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16,239 results on '"Lacoste A."'

105. Branching processes with resetting as a model for cell division

Author

Genthon, Arthur, García-García, Reinaldo, and Lacoste, David

Subjects
Condensed Matter - Statistical Mechanics
Abstract

We study the Stochastic Thermodynamics of cell growth and division using a theoretical framework based on branching processes with resetting. Cell division may be split into two sub-processes: branching, by which a given cell gives birth to an identical copy of itself, and resetting, by which some properties of the daughter cells (such as their size or age) are reset to new values following division. We derive the first and second laws of Stochastic Thermodynamics for this process, and identify separate contributions due to branching and resetting. We apply our framework to well-known models of cell size control, such as the sizer, the timer, and the adder. We show that the entropy production of resetting is negative and that of branching is positive for these models in the regime of exponential growth of the colony. This property suggests an analogy between our model for cell growth and division and heat engines, and the introduction of a thermodynamic efficiency, which quantifies the conversion of one form of entropy production to another.

Published
2021
Full Text
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106. A Survey of Self-Supervised and Few-Shot Object Detection

Author

Huang, Gabriel, Laradji, Issam, Vazquez, David, Lacoste-Julien, Simon, and Rodriguez, Pau

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

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page at https://gabrielhuang.github.io/fsod-survey/, Comment: To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. Awesome Few-Shot Object Detection (Leaderboard) at https://github.com/gabrielhuang/awesome-few-shot-object-detection

Published
2021

107. Stabilization of phase noise in spin torque nano oscillators by a phase locked loop

Author

Wittrock, Steffen, Kreißig, Martin, Lacoste, Bertrand, Litvinenko, Artem, Talatchian, Philippe, Protze, Florian, Ellinger, Frank, Ferreira, Ricardo, Lebrun, Romain, Bortolotti, Paolo, Buda-Prejbeanu, Liliana, Ebels, Ursula, and Cros, Vincent

Subjects
Physics - Applied Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The main limitation in order to exploit spin torque nano-oscillators (STNOs) in various potential applications is their large phase noise. In this work, we demonstrate its efficient reduction by a highly reconfigurable, compact, specifically on-chip designed PLL based on custom integrated circuits. First, we thoroughly study the parameter space of the PLL+STNO system experimentally. Second, we present a theory which describes the locking of a STNO to an external signal in a general sense. In our developed theory, we do not restrict ourselves to the case of a perfect phase locking but also consider phase slips and the corresponding low offset frequency $1/f^2$ noise, so far the main drawback in such systems. Combining experiment and theory allows us to reveal complex parameter dependences of the system's phase noise. The results provide an important step for the optimization of noise properties and thus leverage the exploitation of STNOs in prospective real applications.

