Author: "Jin, Yi" / Topic: computer science - Searchworks@Jio Institute Digital Library Search Results

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367 results on '"Jin, Yi"'

1. Semisupervised Semantic Segmentation by Improving Prediction Confidence

Author: Changan Zhu, Guoqiang Jin, Enhong Chen, Huaian Chen, and Jin Yi
Subjects: Discriminator, Computer Networks and Communications, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Boundary (topology), Contrast (statistics), Pattern recognition, Image segmentation, Pascal (programming language), Class (biology), Computer Science Applications, Artificial Intelligence, Segmentation, Artificial intelligence, business, computer, Software, Generator (mathematics), computer.programming_language
Abstract: Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.
Published: 2022

2. EdgePipe: Tailoring Pipeline Parallelism With Deep Neural Networks for Volatile Wireless Edge Devices

Author: Jin Yi Yoon, Jeewoon Kim, HyungJune Lee, and Yeongsin Byeon
Subjects: Edge device, Computer Networks and Communications, business.industry, Computer science, Pipeline (computing), Distributed computing, Deep learning, Bandwidth (signal processing), Computer Science Applications, Hardware and Architecture, Signal Processing, Scalability, Overhead (computing), Wireless, Artificial intelligence, Enhanced Data Rates for GSM Evolution, business, Information Systems
Abstract: As intelligence recently moves to the edge to tackle the problems of privacy, scalability, and network bandwidth in the centralized intelligence, it is necessary to construct an efficient yet robust deep learning model viable at edge devices, which are usually volatile in wireless links and device functionality. The intensive computation burden for deep learning at the edge side necessitates some level of parallel processing via acceleration. We propose EdgePipe, a deep learning framework based on deep neural networks (DNNs) with a mixture of model parallelism and pipeline training for high resource utilization over volatile wireless edge devices. To tackle the volatility problem in wireless links and device functionality, a concept of super neuron is defined to be a group of neurons across adjacent layers, which is the basis of model partitioning at edge devices. The relatively loss-resilient neuron structure prevents the entire forward or backward training paths from being totally broken down due to only some intermittent link or device failure caused by one or few devices. Further, we design a subsequent pipeline training mechanism based on the prior super neuron-based model partitioning for fast convergence with more training data in a fixed timeline. The experimental results have demonstrated that EdgePipe outperforms several counterpart algorithms including PipeDream under the volatile wireless lossy or device malfunctioning environments, while preserving the low interlayer communication overhead.
Published: 2022

3. A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory

Author: Cai, Jin-Yi, Fu, Zhiguo, Girstmair, Kurt, and Kowalczyk, Michael
Subjects: Computational Mathematics, 000 Computer science, knowledge, general works, Computational Theory and Mathematics, General Mathematics, Computer Science, Theoretical Computer Science
Abstract: Suppose \varphi and \psi are two angles satisfying \tan(\varphi) = 2 \tan(\psi) > 0. We prove that under this condition \varphi and \psi cannot be both rational multiples of \pi. We use this number theoretic result to prove a classification of the computational complexity of spin systems on k-regular graphs with general (not necessarily symmetric) real valued edge weights. We establish explicit criteria, according to which the partition functions of all such systems are classified into three classes: (1) Polynomial time computable, (2) \#P-hard in general but polynomial time computable on planar graphs, and (3) \#P-hard on planar graphs. In particular problems in (2) are precisely those that can be transformed to a form solvable by the Fisher-Kasteleyn-Temperley algorithm by a holographic reduction.
Published: 2023

4. A Knowledge-Based Multiobjective Memetic Algorithm for Green Job Shop Scheduling With Variable Machining Speeds

Author: Liang Gao, Jin Yi, Biao Zhang, Jianhui Mou, and Chao Lu
Subjects: Mathematical optimization, Job shop scheduling, Linear programming, Computer Networks and Communications, business.industry, Computer science, Scheduling (production processes), Genetic operator, Multi-objective optimization, Computer Science Applications, Variable (computer science), Control and Systems Engineering, Memetic algorithm, Local search (optimization), Electrical and Electronic Engineering, business, Information Systems
Abstract: Nowadays, green manufacturing has become one of the hot topics in both academia and industry because of global warming and the greenhouse effect. Among manufacturing systems, job-shop scheduling plays a key role due to its wide applications. In this article, we study a green job-shop scheduling problem with variable machining speeds (JSPVMS) aiming at minimizing the makespan as well as the total energy consumption (TEC). First, a new mixed-integer linear programming model is formulated for this green JSPVMS. Then, a new knowledge-based multiobjective memetic algorithm (MOMA) is developed to address this problem. In our MOMA, a novel decoding scheme based on the problem property is well designed to obtain better tradeoff solutions between makespan and TEC. Furthermore, a novel local search is proposed to search for promising nondominated solutions by discovering problem-specific knowledge. Additionally, the proposed MOMA utilizes the advantage of the genetic operator and local search to balance the exploration and exploitation. The effectiveness of each improvement component (decoding scheme and local search) in our MOMA is verified by comparing experiments among different MOMA variants. Finally, we compare our MOMA with several well-known multiobjective optimization algorithms (i.e., NSGA-II, SPEA2, and MOEA/D) on JSPVMS instances. Experimental results indicate that our MOMA significantly outperforms the other algorithms on most of the instances.
Published: 2022

5. Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

Author: Chao Lu, Jin Yi, Xinyu Li, and Liang Gao
Subjects: Mathematical optimization, Optimization problem, Job shop scheduling, business.industry, Computer science, Tardiness, 020208 electrical & electronic engineering, Scheduling (production processes), 02 engineering and technology, Energy consumption, Flow shop scheduling, Multi-objective optimization, Computer Science Applications, Scheduling (computing), Taguchi methods, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, Local search (optimization), Electrical and Electronic Engineering, business, Greedy algorithm, Information Systems
Abstract: Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.
Published: 2021

6. ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

Author: Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Jabal, Kamal Abu, Zayyad, Hiba Abu, Acharya, Subhash, Acker, Andrew, Adachi, Shingo, Adam, Elisabeth, Adriano, Enrico, Adrião, Diana, Ageel, Saleh Al, Ahmed, Shakeel, Aiello, Marina, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Qasim, Eman Al, Al-dabbous, Tala, Al-Fares, Abdulrahman, Alalqam, Razi, Alberti, Angela, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Alfoudri, Huda, Ali, Adam, Ali, Imran, Shah, Naseem Ali, Sheikh, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, João Melo, Alves, Rita, Amaral, Maria, Amira, Nur, Ammerlaan, Heidi, Ampaw, Phoebe, Andini, Roberto, Andrejak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Anwar, Kazi Rubayet, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Arancibia, Francisco, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean-Benoît, Arnold-Day, Christel, Aroca, Ana, Arora, Lovkesh, Arora, Rakesh, Artaud-Macari, Elise, Aryal, Diptesh, Asaki, Motohiro, Asensio, Angel, Ashley, Elizabeth, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W., Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre-Jensen, Roar, Baig, Nadia, Baillie, J. Kenneth, Baird, J Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani-Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S., Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Bartone, Cheryl, Baruch, Joaquín, Bashir, Mustehan, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beiruti, Karine, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Benkerrou, Dehbia, Bennett, Suzanne, Bento, Luís, Berdal, Jan-Erik, Bergeaud, Delphine, Bergin, Hazel, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Betz, Adam, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianchi, Isabella, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Bin Humaid, Felwa, Bin Kamarudin, Mohd Nazlin, Bissuel, François, Biston, Patrick, Bitker, Laurent, Bitton, Jonathan, Blanco-Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Blumberg, Lucille, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne-Hélène, Bolze, Pierre-Adrien, Bompart, François, Bonelli, Patrizia, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Bounphiengsy, Thipsavanh, Bountthasavong, Latsaniphone, Boureau, Anne-Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer-Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Alex, Browne, Shaunagh, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse-Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G., Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Callahan, Mia, Calligy, Kate, Calvache, Jose Andres, Caminiti, Caterina, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux-Paz, Pauline, Cárcel, Sheila, Cardellino, Chiara Simona, Cardoso, Filipa, Cardoso, Filipe, Cardoso, Nelson, Cardoso, Sofia, Carelli, Simone, Carlacci, Francesca, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie-Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Carvalho, Leonor, Casanova, Maire-Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor-Alexandre, Guylaine, Castrillón, Henry, Castro, Ivo, Catarino, Ana, Catherine, François-Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Cervantes-Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Auger, Christelle Chantalat, Chapplain, Jean-Marc, Chas, Julie, Chatterjee, Allegra, Chaudry, Mobin, Iñiguez, Jonathan Samuel Chávez, Chen, Anjellica, Chen, Yih-Sharng, Cheng, Matthew Pellan, Cheret, Antoine, Chetta, Alfredo Antonio, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Hwa Jin, Cho, Sung-Min, Cholley, Bernard, Chommanam, Danoy, Chopin, Marie-Charlotte, Chow, Ting Soo, Chow, Yock Ping, Christy, Nathaniel, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Herreros, José Miguel Cisneros, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Emma, Clarke, Jennifer, Granado, Rolando Claure-Del, Clohisey, Sara, Cobb, Perren J., Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connor, Marie, Conrad, Anne, Contreras, Sofía, Conway, Elaine, Cooke, Graham S., Copland, Mary, Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Costigan, Emma, Couffignal, Camille, Couffin-Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, D’Orleans, Charles Crepy, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Berm, Juan Luis Cruz, Rojo, Jaime Cruz, Csete, Marc, Cucino, Alberto, Cullen, Ailbhe, Cummings, Matthew, Curley, Ger, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, D’Amico, Federico, D’Aragon, Frédérick, D’Ortenzio, Eric, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al-Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A, Dantas, Jorge, de Boer, Mark, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, Oliveira, Ana Isabel de Pinho, De Rosa, Rosanna, De Rose, Cristina, de Silva, Thushan, de Vries, Peter, Deacon, Jillian, Dean, David, Debard, Alexa, DeBenedictis, Bianca, Debray, Marie-Pierre, DeCastro, Nathalie, Dechert, William, Deconninck, Lauren, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delaveuve, Eric, Delavigne, Karen, Delfos, Nathalie M., Deligiannis, Ionna, Dell’Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Dias, Andrea, Diaz, Juan Jose, Diaz, Priscila, Diaz, Rodrigo, Didier, Kévin, Diehl, Jean-Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djossou, Félix, Docherty, Annemarie B., Doherty, Helen, Dondorp, Arjen M, Dong, Andy, Donnelly, Christl A., Donnelly, Maria, Donohue, Chloe, Donohue, Sean, Donohue, Yoann, Doran, Peter, Dorival, Céline, Douangdala, Phouvieng, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dror, Amiel A., Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, Dubot-Pérès, Audrey, Ducancelle, Alexandre, Duculan, Toni, Dudman, Susanne, Duggal, Abhijit, Dunand, Paul, Dunning, Jake, Duplaix, Mathilde, Durante-Mangoni, Emanuele, Durham III, Lucian, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol-Riise, Anne Margarita, Ean, Sim Choon, Echeverria-Villalobos, Marco, Economopoulos, Giorgio, Edelstein, Michael, Egan, Siobhan, Eggesbø, Linn Margrete, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord-Dale, Merete, Eloy, Philippine, Elshazly, Tarek, Elyazar, Iqbal, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Esposito-Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Isabelle, Fabre, Marc, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J., Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Feeney, Laura, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François-Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferrari, Carlo, Ferraz, Mário, Ferreira, Benigno, Ferreira, Isabel, Ferreira, Sílvia, Ferrer-Roca, Ricard, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Finlayson, William, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florio, Letizia Lucia, Flynn, Brigid, Flynn, Deirdre, Fogliazza, Federica, Foley, Claire, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A., Fraher, Marianne, Franch-Llasat, Diego, Fraser, Christophe, Fraser, John F, Freire, Marcela Vieira, Ribeiro, Ana Freitas, French, Craig, Friedrich, Caren, Fritz, Ricardo, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, Argin, G, Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean-Charles, Gagné, Nathalie, Gagneux-Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Curto, Elena Gallego, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia-Diaz, Julia, Garcia-Gallo, Esteban, Garimella, Navya, Garofalo, Federica, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Giaquinto, Carlo, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Gitaka, Jesse, Giwangkancana, Gezy, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean-Christophe, Goh, Jin Yi, Golob, Jonathan, Gomes, Rui, Gomez, Kyle, Gómez-Junyent, Joan, Gominet, Marie, Gonçalves, Bronner P., Gonzalez, Alicia, Gordon, Patricia, Gorelik, Yanay, Gorenne, Isabelle, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Grazioli, Lorenzo, Green, Christopher A., Greene, Courtney, Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Lordemann, Anja Grosse, Gruner, Heidi, Gu, Yusing, Guarracino, Fabio, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerguerian, Anne-Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, de Castro, Maisa Guimarães, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hackmann, Amy, Hadri, Nadir, Haidri, Fakhir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L., Hamidfar, Rebecca, Hammarström, Bato, Hammond, Terese, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Harrison, Samuel Bernard Ekow, Hartman, Alan, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hashmi, Madiha, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez-Trujillo, Aquiles, Hentzien, Maxime, Hernandez-Montfort, Jaime, Herr, Daniel, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Dawn, Higgins, Eibhilin, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hirkani, Haider, Hitoto, Hikombo, Ho, Antonia, Ho, Yi Bin, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Horcajada, Juan Pablo, Hoshino, Koji, Hoshino, Kota, Houas, Ikram, Hough, Catherine L., Houltham, Stuart, Hsu, Jimmy Ming-Yang, Hulot, Jean-Sébastien, Huo, Stella, Hussain, Iqbal, Ijaz, Samreen, Illes, Hajnal-Gabriela, Imbert, Patrick, Imran, Mohammad, Sikander, Rana Imran, Imtiaz, Aftab, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ito, Asami, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jamieson, Nina, Jaquet, Pierre, Jassat, Waasila, Jaud-Fischer, Coline, Jaureguiberry, Stéphane, Javidfar, Jeffrey, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo-Sotomayor, Ruth, Joe, Ong Yiaw, García, Ruth N. Jorge, Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, June, Jennifer, Jung, Anna, Jung, Hanna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalicinska, Alina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Kamaluddin, Muhammad Aisar Ayadi, Kamaruddin, Zul Amali Che, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Kanwal, Darakhshan, Kanyawati, Dyah, Karpayah, Pratap, Karsies, Todd, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kawasaki, Tatsuya, Kay, Christy, Keane, Hannah, Keating, Seán, Kedia, Pulak, Kelly, Andrea, Kelly, Aoife, Kelly, Claire, Kelly, Niamh, Kelly, Sadie, Kelly, Yvelynne, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Keomany, Sommay, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Kestelyn, Evelyne, Khalid, Imrana, Khalid, Osama, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Khawaja, Amin, Kherajani, Krish, Kho, Michelle E., Khoo, Denisa, Khoo, Ryan, Khoo, Saye, Khoso, Nasir, Kiat, Khor How, Kida, Yuri, Kihuga, Harrison, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kindgen-Milles, Detlef, King, Alexander, Kitamura, Nobuya, Kjetland, Eyrun Floerecke Kjetland, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R, Kobbe, Robin, Kodippily, Chamira, Vasconcelos, Malte Kohns, Koirala, Sabin, Komatsu, Mamoru, Korten, Volkan, Kosgei, Caroline, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Sudhir, Krishnan, Vinothini, Kruglova, Oksana, Kumar, Deepali, Kumar, Ganesh, Kumar, Mukesh, Vecham, Pavan Kumar, Kuriakose, Dinesh, Kurtzman, Ethan, Kusumastuti, Neurinda Permata, Kutsogiannis, Demetrios, Kutsyna, Galyna, Kyriakoulis, Konstantinos, L’Her, Erwan, Lachatre, Marie, Lacoste, Marie, Laffey, John G., Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lakhey, Sanjay, Lalueza, Antonio, Lambert, Marc, Lamontagne, François, Langelot-Richard, Marie, Langlois, Vincent, Lantang, Eka Yudha, Lanza, Marina, Laouénan, Cédric, Laribi, Samira, Lariviere, Delphine, Lasry, Stéphane, Lath, Sakshi, Latif, Naveed, Launay, Odile, Laureillard, Didier, Lavie-Badie, Yoan, Law, Andrew, Law, Andy, Lawrence, Cassie, Lawrence, Teresa, Le, Minh, Le Bihan, Clément, Le Bris, Cyril, Le Falher, Georges, Le Fevre, Lucie, Le Hingrat, Quentin, Le Maréchal, Marion, Le Mestre, Soizic, Le Moal, Gwenaël, Le Moing, Vincent, Le Nagard, Hervé, Le Turnier, Paul, Leal, Ema, Santos, Marta Leal, Lee, Biing Horng, Lee, Heng Gee, Lee, James, Lee, Su Hwan, Lee, Todd C., Lee, Yi Lin, Leeming, Gary, Lefebvre, Bénédicte, Lefebvre, Laurent, Lefevre, Benjamin, LeGac, Sylvie, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Leone, Michela, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letizia, Andrew, Levy, Bruno, Levy, Yves, Levy-Marchal, Claire, Lewandowska, Katarzyna, Bassi, Gianluigi Li, Liang, Janet, Liaquat, Ali, Liegeon, Geoffrey, Lim, Kah Chuan, Lim, Wei Shen, Lima, Chantre, Lina, Bruno, Lina, Lim, Lind, Andreas, Lingad, Maja Katherine, Lingas, Guillaume, Lion-Daolio, Sylvie, Lissauer, Samantha, Liu, Keibun, Livrozet, Marine, Lizotte, Patricia, Loforte, Antonio, Lolong, Navy, Loon, Leong Chee, Lopes, Diogo, Lopez-Colon, Dalia, Loschner, Anthony L., Loubet, Paul, Loufti, Bouchra, Louis, Guillame, Lourenco, Silvia, Lovelace-Macon, Lara, Low, Lee Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M., Lungu, Olguta, Luong, Liem, Luque, Nestor, Luton, Dominique, Lwin, Nilar, Lyons, Ruth, Maasikas, Olavi, Mabiala, Oryane, MacDonald, Sarah, Machado, Moïse, Macheda, Gabriel, Sanchez, Juan Macias, Madhok, Jai, Madiha, Hashmi, de la Calle, Guillermo Maestro, Magara, Jacob, Maglietta, Giuseppe, Mahieu, Rafael, Mahy, Sophie, Maia, Ana Raquel, Maier, Lars S., Maillet, Mylène, Maitre, Thomas, Majori, Maria, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Maltez, Fernando, Malvy, Denis, Mammi, Patrizia, Manda, Victoria, Mandei, Jose M., Mandelbrot, Laurent, Manetta, Frank, Mankikian, Julie, Manning, Edmund, Manuel, Aldric, Malaque, Ceila Maria Sant’Ana, Marino, Daniel, Marino, Flávio, Markowicz, Samuel, Eid, Charbel Maroun, Marques, Ana, Marquis, Catherine, Marsh, Brian, Marsh, Laura, Marshal, Megan, Marshall, John, Martelli, Celina Turchi, Martin, Dori-Ann, Martin, Emily, Martin-Blondel, Guillaume, Martin-Loeches, Ignacio, Martin-Quiros, Alejandro, Martinelli, Alessandra, Martinot, Martin, Martins, Ana, Martins, João, Martins, Nuno, Rego, Caroline Martins, Martucci, Gennaro, Martynenko, Olga, Marwali, Eva Miranda, Marzukie, Marsilla, Jimenez, Juan Fernado Masa, Maslove, David, Mason, Phillip, Mason, Sabina, Masood, Sobia, Nor, Basri Mat, Matan, Moshe, Fernandes, Henrique Mateus, Mathew, Meghena, Mathieu, Daniel, Mattei, Mathieu, Matulevics, Romans, Maulin, Laurence, Maxwell, Michael, Maynar, Javier, Mayxay, Mayfong, Mazzoni, Thierry, Mc Evoy, Natalie, Mc Sweeney, Lisa, McArthur, Colin, McCarthy, Aine, McCarthy, Anne, McCloskey, Colin, McConnochie, Rachael, McDermott, Sherry, McDonald, Sarah E., McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A., McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Meher, Maqsood, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif, Mendes, Joao, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Emmanuelle, Mercier, Noémie, Merckx, Antoine, Mergeay-Fabre, Mayka, Mergler, Blake, Merson, Laura, Meschi, Tiziana, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M., Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelagnoli, Giuliano, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miranda-Maldonado, Hugo, Misnan, Nor Arisah, Mohamed, Nik Nur Eliza, Mohamed, Tahira Jamal, Moin, Asma, Molina, David, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Sarah, Moore, Shona C., Cely, Lina Morales, Moro, Lucia, Tutillo, Diego Rolando Morocho, Morton, Ben, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Muneeb, Syed, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murphy, Lorna, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Mutti, Carlotta, Muvindi, Himasha, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Mohd-Hanafiah, Farah Nadia, Nagpal, Dave, Narasimhan, Mangala, Narayanan, Nageswaran, Khan, Rashid Nasim, Nazerali-Maitland, Alasdair, Neant, Nadège, Neb, Holger, Necsoi, Coca, Nekliudov, Nikita A., Nelwan, Erni, Neto, Raul, Neumann, Emily, Neves, Bernardo, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Ngure, Jane, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair, Nitayavardhana, Prompak, Nonas, Stephanie, Noordin, Nurul Amani Mohd, Noret, Marion, Norharizam, Nurul Faten Izzati, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nowicka, Karolina, Nowinski, Adam, Nseir, Saad, Nunez, Jose I., Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O., Callaghan, Annmarie O., O’Callaghan, Annmarie, O’Donnell, Max, O’Halloran, Sophie, O’Hearn, Katie, O’Neil, Conar, O’Shea, Linda, O’Sullivan, Miriam, Occhipinti, Giovanna, OConnor, Derbrenn, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak-Hyuk, Ohshimo, Shinichiro, Oldakowska, Agnieszka, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L., Ong, David S. Y., Ong, Jee Yan, Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco-Chamorro, Claudia Milena, Orquera, Andrés, Ortoleva, Jamel, Osatnik, Javier, Othman, Siti Zubaidah, Otiku, Paul, Ouamara, Nadia, Ouissa, Rachida, Owyang, Clark, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Amanda, Palacios, Mario, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parducci, Paolo, Oña, Edwin