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3,624 results on '"Rosenberg, David"'

1. Large-scale annotated dataset for cochlear hair cell detection and classification

Author

Buswinka, Christopher J, Rosenberg, David B, Simikyan, Rubina G, Osgood, Richard T, Fernandez, Katharine, Nitta, Hidetomi, Hayashi, Yushi, Liberman, Leslie W, Nguyen, Emily, Yildiz, Erdem, Kim, Jinkyung, Jarysta, Amandine, Renauld, Justine, Wesson, Ella, Wang, Haobing, Thapa, Punam, Bordiga, Pierrick, McMurtry, Noah, Llamas, Juan, Kitcher, Siân R, López-Porras, Ana I, Cui, Runjia, Behnammanesh, Ghazaleh, Bird, Jonathan E, Ballesteros, Angela, Vélez-Ortega, A Catalina, Edge, Albert SB, Deans, Michael R, Gnedeva, Ksenia, Shrestha, Brikha R, Manor, Uri, Zhao, Bo, Ricci, Anthony J, Tarchini, Basile, Basch, Martín L, Stepanyan, Ruben, Landegger, Lukas D, Rutherford, Mark A, Liberman, M Charles, Walters, Bradley J, Kros, Corné J, Richardson, Guy P, Cunningham, Lisa L, and Indzhykulian, Artur A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Prevention, Neurosciences, Ear, Animals, Mice, Guinea Pigs, Humans, Rats, Swine, Cochlea, Hair Cells, Auditory, Microscopy, Fluorescence, Machine Learning
Abstract

Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

Published
2024

3. DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

Author

Castellon, Rodrigo, Gopal, Achintya, Bloniarz, Brian, and Rosenberg, David

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep learning-based approaches tend to lag behind. In this work, we present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a transformer-based autoregressive model that maintains differential privacy and achieves performance competitive with marginal-based methods on a wide variety of datasets, capable of even outperforming state-of-the-art methods in certain settings. We also provide a theoretical framework for understanding the limitations of marginal-based approaches and where deep learning-based approaches stand to contribute most. These results suggest that deep learning-based techniques should be considered as a viable alternative to marginal-based methods in the generation of differentially private synthetic tabular data.

Published
2023

4. MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies

Author

Zhang, Shiyue, Wu, Shijie, Irsoy, Ozan, Lu, Steven, Bansal, Mohit, Dredze, Mark, and Rosenberg, David

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

Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may "over-generalize", in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies. Our code and models are publicly available at https://github.com/bloomberg/mixce-acl2023, Comment: ACL 2023 (22 pages)

Published
2023

5. Large-scale annotated dataset for cochlear hair cell detection and classification

Author

Buswinka, Christopher J, Rosenberg, David B, Simikyan, Rubina G, Osgood, Richard T, Fernandez, Katharine, Nitta, Hidetomi, Hayashi, Yushi, Liberman, Leslie W, Nguyen, Emily, Yildiz, Erdem, Kim, Jinkyung, Jarysta, Amandine, Renauld, Justine, Wesson, Ella, Thapa, Punam, Bordiga, Pierrick, McMurtry, Noah, Llamas, Juan, Kitcher, Siân R, López-Porras, Ana I, Cui, Runjia, Behnammanesh, Ghazaleh, Bird, Jonathan E, Ballesteros, Angela, Vélez-Ortega, A Catalina, Edge, Albert SB, Deans, Michael R, Gnedeva, Ksenia, Shrestha, Brikha R, Manor, Uri, Zhao, Bo, Ricci, Anthony J, Tarchini, Basile, Basch, Martin, Stepanyan, Ruben S, Landegger, Lukas D, Rutherford, Mark, Liberman, M Charles, Walters, Bradley J, Kros, Corné J, Richardson, Guy P, Cunningham, Lisa L, and Indzhykulian, Artur A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Neurosciences, Ear
Abstract

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90,000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

Published
2023

10. BloombergGPT: A Large Language Model for Finance

Author

Wu, Shijie, Irsoy, Ozan, Lu, Steven, Dabravolski, Vadim, Dredze, Mark, Gehrmann, Sebastian, Kambadur, Prabhanjan, Rosenberg, David, and Mann, Gideon

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

The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets. We validate BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. Additionally, we explain our modeling choices, training process, and evaluation methodology. We release Training Chronicles (Appendix C) detailing our experience in training BloombergGPT., Comment: Updated to include Training Chronicles (Appendix C)

Published
2023

12. Teacher Turnover before, during, & after COVID

Author

Education Resource Strategies, Rosenberg, David, and Anderson, Tara

Abstract

Before COVID, the shortage of qualified, skilled teachers was among the top challenges facing education leaders. And with the stress of the pandemic, survey data showed that almost half of the public school teachers who left the profession since March 2020 cite COVID-19 as the main reason. As school systems ramp up hiring for next fall, concrete data on actual teacher turnover is scarce. To fill in that gap, Education Resource Strategies (ERS) worked with six district partners -- all large, urban districts, spread across the country -- to understand their actual teacher turnover patterns in 2020. In the six districts ERS studied, teacher turnover declined from an average of 17.3 percent over the prior three years, to 12.6 percent in 2020. Students in the highest-poverty schools experienced the greatest "increase" in staff stability compared to prior years. With new federal funding, state, district, and school leaders should be exploring how to use stimulus dollars in ways that improve the teaching job. Districts and schools can build toward making teachers' jobs more rewarding, collaborative, and sustainable by investing in the kinds of structures and conditions that matter most -- such as competitive compensation with opportunities to grow over time, supportive school leadership, sufficient time for collaboration, and teaching loads that make it possible to build relationships with their students and adjust approaches to meet their needs.

