Search

Your search keyword '"Collins, Michael A."' showing total 9,653 results
Start Over Author "Collins, Michael A."
9,653 results on '"Collins, Michael A."'

1. The Lowell Observatory Solar Telescope: A fiber feed into the EXtreme PREcision Spectrometer

Author

Llama, Joe, Zhao, Lily L., Brewer, John M., Szymkowiak, Andrew, Fischer, Debra A., Collins, Michael, Tiegs, Jake, and Cornelius, Frank

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

The signal induced by a temperate, terrestrial planet orbiting a Sun-like star is an order of magnitude smaller than the host stars' intrinsic variability. Understanding stellar activity is, therefore, a fundamental obstacle in confirming the smallest exoplanets. We present the Lowell Observatory Solar Telescope (LOST), a solar feed for the EXtreme PREcision Spectrometer (EXPRES) at the 4.3-m Lowell Discovery Telescope (LDT). EXPRES is one of the newest high-resolution spectrographs that accurately measure extreme radial velocity. With LOST/EXPRES, we observe disk-integrated sunlight autonomously throughout the day. In clear conditions, we achieve a ~137,500 optical spectrum of the Sun with a signal-to-noise of 500 in ~150s. Data is reduced using the standard EXPRES pipeline with minimal modification to ensure the data are comparable to the observations of other stars with the LDT. During the first three years of operation, we find a daily RMS of 71 cm/s. Additionally, having two EPRV spectrometers located in Arizona gives us an unprecedented opportunity to benchmark the performance of these planet-finders. We find a RMS of just 55 cm/s when comparing data taken simultaneously with EXPRES and NEID., Comment: SPIE Astronomical Telescopes & Instrumentation proceedings paper

Published
2024

2. Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation

Author

Bohnet, Bernd, Swersky, Kevin, Liu, Rosanne, Awasthi, Pranjal, Nova, Azade, Snaider, Javier, Sedghi, Hanie, Parisi, Aaron T, Collins, Michael, Lazaridou, Angeliki, Firat, Orhan, and Fiedel, Noah

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datasets relied on crowd-sourcing, but the emergence of transformers with a context size of 1 million or more tokens now enables entirely automatic approaches. Our objective is to test the capabilities of LLMs to analyze, understand, and reason over problems that require a detailed comprehension of long spans of text, such as questions involving character arcs, broader themes, or the consequences of early actions later in the story. We propose a holistic pipeline for automatic data generation including question generation, answering, and model scoring using an ``Evaluator''. We find that a relative approach, comparing answers between models in a pairwise fashion and ranking with a Bradley-Terry model, provides a more consistent and differentiating scoring mechanism than an absolute scorer that rates answers individually. We also show that LLMs from different model families produce moderate agreement in their ratings. We ground our approach using the manually curated NarrativeQA dataset, where our evaluator shows excellent agreement with human judgement and even finds errors in the dataset. Using our automatic evaluation approach, we show that using an entire book as context produces superior reading comprehension performance compared to baseline no-context (parametric knowledge only) and retrieval-based approaches.

Published
2024

3. Superflows: A New Tool for Forensic Network Flow Analysis

Author

Collins, Michael, Deshmukh, Jyotirmoy V., Dinesh, Dristi, Raghothaman, Mukund, Ravi, Srivatsan, and Xia, Yuan

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Network security analysts gather data from diverse sources, from high-level summaries of network flow and traffic volumes to low-level details such as service logs from servers and the contents of individual packets. They validate and check this data against traffic patterns and historical indicators of compromise. Based on the results of this analysis, a decision is made to either automatically manage the traffic or report it to an analyst for further investigation. Unfortunately, due rapidly increasing traffic volumes, there are far more events to check than operational teams can handle for effective forensic analysis. However, just as packets are grouped into flows that share a commonality, we argue that a high-level construct for grouping network flows into a set a flows that share a hypothesis is needed to significantly improve the quality of operational network response by increasing Events Per Analysts Hour (EPAH). In this paper, we propose a formalism for describing a superflow construct, which we characterize as an aggregation of one or more flows based on an analyst-specific hypothesis about traffic behavior. We demonstrate simple superflow constructions and representations, and perform a case study to explain how the formalism can be used to reduce the volume of data for forensic analysis.