Published
2021

111. Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Author

Moharam, S.A., Abdalasalam, Sabriya, Abdalhadi, Alaa Abdalfattah, Abdalla, Naana Reyam, Abdalla, Walaa, Abdalrheem, Almthani Hamza, Abdalsalam, Ashraf, Abdeewi, Saedah, Abdelgaum, Esraa Hassan, Abdelhalim, Mohamed, Abdelkabir, Mohammed, Abdelrahman, Israa, Abdukahil, Sheryl Ann, Abdulbaqi, Lamees Adil, Abdulhamid, Salaheddin, Abdulhamid, Widyan, Abdulkadir, Nurul Najmee, Abdulwahed, Eman, Abdunabi, Rawad, Abe, Ryuzo, Abel, Laurent, Abodina, Ahmed Mohammed, Abrous, Amal, Absil, Lara, Jabal, Kamal Abu, Salah, Nashat Abu, Abusalama, Abdurraouf, Abuzaid, Tareg Abdallah, Acharya, Subhash, Acker, Andrew, Adam, Elisabeth, Adem, Safia, Ademnou, Manuella, Adewhajah, Francisca, Adrião, Diana, Afum-Adjei Awuah, Anthony, Agbogbatey, Melvin, Al Ageel, Saleh, Ahmed, Aya Mustafa, Ahmed, Musaab Mohammed, Ahmed, Shakeel, Alaraji, Zainab Ahmed, Elhefnawy Enan, Abdulrahman Ahmed, Ahmed Khalil, Reham Abdelhamid, Ahmed Mohamed Abdelaziz, Ali Mostafa, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Aisha, Ali, Aisha, Bugila, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Akwani, Chika, Al Qasim, Eman, Alajeeli, Ahmed, Alali, Ahmed, Alalqam, Razi, Alameen, Aliya Mohammed, Al-Aquily, Mohammed, Alaraji, Zinah A., Albakry, Khalid, Albatni, Safa, Alberti, Angela, Aldabbourosama, Osama, Al-dabbous, Tala, Aldhalia, Amer, Aldoukali, Abdulkarim, Alegesan, Senthilkumar, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Al-Fares, Abdulrahman, Alflite, Asil, Alfoudri, Huda, Alhadad, Qamrah, Alhaddad, Hoda Salem, Mohamed Abdalla Alhasan, Maali Khalid, Alhouri, Ahmad Nabil, Alhouri, Hasan, Ali, Adam, Ali, Imran, TagElser Mohammed Ali, Maha, Abbas, Syed Ali, Abdelghafar, Yomna Ali, Sheikh, Naseem Ali, Alidjnou, Kazali Enagnon, Aljadi, Mahmoud, Aljamal, Sarah, Alkahlout, Mohammed, Alkaseek, Akram, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Almasri, Lana, Almjersah, Abdulrahman, Alqandouz, Raja Ahmed, Alrfaea, Walaa, Alrifaee, Moayad, Alsaadi, Rawan, Al-Saba'a, Yousef, Alshareea, Entisar, Alshenawy, Eslam, Altaf, Aneela, Alves, João Melo, Alves, João, Alves, Rita, Cabrita, Joana Alves, Amaral, Maria, Amer, Amro Essam, Amira, Nur, Adusei, Amos Amoako, Amuasi, John, Andini, Roberto, Andrejak, Claire, Angheben, Andrea, Angoulvant, François, Ankrah, Sophia, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, Antunes de Brito, Carlos Alexandre, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Arancibia, Francisco, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean-Benoît, Arnold-Day, Christel, Arora, Lovkesh, Arora, Rakesh, Artaud-Macari, Elise, Aryal, Diptesh, Asensio, Angel, Ashley, Elizabeth A., Ashraf, Muhammad, Ashraf, Muhammad Sheharyar, Ben Ashur, Abir, Asiedu-Bekoe, Franklin, Asif, Namra, Asim, Mohammad, Assi, Grace, Assie, Jean Baptiste, Asyraf, Amirul, Atangana, Fouda, Atia, Ahmed, Atif, Minahel, Abdelrhman Abdallahrs, Asia Atif, Atique, Anika, Atlowly, Moad, Attanyake, AM Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Ali Mohammed, Abdelmalek Awad, Axelsen, Eyvind W., Ayad, Ared, Hassan Helmi, Ahmed Ayman, Azemar, Laurène, Azizeldin, Mohammed, Azoulay, Cecile, Babatunde, Hakeem, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre-Jensen, Roar, Baig, Nadia, Baillie, John Kenneth, Baird, J Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bakeer, Hibah Bileid, Bakri, Ashraf, Bal, Andriy, Balakrishnan, Mohanaprasanth, Bandoh, Irene, Bani-Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S., Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Batool, AbdAlkarim, Battaglini, Denise, Bauer, Jules, Bautista Rincon, Diego Fernando, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Bennett, Suzanne, Bento, Luís, Berdal, Jan-Erik, Berdeweel, Lamis, Bergeaud, Delphine, Bergin, Hazel, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianchi, Isabella, Bianco, Claudia, Bichoka, Sandra, Bidin, Farah Nadiah, Humaid, Felwa Bin, Bin Kamarudin, Mohd Nazlin, Binnawara, Muhannud, Bisoffi, Zeno, Biston, Patrick, Bitker, Laurent, Bittaye, Mustapha, Bitton, Jonathan, Blanco-Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Boccia, Filomena, Bodenes, Laetitia, Bogaert, Debby, Boivin, Anne-Hélène, Bolanga, Ariel, Bolaños, Isabela, Bolze, Pierre-Adrien, Bompart, François, Bonifasius, Aurelius, Bonney, Joe, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Bountthasavong, Latsaniphone, Boureau, Anne-Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Boyer-Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Dorothy, Breen, Patrick, Brewster, David, Brickell, Kathy, Broadley, Tessa, Brotherton, Helen, Browne, Alex, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse-Keizer, Marjolein, Bryda, Petra, Buchtele, Nina, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Buri, Donald, Burrell, Aidan, Bustos, Ingrid G., Butnaru, Denis, Cabie, André, Cabral, Susana, Cabrita, Joana, Caceres, Eder, Cadoz, Cyril, Garcês, Rui Caetano, Calligy, Kate, Calvache, Jose Andres, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caoili, Janice, Caraux-Paz, Pauline, Cárcel, Sheila, Cardoso, Filipa, Cardoso, Filipe, Cardoso, Nelson, Cardoso, Sofia, Carelli, Simone, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie-Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Casanova, Maire-Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor-Alexandre, Guylaine, Castro, Ivo, Catarino, Ana, Catherine, François-Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Ceesay, Masaneh, Cerkovnik, Shelby, Cervantes-Gonzalez, Minerva, Cevik, Muge, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Chapplain, Jean-Marc, Charpentier, Charlotte, Chas, Julie, Chaudry, Muhammad Mobin, Chávez Iñiguez, Jonathan Samuel, Chen, Anjellica, Chen, Yih-Sharng, Chenard, Léo, Cheng, Matthew Pellan, Cheret, Antoine, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Sung-Min, Cholley, Bernard, Chommanam, Danoy, Chopin, Marie-Charlotte, Chow, Yock Ping, Chow, Ting Soo, Christy, Nathaniel, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Cisneros Herreros, José Miguel, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Jennifer, Claure-Del Granado, Rolando, Clohisey, Sara, Codan, Cassidy, Cody, Caitriona, Coles, Jennifer, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Combs, Pamela, Connolly, Jennifer, Connor, Marie, Conrad, Anne, Conway, Elaine, Cooke, Graham S., Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Couffignal, Camille, Couffin-Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cowan, Juthaporn, Cregan, Rachel, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Csete, Marc, Cullen, Ailbhe, Cummings, Matthew, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al-Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, D'Alessandro, Umberto, Daley, Peter, Dalloul, Zaina, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Damas, Juliana, Dame, Joycelyn, Damien, Cammandji, Daneman, Nick, Dantas, Jorge, D'Aragon, Frédérick, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, França, Rafael Freitas de Oliveira, Isabel de Pinho Oliveira, Ana, 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, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delamou, Alexandre, 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, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhangar, Pathik, Diallo, Alpha, Diallo, Souleymane Taran, Diamantis, Sylvain, Dias, André, Da Silva, Fernanda Dias, Diaz, Rodrigo, Diaz, Juan Jose, Diaz, Priscila, Dibba, Bakary K., Didier, Kévin, Diehl, Jean-Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djadda, Cedric, Djossou, Félix, Docherty, Annemarie B., Doherty, Helen, Dondorp, Arjen M., Donnelly, Christl A., Donohue, Yoann, Donohue, Sean, Doran, Peter, Dorival, Céline, D'Ortenzio, Eric, Doshi, Yash, Douangdala, Phouvieng, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downey, Joanne, Downing, Mark, Drake, Thomas, Driscoll, Aoife, Duah, Ibrahim Kwaku, 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, Lucian, III, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol-Riise, Anne Margarita, Ean, Sim Choon, Ebo, Ada, Echeverria-Villalobos, Marco, Edelstein, Michael, Egan, Siobhan, Eggesbø, Linn Margrete, Ehzaz, Khadeja, Eira, Carla, El Sanharawi, Mohammed, El Sayed, Marwan, Elabid, Mohammed, Elagili, Mohamed Bashir, Elapavaluru, Subbarao, Elbahnasawy, Mohammad, Elboshra, Sohail, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord-Dale, Merete, ELMagrahi, Hamida, Elmubark, Mohammad Muatasm, Elotmani, Loubna, Eloundou, Lauren, Eloy, Philippine, Elshaikhy, Basma, Elshazly, Tarek, Elsokni, Wafa, Eltayeb, Aml Ahmed, Elyazar, Iqbal, Emad, Zarief Kamel, Embarek, Hussein, Enderle, Isabelle, Endo, Tomoyuki, Eneli, Gervais, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Esaadi, Haneen, Esperatti, Mariano, Esperou, Hélène, Santo, Catarina Espírito, Esposito-Farese, Marina, Essaka, Rachel, Essuman, Lorinda, Estevão, João, Etienne, Manuel, Everding, Anna Greti, Evers, Mirjam, Fabre, Isabelle, Fabre, Marc, Fadera, Ismaila, Abdalla Fadlalla, Asgad