Published
2021

14. Moving to the Next Phase of School Reopening: Three Pivotal Questions for District Leaders

Author

Education Resource Strategies and Rosenberg, David

Abstract

Nearly half of the 907 public school districts tracked by Education Week opened school fully remote this year. This includes more than three-quarters of the nation's largest districts, which serve high proportions of students with the greatest learning and social-emotional needs stemming from the pandemic. Now, many district leaders are working feverishly to determine if they can transition to some in-person learning for their students as the school year goes on. For more than six months, our team at Education Resource Strategies (ERS) has partnered closely with eight large school districts in developing prototypes, models and tools to support district planning, grounded in guiding principles. From that work, we have now zeroed in on three challenging questions that will have an outsized impact on student and educator experiences in a transition to any in-person school: (1) What should be our next step--hybrid or in-person?; (2) How do we meet the needs of in-person and remote students simultaneously?; and (3) How should we handle teacher opt-outs from in-person instruction?

Published
2020

15. Richard K. Smith: An appreciation

Author

Hall, R. Cargill and Rosenberg, David Alan

Subjects
BIOGRAPHY, OFFICERS - Navy - United States - Biography, AERONAUTICS - United States - History, MILITARY HISTORIANS
Abstract

illus por bibliog

Published
2004

17. Identifying and mitigating bias in algorithms used to manage patients in a pandemic

Author

Li, Yifan, Yoon, Garrett, Nasir-Moin, Mustafa, Rosenberg, David, Neifert, Sean, Kondziolka, Douglas, and Oermann, Eric Karl

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

Numerous COVID-19 clinical decision support systems have been developed. However many of these systems do not have the merit for validity due to methodological shortcomings including algorithmic bias. Methods Logistic regression models were created to predict COVID-19 mortality, ventilator status and inpatient status using a real-world dataset consisting of four hospitals in New York City and analyzed for biases against race, gender and age. Simple thresholding adjustments were applied in the training process to establish more equitable models. Results Compared to the naively trained models, the calibrated models showed a 57% decrease in the number of biased trials, while predictive performance, measured by area under the receiver/operating curve (AUC), remained unchanged. After calibration, the average sensitivity of the predictive models increased from 0.527 to 0.955. Conclusion We demonstrate that naively training and deploying machine learning models on real world data for predictive analytics of COVID-19 has a high risk of bias. Simple implemented adjustments or calibrations during model training can lead to substantial and sustained gains in fairness on subsequent deployment., Comment: 4 pages, 1 tables

Published
2021

18. Opportunities Abound: How Massachusetts School Districts Can Use New State Funding to Re-imagine Schools and Systems. ERS Briefing

Author

Education Resource Strategies, Rosenberg, David, Trawick-Smith, Joseph, and Miles, Karen Hawley

Abstract

The 2019 Student Opportunity Act creates a once-in-a-generation moment for Massachusetts students: the Commonwealth has committed $1.5 billion dollars over seven years to make good on the promise that best-in-the-nation education can be a reality for all Massachusetts children, especially English learners and students from low-income families. But daily demands don't evaporate with expanded funding, and it can be difficult for district leaders to know: can we really afford the kinds of changes that will transform school for our kids? Our team at ERS examined real Massachusetts districts to test what's possible with expanded SOA funding. Using state funding allocations and school data, we found a set of strategies to support new teachers, give attention to students who are struggling, and help kids build the social-emotional skills to succeed -- all made possible by funds districts didn't have a year ago. This briefing shares strategic practices that take advantage of this moment to make big changes and help students succeed.

Published
2020

19. 3 Indie Children's Books for Aspiring Astronauts

Author

Rey, H.A., Jeffries, Jo Ann, Bob, Lukas Kaiolohia, Rosenberg, David Faber, Kimsly, Ezra Dutch, Cañas, Mike, and Davis, Marc Eliot

Subjects
Children's books, Outer space -- Discovery and exploration, Astronauts
Abstract

At some point, every child considers a career as an astronaut; it must be among the top five answers to the perennial question, 'What do you want to be when [...]

