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1. Black-box Uncertainty Quantification Method for LLM-as-a-Judge

Author

Wagner, Nico, Desmond, Michael, Nair, Rahul, Ashktorab, Zahra, Daly, Elizabeth M., Pan, Qian, Cooper, Martín Santillán, Johnson, James M., and Geyer, Werner

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.

Published
2024

2. Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences

Author

Ashktorab, Zahra, Desmond, Michael, Pan, Qian, Johnson, James M., Cooper, Martin Santillan, Daly, Elizabeth M., Nair, Rahul, Pedapati, Tejaswini, Achintalwar, Swapnaja, and Geyer, Werner

Subjects
Computer Science - Human-Computer Interaction
Abstract

Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as evaluators to filter training data, evaluate model performance or assist human evaluators with detailed assessments. To support this process, effective front-end tools are critical for evaluation. Two common approaches for using LLMs as evaluators are direct assessment and pairwise comparison. In our study with machine learning practitioners (n=15), each completing 6 tasks yielding 131 evaluations, we explore how task-related factors and assessment strategies influence criteria refinement and user perceptions. Findings show that users performed more evaluations with direct assessment by making criteria task-specific, modifying judgments, and changing the evaluator model. We conclude with recommendations for how systems can better support interactions in LLM-assisted evaluations.

Published
2024

3. Human-Centered Design Recommendations for LLM-as-a-Judge

Author

Pan, Qian, Ashktorab, Zahra, Desmond, Michael, Cooper, Martin Santillan, Johnson, James, Nair, Rahul, Daly, Elizabeth, and Geyer, Werner

Subjects
Computer Science - Human-Computer Interaction
Abstract

Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human's intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners' preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems., Comment: 14 pages, 6 figures, Accepted for publication in ACL 2024 Workshop HuCLLM

Published
2024

4. On Efficient and Statistical Quality Estimation for Data Annotation

Author

Klie, Jan-Christoph, Haladjian, Juan, Kirchner, Marc, and Nair, Rahul

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

Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if quality is insufficient during the annotation process, rectifying measures can be taken to improve it. Quality estimation is often performed by having experts manually label instances as correct or incorrect. But checking all annotated instances tends to be expensive. Therefore, in practice, usually only subsets are inspected; sizes are chosen mostly without justification or regard to statistical power and more often than not, are relatively small. Basing estimates on small sample sizes, however, can lead to imprecise values for the error rate. Using unnecessarily large sample sizes costs money that could be better spent, for instance on more annotations. Therefore, we first describe in detail how to use confidence intervals for finding the minimal sample size needed to estimate the annotation error rate. Then, we propose applying acceptance sampling as an alternative to error rate estimation We show that acceptance sampling can reduce the required sample sizes up to 50% while providing the same statistical guarantees., Comment: Accepted to ACL 2024

Published
2024

6. Ranking Large Language Models without Ground Truth

Author

Dhurandhar, Amit, Nair, Rahul, Singh, Moninder, Daly, Elizabeth, and Ramamurthy, Karthikeyan Natesan

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

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In this paper, we provide a novel perspective where, given a dataset of prompts (viz. questions, instructions, etc.) and a set of LLMs, we rank them without access to any ground truth or reference responses. Inspired by real life where both an expert and a knowledgeable person can identify a novice our main idea is to consider triplets of models, where each one of them evaluates the other two, correctly identifying the worst model in the triplet with high probability. We also analyze our idea and provide sufficient conditions for it to succeed. Applying this idea repeatedly, we propose two methods to rank LLMs. In experiments on different generative tasks (summarization, multiple-choice, and dialog), our methods reliably recover close to true rankings without reference data. This points to a viable low-resource mechanism for practical use., Comment: Accepted to ACL 2024

