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141 results on '"Cerçel A"'

1. RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation

Author: Avram, Andrei-Marius, Timpuriu, Mircea, Iuga, Andreea, Matei, Vlad-Cristian, Tăiatu, Iulian-Marius, Găină, Tudor, Cercel, Dumitru-Clementin, Pop, Florin, and Cercel, Mihaela-Claudia
Subjects: Computer Science - Computation and Language
Abstract: Using supervised automatic summarisation methods requires sufficient corpora that include pairs of documents and their summaries. Similarly to many tasks in natural language processing, most of the datasets available for summarization are in English, posing challenges for developing summarization models in other languages. Thus, in this work, we introduce RoLargeSum, a novel large-scale summarization dataset for the Romanian language crawled from various publicly available news websites from Romania and the Republic of Moldova that were thoroughly cleaned to ensure a high-quality standard. RoLargeSum contains more than 615K news articles, together with their summaries, as well as their headlines, keywords, dialect, and other metadata that we found on the targeted websites. We further evaluated the performance of several BART variants and open-source large language models on RoLargeSum for benchmarking purposes. We manually evaluated the results of the best-performing system to gain insight into the potential pitfalls of this data set and future development., Comment: Accepted at COLING 2024 (long papers)
Published: 2024

2. GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering

Author: Crăciun, Cristian-George, Smădu, Răzvan-Alexandru, Cercel, Dumitru-Clementin, and Cercel, Mihaela-Claudia
Subjects: Computer Science - Computation and Language
Abstract: Pre-trained Language Models (PLMs) have shown remarkable performances in recent years, setting a new paradigm for NLP research and industry. The legal domain has received some attention from the NLP community partly due to its textual nature. Some tasks from this domain are represented by question-answering (QA) tasks. This work explores the legal domain Multiple-Choice QA (MCQA) for a low-resource language. The contribution of this work is multi-fold. We first introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising three different examinations and a number of 10,836 total questions. Along with this dataset, we introduce CROL, an organized corpus of laws that has a total of 93 distinct documents with their modifications from 763 time spans, that we leveraged in this work for Information Retrieval (IR) techniques. Moreover, we are the first to propose Law-RoG, a Knowledge Graph (KG) for the Romanian language, and this KG is derived from the aforementioned corpus. Lastly, we propose a novel approach for MCQA, Graph Retrieval Augmented by Facts (GRAF), which achieves competitive results with generally accepted SOTA methods and even exceeds them in most settings.
Published: 2024

3. Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups

Author: Smădu, Răzvan-Alexandru, Ion, David-Gabriel, Cercel, Dumitru-Clementin, Pop, Florin, and Cercel, Mihaela-Claudia
Subjects: Computer Science - Computation and Language
Abstract: Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5-turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller., Comment: 37 pages, 16 figures, Accepted by EMNLP 2024
Published: 2024

4. A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition

Author: Ion, David-Gabriel, Smădu, Răzvan-Alexandru, Cercel, Dumitru-Clementin, Pop, Florin, and Cercel, Mihaela-Claudia
Subjects: Computer Science - Computation and Language
Abstract: Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is even more prevalent in speech emotion recognition. Our work explores the model's performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. This result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning., Comment: 16 pages, 1 figure, Accepted by WISE 2024
Published: 2024

5. RoQLlama: A Lightweight Romanian Adapted Language Model

Author: Dima, George-Andrei, Avram, Andrei-Marius, Crăciun, Cristian-George, and Cercel, Dumitru-Clementin
Subjects: Computer Science - Computation and Language
Abstract: The remarkable achievements obtained by open-source large language models (LLMs) in recent years have predominantly been concentrated on tasks involving the English language. In this paper, we aim to advance the performance of Llama2 models on Romanian tasks. We tackle the problem of reduced computing resources by using QLoRA for training. We release RoQLlama-7b, a quantized LLM, which shows equal or improved results compared to its full-sized counterpart when tested on seven Romanian downstream tasks in the zero-shot setup. Also, it consistently achieves higher average scores across all few-shot prompts. Additionally, we introduce a novel Romanian dataset, namely RoMedQA, which contains single-choice medical questions in Romanian., Comment: Accepted at EMNLP Findings 2024 (short papers)
Published: 2024

6. Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation

Author: Matei, Vlad-Cristian, Tăiatu, Iulian-Marius, Smădu, Răzvan-Alexandru, and Cercel, Dumitru-Clementin
Subjects: Computer Science - Computation and Language
Abstract: This paper highlights the significance of natural language processing (NLP) within artificial intelligence, underscoring its pivotal role in comprehending and modeling human language. Recent advancements in NLP, particularly in conversational bots, have garnered substantial attention and adoption among developers. This paper explores advanced methodologies for attaining smaller and more efficient NLP models. Specifically, we employ three key approaches: (1) training a Transformer-based neural network to detect offensive language, (2) employing data augmentation and knowledge distillation techniques to increase performance, and (3) incorporating multi-task learning with knowledge distillation and teacher annealing using diverse datasets to enhance efficiency. The culmination of these methods has yielded demonstrably improved outcomes., Comment: Accepted by NLDB2024
Published: 2024
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7. A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition

Author: Ion, David-Gabriel, Smădu, Răzvan-Alexandru, Cercel, Dumitru-Clementin, Pop, Florin, Cercel, Mihaela-Claudia, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barhamgi, Mahmoud, editor, Wang, Hua, editor, and Wang, Xin, editor
Published: 2025
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8. Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language

Author: Nicola, Elena-Beatrice, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computation and Language
Abstract: Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others., Comment: Accepted at KES 2024
Published: 2024

9. Antibiotic prescribing in dental medicine-best practices for successful implementation

Author: Sandulescu, Oana, Preotescu, Liliana Lucia, Streinu-Cercel, Adrian, Sahin, Gulsen Ozkaya, and Sandulescu, Mihai
Published: 2024

10. HistNERo: Historical Named Entity Recognition for the Romanian Language

Author: Avram, Andrei-Marius, Iuga, Andreea, Manolache, George-Vlad, Matei, Vlad-Cristian, Micliuş, Răzvan-Gabriel, Muntean, Vlad-Andrei, Sorlescu, Manuel-Petru, Şerban, Dragoş-Andrei, Urse, Adrian-Dinu, Păiş, Vasile, and Cercel, Dumitru-Clementin
Subjects: Computer Science - Computation and Language
Abstract: This work introduces HistNERo, the first Romanian corpus for Named Entity Recognition (NER) in historical newspapers. The dataset contains 323k tokens of text, covering more than half of the 19th century (i.e., 1817) until the late part of the 20th century (i.e., 1990). Eight native Romanian speakers annotated the dataset with five named entities. The samples belong to one of the following four historical regions of Romania, namely Bessarabia, Moldavia, Transylvania, and Wallachia. We employed this proposed dataset to perform several experiments for NER using Romanian pre-trained language models. Our results show that the best model achieved a strict F1-score of 55.69%. Also, by reducing the discrepancies between regions through a novel domain adaption technique, we improved the performance on this corpus to a strict F1-score of 66.80%, representing an absolute gain of more than 10%., Comment: Accepted at the International Conference on Document Analysis and Recognition (ICDAR 2024)
Published: 2024

11. Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification

Author: Gheorghiu, Adrian, Tăiatu, Iulian-Marius, Cercel, Dumitru-Clementin, Marin, Iuliana, and Pop, Florin
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification., Comment: Accepted by ICAART 2024
Published: 2024

12. Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation

Author: Echim, Sebastian-Vasile, Tăiatu, Iulian-Marius, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models' robustness against adversarial attacks is enhanced through adversarial training, ensuring accurate classification even in the presence of threats. Leveraging explainability techniques, we gain insights into the model's decision-making process, improving trust and transparency. Additionally, we explore model compression techniques to optimize computational efficiency while maintaining classification performance. Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests. We also demonstrate that a student model can be 15-25 times more computationally efficient for a slight performance reduction, distilling the knowledge of more complex models., Comment: 10 pages, 8 figures, Accepted by ICAART 2024
Published: 2023

13. End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition

Author: Mănescu, Emilian-Claudiu, Smădu, Răzvan-Alexandru, Avram, Andrei-Marius, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract: Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and optimizations on the underrepresented, short-scale Romanian language dataset called Wild LRRo. Most notably, we compare different backend modules, demonstrating the effectiveness of adding ample regularization methods. We obtain state-of-the-art results using our proposed method, namely cross-lingual domain adaptation and unlabeled videos from English and German datasets to help the model learn language-invariant features. Lastly, we assess the performance of adding a layer inspired by the neural inhibition mechanism., Comment: 7 pages, 4 figures, Accepted by WI-IAT 2023
Published: 2023

14. SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification

Author: Lungu-Stan, Vlad-Constantin, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Skin cancer is a treatable disease if discovered early. We provide a production-specific solution to the skin cancer classification problem that matches human performance in melanoma identification by training a vision transformer on melanoma medical images annotated by experts. Since inference cost, both time and memory wise is important in practice, we employ knowledge distillation to obtain a model that retains 98.33% of the teacher's balanced multi-class accuracy, at a fraction of the cost. Memory-wise, our model is 49.60% smaller than the teacher. Time-wise, our solution is 69.25% faster on GPU and 97.96% faster on CPU. By adding classification heads at each level of the transformer and employing a cascading distillation process, we improve the balanced multi-class accuracy of the base model by 2.1%, while creating a range of models of various sizes but comparable performance. We provide the code at https://github.com/Longman-Stan/SkinDistilVit., Comment: Accepted at ICANN 2023
Published: 2023

15. From Fake to Hyperpartisan News Detection Using Domain Adaptation

Author: Smădu, Răzvan-Alexandru, Echim, Sebastian-Vasile, Cercel, Dumitru-Clementin, Marin, Iuliana, and Pop, Florin
Subjects: Computer Science - Computation and Language
Abstract: Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup., Comment: 15 pages, 3 figures, Accepted by RANLP 2023
Published: 2023

16. UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

Author: Preda, Andrei-Alexandru, Cercel, Dumitru-Clementin, Rebedea, Traian, and Chiru, Costin-Gabriel
Subjects: Computer Science - Computation and Language, I.2.7
Abstract: This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset., Comment: 10 pages. Accepted for publication in the IberLEF 2023 Proceedings, at https://ceur-ws.org/
Published: 2023

17. Towards Improving the Performance of Pre-Trained Speech Models for Low-Resource Languages Through Lateral Inhibition

Author: Avram, Andrei-Marius, Smădu, Răzvan-Alexandru, Păiş, Vasile, Cercel, Dumitru-Clementin, Ion, Radu, and Tufiş, Dan
Subjects: Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: With the rise of bidirectional encoder representations from Transformer models in natural language processing, the speech community has adopted some of their development methodologies. Therefore, the Wav2Vec models were introduced to reduce the data required to obtain state-of-the-art results. This work leverages this knowledge and improves the performance of the pre-trained speech models by simply replacing the fine-tuning dense layer with a lateral inhibition layer inspired by the biological process. Our experiments on Romanian, a low-resource language, show an average improvement of 12.5% word error rate (WER) using the lateral inhibition layer. In addition, we obtain state-of-the-art results on both the Romanian Speech Corpus and the Robin Technical Acquisition Corpus with 1.78% WER and 29.64% WER, respectively., Comment: Accepted at TSP2023
Published: 2023

18. Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation

Author: Avram, Andrei-Marius, Mititelu, Verginica Barbu, Păiş, Vasile, Cercel, Dumitru-Clementin, and Trăuşan-Matu, Ştefan
Subjects: Computer Science - Computation and Language
Abstract: Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword expressions. The evaluation of our models shows that the approach employed in this work achieves better results compared to the best system of the PARSEME 1.2 competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE identification and on 12 out of 14 languages for unseen MWE identification. Additionally, averaged across all languages, our best approach outperforms the MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen global MWE identification., Comment: Accepted at Mathematics 2023
Published: 2023

19. Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis

Author: Echim, Sebastian-Vasile, Smădu, Răzvan-Alexandru, Avram, Andrei-Marius, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computation and Language
Abstract: Satire detection and sentiment analysis are intensively explored natural language processing (NLP) tasks that study the identification of the satirical tone from texts and extracting sentiments in relationship with their targets. In languages with fewer research resources, an alternative is to produce artificial examples based on character-level adversarial processes to overcome dataset size limitations. Such samples are proven to act as a regularization method, thus improving the robustness of models. In this work, we improve the well-known NLP models (i.e., Convolutional Neural Networks, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Units (GRUs), and Bidirectional GRUs) with adversarial training and capsule networks. The fine-tuned models are used for satire detection and sentiment analysis tasks in the Romanian language. The proposed framework outperforms the existing methods for the two tasks, achieving up to 99.08% accuracy, thus confirming the improvements added by the capsule layers and the adversarial training in NLP approaches., Comment: 15 pages, 3 figures, Accepted by NLDB 2023
Published: 2023
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20. RoBERTweet: A BERT Language Model for Romanian Tweets

Author: Tăiatu, Iulian-Marius, Avram, Andrei-Marius, Cercel, Dumitru-Clementin, and Pop, Florin
Subjects: Computer Science - Computation and Language
Abstract: Developing natural language processing (NLP) systems for social media analysis remains an important topic in artificial intelligence research. This article introduces RoBERTweet, the first Transformer architecture trained on Romanian tweets. Our RoBERTweet comes in two versions, following the base and large architectures of BERT. The corpus used for pre-training the models represents a novelty for the Romanian NLP community and consists of all tweets collected from 2008 to 2022. Experiments show that RoBERTweet models outperform the previous general-domain Romanian and multilingual language models on three NLP tasks with tweet inputs: emotion detection, sexist language identification, and named entity recognition. We make our models and the newly created corpus of Romanian tweets freely available., Comment: Accepted at NLDB2023
Published: 2023