Published
2024

21. Dual Reinforcement-Based Specification Generation for Image De-Rendering

Author

Pasunuru, Ramakanth, Rosenberg, David, Mann, Gideon, and Bansal, Mohit

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Advances in deep learning have led to promising progress in inferring graphics programs by de-rendering computer-generated images. However, current methods do not explore which decoding methods lead to better inductive bias for inferring graphics programs. In our work, we first explore the effectiveness of LSTM-RNN versus Transformer networks as decoders for order-independent graphics programs. Since these are sequence models, we must choose an ordering of the objects in the graphics programs for likelihood training. We found that the LSTM performance was highly sensitive to the sequence ordering (random order vs. pattern-based order), while Transformer performance was roughly independent of the sequence ordering. Further, we present a policy gradient based reinforcement learning approach for better inductive bias in the decoder via multiple diverse rewards based both on the graphics program specification and the rendered image. We also explore the combination of these complementary rewards. We achieve state-of-the-art results on two graphics program generation datasets., Comment: AAAI 2021 Scientific Document Understanding Workshop (9 pages)

Published
2021

22. David Rosenberg: Love him or hate him, an economy under Trump should fare better than under Harris

Author

Rosenberg, David

Subjects
Harris, Kamala -- Tax policy -- Political activity, Trump, Donald -- Political activity -- Tax policy
Abstract

Here’s the U.S. election outcome all investors should be hoping for, Mark Twain, if he were alive today, would likely update his famous refrain to “Lies, damned lies, statistics and politics.” I want to state emphatically that I am not “political,” [...]

Published
2024

23. The Bank of Canada has now offset the policy mistake it made a year ago

Author

Rosenberg, David

Subjects
Bank of Canada -- Powers and duties, Monetary policy -- Political aspects, Inflation (Finance) -- Forecasts and trends -- Canada, Gross domestic product -- Forecasts and trends, Interest rates -- Laws, regulations and rules, Government regulation, Market trend/market analysis, General interest, News, opinion and commentary
Abstract

Byline: DAVID ROSENBERG Founder of Rosenberg Research The Bank of Canada did the expected (and the right thing) on Wednesday by trimming the policy rate for the second meeting in [...]

Published
2024

24. Growing Great Teachers: How School System Leaders Can Use Existing Resources to Better Develop, Support, and Retain New Teachers--and Improve Student Outcomes

Author

Education Resource Strategies, Rosenberg, David, and Miles, Karen Hawley

Abstract

Every year, school districts welcome -- and then lose -- thousands of promising new teachers. For too many, the job is overwhelming. How can we improve instruction and reduce teacher turnover -- especially with limited resources, in our highest-needs schools? In the "Growing Great Teachers" report, we use research-based "impact modeling" to show how a strategic approach to recruiting and supporting rookie teachers could yield as much as 4.2 extra months of student learning. We provide 5 recommendations for school systems to leverage their investment in structures that provide rookie teachers with both shelter and development.

