Search

Your search keyword '"KANTER, David"' showing total 289 results
Start Over Author "KANTER, David"
289 results on '"KANTER, David"'

4. Speech Wikimedia: A 77 Language Multilingual Speech Dataset

Author

Gómez, Rafael Mosquera, Eusse, Julián, Ciro, Juan, Galvez, Daniel, Hileman, Ryan, Bollacker, Kurt, and Kanter, David

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

The Speech Wikimedia Dataset is a publicly available compilation of audio with transcriptions extracted from Wikimedia Commons. It includes 1780 hours (195 GB) of CC-BY-SA licensed transcribed speech from a diverse set of scenarios and speakers, in 77 different languages. Each audio file has one or more transcriptions in different languages, making this dataset suitable for training speech recognition, speech translation, and machine translation models., Comment: Data-Centric Machine Learning Workshop at the International Machine Learning Conference 2023 (ICML)

Published
2023

5. Research needs for a food system transition

Author

McDermid, Sonali Shukla, Hayek, Matthew, Jamieson, Dale W, Hale, Galina, and Kanter, David

Subjects
Environmental Sciences, Environmental Management, Zero Hunger, Life on Land, Animal agriculture, Plant based, Scenarios, Meteorology & Atmospheric Sciences
Abstract

The global food system, and animal agriculture in particular, is a major and growing contributor to climate change, land system change, biodiversity loss, water consumption and contamination, and environmental pollution. The copious production and consumption of animal products are also contributing to increasingly negative public health outcomes, particularly in wealthy and rapidly industrializing countries, and result in the slaughter of trillions of animals each year. These impacts are motivating calls for reduced reliance on animal-based products and increased use of replacement plant-based products. However, our understanding of how the production and consumption of animal products, as well as plant-based alternatives, interact with important dimensions of human and environment systems is incomplete across space and time. This inhibits comprehensively envisioning global and regional food system transitions and planning to manage the costs and synergies thereof. We therefore propose a cross-disciplinary research agenda on future target-based scenarios for food system transformation that has at its core three main activities: (1) data collection and analysis at the intersection of animal agriculture, the environment, and societal well-being, (2) the construction of target-based scenarios for animal products informed by these new data and empirical understandings, and (3) the evaluation of impacts, unintended consequences, co-benefits, and trade-offs of these target-based scenarios to help inform decision-making.

Published
2023

6. DataPerf: Benchmarks for Data-Centric AI Development

Author

Mazumder, Mark, Banbury, Colby, Yao, Xiaozhe, Karlaš, Bojan, Rojas, William Gaviria, Diamos, Sudnya, Diamos, Greg, He, Lynn, Parrish, Alicia, Kirk, Hannah Rose, Quaye, Jessica, Rastogi, Charvi, Kiela, Douwe, Jurado, David, Kanter, David, Mosquera, Rafael, Ciro, Juan, Aroyo, Lora, Acun, Bilge, Chen, Lingjiao, Raje, Mehul Smriti, Bartolo, Max, Eyuboglu, Sabri, Ghorbani, Amirata, Goodman, Emmett, Inel, Oana, Kane, Tariq, Kirkpatrick, Christine R., Kuo, Tzu-Sheng, Mueller, Jonas, Thrush, Tristan, Vanschoren, Joaquin, Warren, Margaret, Williams, Adina, Yeung, Serena, Ardalani, Newsha, Paritosh, Praveen, Bat-Leah, Lilith, Zhang, Ce, Zou, James, Wu, Carole-Jean, Coleman, Cody, Ng, Andrew, Mattson, Peter, and Reddi, Vijay Janapa

Subjects
Computer Science - Machine Learning
Abstract

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community to iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vision, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, online evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry., Comment: NeurIPS 2023 Datasets and Benchmarks Track

Published
2022

8. Federated benchmarking of medical artificial intelligence with MedPerf

Author

Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Wuest, Anna, Pati, Sarthak, Kassem, Hasan, Zenk, Maximilian, Baid, Ujjwal, Narayana Moorthy, Prakash, Chowdhury, Alexander, Guo, Junyi, Nalawade, Sahil, Rosenthal, Jacob, Kanter, David, Xenochristou, Maria, Beutel, Daniel J., Chung, Verena, Bergquist, Timothy, Eddy, James, Abid, Abubakar, Tunstall, Lewis, Sanseviero, Omar, Dimitriadis, Dimitrios, Qian, Yiming, Xu, Xinxing, Liu, Yong, Goh, Rick Siow Mong, Bala, Srini, Bittorf, Victor, Puchala, Sreekar Reddy, Ricciuti, Biagio, Samineni, Soujanya, Sengupta, Eshna, Chaudhari, Akshay, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Huang, Xinyuan, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Moraes, Cassiano Ferro, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G. Anthony, Ribalta, Pablo, Singh, Abhishek, Thiagarajan, Jayaraman J., Albrecht, Jacob, Wolf, Thomas, Miller, Geralyn, Fu, Huazhu, Shah, Prashant, Xu, Daguang, Yadav, Poonam, Talby, David, Awad, Mark M., Howard, Jeremy P., Rosenthal, Michael, Marchionni, Luigi, Loda, Massimo, Johnson, Jason M., Bakas, Spyridon, and Mattson, Peter

Published
2023
Full Text
View/download PDF

9. LSH methods for data deduplication in a Wikipedia artificial dataset

Author

Ciro, Juan, Galvez, Daniel, Schlippe, Tim, and Kanter, David

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

This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset. To evaluate the different models, we create an artificial dataset for data deduplication using English Wikipedia articles. Area-Under-Curve (AUC) over 0.9 were observed for most models, with the best model reaching 0.96. Deduplication enables more effective model training by preventing the model from learning a distribution that differs from the real one as a result of the repeated data.

