Author: "Protopapas AN" - Searchworks@Jio Institute Digital Library Search Results

3,623 results on '"Protopapas AN"'

1. Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks

Author: Liu, Shuheng, Protopapas, Pavlos, Sondak, David, and Chen, Feiyu
Subjects: Computer Science - Machine Learning
Abstract: Solving differential equations is a critical challenge across a host of domains. While many software packages efficiently solve these equations using classical numerical approaches, there has been less effort in developing a library for researchers interested in solving such systems using neural networks. With PyTorch as its backend, NeuroDiffEq is a software library that exploits neural networks to solve differential equations. In this paper, we highlight the latest features of the NeuroDiffEq library since its debut. We show that NeuroDiffEq can solve complex boundary value problems in arbitrary dimensions, tackle boundary conditions at infinity, and maintain flexibility for dynamic injection at runtime., Comment: 13 pages, 6 figures, submitted to Journal of Open Research Software
Published: 2025

2. Astromer 2

Author: Donoso-Oliva, Cristobal, Becker, Ignacio, Protopapas, Pavlos, Cabrera-Vives, Guillermo, Cádiz-Leyton, Martina, and Moreno-Cartagena, Daniel
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively to diverse downstream applications such as classification. In this paper, we present Astromer 2 a foundational model specifically designed for extracting light curve embeddings. We introduce Astromer 2 as an enhanced iteration of our self-supervised model for light curve analysis. This paper highlights the advantages of its pre-trained embeddings, compares its performance with that of its predecessor, Astromer 1, and provides a detailed empirical analysis of its capabilities, offering deeper insights into the model's representations. Astromer 2 is pretrained on 1.5 million single-band light curves from the MACHO survey using a self-supervised learning task that predicts randomly masked observations within sequences. Fine-tuning on a smaller labeled dataset allows us to assess its performance in classification tasks. The quality of the embeddings is measured by the F1 score of an MLP classifier trained on Astromer-generated embeddings. Our results demonstrate that Astromer 2 significantly outperforms Astromer 1 across all evaluated scenarios, including limited datasets of 20, 100, and 500 samples per class. The use of weighted per-sample embeddings, which integrate intermediate representations from Astromer's attention blocks, is particularly impactful. Notably, Astromer 2 achieves a 15% improvement in F1 score on the ATLAS dataset compared to prior models, showcasing robust generalization to new datasets. This enhanced performance, especially with minimal labeled data, underscores the potential of Astromer 2 for more efficient and scalable light curve analysis., Comment: 10 pages, 17 figures
Published: 2025

3. Stiff Transfer Learning for Physics-Informed Neural Networks

Author: Seiler, Emilien, Lei, Wanzhou, and Protopapas, Pavlos
Subjects: Computer Science - Machine Learning, Mathematics - Analysis of PDEs
Abstract: Stiff differential equations are prevalent in various scientific domains, posing significant challenges due to the disparate time scales of their components. As computational power grows, physics-informed neural networks (PINNs) have led to significant improvements in modeling physical processes described by differential equations. Despite their promising outcomes, vanilla PINNs face limitations when dealing with stiff systems, known as failure modes. In response, we propose a novel approach, stiff transfer learning for physics-informed neural networks (STL-PINNs), to effectively tackle stiff ordinary differential equations (ODEs) and partial differential equations (PDEs). Our methodology involves training a Multi-Head-PINN in a low-stiff regime, and obtaining the final solution in a high stiff regime by transfer learning. This addresses the failure modes related to stiffness in PINNs while maintaining computational efficiency by computing "one-shot" solutions. The proposed approach demonstrates superior accuracy and speed compared to PINNs-based methods, as well as comparable computational efficiency with implicit numerical methods in solving stiff-parameterized linear and polynomial nonlinear ODEs and PDEs under stiff conditions. Furthermore, we demonstrate the scalability of such an approach and the superior speed it offers for simulations involving initial conditions and forcing function reparametrization.
Published: 2025

4. Multiband Embeddings of Light Curves

Author: Becker, I., Protopapas, P., Catelan, M., and Pichara, K.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our proposed model consists of an ensemble of RNNs, which do not require the entire light curve to perform inference, making the inference process simpler. The ensemble is able to adapt to varying numbers of bands, tested on three real light curve datasets, namely Gaia, Pan-STARRS1, and ZTF, to demonstrate its potential for generalization. We also show the capabilities of deep learning to perform not only classification, but also regression of physical parameters such as effective temperature and radius. Our ensemble model demonstrates superior performance in scenarios with fewer observations, thus providing potential for early classification of sources from facilities such as Vera C. Rubin Observatory's LSST. The results underline the model's effectiveness and flexibility, making it a promising tool for future astronomical surveys. Our research has shown that a multitask learning approach can enrich the embeddings obtained by the models, making them instrumental to solve additional tasks, such as determining the orbital parameters of binary systems or estimating parameters for object types beyond periodic ones., Comment: 15 pages, 10 figures, accepted at A&A
Published: 2025

5. Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space

Author: Tarancón-Álvarez, Pedro, Tejerina-Pérez, Pablo, Jimenez, Raul, and Protopapas, Pavlos
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, High Energy Physics - Theory, Mathematics - Analysis of PDEs
Abstract: We present a machine learning framework to facilitate the solution of nonlinear multiscale differential equations and, especially, inverse problems using Physics-Informed Neural Networks (PINNs). This framework is based on what is called multihead (MH) training, which involves training the network to learn a general space of all solutions for a given set of equations with certain variability, rather than learning a specific solution of the system. This setup is used with a second novel technique that we call Unimodular Regularization (UR) of the latent space of solutions. We show that the multihead approach, combined with the regularization, significantly improves the efficiency of PINNs by facilitating the transfer learning process thereby enabling the finding of solutions for nonlinear, coupled, and multiscale differential equations.
Published: 2025

6. The Reconstruction of Theaetetus' Theory of Ratios of Magnitudes

Author: Negrepontis, Stelios and Protopapas, Dimitrios
Subjects: Mathematics - History and Overview
Abstract: In the present chapter, we obtain the reconstruction of Theaetetus' theory of ratios of magnitudes based, according to Aristotle's Topics 158b, on the definition of proportion in terms of equal anthyphairesis. Our reconstruction is built on the anthyphairetic interpretation of the notoriously difficult Theaetetus 147d6-e1 passage on Theaetetus' mathematical discovery of quadratic incommensurabilities, itself based on the traces it has left on Plato's philosophical definition of Knowledge in his dialogues Theaetetus, Sophist and Meno. Contrary to earlier reconstructions by Becker, van der Waerden and Knorr, our reconstruction reveals a theory that (a) applies only to the restricted class of pairs of magnitudes whose anthyphairesis is finite or eventually periodic, and (b) avoids the problematic use of Eudoxus' definition 4 of Book V of Euclid's Elements. The final version of this paper will appear as a chapter in the book Essays on Topology: Dedicated to Valentin Po\'enaru, ed. L. Funar and A. Papadopoulos, Springer, 2025.
Published: 2025

7. Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars

Author: Cádiz-Leyton, Martina, Cabrera-Vives, Guillermo, Protopapas, Pavlos, Moreno-Cartagena, Daniel, Donoso-Oliva, Cristobal, and Becker, Ignacio
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the state-of-the-art. While achieving high accuracy is crucial, enhancing model interpretability and uncertainty estimation is equally important to ensure that insights are both reliable and comprehensible. We aim to enhance transformer-based models for classifying astronomical light curves by incorporating uncertainty estimation techniques to detect misclassified instances. We tested our methods on labeled datasets from MACHO, OGLE-III, and ATLAS, introducing a framework that significantly improves the reliability of automated classification for the next-generation surveys. We used Astromer, a transformer-based encoder designed for capturing representations of single-band light curves. We enhanced its capabilities by applying three methods for quantifying uncertainty: Monte Carlo Dropout (MC Dropout), Hierarchical Stochastic Attention (HSA), and a novel hybrid method combining both approaches, which we have named Hierarchical Attention with Monte Carlo Dropout (HA-MC Dropout). We compared these methods against a baseline of deep ensembles (DEs). To estimate uncertainty estimation scores for the misclassification task, we selected Sampled Maximum Probability (SMP), Probability Variance (PV), and Bayesian Active Learning by Disagreement (BALD) as uncertainty estimates. In predictive performance tests, HA-MC Dropout outperforms the baseline, achieving macro F1-scores of 79.8+-0.5 on OGLE, 84+-1.3 on ATLAS, and 76.6+-1.8 on MACHO. When comparing the PV score values, the quality of uncertainty estimation by HA-MC Dropout surpasses that of all other methods, with improvements of 2.5+-2.3 for MACHO, 3.3+-2.1 for ATLAS and 8.5+-1.6 for OGLE-III.
Published: 2024

8. Exact and approximate error bounds for physics-informed neural networks

Author: Chantada, Augusto T., Protopapas, Pavlos, Bachar, Luca Gomez, Landau, Susana J., and Scóccola, Claudia G.
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract: The use of neural networks to solve differential equations, as an alternative to traditional numerical solvers, has increased recently. However, error bounds for the obtained solutions have only been developed for certain equations. In this work, we report important progress in calculating error bounds of physics-informed neural networks (PINNs) solutions of nonlinear first-order ODEs. We give a general expression that describes the error of the solution that the PINN-based method provides for a nonlinear first-order ODE. In addition, we propose a technique to calculate an approximate bound for the general case and an exact bound for a particular case. The error bounds are computed using only the residual information and the equation structure. We apply the proposed methods to particular cases and show that they can successfully provide error bounds without relying on the numerical solution., Comment: 10 pages, 1 figure, accepted to NeurIPS 2024 Workshop on Machine Learning and the Physical Sciences
Published: 2024

9. Generating strongly 2-connected digraphs

Author: Hatzel, Meike, Kreutzer, Stephan, Protopapas, Evangelos, Reich, Florian, Stamoulis, Giannos, and Wiederrecht, Sebastian
Subjects: Mathematics - Combinatorics, 05C20, 05C75
Abstract: We prove that there exist four operations such that given any two strongly $2$-connected digraphs $H$ and $D$ where $H$ is a butterfly-minor of $D$, there exists a sequence $D_0,\dots, D_n$ where $D_0=H$, $D_n=D$ and for every $0\leq i\leq n-1$, $D_i$ is a strongly $2$-connected butterfly-minor of $D_{i+1}$ which is obtained by a single application of one of the four operations. As a consequence of this theorem, we obtain that every strongly $2$-connected digraph can be generated from a concise family of strongly $2$-connected digraphs by using these four operations., Comment: 42 pages
Published: 2024

10. Transformer-Based Astronomical Time Series Model with Uncertainty Estimation for Detecting Misclassified Instances

Author: Cádiz-Leyton, Martina, Cabrera-Vives, Guillermo, Protopapas, Pavlos, Moreno-Cartagena, Daniel, and Donoso-Oliva, Cristobal
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: In this work, we present a framework for estimating and evaluating uncertainty in deep-attention-based classifiers for light curves for variable stars. We implemented three techniques, Deep Ensembles (DEs), Monte Carlo Dropout (MCD) and Hierarchical Stochastic Attention (HSA) and evaluated models trained on three astronomical surveys. Our results demonstrate that MCD and HSA offers a competitive and computationally less expensive alternative to DE, allowing the training of transformers with the ability to estimate uncertainties for large-scale light curve datasets. We conclude that the quality of the uncertainty estimation is evaluated using the ROC AUC metric., Comment: Accepted for LatinX in AI (LXAI) workshop at the 41 st International Conference on Machine Learning (ICML), Vienna, Austria. PMLR 235, 2024
Published: 2024

11. Obstructions to Erd\H{o}s-P\'osa Dualities for Minors

Author: Paul, Christophe, Protopapas, Evangelos, Thilikos, Dimitrios M., and Wiederrecht, Sebastian
Subjects: Mathematics - Combinatorics, Computer Science - Data Structures and Algorithms, 05C83, 05C85, 05C10, 05C75, 68R10, G.2.2
Abstract: Let ${\cal G}$ and ${\cal H}$ be minor-closed graph classes. The pair $({\cal H},{\cal G})$ is an Erd\H{o}s-P\'osa pair (EP-pair) if there is a function $f$ where, for every $k$ and every $G\in{\cal G},$ either $G$ has $k$ pairwise vertex-disjoint subgraphs not belonging to ${\cal H},$ or there is a set $S\subseteq V(G)$ where $|S|\leq f(k)$ and $G-S\in{\cal H}.$ The classic result of Erd\H{o}s and P\'osa says that if $\mathcal{F}$ is the class of forests, then $({\cal F},{\cal G})$ is an EP-pair for every ${\cal G}$. The class ${\cal G}$ is an EP-counterexample for ${\cal H}$ if ${\cal G}$ is minimal with the property that $({\cal H},{\cal G})$ is not an EP-pair. We prove that for every ${\cal H}$ the set $\mathfrak{C}_{\cal H}$ of all EP-counterexamples for ${\cal H}$ is finite. In particular, we provide a complete characterization of $\mathfrak{C}_{\cal H}$ for every ${\cal H}$ and give a constructive upper bound on its size. Each class ${\cal G}\in \mathfrak{C}_{\cal H}$ can be described as all minors of a sequence of grid-like graphs $\langle \mathscr{W}_{k} \rangle_{k\in \mathbb{N}}.$ Moreover, each $\mathscr{W}_{k}$ admits a half-integral packing: $k$ copies of some $H\not\in{\cal H}$ where no vertex is used more than twice. This gives a complete delineation of the half-integrality threshold of the Erd\H{o}s-P\'osa property for minors and yields a constructive proof of Thomas' conjecture on the half-integral Erd\H{o}s-P\'osa property for minors (recently confirmed, non-constructively, by Liu). Let $h$ be the maximum size of a graph in ${\cal H}.$ For every class ${\cal H},$ we construct an algorithm that, given a graph $G$ and a $k,$ either outputs a half-integral packing of $k$ copies of some $H \not\in {\cal H}$ or outputs a set of at most ${2^{k^{\cal O}_h(1)}}$ vertices whose deletion creates a graph in ${\cal H}$ in time $2^{2^{k^{{\cal O}_h(1)}}}\cdot |G|^4\log |G|.$, Comment: Accepted to FOCS 2024
Published: 2024

12. Delineating Half-Integrality of the Erd\H{o}s-P\'osa Property for Minors: the Case of Surfaces

Author: Paul, Christophe, Protopapas, Evangelos, Thilikos, Dimitrios M., and Wiederrecht, Sebastian
Subjects: Mathematics - Combinatorics, Computer Science - Discrete Mathematics, 05C83, 05C75, 05C10, 05C85, 68R10, G.2.2
Abstract: In 1986 Robertson and Seymour proved a generalization of the seminal result of Erd\H{o}s and P\'osa on the duality of packing and covering cycles: A graph has the Erd\H{o}s-P\'osa property for minors if and only if it is planar. In particular, for every non-planar graph $H$ they gave examples showing that the Erd\H{o}s-P\'osa property does not hold for $H.$ Recently, Liu confirmed a conjecture of Thomas and showed that every graph has the half-integral Erd\H{o}s-P\'osa property for minors. Liu's proof is non-constructive and to this date, with the exception of a small number of examples, no constructive proof is known. In this paper, we initiate the delineation of the half-integrality of the Erd\H{o}s-P\'osa property for minors. We conjecture that for every graph $H,$ there exists a unique (up to a suitable equivalence relation) graph parameter ${\textsf{EP}}_H$ such that $H$ has the Erd\H{o}s-P\'osa property in a minor-closed graph class $\mathcal{G}$ if and only if $\sup\{\textsf{EP}_H(G) \mid G\in\mathcal{G}\}$ is finite. We prove this conjecture for the class $\mathcal{H}$ of Kuratowski-connected shallow-vortex minors by showing that, for every non-planar $H\in\mathcal{H},$ the parameter ${\sf EP}_H(G)$ is precisely the maximum order of a Robertson-Seymour counterexample to the Erd\H{o}s-P\'osa property of $H$ which can be found as a minor in $G.$ Our results are constructive and imply, for the first time, parameterized algorithms that find either a packing, or a cover, or one of the Robertson-Seymour counterexamples, certifying the existence of a half-integral packing for the graphs in $\mathcal{H}.$
Published: 2024

13. Effects of word frequency and length in discrete and serial word reading

Author: Romero, Sandra, Georgiou, George K., Altani, Angeliki, and Protopapas, Athanassios
Published: 2024
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14. Gravitational Duals from Equations of State

Author: Bea, Yago, Jimenez, Raul, Mateos, David, Liu, Shuheng, Protopapas, Pavlos, Tarancón-Álvarez, Pedro, and Tejerina-Pérez, Pablo
Subjects: High Energy Physics - Theory, Astrophysics - Cosmology and Nongalactic Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, General Relativity and Quantum Cosmology
Abstract: Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein's equations to determine the equation of state is an algorithmic, direct problem. Determining the gravitational theory that gives rise to a prescribed equation of state is a much more challenging, inverse problem. We present a novel approach to solve this problem based on physics-informed neural networks. The resulting algorithm is not only data-driven but also informed by the physics of the Einstein's equations. We successfully apply it to theories with crossovers, first- and second-order phase transitions.
Published: 2024

15. Policy Mirror Descent with Lookahead

Author: Protopapas, Kimon and Barakat, Anas
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract: Policy Mirror Descent (PMD) stands as a versatile algorithmic framework encompassing several seminal policy gradient algorithms such as natural policy gradient, with connections with state-of-the-art reinforcement learning (RL) algorithms such as TRPO and PPO. PMD can be seen as a soft Policy Iteration algorithm implementing regularized 1-step greedy policy improvement. However, 1-step greedy policies might not be the best choice and recent remarkable empirical successes in RL such as AlphaGo and AlphaZero have demonstrated that greedy approaches with respect to multiple steps outperform their 1-step counterpart. In this work, we propose a new class of PMD algorithms called $h$-PMD which incorporates multi-step greedy policy improvement with lookahead depth $h$ to the PMD update rule. To solve discounted infinite horizon Markov Decision Processes with discount factor $\gamma$, we show that $h$-PMD which generalizes the standard PMD enjoys a faster dimension-free $\gamma^h$-linear convergence rate, contingent on the computation of multi-step greedy policies. We propose an inexact version of $h$-PMD where lookahead action values are estimated. Under a generative model, we establish a sample complexity for $h$-PMD which improves over prior work. Finally, we extend our result to linear function approximation to scale to large state spaces. Under suitable assumptions, our sample complexity only involves dependence on the dimension of the feature map space instead of the state space size.
Published: 2024

16. Generating Images of the M87* Black Hole Using GANs

Author: Mohan, Arya, Protopapas, Pavlos, Kunnumkai, Keerthi, Garraffo, Cecilia, Blackburn, Lindy, Chatterjee, Koushik, Doeleman, Sheperd S., Emami, Razieh, Fromm, Christian M., Mizuno, Yosuke, and Ricarte, Angelo
Subjects: Astrophysics - Astrophysics of Galaxies, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [-1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented dataset while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost effective models for black hole image generation and reliably augment training datasets for other parameterization algorithms., Comment: 11 pages, 7 figures. Accepted by Monthly Notices of the Royal Astronomical Society Journal
Published: 2023

17. Faster Bayesian inference with neural network bundles and new results for $f(R)$ models

Author: Chantada, Augusto T., Landau, Susana J., Protopapas, Pavlos, Scóccola, Claudia G., and Garraffo, Cecilia
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract: In the last few years, there has been significant progress in the development of machine learning methods tailored to astrophysics and cosmology. We have recently applied one of these, namely, the neural network bundle method, to the cosmological scenario. Moreover, we showed that in some cases the computational times of the Bayesian inference process can be reduced. In this paper, we present an improvement to the neural network bundle method that results in a significant reduction of the computational times of the statistical analysis. The novel aspect consists of the use of the neural network bundle method to calculate the luminosity distance of type Ia supernovae, which is usually computed through an integral with numerical methods. In this work, we have applied this improvement to the Hu-Sawicki and Starobinsky $f(R)$ models. We also performed a statistical analysis with data from type Ia supernovae of the Pantheon+ compilation and cosmic chronometers. Another original aspect of this work is the different treatment we provide for the absolute magnitude of type Ia supernovae during the inference process, which results in different estimates of the distortion parameter than the ones obtained in the literature. We show that the statistical analyses carried out with our new method require lower computational times than the ones performed with both the numerical and the neural network method from our previous work. This reduction in time is more significant in the case of a difficult computational problem such as the ones addressed in this work., Comment: 14 pages, 5 figures, 3 tables
Published: 2023
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18. Incorrect conclusions drawn for plausible looking diagrams

Author: Eleftherios, Protopapas
Subjects: Mathematics - History and Overview, 97D50, 97G99
Abstract: In Mathematics is common to make a mistake and therefore a false conclusion arises. In each case it is important to recognize the mistake in order to avoid a similar one in the future. Geometric figures provide decisive help in order to have a strict mathematical proof, but also can easily lead to wrong conclusions without a mathematical proof. In this paper, several incorrect conclusions drawn for plausible looking diagrams are presented, motivated by a well-known faulty model for measuring the length of a segment. Similar models that lead to a contradiction are developed and a model that leads to the correct result is derived. The presented models prove the usefulness of paradoxes and can be implemented in a classroom in order to point out to students the significance of a strict mathematical proof as well as the construction of a correct mathematical model. The geometric nature of the problems provides the opportunity to use a dynamic geometric software., Comment: 16 pages, 15 figures
Published: 2023

19. Positional Encodings for Light Curve Transformers: Playing with Positions and Attention

Author: Moreno-Cartagena, Daniel, Cabrera-Vives, Guillermo, Protopapas, Pavlos, Donoso-Oliva, Cristobal, Pérez-Carrasco, Manuel, and Cádiz-Leyton, Martina
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We conducted empirical experiments to assess the transferability of a light curve transformer to datasets with different cadences and magnitude distributions using various positional encodings (PEs). We proposed a new approach to incorporate the temporal information directly to the output of the last attention layer. Our results indicated that using trainable PEs lead to significant improvements in the transformer performances and training times. Our proposed PE on attention can be trained faster than the traditional non-trainable PE transformer while achieving competitive results when transfered to other datasets., Comment: In Proceedings of the 40th International Conference on Machine Learning (ICML), Workshop on Machine Learning for Astrophysics, PMLR 202, 2023, Honolulu, Hawaii, USA
Published: 2023

20. Residual-based error bound for physics-informed neural networks

Author: Liu, Shuheng, Huang, Xiyue, and Protopapas, Pavlos
Subjects: Computer Science - Computational Engineering, Finance, and Science, Mathematics - Numerical Analysis
Abstract: Neural networks are universal approximators and are studied for their use in solving differential equations. However, a major criticism is the lack of error bounds for obtained solutions. This paper proposes a technique to rigorously evaluate the error bound of Physics-Informed Neural Networks (PINNs) on most linear ordinary differential equations (ODEs), certain nonlinear ODEs, and first-order linear partial differential equations (PDEs). The error bound is based purely on equation structure and residual information and does not depend on assumptions of how well the networks are trained. We propose algorithms that bound the error efficiently. Some proposed algorithms provide tighter bounds than others at the cost of longer run time., Comment: 10 page main artichle + 5 page supplementary material
Published: 2023