Published
2018

25. Risk of COVID-19 after natural infection or vaccination

Author

Adams, Atoya, Miller, Eric, Rankin, Bruce G., Shinn, Steven, Nash, Marshall, Green, Sinikka L., Jacobsen, Colleen, Krishnankutty, Jayasree, Phungwayo, Sikhongi, Glover, Richard M., II, Slechta, Stacy, Holdeman, Troy, Hartvickson, Robyn, Grant, Amber, Poling, Terry L., Klein, Terry D., Klein, Thomas C., Klein, Tracy R., Smith, William B., Gibson, Richard L., Winbigler, Jennifer, Parker, Elizabeth, Wijewardane, Priyantha N., Bravo, Eric, Thessing, Jeffrey, Maxwell, Michelle, Horn, Amanda, Healy, Catherine Mary, Akamine, Christine, Chu, Laurence, Chouteau, R. Michelle, Cotugno, Michael J., Bauer, George H., Jr., Hachigian, Greg, Oshita, Masaru, Cancilla, Michael, Kiersey, Kristen, Seger, William, Antwi, Mohammed, Green, Allison, Kim, Anthony, Desjardins, Michael, Johnson, Jennifer A., Sherman, Amy, Walsh, Stephen R., Borger, Judith, Saleem, Nafisa, Solis, Joel, Medina, Martha Carmen, Keating, Westly, Garcia, Edgar, Bueno, Cynthia, Segall, Nathan, Denham, Douglas S., Weiss, Thomas, Avworo, Ayoade, Hedges, Parke, Strout, Cynthia Becher, Santiago, Rica, Davis, Yvonne, Howenstine, Patty, Bondell, Alison, Marks, Kristin, Wang, Tina, Wilkin, Timothy, Vogler, Mary, Johnston, Carrie, Andrasik, Michele P., Andriesen, Jessica G., Broder, Gail, Eaton, Niles, Gelderblom, Huub G., McClennen, Rachael, Michael, Nelson, Robb, Merlin, Sopher, Carrie, Miller, Vicki E., Santiago, Fredric, Gomez, Blanca, Valika, Insiya, Starr, Amy, Cantos, Valeria D., Kandiah, Sheetal, Rio, Carlos del, Rouphael, Nadine, Edupuganti, Srilatha, Anderson, Evan J., Camacho-Gonzalez, Andres, Kamidani, Satoshi, Teherani, Meghan, Diemert, David J., Malkin, Elissa, Siegel, Marc, Roberts, Afsoon, Simon, Gary, Balani, Bindu, Stephenson, Carolene, Sperber, Steven, Cicogna, Cristina, Zervos, Marcus J., Kilgore, Paul, Ramesh, Mayur, Herc, Erica, Zenlea, Kate, Burgher, Abram, Milliken, Ann M., Davis, Joseph D., Levy, Brendan, Kelman, Sandra, Doust, Matthew W., Sample, Denise, Erickson, Sandra, Christensen, Shane G., Matich, Christopher, Longe, James, Witbeck, John, Peterson, James T., Clark, Alexander, Kelty, Gerald, Pena-Renteria, Issac, Koren, Michael J., Bartilucci, Darlene, Patel, Alpa, Tran, Carolyn, Kennelly, Christina, Brownlee, Robert, Coleman, Jacob, Webster, Hala, Fierro, Carlos A., Leistner, Natalia, Thompson, Amy, Gonzalez, Celia, Jackson, Lisa A., Suyehira, Janice, Haber, Milton, Regalado, Maria M., Procasky, Veronica, Lutat, Alisha, Griffin, Carl P., Hollister, Ripley R., Brown, Jeremy, Ronk, Melody, Harper, Wayne L., Cohen, Lisa, Eckert, Lynn, Hong, Matthew, Rouhbakhsh, Rambod, Danford, Elizabeth, Johnson, John, Calderone, Richard, Khetan, Shishir K., Olanrewaju, Oyebisi, Zhai, Nan, Nieves, Kimberly, O'Brien, Allison, Bradley, Paul S., Lilienthal, Amanda, Callis, Jim, Brosz, Adam B., Clement, Andrea, West, Whitney, Friesen, Luke, Cramer, Paul, Eder, Frank S., Little, Ryan, Engler, Victoria, Rattenbury-Shaw, Heather, Ensz, David J., Oplinger, Allie, Essink, Brandon J., Meyer, Jay, Raiser, Frederick, III, Mueller, Kimberly, Vrbicky, Keith W., Harper, Charles, Nutsch, Chelsie, Lewis, Wendell, III, Laflan, Cathy, Whatley, Jordan L., Harrell, Nicole, Shannon, Amie, Rowell, Crystal, Dedon, Christopher, Makhene, Mamodikoe, Gottschlich, Gregory M., Harden, Kate, Gottschlich, Melissa, Smith, Mary, Powell, Richard, Kimmel, Murray A., Pinto, Simmy, Vachris, Timothy P., Hutchens, Mark, Daniels, Stephen, Wells, Margaret, Van Der Leden, Mimi, Jackson-Booth, Peta-Gay, Baron, Mira, Kane, Pamela, Seversen, Shannen, Kryvicky, Mara, Lord, Julia, Saleh, Jamshid, Miles, Matthew, Lupercio, Rafael, McGettigan, John W., Jr., Patton, Walter, Brakema, Riemke, Choquette, Karin, McGettigan, Jonlyn, Kirstein, Judith L., Bernard, Marcia, Manning, Mary