21. Romanian Multiword Expression Detection Using Multilingual Adversarial Training and Lateral Inhibition

Author: Avram, Andrei-Marius, Mititelu, Verginica Barbu, and Cercel, Dumitru-Clementin
Subjects: Computer Science - Computation and Language
Abstract: Multiword expressions are a key ingredient for developing large-scale and linguistically sound natural language processing technology. This paper describes our improvements in automatically identifying Romanian multiword expressions on the corpus released for the PARSEME v1.2 shared task. Our approach assumes a multilingual perspective based on the recently introduced lateral inhibition layer and adversarial training to boost the performance of the employed multilingual language models. With the help of these two methods, we improve the F1-score of XLM-RoBERTa by approximately 2.7% on unseen multiword expressions, the main task of the PARSEME 1.2 edition. In addition, our results can be considered SOTA performance, as they outperform the previous results on Romanian obtained by the participants in this competition., Comment: Accepted at the 19th Workshop on Multiword Expressions (MWE 2023). The arxiv version contains some minor corrections
Published: 2023

22. TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)

Author: Smădu, Răzvan-Alexandru, Zaharia, George-Eduard, Avram, Andrei-Marius, Cercel, Dumitru-Clementin, Dascalu, Mihai, and Pop, Florin
Subjects: Computer Science - Computation and Language
Abstract: Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score., Comment: Accepted by AAAI 2023 Student Abstract
Published: 2022

23. 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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24. UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification

Author: Paraschiv, Andrei, Dascalu, Mihai, and Cercel, Dumitru-Clementin
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub, Comment: Semeval 2022, Task 5 submission 8 pages, 3 figures
Published: 2022

25. Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Author: Zaharia, George-Eduard, Smădu, Răzvan-Alexandru, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples. In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations. This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets. Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction. Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset. At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset. In addition, our model yields state-of-the-art results in terms of Mean Absolute Error., Comment: 9 pages, 1 figure, Accepted at ACL 2022 main conference (Long Paper)
Published: 2022