Published
2024

4. A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

Author

Jacovi, Alon, Bitton, Yonatan, Bohnet, Bernd, Herzig, Jonathan, Honovich, Or, Tseng, Michael, Collins, Michael, Aharoni, Roee, and Geva, Mor

Subjects
Computer Science - Computation and Language
Abstract

Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction. We introduce REVEAL: Reasoning Verification Evaluation, a dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question-answering settings. REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a variety of datasets and state-of-the-art language models. Evaluation on REVEAL shows that verifiers struggle at verifying reasoning chains - in particular, verifying logical correctness and detecting contradictions. Available at https://reveal-dataset.github.io/ ., Comment: Accepted to ACL 2024

Published
2024

6. Controlled Decoding from Language Models

Author

Mudgal, Sidharth, Lee, Jong, Ganapathy, Harish, Li, YaGuang, Wang, Tao, Huang, Yanping, Chen, Zhifeng, Cheng, Heng-Tze, Collins, Michael, Strohman, Trevor, Chen, Jilin, Beutel, Alex, and Beirami, Ahmad

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective and propose a modular solver for it, called controlled decoding (CD). CD exerts control through a separate prefix scorer module, which is trained to learn a value function for the reward. The prefix scorer is used at inference time to control the generation from a frozen base model, provably sampling from a solution to the RL objective. We empirically demonstrate that CD is effective as a control mechanism on popular benchmarks. We also show that prefix scorers for multiple rewards may be combined at inference time, effectively solving a multi-objective RL problem with no additional training. We show that the benefits of applying CD transfer to an unseen base model with no further tuning as well. Finally, we show that CD can be applied in a blockwise decoding fashion at inference-time, essentially bridging the gap between the popular best-of-K strategy and tokenwise control through reinforcement learning. This makes CD a promising approach for alignment of language models., Comment: ICML 2024

Published
2023

8. Lipid Nanoparticles Deliver mRNA to the Brain after an Intracerebral Injection

Author

Tuma, Jan, Chen, Yu-Ju, Collins, Michael G, Paul, Abhik, Li, Jie, Han, Hesong, Sharma, Rohit, Murthy, Niren, and Lee, Hye Young

Subjects
Medical Biotechnology, Biomedical and Clinical Sciences, Nanotechnology, Gene Therapy, Neurodegenerative, Biotechnology, Genetics, Brain Disorders, Bioengineering, Neurosciences, 5.2 Cellular and gene therapies, Neurological, Animals, Mice, RNA, Guide, CRISPR-Cas Systems, Brain, RNA, Messenger, Nervous System Diseases, Nanoparticles, RNA, Small Interfering, Medicinal and Biomolecular Chemistry, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Biochemistry & Molecular Biology, Biochemistry and cell biology, Medical biochemistry and metabolomics, Medicinal and biomolecular chemistry
Abstract

Neurological disorders are often debilitating conditions with no cure. The majority of current therapies are palliative rather than disease-modifying; therefore, new strategies for treating neurological disorders are greatly needed. mRNA-based therapeutics have great potential for treating such neurological disorders; however, challenges with delivery have limited their clinical potential. Lipid nanoparticles (LNPs) are a promising delivery vector for the brain, given their safer toxicity profile and higher efficacy. Despite this, very little is known about LNP-mediated delivery of mRNA into the brain. Here, we employ MC3-based LNPs and successfully deliver Cre mRNA and Cas9 mRNA/Ai9 sgRNA to the adult Ai9 mouse brain; greater than half of the entire striatum and hippocampus was found to be penetrated along the rostro-caudal axis by direct intracerebral injections of MC3 LNP mRNAs. MC3 LNP Cre mRNA successfully transfected cells in the striatum (∼52% efficiency) and hippocampus (∼49% efficiency). In addition, we demonstrate that MC3 LNP Cas9 mRNA/Ai9 sgRNA edited cells in the striatum (∼7% efficiency) and hippocampus (∼3% efficiency). Further analysis demonstrates that MC3 LNPs mediate mRNA delivery to multiple cell types including neurons, astrocytes, and microglia in the brain. Overall, LNP-based mRNA delivery is effective in brain tissue and shows great promise for treating complex neurological disorders.