Beth, Rothenberg, Joan, Briskin, Toby, Roadman, Denise, Tedder-Edwards, Sharita, Schwartz, Howard I., Mederos, Surisday, Swaminathan, Shobha, Nyaku, Amesika, Varughese, Tilly, DallaPiazza, Michelle, Frey, Sharon E., Graham, Irene, Abate, Getahun, Hoft, Daniel, Allen, Leland N., III, Edwards, Leslie A., Davis, William S., Jr., Mena, Jessica M., Kutner, Mark E., Caso, Jorge, Moran, Maria Hernandez, Carvajal, Marianela, Mendez, Janet, Wadsworth, Larkin T., III, Adams, Michael R., Iverson, Leslie, Newberg, Joseph L., Pearlman, Laura, Nugent, Paul J., Reynolds, Michele D., Bashour, Jennifer, Schmidt, Robert, Sheth, Neil P., Steil, Kenneth, Toma, Ramy J., Kirby, William, Folmar, Pink, Williams, Samantha, Pickrell, Paul, Mott, Stefanie, Linebarger, Carol Ann, Malbari, Hussain, Pampe, David, Fragoso, Veronica G., Holloway, Lisa, McKeown-Bragas, Cecilia, Becker, Teresa, Williams, Barton G., Jones, William H., Clark, Jesse L., Shoptaw, Steven, Vertucci, Michele, Hernandez, Will, Spector, Stephen A., Moodley, Amaran, Blumenthal, Jill, Stangl, Lisa, Deutsch, Karen, Mullane, Kathleen M., Pitrak, David, Nuss, Cheryl, Pi, Judy, Fichtenbaum, Carl, Powers-Fletcher, Margaret, Saemann, Michelle, Kohrs, Sharon, Campbell, Thomas B., Lauria, Andrew, Mancilla, Jose C., Dunlevy, Hillary, Novak, Richard M., Wendrow, Andrea, Borgetti, Scott, Ladner, Ben, Chrisley, Lisa, Young, Cheryl, Doblecki-Lewis, Susanne, Alcaide, Maria L., Gonzales-Zamora, Jose, Morris, Stephen, Wohl, David, Eron, Joseph, Jr., Frank, Ian, Dunbar, Debora, Metzger, David, Momplaisir, Florence, Martin, Judith, Hoberman, Alejandro, Shope, Timothy, Muniz, Gysella, Rupp, Richard, Stanford, Amber, Berman, Megan, Porterfield, Laura, Lewis, Michael, Ghadishah, Elham, Yusin, Joseph, Pham, Mai, Creech, Clarence B., II, Walker, Shannon, Rolsma, Stephanie, Samuels, Robert, Thomsen, Isaac, Kalams, Spyros A., Wilson, Greg, Lucksinger, Gregg H., Parks, Kevin, Israelsen, Ryan, Ostovar, Jaleh, Kelly, Kary, Overcash, Jeffrey S., Chu, Hanh, Lee, Kia, De La Cruz, Luis I., Clemons, Steve, Everette, Elizabeth, Studdard, Suzanna, Mohan, Gowdhami, Tyson, Stefanie, Peay, Alyssa-Kay, Johnson, Danyel, Feldman, Gregory J., Suen, May-Yin, Muenzner, Jacqueline, Boscia, Joseph, Siddiqui, Farhan, Sanders, John, Peacock, James, Nasim, Julio, Levin, Michael L., Hussey, Julie, Kulic, Marcy, McKenzie, Mark M., Deese, Teresa, Osmundsen, Erica, Sweet, Christy, Ebuh, Valentine M., Elnagar, Elwaleed, Ebuh, Georgette, Iwuala, Genevieve, Han-Conrad, Laurie J., Simmons, Todd, Tarakjian, Denis, Ackermann, Jeremy, Adams, Mark S., Alemán, José O., Al-Ibrahim, Mohamed S., Andes, David R., Andrews, Jeb, Arduino, Roberto C., Bäcker, Martín, Badillo, Diana, Bainbridge, Emma, Batteiger, Teresa A., Bazan, Jose A., Bedimo, Roger J., Benitez, Jorge A., Bennett, Annette R., Bernstein, David I., Bialobok, Kristin, Boas, Rebecca, Brady, Judith, Brown, Cynthia, Bunce, Catherine A., Call, Robert S., Campbell, Wesley, Carmody, Ellie, Carpenter, Christopher, Carsons, Steven E., Castellon, Marvin, Castro, Mario, Catan, Hannah, Chang, Jennifer, Chebib, Mouna G., Chen, Corey M., Cheng, Margaret, Chow, Brian D.W., Ciambruschini, Annie, Connor, Joseph P., Conway, James H., Cooney, Maureen, Curlin, Marcel, De La Matta Rodriguez, Claudia, Dedon, Jon F., Degan, Emily, Dickey, Michelle, Dietz, Craig, Dong, Jennifer L., Dorcely, Brenda, Dube, Michael P., Dyer, Carmel B., Eckhardt, Benjamin, Ellerbeck, Edward, Ewers, Evan C., Falk, Amy, Feijoo, Brittany, Felsen, Uriel R., Fiel, Tom, Fitz-Patrick, David, Fogarty, Charles M., Ford, Stacy, Forero, Lina M., Formentini, Elizabeth, Franco-Vitteri, Doris, Frenck, Robert W., Jr., Gharib, Elie, Gharib, Suzanne, Rucker, Rola G., Goldenberg, James N., González, Luis H., Gray, Brett, Greene, Rusty, Grossberg, Robert M., Guanira-Carranza, Juan V., Guerreros Benavides, Alfredo Gilberto, Guillory, Clint C., Gunaratne, Shauna