26. A multi-country analysis of COVID-19 hospitalizations by vaccination status

Author: Abdukahil, Sheryl Ann, Jabal, Kamal Abu, Salah, Nashat Abu, Airlangga, Eka, Hssain, Ali Ait, Akwani, Chika, Al Qasim, Eman, Alberti, Angela, Aldabbourosama, Osama, Alessi, Marta, Alex, Beatrice, Al-Fares, Abdulrahman, Aliudin, Jeffrey, Alkahlout, Mohammed, Almasri, Lana, Al-Saba’a, Yousef, Alves, Rita, Cabrita, Joana Alves, Amaral, Maria, Ampaw, Phoebe, Anchan, Aditya John, Angheben, Andrea, Arabi, Yaseen, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arora, Rakesh, Ashley, Elizabeth A., Atique, Anika, Atlowly, Moad, Bach, Benjamin, Baillie, John Kenneth, Baird, J. Kevin, Balan, Valeria, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S., Barnikel, Michaela, Beer, Netta, Begum, Husna, Bellemare, David, Beltrame, Anna, Bertoli, Giulia, Bianco, Claudia, Humaid, Felwa Bin, Bitton, Jonathan, Blier, Catherine, Bogaert, Debby, Borges, Diogo, Bouhmani, Dounia, Bounphiengsy, Thipsavanh, Bountthasavong, Latsaniphone, Klerk, Bianca Boxma-de, Monteiro, Filipa Brás, Brazzi, Luca, Buchtele, Nina, Buonsenso, Danilo, Burrell, Aidan, Bustos, Ingrid G., Cabrita, Joana, Caceres, Eder, Garcês, Rui Caetano, Campisi, Josie, Canepa, Cecilia, Caoili, Janice, Cardellino, Chiara Simona, Cardoso, Filipa, Cardoso, Filipe, Cardoso, Sofia, Carney, Gayle, Carrier, François Martin, Carson, Gail, Cascão, Mariana, Casimiro, José, Castañeda, Silvia, Castanheira, Nidyanara, Cattaneo, Paolo, Cavalin, Roberta, Cavayas, Alexandros, Cevik, Muge, Chaleunphon, Bounthavy, Chan, Adrienne, Chand, Meera, Chen, Anjellica, Cheng, Matthew Pellan, Chommanam, Danoy, Chow, Yock Ping, Christy, Nathaniel, Claure-Del Granado, Rolando, Clohisey, Sara, Codan, Cassidy, Connor, Marie, Cooke, Graham S., Copland, Mary, Corley, Amanda, Cortegiani, Andrea, Crowl, Gloria, Cruz, Claudina, Csete, Marc, Custodio, Paula, da Silva Filipe, Ana, Dagens, Andrew, Daley, Peter, Dalloul, Zaina, Dalton, Heidi, Dalton, Jo, Damas, Juliana, Daneman, Nick, Dankwa, Emmanuelle A., Dantas, Jorge, D'Aragon, Frédérick, De Rose, Cristina, de Silva, Thushan, Dechert, William, Denis, Emmanuelle, Dishon, Yael, k Dhangar, Docherty, Annemarie B., Donnelly, Christl A., Donohue, Chloe, Douangdala, Phouvieng, Douglas, James Joshua, Downer, Triona, Downing, Mark, Drake, Thomas, Dryden, Murray, Dubot-Pérès, Audrey, Dudman, Susanne, Dunning, Jake, Duplaix, Mathilde, Durham, Lucian, III, Dyrhol-Riise, Anne Margarita, Edelstein, Michael, Escher, Martina, Esperatti, Mariano, Santo, Catarina Espírito, Estevão, João, Faheem, Amna, Fairfield, Cameron J., Faria, Pedro, Farshait, Nataly, Fernandes, Jorge, Fernandes, Marília Andreia, Ferrão, Joana, Ferraz, Mário, Ferreira, Bernardo, Figueiredo-Mello, Claudia, Fletcher, Tom, Flynn, Brigid, Fontela, Patricia, Forsyth, Simon, Foti, Giuseppe, Fowler, Robert A., Franch-Llasat, Diego, Fraser, Christophe, Fraser, John F., Ribeiro, Ana Freitas, Friedrich, Caren, Fuentes, Nora, G, Argin, Skeie, Linda Gail, Gamble, Carrol, Garcês, Rui, Garcia-Diaz, Julia, Garcia-Gallo, Esteban, Gavin, Aisling, Germano, Nuno, Ghadimi, Moji, Ghisulal, Praveen Kumar, Giani, Marco, Gibson, Jess, Girvan, Michelle, Goco, Geraldine, Gómez-Junyent, Joan, Grable, Margarite, Green, Christopher A., Greenhalf, William, Griffiths, Fiona, Gruner, Heidi, Gu, Yusing, Guerguerian, Anne-Marie, Guerreiro, Daniela, Haber, Daniel, Habraken, Hannah, Hafez, Wael, Hall, Matthew, Halpin, Sophie, Hamdan, Shaher, Hamers, Raph L., Hamza, Summer, Hardwick, Hayley, Harrison, Janet, Hartman, Alan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Hesstvedt, Liv, Higgins, Dawn, Higgins, Rupert, Hinton, Samuel, Ho, Antonia, Holter, Jan Cato, Horcajada, Juan Pablo, Ming-Yang Hsu, Jimmy, Hurd, Abby, Ijaz, Samreen, Illana, Carlos Cañada, Inácio, Hugo, Ippolito, Mariachiara, Isidoro, Tiago, Jaber, Hamza, Jackson, Clare, Jaworsky, Denise, Jenum, Synne, Jouvet, Philippe, Kalicinska, Alina, Kandel, Chris, Katz, Kevin, Kaur, Aasmine, Keating, Seán, Kelly, Andrea, Kelly, Sadie, Kennon, Kalynn, Keomany, Sommay, Khalid, Imrana, Kho, Michelle E., Khoo, Saye, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Klenerman, Paul, Bekken, Gry Kloumann, Knight, Stephen R., Korten, Volkan, Kosgei, Caroline, Kumar, Deepali, Kutsogiannis, Demetrios, Lamontagne, François, Lanza, Marina, Law, Andrew, Law, Andy, Lawrence, Teresa, Lee, James, Lee, Jennifer, Lee, Todd C., Leeming, Gary, Letizia, Andrew, Bassi, Gianluigi Li, Liang, Janet, Lim, Wei Shen, Lind, Andreas, Lissauer, Samantha, Lopes, Diogo, Lyons, Ruth, Machado, Sara, Majeed, Nimisha Abdul, Manetta, Frank, Sant`Ana Malaque, Ceila Maria, Marquis, Catherine, Marsh, Laura, Marshall, John, Martín-Quiros, Alejandro, Martins, Ana, Rego, Caroline Martins, Martucci, Gennaro, Maslove, David, Matthew, Christina, Mayxay, Mayfong, McArthur, Colin, McCarthy, Anne, McConnochie, Rachael, McDonald, Sarah E., McGeer, Allison, McKay, Chris, McLean, Kenneth A., Menon, Kusum, Mentzer, Alexander J., Mesquita, António, Meyer, Dan, Meynert, Alison M., Mihelis, Efstathia, Monteiro, Agostinho, Montrucchio, Giorgia, Moore, Sarah, Moore, Shona C., Cely, Lina Morales, Moro, Lucia, Morton, Ben, Mudara, Caroline, Muhaisen, Mo’nes, Müller, Fredrik, Müller, Karl Erik, Munshi, Laveena, Murthy, Srinivas, Mustafa, Dana, Nagpal, Dave, Narasimhan, Mangala, Nasa, Prashant, Nelder, Matthew, Neumann, Emily, Ng, Pauline Yeung, Nichol, Alistair D., Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nusantara, Dwi Utomo, Occhipinti, Giovanna, O'Hearn, Katie, Oliveira, Larissa, Ong, David S.Y., Oosthuyzen, Wilna, Openshaw, Peter, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Parke, Rachael, Patricio, Patricia, Patterson, Lisa, Paul, Mical, Paulos, Jorge, Paxton, William A., Pejkovska, Mare, Pereira, Rui, Petrovic, Michele, Pettersen, Frank Olav, Pharand, Scott, Phonemixay, Ooyanong, Phoutthavong, Soulichanya, de Piero, Maria, Pimentel, Carlos, Pires, Catarina, Pitaloka, Ayodhia, Pius, Riinu, Poli, Sergio, Pollakis, Georgios, Post, Andra-Maris, Póvoas, Diana, Powis, Jeff, Praphasiri, Viladeth, Pritchard, Mark G., Semedi, Bambang Pujo, Purcell, Gregory, Quesada, Luisa, Quist-Paulsen, Else, Rafael, Aldo, Rahardjani, Mutia, Ramalho, José, Rangappa, Rajavardhan, Rätsep, Indrek, Reeve, Brenda, Reikvam, Dag Henrik, Ren, Hongru, Rewa, Oleksa, Ricchiuto, Antonia, Rishu, Asgar, Rivera Nuñez, Maria Angelica, Roberts, Stephanie, Robertson, David L., Roche-Campo, Ferran, Rodari, Paola, Roessler, Bernhard, Rossanese, Andrea, Rossetti, Matteo, Russell, Clark D., Holten, Aleksander Rygh, Saba, Isabela, Sadat, Musharaf, Sahraei, Valla, Salazar, Leonardo, Sales, Gabriele, Sanchez, Emely, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Sarmiento, Iam Claire E., Saviciute, Egle, Schaffer, Justin, Schwameis, Michael, Schwartz, Gary, Scott, Janet T., Scott-Brown, James, Semple, Malcolm G., Sequeira, Tânia, Shadowitz, Ellen, Shankar, Anuraj, Shaw, Catherine A., Shaw, Victoria, Mohan Shetty, Dr. Rajesh, Sibounheuang, Bountoy, Sigfrid, Louise, Sillaots, Piret, Sin, Wai Ching, Sinatti, Dario, Singh, Mahendra, Skogen, Vegard, Smith, Sue, Solomon, Joshua, Solomon, Tom, Song, Rima, Spinuzza, Elisabetta, Sriskandan, Shiranee, Staudinger, Thomas, Stecher, Stephanie-Susanne, Steinsvik, Trude, Stiksrud, Birgitte, Streinu-Cercel, Adrian, Streinu-Cercel, Anca, Stuart, David, Subekti, Decy, Suen, Jacky Y., Sultana, Asfia, Summers, Charlotte, Suwarti, Atie, Sztajnbok, Jaques, Tabrizi, Shirin, Taleb, Sara, Tedder, Richard S., Teixeira, João, Tessier-Grenier, Hubert, Thompson, Shaun, Thomson, Emma C., Thorpe, Mathew, Thwaites, Ryan S., Tonby, Kristian, Torre, Marta, Torres Santos-Olmo, Rosario Maria, Turgeon, Alexis F., Turtle, Lance C.W., Tveita, Anders, Twardowski, Pawel, Ullrich, Roman, Uyeki, Timothy M., Valentini, Piero, Val-Flores, Luís, Varrone, Michael, Vidal, José Ernesto, Vieira, César, Villanueva, Joy Ann, Villar, Judit, Villoldo, Andrea, Vitiello, Chiara, Vongsouvath, Manivanh, Wainstein, Marina, Webb, Steve, Wei, Jia, Wesselius, Sanne, Wham, Murray, White, Nicole, Willems, Sue, Williams, Bailey, Williams, Virginie, Wils, Evert-Jan, Wittman, Jessica, Yerkovich, Stephanie, Yiaye, Touxiong, Zahran, Maram, Zambon, Maria, Gonçalves, Bronner P., Jassat, Waasila, Baruch, Joaquín, Hashmi, Madiha, Rojek, Amanda, Dasgupta, Abhishek, Martin-Loeches, Ignacio, Reyes, Luis Felipe, Piubelli, Chiara, Citarella, Barbara Wanjiru, Kartsonaki, Christiana, Lefèvre, Benjamin, López Revilla, José W., Lunn, Miles, Harrison, Ewen M., Kraemer, Moritz U.G., Shrapnel, Sally, Horby, Peter, Bisoffi, Zeno, Olliaro, Piero L., and Merson, Laura
Published: 2023
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27. Distilling the Knowledge of Romanian BERTs Using Multiple Teachers

Author: Avram, Andrei-Marius, Catrina, Darius, Cercel, Dumitru-Clementin, Dascălu, Mihai, Rebedea, Traian, Păiş, Vasile, and Tufiş, Dan
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro. The first two models resulted from the individual distillation of knowledge from two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity, and dialect identification. Our experimental results argue that the three distilled models offer performance comparable to their teachers, while being twice as fast on a GPU and ~35% smaller. In addition, we further test the similarity between the predictions of our students versus their teachers by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work., Comment: 10 pages, accepted to LREC2022 in the main conference
Published: 2021

