Author: "Li, Andrew A." / Search Limiters: Available in Library Collection - Searchworks@Jio Institute Digital Library Search Results

849 results on '"Li, Andrew A."'

1. Stepping Forward on the Last Mile

Author: Feng, Chen, Zhuo, Shaojie, Zhang, Xiaopeng, Ramakrishnan, Ramchalam Kinattinkara, Yuan, Zhaocong, and Li, Andrew Zou
Subjects: Computer Science - Machine Learning
Abstract: Continuously adapting pre-trained models to local data on resource constrained edge devices is the $\emph{last mile}$ for model deployment. However, as models increase in size and depth, backpropagation requires a large amount of memory, which becomes prohibitive for edge devices. In addition, most existing low power neural processing engines (e.g., NPUs, DSPs, MCUs, etc.) are designed as fixed-point inference accelerators, without training capabilities. Forward gradients, solely based on directional derivatives computed from two forward calls, have been recently used for model training, with substantial savings in computation and memory. However, the performance of quantized training with fixed-point forward gradients remains unclear. In this paper, we investigate the feasibility of on-device training using fixed-point forward gradients, by conducting comprehensive experiments across a variety of deep learning benchmark tasks in both vision and audio domains. We propose a series of algorithm enhancements that further reduce the memory footprint, and the accuracy gap compared to backpropagation. An empirical study on how training with forward gradients navigates in the loss landscape is further explored. Our results demonstrate that on the last mile of model customization on edge devices, training with fixed-point forward gradients is a feasible and practical approach.
Published: 2024

2. EmbedLLM: Learning Compact Representations of Large Language Models

Author: Zhuang, Richard, Wu, Tianhao, Wen, Zhaojin, Li, Andrew, Jiao, Jiantao, and Ramchandran, Kannan
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream, tasks has become increasingly critical. Many existing methods repeatedly learn task-specific representations of Large Language Models (LLMs), which leads to inefficiencies in both time and computational resources. To address this, we propose EmbedLLM, a framework designed to learn compact vector representations, of LLMs that facilitate downstream applications involving many models, such as model routing. We introduce an encoder-decoder approach for learning such embeddings, along with a systematic framework to evaluate their effectiveness. Empirical results show that EmbedLLM outperforms prior methods in model routing both in accuracy and latency. Additionally, we demonstrate that our method can forecast a model's performance on multiple benchmarks, without incurring additional inference cost. Extensive probing experiments validate that the learned embeddings capture key model characteristics, e.g. whether the model is specialized for coding tasks, even without being explicitly trained on them. We open source our dataset, code and embedder to facilitate further research and application.
Published: 2024

3. Thermodynamic Relations between Free Energy and Mobility

Author: Li, Andrew Boshi and Sinno, Talid
Subjects: Condensed Matter - Statistical Mechanics
Abstract: Stochastic and dynamical processes lie at the heart of all physical, chemical, and biological systems. However, kinetic and thermodynamic properties which characterize these processes have largely been treated separately as they can be obtained independently for many systems at thermodynamic equilibrium. In this work we demonstrate the existence of a class of relations between kinetic and thermodynamic factors which holds even in the hydrodynamic limit, and which must be satisfied for all systems that satisfy detailed balance and Boltzmann distribution at equilibrium. We achieve this by proving that for systems with inhomogeneous equilibrium states governed by dynamics such as the Cahn-Hilliard (CH) dynamics, the chemical potential and self-diffusivity must mutually constrain each other. We discuss common issues in the literature which result in inconsistent formulations, construct the consistency requirement mathematically, develop a class of self-diffusivities that guarantee consistency, and discuss how the requirement originates from detailed balance and Boltzmann distribution, and is therefore applicable to both conserved and non-conserved dynamics.
Published: 2024

4. Reward Machines for Deep RL in Noisy and Uncertain Environments

Author: Li, Andrew C., Chen, Zizhao, Klassen, Toryn Q., Vaezipoor, Pashootan, Icarte, Rodrigo Toro, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Formal Languages and Automata Theory, I.2.0, I.2.6, I.2.4, F.4.3
Abstract: Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the underlying structure of a reward function, they enable the decomposition of an RL task, leading to impressive gains in sample efficiency. Although Reward Machines and similar formal specifications have a rich history of application towards sequential decision-making problems, they critically rely on a ground-truth interpretation of the domain-specific vocabulary that forms the building blocks of the reward function--such ground-truth interpretations are elusive in the real world due in part to partial observability and noisy sensing. In this work, we explore the use of Reward Machines for Deep RL in noisy and uncertain environments. We characterize this problem as a POMDP and propose a suite of RL algorithms that exploit task structure under uncertain interpretation of the domain-specific vocabulary. Through theory and experiments, we expose pitfalls in naive approaches to this problem while simultaneously demonstrating how task structure can be successfully leveraged under noisy interpretations of the vocabulary.
Published: 2024

5. Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention

Author: Li, Andrew, Feng, Xianle, Narang, Siddhant, Peng, Austin, Cai, Tianle, Shah, Raj Sanjay, and Varma, Sashank
Subjects: Computer Science - Computation and Language
Abstract: When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in psycholinguistic experiments using online measures such as reading times and offline measures such as comprehension questions. Here, we investigate the processing of garden-path sentences and the fate of lingering misinterpretations using four large language models (LLMs): GPT-2, LLaMA-2, Flan-T5, and RoBERTa. The overall goal is to evaluate whether humans and LLMs are aligned in their processing of garden-path sentences and in the lingering misinterpretations past the point of disambiguation, especially when extra-syntactic information (e.g., a comma delimiting a clause boundary) is present to guide processing. We address this goal using 24 garden-path sentences that have optional transitive and reflexive verbs leading to temporary ambiguities. For each sentence, there are a pair of comprehension questions corresponding to the misinterpretation and the correct interpretation. In three experiments, we (1) measure the dynamic semantic interpretations of LLMs using the question-answering task; (2) track whether these models shift their implicit parse tree at the point of disambiguation (or by the end of the sentence); and (3) visualize the model components that attend to disambiguating information when processing the question probes. These experiments show promising alignment between humans and LLMs in the processing of garden-path sentences, especially when extra-syntactic information is available to guide processing., Comment: Accepted by CogSci-24
Published: 2024

6. Color code decoder with improved scaling for correcting circuit-level noise

Author: Lee, Seok-Hyung, Li, Andrew, and Bartlett, Stephen D.
Subjects: Quantum Physics
Abstract: Two-dimensional color codes are a promising candidate for fault-tolerant quantum computing, as they have high encoding rates, transversal implementation of logical Clifford gates, and high feasibility of magic state constructions. However, decoding color codes presents a significant challenge due to their structure, where elementary errors violate three checks instead of just two (a key feature in surface code decoding), and the complexity in extracting syndrome is greater. We introduce an efficient color-code decoder that tackles these issues by combining two matching decoders for each color, generalized to handle circuit-level noise by employing detector error models. We provide comprehensive analyses of the decoder, covering its threshold and sub-threshold scaling both for bit-flip noise with ideal measurements and for circuit-level noise. Our simulations reveal that this decoding strategy nearly reaches the best possible scaling of logical failure ($p_\mathrm{fail} \sim p^{d/2}$) for both noise models, where $p$ is the noise strength, in the regime of interest for fault-tolerant quantum computing. While its noise thresholds are comparable with other matching-based decoders for color codes (8.2% for bit-flip noise and 0.46% for circuit-level noise), the scaling of logical failure rates below threshold significantly outperforms the best matching-based decoders., Comment: 27 pages, 16 figures
Published: 2024

7. Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial

Author: Li, Mengzhou, Niu, Chuang, Wang, Ge, Amma, Maya R, Chapagain, Krishna M, Gabrielson, Stefan, Li, Andrew, Jonker, Kevin, de Ruiter, Niels, Clark, Jennifer A, Butler, Phil, Butler, Anthony, and Yu, Hengyong
Subjects: Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract: The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality., Comment: 9 figures, 5 tables
Published: 2024

8. On the set partitions that require maximum sorts through the $aba-$avoiding stack

Author: Choi, Yunseo, Gan, Katelyn, Li, Andrew, and Zhu, Tiffany
Subjects: Mathematics - Combinatorics
Abstract: Recently, Xia introduced a deterministic variation $\phi_{\sigma}$ of Defant and Kravitz's stack-sorting maps for set partitions and showed that any set partition $p$ is sorted by $\phi^{N(p)}_{aba}$, where $N(p)$ is the number of distinct alphabets in $p$. Xia then asked which set partitions $p$ are not sorted by $\phi_{aba}^{N(p)-1}$. In this note, we prove that the minimal length of a set partition $p$ that is not sorted by $\phi_{aba}^{N(p)-1}$ is $2N(p)$. Then we show that there is only one set partition of length $2N(p)$ and ${{N(p) + 1} \choose 2} + 2{N(p) \choose 2}$ set partitions of length $2N(p)+1$ that are not sorted by $\phi_{aba}^{N(p)-1}$.
Published: 2024

9. Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model

Author: An, Lin, Li, Andrew A., Moseley, Benjamin, and Visotsky, Gabriel
Subjects: Mathematics - Optimization and Control, Computer Science - Machine Learning
Abstract: Online decision-makers often obtain predictions on future variables, such as arrivals, demands, inventories, and so on. These predictions can be generated from simple forecasting algorithms for univariate time-series, all the way to state-of-the-art machine learning models that leverage multiple time-series and additional feature information. However, the prediction accuracy is unknown to decision-makers a priori, hence blindly following the predictions can be harmful. In this paper, we address this problem by developing algorithms that utilize predictions in a manner that is robust to the unknown prediction accuracy. We consider the Online Resource Allocation Problem, a generic model for online decision-making, in which a limited amount of resources may be used to satisfy a sequence of arriving requests. Prior work has characterized the best achievable performances when the arrivals are either generated stochastically (i.i.d.) or completely adversarially, and shown that algorithms exist which match these bounds under both arrival models, without ``knowing'' the underlying model. To this backdrop, we introduce predictions in the form of shadow prices on each type of resource. Prediction accuracy is naturally defined to be the distance between the predictions and the actual shadow prices. We tightly characterize, via a formal lower bound, the extent to which any algorithm can optimally leverage predictions (that is, to ``follow'' the predictions when accurate, and ``ignore'' them when inaccurate) without knowing the prediction accuracy or the underlying arrival model. Our main contribution is then an algorithm which achieves this lower bound. Finally, we empirically validate our algorithm with a large-scale experiment on real data from the retailer H&M.
Published: 2024

10. Short-lived High-volume Multi-A(rmed)/B(andits) Testing

Author: Jia, Su, Li, Andrew, Ravi, R., Oli, Nishant, Duff, Paul, and Anderson, Ian
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Modern platforms leverage randomized experiments to make informed decisions from a given set of items (``treatments''). As a particularly challenging scenario, these items may (i) arrive in high volume, with thousands of new items being released per hour, and (ii) have short lifetime, say, due to the item's transient nature or underlying non-stationarity that impels the platform to perceive the same item as distinct copies over time. Thus motivated, we study a Bayesian multiple-play bandit problem that encapsulates the key features of the multivariate testing (or ``multi-A/B testing'') problem with a high volume of short-lived arms. In each round, a set of $k$ arms arrive, each available for $w$ rounds. Without knowing the mean reward for each arm, the learner selects a multiset of $n$ arms and immediately observes their realized rewards. We aim to minimize the loss due to not knowing the mean rewards, averaged over instances generated from a given prior distribution. We show that when $k = O(n^\rho)$ for some constant $\rho>0$, our proposed policy has $\tilde O(n^{-\min \{\rho, \frac 12 (1+\frac 1w)^{-1}\}})$ loss on a sufficiently large class of prior distributions. We complement this result by showing that every policy suffers $\Omega (n^{-\min \{\rho, \frac 12\}})$ loss on the same class of distributions. We further validate the effectiveness of our policy through a large-scale field experiment on {\em Glance}, a content-card-serving platform that faces exactly the above challenge. A simple variant of our policy outperforms the platform's current recommender by 4.32\% in total duration and 7.48\% in total number of click-throughs.
Published: 2023

11. Markdown Pricing Under an Unknown Parametric Demand Model

Author: Jia, Su, Li, Andrew, and Ravi, R.
Subjects: Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory
Abstract: Consider a single-product revenue-maximization problem where the seller monotonically decreases the price in $n$ rounds with an unknown demand model coming from a given family. Without monotonicity, the minimax regret is $\tilde O(n^{2/3})$ for the Lipschitz demand family and $\tilde O(n^{1/2})$ for a general class of parametric demand models. With monotonicity, the minimax regret is $\tilde O(n^{3/4})$ if the revenue function is Lipschitz and unimodal. However, the minimax regret for parametric families remained open. In this work, we provide a complete settlement for this fundamental problem. We introduce the crossing number to measure the complexity of a family of demand functions. In particular, the family of degree-$k$ polynomials has a crossing number $k$. Based on conservatism under uncertainty, we present (i) a policy with an optimal $\Theta(\log^2 n)$ regret for families with crossing number $k=0$, and (ii) another policy with an optimal $\tilde \Theta(n^{k/(k+1)})$ regret when $k\ge 1$. These bounds are asymptotically higher than the $\tilde O(\log n)$ and $\tilde \Theta(\sqrt n)$ minimax regret for the same families without the monotonicity constraint.
Published: 2023

12. Modeling Choice via Self-Attention

Author: Ko, Joohwan and Li, Andrew A.
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Models of choice are a fundamental input to many now-canonical optimization problems in the field of Operations Management, including assortment, inventory, and price optimization. Naturally, accurate estimation of these models from data is a critical step in the application of these optimization problems in practice. Concurrently, recent advancements in deep learning have sparked interest in integrating these techniques into choice modeling. However, there is a noticeable research gap at the intersection of deep learning and choice modeling, particularly with both theoretical and empirical foundations. Thus motivated, we first propose a choice model that is the first to successfully (both theoretically and practically) leverage a modern neural network architectural concept (self-attention). Theoretically, we show that our attention-based choice model is a low-rank generalization of the Halo Multinomial Logit (Halo-MNL) model. We prove that whereas the Halo-MNL requires $\Omega(m^2)$ data samples to estimate, where $m$ is the number of products, our model supports a natural nonconvex estimator (in particular, that which a standard neural network implementation would apply) which admits a near-optimal stationary point with $O(m)$ samples. Additionally, we establish the first realistic-scale benchmark for choice model estimation on real data, conducting the most extensive evaluation of existing models to date, thereby highlighting our model's superior performance.
Published: 2023

13. STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent

Author: Lee, Yewon, Li, Andrew Z., Huang, Philip, Heiden, Eric, Jatavallabhula, Krishna Murthy, Damken, Fabian, Smith, Kevin, Nowrouzezahrai, Derek, Ramos, Fabio, and Shkurti, Florian
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, I.2.9
Abstract: Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. This can be inefficient because, typically, candidate task plans resulting from the tree search ignore geometric information. This often leads to motion planning failures that require expensive backtracking steps to find alternative task plans. We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes the hybrid optimization problem into a continuous domain. This allows us to leverage gradients from differentiable physics simulation to fully optimize discrete and continuous plan parameters for TAMP. In particular, we solve the optimization problem using a gradient-based variational inference algorithm called Stein Variational Gradient Descent. This allows us to find a distribution of solutions within a single optimization run. Furthermore, we use an off-the-shelf differentiable physics simulator that is parallelized on the GPU to run parallelized inference over diverse plan parameters. We demonstrate our method on a variety of problems and show that it can find multiple diverse plans in a single optimization run while also being significantly faster than existing approaches., Comment: 14 pages, 9 figures, Learning Effective Abstractions for Planning (LEAP) Workshop at CoRL 2023
Published: 2023

14. FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search

Author: Dotzel, Jordan, Wu, Gang, Li, Andrew, Umar, Muhammad, Ni, Yun, Abdelfattah, Mohamed S., Zhang, Zhiru, Cheng, Liqun, Dixon, Martin G., Jouppi, Norman P., Le, Quoc V., and Li, Sheng
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With improved numerical support in recent hardware, including multiple variants of integer and floating point, mixed-precision quantization has become necessary to achieve high-quality results with low model cost. Prior mixed-precision methods have performed either a post-training quantization search, which compromises on accuracy, or a differentiable quantization search, which leads to high memory usage from branching. Therefore, we propose the first one-shot mixed-precision quantization search that eliminates the need for retraining in both integer and low-precision floating point models. We evaluate our search (FLIQS) on multiple convolutional and vision transformer networks to discover Pareto-optimal models. Our approach improves upon uniform precision, manual mixed-precision, and recent integer quantization search methods. With integer models, we increase the accuracy of ResNet-18 on ImageNet by 1.31% and ResNet-50 by 0.90% with equivalent model cost over previous methods. Additionally, for the first time, we explore a novel mixed-precision floating-point search and improve MobileNetV2 by up to 0.98% compared to prior state-of-the-art FP8 models. Finally, we extend FLIQS to simultaneously search a joint quantization and neural architecture space and improve the ImageNet accuracy by 2.69% with similar model cost on a MobileNetV2 search space., Comment: Accepted to AutoML 2024
Published: 2023

15. Repellent activity of essential oils to the Lone Star tick, Amblyomma americanum

Author: Le Mauff, Anais, Norris, Edmund J., Li, Andrew Y., and Swale, Daniel R.
Published: 2024
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16. Leadership turnover, winning coalition size and foreign policy realignment

Author: Li, Andrew X. and Zha, Wen
Published: 2024
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17. Study protocol: infectious diseases consortium (I3D) for study on integrated and innovative approaches for management of respiratory infections: respiratory infections research and outcome study (RESPIRO)

Author: Ng, Dorothy Hui Lin, Chia, Travis Ren Teen, Young, Barnaby Edward, Sadarangani, Sapna, Puah, Ser Hon, Low, Jenny Guek Hong, Yan, Gabriel Zherong, Mo, Yin, Ngiam, Nicholas Jinghao, Wang, Samuel Sherng Young, Loo, Yan Tong, Ong, Faith Evangeline Jie Qi, Li, Andrew Yunkai, Ho, Sharlene, Ng, Lisa, Tambyah, Paul Anantharajah, and Yeo, Tsin Wen
Published: 2024
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18. Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings

Author: Li, Andrew, Ling, Lowell, Qin, Hanyu, Arabi, Yaseen M., Myatra, Sheila Nainan, Egi, Moritoki, Kim, Je Hyeong, Nor, Mohd Basri Mat, Son, Do Ngoc, Fang, Wen-Feng, Wahyuprajitno, Bambang, Hashmi, Madiha, Faruq, Mohammad Omar, Patjanasoontorn, Boonsong, Al Bahrani, Maher Jaffer, Shrestha, Babu Raja, Shrestha, Ujma, Nafees, Khalid Mahmood Khan, Sann, Kyi Kyi, Palo, Jose Emmanuel M., Mendsaikhan, Naranpurev, Konkayev, Aidos, Detleuxay, Khamsay, Chan, Yiong Huak, Du, Bin, Divatia, Jigeeshu Vasishtha, Koh, Younsuck, and Phua, Jason
Published: 2024
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19. The Optical Phase Curves of CoRoT-1 b

Author: Li, Andrew
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Of the three space telescopes launched so far to survey transiting extrasolar planets, CoRoT is unique in that it was the only one with spectral resolution, allowing for an extraordinary opportunity to study the reflective properties of exoplanets at different wavelengths. In this work, I present a systematic lightcurve analysis of the white-light and chromatic CoRoT lightcurves of CoRoT-1 in order to search for the secondary eclipse and orbital phase variation of the transiting extrasolar planet CoRoT-1 b, as well at search for any chromatic difference in the aforementioned effects. I manage to detect a significant secondary eclipse in the white lightcurve, and detect the eclipse marginally in all three of the color channels. However I am only able to significantly detect the planetary phase variation in the red channel lightcurve. The retrieved secondary eclipse depth is higher in the blue and green channels compared to the white and red, suggesting that CoRoT-1 b has a higher geometric albedo at shorter wavelengths. I also attempt to detect the secondary eclipse using TESS, but show that the available volume and precision of the data is not high enough to allow detection of the secondary eclipse.
Published: 2023