H., Halpert, David, Hamilton, Holli, Hartman, William R., Henderson, Sheryl L., Herati, Ramin, Guarin, Laura Hernandez, Hilder, Robin, Ho, Ken, Hojat, Leila, Hosek, Sybil G., Jacobson, Jeffrey M., Jay, Melanie, Johnson, Diane H., Jones, Kathleen S., Jones-López, Edward C., Justman, Jessica E., Kahney, Scott, Katz, Lois, Katz, Melinda, Kaul, Daniel, Keefer, Michael C., Kennedy, Ashley, Knishinsky, Jennifer, Kogelman, Laura, Koletar, Susan L., Kottkamp, Angelica, Laguio-Vila, Maryrose, Landovitz, Raphael J., Lee, Jessica L., Liu, Albert, Llerena Zegarra, Eneyda Giuvanela, Lok, Anna S., Lovell, James, Lubelchek, Ronald, Lucaj, John, Luckasen, Gary, Luetkemeyer, Annie, Lugogo, Njira Lucia, Maenza, Janine, Malvestutto, Carlos, Mauri, Monica, Maves, Ryan C., Mayer, Kenneth H., McCartney, Michael J., McCort, Margaret E., McElrath, M. Juliana, McNairy, Meredith, Merino, Fernando L., Meyerowitz, Eric A., Mitchell, Carol L., Monaco, Cynthia L., Muhammad, Sauda, Muñoz-Gómez, Sigridh, Munsiff, Sonal, Nee, Paul, Nollen, Nicole L., Noor, Asif, Lagos, Claudio Nuñez, Okulicz, Jason F., Oliver, Patrick A., Ortega, Jessica, Palmer, Steven, Parameswaran, Lalitha, Parikh, Purvi, Parker, Susan, Parungao, Reza, Pavie, Juana R., Madan, Rebecca P., Peralta, Henry, Petts, Jennifer, Pierce, Kristen K., Pretell Alva, E. Javier, Purpura, Lawrence J., Raabe, Vanessa, Recuenco, Sergio E., Richards, Tamara, Riddler, Sharon A., Rizzardi, Barbara, Rokser, Rachel, Rolle, Charlotte-Paige, Rosen, Adam, Rosen, Jeffrey, Freese, Lena R., Santolaya, María E., Schipani, Linda M., Schwartz, Adam, Schwasinger-Schmidt, Tiffany, Scott, Hyman, Sha, Beverly E., Shankaran, Shivanjali, Shapiro, Adrienne E., Sharp, Stephan C., Shopsin, Bo, Sims, Matthew D., Skipper, Stephanie, Smith, Derek M., Smith, Michael J., Sobhanie, M. Mahdee, Sovic, Brit, Sterling, Stephanie, Striker, Robert, Tafur Bances, Karla Beatriz, Talaat, Kawsar R., Tavel, Edward M., Jr., Tieu, Hong V., Tomaszewski, Christian, Tomlinson, Ryan, Torres, Juan P., Torres, Julian A., Treanor, John J., Tukuru, Sade, Ulrich, Robert J., Utz, Gregory C., Viar, Veronica, Viau Colindres, Roberto A., Walsh, Edward E., Walsh, Mary C., Walter, Emmanuel B., Weidler, Jessica L., Wu, Yi H., Yang, Kinara S., Yrivarren Giorza, Juan Luis, Zemanek, Arthur L., Zhang, Kevin, Zingman, Barry S., Gorman, Richard, Paez, Carmen A., Swann, Edith, Takuva, Simbarashe G., Greninger, Alex, Roychoudhury, Pavitra, Coombs, Robert W., Jerome, Keith R., Castellino, Flora, Tong, Xiaomi, Pavetto, Corrina, Gipson, Teletha, Tong, Tina, Lee, Marina, Zhou, James, Fay, Michael, McQuarrie, Kelly, Nnadi, Chimeremma, Sogbetun, Obiageli, Ahmad, Nina, De Proost, Ian, Hoseyni, Cyrus, Coplan, Paul, Khan, Najat, Ronco, Peter, Furey, Dawn, Meck, Jodi, Vingerhoets, Johan, Brandenburg, Boerries, Custers, Jerome, Hendriks, Jenny, Juraszek, Jarek, Marit de Groot, Anne, Van Roey, Griet, Heerwegh, Dirk, Van Dromme, Ilse, Méndez Galván, Jorge F., Carrascal, Monica B., Duran, Adriana Sordo, Sanchez Guerrero, Laura Ruy, Gómora Madrid, Martha Cecilia, Barrat Hernández, Alejandro Quintín, Guizar, Sharzhaad Molina, González Estrada, Denisse Alejandra, Martínez Pérez, Silvano Omar, Zárate Hinojosa, Zindy Yazmín, Ruiz-Palacios, Guillermo Miguel, Cruz-Valdez, Aurelio, Pacheco-Flores, Janeth, Lara, Anyela, Díaz-Miralrio, Secia, Reyes Fentanes, María José, Olmos Vega, Jocelyn Zuleica, Méndez, Daniela Pineda, Martínez, Karina Cano, Alvarez León, Winniberg Stephany, Ruiz Herrera, Vida Veronica, Vázquez Saldaña, Eduardo Gabriel, Camacho Choza, Laura Julia, Vega Orozco, Karen Sofia, Ortega Domínguez, Sandra Janeth, Chacón, Jorge A., Rivera, Juan J., Cutz, Erika A., Ortegón, Maricruz E., Rivera, María I., Browder, David, Burch, Cortney, Moye, Terri, Bondy, Paul, Browder, Lesley, Manning, Rickey D., Hurst, James W., Sturgeon, Rodney E., Wakefield, Paul H., Kirby, John