen, Mohammed, Mustapha, Mohammed, Shafiu, Mokdad, Ali H, Molokhia, Mariam, Momani, Shaher Mohammad, Momtazmanesh, Sara, Monasta, Lorenzo, Mondello, Stefania, Moni, Mohammad Ali, Montazeri, Fateme, Moodi Ghalibaf, AmirAli, Moradi, Maryam, Moradi, Yousef, Moraga, Paula, Morawska, Lidia, Moreira, Rafael Silveira, Morovatdar, Negar, Morrison, Shane Douglas, Mosapour, Abbas, Mosser, Jonathan F, Mossialos, Elias, Motappa, Rohith, Mougin, Vincent, Mousavi, Parsa, Mrejen, Matías, Mubarik, Sumaira, Mueller, Ulrich Otto, Mulita, Francesk, Munjal, Kavita, Murillo-Zamora, Efrén, Musallam, Khaled M, Musina, Ana-Maria, Mustafa, Ghulam, Myung, Woojae, Nafei, Ayoub, Nagarajan, Ahamarshan Jayaraman, Naghavi, Pirouz, Naik, Ganesh R, Naik, Gurudatta, Nainu, Firzan, Najdaghi, Soroush, Nakhostin Ansari, Noureddin, Nangia, Vinay, Narasimha Swamy, Sreenivas, Nargus, Shumaila, Narimani Davani, Delaram, Nascimento, Bruno Ramos, Nascimento, Gustavo G, Naser, Abdallah Y, Nashwan, Abdulqadir J, Natto, Zuhair S, Nauman, Javaid, Navaratna, Samidi N K, Naveed, Muhammad, Nawsherwan, null, Nayak, Biswa Prakash, Nayak, Vinod C, Negash, Hadush, Negoi, Ionut, Negoi, Ruxandra Irina, Nejadghaderi, Seyed Aria, Nejjari, Chakib, Nematollahi, Soroush, Netsere, Henok Biresaw, Ng, Marie, Nguefack-Tsague, Georges, Ngunjiri, Josephine W, Nguyen, Anh Hoang, Nguyen, Dang H, Nguyen, Duc Hoang, Nguyen, Hau Thi Hien, Nguyen, Nhan, Nguyen, Nhien Ngoc Y, Nguyen, Phat Tuan, Nguyen, QuynhAnh P, Nguyen, Van Thanh, Nguyen Tran Minh, Duc, Niazi, Robina Khan, Nigatu, Yeshambel T, Niknam, Mahdieh, Nikoobar, Ali, Nikpoor, Amin Reza, Nikravangolsefid, Nasrin, Noman, Efaq Ali, Nomura, Shuhei, Noor, Syed Toukir Ahmed, Noroozi, Nafise, Nouri, Mehran, Nozari, Majid, Nri-Ezedi, Chisom Adaobi, Ntaios, George, Nunemo, Mengistu H, Nurrika, Dieta, Nutor, Jerry John, Nzoputam, Chimezie Igwegbe, Nzoputam, Ogochukwu Janet, Oancea, Bogdan, Obamiro, Kehinde O, Odetokun, Ismail A, Oduro, Michael Safo, Ogundijo, Oluwaseun Adeolu, Ogunfowokan, Adesola Adenike, Ogunkoya, Abiola, Oguntade, Ayodipupo Sikiru, Oh, In-Hwan, Ojo-Akosile, Tolulope R, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okonji, Osaretin Christabel, Olagunju, Andrew T, Olatubi, Matthew Idowu, Oliveira, Gláucia Maria Moraes, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Oluwafemi, Yinka Doris, Omar, Hany A, Omer, Goran Latif, Ong, Sokking, Onie, Sandersan, Onwujekwe, Obinna E, Opejin, Abdulahi Opejin, Ordak, Michal, Orish, Verner N, Ortiz, Alberto, Ortiz-Prado, Esteban, Osman, Wael M S, Ostojic, Sergej M, Ostroff, Samuel M, Osuagwu, Uchechukwu Levi, Otoiu, Adrian, Otstavnov, Stanislav S, Ouyahia, Amel, Owolabi, Mayowa O, Oyeyemi, Oyetunde T, Ozair, Ahmad, P A, Mahesh Padukudru, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pal, Pramod Kumar, Palicz, Tamás, Pan, Feng, Pan, Hai-Feng, Panda-Jonas, Songhomitra, Pandey, Anamika, Pando-Robles, Victoria, Pangaribuan, Helena Ullyartha, Panos, Georgios D, Panos, Leonidas D, Pantazopoulos, Ioannis, Pantea Stoian, Anca Mihaela, Parikh, Romil R, Park, Eun-Kee, Park, Seoyeon, Park, Sungchul, Parsons, Nicholas, Parthasarathi, Ashwaghosha, Pasovic, Maja, Passera, Roberto, Patel, Jay, Pathan, Aslam Ramjan, Patil, Shankargouda, Patoulias, Dimitrios, Pawar, Shrikant, Pazoki Toroudi, Hamidreza, Pease, Spencer A, Peden, Amy E, Pedersini, Paolo, Pensato, Umberto, Pepito, Veincent Christian Filipino, Peprah, Prince, Pereira, Marcos, Pereira, Maria Odete, Perianayagam, Arokiasamy, Perico, Norberto, Perna, Simone, Pesudovs, Konrad, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Philip, Anil K, Phillips, Michael R, Pigeolet, Manon, Piradov, Michael A, Pisoni, Enrico, Plotnikov, Evgenii, Poddighe, Dimitri, Polibin, Roman V, Poluru, Ramesh, Ponkilainen, Ville T, Popovic, Djordje S, Postma, Maarten J, Pour-Rashidi, Ahmad, Prabhu, Disha, Prada, Sergio I, Pradhan, Jalandhar, Pradhan, Pranil Man Singh, Prashant, Akila, Prates, Elton Junio Sady, Priscilla, Tina, Purnobasuki, Hery, Purohit, Bharathi M, Puvvula, Jagadeesh, Qasim, Nameer Hashim, Qattea, Ibrahim, Qazi, Asma Saleem, Qian, Gangzhen, Rabiee Rad, Mehrdad, Radhakrishnan, Venkatraman, Raeisi Shahraki, Hadi, Rafferty, Quinn, Raggi, Alberto, Raggi, Cat, Raheem, Nasiru, Rahim, Fakher, Rahim, Md Jillur, Rahimibarghani, Sarvenaz, Rahman, Md Mijanur Mijanur Rahman, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahman, Tafhimur, Rahmani, Amir Masoud, Rahmanian, Mohammad, Rahmanian, Nazanin, Rahmati, Rahem, Rahmawaty, Setyaningrum, Raimondo, Diego, Raja, Adarsh, Rajput, Prashant, Ramadan, Majed, Ramasamy, Shakthi Kumaran, Ramazanu, Sheena, Ramteke, Pramod W, Rana, Kritika, Rana, Rishabh Kumar, Ranabhat, Chhabi Lal, Rane, Amey, Rao, Chythra R, Rao, Mithun, Rasella, Davide, Rashedi, Vahid, Rashid, Ahmed Mustafa, Rasouli-Saravani, Ashkan, Rastogi, Prateek, Rasul, Azad, Rathish, Devarajan, Rathnaiah Babu, Giridhara, Rauniyar, Santosh Kumar, Ravangard, Ramin, Rawaf, David Laith, Rawaf, Salman, Raza, Rabail Zehra, Redwan, Elrashdy Moustafa Mohamed, Reifels, Lennart, Reitsma, Marissa B, Remuzzi, Giuseppe, Rengasamy, Kannan RR, Reshmi, Bhageerathy, Resnikoff, Serge, Restaino, Stefano, Reyes, Luis Felipe, Rezaei, Nazila, Rezaei, Negar, Rezaei, Zahra Sadat, Rezaeian, Mohsen, Rhee, Taeho Gregory, Rickard, Jennifer, Robalik, Toshana, Robinson-Oden, Hannah Elizabeth, Rocha, Hermano Alexandre Lima, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Romadlon, Debby Syahru, Ronfani, Luca, Rony, Moustaq karim khan, Roshandel, Gholamreza, Rotimi, Kunle, Rout, Himanshu Sekhar, Roy, Bedanta, Rubagotti, Enrico, Ruela, Guilherme de Andrade, Rumisha, Susan Fred, Runghien, Tilleye, Russo, Michele, Saad, Aly M A, Saber, Korosh, Saber-Ayad, Maha Mohamed, Sabet, Cameron