28. 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. 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Rizo-Topete, Lilia, Pole, Jason D., and Johnson, David W.
Published: 2023
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29. Thrombotic and hemorrhagic complications of COVID-19 in adults hospitalized in high-income countries compared with those in adults hospitalized in low- and middle-income countries in an international registry

Author: Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Abrous, Amal, Absil, Lara, Acker, Andrew, Adam, Elisabeth, Adrião, Diana, Al Ageel, Saleh, Ahmed, Shakeel, Ainscough, Kate, 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, de Brito, Carlos Alexandre Antunes, 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, Baig, Nadia, Baillie, J. Kenneth, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani-Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S., Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Beane, Abigail, Bedossa, Alexandra, Hong Bee, Ker, 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, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianco, Claudia, Bidin, Farah Nadiah, Singh, 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Petra, Buchtele, Nina, Buisson, Marielle, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G., Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Garcês, Rui Caetano, Calligy, Kate, Calvache, Jose Andres, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux-Paz, Pauline, Cárcel, Sheila, 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, Castañeda, Silvia, 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, Chin Tho, Leong, Chirouze, Catherine, Chiumello, Davide, Cho, Sung-Min, Cholley, Bernard, Chopin, Marie-Charlotte, Chow, Ting Soo, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Cisneros Herreros, José Miguel, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Jennifer, Clarke, Emma, Claure-Del Granado, Rolando, Clohisey, Sara, 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, Costigan, Emma, Couffignal, Camille, Couffin-Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, Cristella, Cosimo, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Cruz Bermúdez, Juan Luis, Cruz Rojo, Jaime, 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, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Damas, Juliana, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A., Dantas, Jorge, D'Aragon, Frédérick, de Jong, Menno, de Loughry, Gillian, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, de Pinho Oliveira, Ana Isabel, 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, Delfos, Nathalie M., Deligiannis, Ionna, Dell'Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Da Silva, Fernanda Dias, Diaz, Juan Jose, Diaz, Rodrigo, Diaz, Priscila, 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, Egan, Siobhan, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, 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, Isabelle, Fabre, Marc, 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, Ferreira, Isabel, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florence, Aline-Marie, Florio, Letizia Lucia, Flynn, Deirdre, Foley, Claire, Foley, Jean, 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, Fritz, Ricardo, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, 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, Gnall, Eric, Goehringer, François, Goepel, Siri, Goffard, Jean-Christophe, Goh, Jin Yi, Golob, Jonathan, Gómez-Junyent, Joan, Gominet, Marie, Gonzalez, Alicia, Gordon, Patricia, Gorenne, Isabelle, Gormley, Conor, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Green, Christopher A., Greene, Courtney, 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, de Castro, Maisa Guimarães, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hadri, Nadir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hamer, Ansley, Hamidfar, Rebecca, Hammond, Terese, Hammond, Naomi, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Ekow Harrison, Samuel Bernard, Hartman, Alan, Hashmi, Junaid, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez-Trujillo, Aquiles, Hentzien, Maxime, Hernandez, Diana, Hernandez-Montfort, Jaime, 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, Horcajada, Juan Pablo, Hoshino, Koji, 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, Infante Dominguez, Carmen, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Itai, Junji, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Janes, Victoria, Jaquet, Pierre, Jassat, Waasila, Jaud-Fischer, Coline, Jaureguiberry, Stéphane, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo-Sotomayor, Ruth, Joe, Ong Yiaw, Jorge García, Ruth N., Joseph, Cédric, Joseph, Mark, Jourdain, Mercé, Jouvet, Philippe, Jung, Anna, Jung, Hanna, 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, Keane, Hannah, 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, Kimmoun, Antoine, Kindgen-Milles, Detlef, King, Alexander, Kitamura, Nobuya, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R., Vasconcelos, Malte Kohns, Komatsu, Mamoru, Kosgei, Caroline, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Sudhir, Krishnan, Vinothini, Kruglova, Oksana, Kumar, Deepali, Kumar, Ganesh, 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, Lamontagne, François, 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, Mestre, Soizic Le, Le Moal, Gwenaël, Le Moing, Vincent, Le Nagard, Hervé, LeTurnier, Paul, Leal, Ema, Santos, Marta Leal, Lee, Todd C., Lee, Su Hwan, Lee, James, Lee, Jennifer, Lee, Heng Gee, Lee, Biing Horng, Lee, Yi Lin, Leeming, Gary, Lefebvre, Bénédicte, Lefebvre, Laurent, Lefèvre, Benjamin, LeGac, Sylvie, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Leone, Michela, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letrou, Sophie, Levy, Bruno, Levy, Yves, Levy-Marchal, Claire, Lewandowska, Katarzyna, L'Her, Erwan, Bassi, Gianluigi Li, Liegeon, Geoffrey, Lim, Wei Shen, Lim, Kah Chuan, Lima, Chantre, Lina, Bruno, Lina, Lim, Lind, Andreas, Lingas, Guillaume, Lion-Daolio, Sylvie, Lissauer, Samantha, 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, Lee Low, Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean-Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M., Lungu, Olguta, Luong, Liem, Luque, Nestor, Luton, Dominique, Lwin, Nilar, Lyons, Ruth, Maasikas, Olavi, Mabiala, Oryane, Machado, Moïse, Machado, Sara, Macheda, Gabriel, Madiha, Hashmi, de la Calle, Guillermo Maestro, Mahieu, Rafael, Mahy, Sophie, Maia, Ana Raquel, Maier, Lars S., Maillet, Mylène, Maitre, Thomas, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Maltez, Fernando, 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, Marquis, Catherine, 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, Martins, Nuno, 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, Maynar, Javier, 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., McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Mendes, Joao Joao, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Emmanuelle, Mercier, Noémie, 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, Miranda-Maldonado, Hugo, 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, Morton, Ben, 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, 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, Nazerali-Maitland, Alasdair, Neant, Nadège, Neb, Holger, 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, Noursadeghi, Mahdad, Nowinski, Adam, Nseir, Saad, Nunez, Jose I., Nyamankolly, Elsa, Brien, Fionnuala O., Callaghan, Annmarie O., O'Callaghan, Annmarie, Occhipinti, Giovanna, OConnor, Derbrenn, O'Donnell, Max, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak-Hyuk, O'Halloran, Sophie, O'Hearn, Katie, Ohshimo, Shinichiro, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L., 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, Palacios, Amanda, 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, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patterson, Lisa, Paul, Christelle, Paul, Mical, 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, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Piquard, Valentine, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, 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, Pritchard, Mark G., 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, 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, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, 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, Roger, Claire, Roilides, Emmanuel, 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, Olivier, Sanchez, Emely, Sanchez-Miralles, Angel, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre-François, Sandulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Sarmento Banheiro, Bruno, Sarmiento, Iam Claire E., Sarton, Benjamin, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Savvidou, Parthena, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Scicluna, Brendan, 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, Sharjeel, Shaikh, Sharma, Pratima, Shaw, Catherine A., Shaw, Victoria, Sheenan, John Robert, Shi, Haixia, Shime, Nobuaki, 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, Sonntagbauer, Michael, Soom, Azlan Mat, 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, Suen, Jacky Y., Suen, Gabriel, Sukumar, Prasanth, Sultana, Asfia, Summers, Charlotte, Supic, Dubravka, Suppiah, Deepashankari, Surovcová, Magdalena, 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, Tejada, Sofia, 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, Timsit, Jean-François, Tissot, Noémie, Toal, Fiona, Yang Toh, Jordan Zhien, Toki, Maria, Tonby, Kristian, Tonnii, Sia Loong, Torre, Marta, Torres, Margarida, Torres, Antoni, Torres-Zevallos, Hernando, Towers, Michael, Trapani, Tony, Trioux, Théo, 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, 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 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 Someren Gréve, Frank, van Veen, Ilonka, Van Willigen, Hugo, 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, Villar, Judit, Villeneuve, Pierre-Marc, Villoldo, Andrea, Visseaux, Benoit, Visser, Hannah, 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, Weil, Katharina, 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, Zacharowski, Kai, Zahid, Masliza, Zahran, Maram, Binti Zaidan, Nor Zaila, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Griffee, Matthew J., Bozza, Patricia T., Eddington, Devin P., Fanning, Jonathon P., Alexander, Peta M.A., and Fraser, John
Published: 2023
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30. UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection

Author: Paraschiv, Andrei, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: The real-world impact of polarization and toxicity in the online sphere marked the end of 2020 and the beginning of this year in a negative way. Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of a subset of the Jigsaw Unintended Bias dataset and is the first language toxicity detection task dedicated to identifying the toxicity-level spans. For this task, participants had to automatically detect character spans in short comments that render the message as toxic. Our model considers applying Virtual Adversarial Training in a semi-supervised setting during the fine-tuning process of several Transformer-based models (i.e., BERT and RoBERTa), in combination with Conditional Random Fields. Our approach leads to performance improvements and more robust models, enabling us to achieve an F1-score of 65.73% in the official submission and an F1-score of 66.13% after further tuning during post-evaluation.
Published: 2021

31. UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction

Author: Zaharia, George-Eduard, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: Reading is a complex process which requires proper understanding of texts in order to create coherent mental representations. However, comprehension problems may arise due to hard-to-understand sections, which can prove troublesome for readers, while accounting for their specific language skills. As such, steps towards simplifying these sections can be performed, by accurately identifying and evaluating difficult structures. In this paper, we describe our approach for the SemEval-2021 Task 1: Lexical Complexity Prediction competition that consists of a mixture of advanced NLP techniques, namely Transformer-based language models, pre-trained word embeddings, Graph Convolutional Networks, Capsule Networks, as well as a series of hand-crafted textual complexity features. Our models are applicable on both subtasks and achieve good performance results, with a MAE below 0.07 and a Person correlation of .73 for single word identification, as well as a MAE below 0.08 and a Person correlation of .79 for multiple word targets. Our results are just 5.46% and 6.5% lower than the top scores obtained in the competition on the first and the second subtasks, respectively., Comment: 8 pages, 1 figure, 4 tables
Published: 2021

32. UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense

Author: Smădu, Răzvan-Alexandru, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Detecting humor is a challenging task since words might share multiple valences and, depending on the context, the same words can be even used in offensive expressions. Neural network architectures based on Transformer obtain state-of-the-art results on several Natural Language Processing tasks, especially text classification. Adversarial learning, combined with other techniques such as multi-task learning, aids neural models learn the intrinsic properties of data. In this work, we describe our adversarial multi-task network, AMTL-Humor, used to detect and rate humor and offensive texts from Task 7 at SemEval-2021. Each branch from the model is focused on solving a related task, and consists of a BiLSTM layer followed by Capsule layers, on top of BERTweet used for generating contextualized embeddings. Our best model consists of an ensemble of all tested configurations, and achieves a 95.66% F1-score and 94.70% accuracy for Task 1a, while obtaining RMSE scores of 0.6200 and 0.5318 for Tasks 1b and 2, respectively., Comment: 7 pages, 2 figures, Accepted at SemEval-2021 Workshop, ACL-IJCNLP 2021
Published: 2021

33. UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering

Author: Avram, Andrei-Marius, Zaharia, George-Eduard, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models to extract meaningful information on measurements from scientific texts. The competition is composed of five subtasks that build on top of each other: (1) quantity span identification, (2) unit extraction from the identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and (5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers. We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multi-turn question answering manner. Our best performing model obtained an overlapping F1-score of 36.91% on the test set., Comment: 5 pages, 3 figures, SemEval-2021 Workshop, ACL-IJCNLP 2021
Published: 2021

34. Safety and efficacy of doravirine as first-line therapy in adults with HIV-1: week 192 results from the open-label extensions of the DRIVE-FORWARD and DRIVE-AHEAD phase 3 trials