Osman, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J., Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fathi, Mohamed, Fatima, Salem, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François-Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferraz, Mário, Ferreira, Benigno, Ferreira, Isabel, Ferreira, Bernardo, Ferreira, Sílvia, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Finlayson, William, Flament, Thomas, Fletcher, Tom, Florence, Aline-Marie, Florio, Letizia Lucia, Flynn, Brigid, Flynn, Deirdre, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forrest, Karen, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fotso, Berline, 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, Gagneux-Brunon, Amandine, Gai, Abdou, Gaião, Sérgio, Skeie, Linda Gail, Galal Mohamed Ramadan, Adham Mohamed, Gallagher, Phil, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Garcia-Diaz, Julia, Garcia-Gallo, Esteban, Garimella, Navya, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghemmeid, Malak, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Gigante, Tristan, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Giwangkancana, Gezy, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean-Christophe, Goh, Jin Yi, Golács, Brigitta, Golob, Jonathan, Gomez, Kyle, Gómez-Junyent, Joan, Gominet, Marie, Gonzalez, Alicia, Gordon, Patricia, 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, Grovogui, Fassou Mathias, Gruner, Heidi, Gu, Yusing, Guedj, Jérémie, Guego, Martin, Guerguerian, Anne-Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, Guimarães de Castro, Maisa, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Hachemi, Ali, Haddud, Abdurrahman, Hadri, Nadir, Hafez, Wael, Haidri, Fakhir Raza, Rida Hajij, Fatima Mhd, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hamdan, Shaher, Hamdi, Abdelhafeez, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L., Hamidfar, Rebecca, Hammarström, Bato, Hammond, Naomi, Hammond, Terese, Han, Lim Yuen, Hanan, Matly, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Ekow Harrison, Samuel Bernard, Hartman, Alan, Hasan, Sulieman, Nabil Hasan, Mohammad Ali, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hashmi, Madiha, Hassan, Amoni, Hassanin, Ebtisam, Hayat, Muhammad, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Helmi, Ahmed, Hendry, Ross, Hennessy, Martina, Henriquez-Trujillo, Aquiles Rodrigo, Hentzien, Maxime, Hernandez, Diana, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Eibhlin, Higgins, Rupert, 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, Houas, Ikram, Houderi, Mabrouka, Hough, Catherine L., Houltham, Stuart, Ming-Yang Hsu, Jimmy, Hulot, Jean-Sébastien, Hurd, Abby, Hussain, Iqbal, Hussein, Aliae Mohamed, Hussein, Mahmood, Ibrahim, Fatima, Ibran, Bashir, Ijaz, Samreen, Ikram, M. Arfan, Illana, Carlos Cañada, Imbert, Patrick, Ansari, Muhammad Imran, Sikander, Rana Imran, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Ippolito, Mariachiara, Irawany, Vera, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jaber, Hamza, Jabot, Julien, Jackson, Clare, Jagne, Abubacarr, Jaureguiberry, Stéphane, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo-Sotomayor, Ruth, Joe, Ong Yiaw, Jorge García, Ruth Noemí, Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, Jung, Anna, Jung, Hanna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Ayadi Kamaluddin, Muhammad Aisar, Kamano, Armand Saloun, Che Kamaruddin, Zul Amali, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kang, Kong Yeow, Kanwal, Darakhshan, Kanyawati, Dyah, Karghul, Mohamed, Karpayah, Pratap, Karsies, Todd, Kartsonaki, Christiana, Kasugai, Daisuke, Katz, Kevin, Kay, Christy, Kayyali, Lamees, Keating, Seán, Kedia, Pulak, Kelly, Andrea, Kelly, Aoife, Kelly, Claire, Kelly, Niamh, Kelly, Sadie, Kelly, Yvelynne, Kelsey, Maeve, Kennon, Kalynn, Keomany, Sommay, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Khail, Shams, Khaled, Sarah, Khalid, Imrana, Khalil, Antoine, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Kherajani, Krish, Kho, Michelle E., Khoo, Denisa, Khoo, Ryan, Khoo, Saye, Khoso, Muhammad Nasir, Khuwaja, Amin, Kiat, Khor How, Kida, Yuri, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kindgen-Milles, Detlef, Kitamura, Nobuya, Kjetland Kjetland, Eyrun Floerecke, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R., Kobbe, Robin, Forson, Paa Kobina, Kodippily, Chamira, Vasconcelos, Malte Kohns, Koirala, Sabin, Komatsu, Mamoru, Abebrese, Franklina Korkor, Korten, Volkan, Kouba, Stephanie, Kourouma, Mohamed Lamine, Kourouma, Karifa, Kpangon, Arsène, Krawczyk, Karolina, Kredan, Ali, Krishnan, Vinothini, Krishnan, Sudhir, Kruglova, Oksana, Krund, Anneli, Kuan, Pei Xuan, Kumar, Ashok, Kumar, Deepali, Kumar, Ganesh, Kumar, Mukesh, Kuriakose, Dinesh, Kurtzman, Ethan, Kutsogiannis, Demetrios, Kutsyna, Galyna, Bedu-Addo, Ama Kwakyewaa, Kwedi, Sylvie, Kyriakoulis, Konstantinos, Lachatre, Marie, Lacoste, Marie, Laffey, John G., Lafhej, Nadhem, Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lakhey, Sanjay, 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, Latifeh, Youssef, Launay, Odile, Laureillard, Didier, Lavie-Badie, Yoan, 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, Leal, Ema, Santos, Marta Leal, Lee, Biing Horng, Lee, Heng Gee, Lee, Su Hwan, Lee, James, Lee, Jennifer, Lee, Todd C., Lee, Yi Lin, Leeming, Gary, Lefebvre, Bénédicte, Lefebvre, Laurent, Lefèvre, Benjamin, LeGac, Sylvie, Lehiste, Merili-Helen, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Lepik, Tanel, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letizia, Andrew, Letrou, Sophie, Levy, Bruno, Levy, Yves, Levy-Marchal, Claire, Lewandowska, Katarzyna, L'Her, Erwan, 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, 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, Lee Low, Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Luna, Carlos M., Lungu, Olguta, Lunn, Miles, Luong, Liem, Luque, Nestor, Luton, Dominique, Maasikas, Olavi, Machado, Moïse, Machado, Sara, Macheda, Gabriel, Magzoub, Mustafa, Mahieu, Rafael, Mahy, Sophie, Maia, Ana Raquel, Maier, Lars S., Ascofare, Oumou Maiga, Maillet, Mylène, Maitre, Thomas, Majeed, Nimisha Abdul, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Maltez, Fernando, Malvy, Denis, Manda, Victoria, Mandelbrot, Laurent, Manetta, Frank, Mankikian, Julie, Manning, Edmund, Manuel, Aldric, Maráczi, Veronika, Sant′Ana Malaque, Ceila Maria, Marino, Flávio, Markowicz, Samuel, Marques, Ana, Marquis, Catherine, Marsh, Laura, Marsh, Brian, Marshal, Megan, Marshall, John, Martelli, Celina Turchi, Martin, Dori-Ann, Martin, Emily, Martin-Blondel, Guillaume, Martinelli, Alessandra, Martinez, F. Eduardo, Martin-Loeches, Ignacio, Martinot, Martin, Martín-Quiros, Alejandro, Martins, Ana, Martins, João, Martins, Nuno, Rego, Caroline Martins, Martucci, Gennaro, Martynenko, Olga, Marwali, Eva Miranda, Marzukie, Marsilla, Maslove, David, Mason, Sabina, Masood, Sobia, Masoud, Fatma, Massoma, Moise, Masumbe, Palmer, Mat Nor, Mohd Basri, Matan, Moshe, Fernandes, Henrique Mateus, Mathew, Meghena, Mathew, Christina, Mattei, Mathieu, Maulin, Laurence, May, Juergen, Maynar, Javier, Mayxay, Mayfong, Mazzoni, Thierry, Sweeney, Lisa Mc, McArthur, Colin, McCann, Naina, McCanny, Peter, McCarthy, Aine, McCarthy, Anne, McCloskey, Colin, McConnochie, Rachael, McDermott, Sherry, McDonald, Sarah E., McElroy, Aine, McElwee, Samuel, McEvoy, Natalie, McGeer, Allison, McLean, Kenneth A., McNally, Paul, McNicholas, Bairbre, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Medombou, Nastia, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif Ali, Mendes, João João, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Emmanuelle, Mercier, Noémie, Merckx, Antoine, Mergeay-Fabre, Mayka, Mergler, Blake, Merson, Laura, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M., Meysonnier, Vanina, Mezidi, Mehdi, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Abdullah, Duha Milad, Miller, Jennene, Miranda-Maldonado, Hugo, Misnan, Nor Arisah, Eliza Mohamed, Nik Nur, Mohamed, Nouralsabah, Mohamed, Tahira Jamal, Ads, Alaa Mohamed, Mohamed Elsayed Abdelhalim, Ahmed Reda, Mohammed, Libya, Mohammed Mostafa, Shrouk Fawze, Abdelrahman Mohammedahmed, Manahil Omer, Mohammedelhassan, Omer Abdullah, Moin, Asma, Mokhtar, Walaa, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Sarah, Moore, Shona C., Cely, Lina Morales, Morgom, Marwa, Moro, Lucia, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mualla, Suleiman Haitham, Muftah, Mohamed, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhaisen, Mo'nes, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Ali, Syed Muneeb, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murray, Patrick, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Mustafa, Alamin, Mustafa, Mus'ab, Mustafa, Dana, Muvindi, Himasha, Myrodia, Dimitra Melia, Mohd-Hanafiah, Farah Nadia, Nadjm, Behzad, Nagpal, Dave, Nagrebetsky, Alex, Nagybányai-Nagy, Blanka, Boudoin, Herwin Nanda, Narasimhan, Mangala, Narayanan, Nageswaran, Nasa, Prashant, Khan, Rashid Nasim, Nasrallah, Ahmad, Nassif Metri, Adel Gerges, Nazerali-Maitland, Alasdair, Neant, Nadège, Neb, Holger, Nekliudov, Nikita, Nelder, Matthew, Nelwan, Erni, Neto, Raul, Neumann, Emily, Ng, Wing Yiu, Ng, Pauline Yeung, Nghi, Anthony, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Niba, Nerissa, Nichol, Alistair D., Nitayavardhana, Prompak, Nonas, Stephanie, Mohd Noordin, Nurul Amani, Izzati Norharizam, Nurul