John, Sabour, Siamak, Sachdev, Perminder S, Saddler, Adam, Sadee, Bashdar Abuzed, Sadeghi, Masoumeh, Saeb, Mohammad Reza, Saeed, Umar, Safi, Sher Zaman, Sagar, Rajesh, Saghafi, Alireza, Sagoe, Dominic, Sahebkar, Amirhossein, Sahoo, Pragyan Monalisa, Sajid, Mirza Rizwan, Salam, Nasir, Salamati, Payman, Salami, Afeez Abolarinwa, Saleh, Mohamed A, Salehi, Leili, Salem, Marwa Rashad, Salemcity, Aanuoluwa James, Salimi, Sohrab, Samadi Kafil, Hossein, Samargandy, Saad, Samodra, Yoseph Leonardo, Samy, Abdallah M, Sanabria, Juan, Sanna, Francesca, Santric-Milicevic, Milena M, Sao Jose, Bruno Piassi, Saraswathy, Sivan Yegnanarayana Iyer, Saravanan, Aswini, Sarmiento-Suárez, Rodrigo, Sarode, Gargi Sachin, Sarode, Sachin C, Sartorius, Benn, Satpathy, Maheswar, Sayeed, Abu, Scarmeas, Nikolaos, Schaarschmidt, Benedikt Michael, Schinckus, Christophe, Schuermans, Art, Schumacher, Austin E, Schutte, Aletta Elisabeth, Schwebel, David C, Schwendicke, Falk, Selvaraj, Siddharthan, Semreen, Mohammad H, Senapati, Sabyasachi, Sengupta, Pallav, Senthilkumaran, Subramanian, Serban, Dragos, Sethi, Yashendra, Seylani, Allen, Shafie, Mahan, Shah, Pritik A, Shahbandi, Ataollah, Shahid, Samiah, Shahid, Wajeehah, Shahsavari, Hamid R, Shahwan, Moyad Jamal, Shaikh, Masood Ali, Shalash, Ali S, Shamekh, Ali, Shamim, Muhammad Aaqib, Shanawaz, Mohd, Shankar, Abhishek, Shannawaz, Mohammed, Sharath, Medha, Sharfaei, Sadaf, Sharifan, Amin, Sharifi-Rad, Javad, Sharma, Anupam, Sharma, Manoj, Sharma, Saurab, Sharma, Vishal, Shastry, Rajesh P, Shayan, Maryam, Shekhar, Shashank, Shenoy, Rekha R, Shetty, Mahabalesh, Shetty, Pavanchand H, Shetty, Premalatha K, Shi, Peilin, Shiani, Amir, Shigematsu, Mika, Shimels, Tariku, Shiri, Rahman, Shittu, Aminu, Shiue, Ivy, Shivakumar, K M, Shool, Sina, Shorofi, Seyed Afshin, Shrestha, Sunil, Shuval, Kerem, Si, Yafei, Siddig, Emmanuel Edwar, Sidhu, Jaspreet Kaur, Silva, João Pedro, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Silva, Thales Philipe R, Simpson, Colin R, Simpson, Kyle E, Singh, Abhinav, Singh, Balbir Bagicha, Singh, Baljinder, Singh, Harmanjit, Singh, Jasbir, Singh, Paramdeep, Singh, Puneetpal, Skou, Søren T, Smith, Georgia, Sobia, Farrukh, Socea, Bogdan, Solanki, Shipra, Soleimani, Hamidreza, Soliman, Sameh S M, Song, Yi, Soyiri, Ireneous N, Spartalis, Michael, Spearman, Sandra, Sreeramareddy, Chandrashekhar T, Stanaway, Jeffrey D, Stanikzai, Muhammad Haroon, Starodubova, Antonina V, Stein, Dan J, Steiner, Caitlyn, Steiropoulos, Paschalis, Stockfelt, Leo, Stokes, Mark A, Straif, Kurt, Subedi, Narayan, Suliankatchi Abdulkader, Rizwan, Sultana, Abida, Sun, Jing, Sundström, Johan, Swain, Chandan Kumar, Szarpak, Lukasz, Szeto, Mindy D, Tabaee Damavandi, Payam, Tabarés-Seisdedos, Rafael, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabb, Karen M, Tabche, Celine, Tabish, Mohammad, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Taiba, Jabeen, Talaat, Iman M, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tat, Nathan Y, Tavakoli Oliaee, Razieh, Tavangar, Seyed Mohammad, Taveira, Nuno, Tbakhi, Abdelghani, Tehrani, Hadi, Temsah, Mohamad-Hani, Teramoto, Masayuki, Tesfaye, Behailu Terefe, Teye-Kwadjo, Enoch, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapar, Rekha, Thayakaran, Rasiah, Thirunavukkarasu, Sathish, Thomas, Nihal, Thygesen, Lau Caspar, Ticoalu, Jansje Henny Vera, Timalsena, Dinesh, Tiruye, Tenaw Yimer, Tiwari, Krishna, Tomo, Sojit, Tonelli, Marcello, Topor-Madry, Roman, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Tran, An Thien, Tran, Jasmine T, Tran, Nghia Minh, Tran, Thang Huu, Trico, Domenico, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Turnock, Steven T, Udoh, Arit, Ullah, Atta, Ullah, Saeed, Ullah, Sana, Umakanthan, Srikanth, Umar, Muhammad, Umar, Shehu Salihu, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Usman, Jibrin Sammani, Vahdati, Sanaz, Vaithinathan, Asokan Govindaraj, Vakili, Omid, Valizadeh, Rohollah, Van den Eynde, Jef, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vasic, Milena, Venketasubramanian, Narayanaswamy, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Vijayageetha, Mathavaswami, Villafañe, Jorge Hugo, Vinayak, Manish, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Vohra, Karn, Vos, Theo, Wadood, Abdul Wadood, Waheed, Yasir, Wang, Fang, Wang, Shaopan, Wang, Shu, Wang, Yanqing, Wang, Yanzhong, Wang, Yuan-Pang, Wanjau, Mary Njeri, Waqas, Muhammad, Ward, Paul, Waris, Abdul, Wassie, Emebet Gashaw, Watson, Stefanie, Weaver, Marcia R, Weerakoon, Kosala Gayan, Weintraub, Robert G, Weldetinsaa, Haftom Legese Legese, Wells, Katherine M, Wen, Yi Feng, Westerman, Ronny, Wiangkham, Taweewat, Wickramasinghe, Dakshitha Praneeth, Widowati, Evi, Wojewodzic, Marcin W, Woldeyes, Dawit Habte, Wolf, Axel Walter, Wolfe, Charles D A, Wu, Chenkai, Wu, Dongze, Wu, Felicia, Wu, Jiayuan, Wu, Zenghong, Wulf Hanson, Sarah, Xiao, Hong, Xu, Suowen, Yadav, Rakesh, Yamagishi, Kazumasa, Yang, Danting, Yano, Yuichiro, Yarahmadi, Amir, Yazdani Nia, Iman, Ye, Pengpeng, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yezli, Saber, Yiğit, Arzu, Yiğit, Vahit, Yigzaw, Zeamanuel Anteneh, Yin, Dehui, Yip, Paul, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yu, Chuanhua, Yu, Elaine A, Yu, Yong, Yuan, Chun-Wei, Yusuf, Hadiza, Zafar, Uzma, Zafari, Nima, Zahid, Mondal Hasan, Zakham, Fathiah, Zaki, Nazar, Zerfu, Taddese Alemu, Zhang, Haijun, Zhang, Jingya, Zhang, Liqun, Zhang, Yunquan, Zhang, Zhiqiang, Zhao, Xiu-Ju George, Zhao, Yang, Zhao, Zhongyi, Zhong, Chenwen, Zhou, Bolun, Zhou, Juexiao, Zhou, Shangcheng, Zhu, Bin, Zhumagaliuly, Abzal, Zielińska, Magdalena, Zoghi, Ghazal, Zumla, Alimuddin, Zyoud, Sa'ed H, Zyoud, Samer H, Smith, Amanda E, and Murray, Christopher J L
Published: 2024