A., Andersen, James, Fearon, Szheckera, Negron, Rosa, Medina, Amy, Hill, John M., Rajasekhar, Vivek, Williams, Hayes, Cade, LaShondra, Fouts, Rhodna, Moya, Connie, Anderson, Corey G., Devine, Naomi, Ramsey, James, Perez, Ashley, Tatelbaum, David, Jacobs, Michael, Menasche, Kathleen, Mirkil, Vincent, Winkle, Peter J., Haggag, Amina Z., Haynes, Michelle, Villegas, Marysol, Raja, Sabina, Riesenberg, Robert, Plavin, Stanford, Lerman, Mark, Woodside, Leana, Johnson, Maria, Healy, C. Mary, Whitaker, Jennifer A., Keitel, Wendy A., Atmar, Robert L., Horwith, Gary, Mason, Robin, Johnson, Lisa, Dora, Tambra, Murray, Deborah, Ledbetter, Logan, Ewing, Beverly, Stephenson, Kathryn E., Tan, Chen S., Zash, Rebecca, Ansel, Jessica L., Jaegle, Kate, Guiney, Caitlin J., Henderson, Jeffrey A., O'Leary, Marcia, Enright, Kendra, Kessler, Jill, Ducheneaux, Pete, Inniss, Asha, Brandon, Donald M., Davis, William B., Lawler, Daniel T., Oppong, Yaa D., Starr, Ryan P., Syndergaard, Scott N., Shelly, Rozeli, Majumder, Mashrur Islam, Sugimoto, Danny, Dugas, Jeffrey, Sr., Rijos, Dolores, Shelton, Sandra, Hong, Stephan, Schwartz, Howard, Sanchez-Crespo, Nelia, Schwartz, Jennifer, Piedra, Terry, Corral, Barbara, Medina, Carmen, Dever, Michael E., Shah, Mitul, Delgado, Michael, Scott, Tameika, Usdan, Lisa S., McGill, Lora J., Arnold, Valerie K., Scatamacchia, Carolyn, Anthony, Codi M., Merchant, Rajan, Yoon, Anelgine C., Hill, Janet, Ng-Price, Lucy, Thompson-Seim, Teri, Ackerman, Ronald, Ackerman, Jamie, Aristy, Florida, Ketter, Nzeera, Finley, Jon, Stull, Mildred, Murray, Monica, Rizvi, Zainab, Guerrero, Sonia, Paliwal, Yogesh K., Paliwal, Amit, Gordon, Sarah, Gordon, Bryan, Montano-Pereira, Cynthia, Galloway, Christopher, Montros, Candice, Aleman, Lily, Shairi, Samira, Van Ever, Wesley, Freeman, George H., Harmon, Esther L., Cross, Marshall A., Sales, Kacie, Gular, Catherine Q., Hepburn, Matthew, Alderson, Nathan, Harshell, Shana, Mahgoub, Siham, Maxwell, Celia, Mellman, Thomas, Thompson, Karl M., Wortman, Glenn, Kingsley, Jeff, Pixler, April, Curry, LaKondria, Afework, Sarah, Swanson, Austin, Jacqmein, Jeffry, Bowers, Maggie, Robison, Dawn, Mosteller, Victoria, Garvey, Janet, Easley, Mary, Kurnat, Rebecca J., Cornelison, Raymond, Gower, Shanda, Schnitz, William, Heinzig-Cartwright, Destiny S., Lewis, Derek, Newton, Fred E., Duhart, Aeiress, Watkins, Breanz, Ball, Brandy, York, Jill, Pickle, Shelby, Musante, David B., Silver, William P., Belhorn, Linda R., Viens, Nicholas A., Dellaero, David, Patel, Priti, Lisec, Kendra, Safirstein, Beth, Zapata, Luz, Gonzalez, Lazaro, Quevedo, Evelyn, Irani, Farah, Grillo, Joseph, Potts, Amy, White, Julie, Flume, Patrick, Headden, Gary, Taylor, Brandie, Warden, Ashley, Chamberlain, Amy, Jeanfreau, Robert, Jeanfreau, Susan, Matherne, Paul G., Caldwell, Amy, Stahl, Jessica, Vowell, Mandy, Newhouse, Lauren, Berthaud, Vladimir, Takizala, Zudi-Mwak, Beninati, Genevieve, Snell, Kimberly, Baker, Sherrie, Walker, James, Harrison, Tavane, Miller, Meagan, Otto, Janet, Gray, Roni, Wilson, Christine, Nemecek, Tiffany, Harrington, Hannah, Eppenbach, Sally, Lewis, Wendell, Bourgeois, Tana, Folsom, Lyndsea, Holt, Gregory, Mirsaeidi, Mehdi, Calderon, Rafael, Lichtenberger, Paola, Quintero, Jalima, Martinez, Becky, Immergluck, Lilly, Johnson, Erica, Chan, Austin, Fas, Norberto, Thomas-Seaton, LaTeshia, Khizer, Saadia, Staben, Jonathan, Beresnev, Tatiana, Jahromi, Maryam, Marovich, Mary A., Hutter, Julia, Nason, Martha, Ledgerwood, Julie, Mascola, John, Leibowitz, Mark, Morales, Fernanda, Delgado, Mike, Sanchez, Rosario, Vega, Norma, Áñez, Germán, Albert, Gary, Coston, Erin, Desai, Chinar, Dunbar, Haoua, Eickhoff, Mark, Garcia, Jenina, Kautz, Margaret, Lee, Angela, Lewis, Maggie, McGarry, Alice, McKnight, Irene, Nelson, Joy, Newingham, Patrick, Price-Abbott, Patty, Reed, Patty, Vegas, Diana, Wilkinson, Bethanie, Smith, Katherine, Woo, Wayne, Cho, Iksung, Glenn, Gregory