Published
2024

7. Explaining Knock-on Effects of Bias Mitigation

Author

Nizhnichenkov, Svetoslav, Nair, Rahul, Daly, Elizabeth, and Mac Namee, Brian

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

In machine learning systems, bias mitigation approaches aim to make outcomes fairer across privileged and unprivileged groups. Bias mitigation methods work in different ways and have known "waterfall" effects, e.g., mitigating bias at one place may manifest bias elsewhere. In this paper, we aim to characterise impacted cohorts when mitigation interventions are applied. To do so, we treat intervention effects as a classification task and learn an explainable meta-classifier to identify cohorts that have altered outcomes. We examine a range of bias mitigation strategies that work at various stages of the model life cycle. We empirically demonstrate that our meta-classifier is able to uncover impacted cohorts. Further, we show that all tested mitigation strategies negatively impact a non-trivial fraction of cases, i.e., people who receive unfavourable outcomes solely on account of mitigation efforts. This is despite improvement in fairness metrics. We use these results as a basis to argue for more careful audits of static mitigation interventions that go beyond aggregate metrics., Comment: This paper was accepted at NeurIPS 2023 workshop

Published
2023

10. Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification

Author

Gajcin, Jasmina, McCarthy, James, Nair, Rahul, Marinescu, Radu, Daly, Elizabeth, and Dusparic, Ivana

Subjects
Computer Science - Artificial Intelligence
Abstract

A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging task, especially in complex, multi-objective environments. Developers often have to resort to starting with an initial, potentially misspecified reward function, and iteratively adjusting its parameters, based on observed learned behavior. In this work, we aim to automate this process by proposing ITERS, an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration. We also allow the user to provide explanations of their feedback, which are used to augment the feedback and reduce user effort and feedback frequency. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions., Comment: 7 pages, 2 figures

Published
2023

11. Co-creating a globally interpretable model with human input

Author

Nair, Rahul

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

We consider an aggregated human-AI collaboration aimed at generating a joint interpretable model. The model takes the form of Boolean decision rules, where human input is provided in the form of logical conditions or as partial templates. This focus on the combined construction of a model offers a different perspective on joint decision making. Previous efforts have typically focused on aggregating outcomes rather than decisions logic. We demonstrate the proposed approach through two examples and highlight the usefulness and challenges of the approach., Comment: Paper at Artificial Intelligence & Human-Computer Interaction Workshop at ICML 2023

Published
2023

12. Interpretable Differencing of Machine Learning Models

Author

Haldar, Swagatam, Saha, Diptikalyan, Wei, Dennis, Nair, Rahul, and Daly, Elizabeth M.

Subjects
Computer Science - Machine Learning
Abstract

Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyond differences in overall metrics such as accuracy to identify where in the feature space do the differences occur. We formalize this problem of model differencing as one of predicting a dissimilarity function of two ML models' outputs, subject to the representation of the differences being human-interpretable. Our solution is to learn a Joint Surrogate Tree (JST), which is composed of two conjoined decision tree surrogates for the two models. A JST provides an intuitive representation of differences and places the changes in the context of the models' decision logic. Context is important as it helps users to map differences to an underlying mental model of an AI system. We also propose a refinement procedure to increase the precision of a JST. We demonstrate, through an empirical evaluation, that such contextual differencing is concise and can be achieved with no loss in fidelity over naive approaches., Comment: UAI 2023

Published
2023

15. Geometric Poisson distribution of photons produced in the ultrarelativistic hadronic collisions

Author

Nair, Rahul R, Wilk, Grzegorz, and Włodarczyk, Zbigniew

Subjects
High Energy Physics - Phenomenology
Abstract

We show that the multiplicity distribution of photons produced with enhanced void probability in inelastic proton-proton collisions at $\sqrt{s} =$ 900 GeV, 2.76 TeV, and 7 TeV, measured at forward rapidities by the ALICE experiment at LHC, can be described by the geometric Poisson distribution. The traditionally used negative binomial distribution fails to reproduce the enhanced void probability and the shape of the modified combinants simultaneously. Our findings are relevant for the theoretical modeling of photon production processes in high-energy hadronic collisions., Comment: 6 pages, 2 figures, Accepted by EPJA

Published
2023
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17. Signatures of deuteron synthesis on the modified combinants in nuclear collisions

Author

Nair, Rahul R., Wilk, Grzegorz, and Włodarczyk, Zbigniew

Subjects
Nuclear Theory, High Energy Physics - Phenomenology
Abstract

The mechanism of deuteron production in heavy-ion collisions is shown to have a significant impact on the shape of the modified combinants of their multiplicity distribution. In light of this observation, an experimental study is proposed to tackle the long-standing problem of nuclei synthesis in hadronic and nuclear collisions. The proposed approach has the potential to provide stringent constraints on the models of deuteron synthesis.