3. Continual Few-shot Event Detection via Hierarchical Augmentation Networks

Author: Zhang, Chenlong, Cao, Pengfei, Chen, Yubo, Liu, Kang, Zhang, Zhiqiang, Sun, Mengshu, Zhao, Jun, Zhang, Chenlong, Cao, Pengfei, Chen, Yubo, Liu, Kang, Zhang, Zhiqiang, Sun, Mengshu, and Zhao, Jun
Abstract: Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples. To mitigate these challenges, we propose a memory-based framework: Hierarchical Augmentation Networks (HANet). To memorize previous event types with limited memory, we incorporate prototypical augmentation into the memory set. For the issue of learning new event types in few-shot scenarios, we propose a contrastive augmentation module for token representations. Despite comparing with previous state-of-the-art methods, we also conduct comparisons with ChatGPT. Experiment results demonstrate that our method significantly outperforms all of these methods in multiple continual few-shot event detection tasks., Comment: Accepted to LREC-COLING 2024
Published: 2024

4. Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm

Author: Liu, Lei, Yang, Xiaoyan, Li, Fangzhou, Chi, Chenfei, Shen, Yue, Zhang, Shiwei Lyu Ming, Ma, Xiaowei, Lyu, Xiangguo, Ma, Liya, Zhang, Zhiqiang, Xue, Wei, Huang, Yiran, Gu, Jinjie, Liu, Lei, Yang, Xiaoyan, Li, Fangzhou, Chi, Chenfei, Shen, Yue, Zhang, Shiwei Lyu Ming, Ma, Xiaowei, Lyu, Xiangguo, Ma, Liya, Zhang, Zhiqiang, Xue, Wei, Huang, Yiran, and Gu, Jinjie
Abstract: Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural language. Ensuring the safe and reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.
Published: 2024

5. Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning

Author: Wang, Daixin, Zhang, Zhiqiang, Zhao, Yeyu, Huang, Kai, Kang, Yulin, Zhou, Jun, Wang, Daixin, Zhang, Zhiqiang, Zhao, Yeyu, Huang, Kai, Kang, Yulin, and Zhou, Jun
Abstract: User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user individual features regarding his own profiles and behaviors and build a binary-classification model to make default predictions. However, these methods cannot get satisfied results, especially for users with limited information. Although recent efforts suggest that default prediction can be improved by social relations, they fail to capture the higher-order topology structure at the level of small subgraph patterns. In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction. Specifically, to solve the problem of weak connectivity in motif-based graphs, we design the motif-based gating mechanism. It utilizes the information learned from the original graph with good connectivity to strengthen the learning of the higher-order structure. And considering that the motif patterns of different samples are highly unbalanced, we propose a curriculum learning mechanism on the whole learning process to more focus on the samples with uncommon motif distributions. Extensive experiments on one public dataset and two industrial datasets all demonstrate the effectiveness of our proposed method.
Published: 2024

6. Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling

Author: Zhu, Dingyuan, Wang, Daixin, Zhang, Zhiqiang, Kuang, Kun, Zhang, Yan, Kang, Yulin, Zhou, Jun, Zhu, Dingyuan, Wang, Daixin, Zhang, Zhiqiang, Kuang, Kun, Zhang, Yan, Kang, Yulin, and Zhou, Jun
Abstract: Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informative enough to capture the unobserved and complex hidden factors regarding the uplift. Furthermore, uplift modeling scenario usually has scarce labeled data, especially for the treatment group, which also poses a great challenge for model training. Considering that the neighbors' features and the social relationships are very informative to characterize a user's uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. Specifically, we design the first estimator based on a class-transformed target. The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift. When the outcome is discrete, we further design the other uplift estimator based on our defined partial labels, which is able to utilize more labeled data from both the treatment and control groups, to further alleviate the label scarcity problem. Comprehensive experiments on a public dataset and two industrial datasets show a superior performance of our proposed framework over state-of-the-art methods under various evaluation metrics. The proposed algorithms have been deployed online to serve real-world uplift estimation scenarios.
Published: 2024

7. ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models

Author: Xu, Jun, Sun, Mengshu, Zhang, Zhiqiang, Zhou, Jun, Xu, Jun, Sun, Mengshu, Zhang, Zhiqiang, and Zhou, Jun
Abstract: Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction, have certain limitations. Extracting structured information from natural language that deviates from known schemas or instructions has proven challenging for previous prompt-based methods. This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured information from natural language. In this paper, we present ChatUIE, an innovative unified information extraction framework built upon ChatGLM. Simultaneously, reinforcement learning is employed to improve and align various tasks that involve confusing and limited samples. Furthermore, we integrate generation constraints to address the issue of generating elements that are not present in the input. Our experimental results demonstrate that ChatUIE can significantly improve the performance of information extraction with a slight decrease in chatting ability., Comment: Accepted by LREC-COLING 2024
Published: 2024

8. Can Small Language Models be Good Reasoners for Sequential Recommendation?

Author: Wang, Yuling, Tian, Changxin, Hu, Binbin, Yu, Yanhua, Liu, Ziqi, Zhang, Zhiqiang, Zhou, Jun, Pang, Liang, Wang, Xiao, Wang, Yuling, Tian, Changxin, Hu, Binbin, Yu, Yanhua, Liu, Ziqi, Zhang, Zhiqiang, Zhou, Jun, Pang, Liang, and Wang, Xiao
Abstract: Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations empowered by LLMs. Firstly, user behavior patterns are often complex, and relying solely on one-step reasoning from LLMs may lead to incorrect or task-irrelevant responses. Secondly, the prohibitively resource requirements of LLM (e.g., ChatGPT-175B) are overwhelmingly high and impractical for real sequential recommender systems. In this paper, we propose a novel Step-by-step knowLedge dIstillation fraMework for recommendation (SLIM), paving a promising path for sequential recommenders to enjoy the exceptional reasoning capabilities of LLMs in a "slim" (i.e., resource-efficient) manner. We introduce CoT prompting based on user behavior sequences for the larger teacher model. The rationales generated by the teacher model are then utilized as labels to distill the downstream smaller student model (e.g., LLaMA2-7B). In this way, the student model acquires the step-by-step reasoning capabilities in recommendation tasks. We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios. Extensive experiments demonstrate the effectiveness of SLIM over state-of-the-art baselines, and further analysis showcasing its ability to generate meaningful recommendation reasoning at affordable costs., Comment: Accepted by TheWebConf (WWW) 2024
Published: 2024

9. Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning

Author: He, Zhitao, Cao, Pengfei, Jin, Zhuoran, Chen, Yubo, Liu, Kang, Zhang, Zhiqiang, Sun, Mengshu, Zhao, Jun, He, Zhitao, Cao, Pengfei, Jin, Zhuoran, Chen, Yubo, Liu, Kang, Zhang, Zhiqiang, Sun, Mengshu, and Zhao, Jun
Abstract: Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resource languages, leaving more challenging document-level ECI (DECI) with low-resource languages under-explored. In this paper, we propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning (GIMC) for zero-shot cross-lingual document-level ECI. Specifically, we introduce a heterogeneous graph interaction network to model the long-distance dependencies between events that are scattered over a document. Then, to improve cross-lingual transferability of causal knowledge learned from the source language, we propose a multi-granularity contrastive transfer learning module to align the causal representations across languages. Extensive experiments show our framework outperforms the previous state-of-the-art model by 9.4% and 8.2% of average F1 score on monolingual and multilingual scenarios respectively. Notably, in the multilingual scenario, our zero-shot framework even exceeds GPT-3.5 with few-shot learning by 24.3% in overall performance., Comment: Accepted at LREC-COLING 2024
Published: 2024

10. Non-autoregressive Generative Models for Reranking Recommendation

Author: Ren, Yuxin, Yang, Qiya, Wu, Yichun, Xu, Wei, Wang, Yalong, Zhang, Zhiqiang, Ren, Yuxin, Yang, Qiya, Wu, Yichun, Xu, Wei, Wang, Yalong, and Zhang, Zhiqiang
Abstract: Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items. The key challenge of reranking lies in the exploration of optimal sequences within the combinatorial space of permutations. Recent research proposes a generator-evaluator learning paradigm, where the generator generates multiple feasible sequences and the evaluator picks out the best sequence based on the estimated listwise score. The generator is of vital importance, and generative models are well-suited for the generator function. Current generative models employ an autoregressive strategy for sequence generation. However, deploying autoregressive models in real-time industrial systems is challenging. To address these issues, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. To tackle challenges such as sparse training samples and dynamic candidates, we introduce a matching model. Considering the diverse nature of user feedback, we employ a sequence-level unlikelihood training objective to differentiate feasible sequences from unfeasible ones. Additionally, to overcome the lack of dependency modeling in non-autoregressive models regarding target items, we introduce contrastive decoding to capture correlations among these items. Extensive offline experiments validate the superior performance of NAR4Rec over state-of-the-art reranking methods. Online A/B tests reveal that NAR4Rec significantly enhances the user experience. Furthermore, NAR4Rec has been fully deployed in a popular video app Kuaishou with over 300 million daily active users., Comment: Accepted by KDD 2024
Published: 2024

11. MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

Author: Qian, Hao, Zhou, Hongting, Zhao, Qian, Chen, Hao, Yao, Hongxiang, Wang, Jingwei, Liu, Ziqi, Yu, Fei, Zhang, Zhiqiang, Zhou, Jun, Qian, Hao, Zhou, Hongting, Zhao, Qian, Chen, Hao, Yao, Hongxiang, Wang, Jingwei, Liu, Ziqi, Yu, Fei, Zhang, Zhiqiang, and Zhou, Jun
Abstract: The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment. Traditional sequential methods and graph-based models have been applied in stock movement prediction, but they have limitations in capturing the multifaceted and temporal influences in stock price movements. To address these challenges, the Multi-relational Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a discrete dynamic graph to comprehensively capture multifaceted relations among stocks and their evolution over time. The representation generated from the graph offers a complete perspective on the interrelationships among stocks and associated entities. Additionally, the power of the Transformer structure is leveraged to encode the temporal evolution of multiplex relations, providing a dynamic and effective approach to predicting stock investment. Further, our proposed MDGNN framework achieves the best performance in public datasets compared with state-of-the-art (SOTA) stock investment methods., Comment: 9 pages, 3 figures, accepted by AAAI 2024
Published: 2024

12. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

Author: Wang, Junjie, Yang, Dan, Hu, Binbin, Shen, Yue, Liu, Ziqi, Zhang, Wen, Gu, Jinjie, Zhang, Zhiqiang, Wang, Junjie, Yang, Dan, Hu, Binbin, Shen, Yue, Liu, Ziqi, Zhang, Wen, Gu, Jinjie, and Zhang, Zhiqiang
Abstract: In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. Past research indicates that the reasoning ability of LLMs can be effectively enhanced through chain-of-thought (CoT) prompting. But existing methods still have some limitations: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and questions, making LLMs ineffective in some complex reasoning tasks such as structured language transformation. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation., Comment: Under review
Published: 2024

13. A Comprehensive Study of Knowledge Editing for Large Language Models

Author: Zhang, Ningyu, Yao, Yunzhi, Tian, Bozhong, Wang, Peng, Deng, Shumin, Wang, Mengru, Xi, Zekun, Mao, Shengyu, Zhang, Jintian, Ni, Yuansheng, Cheng, Siyuan, Xu, Ziwen, Xu, Xin, Gu, Jia-Chen, Jiang, Yong, Xie, Pengjun, Huang, Fei, Liang, Lei, Zhang, Zhiqiang, Zhu, Xiaowei, Zhou, Jun, Chen, Huajun, Zhang, Ningyu, Yao, Yunzhi, Tian, Bozhong, Wang, Peng, Deng, Shumin, Wang, Mengru, Xi, Zekun, Mao, Shengyu, Zhang, Jintian, Ni, Yuansheng, Cheng, Siyuan, Xu, Ziwen, Xu, Xin, Gu, Jia-Chen, Jiang, Yong, Xie, Pengjun, Huang, Fei, Liang, Lei, Zhang, Zhiqiang, Zhu, Xiaowei, Zhou, Jun, and Chen, Huajun
Abstract: Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficiencies or undesirable behaviors. There is an increasing interest in efficient, lightweight methods for on-the-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs' behaviors within specific domains while preserving overall performance across various inputs. In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches. Drawing inspiration from educational and cognitive research theories, we propose a unified categorization criterion that classifies knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches. Additionally, we provide an in-depth analysis of knowledge location, which can give a deeper understanding of the knowledge structures inherent within LLMs. Finally, we discuss several potential applications of knowledge editing, outlining its broad and impactful implications., Comment: Ongoing work; 52 pages, 282 citations; benchmark is available at https://huggingface.co/datasets/zjunlp/KnowEdit code is available at https://github.com/zjunlp/EasyEdit paper list is available at https://github.com/zjunlp/KnowledgeEditingPapers
Published: 2024

14. A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions

Author: Liu, Lei, Yang, Xiaoyan, Lei, Junchi, Liu, Xiaoyang, Shen, Yue, Zhang, Zhiqiang, Wei, Peng, Gu, Jinjie, Chu, Zhixuan, Qin, Zhan, Ren, Kui, Liu, Lei, Yang, Xiaoyan, Lei, Junchi, Liu, Xiaoyang, Shen, Yue, Zhang, Zhiqiang, Wei, Peng, Gu, Jinjie, Chu, Zhixuan, Qin, Zhan, and Ren, Kui
Abstract: Large language models (LLMs), such as GPT series models, have received substantial attention due to their impressive capabilities for generating and understanding human-level language. More recently, LLMs have emerged as an innovative and powerful adjunct in the medical field, transforming traditional practices and heralding a new era of enhanced healthcare services. This survey provides a comprehensive overview of Medical Large Language Models (Med-LLMs), outlining their evolution from general to the medical-specific domain (i.e, Technology and Application), as well as their transformative impact on healthcare (e.g., Trustworthiness and Safety). Concretely, starting from the fundamental history and technology of LLMs, we first delve into the progressive adaptation and refinements of general LLM models in the medical domain, especially emphasizing the advanced algorithms that boost the LLMs' performance in handling complicated medical environments, including clinical reasoning, knowledge graph, retrieval-augmented generation, human alignment, and multi-modal learning. Secondly, we explore the extensive applications of Med-LLMs across domains such as clinical decision support, report generation, and medical education, illustrating their potential to streamline healthcare services and augment patient outcomes. Finally, recognizing the imperative and responsible innovation, we discuss the challenges of ensuring fairness, accountability, privacy, and robustness in Med-LLMs applications. Finally, we conduct a concise discussion for anticipating possible future trajectories of Med-LLMs, identifying avenues for the prudent expansion of Med-LLMs. By consolidating above-mentioned insights, this review seeks to provide a comprehensive investigation of the potential strengths and limitations of Med-LLMs for professionals and researchers, ensuring a responsible landscape in the healthcare setting.
Published: 2024

15. Efficient Knowledge Infusion via KG-LLM Alignment

Author: Jiang, Zhouyu, Zhong, Ling, Sun, Mengshu, Xu, Jun, Sun, Rui, Cai, Hui, Luo, Shuhan, Zhang, Zhiqiang, Jiang, Zhouyu, Zhong, Ling, Sun, Mengshu, Xu, Jun, Sun, Rui, Cai, Hui, Luo, Shuhan, and Zhang, Zhiqiang
Abstract: To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategyto enhance the LLM's capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines., Comment: ACL2024 Findings
Published: 2024

16. Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

Author: Gan, Chunjing, Yang, Dan, Hu, Binbin, Zhang, Hanxiao, Li, Siyuan, Liu, Ziqi, Shen, Yue, Ju, Lin, Zhang, Zhiqiang, Gu, Jinjie, Liang, Lei, Zhou, Jun, Gan, Chunjing, Yang, Dan, Hu, Binbin, Zhang, Hanxiao, Li, Siyuan, Liu, Ziqi, Shen, Yue, Ju, Lin, Zhang, Zhiqiang, Gu, Jinjie, Liang, Lei, and Zhou, Jun
Abstract: In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag., Comment: 12 pages
Published: 2024

17. Your decision path does matter in pre-training industrial recommenders with multi-source behaviors

Author: Gan, Chunjing, Hu, Binbin, Huang, Bo, Liu, Ziqi, Ma, Jian, Zhang, Zhiqiang, Zhong, Wenliang, Zhou, Jun, Gan, Chunjing, Hu, Binbin, Huang, Bo, Liu, Ziqi, Ma, Jian, Zhang, Zhiqiang, Zhong, Wenliang, and Zhou, Jun
Abstract: Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER.
Published: 2024

18. Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks

Author: Zhang, Yichi, Hu, Binbin, Chen, Zhuo, Guo, Lingbing, Liu, Ziqi, Zhang, Zhiqiang, Liang, Lei, Chen, Huajun, Zhang, Wen, Zhang, Yichi, Hu, Binbin, Chen, Zhuo, Guo, Lingbing, Liu, Ziqi, Zhang, Zhiqiang, Liang, Lei, Chen, Huajun, and Zhang, Wen
Abstract: Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enhance different downstream tasks, which is a key direction for KG management, maintenance, and applications. Existing works often focus on purely research questions in open domains, or they are not open source due to data security and privacy in real scenarios. Meanwhile, existing studies have not explored the training efficiency and transferability of KGP models in depth. To address these problems, We propose a framework MuDoK to achieve multi-domain collaborative pre-training and efficient prefix prompt tuning to serve diverse downstream tasks like recommendation and text understanding. Our design is a plug-and-play prompt learning approach that can be flexibly adapted to different downstream task backbones. In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research. We evaluated our approach based on constructed KPI benchmarks using diverse backbone models in heterogeneous downstream tasks. The experimental results show that our framework brings significant performance gains, along with its generality, efficiency, and transferability., Comment: Work in progress. Code and data will be open-sourced at https://github.com/zjukg/MuDoK
Published: 2024

19. Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven

Author: Zhang, Dandan, Zhang, Zhiqiang, Chen, Nanguang, Wang, Yun, Zhang, Dandan, Zhang, Zhiqiang, Chen, Nanguang, and Wang, Yun
Abstract: Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, during the time series forecasting process, extracting the local and global time series patterns and understanding the potential nonlinear features among different time observations are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information at different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture potential nonlinear features among time observations. Building upon this, we propose ACNet, an adaptive convolutional network designed to effectively model the local and global temporal dependencies and the nonlinear features between observations in multivariate time series. Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series. The designed nonlinear feature adaptive extraction module captures the nonlinear features among different time observations in the time series. We evaluated the performance of ACNet across twelve real-world datasets. The results indicate that ACNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks with favorable runtime efficiency.
Published: 2024

20. Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

Author: Wei, Shaowei, Wu, Zhengwei, Li, Xin, Wu, Qintong, Zhang, Zhiqiang, Zhou, Jun, Gu, Lihong, Gu, Jinjie, Wei, Shaowei, Wu, Zhengwei, Li, Xin, Wu, Qintong, Zhang, Zhiqiang, Zhou, Jun, Gu, Lihong, and Gu, Jinjie
Abstract: Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the original user behavior sequence. Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation ($S^4$Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. Specifically, we employ online clustering to proficiently group users by their distinct latent intents. Additionally, an adversarial learning strategy is utilized to ensure that the clustering procedure is not affected by the behavior length factor. Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students). Experiments conducted on four real-world datasets validate the effectiveness of the proposed method.
Published: 2024

21. HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning

Author: Han, Rong, Huang, Wenbing, Luo, Lingxiao, Han, Xinyan, Shen, Jiaming, Zhang, Zhiqiang, Zhou, Jun, Chen, Ting, Han, Rong, Huang, Wenbing, Luo, Lingxiao, Han, Xinyan, Shen, Jiaming, Zhang, Zhiqiang, Zhou, Jun, and Chen, Ting
Abstract: Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction tasks, current methods usually employ distinct training for each task. However, each of the tasks is of small size, and such a single-task strategy hinders the models' performance and generalization ability. As some labeled 3D protein datasets are biologically related, combining multi-source datasets for larger-scale multi-task learning is one way to overcome this problem. In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT, consisting of 6 biologically relevant tasks, including affinity prediction and property prediction, integrated from 4 public datasets. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet), which is E(3) equivariant and able to capture heterogeneous relationships between different atoms. Besides, HeMeNet can achieve task-specific learning via the task-aware readout mechanism. Extensive evaluations on our benchmark verify the effectiveness of multi-task learning, and our model generally surpasses state-of-the-art models.
Published: 2024

22. A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data

Author: Chen, Huiling, Shi, Yue, Han, Liangxiu, González-Moreno, Pablo, Dancey, Darren, Huang, Wenjiang, Zhang, Zhiqiang, Liu, Yuanyuan, Huang, Mengning, Miao, Hong, Dai, Min, Chen, Huiling, Shi, Yue, Han, Liangxiu, González-Moreno, Pablo, Dancey, Darren, Huang, Wenjiang, Zhang, Zhiqiang, Liu, Yuanyuan, Huang, Mengning, Miao, Hong, and Dai, Min
Abstract: Introduction: Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. Methods: In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. Results and discussion: The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow
Published: 2023

23. Changes in Physicochemical Properties of Organic Aerosol During Photochemical Aging of Cooking and Burning Emissions

Author: Xu, Weiqi, Li, Zhijie, Zhang, Zhiqiang, Li, Jinjian, Karnezi, Eleni, Lambe, Andrew T., Zhou, Wei, Sun, Jiaxing, Du, Aodong, Li, Ying, Sun, Yele, Xu, Weiqi, Li, Zhijie, Zhang, Zhiqiang, Li, Jinjian, Karnezi, Eleni, Lambe, Andrew T., Zhou, Wei, Sun, Jiaxing, Du, Aodong, Li, Ying, and Sun, Yele
Abstract: Photochemical aging is a key atmospheric processing, yet the changes in physicochemical properties of organic aerosol (OA) during photochemical aging of primary emissions from cooking and burning are less understood. Here we conducted 12 burning (straw, wood, and coal) and cooking experiments to characterize the evolution of size distributions, volatility, and glass transition temperature (Tg) from fresh smoke to aged OA with an equivalent photochemical age of ∼1.5 days using an oxidation flow reactor-thermodenuder-aerosol mass spectrometer system. The mass spectra of OA showed significant changes during photochemical aging, for example, the rapid degradation of m/z 60 for straw and wood burning OA, and the large increase in f44 (fraction of m/z 44 in OA) for all OA. The contributions of non-volatile compounds to the total OA in aged burning OA (1.6%–5.3%) decreased considerably compared with those in fresh burning smoke (2.3%–17.1%), suggesting that photochemical aging of primary emissions for ∼1.5 days produced more volatile secondary OA (SOA). Consistently, the pronounced formation of SOA below 150 nm was observed, and it showed more volatile properties than aged large particles. The Tg of OA under dry conditions (Tg,org) was estimated based on volatility distributions, and the results showed increased Tg,org during photochemical aging of biomass burning and coal combustion emissions, while decreased Tg,org for aged cooking OA. Overall, our results illustrate the different changes in size distributions, volatility, and Tg,org through photochemical aging of different primary emissions, which in turn affect their impacts on radiative forcing and human health.
Published: 2023

24. Improving Recommendation Fairness via Data Augmentation

Author: Chen, Lei, Wu, Le, Zhang, Kun, Hong, Richang, Lian, Defu, Zhang, Zhiqiang, Zhou, Jun, Wang, Meng, Chen, Lei, Wu, Le, Zhang, Kun, Hong, Richang, Lian, Defu, Zhang, Zhiqiang, Zhou, Jun, and Wang, Meng
Abstract: Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA., Comment: The paper is accepted by WWW 2023
Published: 2023

25. Resveratrol Reverses Cigarette Smoke-Induced Urocystic Epithelial–Mesenchymal Transition via Suppression of STAT3 Phosphorylation in SV-HUC-1-Immortalized Human Urothelial Cells [Retraction]

Author: Sun,Hongliang, Zhang,Zhiqiang, Zhang,Taotao, Geng,Hao, Xie,Dongdong, Wang,Yi, Ding,Demao, Zhang,Tao, Yu,Dexin, Sun,Hongliang, Zhang,Zhiqiang, Zhang,Taotao, Geng,Hao, Xie,Dongdong, Wang,Yi, Ding,Demao, Zhang,Tao, and Yu,Dexin
Abstract: Sun H, Zhang Z, Zhang T, et al. Onco Targets Ther. 2019;12:10227–10237 The Editor and Publisher of OncoTargets and Therapy are retracting the published article. Concerns were raised regarding duplicated images in Figures 1 and 3. An investigation found, specifically, The image for Figure 1D, CSE(%) 0, has been duplicated with the same image for Figure 3C, CSE(%) 0; Stattic(µM) 0.5. The image for Figure 3A, CSE(%) 0; Stattic(µM) 0 has been duplicated with the same image for Figure 3A CSE(%) 0.5; Stattic(µM) 0.5. The authors cooperated with the investigation and provided data associated with the reported study. However, despite the authors’ assistance, an adequate explanation for how the images came to be duplicated could not be provided, and the validity of the published work could not be verified. The decision was made to retract the article and the authors wish to apologize for any confusion resulting from the errors that were made. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.
Published: 2023

26. Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

Author: Yang, Dan, Hu, Binbin, Yang, Xiaoyan, Shen, Yue, Zhang, Zhiqiang, Gu, Jinjie, Zhang, Guannan, Yang, Dan, Hu, Binbin, Yang, Xiaoyan, Shen, Yue, Zhang, Zhiqiang, Gu, Jinjie, and Zhang, Guannan
Abstract: With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System., Comment: Accepted by ICDE 2023
Published: 2023

27. GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning

Author: Wang, Weifan, Hu, Binbin, Peng, Zhicheng, Zhong, Mingjie, Zhang, Zhiqiang, Liu, Zhongyi, Zhang, Guannan, Zhou, Jun, Wang, Weifan, Hu, Binbin, Peng, Zhicheng, Zhong, Mingjie, Zhang, Zhiqiang, Liu, Zhongyi, Zhang, Guannan, and Zhou, Jun
Abstract: Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience by quickly obtaining desirable results via textual queries. Unfortunately, users' uncontrollable search customs usually bring vast amounts of long-tail queries, which severely threaten the capability of search models. Inspired by recently emerging graph neural networks (GNNs) and contrastive learning (CL), several efforts have been made in alleviating the long-tail issue and achieve considerable performance. Nevertheless, they still face a few major weaknesses. Most importantly, they do not explicitly utilize the contextual structure between heads and tails for effective knowledge transfer, and intention-level information is commonly ignored for more generalized representations. To this end, we develop a novel framework GARCIA, which exploits the graph based knowledge transfer and intention based representation generalization in a contrastive setting. In particular, we employ an adaptive encoder to produce informative representations for queries and services, as well as hierarchical structure aware representations of intentions. To fully understand tail queries and services, we equip GARCIA with a novel multi-granularity contrastive learning module, which powers representations through knowledge transfer, structure enhancement and intention generalization. Subsequently, the complete GARCIA is well trained in a pre-training&fine-tuning manner. At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios., Comment: Accepted by ICDE 2023
Published: 2023