Faten, North, Anita, Notari, Alessandra, Noursadeghi, Mahdad, Nowinski, Adam, Nseir, Saad, Numfor, Leonard, Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O., Callaghan, Annmarie O., O'Callaghan, Annmarie, Occhipinti, Giovanna, OConnor, Derbrenn, O'Donnell, Max, Ofori-Boadu, Lawrence, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak-Hyuk, O'Halloran, Sophie, O'Hearn, Katie, Ohene, Sally-Ann, Ohshimo, Shinichiro, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L., Rageh Elnaggar, Cinderella Omar, Mohammed Omer, Alsarrah Ali, Ondobo, Pierre, Ong, David S.Y., Ong, Jee Yan, Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco-Chamorro, Claudia Milena, Ortoleva, Jamel, Elsayed Soliman, Mohamed Osama, Osatnik, Javier, O'Shea, Linda, O'Sullivan, Miriam, Othman, Eman, Othman, Siti Zubaidah, Ouamara, Nadia, Ouissa, Rachida, Owusu, Micheal, Owusu-Asare, Ama Akyampomaa, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Amanda, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parke, Rachael, Parker, Melissa, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Christelle, Paul, Mical, Paulos, Jorge, Paxton, William A., Payen, Jean-François, Peake, Sandra L., Peariasamy, Kalaiarasu, Peek, Giles J., Peelman, Florent, Peiffer-Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D., Pereira, Rui, Perez, Daniel, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrovic, Michele, Petrov-Sanchez, Ventzislava, Pettersen, Frank Olav, Peytavin, Gilles, Philips, Richard Odame, Phonemixay, Ooyanong, Phoutthavong, Soulichanya, Piagnerelli, Michael, Picard, Walter, Picone, Olivier, de Piero, Maria, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Piroth, Lionel, Pitaloka, Ayodhia, Piubelli, Chiara, Pius, Riinu, Piva, Simone, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, 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., Dilanthi Priyadarshani, Gamage Dona, Proença, Lucia, Pudota, Sravya, Semedi, Bambang Pujo, Pulicken, Mathew, Puplampu, Peter, Purcell, Gregory, Quesada, Luisa, Quinones-Cardona, Vilmaris, Quist-Paulsen, Else, Quraishi, Mohammed, Qutishat, Fadi, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Ragab, Abdelrahman, Rahardjani, Mutia, Ullah, Arslan Rahat, Haji Ab Rahman, Ahmad Kashfi, Rahman, Rozanah Abd, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramalho, José, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Rehman Khalid, Muhammad Osama, 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, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Atikah Ahmad Ridzuan, Siti Nurul, Rios, Ana L., Rishu, Asgar, Rispal, Patrick, Risso, Karine, Rivera Nuñez, Maria Angelica, Robba, Chiara, Roberto, André, Roberts, Stephanie, Roberts, Charles, Robertson, David L., Robineau, Olivier, Roca, Anna, Roche-Campo, Ferran, Rodari, Paola, Rodeia, Simão, Roessler, Bernhard, Roger, Claire, Roger, Pierre-Marie, Rojek, Amanda, Roncon-Albuquerque, Roberto, Jr., Roriz, Mélanie, Rosa-Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Rossanese, Andrea, Rossetti, Matteo, Rossignol, Patrick, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D., Ryan, Maeve, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Said, Abdurraouf, Saidani, Nadia, Sakiyalak, Pranya, Sako, Fodé Bangaly, Salah, Moamen, Salah Eldin Mohamed Abbas, Ali Alaa, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Alyasiri, Mohammed Saleh, Abu Salem, Talat Ahmed, Sales, Gabriele, Gandonniere, Charlotte Salmon, Salvator, Hélène, Samardali, Dana, Samardali, Shaden, Shaaban Aly Orabi, Yehia Samir, Sanchez, Emely, Sanchez, Olivier, Sanchez de Oliveira, Kizy, Sanchez-Miralles, Angel, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre-François, Săndulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E., Sarton, Benjamine, Satya, Ankana, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schnur, János, Schroll, Stephan, Schwameis, Michael, Schwartz, Gary, Scott, Janet T., Scott-Brown, James, Sedillot, Nicholas, Seitz, Tamara, Selvanayagam, Jaganathan, Selvarajoo, Mageswari, Semple, Malcolm G., Senian, Rasidah Bt, Senneville, Eric, Sepulveda, Claudia, Sequeira, Filipa, Sequeira, Tânia, Neto, Ary Serpa, Shadowitz, Ellen, Shahidan, Syamin Asyraf, Shahla, Hamza, Shalabi, Laila, Shames, Haitam, Shankar, Anuraj, Sharjeel, Shaikh, Sharma, Pratima, Shaw, Catherine A., Shaw, Victoria, Sheenan, John Robert, Mohan Shetty, Dr. Rajesh, Shetty, Rohan, Shiekh, Mohiuddin, Shime, Nobuaki, Shimizu, Keiki, Shrapnel, Sally, Shrestha, Shubha Kalyan, Shrestha, Pramesh Sundar, Shum, Hoi Ping, Mohammed, Nassima Si, Siang, Ng Yong, Siaw-Frimpong, Moses, Sibiude, Jeanne, Sibounheuang, Bountoy, Siddig, Nidhal, Siddiqui, Atif, Siddiqui, Maqsood Ahmed, Sigfrid, Louise, Sillah, Fatoumata, Sillaots, Piret, Silva, Catarina, Silva, Maria Joao, Silva, Rogério, Lim Heng, Benedict Sim, Sin, Wai Ching, Sinatti, Dario, Singh, Mahendra, Singh, Punam, Sitompul, Pompini Agustina, Sivam, Karisha, Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, 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, Søraas, Arne, Sotto, Albert, Soum, Edouard, Sousa, Ana Chora, Sousa, Marta, Uva, Maria Sousa, Souza-Dantas, Vicente, Sow, Mamadou Saliou, Sperry, Alexandra, Spinuzza, Elisabetta, Ruwan Sri Darshana, B. P. Sanka, Sriskandan, Shiranee, Stabler, Sarah, Staudinger, Thomas, Stecher, Stephanie-Susanne, Steinsvik, Trude, Stienstra, Ymkje, Stiksrud, Birgitte, Stolz, Eva, Stone, Amy, Streinu-Cercel, Anca, Streinu-Cercel, Adrian, Strong, Geoff, Stuart, Ami, Stuart, David, Su, Richa, Subekti, Decy, Suen, Gabriel, Suen, Jacky Y., Sukumar, Prasanth, Sultana, Asfia, Summers, Charlotte, Supic, Dubravka, Suppiah, Deepashankari, Surovcová, Magdalena, Suwarti, Atie, Svistunov, Andrey, Syahrin, Sarah, Sylverken, Augustina, Syrigos, Konstantinos, Sztajnbok, Jaques, Szuldrzynski, Konstanty, Tabrizi, Shirin, Taccone, Fabio S., Tagherset, Lysa, Taib, Shahdattul Mawarni, Taleb, Sara, Talla, Cheikh, Talsma, Jelmer, Tamisier, Renaud, Tampubolon, Maria Lawrensia, Tan, Kim Keat, Tan, Yan Chyi, Tanaka, Hiroyuki, Tanaka, Taku, Taniguchi, Hayato, Taqdees, Huda, Taqi, Arshad, Tardivon, Coralie, Kamal Mostafa, Yousef Tarek, Tarhabat, Ali, Tattevin, Pierre, Taufik, M Azhari, Tawfik, Hassan, Tee, Tze Yuan, Teixeira, João, Tejada, Sofia, Tellier, Marie-Capucine, Teoh, Sze Kye, Teotonio, Vanessa, Téoulé, François, Terrier, Olivier, Terzi, Nicolas, Tessier-Grenier, Hubert, Tey, Adrian, Mohd Thabit, Alif Adlan, Thakur, Anand, Tham, Zhang Duan, Thangavelu, Suvintheran, Theron, Elmi, Thibault, Vincent, Thiberville, Simon-Djamel, Thill, Benoît, Thirumanickam, Jananee, Thompson, Niamh, Thompson, Shaun, Thomson, Emma C., Thomson, David, Thorpe, Mathew, Thurai, Surain Raaj Thanga, Thwaites, Ryan S., Tierney, Paul, Tieroshyn, Vadim, Timashev, Peter S., Timsit, Jean-François, Tirupakuzhi Vijayaraghavan, Bharath Kumar, Tissot, Noémie, Toal, Fiona, Yang Toh, Jordan Zhien, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torre, Marta, Torres, Antoni, Torres, Margarida, Santos-Olmo, Rosario Maria Torres, Torres-Zevallos, Hernando, Tounkara, Aboubacar, Towers, Michael, Traoré, Fodé Amara, Trapani, Tony, Tromeur, Cécile, Trontzas, Ioannis, Trouillon, Tiffany, Truong, Jeanne, Tual, Christelle, Tubiana, Sarah, Tuite, Helen, Turgeon, Alexis F., Turmel, Jean-Marie, Turtle, Lance C.W., Tveita, Anders, Twardowski, Pawel, Uchiyama, Makoto, Udayanga, PG Ishara, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad, Usuf, Effua, Uyeki, Timothy M., Vajdovics, Cristinava, Valentini, Piero, Val-Flores, Luís, 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 Gulik, Laura, Van Hattem, Jarne, van Netten, Carolien, van Veen, Ilonka, Vanel, Noémie, Vanoverschelde, Henk, Varrone, Michael, Vasudayan, Shoban Raj, Vauchy, Charline, Vecham, Pavan Kumar, Veeran, Shaminee, Veislinger, Aurélie, Vencken, Sebastian, Ventura, Sara, Verbon, Annelies, Vidal, José Ernesto, Vieira, César, Vijayan, Deepak, Villar, Judit, Villeneuve, Pierre-Marc, Villoldo, Andrea, Vishwanathan, Gayatri, Visseaux, Benoit, Visser, Hannah, Vitiello, Chiara, Vongsouvath, Manivanh, Vonkeman, Harald, Vuotto, Fanny, Wahab, Suhaila Abdul, Wahab, Noor Hidayu, Wahid, Nadirah Abdul, Wainstein, Marina, Walsh, Laura, Shukeri, Wan Fadzlina Wan Muhd, Wang, Chih-Hsien, Webb, Steve, Wei, Jia, Weil, Katharina, Wen, Tan Pei, Wesam, Hassi, Wesselius, Sanne, West, T. Eoin, Wham, Murray, Whelan, Bryan, White, Nicole, Wicky, Paul Henri, Wiedemann, Aurélie, Wijaya, Surya Otto, Wille, Keith, Willems, Sue, Williams, Bailey, Williams, Patricia J., Williams, Virginie, Wils, Evert-Jan, Wittman, Jessica, Wong, Calvin, Wong, Xin Ci, Wong, Yew Sing, Wong, Teck Fung, Wright, Natalie, Xian, Lim Saio, Xynogalas, Ioannis, Binti Mohd Yakop, Siti Rohani, Yamazaki, Masaki, Yarad, Elizabeth, Yazdanpanah, Yazdan, Hing, Nicholas Yee Liang, Mahmoud Abdelaal, Abdelrahman Yehia, Yelnik, Cécile, Yeoh, Chian Hui, Yerkovich, Stephanie, Yiaye, Touxiong, Yokoyama, Toshiki, Yonis, Hodane, Yousif, Obada, Yuliarto, Saptadi, Zaaqoq, Akram, Zabbe, Marion, Zabert, Gustavo E., Zacharowski, Kai, Zahid, Masliza, Zahran, Maram, Binti Zaidan, Nor Zaila, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Ibáñez-Prada, Elsa D., Gonçalves, Bronner P., Baruch, Joaquin, Escher, Martina, Philippy, Fred, Kutsogiannis, Demetrios James, Vaillant, Michel, and Olliaro, Piero