20. The Nonstationary Newsvendor with (and without) Predictions

Author: An, Lin, Li, Andrew A., Moseley, Benjamin, and Ravi, R.
Subjects: Mathematics - Optimization and Control, Economics - Econometrics
Abstract: The classic newsvendor model yields an optimal decision for a "newsvendor" selecting a quantity of inventory, under the assumption that the demand is drawn from a known distribution. Motivated by applications such as cloud provisioning and staffing, we consider a setting in which newsvendor-type decisions must be made sequentially, in the face of demand drawn from a stochastic process that is both unknown and nonstationary. All prior work on this problem either (a) assumes that the level of nonstationarity is known, or (b) imposes additional statistical assumptions that enable accurate predictions of the unknown demand. We study the Nonstationary Newsvendor, with and without predictions. We first, in the setting without predictions, design a policy which we prove (via matching upper and lower bounds) achieves order-optimal regret -- ours is the first policy to accomplish this without being given the level of nonstationarity of the underlying demand. We then, for the first time, introduce a model for generic (i.e. with no statistical assumptions) predictions with arbitrary accuracy, and propose a policy that incorporates these predictions without being given their accuracy. We upper bound the regret of this policy, and show that it matches the best achievable regret had the accuracy of the predictions been known. Finally, we empirically validate our new policy with experiments based on three real-world datasets containing thousands of time-series, showing that it succeeds in closing approximately 74% of the gap between the best approaches based on nonstationarity and predictions alone.
Published: 2023

21. No Evidence for Additional Planets at GJ 3470 from TESS and Archival Radial Velocities

Author: Tarrants, Thomas and Li, Andrew
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The nearby M2 dwarf GJ 3470 has been the target of considerable interest after the discovery of a transiting short-period Neptune-sized planet. Recently, claims regarding the existence of additional transiting planets has gotten some attention, suggesting both the presence of a gas giant in the habitable zone, and that the system hosts a remarkable co-orbital gas giant configuration. We show that the existence of these three additional planets are readily amenable to testing with available data from both ground-based radial velocity data and space-based TESS photometry. A periodogram search of the available radial velocities show no compelling signals at the claimed periods, and the TESS photometry effectively rules out these planets assuming a transiting configuration. While it is doubtlessly possible that additional planets orbit GJ 3470, there is no evidence to date for their existence, and the available data conclusively rule out any planets similar to those considered in this text.
Published: 2023

22. Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation

Author: Jeong, Jaehwan, Tian, Katherine, Li, Andrew, Hartung, Sina, Behzadi, Fardad, Calle, Juan, Osayande, David, Pohlen, Michael, Adithan, Subathra, and Rajpurkar, Pranav
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoherent and incorrect text due to their lack of relevant domain knowledge, while retrieval-based attempts frequently retrieve reports that are irrelevant to the input image. In this work, we propose Contrastive X-Ray REport Match (X-REM), a novel retrieval-based radiology report generation module that uses an image-text matching score to measure the similarity of a chest X-ray image and radiology report for report retrieval. We observe that computing the image-text matching score with a language-image model can effectively capture the fine-grained interaction between image and text that is often lost when using cosine similarity. X-REM outperforms multiple prior radiology report generation modules in terms of both natural language and clinical metrics. Human evaluation of the generated reports suggests that X-REM increased the number of zero-error reports and decreased the average error severity compared to the baseline retrieval approach. Our code is available at: https://github.com/rajpurkarlab/X-REM
Published: 2023

23. Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior

Author: Christoffersen, Phillip J. K., Li, Andrew C., Icarte, Rodrigo Toro, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed. If the reward-worthy behavior is known, it can be specified in terms of a non-Markovian reward function - a function that depends on aspects of the state-action history, rather than just the current state and action. Such reward functions yield sparse rewards, necessitating an inordinate number of experiences to find a policy that captures the reward-worthy pattern of behavior. Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR. Providing such a decomposition has been shown to vastly improve learning rates, especially when coupled with algorithms that exploit automaton structure. Nevertheless, such techniques rely on a priori knowledge of the KR. In this work, we explore how to automatically discover useful state abstractions that support learning automata over the state-action history. The result is an end-to-end algorithm that can learn optimal policies with significantly fewer environment samples than state-of-the-art RL on simple non-Markovian domains., Comment: 7 pages, 2 figures, presented at KR2ML workshop at NeurIPS 2020
Published: 2023

24. Chemistry Lab Automation via Constrained Task and Motion Planning

Author: Yoshikawa, Naruki, Li, Andrew Zou, Darvish, Kourosh, Zhao, Yuchi, Xu, Haoping, Kuramshin, Artur, Aspuru-Guzik, Alán, Garg, Animesh, and Shkurti, Florian
Subjects: Computer Science - Robotics
Abstract: Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process, we propose a robot framework for manipulation that autonomously performs chemistry experiments. Our framework receives high-level abstract descriptions of chemistry experiments, perceives the lab workspace, and autonomously plans multi-step actions and motions. The robot interacts with a wide range of lab equipment and executes the generated plans. A key component of our method is constrained task and motion planning using PDDLStream solvers. Preventing collisions and spillage is done by introducing a constrained motion planner. Our planning framework can conduct different experiments employing implemented actions and lab tools. We demonstrate the utility of our framework on pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization., Comment: Equal author contribution from Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish, Yuchi Zhao and Haoping Xu
Published: 2022

25. Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines

Author: Li, Andrew C., Chen, Zizhao, Vaezipoor, Pashootan, Klassen, Toryn Q., Icarte, Rodrigo Toro, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Formal Languages and Automata Theory
Abstract: Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward functions are specified in a symbolic language captured by Reward Machines, an increasingly popular automaton-inspired structure. We are interested in the case where the mapping of environment state to a symbolic (here, Reward Machine) vocabulary -- commonly known as the labelling function -- is uncertain from the perspective of the agent. We formulate the problem of policy learning in Reward Machines with noisy symbolic abstractions as a special class of POMDP optimization problem, and investigate several methods to address the problem, building on existing and new techniques, the latter focused on predicting Reward Machine state, rather than on grounding of individual symbols. We analyze these methods and evaluate them experimentally under varying degrees of uncertainty in the correct interpretation of the symbolic vocabulary. We verify the strength of our approach and the limitation of existing methods via an empirical investigation on both illustrative, toy domains and partially observable, deep RL domains., Comment: NeurIPS Deep Reinforcement Learning Workshop 2022
Published: 2022

26. Learning to Follow Instructions in Text-Based Games

Author: Tuli, Mathieu, Li, Andrew C., Vaezipoor, Pashootan, Klassen, Toryn Q., Sanner, Scott, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. We conduct experiments that show that the performance of state-of-the-art text-based game agents is largely unaffected by the presence or absence of such instructions, and that these agents are typically unable to execute tasks to completion. To further study and address the task of instruction following, we equip RL agents with an internal structured representation of natural language instructions in the form of Linear Temporal Logic (LTL), a formal language that is increasingly used for temporally extended reward specification in RL. Our framework both supports and highlights the benefit of understanding the temporal semantics of instructions and in measuring progress towards achievement of such a temporally extended behaviour. Experiments with 500+ games in TextWorld demonstrate the superior performance of our approach., Comment: NeurIPS 2022
Published: 2022

27. Large language models for chemistry robotics

Author: Yoshikawa, Naruki, Skreta, Marta, Darvish, Kourosh, Arellano-Rubach, Sebastian, Ji, Zhi, Bjørn Kristensen, Lasse, Li, Andrew Zou, Zhao, Yuchi, Xu, Haoping, Kuramshin, Artur, Aspuru-Guzik, Alán, Shkurti, Florian, and Garg, Animesh
Published: 2023
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28. Markovian Interference in Experiments

Author: Farias, Vivek F., Li, Andrew A., Peng, Tianyi, and Zheng, Andrew
Subjects: Computer Science - Machine Learning, Economics - Econometrics, Statistics - Machine Learning
Abstract: We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited inventory). Despite outsize practical importance, the best estimators for this `Markovian' interference problem are largely heuristic in nature, and their bias is not well understood. We formalize the problem of inference in such experiments as one of policy evaluation. Off-policy estimators, while unbiased, apparently incur a large penalty in variance relative to state-of-the-art heuristics. We introduce an on-policy estimator: the Differences-In-Q's (DQ) estimator. We show that the DQ estimator can in general have exponentially smaller variance than off-policy evaluation. At the same time, its bias is second order in the impact of the intervention. This yields a striking bias-variance tradeoff so that the DQ estimator effectively dominates state-of-the-art alternatives. From a theoretical perspective, we introduce three separate novel techniques that are of independent interest in the theory of Reinforcement Learning (RL). Our empirical evaluation includes a set of experiments on a city-scale ride-hailing simulator.
Published: 2022

29. Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks

Author: Li, Andrew C., Vaezipoor, Pashootan, Icarte, Rodrigo Toro, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract: Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is less often observed in long-horizon, continuous domains with high-dimensional observations, where instead RL research has predominantly focused on problems with simple high-level structure (e.g. opening a drawer or moving a robot as fast as possible). Inspired by combinatorially hard optimization problems, we propose a set of robotics tasks which admit many distinct solutions at the high-level, but require reasoning about states and rewards thousands of steps into the future for the best performance. Critically, while RL has traditionally suffered on complex, long-horizon tasks due to sparse rewards, our tasks are carefully designed to be solvable without specialized exploration. Nevertheless, our investigation finds that standard RL methods often neglect long-term effects due to discounting, while general-purpose hierarchical RL approaches struggle unless additional abstract domain knowledge can be exploited.
Published: 2022

30. Molecular Basis of Surgical Coaptation Techniques in Peripheral Nerve Injuries.

Author: Pereira, Clifford T, Hill, Elise E, Stasyuk, Anastasiya, Parikh, Neil, Dhillon, Jannat, Wang, Aijun, and Li, Andrew
Subjects: end-to-end coaptation, end-to-side coaptation, molecular mechanisms, nerve coaptation, nerve regeneration, peripheral nerve injury, side-to-side coaptation, surgical repair, Regenerative Medicine, Physical Injury - Accidents and Adverse Effects, Neurosciences, Clinical Sciences
Abstract: Peripheral nerve injuries requiring surgical repair affect over 100,000 individuals in the US annually. Three accepted methods of peripheral repair include end-to-end, end-to-side, and side-to-side neurorrhaphy, each with its own set of indications. While it remains important to understand the specific circumstances in which each method is employed, a deeper understanding of the molecular mechanisms underlying the repair can add to the surgeon's decision-making algorithm when considering each technique, as well as help decide nuances in technique such as the need for making epineurial versus perineurial windows, length and dept of the nerve window, and distance from target muscle. In addition, a thorough knowledge of individual factors that are active in a particular repair can help guide research into adjunct therapies. This paper serves to summarize the similarities and divergences of the three commonly used nerve repair strategies and the scope of molecular mechanisms and signal transduction pathways in nerve regeneration as well as to identify the gaps in knowledge that should be addressed if we are to improve clinical outcomes in our patients.
Published: 2023

31. Engineered multi-functional, pro-angiogenic collagen-based scaffolds loaded with endothelial cells promote large deep burn wound healing

Author: Song, Hengyue, Gao, Kewa, Hao, Dake, Li, Andrew, Liu, Ruiwu, Anggito, Bryan, Yin, Boyan, Jin, Qianyu, Dartora, Vanessa, Lam, Kit S, Smith, Lucas R, Panitch, Alyssa, Zhou, Jianda, Farmer, Diana L, and Wang, Aijun
Subjects: Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Wound Healing and Care, Physical Injury - Accidents and Adverse Effects, deep burn wound, endothelial cell, wound healing, vascularization, ECM scaffold, Pharmacology and pharmaceutical sciences
Abstract: The lack of vascularization associated with deep burns delays the construction of wound beds, increases the risks of infection, and leads to the formation of hypertrophic scars or disfigurement. To address this challenge, we have fabricated a multi-functional pro-angiogenic molecule by grafting integrin αvβ3 ligand LXW7 and collagen-binding peptide (SILY) to a dermatan sulfate (DS) glycosaminoglycan backbone, named LXW7-DS-SILY (LDS), and further employed this to functionalize collagen-based Integra scaffolds. Using a large deep burn wound model in C57/BLK6 mice (8-10 weeks old, 26-32g, n = 39), we demonstrated that LDS-modified collagen-based Integra scaffolds loaded with endothelial cells (ECs) accelerate wound healing rate, re-epithelialization, vascularization, and collagen deposition. Specifically, a 2 cm × 3 cm full-thickness skin burn wound was created 48 h after the burn, and then wounds were treated with four groups of different dressing scaffolds, including Integra + ECs, Integra + LDS, and Integra + LDS + ECs with Integra-only as the control. Digital photos were taken for wound healing measurement on post-treatment days 1, 7, 14, 21, 28, and 35. Post-treatment photos revealed that treatment with the Intgera + LDS + ECs scaffold exhibited a higher wound healing rate in the proliferation phase. Histology results showed significantly increased re-epithelialization, increased collagen deposition, increased thin and mixed collagen fiber content, increased angiogenesis, and shorter wound length within the Integra + LDS + ECs group at Day 35. On Day 14, the Integra + LDS + ECs group showed the same trend. The relative proportions of collagen changed from Day 14 to Day 35 in the Integra + LDS + ECs and Integra + ECs groups demonstrated decreased thick collagen fiber deposition and greater thin and mixed collagen fiber deposition. LDS-modified Integra scaffolds represent a promising novel treatment to accelerate deep burn wound healing, thereby potentially reducing the morbidity associated with open burn wounds. These scaffolds can also potentially reduce the need for autografting and morbidity in patients with already limited areas of harvestable skin.
Published: 2023

32. DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers

Author: Gull, Nicole, Jones, Michelle R, Peng, Pei-Chen, Coetzee, Simon G, Silva, Tiago C, Plummer, Jasmine T, Reyes, Alberto Luiz P, Davis, Brian D, Chen, Stephanie S, Lawrenson, Kate, Lester, Jenny, Walsh, Christine, Rimel, Bobbie J, Li, Andrew J, Cass, Ilana, Berg, Yonatan, Govindavari, John-Paul B, Rutgers, Joanna KL, Berman, Benjamin P, Karlan, Beth Y, and Gayther, Simon A
Subjects: Human Genome, Genetics, Ovarian Cancer, Clinical Research, Cancer, Rare Diseases, Orphan Drug, Aetiology, 2.1 Biological and endogenous factors, DNA Methylation, Drug Resistance, Neoplasm, Female, Humans, Neoplasm Recurrence, Local, Ovarian Neoplasms, Transcriptome, High grade serous ovarian cancer, Methylation, Chemoresistance, Epigenetics, Whole genome bisulfite sequencing, Computational methods, Translational research, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract: BackgroundLittle is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC).MethodsWe performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations.ResultsLandscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers.ConclusionThese findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.
Published: 2022

33. Boreas: A Multi-Season Autonomous Driving Dataset

Author: Burnett, Keenan, Yoon, David J., Wu, Yuchen, Li, Andrew Zou, Zhang, Haowei, Lu, Shichen, Qian, Jingxing, Tseng, Wei-Kang, Lambert, Andrew, Leung, Keith Y. K., Schoellig, Angela P., and Barfoot, Timothy D.
Subjects: Computer Science - Robotics
Abstract: The Boreas dataset was collected by driving a repeated route over the course of one year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset includes over 350km of driving data featuring a 128-channel Velodyne Alpha Prime lidar, a 360$^\circ$ Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. Our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at https://www.boreas.utias.utoronto.ca, Comment: Accepted in IJRR as a data paper
Published: 2022

34. A Unified Theory of Free Energy Functionals and Applications to Diffusion

Author: Li, Andrew B., Miroshnik, Leonid, Rummel, Brian D., Balakrishnan, Ganesh, Han, Sang M., and Sinno, Talid
Subjects: Condensed Matter - Statistical Mechanics
Abstract: Free energy functionals of Ginzburg-Landau type lie at the heart of a broad class of continuum dynamical models, such as the Cahn-Hilliard and Swift-Hohenberg equations. Despite the wide use of such models, the assumptions embodied in the free energy functionals are frequently either poorly justified or lead to physically opaque parameters. Here, we introduce a mathematically rigorous pathway for constructing free energy functionals that generalizes beyond the constraints of Ginzburg-Landau gradient expansions. We show that the new formalism unifies existing free energetic descriptions under a single umbrella by establishing the criteria under which the generalized free energy reduces to gradient-based representations. Consequently, we derive a precise physical interpretation of the gradient energy parameter in the Cahn-Hilliard model as the product of an interaction length scale and the free energy curvature. The practical impact of our approach is demonstrated using both a model free energy function and the silicon-germanium alloy system.
Published: 2022
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35. Administration of low intensity vibration and a RANKL inhibitor, alone or in combination, reduces bone loss after spinal cord injury-induced immobilization in rats

Author: Peng, Yuanzhen, Bramlett, Helen M., Dietrich, W. Dalton, Marcillo, Alex, Sanchez-Molano, Juliana, Furones-Alonso, Ofelia, Cao, Jay J., Huang, Jenney, Li, Andrew A., Feng, Jian Q., Bauman, William A., and Qin, Weiping
Published: 2024
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36. Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise

Author: Farias, Vivek F., Li, Andrew A., and Peng, Tianyi
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: The problem of low-rank matrix completion with heterogeneous and sub-exponential (as opposed to homogeneous and Gaussian) noise is particularly relevant to a number of applications in modern commerce. Examples include panel sales data and data collected from web-commerce systems such as recommendation engines. An important unresolved question for this problem is characterizing the distribution of estimated matrix entries under common low-rank estimators. Such a characterization is essential to any application that requires quantification of uncertainty in these estimates and has heretofore only been available under the assumption of homogenous Gaussian noise. Here we characterize the distribution of estimated matrix entries when the observation noise is heterogeneous sub-exponential and provide, as an application, explicit formulas for this distribution when observed entries are Poisson or Binary distributed.
Published: 2021

37. Access to and quality of elective care: a prospective cohort study using hernia surgery as a tracer condition in 83 countries