21. Preschool Morphological Awareness and Developmental Change in Early Reading Ability

Author: Vassiliki Diamanti, Germán Grande, Athanassios Protopapas, Monica Melby-Lervåg, and Arne Lervåg
Abstract: Purpose: This longitudinal study examined the contribution of preschool morphological awareness to word reading skills and reading comprehension, as well as to the developmental change of reading ability beyond other well-established oral language and cognitive predictors. A distinction was made between the domains of inflectional and derivational morphology. Method: Two hundred and fifty-nine Norwegian-speaking children (46% female) with a mean age of 5.5 years were assessed in preschool on language measures and again in Grades 1 and 3 on measures of word reading accuracy and fluency and in Grades 3 and 4 on reading comprehension. We fit latent change score models with preschool predictors using parceling to control for measurement error. Results: We found a unique contribution of preschool morphological awareness to reading comprehension in Grade 3, but no unique contribution to Grade 1 decoding. Neither awareness of inflections nor awareness of derivations predicted developmental change in word reading fluency between Grades 1 and 3 or change in reading comprehension between Grades 3 and 4 beyond the effect of control variables. Conclusion: Our findings confirm the relevance of morphological awareness only for early attainment in reading comprehension and highlight the importance of accounting for measurement error in studying associations among variables aiming to discover specific contributions.
Published: 2024
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22. Beyond Word Recognition: The Role of Efficient Sequential Processing in Word- and Text-Reading Fluency Development

Author: Sandra Romero, George K. Georgiou, Angeliki Altani, Guher Gorgun, and Athanassios Protopapas
Abstract: Purpose: Previous studies examining the inter-relations between serial and discrete naming with reading have found that the ability to efficiently process multiple items presented in a sequence (indexed by serial naming) is a unique predictor of word- and text-reading fluency. However, conclusions have been tempered by the concurrent nature of the available data and the uniformly low demands of the materials (words and texts). Here we go beyond previous studies by using more varied materials to examine the relations of serial and discrete naming with the discrete reading of words and the serial reading of word lists and connected text over time. Method: Two hundred and eight English-speaking Canadian children (51% female, M[subscript age] = 7.2 years) were followed from Grade 2 to Grade 5 and were assessed on serial and discrete digit naming and serial and discrete word reading at both measurement points. Results: Strong associations between discrete naming and discrete reading already from Grade 2 indicated that short and high-frequency words were processed in parallel early in development. By Grade 5, when word recognition was presumably automatized, serial naming accounted for unique variance in serial reading of word lists and connected texts after controlling for discrete word reading. More importantly, Latent Change Score modeling indicated that serial naming was the main predictor of growth in serial reading from Grade 2 to Grade 5. Conclusion: These findings suggest that, beyond individual word recognition, reading fluency development also requires efficient processing of multiple items presented in serial format (termed "cascaded processing").
Published: 2024
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23. How RAN Stimulus Type and Repetition Affect RAN's Relation with Decoding Efficiency and Reading Comprehension

Author: Mads Poulsen, Athanassios Protopapas, and Holger Juul
Abstract: Purpose: This study investigated how correlations between rapid automatized naming (RAN) and reading depend on characteristics of the stimuli. RAN tasks using stimuli with high phonological demands were predicted to be the strongest correlates of decoding efficiency, while high semantic demands were predicted to lead to stronger correlations with comprehension. Method: At two time points, 132 Grade 2 children completed four different RAN versions, two using letter stimuli (low semantic load) and two using object stimuli (high semantic load). Both types of stimuli were used in either a repeated version, where a set of four items were repeated multiple times (low semantic load), or in a unique version, where each item appeared only once (high semantic load). Decoding efficiency and reading comprehension were assessed in Grade 5. Results: Analyses showed that confirmatory factor models with separate factors for each version provided better fit than grouping factors according to time point. Repetition (lowering semantic load) increased the longitudinal association between RAN objects and decoding efficiency. There was a tendency for conditions with higher semantic load to correlate more strongly with reading comprehension after control for decoding efficiency, but the differences were not significant. Conclusion: The results indicate that increasing semantic load weakens the relationship with decoding efficiency.
Published: 2024
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24. An Overview of Universal Obstructions for Graph Parameters

Author: Paul, Christophe, Protopapas, Evangelos, and Thilikos, Dimitrios M.
Subjects: Computer Science - Discrete Mathematics, Mathematics - Combinatorics, 05C85, 05C83, 05C75, G.2.2
Abstract: In a recent work, we introduced a parametric framework for obtaining obstruction characterizations of graph parameters with respect to a quasi-ordering $\leqslant$ on graphs. Towards this, we proposed the concepts of class obstruction, parametric obstruction, and universal obstruction as combinatorial objects that determine the approximate behaviour of a graph parameter. In this work, we explore its potential as a unifying framework for classifying graph parameters. Under this framework, we survey existing graph-theoretic results on many known graph parameters. Additionally, we provide some unifying results on their classification.
Published: 2023

25. Graph Parameters, Universal Obstructions, and WQO

Author: Paul, Christophe, Protopapas, Evangelos, and Thilikos, Dimitrios M.
Subjects: Mathematics - Combinatorics, Computer Science - Discrete Mathematics, 06A07, 05C83, 05C85, G.2.1, F.2.2, G.2.2
Abstract: We establish a parametric framework for obtaining obstruction characterizations of graph parameters with respect to a quasi-ordering $\leqslant$ on graphs. At the center of this framework lies the concept of a $\leqslant$-parametric graph: a non $\leqslant$-decreasing sequence $\mathscr{G} = \langle \mathscr{G}_{t} \rangle_{t \in \mathbb{N}}$ of graphs indexed by non-negative integers. Parametric graphs allow us to define combinatorial objects that capture the approximate behaviour of graph parameters. A finite set $\mathfrak{G}$ of $\leqslant$-parametric graphs is a $\leqslant$-universal obstruction for a parameter $\mathsf{p}$ if there exists a function $f \colon \mathbb{N} \to \mathbb{N}$ such that, for every $k \in \mathbb{N}$ and every graph $G$, 1) if $\mathsf{p}(G) \leq k$, then for every $\mathscr{G} \in \mathfrak{G},$ $\mathscr{G}_{f(k)} \not\leqslant G$, and 2) if for every $\mathscr{G} \in \mathfrak{G},$ $\mathscr{G}_{k} \not\leqslant G$, then $\mathsf{p}(G) \leq f(k).$ To solidify our point of view, we identify sufficient order-theoretic conditions that guarantee the existence of universal obstructions and in this case we examine algorithmic implications on the existence of fixed-parameter tractable algorithms. Our parametric framework has further implications related to finite obstruction characterizations of properties of graph classes. A $\leqslant$-class property is defined as any set of $\leqslant$-closed graph classes that is closed under set inclusion. By combining our parametric framework with established results from order theory, we derive a precise order-theoretic characterization that ensures $\leqslant$-class properties can be described in terms of the exclusion of a finite set of $\leqslant$-parametric graphs.
Published: 2023

26. A split-GAL4 driver line resource for Drosophila neuron types

Author: Geoffrey W Meissner, Allison Vannan, Jennifer Jeter, Kari Close, Gina M DePasquale, Zachary Dorman, Kaitlyn Forster, Jaye Anne Beringer, Theresa Gibney, Joanna H Hausenfluck, Yisheng He, Kristin Henderson, Lauren Johnson, Rebecca M Johnston, Gudrun Ihrke, Nirmala A Iyer, Rachel Lazarus, Kelley Lee, Hsing-Hsi Li, Hua-Peng Liaw, Brian Melton, Scott Miller, Reeham Motaher, Alexandra Novak, Omotara Ogundeyi, Alyson Petruncio, Jacquelyn Price, Sophia Protopapas, Susana Tae, Jennifer Taylor, Rebecca Vorimo, Brianna Yarbrough, Kevin Xiankun Zeng, Christopher T Zugates, Heather Dionne, Claire Angstadt, Kelly Ashley, Amanda Cavallaro, Tam Dang, Guillermo A Gonzalez III, Karen L Hibbard, Cuizhen Huang, Jui-Chun Kao, Todd Laverty, Monti Mercer, Brenda Perez, Scarlett Rose Pitts, Danielle Ruiz, Viruthika Vallanadu, Grace Zhiyu Zheng, Cristian Goina, Hideo Otsuna, Konrad Rokicki, Robert R Svirskas, Han SJ Cheong, Michael-John Dolan, Erica Ehrhardt, Kai Feng, Basel EI Galfi, Jens Goldammer, Stephen J Huston, Nan Hu, Masayoshi Ito, Claire McKellar, Ryo Minegishi, Shigehiro Namiki, Aljoscha Nern, Catherine E Schretter, Gabriella R Sterne, Lalanti Venkatasubramanian, Kaiyu Wang, Tanya Wolff, Ming Wu, Reed George, Oz Malkesman, Yoshinori Aso, Gwyneth M Card, Barry J Dickson, Wyatt Korff, Kei Ito, James W Truman, Marta Zlatic, Gerald M Rubin, and FlyLight Project Team
Subjects: split-GAL4, GAL4, central nervous system, driver line, targeting, confocal microscopy, Medicine, Science, Biology (General), QH301-705.5
Abstract: Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system (CNS) have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila CNS and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell-type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.
Published: 2025
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27. Recurrent adnexal torsion in a teenager with hypermobile Ehlers-Danlos syndrome: A case report

Author: Michail Panagiotopoulos, Maria Tsiriva, Lito Vogiatzi-Vokotopoulou, Konstantinos Koukoumpanis, Nikolaos Kathopoulis, Athanasios Douligeris, Athanasios Protopapas, and Lina Michala
Subjects: Hypermobile Ehlers-Danlos syndrome, hEDS, Adnexal torsion, Adolescent, Case report, Surgery, RD1-811, Gynecology and obstetrics, RG1-991
Abstract: Hypermobile Ehlers-Danlos syndrome (hEDS) is the most common type of EDS, characterized by joint hypermobility, frequent dislocations, and chronic pain. Genetic markers are not typically used in diagnosis.A 17-year-old clinically diagnosed with hEDS presented with recurrent lower abdominal pain, later attributed to intermittent partial adnexal torsion. Whole-genome sequencing revealed a missense mutation c.1691G > A (p.Arg564His) in the COL1A1 gene. She had undergone two exploratory laparotomies at ages 8 and 10 due to acute pain, resulting in a left adnexectomy and right detorsion with hydrosalpinx drainage. It was suspected that the recurrent adnexal torsion was linked to hEDS-related tissue elasticity, and so a laparoscopic right oophoropexy by shortening the utero-ovarian ligament was performed. At one-year follow-up, she was asymptomatic.This case highlights the potential connection between hEDS and adnexal torsion, which may contribute to chronic abdominal pain, often misattributed to other conditions, such as irritable bowel syndrome.
Published: 2024
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28. Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks

Author: Graf, Olga, Flores, Pablo, Protopapas, Pavlos, and Pichara, Karim
Subjects: Computer Science - Machine Learning
Abstract: Physics-Informed Neural Networks (PINNs) are gaining popularity as a method for solving differential equations. While being more feasible in some contexts than the classical numerical techniques, PINNs still lack credibility. A remedy for that can be found in Uncertainty Quantification (UQ) which is just beginning to emerge in the context of PINNs. Assessing how well the trained PINN complies with imposed differential equation is the key to tackling uncertainty, yet there is lack of comprehensive methodology for this task. We propose a framework for UQ in Bayesian PINNs (B-PINNs) that incorporates the discrepancy between the B-PINN solution and the unknown true solution. We exploit recent results on error bounds for PINNs on linear dynamical systems and demonstrate the predictive uncertainty on a class of linear ODEs.
Published: 2022

29. Between-word processing and text-level skills contributing to fluent reading of (non)word lists and text

Author: van Viersen, Sietske, Altani, Angeliki, de Jong, Peter F., and Protopapas, Athanassios
Published: 2024
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30. Gravitational duals from equations of state

Author: Yago Bea, Raul Jimenez, David Mateos, Shuheng Liu, Pavlos Protopapas, Pedro Tarancón-Álvarez, and Pablo Tejerina-Pérez
Subjects: AdS-CFT Correspondence, Gauge-Gravity Correspondence, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein’s equations to determine the equation of state is an algorithmic, direct problem. Determining the gravitational theory that gives rise to a prescribed equation of state is a much more challenging, inverse problem. We present a novel approach to solve this problem based on physics-informed neural networks. The resulting algorithm is not only data-driven but also informed by the physics of the Einstein’s equations. We successfully apply it to theories with crossovers, first- and second-order phase transitions.
Published: 2024
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31. Clostridioides difficile infection in patients with and without COVID-19 during the pandemic: A retrospective cohort study from a tertiary referral hospital

Author: Tsankof, Alexandra, Protopapas, Adonis A., Mantzana, Paraskevi, Protonotariou, Efthymia, Skoura, Lemonia, Protopapas, Andreas N., Savopoulos, Christos, and Mimidis, Konstantinos
Published: 2024
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32. Higher dose corticosteroids in hospitalised COVID-19 patients requiring ventilatory support (RECOVERY): a randomised, controlled, open-label, platform trialResearch in context