Published
2021

10. The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

Author

Galvez, Daniel, Diamos, Greg, Ciro, Juan, Cerón, Juan Felipe, Achorn, Keith, Gopi, Anjali, Kanter, David, Lam, Maximilian, Mazumder, Mark, and Reddi, Vijay Janapa

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

The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). The data is collected via searching the Internet for appropriately licensed audio data with existing transcriptions. We describe our data collection methodology and release our data collection system under the Apache 2.0 license. We show that a model trained on this dataset achieves a 9.98% word error rate on Librispeech's test-clean test set.Finally, we discuss the legal and ethical issues surrounding the creation of a sizable machine learning corpora and plans for continued maintenance of the project under MLCommons's sponsorship., Comment: Part of 2021 Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks

Published
2021

11. MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, and Yin, Junqi

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning applications that are representative of real-world scientific use cases. MLPerf is a community-driven standard to benchmark machine learning workloads, focusing on end-to-end performance metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of large-scale scientific machine learning training applications driven by the MLCommons Association. We present the results from the first submission round, including a diverse set of some of the world's largest HPC systems. We develop a systematic framework for their joint analysis and compare them in terms of data staging, algorithmic convergence, and compute performance. As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling, enabling overall $>10 \times$ (end-to-end) performance improvements through system scaling. Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near-compute storage. To overcome the data-parallel scalability challenge at large batch sizes, we discuss specific learning techniques and hybrid data-and-model parallelism that are effective on large systems. We conclude by characterizing each benchmark with respect to low-level memory, I/O, and network behavior to parameterize extended roofline performance models in future rounds.

Published
2021

12. MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Author

Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Bala, Srini, Beutel, Daniel J., Bittorf, Victor, Chaudhari, Akshay, Chowdhury, Alexander, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Guo, Junyi, Huang, Xinyuan, Kanter, David, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G Anthony, Ribalta, Pablo, Rosenthal, Jacob, Singh, Abhishek, Thiagarajan, Jayaraman J., Wuest, Anna, Xenochristou, Maria, Xu, Daguang, Yadav, Poonam, Rosenthal, Michael, Loda, Massimo, Johnson, Jason M., and Mattson, Peter

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance, Computer Science - Software Engineering
Abstract

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.

Published
2021
Full Text
View/download PDF

13. MLPerf Tiny Benchmark

Author

Banbury, Colby, Reddi, Vijay Janapa, Torelli, Peter, Holleman, Jeremy, Jeffries, Nat, Kiraly, Csaba, Montino, Pietro, Kanter, David, Ahmed, Sebastian, Pau, Danilo, Thakker, Urmish, Torrini, Antonio, Warden, Peter, Cordaro, Jay, Di Guglielmo, Giuseppe, Duarte, Javier, Gibellini, Stephen, Parekh, Videet, Tran, Honson, Tran, Nhan, Wenxu, Niu, and Xuesong, Xu

Subjects
Computer Science - Machine Learning, Computer Science - Hardware Architecture
Abstract

Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted and easily reproducible benchmark for these systems. To meet this need, we present MLPerf Tiny, the first industry-standard benchmark suite for ultra-low-power tiny machine learning systems. The benchmark suite is the collaborative effort of more than 50 organizations from industry and academia and reflects the needs of the community. MLPerf Tiny measures the accuracy, latency, and energy of machine learning inference to properly evaluate the tradeoffs between systems. Additionally, MLPerf Tiny implements a modular design that enables benchmark submitters to show the benefits of their product, regardless of where it falls on the ML deployment stack, in a fair and reproducible manner. The suite features four benchmarks: keyword spotting, visual wake words, image classification, and anomaly detection., Comment: TinyML Benchmark

Published
2021

14. Data Engineering for Everyone

Author

Reddi, Vijay Janapa, Diamos, Greg, Warden, Pete, Mattson, Peter, and Kanter, David

Subjects
Computer Science - Machine Learning
Abstract

Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data than individual teams of data engineers can readily produce, which presents a severe challenge to ML deployment at scale. Much like the software-engineering revolution, where mass adoption of open-source software replaced the closed, in-house development model for infrastructure code, there is a growing need to enable rapid development and open contribution to massive machine learning data sets. This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations. Our analysis of nearly 2000 research publications from Facebook, Google and Microsoft over the past five years shows the widespread use and adoption of open data sets. Open data sets that are easily accessible to the public are vital to accelerating ML innovation for everyone. But such open resources are scarce in the wild. So, what if we are able to accelerate data-set creation via automatic data set generation tools?

Published
2021

15. MLPerf Mobile Inference Benchmark

Author

Reddi, Vijay Janapa, Kanter, David, Mattson, Peter, Duke, Jared, Nguyen, Thai, Chukka, Ramesh, Shiring, Ken, Tan, Koan-Sin, Charlebois, Mark, Chou, William, El-Khamy, Mostafa, Hong, Jungwook, John, Tom St., Trinh, Cindy, Buch, Michael, Mazumder, Mark, Markovic, Relia, Atta, Thomas, Cakir, Fatih, Charkhabi, Masoud, Chen, Xiaodong, Chiang, Cheng-Ming, Dexter, Dave, Heo, Terry, Schmuelling, Gunther, Shabani, Maryam, and Zika, Dylan

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks. The benchmark draws from the expertise of leading mobile-SoC vendors, ML-framework providers, and model producers. It comprises a suite of models that operate with standard data sets, quality metrics and run rules. We describe the design and implementation of this domain-specific ML benchmark. The current benchmark version comes as a mobile app for different computer vision and natural language processing tasks. The benchmark also supports non-smartphone devices, such as laptops and mobile PCs. Benchmark results from the first two rounds reveal the overwhelming complexity of the underlying mobile ML system stack, emphasizing the need for transparency in mobile ML performance analysis. The results also show that the strides being made all through the ML stack improve performance. Within six months, offline throughput improved by 3x, while latency reduced by as much as 12x. ML is an evolving field with changing use cases, models, data sets and quality targets. MLPerf Mobile will evolve and serve as an open-source community framework to guide research and innovation for mobile AI.