Author: Dönmez, A Eylül, Goswami, Aakansha Giri, Raheja, Aashna, Bhadani, Aayush, El Kady, Abd Elrahman Safwat, Alniemi, Abdalaziz, Awad, Abdalkarim, Aladl, Abdalla, Younis, Abdalla, Alwali, Abdallah, Khalil, Abdallah, Mansour, Abdallah, Elkhouly, Abdallah Mohamed, Rashad Temerik, Abdallah, Hasan, Abdallatef, Chied, Abdelhafid, Abdraba, Abdelhamid, Hrora, Abdelmalek, Abdelshafi, Abdelrahman, Alwali, Abdelrahman, Maraqa, Abdelrahman, Ghaffar, Abdul, Chekfa, Abdul Jabar, Al Balushi, Abdul Majeed, Quddus, Abdul, Hammadieh, Abdul Rahman, Muntaka, Abdul-Jalilu Mohammed, Asekun, Abdulazeez, Alshahrani, Abdulaziz, Jowharji, Abdulaziz, Alshukre, Abdulbaset, Khair Etareig, Abdulhafid, Abubakar Abdulkarim, Abdulhafiz, Ashiru, Abdulhafiz, Alailesh, Abdulhamid, Al-Mallah, Abdullah, Baydoun, Abdullah, Chaudhry, Abdullah, Askin, Abdullah Emre, Almutairi, Abdulmjeed, Belkhair, Abdulmunem, Abdllah, Abdulrahman, Alhammali, Abdulrahman, Basalim, Abdulrahman, Abouelnagah, Abdulrahman Galal Mohamed Mostafa, Ghazal, Abdulrahman, Mohammed, Abdulrahman, Qasem, Abdulrahman, Anadani, Abdulrazak, Jimoh, Abdulrazaq, Lawal, Abdulrazzaq Oluwagbemiga, Khaity, Abdulrhman, Lawal, Abdulwahab, Elzoubi, Abdurrahim Esseid, Sheshe, Abdurrahman Abba, Cetişli, Abdurrahman Furkan, Oyedele, Abeeb, Aljahdali, Abeer, Aboelnasr, Abeer Gamal Elsyed, Demessie, Abel, Bhargava, Abhilasha, Sonkar, Abhinav Arun, Bavaharan, Abhirame, Khan, Abid, Campbell, Abigail, Oyelekan, Abimbola Adeola Akintunde, Okunlola, Abiodun Idowu, Ojewuyi, Abiodun, Ekerin, Abiola, Saleh, Abobakr, Khairy, Abouelatta, Muthunayagam, Abraham Benjamin, Botha, Abraham, Teshome Sahilemariam, Abraham, Nawawi, Abrar, Muhammad, Abubakar Bala, Dahiru, Abubakar, Yahaya, Abubakar, Abdelmalik, Abubaker, Anuar, Abul Akmal Arif, Ousseini, Adakal, Leon -Del- Angel, Adalberto, Frankel, Adam, Gyedu, Adam, Lim, Adam, Lukáč, Adam, Peckham-Cooper, Adam, Sabbah, Adam, Varga, Adam, Sanou, Adama, Issaka, Adamu, Ningi, Adamu, Yeshitila, Addis, Yingess, Addisu, Olugbami, Adebayo, Moses, Damola-Okesiji, Adebola, Lawal, Adedayo, Ojo, Adedoyin, Hashmi, Adeel, Fakoya, Adegbolahan, Ishola, Adegoke, Abiodun, Adekunle, Ajayi, Adekunle, Mouffokes, Adel, Bonelli, Adele, Adekoya, Adeleke, Akeem Aderogba, Adeleke, Özcan, Adem, Ayoade, Ademidun, Adeyeye, Ademola, Agbaje, Ademola, Talabi, Ademola, Ademuyiwa, Adesoji, Adewumiya, Adesoji, Ademuyiwa, Adesoji O, Aderounmu, Adewale, Adisa, Adewale, Oke, Adewale, Ragab, Adham, Alzadjali, Adil, Baksi, Aditya, Jayapalan, Aditya, Md Yunos, Adnan, Pisanu, Adolfo, Salazar, Adolfo, Salinas Barragan, Adrian Alberto, Fox, Adrian, Stollberg, Adrian, Franco, Adriana, Neves, Adriana, Vricheva, Adriana, Hodonou, Adrien Montcho, Aghayeva, Afag, Liaquat, Affifa, Alsultan, Afnan, Morad, Afnan, Kalidis, Afroditi Angella, Gkouniaroudi, Agathi, Karakosta, Agathi, Lovi, Agbenya, Yevide, Agossou Barthélémy, Duro, Agustin, Rodriguez Gonzalez, Agustin, Tariq, Ahbala, Abaidalla, Ahmad, Abuhammad, Ahmad, Abuhantash, Ahmad, Mansour, Ahmad Adel, Ahmad Zaidi, Ahmad Al Hafeez, Alahmad Alismael, Ahmad, Alayed, Ahmad, Alfarwan, Ahmad, Alhaj, Ahmad, Ali, Ahmad, Alroobi, Ahmad, Alsheikh, Ahmad, Alusef, Ahmad, Alzu'Bi, Ahmad, Kayali, Ahmad Amir, Bouhuwaish, Ahmad, Doghaim, Ahmad, El-Sherbiney, Ahmad, Ghazal, Ahmad, Ramly, Ahmad Nasir, Raza, Ahmad, Qureshi, Ahmad Uzair, Arnaout, Ahmad Yamen, Abdelhamid, Ahmed, Abuthaher, Ahmed, Adel, Ahmed, Ahmayda, Ahmed, Al-Mouahhed, Ahmed, Alagha, Ahmed, Albalawi, Ahmed, Albhaisi, Ahmed, Alrifaee, Ahmed, Azam, Ahmed, Barakat, Ahmed, Al-Hajjaj, Ahmed Basim Abed, Dawoud, Ahmed, El Shamarka, Ahmed, El-Bastwesy, Ahmed, Elghrieb, Ahmed, Elkhalifa, Ahmed, Elmosalamy, Ahmed, Elshazli Mahmoud, Ahmed, Montaser, Ahmed G., Gbeadese, Ahmed, Haty, Ahmed, K. Ali, Ahmed, Awad, Ahmed K., Kedwany, Ahmed, Khalleefah, Ahmed, Abbas, Ahmed M., Allam, Ahmed M., Maher, Ahmed, Elsayed, Ahmed Mohamed, Mohamed Ibrahim Mohamed, Ahmed, Mostafa, Ahmed, Nabil, Ahmed, Nada, Ahmed, Ragheb, Ahmed, Abdelrahman, Ahmed Saber Mohamed, Sabry, Ahmed, Sakr, Ahmed, Elkhodary, Ahmed Salah, Shaheen, Ahmed, Ammar, Ahmed Siddique, Hameed, Ahmer, Akmercan, Ahmet, Aslan, Ahmet, Barcin, Ahmet, Ciftci, Ahmet Burak, Bozkurt, Ahmet Çağlar, Sarı, Ahmet Can, Yaşar, Ahmet Can, Develioğlu, Ahmet, Öztürk, Ahmet Mücteba, Sanli, Ahmet Necati, Rencuzogullari, Ahmet, Yildiz, Ahmet, Kaan, Ahmet Zahit, Toi, Ahouefa Chourika Sakinath, Aljamoudi, Aida, Cheung, Aiden Chak Kei, Gkoutoula, Aikaterini, Kalogeropoulou, Aikaterini, Sarafi, Aikaterini, Abo Al Shamat, Aiman, Sanon, Aimé Serge Titianma, Sanon, Aimée Florence, Hiller, Aimée, Costas-Chavarri, Ainhoa, Lombardero, Ainhoa, Fraile, Ainoa, Bojazyah, Aisha, Kamal, Aisha, Gill, Aisling, Hada, Aïssatou, Gulla, Aiste, Mačiulaitytė, Aistė, Sukumar, Aiswarya, Landaluce-Olavarria, Aitor, Sainz Lete, Aitor, Pal, Ajay Kumar, Malik, Ajaz Ahmad, Horiguchi, Akihiko, Kocaay, Akin Firat, Akinkuolie, Akinbolaji, Olabode, Akingunloye, Fadipe, Akinniyi, Akinmade, Akinola, Darkwa Boateng, Akosua, Appiah-Kubi, Akosua Yeboaa, Alkaseek, Akram, Kadamani Abiyomaa, Akram, Anand, Akshay, Oppong, Akua, Opoku-Agyapong, Akwasi, Asharaf, Al Ameen, Ahmed, Al Samoul, Andani, Al-Hassan Dasana, Alkharusi, Al-Salt, Abd Elsattar, Alaa, Alragheai, Alaa, Alsalahat, Alaa Atta, Hawarah, Alaa, Aldirani, Alaa Nasib, Shabkah, Alaa, Nixon Fulli, Alafi Francis, Nguyen, Alain, Mustafa, Alamin, Joseph, Alan, Huws, Alaw, Aregawi, Alazar Berhe, Sanchez Gallego, Alba, Torroella, Alba, Vazquez Melero, Alba, Dauksa, Albertas, Abreu Da Silva, Alberto, Brun-Peressut, Alberto, Barranquero, Alberto G, Gegúndez Simón, Alberto, Gerundo, Alberto, Navarrete-Peón, Alberto, Viacava, Alberto, Vilar, Alberto, Adam, Albushra Altayeb, Jarimba, Aldo, Johnson, Aleena, Nuñez Venzor, Alejandra, Gonzalez Ojeda, Alejandro, Romero De Diego, Alejandro, Karamarkovic, Aleksandar, Butyrskii, Aleksandr, Edmundson, Aleksandra, Sidorova, Aleksandra, Thomas, Alen, Tomazic, Ales, Giuliani, Alessandra, Iodice, Alessandra, Marano, Alessandra, Marello, Alessandra, Ongaro, Alessandra, Saba, Alessandra, Anastasi, Alessandro, Borello, Alessandro, Broglia, Alessandro, Carta, Alessandro, Finocchio, Alessandro, Francia, Alessandro, Iacomino, Alessandro, Izzo, Alessandro, Ottaiano, Alessandro, Spaziani, Alessandro, Strumia, Alessandro, Biancafarina, Alessia, D'Ignazio, Alessia, Davolio, Alessia, Morello, Alessia, Licciardello, Alessio, Mazzoni, Alessio, Wilkins, Alex, Arekhandia, Alexander, Barlas, Alexandros, Chamzin, Alexandros, Demetriou, Alexandros, Giakoustidis, Alexandros, Kozadinos, Alexandros, Kyriakidis, Alexandros, Sarakatsanos, Alexandros, Rojas, Alexei, Saridaki, Alexia, Litchinko, Alexis, Arnaud, Alexis P, Binder, Alf-Dorian, Yakubu, Alfa, Kavalakat, Alfie, Shamsutdinova, Alfiya, Alberico, Alfonso, Amendola, Alfonso, Sánchez-Terán, Alfonso Iván, Lapergola, Alfonso, Baiden Amissah, Alfred, Königsrainer, Alfred, Twumasi, Alfred, Alhebshi, Alhasan, Abbas, Ali, Abdul Rahman, Ali, Abidali, Ali, Abu Tair, Ali, Ahmad, Ali, Al Mukhtar, Ali, Sharq, Ali Al, Alloush, Ali, Amaigl, Ali, Kheirkhah Vakilabad, Ali Asghar, Bassma, Ali, Hacioglu, Ali Doruk, Elzanaty, Ali, Fani, Ali, Farsi, Ali, Ghummied, Ali, Hajali, Ali, Al Sharqi, Ali Hamed, Hammed, Ali, Hewari, Ali, Isler, Ali, Marouf, Ali, Uysal, Ali Mert, Murtada, Ali, Syed, Ali Naqi, Ozkomec, Ali, Sapmaz, Ali, Wassouf, Ali, Aldurssi, Alia, Polatbekov, Alibek, Caltagirone, Alice, Gori, Alice, Lim, Alicia, Bhatt, Alisha, Mohammed, Aljarrah, Maxwell, Allison, Kiyabayev, Almaz, Eltayeb, Almoutaz, Khamees, Almu'atasim, Abdalla, Alnazir, Pikarsky, Alon, Trigos Díaz, Alonso, A Elhars, Alshymaa, Alhaj Zain, Altayeb, Eltahir, Altayeb, Robin Valle De Lersundi, Alvaro, Sanad, Aly, Abdalhadi, Alya, Sparke, Alyssa, Maqsood-Shah, Amaal, Ali, Amal, Ouachhou, Amal, Ahmad, Aman, Kezze, Amana, Dawson, Amanda Caroline, Ali, Amani, Mohsen, Amani, Brar, Amanpreet, Dembele, Amara, Tansawet, Amarit, Obbeng, Ambe, Chiappini, Ambra, Davis, Amelia, Awidan, Amera, Tibude, Ameya, Shah, Ami, Hilder, Amie, Huremovic, Amila, Alamin, Amin, Egdeer, Amin, Gharib, Amin, Choudhary, Amin Mazhar, Amin, Amina, Ibrahim Hassan, Amina, Rashid, Amina, Patel, Aminah, Sebai, Amine, Asla, Amir, Ghanbari, Amir, Shweiki, Amir, Tursunovic, Amir, Aboshosha, Amira Abdelbadea Abdelhamed, Elbalal, Amira, Abdelhalim, Amira Ramadan, Gupta, Amit, Sinha, Amit, Al-Fandi, Amjad, Awwad, Amjad, Belaid, Amjad, Khatib, Amjad, Al-Zubeidy, Ammar, Niazi, Ammar, Zamareh, Ammar, Salam, Ammara, Ayodele, Amoda, Al Hammoud, Amr, Khouli, Amro, Crowe, Amy, Surkitt, Amy Louise, Alshiteewi, Amyirah, Alvarez Cuiñas, Ana, Cabral, Ana, Dupont, Ana, Faustino, Ana, Carreira-Marques, Ana Lúcia, Preto Barreira, Ana Lúcia, Minaya Bravo, Ana Maria, Pinheiro Pereira, Ana Marta, Minaya-Bravo, Ana, Lourenço, Ana Rita, Mateus Loureiro, Ana Rita, Sánchez Gollarte, Ana, Tvaladze, Ana, Veira, Ana, Perja, Ana-Maria, Dusabimana, Anaclet, Herewini, Anahera, Fatima, Anam, Zafar, Anam, Bangar, Anant, Kutma, Ananta, Mehta, Ananya, Alqerem, Anas, Jaber, Anas, Mourtzouni, Anastasia, Theochari, Anastasia, Zarafidou, Anastasia, Smyrnis, Anastasios, Tsechpenakis, Anastasios, Zakaria, Andee Dzulkarnaen, Cardoso Almeida, Andre, Pita, André, Torbey, André, Abreu, Andrea, Barberis, Andrea, Bartalini Cinughi De Pazzi, Andrea, Belli, Andrea, Sironi, Andrea Carlo, Damigella, Andrea, Manetti, Andrea, Mingoli, Andrea, Natili, Andrea, Police, Andrea, Razzore, Andrea, Scacchi, Andrea, Vazquez Fernandez, Andrea, Luzzi, Andrea-Pierre, Brosin, Andreas, Efstathiou, Andreas, Kirschniak, Andreas, Panagakis, Andreas, Pilavas, Andreas, Scheiwiller, Andreas, Skarpas, Andreas, Chitul, Andrei, Trif, Andrei, Ferreira, Andreia, Silva, Andreia, Subocius, Andrejus, Cembellin, Andres, García Marín, Andrés, Ang, Andrew, Gray, Andrew, Li, Andrew, Skreka, Androniki-Maria, Abdul Rahim, Andy Adib, Bhangu, Aneel, Moncy, Aneena, Mwanjoka, Anganile, Fernández Camuñas, Ángel, De Jesús Gil, Ángela, Molero, Ángela, Sultana, Angela, Suárez Álvarez, Angeles Anabel, Nava Franco, Angelica Magaly, Autiak Ayii Chol, Angelo, Serao, Angelo, Nikolaou, Angelos I., Hassanin, Angham, Evans, Angharad, Vu, Anh, Lala, Anil Kumar, Luther, Anil, Sundaram, Anil, Cerovac, Anis, Abgeko, Anita Eseenam, Agbeko, Anita Eseenam, Sharma, Anju, Gupta, Ankit, Jain, Ankit, Attili, Ann, Alecci, Anna, D'Amore, Anna, Domenech Plana, Anna, Gut, Anna Eleonora, John, Anna, Lareida, Anna, Pesce, Anna Lisa, Paspala, Anna, Sharp, Anna, Victor, Anna, Comandatore, Annalisa, Olsen, Annelisse, Yiallourou, Anneza, Loizou, Annita, Sharma, Anshuman, Asare Twumasi, Anthony, Davor, Anthony, Tabiim, Anthony Jagri, Lin, Anthony, Szpytko, Anthony, Walfor, Anthony, Oosterkamp, Antje, Kachi, Antoine, Khoneisser, Antoine, Bediako 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Aristeidis-Panagiotis, Bang, Aristide, Nikitaras, Aristotelis, Akiba, Arit, Jindal, Arjun, Bedzhanyan, Arkady, Fajardo, Arlene, Antinori, Armando, Galvan, Armando, Belarmino, Armin, Mustafa, Arooba, Mathew, Arpit, Pillai, Arpit, Alrababah, Arqam, Shoker, Arran, Hai, Arrham, Anand, Arun, Bhatti, Arun, Nair, Arun, Prasath S V, Arun, Krishna, Arunachalam, Sinha, Arvind, Abdulnabi, Arwa, Aboumedian, Arwa, Faisal, Arwa, Haidar, Arwa, J, Arya, Ahmed, Asaad, Choudhry, Asad, Albager, Asawer, Jha, Ashesh Kumar, Chhabra, Ashish, Choudhrie, Ashish, Gupta, Ashish, Kumar, Ashish, Tirkey, Ashish, Oommen, Ashok Ninan, Al-Bahla, Ashraf, Bakri, Ashraf, Maghrabi, Ashraf, Mahendran, Ashuvini, Kumar, Ashwani, Das, Asish, Eisa, Assem, Comella, Assia, Shanmugalingam, Aswin, Boztaş Demir, Asya Eylem, Zubillaga-Mares, Asya, Altawaiha, Ata'O Allah, Mitsala, Athanasia, Katsiou, Athanasia-Faidra, Bakalis, Athanasios, Marinis, Athanasios, Migdanis, Athanasios, Soumpasis, Athanasios, Syllaios, Athanasios, 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Alhaj, Baraa, Binda, Barbara, Carvalho, Bárbara, Demurtas, Barbara, Dieck, Bárbara, Freire, Bárbara, Gama, Bárbara, Habrat, Barbara, Tinoco, Bárbara, Hernandez Cervantes, Barbara Yordanis, East, Barbora, Maguire, Barry, Mckay, Bartholomew, Żaczek, Bartłomiej, Dawud, Bashar, Allbakosh, Bashir, Yunusa, Bashir, Aminu, Bashiru, Ibi, Bashkim, D'Souza, Basil, Alazabi, Basma, Algettawi, Basma, Sharaf Eldin, Basma, Hameed, Basmah Hilal, Abboud, Bassam, Al Soudi, Bassam, Fahmy, Bassam, Ranjous, Batoul, Bereket Araya, Batseba, Eyduran, Batuhan, Alsaid, Bayan, Guerra, Bayron, Markowska, Beata, De Zolt Ponte, Beatrice, Robertson-Jones, Beatrice, Sperotto, Beatrice, Cordeiro, Beatriz, Cuneo, Beatriz, De Andrés-Asenjo, Beatriz, Louro, Beatriz, Estraviz, Begoña, Khan, Belawl, Abdennour, Belkadi, Calcaño, Bellanirys, Kigwe, Ben, Boakye, Benedict, Ağca, Bengi, Togay, Bengü, Esposito, Beniamino, Chiman, Benjamin Guarken, Lulham-Robinson, Benjamin, Martin, Benjamin, Peter, Bennett, Özcan, Berkay, Seshie, Bernard Kofi, Maanikuu, Bernard, Lopes Patrício, Bernardo, Khattab, Besher, Johnson, Bethan, Mcdermott, Bethany, Neall, Bethany, Wang, Betty, Roopavathana. S, Beulah, Fosh, Beverley, Sarang, Bhakti, Sv, Bharath, R Budihal, Bhargavi, Devkaran, Bhavesh, Van Zyl, Bianca, Kharga, Bikram, Al-Sarireh, Bilal, Ali, Bilal, Duman, Bilgesu, Baca, Bilgi, Down, Billy, Mengesha, Birhanu, Owusu Ansah, Bismark, Radja, Bitour, Sime, Bizuneh, Capdevila Vilaró, Blanca, Izedomi, Blessing Ohikhuare, Hao, Bo Zhen, Nimako, Boateng, Phakathi, Boitumelo, Sehgal, Boney, Cakpo, Boniface, Alrayes, Bourhan, Mao, Boyuan, Bogdan-Gabriel, Bratu, Bengaly, Bréhima, Williams, Brielle, Cismasiu, Brigitta, Helou, Brigitte, Davies, Brooke, Arya, Brundha, Cirillo, Bruno, Nardo, Bruno, Pinto, Bruno, Scotto, Bruno, Vieira, Bruno, Kadir, Bryar, Albaihani, Bshaer, Calik, Bulent, Citgez, Bulent, Celik, Burak, Azhar, Burhansyah, Alshaikh, Bushra, Kum, Büşra, Killoran, Callie, Nwachukwu, Callistus, Hidalgo Salinas, Camila, Brasset, Camille, Bertoglio, Camillo Leonardo, Vasquez Maya, Camilo, Zapata Syro, Camilo, Akgün, Can, Yurttas, Can, Semeraro, Candy, Lategan, Carina, Boven, Carine, Uwakunda, Carine, Leal Ferrandis, Carla, Margiani, Carla, Piras, Carla, Samojeden, Carla, Vitiello, Carla, Feo, Carlo V., Bôto, Carlos, Roman Ortega, Carlos Fernando, Ferreras García, Carlos, Figueiredo, Carlos, García-Salas, Carlos, González, Carlos, González Baez, Carlos, Gracia-Roche, Carlos, Guijarro Moreno, Carlos, Hernandez Diaz, Carlos, Ferrero, Carlos Ignacio, Perez Rivera, Carlos J, Jezieniecki, Carlos, Pérez - Padrón, Carlos Josué, León-Espinoza, Carlos, Nuño-Guzmán, Carlos M., Macedo Cardoso De Oliveira, Carlos, Shiraishi Zapata, Carlos, Yanowsky-Gonzalez, Carlos, Sorrentino, Carmen, Tung, Carmen, Cardoso, Carolina, Chavarría Noya, Carolina, Coutinho, Carolina, Korzin, Carolina, Maya, Carolina, Moreno-Licea, Carolina, Panis, Carolina, Righetti, Carolina, Silva, Carolina, Soares-Aquino, Carolina, Golliez, Caroline, Chiam, Carolyn, Kamphues, Carsten, Rahantasoa Finaritra, Casimir Fp, Assis, Catarina, Henriques, Catarina, Quintela, Catarina, Rio Ferreira, Catarina, Rolo 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Daniel, Montwedi, Daniel, Muñoz, Daniel, Patterson, Daniel, Reim, Daniel, Agbley, Daniel Yao Dodzie, Patiño García, Daniela Cristina, Di Pietrantonio, Daniela, Pais, Daniela, Rosado, Daniela, Tavares, Daniela, Alberti, Daniele, Fusario, Daniele, Massaro, Daniele, Paglione, Daniele, Pironi, Daniele, Kurochka, Daniil, Centonze, Danilo Corrado, Hackner, Danilo, Herrera, Danilo, Amangaliyev, Daniyar, Silva-Cano, Danny Wladimir, Chaudhry, Daoud, Palmisano, Dario, Ribero, Dario, Vukosav, Dario, Misca, Darius, Lowen, Darren, Rattray, Darren, Walia, Darshanjit Singh, Maan, Darvesh, Muyenzi, Daryle, Verdi, Daunia, Bardsley, David, Reyes Rodríguez, David Estuardo, Fra Corral, David, Garcia López De Goicoechea, David, Gavit, David, Gómez, David, Idowu, David, Konkin, David, Labadah, David Kplorla Kekeli, Liu, David, Merlini, David, Mitchell, David, Moro-Valdezate, David, Osifo, David, Proud, David, Ridder, David, Gobatti, Davide, Muschitiello, Davide, Pertile, Davide, Zulian, Davide, Mengiste, 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Rabaia, Dima, Georgiev, Dimitar, Hadzhiev, Dimitar, Alexandrou, Dimitrios, Balalis, Dimitrios, Giakoustidis, Dimitrios, Manatakis, Dimitrios K., Korkolis, Dimitrios, Lytras, Dimitrios, Massaras, Dimitrios, Moris, Dimitrios, Politis, Dimitrios, Sampanis, Dimitrios, Schizas, Dimitrios, Tsiardas, Dimitrios, Vardakostas, Dimitrios, Kotsaris, Dimitris, Khattab, Dina Hesham, M. 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Ehioghae, Afeikhena, Ehis, Elshennawy, Eiad, Athanasopoulou, Eirini, Synekidou, Eirini, Ekaladze, Eka, Tampaki, Ekaterini Christina, Adam, Elaf, Adel Hamdoun Aziz, Elaf, Yousuf, Elamin, Gialamas, Eleftherios, Spartalis, Eleftherios, Bedzhanyan, Elen, Toma, Elena Adelina, Daukšaitė, Elena, Desiato, Elena, Dvivedi, Elena, Hadzhieva, Elena, Martí Cuñat, Elena, Montalbán Martínez, Elena, Montanari, Elena, Signaroli, Elenasofia, Efremidou, Eleni, Gavriil, Eleni, Karakeke, Eleni, Mavrodimitraki, Eleni, Papamattheou, Eleni, Jose, Elenza, Cianci, Eleonora, Ciccioli, Eleonora, Daleva, Eleonora, Locci, Eleonora, Lori, Eleonora, Silanos, Eleonora, Ayala, Elí, Bankart, Eliana, Brolese, Eliane, Efstathiou, Elias, Salloum, Elias, Colak, Elif, Terzi, Elif Didem, Ametefe, Elikem, Cagigal Ortega, Elima Pilar, Donnarumma, Elio, Mina, Elisa, Reitano, Elisa, Wallner, Elisabeth, Moggia, Elisabetta, Betz, Elise, Kontis, Elissaios, Anestiadou, Elissavet, Gimson, Eliza, Lun, Elizabeth, Fraser, Elizabeth, Li, Elizabeth, Torres De Anda, Elizabeth Magaly, Murphy, Elizabeth Mary Ann, O'Connell, Elizabeth, Redondo, Elizabeth, Lun, Elizabeth Weng Yan, Francis, Ella, Haiden, Elmar, Hamed, Elmonzir, Alkhalifa, Elmustafa, Dexter, Eloise, Daketsey, Elorm, Setsoafia, Elorm, Mohamed, Elsagad, Mohammed, Elsamoual, Hegab, Elsayed, Ay, Elvin, Adinolfi, Elvira, Mbanzabugabo, Elvis, Chin, Elynn, Ahmed, Emad Ali, Younes, Eman, Basile, Emanuela, Gessa, Emanuela, Pontecorvi, Emanuele, Romairone, Emanuele, Hatangimana, Eméry, Ndizeye, Emile, Ciofic, Emilia, Dijan, Emilio, López-Negrete Cueto, Emilio, Baker, Emily, Reid, Emily, Schmidt, Emily, Alić, Emir, Bičakčić, Emir, Agbowada, Emma, Colton, Emma, Downing, Emma, Haege, Emma, O'Neill, Emma, Roussos, Emmanouel, Nachelleh, Emmanuel Aadereyir, Adebunmi, Emmanuel, Obiesie, Emmanuel Ahuizechukwu, Akakpo, Emmanuel Bismark, Konlan, Emmanuel Dammigu Francis, Nwangwu, Emmanuel Ifeanyi, Kafui Ayodeji, Emmanuel, Ofori, Emmanuel Owusu, Spanoudakis, Emmanuel, Abate, 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Mohammed Daoud, Fatima Ahmed, Alowjaly, Fatima, Oni, Fatima, Rauf, Fatima, Elkhafeefi, Fatimah, Hourri, Fatine, Monib, Fatma A., Alshreef, Fatma, Benghalbon, Fatma, Ntirenganya, Faustin, Ashraf, Fauziyah, Chahrour, Fayez, Ahmad, Fazal, Parray, Fazlul Qadir, Philips, Febi, Campus, Federica, Di Marco, Federica, Ferraina, Federica, Frongia, Federica, Maffeis, Federica, Natali, Federica, Pederiva, Federica, Scolari, Federica, Tofani, Federica, Marin, Federico Alberto Niccolò, Biolchini, Federico, Cappellacci, Federico, Corronca, Federico, Mazzola, Federico, Suldrup, Federico, Nyarko, Felicia, Casas J, Felipe, Zapata, Felipe, Aigner, Felix, Alakaloko, Felix, Amoako, Felix, Camacho, Felix, Hammett, Felix, Kuubetersob B. N, Felix, Alahmad, Feras, Alnimri, Feras, Haddad, Feras, Gool, Ferhana, Castro, Fernanda, Diaz, Fernanda, Lucero, Fernanda, Vovola, Fernanda, Mahnic, Fernando, Mercado Sanchez, Fernando, Prieto La Noire, Fernando, Soggiu, Fiammetta, Tsige, Fikreab, Brzeszczyński, Filip, Rudisch, Filip, Valente Costa Pinto, Filipa, Ramalho De Almeida, Filipe, Banchini, Filippo, Palomino Escalante, Fiorella, Fadzlullah, Fitri, Terefe, Fitsum, Sulo, Fjorenta, Ciccarone, Flavia, Tirelli, Flavio, Krämer, Florian, Grama, Florin, Spanos, Foivos, Solimene, Folco, Akinwande, Foluso, Osman, Forzia, Stefou, Foteini, Kyramargios, Fotios, Stavratis, Fotios, Alassani, Fousseni, Ascari, Francesca, Cammelli, Francesca, D'Agostino, Francesca, Tropeano, Francesca Paola, Peters, Francesca, Ré, Francesca, Brucchi, Francesco, Casti, Francesco, Coratti, Francesco, Ferrara, Francesco, Floris, Francesco, Izzo, Francesco, Maffei, Francesco, Palmieri, Francesco, Pata, Francesco, Porcelli, Francesco, Selvaggi, Francesco, Velluti, Francesco, Mulita, Francesk, Bello, Francis, Caiquo, Francis, Dossou, Francis, Eshun, Francis Junior, Opoku Twene, Francis, Tierenye, Francis, Quayson, Francis William, Romo Rosales, Francisco David, Ibañez Ortiz, Francisco, León Frutos, Francisco Javier, Redondo Calvo, Francisco Javier, Silva Rivera, Francisco Javier, Barbosa Camacho, Francisco José, Bujalance Cabrera, Francisco Manuel, Ruescas, Francisco, Irakiza, Franck Fanon, Corvatta, Franco, Pascual, Franco Raúl, Roviello, Franco, Roodt, Francois, Gyamfi, Frank