28. COUPA: An Industrial Recommender System for Online to Offline Service Platforms

Author: Xie, Sicong, Hu, Binbin, Li, Fengze, Liu, Ziqi, Zhang, Zhiqiang, Zhong, Wenliang, Zhou, Jun, Xie, Sicong, Hu, Binbin, Li, Fengze, Liu, Ziqi, Zhang, Zhiqiang, Zhong, Wenliang, and Zhou, Jun
Abstract: Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation, Comment: The short version has been accepted by the SIGIR 2023 Industrial track
Published: 2023
Full Text: View/download PDF

29. Adversarially Robust Neural Architecture Search for Graph Neural Networks

Author: Xie, Beini, Chang, Heng, Zhang, Ziwei, Wang, Xin, Wang, Daixin, Zhang, Zhiqiang, Ying, Rex, Zhu, Wenwu, Xie, Beini, Chang, Heng, Zhang, Ziwei, Wang, Xin, Wang, Daixin, Zhang, Zhiqiang, Ying, Rex, and Zhu, Wenwu
Abstract: Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks., Comment: Accepted as a conference paper at CVPR 2023
Published: 2023

30. Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

Author: Wang, Bikun, Wang, Zhipeng, Zhu, Chenhao, Zhang, Zhiqiang, Wang, Zhichen, Lin, Penghong, Liu, Jingchu, Zhang, Qian, Wang, Bikun, Wang, Zhipeng, Zhu, Chenhao, Zhang, Zhiqiang, Wang, Zhichen, Lin, Penghong, Liu, Jingchu, and Zhang, Qian
Abstract: Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works have been studied in the decision-making and motion planning module. However, the reliability and the stability of the neural network is still full of uncertainty. In this paper, we introduce a hierarchical planning architecture including a high-level grid-based behavior planner and a low-level trajectory planner, which is highly interpretable and controllable. As the high-level planner is responsible for finding a consistent route, the low-level planner generates a feasible trajectory. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios., Comment: 6 pages, 8 figures, accepted by IROS2023
Published: 2023

31. RJUA-QA: A Comprehensive QA Dataset for Urology

Author: Lyu, Shiwei, Chi, Chenfei, Cai, Hongbo, Shi, Lei, Yang, Xiaoyan, Liu, Lei, Chen, Xiang, Zhao, Deng, Zhang, Zhiqiang, Lyu, Xianguo, Zhang, Ming, Li, Fangzhou, Ma, Xiaowei, Shen, Yue, Gu, Jinjie, Xue, Wei, Huang, Yiran, Lyu, Shiwei, Chi, Chenfei, Cai, Hongbo, Shi, Lei, Yang, Xiaoyan, Liu, Lei, Chen, Xiang, Zhao, Deng, Zhang, Zhiqiang, Lyu, Xianguo, Zhang, Ming, Li, Fangzhou, Ma, Xiaowei, Shen, Yue, Gu, Jinjie, Xue, Wei, and Huang, Yiran
Abstract: We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}., Comment: An initial version
Published: 2023

32. Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation

Author: Gan, Chunjing, Yang, Dan, Hu, Binbin, Liu, Ziqi, Shen, Yue, Zhang, Zhiqiang, Gu, Jinjie, Zhou, Jun, Zhang, Guannan, Gan, Chunjing, Yang, Dan, Hu, Binbin, Liu, Ziqi, Shen, Yue, Zhang, Zhiqiang, Gu, Jinjie, Zhou, Jun, and Zhang, Guannan
Abstract: Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user preferences and desired marketing campaigns where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG) could serve as the critical "bridge" for preference propagation. In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i.e., uncontrollable relation generation of LLMs,insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs. To this end, we propose PAIR, a novel Progressive prompting Augmented mIning fRamework for harvesting marketing-oriented knowledge graph with LLMs. In particular, we reduce the pure relation generation to an LLM based adaptive relation filtering process through the knowledge-empowered prompting technique. Next, we steer LLMs for entity expansion with progressive prompting augmentation,followed by a reliable aggregation with comprehensive consideration of both self-consistency and semantic relatedness. In terms of online serving, we specialize in a small and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality corpus provided by a strong teacher-LLM. Extensive experiments and practical applications in audience targeting verify the effectiveness of the proposed (Light)PAIR.
Published: 2023

33. Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction

Author: Wang, Yakun, Hu, Binbin, Yang, Shuo, Zhu, Meiqi, Zhang, Zhiqiang, Zhang, Qiyang, Zhou, Jun, Ye, Guo, He, Huimei, Wang, Yakun, Hu, Binbin, Yang, Shuo, Zhu, Meiqi, Zhang, Zhiqiang, Zhang, Qiyang, Zhou, Jun, Ye, Guo, and He, Huimei
Abstract: The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a comprehensive analysis, we surprisingly find that current link predictors with dynamic negative samplers (DNSs) suffer from the migration phenomenon between "easy" and "hard" samples, which goes against the preference of DNS of choosing "hard" negatives, thus severely hindering capability. Towards this end, we propose the MeBNS framework, serving as a general plugin that can potentially improve current negative sampling based link predictors. In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner. To effectively guide the learning of MST-GNN, we prepare a Structure enhanced Training Data Generator (STD-Generator) and an Uncertainty based Meta Data Collector (UMD-Collector) for supporting the teacher and student GNN, respectively. Extensive experiments show that the MeBNS achieves remarkable performance across six link prediction benchmark datasets.
Published: 2023

34. PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation

Author: Gan, Chunjing, Huang, Bo, Hu, Binbin, Ma, Jian, Liu, Ziqi, Zhang, Zhiqiang, Zhou, Jun, Zhang, Guannan, Zhong, Wenliang, Gan, Chunjing, Huang, Bo, Hu, Binbin, Ma, Jian, Liu, Ziqi, Zhang, Zhiqiang, Zhou, Jun, Zhang, Guannan, and Zhong, Wenliang
Abstract: To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent. However, the non-negligible gap between the source and diversified target domains poses a considerable challenge to cross-domain recommendation systems, which often leads to performance bottlenecks in industrial settings. While entity graphs have the potential to serve as a bridge between domains, rudimentary utilization still fail to distill useful knowledge and even induce the negative transfer issue. To this end, we propose PEACE, a Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation. For domain gap bridging, PEACE is built upon a multi-interest and entity-oriented pre-training architecture which could not only benefit the learning of generalized knowledge in a multi-granularity manner, but also help leverage more structural information in the entity graph. Then, we bring the prototype learning into the pre-training over source domains, so that representations of users and items are greatly improved by the contrastive prototype learning module and the prototype enhanced attention mechanism for adaptive knowledge utilization. To ease the pressure of online serving, PEACE is carefully deployed in a lightweight manner, and significant performance improvements are observed in both online and offline environments., Comment: Accepted by WSDM 2024
Published: 2023

35. Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework

Author: Gan, Chunjing, Hu, Binbin, Huang, Bo, Zhao, Tianyu, Lin, Yingru, Zhong, Wenliang, Zhang, Zhiqiang, Zhou, Jun, Shi, Chuan, Gan, Chunjing, Hu, Binbin, Huang, Bo, Zhao, Tianyu, Lin, Yingru, Zhong, Wenliang, Zhang, Zhiqiang, Zhou, Jun, and Shi, Chuan
Abstract: In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel M ulti-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, i.e., fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL., Comment: Accepted by SIGIR 2023
Published: 2023

36. Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory

Author: Liu, Lei, Yang, Xiaoyan, Shen, Yue, Hu, Binbin, Zhang, Zhiqiang, Gu, Jinjie, Zhang, Guannan, Liu, Lei, Yang, Xiaoyan, Shen, Yue, Hu, Binbin, Zhang, Zhiqiang, Gu, Jinjie, and Zhang, Guannan
Abstract: Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses. However, such repeated recall-reason steps easily produce biased thoughts, \textit{i.e.}, inconsistent reasoning results when recalling the same history for different questions. On the contrary, humans can keep thoughts in the memory and recall them without repeated reasoning. Motivated by this human capability, we propose a novel memory mechanism called TiM (Think-in-Memory) that enables LLMs to maintain an evolved memory for storing historical thoughts along the conversation stream. The TiM framework consists of two crucial stages: (1) before generating a response, a LLM agent recalls relevant thoughts from memory, and (2) after generating a response, the LLM agent post-thinks and incorporates both historical and new thoughts to update the memory. Thus, TiM can eliminate the issue of repeated reasoning by saving the post-thinking thoughts as the history. Besides, we formulate the basic principles to organize the thoughts in memory based on the well-established operations, (\textit{i.e.}, insert, forget, and merge operations), allowing for dynamic updates and evolution of the thoughts. Furthermore, we introduce Locality-Sensitive Hashing into TiM to achieve efficient retrieval for the long-term conversations. We conduct qualitative and quantitative experiments on real-world and simulated dialogues covering a wide range of topics, demonstrating that equipping existing LLMs with TiM significantly enhances their performance in generating responses for long-term interactions.
Published: 2023