Author: O. Abani, A. Abbas, F. Abbas, J. Abbas, K. Abbas, M. Abbas, S. Abbasi, H. Abbass, A. Abbott, N. Abdallah, A. Abdelaziz, M. Abdelfattah, B. Abdelqader, A. Abdul, B. Abdul, S. Abdul, A. Abdul Rasheed, A. Abdulakeem, R. Abdul-Kadir, A. Abdullah, A. Abdulmumeen, R. Abdul-Raheem, N. Abdulshukkoor, K. Abdusamad, Y. Abed El Khaleq, M. Abedalla, A. Abeer Ul Amna, L. Abel, K. Abernethy, M. Abeywickrema, C. Abhinaya, A. Abidin, A. Aboaba, A. Aboagye-Odei, C. Aboah, H. Aboelela, H. Abo-Leyah, K. Abouelela, A. Abou-Haggar, M. Abouibrahim, A. Abousamra, M. Abouzaid, M. Abraham, T. Abraham, A. Abraheem, J. Abrams, R. Abrams, H.J. Abu, A. Abu-Arafeh, S.M. Abubacker, A. Abung, Y. Abusamra, Y. Aceampong, A. Achara, D. Acharya, F. Acheampong, P. Acheampong, S. Acheampong, J. Acheson, S. Achieng, A. Acosta, R. Acquah, C. Acton, J. Adabie-Ankrah, P. Adair, A.S. Adam, F. Adam, M. Adam, H. Adamali, M. Adamczyk, C. Adams, D. Adams, K. Adams, L. Adams, N. Adams, R. Adams, T. Adams, L. Adamu-Ikeme, K. Adatia, K. Adcock, L. Addai-Boampong, A. Addo, O. Adeagbo, A. Adebiyi, O. Adedeji, Y. Adegeye, K. Adegoke, V. Adell, S. Adenwalla, F.W. Adeoye, O.A. Adesemoye, E.O. Adewunmi, A. Adeyanju, J. Adeyemi, T. Adeyemo, B. Adhikari, S.A. Adhikari, R. Adhikary, A. Aditya, P. Adjepong, G. Adkins, A. Adnan, M. Adriaanse, J. Aeron-Thomas, D. Affleck, C. Afnan, M. Afridi, P. Afrim, F.A. Afriyie, Z.A. Aftab, A. Afum-Adjei Awuah, M. Agarwal, P.N. Agasiya, R. Agbeko, C. Agbo, S. Aggarwal, A. Aghababaie, L. Aguilar Jimenez, J.A. Agyekum, K. Agyen, E.K. Ahadome, S. Ahamed Sadiq, M.H. Ahammed Nazeer, M. Ahmad, S. Ahmad, A. Ahmed, B.A.R. Ahmed, B. Ahmed, F. Ahmed, H. Ahmed, I. Ahmed, K. Ahmed, L. Ahmed, M. Ahmed, M.C. Ahmed, M.S. Ahmed, N. Ahmed, O. Ahmed, R.A. Ahmed, R. Ahmed, S. Ahmed, S.G. Ahmed, S.H. Ahmed, R. Ahmed Ali, B. Ahmed Mohamud, S. Ahmer, A. Ahonia, C. Aidoo, C. Aiken, D. Ail, M. Ainsworth, M. Aissa, L. Aitken, B. Ajay, A. Ajibode, A. Ajmi, N. Akhtar, S. Akili, B. Akinbiyi, O. Akindolie, Y. Akinfenwa, O. Akinkugbe, I. Akinpelu, M. Akram, O. Aktinade, U. Akudi, A.S.A.R. Al Aaraj, A. Al Balushi, M. Al Dakhola, A. Al Swaifi, E. Al-Abadi, A. Alabi, N. Aladangady, M. Alafifi, A. Alam, S. Alam, A. Al-Asadi, K. Alatzoglou, P. Albert, A. Albertus, L. Albon, A. Alcala, G. Alcorn, S. Alcorn, A. Aldana, D. Alderdice, A. Aldesouki, R. Aldouri, J. Aldridge, N. Aldridge, R. Ale, R.M. Ale, A. Alegria, A. Alexander, C. Alexander, J. Alexander, P.D.G. Alexander, J. Al-fori, L. Alghazawi, O. Alhabsha, B. Al-Hakim, R. Alhameed, M. Al-Hayali, S. Al-Hity, A. Ali, F.R. Ali, J. Ali, M. Ali, M.S. Ali, N. Ali, O. Ali, R. Ali, S. Ali, E. Aliberti, J. Alin, A. Alina, A. Alipustain, B. Alisjahbana, F. Aliyuda, K. Alizadeh, M. Al-Jibury, S. Al-Juboori, M. Al-Khalil, A. Alkhudhayri, M. Alkhusheh, F. Allan, N. Allan, A. Allanson, R. Allcock, E. Allen, J. Allen, K. Allen, L. Allen, P. Allen, R. Allen, S. Allen, T. Allen, A. Alli, K. Allison, B. Allman, H.K. Allsop, L. Allsop, D. Allsup, A.F.T. Almahroos, H. Al-Moasseb, M. Al-Obaidi, L. Alomari, A. Al-Rabahi, B. Al-Ramadhani, Z. Al-Saadi, R. Al-Sammarraie, I. Alshaer, R. Al-Shahi Salman, W. Al-Shamkhani, F. Alsheikh, B. Al-Sheklly, S. Altaf, A. Alty, M. Alvarez, M. Alvarez Corral, E. Alveyn, M. Alzetani, S. Amamou, N. Amar, S. Ambalavanan, R. Ambrogetti, C. Ambrose, A. Ameen, A. Amelia Ganefianty, K. Ames, M.R. Amezaga, A. Amin, K. Amin, S. Amin, T. Amin, B. Amit, A. Amjad, N. Amjad, J. Amoah-Dankwa, A. Amoako-Adusei, V. Amosun, M. Amsal, K. Amsha, J. Amuasi, N. Amutio Martin, P. Amy, A. Anada, A. Anand, S. Anandappa, S.D. Anantapatnaikuni, N.K.N. Andari, E. Anderson, J. Anderson, L. Anderson, M. Anderson, N. Anderson, R. Anderson, S. Anderson, W. Anderson, P. Andreou, A. Andrews, J. Andrews, K. Aneke, A. Ang, W.W. Ang, T. Angel, A. Angela, P. Angelini, L. Anguvaa, O. Anichtchik, M. Anim-Somuah, K. Aniruddhan, J. Annett, L. Anning, M. Ansah, P.J. Anstey, R. Anstey, A. Anthony, A. Anthony-Pillai, P. Antill, Z. Antonina, V. Anu, M. Anwar, S. Anwar, E. Apetri, A. Apostolopoulos, S. Appleby, D. Appleyard, M.F. Aquino, B. Araba, S. Aransiola, M. Araujo, A. Archer, D. Archer, S. Archer, D. Arcoria, C. Ardley, G. Arhin-Sam, A.-M. Arias, O. Aribike, R. Arimoto, N.L.P.E. Arisanti, C. Arkley, C. Armah, I. Armata, J. Armistead, A. Armitage, C. Armstrong, M. Armstrong, S. Armstrong, W. Armstrong, P. Armtrong, H. Arndt, C. Arnison-Newgass, D. Arnold, R. Arnold, A. Arnott, D. Arora, K. Arora, P. Arora, R. Arora, A. Arter, A. Arthur, N.M. Artini, A. Arumaithurai, A. Arya, R. Arya, D. Aryal, D. Asandei, G.A. Asare, A. Asghar, M. Asghar, A. Ashab, C. Ashbrook-Raby, H. Ashby, J. Ashcroft, S. Ashcroft, G. Asher, Z. Ashfak, A. Ashfaq, H.A. Asiamah, A. Ashish, D. Ashley, S. Ashman-Flavell, S. Ashok, A.-E.-A. Ashour, M.Z. Ashraf, S. Ashraf, M.B. Ashraq, D. Ashton, S. Ashton, A. Ashworth, F.J. Ashworth, R. Ashworth, A. Aslam, I. Aslam, S. Aslam, L. Aslett, H. Asogan, A. Asrar, O. Assaf, R. Astin-Chamberlain, Y.E. Atabudzi, P. Athavale, D. Athorne, B. Atkins, C. Atkins, S. Atkins, J. Atkinson, V. Atkinson, A. Atomode, B. Atraskiewicz, A.A. Attia, E. Attubato, M. Attwood, P. Aubrey, Z. Auer, A. Aujayeb, A.T. Aung, H. Aung, H.W.W. Aung, K.K. Aung, K.T. Aung, N. Aung, Y. Aung, Z.M. Aung, E. Austin, K. Austin, A. Auwal, M. Avari, M. Avery, N. Aveyard, J. Avis, G. Aviss, C. Avram, P. Avram, A. Awadelkareem, G. Awadzi, M. Awaly, A. Awan, S. Awisi, A. Aya, E. Ayaz, J.M. Ayerh, A. Ayers, J. Azam, A. Azeem, M. Azharuddin, A. Aziz, G. Aziz, I. Aziz, N. Aziz, A. Azkoul, A. Azman Shah, G. Azzopardi, H. Azzoug, F. Babatunde, M. Babi, B. Babiker, G. Babington, M. Babirecki, M. Babores, A.O. Babs-Osibodu, T. Bac, S. Bacciarelli, R. Bachar, M.-E. Bachour, A. Bachti, G. Bacon, J. Bacon, B. Badal, A. Badat, M. Bader, G.R. Badhan, S. Badhrinarayanan, J.P. Bae, A. Baggaley, A. Baggott, G. Bagley, D. Bagmane, L. Bagshaw, K. Bahadori, Y. Bahurupi, A. Bailey, J. Bailey, K. Bailey, L. Bailey, M.A. Bailey, M. Bailey, P. Bailey, S. Bailey, H. Baillie, J.K. Baillie, J. Bain, V. Bains, D. Baird, E. Baird, K. Baird, S. Baird, T. Baird, Y. Baird, A. Bajandouh, M. Bajracharya, D.C. Baker, E. Baker, J. Baker, K. Baker, M. Baker, R. Baker, T.-A. Baker, V. Baker, H. Bakere, N. Bakerly, M. Baker-Moffatt, A. Bakhai, N. Bakhtiar, P. Bakoulas, D. Bakthavatsalam, N. Balachandran, A. Balan, P. Balasingam, T. Balaskas, M. Balasubramaniam, N. Balatoni, A. Balcombe, A. Baldwin, C. Baldwin, D. Baldwin, F. Baldwin, R. Baldwin-Jones, N. Bale, J. Balfour, M. Ball, Ro Ball, K. Ballard, I. Balluz, C. Balmforth, E. Balogh, A. Baltmr, A. Baluwala, G. Bambridge, A. Bamford, P. Bamford, A. Bamgboye, E. Bancroft, H. Bancroft, J. Banda, K. Bandaru, S. Bandi, N. Bandla, S. Bandyopadhyam, A. Banerjee, R. Banerjee, P. Bang, S. Baniya, O. Bani-Saad, H. Banks, L. Banks, P. Banks, C. Bann, H. Bannister, O. Bannister, L. Banton, D.G. Bao, T. Bao, M. Baptist, T. Baqai, A.M. Baral, S.C. Baral, D. Baramova, R. Barber, E. Barbon, M. Barbosa, J. Barbour, A. Barclay, C. Barclay, G. Bardsley, S. Bareford, S. Bari, M. Barimbing, A. Barker, D. Barker, E. Barker, H. Barker, J. Barker, L. Barker, O. Barker, K. Barker-Williams, S. Barkha, J. Barla, G. Barlow, R. Barlow, V. Barlow, J. Barnacle, A. Barnard, D. Barnes, N. Barnes, R. Barnes, T. Barnes, C. Barnetson, A. Barnett, A. Barnett-Vanes, P.G. Barning, W. Barnsley, A. Barr, D. Barr, J. Barr, C. Barr, N. Barratt, S. Barratt, M. Barrera, A. Barrett, Fi Barrett, J. Barrett, S. Barrett, E. Barrow, J. Bartholomew, M.S. Barthwal, C. Bartlett, G. Bartlett, J. Bartlett, L. Bartlett, S. Bartley, S. Bartolmeu-Pires, A. Barton, G. Barton, J. Barton, L. Barton, R. Barton, R. Baruah, S. Baryschpolec, H. Bashir, A. Bashyal, B. Basker, S. Basnet, B. Basnyat, A. Basoglu, A. Basran, J. Bassett, G. Bassett, C. Bassford, B. Bassoy, V. Bastion, A. Bastola, A. Basumatary, P. Basvi, J.A. Batac, V.R. Bataduwaarachchi, T. Bate, H.J. Bateman, K. Bateman, V. Bateman, E. Bates, H. Bates, M. Bates, S. Bates, S. Batham, A. Batista, A. Batla, D. Batra, H. Batty, T. Batty, A. Batty, M. Baum, R. Baumber, C. Bautista, F. Bawa, T. Bawa, F.S. Bawani, S. Bax, M. Baxter, N. Baxter, Z. Baxter, H. Bayes, L.-A. Bayo, F. Bazari, R. Bazaz, A. Bazli, L. Beacham, W. Beadles, K. Beadon, P. Beak, A. Beale, K. Beard, J. Bearpark, A. Beasley, S. Beattie, K. Beaumont, D. Beaumont-Jewell, T. Beaver, S. Beavis, C. Beazley, S. Beck, V. Beckett, R. Beckitt, S. Beckley, H. Beddall, S. Beddows, D. Beeby, S. Beeby, G. Beech, M. Beecroft, N. Beer, Sa Beer, J. Beety, G. Bega, A. Begg, S. Begg, S. Beghini, A. Begum, S. Begum, T. Behan, R. Behrouzi, J. Beishon, C. Beith, J. Belcher, H. Belfield, K. Belfield, A. Belgaumkar, D. Bell, G. Bell, J. Bell, L. Bell, N. Bell, P. Bell, S. Bell, J. Bellamu, M. Bellamy, T. Bellamy, A. Bellini, A. Bellis, F. Bellis, L. Bendall, N. Benesh, N. Benetti, S.A. Bengu, L. Benham, G. Benison-Horner, S. Benkenstein, T. Benn, A. Bennett, C. Bennett, D. Bennett, G. Bennett, K. Bennett, L. Bennett, M.R. Bennett, S. Bennett, K. Bennion, G. Benoy, V. Benson, A. Bentley, J. Bentley, I. Benton, E. Beranova, M. Beresford, C. Bergin, M. Bergstrom, J. Bernatoniene, T. Berriman, Z. Berry, F. Best, K. Best, A.-M. Bester, Y. Beuvink, E. Bevan, S. Bevins, T. Bewick, A. Bexley, S. Beyatli, F. Beynon, A. Bhadi, S. Bhagani, S. Bhakta, R. Bhalla, K. Bhandal, A. Bhandari, L. Bhandari, L.N. Bhandari, S. Bhandari, J. Bhanich Supapol, A. Bhanot, R. Bhanot, S. Bhasin, A. Bhat, P. Bhat, R. Bhatnagar, K. Bhatt, J. Bhayani, D. Bhojwani, P. Bhuie, M.S. Bhuiyan, S. Bhuiyan, A. Bibby, F. Bibi, N. Bibi, S. Bibi, T. Bicanic, S. Bidgood, J. Bigg, S. Biggs, A. Biju, A. Bikov, S. Billingham, J. Billings, P. Binh, A. Binns, M. BinRofaie, O. Bintcliffe, C. Birch, J. Birch, K. Birchall, S. Bird, M. Birt, C. Bishop, K. Bishop, L. Bishop, K. Bisnauthsing, N. Biswas, M. Bittaye, S. Biuk, K. Blachford, E. Black, H. Black, K. Black, M. Black, P. Black, V. Black, H. Blackgrove, B. Blackledge, J. Blackler, S. Blackley, H. Blackman, C. Blackstock, C. Blair, F. Blakemore, H. Blamey, A. Bland, S. Blane, S. Blankley, P. Blaxill, K. Blaylock, J. Blazeby, N. Blencowe, B. Bloom, J. Bloomfield, A. Bloss, A. Blowers, S. Blows, H. Bloxham, S. Blrd, L. Blundell, A. Blunsum, M. Blunt, T. Blunt, I. Blyth, K. Blyth, A. Blythe, K. Blythe, K.A. Boahen, M. Boampoaa, S. Board, E. Boatemah, B. Bobie, K. Bobruk, P.N. Bodalia, N. Bodasing, M. Boden, T. Bodenham, G. Boehmer, M. Boffito, K. Bohanan, K. Bohmova, N. Bohnacker, S. Bokhandi, M. Bokhar, S. Bokhari, S.O. Bokhari, I. Bokobza, A. Boles, C. Bolger, C. Bonaconsa, C. Bond, H. Bond, S. Bond, T. Bond, A. Bone, G. Boniface, J. Bonney, L. Bonney, L. Booker, S. Boot, M. Boothroyd, J. Borbone, N. Borman, S. Bosence, K. Bostock, N. Botting, F. Bottrill, H. Bouattia, L. Bough, H. Boughton, Z. Boult, T. Boumrah, M. Bourke, S. Bourke, M. Bourne, R. Bousfield, L. Boustred, A. Bowes, P. Bowker, T. Bowker, H. Bowler, L. Bowman, S. Bowman, R. Bowmer, A. Bowring, H. Bowyer, A. Boyd, J. Boyd, L. Boyd, N. Boyer, N. Boyle, P. Boyle, R. Boyle, L. Boyles, L. Brace, A. Bracken, J. Bradder, C.J. Bradley, P. Bradley, J. Bradley-Potts, L. Bradshaw, Z. Bradshaw, C. Brady, R. Brady, S. Brady, P. Braga Sardo, D. Braganza, M. Braithwaite, S. Brammer, M. Branch, T. Brankin-Frisby, J. Brannigan, S. Brattan, F. Bray, N. Bray, M. Brazil, L. Brear, Tr Brear, S. Brearey, L. Bremner, M. Brend, C. Bresges, C. Bressington, G. Bretland, C. Brewer, M. Bridgett, G. Bridgwood, S. Brigham, J. Bright, C. Brightling, T. Brigstock, L. Brimfield, P. Brinksman, E. Brinkworth, R. Brittain-Long, V. Britten, H. Britton, L. Broad, S. Broadhead, R. Broadhurst, A. Broadley, M. Broadway, C. Brockelsby, M. Brocken, T. Brockley, M. Brodsky, F. Brogan, L. Brohan, F. Brokke, J. Brolly, D. Bromley, H. Brooke-Ball, V. Brooker, M. Brookes, D. Brooking, A. Brooks, D. Brooks, J. Brooks, K. Brooks, N. Brooks, P. Brooks, R. Brooks, S. Brooks, M. Broom, N. Broomhead, C. Broughton, N. Broughton, M. Brouns, A. Brown, C. Brown, E. Brown, H. Brown, J. Brown, L. Brown, N. Brown, P. Brown, R. Brown, S. Brown, T. Brown, B. Browne, C. Browne, D. Browne, M. Browne, S. Brownlee, A. Brraka, J. Bruce, M. Bruce, W. Brudlo, A. Brunchi, N. Brunskill, A. Brunton, M. Brunton, M. Bryant, E. Bryden, H. Brzezicki, A. Buazon, M.H. Buch, R. Buchan, R. Buchanan, D. Buche, A. Buck, L. Buck, M. Buckland, C. Buckley, L. Buckley, P. Buckley, S. Buckley, C. Buckman, A. Budds, G. Bugg, R. Bujazia, M. Bukhari, S. Bukhari, R. Bulbulia, A. Bull, D. Bull, K. Bull, R. Bull, Th Bull, E. Bullock, S. Bullock, N. Bulteel, K. Bumunarachchi, R. Bungue-Tuble, O. Burbidge, C. Burchett, D. Burda, C. Burden, T.G. Burden, Mi Burgess, R. Burgess, S. Burgess, E. Burhan, H. Burhan, H. Burke, K. Burke, A. Burman, S. Burnard, C. Burnett, S. Burnett, A. Burns, C. Burns, J. Burns, K. Burns, D. Burrage, K. Burrows, C. Burston, A. Burton, B. Burton, F. Burton, H. Burton, M. Burton, M. Butar butar, D. Butcher, A. Butler, E. Butler, J. Butler, P. Butler, S. Butler, A.-T. Butt, M. Butt, M.M. Butt, C. Butterworth, N. Butterworth-Cowin, R. Buttery, T. Buttle, H. Button, D. Buttress, H. Bye, J. Byrne, W. Byrne, V. Byrne-Watts, N.K. C, A. Cabandugama, L. Cabrero, S. Caddy, R. Cade, A. Cadwgan, Z. Cahilog, A. Cahyareny, D. Cairney, J. Calderwood, D. Caldow, E. Cale, G. Calisti, D. Callaghan, J. Callaghan, C. Callens, D. Callum, C. Calver, M. Cambell-Kelly, T. Camburn, D.R. Cameron, E. Cameron, F. Cameron, S. Cameron, C. Camm, F.D. Cammack, A. Campbell, B. Campbell, D. Campbell, H. Campbell, J. Campbell, K. Campbell, M. Campbell, R. Campbell, W. Campbell, Q. Campbell Hewson, J. Camsooksai, L. Canclini, S.M. Candido, J. Candlish, C. Caneja, A. Cann, J. Cann, R. Cannan, A. Cannon, E. Cannon, M. Cannon, P. Cannon, V. Cannons, E. Canonizado, J. Cantliff, N. Cap, N.T. Cap, B. Caplin, S. Capocci, N. Caponi, A. Capp, R. Capstick, T. Capstick, C. Caraenache, A. Card, M. Cardwell, C. Carey, R. Carey, S. Carley, F. Carlin, T. Carlin, S. Carmichael, M. Carmody, M. Carnahan, C. Caroline, J. Carpenter, S. Carr, A. Carrasco, Z. Carrington, A. Carroll, P. Carroll, R. Carson, C. Cart, E. Carter, J. Carter, M. Carter, N. Carter, P. Carter, D. Cartwright, J.-A. Cartwright, C. Carty, L. Carty, J. Carungcong, C. Carver, E. Carver, R. Carver, S. Casey, A. Cassells, T. Castiello, G. Castle, B. Castles, M. Caswell, A.M. Catana, H. Cate, A. Catelan Zborowski, S. Cathcart, K. Cathie, D. Catibog, C. Catley, L. Catlow, M. Caudwell, A. Cavazza, A. Cave, L. Cave, S. Cavinato, F. Cawa, K. Cawley, C. Caws, K. Cawthorne, H. Cendl, H. Century, J. Cernova, M. Cesay, E. Cetti, S. Chabane, M. Chablani, C. Chabo, J. Chacko, D. Chadwick, J. Chadwick, R. Chadwick, E. Chakkarapani, A. Chakraborty, M. Chakraborty, M. Chakravorty, P. Chalakova, B. Chalise, B.S. Chalise, J. Chalmers, R. Chalmers, G. Chamberlain, S. Chamberlain, E. Chambers, J. Chambers, L. Chambers, N. Chambers, A. Chan, C. Chan, E. Chan, M. Chan, K. Chan, P. Chan, R. (P-C). Chan, X.H.S. Chan, C. Chandler, H. Chandler, K.J. Chandler, S. Chandler, Z. Chandler, S. Chandra, N. Chandran, B. Chandrasekaran, Y. Chang, H. Chanh, H.Q. Chanh, G. Chaplin, J. Chaplin, G. Chapman, J. Chapman, K. Chapman, L. Chapman, M. Chapman, P. Chapman, T. Chapman, L. Chappell, A. Charalambou, B. Charles, D. Charlton, S. Charlton, K. Chatar, C. Chatha, D. Chatterton, N. Chau, R. Chaube, A. Chaudhary, M.Y.N. Chaudhary, B. Chaudhary, I. Chaudhry, Z. Chaudhry, K. Chaudhuri, N. Chaudhuri, M. Chaudhury, A. Chauhan, R.S. Chauhan, L. Chaulagain, A. Chavasse, N. Chavasse, V. Chawla, L. Cheater, J. Cheaveau, C. Cheeld, M. Cheeseman, F. Chen, H.M. Chen, T. Chen, F. Cheng, L.Y. Cheng, Z. Cheng, H. Chenoweth, C.H. Cheong, J.J. Cherian, S. Cherian, M. Cherrie, H. Cheshire, C.K. Cheung, E. Cheung, K. Cheung, M. Cheung, C. Cheyne, S. Chhabra, W.L. Chia, E. Chiang, A. Chiapparino, R. Chicano, G. Chikara, M. Chikungwa, Z.A. Chikwanha, G. Chilcott, S. Chilcott, A. Chilvers, P. Chimbo, K.W. Chin, W.J. Chin, R. Chineka, A. Chingale, E. Chinonso, C. Chin-Saad, M. Chirgwin, H. Chisem, C. Chisenga, C. Chisholm, B. Chisnall, C. Chiswick, S. Chita, N. Chitalia, M. Chiu, L. Chiverton, B. Chivima, C. Chmiel, S. Choi, W. Choon Kon Yune, M. Chopra, V. Choudhary, O. Choudhury, S. Choudhury, B.-L. Chow, M. Chowdhury, S. Chowdhury, A. Chrisopoulou, V. Christenssen, P. Christian, A. Christides, F. Christie, D. Christmas, G. Christoforou, T. Christopherson, A. Christou, M. Christy, P. Chrysostomou, Y. Chua, D. Chudgar, R. Chudleigh, S. Chukkambotla, M.E. Chukwu, I. Chukwulobelu, C.Y. Chung, E. Church, S.R. Church, D. Churchill, N. Cianci, P. Cicconi, P. Cinardo, Z. Cipinova, B. Cipriano, S. Clamp, B. Clancy, M. Clapham, E. Clare, S. Clare, A. Clark, C. Clark, D. Clark, E. Clark, F. Clark, G. Clark, J. Clark, K. Clark, L. Clark, M. Clark, N. Clark, P. Clark, R. Clark, T. Clark, Z. Clark, A. Clarke, J. Clarke, P. Clarke, R. Clarke, S. Clarke, A. Claxton, L. Claxton, K. Clay, C. Clayton, E. Clayton, O. Clayton, J. Clayton-Smith, B. Clearyb, C. Cleaver, R. Cleeton, I. Clement, C. Clemente de la Torre, J. Clements, S. Clements, S. Clenton, S. Cliff, R. Clifford, S. Clifford, A. Clive, J. Clouston, V. Clubb, S. Clueit, L. Clutterbuck, A. Clyne, M. Coakley, P.G.L. Coakley, K. Cobain, A. Cochrane, P. Cochrane, L. Cockayne, M. Cockerell, H. Cockerill, S. Cocks, R. Codling, A. Coe, S. Coetzee, D. Coey, D. Cohen, J. Cohen, O. Cohen, M. Cohn, L. Coke, O. Coker, N. Colbeck, R. Colbert, E. Cole, J. Cole, G. Coleman, M. Coleman, N. Coleman, H. Coles, M. Colin, A. Colino-Acevedo, J. Colley, K. Collie, A. Collier, D. Collier, H. Collier, T. Collingwood, P. Collini, E. Collins, J. Collins, K. Collins, M. Collins, N. Collins, S. Collins, V. Collins, A. Collinson, B. Collinson, J. Collinson, M. Collis, M. Colmar, H.E. Colton, J. Colton, K. Colville, C. Colvin, E. Combes, D. Comer, A. Comerford, D. Concannon, A. Condliffe, R. Condliffe, E. Connell, L. Connell, N. Connell, K. Connelly, G. Connolly, E. Connor, A. Conroy, K. Conroy, V. Conteh, R. Convery, F. Conway, G. Conway, R. Conway, J.-A. Conyngham, A. Cook, C. Cook, E. Cook, G. Cook, H. Cook, J. Cook, M. Cook, S. Cook, D. Cooke, G. Cooke, H. Cooke, J. Cooke, K. Cooke, T. Cooke, V. Cooke, A. Cooper, C. Cooper, D. Cooper, H. Cooper, J. Cooper, L. Cooper, N. Cooper, R. Cooper, S. Cooray, T. Cope, S. Corbet, C. Corbett, A. Corbishley, J. Corcoran, C. Cordell, J. Cordle, A. Corfield, J. Corless, A. Corlett, J. Cornwell, M. Cornwell, D. Corogeanu, A. Corr, M. Corredera, R. Corrigan, P. Corry, R. Corser, J. Cort, D. Cosgrove, T. Cosier, P. Costa, T. Costa, C. Coston, S. Cotgrove, Z. Coton, L.