Enoch, Olaniru, Frank, Owusu, Frank, Bader, Franz G., Anzinger, Franz-Xaver, Agbodo, Fred David Anku, Ris, Frederic, Agbemafoh, Frederick Mawuli, Afonso, Frederico, Nazareth, Frederico, Berrevoet, Frederik, Hounde, Fréjus, Demiral, Furkan, Hassooni, Furqan Ridha Hassooni, Kafa, Gabi, Cárdenas Rivera, Gabriel, Ntwari, Gabriel, Szydlo Shein, Gabriel, Ambriz González, Gabriela, Nita, Gabriela Elisa, Bellio, Gabriele, Moitzi, Gabriele, Urdang, Gabriella, Marom, Gad, Gallo, Gaetano, Luglio, Gaetano, Poillucci, Gaetano, Singh, Gagandeep, Saletta, Gaia, Abouelnagah, Galal, Yarovenko, Galina, Kazobinka, Gallina, Ahmed, Gamal Mutwakil Gamal, Rahman, Ganiyu Adebisi, Smith, Garett, Pillai, Gargi, Saxena, Garima, Gbessi, Gaspard, Nkunguzi, Gaston, Jalal, Gaurav, Chillarge, Gauri, Singh, Gautam, Mevognon, Gedeon, Getachew, Gedion, George, Gejoe, De La Peña González, Gema, Martínez Izquierdo, Gema, Grande, Gennaro, Birikorang, Geoffrey, Weber, Georg F., Singer, Georg, Ansong, George, Bashour, George, Brown, George Darko, Hneino, George, Kavalieratos, George, Kenchadze, George, Nagy, George, Pappas Gogos, George, Skroubis, George, Waiyaki, George, Cox, Georgia, Zeringa, Georgia, De Wee, Georgin, Hart, Georgina, Bekakos, Georgios, Fragulidis, Georgios, Kapogiannatos, Georgios, Karakaidos, Georgios, Kokkinos, Georgios, Kostoulas, Georgios, Koukoulis, Georgios, Peros, Georgios, Rados, Georgios, Tsoulfas, Georgios, Verras, Georgios-Ioannis, Berger, Geraint, Adoro, Gerard Ayuka, Chamoso Mialdea, Gerardo, Palmieri, Gerardo, Procaccini, Gerardo, Steenkamp, Gerhard, Viscido, Germán Rodrigo, Dajti, Gerti, Alemayehu, Getachew, Seyfu, Gezachew, Birqeeq, Ghada, Alsheikh, Ghadeer, Alrayyes, Ghaith, El Hunjul, Ghalib Nashaat, Zaza, Ghayath, Matroud, Ghazlah, Alhadwah, Ghena, Maltinti, Gherardo, Awais, Ghina, Shamsi, Ghina, Calini, Giacomo, Carganico, Giacomo, Zanus, Giacomo, Canu, Gian Luigi, Mendiola, Gian, Ibero Casadiego, Giana Pamela, Cocorullo, Gianfranco, Baronio, Gianluca, Pellino, Gianluca, Zanni, Gianluca, Poto, Gianmario Edoardo, Gravante, Gianpiero, Aduroja, Gideon, Almogy, Gidon, Bond-Smith, Giles, Natchagande, Gilles, Bechara, Gio, Brachini, Gioia, Riccioli, Giordana, Ercolani, Giorgio, Farris, Giorgio, Giraudo, Giorgio, Mammolo, Giovanna, Pavone, Giovanna, Sgarzini, Giovanna, Aprea, Giovanni, Fonsi, Giovanni Battista, Boroni, Giovanni, De Angeli, Giovanni, De Palma, Giovanni Domenico, Emiliani, Giovanni, Grava, Giovanni, Guercio, Giovanni, De Carlo, Giulia, Del Corpo, Giulia, Duranti, Giulia, Galatioto, Giulia, Grassi, Giulia, Graziano, Giulia, Marini, Giulia Maria Francesca, Munzi, Giulia, Tarantino, Giulia, Borda-Luque, Giuliano, Pellicano', Giuseppe Antonino, Canonico, Giuseppe, Carollo, Giuseppe, Ietto, Giuseppe, Palomba, Giuseppe, Pascarella, Giuseppe, Salamone, Giuseppe, Sica, Giuseppe, Sorrentino, Giuseppe, Orlando, Giuseppina, Pignata, Giusto, Pisarevi, Givi, Akcakoca, Gizem, Owusu, Godfred, Yohanna Abrak, Godwin, Kurtoglu, Gokalp Kagan, Kurtoglugk, Gokalp Kagan, Alıcı, Gökhan, Kurtoglu, Goktug Mert, Delgado Hernandez, Gonzalo, Augustin, Goran, Bhat, Gowhar Aziz, Sundaram Venkatesan, Gowtham, Chang, Grace, Eke, Grace, Lee, Grace, Massarra, Grace, Travaglini, Grazia, Scialandrone, Grazisa, Stavrou, Gregor A., Di Franco, Gregorio, Charalambous, Gregory, Christodoulidis, Gregory, Kouraklis, Gregory, Goodwin, Gregory Richard, Devidze, Grigol, Fialho, Guilherme, Santos, Guilherme, Cabezudo, Guillermo, García-Santos, Guillermo, Pou, Guillermo, Romero Reyna, Guillermo, Yanowsky-Reyes, Guillermo, Ozyuksel, Gul, Karadeniz Cakmak, Guldeniz, Kucuk, Gultekin Ozan, Bulbuloglu, Guney Cem, Sarp, Guray, Kaur, Gurleen, Nibogora, Guy Darcy, Hamdan, Gymar, Ogundipe, Habeeb, Yunes, Habib, Ayesh, Hadeel, Al Atassi, Hadi, Jalilehvand, Hadis, Masalma, Hafez, Jafarkhan, Hafis, Shahid, Hafiz Huzaifa, Younus, Hafsa, Al-Derume, Hail, Shames, Haitam, Mimouni, Haithem Ahmed, Aldare, Hajar, Odah Bashi, Hajar Omar, Abusnina, Hajer, Altounsi, Hajir, Qayum, Hajra, Baysal, Hakan, Bolukbasi, Hakan, Yigitbas, Hakan, Alkhatib, Hala, Afzal, Haleema, Mftah, Halima, Oyinlola, Halimah, Tümer, Haluk, Dolo, Hamadoun, Braham, Hamdi, Ozsahin, Hamdi, Abu-Arish, Hamdoon, Al-Aamri, Hamed, Fadlalmola, Hammad Ali, Abu Obead, Hamza, Al-Fahel, Hamza, Al-Zoubi, Hamza, Asaad, Hamza, Ejaz, Hamza, Jaber, Hamza, Bhatti, Hamza Waqar, Mohammadi Sardoo, Hamzeh, Farhat, Hana, Idheiraj, Hanadi, Zwaraa, Hanady, Al Sohabi, Hanan, Basharat, Hanan, Said, Hanan, Al-Abdallat, Haneen, Foad, Haneen, Daaboul, Hanen, Al Qadhi, Hani, Elhallaq, Hani, Alhusaini, Hanin, Lidbetter, Hannah, Reilly, Hannah, Moya- Ambriz, Hannia Betzabeth, Lederhuber, Hans, Fehrer, Hans-Jörg, Xiao, Haotian, Iswariah, Harish, Kaur, Harmeena, Cheema, Harnoor Kaur, Foda, Haroun, Chauhan, Harpreet, Kaur, Harpreet, Sagar, Harry, Unwin, Harry, Kaur, Harsharan, Panga, Harshavardhan, Gupta, Harshit, Prabhakar, Harshit Kumar, Anand, Harshitha, Pahwa, Harvinder Singh, Abbadi, Hasan, Amoudi, Hasan, Zaini, Hasan Hadi Zaini, Škiljo, Hasan, Miyasika, Hasangi, Altabbaa, Hashem, Haj Freej, Hasn, Abdelazim, Hassan, Habiba, Hassan, Kamal, Hassan, Sabir, Hassan, Zaigham, Hassan, Pehlivan, Hatice, Telci, Hatice, Abuobaida, Hayat, Tetley, Heather, Taher, Heba, Aguado López, Héctor, Askarpour, Hedyeh, Sherif Farouk Ahmed Hassan, Heidi, Devesa, Helena, Arbogast, Helmut, Nuñez Del Barrio, Henar, Abiyere, Henry, Delacave, Henry, Rice, Henry E., Pleass, Henry, Bradly, Hera, 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Özata, ıbrahim Halil, Kandil, Ibrahim, Ahmed, Ibrahim Mutwakil Gamal, Tagreda, Ibrahim, Garzali, Ibrahim Umar, Nadi, Ibtihal, Ogundele, Ibukunolu, Pius Ogolekwu, Idoko, Shariff, Idriss, Jakaityte, Ieva, Abdullah, Ifra, Babajimi-Joseph, Igbagbolere, Guirguis, Ihab, Orji, Iheanyi, Okoro, Iheanyichukwu, Ahmed, Ihsan, Obianyo, Ijeoma, Njere, Ike, Ugwueke, Ikechukwu, Nzenwa, Ikemsinachi C, Zerrouq, Ikram, Clementi, Ilaria, Neri, Ilaria, Yavuz, Ilayda Esma, Hoxhaj, Ilda, Fakhradiyev, Ildar, R Fakhradiyev, Ildar, Gomatos, Ilias, Fidoshev, Iliya, Gouazar, Iltimass, Alnimer, Imad, Boudis, Imad Eddine, Boumakhlouf, Imad Eddine, Abdalla, Iman, Herrero, Imanol, Osman, Imoro, Nasser, Inas, Singh, Indra, P S, Induchoodan, Borges Da Costa, Inês, Colaço, Ines, Lourenço, Inês Mariana, Mogárrio, Inês, Königsrainer, Ingmar, Ahmed, Intezar, Hamdun, Intisar Hisham Said, Tanase, Ioan, Bokos, Ioannis, Contis, Ioannis, Demiris, Ioannis, Gerogiannis, Ioannis, Gogoulis, Ioannis, Karatsolis, Ioannis, Katsaros, Ioannis, Kozadinos, Ioannis, Margaris, Ioannis, Maroulis, Ioannis, Papaconstantinou, Ioannis, Siannis, Ioannis, Sougkas, Ioannis, Spyridakis, Ioannis, Tierris, Ioannis, Tsakiridis, Ioannis, Imihteev, Ion, Negoi, Ionut, Chatzis, Iosif, Shahbaz, Iqra, Didier, Irabishohoje, Pipia, Irakli, Parwaiz, Iram, Durán Sánchez, Iran Irani, Norman, Irena, Bakaweri, Irenaeus Nutumie, Santos, Irene, Mylona, Irene Chrysovalanto, De Haro Jorge, Irene, Cervera, Iria, Dajti, Irida, Datji, Irida, Matache, Irina Mihaela, Russo, Iris Shari, Aremu, Is'haq Ishola, Amankwaa, Isaac, Caitens, Isaac, Esparza Estrada, Isaac, Mubezi, Isaac, Gascon Ferrer, Isabel, Serrano, Isabel, Dong, Isabella, Mondi, Isabella, Serfontein, Isabella, Rakshit, Ishani, Kyei, Ishmael, Murshed, Ishraq, Cabrera, Ishtar, Abo Elhagag, Islam, Alsoubie, Ismael, Lawani, Ismail, Bilgin, Ismail Ahmet, Cholah, Ismail, Hamzaoglu, Ismail, Lawani, Ismaïl, Maharem, Israa, Shehadeh, Israa, Hagbevor, Israel, Bahikoro, Issa Wendpouiré, Laopeamthong, Issaree, Mikulic, Iva, Bacic, Ivan, Rakvin, Ivan, Separovic, Ivan, Vidić, Ivan, Conversano, Ivana, Mrazkova, Ivana, Yotsov, Ivelin, Ilieva, Ivelina, Ćoza, Ivo, Ali, Iyad, Aljada, Iyad, Hussein, Iyad, Tesfahun, Iyasu, Sadiq, Izzah, Martinez Martin Del Campo, J Francisco, Mackenzie, Jack, Maida, Jack Vittorio, Kaippally, Jacob, Montero García, Jacobo, Andreuccetti, Jacopo, Martellucci, Jacopo, Secchi, Jacopo, Van Niekerk, Jacques, Saunders, Jada, Duffield, Jaime A., Tavares-Ortega, Jaime Alberto, Orozco-Perez, Jaime, Mihanovic, Jakov, Woleský, Jakub, Bryan, James, Bundred, James, Cui, James, Glasbey, James, Casella Mariolo, James Rossario, Suliman, Jamil, Mohammad Mohammad, Jamiu, Bolanowski, Jan, Goedeke, Jan, Grosek, Jan, Moravik, Jan, Ringers, Jan, Roman, Jan, Chew, Janae, Maritz, Jancke, Martin, Janet, Velkoski, Jaqueline, Wong, Jason, García-Quijada, Javier, Tk, Jayakumar, Siby, Jayas, Mcnab-Hand, Jaymee, Gusa, JB, Gusa, Jean Bosco, Allen, Jean Claude, Mbonicura, Jean Claude, Rugambwa, Jean Paul, Jordaan, Jean- Jacques, Razafindrahita, Jeannie Ba, Do, Jee Eun, Senavirathna, Jeewantha, Kalyanapu, Jeffrey A, Chan, Jeffrey, Ofori, Jeffrey, Otote, Jeffrey, Akiely, Jehad, Cassiano Neves, Jelena, Gillingham, Jemima, Herrera Batres, Jenifer Olga María, Ezeh, Jennifer, Okei, Jennifer, Vu, Jennifer, Guevara, Jenny, Siu, Jenny, Rolinger, Jens, Skaff, Jeolle, Uys, Jernelle, Makama, Jerry Godfrey, Barklimore, Jessica, Dei-Asamoa, Jessica, Jahjah, Jessica, Jones, Jessica, Ng, Jessica, Ricardo, Jéssica, Schembri Higgans, Jessica, You, Jessica, Annan, Jessie, Aguilar, Jesus Antonio, Pizarro Lozano, Jesús, Lim, Jia Hui, Zhu, Jiang, Wang, Jianxue, Abbasy, Jibran, Ugwu, Jideofor, Rathod, Jignesh, Albalushi, Jihan, Najajra, Jinan, Fu, Jing Hui, Tefay, Joan Sia Tan, Domingues, Joana Cristina, Simoes, Joana FF, Figueiredo, Joana, Fontaínhas, Joana, Frazão, Joana, Marques Antunes, Joana, Simoes, Joana, Marx, Joanna Naemi, Dias-Ferreira, João, Fernandes, João, Mendes, João, Pinho, João, Ribeiro, João, Vaz, João, Dominguez, Joaquina, Abutu John, Job, Chen, Joe Jun Qiao, Enaholo, Joel, Alaji, Joelle, Miller, Johanna, Binder, Johannes, Daleku, John, Katogiritis, John, Khoury, John, Anyorigiya, John Noah, Reilly, John, Abebrese, John Tabiri, Ahale, Johnny Lordson, Tabora-Zepeda, Johnny, Barrutia Leonardo, Jon, Demma, Jonathan Abraham, Hong, Jonathan, Chejfec-Ciociano, Jonathan Matias, Oppong, Jonathan, Quansah, Jonathan, Sutcliffe, Jonathan, Taiwo, Jones, Nyamekye - Baidoo, Joojo, Prat-Ortells, Jordi, Dongmo, Jordy, Kleeff, Jorg, Jimenez Flores, Jorge Alberto, Pellecer Cano, Jorge Bernardo, Chóliz, Jorge, Giubi Bobeda, Jorge Eduardo, Escartin, Jorge, Febré, Jorge, Gass, Jörn-Markus, Calvache, Jose A, Guzman Barba, Jose Aldo, Gutiérrez Gómez, José Alonso, Calvache, Jose Andres, Acevedo Parrales, José Danilo, Orozco Navarro, José Esteban, Viñas, José Federico, Gomez, José, Goncalves-Nobre, Jose Guilherme, Valenzuela, José Ignacio, Garcia Galocha, Jose Luis, Rodicio Miravalles, Jose Luis, Faylona, Jose Macario, Alvarez Hernandez, Jose Manuel, De Francisco Rios, Jose Mario, Martin Fernandez, Jose, Michel, José, Carlos, José Miguel, Mondino, Jose, Vieira De Sousa, José Pedro, Asturias Luna, José Rodrigo, Hernández, Jose Rodrigo, García, José Sebastian, Galea, Joseph, Hwang, Joseph, Leong, Joseph, Yorke, Joseph, Psaila, Josephine, Walshaw, Josephine, Ampadu, Joshua, Linker, Joshua, Ng-Kamstra, Joshua, Olorunfunmi, Joshua, Mavrek, Josip, Miskovic, Josip, Petric, Josipa, Avakoudjo, Josue, Al Karmi, Joud, Fahed, Joud, Suleiman Hadidi, Joudd, Alkharish, Joul, Juloski, Jovan, Vaitekūnas, Jovydas, Wang, Joyce, Flores Prado, Juan Armando, Beltrán De Heredia, Juan, Garcia Vera, Juan Carlos, Molina Marin, Juan David, Gonzalez Aboytes, Juan Eduardo, Ulloa Robles, Juan Jose, Blas Laina, Juan Luis, Carranza Rosales, Juan Manuel, Morales, Juan, Naranjo Fernández, Juan Ramón, Al Daradkah, Jude, Aseme, Jude, Ede, Jude, Kahn, Judith, Lindert, Judith, Deeb, Judy, Mühlhäusser, Julia, Sidorovskaia, Julia, Carrizo, Julian, Napoli, Juliana, Pereira-Macedo, Juliana, Ribeiro, Juliana, De Deken, Julie, Nnoli, Juliet, Paredes, Julieta, Zevallos-Quiroz, Julio Cesar, Gonzalez Garcia, Julio, Adjei, Julius, Olaogun, Julius, Kutkevičius, Juozas, Metzger, Jürg, Košir, Jurij Aleš, Kosir, Jurij, Seyi-Olajide, Justina, Zajac, Justyna Karolina, Dhima, Jyoti, Dhiman, Jyoti, Shreyas, K, Türker, Kadir Yağız, Lee, Kai, Khutsishvili, Kakhi, Yu, Kam Keung Auston, Das, Kamala, Özdoğan, Kamil, Reddy, Kamireddy Madana Raghava, Kabulov, Kamran, Nahar, Kamrun, Rajaratnam, Kanapathippillai, Wijesinghe, Kanchana, Sevnaran, Kapil, Khalil, Karam, Singh, Karanbir, Fayed, Kareem, Booyse, Karien, Geiger-Timm, Karin, Maharaj, Karl, Bhutia, Karma, Richter, Karolina, Komorowska, Katarzyna, Urbańska, Katarzyna, Waddell, Kate, Kambouri, Katerina, Tata, Katerina, Richetti, Katharina, Griffin, Katherine, McKenzie, Katherine, Tambudze, Katherine, Hall, Katheryn, Chu, Kathryn, Theodoropoulou, Katina, Kröning, Katrin, Bozada-Gutiérrez, Katya, Raghuwanshi, Kaustubh, J B, Kavitha, Ahmed Ibrahim, Kefah, Bwala, Kefas John, Iles, Kelly, Jasso García, Kelvin, Oğur, Kemal, Fretwell, Kenneth, Oluchukwu, Kenneth, Onyekachi, Kenneth, Voon, Kenneth, Oderoha, Kevin Ewomazino, Nieto Yrigoin, Kevin, Quispe De La Roca, Kewin, Anaam, Khadega, Bensmain, Khadija, Shwail, Khadija, Akter, Khadiza, Sawaftah, Khaled Ahmad, Shamiyah, Khalid Adnan, Bhatti, Khalid, Mohammed, Khalid Miftah Ghuraybeel, Ayyoub, Khalil, Cholah, Khalil, Rahman, Khandakar Rezwanur, Abdulrahman, Khawla, Al-Shami, Khayry, Hayat, Khizar, Al Hinai, Khulood, Taghavi, Kiarash, Shodunke, Kikelomo, Abo Zaal, Kinaz, Ewool, Kingsley, Gaikwad, Kiran, Shemyatovsky, Kirill, Dey, Kirsty Elaine, Mishra, Kirti Kumar, Rathod, Kirtikumar J, Senanayake, Kithsiri Janakantha, Noguchi, Kiyonari, Kordeni, Kleoniki C., Dembele, Klétigui Samuel, Spassov, Kolyo, Khalid, Komal, Petrenko, Konstantin, Bekiaridou, Konstantina, Rekouna, Konstantina, Athanassiou, Konstantinos, Avgerinos, Konstantinos, Bouchagier, Konstantinos, Bouliaris, Konstantinos, Bramis, Konstantinos, Iliakopoulos, Konstantinos, Kontzoglou, Konstantinos, Ntatsis, Konstantinos, Pavlopoulos, Konstantinos, Perivoliotis, Konstantinos, Polychronopoulos, Konstantinos, Giannakopoulos, Konstantinos S., Zakkas, Konstantinos, Zapsalis, Konstantinos, Koumarelas, Konstantinos-Eleftherios, Mori, Krinal, Ooi, Krisada, Ascencio Diaz, Kriscia Vanessa, Jasaitis, Kristijonas, Nieuwenhuys, Kristin, Atherton, Kristy, Smith, Kristy, Sharma, Kruti, Jaffry, Kumail, Lehmann, Kuno, Ayad, Kusay, Luthra, Kush Kumar, Nanayakkara, Kushan, Agbedinu, Kwabena, Oduro-Boateng, Kwaku Boakye, Boakye-Acheampong, Kwame, Sim, Kwang Kiat, Yalley, Kwekuma, Collins, Kyeremeh, Muscat, Kyle, Hofmann, Kyra, Lambri, Kyriaki, Bananis, Kyriakos, Nassim, Kyrillos, Allan, Lachlan, Codina Corrons, Laia, Hijazein, Laith, Moolla, Laith, Sweidan, Laith, Rai, Lajpat, Devi, Lakshmi, Fountoulis, Lampros, Andreski, Lara, Bonello, Lara, Rodrigues Madeira, Lara, Tafesh, Lara, Yasin, Lara, Martinez, Laura, Fortuna, Laura, Peña Balboa, Laura Gabriela, García-Zambrano, Laura, Bolaños, Laura Isabela, Jiménez, Laura, Lely, Laura, Ortega Lechuga, Laura, Rossi, Laura, Rubin, Laura, Ruiz-Villa, Laura, Sequi, Laura, Sahan, Laura-Ann, Johnstone, Lauren, Monteiro, Lauren, Abdullahi, Lawal Barau, Jovcheski, Lazo, Siragusa, Leandro, Kang, Leannedra, Elgebaly, Leenah, Mansour, Lefteris, Robine-Durnell, Lélia, Bray, Lemuel Davies, Mohammed, Lena, Licari, Leo, Onyebulu, Leonard, Chieng, Leonard Ron, Solaini, Leonardo, Chardalias, Leonidas, Fountarlis, Leonidas Ilias, Van Den Hil, Leontine, Tapia Moral, Leopoldo, Petashvili, Leri, Heeren, Letícia, Laface, Letizia, Niyidukunda, Lewis, Barnard, Liam, Torres Bavestrello, Lidice Veronica, Hasan, Lilav, Dyab, Lima, Venclauskas, Linas, Beukes, Linda, Gabellini, Linda, Liepa, Linda, Zeng, Lingjuan, Dubs, Linus, Rampersad, Lisa, Catozzi, Livio, Castro, Lizeth, Milic, Ljiljana, Shawesh, Lobna, Anyanwu, Lofty-John, Khalifa, Lojina, Cheng, Lok Pong, Manoj Joshua, Lokavarapu, Shammas, Lora, Fuentes Rivera Lau, Lorena, Rodríguez Gómez, Lorena, Capello, Lorenzo, Epis, Lorenzo, Fatucchi, Lorenzo Maria, Rende, Lorenzo, Ripamonti, Lorenzo, Ackam, Louis, Moniz, Lourenço, Bains, Lovenish, Alriyami, Lubna, Alokshi, Lubnah, Martinek, Lubomir, Tulinsky, Lubomir, Samison, Luc, Lo Piccolo, Luca Benedetto, Cardinali, Luca, Nespoli, Luca Carlo, Casingena, Luca, Degrate, Luca, Bonomo, Luca Domenico, Morelli, Luca, Ottaviani, Luca, Resca, Luca, Scaravilli, Luca, Schiavone, Luca, Theuil, Luca, Casalduero, Lucas, Lee, Lucas D., Granero, Lucas, Caram, Lucas Jose, Barni, Lucia, Cidade Costa, Luciana, Paredes, Luciana, Poggi, Luciano, Marquez, Lucila, Garciandia, Lucio, Taglietti, Lucio, Pramod, Ludiya, Stella, Ludovica, Carbone, Ludovico, Bracciano, Luigi, Cobellis, Luigi, Conti, Luigi, Fedele, Luigi, Ragazzini, Luigi, Simonelli, Luigi, Verre, Luigi, Flores Chávez, Luis Adrián, Rea Bocanegra, Luis Alberto, Pelayo Orozco, Luis Ángel, Asensio Gomez, Luis, Bravo, Luis, Flores Chávez, Luis Manuel, Poggi, Luis, Cabezudo, Luis Ramón, Sante Serna, Luis, Tallon-Aguilar, Luis, Talé-Rosales, Luis-Fernando, Chantada, Luisa, Moreira, Luísa, Hasan, Lujain, M Mheidat, Lujain, Eisner, Lukas, Gantner, Lukas, Havranek, Lukas, Majerčák, Lukáš, Pánči, Lukáš, Nawacki, Łukasz, Adagrah Aniakwo, Luke, Bromley, Luke, Green, Luke, Paterson, Luke, Peters, Luke, Abdur-Rahman, Lukman, Omomeji, Lukman, Amosu, Lukmon, Kolongi, Lungushinga Christopher, Bin Aizan, Luqman Naim, Imchen, Lurong, Chang, Lydia, Mukamazera, Lydie, Katsiaras, Lykoyrgos, Al-Boukhari, Lynn, Chong, Lynn, Karydakis, Lysandros, Peris, M. Roosindu, Cancelas, Mª Dolores, Kariem, Maahir, Amoako-Boateng, Mabel, Alshamikh, Mabroukah, Ishak, Mabruok, Narayanan, Machingal, Matyja, Maciej, Walędziak, Maciej, Zounon, Mack-Arthur, Trindade, Madalena, Shahid, Madeeha, Kelly, Madeleine, Bajwa, Madiha, Bowles, Madison, Roussos, Madison, Sampaio-Alves, Mafalda, Manfredini, Magda, Kołomańska, Magdalena, Redynk, Magdalena, Farooq, Maham, Ali, Mahamudu Ayamba, Rodha, Mahaveer Singh, Alsalawi, Mahdi, Lodha, Mahendra, Sethi, Mahesh Kumar, Khalil, Mahfoud, Singh, Mahima, Abdualqader, Mahmmoud yehia, Abdelhafez, Mahmood, Sersarah, Mahmood, Abdelreheem, Mahmoud, Abu Al Amrain, Mahmoud, Barbarawi, Mahmoud, Diab, Mahmoud, Eissa, Mahmoud, ElFiky, Mahmoud, Elmesalmi, Mahmoud, Elmousili, Mahmoud, Fahmy, Mahmoud, Faisal, Mahmoud, Jabal, Mahmoud, Saad, Mahmoud M., Mahafdah, Mahmoud, Marei, Mahmoud Marei Abdelaziz, Mostafa, Mahmoud, Reda, Mahmoud, Zahran, Mahmoud, Suleman, Mahmowd, Genç, Mahmut Salih, Haghighat Ghahfarokhi, Mahta, Abubakar, Maimuna, Haimona, Mairarangi, Camuera, Maite, H. Oweidat, Majd, Mansour, Majd, Kalabić, Majda, MohammedAli, Majdeddin, Abu Daoud, Majdi, Almahjoub, Majdolin, Bilfaqirah, Majed, Yigah, Makafui, Akkawe, Malak, Dery, Malcolm, West, Malcolm, Rius, Malena, Nandasena, Malith, Aljarawn, Mallak, Masaaod, Mallak, Manangi, Mallikarjuna, Windsouri, Mamadou, Battili, Mamata, Rakotonaivo, Mamisoa Judicaël, Benghazi, Manaji, Al-Dhaheri, Manal, Almaqrahi, Manal, Katif, Manal, Alshami, Manar, Ibrahim, Manar, Ramazanov, Manas, Kaur, Mandeep, Agrawal, Manish Kumar, Pathak, Manish, Aggarwal, Manisha, Chandrasegaram, Manju, Anandan, Manoj, K S D, Manoj Kishan Lal, Kumar, Manoj, Sarda, Manoj, K B, Manojkumar, Jokubauskas, Mantas, Moreno Gonzales, Manuel Arturo, Cruz, Manuel, Diez Alonso, Manuel, Garcia, Manuel, Perez, Manuel, Mosquera Paz, Manuel Santiago, Cammelli, Manuela, Mercurio, Manuela, Singh, Manvinder, Taamreh, Manwa, Canals Sin, Mªpilar, Carbonell Pradas, Mar, Nunes, Mara, Sawaftah, Marah Ahmad, Khalil, Maram, Schneider, Marcel André, Steffani, Marcella, Pisano, Marcello, Doniquian, Marcelo Alejandro, Martínez Bareiro, Marcelo Nicolás, Pagani, Marcelo, Scortechini, Marcelo, Carvalho, Márcia, Sánchez Audelo, Marco Antonio, Brizzolari, Marco, Ceresoli, Marco, De Prizio, Marco, Amisano, Marco Francesco, Gramellini, Marco, Marcianò, Marco, Mariani, Marco, Milone, Marco, Montagna, Marco, Piccino, Marco, Santos, Marco, Dyer, Marcus, Kang, Marcus, Mahadi, Mardhiana, Zawadzki, Marek, Yinusa, Margaret, Bernardo, Margarida, Nunes Luís, Margarida, Gambelli, Margherita, Flint, Margot, Aggelidou, Maria, Bequis, Maria Agustina, Sotiriou, Maria Aikaterini, Ñañez, Maria Alejandra, Tahir, Maria Ambreen, Gascón Domínguez, María Ángeles, Asimakidou, Maria, Mourato, Maria Beatriz, Blanco, Maria, Christou, Maria, Coronas Soucheiron, Maria, Pignanelli, Maria Costanza, Kalaydjian, María Cristina, Martí-Ejarque, Maria del Mar, Olmedo Reinoso, María Del Mar, Cebollero, María Del Pilar, Derattani, Maria, Gama Caldeira, Maria Do Carmo, Drogouti, Maria, Gonzalez-Gonzalez, Maria Elena, Muñoz Fernández, María Elena, Muriel, María Emilia, Duran, María Eugenia, Zarate Casas, María Fernanda, Florou, Maria, Piacentini, Maria Gaia, Seixo, Maria Inês, Bellini, Maria Irene, Peña Soria, Maria Jesús, Martínez, María José, Medina, Maria José, Rueda Medécigo, María José, Karakeke, Maria, Kausar, Maria, Kyriazi, Maria, Marqueta De Salas, Maria, Modolo, Maria Marta, Mateu, Maria, Mavri, Maria, El Ghoul Miliotou, Maria Melina, Moreno Gijon, Maria, Papadoliopoulou, Maria, Proclamà, Maria