Published
2020

16. Benchmarking TinyML Systems: Challenges and Direction

Author

Banbury, Colby R., Reddi, Vijay Janapa, Lam, Max, Fu, William, Fazel, Amin, Holleman, Jeremy, Huang, Xinyuan, Hurtado, Robert, Kanter, David, Lokhmotov, Anton, Patterson, David, Pau, Danilo, Seo, Jae-sun, Sieracki, Jeff, Thakker, Urmish, Verhelst, Marian, and Yadav, Poonam

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

Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems. Benchmarking allows us to measure and thereby systematically compare, evaluate, and improve the performance of systems and is therefore fundamental to a field reaching maturity. In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads. Furthermore, we present our four benchmarks and discuss our selection methodology. Our viewpoints reflect the collective thoughts of the TinyMLPerf working group that is comprised of over 30 organizations., Comment: 6 pages, 1 figure, 3 tables

Published
2020

17. MLPerf Inference Benchmark

Author

Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St., Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, and Zhou, Yuchen

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

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability., Comment: ISCA 2020

Published
2019

18. MLPerf Training Benchmark

Author

Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St., Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, and Zaharia, Matei

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

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors., Comment: MLSys 2020

Published
2019

21. Global Challenges for Nitrogen Science-Policy Interactions: Towards the International Nitrogen Management System (INMS) and Improved Coordination Between Multi-lateral Environmental Agreements

Author

Sutton, Mark A., Howard, Clare M., Brownlie, Will J., Kanter, David R., de Vries, Wim, Adhya, T. K., Ometto, Jean P., Baron, Jill S., Winiwarter, Wilfried, Ju, Xiaotang, Masso, Cargele, Oenema, Oene, Raghuram, N., van Grinsven, Hans J. M., Van der Beck, Isabelle, Cox, Christopher, Hansen, Steffen C. B., Ramachandran, Ramesh, Hicks, W. Kevin, Sutton, Mark A., editor, Mason, Kate E., editor, Bleeker, Albert, editor, Hicks, W. Kevin, editor, Masso, Cargele, editor, Raghuram, N., editor, Reis, Stefan, editor, and Bekunda, Mateete, editor

Published
2020
Full Text
View/download PDF

24. Improving the social cost of nitrous oxide

Author

Kanter, David R., Wagner-Riddle, Claudia, Groffman, Peter M., Davidson, Eric A., Galloway, James N., Gourevitch, Jesse D., van Grinsven, Hans J. M., Houlton, Benjamin Z., Keeler, Bonnie L., Ogle, Stephen M., Pearen, Holly, Rennert, Kevin J., Saifuddin, Mustafa, Sobota, Daniel J., and Wagner, Gernot

Published
2021
Full Text
View/download PDF

27. A framework for nitrogen futures in the shared socioeconomic pathways

Author

Kanter, David R., Winiwarter, Wilfried, Bodirsky, Benjamin L., Bouwman, Lex, Boyer, Elizabeth, Buckle, Simon, Compton, Jana E., Dalgaard, Tommy, de Vries, Wim, Leclère, David, Leip, Adrian, Müller, Christoph, Popp, Alexander, Raghuram, Nandula, Rao, Shilpa, Sutton, Mark A., Tian, Hanqin, Westhoek, Henk, Zhang, Xin, and Zurek, Monika

Published
2020
Full Text
View/download PDF

37. A Hierarchical Framework for Unpacking the Nitrogen Challenge

Author

Li, Tingyu, primary, Zhang, Xin, additional, Zhong, Yuxiu, additional, Davidson, Eric A., additional, Dou, Zhengxia, additional, Zhang, Weifeng, additional, Pavinato, Paulo S., additional, Martinelli, Luiz A., additional, Kanter, David R., additional, Liu, Jianguo, additional, and Zhang, Fusuo, additional

Published
2022
Full Text
View/download PDF

38. Removal plus timely assertion: a better rule for the intersection of removal and state sovereign immunity

Author

Kanter, David

Subjects
Judicial power -- Laws, regulations and rules, Balancing tests (Law) -- Analysis, Sovereign immunity -- Laws, regulations and rules, Exceptions (Law) -- Laws, regulations and rules, Removal of causes -- Laws, regulations and rules, Government regulation, Law, Lapides v. Regents of the University System of Georgia (535 U.S. 613 (2002)), United States Constitution (U.S. Const. art. 3) (U.S. Const. amend. 11)
Abstract

TABLE OF CONTENTS INTRODUCTION I. State Sovereign Immunity and Waiver Background A. STATE SOVEREIGN IMMUNITY'S HISTORICAL DEVELOPMENT B. EXCEPTIONS TO THE DOCTRINE OF STATE SOVEREIGN IMMUNITY II. LAPIDES V. BOARD [...]

Published
2017

41. The Impact of a Project-Based Science Curriculum on Minority Student Achievement, Attitudes, and Careers: The Effects of Teacher Content and Pedagogical Content Knowledge and Inquiry-Based Practices

Author

Kanter, David E. and Konstantopoulos, Spyros

Abstract

Project-based science (PBS) curricula have project- and inquiry-based aspects that leverage the strengths of urban students from ethnic and racial groups underrepresented in science careers, potentially impacting positively these students' science achievement and attitudes and thus their college and career plans. We aimed to determine the extent to which a PBS curriculum would show this. We provided professional development to bolster urban teachers' science content knowledge (CK) and science pedagogical content knowledge (PCK) to observe the maximal impact of the PBS curriculum. We found that students' science achievement improved with the PBS curriculum, but that their attitudes toward science and plans to pursue science did not. Increases in teachers' CK and PCK with the professional development correlated with the improvements in student science achievement but did not correlate with improvements in student science attitudes or plans. However, the frequency of teachers' use of specific inquiry-based activities did correlate with improvements in students' science attitudes and plans. In sum, the extent of the success of a PBS curriculum with students from groups underrepresented in science careers appears to be dependent on elements of both teacher knowledge (CK and PCK) and teachers' frequency of use of inquiry-based activities that are consistent with culturally relevant pedagogical practices. (Contains 8 tables.)