Published
2023

14. Learning to Reject with a Fixed Predictor: Application to Decontextualization

Author

Mohri, Christopher, Andor, Daniel, Choi, Eunsol, and Collins, Michael

Subjects
Computer Science - Machine Learning
Abstract

We study the problem of classification with a reject option for a fixed predictor, applicable in natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We provide a complete theoretical analysis of the surrogate loss function with a strong $H$-consistency guarantee. For evaluation, we choose the decontextualization task, and provide a manually-labelled dataset of $2\mathord,000$ examples. Our algorithm significantly outperforms the baselines considered, with a $\sim\!\!25\%$ improvement in coverage when halving the error rate, which is only $\sim\!\! 3 \%$ away from the theoretical limit.

Published
2023

16. Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Author

Bohnet, Bernd, Tran, Vinh Q., Verga, Pat, Aharoni, Roee, Andor, Daniel, Soares, Livio Baldini, Ciaramita, Massimiliano, Eisenstein, Jacob, Ganchev, Kuzman, Herzig, Jonathan, Hui, Kai, Kwiatkowski, Tom, Ma, Ji, Ni, Jianmo, Saralegui, Lierni Sestorain, Schuster, Tal, Cohen, William W., Collins, Michael, Das, Dipanjan, Metzler, Donald, Petrov, Slav, and Webster, Kellie

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting. We formulate and study Attributed QA as a key first step in the development of attributed LLMs. We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures. We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development. Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).

Published
2022

17. Coreference Resolution through a seq2seq Transition-Based System

Author

Bohnet, Bernd, Alberti, Chris, and Collins, Michael

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underlying language model. We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83.3 F1-score for English (a 2.3 higher F1-score than previous work (Dobrovolskii, 2021)) using only CoNLL data for training, 68.5 F1-score for Arabic (+4.1 higher than previous work) and 74.3 F1-score for Chinese (+5.3). In addition we use the SemEval-2010 data sets for experiments in the zero-shot setting, a few-shot setting, and supervised setting using all available training data. We get substantially higher zero-shot F1-scores for 3 out of 4 languages than previous approaches and significantly exceed previous supervised state-of-the-art results for all five tested languages.

Published
2022

18. Towards Computationally Verifiable Semantic Grounding for Language Models

Author

Alberti, Chris, Ganchev, Kuzman, Collins, Michael, Gehrmann, Sebastian, and Chelba, Ciprian

Subjects
Computer Science - Computation and Language
Abstract

The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in an auto-encoder by feeding its output to a semantic parser whose output is in the same representation domain as the input message. Compared to a baseline that generates text using greedy search, we demonstrate two techniques that improve the fluency and semantic accuracy of the generated text: The first technique samples multiple candidate text sequences from which the semantic parser chooses. The second trains the language model while keeping the semantic parser frozen to improve the semantic accuracy of the auto-encoder. We carry out experiments on the English WebNLG 3.0 data set, using BLEU to measure the fluency of generated text and standard parsing metrics to measure semantic accuracy. We show that our proposed approaches significantly improve on the greedy search baseline. Human evaluation corroborates the results of the automatic evaluation experiments.

Published
2022

19. Query Refinement Prompts for Closed-Book Long-Form Question Answering

Author

Amplayo, Reinald Kim, Webster, Kellie, Collins, Michael, Das, Dipanjan, and Narayan, Shashi

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficult to evaluate. We resolve the difficulties to evaluate long-form output by doing both tasks at once -- to do question answering that requires long-form answers. Such questions tend to be multifaceted, i.e., they may have ambiguities and/or require information from multiple sources. To this end, we define query refinement prompts that encourage LLMs to explicitly express the multifacetedness in questions and generate long-form answers covering multiple facets of the question. Our experiments on two long-form question answering datasets, ASQA and AQuAMuSe, show that using our prompts allows us to outperform fully finetuned models in the closed book setting, as well as achieve results comparable to retrieve-then-generate open-book models.