Fernando Paredes, Parke, Rachael, Parker, Melissa, Parra, Briseida, Parrini, Vieri, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patrizi, Laura, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Christelle, Paul, Mical, Paulos, Jorge, Paxton, William A., Payen, Jean-François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J., Peelman, Florent, Peiffer-Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D., Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrov-Sanchez, Ventzislava, Petrovic, Michele, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Phonemixay, Ooyanong, Piagnerelli, Michael, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pisi, Roberta, Pitaloka, Ayodhia, Pius, Riinu, Piva, Simone, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska-Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra-Maris, Postma, Douwe F., Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Praphasiri, Viladeth, Preau, Sébastien, Prebensen, Christian, Preiser, Jean-Charles, Prinssen, Anton, Pritchard, Mark G., Priyadarshani, Gamage Dona Dilanthi, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Pulicken, Mathew, Puntoni, Matteo, Purcell, Gregory, Quesada, Luisa, Quinones-Cardona, Vilmaris, González, Víctor Quirós, Quist-Paulsen, Else, Quraishi, Mohammed, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Ragazzo, Gabrielle, Rahardjani, Mutia, Ab Rahman, Ahmad Kashfi Haji, Rahman, Rozanah Abd, Rahutullah, Arsalan, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramanathan, Kollengode, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rampello, Anais, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Ranza, Elena, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Rausa, Francesco, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, Resseguier, Anne-Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Luis Felipe, Reyes, Tiago, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Ridzuan, Siti Nurul Atikah Ahmad, Riera, Jordi, Rios, Ana L., Rishu, Asgar, Rispal, Patrick, Risso, Karine, Nuñez, Maria Angelica Rivera, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L., Robineau, Olivier, Roche-Campo, Ferran, Rodari, Paola, Rodeia, Simão, Abreu, Julia Rodriguez, Roessler, Bernhard, Roger, Claire, Roger, Pierre-Marie, Roilides, Emmanuel, Rojek, Amanda, Romaru, Juliette, Roncon-Albuquerque Jr, Roberto, Roriz, Mélanie, Rosa-Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Roslan, Nurul Hidayah Mohammad, Rossanese, Andrea, Rossetti, Matteo, Rossi, Sandra, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D., Ryan, Maeve, Ryan, Maria, Ryckaert, Mazankowski Heart Institute, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sacchelli, Luca, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Saidani, Nadia, Saint-Gilles, Maximilien, Sakiyalak, Pranya, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Saleh, Nazal, Sales, Gabriele, Sallaberry, Stéphane, Gandonniere, Charlotte Salmon, Salvator, Hélène, Sanchez, Olivier, Choez, Xavier Sánchez, de Oliveira, Kizy Sanchez, Sanchez-Miralles, Angel, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre-François, Sandulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E., Sarton, Benjamine, Satya, Ankana, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Savvidou, Parthena, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Schwartz, Gary, Scott, Janet T., Scott-Brown, James, Sedillot, Nicholas, Seitz, Tamara, Selvanayagam, Jaganathan, Selvarajoo, Mageswari, Semaille, Caroline, Semple, Malcolm G., Senian, Rasidah Bt, Senneville, Eric, Sepulveda, Claudia, Sequeira, Filipa, Sequeira, Tânia, Neto, Ary Serpa, Balazote, Pablo Serrano, Shadowitz, Ellen, Shahidan, Syamin Asyraf, Shamsah, Mohammad, Shankar, Anuraj, Sharjeel, Shaikh, Sharma, Pratima, Shaw, Catherine A., Shaw, Victoria, Sheharyar, Ashraf, Shetty, Dr. Rajesh Mohan, Shetty, Rohan, Shi, Haixia, Shiban, Nisreen, Shiekh, Mohiuddin, Shiga, Takuya, Shime, Nobuaki, Shimizu, Hiroaki, Shimizu, Keiki, Shimizu, Naoki, Shrapnel, Sally, Shrestha, Pramesh Sundar, Shrestha, Shubha Kalyan, Shum, Hoi Ping, Mohammed, Nassima Si, Siang, Ng Yong, Sibiude, Jeanne, Sibounheuang, Bountoy, Siddiqui, Atif, Sigfrid, Louise, Sillaots, Piret, Silva, Catarina, Silva, Maria Joao, Silva, Rogério, Heng, Benedict Sim Lim, Sin, Wai Ching, Sinatti, Dario, Singh, Budha Charan, Singh, Punam, Sitompul, Pompini Agustina, Sivam, Karisha, Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, Smyth, Michelle, Snacken, Morgane, So, Dominic, Soh, Tze Vee, Solberg, Lene Bergendal, Solomon, Joshua, Solomon, Tom, Somers, Emily, Sommet, Agnès, Song, Myung Jin, Song, Rima, Song, Tae, Chia, Jack Song, Sonntagbauer, Michael, Soom, Azlan Mat, Søraas, Arne, Søraas, Camilla Lund, Sotto, Alberto, Soum, Edouard, Sousa, Ana Chora, Sousa, Marta, Uva, Maria Sousa, Souza-Dantas, Vicente, Sperry, Alexandra, Spinuzza, Elisabetta, Darshana, B. P. Sanka Ruwan Sri, Sriskandan, Shiranee, Stabler, Sarah, Staudinger, Thomas, Stecher, Stephanie-Susanne, Steinsvik, Trude, Stienstra, Ymkje, Stiksrud, Birgitte, Stolz, Eva, Stone, Amy, Streinu-Cercel, Adrian, Streinu-Cercel, Anca, Strudwick, Samantha, Stuart, Ami, Stuart, David, Subekti, Decy, Suen, Gabriel, Suen, Jacky Y., Sultana, Asfia, Summers, Charlotte, Supic, Dubravka, Suppiah, Deepashankari, Surovcová, Magdalena, Suwarti, Suwarti, Svistunov, Andrey A., Syahrin, Sarah, Syrigos, Konstantinos, Sztajnbok, Jaques, Szuldrzynski, Konstanty, Tabrizi, Shirin, Taccone, Fabio S., Tagherset, Lysa, Taib, Shahdattul Mawarni, Talarek, Ewa, Taleb, Sara, Talsma, Jelmer, Tamisier, Renaud, Tampubolon, Maria Lawrensia, Tan, Kim Keat, Tan, Le Van, Tan, Yan Chyi, Tanaka, Clarice, Tanaka, Hiroyuki, Tanaka, Taku, Taniguchi, Hayato, Taqdees, Huda, Taqi, Arshad, Tardivon, Coralie, Tattevin, Pierre, Taufik, M Azhari, Tawfik, Hassan, Tedder, Richard S., Tee, Tze Yuan, Teixeira, João, Tejada, Sofia, Tellier, Marie-Capucine, Teoh, Sze Kye, Teotonio, Vanessa, Téoulé, François, Terpstra, Pleun, Terrier, Olivier, Terzi, Nicolas, Tessier-Grenier, Hubert, Tey, Adrian, Thabit, Alif Adlan Mohd, Thakur, Anand, Tham, Zhang Duan, Thangavelu, Suvintheran, Thibault, Vincent, Thiberville, Simon-Djamel, Thill, Benoît, Thirumanickam, Jananee, Thompson, Shaun, Thomson, David, Thomson, Emma C., Thurai, Surain Raaj Thanga, Thuy, Duong Bich, Thwaites, Ryan S., Ticinesi, Andrea, Tierney, Paul, Tieroshyn, Vadim, Timashev, Peter S., Timsit, Jean-François, Vijayaraghavan, Bharath Kumar Tirupakuzhi, Tissot, Noémie, Toh, Jordan Zhien Yang, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torres, Antoni, Torres, Margarida, Santos-Olmo, Rosario Maria Torres, Torres-Zevallos, Hernando, Towers, Michael, Trapani, Tony, Treoux, Théo, Trieu, Huynh Trung, Tromeur, Cécile, Trontzas, Ioannis, Trouillon, Tiffany, Truong, Jeanne, Tual, Christelle, Tubiana, Sarah, Tuite, Helen, Turmel, Jean-Marie, Turtle, Lance C. W., Tveita, Anders, Twardowski, Pawel, Uchiyama, Makoto, Udayanga, P G Ishara, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad, Uyeki, Timothy M., Vajdovics, Cristinava, Val-Flores, Luís, Valentini, Piero, Valle, Ana Luiza, Valran, Amélie, Valzano, Ilaria, Van de Velde, Stijn, van den Berge, Marcel, Van der Feltz, Machteld, van der Palen, Job, van der Valk, Paul, Van Der Vekens, Nicky, Van der Voort, Peter, Van Der Werf, Sylvie, van Dyk, Marlice, van Gulik, Laura, Van Hattem, Jarne, van Netten, Carolien, Van Twillert, Gitte, van Veen, Ilonka, Vanel, Noémie, Vanoverschelde, Henk, Varghese, Pooja, Varrone, Michael, Vasudayan, Shoban Raj, Vauchy, Charline, Veeran, Shaminee, Veislinger, Aurélie, Vencken, Sebastian, Ventura, Sara, Verbon, Annelies, Vickers, James, Vidal, José Ernesto, Vieira, César, Vijayan, Deepak, Villanueva, Joy Ann, Villar, Judit, Villeneuve, Pierre-Marc, Villoldo, Andrea, Van Vinh Chau, Nguyen, Vishwanathan, Gayatri, Visseaux, Benoit, Visser, Hannah, Vitiello, Chiara, Vongsouvath, Manivanh, Vonkeman, Harald, Vuotto, Fanny, Wahab, Noor Hidayu, Wahab, Suhaila Abdul, Wahid, Nadirah Abdul, Wainstein, Marina, Shukeri, Wan Fadzlina Wan Muhd, Wang, Chih-Hsien, Webb, Steve, Wei, Jia, Weil, Katharina, Wen, Tan Pei, Wesselius, Sanne, West, T. Eoin, Wham, Murray, Whelan, Bryan, White, Nicole, Wicky, Paul Henri, Wiedemann, Aurélie, Wijaya, Surya Otto, Wille, Keith, Willems, Suzette, Williams, Virginie, Wils, Evert-Jan, Wong, Calvin, Wong, Teck Fung, Wong, Xin Ci, Wong, Yew Sing, Wright, Natalie, Xian, Gan Ee, Xian, Lim Saio, Xuan, Kuan Pei, Xynogalas, Ioannis, Yacoub, Sophie, Yakop, Siti Rohani Binti Mohd, Yamazaki, Masaki, Yazdanpanah, Yazdan, Hing, Nicholas Yee Liang, Yelnik, Cécile, Yeoh, Chian Hui, Yerkovich, Stephanie, Yiaye, Touxiong, Yokoyama, Toshiki, Yonis, Hodane, Yousif, Obada, Yuliarto, Saptadi, Zaaqoq, Akram, Zabbe, Marion, Zacharowski, Kai, Zahid, Masliza, Zahran, Maram, Zaidan, Nor Zaila Binti, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zawadka, Konrad, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Fryer, Daniel Vidali, Duque, Sara, Fuentes, Yuli V., Baruch, Joaquin, Beane, Abi, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), CHU Pontchaillou [Rennes], ARN régulateurs bactériens et médecine (BRM), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR - laboratoire coordonnateur), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), University of La Sabana = Universitad de la Sabana, University of Queensland [Brisbane], University of British Columbia (UBC), University of Oxford, The investigators acknowledge: This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund, UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z], the Bill & Melinda Gates Foundation [OPP1209135], the National Institute for Health Research (NIHR, award CO-CIN-01), the Medical Research Council (MRC, grant MC_PC_19059), the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), NIHR Clinical Research Network (infrastructure support), CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute, the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12], Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589], European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313], COVID clinical management team, AIIMS, Rishikesh, India, Cambridge NIHR Biomedical Research Centre, the dedication and hard work of the Groote Schuur Hospital Covid ICU Team, the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town, Wellcome Trust fellowship [205228/Z/16/Z], the Liverpool School of Tropical Medicine, the University of Oxford, the dedication and hard work of the Norwegian SARS-CoV-2 study team, the Research Council of Norway grant no 312780, a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner, Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution, preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection, Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM, Italian Ministry of Health 'Fondi Ricerca corrente–L1P6' to IRCCS Ospedale Sacro Cuore–Don Calabria, Australian Department of Health grant (3273191), Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009), the Prince Charles Hospital Foundation, Australia, UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1, Instituto de Salud Carlos III, Ministerio de Ciencia, Spain, Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7, Firland Foundation, Shoreline, Washington, USA, the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424). This work uses Data/Material provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The Data/materials used for this research were obtained from ISARIC4C. ISARIC4C Investigators collated the COVID-19 Clinical Information Network (CO-CIN) data., European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), Bill & Melinda Gates Foundation, Minderoo Foundation, Commonwealth Foundation, Wellcome Trust, National Institute for Health Research (UK), Medical Research Council (UK), University of Liverpool, Imperial College London, Canadian Institutes of Health Research, Sunnybrook Research Institute, Health Research Board (Ireland), European Commission, All India Institute of Medical Science, University of Cape Town, Research Council of Norway, Innovative Medicines Initiative, Governo Italiano, University of Queensland, Australian Research Council, Prince Charles Hospital Foundation, Instituto de Salud Carlos III, Institut National de la Santé et de la Recherche Médicale (France), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Garcia-Gallo, Esteban, Merson, Laura, Kennon, Kalynn, Kelly, Sadie, Citarella, Barbara Wanjiru, Fryer, Daniel Vidali, Shrapnel, Sally, Lee, Jame, Duque, Sara, Fuentes, Yuli V, Balan, Valeria, Smith, Sue, Wei, Jia, Gonçalves, Bronner P, Russell, Clark D, Sigfrid, Louise, Dagens, Andrew, Olliaro, Piero L, Baruch, Joaquin, Kartsonaki, Christiana, Dunning, Jake, Rojek, Amanda, Rashan, Aasiyah, Beane, Abi, Murthy, Sriniva, Reyes, Luis Felipe, ISARIC Clinical Characterization Group: Ali Abbas, Sheryl Ann Abdukahil, Nurul Najmee Abdulkadir, Ryuzo Abe, Laurent Abel, Lara Absil, Kamal Abu Jabal, Hiba Abu Zayyad, Subhash Acharya, Andrew Acker, Shingo Adachi, Elisabeth Adam, Enrico Adriano, Diana Adrião, Saleh Al Ageel, Shakeel Ahmed, Marina Aiello, Kate Ainscough, Eka Airlangga, Tharwat Aisa, Ali Ait Hssain, Younes Ait Tamlihat, Takako Akimoto, Ernita Akmal, Eman Al Qasim, Tala Al-Dabbous, Abdulrahman Al-Fares, Razi Alalqam, Angela Alberti, Senthilkumar Alegesan, Cynthia Alegre, Marta Alessi, Beatrice Alex, Kévin Alexandre, Huda Alfoudri, Adam Ali, Imran Ali, Naseem Ali Shah, Naseem Ali Sheikh, Kazali Enagnon Alidjnou, Jeffrey Aliudin, Qabas Alkhafajee, Clotilde Allavena, Nathalie Allou, Aneela Altaf, João Alves, João Melo Alves, Rita Alves, Maria Amaral, Nur Amira, Heidi Ammerlaan, Phoebe Ampaw, Roberto Andini, Claire Andrejak, Andrea Angheben, François Angoulvant, Séverine Ansart, Sivanesen Anthonidass, Massimo Antonelli, Carlos Alexandre Antunes de Brito, Kazi Rubayet Anwar, Ardiyan Apriyana, Yaseen Arabi, Irene Aragao, Francisco Arancibia, Carolline Araujo, Antonio Arcadipane, Patrick Archambault, Lukas Arenz, Jean-Benoît Arlet, Christel Arnold-Day, Ana Aroca, Lovkesh Arora, Rakesh Arora, Elise Artaud-Macari, Diptesh Aryal, Motohiro Asaki, Angel Asensio, Elizabeth Ashley, Muhammad Ashraf, Namra Asif, Mohammad Asim, Jean Baptiste Assie, Amirul Asyraf, Anika Atique, A M Udara Lakshan Attanyake, Johann Auchabie, Hugues Aumaitre, Adrien Auvet, Eyvind W Axelsen, Laurène Azemar, Cecile Azoulay, Benjamin Bach, Delphine Bachelet, Claudine Badr, Roar 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Christy Kay, Hannah Keane, Seán Keating, Pulak Kedia, Andrea Kelly, Aoife Kelly, Claire Kelly, Niamh Kelly, Sadie Kelly, Yvelynne Kelly, Maeve Kelsey, Ryan Kennedy, Kalynn Kennon, Sommay Keomany, Maeve Kernan, Younes Kerroumi, Sharma Keshav, Evelyne Kestelyn, Imrana Khalid, Osama Khalid, Antoine Khalil, Coralie Khan, Irfan Khan, Quratul Ain Khan, Sushil Khanal, Abid Khatak, Amin Khawaja, Krish Kherajani, Michelle E Kho, Denisa Khoo, Ryan Khoo, Saye Khoo, Nasir Khoso, Khor How Kiat, Yuri Kida, Harrison Kihuga, Peter Kiiza, Beathe Kiland Granerud, Anders Benjamin Kildal, Jae Burm Kim, Antoine Kimmoun, Detlef Kindgen-Milles, Alexander King, Nobuya Kitamura, Eyrun Floerecke Kjetland Kjetland, Paul Klenerman, Rob Klont, Gry Kloumann Bekken, Stephen R Knight, Robin Kobbe, Chamira Kodippily, Malte Kohns Vasconcelos, Sabin Koirala, Mamoru Komatsu, Volkan Korten, Caroline Kosgei, Arsène Kpangon, Karolina Krawczyk, Sudhir Krishnan, Vinothini Krishnan, Oksana Kruglova, Deepali Kumar, Ganesh Kumar, Mukesh Kumar, Pavan Kumar Vecham, Dinesh Kuriakose, Ethan Kurtzman, Neurinda Permata Kusumastuti, Demetrios Kutsogiannis, Galyna Kutsyna, Konstantinos Kyriakoulis, Erwan L'Her, Marie Lachatre, Marie Lacoste, John G Laffey, Marie Lagrange, Fabrice Laine, Olivier Lairez, Sanjay Lakhey, Antonio Lalueza, Marc Lambert, François Lamontagne, Marie Langelot-Richard, Vincent Langlois, Eka Yudha Lantang, Marina Lanza, Cédric Laouénan, Samira Laribi, Delphine Lariviere, Stéphane Lasry, Sakshi Lath, Naveed Latif, Odile Launay, Didier Laureillard, Yoan Lavie-Badie, Andrew Law, Andy Law, Cassie Lawrence, Teresa Lawrence, Minh Le, Clément Le Bihan, Cyril Le Bris, Georges Le Falher, Lucie Le Fevre, Quentin Le Hingrat, Marion Le Maréchal, Soizic Le Mestre, Gwenaël Le Moal, Vincent Le Moing, Hervé Le Nagard, Paul Le Turnier, Ema Leal, Marta Leal Santos, Biing Horng Lee, Heng Gee Lee, James Lee, Su Hwan Lee, Todd C Lee, Yi Lin Lee, Gary Leeming, Bénédicte Lefebvre, Laurent Lefebvre, Benjamin Lefevre, Sylvie LeGac, Jean-Daniel Lelievre, François Lellouche, Adrien Lemaignen, Véronique Lemee, Anthony Lemeur, Gretchen Lemmink, Ha Sha Lene, Jenny Lennon, Rafael León, Marc Leone, Michela Leone, Quentin Lepiller, François-Xavier Lescure, Olivier Lesens, Mathieu Lesouhaitier, Amy Lester-Grant, Andrew Letizia, Bruno Levy, Yves Levy, Claire Levy-Marchal, Katarzyna Lewandowska, Gianluigi Li Bassi, Janet Liang, Ali