Published
2010
Full Text
View/download PDF

42. Doing the Project and Learning the Content: Designing Project-Based Science Curricula for Meaningful Understanding

Author

Kanter, David E.

Abstract

Project-based science curricula can improve students' usable or meaningful understanding of the science content underlying a project. However, such curricula designed around "performances" wherein students design or make something do not always do this. We researched ways to design performance project-based science curricula (pPBSc) to better support the meaningful understanding of science content. Using existing curriculum design frameworks, we identified the learner's need to "create the demand" for the science content, anticipating how to use it in the performance, and to "apply" the science content, both being necessary to ensure meaningful understanding. Designing the pPBSc "I, Bio" we discovered how these guiding principles manifested as curriculum design challenges. We generalized from the design of "I, Bio" and related literature design approaches for addressing each challenge. Finally, we measured the extent to which a pPBSc incorporating these design approaches developed meaningful understanding. 652 middle grades students using "I, Bio" completed pre- and posttests on the science content behind the "I, Bio" performance. Our findings provide preliminary evidence that a pPBSc that incorporates these design approaches is consistent with gains in meaningful understanding. We discuss how the results of this work can be used to improve systematic experiments on instructional supports. (Contains 3 figures and 5 tables.)

Published
2010
Full Text
View/download PDF

45. Learning Content Using Complex Data in Project-Based Science: An Example from High School Biology in Urban Classrooms

Author

Kanter, David E. and Schreck, Melissa

Abstract

This chapter explores the extent to which project-based science (PBS) curricula designed with supports for students' inquiry into complex scientific data can help urban students make sense of such data and promote their deep understanding of standards-based content. We review qualitative and quantitative data from classroom enactments of a PBS high school biology curriculum called Disease Detectives. (Contains 6 tables and 2 figures.)

Published
2006
Full Text
View/download PDF

46. Policies to combat nitrogen pollution in South Asia: gaps and opportunities

Author

Yang, Anastasia L, primary, Raghuram, Nandula, additional, Adhya, Tapan Kumar, additional, Porter, Stephen D, additional, Panda, Ananta Narayan, additional, Kaushik, Himadri, additional, Jayaweera, Anuradha, additional, Nissanka, Sarath Premalal, additional, Anik, Asif Reza, additional, Shifa, Sharmin, additional, Sharna, Shaima Chowdhury, additional, Joshi, Rajendra, additional, Arif Watto, Muhammad, additional, Pokharel, Anju, additional, Shazly, Aminath, additional, Hassan, Rifaath, additional, Bansal, Sangeeta, additional, Kanter, David, additional, Das, Smriti, additional, and Jeffery, Roger, additional

Published
2022
Full Text
View/download PDF

48. Nitrous Oxides Ozone Destructiveness Under Different Climate Scenarios

Author

Kanter, David R and McDermid, Sonali P

Subjects
Geophysics, Environment Pollution
Abstract

Nitrous oxide (N2O) is an important greenhouse gas and ozone depleting substance as well as a key component of the nitrogen cascade. While emissions scenarios indicating the range of N2O's potential future contributions to radiative forcing are widely available, the impact of these emissions scenarios on future stratospheric ozone depletion is less clear. This is because N2O's ozone destructiveness is partially dependent on tropospheric warming, which affects ozone depletion rates in the stratosphere. Consequently, in order to understand the possible range of stratospheric ozone depletion that N2O could cause over the 21st century, it is important to decouple the greenhouse gas emissions scenarios and compare different emissions trajectories for individual substances (e.g. business-as-usual carbon dioxide (CO2) emissions versus low emissions of N2O). This study is the first to follow such an approach, running a series of experiments using the NASA Goddard Institute for Space Sciences ModelE2 atmospheric sub-model. We anticipate our results to show that stratospheric ozone depletion will be highest in a scenario where CO2 emissions reductions are prioritized over N2O reductions, as this would constrain ozone recovery while doing little to limit stratospheric NOx levels (the breakdown product of N2O that destroys stratospheric ozone). This could not only delay the recovery of the stratospheric ozone layer, but might also prevent a return to pre-1980 global average ozone concentrations, a key goal of the international ozone regime. Accordingly, we think this will highlight the importance of reducing emissions of all major greenhouse gas emissions, including N2O, and not just a singular policy focus on CO2.

Published
2016

49. MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, primary, Emani, Murali, additional, Balma, Jacob, additional, Drescher, Lukas, additional, Drozd, Aleksandr, additional, Fink, Andreas, additional, Fox, Geoffrey, additional, Kanter, David, additional, Kurth, Thorsten, additional, Mattson, Peter, additional, Mu, Dawei, additional, Ruhela, Amit, additional, Sato, Kento, additional, Shirahata, Koichi, additional, Tabaru, Tsuguchika, additional, Tsaris, Aristeidis, additional, Balewski, Jan, additional, Cumming, Ben, additional, Danjo, Takumi, additional, Domke, Jens, additional, Fukai, Takaaki, additional, Fukumoto, Naoto, additional, Fukushi, Tatsuya, additional, Gerofi, Balazs, additional, Honda, Takumi, additional, Imamura, Toshiyuki, additional, Kasagi, Akihiko, additional, Kawakami, Kentaro, additional, Kudo, Shuhei, additional, Kuroda, Akiyoshi, additional, Martinasso, Maxime, additional, Matsuoka, Satoshi, additional, Mendonca, Henrique, additional, Minami, Kazuki, additional, Ram, Prabhat, additional, Sawada, Takashi, additional, Shankar, Mallikarjun, additional, John, Tom St., additional, Tabuchi, Akihiro, additional, Vishwanath, Venkatram, additional, Wahib, Mohamed, additional, Yamazaki, Masafumi, additional, and Yin, Junqi, additional