Published
2023

18. AutoDOViz: Human-Centered Automation for Decision Optimization

Author

Weidele, Daniel Karl I., Afzal, Shazia, Valente, Abel N., Makuch, Cole, Cornec, Owen, Vu, Long, Subramanian, Dharmashankar, Geyer, Werner, Nair, Rahul, Vejsbjerg, Inge, Marinescu, Radu, Palmes, Paulito, Daly, Elizabeth M., Franke, Loraine, and Haehn, Daniel

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO, with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks, we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.

Published
2023
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19. A Cost-Effective Autonomous Beach Cleaning Rover Using Deep Learning

Author

Girish, S., Harikrishnan, S., Nair, Rahul H., Sreerajan, Midhun, Sathyan, Soumya, Pandi, V. Ravikumar, Valsan, Vipina, Suresh, Kavya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Garg, Lalit, editor, Kesswani, Nishtha, editor, Brigui, Imene, editor, Dewangan, Bhupesh Kr., editor, Shukla, R. N., editor, and Sisodia, Dilip Singh, editor

Published
2024
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22. On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Author

Wei, Dennis, Nair, Rahul, Dhurandhar, Amit, Varshney, Kush R., Daly, Elizabeth M., and Singh, Moninder

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a supervised learning model from a reference model regarded as safe. We then show how interpretability facilitates this safety assessment. For models including decision trees, generalized linear and additive models, the maximum deviation can be computed exactly and efficiently. For tree ensembles, which are not regarded as interpretable, discrete optimization techniques can still provide informative bounds. For a broader class of piecewise Lipschitz functions, we leverage the multi-armed bandit literature to show that interpretability produces tighter (regret) bounds on the maximum deviation. We present case studies, including one on mortgage approval, to illustrate our methods and the insights about models that may be obtained from deviation maximization., Comment: Published at NeurIPS 2022

Published
2022

23. The Use of iPad and eBooks to Perform Daily Living Skills among Adolescents with Autism Spectrum Disorder and Intellectual Disability

Author

Yeong, Adeline M. Y., Dutt, Anuradha S., Yong, Yvonne H. L., and Nair, Rahul

Abstract

In the current study, three adolescents with autism spectrum disorder and moderate intellectual disability were first taught to independently use an iPad to access instructional material (i.e., video models) via electronic books or eBooks. After iPad training, participants were required to apply their newly acquired skills to independently operate the iPad and eBooks to learn and perform other novel daily living tasks. The study employed a single case, non-concurrent multiple baseline design across participants during the iPad training phase and across tasks for each participant during the application phase. Results based on visual analysis indicated that all participants learned to independently operate the iPad and access instructional materials presented via eBooks. Furthermore, they applied these skills to learn and independently perform other novel daily living tasks. Effect size estimates using between case standardized mean difference showed improvements in responding subsequent to iPad training.

Published
2023
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24. Boolean Decision Rules for Reinforcement Learning Policy Summarisation

Author

McCarthy, James, Nair, Rahul, Daly, Elizabeth, Marinescu, Radu, and Dusparic, Ivana

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and intentions of an RL policy offer avenues to incorporate safety into the policy by limiting undesirable actions. We propose the use of a Boolean Decision Rules model to create a post-hoc rule-based summary of an agent's policy. We evaluate our proposed approach using a DQN agent trained on an implementation of a lava gridworld and show that, given a hand-crafted feature representation of this gridworld, simple generalised rules can be created, giving a post-hoc explainable summary of the agent's policy. We discuss possible avenues to introduce safety into a RL agent's policy by using rules generated by this rule-based model as constraints imposed on the agent's policy, as well as discuss how creating simple rule summaries of an agent's policy may help in the debugging process of RL agents.