Liaquat, Geoffrey Liegeon, Kah Chuan Lim, Wei Shen Lim, Chantre Lima, Bruno Lina, Lim Lina, Andreas Lind, Maja Katherine Lingad, Guillaume Lingas, Sylvie Lion-Daolio, Samantha Lissauer, Keibun Liu, Marine Livrozet, Patricia Lizotte, Antonio Loforte, Navy Lolong, Leong Chee Loon, Diogo Lopes, Dalia Lopez-Colon, Anthony L Loschner, Paul Loubet, Bouchra Loufti, Guillame Louis, Silvia Lourenco, Lara Lovelace-Macon, Lee Lee Low, Marije Lowik, Jia Shyi Loy, Jean Christophe Lucet, Carlos Lumbreras Bermejo, Carlos M Luna, Olguta Lungu, Liem Luong, Nestor Luque, Dominique Luton, Nilar Lwin, Ruth Lyons, Olavi Maasikas, Oryane Mabiala, Sarah MacDonald, Moïse Machado, Gabriel Macheda, Juan Macias Sanchez, Jai Madhok, Hashmi Madiha, Guillermo Maestro de la Calle, Jacob Magara, Giuseppe Maglietta, Rafael Mahieu, Sophie Mahy, Ana Raquel Maia, Lars S Maier, Mylène Maillet, Thomas Maitre, Maria Majori, Maximilian Malfertheiner, Nadia Malik, Paddy Mallon, Fernando Maltez, Denis Malvy, Patrizia Mammi, Victoria Manda, Jose M Mandei, Laurent Mandelbrot, Frank Manetta, Julie Mankikian, Edmund Manning, Aldric Manuel, Ceila Maria Sant'Ana Malaque, Daniel Marino, Flávio Marino, Samuel Markowicz, Charbel Maroun Eid, Ana Marques, Catherine Marquis, Brian Marsh, Laura Marsh, Megan Marshal, John Marshall, Celina Turchi Martelli, Dori-Ann Martin, Emily Martin, Guillaume Martin-Blondel, Ignacio Martin-Loeches, Alejandro Martin-Quiros, Alessandra Martinelli, Martin Martinot, Ana Martins, João Martins, Nuno Martins, Caroline Martins Rego, Gennaro Martucci, Olga Martynenko, Eva Miranda Marwali, Marsilla Marzukie, Juan Fernado Masa Jimenez, David Maslove, Phillip Mason, Sabina Mason, Sobia Masood, Basri Mat Nor, Moshe Matan, Henrique Mateus Fernandes, Meghena Mathew, Daniel Mathieu, Mathieu Mattei, Romans Matulevics, Laurence Maulin, Michael Maxwell, Javier Maynar, Mayfong Mayxay, Thierry Mazzoni, Natalie Mc Evoy, Lisa Mc Sweeney, Colin McArthur, Aine McCarthy, Anne McCarthy, Colin McCloskey, Rachael McConnochie, Sherry McDermott, Sarah E McDonald, Aine McElroy, Samuel McElwee, Victoria McEneany, Allison McGeer, Chris McKay, Johnny McKeown, Kenneth A McLean, Paul McNally, Bairbre McNicholas, Elaine McPartlan, Edel Meaney, Cécile Mear-Passard, Maggie Mechlin, Maqsood Meher, Omar Mehkri, Ferruccio Mele, Luis Melo, Kashif Memon, Joao Mendes, Ogechukwu Menkiti, Kusum Menon, France Mentré, Alexander J Mentzer, Emmanuelle Mercier, Noémie Mercier, Antoine Merckx, Mayka Mergeay-Fabre, Blake Mergler, Laura Merson, Tiziana Meschi, António Mesquita, Roberta Meta, Osama Metwally, Agnès Meybeck, Dan Meyer, Alison M Meynert, Vanina Meysonnier, Amina Meziane, Mehdi Mezidi, Giuliano Michelagnoli, Céline Michelanglei, Isabelle Michelet, Efstathia Mihelis, Vladislav Mihnovit, Hugo Miranda-Maldonado, Nor Arisah Misnan, Nik Nur Eliza Mohamed, Tahira Jamal Mohamed, Asma Moin, David Molina, Elena Molinos, Brenda Molloy, Mary Mone, Agostinho Monteiro, Claudia Montes, Giorgia Montrucchio, Sarah Moore, Shona C Moore, Lina Morales Cely, Lucia Moro, Diego Rolando Morocho Tutillo, Ben Morton, Catherine Motherway, Ana Motos, Hugo Mouquet, Clara Mouton Perrot, Julien Moyet, Caroline Mudara, Aisha Kalsoom Mufti, Ng Yong Muh, Dzawani Muhamad, Jimmy Mullaert, Fredrik Müller, Karl Erik Müller, Daniel Munblit, Syed Muneeb, Nadeem Munir, Laveena Munshi, Aisling Murphy, Lorna Murphy, Marlène Murris, Srinivas Murthy, Himed Musaab, Carlotta Mutti, Himasha Muvindi, Gugapriyaa Muyandy, Dimitra Melia Myrodia, Farah Nadia Mohd-Hanafiah, Dave Nagpal, Mangala Narasimhan, Nageswaran Narayanan, Rashid Nasim Khan, Alasdair Nazerali-Maitland, Nadège Neant, Holger Neb, Coca Necsoi, Nikita A Nekliudov, Erni Nelwan, Raul Neto, Emily Neumann, Bernardo Neves, Pauline Yeung Ng, Wing Yiu Ng, Anthony Nghi, Jane Ngure, Duc Nguyen, Orna Ni Choileain, Niamh Ni Leathlobhair, Alistair Nichol, Prompak Nitayavardhana, Stephanie Nonas, Nurul Amani Mohd Noordin, Marion Noret, Nurul Faten Izzati Norharizam, Lisa Norman, Alessandra Notari, Mahdad Noursadeghi, Karolina Nowicka, Adam Nowinski, Saad Nseir, Jose I Nunez, Nurnaningsih Nurnaningsih, Dwi Utomo Nusantara, Elsa Nyamankolly, Anders Benteson Nygaard, Fionnuala O Brien, Annmarie O Callaghan, Annmarie O'Callaghan, Max O'Donnell, Sophie O'Halloran, Katie O'Hearn, Conar O'Neil, Linda O'Shea, Miriam O'Sullivan, Giovanna Occhipinti, Derbrenn OConnor, Tawnya Ogston, Takayuki Ogura, Tak-Hyuk Oh, Shinichiro Ohshimo, Agnieszka Oldakowska, João Oliveira, Larissa Oliveira, Piero L Olliaro, David S Y Ong, Jee Yan Ong, Wilna Oosthuyzen, Anne Opavsky, Peter Openshaw, Saijad Orakzai, Claudia Milena Orozco-Chamorro, Andrés Orquera, Jamel Ortoleva, Javier Osatnik, Siti Zubaidah Othman, Paul Otiku, Nadia Ouamara, Rachida Ouissa, Clark Owyang, Eric Oziol, Maïder Pagadoy, Justine Pages, Amanda Palacios, Mario Palacios, Massimo Palmarini, Giovanna Panarello, Prasan Kumar Panda, Hem Paneru, Lai Hui Pang, Mauro Panigada, Nathalie Pansu, Aurélie Papadopoulos, Paolo Parducci, Edwin Fernando Paredes Oña, Rachael Parke, Melissa Parker, Briseida Parra, Vieri Parrini, Taha Pasha, Jérémie Pasquier, Bruno Pastene, Fabian Patauner, Drashti Patel, Mohan Dass Pathmanathan, Luís Patrão, Patricia Patricio, Juliette Patrier, Laura Patrizi, Lisa Patterson, Rajyabardhan Pattnaik, Christelle Paul, Mical Paul, Jorge Paulos, William A Paxton, Jean-François Payen, Kalaiarasu Peariasamy, Miguel Pedrera Jiménez, Giles J Peek, Florent Peelman, Nathan Peiffer-Smadja, Vincent Peigne, Mare Pejkovska, Paolo Pelosi, Ithan D Peltan, Rui Pereira, Daniel Perez, Luis Periel, Thomas Perpoint, Antonio Pesenti, Vincent Pestre, Lenka Petrou, Ventzislava Petrov-Sanchez, Michele Petrovic, Frank Olav Pettersen, Gilles Peytavin, Scott Pharand, Ooyanong Phonemixay, Michael Piagnerelli, Walter Picard, Olivier Picone, Maria de Piero, Carola Pierobon, Djura Piersma, Carlos Pimentel, Raquel Pinto, Catarina Pires, Isabelle Pironneau, Lionel Piroth, Roberta Pisi, Ayodhia Pitaloka, Riinu Pius, Simone Piva, Laurent Plantier, Hon Shen Png, Julien Poissy, Ryadh Pokeerbux, Maria Pokorska-Spiewak, Sergio Poli, Georgios Pollakis, Diane Ponscarme, Jolanta Popielska, Diego Bastos Porto, Andra-Maris Post, Douwe F Postma, Pedro Povoa, Diana Póvoas, Jeff Powis, Sofia Prapa, Viladeth Praphasiri, Sébastien Preau, Christian Prebensen, Jean-Charles Preiser, Anton Prinssen, Mark G Pritchard, Gamage Dona Dilanthi Priyadarshani, Lucia Proença, Sravya Pudota, Oriane Puéchal, Bambang Pujo Semedi, Mathew Pulicken, Matteo Puntoni, Gregory Purcell, Luisa Quesada, Vilmaris Quinones-Cardona, Víctor Quirós González, Else Quist-Paulsen, Mohammed Quraishi, Maia Rabaa, Christian Rabaud, Ebenezer Rabindrarajan, Aldo Rafael, Marie Rafiq, Gabrielle Ragazzo, Mutia Rahardjani, Ahmad Kashfi Haji Ab Rahman, Rozanah Abd Rahman, Arsalan Rahutullah, Fernando Rainieri, Giri Shan Rajahram, Pratheema Ramachandran, Nagarajan Ramakrishnan, Kollengode Ramanathan, Ahmad Afiq Ramli, Blandine Rammaert, Grazielle Viana Ramos, Anais Rampello, Asim Rana, Rajavardhan Rangappa, Ritika Ranjan, Elena Ranza, Christophe Rapp, Aasiyah Rashan, Thalha Rashan, Ghulam Rasheed, Menaldi Rasmin, Indrek Rätsep, Cornelius Rau, Francesco Rausa, Tharmini Ravi, Ali Raza, Andre Real, Stanislas Rebaudet, Sarah Redl, Brenda Reeve, Attaur Rehman, Liadain Reid, Dag Henrik Reikvam, Renato Reis, Jordi Rello, Jonathan Remppis, Martine Remy, Hongru Ren, Hanna Renk, Anne-Sophie Resseguier, Matthieu Revest, Oleksa Rewa, Luis Felipe Reyes, Tiago Reyes, Maria Ines Ribeiro, Antonia Ricchiuto, David Richardson, Denise Richardson, Laurent Richier, Siti Nurul Atikah Ahmad Ridzuan, Jordi Riera, Ana L Rios, Asgar Rishu, Patrick Rispal, Karine Risso, Maria Angelica Rivera Nuñez, Nicholas Rizer, Chiara Robba, André Roberto, Stephanie Roberts, David L Robertson, Olivier Robineau, Ferran Roche-Campo, Paola Rodari, Simão Rodeia, Julia Rodriguez Abreu, Bernhard Roessler, Claire Roger, Pierre-Marie Roger, Emmanuel Roilides, Amanda Rojek, Juliette Romaru, Roberto Roncon-Albuquerque Jr, Mélanie Roriz, Manuel Rosa-Calatrava, Michael Rose, Dorothea Rosenberger, Nurul Hidayah Mohammad Roslan, Andrea Rossanese, Matteo Rossetti, Sandra Rossi, Bénédicte Rossignol, Patrick Rossignol, Stella Rousset, Carine Roy, Benoît Roze, Desy Rusmawatiningtyas, Clark D Russell, Maeve Ryan, Maria Ryan, Mazankowski Heart Institute Ryckaert, Steffi Ryckaert, Aleksander Rygh Holten, Isabela Saba, Luca Sacchelli, Sairah Sadaf, Musharaf Sadat, Valla Sahraei, Nadia Saidani, Maximilien Saint-Gilles, Pranya Sakiyalak, Nawal Salahuddin, Leonardo Salazar, Jodat Saleem, Nazal Saleh, Gabriele Sales, Stéphane Sallaberry, Charlotte Salmon Gandonniere, Hélène Salvator, Olivier Sanchez, Xavier Sánchez Choez, Kizy Sanchez de Oliveira, Angel Sanchez-Miralles, Vanessa Sancho-Shimizu, Gyan Sandhu, Zulfiqar Sandhu, Pierre-François Sandrine, Oana Sandulescu, Marlene Santos, Shirley Sarfo-Mensah, Bruno Sarmento Banheiro, Iam Claire E Sarmiento, Benjamine Sarton, Ankana Satya, Sree Satyapriya, Rumaisah Satyawati, Egle Saviciute, Parthena Savvidou, Yen Tsen Saw, Justin Schaffer, Tjard Schermer, Arnaud Scherpereel, Marion Schneider, Stephan Schroll, Michael Schwameis, Gary Schwartz, Janet T Scott, James Scott-Brown, Nicholas Sedillot, Tamara Seitz, Jaganathan Selvanayagam, Mageswari Selvarajoo, Caroline Semaille, Malcolm G Semple, Rasidah Bt Senian, Eric Senneville, Claudia Sepulveda, Filipa Sequeira, Tânia Sequeira, Ary Serpa Neto, Pablo Serrano Balazote, Ellen Shadowitz, Syamin Asyraf Shahidan, Mohammad Shamsah, Anuraj Shankar, Shaikh Sharjeel, Pratima Sharma, Catherine A Shaw, Victoria Shaw, Ashraf Sheharyar, Dr Rajesh Mohan Shetty, Rohan Shetty, Haixia Shi, Nisreen Shiban, Mohiuddin Shiekh, Takuya Shiga, Nobuaki Shime, Hiroaki Shimizu, Keiki Shimizu, Naoki Shimizu, Sally Shrapnel, Pramesh Sundar Shrestha, Shubha Kalyan Shrestha, Hoi Ping Shum, Nassima Si Mohammed, Ng Yong Siang, Jeanne Sibiude, Bountoy Sibounheuang, Atif Siddiqui, Louise Sigfrid, Piret Sillaots, Catarina Silva, Maria Joao Silva, Rogério Silva, Benedict Sim Lim Heng, Wai Ching Sin, Dario Sinatti, Budha Charan Singh, Punam Singh, Pompini Agustina Sitompul, Karisha Sivam, Vegard Skogen, Sue Smith, Benjamin Smood, Coilin Smyth, Michelle Smyth, Morgane Snacken, Dominic So, Tze Vee Soh, Lene Bergendal Solberg, Joshua Solomon, Tom Solomon, Emily Somers, Agnès Sommet, Myung Jin Song, Rima Song, Tae Song, Jack Song Chia, Michael Sonntagbauer, Azlan Mat Soom, Arne Søraas, Camilla Lund Søraas, Alberto Sotto, Edouard Soum, Ana Chora Sousa, Marta Sousa, Maria Sousa Uva, Vicente Souza-Dantas, Alexandra Sperry, Elisabetta Spinuzza, B P Sanka Ruwan Sri Darshana, Shiranee Sriskandan, Sarah Stabler, Thomas Staudinger, Stephanie-Susanne Stecher, Trude Steinsvik, Ymkje Stienstra, Birgitte Stiksrud, Eva Stolz, Amy Stone, Adrian Streinu-Cercel, Anca Streinu-Cercel, Samantha Strudwick, Ami Stuart, David Stuart, Decy Subekti, Gabriel Suen, Jacky Y Suen, Asfia Sultana, Charlotte Summers, Dubravka Supic, Deepashankari Suppiah, Magdalena Surovcová, Suwarti Suwarti, Andrey A Svistunov, Sarah Syahrin, Konstantinos Syrigos, Jaques Sztajnbok, Konstanty Szuldrzynski, Shirin Tabrizi, Fabio S Taccone, Lysa Tagherset, Shahdattul Mawarni Taib, Ewa Talarek, Sara Taleb, Jelmer Talsma, Renaud Tamisier, Maria Lawrensia Tampubolon, Kim Keat Tan, Le Van Tan, Yan Chyi Tan, Clarice Tanaka, Hiroyuki Tanaka, Taku Tanaka, Hayato Taniguchi, Huda Taqdees, Arshad Taqi, Coralie Tardivon, Pierre Tattevin, M Azhari Taufik, Hassan Tawfik, Richard S Tedder, Tze Yuan Tee, João Teixeira, Sofia Tejada, Marie-Capucine Tellier, Sze Kye Teoh, Vanessa Teotonio, François Téoulé, Pleun Terpstra, Olivier Terrier, Nicolas Terzi, Hubert Tessier-Grenier, Adrian Tey, Alif Adlan Mohd Thabit, Anand Thakur, Zhang Duan Tham, Suvintheran Thangavelu, Vincent Thibault, Simon-Djamel Thiberville, Benoît Thill, Jananee Thirumanickam, Shaun Thompson, David Thomson, Emma C Thomson, Surain Raaj Thanga Thurai, Duong Bich Thuy, Ryan S Thwaites, Andrea Ticinesi, Paul Tierney, Vadim Tieroshyn, Peter S Timashev, Jean-François Timsit, Bharath Kumar Tirupakuzhi Vijayaraghavan, Noémie Tissot, Jordan Zhien Yang Toh, Maria Toki, Kristian Tonby, Sia Loong Tonnii, Antoni Torres, Margarida Torres, Rosario Maria Torres Santos-Olmo, Hernando Torres-Zevallos, Michael Towers, Tony Trapani, Théo Treoux, Huynh Trung Trieu, Cécile Tromeur, Ioannis Trontzas, Tiffany Trouillon, Jeanne Truong, Christelle Tual, Sarah Tubiana, Helen Tuite, Jean-Marie Turmel, Lance C W Turtle, Anders Tveita, Pawel Twardowski, Makoto Uchiyama, P G Ishara Udayanga, Andrew Udy, Roman Ullrich, Alberto Uribe, Asad Usman, Timothy M Uyeki, Cristinava Vajdovics, Luís Val-Flores, Piero Valentini, Ana Luiza Valle, Amélie Valran, Ilaria Valzano, Stijn Van de Velde, Marcel van den Berge, Machteld Van der Feltz, Job van der Palen, Paul van der Valk, Nicky Van Der Vekens, Peter Van der Voort, Sylvie Van Der Werf, Marlice van Dyk, Laura van Gulik, Jarne Van Hattem, Carolien van Netten, Gitte Van Twillert, Ilonka van Veen, Noémie Vanel, Henk Vanoverschelde, Pooja Varghese, Michael Varrone, Shoban Raj Vasudayan, Charline Vauchy, Shaminee Veeran, Aurélie Veislinger, Sebastian Vencken, Sara Ventura, Annelies Verbon, James Vickers, José Ernesto Vidal, César Vieira, Deepak Vijayan, Joy Ann Villanueva, Judit Villar, Pierre-Marc Villeneuve, Andrea Villoldo, Nguyen Van Vinh Chau, Gayatri Vishwanathan, Benoit Visseaux, Hannah Visser, Chiara Vitiello, Manivanh Vongsouvath, Harald Vonkeman, Fanny Vuotto, Noor Hidayu Wahab, Suhaila Abdul Wahab, Nadirah Abdul Wahid, Marina Wainstein, Wan Fadzlina Wan Muhd Shukeri, Chih-Hsien Wang, Steve Webb, Jia Wei, Katharina Weil, Tan Pei Wen, Sanne Wesselius, T Eoin West, Murray Wham, Bryan Whelan, Nicole White, Paul Henri Wicky, Aurélie Wiedemann, Surya Otto Wijaya, Keith Wille, Suzette Willems, Virginie Williams, Evert-Jan Wils, Calvin Wong, Teck Fung Wong, Xin Ci Wong, Yew Sing Wong, Natalie Wright, Gan Ee Xian, Lim Saio Xian, Kuan Pei Xuan, Ioannis Xynogalas, Sophie Yacoub, Siti Rohani Binti Mohd Yakop, Masaki Yamazaki, Yazdan Yazdanpanah, Nicholas Yee Liang Hing, Cécile Yelnik, Chian Hui Yeoh, Stephanie Yerkovich, Touxiong Yiaye, Toshiki Yokoyama, Hodane Yonis, Obada Yousif, Saptadi Yuliarto, Akram Zaaqoq, Marion Zabbe, Kai Zacharowski, Masliza Zahid, Maram Zahran, Nor Zaila Binti Zaidan, Maria Zambon, Miguel Zambrano, Alberto Zanella, Konrad Zawadka, Nurul Zaynah, Hiba Zayyad, Alexander Zoufaly, David Zucman, Durante-Mangoni, Emanuele, Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Microbes in Health and Disease (MHD), Garcia-Gallo E., Merson L., Kennon K., Kelly S., Citarella B. W. , Fryer D. V. , Shrapnel S., Lee J., Duque S., Fuentes Y. V. , et al., and Medical Microbiology & Infectious Diseases
Subjects: Eğitim, Social Sciences and Humanities, Information Security and Reliability, Social Sciences (SOC), Sosyal Bilimler ve Beşeri Bilimler, Epidemiology, EDUCATION & EDUCATIONAL RESEARCH, Temel Bilimler (SCI), BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ, MATHEMATICS, Sociology, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Prospective Studies, COMPUTER SCIENCE, INFORMATION SYSTEMS, STATISTICS & PROBABILITY, Matematik, Bilgisayar Bilimi Uygulamaları, Computer Sciences, Bilgi Güvenliği ve Güvenilirliği, EĞİTİM VE EĞİTİM ARAŞTIRMASI, BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ, Bilgi sistemi, Computer Science Applications, Kütüphane ve Bilgi Bilimleri, Hospitalization, Natural Sciences (SCI), Physical Sciences, Engineering and Technology, Sosyal Bilimler (SOC), Bilgisayar Bilimi, Statistics, Probability and Uncertainty, Information Systems, Human, Statistics and Probability, Humans, Pandemics, SARS-CoV-2, COVID-19, SOCIAL SCIENCES, GENERAL, Library and Information Sciences, Education, SDG 3 - Good Health and Well-being, Library Sciences, INFORMATION SCIENCE & LIBRARY SCIENCE, İstatistik ve Olasılık, Sosyal ve Beşeri Bilimler, Bilgisayar Bilimleri, Social Sciences & Humanities, Engineering, Computing & Technology (ENG), Sosyoloji, Pandemic, İSTATİSTİK & OLASILIK, Mühendislik, Bilişim ve Teknoloji (ENG), İstatistik, Olasılık ve Belirsizlik, Sosyal Bilimler Genel, COMPUTER SCIENCE, Prospective Studie, Fizik Bilimleri, Viral infection, Mühendislik ve Teknoloji, Kütüphanecilik
Abstract: The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use., This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the National Institute for Health Research (NIHR; award CO-CIN-01); the Medical Research Council (MRC; grant MC_PC_19059); the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907); NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153); NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013); NIHR Clinical Research Network (infrastructure support); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland; Artificial Intelligence for Pandemics (A14PAN) at University of Queensland; the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424).
Published: 2022