Pia, Picciochi, Maria, Reis, Maria, Rennis, Maria, D'Anna, Maria Rosa, Ruffoli, Maria, Ruiz Soriano, María, Sánchez Ramirez, Maria, Sánchez-Rubio, María, Schüler, Maria, Sotiropoulou, Maria, Barrio Renteria, Maria Teresa, Tsopozidi, Maria, Vasconcelos, Maria, Facchino, Maria Vittoria, Wikar, Maria, Aguilera-Arevalo, Maria-Lorena, Abdu, Mariam, Asarbakhsh, Mariam, Elmiesiry, Mariam, Ashong, Marian Esi, Gomes, Mariana, Capuano, Marianna, Lill, Marianne, Nieto Galvan, Mariano, Osso, Mariasara, Olivos, Maricarmen, Sacdalan, Marie Dione, Weber, Marie-Christin, Gonzalez, Marijose De Cristo, Ferreira, Marília, Infantes Ormad, Marina, Kouliou, Marina Nektaria, Ransome, Marina, Vicario Bravo, Marina, Cuesta Argos, Mario, Faletar, Mario, Giuffrida, Mario, Lainez Escribano, Mario, Pacilli, Mario, Pannullo, Mario, Rodriguez-Lopez, Mario, Trejo-Avila, Mario, Merrakos, Marios, Tello Jimenez, Marisol, Kalisvaart, Marit, Quaunine, Marium, Perera, Mariyan, Harris, Mark, Youssef, Mark Melad Thabet, Peter, Mark, Philipp, Mark, Portelli, Mark, Jesudason, Mark Ranjan, Sammut, Mark, Tomlinson, Mark, Zammit Vincenti, Mark, Despotidis, Markos, Albertsmeier, Markus, Quante, Markus, Löffler, Markus W., Gurung, Markush, Gumsheimer, Marlena, Benamrouche, Maroua, Chaccour, Maroun, Ahmed, Marriam, Farooqui, Marryam Riaz, Angelini, Marta, Ortega Escudero, Marta, Martin Gimenez, Marta Pilar, Reia, Marta, Scricciolo, Marta, Caramantin Obando, Martha Maria, Gosselink, Martijn, Bertrand, Martin, Dum, Martin, Kajic, Martin, Karamanliev, Martin, Kyereh, Martin, Lucchini, Martin, Schön, Martin, Morna, Martin Tangnaa, Binda, Martina, Ceolin, Martina, Martorana, Martina, Zambon, Martina, Shams, Marvi, Alansary, Marwa, Alnuwayli, Marwa, Khalifa, Marwa, Bahnacy, Marwan, Iqreewi, Marway, Nnabagulanyi, Mary, Saleem, Marya, Alhimyar, Maryam, Kamal, Maryam, Masood, Maryam, Razaq, Maryam, Siddique, Maryam, Lazo Ramírez, Maryzela, Edeeb, Masaoud, Solkar, Maseera, Adetoyi, Mashood, Adeniyi, Caccetta, Massimiliano, Casati, Massimiliano, Veroux, Massimiliano, Dugo, Massimo, Bevanda, Mateo, Santillan, Mateo, Mietła, Mateusz, Pryt, Mateusz, Muir, Mathew, Bei, Matilda, McGuinness, Matt, Aulicino, Matteo, Castrovillari, Matteo, Filardo, Matteo, Palmeri, Matteo, Pisano, Matteo, Rottoli, Matteo, Gjoka, Mattheus, Binks, Matthew, Corcos, Matthew, Kwon, Matthew, Maybury, Matthew, Sammut, Matthew, Tutton, Matthew, Watson, Matthew, Biebl, Matthias, Janda, Matthias, Maak, Matthias, Wichmann, Matthias, Berselli, Mattia, Gritti, Mattia, Molteni, Mattia, Pacilli, Maurizio, Favoriti, Mauro, Garino, Mauro, Podda, Mauro, Santarelli, Mauro, Alfaid, Mawada, Albert, Mawadri Jimmy Isaac, Oyortey, Mawuenyo Attawa, Enßlin, Max, Berlet, Maximilian, Brunner, Maximilian, Kießler, Maximilian, Scheiterle, Maximilian, Sohn, Maximilian, Busch, Maximiliane, Perkins, Maya, Sabboh, Maya, Badkur, Mayank, Okudero, Mayowa, Mohamedshafee, Maysoon, Al-Juaifari, Maytham Aqeel, Elesseily, Mazen, Mohammad, Mazen, Nikberg, Maziar, Jean Claude, Mbonicura, Şermet, Medeni, Chisthi, Meer, Luthra, Meera, Sulciner, Megan, Kaple, Meghali, Bhat, Meghana, Koç, Mehmet Ali, Avci, Mehmet Alperen, Kizilkaya, Mehmet Celal, Kara, Mehmet Emin, Ozcelik, Mehmet Faik, Ozsoy, Mehmet Sait, Zaigham, Mehreen, Mesfin, Mekdelawit, Kalender, Mekselina, Mellado Tellez, Mel Paul, González De Miguel, Melania, Farrugia, Melanie, Theodoridou, Melanie, Barcelona, Melden Darrell, Aktas, Melik Kagan, Fernández Zurita, Melissa Andrea, Bautista, Melissa, Park, Melissa, Rodríguez-Ordoñez, Melissa-Zohemia, Yashar, Meltem, Worku, Mengistu, Al-Fraijat, Meran, Estaire Gómez, Mercedes, Shoshkova, Meri Danailova, Kruščica, Merima, Tanal, Mert, Çuhadar, Merve, Tokocin, Merve, Agyapong, Meshach Manu, Erkaya, Metincan, Okechukwu, Mgbako, Adinku, Michael, Bickford, Michael, Ediale, Michael, Issa, Michael, Konstantinidis, Michael K., Lie, Michael, M. Nathan, Michael, Maclean, Michael, Nortey, Michael, Pellen, Michael, Pham, Michael, Pitiakoudis, Michael, Singh, Michael Prodip, Spartalis, Michael, Van Der Colf, Michael, Wilson, Michael, Billis, Michail, Karanikas, Michail, Papamichail, Michail, Vailas, Michail, Kisielewski, Michał, Kusiński, Michał, Nycz, Michał, Święch, Michał, Fagbayimu, Micheal, Adamina, Michel, Fiogbe, Michel, Prudhomme, Michel, Angelucci, Michela, Campanelli, Michela, Cricrì, Michele, Grande, Michele, Manigrasso, Michele, Scopelliti, Michele, George, Michelle, Mozel, Michelle, Galasyuk, Mick, Chevallay, Mickael, Giuliano, Micol, Dobón Rascón, Miguel Ángel, Fernandez Romero, Miguel Angel, García Ureña, Miguel Ángel, Zaragoza Mendieta, Miguel Ángel, Bejarano Serrano, Miguel, Brito, Miguel, Calderon, Miguel, Jurado Román, Miguel, Palas, Miguel, Li Valencia, Miguel Roberto, Sebastián-Mendoza, Miguel-Julián, Muresan, Mihai-Stefan, Slavchev, Mihail, Agapov, Mikhail, Milentijević, Milica, Catterall, Millie, Stjepanovic, Milos, Doss, Mina, Natey, Mina, Aikins, Minas, Mattis, Minca, Ekanayake, Minoli Tharushika, Tora, Mir Mehrab Sharmin, Alsharayri, Mira, Shalaby, Mira, Boras, Miran, Riba Martínez, Mireia, Mogiatti, Mirella, Salibašić, Mirhan, Biancu, Miriam, Cappiello, Miriam, Koschke, Miriam Luise, Sadek, Mirna, Szura, Miroslaw, Horga, Miruna, Dason, Mishael, Tayyab, Mishal, Imirski, Mitko, Pejović, Mladen, Tabaza, Mo'Ath, Alneasan, Moaaz, Shalkamy, Moamen, Issa, Moammad Issa, Erfan, Moatasem, Ewedah, Moataz, Srour, Moayad, Beletachew, Moges, Abbas, Mohamad, Farho, Mohamad Ali, Marawy, Mohamad Kher, Klib, Mohamad, Morjan, Mohamad, Sawas, Mohamad Nabhan, Elghriani, Mohamed Abdelbaset Masoud, Abdelmaboud, Mohamed, Abdelmaksoud, Mohamed, Abdullahi, Mohamed, Abo Abdo, Mohamed, Ahmed, Mohamed, Ahmoud, Mohamed, Al Sayed, Mohamed, Suliman, Mohamed Almouafaq, Alsori, Mohamed, Altajouri, Mohamed, Assalhi, Mohamed, Berrakkouch, Mohamed, Elnour, Mohamed, Abdelsalam, Mohamed Elsayed Elaraby Abdelrahim, Elsayed Metwally, Mohamed, Hajalamin, Mohamed, Kamal Matter, Mohamed, Khaled, Mohamed, Kouta, Mohamed, Masoud, Mohamed, Almihashhish, Mohamed Miftah, Mokhtar, Mohamed, Mostafa, Mohamed, Yassin, Mohamed Musa, Bouhafs, Mohamed Nedjmeddine, Herdan, Mohamed Omar, Omer, Mohamed, Osman, Mohamed, Zaazou, Mohamed, Zahed Abdalla, Mohamed, Melouane, Mohamed-Sami, Abed, Mohammad, Sawaftah, Mohammad Ahmad Mohammad, Al-Masri, Mohammad, Al-Qannas, Mohammad, Ali-Hassanzadeh, Mohammad, Yunus, Mohammad Alif, Aloulou, Mohammad, Alrantisi, Mohammad, Araydah, Mohammad, Bani Hani, Mohammad, Barakat, Mohammad, Durrani, Mohammad, Khan, Mohammad Faisal, Dwikat, Mohammad Fuad, Hasan, Mohammad, Nabhan, Mohammad Hassan, Hijazi, Mohammad, Abu Albahrain, Mohammad Karam, Barakat, Mohammad Mounzer, Mubarak, Mohammad, Omar, Mohammad, Pourfridoni, Mohammad, Mslmani, Mohammad Rabee, Salah, Mohammad, Tanashat, Mohammad, Yahmad, Mohammad, Elmujtaba, Mohammed, Abd Al-Fattah, Mohammed, Abdelkabir, Mohammed, Abosedra, Mohammed, Sarker, Mohammed Abu Kawsar, Abudabbous, Mohammed, Abuwarda, Mohammed, Elgak, Mohammed Ahmed, Al Hinai, Mohammed, Al Zebda, Mohammed, Al-Shehari, Mohammed, Alharthi, Mohammed, Ali, Mohammed, Ismael Alamin, Mohammed Ali, Alqadasi, Mohammed, Alshanwani, Mohammed, Alshehri, Mohammed, Asaad, Mohammed, Bashir, Mohammed, Bohlala, Mohammed, Meghaizerou, Mohammed El Amine, Albanna, Mohammed Emad Hashem, Ghunaim, Mohammed, Goga, Mohammed, Hajhamad, Mohammed, Hamada Takrouney, Mohammed, Hammoda, Mohammed, Hamoud Almahly, Mohammed, Muragi, Mohammed, Mohammed, Mohammed Mustafa Hassan, Obaid, Mohammed, Omar, Mohammed, Said, Mohammed, Salah, Mohammed, Saleh Khatab, Mohammed, Aliyu, Mohammed Salele, Shakhshir, Mohammed, Sheriff, Mohammed, Tageldin, Mohammed, Abdelfattah, Mohanad, Daher, Mohanad, Lotfy, Mohanad, Alqedrh, Mohannad, Nasani, Mohannad, Mohd Shah, Mohd Shahrulsalam, Madany, Mohie El-Din Mostafa, Mobolaji-Ojibara, Mojisola, Nkogatse, Moleseng, Alazabi, Mona, Gupta, Money, Bogdan, Monica, Guerrero, Monica, Noguez Castillo, Monica, Gureh, Monika, Kacem, Montasser Jamaleddine, Tarar, Moosa Feroze, Seto, Morel, Steinruecke, Moritz, Edena, Morrison, Abdulsalam, Moruf, Abdelhadi Suliman Adam, Mosab, Saleh, Mosab, Anati, Mosaikah, Dokurugu, Moses Abdulai, Agunloye, Moses, Ibadin, Moses, Momoh, Moses, Abdelkarim, Mostafa, Kamar, Mostafa, Kotb, Mostafa, Mahran, Mostafa, Mohamad Amin, Mostafa, Shalaby, Mostafa, Waseem, Mostafa, Almaletti, Motasem, Saifi, Motaz, Tsuruta, Motohisa, Agbadebo, Mouhamed, Arnaout, Moumen, Ojo, Moyosoluwa, Sharma, Mrthyunjayani, Azzuz, Muad Mohamed Ramadan, Haj Hussein, Mudar, Ameen, Muftiat Omolola, Nkoronko, Mugisha, Fitri, Muhamad, Abdullah, Muhammad, Bashir, Muhammad Ahmad, Sadiq, Muhammad Ahmad, Ahmed, Muhammad, Ali, Muhammad, Al-Yusuf, Muhammad Ali, Haider, Muhammad Aliyan, Alshaar, Muhammad, Mirza, Muhammad Bilal, Che Yaacob, Muhammad, Daniyan, Muhammad, Elzayat, Muhammad, Butt, Muhammad Faizan, Sadiq, Muhammad Hamza, Chishti, Muhammad Haris, Ismail, Muhammad Izzat Shufphi, Kashif, Muhammad, Khalid, Muhammad, Mansoor Iqbal, Muhammad, Bin Khalid, Muhammad Moaz, Rafique, Muhammad Nouman, Raheem, Muhammad, Khalid, Muhammad Saud, Usama, Muhammad, Usman Malik, Muhammad, Saqib, Muhammad Waleed, Elhadi, Muhammed, Ateş, Muhammed Ikbal, Demirpolat, Muhammed Taha, Ergenç, Muhammer, Bello, Muideen, Kulimbet, Mukhtar, Nyirenda, Mulinda, Taeme, Mulugeta, Denini, Muna, Aziz, Munazzah, Yahaya, Mundashiru, Eugene, Muneza, Amadu, Munira, Chauhan, Munish, Acar, Murat, Teressa, Murti, Dauphin, Muryango, Arafat, Musa, Althomali, Musab, Rayzah, Musaed, Husain, Musharraf, Tolani, Musliu Adetola, Abu Jayyab, Mustafa, Al-Attraqchi, Mustafa, Kara, Mustafa Alper, Seker, Mustafa Ege, Gün, Mustafa, Hamed, Mustafa, Mehanny, Mustafa, Oncel, Mustafa, Sabri Massadi, Mustafa, Uyanik, Mustafa Safa, Uzunoglu, Mustafa Yener, Abdullahi, Mustapha, Saidani, Mustapha, Nofal, Mutaz, San, Myat, Ishak, Myrna, Keramida, Myrto D., Nik Burhanuddin, N.M. Syahrin, Talat, Nabila, Shehata, Nada, Alasbahi, Nadeen, Marcos, Nádia, Al Amri, Nadiah, Absy, Nadine, Parker, Naeelah, Lindi, Naeimah, Omer, Nafisa, Papadogianni, Nafsika, Rawal, Nagendra, Ismaiel, Nahar, Ben Hasan, Najat, Albahloul, Najla, Gokhare Viswanath, Nakul, Srinivas, Namitha, Christian, Nana Ama, Obuobi, Nana Kwame Asare, Beharry, Naomi, Yousefzadeh Kandevani, Naser, Alnamari, Nashwan, Oyelowo, Nasir, Oloko, Nasirudeen, Alzerwi, Nasser, Mosbah, Nasser, Shulman, Nastassia, Andrade, Natacha, Boumas, Natacha, Cobeño Tamayo, Natalia, Barrera Lopez, Natalia Guadalupe, Palamara, Natalia, Blencowe, Natalie, Marzouqa, Natalie, Blencowe, Natalie S, Salgadoe, Natalie, Techapongsatorn, Nathakorn, Paez, Nathaly, Ekwo, Nathan, Saini, Navdeep, Malik, Naveed Akhtar, Sharma, Naveen, Kumar, Navin, Chaudhry, Navneet Kumar, Karimbocus, Nawaaz, Rasool, Nazeerah, Lodhi, Nazia, Kavak, Nazli, Ildephonse, Ndayiragije, Diomede, Nduwimana, Bayleyegn, Nebiyou S., S.Bayleyegn, Nebiyou, Jović, Nediljko, Mosleh, Neebal, Aruldas, Neerav, Kapur, Neeti, Paul Sigamony, Negine Paul, Karthikeyan, Neha, Tabatabaian, Nelia, Affram, Nelson, Agboadoh, Nelson, Tran, Nelson, Lalovic, Nenad, Agugua-Obianyo, Nene, Kullab, Nermen, Lahmer, Nesrin, Raiq, Neven, Soldo, Neven, Spiteri, Neville, Chen, Nevin, Al Rabadi, Nezar, Christian, Ngarambe, Cabrel, Ngongang Ngansop, Leonelle Lore, Nguefack Noudem, Furbetta, Niccolò, Ensor, Nicholas, Tsantikos, Nick, Seeger, Nico, Cillara, Nicola, Eardley, Nicola, Petrarota, Nicola, Tartaglia, Nicola, Feijoo, Nicolas, Finocchiaro, Nicole, Koter, Nicole, De Santis, Nicolò, Fabbri, Nicolò, Schiavon, Nicolò, Tamini, Nicolò, Sharma, Nidhi, Sánchez Fuentes, Nieves, Solomon, Nigus, Bugdayci, Nihat, Lekiashvili, Nika, Gupta, Nikhil, Narain, Nikhil, Uttam, Nikita, Kłos, Nikola, Ververidis, Nikolaos Adrianos, Arkadopoulos, Nikolaos, Dimitrokallis, Nikolaos, Garmpis, Nikolaos, Gouvas, Nikolaos, Kalampokis, Nikolaos, Kopanakis, Nikolaos, Kydonakis, Nikolaos, Machairas, Nikolaos, Memos, Nikolaos, Michalopoulos, Nikolaos, Mathioudakis, Nikolaos Nestor, Roukounakis, Nikolaos, Stamos, Nikolaos, Tasis, Nikolaos, Tsakiridis, Nikolaos, Vlachakos, Nikolaos, Zampitis, Nikolaos, Kouzakos, Nikolas, Börner, Nikolaus, Dimitriou, Nikoletta, Dafnios, Nikos, Banerjee, Niladri, Klammer, Nils, Maharjan, Nima, Rabai, Nimah, Oscar, Nimpaye, Harrison, Nina, Kheyrbek, Nina, Kansakar, Nirajan, Bhat, Nisar, Krishnappa, Nischal, Pahuja, Nisha, Paul Ambrose, Nitin, Renovat, Niyongabo, Musawa, Nizar, Nwanne, Njideka, Steve, Nkurunziza, Chidumije, Nnaemeka, Nwafulume, Nnaemeka, Celestine, Nnamani, Duru, Nnamdi, López Bernal, Noelia Esthela, Laquatra, Noemi, Pasqua, Noemi, Awwad, Noor Al-Deen, Ali, Nora, Hielscher, Nora, Trabulsi, Nora, Hüser, Norbert, Muñoz Montes, Norberto, Eshak, Norma-Sol, Rajab, Nosaiba, Lahnaoui, Nouhaila, Ouachou, Nouhaila, Ali, Nour, Dadoush, Nour, Fazili, Noureen, Theophile, Ntahompagaze, Ntanzi, Ntuthuko, Bibila, Nubia Samuel, Alwaer, Nuhay, Naabo, 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Ismail, Srraa, Zourntou, Stamatina-Emmanouela, Nwabuoku, Stanley Emeka, Ideh, Stanley, Charitonos, Stavria, Liapis, Stavros Chrysovalantis, Faber, Stefan, Piccioni, Stefania Angela, Romano, Stefania, Silvestre, Stefania, Jarmusch, Stefanie, Soelling, Stefanie, Cardelli, Stefano, Chaifouroosh Mamagany, Stefano, Corso, Stefano, Basso, Stefano Maria Massimiliano, Megna, Stefano, Bitsianis, Stefanos, Tsatsos, Stefanos, Morales, Steffanía, Gerdes, Stephan, Göller, Stephanie, Hanna, Stephanie, Tabiri, Stephen, Agana, Stephen, Theivendran, Stephen Arthur, Kache, Stephen, Knight, Stephen R, Smith, Stephen, Stonelake, Stephen, Wrenn, Stephen, Gopaul, Steven, Vederaki, Styliani, Kapiris, Stylianos, Kykalos, Stylianos, Hlaing, Su Su, Chia, Su Yin Denise, Abu Khousa, Suad, M. Udwan, Suad, Raul, Subrat, Chase, Suchita, Nair, Sudharrshan, Joseph, Sudhir, Singh, Sudhir Kumar, Singh, Sudhir, Timmalah, Suhaylah, Alsuwiyah, Suhir, Thalgaspitiya, Sujeewa Priyantha Bandara, Sellahewa, Sujeewa, Neogi, Sujoy, Burger, Sule, Polat, Süleyman, Alkadi, Sulman, Asiri, Sultan, Otho, Sumaira, Mewa Kinoo, Suman, Ebrahim, Sumayyah, Tontus, Sumeyya, Midya, Sumit, Thuraisamy Sarma, Sundaramoorthy Iyer, Payyanur Thotan, Sundeep, Kumar Venkatappa, Sunil, Techapongsatorn, Suphakarn, Kaur, Supreet, Jp, Supritha, Tan, Suqi, Djote, Surafel Mulatu, Surendran, Suraj, Singh, Surendra, Chilaka, Suresh, Lee, Susan, Henriques, Susana, Giridaran, Suseendirarajah, Achugatla, Sushma, Kumar, Suvam, Brincat, Svetlana, Deshpande, Swati, Batista, Sylvia, Akharaekpanya, Tachporn, Moges, Tadesse Girma, Türkmen, Taha Berkay, Alhaj Hasan, Taima, Alshawabkeh, Taima', Majed, Taima', Lawal, Taiwo Akeem, Ibiyeye, Taiye Taibat, Rashid, Tajamul, Košir Božič, Tajda, Mohammed, Tajudeen, Khewater, Talal, Abelman, Talia, Al Barhi, Tamadher, Mogne, Tamiris, Salazar-Lorenzana, Tania Abril, Akter, Tania, Gómez Sanz, Tania, Motiwala, Tanmay, Valizadeh Elizeh, Tanya, Almahdi, Tanzeel, Albushary, Tanzel Alrahman, Abdulrahman, Taofeeq, Finlay, Tara, Chouari, Tarak, Awad, Tarek, Sabra, Tarek, M. Abubasheer, Tareq, Alhusban, Tariq, Yılmaz, Tarık Emre, Aloqaili, Tasneem, Aldirawi, Tayef, Karahasanoğlu, Tayfun, Gülşen, Taygun, Jemal, Tebarek, Nadeem, Tehmina, Singh, Tejinder Paul, Alinger, Temsula, Longkumer, Temsutoshi, Lo, Terence, Gimenez Maurel, Teresa, Uprak, Tevfik Kıvılcım, Petropoulou, Thalia, Rawther, Thampi, Techapongsatorn, Thawin, Manickam, Thendral, Padioti, Theodora, Kozonis, Theodoros, Sidiropoulos, Theodoros, Tsirlis, Theodoros, Theodosopoulos, Theodosios, Hessou, Thierry Kinnongni, Uiyapat, Thitapon, Agyen, Thomas, Harris, Thomas, Hugh, Thomas J, Jorgensen, Thomas W., Theivendrampillai, Thrshegan, Pham, Thuy Vy, Branco, Tiago, Nagwamutse, Tijjani Nasiru, Tony V, Tina, Devabalan Koil, Titus, Pilia, Tiziana, Abdulmola, Tmador Mahmed Ali, Oyeyemi, Tobiloba, Kebede, Tolera, Ibarra-Hurtado, Tomás Ramón, Reichelt, Tomas, Gach, Tomasz, Stefura, Tomasz, Wojewoda, Tomasz, Soric, Tomislav, Risteski, Toni, Tridip, Tridip Dutta Baruah, Yotsov, Tsanko, Doma Bhutia, Tshering, Girma, Tsion, Kwan, Tsun Yu, Ivanov, Tsvetomir, Iahmo, TT, Ashkar, Tuleen, Mabogoane, Tumi, Olobatoke, Tunde, Sholadoye, Tunde T., Mishra, Tushar Subhadarshan, Parker, Tyla, Ezomike, Uchechukwu, Dilibe, Uchenna, Kumbhar, Uday, Una, Uduak-Abasi, Grossi, Ugo, Ezidiegwu, Ugochukwu, Edith, Ugwuoke, Daspal, Ujjwal, Kalu, Ukoha Agwu, Dietz, Ulrich, Ronellenfitsch, Ulrich, Wirth, Ulrich, Abdullah, Umar, Rivolta, Umberto, Jayarajah, Umesh, Kanyan Kassim, Umu-Hera, Govindan, Unnikrishnan, Fernandes, Urânia, Akram, Usman, Mohammed Bello, Usman, Omara, Usra, Naidoo, Uvarasen, Mahendravarman, Vaishnaivy, Negoita, Valentina Madalina, Mariottini, Valentina, Murzi, Valentina, Zucchini, Valentina, Agbakwuru, Valentine, Armenta Tapia, Valeria Estefania, Gentilino, Valerio, Borgogni, Vanessa, Ng, Vanessa, Wolfschluckner, Vanessa, Silvestri, Vania, Žufić, Vanja, Gunasaegaram, Varman, Kamalathevan, Vasan, Pillay, Vasanthan, Neykov, Vasil, Kalles, Vasileios, Mousafeiris, Vasileios, Papadopoulos, Vasileios, Tsaousis, Vasileios, Tselepidis, Vasileios, Vougas, Vasileios, Georgilaki, Vasiliki, Nikolaou, Vasiliki, Themelidi, Vasiliki, Manchev, Vassil, Ioannou, Veatriki, Jha, Vedant Shekhar, Dragisic, Vedran, Sutharshan, Vengadasalam, Alonso, Veronica, Brocco, Veronica, Ongil Rodríguez, Verónica, Schirinzi, Veronica, Budyakova, Veronika, Naunova, Vesna, Panduro-Correa, Vicky, Ruiz López, Víctor Andrés, Bill, Victor, Dassah, Victor, Gonzabay, Víctor Eduardo, Etwire, Victor, Modekwe, Victor, Serna-Alarcon, Victor, De Donato, Victor Ugo, Duque Mallén, Victoria, Liu, Victoria, Sharma, Vidushi, Kumar, Vijaya, Muralidharan, Vijayaragavan, Kakotkin, Viktor, Enemuo, Vincent, Kudoh, Vincent, Osoka, Vincent, Siepaal, Vincent, Granata, Vincenza, Dinuzzi, Vincenza Paola, Lizzi, Vincenzo, Ricchiuti, Vincenzo, Mosca, Vinicio, Kanna D, Vinoth, Nair, Vipin Venugopal, Ledda, Virginia, Michael, Vishal, Singh, Vishavdeep, Govindasamy, Vishendran, Kollengode, Viswanathan V, Giordano, Vittoria, Morinelli, Vittoria, Lin, Vivian, Cuk, Vladica, Cijan, Vladimir, Çalik, Volkan, Bakshi, Vrushank, Ten, Vyacheslav, Ratheesh, Vysakh, Alsado, Wael, Mayo, Wafik, Yang, Wah, Rehman, Wajeeh Ur, Abbas, Walaa, Al-Khyatt, Waleed, Mabood, Waleed, Santucci, Walter, Yew, Wan Ting, Tahir, Warda, De Silva, Warusha Dhammika Dulantha, Wijenayake, Wasantha, Almadhoun, Waseem J.N., Alhroub, Wasef, Ebrahim, Wesam, Jabłoński, Wiktor, Krawczyk, Wiktor, Bhatti, William, Lai, William Kwan Ho, Petrushnko, Wilson, Afedo, Wisdom, Kasetsermwiriya, Wisit, Serednicki, Wojciech, Wysocki, Wojciech, Zerefa, Wondwossen Amtataw, Vagena, Xanthoula, Tarrado, Xavier, Papazarkadas, Xenofon, Po, Xiang Yuen, Ouadi, Yacine, Myla, Yacob, Haido, Yacoub, Hasan, Yafea, Karataş, Yağmur, Carpio Colmenares, Yahaira, Galvañ Félix, Yaiza, Balogun, Yakub Lekan, Maktabi, Yaman, Nerabani, Yaman, Fulgence, Yamuremye, De Silva, Yannick, Asar, Yara, Samer Morsy, Yara, Ghaleb, Yasameen Saif Saeed Mohammed, Vashishth, Yash, M S, Yashas, Manickchund, Yashoda, Sakaray, Yashwant, Kara, Yasin, Tosun, Yasin, Zwain, Yasir, Farag, Yasmeen, Jedidi, Yasmine, Seada, Yasmine, Aldebasi, Yasser, Ashour, Yasser, Badr, Yassin, Sarpong, Yaw, Alawneh, Yazan, Aung, Yee Lay, Orabi, Yehia, Lam, Yick Ho, Şen, Yigit Kaan, Koyluoglu, Yilmaz Onat, Huang, Yining, Mintz, Yoav, Perez, Yocarli, Chamara, Yohan, Melkamu, Yohannes, Maestre González, Yolanda, Dean, Yomna, Metaferia, Yonas Yilma, Malki, Yosra, Mohamed, Younis, A. Omar, Yousef, Alhammoud, Yousef, Allaoua, Yousef, Tanas, Yousef, El Okazy, Youssef, Kerolous, Youssef, Mohamed, Youssef, Al-Junaidi, Youssuf, Lakdawala, Yousuf, Lu, Yuann, Asano, Yukio, Altinel, Yuksel, Aktimur, Yunus Emre, Fishman, Yuri, Kudryavcev, Yurii, Tandoğan, Yusuf Iskender, Razafimandimby, Yves Michaël, Adofo-Asamoah, Yvonne, Şenol, Zafer, Odeh, Zahra, Benamrouche, Zahra Rayane, Alkhaier, Zahraa, Al-Sheikh Ali, Zaid, Zaher, Zain Aldin, Alnajjar, Zaina, Al-Balushi, Zainab, Alkhuzaie, Zainab, Khraim, Zakaria, Toutounji, Zakaria, Djama, Zakarya, Farraj, Zamil, Shah, Zamir Ahmad, Khan, Zara, Khan, Zarnigar, Abo Alarous, Zayn Alabden, Asaad, Zaynab, Demetrashvili, Zaza, Lorenc, Zbigniew, Brekalo, Zdrinko, Hannouneh, Zein Alabdin, Robertson, Zelda, Bady, Zeyad, Durna, Zeynep, Wong, Zhen Yu, Fozo, Zien Alabdin, Klib, Zienab, Chin, Ziling, Tamou, Zime Farid, Johnson, Zina, Shabello, Ziyad, Orzeszko, Zofia, Aljalabi, Zohal, Al-Azher El-Hamel, Zohour, Karampotaki, Zoi Rafaila, Valera Sanchez, Zoraida, Matkovic, Zoran, Azhar, Zubair, Qureshi, Zubaria, Correa López, Zulema Margarita, Balciscueta, Zutoia, Musilová, Zuzana, Katusic, Zvonimir, Osama, Zyad, and Dauksa, Zygimantas
Published: 2024
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38. Parasitoid-induced changes in metabolic rate and feeding activity of the emerald ash borer (Coleoptera: Buprestidae): implications for biological control