Published
2022

27. User Driven Model Adjustment via Boolean Rule Explanations

Author

Daly, Elizabeth M., Mattetti, Massimiliano, Alkan, Öznur, and Nair, Rahul

Subjects
Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to interact with interpretable explanations of ML predictions in order to inject modifications or constraints that more accurately reflect current realities of the system. In this paper, we present a solution which leverages the predictive power of ML models while allowing the user to specify modifications to decision boundaries. Our interactive overlay approach achieves this goal without requiring model retraining, making it appropriate for systems that need to apply instant changes to their decision making. We demonstrate that user feedback rules can be layered with the ML predictions to provide immediate changes which in turn supports learning with less data.

Published
2022

30. FROTE: Feedback Rule-Driven Oversampling for Editing Models

Author

Alkan, Öznur, Wei, Dennis, Mattetti, Massimiliano, Nair, Rahul, Daly, Elizabeth M., and Saha, Diptikalyan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries. While work has been done to reinforce existing decision boundaries, very little has been done to cover these scenarios where decision boundaries of the ML models should change in order to reflect new rules. In this paper, we focus on user-provided feedback rules as a way to expedite the ML models update process, and we formally introduce the problem of pre-processing training data to edit an ML model in response to feedback rules such that once the model is retrained on the pre-processed data, its decision boundaries align more closely with the rules. To solve this problem, we propose a novel data augmentation method, the Feedback Rule-Based Oversampling Technique. Extensive experiments using different ML models and real world datasets demonstrate the effectiveness of the method, in particular the benefit of augmentation and the ability to handle many feedback rules., Comment: 23 pages

Published
2022

32. Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents

Author

Gajcin, Jasmina, Nair, Rahul, Pedapati, Tejaswini, Marinescu, Radu, Daly, Elizabeth, and Dusparic, Ivana

Subjects
Computer Science - Artificial Intelligence
Abstract

In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function. Understanding the differences in strategies between policies is necessary to enable users to choose between offered policies, and can help developers understand different behaviors that emerge from various reward functions and training hyperparameters in RL systems. In this work we compare behavior of two policies trained on the same task, but with different preferences in objectives. We propose a method for distinguishing between differences in behavior that stem from different abilities from those that are a consequence of opposing preferences of two RL agents. Furthermore, we use only data on preference-based differences in order to generate contrasting explanations about agents' preferences. Finally, we test and evaluate our approach on an autonomous driving task and compare the behavior of a safety-oriented policy and one that prefers speed., Comment: 7 pages, 3 figures

Published
2021

33. Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

Author

Görtler, Jochen, Hohman, Fred, Moritz, Dominik, Wongsuphasawat, Kanit, Ren, Donghao, Nair, Rahul, Kirchner, Marc, and Patel, Kayur

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, H.2.m, I.7.m
Abstract

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo's utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions., Comment: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Published
2021
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34. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
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36. Effectiveness of mRNA Vaccines Against COVID-19 Hospitalization by Age and Chronic Medical Conditions Burden Among Immunocompetent US Adults, March-August 2021