-J. Cottam, R. Cotter, D. Cotterill, C. Cotton, G. Couch, M. Coulding, A. Coull, D. Counsell, D. Counter, C. Coupland, E. Courtney, J. Courtney, R. Cousins, A.J. Coutts, A. Cowan, E. Cowan, R. Cowan, R. Cowell, L. Cowen, S. Cowman, A. Cowton, E. Cox, G. Cox, H. Cox, K. Cox, M. Cox, K. Coy, A. Cradduck-Bamford, H. Craig, J. Craig, V. Craig, F. Craighead, M. Cramp, H. Cranston, S.S. Crasta, J. Crause, A. Crawford, E. Crawford, I. Crawford, S. Crawshaw, B. Creagh-Brown, A. Creamer, A. Creaser-Myers, J. Cremona, S. Cremona, A. Crepet, J. Cresswell, M. Cribb, C. Crichton, D. Crilly, L. Crisp, N. Crisp, D. Crocombe, M. Croft, J. Crooks, H. Crosby, E. Cross, T. Cross, A. Crothers, S. Crotty, S. Crouch, M. Crow, A. Crowder, K. Crowley, T. Crowley, R. Croysdill, C. Cruickshank, I. Cruickshank, J. Cruise, C. Cruz, T. Cruz Cervera, D. Cryans, G. Cui, H. Cui, L. Cullen, G. Cummings-Fosong, V. Cunliffe, N. Cunningham, J. Cupitt, H. Curgenven, G. Curnow, D. Curran, S. Curran, C. Currie, J. Currie, S. Currie, J. Curtis, K. Curtis, M. Curtis, O. Curtis, T. Curtis, R. Cuthbertson, J. Cuthill, S. Cutler, S. Cutts, M. Czekaj, P. Czylok, S. D’Souza, J. da Rocha, G.S. Dadzie, M. Dafalla, A. Dagens, H. Daggett, J. Daglish, S. Dahiya, A. Dale, K. Dale, M. Dale, S. Dale, J. Dales, U. D'Alessandro, H. Dalgleish, H. Dallow, C. D'aloia, D. Dalton, M. Dalton, Z. Daly, M. Damani, E. Damm, L. Dan, A. Danga, J. Dangerfield, A. Daniel, P. Daniel, A. Daniels, A. Dann, K.G. Danso, S. Danso-Bamfo, Q.T. Dao, S. Darby, A. Darbyshire, J. Darbyshire, P. Dargan, P. Dark, K. Darlington, S. Darnell, T. Darton, G. Darylile, A. Das, M. Das, S. Das, M. Daschel, J. Dasgin, D. Datta, A. Daunt, V. Dave, E. Davenport, M. Davey, M. David, A. Davidson, L. Davidson, N. Davidson, R. Davidson, A. Davies, B. Davies, C. Davies, D. Davies, E. Davies, F. Davies, G. Davies, H. Davies, J. Davies, K. Davies, L. Davies, M. Davies, N. Davies, O. Davies, P. Davies, R. Davies, S. Davies, A. Davis, J.-A. Davis, K. Davis, P. Davis, A. Davis-Cook, A. Davison, C. Dawe, H. Dawe, M. Dawkins, A. Dawson, D. Dawson, E. Dawson, J. Dawson, L. Dawson, M. Dawson, S. Dawson, T. Dawson, I. Dawson, A. Daxter, A. Day, J. Day, J. D'Costa, P. De, D. de Fonseka, T. de Freitas, P. De Los Santos Dominguez, R. De Pretto, F. De Santana Miranda, E. de Sausmarez, S. de Silva, T. de Silva, J. De Sousa, P. De Sousa, J. de Souza, P. De Souza, A. De Soyza, N. de Vere, J. de Vos, B. Deacon, S. Dealing, A. Dean, J. Dean, K. Dean, S. Dean, T. Dean, J. Deane, J. Dear, E. Dearden, C. Deas, S. Debbie, G. Debreceni, V. Deelchand, M. Deeley, J. Deery, E. Defever, M. Del Forno, A. Dela Rosa, G. De-La-Cedra, A. Dell, C. Demetriou, D. DeMets, J. Democratis, J. Denham, E. Denis, L. Denley, C. Denmade, A. Dent, K. Dent, M. Dent, E. Denton, T. Denwood, N. Deole, D. Depala, M. Depante, S. Dermody, A. Desai, P. Desai, S. Deshpande, V. Deshpande, S. Devkota, U. Devkota, D. Devonport, M. Devonport, P. Dey, V. Dey, R. Deylami, K. Dhaliwal, P. Dhangar, S. Dhani, A. Dhanoa, M. Dhar, A. Dhariwal, D. Dharmasena, D. Dhasmana, E. Dhillon, R. Dhillon, S. Dhillon, M. Dhimal, D. Dhiru, T. Dhorajiwala, P. Dias, S. Diaz, K. Diaz-Pratt, M. Dibas, D. Dickerson, P. Dicks, M. Dickson, S. Dickson, J. Digby, R. Digpal, S. Dillane, S. Diment, P. Dimitri, G. Dimitriadis, S. Din, T.H. Dinh, T.T.T. Dinh, C. Dipheko, A. Dipper, S. Dipro, L. Dirmantaite, L. Dismore, L. Ditchfield, S. Diver, L. Diwakar, P. Diwan, C. Dixon, G. Dixon, K. Dixon, B. Djeugam, S. Dlamini, P. Dlouhy, A. D'Mello, P. Dmitri, T. Do, T.T. Do, L. Dobbie, M. Dobranszky Oroian, C. Dobson, L. Dobson, M. Docherty, D. Dockrell, J. Dodd, J. Dodds, R. Dodds, S. Dodds, R. Dogra, C. Doherty, E. Doherty, W. Doherty, Y. Doi, I. Doig, E. Doke, D. Dolan, M. Dolman, R. Dolman, L. Donald, K. Donald, C. Donaldson, D. Donaldson, G. Donaldson, K. Donaldson, P. Dong, P.K. Dong, M. Donkor, S. Donlon, J. Donnachie, E. Donnelly, R. Donnelly, P. Donnison, A. Donohoe, G. Donohoe, B. Donohue, E. Dooks, R. Doonan, R. Doorn, G. Doran, R. Dore, K. Dorey, S. Dorgan, K. Dos Santos, M. Dosani, D. Dosanjh, P. Dospinescu, I. Doss, T. Doudouliaki, A. Dougherty, J. Doughty, K. Douglas, J. Douse, A. Dow, L. Dowden, M. Dower, S. Dowling, N. Downer, C. Downes, R. Downes, T. Downes, D. Downey, R. Downey, C. Downing, L. Downs, S. Dowson, C. Dragan, C. Dragos, M. Drain, C. Drake, V. Drew, O. Drewett, A. Drexel, C. Driscoll, H. Drogan, O. Drosos, G. Drummond, K. Drury, K. Druryk, R. Druyeh, J. Dryburgh-Jones, S. Drysdale, P. Dsouza, A. Du Thinh, I.K. Duah, H. Dube, J. Dube, S. Duberley, P. Duckenfield, H. Duckles-Leech, N. Duff, E. Duffield, H. Duffy, K. Duffy, L. Dufour, A. Duggan, P. Dugh, R. Duhoky, J. Duignan, J. Dulay, S. Dummer, A. Duncan, C. Duncan, F. Duncan, G. Duncan, H. Duncan, R. Duncan, S. Dundas, D.V. Dung, N.T.P. Dung, A. Dunleavy, J. Dunleavy, A. Dunn, C. Dunn, D. Dunn, L. Dunn, P. Dunn, C. Dunne, K. Dunne, F. Dunning, A. Dunphy, T. Duong, T.T.H. Duong, V. Duraiswamy, B. Duran, I. DuRand, L. Durdle, N. Duric, A. Durie, E. Durie, S. Durogbola, C. Durojaiye, L. Durrans, K. Durrant, H. Durrington, I. Duru, H. Duvnjak, A. Dwarakanath, L. Dwarakanath, D. Dwomoh Nkrumah, E. Dwyer, Z. Dyar, C. Dyball, K. Dyer, H. Dymond, T. Dymond, E.D. Dzidzomu, C. Eades, L. Eadie, R. Eadie, L. Eagles, B. Eapen, N. Earl, J. Early, M. Earwaker, N. Easom, C. East, A. Easthope, F. Easton, J. Easton, P. Easton, R. Eatough, O. Ebigbola, M. Ebon, A. Eccles, S. Eccles, C. Eddings, M. Eddleston, M. Edgar, K. Edgerley, N. Edmond, M. Edmondson, T. Edmunds, A. Edwards, C. Edwards, J. Edwards, K. Edwards, M. Edwards, S. Edwards, J. Eedle, A. Eggink, S. Eggleston, L. Ehiorobo, S. Eisen, M. Ekoi, A. Ekunola, N. Elashbar, L. Elawamy, D. Eleanor, S. El Behery, M. Elbeshy, K. El-Bouzidi, M. El-Din, E. Eldridge, U. Elenwa, I. Eletu, E. Elfar, M. Elgamal, A. Elgohary, N. Elkaram, R. Elmahdi, S. Eliammmknjhhhh, J. Elias, T. Elias, A. Elkins, J. Ellam, L. Ellerton, L. Elliot, A. Elliott, F. Elliott, K. Elliott, S. Elliott, A. Ellis, C. Ellis, K. Ellis, L. Ellis, R. Ellis, T. Ellis, T.-Y. Ellis, Y. Ellis, A. Ellwood, E. Elmahi, H.-M. Elmasry, A. Emery, M. El-Naggar, N. Elndari, O. Elneima, M. Elokl, A. Elradi, M. Elsaadany, M.A.S.A. Elsayed, S. El-Sayeh, H. El-Sbahi, M. Elsebaei, T. Elsefi, K. El-Shakankery, A. Elsheikh, H. El-Taweel, S. Elyoussfi, J. Emberey, J.R. Emberson, J. Emberton, J. Emmanuel, I. Emmerson, M. Emms, F. Emond, M. Emonts, N. Enachi, D. Enenche, A. Engden, K. English, C. Enimpah, E. Entwistle, H. Enyi, M. Erotocritou, P. Eskander, H. Esmail, F. Essa, B. Evans, C. Evans, D. Evans, E. Evans, G. Evans, I. Evans, J. Evans, L. Evans, M. Evans, R. Evans, S. Evans, T. Evans, C. Everden, S. Everden, L. Every, H. Evison, L. Evison, C. Ezenduka, J. Faccenda, L. Fahel, Y. Fahmay, I. Fairbairn, S. Fairbairn, T. Fairbairn, A. Fairclough, L. Fairlie, M. Fairweather, A. Fajardo, N. Falcone, E. Falconer, J. Fallon, A. Fallow, D. Faluyi, V. Fancois, A. Farah, M. Farah, Q. Farah, N.Z. Fard, L. Fares, A. Farg, A. Farmer, K. Farmer, T. Farmery, S. Farnworth, F. Farook, H. Farooq, S. Farooq, F. Farquhar, H. Farr, A. Farrell, B. Farrell, F. Farrukh, J. Farthing, S. Farzana, R. Fasina, A. Fatemi, M. Fatemi, S. Fathima, N. Fatimah, M. Faulkner, S. Faust, C. Favager, A. Fawad, J. Fawke, S. Fawohunre, A. Fazal, A. Fazleen, S. Fearby, C. Fearnley, A. Feben, F. Fedel, D. Fedorova, C. Fegan, M. Felongo, L. Felton, T. Felton, K. Fenlon, A. Fenn, R. Fennelly, I. Fenner, C. Fenton, M. Fenton, G. Ferenando, C. Ferguson, J. Ferguson, K. Ferguson, S. Ferguson, V. Ferguson, D. Fernandes, C. Fernandez, E. Fernandez, M. Fernandez, S. Fernandez Lopez, J. Fernandez Roman, C.J. Fernando, J. Fernando, A. Feroz, P. Ferranti, T. Ferrari, E. Ferrelly, A. Ferrera, E. Ferriman, S. Ferron, N. Fethers, B. Field, J. Field, R. Field, K. Fielder, L. Fieldhouse, A. Fielding, J. Fielding, S. Fielding, A. Fikree, S. Filipa, S. Filson, S. Finan, S. Finbow, D.J. Finch, J. Finch, L. Finch, S. Finch, N. Fineman, J. Finlayson, L. Finlayson, A. Finn, J. Finn, D. Finnerty, C. Finney, D. Finucane, S. Fiouni, J. Fiquet, P. Firi, J. Fisher, N. Fisher, D. Fishman, K. Fishwick, C. Fitton, F. Fitzgerald, K. Fitzjohn, J. Flaherty, M. Flanagan, C. Flanders, N. Flaris, G. Fleming, J. Fleming, L. Fleming, P. Fleming, W. Flesher, A. Fletcher, J. Fletcher, L. Fletcher, S. Fletcher, F. Flett, K. Flewitt, S. Flockhart, C. Flood, I. Floodgate, J. Flor, V. Florence, M. Flowerdew, S. Floyd, M.J. Flynn, R. Flynn, C. Foden, A. Fofana, G. Fogarty, P. Foley, L. Folkes, T. Fong, D.M. Font, A. Foo, J. Foo, A. Foot, H.R. Foot, J. Foot, J. Forbes, A. Ford, J. Ford, I. Fordham, J. Foreman, M. Forester, M. Forkan, C. Fornolles, A. Forrest, E. Forsey, M. Forsey, T. Forshall, E. Forster, A. Forsyth, J. Forton, C. Foster, E. Foster, J. Foster, R.A. Foster, T. Foster, A. Foulds, I. Foulds, F. Fowe, N. Fowkes, E. Fowler, R. Fowler, S. Fowler, A. Fox, C. Fox, H. Fox, J. Fox, L. Fox, N. Fox, O. Fox, S. Fox, S.-J. Foxton, E. Fraile, R. Frake, A. Francioni, O. Francis, R. Francis, S. Francis, T. Francis-Bacon, H. Frankland, J. Franklin, S. Franklin, C. Fraser, A. Fratila, S. Frayling, M. Fredlund, A. Freeman, C. Freeman, E. Freeman, H. Freeman, N. Freeman, C. Freer, E. French, T. French, K. Freshwater, M. Frise, R. Fromson, A. Frosh, J. Frost, V. Frost, O. Froud, R. Frowd, A. Fryatt, A. Frygier, B. Fuller, L. Fuller, T. Fuller, D. Fullerton, C. Fung, G. Fung, S. Funnell, J. Furness, A. Fyfe, N. G, E. Gabbitas, C. Gabriel, Z. Gabriel, H. Gachi, S. Gaffarena, S. Gage, J. Gahir, S. Gajebasia, K. Gajewska-Knapik, B. Gajmer, Z. Galani, C. Gale, H. Gale, L. Gale, R. Gale, S. Gali, B. Gallagher, J. Gallagher, R. Gallagher, W. Gallagher, F. Gallam, J. Galliford, C. Galloway, E. Galloway, J. Galloway, A. Galvin, V. Galvis, G. Gamble, L. Gamble, B. Gammon, C.N. Gan, M.B. Ganaie, J. Ganapathi, R. Ganapathy, K. Gandhi, S. Gandhi, U. Ganesh, T. Ganeshanathan, S. Ganguly, A. Gani, P. Ganley, U. Garcia, E.-J. Garden, A.D. Gardener, E. Gardiner, M. Gardiner, P. Gardiner, S. Gardiner, C. Gardiner-Hill, J. Gardner, L. Gardner, M. Garfield, A. Garg, I. Garg, N. Garlick, D. Garner, J. Garner, L. Garner, Z. Garner, R. Garr, K.A. Garrero, M. Gartaula, F. Garty, R. Gascoyne, H. Gashau, A. Gatenby, E. Gaughan, A. Gaurav, M. Gavrila, J. Gaylard, S. Gayle, C. Geddie, I. Gedge, S. Gee, F. Geele, K. Geerthan, M. Gellamucho, K. Gelly, L. Gelmon, S. Gelves-Zapata, G. Genato, N. Gent, S. Gent, N. Geoghegan, A. George, B. George, S. George, T. George, V.P. George, S. Georges, D. Georgiou, P. Gerard, L. Gerdes, L. Germain, H. Gerrish, A. Getachew, L. Gethin, S. Gettings, H. Ghanayem, B. Ghavami Kia, S. Ghazal, A. Gherman, A. Ghosh, D. Ghosh, J. Ghosh, S. Ghosh, T. Giang, T.V. Giang, S. Giannopoulou, M. Gibani, C. Gibb, B. Gibbison, K. Gibbons, A. Gibson, B. Gibson, J. Gibson, K. Gibson, S. Gibson, M. Gigi, C. Gilbert, J. Gilbert, K. Gilbert, B. Giles, J. Gilham, M. Gill, L. Gill, P. Gillen, A. Gillesen, K. Gillespie, E. Gillham, A. Gillian, D. Gilliland, R. Gillott, D. Gilmour, K. Gilmour, L. Gilmour, L. Ginn, F. Ginting, T. Giokanini-Royal, A. Gipson, B. Giri, J. Girling, R. Gisby, A. Gkioni, A. Gkoritsa, E. Gkrania-Klotsas, A. Gladwell, J. Glanville, J. Glasgow, S. Glasgow, J. Glass, L. Glass, S. Glaysher, L. Gledhill, E. Glenday, A. Glennon, J. Glossop, J. Glover, K. Glover, M. Glover, J. Glover Bengtsson, D. Glowski, S. Glynn, C. Gnanalingam, J. Goddard, W. Goddard, E. Godden, J. Godden, S. Godlee, E. Godson, G. Godwin, S. Gogoi, A. Goh, M. Gohel, R. Goiriz, S. Gokaraju, R. Goldacre, A. Goldsmith, P. Goldsmith, D. Gomersall, L. Gomez, R. Gomez-Marcos, A. Gondal, C. Gonzalez, J. Goodall, V. Goodall, B. Goodenough, A. Goodfellow, L. Goodfellow, J. Goodlife, C. Goodwin, E. Goodwin, J. Goodwin, P. Goodyear, R. Gooentilleke, M. Goonasekara, S. Gooseman, S. Gopal, C. Gordon, S. Gordon, R. Gore, H. Gorick, C. Gorman, S. Gormely, M. Gorniok, D. Gorog, M. Gorst, T. Gorsuch, J. Gosai, R. Gosling, S. Gosling, G. Gosney, V. Goss, D. Gotham, N. Gott, E. Goudie, N. Gould, S. Gould, C. Goumalatsou, L. Gourbault, A. Govind, R. Govindan, S. Gowans, G. Gowda, R. Gowda, H. Gower, P. Goyal, S. Goyal, C. Graham, J. Graham, L. Graham, R. Graham, S. Graham, M. Graham-Brown, J. Grahamslaw, G. Grana, T. Grandison, L. Grandjean, A. Grant, D. Grant, K. Grant, M. Grant, P. Grant, R. Gravell, J. Graves, A. Gray, C. Gray, G. Gray, J. Gray, K. Gray, N. Gray, R. Gray, S. Gray, A. Grayson, F. Greaves, P. Greaves, A. Green, A.S. Green, C. Green, C.A. Green, D. Green, F. Green, J. Green, M. Green, N. Green, S. Green, D. Greene, P. Greenfield, A. Greenhalgh, D. Greenwood, S. Greer, J. Gregory, K. Gregory, T. Gregory, J. Greig, R. Grenfell, T. Grenier, J. Grenville, J. Gresty, S. Grevatt, G. Grey, S. Gribben, A. Gribbin, A. Gribble, N. Grieg, D. Grieve, B. Griffin, D. Griffin, M. Griffin, S. Griffith, A. Griffiths, D. Griffiths, I. Griffiths, M. Griffiths, N. Griffiths, O. Griffiths, S. Griffiths, Y. Griffiths, S. Grigoriadou, S. Grigsby, P. Grist, E. Grobovaite, D. Grogono, C. Grondin, R. Groome, P. Grose, L. Grosu, J. Grounds, M. Grout, H. Grover, J. Groves, N. Grubb, J. Grundy, F. Guarino, S. Gudur, J. Guerin, S. Guettari, S. Gulati, V. Gulia, H. Gunasekara, P. Gunasekera, M. Gunawardena, K. Gunganah, J. Gunn, E. Gunter, A. Gupta, A.K. Gupta, R. Gupta, T. Gupta, V. Gupta, A. Gupta-Wright, V. Guratsky, A. Gureviciute, S. Gurram, A. Gurung, B. Gurung, L. Gurung, S. Gurung, S. Gurung Rai, H. Guth, N. Guthrine, S. Gyambrah, P. Gyanwali, S. Gyawali, N. Ha, N.T. Ha, N.X. Ha, R. Habibi, B. Hack, J. Hackett, P. Hackney, C. Hacon, A. Haddad, D. Hadfield, N. Hadfield, S. Hadfield, M. Hadjiandreou, N. Hadjisavvas, A. Haestier, N. Hafiz, R. Hafiz-Ur-Rehman, J. Hafsa, S. Hagan, J.W. Hague, R. Hague, N. Haider, K. Haigh, V. Haile, J. Hailstone, C. Haines, S. Hainey, M. Hair, B. Hairsine, J. Hajnik, D. Hake, L. Hakeem, A. Haldeos, W. Halder, E. Hale, J. Hale, C. Halevy, P. Halford, W. Halford, A. Halim, A. Hall, C. Hall, E. Hall, F. Hall, H. Hall, J. Hall, K. Hall, L. Hall, J. Hallas, K. Hallas, C. Hallett, J. Halliday, A. Hallman, H. Halls, M. Hamdollah-Zadeh, I.A. Hamed-Adekale, B. Hameed, M. Hameed, R. Hamers, I. Hamid, M. Hamie, R. Hamill, B. Hamilton, F. Hamilton, G. Hamilton, L. Hamilton, M. Hamilton, N. Hamilton, S. Hamilton, R. Hamlin, E. Hamlyn, B. Hammans, S. Hammersley, K. Hammerton, B. Hammond, E. Hammond, L. Hammond, S. Hammond, F. Hammonds, I. Hamoodi, K. Hampshire, J.A. Hampson, J. Hampson, L. Hampson, L. Hamzah, J. Han, O. Hanci, S. Hand, L. Handayani, J. Handford, S. Handrean, N.K. Handzewniak, S. Haney, D.T.T. Hang, V. Hang, V.T.K. Hang, D. Hanh, S. Hanif, E. Hanison, J. Hannah, A. Hannington, M. Hannun, A. Hanrath, H. Hanratty, D. Hansen, A. Hanson, H. Hanson, J. Hanson, K. Hanson, S. Hanson, N. Hao, A. Haqiqi, M. Haque, H. Harcourt, L. Harden, Z. Harding, S. Hardman, M. Hardwick, G. Hardy, J. Hardy, Y. Hardy, K. Haresh, R. Harford, B. Hargadon, J. Hargraves, C. Hargreaves, A. Harin, M. Haris, E. Harlock, P. Harman, T. Harman, M. Harmer, M.A. Haroon, C. Harper, H. Harper, J. Harper, P. Harper, R. Harper, S. Harrhy, K. Harrington, S. Harrington, Y. Harrington-Davies, J. Harris, L. Harris, M.-C. Harris, N. Harris, S. Harris, D. Harrison, L. Harrison, M. Harrison, O.A. Harrison, R. Harrison, S. Harrison, T. Harrison, W. Harrison, E. Harrod, C. Hart, D. Hart, J. Hartley, L. Hartley, R. Hartley, T. Hartley, W. Hartrey, P. Hartridge, S. Hartshorn, A. Harvey, M. Harvey, C. Harwood, H. Harwood, Z. Harzeli, B. Haselden, H. Hasford, K. Hashem, M. Hashimm, T. Hashimoto, I. Hashmi, J. Haslam, Z. Haslam, G. Hasnip, A. Hassan, Z. Hassan, S. Hassasing, J. Hassell, P. Hassell, A. Hastings, B. Hastings, J. Hastings, S. Hathaway-Lees, J. Hatton, J. Hau, M. Havinden-Williams, S. Havlik, D.B. Hawcutt, K. Hawes, L. Hawes, N. Hawes, L. Hawker, A. Hawkins, J. Hawkins, N. Hawkins, W. Hawkins, D. Hawley, E. Hawley-Jones, E. Haworth, A.W. Hay, C. Hay, A. Hayat, J. Hayat, M.-R. Hayathu, A. Hayes, J. Hayes, K. Hayes, M. Hayes, F. Hayes, P. Hayle, C. Haylett, A. Hayman, M. Hayman, M. Haynes, R. Haynes, R. Hayre, C. Hays, S. Haysom, J. Hayward, P. Haywood, H. Haywood Hasford, T. Hazelton, P. Hazenberg, Z. He, E. Headon, C. Heal, B. Healy, J.L. Healy, A. Hearn, D. Heasman, A. Heath, D. Heath, R. Heath, D. Heaton, A. Heavens, K. Hebbron, C. Heckman, G. Hector, S. Heddon, A. Hedges, K. Hedges, C. Heeley, E. Heeney, R. Heinink, R. Heire, I. Helgesen, J. Hemingway, U. Hemmila, B. Hemmings, S. Hemphill, D. Hemsley, A. Henderson, E. Henderson, J. Henderson, S. Henderson, J. Henry, K. Henry, L. Henry, M. Henry, N. Henry, D. Henshall, G. Herdman, R. Herdman-Grant, M. Herkes, L.E. Hermans, F. Hernandez, E. Heron, L. Heron, W. Herrington, E. Heselden, P. Heslop, T. Heslop, S. Hester, E. Hetherington, J. Hetherington, C. Hettiarachchi, P. Hettiarachchi, H. Hewer, J. Hewertson, A. Hewetson, S. Hewins, N. Hewitson, C. Hewitt, D. Hewitt, R. Hewitt, S. Hey, R.S. Heyderman, M. Heydtmann, J. Heys, J. Heywood, M. Hibbert, J. Hickey, N. Hickey, P. Hickey, N. Hickman, A. Hicks, J. Hicks, S. Hicks, P. Hien, T. Hien, T.T. Hien, D. Higbee, L. Higgins, A. Higham, M. Highcock, J. Highgate, M. Hikmat, A. Hill, H. Hill, J. Hill, L. Hill, P. Hill, U. Hill, A. Hilldrith, C. Hillman-Cooper, J. Hilton, Z. Hilton, S. Hinch, A. Hindle, E. Hindley, A. Hindmarsh, P. Hine, K. Hinshaw, C. Hird, C. Hirst, L. Hirst, J. Hives, H.M. Hlaing, B. Ho, D.K.K. Ho, R. Ho, L. Hoa, L.N.M. Hoa, M. Hoare, D. Hobden, G. Hobden, M. Hobrok, S. Hobson, C. Hodge, S. Hodge, L. Hodgen, G. Hodgetts, H. Hodgkins, S. Hodgkinson, D. Hodgson, H. Hodgson, L. Hodgson, S. Hodgson, G. Hodkinson, K. Hodson, M. Hodson, A. Hogan, M. Hogben, L. Hogg, L. Hoggett, A. Holborow, C. Holbrook, R. Holbrook, C. Holden, J. Holden, M. Holden, S. Holden, T. Holder, N. Holdhof, H. Holdsworth, L. Holland, M. Holland, N. Holland, P. Holland, S. Holland, M.L. Hollands, E. Holliday, K. Holliday, M. Holliday, N. Holling, L. Hollos, N. Hollos, L. Holloway, S. Holloway, M. Hollowday, M. Hollyer, A. Holman, A. Holmes, M. Holmes, R. Holmes, K. Holroyd, B. Holroyd-Hind, L. Holt, S. Holt, A. Holyome, M. Home, R. Homewood, K. Hong, L. Hoole, C. Hooper, S. Hope, B. Hopkins, P.W. Horby, S. Horler, A. Hormis, D. Hornan, N. Hornby, T. Horne, Z. Horne, R. Horner, C. Horrobin, L. Horsford, M. Horsford, V. Horsham, A. Horsley, E. Horsley, S. Horton, J. Hosea, T. Hoskins, M.S. Hossain, R. Hossain, M. Hough, S. Hough, C. Houghton, K. Houghton, R. Houlihan, K. Housely, H. Houston, R. Hovvels, L. How, L. Howaniec, J. Howard, K. Howard, L. Howard, M. Howard, S. Howard, R. Howard-Griffin, L. Howard-Sandy, S. Howe, A. Howell, M. Howells, L. Howie, K. Howlett, S. Howlett, R. Howman, J. Hrycaiczuk, H. Htet, N.Z. Htoon, S. Htwe, Y. Hu, C.O.H. Huah, S. Huang, K. Hubbard, A. Huckle, S. Huda, A. Hudak, L. Hudig, H. Hudson, S. Hudson, O. Hudson, A. Hufton, C. Huggins, A. Hughes, C. Hughes, E. Hughes, G. Hughes, H. Hughes, L. Hughes, M. Hughes, R. Hughes, S. Hughes, V. Hughes, W. Hughes, L. Huhn, C. Hui, R. Hulbert, D. Hull, G. Hull, R. Hull, A. Hulme, P. Hulme, W. Hulse, G. Hulston, R. Hum, M. Hume, C. Humphrey, A. Humphries, J. Humphries, T. Hung, C. Hunt, F. Hunt, J. Hunt, K. Hunt, L. Hunt, M. Hunt, S. Hunt, A. Hunter, C. Hunter, E. Hunter, K. Hunter, N. Hunter, R. Hunter, S. Hunter, G. Huntington, F. Huq, E. Hurditch, J. Hurdman, C. Hurley, K. Hurley, M.A. Husain, S. Husaini, C. Huson, A. Hussain, C. Hussain, I. Hussain, M. Hussain, R. Hussain, S. Hussain, Y. Hussain, M. Hussam