Published
2024
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113. Latest results from the RD42 collaboration on the radiation tolerance of polycrystalline diamond detectors

Author

Mali, M., Artuso, M., Bäni, L., Bartosik, M., Bellini, V., Bentele, B., Bergonzo, P., Bes, A., Brom, J-M., Chiodini, G., Chren, D., Cindro, V., Claus, G., Collot, J., Cumalat, J., Dabrowski, A., Dauvergne, D., Tchernij, S. Ditalia, Eigen, G., Eremin, V., Everaere, P., Forneris, J., Gallin-Martel, L., Gallin-Martel, M-L., Gan, K.K., Gastal, M., Gentry, A., Goffe, M., Goldstein, J., Golubev, A., Gorišek, A., Grigoriev, E., Grosse-Knetter, J., Hiti, B., Hits, D., Hoarau, C., Hoeferkamp, M., Hosslet, J., Hügging, F., Hutson, C., Jackman, R., Jennings-Moors, R., Kagan, H., Kanxheri, K., Kis, M., Kramberger, G., Kruger, M., Kuleshov, S., Lacoste, A., Lukosi, E., Maazouzi, C., Mandić, I., Marcatili, S., Marino, A., Mathieu, C., Menichelli, M., Mikuž, M., Molle, R., Morozzi, A., Moscatelli, F., Moss, J., Mountain, R., Muraz, J-F., Narazyanan, E.A., Oh, A., Olivero, P., Passeri, D., Pernegger, H., Perrino, R., Picollo, F., Porter, A., Portier, A., Potenza, R., Quadt, A., Rarbi, F., Re, A., Reichmann, M., Roe, S., Rossetto, O., Salter, P., Becerra, D.A. Sanz, Schmidt, C.J., Schnetzer, S., Seidel, S., Servoli, L., Shivaraman, R., Smith, D.S., Sopko, B., Sopko, V., Sorenson, J., Spagnolo, S., Spanier, S., Stenson, K., Stone, R., Stugu, B., Sutera, C., Traeger, M., Trischuk, W., Truccato, M., Tuve, C., Velthuis, J., Verbitskaya, E., Wagner, S., Wallny, R., Welch, J., Wengler, T., Yamouni, M., Zalieckas, J., and Zavrtanik, M.

Published
2024
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115. Differentiable Causal Discovery from Interventional Data

Author

Brouillard, Philippe, Lachapelle, Sébastien, Lacoste, Alexandre, Lacoste-Julien, Simon, and Drouin, Alexandre

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning, I.2.6, I.5.1
Abstract

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown., Comment: Appears in: Advances in Neural Information Processing Systems 34 (NeurIPS 2020). 46 pages

Published
2020

119. A Vascular-Centric Approach to Autism Spectrum Disorders

Author

Julie Ouellette, Elizabeth E Crouch, Jean-Luc Morel, Vanessa Coelho-Santos, and Baptiste Lacoste

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Brain development and function are highly reliant on adequate establishment and maintenance of vascular networks. Early impairments in vascular health can impact brain maturation and energy metabolism, which may lead to neurodevelopmental anomalies. Our recent work not only provides novel insights into the development of cerebrovascular networks but also emphasizes the importance of their well-being for proper brain maturation. In particular, we have demonstrated that endothelial dysfunction in autism spectrum disorders (ASD) mouse models is causally related to altered behavior and brain metabolism. In the prenatal human brain, vascular cells change metabolic states in the second trimester. Such findings highlight the need to identify new cellular and molecular players in neurodevelopmental disorders, raising awareness about the importance of a healthy vasculature for brain development. It is thus essential to shift the mostly neuronal point of view in research on ASD and other neurodevelopmental disorders to also include vascular and metabolic features.

Published
2024
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120. Protocol for venoarterial ExtraCorporeal Membrane Oxygenation to reduce morbidity and mortality following bilateral lung TransPlantation: the ECMOToP randomised controlled trial

Author

Richard Galliot, Jonathan Messika, Jérôme Devaquet, Jérome Ridolfo, Hervé Mal, Séverine Feuillet, François Tronc, Pascal-Alexandre Thomas, Edouard Sage, Philippe Montravers, Jacques Jougon, Elodie Blanchard, Enora Atchade, Philippine Eloy, Bruno Pastene, Nassima Si Mohammed, Pierre Gazengel, Charles Cerf, David Boulate, Justin Issard, Elie Fadel, Olaf Mercier, Brice Lortat-Jacob, Sylvain Jean-Baptiste, Aurelie Snauwaert, Yves Castier, Elie Kantor, Sandrine Boudinet, Pierre Mordant, Antoine Girault, Arnaud Roussel, Aude Charvet, Julien Fessler, Philippe Lacoste, Philippe Portran, Hadrien Roze, Jacques Thes, Mickael Vourc'h, Pierre Cerceau, Vincent Bunel, Isabelle Pavlakovic, Delphine Chesnel, Léa Didier, Matthias Jacquet Lagreze, Eva Chatron, Claire Merveilleux Du Vignaux, Gabrielle Drevet, Jean Michel Maury, Valentin Soldea, Xavier Demant, Julie Macey, Christelle Pellerin, Clément Boisselier, Claire Bon, Benjamin Chevalier, Eloïse Gallo, Benjamin Repusseau, Arnaud Rodriguez, Regisse Seramondi, Matthieu Thumerel, Gaelle Dauriat, Amélie Delaporte, Samuel Dolidon, Jerome Estephan, Sylvain Diop, Dominique Fabre, Avit Guirimand, Iolanda Ion, Christian Ionescu, Jérome Le Pavec, Chahine Medraoui, Jean-Baptiste Menager, Delphine Mitilian, Andy Musat, Marwan Nader, Geoffrey Brioude, Xavier Djourno, Ambroise Labarriere, Pierre Mora, Adrien Rivory, Julien Cadiet, Nicolas Groleau, Thierry Lepoivre, Antoine Roux, Sandra de Miranda, Clément Picard, Laurence Beaumont, Olivier Brugière, Sylvie Colin de Verdière, Abdul-Momen Hamid, François Parquin, Amer Hamdan, Benjamin Zuber, Guillaume Tachon, Nicolas Mayenco-Cardenal, Mathilde Phillips-Houlbracq, David Cortier, Johanna Cohen, Alexis Paternot, Ciprian Pricopi, Francesco Cassiano, Matthieu Glorion, Julien De Wolf, Chloé Mimbimi, Morgan Le Guen, Virginie Dumans, Sébastien Jacqmin, Michael Finet, Sindia Goncalves, Louis Grosz, Charles Hickel, Julien Josserand, Julien Richard, and Gaëlle Weisenburger

Subjects
Medicine
Abstract

Introduction Lung transplantation (LTx) aims at improving survival and quality of life for patients with end-stage lung diseases. Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is used as intraoperative support for LTx, despite no precise guidelines for its initiation. We aim to evaluate two strategies of VA-ECMO initiation in the perioperative period in patients with obstructive or restrictive lung disease requiring bilateral LTx. In the control ‘on-demand’ arm, high haemodynamic and respiratory needs will dictate VA-ECMO initiation; in the experimental ‘systematic’ arm, VA-ECMO will be pre-emptively initiated. We hypothesise a ‘systematic’ strategy will increase the number of ventilatory-free days at day 28.Methods and analysis We designed a multicentre randomised controlled trial in parallel groups. Adult patients with obstructive or restrictive lung disease requiring bilateral LTx, without a formal indication for pre-emptive VA-ECMO before LTx, will be included. Patients with preoperative pulmonary hypertension with haemodynamic collapse, ECMO as a bridge to transplantation, severe hypoxaemia or hypercarbia will be secondarily excluded. In the systematic group, VA-ECMO will be systematically implanted before the first pulmonary artery cross-clamp. In the on-demand group, VA-ECMO will be implanted intraoperatively if haemodynamic or respiratory indices meet preplanned criteria. Non-inclusion, secondary exclusion and VA-ECMO initiation criteria were validated by a Delphi process among investigators. Postoperative weaning of ECMO and mechanical ventilation will be managed according to best practice guidelines. The number of ventilator-free days at 28 days (primary endpoint) will be compared between the two groups in the intention-to-treat population. Secondary endpoints encompass organ failure occurrence, day 28, day 90 and year 1 vital status, and adverse events.Ethics and dissemination The sponsor is the Assistance Publique–Hôpitaux de Paris. The ECMOToP protocol version 2.1 was approved by Comité de Protection des Personnes Ile de France VIII. Results will be published in international peer-reviewed medical journals.Trial registration number NCT05664204.

Published
2024
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121. Challenges of rabies surveillance in Madagascar based on a mixed method survey amongst veterinary health officers

Author

Anou Dreyfus, Marie Hermelienne Volasoa, Hélène Guis, Nivohanitra Perle Razafindraibe, Mino Harimbola Razafindramparany, Nomenjanahary Lalaina Arivony, Naltiana Rakotondrabe, Mamitiana Aimé Andriamananjara, Philippe Dussart, Daouda Kassie, Vincent Lacoste, and Soa Fy Andriamandimby

Subjects
rabies, surveillance, survey, veterinary health officer, Madagascar, dog-mediated rabies, Veterinary medicine, SF600-1100
Abstract

IntroductionIn Madagascar, rabies is endemic and a notifiable disease. The objective of this mixed study was to understand the challenges faced by the Veterinary Health Officers (VHOs) in the current rabies surveillance system in Madagascar.MethodsA survey was conducted from mid-April to the end of July 2021 during which all officially-listed VHOs (N = 150) were contacted by phone at least twice. Participants, representative of the 22 regions of Madagascar, were interviewed by phone based on a semi-structured questionnaire on (1) their knowledge of rabies epidemiology in their area of activities, (2) the occurrence of human and animal rabies and the species affected in the region where they work, (3) the factors that might influence rabies surveillance depending on (a) their activities and area of operation, (b) the socio-cultural aspects of local communities, and (c) the general organization of animal rabies surveillance.ResultsThe majority (80/90) of VHOs declared having been informed of at least one suspected or confirmed case of human and/or animal rabies in their area of activity during their work as VHOs: at least once a month for 11 of them, at least once a year for 40 and with undetermined frequency for 29. Several obstacles hinder the surveillance of rabies resulting in under-reporting. The lack of funds to access suspect animals, collect, pack and ship samples in compliance with biosecurity measures and the cold chain, was mentioned as a major obstacle to surveillance. The second barrier is socio-cultural: in many large coastal regions, dogs are taboo and VHOs fear rejection by the community if they treat dogs.Discussion and conclusionWhile the general population requires information on the rabies situation in Madagascar and on how to proceed in the event of a bite, veterinarians and decision-makers need to be fully aware of an evidence-based approach to rabies surveillance, prevention and control. Communication between the human and animal health sectors should be improved. Politicians need to be persuaded of the importance of funding to eliminate rabies in Madagascar. The adoption, in early 2023, of a national strategic plan for rabies control is a first step in this direction.