7. Impact of Driver Age and Experience in Software Usage on Driving Safety and Usability of Car-Sharing Software

Author: Wei Wang, Suixian Yang, Jin-Yi Zhi, and Chunhui Jing
Subjects: Economics and Econometrics, Article Subject, Computer science, Strategy and Management, Applied psychology, Sample (statistics), Software, User experience design, Distraction, 0502 economics and business, 0501 psychology and cognitive sciences, 050107 human factors, HE1-9990, 050210 logistics & transportation, TA1001-1280, business.industry, Mechanical Engineering, 05 social sciences, Driving simulator, Usability, Driving safety, Computer Science Applications, Transportation engineering, Automotive Engineering, business, Transportation and communications, Cognitive load
Abstract: Car-sharing economy has caused new driving safety and usability problems, which have not been well studied. This study aims at analyzing the effects of users age and the user experience (UX) of the car-sharing software (e.g., DiDi travel app) on overall usability and the level of distraction for drivers. To this end, 48 experienced Chinese drivers were recruited to perform various tasks with the car-sharing software using a driving simulator. The variables of driving safety and usability were analyzed by two-way analysis of variance (ANOVA) and independent sample Kruskal–Wallis nonparametric test. As expected, it was found that car-sharing software has a significant negative impact on driving distraction and usability. The overall performance of young drivers is better than that of the elderly, but it seems that young drivers are more likely to be led to errors by car-sharing software. In most aspects, experienced drivers perform better than inexperienced drivers and have a better in-depth understanding of car-sharing software weaknesses. However, inexperienced drivers performed better regarding braking time and interaction time. Although young inexperienced drivers performed worst in driving safety, they exhibited the lowest cognitive load and the highest interaction efficiency. The experience of using car-sharing software may improve driver’s ability to deal with driving distractions. The above conclusions provide theoretical support for optimizing the UX of car-sharing software and some references for driver’s screening and training.
Published: 2021

8. Adversarial auto-encoder for rating prediction with ratings and reviews

Author: Jiajin Huang, Jin Qin, and Jin Yi
Subjects: Adversarial system, Artificial Intelligence, Computer Networks and Communications, Computer science, 020204 information systems, Speech recognition, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, Autoencoder, Software
Abstract: Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users’ historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users’ preferences for higher recommendation quality. Similar to rating data, review data also contain rich information about users’ preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance.
Published: 2021

9. A Study on the Re-establishment and Connection between Busan Port and Busan

Author: Jin-Yi Choi
Subjects: Computer science, business.industry, General Medicine, Telecommunications, business, Port (computer networking), Connection (mathematics)
Published: 2021

10. Enhancing aircraft cabin comfort to compete with high‐speed trains: A survey in China

Author: Jun Du, Ze-Rui Xiang, Si-Jun He, Yang Du, Jin Wang, Jin-Yi Zhi, and Jian-Ping Chen
Subjects: Computer science, Human Factors and Ergonomics, Train, High speed train, China, Industrial and Manufacturing Engineering, Automotive engineering
Published: 2021

11. A method identifying key optimisation points for aircraft seat comfort

Author: Jin-Yi Zhi, Jin Wang, Ze-Rui Xiang, Yang Du, Shu Feng, and Jian-Ping Chen
Subjects: Aircraft, Computer science, Posture, Physical Therapy, Sports Therapy and Rehabilitation, Human Factors and Ergonomics, Latent variable, Structural equation modeling, 03 medical and health sciences, 0302 clinical medicine, Surveys and Questionnaires, Statistics, Humans, 0501 psychology and cognitive sciences, Path analysis (statistics), 050107 human factors, Reliability (statistics), 05 social sciences, Reproducibility of Results, Equipment Design, 030229 sport sciences, Variance (accounting), Exploratory factor analysis, Confirmatory factor analysis, Variable (computer science), Ergonomics, human activities
Abstract: Seating is the overriding factor influencing aircraft cabin comfort. To efficiently enhance seat comfort, this paper proposes a method to identify key optimisation points for seat comfort. Seat discomfort indicators are recognised based on a comparison of perceived performance with expectation. Confirmatory factor analysis is used to explore the latent variables of discomfort indicators, and a structural model was used to analyse correlations between latent variables. Finally, the most important latent variable influencing seat comfort was clarified. Analysis results of survey data from narrow-body aircraft show that seat discomfort indicators centre on the physical performance of the seat and include four latent variables: support performance, personal space, contact surface features, and safety and stability. Support performance determines body posture while travelling and is the overriding latent variable influencing seat comfort. This research establishes aircraft seat discomfort indicators, latent variables formed through the mutual linkage of discomfort indicators, and the structural relations between latent variables. The results can assist in the formulation of comfort optimisation procedures for aircraft seats. Practitioner summary: A method identifying the key points of aircraft seat comfort optimisation was proposed, which includes three steps: recognising discomfort indicators, exploring the relationship between discomfort indicators, and confirming the most important variable influencing seat comfort. Results provide guidance for aircraft seat optimisation. Abbreviations: SEM: structural equation modelling; EFA: exploratory factor analysis; CFA: confirmatory factor analysis; PA-OV: path analysis with observed variables; CR: construct reliability; AVE: average variance extracted; CMIN: likelihood-ratio chi-square; DF: degrees of freedom; GFI: goodness-of -fit index; AGFI: adjusted goodness-of -fit index; RMSEA: root mean square error of approximation; NNFI: non-normed fit index; RFI: relative fit index; CFI: comparative fit index; CN: critical N.
Published: 2021

12. Nowcasting Multicell Short-Term Intense Precipitation Using Graph Models and Random Forests

Author: Ping Wang, Di Wang, Bing Xue, Cong Wang, and Jin-Yi Hou
Subjects: Atmospheric Science, 010504 meteorology & atmospheric sciences, Nowcasting, Meteorology, Computer science, 0207 environmental engineering, Graph (abstract data type), 02 engineering and technology, 020701 environmental engineering, 01 natural sciences, 0105 earth and related environmental sciences, Random forest
Abstract: Short-term intense precipitation (SIP; i.e., convective precipitation exceeding 20 mm h−1) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms.
Published: 2020

13. A Generalized High-Efficiency Broadband Class-E/F3 Power Amplifier Based on Design Space Expanding of Load Network

Author: Zhenxing Yang, Fang Tang, Tian Li, Mingyu Li, Zhijiang Dai, Jin Yi, and Xu Changzhi
Subjects: Radiation, Computer science, Initial phase, Amplifier, dBm, Broadband, Bandwidth (signal processing), Electronic engineering, Waveform, Electrical and Electronic Engineering, Condensed Matter Physics, Design space, Voltage
Abstract: This article presents an analytical and coherent design method for the generalized high-efficiency broadband parallel-circuit (PC) Class-E/F 3 power amplifier (PA). By importing the initial phase shift of the third harmonic and adding series reactive element to the load network, a free design parameter can be defined and the design space of the load network parameters are expanded. The design equations of the idealized waveforms and the optimum values of the load network components are derived in detail, and the performance of the peak drain voltage, maximum operating frequency, and power output capability can be improved. In addition, with the expanding space of load network, the generalized PC Class-E/F 3 structure exhibits broadband capability. A matching method with the real frequency technique (RFT) and the de-embedding technique is presented to design the optimal broadband matching network. To verify the validity of the proposed method, a broadband high-efficiency PA working from 1.7 to 2.6 GHz (fractional bandwidth of 42%) is designed. The experimental results of the fabricated PA present high-efficiency characteristics of 68.5%-81% drain efficiency and 62%-75.8% power-added efficiency with 38.8-40.5 dBm output power over the whole operation bandwidth.
Published: 2020

14. A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

Author: Yichi Shen, Christine A. Shoemaker, and Jin Yi
Subjects: Mathematical optimization, Control and Optimization, Optimization problem, Computer science, 020206 networking & telecommunications, 02 engineering and technology, Computer Graphics and Computer-Aided Design, Computer Science Applications, Capacity planning, Control and Systems Engineering, Search algorithm, Algorithmic efficiency, 0202 electrical engineering, electronic engineering, information engineering, Test suite, Benchmark (computing), 020201 artificial intelligence & image processing, Radial basis function, Engineering design process, Software
Abstract: This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.
Published: 2020

15. Offline Selective Harmonic Elimination With (2N+1) Output Voltage Levels in Modular Multilevel Converter Using a Differential Harmony Search Algorithm

Author: Jian Xiong, Kai Zhang, Canfeng Chen, Jin Yi, and Yayun Xin
Subjects: 0209 industrial biotechnology, Total harmonic distortion, General Computer Science, Computer science, Ant colony optimization algorithms, Bayesian optimization, General Engineering, Particle swarm optimization, 02 engineering and technology, 020901 industrial engineering & automation, Metaheuristic algorithms, Differential evolution, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Harmony search, 020201 artificial intelligence & image processing, General Materials Science, Algorithm
Abstract: (2N+1) selective harmonic elimination pulse-width modulation (SHE-PWM) is an effective switching strategy of the modular multilevel converter in medium-voltage cases. In these cases, the number of sub-modules (SMs) is not high. Compared to the traditional (N+1) SHE-PWM, (2N+1) SHE-PWM has N more voltage levels, thus it can yield much better harmonic performance. However, the task of selective harmonic elimination also becomes much more complicated since there are more switching angles to be determined. This paper proposes a differential harmony search algorithm (DHS) with a novel harmony improvisation procedure for solving this problem. The Bayesian optimization method is applied to find the optimal parameter configuration for DHS. The performance of DHS is compared with 6 other metaheuristic algorithms including differential evolution (DE), harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), teaching and learning-based optimization (TLBO), and ant colony optimization (ACO). The comparison is conducted on a set of 100 (2N +1) SHE-PWM instances by varying the modulation index from 0.01 to 1.0 with a step of 0.01. The numerical results show that the proposed DHS outperforms other compared methods in terms of objective function values, algorithm robustness, the magnitude of fundamental harmonic, and the calculated total harmonic distortion values. The switching angles obtained by DHS are further validated by both Matlab/Simulink simulation and hardware experiment.
Published: 2020

16. Convolutional Neural Networks For Apical Lesion Segmentation From Panoramic Radiographs

Author: Min-Suk Heo, Kyung-Hoe Huh, Hak-Kyun Shin, Jo-Eun Kim, Ju Hee Kang, Sam-Sun Lee, Won-Jin Yi, and Il-Seok Song
Subjects: Lesion segmentation, Computer science, business.industry, Radiography, Pattern recognition, Artificial intelligence, business, Convolutional neural network
Abstract: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising next-generation artificial intelligence (AI) in the field of medical and dental researches, which can further provide an effective diagnostic methodology allowing for detection of diseases at early age. This study was, thus, aimed to evaluate performances for apical lesion segmentation from panoramic radiographs using two CNN algorithms including U-Net and FPN. A total of 1000 panoramic radiographs showing apical lesions were separated into training (n = 800, 80%), validation (n = 100, 10%), and test (n = 100, 10%) dataset, respectively. These datasets were further incorporated to construct CNN models using two algorithms, respectively. The performances of identifying apical lesions were evaluated after calculating precision, recall, and F1-score from both CNN models. Both U-Net and FPN algorithms provided considerably good performances in identifying apical lesions in panoramic radiographs.
Published: 2021