El-Din, S.F.E.M. Hussein, Z. Hussein, R. Hussey, A.H. Hussien, A. Hutchinson, C. Hutchinson, D. Hutchinson, E. Hutchinson, J. Hutchinson, C. Hutsby, P. Hutton, N. Huy, N.Q. Huy, N. Huyen, N.T.T. Huyen, T. Huyen, T.B. Huyen, N.T. Huyen Thuong, H. Huynh, D. Hydes, J. Hyde-Wyatt, N. Hynes, M. Hyslop, A. Iakovou, K. Ibison, M. Ibraheim, A. Ibrahim, J. Ibrahim, M. Ibrahim, W. Ibrahim, B. Icke, A.I. Idowu, M. Idrees, N. Idrees, H. Iftikhar, M. Iftikhar, C. Igwe, O. Igwe, M. Ijaz, A. Ikomi, C. Iles, S. Iliodromiti, M. Ilsley, L. Ilves, F.M. Ilyas, L. Imam-Gutierrez, M. Iman, C. Imray, H. Imtiaz, J. Ingham, R. Ingham, T. Ingle, J. Inglis, S. Inglis-Hawkes, A. Ingram, L. Ingram, T. Ingram, N. Innes, P. Inns, V. Inpadhas, K. Inweregbu, A.A. Ionescu, A. Ionita, I. Iordanov, A. Ipe, J. Iqbal, M. Iqbal, F. Iqbal Sait, I. Irabor, J. Irisari, R. Irons, M. Irshad, M.S. Irshad, J. Irvine, V. Irvine, R. Irving, M. Ishak, E. Isherwood, G. Isitt, A. Islam, M.D.T. Islam, S. Islam, A. Ismail, O. Ismail, C. Ison, M. Israa, S. Isralls, H. Istiqomah, M. Ivan, C. Ivenso, N. Ivin, A. Ivy, S. Iwanikiw, K. Ixer, M. Iyer, K. Jabbar, C. Jack, J. Jackman, S. Jackman, A. Jackson, B. Jackson, E. Jackson, H. Jackson, L. Jackson, M. Jackson, N. Jackson, S. Jackson, Y. Jackson, J. Jacob, P. Jacob, R. Jacob, N. Jacques, H. Jadhav, A. Jafar, D. Jafferji, A. Jaffery, C. Jagadish, V. Jagannathan, A. Jagne, M. Jagpal, N. Jain, S. Jain, S. Jaiswal, D. Jajbhay, T. Jaki, P. Jali, B. Jallow, Y. Jaly, R. Jama, A. Jamal, S. Jamal, Z. Jamal, Y. Jameel, A. James, C. James, K. James, L. James, M. James, N. James, O. James, P. James, R. James, S. James, T. James, J. Jameson, L. Jamieson, A. Jamison, P. Jane, K. Janes, A. Janmohamed, D. Japp, P. Jaques, V. Jardim, C. Jardine, C. Jarman, E. Jarnell, E. Jarvie, C. Jarvis, R. Jarvis, P. Jastrzebska, H. Javed, A. Javier, M. Jawad, L. Jawaheer, A. Jayachandran, D. Jayachandran, A. Jayadev, A. Jayakumar, D. Jayaram, R. Jayaram, G. Jayasekera, T. Jayatilleke, A. Jayebalan, J. Jeater, S. Jeddi, V. Jeebun, M.S. Jeelani, K. Jeffery, H. Jeffrey, R. Jeffrey, N. Jeffreys, B. Jeffs, C. Jeffs, J.P. Jeganathan Ponraj, D. Jegede, T. Jemima, I. Jenkin, A. Jenkins, C. Jenkins, D. Jenkins, E. Jenkins, I. Jenkins, P. Jenkins, S. Jenkins, F. Jennings, J. Jennings, L. Jennings, V. Jennings, E. Jerome, D. Jerry, G. Jervis, E. Jessup-Dunton, J. Jesus Silva, C. Jetha, K. Jethwa, R. Jha, S. Jhanji, K. Jian, Z. Jiao, L. Jimenez, A. Jimenez Gil, J. Jith, T. Joefield, N. Johal, S. Johal, K. Johannessen, A. Johari, A. John, M. John, N. John, E. Johns, M. Johns, A. Johnson, E. Johnson, G. Johnson, K. Johnson, L. Johnson, M. Johnson, N. Johnson, O. Johnson, R. Johnson, B. Johnston, C. Johnston, J. Johnston, S. Johnston, V. Johnston, D. Johnstone, E. Johnstone, J. Johnstone, M. Joishy, A. Jones, B. Jones, C. Jones, C.E. Jones, D. Jones, E. Jones, G. Jones, J. Jones, K. Jones, K.E. Jones, L. Jones, L.M. Jones, M. Jones, N. Jones, O. Jones, P. Jones, P.H. Jones, R. Jones, R.E. Jones, S. Jones, T. Jones, R. Jonnalagadda, R. Jordache, M. Jordan, S. Jordan, A. Jose, L. Jose, S. Jose, A. Joseph, G. Joseph, P.A. Joseph, R. Joseph, S. Joseph, D. Joshi, M. Joshi, P. Joshi, T. Joshi, B. Josiah, D.K. Joy, L. Joy, T. Joyce, H. Ju, A. Ju Wen Kwek, A. Judd, E. Jude, P. Judge, J. Juhl, S. Jujjavarapu, M. Juniper, E. Juszczak, D. Jyothish, R. Jaiswal, R.K. Jha, K. Kabiru Dawa, M. Kacar, D. Kadad, N. Kadam, N. Kader, A. Kailey, M. Kain, G. Kakoullis, A. Kakrani, A. Kala Bhushan, R.J.K. Kalayi, R. Kaliannan Periyasami, D. Kalita, I. Kalla, E. Kallistrou, T. Kalmus Eliasz, S. Kalsoom, E. Kam, J. Kamara, A. Kamath, P. Kamath, R. Kamath, S.A. Kamerkar, N. Kametas, M. Kamfose, C. Kamundi, D. Kanabar, L. Kane, S. Kanitkar, O. Kankam, T. Kannan, A. Kant, V. Kapil, R. Kapoor, S. Kapoor, S. Kaprapina, S. Kar, J. Kara, E. Karbasi, S. Karelia, R. Kark, A. Karkey, A. Karki, S. Karki, S. Karmali, V. Karunanithi, N. Karunaratne, N. Kasianczuk, A. Kasiappan Balasubramanian, V. Kasipandian, R. Kassam, J. Kathirgamachelvam, M. Kati, V. Katsande, K. Kaul, D. Kaur, J. Kaur, S. Kaur, Z. Kausar, L. Kavanagh, S. Kavanagh, M.A.A. Kawser, A. Kay, J. Kay, R. Kay, S. Kay, J.N. Kayappurathu, S. Kayastha, C. Kaye, A. Kazeem, P. KC, M. Ke, T. Keady, R. Kearns, N. Kearsley, J. Keating, L. Keating, E. Keddie-Gray, B. Keegan, R. Keen, N. Keenan, J. Kefas, S. Kegg, L. Keith, U. Keke, J. Kellett, J. Kelliher, A. Kelly, D. Kelly, E. Kelly, H. Kelly, L. Kelly, M. Kelly, R. Kelly, S. Kelly, M. Kelly-Baxter, O. Kelsall, M. Keltos, T. Kemp, E. Kendall, A. Kendall-Smith, S. Kennard, A. Kennedy, J. Kennedy, M. Kennedy, S. Kennedy-Hay, J. Kenny, M. Kent, L. Keogan, A. Keough, D. Kernaghan, A. Kerr, C. Kerrison, A. Kerry, H. Kerslake, I. Kerslake, H. Kerss, J. Keshet-Price, E. Kestelyn, G. Keyte, A. Khadar, D. Khadka, N.V. Khai, P. Khairunnisa, A. Khalid, H. Khalid, M. Khalid, M.U. Khalid, S. Khalid, T. Khalifa, A. Khalil, S. Khalil, A. Khan, B. Khan, F. Khan, M. Khan, K. Khan, M.A. Khan, N. Khan, O. Khan, R. Khan, S. Khan, T. Khan, W. Khan, Z. Khan, M.S. Khan Tharin, N.H. Khanh, U. Khatana, J. Khatri, H. Khatun, T. Khatun, M. Kheia, J. Khera, D. Khiem, D.P. Khiem, H.H.E. Khin, T.D. Khoa, N. Khoja, K. Khokhar, M.Q. Khong, J. Khoo, V.T. Khuong, C. Khurana, J. Kibaru, F. Kibutu, A. Kidd, M. Kidd, J. Kidney, S. Kidney, W. Kieffer, T. Kien, T.V. Kien, J. Kilbane, C. Kilby, E. Killen, B. Kilner, S. Kilroy, B. Kim, J.W. Kim, M. Kim, A. Kimber, S. Kimber, A. King, B. King, H. King, J. King, K. King, M. King, R. King, S. King, V. King, E. King-Oakley, L. Kingsmore, D.J. Kinnear, F. Kinney, S. Kiran, A. Kirby, A. Kirk, J. Kirk, A. Kirkby, E. Kirkham, G. Kirkman, L. Kirkpatrick, U. Kirwan, T. Kitching, L. Kitto, L. Kittridge, T. Kjoa, S. Klaczek, F. Kleemann, S. Kmachia, V. KN, C.P. Knapp, L. Knibbs, A. Knight, F. Knight, M. Knight, S. Knight, T. Knight, E. Knights, J. Knights, M. Knolle, P. Knopp, C. Knowles, K. Knowles, L. Knowles, E. Knox, L. Knox, O. Koch, M. Kocsor, R. Kodituwakku, G. Koduri, Y.J. Koe, J. Koirala, A. Koirata, E. Kolakaluri, M. Kolodziej, E. Kolokouri, S. Kon, N. Konar, M. Kononen, A. Konstantinidis, R. Kontogonis, H. Koo, I. Koopmans, E. Kopyj, L. Korcierz, J. Korolewicz, G. Koshy, C. Kosmidis, C. Kosztolanyi, J. Kotecha, E. Kothandaraman, R. Kothavale, K. Koukou, A. Kountourgioti, K. Kouranloo, R. Kousar, M. Kousteni, A. Koutalopoula, M. Kovac, A. Kozak Eskenazia, K. Krasauskas, R. Krishnamurthy, V. Krishnamurthy, M. Krishnan, H. Krishnan, N. Krishnapalli, S. Krizak, S. Krueper, S. Krupej, J. Krzowski, R. Kubaisi, S. Kubheka, A. Kubisz-Pudelko, S. Kuckreja, S. Kudsk-Iversen, A. Kudzinskas, C. Kukadiya, N. Kulkarni, S. Kumala Dewi, M. Kuma-Mintah, A. Kumar, G. Kumar, M. Kumar, R. Kumar, S. Kumar, V. Kumar, P. Kumar Panda, A. Kundu, H. Kunst, S.S. Kunwar, K. Kupiec, A. Kurani, M. Kurdy, K. Kuriakose, R. Kurian, V. Kurmars, C. Kuronen-Stewart, R.S. Kusangaya, V. Kushakovsky, A. Kutera, A. Kuverji, A. Kyei-Mensah, H. Kyepa, T. Kyere-Diabour, M. Kyi, N.M. Kyi, L. Kyle, K.-T. Kyriaki, J. Labao, L. Labuschagne, L. Lacey, N. Lack, M. Lacson, Z. Ladan, E. Ladlow, H. Lafferty, A. Lagnado, S. Laha, S. Lahane, C. Lai, J. Lai, P. Laidler, R. Laing, I. Laing-Faiers, E. Laity, K. Lake, N. Lakeman, D. Lalloo, F. Lalloo, A. Lam, C. Lam, F. Lamb, L. Lamb, T. Lamb, O. Lambert, P. Lambert, C. Lameirinhas, M.K.G. Lami, H. Lamont, M. Lamparski, D. Lamrani, C. Lanaghan, I. Lancona-Malcolm, G. Landers, M.J. Landray, M. Lane, N. Lane, A. Lang, S. Lang, D. Langer, M. Langley, C. Langoya, E. Langridge, E. Langthorne, H. Langton, B. Lara, T. Large, L.N. Lartey, S. Lassa, A. Last, S. Latham, V. Latham, A. Latheef, L. Latif, N. Latt, C. Lau, D. Lau, E. Lau, G.G. Laura, M. Laurenson, E. Lavington, H. Law, J. Law, K.Y. Law, P. Law, R. Law, L. Lawless, C. Lawrence, E. Lawrence, G. Lawrence, H.M. Lawrence, N. Lawrence, R. Lawrie, L. Lawson, N. Lawson, R. Lawson, M. Lay, S. Laybourne, C. Laycock, R. Layug, M. Lazo, D.H. Le, T.T. Le, V. Le, A. Lea, W. Lea, L.E. Leach, I. Leadbitter, T. Leahy, R. Lean, L. Leandro, D. Leaning, R. Leary, S. Leason, M.A. Ledingham, C. Lee, E. Lee, G. Lee, H. Lee, I. Lee, J. Lee, S. Lee, S.H. Lee, T. Lee, X. Lee, R. Lee, D. Lees, J. Lees, H. Legge, J. Leggett, K. Leigh-Ellis, D. Leitch, N. Leitch, E. Lekoudis, P. Lemessy, N. Lemoine, R. Lenagh, K. Leng, K. Lennon, L. Lennon, B. Leonard, K. Leonard, W. Leong, N. Leopold, O. Lepiarczyk, I. Leslie, E.N. Lestari, E. Lester, E. Levell, C. Levett, A. Levynska, A. Lewin, A. Lewis, C. Lewis, D. Lewis, H. Lewis, J. Lewis, K. Lewis, L. Lewis, M. Lewis, N. Lewis, R. Lewis, C. Lewis-Clarke, A. Lewszuk, P. Lewthwaite, S. Ley, A. Liao, V. Licence, D. Lieberman, S. Liebeschuetz, T. Light, N. Lightfoot, P. Lillie, A. Lillis, B. Lim, C. Lim, E.T. Lim, I. Lim, T. Lim, W. Lim, W.S. Lim, J. Limb, D. Limbu, U. Limbu, C. Linares, D. Linden, G. Lindergard, K. Lindley, C. Lindsay, E. Lindsay, M. Lindsay, H. Lindsay-Clarke, M. Ling, C. Lingam, N.V.H. Linh, V.D. Linh, L. Linkson, T. Linn, M. Linney, C. Lippold, G. Lipscomb, K. Lipscomb, L. Lipskis, A. Lisboa, E. Lister, J. Little, S. Little, L. Littlejohn, S. Liu, X. Liu, D.K. Llanera, R. Llewellyn, M. Llewelyn, A. Lloyd, O. Lloyd, R. Lloyd, S. Lo, D. Loader, C. Loan, L. Lobosco, L. Lock, S. Lock, A. Locke, J. Locke, T. Locke, T. Lockett, J. Lodge, K. Lodhia, M. Lofthouse, H. Loftus, M. Logan, C. Logue, S.Y. Loh, S. Lokanathan, K. Lomme, E. London, G. Long, N. Long, K. Longbottom, B. Longhurst, M. Longshaw, S. Longstaffe, J. Lonnen, C. Lonsdale, L. Looby, R. Loosley, L. Lopes, P. Lopez, R.W. Lord, S. Lord, C. Lorimer, F. Loro, R. Lorusso, C. Loughlin, W. Lovegrove, R. Loveless, M. Lovell, A. Loverdou, A. Low, J. Low, S. Low, A. Lowe, C. Lowe, E. Lowe, F. Lowe, L. Lowe, M. Lowe, R. Lowsby, V. Lowthorpe, G. Lubimbi, A. Lubina Solomon, G. Lucas, J. Lucas, A. Lucey, O. Lucey, S. Luck, L.H. Lui, A. Luintel, H. Luke, J. Luke, N. Lungu, A. Lunia, M. Lunn, J. Luo, M. Luscombe, J. Luveta, C.N. Luximon, K. Lwin, M. Lwin, A. Lye, B. Lyell, E. Lyka, A. Lynas, C. Lynch, D. Lynch, S. Lynch, R.-G. Maamari, H. Mabb, L. Mabelin, G. Mabeza, J. Macaro, K. Macconaill, C. Macdonald, A. Macduff, C. Macfadyen, J.G. Macfarlane, J. Macfarlane, L. Macfarlane, I. Macharia, L. MacInnes, I. MacIntyre, J. MacIntyre, K. Mack, C. Mackay, E. Mackay, L. Mackay, A. Mackenzie, M. Mackenzie, R. MacKenzie Ross, A. Mackey, F. Mackie, J. Mackie, R. Mackie, C. Mackinlay, C. Mackintosh, K. Mackintosh, M.J. MacLeod, S. Macleod, M. Macmahon, A. MacNair, C. Macphee, I. Macpherson, C. Macrae, A. MacRaild, Y. Madani, A. Madden, M. Madden, C. Madden-McKee, S. Maddison, N. Madeja, P. Madhivathanan, M. Madhusudhana, A. Madu, L. Madziva, M. Mafham, S. Magar, N. Magee, F. Magezi, N. Maghsoodi, C. Magier, L.M. Magnaye, M. Magriplis, M. Magtalas, N.P. Magula, N. Mahabir, S. Mahadevan-Bava, S. Mahajan, A. Maharajh, K. Maharjan, M. Maharjan, A. Mahaveer, B. Mahay, K. Mahay, A. Mahdi, H. Mahdi, N. Mahdi, T. Mahendiran, S. Mahendran, S. Maher, A. Maheswaran, S. Maheswaran, T. Maheswaran, P. Mahjoob-Afag, A. Mahmood, F. Mahmood, H. Mahmood, W. Mahmood, Z. Mahmood, H. Mahmoud, M. Mahmud, E. Mahony, T. Mahungu, O. Maiga, L. Mair, T. Majekdunmi, K. Majid, A. Major, R. Major, J. Majumdar, M.K.H. Majumder, T.L.A. Mak, A. Makan, E. Makanju, S. Makin, W.-O. Makinde, Y. Makkeyah, O.N. Makoetlane, M. Malanca, H. Malcolm, F. Malein, N. Malhan, A. Malicka, A. Malik, G. Malik, M. Maljk, P. Mallett, P. Mallinder, G. Mallison, L. Mallon, E. Malone, G. Maloney, M. Mamman, I. Man, K. Man, R. Mancinelli, M. Mancuso-Marcello, S. Mandal, S.K. Mandal, T. Manders, L. Manderson, J. Mandeville, T. Mane, R. Manhas, C. Maniero, R. Manikonda, I. Manjra, R. Mankiewitz, B. Mann, J. Manning, S. Manning, P. Mannion, K. Mansi, K. Manso, D. Mansour, M. Mansour, R. Mansour, I.T. Mapfunde, P. Mappa, A. Maqsood, H. Maraj, C. Marchand, N. Marcus, A. Marcyniuk, M. Marecka, D. Maren, G. Margabanthu, J. Margalef, L. Margarit, G. Margaritopoulos, M. Margarson, F. Maria del Rocio, T. Maria Pfyl, V. Mariano, A. Maric, G. Markham, B. Marks, M. Marks, P. Marks, E. Marler, E. Marouzet, A. Marriott, C. Marriott, N. Marriott, C. Marsden, K. Marsden, P. Marsden, S. Marsden, T. Marsden, C. Marsh, G. Marsh, R. Marsh, A. Marshall, G. Marshall, H. Marshall, J. Marshall, N. Marshall, R. Marshall, S. Marshall, E. Martin, G. Martin, H. Martin, J. Martin, K. Martin, L. Martin, M. Martin, N. Martin, T. Martin, W. Martin, S. Martin, T. Martindale, M. Martineau, L. Martinez, J.C. Martinez Garrido, J. Martin-Lazaro, V.K. Maruthamuthu, B. Marwan, G. Maryan, R. Mary-Genetu, S. Maryosh, V. Masani, A. Mascagni, D. Maseda, Z. Maseko, S. Mashate, Y. Mashhoudi, A. Mashta, I. Masih, S. Masih, N. Maskell, P. Maskell, P. Maskey, M. Masoli, J. Mason, R. Mason, C. Mason, M. Masood, M.T. Masood, S.S.M.E. Masood, T. Massa, I. Massey, J. Masters, A. Masud, L. Matapure, C. Matei, R. Matewe, E. Matey, M. Matharu, S. Mathen, A. Mather, N. Mather, J. Mathers, J. Matheson, A. Mathew, M. Mathew, V. Mathew, J. Mathews, K. Mathias, A. Mathioudakis, S. Matibela, D. Matila, W. Matimba-Mupaya, N. Matin, E. Matisa, E. Matkins, M. Matonhodze, E. Matovu, J. Mattappillil, A.J. Matthews, C. Matthews, H. Matthews, L. Mattocks, C. Maughan, T.T. Maulidya, E. Mawson, F. Maxton, A. Maxwell, V. Maxwell, E. May, J. May, P. May, I. Mayanagao, M. Maycock, J. Mayer, G. Mayers, V.A. Maynard, K. Mayne, T. Mayo, L. Mayola, S. Mayor, I. Mazen, T. Mazhani, A. Mazzella, N. Mburu, A. Mbuyisha, C. Mc Cague, E. McAleese, P. McAlinden, L. McAllister, A. McAlpine, G. McAlpine, J. McAndrew, H. McAuley, S. McAuliffe, C. McBrearty, E. McBride, M. McBuigan, J. McBurney, L. McCabe, G.L. McCafferty, L. McCafferty, A. McCairn, J. McCammon, N. McCammon, C. McCann, E. McCann, A. McCarrick, B. McCarron, E. McCarthy, M. McCarthy, N. McCarthy, S. McCaughey, T. McClay, B. McClelland, D. McClintock, M. McCloskey, K. McCollum, A. McCorkindale, P. McCormack, J. McCormick, W. McCormick, P. McCourt, J. McCrae, S. McCready, G. McCreath, H. McCreedy, C. McCue, I.J. McCullagh, L. McCullagh, M. McCullagh, C. McCullough, K. McCullough, N. McCullough, S. McCullough, F. McCurrach, J. McDermott, P. McDermott, R. McDermott, K. McDevitt, H. McDill, B. McDonald, C. McDonald, D. McDonald, R. McDonald, S. McDonald, N. McDonnell, C. McDougall, L. McDougall, R. McDougall, I. McEleavy, F. McElwaine, J. McEntee, E. McEvoy, C. McEwan, R. McEwen, M. McFadden, D. McFarland, M. McFarland, R. McFarland, J. McFlynn, E. McGarry, L. McGarvey, A. McGeachan, F. McGee, L. McGenily, C. McGettigan, M. McGettrick, C. McGhee, F. McGill, S. McGinnity, N. McGlinchey, P. McGlone, D. McGlynn, C. McGoldrick, E. McGough, C. McGovern, R. McGovern, A. McGowan, A. McGown, B. McGrath, A. McGregor, M.P. McGuigan, H. McGuinness, S. McGuire, T. McHugh, C. McInnes, N. McInnes, J. McIntosh, K. McIntyre, M. McIntyre, L. McKay, C.P. McKeag, J. McKeane, M. McKee, J. McKeever, J. McKenna, S. McKenna, M. McKenzie, D. McKeogh, C. McKerr, A.M. McKie, H. Mckie, L. Mckie, G. McKnight, H. McLachlan, A. McLaren, B. McLaren, N. McLarty, D. Mclaughlan, M. McLaughlin, J. McLay, M. McLeish, T. McLennan, S. McLure, A.M. McMahon, G. McMahon, M. McMahon, S. McMahon, T. McManus, M. McMaster, P. McMaster, F. Mcmeeken, S. McMeekin, N. McMillan, K. McMillen, J. McMinn, L. McMorrow, H. McMullen, C. McMurran, H. McNally, F. McNeela, L. McNeil, C. McNeill, J. McNeill, S. McNeill, U. McNelis, M. McNulty, R. McNulty, C. McParland, M. McPhail, A. McQueen, A. McSkeane, D. McSorland, T. McSorley, G. McTaggart, J. McTaggart, J. Mead, P. Mead, E. Meadows, O. Meakin, B. Mearns, C. Mearns, K. Mears, W. Mears, M. Meda, A. Mediana, R. Medine, T. Medveczky, S. Meehan, E. Meeks, A. Megan, N. Meghani, S. Meghjee, S. Megson, A. Mehar, M.N. Mehmood, R. Mehra, R. Mehta, G. Meintjes, J. Meirill, J. Meiring, R. Mejri, E. Mekonnen, S. Melander, A.-S. Melinte, J. Mellersh, L. Melling, C. Mellish, F. Mellor, J. Mellor, S. Mellor, Z. Mellor, K. Mellows, V. Melnic, A. Melville, D. Melville, J. Melville, H. Membrey, M. Mencias, A. Mendelski, M. Mendelson, C. Mendonca, C. Meney, C. Menezes, W. Mensah, J.E. Mensshan, A. Mentzer, D. Menzies, S. Menzies, S. Mepham, O. Mercer, P. Mercer, A. Merchant, F. Merchant, M. Mercioniu, M. Meredith, M. Merida Morillas, B. Merrick, J. Merritt, S. Merritt, P. Merron, E. Merwaha, S. Message, J. Messenger, G. Metcalf-Cuenca, A. Metcalfe, B. Metcalfe, K. Metcalfe, S. Metherell, A. Metryka, L. Mew, S. Meyrick, N. Mguni, J. Mhlongo, A. Miah, J. Miah, N. Miah, A. Mian, G. Mic, L. Micah-Amuah, D. Micallef, A. Michael, S. Michael, N. Michalak, L. Michalca-Mason, O. Michalec, J. Middle, H. Middleton, J. Middleton, M. Middleton, S. Middleton, S. Mieres, L. Mihalca-Mason, T. Mikolasch, S. Milgate, C. Millar, J. Millar, J. Millard, D. Miller, J. Miller, L. Miller, R. Miller, N. Miller-Biot, A. Miller-Fik, L. Millett, B. Milligan, H. Milligan, I. Milligan, C. Milliken, K. Millington, R. Millington, S. Millington, H. Mills, J. Mills, R. Mills, H. Millward, R. Miln, A. Milne, C. Milne, L. Milne, J. Milner, L. Milner, Z. Min, S. Mindel, N. Minh, P.A. Minkah, C. Minnis, P. Minnis, K. Minou, N. Minskip, J. Minton, F. Miranda, M. Mirela, T. Mirza, A. Misbahuddin, A. Mishra, B. Mishra, E. Mishra, R. Mishra, S. Misra, D. Mistry, H. Mistry, D. Mital, S. Mitchard, B. Mitchell, C. Mitchell, L.J. Mitchell, P. Mitchell, P. Mitchelmore, A. Mitra, S. Mitra, N. Mlambo, E. Moakes, K. Moar, E. Moatt, D. Mock Font, G. Modgil, A. Mohamed, O. Mohamed, A. Mohammad, W. Mohammad, A. Mohammed, O. Mohammed, Y.N.S. Mohammed, B. Mohamud, A. Moharram, H.-P. Mok, J. Mok, L. Mokogwu, M. Molina, C. Moller-Christensen, M. Mollet, M. Molloholli, A. Molloy, L. Molloy, A. Molyneux, R. Molyneux, T. Momoniat, H. Monaghan, K. Monaghan, S. Mongolu, T. Monika, K. Monsell, M. Montasser, A. Montgomery, H. Montgomery, P. Moodley, M. Moody, N. Moody, A. Moon, J. Moon, J.-H. Moon, M. Moon, M. Moonan, P. Moondi, S. Moorby, J. Moorcroft, A. Moore, C. Moore, D.A.J. Moore, F. Moore, J. Moore, L. Moore, N. Moore, S. Moore, V. Moore, R. Moores, E. Morab, J. Morales, N. Moramorell, L. Moran, G. Moray, J. Moreno-Cuesta, A. Morgan, C. Morgan, H. Morgan, K. Morgan, L. Morgan, M. Morgan, P. Morgan, K. Morgan-Jones, E. Morgan-Smith, J. Morilla, A. Morley, T. Morley, W. Morley, A. Morris, D. Morris, F. Morris, H. Morris, J. Morris, K. Morris, L. Morris, M.-A. Morris, N. Morris, P. Morris, S. Morris, D. Morrison, M. Morrison, S. Morrison, M. Morrissey, A.C. Morrow, A. Morrow, F. Morselli, G. Mortem, V. Mortland, C. Morton, G. Morton, P. Morzaria, D. Mosby, L. Moseley, K. Moshal, B. Moshy, A. Moss, C. Moss, J. Moss, S. Moss, O. Mostafa, G. Moth, N. Motherwell, S. Mottershaw, H. Moudgil, J. Mouland, C. Moulds, H. Moulton, G. Mounce, E. Mousley, C. Mowatt, K. Moxham, B. Moya, Q. Moyo, E. Mshengu, S. Mtuwa, A. Muazzam, I.A. Muazzam, N. Muchenje, D. Mudawi, G. Muddegowda, R. Mufti, I. Mugal, A. Mughal, J. Muglu, F. Muhammad, J. Muhammad, C. Muir, A. Mukherjee, D. Mukherjee, J. Mukhtar, S.A.A. Mukhtar, D. Mukimbiri, J. Mulcahy, M. Mulcahy, P. Mulgrew, B. Mulhearn, A. Mulla, D. Mullan, D. Mullasseril Kutten, N. Mullen, R. Mullett, C. Mulligan, S. Mulligan, L. Mumelj, A. Mumford, M. Munavvar, H. Munby, H. Munday, A. Munro, S. Munt, M. Mupudzi, A. Murad, O.H. Muraina, K. Muralidhara, M. Murdoch, J. Murira, A. Murphy, B. Murphy, C. Murphy, E. Murphy, G. Murphy, H. Murphy, P. Murphy, R. Murphy, S. Murphy, C. Murray, D. Murray, E. Murray, K. Murray, L. Murray, T. Murray, E. Murtagh, M. Murthy, C. Murton, R. Murton, N. Muru, R. Musanhu, M. Mushabe, O. Mushtaq, S. Musini, A.M.M. Mustafa, E. Mustafa, M. Mustafa, I. Mustapha, N. Mustfa, Z. Mustufvi, C. Mutch, R. Mutch, E. Mutema, B. Muthukrishnan, S. Mutton, N. Muzengi, M. Mwadeyi, B. Mwale, E. Mwaura, R. Myagerimath, A. Myers, S. Myers, J.S. Myerson, K. Myint, Y. Myint, G. Mynott, L. Myslivecek, P. Nabayego, E. Nadar, I. Nadeem, M. Nadheem, B. Nadjm, A. Naeem, H. Naeem, S. Naeem, S. Nafees, M. Nafei, W. Naftalia, T. Nagarajan, L. Naglik, I. Nagra, D. Nagra, M. Naguib, K. Naguleswaran, K.S. Nagumantry, K. Naicker, S. Naidoo, V. Naidoo, G. Naik, R. Naik, S. Naik, D.S. Nair, R. Nair, T. Nair, J. Naisbitt, K. Naismith, D. Nakiboneka-Ssenabulya, S. Nallapareddy, S. Nallapeta, A. Nallasivan, H. Nam, U. Nanda, A. Nandani, T. Nandwani, A.R. Naqvi, A. Naqvi, S. Naqvi, S. Nasa, D. Nash, N. Nasheed, A. Nasimudeen, U. Nasir, N. Nasronudin, T. Nasser, A. Natarajan, G. Natarajan, N. Natarajan, R. Natarajan, P. Nath, N. Nathaniel, M. Nathvani, P. Nathwani, G. Nava, N. Navaneetham, J. Navaratnam, H. Navarra, S. Naveed, J. Navin, K. Nawaz, S. Nawaz, B. Nayar, S. Naylor, M. Nayyar, F. Naz, M. Naz, B. Nazari, A. Nazir, S. Nazir, D. Ncomanzi, O. Ndefo, N.B. Ndoumbe, A. Neal, E. Neary, M. Negmeldin, J. Neil, P. Neill, H.E. Neils, A. Nejad, J. Nel, L. Nel, A. Nelson, B. Nelson, L. Nelson, M. Nelson, R. Nelson, S. Nelson, E. Nelwan, E.J. Nelwan, R. Nemane, S. Nepal, D. Nethercott, K. Netherton, K. Nettleton, J. Neupane, K. Neupane, A. Newby, D. Newby, T. Newcombe, H. Newell, C. Newman, D. Newman, H. Newman, J. Newman, O. Newman, T. Newman, R. Newport, M. Newton, A.Y.K.C. Ng, H.E.J. Ng, K.W. Ng, M. Ng, S. Ng, W.J. Ng, Y.W.M. Ng, T. Ngan, T.H. Ngo, G.C.E. Ngui, A. Ngumo, H.K. Nguyen, M.T. Nguyen, N. Nguyen, N.T. Nguyen, N.T.T. Nguyen, Q. Nguyen, T.H. Nguyen, T.H.T. Nguyen, T.T. Nguyen, T.T.N. Nguyen, T.T.P. Nguyen, K. Ngwenya, N.Y. Nhi, C. Nic Fhogartaigh, N. Nicholas, P. Nicholas, R. Nicholas, D. Nicholls, L. Nicholls, S. Nicholls, A. Nicholson, I. Nickson, E. Nicol, R. Nicol, P. Nicola, A. Nicoll, T. Nightingale, F. Nikita, P. Nikolaos, G. Nikonovich, A. Nilsson, K. Nimako, L. Nimako, C. Nimmo, P. Ninan, T. Ninh, M. Nirmalan, R. Niroula, A. Nisar, M. Nisar, T. Nisar, T. Nisbett, A. Nisha James, S. Nishat, T. Nishiyama, S. Nix, J. Nixon, M. Nixon, K. Nizam Ud Din, M. Nizami, S. Nizamis, R. Njafuh, I. Noakes, L. Noba, J. Noble, H. Noble, H.M. Noe, J. Nolan, J. Nolasco, Z. Noor, Z. Noori, J. Norcliffe, L. Norman, R. Norman, E. Norris, K. Norris, L. Norris, S.A. Nortcliffe, F. North, J. North, T. North, J. Northfield, S. Northover, J. Nortje, D. Norton, R. Norton, H. Notman, K. Nourein, T. Novak, N. Novas Duarte, C. Novis, J.A. Nowak, K.P. Nu, M. Nugdallah, A. Nugent, J. Nugent, C. Nugroho, N. Numbere, K. Nundlall, A. Nune, K. Nunn, M. Nunn, J. Nunnick, Y. Nupa, F. Nur, Z. Nurgat, R. Nurpeni, A. Nuttall, L. Nwafor, P. Nwajiugo, G. Nyamugunduru, L. Nyanor, M. Nyirenda, K. Nyland, D.O. Rinn, D.O. Shea, M. O Toole, M. O’Hara, C. O’Hara, L. O’Keefe, K. O’Reilly, W. O’Rourke, C. Oakley, N. Oakley, S. Oakley, H.T.K. Oanh, B. Obale, C. Oboh, C. O'Brien, J. O'Brien, K. O'Brien, L. O'Brien, N. O'Brien, R. O'Brien, T. O'Brien, E. O'Bryan, R. Obukofe, C. O'Callaghan, L. O'Connell, T. OConnor, C. O'Connor, G. O'Connor, M. Odam, S. Oddie, S. Oddy, Y. Odedina, K. Odedra, S. Odelberg, N. Odell, O. Oderinde, J. Odone, L. O'Donohoe, C. O'Donovan, I. Odysseos-Beaumont, S. O'Farrell, P. Offord, M. O'Flaherty, E. Ofori, T. Ogbara, C. Ogilvie, C. O'Gorman, I. Ogunjembola, O. Ogunkeye, U. Ohia, S. Ojha, O. Ojo, F. O'kane, M. O'Kane, T. Okeke, E. OKell, A. Okines, I. Okpala, E. Okpo, F. Okpoko, M. Okubanjo, C. Oladipo, L. Olaivar, R. Olaiya, J. Olatujoye, T. Old, G. Oleszkiewicz, A. Oliver, C. Oliver, J. Oliver, L. Oliver, M. Oliver, Z. Oliver, J. Oliver-Commey, N.O. Olokoto, F. Olonipile, O. Olufuwa, O. Olukoya, A. Oluwole-Ojo, L. O'Malley, I.V. Omale, P.K. Omane-Donkor, M. Omar, Z. Omar, N. Omer, E. Omoregie, C. O'Neill, L. O'Neill, C. Ong, O. Onuoha, C. Onyeagor, C.N. Oo, Z. Oo, H.C. Ooi, S.H. Ooi, A. Oomatia, A. Opata, M. Opena, R. Oram, C. Ord, J. Ord, C. Oreilly, L. Orekoya, D. O'Riordan, S. O'Riordan, I. Orlikowska, A. Orme, H. Orme, L. O'Rourke, C. Orr, S. Orr, C. Orton, A. Osadcow, R. Osagie, R. Osanlou, L. Osborne, N. Osborne, R. Osborne, W. Osborne, C. Osbourne, J. Osei-Bobie, J. Osman, W. Osman, B. Osman, G. Osoata, M. Ostermann, E. O'Sullivan, S. O'Sullivan, M.A. Oteng, N. Otey, O.K. Otite, M. O'Toole, J. Ouyang, R. Owen, S. Owen, E. Owens, C. Owoo, Y. Owoseni, M. Owston, R. Oxlade, F. Ozdes, J. Pack, A. Packham, S. Packham, P. Paczko, G. Padden, A. Padmakumar, C. Page, I. Page, J. Page, S. Page, V. Page, J. Paget, K. Pagett, V. Pai, L. Paisley, S. Pajak, G. Pakou, A. Pakozdi, S. Pal, A. Palacios, V.B. Palagiri Sai, V. Palaniappan, P. Palanivelu, A. Palfreeman, H. Palfrey, V. Palissery, D. Palit, S. Pallipparambil Antony, J. Palman, A. Palmer, H. Palmer, J. Palmer, L. Palmer, R. Palmer, A. Pambouka, I. Pamphlett, D. Pan, A. Pandey, N. Pandian, K. Pandya, T. Pandya, H.R. Paneru, A. Panes, J. Pang, Y.W. Pang, R. Pangeni, L. Pannell, K. Pannu, S. Pant, S. Panthakalam, C.T. Pantin, N. Pao, H. Papaconstantinou, N.S. Papavarnavas, P. Papineni, K. Paques, A.W. Paracha, K. Paradowski, V. Parambil, S. Paranamana, S.R. Parashar, I. Parberry, A. Parekh, D. Parekh, L. Parfitt, H. Parfrey, O. Parikh, G. Parish, J. Park, V. Parkash, A. Parker, B. Parker, E. Parker, H. Parker, J. Parker, L. Parker, N. Parker, S. Parker, K. Parkin, A. Parkinson, M. Parkinson, V. Parkinson, C. Parmar, V. Parmar, V. Parris, C. Parrish, B. Parry, H.C. Parry, S. Parslow-Williams, M. Parsonage, G. Parsons, J. Parsons, P. Parsons, R. Partridge, Z. Parvez, K. Parvin, L. Passby, S. Passey, H. Passmore, J. Pastrana, J. Patachako, M. Patal, S. Patch, A. Patel, B. Patel, D. Patel, H. Patel, J. Patel, K. Patel, M. Patel, N. Patel, P. Patel, S. Patel, T. Patel, Z. Patel, V. Patel, K. Paterson, S. Pathak, N. Pathan, A. Patience, D. Patience, B. Patil, R. Patmore, S. Patole, L. Paton, A. Patrick, G. Patrick, J. Patrick, S. Patten, B. Pattenden, C. Patterson, J. Patterson, L. Patterson, M. Patterson, R. Patterson, M. Pattrick, D. Paudel, K. Paudel, M. Paudel, S. Paudel, M. Paul, S. Paul, L. Pauls, S. Paulus, A. Pavely, M.J. Pavitt, S. Pavord, B. Payne, E. Payne, M. Payne, R. Payne, L. Peacock, S. Peacock, H. Peake, J. Pearce, R. Pearse, A. Pearson, D. Pearson, H. Pearson, K. Pearson, S. Pearson, S.A. Pearson, A. Peasley, H. Peddie, S. Peebles, R. Peek, A. Peer, S. Peerbhoy, C. Pegg, E. Peggie, H. Peggie, S. Peglar, B.H. Peirce, M. Peirse, C. Pelham, A. Pemberton, M. Penacerrada, A. Pender, C. Pendlebury, J. Pendlebury, R. Penfold, C. Penman, J. Penman, R. Penman, J. Penner, K. Penney, A. Pennington, J. Penny, J. Pepperell, R. Percival, A. Pereira, R. Pereira, C. Pereira Dias Alves, I. Perera, M. Perera, E. Perez, J. Perez, T. Perinpanathan, L. Periyasamy, E. Perkins, I. Pernicova, E. Perritt, A. Perry, E. Perry, M. Perry, T.M. Perumpral, G. Pessoa-Amorim, R. Petch, L. Peter, C. Peters, L. Peters, M. Peters, S. Peters, T. Peters, A. Peterson, R. Petersen, L. Peto, I. Petras, B. Petrova, M. Petrova, E. Petrovics, T. Pettigrew, M. Pezard-Snell, P. Pfeffer, G. Phalod, N.T. Pham, V.P. Pham, T.T.H. Phan, M. Phanish, P. Phelan, C. Philbey, J. Philbin, A. Phillips, B. Phillips, D. Phillips, N. Phillips, P. Phillips, R. Phillips, T. Phillips, M. Phipps, N. Phong, N.T. Phong, V. Phongsathorn, P. Phuc, P.V. Phuc, M. Phull, H. Phung, H.T.K. Phung, H.M. Phuong, N. Phuong, A. Phuyal, A.K. Phyo, M.T.T. PI, S. Pick, J. Pickard, C. Pickering, F. Pickering, G. Pickering, T. Pickett, J. Pickles, S. Pickstock, B. Pickwell-Smith, N. Pieniazek, C. Piercy, A. Pieris, S. Pilgrim, P.A. Pillai, S. Pillay, L. Pilling, Z. Pilsworth, H. Pinches, S. Pinches, K. Pine, M.T. Pinjala, S. Pintus, G. Piper, T. Pirani, M. Pitchford, M. Pittman, S. Pitts, N. Plaatjies, N. Platt, R. Pleass, M. Plowright, L. Plummer, C. Plumptre, J. Pobjoy, T. Pogreban, C. Poku, S. Poku, P. Polgarova, R. Pollard, L. Pollock, O. Poluyi, G.J. Polwarth, F. Pomery, I.M.F. Ponce, P. Ponnusamy, S. Ponnusamy, A. Ponnuswamy, I. Ponte Bettencourt dos Reis, S. Pooboni, A. Poole, L. Poole, M. Poole, S. Poon, T. Poonian, J. Porteous, M. Porteous, D. Porter, J. Porter, L. Porter, R. Porter, A. Posada, K. Postlethwaite, M. Potdar, C. Pothecary, N. Pothina, P. Potla, D. Potoczna, J. Pott, A. Potter, J. Potter, S. Potter, T. Potter, E. Potton, J.B. Potts, J. Potts, K. Potts, K. Poudel, B. Poudyal, U. Poultney, K. Poulton, V. Poustie, J. Powell, N. Powell, S. Powell, D. Power, N. Power, S. Power, J. Poxon, E. Poyner, R. Poyner, A. Prabhu, S. Prabowo, V. Pradhan, G. Pradip, H. Prady, R. Prananingtias, A. Prasad, K. Prasad, U. Prasad, F. Prasanth Raj, S. Prasath, Prathima, N. Pratiwi, A. Pratley, S. Pratt, C.B. Prayuda, D. Preiss, C. Prendergast, L. Prentice, P. Prentice, V. Prescott, L. Presland, C. Prest, S. Preston, M. Pretorius, N. Prevatt, S. Prew, A. Price, C. Price, D. Price, E. Price, K. Price, L.J. Price, N. Price, V. Price, R. Price-Eland, A. Priest, J. Prieto, L. Primrose, C. Prince, J. Prince, L. Prince, S. Pringle, M. Prior-Ong, V. Pristopan, K. Pritchard, L. Pritchard, S. Pritchard, V. Priyash, A. Procter, C. Proctor, M. Protopapas, R. Proudfoot, B. Prudon, D. Pryor, S. Pudi, A. Puffett, J. Pugh, L. Pugh, M.T. Pugh, N. Pugh, R. Pugh, V. Puisa, E. Puji Lestari, S. Puliyakkadi, J. Pullen, K. Punia, S. Punnilath Abdulsamad, L. Purandare, D. Purchase, C. Purdue, R. Purdy, B. Purewal, R. Purnell, M. Pursell, G. Purssord, R. Purves, S. Purvis, K. Puspatriani, D. Putensen, S.I. Putu, B. Puvaneswaran, A. Puxty, K. Puxty, Z. Puyrigaud, E. Pyart, E. Pye, M. Pynn, T. Qadeer, M. Qayum, C. Quah, S. Quaid, N. Quail, C. Quamina, K. Quang, N.N. Quang, L. Quarm, G. Quartermaine, R. Quartey, T. Quasim, S. Quaye, A. Quayle, E. Quek, S. Quenby, P. Qui, X. Qui, V. Quick, J. Quigley, J.-C. Quijano-Campos, J. Quindoyos, A. Quinn, J. Quinn, T. Quinn, L.J. Quist, Q. Quratulain, D. Qureshi, E. Qureshi, H. Qureshi, I. Qureshi, K. Qureshi, N. Qureshi, Q. Qurratulain, S. Qutab, D.T.H. Quyen, D.T.N. Quyen, N.T.H. Quyen, M.S. Rabbani, S. Rabinowicz, M. Raceala, A. Rachid, B. Rachman, R. Rachman, L. Rad, J. Radford, L. Radford, J. Radhakrishnan, H. Rafferty, M.Y. Rafiq, S. Rafiq, C. Rafique, J. Rafique, M. Rafique, R. Ragatha, A. Raghunathan, A. Raguro, S.D. Raha, S. Rahama, M. Rahardjani, K. Rahilly, F. Rahim, A.H. Rahimi, H.R. Rahimi, M. Rahman, S.U. Rahman, S. Rahmany, P. Rai, S. Rai, L. Raisova, A. Raithatha, A. Raj, A. Rajagopal, P. Rajagopalan, N. Rajaiah, K. Rajalingam, A. Rajasekaran, A. Rajasri, B. Rajbhandari, S. Rajbhandari, T. Rajeswaran, J. Rajeswary, J. Rajkanna, I. Rajkumar, G. Rajmohan, R. Rallan, K. Ralston, M. Ralston, M. Ram, B. Ramabhadran, F. Ramali, M. Ramali, A. Ramanan, S. Ramanna, M. Ramasamy, I. Rambe, A. Ramchandani, D. Ramdin, J. Ramirez, M. Ramirez, G. Ramnarain, A. Ramnarine, L. Ramos, T. Rampling, S. Ramraj, J. Ramsay, A. Ramshaw, A. Rana, G.F. Rana, N. Rana, R. Rana, A. Rand, J. Rand, H. Randheva, P. Ranga, M. Rangar, H. Rangarajan, S. Ranjan, H. Rank, P. Ranka, R. Rankhelawon, A. Rankin, A. Rao, S. Rao, D. Rao, A.A. Rasheed, K. Rashid, M. Rason, V. Raspa, S. Rastogi, F. Rasul, S. Ratcliff, S. Ratcliffe, P. Rath, S. Rath, M.I. Rather, K. Rathod, S. Rathore, A. Ratnakumar, J. Ratoff, D. Rattehalli, D. Ravaccia, M. Raval, P. Ravencroft, J. Raw, R. Raw, M. Rawal, S.A. Rawashdeh, H. Rawlins, G. Ray, A. Raymond-White, D. Raynard, H. Rayner, N. Rayner, A. Raynsford, S. Razvi, Z. Razvi, K. Read, S. Read, M. Reay, A. Reddington, A. Reddy, H. Reddy, H. Redfearn, A. Redfern-Walsh, I. Redknap, N. Redman, A. Redome, J. Redome, A. Reed, J. Reed, A. Rees, C. Rees, H. Rees, J. Rees, M. Rees, S. Rees, T. Rees, E. Rees-Jones, F. Regan, K. Regan, M. Regan, S. Regan, K. Rege, A. Regmi, A. Rehan, A. Rehman, H. Rehman, S. Rehman, Z. Rehman, A. Reid, J. Reid, S. Reid, M. Reilly, S. Reilly, C. Reith, A. Reka, A. Remegoso, D. Rengan, L. Renouf, S. Renshaw, R. Renu Vattekkat, H. Reschreiter, M. Revels, A. Revill, G. Rewitzky, S. Rey, C. Reynard, D. Reynish, H. Reynolds, P. Reynolds, J. Rhodes, N. Riaz, P. Ribeiro, E. Rice, M. Rice, N. Rice, M. Rich, A. Richards, L. Richards, S. Richards, C. Richardson, E. Richardson, F. Richardson, J. Richardson, M. Richardson, N. Richardson, J. Riches, K. Riches, L. Richmond, R. Richmond, W. Ricketts, H. Rickman, A. Riddell, S. Ridgway, M. Ridha, C. Ridley, P. Ridley, G. Rieck, L. Rigby, M. Rigby, D. Rigler, S. Rijal, N. Rika, H. Riley, M. Riley, P. Riley, A. Rimainar, Z.V.P. Rimba, D. Rimmer, W. Rina, R. Rintoul, A. Riordan, D. Ripley, N. Rippon, C. Rishton, M. Riste, D. Ritchie, J. Ritchie, A. Ritchings, P. Rivera Ortega, V. Rivers, B. Rizvi, S.A.S. Rizvi, S.H.M. Rizvi, J. Robb, E. Robbins, C. Roberts, G. Roberts, I. Roberts, J. Roberts, K. Roberts, M. Roberts, N. Roberts, P. Roberts, R. Roberts, V. Roberts, C. Robertson, J. Robertson, K. Robertson, N. Robertson, S. Robertson, M. Robertson, N. Robin, C. Robinson, E. Robinson, G. Robinson, H. Robinson, J. Robinson, K. Robinson, L. Robinson, M. Robinson, N. Robinson, R. Robinson, S. Robinson, A. Robinson, S. Robson, A. Rocca, L. Roche, S. Roche, N. Rodden, A. Roddick, E. Roddy, J. Roddy, M. Roderick, A. Rodger, F. Rodger, M. Rodger, A. Rodgers, D. Rodgers, N. Rodgers, P. Rodgers, R. Rodriguez-Belmonte, N. Roe, C. Roehr, G. Rogers, J. Rogers, L. Rogers, M. Rogers, P. Rogers, S. Rogers, T. Rogers, J. Rojkova, K.K. Roka, S. Rokadiya, L. Rollins, J. Rollo, C. Rolls, A. Rond-Alliston, C. Rook, K. Rooney, L. Rooney, L.P. Rosaroso, E.J. Rosby, A. Rose, S. Rose, Z. Rose, J. Rosier, A. Roskilly, G.A. Ross, I. Ross, J. Ross, J. Rossdale, A. Ross-Parker, A. Rostron, A. Rosyid, A. Rothman, J. Rothwell, L. Roughley, C.A. Rourke, K. Rowan, N. Rowan, S. Rowan, A. Rowe, N. Rowe, L. Rowe-Leete, B. Rowlands, E. Rowlands, M. Rowley, S. Roy, M. Roycroft, A. Roynon-Reed, A.R. Royson, S. Rozewicz, A. Rudenko, S. Rudrakumar, B. Rudran, S. Ruff, P. Rughani, R. Rule, S. Rundell, E. Rushforth, J. Rushmer, D. Rusk, P. Russell, R. Russell, C. Russo, M. Rutgers, K. Rutkowski, A. Ryan, B. Ryan, K. Ryan, L. Ryan, M. Ryan, P. Ryan, D. Ryan-Wakeling, E. Rybka, M. Ryder, S. Ryder, M. Saad, G. Saalmink, J. Sabale, S. Sabaretnam, N. Sadiq, E. Sadler, A. Saffy, B. Sage, H. Sagoo, S. Sagrir, R. Saha, S. Saha, N. Sahdev, S. Sahedra, J. Sahota, N. Said, S. Saini, V. Saini, B. Saint, N. Sairam, A. Sajid, S. Sakthi, H. Sakuri, M. Saladi, A. Salam, A. Salberg, E. Salciute, G. Saleeb, M. Saleh, H. Salih, L. Salih, D. Salim, S. Salisbury, S. Saliu, R. Salman, J. Salmon, R. Salmon, D. Salutous, M. Sam, S. Sam, T. Samakomva, R. Saman, S. Samar, S. Saminathan, R. Samlal, E. Sammons, D. Sammut, M. Sammut, S. Sammut, T. Sammut, S. Sampath, C. Sampson, J. Sampson, A. Samson, J. Samuel, M. Samuel, R. Samuel, T.D.L. Samuel, Y. Samuel, K. Samuels, T. Samuels, J. Samways, M. Samyraju, I. Sana, V. Sanchez, A. Sanchez Gonzalez, A. Sanda-Gomez, P. Sandercock, J. Sanders, A. Sanderson, T. Sanderson, K. Sandhu, L. Sandhu, S. Sandow, V. Sandrey, S. Sands, L. Sanga, H. Sangha, J. Sanghera, M. Sangombe, M. Sanju, L. Sankaran, F. Santos, C. Santos Ferreira De Almeida, R. Santosh, J. Sanyal, A.F. Sanz-Cepero, Y. Sapkota, D. Saragih, D. Saralaya, A. Saraswati, A. Saraswatula, P. Saravanamuthu, S. Sarawade, J. Sarella, A. Sarfatti, R. Sargent, B. Sari, D. Sari, D. Sarkar, K. Sarkar, N. Sarkar, R. Sarkar, S. Sarma, P. Sarmiento, Z. Sarwar, T. Sass, K. Satchithananthasivam, S. Sathe, S. Sathianandan, A. Sathyanarayanan, S.J.P. Sathyanarayanan, T. Sathyapalan, P. Satodia, V. Saulite, A. Saunders, R. Saunders, S. Saunders, A. Saunderson, H. Savill, K. Savlani, G. Saxena, M. Saxton, A. Sayan, I. Sayers, D. Scaletta, D. Scanlon, J. Scanlon, L. Scarratt, S. Scattergood, A. Schadenberg, J. Schafers, W. Schneblen, E. Schofield, R. Schofield, S. Schofield, D. Scholes, K. Scholes, A. Schoolmeesters, N. Schumacher, N. Schunke, M. Schuster Bruce, K. Schwarz, A. Scobie, T. Scoones, T. Scorrer, A. Scott, C. Scott, E. Scott, K. Scott, L. Scott, M. Scott, S. Scott, T. Scott, Z. Scott, S. Scourfield, W. Scrase, N.A. Scriven, A. Scullion, T. Scullion, E. Seager, C. Seagrave, R. Seaman, E. Sear, I. Seaton, A. Seatter, A. Seckington, J. Sedano, G. Seddon, G. Sedgwick, Y. See, M.A. Seelarbokus, C. Sefton, M. Segovia, F. Seidu, G. Sekadde, F. Selby, G. Selby, C. Sellar, R. Sellars, K. Sellers, J. Selley, V. Sellick, G. Selvadurai, B. Selvarajah, H. Selvaskandan, S.S. Selvendran, J. Selwyn, A. Semmens, G. Semple, M. Sen, N. Sen, S. Sen, A. Sengupta, N. Sengupta, S. Senra, H. Senya, T. Serafimova, E. Sernicola, D. Sethi, S. Sethi, N. Setty, A. Seward, T. Sewdin, T.-A. Sewell, J. Seymour, K. Seymour, H. Shabbir, F. Shackley, T. Shafi, F. Shafique, A. Shah, B. Shah, H.-A. Shah, M. Shah, N. Shah, P. Shah, Q. Shah, R. Shah, S. Shah, S.H. Shah, W. Shah, S. Shahad, S. Shahi, S. Shahnazari, N. Shahzad, M. Shahzeb, A. Shaibu, Z. Shaida, A.Y. Shaikh, M. Shaikh, R. Shail, M. Shaji, M. Shakeel, R. Shakya, K. Shalan, M. Shameem, N. Shamim, U. Shamji, A. Shams, K. Shams, R. Shamsah, T. Shanahan, H. Sharaf, A. Sharif, A. Sharma, B. Sharma, M. Sharma, O. Sharma, P. Sharma, R. Sharma, S. Sharma, S.D. Sharma, A. Sharp, C. Sharp, G. Sharp, K. Sharp, L.M. Sharp, P. Sharratt, K. Sharrocks, S. Shashaa, A. Shaw, C. Shaw, D. Shaw, J. Shaw, L. Shaw, M. Shaw, T.G. Shaw, A. Shawcross, J. Shawcross, J. Shawe, L. Shayler, S. Shedwell, J. Sheffield, Z. Shehata, A. Sheik, A. Sheikh, N. Sheikh, B. Shelley, S. Shelton, A. Shenoy, J. Shenton, S. Shephardson, A. Shepherd, K. Shepherd, L. Shepherd, S. Shepherd, G. Sheppard, R. Sheppeard, H. Sheridan, R. Sheridan, S. Sherridan, L. Sherris, S. Sherwin, S. Shibly, F.F. Shiham, C. Shilladay, B. Shillitoe, D. Shingadia, C. Shioi, A. Shirgaonkar, K. Shirley, H. Shirt, A. Shonubi, J. Shoote, R. Shorrocks, R. Shortman, R. Shotton, S. Shotton, C. Shovelton, E. Shpuza, A. Shrestha, G. Shrestha, N. Shrestha, R. Shrestha, S. Shrestha, K. Shuker, J. Shurlock, J. Shurmer, E.R. Shuvo, S.K. Siabi, G. Siame, L. Siamia, M. Siaw-Frimpong, S. Siddavaram, N. Siddique, S. Siddique, E. Siddle, E. Sidebotham, J. Sidebottom, R. Sievers, K. Siggens, N. Sikondari, I. Silanas, S.V. Silva, C. Silva Moniz, M. Sim, T. Simangan, V. Simbi, R. Sime, G. Simmons, O. Simmons, R. Simms, L. Simon, M. Simon, N. Simon, S. Simpkins, A. Simpson, D. Simpson, G. Simpson, J. Simpson, K. Simpson, M. Simpson, P. Simpson, T. Simpson, S. Sinclair, C. Sing, A. Singh, C. Singh, D. Singh, J. Singh, L. Singh, M. Singh, N. Singh, P. Singh, S. Singh, P. Singhal, B. Singizi, V. Singler, M. Sinha, P. Sinha, S. Sinha, U. Sinha, G. Sisson, S. Sithiravel, K. Sivakumar, S. Sivakumar, D. Sivakumran, S. Sivanadarajah, P.-R. Sivasothy, A. Skaria, N. Skehan, R. Skelly, O. Skelton, I. Skene, D. Skinner, T. Skinner, V. Skinner, A. Skorko, I. Skorupinska, M. Skorupinska, A. Slack, K. Slack, H. Slade, M. Slade, L. Slater, N. Slawson, R. Slingsby, A. Sloan, B. Sloan, D. Sloan, G. Sloane, M. Slowinska, B. Small, E. Small, S. Small, A. Smallridge, D. Smalls, K.D. Smallshaw, A. Smallwood, B. Smart, L. Smart, J. Smeaton, C. Smit, A. Smith, C. Smith, D. Smith, E. Smith, H. Smith, I. Smith, J. Smith, K. Smith, L. Smith, M. Smith, M.A. Smith, N. Smith, O. Smith, P. Smith, R. Smith, S. Smith, T. Smith, V. Smith, S. Smolen, S. Smuts, N. Smyth, A. Snell, D. Snell, L. Snell, A. So, B. So, M. Soan, R.F. Sobama, T. Sobande, S. Sobowiec Kouman, A. Sobrino Diaz, B. Sohail, H. Sohal, R. Soiza, O. Solademi, B. Soleimani, A. Solesbury, M. Soliman, B. Solis, R. Solly, L. Solomon, S. Somalanka, C. Somashekar, S. Sommerfield, G. Soni, R. Sonia, T. Sonoiki, S.-C. Soo, P. Soor, G. Soothill, J. Soren, A. Sothinathan, P. Sothirajah, J. Sousa, N. Soussi, D. Southam, D. Southern, I. Southern, L. Southern, S.M. Southin, J. Southwell, T. Southworth, S. Sowden, J. Sowter, C. Spalding, E. Spata, C. Speare, K. Spears, M. Spears, L. Speirs, S. Speirs, M. Spence, N. Spence, B. Spencer, G. Spencer, R. Spencer, S. Spencer, T. Spencer, H. Spickett, J. Spillane, W. Spiller, K. Spinks, M. Spinks, N. Spittle, S. Spray, J. Spriggs, O. Spring, G. Squires, J. Squires, R. Squires, R. Sreenivasan, S. Sreenivasan, M. Sri, K. Sri Paranthamen, R. Srikantaiah, K. Srinivasan, R. Srinivasan, A. Srirajamadhuveeti, V. Srirathan, S.K. Ssiabi, R. Stacey, S. Stacpoole, L. Stadon, W.J. Stagg, J. Staines, N. Staines, K. Stammers, R. Stanciu, G. Stanczuk, T. Standley, B. Staniforth, A. Stanton, L. Stanton, R. Staples, S. Stapley, N. Staplin, A. Stark, E. Starkey, D.S. Starnes, M. Starr, R. Stead, C. Stebbing, C. Steele, H. Steer, J. Steer, V. Stefania, P. Stefanowska, F. Steffensen, C. Stemp, E. Stenson, A. Stephens, D. Stephensen, E. Stephenson, M. Sterrenburg, J. Stevens, M. Stevens, W. Stevens, A. Stevenson, L. Stevenson, S. Stevenson, M. Steward, C. Stewart, D.A. Stewart, K. Stewart, M. Stewart, R. Stewart, J. Stickley, G. Stiller, S. Stirrup, S. Stock, A. Stockdale, D. Stocker, L. Stockham, P. Stockton, E. Stoddard, K. Stoffberg, C. Stokes, B. Stone, R. Stone, S. Stone, E.-J. Stoner, I. Storey, K. Storton, F. Stourton, A. Strachan, C. Strait, E. Stratton, J. Stratton, S. Straw, D. Streit, E. Stride, S. Stringer, S. Strong-Sheldrake, S. Struik, C. Stuart, A. Stubbs, H. Stubbs, A. Sturdy, S. Sturney, M. Stuttard, C. Suarez, K. Subba, C.P. Subbe, K. Subramaniam, M. Subramanian, V. Subramanian, C. Subudhi, R. Suckling, S. Sudershan, P. Sugden, P.A. Suherman, R. Sukla, A. Sukumaran, E. Suleiman, A. Suliman, F. Suliman, S. Sultan, U. Sumardi, S. Sundar, R. Sundaram, R. Sundhar, E. Sung, N. Sunni, J. Suntharalingam, A. Sur, D. Suresh, N. Suresh, S. Suresh, M. Surtees, C. Susan, D. Suter, R. Suthar, H. Sutherland, R. Sutherland, S. Sutherland, D. Sutinyte, D. Sutton, S. Sutton, M. Sutu, M.-L. Svensson, S. Svirpliene, A. Swain, R. Swain, T. Swaine, C. Swales, C. Swanson-Low, T. Swart, S. Sweetman, E. Swift, P. Swift, R. Swift, R. Swingler, S. Swinhoe, K. Swist-Szulik, L. Swithenbank, O. Syed, C. Sykes, D. Sykes, E. Sykes, L. Sylvester, D. Symington, D. Symon, A. Syndercombe, Z. Syrimi, J. Syson, G. Szabo, D. Szabó, T. Szakmany, N. Szarazova, M. Szekely, A. Szekeres, M. Szeto, K. Szymiczek, M. Tabish, M. Tadros, A. Tageldin, L. Tague, H. Tahir, M. Tahir, M. Tai, J. Tait, A. Takyi, P. Talbot, A. Talbot-Smith, J. Talbot-Ponsonby, R. Tallent, B. Tallon, A. Talukdar, A. Tan, B.T. Tan, H. Tan, J. Tan, J.S. Tan, K. Tan, W.T. Tan, A. Tana, A. Tanner, C. Tanney, T. Tanqueray, E. Tanton, A. Tantri, T. Tanzil-Al-Imran, H. Tarft, P. Taribagil, O. Tarin, S. Tariq, D. Tarpey, E. Tarr, L. Tarrant, A. Tasiou, A. Tate, M. Tate, M.L. Tate, P. Tate, K. Tatham, S.S. Tavares, V. Tavoukjian, S.A.I. Tay, A. Taylor, B. Taylor, C. Taylor, C.A. Taylor, D. Taylor, E. Taylor, H. Taylor, J. Taylor, K. Taylor, L. Taylor, M. Taylor, N. Taylor, R. Taylor, S. Taylor, T. Taylor, V. Taylor, M. Taylor-Siddons, T. Taynton, A. Te, F. Teasdale, J. Teasdale, K. Teasdale, J. Tebbutt, C. Tee, I. Teeluck, B. Tejero Moya, R. Tejwani, A. Telfer, V. Teli, J. Tempany, J. Temple, N. Temple, H. Tench, Y.H. Teoh, R. Tereszkowski-Kaminski, L. Terrett, L. Terry, T.I.M. Tesha, D. Tetla, S. Tewari, D. Tewkesbury, J. Texeira, C. Tey, P.N. Thach, M. Thake, C. Thakker, M. Thakker, J. Thakrar, B.J. Thakuri, B. Thamu, H. Thao, H.P. Thao, N.N. Thao, N. Thao, A. Thapa, H. Thatcher, A. Thayanandan, K. Thazhatheyil, E. Thein, L. Theocharidou, P. Thet, K. Thevarajah, M. Thevendra, V.T.K. Thi, D. Thien, N. Thiri Phoo, Y. Thirlwall, M. Thirumaran, A. Thomas, C. Thomas, E. Thomas, H. Thomas, J. Thomas, J.L. Thomas, K. Thomas, L. Thomas, R. Thomas, S. Thomas, T. Thomas, V. Thomas, K. Thomasson, R. Thomas-Turner, C. Thompson, E. Thompson, F. Thompson, H. Thompson, J. Thompson, K. Thompson, L. Thompson, M. Thompson, O. Thompson, R. Thompson, Y. Thompson, B.G. Thomson, N. Thomson, P. Thorburn, N. Thorn, C. Thorne, N. Thorne, A. Thornton, D. Thornton, J. Thornton, R. Thornton, S. Thornton, T. Thornton, C. Thorpe, N. Thorpe, S. Thorpe, P. Thozthumparambil, L. Thrasyvoulou, H. Thraves, N. Thu, N.M. Thu, G. Thueux, N. Thuong, P. Thu-Ta, D. Thuy, D.T.T. Thuy, V. Thwaiotes, C. Thwaites, C.L. Thwaites, G. Thwaites, S. Tiberi, S. Tieger, C. Tierney, M. Tighe, S. Tilbey, C. Till, A. Tiller, H. Tiller, J. Timerick, E. Timlick, A. Timmins, A. Timmis, H. Timms, A.-M. Timoroksa, S. Tinashe, S. Tingley, N. Tinker, H. Tinkler, M. Tinkler, J. Tipper, A. Tirumalai Adisesh, H. Tivenan, K. Tluchowska, H. T-Michael, A. Todd, J. Todd, S. Todd, O. Toffoletti, M. Tohfa, S. Tohill, M. Tolson, A. Tomas, N. Tomasova, S. Tomlin, S. Tomlins, J. Tomlinson, K. Tomlinson, J. Tonkin, I. Tonna, C. Toohey, K. Topham, M. Topping, A. Torokwa, C. Torrance, O. Touma, L. Tous Sampol, R. Tousis, M. Tout, P. Tovey, G. Towersey, J. Townley, R. Tozer, D.K. Tran, H. Tran, H.B. Tran, M. Tran, N. Tran, V.G. Tran, V.K. Tran, N.T.H. Trang, H. Tranter, J. Travers, C. Travill, S. Traynor, L. Trethowan, E. Treus Gude, M. Trevelyan, N.A. Trewick, A. Tridente, H. Trieu, S. Triggs, F. Trim, A. Trimmings, T. Trinick, S. Tripathy, K. Trivedi, S. Troedson, E. Tropman, A. Trotter, S. Trous, H. Trower, M. Trowsdale Stannard, N. Trudgill, R. Truell, N. Truman, M. Truslove, S. Trussell, T. Trussell, K. Tsakiridou, C. Tsang, P. Tsang, T. Tsawayo, K.K. Tsilimpari, G. Tsinaslanidis, M. Tsitsi, S. Tso, H.T.C. Tu, N. Tucker, S. Tucker, D.E. Tudor, A. Tufail, J. Tuff, J. Tuffney, R. Tully, T. Tulus Satriasih, G. Tunesi, D. Tung, D.Q. Tung, K. Turbitt, R. Turel, T. Turgut, C. Turley, A. Turnbull, A. Turner, C. Turner, G. Turner, K. Turner, L. Turner, L.C. Turner, M. Turner, P. Turner, S. Turner, V. Turner, I. Turner-bone, S. Turney, J. Turvey, N.T.M. Tuyen, C. Tweed, D. Tweed, R. Twemlow, E. Twohey, B. Tyagi, V. Tyagi, A. Tyer, A. Tyler, J. Tyler, A. Tyzack, P. Tzavaras, I. Tzinieris, A.W. Uddin, M.S. Uddin, R. Uddin, J. Ugoji, E. Ukaegbu, M. Ul Haq, W. Ul Hassan, Z. Ul-Haq, S. Ullah, J. Um, A. Umaipalan, A. Umate, J. Umeadi, A. Umeh, W. Umeojiako, B. Ummat, E. Underhill, C. Underwood, J. Underwood, A. Unsworth, V. Uppal, V.S. Uppal, G. Upson, M. Ur Rasool, A. Uriel, S. Urruela, H. Uru, J. Usher, M. Usher, R. Usher, A. Usher-Rea, A. Ustianowski, E. Usuf, F. Utomo, H. Uzu, L.C. Vaccari, U. Vaghela, A. Vaidya, D. Vail, B. Valecka, J. Valentine, B. Valeria, P. Vallabhaneni, T. Valleri, N. Vallotton, L. Vamplew, E. Vamvakiti, J. Vamvakopoulos, C.T.C. Van, S. Van Blydenstein, L. van Bruggen, M. van de Venne, A. van der Meer, N. van der Stelt, R. Van Doorn, L. van Koutrik, A. Van Loggerenberg, J. Vance-Daniel, R. Vancheeswaran, S.I. Vandeyoon, P. Vankayalapati, P. Vanmali, C. Vansomeren, W. Van't Hoff, S. Vara, S.J. Vardy, A. Varghese, M. Varghese, W. Varney, G. Varnier, A.-N. Varouxaki, R. Varquez, V. Vasadi, O. Vass, K. Vassell, V. Vasu, V. Vasudevan, M. Vatish, S. Vaughan, H. Vayalaman, D. Vayapooree, C. Vaz, N. Veale, S. Veerasamy, S. Velankar, L. Velauthar, N. Veli, N. Vella, A. Velugupati, A. Velusamy, I. Venables, M. Venditti, R. Venkataramakrishnan, R. Venn, M. Venter, L. Ventilacion, J. Vere, M. Veres, S. Vergnano, W. Verling, A. Verma, R. Vernall, B. Vernon, M. Vertue, L. Verueco, J. Verula, A. Veterini, N. Vethanayagam, S. Vettikumaran, L. Veys, C. Vickers, S. Victor, S. Victoria, C. Vidaillic, C.P. Vidaillac, J. Vidler, B. Vijayakumar, V.W. Vijayaraghavan Nalini, B. Vilcinskaite, A. Vileito, N. Vilimiene, L. Vinall, S. Vinay, L. Vinayakarao, O. Vincent, R. Vincent, N.Q. Vinh, P. Virdee, E. Virgilio, A.M. Virk, E. Visentin, M. Vitaglione, K. Vithian, S. Vittoria, S. Vivekananthan, E. Vlad, B. Vlies, L. von Oven, C. Vooght, K.T. Vu Thai, K. Vutipongsatorn, A. Vuylsteke, E. Vyras, R. Wach, B. Wadams, S. Wadd, N. Waddington, P. Wade, J. Wadsley, K. Wadsworth, S.E.I. Wafa, D. Wagstaff, L. Wagstaff, D. Wahab, Z. Wahbi, A. Waheed Adigun, S. Waidyanatha, A. Waite, R. Wake, A. Wakefield, W. Wakeford, F. Wakinshaw, E. Waldeck, A. Walden, L. Walding, A. Waldron, J. Waldron, E. Wales, B. Wali, D. Walker, G. Walker, H. Walker, I. Walker, K. Walker, L. Walker, O. Walker, R. Walker, S. Walker, G. Wallace, R. Wallbutton, J. Wallen, K. Wallendszus, A. Waller, R. Waller, G. Wallis, L. Wallis, M. Wallis, E. Walmsley, D. Walsh, E. Walsh, L. Walsh, D. Walstow, D. Walter, A. Walters, H. Walters, J. Walters, E. Walton, L. Walton, M. Walton, O. Walton, S. Walton, M. Wan, J. Wanda, M. Wands, R. Wane, F. Wang, N. Wang, R. Wang, S. Wang, D. Warbrick, S. Warburton, C. Ward, D. Ward, E. Ward, H. Ward, J. Ward, L. Ward, N. Ward, R. Ward, T. Ward, S.A. Warden, G. Wardere, S. Wardle, H. Wardy, G. Waring, S. Waring, J. Warmington, B. Warner, C. Warner, L. Warnock, S. Warran, J. Warren, L. Warren, R. Warren, Y. Warren, D. Warrender, H. Warren-Miell, A. Warris, G. Warwick, H. Wassall, S. Wasserman, E. Wasson, H.J. Watchorn, H. Waterfall, A. Waters, D. Waters, M. Waterstone, A. Watkin, C. Watkins, E. Watkins, K. Watkins, L. Watkins, A. Watson, A.J.R. Watson, E. Watson, F. Watson, J.G.R. Watson, L. Watson, P. Watson, R. Watson, K. Watson, M. Watters, D. Watterson, K. Wattimena, D. Watts, J. Watts, M. Watts, V. Waugh, E. Wayman, M. Wayman, A. Wazir, M. Weatherhead, N. Weatherly, C. Webb, H. Webb, K. Webb, S. Webb, C. Websdale, D. Webster, I. Webster, J. Webster, T. Webster, J. Wedlin, L. Wee, R. Weerakoon, T. Weerasinghe, J. Weeratunga, M. Weetman, S. Wei, I. Weichert, E. Welch, H. Welch, J. Welch, L. Welch, S. Welch, B. Welham, S. Weller, L. Wellings, B. Wells, S. Wellstead, B. Welsh, R. Welsh, I. Welters, R. Welton, V. Wenn, L. Wentworth, J. Wesonga, K. Wesseldine, J. West, M. West, R. West, S. West, L. Western, R. Westhead, H. Weston, A. Westwood, K. Westwood, S. Westwood, B. Wetherill, S. Wheaver, H. Wheeler, B. Whelan, M. Whelband, A. Whileman, A. Whitcher, A. White, B. White, C. White, D. White, J. White, K. White, M. White, N. White, S. White, T. White, C. Whitehead, K. Whitehorn, A. Whitehouse, C. Whitehouse, T. Whitehouse, J. Whiteley, L. Whiteley, S. Whiteley, R. Whitham, G. Whitlingum, D. Whitmore, E. Whittaker, L. Whittam, A. Whittington, H. Whittle, R. Whittle, E. Wiafe, L. Wiblin, O. Wickens, J. Widdrington, J. Wieboldt, H. Wieringa, C. Wiesender, L. Wiffen, A. Wight, A. Wignall, C. Wignall, A. Wilce, D. Wilcock, E. Wilcock, L. Wilcox, B. Wild, L. Wild, S. Wild, M. Wilde, L. Wilding, P. Wilding, T. Wildsmith, J. Wileman, J. Wiles, K. Wiles, E. Wilhelmsen, T. Wiliams, J. Wilkie, D. Wilkin, H. Wilkins, J. Wilkins, S. Wilkins, I. Wilkinson, L. Wilkinson, N. Wilkinson, S. Wilkinson, T. Wilkinson, S. Willetts, A. Williams, C. Williams, C.V. Williams, D. Williams, E. Williams, G. Williams, H. Williams, J. Williams, K. Williams, M. Williams, P. Williams, R. Williams, S. Williams, T. Williams, A. Williamson, C. Williamson, D. Williamson, J. Williamson, J.D. Williamson, R. Williamson, H. Williamson, E. Willis, H. Willis, J. Willis, L. Wills, L. Willsher, C. Willshire, F. Willson, J. Willson, A. Wilson, B. Wilson, D. Wilson, I. Wilson, J. Wilson, K. Wilson, K.-A. Wilson, L. Wilson, M. Wilson, S. Wilson, T. Wilson, K.L.Y. Win, M. Win, T. Win, T.T. Win, W.Y.W. Win, L. Winckworth, L. Winder, P. Winder, S. Winearl, H. Winmill, S. Winn, C. Winpenny, H. Winslow, H. Winter, J. Winter, B. Winter-Goodwin, J. Winterton, H. Winwood, J. Wischhusen, S. Wisdom, M. Wise, M. Wiselka, R. Wiseman, S. Wiseman, S. Wishart, T. WIshlade, E. Witele, N. Withers, J. Wittes, D. Wixted, T. Wodehouse, W. Wolf, N. Wolff, K. Wolffsohn, R. Wolf-Roberts, E. Wolodimeroff, A. Wolstencroft, A. Wong, C. Wong, C.-H. Wong, C.-M. Wong, E. Wong, J.S.Y. Wong, K.Y. Wong, M.Y. Wong, N. Wong, S. Wong, T. Wong, A.A. Wongkyezeng, A. Wood, C. Wood, D. Wood, F. Wood, G. Wood, H. Wood, J. Wood, L. Wood, M. Wood, S. Wood, T. Wood, K. Woodall, R. Woodfield, C. Woodford, E. Woodford, J. Woodford, L. Woodhead, T. Woodhead, P. Woodland, M. Woodman, S. Woodmansey, C. Woods, J. Woods, K. Woods, S. Woods, Z. Woodward, M. Woolcock, G. Wooldridge, R. Woolf, C. Woollard, L. Woollen, E. Woolley, J. Woolley, D. Woosey, D. Wootton, J. Wootton, D. Worley, S. Worton, J. Wraight, M. Wray, K. Wren, L. Wren, C. Wrey Brown, C. Wright, D. Wright, F. Wright, H. Wright, I. Wright, L. Wright, R. Wright, S. Wright, T. Wright, C. Wroe, H. Wroe, H. Wu, P. Wu, J. Wubetu, F. Wulandari, R. Wulandari, S. Wurie, C. Wyatt, F. Wyn-Griffiths, I. Wynter, B. Xavier, A. Xhikola, B.E. Xia, Z. Xia, E. Yacoba, S. Yadav, M. Yakubi, M. Yan, Y. Yanagisawa, F. Yang, Y. Yang, M. Yanney, W.L. Yap, N. Yaqoob, S. Yasmin, B. Yates, D. Yates, E. Yates, H. Yates, T. Yates, M. Yates, J. Ye, C. Yearwood Martin, K. Yein, F. Yelnoorkar, L. Yen, L.M. Yen, A. Yeoh, C.Y. Yeung, P. Yew, D. Yewatkar, L. Ylquimiche Melly, I. Ynter, H. Yong, J. Yorke, J. Youens, A. Younes Ibrahim, E. Young, G. Young, L. Young, A. Yousafzar, S. Youssouf, A. Yousuf, H. Yovita, C. Yu, J.S.J. Yuan, N. Yufaniaputri, B. Yung, D. Yusef, S. Yusef, I. Yusuf, A.-S. Zafar, S. Zagalo, S. Zaher, A. Zahoor, M. Zainab, T. Zak, K. Zaki, N. Zakir, K. Zalewska, A. Zamalloa, M. Zaman, S. Zaman, J. Zamikula, L. Zammit, M. Zammit-Mangion, M. Zawadzka, M. Zayed, E. Zebracki, D. Zehnder, L. Zeidan, D. Zeinali, J. Zhang, X. Zhao, D. Zheng, D. Zhu, M. Zia, O. Zibdeh, R. Zill-E-Huma, E.T. Zin, E. Zincone, G. Zindoga, E. Zinkin, V. Zinyemba, C. Zipitis, L. Zitter, A. Zmierczak, G. Zubikarai, A. Zubir, N. Zuhra, R. Zulaikha, S. Zulfikar, C. Zullo, and A. Zuriaga-Alvaro
Subjects: COVID-19, Corticosteroid, Dexamethasone, Mortality, Clinical trial, Medicine (General), R5-920
Abstract: Summary: Background: Low dose corticosteroids (e.g., 6 mg dexamethasone) have been shown to reduce mortality for hypoxic COVID-19 patients. We have previously reported that higher dose corticosteroids cause harm in patients with clinical hypoxia but not receiving ventilatory support (the combination of non-invasive mechanical ventilation, including high-flow nasal oxygen, continuous positive airway pressure and bilevel positive airway pressure ventilation, and invasive mechanical ventilation or extra-corporeal membrane oxygenation), but the balance of efficacy and safety in patients receiving ventilatory support is uncertain. Methods: This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) assessed multiple possible treatments in patients hospitalised for COVID-19. Eligible and consenting adult patients receiving ventilatory support were randomly allocated (1:1) to either usual care with higher dose corticosteroids (dexamethasone 20 mg once daily for 5 days followed by 10 mg once daily for 5 days or until discharge if sooner) or usual standard of care alone (which includes dexamethasone 6 mg once daily for 10 days or until discharge if sooner). The primary outcome was 28-day mortality; secondary outcomes were duration of hospitalisation and (among participants not on invasive mechanical ventilation at baseline) the composite of invasive mechanical ventilation or death. Recruitment closed on 31 March 2024 when funding for the trial ended. The RECOVERY trial is registered with ISRCTN (50189673) and clinicaltrials.gov (NCT04381936). Findings: Between 25 May 2021 and 9 January 2024, 477 COVID-19 patients receiving ventilatory support were randomly allocated to receive usual care plus higher dose corticosteroids vs. usual care alone (of whom 99% received corticosteroids during the follow-up period). Of those randomised, 221 (46%) were in Asia, 245 (51%) in the UK and 11 (2%) in Africa. 143 (30%) had diabetes mellitus. Overall, 86 (35%) of 246 patients allocated to higher dose corticosteroids vs. 86 (37%) of 231 patients allocated to usual care died within 28 days (rate ratio [RR] 0.87; 95% CI 0.64–1.18; p = 0.37). There was no significant difference in the proportion of patients discharged from hospital alive within 28 days (128 [52%] in the higher dose corticosteroids group vs. 120 [52%] in the usual care group; RR 1.04, 0.81–1.33]; p = 0.78). Among those not on invasive mechanical ventilation at baseline, there was no clear reduction in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (76 [37%] of 206 vs. 93 [45%] of 205; RR 0.79 [95% CI 0.63–1.00]; p = 0.05). Interpretation: In patients hospitalised for COVID-19 receiving ventilatory support, we found no evidence that higher dose corticosteroids reduced the risk of death compared to usual care, which included low dose corticosteroids. Funding: UK Research and Innovation (Medical Research Council) and National Institute for Health Research (Grant ref: MC_PC_19056), and Wellcome Trust (Grant Ref: 222406/Z/20/Z).
Published: 2025
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33. Improving astroBERT using Semantic Textual Similarity