Author

Lewis, Nathaniel M, Naioti, Eric A, Self, Wesley H, Ginde, Adit A, Douin, David J, Talbot, H Keipp, Casey, Jonathan D, Mohr, Nicholas M, Zepeski, Anne, Gaglani, Manjusha, Ghamande, Shekhar A, McNeal, Tresa A, Shapiro, Nathan I, Gibbs, Kevin W, Files, D Clark, Hager, David N, Shehu, Arber, Prekker, Matthew E, Erickson, Heidi L, Gong, Michelle N, Mohamed, Amira, Henning, Daniel J, Steingrub, Jay S, Peltan, Ithan D, Brown, Samuel M, Martin, Emily T, Hubel, Kinsley, Hough, Catherine L, Busse, Laurence W, Lohuis, Caitlin C ten, Duggal, Abhijit, Wilson, Jennifer G, Gordon, Alexandra J, Qadir, Nida, Chang, Steven Y, Mallow, Christopher, Rivas, Carolina, Babcock, Hilary M, Kwon, Jennie H, Exline, Matthew C, Halasa, Natasha, Chappell, James D, Lauring, Adam S, Grijalva, Carlos G, Rice, Todd W, Rhoads, Jillian P, Stubblefield, William B, Baughman, Adrienne, Womack, Kelsey N, Lindsell, Christopher J, Hart, Kimberly W, Zhu, Yuwei, Schrag, Stephanie J, Kobayashi, Miwako, Verani, Jennifer R, Patel, Manish M, Tenforde, Mark W, Collaborators, IVY Network, McNeal, Tresa, Ghamande, Shekhar, Calhoun, Nicole, Murthy, Kempapura, Herrick, Judy, McKillop, Amanda, Hoffman, Eric, Zayed, Martha, Smith, Michael, Settele, Natalie, Ettlinger, Jason, Priest, Elisa, Thomas, Jennifer, Arroliga, Alejandro, Beeram, Madhava, Kindle, Ryan, Kozikowski, Lori-Ann, De Souza, Lesley, Ouellette, Scott, Thornton-Thompson, Sherell, Tyler, Patrick, Mehkri, Omar, Mitchell, Meg, Brennan, Connery, Ashok, Kiran, Poynter, Bryan, Stanley, Nicholas, Hendrickson, Audrey, Caspers, Sean, Scharber, Tyler, Jorgensen, Jeffrey, Bowers, Robert, King, Jennifer, Aston, Valerie, Armbruster, Brent, Rothman, Richard E, Nair, Rahul, Chen, Jen-Ting, Karow, Sarah, Robart, Emily, Maldonado, Paulo Nunes, and Khan, Maryiam

Subjects
Immunization, Aging, Vaccine Related, Good Health and Well Being, Adult, COVID-19, COVID-19 Vaccines, Chronic Disease, Hospitalization, Humans, Vaccines, Synthetic, mRNA Vaccines, chronic medical conditions, preexisting conditions, vaccine effectiveness, IVY Network Collaborators, Biological Sciences, Medical and Health Sciences, Microbiology
Abstract

Vaccine effectiveness (VE) against COVID-19 hospitalization was evaluated among immunocompetent adults (≥18 years) during March-August 2021 using a case-control design. Among 1669 hospitalized COVID-19 cases (11% fully vaccinated) and 1950 RT-PCR-negative controls (54% fully vaccinated), VE was 96% (95% confidence interval [CI], 93%-98%) among patients with no chronic medical conditions and 83% (95% CI, 76%-88%) among patients with ≥ 3 categories of conditions. VE was similar between those aged 18-64 years versus ≥65 years (P > .05). VE against severe COVID-19 was very high among adults without chronic conditions and lessened with increasing comorbidity burden.

Published
2022

38. Ion exchange in atomically thin clays and micas

Author

Zou, Yi-Chao, Mogg, Lucas, Clark, Nick, Bacaksiz, Cihan, Milanovic, Slavisa, Sreepal, Vishnu, Hao, Guang-Ping, Wang, Yi-Chi, Hopkinson, David G., Gorbachev, Roman, Shaw, Samuel, Novoselov, Kostya S., Raveendran-Nair, Rahul, Peeters, Francois M., Lozada-Hidalgo, Marcelo, and Haigh, Sarah J.

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Clays and micas are receiving attention as materials that, in their atomically thin form, could allow for novel proton conductive, ion selective, osmotic power generation, or solvent filtration membranes. The interest arises from the possibility of controlling their properties by exchanging ions in the crystal lattice. However, the ion exchange process itself remains largely unexplored in atomically thin materials. Here we use atomic-resolution scanning transmission electron microscopy to study the dynamics of the process and reveal the binding sites of individual ions in atomically thin and artificially restacked clays and micas. Imaging ion exchange after different exposure time and for different crystal thicknesses, we find that the ion diffusion constant, D, for the interlayer space of atomically thin samples is up to 10^4 times larger than in bulk crystals and approaches its value in free water. Surprisingly, samples where no bulk exchange is expected display fast exchange if the mica layers are twisted and restacked; but in this case, the exchanged ions arrange in islands controlled by the moir\'e superlattice dimensions. We attribute the fast ion diffusion to enhanced interlayer expandability resulting from weaker interlayer binding forces in both atomically thin and restacked materials. Finally, we demonstrate images of individual surface cations for these materials, which had remained elusive in previous studies. This work provides atomic scale insights into ion diffusion in highly confined spaces and suggests strategies to design novel exfoliated clays membranes., Comment: 11 pages, 4 figures, plus 35 pages supplementary information