17. An Improved Vibration Controller for Precision Manufacture Based on Youla Parametrization and Fuzzy Logic

Author: Qimin Li, Huayan Pu, Ruqing Bai, Jie Ma, Li Ke, Jinglei Zhao, Tao Zhu, Jun Luo, Jin Yi, and Shilong Wang
Subjects: Vibration, Noise, Computer science, Control theory, Convergence (routing), Parameterized complexity, Function (mathematics), Fuzzy control system, Fuzzy logic
Abstract: Vibration affects the function of precision instruments and equipment, causing major structural deformation and damage. Strong vibration and noise cause serious public hazards. Therefore, vibration is considered to be a negative factor. There are many methods for designing vibration controllers, such as machine learning algorithms and artificial intelligence algorithms. In order to reduce the harm of vibration, this paper proposes an improved Youla parameterized adaptive controller to suppress fixed or time-varying sinusoidal disturbances. The algorithm uses fuzzy control to adjust the forgetting factor, so that the system quickly reaches a steady-state and keeps the steady-state error unchanged. The paper illustrates that the improved Youla parameterized adaptive controller can suppress the disturbance. The convergence speed of this algorithm is better than the original Youla parameterized adaptive controller, and the steady-state error is basically unchanged.
Published: 2021

18. Optimal Design of 6-DOF Parallel Manipulator with Workspace Maximization using a Constrained Differential Evolution

Author: Jie Ma, Huayan Pu, Tao Zhu, Jin Yi, Hao Cheng, Shilong Wang, Jun Luo, Zhijiang Xie, and Jinglei Zhao
Subjects: Computer Science::Robotics, Optimal design, Optimization problem, Control theory, Computer science, Differential evolution, Genetic algorithm, Parallel manipulator, Workspace, Kinematics, Evolutionary computation
Abstract: The applications of parallel robots in various fields are increasing. In this paper, a research on the design optimization of a 6-PSS parallel manipulator for motion simulating is presented. The optimization problem of maximizing the workspace is formulated according to geometric and kinematic analysis. In particular, the workspace is a totally flexible workspace with 6 degrees-of-freedom (DOF). To maximize the workspace of the 6-PSS parallel robot, we investigated the differential evolution algorithm (DE), and the genetic algorithm (GA) is also adopted as a comparison. Computational results show DE algorithm outperforms GA algorithm at about 48.3% in optimal solution providing and 9.3% in time consuming.
Published: 2021

19. Relook the medium of dance with the concept of Peripersonal Space

Author: Jin Yi Lee
Subjects: Dance, Computer science, Human–computer interaction, Geography, Planning and Development, Management, Monitoring, Policy and Law, Space (commercial competition)
Published: 2019

20. An on-line variable-fidelity surrogate-assisted harmony search algorithm with multi-level screening strategy for expensive engineering design optimization

Author: Chao Lu, Jin Yi, Christine A. Shoemaker, Xinyu Li, and Liang Gao
Subjects: Mathematical optimization, Information Systems and Management, Optimization problem, Computer science, MathematicsofComputing_NUMERICALANALYSIS, Sorting, Particle swarm optimization, 02 engineering and technology, Management Information Systems, Reduction (complexity), Artificial Intelligence, 020204 information systems, Differential evolution, 0202 electrical engineering, electronic engineering, information engineering, Test suite, Harmony search, 020201 artificial intelligence & image processing, Engineering design process, Software
Abstract: This paper presents an on-line variable-fidelity surrogate-assisted harmony search algorithm (VFS-HS) for expensive engineering design optimization problems. VFS-HS employs a novel model-management strategy that uses a multi-level screening mechanism based on non-dominated sorting to strictly control the numbers of low-fidelity and high-fidelity evaluations and to keep a balance between exploration and exploitation. The performance of VFS-HS is validated through comparison not only to those of four single-fidelity surrogate-assisted optimization methods (i.e. the particle swarm optimization algorithm with radial basis function-based surrogate (OPUS-RBF), the two-layer surrogate-assisted particle swarm optimization algorithm (TLSAPSO), the surrogate-assisted hierarchical particle swarm optimization (SHPSO) and the hybrid surrogate-based optimization using space reduction (HSOSR)) but also to that a multi-fidelity surrogate-assisted optimization method (the multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution (MGPMDE)) on the CEC2014 expensive optimization test suite. A real-world problem of the optimal design for a long cylindrical gas-pressure vessel is also investigated. The results show that VFS-HS outperforms all the compared methods.
Published: 2019

21. Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem

Author: Shengqiang Xiao, Xinyu Li, Jin Yi, and Cuiyu Wang
Subjects: Mathematical optimization, Control and Optimization, General Computer Science, Job shop scheduling, Mathematical model, Computer science, business.industry, Complex system, Scheduling (production processes), Automotive industry, Welding, law.invention, Artificial bee colony algorithm, law, business, Aerospace
Abstract: The welding process which is one of the most important assembly processes is widespread in the modern manufacturing industry, including aerospace, automotive and engineering machinery. The welding shop scheduling greatly impacts the efficiency of whole production system. However, few studies on the welding shop scheduling problem (WSSP) were reported. In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP. Firstly, it is defined where multi-machine can process one job at the same time in the WSSP. Secondly, the mathematical models of WSSP have been constructed. Thirdly, an effective DABC is proposed to solve the WSSP, considering job permutation and machine allocation simultaneously. To improve the performance of proposed DABC algorithm, the effective operators have been designed. Three instances with different scales are used to evaluate the effectiveness of proposed algorithm. The comparisons with other two algorithms including genetic algorithm and grey wolf optimizer are also provided. Experimental results show that the proposed model and algorithm achieve good performance. Finally, the proposed model and DABC algorithm are applied in a real-world girder welding shop from a crane company in China. The results show that proposed model and algorithm reduces 55.17% production time comparing with the traditional algorithm and the scheduled machine allocation provides more reasonable arrangements for workers and machine loads.
Published: 2019

22. Failure Analysis of Ultra-High Strength Bolt of Circuit Breaker

Author: Jia Yi Yi, Yi Zhou Jiang, Bo Zhong Wang, Jin Yi, Ying Xiang Zou, and Ming Wei Huang
Subjects: Computer science, 020206 networking & telecommunications, 02 engineering and technology, Microstructure, Mechanism (engineering), Fracture failure, Spring (device), 0202 electrical engineering, electronic engineering, information engineering, Fracture (geology), General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, Composite material, Circuit breaker, General Environmental Science, Hydrogen embrittlement
Abstract: Screw mechanism fixing bolt fracture failure of a 500kV circuit breaker spring mechanism running process. By analyzing the installation position and operating environment, and by means of chemical analysis, metallographic examination and other means, the microstructure macro and micro fracture morphology of the fracture bolt were analyzed. The results show that the surface of the bolt is hydrogen-rich and produces hydrogen embrittlement during surface treatment.
Published: 2019

23. Optimized tool path planning for five-axis flank milling of ruled surfaces using geometric decomposition strategy and multi-population harmony search algorithm

Author: Jin Yi, Xinyu Li, Chih-Hsing Chu, Liang Gao, and Chi-Lung Kuo
Subjects: Surface (mathematics), 0209 industrial biotechnology, Optimization problem, Computer science, Message Passing Interface, 02 engineering and technology, 020901 industrial engineering & automation, Machining, Scalability, 0202 electrical engineering, electronic engineering, information engineering, Decomposition (computer science), Key (cryptography), Harmony search, 020201 artificial intelligence & image processing, Algorithm, Software
Abstract: Tool path planning is a key to ensure high machining quality and productivity in 5-axis flank milling of ruled surfaces. Previous studies have shown that optimization-driven tool path planning can effectively reduce the geometrical errors on the finished surface. However, to solve the corresponding optimization problem is a challenging task involving a large number of decision variables. This paper proposes a novel approach to generating optimized tool path for 5-axis flank finishing cut based on a geometric decomposition strategy and multi-population harmony search algorithm. The proposed approach geometrically divides the surface to be machined into a number of segments. The tool paths on those sub-surfaces are independently optimized by the multi-population harmony search algorithm. Individual tool paths are then combined together to form a complete one. The test results of representative surfaces show that the proposed approach produces higher machining precision with less computational time than compared previous methods. And the computational time is further reduced by Message passing interface based parallel computing techniques. A detailed analysis is conducted to characterize how the number of divisions affects the optimization results. And the proposed approach also shows good scalability with the increasing number of cutter locations.
Published: 2018

24. Sparse Principal Component Thermography for Subsurface Defect Detection in Composite Products

Author: Jin-Yi Wu, Yuan Yao, and Stefano Sfarra
Subjects: Computer science, Active thermography, data analysis, nondestructive testing (NDT), sparse principal component analysis (SPCA), thermographic analysis, data analysis, Carbon fibers, 02 engineering and technology, sparse principal component analysis (SPCA), thermographic analysis, Nondestructive testing, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Active thermography, business.industry, Noise (signal processing), 020208 electrical & electronic engineering, nondestructive testing (NDT), Pattern recognition, Fibre-reinforced plastic, 021001 nanoscience & nanotechnology, Computer Science Applications, Characterization (materials science), Control and Systems Engineering, visual_art, Principal component analysis, Thermography, visual_art.visual_art_medium, Artificial intelligence, 0210 nano-technology, business, Information Systems
Abstract: Active thermography is an efficient and powerful technique for nondestructive testing of products made of composite materials, which enables rapid inspection of large areas, presents results as easily interpreted high-resolution images, and is easy to operate. In recent years, a number of thermographic data analysis methods were developed to enhance the visibility of subsurface defects, among which principal component thermography (PCT) is recommended because of its capability to enhance the contrast between defective and defect-free areas, compress data, and reduce noise. In this study, a sparse principal component thermography (SPCT) method is proposed, which inherits the advantages of PCT and allows more flexibility by introducing a penalization term. Compared to PCT, SPCT provides more interpretable analysis results owing to its structure sparsity. The feasibility and effectiveness of the proposed method are illustrated by the experimental results of the subsurface defect characterization in a carbon fiber reinforced plastic specimen.
Published: 2018

25. Workload Modelling Method of Edge Computing Terminals for Distribution Service Under Power Internet of Things

Author: Jin Yi, Guozheng Huang, Yijia Zhang, Huang Mengzhe, and Gang Chen
Subjects: Service (systems architecture), Resource (project management), Software, Sequential logic, Dependency (UML), Terminal (telecommunication), Computer science, business.industry, Distributed computing, Workload, business, Edge computing
Abstract: Edge computing terminals realize the decoupling between software and hardware, extend the functions of the terminals and meet the needs of diversified distribution service under Power Internet of Things. Workload modeling is the basis of computing resource configuration for edge computing terminals. Thus, this paper proposes a workload modeling method of edge computing terminals for distribution service. According to the timing logic of distribution service, the service is decomposed into several tasks and the sequential logic diagram is constructed. In the sequential logic diagram, directed edges are used to represent the dependency of the tasks. Combining the internal architecture of the edge computing terminals with the sequential logic diagram, the mathematical workload model is formulated. Based on the formulated models, a workload curve generation algorithm is proposed to obtain the computing resource demand of distribution service. Finally, the computing resources of the edge computing terminal are configured according to workload curve. Simulation results verify the effectiveness of the proposed model and method.
Published: 2021

26. Compact CubeSat Gamma-Ray Detector for GRID Mission

Author: Wen, Jia-Xing, Zheng, Xu-Tao, Yu, Jian-Dong, Che, Yue-Peng, Yang, Dong-Xin, Gao, Huai-Zhong, Jin, Yi-Fei, Long, Xiang-Yun, Liu, Yi-Hui, Xu, Da-Cheng, Zhang, Yu-Chong, Zeng, Ming, Tian, Yang, Feng, Hua, Zeng, Zhi, Cang, Ji-Rong, Wu, Qiong, Zhao, Zong-Qing, Zhang, Bin-Bin, An, Peng, and collaboration, GRID
Subjects: Nuclear and High Energy Physics, business.industry, Firmware, Computer science, Physics::Instrumentation and Detectors, media_common.quotation_subject, Astrophysics::High Energy Astrophysical Phenomena, Detector, Electrical engineering, Astrophysics::Instrumentation and Methods for Astrophysics, FOS: Physical sciences, Scintillator, Grid, computer.software_genre, Silicon photomultiplier, Nuclear Energy and Engineering, Sky, CubeSat, Transient (oscillation), Astrophysics - Instrumentation and Methods for Astrophysics, business, computer, Instrumentation and Methods for Astrophysics (astro-ph.IM), media_common
Abstract: Gamma-Ray Integrated Detectors (GRID) mission is a student project designed to use multiple gamma-ray detectors carried by nanosatellites (CubeSats), forming a full-time all-sky gamma-ray detection network that monitors the transient gamma-ray sky in the multi-messenger astronomy era. A compact CubeSat gamma-ray detector, including its hardware and firmware, was designed and implemented for the mission. The detector employs four Gd2Al2Ga3O12 : Ce (GAGG:Ce) scintillators coupled with four silicon photomultiplier (SiPM) arrays to achieve a high gamma-ray detection efficiency between 10 keV and 2 MeV with low power and small dimensions. The first detector designed by the undergraduate student team onboard a commercial CubeSat was launched into a Sun-synchronous orbit on October 29, 2018. The detector was in a normal observation state and accumulated data for approximately one month after on-orbit functional and performance tests, which were conducted in 2019., Comment: final manuscript, 9 pages, 10 figures, published on NST
Published: 2021
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27. Optimizated Simulation of Central Cooling System

Author: Feng Sun, Gang Zheng, Jin-Yi Jiao, Guohu Yu, Xing-Ming Qi, Qing Sun, and Ling Sun
Subjects: Computer science, 020208 electrical & electronic engineering, 02 engineering and technology, iSight, 01 natural sciences, Automotive engineering, 010305 fluids & plasmas, Variable (computer science), Nonlinear system, Control theory, 0103 physical sciences, Heat exchanger, 0202 electrical engineering, electronic engineering, information engineering, Water cooling, Ship load, Energy (signal processing)
Abstract: Optimized simulation of central cooling system can promote ship energy-saving and reduce emission. It will cause energy waste by fix quantity pump with defect on the adjust ability with load fluctuation. An optimization and simulation environment were established with ISIGHT, which accomplish the optimize simulation on center heat exchangers and variable frequency pumps. The simulation and experimental results of one ship show that: the error between simulation and experiment data is 3.78 percent, meeting the requirement of engineering; variable frequency center cooling system achieve the 13$\sim$35 percent energy saving; the optimize simulation on variable frequency pumps and center heat exchangers must base on the ship load distribution and operation conditions.
Published: 2020