Published
2024
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122. Yerba mate, patrimonio inmaterial y poder blando en tiempos de Juan Manuel de Rosas (1829-1852)

Author

Jose Jeffs Munizaga, Pablo Lacoste, Juan Carlos Skewes, and Luis Alegría

Subjects
Patrimonio agroalimentario, Patrimonio cultural material e inmaterial, colonialismo comercial, Mate, Gastropolítica, History (General), D1-2009, Latin America. Spanish America, F1201-3799
Abstract

Se examina la valoración de la yerba mate como patrimonio agroalimentario regional, en el proceso de construcción del ideario americanista durante la gestión de Juan Manuel de Rosas al frente de la Confederación Argentina. Se detecta que la cultura de la yerba mate alcanzó un fuerte arraigo en la sociedad del Cono Sur en los siglos XVII y XVIII, pero, después de la independencia, la estrategia comercial británica procuró sustituirla por el té, producto imperial funcional a sus intereses. Este proyecto tuvo éxito en varios territorios, como por ejemplo en Chile, donde la élite se plegó a la moda impuesta por los ingleses. Pero en el Río de la Plata, Rosas se empeñó en promover y fortalecer la cultura del mate, como medio estratégico de afirmar la identidad americanista y de resistencia a las pretensiones neocoloniales europeas. Con esta actitud, Rosas fue un pionero en la valoración del patrimonio agroalimentario regional.

Published
2024

123. Cohort profile: COBLAnCE: a French prospective cohort to study prognostic and predictive factors in bladder cancer and to generate real-world data on treatment patterns, resource use and quality of life

Author

Simone Benhamou, Julia Bonastre, Bijan Ghaleh, Hélène Blanché, Pascal Eschwege, Arnauld Villers, Arnaud Méjean, François Radvanyi, Xavier Rébillard, Yves Allory, Thierry Lebret, Aldéric Fraslin, Yann Neuzillet, Stéphane Droupy, Dimitri Vordos, Laurent Guy, Marc Schneider, Patrick Coloby, Jean Lacoste, Jacques Lacoste, Jean-Luc Descotes, Guillaume Loison, Odette Mariani, Anthony Mangin, Nanor Sirab, and Karine Groussard

Subjects
Medicine
Abstract

Purpose Bladder cancer is a complex disease with a wide range of outcomes. Clinicopathological factors only partially explain the variability between patients in prognosis and treatment response. There is a need for large cohorts collecting extensive data and biological samples to: (1) investigate gene-environment interactions, pathological/molecular classification and biomarker discovery; and (2) describe treatment patterns, outcomes, resource use and quality of life in a real-world setting.Participants COBLAnCE (COhort to study BLAdder CancEr) is a French national prospective cohort of patients with bladder cancer recruited between 2012 and 2018 and followed for 6 years. Data on patient and tumour characteristics, treatments, outcomes and biological samples are collected at enrolment and during the follow-up.Findings to date We describe the cohort at enrolment according to baseline surgery and tumour type. In total, 1800 patients were included: 1114 patients with non-muscle-invasive bladder cancer (NMIBC) and 76 patients with muscle-invasive bladder cancer (MIBC) had transurethral resection of a bladder tumour without cystectomy, and 610 patients with NMIBC or MIBC underwent cystectomy. Most patients had a solitary lesion (56.3%) without basement membrane invasion (71.7% of Ta and/or Tis). Half of the patients with cystectomy were stage ≤T2 and 60% had non-continent diversion. Surgery included local (n=298) or super-extended lymph node dissections (n=11) and prostate removal (n=492). Among women, 16.5% underwent cystectomy and 81.4% anterior pelvectomy.Future plans COBLAnCE will be used for long-term studies of bladder cancer with focus on clinicopathological factors and molecular markers. It will lead to a much-needed improvement in the understanding of the disease. The cohort provides valuable real-world data, enabling researchers to study various research questions, assess routine medical practices and guide medical decision-making.

Published
2023
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125. A robust transition to homochirality in complex chemical reaction networks

Author

Laurent, Gabin, Lacoste, David, and Gaspard, Pierre

Subjects
Condensed Matter - Statistical Mechanics, Physics - Chemical Physics, Quantitative Biology - Molecular Networks
Abstract

Homochirality, i.e. the dominance across all living matter of one enantiomer over the other among chiral molecules, is thought to be a key step in the emergence of life. Building on ideas put forward by Frank and many others, we proposed recently one such mechanism in G. Laurent et al., PNAS (2021) based on the properties of large out of equilibrium chemical networks. We showed that in such networks, a phase transition towards an homochiral state is likely to occur as the number of chiral species in the system becomes large or as the amount of free energy injected into the system increases. This paper aims at clarifying some important points in that scenario, not covered by our previous work. We first analyze the various conventions used to measure chirality, introduce the notion of chiral symmetry of a network, and study its implications regarding the relative chiral signs adopted by different groups of molecules. We then propose a generalization of Frank's model for large chemical networks, which we characterize completely using methods of random matrices. This analysis can be extended to sparse networks, which shows that the emergence of homochirality is a robust transition.

Published
2021
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126. Typing assumptions improve identification in causal discovery

Author

Brouillard, Philippe, Taslakian, Perouz, Lacoste, Alexandre, Lachapelle, Sebastien, and Drouin, Alexandre

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

Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class. Such classes, which are often large in size, encode uncertainties about the orientation of some edges in the causal graph. In this work, we propose a new set of assumptions that constrain possible causal relationships based on the nature of variables, thus circumscribing the equivalence class. Namely, we introduce typed directed acyclic graphs, in which variable types are used to determine the validity of causal relationships. We demonstrate, both theoretically and empirically, that the proposed assumptions can result in significant gains in the identification of the causal graph. We also propose causal discovery algorithms that make use of these assumptions and demonstrate their benefits on simulated and pseudo-real data., Comment: 30 pages, 13 figures, accepted for the 1st conference on Causal Learning and Reasoning (CLeaR), 2022

Published
2021

127. Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity

Author

Loizou, Nicolas, Berard, Hugo, Gidel, Gauthier, Mitliagkas, Ioannis, and Lacoste-Julien, Simon

Subjects
Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017]. SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption. SCO is used successfully for solving large-scale adversarial problems, but its convergence guarantees are limited to its deterministic variant. In this work, we introduce the expected co-coercivity condition, explain its benefits, and provide the first last-iterate convergence guarantees of SGDA and SCO under this condition for solving a class of stochastic variational inequality problems that are potentially non-monotone. We prove linear convergence of both methods to a neighborhood of the solution when they use constant step-size, and we propose insightful stepsize-switching rules to guarantee convergence to the exact solution. In addition, our convergence guarantees hold under the arbitrary sampling paradigm, and as such, we give insights into the complexity of minibatching., Comment: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Published
2021

128. Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Author

Annadani, Yashas, Rothfuss, Jonas, Lacoste, Alexandre, Scherrer, Nino, Goyal, Anirudh, Bengio, Yoshua, and Bauer, Stefan

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

Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determining a single directed acyclic graph (DAG) or a Markov equivalence class thereof. However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty. For instance, planning interventions to find out more about the causal mechanisms that govern our data requires quantifying epistemic uncertainty over DAGs. While Bayesian causal inference allows to do so, the posterior over DAGs becomes intractable even for a small number of variables. Aiming to overcome this issue, we propose a form of variational inference over the graphs of Structural Causal Models (SCMs). To this end, we introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. Its number of parameters does not grow exponentially with the number of variables and can be tractably learned by maximising an Evidence Lower Bound (ELBO). In our experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior., Comment: 10 pages, 6 figures

Published
2021

129. Structured Convolutional Kernel Networks for Airline Crew Scheduling

Author

Yaakoubi, Yassine, Soumis, François, and Lacoste-Julien, Simon

Subjects
Computer Science - Machine Learning
Abstract

Motivated by the needs from an airline crew scheduling application, we introduce structured convolutional kernel networks (Struct-CKN), which combine CKNs from Mairal et al. (2014) in a structured prediction framework that supports constraints on the outputs. CKNs are a particular kind of convolutional neural networks that approximate a kernel feature map on training data, thus combining properties of deep learning with the non-parametric flexibility of kernel methods. Extending CKNs to structured outputs allows us to obtain useful initial solutions on a flight-connection dataset that can be further refined by an airline crew scheduling solver. More specifically, we use a flight-based network modeled as a general conditional random field capable of incorporating local constraints in the learning process. Our experiments demonstrate that this approach yields significant improvements for the large-scale crew pairing problem (50,000 flights per month) over standard approaches, reducing the solution cost by 17% (a gain of millions of dollars) and the cost of global constraints by 97%., Comment: ICML 2021 (Proceedings of the 38th International Conference on Machine Learning, PMLR 139:11626-11636)

Published
2021

130. Performance of CVD diamond detectors for single ion beam-tagging applications in hadrontherapy monitoring

Author

Curtoni, Sébastien, Gallin-Martel, Marie-Laure, Marcatili, Sara, Abbassi, Latifa, Bes, Alexandre, Bosson, Germain, Collot, Johann, Crozes, Thierry, Dauvergne, Denis, De Nolf, Wout, Everaere, Pierre, Gallin-Martel, Laurent, Ghimouz, Abderrahmane, Haddad, Ferid, Hoarau, Christophe, Hostachy, Jean-Yves, Koumeir, Charbel, Lacoste, Ana, Métivier, Vincent, Morse, John, Motte, Jean-François, Muraz, Jean-François, Poirier, Freddy, Rarbi, Fatah Ellah, Rossetto, Olivier, Salomé, Muriel, Servagent, Noël, Testa, Étienne, and Yamouni, Mahfoud

Subjects
Physics - Instrumentation and Detectors, Physics - Medical Physics
Abstract

In the context of online ion range verification in particle therapy, the CLaRyS collaboration is developing Prompt-Gamma (PG) detection systems. The originality in the CLaRyS approach is to use a beam-tagging hodoscope in coincidence with the gamma detectors to provide both temporal and spatial information of the incoming ions. The ion range sensitivity of such PG detection systems could be improved by detecting single ions with a 100 ps ($\sigma$) time resolution, through a quality assurance procedure at low beam intensity at the beginning of the treatment session. This work presents the investigations led to assess the performance of Chemical Vapor Deposition (CVD) diamond detectors to fulfill these requirements. A $^{90}$Sr beta source, 68 MeV protons, 95 MeV/u carbon ions and a synchrotron X-ray pulsed beam were used to measure the time resolution, single ion detection efficiency and proton counting capability of various CVD diamond samples. An offline technique, based on double-sided readout with fast current preamplifiers and used to improve the signal-to-noise ratio, is also presented. The different tests highlighted Time-Of-Flight resolutions ranging from 13 ps ($\sigma$) to 250 ps ($\sigma$), depending on the experimental conditions. The single 68 MeV proton detection efficiency of various large area polycrystalline (pCVD) samples was measured to be $>$96% using coincidence measurements with a single-crystal reference detector. Single-crystal CVD (sCVD) diamond proved to be able to count a discrete number of simultaneous protons while it was not achievable with a polycrystalline sample. Considering the results of the present study, two diamond hodoscope demonstrators are under development: one based on sCVD, and one of larger size based on pCVD. They will be used for the purpose of single ion as well as ion bunches detection, either at reduced or clinical beam intensities., Comment: 15 pages, 9 figures

Published
2021
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132. Removal of senescent cells reduces the viral load and attenuates pulmonary and systemic inflammation in SARS-CoV-2-infected, aged hamsters

Author

Delval, Lou, Hantute-Ghesquier, Aline, Sencio, Valentin, Flaman, Jean Michel, Robil, Cyril, Angulo, Fabiola Silva, Lipskaia, Larissa, Çobanoğlu, Ozmen, Lacoste, Anne-Sophie, Machelart, Arnaud, Danneels, Adeline, Corbin, Mathieu, Deruyter, Lucie, Heumel, Séverine, Idziorek, Thierry, Séron, Karin, Sauve, Florent, Bongiovanni, Antonino, Prévot, Vincent, Wolowczuk, Isabelle, Belouzard, Sandrine, Saliou, Jean-Michel, Gosset, Philippe, Bernard, David, Rouillé, Yves, Adnot, Serge, Duterque-Coquillaud, Martine, and Trottein, François

Published
2023
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135. Can Active Learning Preemptively Mitigate Fairness Issues?

Author

Branchaud-Charron, Frédéric, Atighehchian, Parmida, Rodríguez, Pau, Abuhamad, Grace, and Lacoste, Alexandre

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset preparation stages therefore becomes an essential part of training fairer algorithms. In particular, active learning (AL) algorithms show promise for the task by drawing importance to the most informative training samples. However, the effect and interaction between existing AL algorithms and algorithmic fairness remain under-explored. In this paper, we study whether models trained with uncertainty-based AL heuristics such as BALD are fairer in their decisions with respect to a protected class than those trained with identically independently distributed (i.i.d.) sampling. We found a significant improvement on predictive parity when using BALD, while also improving accuracy compared to i.i.d. sampling. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD. We found that, while addressing different fairness issues, their interaction further improves the results on most benchmarks and metrics we explored., Comment: Presented at ICLR 2021 Workshop on Responsable AI

Published
2021

136. Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Author

Mañas, Oscar, Lacoste, Alexandre, Giro-i-Nieto, Xavier, Vazquez, David, and Rodriguez, Pau

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there exist vast amounts of remote sensing data, most of it remains unlabeled and thus inaccessible for supervised learning algorithms. Transfer learning approaches can reduce the data requirements of deep learning algorithms. However, most of these methods are pre-trained on ImageNet and their generalization to remote sensing imagery is not guaranteed due to the domain gap. In this work, we propose Seasonal Contrast (SeCo), an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations. The SeCo pipeline is composed of two parts. First, a principled procedure to gather large-scale, unlabeled and uncurated remote sensing datasets containing images from multiple Earth locations at different timestamps. Second, a self-supervised algorithm that takes advantage of time and position invariance to learn transferable representations for remote sensing applications. We empirically show that models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. The datasets and models in SeCo will be made public to facilitate transfer learning and enable rapid progress in remote sensing applications.

Published
2021

137. Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Author

Rodriguez, Pau, Caccia, Massimo, Lacoste, Alexandre, Zamparo, Lee, Laradji, Issam, Charlin, Laurent, and Vazquez, David

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

Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction, providing details about the model's decision-making. Current methods tend to generate trivial counterfactuals about a model's decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations., Comment: ICCV 2021

Published
2021

138. Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning

Author

Kwon, Namyeong, Na, Hwidong, Huang, Gabriel, and Lacoste-Julien, Simon

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

Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set. In practice, training on the meta-training set may not always be an option due to data privacy concerns, intellectual property issues, or merely lack of computing resources. In this paper, we consider the novel problem of repurposing pretrained MAML checkpoints to solve new few-shot classification tasks. Because of the potential distribution mismatch, the original MAML steps may no longer be optimal. Therefore we propose an alternative meta-testing procedure and combine MAML gradient steps with adversarial training and uncertainty-based stepsize adaptation. Our method outperforms "vanilla" MAML on same-domain and cross-domains benchmarks using both SGD and Adam optimizers and shows improved robustness to the choice of base stepsize., Comment: Appears in: Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021). 20 pages

Published
2021

139. Online Adversarial Attacks

Author

Mladenovic, Andjela, Bose, Avishek Joey, Berard, Hugo, Hamilton, William L., Lacoste-Julien, Simon, Vincent, Pascal, and Gidel, Gauthier

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Data Structures and Algorithms
Abstract

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model, and the decisions made by the attacker are irrevocable since they operate on a transient data stream. We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied $k$-secretary problem in theoretical computer science and propose Virtual+, a simple yet practical online algorithm. Our main theoretical result shows Virtual+ yields provably the best competitive ratio over all single-threshold algorithms for $k<5$ -- extending the previous analysis of the $k$-secretary problem. We also introduce the \textit{stochastic $k$-secretary} -- effectively reducing online blackbox transfer attacks to a $k$-secretary problem under noise -- and prove theoretical bounds on the performance of Virtual+ adapted to this setting. Finally, we complement our theoretical results by conducting experiments on MNIST, CIFAR-10, and Imagenet classifiers, revealing the necessity of online algorithms in achieving near-optimal performance and also the rich interplay between attack strategies and online attack selection, enabling simple strategies like FGSM to outperform stronger adversaries., Comment: ICLR 2022

Published
2021

140. SVRG Meets AdaGrad: Painless Variance Reduction

Author

Dubois-Taine, Benjamin, Vaswani, Sharan, Babanezhad, Reza, Schmidt, Mark, and Lacoste-Julien, Simon

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Variance reduction (VR) methods for finite-sum minimization typically require the knowledge of problem-dependent constants that are often unknown and difficult to estimate. To address this, we use ideas from adaptive gradient methods to propose AdaSVRG, which is a more robust variant of SVRG, a common VR method. AdaSVRG uses AdaGrad in the inner loop of SVRG, making it robust to the choice of step-size. When minimizing a sum of n smooth convex functions, we prove that a variant of AdaSVRG requires $\tilde{O}(n + 1/\epsilon)$ gradient evaluations to achieve an $O(\epsilon)$-suboptimality, matching the typical rate, but without needing to know problem-dependent constants. Next, we leverage the properties of AdaGrad to propose a heuristic that adaptively determines the length of each inner-loop in AdaSVRG. Via experiments on synthetic and real-world datasets, we validate the robustness and effectiveness of AdaSVRG, demonstrating its superior performance over standard and other "tune-free" VR methods.