Author: Dang, Ying-Qiao, Duan, Jian J., and Li, Andrew Y.
Published: 2023
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39. Learning Treatment Effects in Panels with General Intervention Patterns

Author: Farias, Vivek F., Li, Andrew A., and Peng, Tianyi
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Economics - Econometrics
Abstract: The problem of causal inference with panel data is a central econometric question. The following is a fundamental version of this problem: Let $M^*$ be a low rank matrix and $E$ be a zero-mean noise matrix. For a `treatment' matrix $Z$ with entries in $\{0,1\}$ we observe the matrix $O$ with entries $O_{ij} := M^*_{ij} + E_{ij} + \mathcal{T}_{ij} Z_{ij}$ where $\mathcal{T}_{ij} $ are unknown, heterogenous treatment effects. The problem requires we estimate the average treatment effect $\tau^* := \sum_{ij} \mathcal{T}_{ij} Z_{ij} / \sum_{ij} Z_{ij}$. The synthetic control paradigm provides an approach to estimating $\tau^*$ when $Z$ places support on a single row. This paper extends that framework to allow rate-optimal recovery of $\tau^*$ for general $Z$, thus broadly expanding its applicability. Our guarantees are the first of their type in this general setting. Computational experiments on synthetic and real-world data show a substantial advantage over competing estimators.
Published: 2021