37. Long-tail Augmented Graph Contrastive Learning for Recommendation

Author: Zhao, Qian, Wu, Zhengwei, Zhang, Zhiqiang, Zhou, Jun, Zhao, Qian, Wu, Zhengwei, Zhang, Zhiqiang, and Zhou, Jun
Abstract: Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usually encounter data sparsity issue in real-world scenarios. To address this issue, GCN-based recommendation methods employ contrastive learning to introduce self-supervised signals. Despite their effectiveness, these methods lack consideration of the significant degree disparity between head and tail nodes. This can lead to non-uniform representation distribution, which is a crucial factor for the performance of contrastive learning methods. To tackle the above issue, we propose a novel Long-tail Augmented Graph Contrastive Learning (LAGCL) method for recommendation. Specifically, we introduce a learnable long-tail augmentation approach to enhance tail nodes by supplementing predicted neighbor information, and generate contrastive views based on the resulting augmented graph. To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ generative adversarial networks to ensure that the distribution of the generated tail/head nodes matches that of the original tail/head nodes. Extensive experiments conducted on three benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the uniformity of learned representations and the superiority of LAGCL on long-tail performance. Code is publicly available at https://github.com/im0qianqian/LAGCL, Comment: 17 pages, 6 figures, accepted by ECML/PKDD 2023 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)
Published: 2023

38. BATINet: Background-Aware Text to Image Synthesis and Manipulation Network

Author: Morita, Ryugo, Zhang, Zhiqiang, Zhou, Jinjia, Morita, Ryugo, Zhang, Zhiqiang, and Zhou, Jinjia
Abstract: Background-Induced Text2Image (BIT2I) aims to generate foreground content according to the text on the given background image. Most studies focus on generating high-quality foreground content, although they ignore the relationship between the two contents. In this study, we analyzed a novel Background-Aware Text2Image (BAT2I) task in which the generated content matches the input background. We proposed a Background-Aware Text to Image synthesis and manipulation Network (BATINet), which contains two key components: Position Detect Network (PDN) and Harmonize Network (HN). The PDN detects the most plausible position of the text-relevant object in the background image. The HN harmonizes the generated content referring to background style information. Finally, we reconstructed the generation network, which consists of the multi-GAN and attention module to match more user preferences. Moreover, we can apply BATINet to text-guided image manipulation. It solves the most challenging task of manipulating the shape of an object. We demonstrated through qualitative and quantitative evaluations on the CUB dataset that the proposed model outperforms other state-of-the-art methods., Comment: Accepted to ICIP2023
Published: 2023

39. A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics

Author: Shi, Yue, Ma, Shuhao, Zhao, Yihui, Zhang, Zhiqiang, Shi, Yue, Ma, Shuhao, Zhao, Yihui, and Zhang, Zhiqiang
Abstract: Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. And a physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM)., Comment: 17 pages, 8 Figures
Published: 2023

40. Generative Contrastive Graph Learning for Recommendation

Author: Yang, Yonghui, Wu, Zhengwei, Wu, Le, Zhang, Kun, Hong, Richang, Zhang, Zhiqiang, Zhou, Jun, Wang, Meng, Yang, Yonghui, Wu, Zhengwei, Wu, Le, Zhang, Kun, Hong, Richang, Zhang, Zhiqiang, Zhou, Jun, and Wang, Meng
Abstract: By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning(VGCL) framework for recommendation. Specifically, we leverage variational graph reconstruction to estimate a Gaussian distribution of each node, then generate multiple contrastive views through multiple samplings from the estimated distributions, which builds a bridge between generative and contrastive learning. Besides, the estimated variances are tailored to each node, which regulates the scale of contrastive loss for each node on optimization. Considering the similarity of the estimated distributions, we propose a cluster-aware twofold contrastive learning, a node-level to encourage consistency of a node's contrastive views and a cluster-level to encourage consistency of nodes in a cluster. Finally, extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed model., Comment: This paper is accepted to SIGIR 2023. Code is avaliable: https://github.com/yimutianyang/SIGIR23-VGCL
Published: 2023

41. InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs

Author: Zhang, Dalong, Song, Xianzheng, Hu, Zhiyang, Li, Yang, Tao, Miao, Hu, Binbin, Wang, Lin, Zhang, Zhiqiang, Zhou, Jun, Zhang, Dalong, Song, Xianzheng, Hu, Zhiyang, Li, Yang, Tao, Miao, Hu, Binbin, Wang, Lin, Zhang, Zhiqiang, and Zhou, Jun
Abstract: GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address the above challenges, we propose a scalable system named InferTurbo to boost the GNN inference tasks in industrial scenarios. Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference. The computation of GNNs is expressed in an iteration manner, in which a vertex would gather messages via in-edges and update its state information by forwarding an associated layer of GNNs with those messages and then send the updated information to other vertexes via out-edges. Following the schema, the proposed InferTurbo can be built with alternative backends (e.g., batch processing system or graph computing system). Moreover, InferTurbo introduces several strategies like shadow-nodes and partial-gather to handle nodes with large degrees for better load balancing. With InferTurbo, GNN inference can be hierarchically conducted over the full graph without sampling and redundant computation. Experimental results demonstrate that our system is robust and efficient for inference tasks over graphs containing some hub nodes with many adjacent edges. Meanwhile, the system gains a remarkable performance compared with the traditional inference pipeline, and it can finish a GNN inference task over a graph with tens of billions of nodes and hundreds of billions of edges within 2 hours., Comment: Accepted by ICDE 2023
Published: 2023

42. A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data

Author: Shi, Yue, Han, Liangxiu, González-Moreno, Pablo, Dancey, Darren, Huang, Wenjiang, Zhang, Zhiqiang, Liu, Yuanyuan, Huan, Mengning, Miao, Hong, Dai, Min, Shi, Yue, Han, Liangxiu, González-Moreno, Pablo, Dancey, Darren, Huang, Wenjiang, Zhang, Zhiqiang, Liu, Yuanyuan, Huan, Mengning, Miao, Hong, and Dai, Min
Abstract: Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series., Comment: 24 pages
Published: 2023

43. Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving

Author: Zhang, Zhiqiang, Zhu, Hong, Xie, Meiyi, Zhang, Zhiqiang, Zhu, Hong, and Xie, Meiyi
Abstract: Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. Currently, the study on combination of machine learning and DP is vibrant. In contrast, another widely used artificial intelligence technique, the swarm intelligence (SI) algorithm, has received little attention in the context of DP even though it also triggers privacy concerns. For this reason, this paper attempts to combine DP and SI for the first time, and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF). Based on the exponential mechanism, this framework can easily develop existing SI algorithms into the private versions. As examples, we apply the proposed DPSIAF to four popular SI algorithms, and corresponding analyses demonstrate its effectiveness. More interestingly, the experimental results show that, for our private algorithms, their performance is not strictly affected by the privacy budget, and one of the private algorithms even owns better performance than its non-private version in some cases. These findings are different from the conventional cognition, which indicates the uniqueness of SI with DP. Our study may provide a new perspective on DP, and promote the synergy between metaheuristic optimization community and privacy computing community.
Published: 2023

44. Description-Enhanced Label Embedding Contrastive Learning for Text Classification

Author: Zhang, Kun, Wu, Le, Lv, Guangyi, Chen, Enhong, Ruan, Shulan, Liu, Jing, Zhang, Zhiqiang, Zhou, Jun, Wang, Meng, Zhang, Kun, Wu, Le, Lv, Guangyi, Chen, Enhong, Ruan, Shulan, Liu, Jing, Zhang, Zhiqiang, Zhou, Jun, and Wang, Meng
Abstract: Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially Pre-trained Language Models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this paper, we employ Self-Supervised Learning (SSL) in model learning process and design a novel self-supervised Relation of Relation (R2) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multi-aspect descriptions for label semantic learning and extend R2-Net to a novel Description-Enhanced Label Embedding network (DELE) from a label embedding perspective. ..., Comment: This paper has been accepted by IEEE Transactions on Neural Networks and Learning Systems
Published: 2023