Published
2021
Full Text
View/download PDF

50. Contributors

Author

Abzug, Mark J., Acharya, Krishna K., Adams, Denise M., Adelson, Stewart, Adrian, Molly C., Ahlfeld, Shawn K., Aiken, John J., Akdis, Cezmi A., Albokhari, Daniah, Alderman, Elizabeth M., Ali, Omar, Allen-Rhoades, Wendy A., Almutlaq, Nourah N., Amos, Louella B., Anari, Jason B., Anderson, Karl E., Anupindi, Sudha A., Appleby, Brian S., Ardoin, Stacy P., Arkader, Alexandre, Armangué, Thaís, Arndt, Carola A.S., Arnold, Danielle E., Artis, Adrianne R., Asher, David M., Asselin, Barbara L., Astley, Christina M., Atkinson, Norrell K., Augustine, Erika F., Augustyn, Marilyn C., Bacharier, Leonard B., Bacino, Carlos A., Bailey, Zinzi D., Balamuth, Frances B., Baldassano, Robert N., Baldwin, Keith D., Bales, Christina B., Balistreri, William F., Balwani, Manisha, Bamba, Vaneeta, Banerji, Aleena, Bang, Janet Y., Barai, Nikita, Baranowski, Katherine, Barclay, Sarah F., Barkoudah, Elizabeth, Barrero-Castillero, Alejandra, Barrett, Katherine J., Barron, Karyl S., Basel, Donald, Bass, Dorsey M., Bassett, Mary T., Bassiri, Hamid, Baum, Rebecca A., Behrens, Edward M., Bell, Michael J., Benjamin, Daniel K., Jr., Bennett, Amanda E., Bergerson, Jenna R.E., Bernstein, Daniel, Bernstein, Henry H., Bice-Urbach, Brittany J., Bielory, Brett P., Bielory, Leonard, Blanchard, Samra S., Blanchette, Eliza, Blatter, Joshua A., Bleyer, Archie, Boas, Steven R., Bock, Margret E., Boggs, Sarah R., Boivin, Michael J., Bonn, Julie, Bonthius, Daniel J., Boppana, Suresh B., Bordini, Brett J., Borst, Alexandra J., Bosse, Kristopher R., Boyer, Kenneth M., Brady, Patrick W., Brady, Rebecca C., Brady, Samuel L., Branchford, Brian R., Brandow, Amanda M., Brandsma, Erik, Breault, David T., Breuner, Cora Collette, Bridgemohan, Carolyn F., Britt, William J., Brower, Laura, Brown, Maria D., Brownell, Jefferson N., Browning, Meghen B., Brunetti-Pierri, Nicola, Bunyavanich, Supinda, Burstein, Danielle S., Bustinduy, Amaya L., Buyon, Jill P., Cabada, Miguel M., Cada, Michaela, Cairo, Mitchell S., Calello, Diane P., Cameron, Lindsay H., Campbell, Angela J.P., Candelaria, Margo, Cannon, Laura, Carlin, Rebecca F., Carlucci, James G., Carr, Michael R., Carrigan, Robert B., Carter, Rebecca G., Carter-Hamilton, Gail V., Case, Abigail, Chang, Pearl W., Chelimsky, Gisela G., Chelimsky, Thomas, Chemaitilly, Wassim, Chiotos, Kathleen, Chiu, Yvonne E., Chong, Hey Jin, Chou, Stella T., Christ, Lori A., Christenson, John C., Chugh, Ankur A., Cieslak, Theodore J., Claes, Donna J., Coates, Thomas D., Sánchez Códez, María I., Coffin, Susan E., Cohen, Mitchell B., Cohen, Susan S., Cole, F. Sessions, III, Collaco, J. Michael, Collins, James W., Jr., Congeni, Joseph A., Conrad, Máire A., Corcoran, Justin N., Corley, Alexandra M.S., Cox, Amanda L., Coyle, Anne M., Coyne-Beasley, Tamera, Craig, Sansanee S., Creighton, Sarah M., Crigger, Chad B., Crowe, James E., Jr., Culbert, Gabriel, Czinn, Steven J., Dalal, Aarti S., Dalmau, Josep, D’Andrea, Lynn A., Danziger-Isakov, Lara A., Darville, Toni, David, Richard J., Davidoff, Katharine, Davidson, Loren T., Davidson, Richard S., Davies, H. Dele, Davis, Stephanie D., Davis-Kankanamge, Christina, Daw, Najat C., Dean, Shannon L., DeBiasi, Roberta L., Delair, Shirley, DeLaroche, Amy M., De León-Crutchlow, Diva D., Oquendo Del Toro, Helen M., Del Valle Mojica, Coralee, DeMaso, David R., Dendrinos, Melina L., Dent, Arlene E., Desnick, Robert J., Deterding, Robin R., Devarajan, Prasad, deVeber, Gabrielle A., Dhar, Vineet K., Dhossche, Julie M., Diab, Liliane K., Di Carlo, Heather N., Dietz, Harry C., III, Dietze-Fiedler, Megan L., DiMeglio, Linda A., Dixon, Bradley P., DiVasta, Amy D., Dlamini, Nomazulu, Dobbs, Katherine R., Dodhia, Sonam N., Doerholt, Katja, Dolin, Cara D., Dominguez, Samuel R., Donohoue, Patricia A., Dow, Jennifer, Downes, Kevin J., Doyle, Daniel A., Doyle, Jefferson J., Dror, Yigal, Dubowitz, Howard, Dumler, J. Stephen, Duncan, Andrea F., Durant, Nefertiti H., Dvergsten, Jeffrey A., Earing, Michael G., Eberly, Col. Matthew