28. An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism

Author: Chen Naiyue, Li Yidong, Jingyi Wang, Jinhui Yu, and Jin Yi
Subjects: Network security, business.industry, Computer science, Deep learning, Feature extraction, Intrusion detection system, Machine learning, computer.software_genre, Autoencoder, Constant false alarm rate, Binary classification, Feature (computer vision), Artificial intelligence, business, computer
Abstract: In recent years, various types of network attacks emerge in endlessly, the protection of network security has been paid more and more attention by our society. Network Intrusion Detection System (NIDS)is used to protect computer systems from malicious attacks and intrusions, thus has also become a hot research field. Due to the great success of deep learning in industry and academia, there is an increasing interest in the application of deep learning methods for feature representations and classification. In this paper,we propose a intrusion detection model based on time-related deep learning approach with attention mechanism. Firstly, we build a stacked sparse autoencoder(SSAE) to extract high-level feature representations of intrusion information. Then we design a two-layer bidirectional gated recurrent unit(BiGRU) network with attention mechanism to classify traffic data. We perform experiments on a benchmark dataset UNSW-NB15, the results in binary classification indicate that using high-dimensional sparse features extracted by SSAE can significantly accelerate the classification progress. Our model can detect network intrusions effectively and outperform other related methods with reduction of false alarm rate, high accuracy rate, reduced training and testing time.
Published: 2020

29. Discriminative repair approach to remove shadow-induced error for typical digital fringe projection

Author: Minghui Duan, Yan Kan, Changan Zhu, Sun Zheng, Jin Yi, and Enhong Chen
Subjects: business.industry, Computer science, Kernel density estimation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Phase (waves), Kalman filter, GeneralLiterature_MISCELLANEOUS, Atomic and Molecular Physics, and Optics, Structured-light 3D scanner, symbols.namesake, Optics, Fourier transform, Discriminative model, Shadow, symbols, Computer vision, Artificial intelligence, business, Phase retrieval, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: In a typical digital fringe projection (DFP) system, the shadows in the fringe images cause errors in the phase map. We propose a novel discriminative repair approach to remove the shadow-induced error in the phase map. The proposed approach first classifies the shadow area in the phase map obtained by the DFP into two categories: valid shadow area and invalid shadow area. Then the valid shadow area is repaired by a proposed neighboring information fusion phase estimation (NIFPE) method, which fuses the phase gradient into the result of kernel density estimation (KDE) through the Kalman filter (KF) algorithm. The invalid shadow area is repaired by a proposed background phase matching (BPM) method. The experimental results demonstrate that the shadow-induced error in the phase map can be removed, which verifies the effectiveness of the proposed approach.
Published: 2020

30. A Novel Robotic Grasp Detection Technique by Integrating YOLO and Grasp Detection Deep Neural Networks

Author: Ying-Jen Chen, Ui-Kai Chen, Kai-Chu Chang, and Jin-Yi Yang
Subjects: Artificial neural network, Computer science, business.industry, 05 social sciences, GRASP, Point cloud, 010501 environmental sciences, Object (computer science), 01 natural sciences, Object detection, 0502 economics and business, Robot, Computer vision, Artificial intelligence, 050207 economics, business, Focus (optics), Robotic arm, 0105 earth and related environmental sciences
Abstract: This paper proposes a robotic grasp detection technique by integrating you only look once (YOLO) deep neural network (DNN) and a grasp detection DNN. In this world, there are many people who cannot move their own bodies. The reason may be an accident or physical deterioration. So we need to invest more human resources to assist their lives. With new technological advances, robots are gradually able to perfectly replicate human movements. Hence, we intend to design a remote-control fetching robot. The system combines internet of things (IoT) technology, and users can use intelligent devices to control this robot with robotic arm to get the items they want. This paper focus on detecting the grasp of robotic arm by integrating YOLO and grasp detection DNNs. At first, YOLO V-v3 is applied to achieve object detection. Then a robotic grasp detection DNN is proposed to detect the robotic grasp. After that, the point cloud information of this object is utilized to calculate the normal vector of the grasp position such that the robotic arm can fetch the target along the normal vector. Finally, experiment results are given to show the practicality of the proposed robotic grasp detection Technique.
Published: 2020

31. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network

Author: Jo-Eun Kim, Odeuk Kwon, Tae-Hoon Yong, Soon-Chul Choi, Se-Ryong Kang, Sam-Sun Lee, Min-Suk Heo, Kyung-Hoe Huh, and Won-Jin Yi
Subjects: medicine.medical_specialty, Panoramic radiograph, Computer science, Radiography, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, parasitic diseases, Radiography, Panoramic, medicine, Humans, Radiology, Nuclear Medicine and imaging, General Dentistry, business.industry, Deep learning, 030206 dentistry, General Medicine, Odontogenic, Otorhinolaryngology, Area Under Curve, Odontogenic Cysts, Artificial intelligence, Radiology, Neural Networks, Computer, business, Research Article
Abstract: Objectives: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation for detection and classification of the multiple diseases. Methods: We developed a deep CNN modified from YOLOv3 for detecting and classifying odontogenic cysts and tumors of both jaws. Our data set of 1282 panoramic radiographs comprised 350 dentigerous cysts (DCs), 302 periapical cysts (PCs), 300 odontogenic keratocysts (OKCs), 230 ameloblastomas (ABs), and 100 normal jaws with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. We evaluated the classification performance of the developed CNN by calculating sensitivity, specificity, accuracy, and area under the curve (AUC) for diseases of both jaws. Results: The overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity,91.3% accuracy, and 0.86 AUC using the CNN with unaugmented data set to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the CNN with augmented data set. CNN using augmented data set had the following sensitivities, specificities, accuracies, and AUCs: 91.4%, 99.2%, 97.8%, and 0.96 for DCs, 82.8%, 99.2%, 96.2%, and 0.92 for PCs, 98.4%,92.3%,94.0%, and 0.97 for OKCs, 71.7%, 100%, 94.3%, and 0.86 for ABs, and 100.0%, 95.1%, 96.0%, and 0.97 for normal jaws, respectively. Conclusion: The CNN method we developed for automatically diagnosing odontogenic cysts and tumors of both jaws on panoramic radiographs using data augmentation showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.
Published: 2020

32. Development and application system based on static and dynamic data for disaster management

Author: Junwoo Lee, Lee Sohee, Ok Ju Kim, and Jin Yi Park
Subjects: Process management, Development (topology), Emergency management, business.industry, Computer science, Dynamic data, business
Abstract: The patterns of recent disasters in Korea such as typhoons, mountain fires and earthquakes are becoming increasingly complex and extensive. It is important to look at the disaster from a unified perspective in order to reduce the damage that will occur from the disaster and promote recovery. The integration between including work systems and information among government agencies that manage disaster situations is one of the important parts in order to respond quickly and reduce damage. But most of the information used to cope with disaster situations is temporarily consumed and volatile. Also there is a lack of periodic updates or management systems. In this study, the information system was established through analysis of the status of the utilization system of static and dynamic data among disaster information used in the Republic of Korea when disaster management. In the event of a typhoon situation, the system operation and on-site survey are to derive the deficiencies to support the assessment of the situation in government agencies and local governments.
Published: 2020

33. Satisfaction Evaluation of the Passenger Interface Design of High-Speed Train Based on G1-Entropy Weight Method

Author: Jin-Yi Zhi, Ze-Rui Xiang, and Si-Jun He
Subjects: Entropy weight method, Computer science, Order (business), media_common.quotation_subject, Interface (computing), High speed train, Interface design, Empowerment, Industrial engineering, Preference, Randomness, media_common
Abstract: High-speed train passenger interface (PI) is of great significance for passengers’ experience. CRH3X is a new type of double-layer high-speed train at a closed testing stage, with independent research and development of China. In order to feed back problems existing in the current PI design of CRH3X, the G1 method was introduced and combined with the entropy weight method for quantification of differences of importance degrees between different indexes based on empirical data of passengers’ satisfaction. The innovation is advantageous in that through combination of experts’ experience and passengers’ actual experience information, randomness in subjective empowerment is avoided. In addition, with reference to those evaluation results, designers can better determine an improved preference strategy.
Published: 2020

34. On a Theorem of Lovász that hom(?, H) Determines the Isomorphism Type of H

Author: Cai, Jin-Yi and Govorov, Artem
Subjects: 000 Computer science, knowledge, general works, Computer Science
Abstract: Graph homomorphism has been an important research topic since its introduction [László Lovász, 1967]. Stated in the language of binary relational structures in that paper [László Lovász, 1967], Lovász proved a fundamental theorem that the graph homomorphism function G ? hom(G, H) for 0-1 valued H (as the adjacency matrix of a graph) determines the isomorphism type of H. In the past 50 years various extensions have been proved by Lovász and others [László Lovász, 2006; Michael Freedman et al., 2007; Christian Borgs et al., 2008; Alexander Schrijver, 2009; László Lovász and Balázs Szegedy, 2009]. These extend the basic 0-1 case to admit vertex and edge weights; but always with some restrictions such as all vertex weights must be positive. In this paper we prove a general form of this theorem where H can have arbitrary vertex and edge weights. An innovative aspect is that we prove this by a surprisingly simple and unified argument. This bypasses various technical obstacles and unifies and extends all previous known versions of this theorem on graphs. The constructive proof of our theorem can be used to make various complexity dichotomy theorems for graph homomorphism effective, i.e., it provides an algorithm that for any H either outputs a P-time algorithm solving hom(?, H) or a P-time reduction from a canonical #P-hard problem to hom(?, H).
Published: 2020
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35. Research on Optimal Design of Metro Driving Interface Based on Driver’s Operating Characteristics

Author: Yang Du, Jin-Yi Zhi, Ze-Rui Xiang, and Jing Kang
Subjects: Optimal design, Computer science, Interface (computing), Design proposal, Simulation
Abstract: For the purposes of relief of drivers’ working strength and spiritual stress and increase of working efficiency, this paper studies design of the metro train’s driving interface and comes up with an optimization proposal aiming. Firstly, investigation survey is conducted on drivers’ driving satisfaction; secondly, importance and use frequency of equipment are surveyed; thirdly, drivers’ driving operation processes are recorded with videos, the tracks of drivers’ operation processes are analyzed; fourthly, based on the above survey analysis, an optimization design proposal for driving interface layout is proposed, while man-machine verification of the proposal is conducted with the JACK virtual simulation technology; and finally, drivers’ overall satisfaction with the optimization proposal is investigated. As shown in the results, the optimized proposal is advantageous in rational layout, convenient operations and clear tracks of operation processes, which can help relieve drivers’ driving strength effectively and increase their satisfaction significantly.
Published: 2020

36. EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

Author: Liang, Tengfei, Jin, Yi, Li, Yidong, Wang, Tao, Feng, Songhe, and Lang, Congyan
Subjects: FOS: Computer and information sciences, Image quality, Computer science, Noise reduction, Computer Vision and Pattern Recognition (cs.CV), Feature extraction, Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Convolutional neural network, 030218 nuclear medicine & medical imaging, Convolution, 03 medical and health sciences, 0302 clinical medicine, FOS: Electrical engineering, electronic engineering, information engineering, business.industry, Deep learning, Image and Video Processing (eess.IV), Sobel operator, Pattern recognition, Edge enhancement, Electrical Engineering and Systems Science - Image and Video Processing, 030220 oncology & carcinogenesis, Computer Science::Computer Vision and Pattern Recognition, Artificial intelligence, Enhanced Data Rates for GSM Evolution, business
Abstract: In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become an important research issue in the field of medical images. In recent years, with the rapid development of deep learning technology, many algorithms have emerged to apply convolutional neural networks to this task, achieving promising results. However, there are still some problems such as low denoising efficiency, over-smoothed result, etc. In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN). In our network, we design an edge enhancement module using the proposed novel trainable Sobel convolution. Based on this module, we construct a model with dense connections to fuse the extracted edge information and realize end-to-end image denoising. Besides, when training the model, we introduce a compound loss that combines MSE loss and multi-scales perceptual loss to solve the over-smoothed problem and attain a marked improvement in image quality after denoising. Compared with the existing low-dose CT image denoising algorithms, our proposed model has a better performance in preserving details and suppressing noise., Comment: 8 pages, 7 figures, 3 tables
Published: 2020
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37. Research on the Influence of Physical Environment in Civil Aircraft Cabin on Seat Comfort

Author: Li-Li Zhang, Jin-Yi Zhi, Jin Wang, Zhi-Ruo Lin, and Jian-Ping Chen
Subjects: Aeronautics, Noise (signal processing), Computer science, Critical condition, Exploratory factor analysis, Structural equation modeling
Abstract: The physical environment of the aircraft cabin is the basic factor influencing the comfort experience of air passengers. In order to clarify the influence path of the cabin physical environment factors on the passenger comfort experience. Exploratory factor analysis methods and structural equation models are used to explore the effects of environmental factors such as temperature, humidity, brightness, noise, vibration, and air pressure on seat comfort and cross-group analysis is used to explore whether the above paths are affected by passenger gender and Influence of body shape. The results show that the overall fitting indexes of the model meet the critical conditions and the routes described in the model have significant effects on passengers of different genders and shapes. The research results provide a reference for the study of the relationship between the factors affecting the comfort. Research are beneficial to the improvement of comfort experience of aircraft cabin.
Published: 2020

38. Research on the Comprehensive Evaluation System of Cabin Comfort of Civil Aircraft

Author: Jin Wang, Li-Li Zhang, Jian-Ping Chen, and Jin-Yi Zhi
Subjects: Evaluation system, Aeronautics, Computer science, ComputerApplications_MISCELLANEOUS, Evaluation methods, Statistical analysis
Abstract: In order to clear the factors and degree that affect the comfort experience of the aircraft cabin, and to make a reasonable strategy for improving the comfort of the aircraft cabin, the comprehensive evaluation system of the comfort in aircraft cabin is studied. An indicators system, weight system, and evaluation method for cabin comfort evaluation are proposed, and the comfort of aircraft cabins is evaluated from four aspects: the indicators of criterion layer, the indicators of attribute layer, the factors of factor layer, and the overall comfort of cabin. Based on the evaluation system, the cabin comfort of a flight was investigated, and 206 valid questionnaires were obtained. Statistical analysis of the data found that the factors and indictors influencing comfort. The comprehensive evaluation system of comfort experience in civil aircraft cabin provides quantitative reference for the selection and optimization of cabin scheme, and provides direction for the improvement of aircraft cabin comfort in future design.
Published: 2020

39. Grey wolf optimizer with cellular topological structure

Author: Chao Lu, Jin Yi, and Liang Gao
Subjects: 0209 industrial biotechnology, Computer science, Covariance matrix, Population size, General Engineering, Evolutionary algorithm, Particle swarm optimization, 02 engineering and technology, Topology, Computer Science Applications, Reduction (complexity), 020901 industrial engineering & automation, Local optimum, Artificial Intelligence, Differential evolution, 0202 electrical engineering, electronic engineering, information engineering, Harmony search, 020201 artificial intelligence & image processing, Metaheuristic
Abstract: Grey wolf optimizer (GWO) is a newly developed metaheuristic inspired by hunting mechanism of grey wolves. The paramount challenge in GWO is that it is prone to stagnation in local optima. This paper proposes a cellular grey wolf optimizer with a topological structure (CGWO). The proposed CGWO has two characteristics. Firstly, each wolf has its own topological neighbors, and interactions among wolves are restricted to their neighbors, which favors exploitation of CGWO. Secondly, information diffusion mechanism by overlap among neighbors can allow to maintain the population diversity for longer, usually contributing to exploration. Empirical studies are conducted to compare the proposed algorithm with different metaheuristics such as success-history based adaptive differential evolution with linear population size reduction (LSHADE), teaching-learning based optimization algorithm (TLBO), effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR), novel dynamic harmony search (NDHS), bat-inspired algorithm (BA), comprehensive learning particle swarm optimizer (CLPSO), evolutionary algorithm based on decomposition (EAD), ring topology PSO (RPSO), crowding-based differential evolution (CDE), neighborhood based crowding differential evolution (NCDE), locally informed particle swarm (LIPS), some improved variants of GWO and GWO. Experimental results show that the proposed method performs better than the other algorithms on most benchmarks and engineering problems.
Published: 2018