45. Topological black holes in Einstein-Maxwell and 4D conformal gravities revisited

Author: Wang, Tao, Zhang, Zhiqiang, Kong, Xiangqing, Zhao, Liu, Wang, Tao, Zhang, Zhiqiang, Kong, Xiangqing, and Zhao, Liu
Abstract: The thermodynamics of charged topological black holes (TBHs) with different horizon geometries in $d$-dimensional Einstein-Maxwell and 4-dimensional conformal gravities is revisited using the restricted phase space formalism. The concept of subsystems for black holes is introduced, which enables a precise description for the thermodynamic behaviors of (non-compact) black holes with infinitely large horizon area. The concrete behaviors can be different for TBHs in the same underlying theory but with different horizon geometries, or for those with the same horizon geometry but from different underlying theories. In some concrete thermodynamic processes, the non-compact black holes in 4-dimensional conformal gravity can reach certain state with zero entropy but nonvanishing temperature, which is physically unsounded. This suggests a novel use of black hole thermodynamics as a tool for testing the viability of gravity models or constraining the model parameters or integration constants. The high and low temperature limits of the TBHs are also considered, and it is found that, at high temperature, all TBHs behave like low temperature phonon gases, while at low temperature, all charged as well as neutral hyperbolic TBHs in Einstein-Maxwell gravity behave like strongly degenerated electron gases. Finally, using the concept of subsystems, some conceptual issues in the description for thermal fluctuations in black hole systems are clarified, and the relative thermal fluctuations for finite subsystems are also analyzed in some detail., Comment: 52 pages, 11 pdf figures. Version matching published version
Published: 2022
Full Text: View/download PDF

46. Restricted phase space thermodynamics of charged AdS black holes in conformal gravity

Author: Kong, Xiangqing, Zhang, Zhiqiang, Zhao, Liu, Kong, Xiangqing, Zhang, Zhiqiang, and Zhao, Liu
Abstract: The thermodynamics of charged spherically symmetric AdS black holes in conformal gravity is revisited using the recently proposed restricted phase space (RPS) formalism. This formalism avoids all the bizarreness that arose in the extended phase space formalism for this model. It is found that the charged AdS black holes in this model may belong to a new universality class which is different from all previously studied cases under the RPS formalism. Besides the distinguished isocharge $T-S$ and isothermal $\Phi-Q_e$ behaviors, the absence of Hawking-Page transition is another notable feature. On the other hand, in the high temperature limit, the thermodynamic behavior of the present model become exactly the same as that of the Einstein gravity and the black hole scan models, which add further evidence for the universality of the recently reported correspondence between high temperature AdS black holes and low temperature quantum phonon gases in nonmetallic crystals., Comment: 22 pages, 11 pdf figures
Published: 2022
Full Text: View/download PDF

47. Esthetic Evaluation and Acceptability of Different Hole Designs on Implant Crowns from the Perspective of Patients and Dentists in China

Author: Wen,Cai, Jiang,Rong, Zhang,Zhiqiang, Lei,Bo, Zhong,Yingquan, Zhou,Huangjun, Wen,Cai, Jiang,Rong, Zhang,Zhiqiang, Lei,Bo, Zhong,Yingquan, and Zhou,Huangjun
Abstract: Cai Wen,1â 3 Rong Jiang,4 Zhiqiang Zhang,4 Bo Lei,4 Yingquan Zhong,4 Huangjun Zhou1,3 1Department of Oral Implantology, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, Peopleâs Republic of China; 2Department of VIP Dental Service, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, Peopleâs Republic of China; 3Orofacial Reconstruction and Regeneration Laboratory,The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, Peopleâs Republic of China; 4Department of Dentistry, Peopleâs Hospital of Aba Tibetan and Qiang Autonomous Prefecture, Maâerkang, Sichuan, Peopleâs Republic of ChinaCorrespondence: Cai Wen, Department of Oral Implantology, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, Peopleâs Republic of China, Email wencai@swmu.edu.cnPurpose: Abutment access hole on dental implant crowns may facilitate crown retrieval and reduce cement overflow but present esthetic obstacle for patients. This study aimed to investigate the esthetic evaluation and acceptability of implant crowns with different hole designs from the perspective of patients and dentists.Materials and Methods: Anterior and posterior implant zirconia crowns were fabricated into three types: no hole (NH), 1 mm micro hole (MH), and 2.5 mm regular hole (RH). The NH crown was set as the control, and the anterior and posterior crowns with MH and RH were evaluated. The subjects, who were recruited randomly, were comprised of lay patients (n=60) and professional dentists (n=30). All subjects were invited to evaluate the esthetic performance of MH and RH crowns on a Visual Analog Scale (VAS), rate them from 0 to 10, state their acceptability of these crowns, and label them as acceptable and unacceptable.Results: The ANOVA analysis of the VAS esthetic evaluation showed that the size of the hole, the position of the teeth, and the professional background of th
Published: 2022

48. Interactive Image Manipulation with Complex Text Instructions

Author: Morita, Ryugo, Zhang, Zhiqiang, Ho, Man M., Zhou, Jinjia, Morita, Ryugo, Zhang, Zhiqiang, Ho, Man M., and Zhou, Jinjia
Abstract: Recently, text-guided image manipulation has received increasing attention in the research field of multimedia processing and computer vision due to its high flexibility and controllability. Its goal is to semantically manipulate parts of an input reference image according to the text descriptions. However, most of the existing works have the following problems: (1) text-irrelevant content cannot always be maintained but randomly changed, (2) the performance of image manipulation still needs to be further improved, (3) only can manipulate descriptive attributes. To solve these problems, we propose a novel image manipulation method that interactively edits an image using complex text instructions. It allows users to not only improve the accuracy of image manipulation but also achieve complex tasks such as enlarging, dwindling, or removing objects and replacing the background with the input image. To make these tasks possible, we apply three strategies. First, the given image is divided into text-relevant content and text-irrelevant content. Only the text-relevant content is manipulated and the text-irrelevant content can be maintained. Second, a super-resolution method is used to enlarge the manipulation region to further improve the operability and to help manipulate the object itself. Third, a user interface is introduced for editing the segmentation map interactively to re-modify the generated image according to the user's desires. Extensive experiments on the Caltech-UCSD Birds-200-2011 (CUB) dataset and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our proposed method can enable interactive, flexible, and accurate image manipulation in real-time. Through qualitative and quantitative evaluations, we show that the proposed model outperforms other state-of-the-art methods., Comment: Accepted to WACV2023
Published: 2022

49. Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification

Author: Wang, Xin, Chang, Heng, Xie, Beini, Bian, Tian, Zhou, Shiji, Wang, Daixin, Zhang, Zhiqiang, Zhu, Wenwu, Wang, Xin, Chang, Heng, Xie, Beini, Bian, Tian, Zhou, Shiji, Wang, Daixin, Zhang, Zhiqiang, and Zhu, Wenwu
Abstract: Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated their vulnerability to potentially existent adversarial examples on graph-structured data. Existing approaches are either limited to structure attacks or restricted to local information, urging for the design of a more general attack framework on graph classification, which faces significant challenges due to the complexity of generating local-node-level adversarial examples using the global-graph-level information. To address this "global-to-local" attack challenge, we present a novel and general framework to generate adversarial examples via manipulating graph structure and node features. Specifically, we make use of Graph Class Activation Mapping and its variant to produce node-level importance corresponding to the graph classification task. Then through a heuristic design of algorithms, we can perform both feature and structure attacks under unnoticeable perturbation budgets with the help of both node-level and subgraph-level importance. Experiments towards attacking four state-of-the-art graph classification models on six real-world benchmarks verify the flexibility and effectiveness of our framework., Comment: 13 pages, 7 figures
Published: 2022

50. Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce

Author: Ye, Borui, Yang, Shuo, Hu, Binbin, Zhang, Zhiqiang, He, Youqiang, Huang, Kai, Zhou, Jun, Fang, Yanming, Ye, Borui, Yang, Shuo, Hu, Binbin, Zhang, Zhiqiang, He, Youqiang, Huang, Kai, Zhou, Jun, and Fang, Yanming
Abstract: E-commerce has gone a long way in empowering merchants through the internet. In order to store the goods efficiently and arrange the marketing resource properly, it is important for them to make the accurate gross merchandise value (GMV) prediction. However, it's nontrivial to make accurate prediction with the deficiency of digitized data. In this article, we present a solution to better forecast GMV inside Alipay app. Thanks to graph neural networks (GNN) which has great ability to correlate different entities to enrich information, we propose Gaia, a graph neural network (GNN) model with temporal shift aware attention. Gaia leverages the relevant e-seller' sales information and learn neighbor correlation based on temporal dependencies. By testing on Alipay's real dataset and comparing with other baselines, Gaia has shown the best performance. And Gaia is deployed in the simulated online environment, which also achieves great improvement compared with baselines., Comment: Accepted by ICDE 2022
Published: 2022

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Publication Year Range

Language

Publication Type

Database

Publisher

155 results on '"Zhang, Zhiqiang"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources