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8. D3: Data Diversity Design for Systematic Generalization in Visual Question Answering

Author

Rahimi, Amir, D'Amario, Vanessa, Yamada, Moyuru, Takemoto, Kentaro, Sasaki, Tomotake, and Boix, Xavier

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

Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence systematic generalization is the diversity of training data. However, diversity can be defined in various ways, as data have many factors of variation. A more granular understanding of how different aspects of data diversity affect systematic generalization is lacking. We present new evidence in the problem of Visual Question Answering (VQA) that reveals that the diversity of simple tasks (i.e. tasks formed by a few subtasks and concepts) plays a key role in achieving systematic generalization. This implies that it may not be essential to gather a large and varied number of complex tasks, which could be costly to obtain. We demonstrate that this result is independent of the similarity between the training and testing data and applies to well-known families of neural network architectures for VQA (i.e. monolithic architectures and neural module networks). Additionally, we observe that neural module networks leverage all forms of data diversity we evaluated, while monolithic architectures require more extensive amounts of data to do so. These findings provide a first step towards understanding the interactions between data diversity design, neural network architectures, and systematic generalization capabilities., Comment: TMLR (https://openreview.net/forum?id=ZAin13msOp)

Published
2023

9. Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Author

Farshidi, Siamak, Rezaee, Kiyan, Mazaheri, Sara, Rahimi, Amir Hossein, Dadashzadeh, Ali, Ziabakhsh, Morteza, Eskandari, Sadegh, and Jansen, Slinger

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

Published
2023

12. The Role of University Teachers' 21st-Century Digital Competence in Their Attitudes toward ICT Integration in Higher Education: Extending the Theory of Planned Behavior

Author

Rahimi, Amir Reza and Tafazoli, Dara

Abstract

Due to the massive integration of Information and Communication Technologies (ICTs) in higher education, teachers need to be highly digital competent and have positive attitudes to manage their classes effectively. In this regard, the European Framework for the Digital Competence of Educators (DigCompEdu) introduces a paradigm known as 21st-century competence that all teachers should acquire. Recently, scholars have explored the dimension of teachers' digital competence in the 21st century relating to a digital context. Thus, this explanatory study wants to take a step forward to add to the literature the role of university teachers' 21st-century digital competence in their attitudes toward ICT integration. To meet the end, a questionnaire was designed and administered to 350 Iranian university teachers who implemented ICTs in their classes. The result of partial least squares analysis (PLS-SEM) reveals the significant relationship between university teachers' information and data literacy, digital content creation, communication and collaboration, and problem-solving skills with three antecedents of behavioral intention and actual behavior for employing ICTs. Related pedagogical implications are addressed, such as running some teacher training programs for teachers to escalate their 21st-century digital competencies and making teachers aware of the role of these factors in managing their classes with ICTs.

Published
2022

20. Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation

Author

Kaviani, Samira, Rahimi, Amir, and Hartley, Richard

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios. To tackle this issue in the context of semi-supervised 3D hand shape and pose estimation, we propose the Pose Alignment network to propagate 3D annotations from labelled frames to nearby unlabelled frames in sparsely annotated videos. We show that incorporating the alignment supervision on pairs of labelled-unlabelled frames allows us to improve the pose estimation accuracy. Besides, we show that the proposed Pose Alignment network can effectively propagate annotations on unseen sparsely labelled videos without fine-tuning., Comment: DICTA 2021

Published
2021

21. CogSense: A Cognitively Inspired Framework for Perception Adaptation

Author

Kwon, Hyukseong, Rahimi, Amir, Lee, Kevin G., Agarwal, Amit, and Bhattacharyya, Rajan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper proposes the CogSense system, which is inspired by sense-making cognition and perception in the mammalian brain to perform perception error detection and perception parameter adaptation using probabilistic signal temporal logic. As a specific application, a contrast-based perception adaption method is presented and validated. The proposed method evaluates perception errors using heterogeneous probe functions computed from the detected objects and subsequently solves a contrast optimization problem to correct perception errors. The CogSense probe functions utilize the characteristics of geometry, dynamics, and detected blob image quality of the objects to develop axioms in a probabilistic signal temporal logic framework. By evaluating these axioms, we can formally verify whether the detections are valid or erroneous. Further, using the CogSense axioms, we generate the probabilistic signal temporal logic-based constraints to finally solve the contrast-based optimization problem to reduce false positives and false negatives.

Published
2021

23. Few-shot Weakly-Supervised Object Detection via Directional Statistics

Author

Shaban, Amirreza, Rahimi, Amir, Ajanthan, Thalaiyasingam, Boots, Byron, and Hartley, Richard

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Detecting novel objects from few examples has become an emerging topic in computer vision recently. However, these methods need fully annotated training images to learn new object categories which limits their applicability in real world scenarios such as field robotics. In this work, we propose a probabilistic multiple instance learning approach for few-shot Common Object Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD). In these tasks, only image-level labels, which are much cheaper to acquire, are available. We find that operating on features extracted from the last layer of a pre-trained Faster-RCNN is more effective compared to previous episodic learning based few-shot COL methods. Our model simultaneously learns the distribution of the novel objects and localizes them via expectation-maximization steps. As a probabilistic model, we employ von Mises-Fisher (vMF) distribution which captures the semantic information better than Gaussian distribution when applied to the pre-trained embedding space. When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images. Our extensive experiments show that the proposed method, despite being simple, outperforms strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.

Published
2021

25. Effect of Grouting on the Resistance of Portland and Blast Furnace Slag Cement Paste Against Chemical Attack by Aggressive Carbon Dioxide

Author

Wagemann, Falk, Schmidt-Döhl, Frank, Rahimi, Amir, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Ilki, Alper, editor, Çavunt, Derya, editor, and Çavunt, Yavuz Selim, editor

Published
2023
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27. Post-hoc Calibration of Neural Networks by g-Layers

Author

Rahimi, Amir, Mensink, Thomas, Gupta, Kartik, Ajanthan, Thalaiyasingam, Sminchisescu, Cristian, and Hartley, Richard

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

Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves. In recent years, there is a surge of research on neural network calibration and the majority of the works can be categorized into post-hoc calibration methods, defined as methods that learn an additional function to calibrate an already trained base network. In this work, we intend to understand the post-hoc calibration methods from a theoretical point of view. Especially, it is known that minimizing Negative Log-Likelihood (NLL) will lead to a calibrated network on the training set if the global optimum is attained (Bishop, 1994). Nevertheless, it is not clear learning an additional function in a post-hoc manner would lead to calibration in the theoretical sense. To this end, we prove that even though the base network ($f$) does not lead to the global optimum of NLL, by adding additional layers ($g$) and minimizing NLL by optimizing the parameters of $g$ one can obtain a calibrated network $g \circ f$. This not only provides a less stringent condition to obtain a calibrated network but also provides a theoretical justification of post-hoc calibration methods. Our experiments on various image classification benchmarks confirm the theory.

Published
2020

28. Calibration of Neural Networks using Splines

Author

Gupta, Kartik, Rahimi, Amir, Ajanthan, Thalaiyasingam, Mensink, Thomas, Sminchisescu, Cristian, and Hartley, Richard

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

Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two empirical distributions. In this work, we introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test in which the main idea is to compare the respective cumulative probability distributions. From this, by approximating the empirical cumulative distribution using a differentiable function via splines, we obtain a recalibration function, which maps the network outputs to actual (calibrated) class assignment probabilities. The spine-fitting is performed using a held-out calibration set and the obtained recalibration function is evaluated on an unseen test set. We tested our method against existing calibration approaches on various image classification datasets and our spline-based recalibration approach consistently outperforms existing methods on KS error as well as other commonly used calibration measures. Our Code is available at https://github.com/kartikgupta-at-anu/spline-calibration., Comment: ICLR 2021

Published
2020

29. Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization

Author

Rahimi, Amir, Shaban, Amirreza, Ajanthan, Thalaiyasingam, Hartley, Richard, and Boots, Byron

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We study the problem of learning localization model on target classes with weakly supervised image labels, helped by a fully annotated source dataset. Typically, a WSOL model is first trained to predict class generic objectness scores on an off-the-shelf fully supervised source dataset and then it is progressively adapted to learn the objects in the weakly supervised target dataset. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well. The combined localization model and the estimated object annotations are jointly learned in an alternating optimization paradigm as is typically done in standard WSOL methods. In contrast to the existing work that learns pairwise similarities, our approach optimizes a unified objective with convergence guarantee and it is computationally efficient for large-scale applications. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function. For instance, in the ILSVRC dataset, the Correct Localization (CorLoc) performance improves from 72.8% to 78.2% which is a new state-of-the-art for WSOL task in the context of knowledge transfer., Comment: ECCV 2020. formerly "In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization"

Published
2020

30. Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Author

Rahimi, Amir, Shaban, Amirreza, Cheng, Ching-An, Hartley, Richard, and Boots, Byron

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

Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy. However, previous post-hoc calibration techniques work only with simple calibration functions, potentially lacking sufficient representation to calibrate the complex function landscape of deep networks. In this work, we aim to learn general post-hoc calibration functions that can preserve the top-k predictions of any deep network. We call this family of functions intra order-preserving functions. We propose a new neural network architecture that represents a class of intra order-preserving functions by combining common neural network components. Additionally, we introduce order-invariant and diagonal sub-families, which can act as regularization for better generalization when the training data size is small. We show the effectiveness of the proposed method across a wide range of datasets and classifiers. Our method outperforms state-of-the-art post-hoc calibration methods, namely temperature scaling and Dirichlet calibration, in several evaluation metrics for the task., Comment: NeurIPS 2020

Published
2020

34. Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Author

Abdukahil, heryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Abrous, Amal, Absil, Lara, Acker, Andrew, Adachi, Shingo, Adrião, Diana, Al Ageel, Saleh, Ahmed, Shakeel, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Akwani, Chika, Al Qasim, Eman, Alalqam, Razi, Alberti, Angela, Al-dabbous, Tala, Alegesan, Senthilkumar, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Al-Fares, Abdulrahman, Alfoudri, Huda, Ali, Imran, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Alves, João, Alves, Rita, Alves, João Melo, Cabrita, Joana Alves, Amaral, Maria, Amira, Nur, Ampaw, Phoebe, Andini, Roberto, Andréjak, Claire, Angheben, Andrea, Angoulvant, François, 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, Ashraf, Muhammad, Assie, Jean Baptiste, Asyraf, Amirul, Atif, Minahel, Atique, Anika, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, 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, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Basmaci, Romain, Hassin Basri, Muhammad Fadhli, Battaglini, Denise, Bauer, Jules, Bautista Rincon, Diego Fernando, 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, Benkerrou, Dehbia, Bennett, Suzanne, Benny, Binny, Bento, Luís, Berdal, Jan-Erik, Bergeaud, Delphine, Bergin, Hazel, Bernal Sobrino, José Luis, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Humaid, Felwa Bin, Bin Kamarudin, Mohd Nazlin, Bisoffi, Zeno, Bissuel, François, Biston, Patrick, Bitker, Laurent, Bitton, Jonathan, Blanco-Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Blumberg, Lucille, Boccia, Filomena, Bodenes, Laetitia, Bogaert, Debby, Boivin, Anne-Hélène, Bolaños, Isabela, Bolze, Pierre-Adrien, Bompart, François, Borges, Diogo, Borie, Raphaël, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, 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, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Patrick, Breen, Dorothy, Brewster, David, Brickell, Kathy, Browne, Shaunagh, Brusse-Keizer, Marjolein, Bryda, Petra, Buchtele, Nina, Bugaeva, Polina, Buisson, Marielle, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G., Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Garcês, Rui Caetano, Calligy, Kate, Calvache, Jose Andres, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Cantero, Mireia, Caraux-Paz, Pauline, Cardellino, Chiara Simona, Cardoso, Sofia, Cardoso, Filipe, Cardoso, Filipa, Cardoso, Nelson, 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, Castanheira, Nidyanara, Castor-Alexandre, Guylaine, Castro, Ivo, Catarino, Ana, Catherine, François-Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavayas, Alexandros, Cervantes-Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Chantalat Auger, Christelle, Chapplain, Jean-Marc, Charpentier, Charlotte, Chas, Julie, 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, Chopin, Marie-Charlotte, Chow, Ting Soo, Chow, Yock Ping, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Jennifer, Claure-Del Granado, Rolando, Clohisey, Sara, Cobb, Perren J., Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Coles, Jennifer, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connolly, Jennifer, Connor, Marie, Conrad, Anne, Conway, Elaine, Cooke, Graham S., Copland, Mary, 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, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Cruz Bermúdez, Juan Luis, Rojo, Jaime Cruz, Csete, Marc, Cullen, Ailbhe, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al-Awwab, Dai, Danyang, Dagens, Andrew, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Damas, Juliana, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A., Dantas, Jorge, de Loughry, Gillian, De Montmollin, Etienne, Freitas de Oliveira França, Rafael, De Rosa, Rosanna, de Silva, Thushan, de Vries, Peter, 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, 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, Da Silva, Fernanda Dias, Diaz, Juan Jose, 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, Donnelly, Maria, Donnelly, Christl A., Donohue, Sean, Donohue, Yoann, Donohue, Chloe, Doran, Peter, Dorival, Céline, D'Ortenzio, Eric, Douglas, James Joshua, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, 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, Echeverria-Villalobos, Marco, Edwards, Eleanor, Egan, Siobhan, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Eloy, Philippine, Elshazly, Tarek, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Santo, Catarina Espírito, Esposito-Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Marc, Fabre, Isabelle, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J., Faria, Pedro, 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, Ferraz, Mário, Ferreira, Sílvia, Ferreira, Bernardo, Ferreira, Benigno, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Fiorda, Juan, Flament, Thomas, Flanagan, Emily, Flateau, Clara, Fletcher, Tom, Florence, Aline-Marie, Florio, Letizia Lucia, Flynn, Deirdre, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A., Fraher, Marianne, Franch-Llasat, Diego, Fraser, John F., Fraser, Christophe, Freire, Marcela Vieira, Ribeiro, Ana Freitas, Friedrich, Caren, Fritz, Ricardo, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, G, Argin, Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean-Charles, Gagneux-Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia-Gallo, Esteban, Garot, Denis, Garrait, Valérie, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghosn, Jade, Giani, Marco, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goehringer, François, Goffard, Jean-Christophe, Goh, Jin Yi, Golob, Jonathan, Gominet, Marie, Gonzalez, Alicia, Gordon, Patricia, Gorenne, Isabelle, Gormley, Conor, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Green, Christopher A., Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Gruner, Heidi, Gu, Yusing, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, Guimarães de Castro, Maisa, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hadri, Nadir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hamer, Ansley, Hamers, Raph L., Hamidfar, Rebecca, Hammond, Terese, Hammond, Naomi, Han, Lim Yuen, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Hartman, Alan, Hashmi, Junaid, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Hentzien, Maxime, Hernandez, Diana, Hershey, Andrew, Hesstvedt, Liv, Higgins, Eibhlin, Higgins, Dawn, Higgins, Rupert, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hitoto, Hikombo, Ho, Antonia, Ho, Yi Bin, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Hoshino, Kota, Houas, Ikram, Hough, Catherine L., Houltham, Stuart, Ming-Yang Hsu, Jimmy, Hulot, Jean-Sébastien, Hurd, Abby, Ijaz, Samreen, Ikram, M. Arfan, Illes, Hajnal-Gabriela, Imbert, Patrick, Inácio, Hugo, Ing, Yun Sii, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Itai, Junji, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jaquet, Pierre, Jassat, Waasila, Jaud-Fischer, Coline, Jaureguiberry, Stéphane, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Joe, Ong Yiaw, Jorge García, Ruth N., Joseph, Mark, Joseph, Cédric, Jourdain, Mercé, Jouvet, Philippe, Jung, Anna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalomoiri, Smaragdi, Ayadi Kamaluddin, Muhammad Aisar, Che Kamaruddin, Zul Amali, Kamarudin, Nadiah, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Karpayah, Pratap, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kay, Christy, Kayyali, Lamees, Keating, Seán, Kelly, Claire, Kelly, Yvelynne, Kelly, Andrea, Kelly, Niamh, Kelly, Aoife, Kelly, Sadie, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Khalid, Imrana, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Kho, Michelle E., Khoo, Saye, Khoo, Ryan, Khoo, Denisa, Kiat, Khor How, Kida, Yuri, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kitamura, Nobuya, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R., Komatsu, Mamoru, Korten, Volkan, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Sudhir, Krishnan, Vinothini, Kruglova, Oksana, Kumar, Deepali, Kumar, Ganesh, Tirupakuzhi Vijayaraghavan, Bharath Kumar, Vecham, Pavan Kumar, Kuriakose, Dinesh, Kurtzman, Ethan, Kutsogiannis, Demetrios, Kutsyna, Galyna, Kyriakoulis, Konstantinos, Lachatre, Marie, Lacoste, Marie, Laffey, John G., Lafhej, Nadhem, Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lalueza, Antonio, Lambert, Marc, Langelot-Richard, Marie, Langlois, Vincent, Lantang, Eka Yudha, Lanza, Marina, Laouénan, Cédric, Laribi, Samira, Lariviere, Delphine, Lasry, Stéphane, Launay, Odile, Laureillard, Didier, Lavie-Badie, Yoan, Law, Andy, Lawrence, Teresa, Lawrence, Cassie, 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, Lee, Todd C., Lee, Su Hwan, Lee, James, Lee, Jennifer, Lee, Heng Gee, Lee, Biing Horng, Lee, Yi Lin, Leeming, Gary, Lefebvre, Laurent, Lefebvre, Bénédicte, Lefèvre, Benjamin, LeGac, Sylvie, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Leone, Michela, Leone, Marc, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letrou, Sophie, Levy, Yves, Levy, Bruno, Levy-Marchal, Claire, L'Her, Erwan, Bassi, Gianluigi Li, Liang, Janet, Liegeon, Geoffrey, Lim, Wei Shen, Lim, Kah Chuan, Lima, Chantre, Lina, Bruno, Lina, Lim, Lind, Andreas, Lingas, Guillaume, Lion-Daolio, Sylvie, Liu, Keibun, Livrozet, Marine, Lizotte, Patricia, Lolong, Navy, Loon, Leong Chee, Lopes, Diogo, Lopez-Colon, Dalia, Loschner, Anthony L., Loubet, Paul, Loufti, Bouchra, Louis, Guillame, Lourenco, Silvia, Low, Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M., Lungu, Olguta, Lunn, Miles, Luong, Liem, Luque, Nestor, Luton, Dominique, Lyons, Ruth, Maasikas, Olavi, Mabiala, Oryane, Machado, Sara, Machado, Moïse, Macheda, Gabriel, Madiha, Hashmi, Maestro de la Calle, Guillermo, Mahieu, Rafael, Mahy, Sophie, Maier, Lars S., Maillet, Mylène, Maitre, Thomas, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Malvy, Denis, Manda, Victoria, Mandelbrot, Laurent, Manetta, Frank, Mankikian, Julie, Manning, Edmund, Manuel, Aldric, Sant`Ana Malaque, Ceila Maria, Marino, Flávio, Markowicz, Samuel, Marques, Ana, Marsh, Brian, Marsh, Laura, Marshal, Megan, Marshall, John, Martelli, Celina Turchi, Martin, Emily, Martin-Blondel, Guillaume, Martinez, F. Eduardo, Martin-Loeches, Ignacio, Martinot, Martin, Martins, João, Martins, Ana, Rego, Caroline Martins, Martucci, Gennaro, Martynenko, Olga, Marwali, Eva Miranda, Marzukie, Marsilla, Maslove, David, Mason, Sabina, Mat Nor, Mohd Basri, Matan, Moshe, Mathieu, Daniel, Mattei, Mathieu, Maulin, Laurence, Maxwell, Michael, Mazzoni, Thierry, Sweeney, Lisa Mc, McArthur, Colin, McCanny, Peter, McCarthy, Anne, McCarthy, Aine, McCloskey, Colin, McConnochie, Rachael, McDermott, Sherry, McDonald, Sarah E., McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McEvoy, Natalie, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A., McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Mehkri, Omar, Mele, Ferruccio, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Noémie, Mercier, Emmanuelle, Merckx, Antoine, Mergeay-Fabre, Mayka, Mergler, Blake, Merson, Laura, Mesquita, António, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M., Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miller, Jennene, Misnan, Nor Arisah, Mohamed, Tahira Jamal, Eliza Mohamed, Nik Nur, Moin, Asma, Molinos, Elena, Molloy, Brenda, Monahan, Sinead, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Shona C., Moore, Sarah, Cely, Lina Morales, Moro, Lucia, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Murphy, Aisling, Murphy, Lorna, Murray, Patrick, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Nagpal, Dave, Nagrebetsky, Alex, Narasimhan, Mangala, Narayanan, Nageswaran, Neant, Nadège, Necsoi, Coca, Nekliudov, Nikita, Neto, Raul, Neumann, Emily, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair D., Nitayavardhana, Prompak, Nonas, Stephanie, Mohd Noordin, Nurul Amani, Noret, Marion, Izzati Norharizam, Nurul Faten, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nseir, Saad, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Brien, Fionnuala O., Callaghan, Annmarie O., O'Callaghan, Annmarie, Occhipinti, Giovanna, OConnor, Derbrenn, Ogura, Takayuki, O'Halloran, Sophie, O'Hearn, Katie, Ohshimo, Shinichiro, Oliveira, João, Olliaro, Piero L., O'Neil, Conar, Ong, David S.Y., Ong, Jee Yan, Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orozco-Chamorro, Claudia Milena, Ortoleva, Jamel, Osatnik, Javier, O'Shea, Linda, O'Sullivan, Miriam, Othman, Siti Zubaidah, Ouamara, Nadia, Ouissa, Rachida, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parke, Rachael, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Pathmanathan, Mohan Dass, Patricio, Patricia, Patrier, Juliette, Patterson, Lisa, Paul, Mical, Paul, Christelle, Paulos, Jorge, Paxton, William A., Payen, Jean-François, Peake, Sandra L., 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, Petrovic, Michele, Petrov-Sanchez, Ventzislava, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Piagnerelli, Michael, Picard, Walter, Picone, Olivier, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Piquard, Valentine, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pitaloka, Ayodhia, Piubelli, Chiara, Pius, Riinu, 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, Preau, Sébastien, Prebensen, Christian, Preiser, Jean-Charles, Prinssen, Anton, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Purcell, Gregory, Quesada, Luisa, González, Víctor Quirós, Quist-Paulsen, Else, Quraishi, Mohammed, Qutishat, Fadi, Rabaud, Christian, Rafael, Aldo, Rafiq, Marie, Rahardjani, Mutia, Rahimi, Amir Kamel, Rahman, Rozanah Abd, Haji Ab Rahman, Ahmad Kashfi, Rainieri, Fernando, Rajahram, Giri Shan, Ramakrishnan, Nagarajan, Ramalho, José, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Ranjan, Ritika, Rapp, Christophe, Rasmin, Menaldi, Rätsep, Indrek, Ravi, Tharmini, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Remy, Martine, Ren, Hongru, Resseguier, Anne-Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Tiago, Reyes, Luis Felipe, Ribeiro, Maria Ines, Richardson, David, Richardson, Denise, Richier, Laurent, Atikah Ahmad Ridzuan, Siti Nurul, Rios, Ana L., Rishu, Asgar, Rispal, Patrick, Risso, Karine, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L., Robineau, Olivier, Roche-Campo, Ferran, Rodari, Paola, Rodeia, Simão, Roessler, Bernhard, Roger, Pierre-Marie, Rojek, Amanda, Romaru, Juliette, Roncon-Albuquerque, Roberto, Jr., Roriz, Mélanie, Rosa-Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Rossanese, Andrea, Rossetti, Matteo, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Russell, Clark D., Ryan, Maria, Ryan, Maeve, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sadat, Musharaf, Sahraei, Valla, Saint-Gilles, Maximilien, Sakiyalak, Pranya, Salazar, Leonardo, Sales, Gabriele, Sallaberry, Stéphane, Gandonniere, Charlotte Salmon, Salvator, Hélène, Sanchez, Emely, Sanchez, Olivier, Sanchez-Miralles, Angel, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre-François, Sandulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Sarmiento, Iam Claire E., Sarton, Benjamine, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Scott, Janet T., Scott-Brown, James, Sedillot, Nicholas, Seitz, Tamara, 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, Sheenan, John Robert, Shi, Haixia, Shime, Nobuaki, Shimizu, Hiroaki, Shimizu, Keiki, Shrapnel, Sally, Shum, Hoi Ping, Mohammed, Nassima Si, Siang, Ng Yong, Sibiude, Jeanne, Siddiqui, Atif, Sigfrid, Louise, Sillaots, Piret, Silva, Catarina, Silva, Rogério, Silva, Maria Joao, Lim Heng, Benedict Sim, Sin, Wai Ching, Singh, Punam, Singh, Budha Charan, Sitompul, Pompini Agustina, Sivam, Karisha, Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, Smyth, Michelle, Snacken, Morgane, So, Dominic, Soh, Tze Vee, Solomon, Joshua, Solomon, Tom, Somers, Emily, Sommet, Agnès, Song, Rima, Song, Myung Jin, Song, Tae, Chia, Jack Song, Sotto, Albert, Soum, Edouard, Sousa, Marta, Sousa, Ana Chora, Uva, Maria Sousa, Souza-Dantas, Vicente, Sperry, Alexandra, Spinuzza, Elisabetta, 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, 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, Svistunov, Suwarti; Andrey, Syahrin, Sarah, Syrigos, Konstantinos, Sztajnbok, Jaques, Szuldrzynski, Konstanty, Tabrizi, Shirin, Taccone, Fabio S., Tagherset, Lysa, Taib, Shahdattul Mawarni, Taleb, Sara, Talsma, Jelmer, Tampubolon, Maria Lawrensia, Tan, Kim Keat, Tan, Yan Chyi, Tanaka, Hiroyuki, Tanaka, Taku, Taniguchi, Hayato, Tardivon, Coralie, Tattevin, Pierre, Taufik, M Azhari, Tawfik, Hassan, Tedder, Richard S., Tee, Tze Yuan, Teixeira, João, Tellier, Marie-Capucine, Teoh, Sze Kye, Teotonio, Vanessa, Téoulé, François, Terpstra, Pleun, Terrier, Olivier, Terzi, Nicolas, Tessier-Grenier, Hubert, Tey, Adrian, Mohd Thabit, Alif Adlan, Tham, Zhang Duan, Thangavelu, Suvintheran, Theron, Elmi, Thibault, Vincent, Thiberville, Simon-Djamel, Thill, Benoît, Thirumanickam, Jananee, Thompson, Shaun, Thomson, David, Thomson, Emma C., Thorpe, Mathew, Thanga Thurai, Surain Raaj, Thwaites, Ryan S., Tierney, Paul, Tieroshyn, Vadim, Timashev, Peter S., Timsit, Jean-François, Tissot, Noémie, Toal, Fiona, Yang Toh, Jordan Zhien, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torre, Marta, Torres, Antoni, Torres-Zevallos, Hernando, Towers, Michael, 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, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad, Uyeki, Timothy M., Vajdovics, Cristinava, Val-Flores, Luís, Valran, Amélie, Van de Velde, Stijn, van den Berge, Marcel, 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, Varghese, Pooja, Varrone, Michael, Vasudayan, Shoban Raj, Vauchy, Charline, Veeran, Shaminee, Veislinger, Aurélie, Vencken, Sebastian, Ventura, Sara, Verbon, Annelies, Vidal, José Ernesto, Vieira, César, Villanueva, Joy Ann, Villoldo, Andrea, Visseaux, Benoit, Vitiello, Chiara, Vonkeman, Harald, Vuotto, Fanny, Wahab, Noor Hidayu, Wahab, Suhaila Abdul, Wahid, Nadirah Abdul, Wainstein, Marina, Walsh, Laura, Wang, Chih-Hsien, Webb, Steve, Wei, Jia, Wen, Tan Pei, Wesselius, Sanne, Wham, Murray, Whelan, Bryan, White, Nicole, Wicky, Paul Henri, Wiedemann, Aurélie, Wijaya, Surya Otto, Wille, Keith, Willems, Sue, Williams, Bailey, Williams, Virginie, Williams, Patricia J., Wils, Evert-Jan, Wittman, Jessica, Wong, Calvin, Wong, Xin Ci, Wong, Yew Sing, Wong, Teck Fung, Xian, Gan Ee, Xian, Lim Saio, Kuan, Pei Xuan, Xynogalas, Ioannis, Binti Mohd Yakop, Siti Rohani, Yamazaki, Masaki, Yarad, Elizabeth, Yazdanpanah, Yazdan, Liang Hing, Nicholas Yee, Yelnik, Cécile, Yeoh, Chian Hui, Yerkovich, Stephanie, Yokoyama, Toshiki, Yonis, Hodane, Yousif, Obada, Zaaqoq, Akram, Zabbe, Marion, Zahid, Masliza, Zahran, Maram, Binti Zaidan, Nor Zaila, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Spyrison, Nicholas, Ghadimi, Moji, Chávez-Iñiguez, Jonathan S., Rizo-Topete, Lilia, Pole, Jason D., and Johnson, David W.

Published
2023
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37. Learning to Find Common Objects Across Few Image Collections

Author

Shaban, Amirreza, Rahimi, Amir, Bansal, Shray, Gould, Stephen, Boots, Byron, and Hartley, Richard

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Given a collection of bags where each bag is a set of images, our goal is to select one image from each bag such that the selected images are from the same object class. We model the selection as an energy minimization problem with unary and pairwise potential functions. Inspired by recent few-shot learning algorithms, we propose an approach to learn the potential functions directly from the data. Furthermore, we propose a fast greedy inference algorithm for energy minimization. We evaluate our approach on few-shot common object recognition as well as object co-localization tasks. Our experiments show that learning the pairwise and unary terms greatly improves the performance of the model over several well-known methods for these tasks. The proposed greedy optimization algorithm achieves performance comparable to state-of-the-art structured inference algorithms while being ~10 times faster. The code is publicly available on https://github.com/haamoon/finding_common_object., Comment: ICCV 2019

Published
2019

39. An Autonomous Driving Control Strategy for Multi-trailer Articulated Heavy Vehicles with Enhanced Active Trailer Safety

Author

Rahimi, Amir, Huang, Wei, Sharma, Tarun, He, Yuping, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Orlova, Anna, editor, and Cole, David, editor

Published
2022
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41. Novel nanobody-based tools for studying the synaptic vesicle life cycle

Author

Rehm Ronja, Mougios Nikolaos, Real Karine Queiroz Zetune Villa, Sograte-Idrissi Shama, Albert László, Rahimi Amir M., Maidorn Manuel, Hentze Jannik, Martinez-Carranza Markel, Hosseini Hassan, Saal Kim-Ann, Oleksiievets Nazar, Prigge Matthias, Tsukanov Roman, Stenmark Pål, Rizzoli Silvio, Opazo Felipe, and Fornasiero Eugenio

Subjects
neuroscience, nanobody, synaptic vesicle, live-imaging, Microbiology, QR1-502, Physiology, QP1-981, Zoology, QL1-991
Published
2024
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43. Rapid Prototyping Model for Healthcare Alternative Payment Models: Replicating the Federally Qualified Health Center Advanced Primary Care Practice Demonstration

Author

Olson, Jarrod, Rahimi, Amir, Chen, Po Hsu Allen, Jackson, J. Elizabeth, Coy, Tyler, Cocci, Adrienne, McMillan, Nancy, and Geppert, Jeff

Subjects
Statistics - Other Statistics, 91C-02
Abstract

Innovation in healthcare payment and service delivery utilizes high cost, high risk pilots paired with traditional program evaluations. Decision-makers are unable to reliably forecast the impacts of pilot interventions in this complex system, complicating the feasibility assessment of proposed healthcare models. We developed and validated a Discrete Event Simulation (DES) model of primary care for patients with Diabetes to allow rapid prototyping and assessment of models before pilot implementation. We replicated four outcomes from the Centers for Medicare and Medicaid Services Federally Qualified Health Center Advanced Primary Care Practice pilot. The DES model simulates a synthetic population's healthcare experience, including symptom onset, appointment scheduling, screening, and treatment, as well as the impact of physician training. A network of detailed event modules was developed from peer-reviewed literature. Synthetic patients' attributes modify the probability distributions for event outputs and direct them through an episode of care; attributes are in turn modified by patients' experiences. Our model replicates the direction of the effect of physician training on the selected outcomes, and the strength of the effect increases with the number of trainings. The simulated effect strength replicates the pilot results for eye exams and nephropathy screening, but over-estimates results for HbA1c and LDL screening. Our model will improve decision-makers' abilities to assess the feasibility of pilot success, with reproducible, literature-based systems models. Our model identifies intervention and healthcare system components to which outcomes are sensitive, so these aspects can be monitored and controlled during pilot implementation. More work is needed to improve replication of HbA1c and LDL screening, and to elaborate sub-models related to intervention components., Comment: Working paper

Published
2018

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