Author: Martins, Marcelo D, Cahn, Pedro E, Lopardo, Gustavo D, Porteiro, Norma, Bloch, Mark Theo, Baker, David Alfred, Roth, Norman, Moore, Richard J, Finlayson, Robert James, McMahon, James, Rieger, Armin, Zoufaly, Alexander, Schmied, Brigitte, Hartl, Sylvia, Zangerle, Robert, Smaill, Fiona, Walmsley, Sharon L, Conway, Brian, Rachlis, Anita, Smith, Graham H R, Perez Cortes, Carlos, Afani, Alejandro, Campos Barker, Maria Isabel E, Chahin Anania, Carolina Eugenia, Reyes, Marcelo J. Wolff, Gerstoft, Jan, Weis, Nina, Laursen, Alex Lund, Molina, Jean-Michel, Yazdanpanah, Yazdan, Cotte, Laurent, Raffi, Francois, Slama, Laurence, Morlat, Philippe, Girard, Pierre-Marie, Katlama, Christine, Rockstroh, Juergen K, Arasteh, Keikawus, Esser, Stefan, Stoehr, Albrecht, Stellbrink, Hans-Juergen, Stoll, Matthias, Schuermann, Dirk, Faetkenheuer, Gerd, Bogner, Johannes Richard, Lutz, Thomas, Baumgarten, Axel, Jaeger, Hans, Wiese, Carmen, Gori, Andrea, Migliorino, Guglielmo Marco, Coltan, Gabriel, Constandis, Felicia, Erscoiu, Simona M, Prisacariu, Liviu-Jany, Rugina, Sorin, Streinu-Cercel, Adrian, Pokrovsky, Vadim V, Zakharova, Natalia, Shuldyakov, Andrey Anatolyevich, Ryamova, Elena Pavlovna, Kulagin, Valeriy Viktorovich, Tsybakova, Olga Aleksandrovna, Orlova-Morozova, Elena, Nagimova, Firaya, Voronin, Evgeniy, Shimonova, Tatiana Evgenyevna, Kozyrev, Oleg Anatolyevich, Orrell, Catherine, Lombaard, Johannes Jurgens, Botes, Margaretha Elizabeth, de Jager, Marleen, Segorb, Joaquin Portilla, Gatell Artigas, Josep Maria, Mallolas Masferrer, Josep, Guillen, Santiago Moreno, Perez Elias, Maria Jesus, Arribas Lopez, Jose R, Puigmal, Eugenia Negredo, Podzamczer Palter, Daniel, Ortega, Frederico Pulido, Garcia, Jesus Troya, de los Santos Gil, Ignacio, Berenguer, Juan, Nelson, Mark Richard, Williams, Ian G, Johnson, Margaret A, Khoo, Saye, Schembri, Gabriel, Clarke, Amanda, Gompels, Mark, Fox, Julie Meriel, Lwanga, Julianne, Taylor, Steven John, Dockrell, David Harold, Kegg, Stephen, Hagins, Debbie P, Osiyemi, Olayemi O, Prelutsky, David James, Ramgopal, Moti N, Scarsella, Anthony J, Dretler, Robin, DeJesus, Edwin, Bettacchi, Christopher J, Sims III, James, Clay, Patrick G, Bellos, Nicholaos C, Thompson, Melanie A, Montero, Jose, McDonald, Cheryl K, Creticos, Catherine, Shamblaw, David, Terrelonge, Antonio E, Valdes, Martin, Tashima, Karen T, Robbins, William J, Elion, Richard A, Goldstein, Deborah, Slim, Jihad, Lalezari, Jacob Paul, Pushkin, Richard, Lalla-Reddy, Sujata N, Win, Sanda S, Ruane, Peter Jerome, Mills, Anthony Martin, Cade, Jerry L, Campo, Rafael, Dietz, Craig A, Hoffman-Terry, Margaret, Blick, Gary, Rubin, David Scott, Mayer, Cynthia, Rondon, Juan Carlos, Cook, Paul P, Daar, Eric, Kumar, Princy N, Swindells, Susan, Castro, Jose Guillermo, Morales-Ramirez, Javier O, Santiago, Lizette, Santana-Bagur, Jorge L, Vandekerckhove, Linos, Florence, Eric, De Wit, Stephane, Derdelinckx, Inge, Vandercam, Bernard, Belkhir, Leila, De Wet, Joseph, Lebouche, Bertrand, Trottier, Benoit, Longpre, Daniele, Szabo, Jason, LeBlanc, Roger P, Jensen, Werner, Gonzalez, Alvaro Rojas, Beltran, Carlos, Sussmann, Otto Alberto, Velez, Juan Diego, Onate, Jose Millan, Nielsen, Henrik, Degen, Olaf, Stephan, Christoph, Arathoon, Eduardo, Lopez, Rudy Manuel, Rojas Alvarado, Evelyn Michelle, Gonzalez Patzan, Luis Demetrio, Meija, Carlos R, Pinzon, Rodolfo, Parchment, Charles, Sthoeger, Zev, Chowers, Michal, Riesenberg, Klaris, Shahar, Eduardo, Levy, Itzchak, Quintero Perez, Nora Patricia, Andrade-Villanueva, Jaime Federico, Crabtree Ramirez, Brenda Eloisa, Rijnders, Bart, den Hollander, Jan G, Handy, Rupert, Morales, Nilo Bonifacio, Hidalgo, Jose Alfredo, Infante, Rosa Mercedes, Matos Prado, Eduardo Demetrio, Campos, Pablo E, Ticona Chaves, Eduardo Romulo, Pinedo, Yvett, Pacheco, Patricia, Maltez, Fernando Manuel, Cunha, Jose, Neves, Isabel, Serrao, Rosario, Melendez-Rivera, Ivan, Mendoza-Rodriguez, Rafael O, Maldonado-Rivera, Sandra, Ortiz-Lasanta, Grisell, Kizhlo, Svetlana, Freud, Hernando Knobel, Moreno, Jose Sanz, Mendez, Francisco Vera, Mohapi, Lerato, Mitha, Essack Aziz, Mahomed, Akbar Anvar, Fouche, Leon Frederik, Kaplan, Richard, Siddique, Naeem, Hoosen, Farzana, Rassool, Mohammed Siddique, Baraldi, Ezio, Calmy, Alexandra, Cavassini, Matthias, Fehr, Jan, Tsai, Hung-Chin, Lin, Hsi-Hsun, Huang, Chun-Kai, Ko, Wen-Chien, Lin, Yu-Hui, Chen, Su-Jung, Hung, Chien-Ching, Avihingsanon, Anchalee, Kiertiburanakul, Sasisopin, Ratanasuwan, Winai, Supparatpinyo, Khuanchai, Chetchotisakd, Ploenchan, Changpradub, Dhitiwat, Orkin, Chloe, Fox, Ashini, Winston, Alan, Ustianowski, Andrew, Yangco, Bienvenido G, Asmuth, David Michael, Vigil, Karen J, Berger, Daniel S, Bhatti, Laveeza, Campbell, Thomas, Casey, Kathleen K, Liu, Edward, Crofoot, Gordon E, Cunningham, Douglas, Feinberg, Judith, Fichtenbaum, Carl, Balamban Felizarta, Franco Antonio, Jefferson, Thomas T, Johnson, Marc Alexander, Lewis, Stanley T, Luque, Amneris E, Novak, Richard M, Sloan, Louis, Sweet, Donna E, Towner, William J, Zane, Ryan, Riedel, David J, Loftus, Richard Anton, Shon, Alyssa So Young, Mogyoros, Miguel, Tebas, Pablo, Scott, Mia Louise, Parenti, David M, Inciarte Portillo, Alexy, Cahn, Pedro, Lombaard, Johannes, Kumar, Sushma, Campbell, Havilland, Wan, Hong, Teal, Valerie, Jin Xu, Zhi, Asante-Appiah, Ernest, Sklar, Peter, Teppler, Hedy, and Lahoulou, Rima
Published: 2024
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35. UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models

Author: Tanase, Mircea-Adrian, Cercel, Dumitru-Clementin, and Chiru, Costin-Gabriel
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Offensive language detection is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This paper describes our Transformer-based solutions for identifying offensive language on Twitter in five languages (i.e., English, Arabic, Danish, Greek, and Turkish), which was employed in Subtask A of the Offenseval 2020 shared task. Several neural architectures (i.e., BERT, mBERT, Roberta, XLM-Roberta, and ALBERT), pre-trained using both single-language and multilingual corpora, were fine-tuned and compared using multiple combinations of datasets. Finally, the highest-scoring models were used for our submissions in the competition, which ranked our team 21st of 85, 28th of 53, 19th of 39, 16th of 37, and 10th of 46 for English, Arabic, Danish, Greek, and Turkish, respectively.
Published: 2020

36. Cross-Lingual Transfer Learning for Complex Word Identification

Author: Zaharia, George-Eduard, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words, or groups of words, in texts from different areas of expertise. The purpose of CWI is to highlight problematic structures that non-native speakers would usually find difficult to understand. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e., Transformers). Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment by relying on the CWI shared task 2018 dataset available for four different languages (i.e., English, German, Spanish, and also French). Our approach surpasses state-of-the-art cross-lingual results in terms of macro F1-score on English (0.774), German (0.782), and Spanish (0.734) languages, for the zero-shot learning scenario. At the same time, our model also outperforms the state-of-the-art monolingual result for German (0.795 macro F1-score)., Comment: accepted at ICTAI 2020, 7 pages, 5 tables
Published: 2020

37. UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

Author: Avram, Andrei-Marius, Cercel, Dumitru-Clementin, and Chiru, Costin-Gabriel
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract: This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional,(2) labeling of definitional sentences, and (3) relation classification. We use various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the competition. Specifically, for each language model variant, we experiment by both freezing its weights and fine-tuning them. We also explore a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks. Our best performing model evaluated on the DeftEval dataset obtains the 32nd place for the first subtask and the 37th place for the second subtask. The code is available for further research at: https://github.com/avramandrei/DeftEval.
Published: 2020

38. UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT

Author: Paraschiv, Andrei, Cercel, Dumitru-Clementin, and Dascalu, Mihai
Subjects: Computer Science - Computation and Language
Abstract: Manipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. This paper describes our participation in the SemEval-2020, Task 11: Detection of Propaganda Techniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060% in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302% for subtask TC, ranking 19th from 32 teams.
Published: 2020

39. UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning

Author: Zaharia, George-Eduard, Vlad, George-Alexandru, Cercel, Dumitru-Clementin, Rebedea, Traian, and Chiru, Costin-Gabriel
Subjects: Computer Science - Computation and Language
Abstract: Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16th out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa., Comment: Accepted at SemEval-2020, 9 pages, 4 tables
Published: 2020

40. UPB at SemEval-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis

Author: Vlad, George-Alexandru, Zaharia, George-Eduard, Cercel, Dumitru-Clementin, Chiru, Costin-Gabriel, and Trausan-Matu, Stefan
Subjects: Computer Science - Computation and Language
Abstract: Users from the online environment can create different ways of expressing their thoughts, opinions, or conception of amusement. Internet memes were created specifically for these situations. Their main purpose is to transmit ideas by using combinations of images and texts such that they will create a certain state for the receptor, depending on the message the meme has to send. These posts can be related to various situations or events, thus adding a funny side to any circumstance our world is situated in. In this paper, we describe the system developed by our team for SemEval-2020 Task 8: Memotion Analysis. More specifically, we introduce a novel system to analyze these posts, a multimodal multi-task learning architecture that combines ALBERT for text encoding with VGG-16 for image representation. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the three subtasks of the current competition, ranking 11th for Subtask A (0.3453 macro F1-score), 1st for Subtask B (0.5183 macro F1-score), and 3rd for Subtask C (0.3171 macro F1-score) while exceeding the official baseline results by high margins., Comment: Accepted at SemEval-2020, 7 pages, 1 figure, 2 tables
Published: 2020

41. Lenin, Vladimir Ilyich: On Justice

Author: Cercel, Cosmin, Tasioulas, John, Section editor, Albrecht, Kristin, Section editor, Sellers, Mortimer, editor, and Kirste, Stephan, editor
Published: 2023
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42. Clinical Ageing

Author: Surugiu, Roxana, Burdusel, Daiana, Ruscu, Mihai-Andrei, Cercel, Andreea, Hermann, Dirk M., Cadenas, Israel Fernandez, Popa-Wagner, Aurel, Harris, J. Robin, Series Editor, Kundu, Tapas K., Advisory Editor, Korolchuk, Viktor, Advisory Editor, Bolanos-Garcia, Victor, Advisory Editor, Marles-Wright, Jon, Advisory Editor, and Korolchuk, Viktor I., editor
Published: 2023
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43. Influence of the Direction of Lateral Load on Fiber-Reinforced Elastomeric Isolators

Author: Sinjari, S., Stratton, N., Cercel, J., Van Engelen, N., 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, Walbridge, Scott, editor, Nik-Bakht, Mazdak, editor, Ng, Kelvin Tsun Wai, editor, Shome, Manas, editor, Alam, M. Shahria, editor, El Damatty, Ashraf, editor, and Lovegrove, Gordon, editor
Published: 2023
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44. Safety, Virologic Efficacy, and Pharmacokinetics of CT-P59, a Neutralizing Monoclonal Antibody Against SARS-CoV-2 Spike Receptor-Binding Protein: Two Randomized, Placebo-Controlled, Phase I Studies in Healthy Individuals and Patients With Mild SARS-CoV-2 Infection

Author: Kim, Jin Yong, Jang, Young Rock, Hong, Jang Hee, Jung, Jin Gyu, Park, Jae-Hyeong, Streinu-Cercel, Adrian, Streinu-Cercel, Anca, Săndulescu, Oana, Lee, Sang Joon, Kim, Sung Hyun, Jung, Na Hyun, Lee, Seul Gi, Park, Jeong Eun, Kim, Min Kyung, Jeon, Da Bee, Lee, Yeo Jin, Kim, Bum Soo, Lee, Yeon Mi, and Kim, Yeon-Sook
Published: 2021
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45. Memory Cultures of War in European War Museums

Author: Berger, Stefan, Cento Bull, Anna, Cercel, Cristian, Clarke, David, Parish, Nina, Rowley, Eleanor, Wóycicka, Zofia, Hoskins, Andrew, Series Editor, Sutton, John, Series Editor, Berger, Stefan, editor, and Kansteiner, Wulf, editor
Published: 2021
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46. 'Krieg. Macht. Sinn.' An Agonistic Exhibition at the Ruhr Museum Essen

Author: Angeli, Daniela De, Kansteiner, Wulf, Cercel, Cristian, O’Neill, Eamonn, Hoskins, Andrew, Series Editor, Sutton, John, Series Editor, Berger, Stefan, editor, and Kansteiner, Wulf, editor
Published: 2021
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47. Performance of eco-friendly mortars made with alkali-activated slag and glass powder as a binder

Author: Cercel, Jonathan, Adesina, Adeyemi, and Das, Sreekanta
Published: 2021
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48. Anti-Biofilm Activity of Viruses, Bacteria, Fungi, and Lichens: Mechanisms and Impact on Clinical Practice

Author: Săndulescu, Oana, Streinu-Cercel, Anca, Săndulescu, Mihai, Streinu-Cercel, Adrian, Duscher, Dominik, editor, and Shiffman, Melvin A., editor
Published: 2019
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49. The influenza season 2016/17 in Bucharest, Romania – surveillance data and clinical characteristics of patients with influenza-like illness admitted to a tertiary infectious diseases hospital

Author: Drăgănescu, Anca, Săndulescu, Oana, Florea, Dragoş, Vlaicu, Ovidiu, Streinu-Cercel, Anca, Oţelea, Dan, Aramă, Victoria, Luminos, Monica Luminiţa, Streinu-Cercel, Adrian, Niţescu, Maria, Ivanciuc, Alina, Bacruban, Rodica, and Piţigoi, Daniela
Published: 2018
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50. JNJ-56136379, an HBV Capsid Assembly Modulator, Is Well-Tolerated and Has Antiviral Activity in a Phase 1 Study of Patients With Chronic Infection

Author: Zoulim, Fabien, Lenz, Oliver, Vandenbossche, Joris J., Talloen, Willem, Verbinnen, Thierry, Moscalu, Iurie, Streinu-Cercel, Adrian, Bourgeois, Stefan, Buti, Maria, Crespo, Javier, Manuel Pascasio, Juan, Sarrazin, Christoph, Vanwolleghem, Thomas, Shukla, Umesh, Fry, John, and Yogaratnam, Jeysen Z.
Published: 2020
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