Published
2021
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40. Effectiveness of a Third Dose of Pfizer-BioNTech and Moderna Vaccines in Preventing COVID-19 Hospitalization Among Immunocompetent and Immunocompromised Adults — United States, August–December 2021

Author

Tenforde, Mark W, Patel, Manish M, Gaglani, Manjusha, Ginde, Adit A, Douin, David J, Talbot, H Keipp, Casey, Jonathan D, Mohr, Nicholas M, Zepeski, Anne, McNeal, Tresa, Ghamande, Shekhar, Gibbs, Kevin W, Files, D Clark, Hager, David N, Shehu, Arber, Prekker, Matthew E, Erickson, Heidi L, Gong, Michelle N, Mohamed, Amira, Johnson, Nicholas J, Srinivasan, Vasisht, Steingrub, Jay S, Peltan, Ithan D, Brown, Samuel M, Martin, Emily T, Monto, Arnold S, Khan, Akram, Hough, Catherine L, Busse, Laurence W, Duggal, Abhijit, Wilson, Jennifer G, Qadir, Nida, Chang, Steven Y, Mallow, Christopher, Rivas, Carolina, Babcock, Hilary M, Kwon, Jennie H, Exline, Matthew C, Botros, Mena, Lauring, Adam S, Shapiro, Nathan I, Halasa, Natasha, Chappell, James D, Grijalva, Carlos G, Rice, Todd W, Jones, Ian D, Stubblefield, William B, Baughman, Adrienne, Womack, Kelsey N, Rhoads, Jillian P, Lindsell, Christopher J, Hart, Kimberly W, Zhu, Yuwei, Naioti, Eric A, Adams, Katherine, Lewis, Nathaniel M, Surie, Diya, McMorrow, Meredith L, Self, Wesley H, Calhoun, Nicole, Murthy, Kempapura, Herrick, Judy, McKillop, Amanda, Hoffman, Eric, Zayed, Martha, Smith, Michael, Kindle, Ryan, Kozikowski, Lori-Ann, De Souza, Lesley, Ouellette, Scott, Thornton-Thompson, Sherell, Mehkri, Omar, Ashok, Kiran, Gole, Susan, King, Alexander, Poynter, Bryan, ten Lohuis, Caitlin, Stanley, Nicholas, Hendrickson, Audrey, Caspers, Sean, Tordsen, Walker, Kaus, Olivia, Scharber, Tyler, Jorgensen, Jeffrey, Bowers, Robert, King, Jennifer, Aston, Valerie, Rothman, Richard E, Ali, Harith, Nair, Rahul, Karow, Sarah, Robart, Emily, Maldonado, Paulo Nunes, Khan, Maryiam, So, Preston, Krol, Olivia, Martinez, Jesus, Zouyed, Zachary, Acosta, Michael, and Bazyarboroujeni, Reihaneh

Subjects
Immunization, Infectious Diseases, Prevention, Vaccine Related, Clinical Research, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Good Health and Well Being, 2019-nCoV Vaccine mRNA-1273, Adult, Aged, BNT162 Vaccine, COVID-19, Female, Hospitalization, Humans, Immunization, Secondary, Immunocompetence, Immunocompromised Host, Male, Middle Aged, SARS-CoV-2, United States, Vaccine Efficacy, IVY Network, General & Internal Medicine
Abstract