D., Egan, Marie E., Eichenwald, Eric C., Elkadri, Abdul-Aziz K., Englander, Elizabeth, Ericson, Jessica E., Erkan, Elif, Etzel, Ruth A., Evans, Sarah Helen, Faherty, Erin, Falk, Marni J., Familiar-Lopez, Itziar, Fargo, John H., Feemster, Kristen A., Fehnel, Katie P., Feigelman, Susan, Feldman, Amy G., Feldman, Heidi M., Fels, Edward C., Felner, Eric I., Feng, Sing-Yi, Ferkol, Thomas W., Jr., Finberg, Karin E., Finder, Jonathan D., Fiorino, Kristin N., Fischer, Philip R., Fitzpatrick, Anne M., Flannery, Dustin D., Fleming, Nicholas L., Flood, Veronica H., Flores, Francisco X., Flynn, Joseph T., Flynn, Patricia M., Foglia, Elizabeth E., Forkey, Heather C., Forman, Joel A., Freeman, Alexandra F., Friedman, Deborah M., Friedman, Susan A., Friehling, Erika D., Fritz, Stephanie A., Frush, Donald P., Fuleihan, Ramsay L., Gahagan, Sheila, Gallagher, Patrick G., Galloway, David P., Gans, Hayley A., Garber, Andrea K., Gardiner, Paula M., Garibaldi, Luigi R., Gauthier, Gregory M., Gerber, Jeffrey S., Gershon, Anne A., Ghadersohi, Saied, Gibbs, Kathleen A., Gibson, Mark, Gigante, Joseph, Gigliotti, Francis, Gilley, Stephanie P., Gilliam, Walter S., Ginde, Salil, Girotto, John A., Goldfarb, Samuel B., Goldman, David L., Goldman, Stanton C., Gómez-Duarte, Oscar G., Good, Misty, Goodbody, Christine M., Goodman, Denise M., Goodman, Tracey, Goodyer, William R., Gordon, Catherine M., Gordon, Leslie B., Gordon, Rebecca J., Gordon-Lipkin, Eliza, Gorelik, Michael, Gower, W. Adam, Graber, Evan G., Graff, Zachary T., Graham, Robert J., Green, Cori M., Green, Michael, Greenbaum, Larry A., Greenbaum, V. Jordan, Greiner, Mary V., Griffiths, Anne G., Grizzle, Kenneth L., Groner, Judith A., Grumach, Anete Sevciovic, Gueye-Ndiaye, Seyni, Guz-Mark, Anat, Haamid, Fareeda, Haddad, Gabriel G., Haddad, Joseph, Jr., Haemer, Matthew A., Hagan, Joseph F., Jr., Haider, Suraiya K., Hakim, Hana, Haldeman-Englert, Chad R., Halstead, Scott B., Hamie, Lamiaa, Hammerschlag, Margaret R., Hammershaimb, E. Adrianne, Hampton, Elisa, Hamvas, Aaron, Hanchard, Neil A., Hanley, Patrick C., Hanna, Melisha G., Harijan, Pooja D., Harrison, Douglas J., Harstad, Elizabeth B., Haslam, David B., Hauck, Fern R., Havers, Fiona P., Hayes, Ericka V., Heard-Garris, Nia J., Hedrick, Holly L., Hemingway, Cheryl, Heneghan, Chelsea, Hernandez, Michelle L., Hernandez-Trujillo, Vivian P., Hernandez Tejada, Fiorela N., Herrick, Heidi M., Hershey, Andrew D., Herzog, Cynthia E., Heston, Sarah M., Hijazi, Ghada, Hill, Samantha V., Hochberg, Jessica, Hodes, Deborah, Hoefgen, Holly R., Holinger, Lauren D., Holland-Hall, Cynthia M., Hollenbach, Laura L., Holler-Managan, Yolanda F., Hooper, David K., Hooven, Thomas A., Hoover-Fong, Julie E., Hopper, Rachel K., Hord, Jeffrey D., Horn, B. David, Horstmann, Helen M., Hotez, Peter J., House, Samantha A., Howard, Ashley C., Howard, Mary Beth, Hsu, Evelyn K., Hsu, Katherine, Huddleston, Heather G., Huh, Winston W., Humphrey, Stephen R., Hunstad, David A., Hunger, Stephen P., Hunt, Carl E., Huppert, Stacey S., Huppler, Anna R., Hurt, Hallam, Izumi, Kosuke, Jackson, Allison M., Jackson, Mary Anne, Jaffe, Ashlee M., James, Kiera M., Janowski, Andrew B., Jenssen, Brian P., Jinnah, H.A., John, Chandy C., Johansen, Kari, Johnson, Susan L., Johnston, Brian D., Jongco, Artemio M., III, Josephson, Cassandra D., Joyce, Joel C., Jyonouchi, Soma, Kabbany, Mohammad Nasser, Kabbouche, Marielle, Kacperski, Joanne, Kadry, Nadia A., Kaj-Carbaidwala, Batul, Kalish, Jennifer M., Kamat, Deepak, Kansra, Alvina R., Kanter, David M., Kao, Carol M., Kapavarapu, Prasanna K., Kattan, Jacob, Kelly, Andrea, Kelly, Desmond P., Kelly, Matthew S., Kelly, Michael E., Kendi, Sadiqa, Kerem, Eitan, Kerr, Julie M., Khan, David A., Khan, Seema, Khatami, Ameneh, Khaytin, Ilya, Kier, Catherine, Kilinsky, Alexandra, Kim, Chong-Tae, Kim, Jung Won, Kim, Rosa K., King, J. Michael, Kirschen, Matthew P., Kishnani, Priya S., Klawonn, Meghan A., Klein, Bruce L., Klein, Bruce S., Kliegman, Alison S., Kliegman, Robert M., Kneyber, Martin C.J., Koch, William