40. Automatic Reproduction of Natural Head Position Using a Portable 3D Scanner Based on Immediate Calibration

Author: Sang-Jeong Lee, Min-Suk Heo, Kyung-Hoe Huh, Soon Jung Hwang, Won-Jin Yi, Sam-Sun Lee, Min-Hyuk Choi, Hoon Joo Yang, and Soon-Chul Choi
Subjects: optical 3d scan, Scanner, Computer science, Coordinate system, Computed tomography, lcsh:Technology, Natural head position, Mean difference, Imaging phantom, natural head position (nhp), lcsh:Chemistry, 03 medical and health sciences, coherent point drift (cpd) registration, 0302 clinical medicine, medicine, Calibration, General Materials Science, Computer vision, Instrumentation, lcsh:QH301-705.5, Fluid Flow and Transfer Processes, medicine.diagnostic_test, business.industry, lcsh:T, Process Chemistry and Technology, orthognathic surgery, General Engineering, 030206 dentistry, lcsh:QC1-999, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 030220 oncology & carcinogenesis, Face (geometry), Artificial intelligence, business, lcsh:Engineering (General). Civil engineering (General), lcsh:Physics
Abstract: This paper developed a new method to easily record and automatically reproduce the 3D natural head position (NHP) of patients using a portable 3D scanner based on immediate calibration. We first optically scanned the patient&rsquo, s face using a portable 3D scanner, and the scanned model was easily aligned with the global horizon based on an immediate calibration procedure using a developed calibration plate. The 3D patient NHP Computed Tomography(CT) model was reproduced automatically by performing registration between the CT model and the optically scanned model in the NHP using a modified coherent point drift (CPD) algorithm. In a phantom experiment, we evaluated the developed method&rsquo, s accuracy using the error between the true and the calculated orientations in roll, pitch, and yaw directions. The mean difference was &minus, 0.05 &plusmn, 0.13&deg, 0.08 &plusmn, 0.22&deg, and &minus, 0.18&deg, in the roll, pitch, and yaw directions, respectively. The measured roll, pitch, and yaw directions were not significantly different from the true directions (p &gt, 0.05). The calibration procedure for aligning the scanner coordinate system was easy enough for an inexperienced user to operate, and the 3D NHP CT model could be reproduced automatically. The developed method could be used for diagnosing and treating orthognathic patients with facial asymmetry accurately and conveniently in dental clinics.
Published: 2019

41. A systematic approach for streamlined head form design and evaluation of Chinese high-speed train

Author: Li Fang, Hong-Jun Kang, Jun-Hui Huang, Tian Li, Jin-Yi Zhi, Peng-Fei Gao, and Ze-Rui Xiang
Subjects: Design assessment, Computer science, media_common.quotation_subject, 05 social sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Transportation, High speed train, 02 engineering and technology, computer.software_genre, Automotive engineering, 020303 mechanical engineering & transports, 0203 mechanical engineering, Mechanics of Materials, Automotive Engineering, Computer Aided Design, Head (vessel), 0501 psychology and cognitive sciences, Train, Quality (business), Product (category theory), computer, 050107 human factors, media_common
Abstract: Product forms can reflect the comprehensive quality of products. High-speed trains are highly technological industrial product, and their streamlined heads (nose shapes) are designed based on aerod...
Published: 2018

42. Active thermography testing and data analysis for the state of conservation of panel paintings

Author: Xavier Maldague, Dario Ambrosini, Susana Lagüela, Stefano Sfarra, Clemente Ibarra-Castanedo, Yuan Yao, and Jin-Yi Wu
Subjects: Computer science, Computation, 02 engineering and technology, 01 natural sciences, Nondestructive testing, Component (UML), Thermographic data processing, Heat transfer, Computer vision, Active thermography, Thermographic data processing, Nondestructive testing, Cultural heritage, Heat transfer, Statistical analysis, Active thermography, Data processing, Pixel, business.industry, 010401 analytical chemistry, General Engineering, 021001 nanoscience & nanotechnology, Condensed Matter Physics, Sample (graphics), 0104 chemical sciences, Statistical analysis, Principal component analysis, Thermography, Cultural heritage, Artificial intelligence, 0210 nano-technology, business
Abstract: The restoration of Cultural Heritage wouldn't be possible without the financial resources to meet Cultural Heritage needs. A smart procedure to reduce in time and funds spent for restoration is linked to a planning of the diagnostic interventions acting to predict incipient defects undetectable to the naked eye. One of the main methods to fulfil this task is infrared thermography (IRT). The aim of this study is to examine the efficiency of various mathematical techniques in thermographic data processing, with respect to the thermal excitation procedure and the type of artificial defect in a panel painting sample. One of the thermographic analyses performed was based on the pixelwise algorithm for time-derivative of temperature (PATDT). With this algorithm, Newton's cooling law was applied pixel per pixel, resulting in the computation of the cooling rate of each pixel. In addition, the capabilities of the multivariate statistical analysis methods, independent component thermography (ICT) and sparse principal component thermography (SPCT) were also investigated. In the present case study, the authors inspected possible pathologies resembling splitting areas (i.e., detachments) in real panel paintings, with the consequent change in the heat transfer coefficient and the heat capacity. The feasibility of the different analysis methods was illustrated with the application results.
Published: 2018

43. Sparse Principal Component Thermography for Structural Health Monitoring of Composite Structures

Author: Yuan Yao, Jin-Yi Wu, and Stefano Sfarra
Subjects: 010302 applied physics, Pixel, Computer science, business.industry, Feature extraction, Pattern recognition, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, Control and Systems Engineering, Nondestructive testing, 0103 physical sciences, Principal component analysis, Thermography, Structural health monitoring, Artificial intelligence, 0210 nano-technology, business, Data compression
Abstract: Non-destructive testing (NDT) techniques play an important role in structural health monitoring (SHM) of composite structures, among which infrared thermography (IRT) is popular because it is easy to operate, enables rapid inspection of large areas, and presents results as easily interpreted thermal images. In order to achieve noise reduction, feature extraction, and data compression, principal component thermography (PCT) was developed for thermographic data processing. However, each principal component in PCT is a linear combination of all the original pixel values, making the results difficult to interpret and hence affecting defect identification. In this work, sparse principal component thermography (SPCT) is proposed as an improved version of PCT, which provides more interpretable analysis results owing to its structure sparsity and leads to a better defect detection. The feasibility of SPCT is illustrated with two case studies.
Published: 2018

44. The influence of mobile phone location and screen orientation on driving safety and the usability of car-sharing software in-car use

Author: Jing Zhang, Nick Bryan-Kinns, Suixian Yang, Jin-Yi Zhi, and Chunhui Jing
Subjects: 030506 rehabilitation, business.industry, Computer science, 05 social sciences, Public Health, Environmental and Occupational Health, Driving simulator, Human Factors and Ergonomics, Usability, Steering wheel, Task (project management), 03 medical and health sciences, Software, Phone, Human–computer interaction, Mobile phone, 0501 psychology and cognitive sciences, The Internet, 0305 other medical science, business, 050107 human factors
Abstract: One of the important parts of the Internet economy is the car-sharing economy. Nevertheless, a few numbers of studies have been dedicated to the issues of safety of driving and usability resulting from the car-sharing software application. The current study aims at analyzing the effect of phone location and screen orientation on usability and safety of driving in Chinese drivers who use car-sharing software (e.g., UBER) and comparing the differences in the safety of driving and usability among various car-sharing tasks. To this end, 24 experienced Chinese Uber drivers were employed using a driving simulator, and three tasks of car-sharing software were investigated. The variables of driving safety and usability were analyzed by Repeat Measurement ANOVA as well as Two-way ANOVA. It was found that when the phone is located on the left side of the steering wheel, usability and driving safety were better compared to locating it on the right side. The orientation of the mobile phone screen had a major impact on usability, while its impact on driving safety was trivial. The left-portrait mode showed the best performance. No significant impact on usability was found in terms of tasks (T1: ordering; T2: route checking; T3: destination search), while tasks showed a significant effect on driving safety. Among these tasks, the T3 task showed the highest impact on driving safety. The T2 task was in the second rank, followed by the T1 task (T1 Relevance to industry This finding can be used as a basis for the optimization design of ca-sharing software and as a foundation for improving the safety and usability of car-sharing software used by drivers in the car.
Published: 2021

45. Immunochemical techniques for multianalyte analysis of chemical residues in food and the environment: A review

Author: Yu-Dong Shen, Shirley J. Gee, Hongtao Lei, Yong-Fang Li, Jin-Yi Yang, Xiulan Sun, Yuanming Sun, Hong Wang, Zhen-Lin Xu, Ross C. Beier, Bruce D. Hammock, and Zhanhui Wang
Subjects: medicine.diagnostic_test, Environmental analysis, Computer science, 010401 analytical chemistry, Nanotechnology, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, Analytical Chemistry, Single test, Immunoassay, medicine, Biochemical engineering, 0210 nano-technology, Spectroscopy
Abstract: Immunoassay is an emerging technique that has been used in the fields of food and environmental analysis to monitor and control hazardous residues. Multianalyte immunoassays (MAIAs) are favored because they can simultaneously determine several targets in a single test. In this review, an overview of various formats for the MAIA is given. New trends on how to produce antibodies, how to improve antibody specificity and how to manipulate assay formats to obtain the desired multi-analysis schemes are summarized. Finally, the limitations and prospects of MAIAs are discussed.
Published: 2017

46. Thermal- and Performance-Aware Address Mapping for the Multi-Channel Three-Dimensional DRAM Systems

Author: Jin-Yi Lin and Shu-Yen Lin
Subjects: 010302 applied physics, Random access memory, General Computer Science, Computer science, General Engineering, 02 engineering and technology, Parallel computing, Function (mathematics), 3-D DRAM, 01 natural sciences, CAS latency, 020202 computer hardware & architecture, Memory management, Feature (computer vision), 0103 physical sciences, Thermal, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, General Materials Science, hotspot, lcsh:Electrical engineering. Electronics. Nuclear engineering, thermal-aware mapping, lcsh:TK1-9971, Dram
Abstract: In this paper, we proposed thermal- and performance-aware address mapping (TPAMAP) for the multi-channel 3-D DRAM systems. TPAMAP reduces the thermal problem from the vertical stacking of the active banks in different DRAM channels and supports the mappings with the design tradeoffs of the temperature and performance. In our experiments, the peak temperature can be reduced by 1.1 °C ~ 12.3 °C for the 3-D DRAM system using TPAMAP. The cost function for the constraints of the temperature and performance is also proposed. In hardware design, we demonstrate the low-cost feature of TPAMAP in our hardware design.
Published: 2017

47. Joint Heterogeneous Pair-wise Loss For Top-N Recommendation

Author: Yuan Luo, Jin Qin, Jiajin Huang, and Jin Yi
Subjects: Pair wise, Ranking, Computer science, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Pairwise comparison, 02 engineering and technology, Data mining, computer.software_genre, Joint (audio engineering), computer
Abstract: We propose a novel pairwise unified recommendation model (short for pairwise URM). The pairwise URM combines two pairwise ranking-oriented collaborative filtering approaches, namely Collaborative Less-is-More Filtering (CLiMF) and Bayesian Personal Ranking (BPR). By sharing common latent features of users and items in BPR and CLiMF, the pairwise URM can benefit from the two methods to improve recommendation qualities. The experimental evaluation is conducted on two real-world datasets with different scales and demonstrates the positive effect of the performance of the pairwise URM.
Published: 2019

48. Dynamic Thermal-Aware Inter-Layer Perpendicular Downward Mapping for Three-Dimensional Convolutional Neural Network Accelerator

Author: Jin-Yi Lin and Shu-Yen Lin
Subjects: 010302 applied physics, Artificial neural network, Computer science, Inter layer, 020208 electrical & electronic engineering, Three-dimensional integrated circuit, 02 engineering and technology, 01 natural sciences, Convolutional neural network, Computational science, Thermal aware, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Perpendicular
Abstract: In this work, we propose an inter-layer perpendicular downward mapping (ILPDM) to avoid the thermal problem for three-dimensional convolutional neural network (3D CNN) accelerator. The workload of the overheated multiply-accumulate units (MACs) in 3D CNN accelerator can be moved to the non-overheated ones by using ILPDM. In our experiments, the peak temperature can be reduced by up to 11.42°C for the 3D CNN accelerator using ILPDM.
Published: 2019

49. Phase-shifting profilometry for the robust 3-D shape measurement of moving objects

Author: Xiaobo Xu, Changan Zhu, Minghui Duan, Jin Yi, Chunmei Xu, and Enhong Chen
Subjects: business.industry, Absolute phase, Computer science, Phase (waves), Motion (geometry), Image processing, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, Atomic and Molecular Physics, and Optics, 010309 optics, symbols.namesake, Optics, Transformation (function), Fourier transform, Motion estimation, Phase correlation, 0103 physical sciences, symbols, Computer vision, Artificial intelligence, 0210 nano-technology, business
Abstract: Arbitrary two-dimensional (2-D) motion introduces coordinate errors and phase errors to three-dimensional (3-D) shape measurement of objects in phase-shifting profilometry (PSP). This paper presents a new robust 3-D reconstruction method for arbitrary 2-D moving objects by introducing an adaptive reference phase map and the motion estimation based on fence image. First, a composite fence image is used to track object motion. Second, to obtain the transformation matrixes and remove the coordinate errors among object images, the angle extraction technique and the 1-D hybrid phase correlation method (1-D HPCM) are integrated to automatically estimate the sub-pixel motion of objects. Third, the phase errors are compensated to obtain the rough absolute phase map of objects by combining the transformation matrixes with the reference phase map. Finally, the absolute phase map is refined to reconstruct the 3-D surfaces of moving objects with adaptive reference phase map. The proposed computational framework can accurately and automatically realize 3-D shape measurement of arbitrary objects with 2-D movement. The results of experiment verify the effectiveness of our computational framework.
Published: 2019

50. Loss functions for denoising compressed images: a comparative study

Author: François Malgouyres, Jin-Yi Wu, Thomas Oberlin, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), IRT Saint Exupéry - Institut de Recherche Technologique, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0040,CIMI,Centre International de Mathématiques et d'Informatique (de Toulouse)(2011), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), and Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Compression artifact, Computer science, Noise reduction, Quantization (signal processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020206 networking & telecommunications, 02 engineering and technology, Data_CODINGANDINFORMATIONTHEORY, Inverse problem, Compression artifacts, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, Bayesian denoising, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Image denoising, 0202 electrical engineering, electronic engineering, information engineering, Com- pression artifacts, 020201 artificial intelligence & image processing, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Algorithm, Image decompression
Abstract: International audience; This paper faces the problem of denoising compressed images, obtained through a quantization in a known basis. The denoising is formulated as a variational inverse problem regularized by total variation, the emphasis being placed on the data-fidelity term which measures the distance between the noisy observation and the reconstruction. The paper introduces two new loss functions to jointly denoise and dequantize the corrupted image, which fully exploit the knowledge about the compression process, i.e., the transform and the quantization steps. Several numerical experiments demonstrate the effectiveness of the proposed loss functions and compare their performance with two more classical ones.
Published: 2019

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