Author: Grezes, Felix, Allen, Thomas, Blanco-Cuaresma, Sergi, Accomazzi, Alberto, Kurtz, Michael J., Shapurian, Golnaz, Henneken, Edwin, Grant, Carolyn S., Thompson, Donna M., Hostetler, Timothy W., Templeton, Matthew R., Lockhart, Kelly E., Chen, Shinyi, Koch, Jennifer, Jacovich, Taylor, and Protopapas, Pavlos
Subjects: Computer Science - Computation and Language, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.
Published: 2022

34. Tree-layout based graph classes: proper chordal graphs

Author: Paul, Christophe and Protopapas, Evangelos
Subjects: Computer Science - Discrete Mathematics
Abstract: Many standard graph classes are known to be characterized by means of layouts (a permutation of its vertices) excluding some patterns. Important such graph classes are among others: proper interval graphs, interval graphs, chordal graphs, permutation graphs, (co-)comparability graphs. For example, a graph $G=(V,E)$ is a proper interval graph if and only if $G$ has a layout $L$ such that for every triple of vertices such that $x\prec_L y\prec_L z$, if $xz\in E$, then $xy\in E$ and $yz\in E$. Such a triple $x$, $y$, $z$ is called an indifference triple and layouts excluding indifference triples are known as indifference layouts. In this paper, we investigate the concept of tree-layouts. A tree-layout $T_G=(T,r,\rho_G)$ of a graph $G=(V,E)$ is a tree $T$ rooted at some node $r$ and equipped with a one-to-one mapping $\rho_G$ between $V$ and the nodes of $T$ such that for every edge $xy\in E$, either $x$ is an ancestor of $y$ or $y$ is an ancestor of $x$. Clearly, layouts are tree-layouts. Excluding a pattern in a tree-layout is defined similarly as excluding a pattern in a layout, but now using the ancestor relation. Unexplored graph classes can be defined by means of tree-layouts excluding some patterns. As a proof of concept, we show that excluding non-indifference triples in tree-layouts yields a natural notion of proper chordal graphs. We characterize proper chordal graphs and position them in the hierarchy of known subclasses of chordal graphs. We also provide a canonical representation of proper chordal graphs that encodes all the indifference tree-layouts rooted at some vertex. Based on this result, we first design a polynomial time recognition algorithm for proper chordal graphs. We then show that the problem of testing isomorphism between two proper chordal graphs is in P, whereas this problem is known to be GI-complete on chordal graphs., Comment: 33 pages, 13 figures
Published: 2022

35. Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

Author: Pellegrin, Raphaël, Bullwinkel, Blake, Mattheakis, Marios, and Protopapas, Pavlos
Subjects: Computer Science - Machine Learning, Physics - Computational Physics
Abstract: Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences. We adopt a recently developed transfer learning approach for PINNs and introduce a multi-head model to efficiently obtain accurate solutions to nonlinear systems of ordinary differential equations with random potentials. In particular, we apply the method to simulate stochastic branched flows, a universal phenomenon in random wave dynamics. Finally, we compare the results achieved by feed forward and GAN-based PINNs on two physically relevant transfer learning tasks and show that our methods provide significant computational speedups in comparison to standard PINNs trained from scratch., Comment: 5 pages, 3 figures
Published: 2022

36. Semi-Supervised Classification and Clustering Analysis for Variable Stars

Author: Pantoja, R., Catelan, M., Pichara, K., and Protopapas, P.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning approaches have become commonplace. However, this needs a considerable collection of expert-labeled light curves to achieve adequate performance, which is costly to construct. To solve this problem, we introduce two approaches. First, a semi-supervised hierarchical method, which requires substantially less trained data than supervised methods. Second, a clustering analysis procedure that finds groups that may correspond to classes or sub-classes of variable stars. Both methods are primarily supported by dimensionality reduction of the data for visualization and to avoid the curse of dimensionality. We tested our methods with catalogs collected from OGLE, CSS, and Gaia surveys. The semi-supervised method reaches a performance of around 90\% for all of our three selected catalogs of variable stars using only $5\%$ of the data in the training. This method is suitable for classifying the main classes of variable stars when there is only a small amount of training data. Our clustering analysis confirms that most of the clusters found have a purity over 90\% with respect to classes and 80\% with respect to sub-classes, suggesting that this type of analysis can be used in large-scale variability surveys as an initial step to identify which classes or sub-classes of variable stars are present in the data and/or to build training sets, among many other possible applications., Comment: 23 pages, 21 figures, 4 tables, submitted to MNRAS
Published: 2022
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37. DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks

Author: Bullwinkel, Blake, Randle, Dylan, Protopapas, Pavlos, and Sondak, David
Subjects: Computer Science - Machine Learning
Abstract: Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving differential equations, but they lack a theoretical justification for the use of any particular loss function. This work presents Differential Equation GAN (DEQGAN), a novel method for solving differential equations using generative adversarial networks to "learn the loss function" for optimizing the neural network. Presenting results on a suite of twelve ordinary and partial differential equations, including the nonlinear Burgers', Allen-Cahn, Hamilton, and modified Einstein's gravity equations, we show that DEQGAN can obtain multiple orders of magnitude lower mean squared errors than PINNs that use $L_2$, $L_1$, and Huber loss functions. We also show that DEQGAN achieves solution accuracies that are competitive with popular numerical methods. Finally, we present two methods to improve the robustness of DEQGAN to different hyperparameter settings., Comment: arXiv admin note: text overlap with arXiv:2007.11133
Published: 2022

38. Obstructions to Erdös-Pósa Dualities for Minors.

Author: Christophe Paul, Evangelos Protopapas, Dimitrios M. Thilikos, and Sebastian Wiederrecht
Published: 2024
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39. Delineating Half-Integrality of the Erdős-Pósa Property for Minors: The Case of Surfaces.

Author: Christophe Paul, Evangelos Protopapas, Dimitrios M. Thilikos, and Sebastian Wiederrecht
Published: 2024
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40. Tree-Layout Based Graph Classes: Proper Chordal Graphs.

Author: Christophe Paul and Evangelos Protopapas
Published: 2024
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41. IoT Malware Data Augmentation using a Generative Adversarial Network.

Author: John Carter, Spiros Mancoridis, Pavlos Protopapas, and Erick Galinkin
Published: 2024

42. Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings.

Author: John Carter, Spiros Mancoridis, Pavlos Protopapas, and Erick Galinkin
Published: 2024

43. Online Decentralised Mechanisms for Dynamic Ridesharing.

Author: Nicos Protopapas, Vahid Yazdanpanah, Enrico H. Gerding, and Sebastian Stein 0001
Published: 2024
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44. A mathematical model for studying the Red Blood Cell magnetic susceptibility

Author: Protopapas, Eleftherios, Vafeas, Panayiotis, and Hadjinicolaou, Maria
Published: 2025
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45. RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization

Author: Joy, Hayden, Mattheakis, Marios, and Protopapas, Pavlos
Subjects: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Physics - Applied Physics
Abstract: Reservoir computers (RCs) are among the fastest to train of all neural networks, especially when they are compared to other recurrent neural networks. RC has this advantage while still handling sequential data exceptionally well. However, RC adoption has lagged other neural network models because of the model's sensitivity to its hyper-parameters (HPs). A modern unified software package that automatically tunes these parameters is missing from the literature. Manually tuning these numbers is very difficult, and the cost of traditional grid search methods grows exponentially with the number of HPs considered, discouraging the use of the RC and limiting the complexity of the RC models which can be devised. We address these problems by introducing RcTorch, a PyTorch based RC neural network package with automated HP tuning. Herein, we demonstrate the utility of RcTorch by using it to predict the complex dynamics of a driven pendulum being acted upon by varying forces. This work includes coding examples. Example Python Jupyter notebooks can be found on our GitHub repository https://github.com/blindedjoy/RcTorch and documentation can be found at https://rctorch.readthedocs.io/., Comment: 18 pages, 12 figures, and 43 citations. GitHub repository and documentation information included and linked
Published: 2022

46. Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems

Author: Liu, Shuheng, Huang, Xiyue, and Protopapas, Pavlos
Subjects: Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis
Abstract: There have been extensive studies on solving differential equations using physics-informed neural networks. While this method has proven advantageous in many cases, a major criticism lies in its lack of analytical error bounds. Therefore, it is less credible than its traditional counterparts, such as the finite difference method. This paper shows that one can mathematically derive explicit error bounds for physics-informed neural networks trained on a class of linear systems of differential equations. More importantly, evaluating such error bounds only requires evaluating the differential equation residual infinity norm over the domain of interest. Our work shows a link between network residuals, which is known and used as loss function, and the absolute error of solution, which is generally unknown. Our approach is semi-phenomonological and independent of knowledge of the actual solution or the complexity or architecture of the network. Using the method of manufactured solution on linear ODEs and system of linear ODEs, we empirically verify the error evaluation algorithm and demonstrate that the actual error strictly lies within our derived bound., Comment: 12 pages + 4 appendices
Published: 2022

47. Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

Author: García-Jara, Germán, Protopapas, Pavlos, and Estévez, Pablo A.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, J.2.3
Abstract: Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine learning algorithms whose performance is limited by the number of existing annotations of astronomical objects and their highly imbalanced class distributions. In this work, we propose a data augmentation methodology based on Generative Adversarial Networks (GANs) to generate a variety of synthetic light curves from variable stars. Our novel contributions, consisting of a resampling technique and an evaluation metric, can assess the quality of generative models in unbalanced datasets and identify GAN-overfitting cases that the Fr\'echet Inception Distance does not reveal. We applied our proposed model to two datasets taken from the Catalina and Zwicky Transient Facility surveys. The classification accuracy of variable stars is improved significantly when training with synthetic data and testing with real data with respect to the case of using only real data., Comment: Accepted to ApJ on May 11, 2022
Published: 2022
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48. Cosmology-informed neural networks to solve the background dynamics of the Universe

Author: Chantada, Augusto T., Landau, Susana J., Protopapas, Pavlos, Scóccola, Claudia G., and Garraffo, Cecilia
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract: The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are $\Lambda \mathrm{CDM}$, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki $f(R)$ model. We use the solutions that the networks provide to perform statistical analyses to estimate the values of each model's parameters with observational data; namely, estimates of the Hubble parameter from cosmic chronometers, type Ia supernovae data from the Pantheon compilation, and measurements from baryon acoustic oscillations. The results we obtain for all models match similar estimations done in the literature using numerical solvers. In addition, we estimate the error of the solutions that the trained networks provide by comparing them with the analytical solution when there is one, or to a high-precision numerical solution when there is not. Through these estimations we find that the error of the solutions is at most $\sim1\%$ in the region of the parameter space that concerns the $95\%$ confidence regions that we find using the data, for all models and all statistical analyses performed in this work., Comment: 28 pages, 10 figures, 10 tables, supplemental material available at https://github.com/at-chantada/Supplemental-Materials
Published: 2022
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49. ASTROMER: A transformer-based embedding for the representation of light curves

Author: Donoso-Oliva, C., Becker, I., Protopapas, P., Cabrera-Vives, G., M., Vishnu, and Vardhan, Harsh
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract: Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to other surveys by re-training ASTROMER on new sources. The power of ASTROMER consists of using the representation to extract light curve embeddings that can enhance the training of other models, such as classifiers or regressors. As an example, we used ASTROMER embeddings to train two neural-based classifiers that use labeled variable stars from MACHO, OGLE-III, and ATLAS. In all experiments, ASTROMER-based classifiers outperformed a baseline recurrent neural network trained on light curves directly when limited labeled data was available. Furthermore, using ASTROMER embeddings decreases computational resources needed while achieving state-of-the-art results. Finally, we provide a Python library that includes all the functionalities employed in this work. The library, main code, and pre-trained weights are available at https://github.com/astromer-science
Published: 2022
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50. Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations

Author: Pérez-Carrasco, Manuel, Protopapas, Pavlos, and Cabrera-Vives, Guillermo
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract: In this work, we present Con$^{2}$DA, a simple framework that extends recent advances in semi-supervised learning to the semi-supervised domain adaptation (SSDA) problem. Our framework generates pairs of associated samples by performing stochastic data transformations to a given input. Associated data pairs are mapped to a feature representation space using a feature extractor. We use different loss functions to enforce consistency between the feature representations of associated data pairs of samples. We show that these learned representations are useful to deal with differences in data distributions in the domain adaptation problem. We performed experiments to study the main components of our model and we show that (i) learning of the consistent and contrastive feature representations is crucial to extract good discriminative features across different domains, and ii) our model benefits from the use of strong augmentation policies. With these findings, our method achieves state-of-the-art performances in three benchmark datasets for SSDA., Comment: Accepted to NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications
Published: 2022

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