C., Kochanek, Patrick M., Kodish, Eric, Kohlhoff, Stephan A., Kortepeter, Mark G., Kotloff, Karen L., Koumbourlis, Anastassios C., Krause, Peter J., Krebs, Nancy F., Kreipe, Richard E., Krug, Steven E., Kwiatkowski, Janet L., Kwon, Jennifer M., Ladisch, Stephan, Lakser, Oren J., Lalor, Leah, Lam, Simon, Lambert, Michele P., Lampe, Christina, Landry, Gregory L., Lane, Wendy G., Larson, A. Noelle, LaRussa, Phillip S., Lawrence, J. Todd R., Lee, Brendan, Lee, Erica H., Leiding, Jennifer W., Lemmon, Monica E., Lesser, Daniel J., Lestrud, Steven O., Leung, Donald Y.M., Levas, Michael N., Liacouras, Chris A., Lipkin, Paul H., Liptzin, Deborah R., Liu, Andrew H., Lo, Mindy S., Lo, Stanley F., Long, Sarah S., Lord, Katherine, Macias, Charles G., Macias, Michelle M., Macumber, Ian R., Magnusson, Mark R., Magoulas, Pilar L., Maguire, Kathleen J., Mahajan, Prashant V., Majzoub, Joseph A., Mamula, Petar, Manak, Colleen K., Mangus, Courtney W., Manoli, Irini, Manzur, Adnan Y., Maqbool, Asim, Maranich, Col. Ashley M., Margetts, Miranda, Margolis, David, Marin, Mona, Marini, Joan C., Markowitz, Morri, Maroushek, Stacene R., Marsh, Justin D., Marshall, Trisha L., Martin, Kari L., Masson, Vicki K., Matalon, Dena R., Matalon, Reuben K., Mathijssen, Irene M.J., Reddy Matta, Sravan Kumar, Maxwell, Elizabeth C., Maybank, Aletha, McCabe, Megan E., McCain, Darla H., McColley, Susanna A., McConnico, Neena, McCormick, Elizabeth M., McDonald, Christine M., McGovern, Margaret M., McGrath-Morrow, Sharon A., McInerney, Alissa, McKinney, Jeffrey S., McLeod, Rima, McVay-Gillam, Marcene R., Meade, Julia C., Meehan, William P., III, Mejias, Asuncion, Melby, Peter C., Melzer-Lange, Marlene D., Merves, Jamie F., Messacar, Kevin B., Michaels, Marian G., Michniacki, Thomas F., Mikati, Mohamad A., Miller-Handley, Hilary E., Mink, Jonathan W., Mirasola, Karolyn, Mistovich, R. Justin, Mohr, Emma L., Montoya-Williams, Diana, Moon, Rachel Y., Morava, Eva, Moreno, Megan A., Morgan, Ryan W., Morrison, Peter E., Morrison, Wynne, Mukhopadhyay, Sagori, Munoz, Flor M., Munson, David A., Murphy, Timothy F., Murray, Karen F., Murray, Thomas S., Mutlu, Levent, Nagata, Jason M., Narula, Sona, Nataro, James P., Navsaria, Dipesh, Nduati, Ruth W., Nehus, Edward J., Nelson, Maureen R., Neri, Caitlin M., Nevin, Mary A., Newburger, Jane W., Newmark, Jonathan, Nield, Linda S., Niermeyer, Susan, Nocton, James J., Nogee, Lawrence M., Noje, Corina, Nowak-Wegrzyn, Anna H., Obaro, Stephen K., Obeid, Makram M., O’Callaghan, Kevin P., Oleszek, Joyce L., Olitsky, Scott E., Olsson, John M., O’Neill, Meghan E., Onigbanjo, Mutiat T., Opoka, Robert O., Orenstein, Walter A., Orkin, Sarah H., Orscheln, Rachel C., Ortega, Camile, O’Toole, Timothy R., Owens, Judith A., Ozen, Seza, Pach, Sophie, Pachter, Lee M., Padhye, Amruta, Pandurangi, Sindhu, Pak-Gorstein, Suzinne, Palla, John, Palmieri, Tina L., Palmieri, Jessica M., Pappas, Diane E., Parent, John J., Parga-Belinkie, Joanna J., Parikh, Bijal A., Parker, Alasdair P.J., Partridge, Emily A., Patel, Ami B., Patel, Trusha, Patrick, Stephen W., Patterson, Briana C., Pelosi, Emanuele, Permar, Sallie R., Perry, Michael, Perry, Tamara T., Peters, Mark J., Peters, Timothy R., Peterson, Stacy J.B., Phelan, Rachel A., Pinto, Anna L., Pipan, Mary, Player, Brittany, Prince, William Benjamin, Proctor, Mark R., Prozora, Stephanie, Pryor, Howard I., II, Pyles, Lee A., Quinn, Molly M., Quint, Elisabeth H., Rabinovich, C. Egla, Raffini, Leslie J., Ragoonanan, Dristhi S., Rahman, Shamima, Ralston, Shawn L., Ram, Sanjay, Ramilo, Octavio, Ramirez, Kacy A., Rand, Casey M., Rasmussen, Sonja A., Rathke, Kevin M., Ratner, Adam J., Ratner, Lee, Reed, Ann M., Reich, Patrick J., Reif, Shimon, Reller, Megan E., Remick, Katherine E., Remiker, Allison S., Reyes, Jorge D., Richardson, Katherine M., Rintoul, Natalie E., Ritchey, A. Kim, Robinson, Angela Byun, Rodrigues, Kristine Knuti, Rogers, Michael E., Romano, Mary E., Roosevelt, Genie E., Roper, Stephen M., Rosenthal, Stephen M., Ross, A. Catharine, Rossano, Joseph W., Rothman, Jennifer