40. In Vitro, In Vivo and Ex Vivo Models for Peripheral Nerve Injury and Regeneration

Author: Li, Andrew, Pereira, Clifford, Hill, Elise Eleanor, Vukcevic, Olivia, and Wang, Aijun
Subjects: Biomedical and Clinical Sciences, Neurosciences, Physical Injury - Accidents and Adverse Effects, Peripheral Neuropathy, Regenerative Medicine, Neurodegenerative, Injuries and accidents, Neurological, Humans, Nerve Regeneration, Peripheral Nerve Injuries, Ex vivo models, in vitro models, in vivo models, organotypic models, peripheral nerve, peripheral nerve injury, peripheral nerve regeneration, Pharmacology and Pharmaceutical Sciences, Psychology, Neurology & Neurosurgery, Pharmacology and pharmaceutical sciences, Biological psychology
Abstract: Peripheral Nerve Injuries (PNI) frequently occur secondary to traumatic injuries. Recovery from these injuries can be expectedly poor, especially in proximal injuries. In order to study and improve peripheral nerve regeneration, scientists rely on peripheral nerve models to identify and test therapeutic interventions. In this review, we discuss the best described and most commonly used peripheral nerve models that scientists have and continue to use to study peripheral nerve physiology and function.
Published: 2022

41. Greedy Approximation Algorithms for Active Sequential Hypothesis Testing

Author: Gan, Kyra, Jia, Su, and Li, Andrew
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $\delta>0$, the goal is to sequentially select the fewest number of actions so as to identify the true hypothesis with probability at least $1 - \delta$. Motivated by applications in which the number of hypotheses or actions is massive (e.g., genomics-based cancer detection), we propose efficient (greedy, in fact) algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Both of our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data, demonstrating that our algorithms outperform previously proposed heuristic policies by large margins.
Published: 2021

42. LTL2Action: Generalizing LTL Instructions for Multi-Task RL

Author: Vaezipoor, Pashootan, Li, Andrew, Icarte, Rodrigo Toro, and McIlraith, Sheila
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Robotics
Abstract: We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language -- linear temporal logic (LTL) -- and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments. Experiments on discrete and continuous domains target combinatorial task sets of up to $\sim10^{39}$ unique tasks and demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions., Comment: International Conference on Machine Learning (ICML) 2021
Published: 2021

43. Searching for Fast Model Families on Datacenter Accelerators

Author: Li, Sheng, Tan, Mingxing, Pang, Ruoming, Li, Andrew, Cheng, Liqun, Le, Quoc, and Jouppi, Norman P.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from previous compound scaling results. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than EfficientNet (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.
Published: 2021

44. Effect of adequacy of empirical antibiotic therapy for hospital-acquired bloodstream infections on intensive care unit patient prognosis: a causal inference approach using data from the Eurobact2 study