COVID-19 mRNA vaccines (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]) provide protection against infection with SARS-CoV-2, the virus that causes COVID-19, and are highly effective against COVID-19-associated hospitalization among eligible persons who receive 2 doses (1,2). However, vaccine effectiveness (VE) among persons with immunocompromising conditions* is lower than that among immunocompetent persons (2), and VE declines after several months among all persons (3). On August 12, 2021, the Food and Drug Administration (FDA) issued an emergency use authorization (EUA) for a third mRNA vaccine dose as part of a primary series ≥28 days after dose 2 for persons aged ≥12 years with immunocompromising conditions, and, on November 19, 2021, as a booster dose for all adults aged ≥18 years at least 6 months after dose 2, changed to ≥5 months after dose 2 on January 3, 2022 (4,5,6). Among 2,952 adults (including 1,385 COVID-19 case-patients and 1,567 COVID-19-negative controls) hospitalized at 21 U.S. hospitals during August 19-December 15, 2021, effectiveness of mRNA vaccines against COVID-19-associated hospitalization was compared between adults eligible for but who had not received a third vaccine dose (1,251) and vaccine-eligible adults who received a third dose ≥7 days before illness onset (312). Among 1,875 adults without immunocompromising conditions (including 1,065 [57%] unvaccinated, 679 [36%] 2-dose recipients, and 131 [7%] 3-dose [booster] recipients), VE against COVID-19 hospitalization was higher among those who received a booster dose (97%; 95% CI = 95%-99%) compared with that among 2-dose recipients (82%; 95% CI = 77%-86%) (p

Published
2022

41. On the thermalisation of leptons from heavy-flavour meson decay in pp collisions

Author

Nair, Rahul Ramachandran

Subjects
High Energy Physics - Phenomenology
Abstract

It is shown that the $\mu^{\pm}$ originating from the decay of heavy-flavour mesons in the simulated proton-proton collisions at $\sqrt{s} = 13$ TeV using the PYTHIA 8 event generator can be made into two groups based on the value of their light front variable such that one among these two groups of $\mu^{\pm}$ follow the Boltzmann statistics with a nearly isotropic angular distribution. A similar effect is not observed for $e^{\pm}$ from the decay of heavy-flavour mesons in these collisions., Comment: 3 Pages, 2 figures, 1 table

Published
2021

42. Light front analysis towards the study of thermalisation in pp collisions at $\sqrt{s}= 13$ TeV

Author

Nair, Rahul Ramachandran

Subjects
High Energy Physics - Phenomenology
Abstract

An analysis involving the light front variables of inclusively produced hadrons in proton-proton (pp) collisions to study the thermalisation and formation of a QGP-like medium is presented in this paper. Two schemes of analysis are discussed and performed with inclusively produced $\pi^{\pm}$, $K^{\pm}$ and $p(\bar{p})$ in the central pp collisions at $\sqrt{s} = 13$ TeV simulated using the PYTHIA 8 event generator. It is shown that a group of $\pi^{\pm}$ and $K^{\pm}$ falling inside a paraboloid defined by a certain constant value of the light front variable in their respective phase space follows the Bose-Einstein statistics while the $p(\bar{p})$ inside the similar paraboloid in its phase space follows the Fermi-Dirac statistics. It is also shown that for the $\pi^{\pm}$, $K^{\pm}$ and $p(\bar{p})$ with transverse momentum $p_{T} < 1$ GeV in these collisions, a polynomial in $p_T$ can be constructed for a specific light front variable of the particle $\zeta_c (p_T)$ such that the $\pi^{\pm}$ and $K^{\pm}$ with their light front variable $\zeta^{\pm} > \zeta_c (p_T)$ follows Bose-Einstein statistics while the $p(\bar{p})$ with $\zeta^{\pm} > \zeta_c (p_T)$ follows the Fermi-Dirac statistics. The analysis can serve as a baseline for an investigation towards the QGP formation and thermalisation in the pp collisions at LHC., Comment: 9 pages, 4 figures, 2 tables