Published
2022

21. Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization

Author

Effland, Thomas and Collins, Michael

Subjects
Computer Science - Computation and Language
Abstract

We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing., Comment: Accepted in TACL 2022, pre-MIT Press publication version

Published
2022

22. Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model

Author

Eisenstein, Jacob, Andor, Daniel, Bohnet, Bernd, Collins, Michael, and Mimno, David

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Explainable question answering systems should produce not only accurate answers but also rationales that justify their reasoning and allow humans to check their work. But what sorts of rationales are useful and how can we train systems to produce them? We propose a new style of rationale for open-book question answering, called \emph{markup-and-mask}, which combines aspects of extractive and free-text explanations. In the markup phase, the passage is augmented with free-text markup that enables each sentence to stand on its own outside the discourse context. In the masking phase, a sub-span of the marked-up passage is selected. To train a system to produce markup-and-mask rationales without annotations, we leverage in-context learning. Specifically, we generate silver annotated data by sending a series of prompts to a frozen pretrained language model, which acts as a teacher. We then fine-tune a smaller student model by training on the subset of rationales that led to correct answers. The student is "honest" in the sense that it is a pipeline: the rationale acts as a bottleneck between the passage and the answer, while the "untrusted" teacher operates under no such constraints. Thus, we offer a new way to build trustworthy pipeline systems from a combination of end-task annotations and frozen pretrained language models., Comment: added details about a human evaluation

Published
2022

23. Making sense of complexity : executive leaders and executive leadership in an English Multi-Academy Trust

Author

Collins, Michael

Abstract

The emergence of new, multi-school organisational forms in England has been accompanied by distinctive new roles for school leaders, extending beyond individual schools. In multi-school organisations with formalised relationships, the new roles are commonly characterised as executive leadership and an increasingly common organisational form is a Multi-Academy Trust (MAT). The new executive roles represent a significant professional challenge for individuals. This study sought to understand and explain the response of individual leaders in new executive roles to the complexity they experience. A single embedded case study design explored how executive leadership was enacted in a MAT in relation to a domain of leadership practice, educational improvement. Executive leadership was considered to extend beyond an individual school and embrace both the principal executive leader (EL) and others involved in leadership in a MAT. The conceptual framework underpinning the study draws on insights from complexity theories, seeing everyone as embedded in ongoing networks of interaction and communication. Leadership is conceptualised as plural and relational, arising from multiple loci, with contextually specific configurations of leadership relationships emerging from peoples' enactment of leadership practices, through processes of organisational sensemaking. The analysis combined qualitative data with analysis of socially constructed instrumental and expressive social networks. Findings show how the actions of the EL cannot be understood in isolation from the networks of relationships and flow of interaction and relating that constitute the organisation, and that leaders draw on familiar practices to bring about educational improvement in novel contexts which nevertheless challenge identities, roles and boundaries. Some distinctive practices associated with executive leadership are identified. The study contributes by proposing the idea of flows of enactment to describe the complexity experienced by leaders, unfolding shifting configurations of relations, emergent figurations of power, and plural sensemaking. Implications for MATs as organisations are also explored.

Published
2023

24. A Simple Proof that Major Index and Inversions are Equidistributed

Author

Collins, Michael J.

Subjects
Mathematics - Combinatorics, 05A05
Abstract

We present a short proof of MacMahon's classic result that the number of permutations with $k$ inversions equals the number whose major index (sum of positions at which descents occur) is $k$

Published
2022

27. Ensuring a More Equitable Future: Addressing Skills Gaps through Multiple, Nuanced Solutions

Author

Postsecondary Value Commission and Collins, Michael

Abstract

Debate over the "skills gap" and how to address it has consumed considerable space in discussions about U.S. higher education, workforce development, and economic strength. The conventional narrative describes a fundamental mismatch between the skills that employers say they need in their employees and the skills that jobseekers possess, most often in what are called STEM fields. The resulting problem is clear: employers can't get the talent they want, and individuals can't get the jobs they want. The implication about higher education is equally clear: U.S. colleges and universities are failing to prepare their students for the workforce. This paper briefly reviews the emergent understanding of skills gaps, examines what some in higher education are doing to address these problems, and then turns to what more can be done to provide equitable access to strategies that improve students' employability, shore up the talent pipeline, and ultimately increase the value of postsecondary credentials for all.