Published
2021

141. Emergence of homochirality in large molecular systems

Author

Laurent, Gabin, Lacoste, David, and Gaspard, Pierre

Subjects
Condensed Matter - Statistical Mechanics
Abstract

The selection of a single molecular handedness, or homochirality across all living matter, is a mystery in the origin of life. Frank's seminal model showed in the fifties how chiral symmetry breaking can occur in non-equilibrium chemical networks. However, an important shortcoming in this classic model is that it considers a small number of species, while there is no reason for the prebiotic system, in which homochirality first appeared, to have had such a simple composition. Furthermore, this model does not provide information on what could have been the size of the molecules involved in this homochiral prebiotic system. Here, we show that large molecular systems are likely to undergo a phase transition towards a homochiral state, as a consequence of the fact that they contain a large number of chiral species. Using chemoinformatics tools, we quantify how abundant are chiral species in the chemical universe of all possible molecules of a given length. Then, we propose that Frank's model should be extended to include a large number of species, in order to possess the transition towards homochirality as confirmed by numerical simulations. Finally, using random matrix theory, we prove that large non-equilibrium reaction networks possess a generic and robust phase transition towards a homochiral state.

Published
2021
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142. QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy

Author

Nelson, Glyn, Boehm, Ulrike, Bagley, Steve, Bajcsy, Peter, Bischof, Johanna, Brown, Claire M, Dauphin, Aurelien, Dobbie, Ian M, Eriksson, John E, Faklaris, Orestis, Fernandez-Rodriguez, Julia, Ferrand, Alexia, Gelman, Laurent, Gheisari, Ali, Hartmann, Hella, Kukat, Christian, Laude, Alex, Mitkovski, Miso, Munck, Sebastian, North, Alison J, Rasse, Tobias M, Resch-Genger, Ute, Schuetz, Lucas C, Seitz, Arne, Strambio-De-Castillia, Caterina, Swedlow, Jason R, Alexopoulos, Ioannis, Aumayr, Karin, Avilov, Sergiy, Bakker, Gert-Jan, Bammann, Rodrigo R, Bassi, Andrea, Beckert, Hannes, Beer, Sebastian, Belyaev, Yury, Bierwagen, Jakob, Birngruber, Konstantin A, Bosch, Manel, Breitlow, Juergen, Cameron, Lisa A, Chalfoun, Joe, Chambers, James J, Chen, Chieh-Li, Conde-Sousa, Eduardo, Corbett, Alexander D, Cordelieres, Fabrice P, Del Nery, Elaine, Dietzel, Ralf, Eismann, Frank, Fazeli, Elnaz, Felscher, Andreas, Fried, Hans, Gaudreault, Nathalie, Goh, Wah Ing, Guilbert, Thomas, Hadleigh, Roland, Hemmerich, Peter, Holst, Gerhard A, Itano, Michelle S, Jaffe, Claudia B, Jambor, Helena K, Jarvis, Stuart C, Keppler, Antje, Kirchenbuechler, David, Kirchner, Marcel, Kobayashi, Norio, Krens, Gabriel, Kunis, Susanne, Lacoste, Judith, Marcello, Marco, Martins, Gabriel G, Metcalf, Daniel J, Mitchell, Claire A, Moore, Joshua, Mueller, Tobias, Nelson, Michael S, Ogg, Stephen, Onami, Shuichi, Palmer, Alexandra L, Paul-Gilloteaux, Perrine, Pimentel, Jaime A, Plantard, Laure, Podder, Santosh, Rexhepaj, Elton, Royon, Arnaud, Saari, Markku A, Schapman, Damien, Schoonderwoert, Vincent, Schroth-Diez, Britta, Schwartz, Stanley, Shaw, Michael, Spitaler, Martin, Stoeckl, Martin T, Sudar, Damir, Teillon, Jeremie, Terjung, Stefan, Thuenauer, Roland, Wilms, Christian D, Wright, Graham D, and Nitschke, Roland

Subjects
Quantitative Biology - Other Quantitative Biology, Physics - Instrumentation and Detectors
Abstract

In April 2020, the QUality Assessment and REProducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models, and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper 1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; 2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers, and observers of such; 3) outlines the current actions of the QUAREP-LiMi initiative, and 4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics., Comment: 17 pages, 3 figures, shortened abstract, Co-Lead Authors: Glyn Nelson and Ulrike Boehm, Corresponding author: Roland Nitschke

Published
2021
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143. Tunable Hydrogen-Related Defects in ZnO Nanowires Using Oxygen Plasma Treatment by Ion Energy Adjustment

Author

Alexandre Dieulesaint, Odette Chaix-Pluchery, Matthieu Weber, Fabrice Donatini, Ana Lacoste, Vincent Consonni, and Eirini Sarigiannidou

Subjects
ZnO nanowires, plasma treatment, hydrogen, chemical bath deposition, Chemistry, QD1-999
Abstract

The chemical bath deposition (CBD) process enables the deposition of ZnO nanowires (NWs) on various substrates with customizable morphology. However, the hydrogen-rich CBD environment introduces numerous hydrogen-related defects, unintentionally doping the ZnO NWs and increasing their electrical conductivity. The oxygen-based plasma treatment can modify the nature and amount of these defects, potentially tailoring the ZnO NW properties for specific applications. This study examines the impact of the average ion energy on the formation of oxygen vacancies (VO) and hydrogen-related defects in ZnO NWs exposed to low-pressure oxygen plasma. Using X-ray photoelectron spectroscopy (XPS), 5 K cathodoluminescence (5K CL), and Raman spectroscopy, a comprehensive understanding of the effect of the oxygen ion energy on the formation of defects and defect complexes was established. A series of associative and dissociative reactions indicated that controlling plasma process parameters, particularly ion energy, is crucial. The XPS data suggested that increasing the ion energy could enhance Fermi level pinning by increasing the amount of VO and favoring the hydroxyl group adsorption, expanding the depletion region of charge carriers. The 5K CL and Raman spectroscopy further demonstrated the potential to adjust the ZnO NW physical properties by varying the oxygen ion energy, affecting various donor- and acceptor-type defect complexes. This study highlights the ability to tune the ZnO NW properties at low temperature by modifying plasma process parameters, offering new possibilities for a wide variety of nanoscale engineering devices fabricated on flexible and/or transparent substrates.

Published
2024
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149. Universal constraints on selection strength in lineage trees

Author

Genthon, Arthur and Lacoste, David

Subjects
Quantitative Biology - Populations and Evolution, Condensed Matter - Statistical Mechanics, Physics - Biological Physics
Abstract

We obtain general inequalities constraining the difference between the average of an arbitrary function of a phenotypic trait, which includes the fitness landscape of the trait itself, in the presence or in the absence of natural selection. These inequalities imply bounds on the strength of selection, which can be measured from the statistics of trait values and divisions along lineages. The upper bound is related to recent generalizations of linear response relations in Stochastic Thermodynamics, and shares common features with Fisher's fundamental theorem of natural selection, and with its generalization by Price, although they define different measures of selection. The lower bound follows from recent improvements on Jensen's inequality, and both bounds depend on the variability of the fitness landscape. We illustrate our results using numerical simulations of growing cell colonies and with experimental data of time-lapse microscopy experiments of bacteria cell colonies.

Published
2020
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150. Geometry-Aware Universal Mirror-Prox

Author

Babanezhad, Reza and Lacoste-Julien, Simon

Subjects
Computer Science - Machine Learning
Abstract

Mirror-prox (MP) is a well-known algorithm to solve variational inequality (VI) problems. VI with a monotone operator covers a large group of settings such as convex minimization, min-max or saddle point problems. To get a convergent algorithm, the step-size of the classic MP algorithm relies heavily on the problem dependent knowledge of the operator such as its smoothness parameter which is hard to estimate. Recently, a universal variant of MP for smooth/bounded operators has been introduced that depends only on the norm of updates in MP. In this work, we relax the dependence to evaluating the norm of updates to Bregman divergence between updates. This relaxation allows us to extends the analysis of universal MP to the settings where the operator is not smooth or bounded. Furthermore, we analyse the VI problem with a stochastic monotone operator in different settings and obtain an optimal rate up to a logarithmic factor.

Published
2020

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