Published
2021

43. A polynomial in transverse momentum manifesting thermalisation in nuclear collisions

Author

Nair, Rahul Ramachandran

Subjects
Nuclear Theory, High Energy Physics - Phenomenology
Abstract

A specific value of light front variable $\zeta_c(p_T,m)$ of the inclusively produced hadrons in a relativistic nuclear collision is constructed as a polynomial in transverse momentum $p_T$ of the particle within the UrQMD transport model. It is shown that those particles with the absolute value of their light front variable $\zeta$ greater than the corresponding $\zeta_c(p_T,m)$ follow a Fermi-Dirac or Bose-Einstein distribution depending upon the spin of the particle., Comment: 3 pages, 2 figures, 1 table

Published
2021

44. A feasibility study of the light front analysis of ultrarelativistic nuclear collisions in ALICE at LHC

Author

Nair, Rahul Ramachandran

Subjects
High Energy Physics - Phenomenology
Abstract

An analysis involving the light front variables is performed over the inclusively produced $\pi^{\pm}$, $K^{\pm}$ and $p(\bar{p})$ in the Pb-Pb collisions simulated using the UrQMD event generator at $\sqrt{s} = 2.76$ TeV to study the possible formation of a thermalised medium in these collisions. It is demonstrated that there exist surfaces defined by constant values of the light front variable in the phase space of these hadrons which can select a group of thermalised particles. The temperatures are extracted for each species and are compared with those obtained from a similar calculation involving the kinematical and acceptance restrictions which are typically bound to the collider experiments. It is shown that the analysis is feasible to perform with the data collected at the ALICE experiment at LHC. The analysis may be considered as a prototypical study to a similar experimental analysis of the data from the heavy-ion collisions at RHIC and LHC after implementing the necessary corrections and calibrations relevant to the experimental apparatuses., Comment: 11 pages, 5 figures, 2 Tables

Published
2021

45. On the light front surfaces in the phase space of hadrons in heavy-ion collisions

Author

Nair, Rahul Ramachandran

Subjects
High Energy Physics - Phenomenology, Nuclear Theory
Abstract

Surfaces corresponding to the constant values of the light front variable forms paraboloids in the phase space of the inclusively produced hadrons in heavy-ion collisions. In this paper, it is demonstrated with the simulated Au-Au collisions at $\sqrt{s} = 200$ GeV using the UrQMD event generator that the paraboloidal surface defined by a certain constant value of the light front variable divides the particles in the phase space into two groups such that the distributions of the square of the transverse momentum, the polar angle and the light front variable of particles belonging to one among them can be described with the Boltzmann distribution parameterised by the mass-dependent temperature., Comment: 6 pages, 4 figures, 1 table

Published
2021

50. Apparent Ferromagnetism in Exfoliated Ultra-thin Pyrite Sheets

Author

Puthirath, Anand B., Balan, Aravind Puthirath, Oliveira, Eliezer F., Sreepal, Vishnu, Hernandez, Francisco C. Robles, Gao, Guanhui, Chakingal, Nithya, Sassi, Lucas M., Thibeorchews, Prasankumar, Costin, Gelu, Vajtai, Robert, Galvao, Douglas S., Nair, Rahul R., and Ajayan, Pulickel M.

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

Experimental evidence for ferromagnetic ordering in isotropic atomically thin two-dimensional crystals has been missing until a bilayer Cr2Ge2Te6, and a three-atom thick monolayer CrI3 are shown to retain ferromagnetic ordering at finite temperatures. Here, we demonstrate successful isolation of a non-van der Waals type ultra-thin nanosheet of FeS2 derived from naturally occurring pyrite mineral (FeS2) by means of liquid-phase exfoliation. Structural characterizations imply that (111) oriented sheets are predominant and is supported theoretically by means of density functional theory surface energy calculations. Spin-polarized density theory calculations further predicted that (111) oriented three-atom thick pyrite sheet has a stable ferromagnetic ground state different from its diamagnetic bulk counterpart. This theoretical finding is evaluated experimentally employing low temperature superconducting quantum interference device measurements and observed an anomalous ferromagnetic kind of behavior., Comment: 21 pages, 5 figures

Published
2020
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