Author: Tabah, Alexis, Lipman, Jeffrey, Pollock, Hamish, Ben Margetts, Udy, Andrew, Young, Meredith, Bhadange, Neeraj, Tyler, Steven, Ledtischke, Anne, Finnis, Mackenzie, Dwivedi, Jyotsna, Saxena, Manoj, Biradar, Vishwanath, Soar, Natalie, Sarode, Vineet, Brewster, David, Regli, Adrian, Weeda, Elizabeth, Ahmed, Samiul, Fourie, Cheryl, Laupland, Kevin, Ramanan, Mahesh, Walsham, James, Meyer, Jason, Litton, Edward, Maria Palermo, Anna, Yap, Timothy, Eroglu, Ege, George Attokaran, Antony, Jaramillo, C'havala, Nafees, Khalid Mk, Nafees, Khalid Mahmood Khan, Aqilah Haji Abd Rashid, Nurhikmahtul, Adi Muhamad Ibnu Walid, Haji, Mon, Tomas, Dhakshina Moorthi, P., Sudhirchandra, Shah, Sridharan, Dhadappa Damodar, Haibo, Qiu, Xie, Jianfeng, Jianfeng, Xie, Wei-Hua, Lu, Zhen, Wang, Qian, Chuanyun, Luo, Jili, Chen, Xiaomei, Wang, Hao, Zhao, Peng, Zhao, Juan, Wusi, Qiu, Mingmin, Chen, Xu, Lei, Yin, Chengfen, Wang, Ruilan, Wang, Jinfeng, Yin, Yongjie, Zhang, Min, Ye, Jilu, Hu, Chungfang, Zhou, Suming, Huang, Min, Yan, Jing, Wang, Yan, Qin, Bingyu, Ye, Ling, Weifeng, Xie, Peije, Li, Geng, Nan, Ling, Lowell, Hayashi, Yoshiro, Karumai, Toshiyuki, Yamasaki, Masaki, Hashimoto, Satoru, Hosokawa, Koji, Makino, Jun, Matsuyoshi, Takeo, Kuriyama, Akira, Shigemitsu, Hidenobu, Mishima, Yuka, Nagashima, Michio, Yoshida, Hideki, Fujitani, Shigeki, Omori, Koichiro, Rinka, Hiroshi, Saito, Hiroki, Atobe, Kaori, Kato, Hideaki, Takaki, Shunsuke, Sulaiman, Helmi, Shahnaz Hasan, M., Fadhil Hadi Jamaluddin, Muhamad, Pheng, Lee See, Visvalingam, Sheshendrasurian, Thing Liew, Mun, Ling Danny Wong, Siong, Khang Fong, Kean, Bt Abdul Rahman, Hamizah, Md Noor, Zuraini, Lee, Kok Tong, Hamid Azman, Abd., Zulfakar Mazlan, Mohd, Ali, Saedah, Hernandez, Aaron Mark, Abello, Anton, Jeon, Kyeongman, Lee, Sang-Min, Park, Sunghoon, Park, Seung Yong, Yoon Lim, Sung, Kwa, Andrea Lay Hoon, Yuan Goh, Qing, Ng, Shin Yi, An Lie, Sui, Junyang Goh, Ken, Yunkai Li, Andrew, Ong, Caroline Yu Ming, Yan Lim, Jia, Lishan Quah, Jessica, Ng, Kangqi, Xiang Long Ng, Louis, Yeh, Tony Yu-Chang, Chang Yeh, Yu, Chou, Nai-Kuan, Cia, Cong-Tat, Hu, Ting-Yu, Kuo, Li-Kuo, Ku, Shih-Chi, Wongsurakiat, Phunsup, Apichatbutr, Yutthana, Chiewroongroj, Supattra, Alsisi, Adel, Nadeem, Rashid, El Houfi, Ashraf, Elhadidy, Amr, Barsoum, Mina, Osman, Nermin, Mostafa, Tarek, Elbahnasawy, Mohamed, Saber, Ahmed, Aldhalia, Amer, Elmandouh, Omar, Elsayed, Ahmed, Elbadawy, Merihan A., Awad, Ahmed K., Hemead, Hanan M., Zand, Farid, Ouhadian, Maryam, Hamid Borsi, Seyed, Mehraban, Zahra, Kashipazha, Davood, Ahmadi, Fatemeh, Savaie, Mohsen, Soltani, Farhad, Rashidi, Mahboobeh, Baghbanian, Reza, Javaherforoosh, Fatemeh, Amiri, Fereshteh, Kiani, Arash, Amin Zargar, Mohammad, Mahmoodpoor, Ata, Aalinezhad, Fatemeh, Dabiri, Gholamreza, Sabetian, Golnar, Sarshad, Hakimeh, Masjedi, Mansoor, Tajvidi, Ramin, Nasirodin (S.M.N.) Tabatabaei, Dr Seyed Mohammad, Ahmed, Abdullah Khudhur, Singer, Pierre, Kagan, Ilya, Rigler, Merav, Belman, Daniel, Levin, Phillip, Harara, Belal, Diab, Adei, Abillama, Fayez, Abilama, Fayez, Ibrahim, Rebecca, Fares, Aya, Elhadi, Muhammed, Buimsaedah, Ahmad, Gamra, Marwa, Aqeelah, Ahmed, Ali Mohammed Ali, Almajdoub, Gaber Sadik Homaidan, Ahmed, Almiqlash, Bushray, Bilkhayr, Hala, Bouhuwaish, Ahmad, Sa Taher, Ahmed, Abdulwahed, Eman, Abousnina, Fathi A., Khaled Hdada, Aisha, Jobran, Rania, Ben Hasan, Hayat, Shaban Ben Hasan, Rabab, Khalid Abidi, Serghini, Issam, Seddiki, Rachid, Boukatta, Brahim, Kanjaa, Nabil, Mouhssine, Doumiri, Ahmed Wajdi, Maazouzi, Dendane, Tarek, Ali Zeggwagh, Amine, Housni, Brahim, Younes, Oujidi, Hachimi, Abdelhamid, Ghannam, A., Belkhadir, Z., Abu Jayyab, Mustafa, Aithssain, Ali, Lance, Marcus, Nissar, Shaikh, Sallam, Hend, Elrabi, Omar, Almekhlafi, Ghaleb A., Awad, Maher, Aljabbary, Ahmed, Karam Chaaban, Mohammad, Abu-Sayf, Natalia, Al-Jadaan, Mohammad, Bakr, Lubna, Mounir Bouaziz, Bouaziz, Mounir, Turki, Olfa, Sellami, Walid, Vidal, Gabriela, Centeno, Pablo, Morvillo, Natalia, Oscar Acevedo, José, Mabel Lopez, Patricia, Fernández, Rubén, Segura, Matías, Aparicio, Marta, Alonzo, Irene, Nuccetelli, Yanina, Montefiore, Pablo, Arias, Mario, Felipe Reyes, Luis, Ñamendys-Silva, Silvio A., Romero-Gonzalez, Juan P., Hermosillo, Mariana, Alejandro Castillo, Roberto, Nicolás Pantoja Leal, Jesús, Garcia Aguilar, Candy, Ocotlan Gonzalez Herrera, Mara, Vladimir Espinoza Villafuerte, Missael, Lomeli-Teran, Manuel, Dominguez-Cherit, Jose G., Davalos-Alvarez, Adrian, Sánchez-Hurtado, Luis, Tejeda-Huezo, Brigitte, Perez-Nieto, Orlando R., Deloya Tomas, Ernesto, De Bus, Liesbet, De Waele, Jan, Francois, Guy, Hollevoet, Isabelle, Denys, Wouter, Bourgeois, Marc, Vanderhaeghen, Sofie F.M., Mesland, Jean-Baptiste, Henin, Pierre, Haentjens, Lionel, Biston, Patrick, Noel, Cindérella, Layos, Nathalie, Misset, Benoît, De Schryver, Nicolas, Serck, Nicolas, Wittebole, Xavier, De Waele, Elisabeth, Opdenacker, Godelive, Kovacevic, Pedja, Zlojutro, Biljana, Ina, Filipovic-Grcic, Custovic, Aida, Filipovic-Grcic, Ina, Radonic, Radovan, Vujaklija Brajkovic, Ana, Persec, Jasminka, Sakan, Sanja, Nikolic, Mario, Lasic, Hrvoje, Leone, Marc, Timsit, Jean-François, Ruppe, Etienne, Ruckly, Stephane, Montravers, Philippe, Arbelot, Charlotte, Patrier, Juliette, Zappela, N., Montravers, P., Dulac, Thierry, Castanera, Jérémy, Auchabie, Johann, Le Meur, Anthony, Marchalot, A., Beuzelin, M., Massri, Alexandre, Guesdon, Charlotte, Escudier, Etienne, Mateu, Philippe, Rosman, Jérémy, Leroy, Olivier, Alfandari, Serge, Nica, Alexandru, Souweine, Bertrand, Coupez, Elisabeth, Duburcq, Thibault, Kipnis, Eric, Bortolotti, Perrine, Le Souhaitier, Mathieu, Mira, Jean-Paul, Garcon, Pierre, Duprey, Matthieu, Thyrault, Martial, Paulet, Rémi, Philippart, François, Tran, Marc, Bruel, Cédric, Weiss, Emmanuel, Janny, Sylvie, Foucrier, Arnaud, Perrigault, Pierre-François, Djanikian, Flora, Barbier, François, Gainnier, Marc, Bourenne, Jérémy, Louis, Guillaume, Smonig, Roland, Argaud, Laurent, Baudry, Thomas, Mekonted Dessap, Armand, Razazi, Keyvan, Kalfon, Pierre, Badre, Gaëtan, Larcher, Romaric, Lefrant, Jean-Yves, Roger, Claire, Sarton, Benjamine, Silva, Stein, Demeret, Sophie, Le Guennec, Loïc, Siami, Shidasp, Aparicio, Christelle, Voiriot, Guillaume, Fartoukh, Muriel, Dahyot-Fizelier, Claire, Imzi, Nadia, Klouche, Kada, Bracht, Hendrik, Hoheisen, Sandra, Bloos, Frank, Thomas-Rueddel, Daniel, Petros, Sirak, Pasieka, Bastian, Dubler, Simon, Schmidt, Karsten, Gottschalk, Antje, Wempe, Carola, Lepper, Philippe, Metz, Carlos, Viderman, Dmitriy, Umbetzhanov, Yerlan, Mugazov, Miras, Bazhykayeva, Yelena, Kaligozhin, Zhannur, Babashev, Baurzhan, Merenkov, Yevgeniy, Temirov, Talgat, Arvaniti, Kostoula, Smyrniotis, Dimitrios, Psallida, Vasiliki, Fildisis, Georgios, Soulountsi, Vasiliki, Kaimakamis, Evangelos, Iasonidou, Cristina, Papoti, Sofia, Renta, Foteini, Vasileiou, Maria, Romanou, Vasiliki, Koutsoukou, Vasiliki, Kristina Matei, Mariana, Moldovan, Leora, Karaiskos, Ilias, Paskalis, Harry, Marmanidou, Kyriaki, Papanikolaou, M., Kampolis, C., Oikonomou, Marina, Kogkopoulos, Evangelos, Nikolaou, Charikleia, Sakkalis, Anastasios, Chatzis, Marinos, Georgopoulou, Maria, Efthymiou, Anna, Chantziara, Vasiliki, Sakagianni, Aikaterini, Athanasa (Athanassa), Zoi (Zoe), Papageorgiou, Eirini, Ali, Fadi, Dimopoulos, Georges, Panagiota Almiroudi, Mariota, Malliotakis, Polychronis, Marouli, Diamantina, Theodorou, Vasiliki, Retselas, Ioannis, Kouroulas, Vasilios, Papathanakos, Georgios, Bassetti, Matteo, Giacobbe, Daniele, Montrucchio, Giorgia, Sales, Gabriele, De Pascale, Gennaro, Maria Montini, Luca, Carelli, Simone, Vargas, Joel, Di Gravio, Valentina, Roberto Giacobbe, Daniele, Gratarola, Angelo, Porcile, Elisa, Mirabella, Michele, Daroui, Ivan, Lodi, Giovanni, Zuccaro, Francesco, Grazia Schlevenin, Maria, Pelosi, Paolo, Battaglini, Denise, Cortegiani, Andrea, Ippolito, Mariachiara, Bellina, Davide, Di Guardo, Andrea, Pelagalli, Lorella, Covotta, Marco, Rocco, Monica, Fiorelli, Silvia, Cotoia, Antonella, Chiara Rizzo, Anna, Adam Mikstacki, Mikstacki, Adam, Tamowicz, Barbara, Kaptur Komorowska, Irmina, Szczesniak, Anna, Bojko, Jozef, Kotkowska, Anna, Walczak-Wieteska, Paulina, Wasowska, Dominika, Nowakowski, Tomasz, Broda, Hanna, Mariusz Peichota, Assoc, Pietraszek-Grzywaczewska, Iwona, Martin-Loeches, Ignacio, Bisanti, Alessandra, Paiva, José Artur, Póvoa, Pedro, Cartoze, Nuno, Pereira, Tiago, Guimarães, Nádia, Alves, Madalena, Josefina Pinheiro Marques, Ana, Rios Pinto, Ana, Krystopchuk, Andriy, Teresa, Ana, Manuel Pereira de Figueiredo, António, Botelho, Isabel, Duarte, Tiago, Costa, Vasco, Pedro Cunha, Rui, Molinos, Elena, Tito da Costa, Ledo, Sara, Queiró, Joana, Pascoalinho, Dulce, Nunes, Cristina, Pedro Moura, José, Pereira, Énio, Carvalho Mendes, António, Valeanu, Liana, Bubenek-Turconi, Serban, Marina Grintescu, Ioana, Cobilinschi, Cristian, Carmen Filipescu, Daniela, Elena Predoi, Cornelia, Tomescu, Dana, Popescu, Mihai, Marcu, Alexandra, Grigoras, Ioana, Lungu, Olguta, Gritsan, Alexey, Anderzhanova, Anastasia, Meleshkina, Yulia, Magomedov, Marat, Zubareva, Nadezhda, Tribulev, Maksim, Gaigolnik, Denis, Eremenko, Aleksandr, Vistovskaya, Natala, Chukina, Maria, Belskiy, Vladislav, Furman, Mikhail, Ferrer Rocca, Ricard, Martinez, Maria, Casares, Vanessa, Mellado Artigas, Ricard, Vera, Paula, Flores, Matias, Amador Amerigo, Joaquin, Gracia Arnillas, Maria Pilar, Munoz Bermudez, Rosana, Armestar, Fernando, Catalan, Beatriz, Roig, Regina, Raguer, Laura, Dolores Quesada, María, Diaz Santos, Emilio, Gomà, Gemma, Ubeda, Alejandro, Salgado, Maria, Forcelledo Espina, Lorena, Garcia Prieto, Emilio, Asensio, Mj, Rodriguez, M., Maseda, Dr Emilio, Suarez De La Rica, Alejandro, Ignacio Ayestaran, J., Novo, Mariana, Blasco-Navalpotro, Miguel Angel, Orejas Gallego, Alberto, Sjovall, Fredrik, Sjövall, Fredrik, Spahic, Dzana, Johan Svensson, Carl, Haney, Michael, Edin, Alicia, Åkerlund, Joyce, De Geer, Lina, Prazak, Josef, Buetti, Niccolò, Jakob, Stephan, Pagani, Jl, Abed-Maillard, S., Akova, Murat, Tarık Aslan, Abdullah, Tarik Aslan, Abdullah, Timuroglu, Arif, Kocagoz, Sesin, Kusoglu, Hulya, Mehtap, Selcuk, Ceyhun, Solakoğlu, Altintas, Dr. Neriman Defne, Talan, Leyla, Kayaaslan, Bircan, Kaya Kalem, Ayşe, Kurt, Dr. Ibrahim, Telli, Murat, Ozturk, Barcin, Erol, Çiğdem, Dindar Demiray, Emine Kubra, Çolak, Sait, Akbas, Türkay, Dr. Kursat Gundogan, Sari, Ali, Agalar, Canan, Çolak, Onur, Baykam, Nurcan (N), Akdogan, Ozlem (O), Yilmaz, Mesut, Tunay, Burcu, Cakmak, Rumeysa, Saltoglu, Nese, Karaali, Ridvan, Iftihar Koksal, Firdevs Aksoy, Eroglu, Ahmet, Tolga Saracoglu, Kemal, Bilir, Yeliz, Guzeldag, Seda, Ersoz, Gulden, Evik, Guliz, Sungurtekin, Hulya, Ozgen, Cansu, Erdoğan, Cem, Gürbüz, Yunus, Altin, Nilgün, Bayindir, Yasar, Ersoy, Yasemin, Goksu, Senay, Akyol, Ahmet, Dr, Kartal, Batirel, Ayse, Cagan Aktas, Sabahat, Morris, Andrew Conway, Conway Morris, Andrew, Routledge, Matthew, Ercole, Ari, Antcliffe, David, Rojo, Roceld, Tizard, Kate, Faulkner, Maria, Cowton, Amanda, Kent, Melanie, Raj, Ashok, Zormpa, Artemis, Tinaslanidis, George, Khade, Reena, Torlinski, Tomasz, Mulhi, Randeep, Goyal, Shraddha, Bajaj, Manan, Soltan, Marina, Yonan, Aimee, Dolan, Rachael, Johnson, Aimee, Macfie, Caroline, Lennard, James, Templeton, Maie, Sousa Arias, Sonia, Franke, Uwe, Hugill, Keith, Angell, Hollie, Benjamin J Parcell, Cobb, Katherine, Cole, Stephen, Smith, Tim, Graham, Clive, Cerman, Jaroslav, Keegan, Allison, Ritzema, Jenny, Sanderson, Amanda, Roshdy, Ashraf, Szakmany, Tamas, Baumer, Tom, Longbottom, Rebecca, Hall, Daniel, Tatham, Kate, Loftus, S., Husain, A., Black, E., Jhanji, S., Rao Baikady, R., Mcguigan, Peter, Mckee, Rachel, Kannan, Santhana, Antrolikar, Supriya, Marsden, Nicholas, Della Torre, Valentina, Banach, Dorota, Zaki, Ahmed, Jackson, Matthew, Chikungwa, Moses, Attwood, Ben, Patel, Jamie, Rebecca E Tilley, Humphreys, Sally K., Jean Renaud, Paul, Sokhan, Anton, Burma, Yaroslava, Sligl, Wendy, Baig, Nadia, McCoshen, Lorena, Kutsogiannis, Demetrios J., Thompson, Patricia, Hewer, Tayne, Rabbani, Raihan, Huq, Shihan Mahmud Redwanul, Hasan, Rajib, Motiul Islam, Mohammad, Gurjar, Mohan, Baronia, Arvind, Kothari, Nikhil, Sharma, Ankur, Karmakar, Saurabh, Sharma, Priya, Nimbolkar, Janardan, Samdani, Pratit, Vaidyanathan, R., Ahmedi Rubina, Noor, Jain, Nikhilesh, Pahuja, Madhumati, Singh, Ritu, Shekhar, Saurav, Syed, Nabeel Muzaffar, Ozair, Ahmad, Sarwar Siddiqui, Suhail, Bose, Payel, Datta, Avijatri, Rathod, Darshana, Patel, Mayur, MK, Renuka, Sailaja, K Baby, Dsilva, Carol, Chandran, Jagadish, Ghosh, Pralay, Mukherjee, Sudipta, Sheshala, Kaladhar, Chandra Misra, Krushna, Adekola, Oyebola O., Yusuf Yakubu, Saidu, Mgbosoro Ugwu, Euphemia, Olatosi, John (O), Desalu, Ibironke, Asiyanbi, Gabriel, Oladimeji, Motunrayo, Idowu, Olusola, Adeola, Fowotade, Mer, Mervyn, Mc Cree, Melanie, El Sanousi, Dr. Bashir, Adil Ali Karar, Ali, Saidahmed, Elfayadh, Hamid, Hytham K.S., Loiodice, Ambre, Bailly, Sébastien, Ruckly, Stéphane, and Staiquly, Quentin
Published: 2024
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45. Which Endoscopic Procedure to Use and in What Patient? Valves, Coils, Foam, and Heat in COPD and Asthma

Author: Li, Andrew and Lee, Pyng
Published: 2023
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46. Optimizing Offer Sets in Sub-Linear Time

Author: Farias, Vivek F., Li, Andrew A., and Sinha, Deeksha
Subjects: Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Statistics - Applications, G.2.1, F.2.0, E.1, H.3.3
Abstract: Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks. While the challenge of estimating user preferences has garnered significant attention, the operational problem of using such preferences to construct personalized offer sets to users is still a challenge, particularly in modern settings where a massive number of items and a millisecond response time requirement mean that even enumerating all of the items is impossible. Faced with such settings, existing techniques are either (a) entirely heuristic with no principled justification, or (b) theoretically sound, but simply too slow to work. Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. Our algorithm works for an extremely general class of problems and models of user choice that includes the mixed multinomial logit model as a special case. We achieve a sub-linear runtime by leveraging the dimensionality reduction from learning an accurate latent factor model, along with existing sub-linear time approximate near neighbor algorithms. Our algorithm can be entirely data-driven, relying on samples of the user, where a `sample' refers to the user interaction data typically collected by firms. We evaluate our approach on a massive content discovery dataset from Outbrain that includes millions of advertisements. Results show that our implementation indeed runs fast and with increased performance relative to existing fast heuristics., Comment: 30 pages, 3 figures. Proceedings of the 21st ACM Conference on Economics and Computation. 2020
Published: 2020

47. Interpretable Sequence Classification via Discrete Optimization

Author: Shvo, Maayan, Li, Andrew C., Icarte, Rodrigo Toro, and McIlraith, Sheila A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. While many state-of-the-art sequence classifiers are neural networks, and in particular LSTMs, our classifiers take the form of finite state automata and are learned via discrete optimization. Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance. Experiments over a suite of goal recognition and behaviour classification datasets show our learned automata-based classifiers to have comparable test performance to LSTM-based classifiers, with the added advantage of being interpretable.
Published: 2020

48. Fixing Inventory Inaccuracies At Scale

Author: Farias, Vivek F., Li, Andrew A., and Peng, Tianyi
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: Inaccurate records of inventory occur frequently, and by some measures cost retailers approximately 4% in annual sales. Detecting inventory inaccuracies manually is cost-prohibitive, and existing algorithmic solutions rely almost exclusively on learning from longitudinal data, which is insufficient in the dynamic environment induced by modern retail operations. Instead, we propose a solution based on cross-sectional data over stores and SKUs, observing that detecting inventory inaccuracies can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. State-of-the-art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that non-anomalous entries be observed with vanishingly small noise (which is not the case in our problem, and indeed in many applications). So motivated, we propose a conceptually simple entry-wise approach to anomaly detection in low-rank Poisson matrices. Our approach accommodates a general class of probabilistic anomaly models. We show that the cost incurred by our algorithm approaches that of an optimal algorithm at a min-max optimal rate. Using synthetic data and real data from a consumer goods retailer, we show that our approach provides up to a 10x cost reduction over incumbent approaches to anomaly detection. Along the way, we build on recent work that seeks entry-wise error guarantees for matrix completion, establishing such guarantees for sub-exponential matrices, a result of independent interest., Comment: The preliminary version titled "Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise" appeared at Proceedings of the 38th International Conference on Machine Learning (ICML 2021)
Published: 2020

49. Causal Inference With Selectively Deconfounded Data

Author: Gan, Kyra, Li, Andrew A., Lipton, Zachary C., and Tayur, Sridhar
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract: Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner must then either (a) collect deconfounded data;(b) run a clinical trial; or (c) elucidate further properties of the causal graph that might render the ATE identifiable. In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE. Our theoretical results suggest that the inclusion of confounded data can significantly reduce the quantity of deconfounded data required to estimate the ATE to within a desired accuracy level. Moreover, in some cases -- say, genetics -- we could imagine retrospectively selecting samples to deconfound. We demonstrate that by actively selecting these samples based upon the (already observed) treatment and outcome, we can reduce sample complexity further. Our theoretical and empirical results establish that the worst-case relative performance of our approach (vs. a natural benchmark) is bounded while our best-case gains are unbounded. Finally, we demonstrate the benefits of selective deconfounding using a large real-world dataset related to genetic mutation in cancer.
Published: 2020

50. Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube

Author: Dinh, Huy Q, Lin, Xianzhi, Abbasi, Forough, Nameki, Robbin, Haro, Marcela, Olingy, Claire E, Chang, Heidi, Hernandez, Lourdes, Gayther, Simon A, Wright, Kelly N, Aspuria, Paul-Joseph, Karlan, Beth Y, Corona, Rosario I, Li, Andrew, Rimel, BJ, Siedhoff, Matthew T, Medeiros, Fabiola, and Lawrenson, Kate
Subjects: Biochemistry and Cell Biology, Biological Sciences, Genetics, Rare Diseases, Cancer, Ovarian Cancer, Aetiology, 2.1 Biological and endogenous factors, Adult, Cell Differentiation, Cell Line, Tumor, Core Binding Factor Alpha 3 Subunit, Endometriosis, Epithelial Cells, Epithelial-Mesenchymal Transition, Fallopian Tubes, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Leiomyoma, Middle Aged, PAX8 Transcription Factor, SOXF Transcription Factors, Signal Transduction, Single-Cell Analysis, Transcriptome, PAX8, RUNX3, SOX17, ciliated epithelial cells, fallopian tube, microenvironment, ovarian cancer, scRNA-seq, secretory epithelial cells, transcription factor, Medical Physiology, Biological sciences
Abstract: The human fallopian tube harbors the cell of origin for the majority of high-grade serous "ovarian" cancers (HGSCs), but its cellular composition, particularly the epithelial component, is poorly characterized. We perform single-cell transcriptomic profiling of around 53,000 individual cells from 12 primary fallopian specimens to map their major cell types. We identify 10 epithelial subpopulations with diverse transcriptional programs. Based on transcriptional signatures, we reconstruct a trajectory whereby secretory cells differentiate into ciliated cells via a RUNX3high intermediate. Computational deconvolution of advanced HGSCs identifies the "early secretory" population as a likely precursor state for the majority of HGSCs. Its signature comprises both epithelial and mesenchymal features and is enriched in mesenchymal-type HGSCs (p = 6.7 × 10-27), a group known to have particularly poor prognoses. This cellular and molecular compendium of the human fallopian tube in cancer-free women is expected to advance our understanding of the earliest stages of fallopian epithelial neoplasia.
Published: 2021

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