Published
2021

30. Combining Cognitive and Machine Learning Models to Mine CPR Training Histories for Personalized Predictions

Author

Sense, Florian, Krusmark, Michael, Fiechter, Joshua, Collins, Michael G., Sanderson, Lauren, Onia, Joshua, and Jastrzembski, Tiffany

Abstract

Cardiopulmonary resuscitation (CPR) is a foundational lifesaving skill for which medical personnel are expected to be proficient. Frequent refresher training is needed to prevent the involved skills from decaying. Regular low-dose, high-frequency training for staff at fixed intervals has proven successful at maintaining CPR competence but does not take into account inherent performance variability across learners. Tailoring refreshers to an individual's past performance would minimize personnel being trained too (in)frequently and would ensure faster knowledge acquisition for new learners. To maximize the benefits of individualized schedules, learning needs gleaned from past training history need to be identified. A recent field study conducted among nursing students showed that a cognitive model-based approach was able to successfully trace the knowledge acquisition and decay of learners and prescriptively devise personalized training regimes that outperformed fixed schedules with regards to both training efficiency and learners' performance. Here, we report a post-hoc analysis of the collected data to investigate whether an alternative modeling approach, blending cognitive modeling and machine learning, could have resulted in even higher quality predictions. Our results reveal modest improvements in predictive accuracy for ensemble models, in which machine learning models predict the prediction errors (i.e., residuals) of the standalone cognitive model. These promising findings reveal strong applied utility for future use in domains where sustained proficiency is a requirement. [For the full proceedings, see ED615472.]

Published
2021

31. A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation

Author

Narayan, Shashi, Simões, Gonçalo, Zhao, Yao, Maynez, Joshua, Das, Dipanjan, Collins, Michael, and Lapata, Mirella

Subjects
Computer Science - Computation and Language
Abstract

We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (Narayan et al, 2021) that are trained to first create a composition of the output and then generate by conditioning on it and the input. Our approach avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. Experiments on summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using existing and newly proposed automatic metrics together with human-based evaluation, demonstrate that Composition Sampling is currently the best available decoding strategy for generating diverse meaningful outputs., Comment: 21 pages, ACL 2022

Published
2022

32. High-Fidelity Ion State Detection Using Trap-Integrated Avalanche Photodiodes

Author

Reens, David, Collins, Michael, Ciampi, Joseph, Kharas, Dave, Aull, Brian F., Donlon, Kevan, Bruzewicz, Colin D., Felton, Bradley, Stuart, Jules, Niffenegger, Robert J., Rich, Philip, Braje, Danielle, Ryu, Kevin K., Chiaverini, John, and McConnell, Robert

Subjects
Quantum Physics, Physics - Atomic Physics
Abstract

Integrated technologies greatly enhance the prospects for practical quantum information processing and sensing devices based on trapped ions. High-speed and high-fidelity ion state readout is critical for any such application. Integrated detectors offer significant advantages for system portability and can also greatly facilitate parallel operations if a separate detector can be incorporated at each ion-trapping location. Here we demonstrate ion quantum state detection at room temperature utilizing single-photon avalanche diodes (SPADs) integrated directly into the substrate of silicon ion trapping chips. We detect the state of a trapped $^{88}\text{Sr}^{+}$ ion via fluorescence collection with the SPAD, achieving $99.92(1)\%$ average fidelity in 450 $\mu$s, opening the door to the application of integrated state detection to quantum computing and sensing utilizing arrays of trapped ions., Comment: 7 pages, 5 figures

Published
2022
Full Text
View/download PDF

38. Measuring Attribution in Natural Language Generation Models

Author

Rashkin, Hannah, Nikolaev, Vitaly, Lamm, Matthew, Aroyo, Lora, Collins, Michael, Das, Dipanjan, Petrov, Slav, Tomar, Gaurav Singh, Turc, Iulia, and Reitter, David