A., Rotta, Alexandre T., Rozenfeld, Ranna A., Russo, Michael E., Ryan, Kelsey S., Ryan, Monique M., Ryu, Julie, Sabbagh, Sara E., Sachdev, H.P.S., Sadarangani, Manish, Sadun, Rebecca E., Sahin, Mustafa, Saint-Cyr, Martine, Salata, Robert A., Salazar, José H., Salvana, Edsel Maurice T., Samelson-Jones, Benjamin J., Sammons, Julia S., Sampson, Hugh A., Samsel, Chase B., Sandora, Thomas J., Sankar, Wudbhav N., Sarnaik, Ashok P., Sato, Alice I., Satter, Lisa Forbes, Scaggs Huang, Felicia A., Schaffzin, Joshua K., Schechter, Michael S., Schilling, Samantha, Schleiss, Mark R., Schluter, W. William, Schondelmeyer, Amanda C., Schroeder, James W., Jr., Schulte, Elaine E., Schuster, Jennifer E., Schuster, Marcy, Schuster, Mark A., Scott, Daryl A., Scott, John P., Seaborg, Kristin A., Seed, Patrick C., Serwint, Janet R., Shah, Dheeraj, Shah, Samir S., Shah, Shivang S., Shamir, Raanan, Shanti, Christina M., Shapiro, Bruce K., Shaywitz, Bennett A., Shaywitz, Sally E., Shchelochkov, Oleg A., Shulman, Stanford T., Sicherer, Scott H., Simmons, Jeffrey M., Simões, Eric A.F., Simonsen, Kari A., Simpson, Tess S., Sinclair-McBride, Keneisha R., Singh, Arunjot, Sink, Jacquelyn R., Sisk, Bryan A., Sivaraman, Vidya, Slattery, Susan M., Slavotinek, Anne M., Smith, Jessica R., Smith-Whitley, Kim, Solensky, Roland, Son, Mary Beth F., Soranno, Danielle E., Sosa, Tina K., Soto-Rivera, Carmen L., Sosinsky, Laura Stout, Souder, Emily E., Souverbielle, Cristina Tomatis, Spearman, Paul, Spiegel, David A., Spinks-Franklin, Adiaha I.A., Sprecher, Alicia J., Squires, James E., Srivastava, Siddharth, St. Geme, Joseph W., III, St. John, Rachel D., Stambough, Kathryn C., Stanberry, Lawrence R., Starke, Jeffrey R., Starr, Taylor B., Steenhoff, Andrew P., Stein, Ronen E., Steinbach, William J., Stillwell, Terri L., Stone, Deborah L., Su, Stefani, Sucato, Gina S., Suchy, Frederick J., Sullivan, Kathleen E., Swami, Sanjeev K., Szafron, Vibha A., Szilagyi, Moira, Taha, Dalal, Tan, Libo, Tantisira, Kelan G., Taylor, Alex M., Tchapyjnikov, Dmitry, Tesini, Brenda L., Theobald, Jillian L., Thielen, Beth K., Thom, Christopher S., Thornburg, Courtney D., Tieder, Joel S., Tissières, Pierre, Tolentino, Victorio R., Jr., Topjian, Alexis A., Tower, Richard L., Trachtman, Rebecca, Triebwasser, Jourdan E., Trowbridge, Sara K., Truglio, Joseph M., Tubergen, David G., Turk, Margaret A., Tymon-Rosario, Joan R., Ufberg, Paul J., Ullrich, Christina, Ullrich, Nicole, Valika, Taher S., Van Hare, George F., Van Mater, Heather A., Varnell, Charles D., Jr., Vash-Margita, Alla, Vece, Timothy J., Vemana, Aarthi P., Venditti, Charles P., Vepraskas, Sarah, Verbsky, James W., Vermilion, Jennifer A., Vickery, Brian P., Vockley, Jerry, Voynow, Judith A., Walch, Abby, Waldrop, Stephanie W., Walker, David M., Walkovich, Kelly J., Walter, Heather J., Wambach, Jennifer A., Wamithi, Susan, Wang, Julie, Wang, Marie E., Wangler, Michael F., Ware, Stephanie M., Washam, Matthew C., Wasserman, Jonathan D., Wassner, Ari J., Watson, Andrew M., Wattier, Rachel L., Weber, David R., Webster, Jennifer, Weese-Mayer, Debra E., Weinberg, Jason B., Weinman, Jason P., Weisman, Steven J., Weiss, Anna K., Weiss, Scott L., Weiss, Pamela F., Weitzman, Carol C., Wells, Lawrence, Wen, Jessica W., Wendel, Danielle R., Werlin, Steven L., Wexler, Isaiah D., Whitaker, Alexander S., White, A. Clinton, Jr., White, Perrin C., Willoughby, Rodney E., Jr., Wilschanski, Michael, Wiley, Susan E., Williams, Brendan A., Wilson, Karen M., Wilson, Pamela E., Winell, Jennifer J., Witters, Peter, Wolf, Joshua, Wolfe, Joanne, Wolfgram, Peter M., Woods, Brandon T., Wright, Benjamin L., Wright, Terry W., Wu, Eveline Y., Yagupsky, Pablo, Yang, Edward, Yang, Kesi C., Yang, Ming, Yaron, Michael, Younger, Sarah B., Yuskaitis, Christopher J., Zachariah, Philip, Zafar, Muhammad S., Zahler, Stacey G., Zajac, Lauren M., Zaky, Wafik, Zaspel, Jennifer A., Zerra, Patricia E., Zhou, Amy, Zuckerman, Barry S., and Zur, Karen B.

Published
2025
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results