Subjects
Computer Science - Computation and Language
Abstract

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world. In this work, we present a new evaluation framework entitled Attributable to Identified Sources (AIS) for assessing the output of natural language generation models, when such output pertains to the external world. We first define AIS and introduce a two-stage annotation pipeline for allowing annotators to appropriately evaluate model output according to AIS guidelines. We empirically validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset) via human evaluation studies that suggest that AIS could serve as a common framework for measuring whether model-generated statements are supported by underlying sources. We release guidelines for the human evaluation studies.

Published
2021

41. Variation in blood microbial lipopolysaccharide (LPS) contributes to immune reconstitution in response to suppressive antiretroviral therapy in HIV

Author

Luo, Zhenwu, Health, Sonya L, Li, Min, Yang, Hyojik, Wu, Yongxia, Collins, Michael, Deeks, Steven G, Martin, Jeffrey N, Scott, Alison, and Jiang, Wei

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Immunology, Infectious Diseases, HIV/AIDS, Emerging Infectious Diseases, Sexually Transmitted Infections, 2.1 Biological and endogenous factors, Aetiology, Infection, Animals, Antiretroviral Therapy, Highly Active, CD4 Lymphocyte Count, CD4-Positive T-Lymphocytes, HIV Infections, Humans, Immune Reconstitution, Lipid A, Lipopolysaccharides, Mice, Mice, Inbred C57BL, HIV, Immune non-responders, Immune responders, Lipopolysaccharide, immune non-responders, immune responders, lipid A, Clinical Sciences, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundIn HIV infection, even under long-term antiretroviral therapy (ART), up to 20% of HIV-infected individuals fail to restore CD4+ T cell counts to the levels similar to those of healthy controls. The mechanisms of poor CD4+ T cell reconstitution on suppressive ART are not fully understood.MethodsHere, we tested the hypothesis that lipopolysaccharide (LPS) from bacteria enriched in the plasma from immune non-responders (INRs) contributes to blunted CD4+ T cell recovery on suppressive ART in HIV. We characterized plasma microbiome in HIV INRs (aviremic, CD4+ T cell counts < 350 cells/μl), immune responders (IRs, CD4+ T cell counts > 500 cells/μl), and healthy controls. Next, we analyzed the structure of the lipid A domain of three bacterial species identified by mass spectrometry (MS) and evaluated the LPS function through LPS induced proinflammatory responses and CD4+ T cell apoptosis in PBMCs. In comparison, we also evaluated plasma levels of proinflammatory cytokine and chemokine patterns in these three groups. At last, to study the causality of microbiome-blunted CD4+ T cell recovery in HIV, B6 mice were intraperitoneally (i.p.) injected with heat-killed Burkholderia fungorum, Serratia marcescens, or Phyllobacterium myrsinacearum, twice per week for total of eight weeks.FindingsINRs exhibited elevated plasma levels of total microbial translocation compared to the IRs and healthy controls. The most enriched bacteria were Burkholderia and Serratia in INRs and were Phyllobacterium in IRs. Further, unlike P. myrsinacearum LPS, B. fungorum and S. marcescens LPS induced proinflammatory responses and CD4+ T cell apoptosis in PBMCs, and gene profiles of bacteria-mediated cell activation pathways in THP-1 cells in vitro. Notably, LPS structural analysis by mass spectrometry revealed that lipid A from P. myrsinacearum exhibited a divergent structure consistent with weak toll-like receptor (TLR) 4 agonism, similar to the biological profile of probiotic bacteria. In contrast, lipid A from B. fungorum and S. marcescens showed structures more consistent with canonical TLR4 agonists stemming from proinflammatory bacterial strains. Finally, intraperitoneal (i.p.) injection of inactivated B. fungorum and S. marcescens but not P. myrsinacearum resulted in cell apoptosis in mesenteric lymph nodes of C57BL/6 mice in vivo.InterpretationThese results suggest that the microbial products are causally associated with INR phenotype. In summary, variation in blood microbial LPS immunogenicity may contribute to immune reconstitution in response to suppressive ART. Collectively, this work is consistent with immunologically silencing microbiome being causal and targetable with therapy in HIV.FundingThis work was supported by the National Institute of Allergy and Infectious Diseases (NIAID; R01 AI128864, Jiang) (NIAID; P30 AI027767, Saag/Health), the Medical Research Service at the Ralph H. Johnson VA Medical Center (merit grant VA CSRD MERIT I01 CX-002422, Jiang), and the National Institute of Aging (R21 AG074331, Scott). The SCOPE cohort was supported by the UCSF/Gladstone Institute of Virology & Immunology CFAR (P30 AI027763, Gandhi) and the CFAR Network of Integrated Clinical Systems (R24 AI067039, Saag). The National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001450 (the pilot grant, Jiang). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Published
2022

42. Balanced crystalloid solution versus saline in deceased donor kidney transplantation (BEST-Fluids): a pragmatic, double-blind, randomised, controlled trial

Author

Chadban, Steven J, Ying, Tracey, Gracey, David, Aouad, Leyla, Wyburn, Kate, Habijanec, Beatriz, Yeo, Hee-eun (Ellen), Lin, Lin, Rosales, Brenda, Hudaly, Julia, Rodrigues, Vera, Wong, Germaine, O'Connell, Philip, Murie, Penelope, Endre, Zoltan, Gray, Zuzana, Spicer, Rebecca, Durkan, Anne, Fahim, Magid A, Jegatheesan, Dev, Leary, Diana, Guo, Yujing (Sarah), Coburn, Amanda, Irvine, Rachael, McTaggart, Steven, Crawford, Aimee, Coates, P Toby, Hockley, Bronwyn, Fischer, Karen, Weinberg, Laurence, Mount, Peter, Baulch, Sarah, Claxton, Gayle, Harris, Saskia, Sidiropoulos, Sofia, Veenendal, Marieke, Kanellis, John, Barbis, Rita, Goodman, David, Brahmbhatt, Anjalee, Le Page, Amelia, Jamboti, Jagadish, Chiam, Anna, Warger, Anne, Lim, Wai H, Collins, Michael, Pilmore, Helen, Dittmer, Ian, Manley, Paul, Ahmed, Jafar, McConnochie, Rachael, Newby, Lynette, Chen, Yan, Simmonds, Catherine, Irvine, John, Usher, Jenny, Clark, Carolyn, Beckett, Claire, Prestidge, Chanel, Erickson, Robin, Rea, Miriam, Scherring, Claire, Collins, Michael G, Pascoe, Elaine M, Hawley, Carmel M, Johnson, David W, Varghese, Julie, Hickey, Laura E, Clayton, Philip A, Dansie, Kathryn B, McConnochie, Rachael C, Vergara, Liza A, Kiriwandeniya, Charani, Reidlinger, Donna, Mount, Peter F, McArthur, Colin J, Endre, Zoltan H, Howard, Kirsten, Howell, Martin, Jamboti, Jagadish S, Laurence, Jerome M, McTaggart, Steven J, O'Connell, Philip J, and Pilmore, Helen L

Published
2023
Full Text
View/download PDF

43. Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss

Author

Effland, Thomas and Collins, Michael

Subjects
Computer Science - Computation and Language
Abstract

We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets., Comment: Accepted in TACL 2021, pre-MIT Press publication version

Published
2021

44. A Biologically Plausible Parser

Author

Mitropolsky, Daniel, Collins, Michael J., and Papadimitriou, Christos H.

Subjects
Computer Science - Computation and Language
Abstract

We describe a parser of English effectuated by biologically plausible neurons and synapses, and implemented through the Assembly Calculus, a recently proposed computational framework for cognitive function. We demonstrate that this device is capable of correctly parsing reasonably nontrivial sentences. While our experiments entail rather simple sentences in English, our results suggest that the parser can be extended beyond what we have implemented, to several directions encompassing much of language. For example, we present a simple Russian version of the parser, and discuss how to handle recursion, embedding, and polysemy.

Published
2021

46. 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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results