M., Dubovsky, Filip, Fried, David L., Haughey, Lynne A., Stanton, Ariana C., Rameaka, Lisa Stevens, Rosenberg, David, Tomatsu, Lee, Gonzalez, Viviana, Manalo, Millie, Grunstra, Bernard, Quinn, Donald, Claybrook, Phillip, Olds, Shelby, Dye, Amy, Cannon, Kevin D., Chadwick, Mesha M., Jordan, Bailey, Hussey, Morgan, Nevarez, Hannah, Kelley, Colleen F., Chung, Michael, Moran, Caitlin, Rebolledo, Paulina, Bacher, Christina, Barranco-Santana, Elizabeth, Rodriguez, Jessica, Mendoza, Rafael, Ruperto, Karen, Olivieri, Odette, Ocaña, Enrique, Wylie, Paul E., Henderson, Renea, Jenson, Natasa, Yang, Fan, Kelley, Amy, Finkelstein, Kenneth, Beckmann, David, Hutchins, Tanya, Escallon, Sebastian Garcia, Johnson, Kristen, Sligh, Teresa S., Desai, Parul, Huynh, Vincent, Lopez, Carlos, Mendoza, Erika, Adelglass, Jeffrey, Naifeh, Jerome G., Kucera, Kristine J., Chughtai, Waseem, Jaffer, Shireen H., Davis, Matthew G., Foley, Jennifer, Burgett, Michelle L., Shlotzhauer, Tammi L., Ingalsbe-Geno, Sarah M., Duncanson, Daniel, Kush, Kelly, Nesbitt, Lori, Sonnier, Cora, McCarter, Jennifer, Butcher, Michael B., Fry, James, Percy, Donna, Freudemann, Karen, Gebhardt, Bruce C., Mangu, Padma N., Schroeck, Debra B., Davit, Rajesh K., Hennekes, Gayle D., Luft, Benjamin J., Carr, Melissa, Nachman, Sharon, Pellecchia, Alison, Smith, Candace, Valenti, Bruno, Bermudez, Maria I., Peraita, Noris, Delgado, Ernesto, Arrazcaeta, Alicia, Ramirez, Natalie, Amador, Carmen, Marafioti, Horacio, Dang, Lyly, Clement, Lauren, Berry, Jennifer, Allaw, Mohammed, Geuss, Georgettea, Miles, Chelsea, Bittner, Zachary, Werne, Melody, Calinescu, Cornell, Rodman, Shannon, Rindt, Joshua, Cooksey, Erin, Harrison, Kristina, Cooper, Deanna, Horton, Manisha, Philyaw, Amanda, Jennings, William, Alvarado, Hilario, Baka, Michele, Regalado, Malina, Murray, Linda, Naguib, Sherif, Singletary, Justin, Richmond, Sha-Wanda, Omodele, Sarah, Oppenheim, Emily, Martinez, Reuben, Andriulis, Victoria, Singer, Leonard, Blevins, Jeanne, Thomas, Meagan, Hull, Christine, Pereira, Isabel, Rivero, Gina, Okonya, Tracy, Downing, Frances, Miller, Paulina, Rhee, Margaret, Stapleton, Katherine, Klein, Jeffrey, Hong, Rosamond, Swan, Suzanne, Wahlin, Tami, Bennett, Elizabeth, Salzl, Amy, Phan, Sharine, White, Jewel J., Occhino, Amanda, Paiano, Ruth, McLaughlin, Morgan, Swieboda, Elisa, Garcia-Fragoso, Veronica, Becerra, Maria G., White, Toni, Turley, Christine B., McWilliams, Andrew, Esinhart, Tiffany, Montoya, Natasha, Huskey, Shamika, Paul, Leena, Tashima, Karen, Johnson, Jennie, Neill, Marguerite, Sanchez, Martha, Rybak, Natasha, Mileno, Maria, Cohen, Stuart H., Ruiz, Monica, Boswell, Dean M., Robison, Elizabeth E., Reynolds, Trina L., Neumeister, Sonja, Zorrilla, Carmen D., Rivera, Juana, Ibarra, Jessica, García, Iris, Sierra, Dianca, Ramon, Wanda, Fiorillo, Suzanne, Pitotti, Rebecca, Anderson, Victoria R., Mancilla, Jose Castillo, Le, Nga, Winokur, Patricia L., Ince, Dilek, Hegmann, Theresa, Meier, Jeffrey, Stapleton, Jack, Stulken, Laura, McArthur, Monica, Berry, Andrea, Tapia, Milagritos, Hammershaimb, Elizabeth, Robinson, Toni, MacBryde, Rosa, Kline, Susan, Billings, Joanne L., Cavert, Winston, Forgosh, Les B., Schacker, Timothy W., Bold, Tyler D., Dandachi, Dima, Nelson, Taylor, Bran, Andres, Geiger, Grant, Naqvi, S. Hasan, Florescu, Diana F., Starlin, Richard, Kline, David, Zimmer, Andrea, Abbas, Anum, Wilson, Natasha, Eron, Joseph J., Sciaudone, Michael, Rosengren, A. Lina, Kizer, John S., Rutstein, Sarah E., Bruce, Elizabeth, Espinosa, Claudia, Sanders, Lisa J., Kim, Kami, Casey, Denise, Taylor, Barbara S., Patterson, Thomas, Pinilla, Ruth S., Bullock, Delia, Ponce, Philip, Patterson, Jan, McClelland, R. Scott, Lane, Dakotah C., Wald, Anna, James, Frank, Duke, Elizabeth, Hauge, Kirsten, Heimonen, Jessica, Goecker, Erin A., Huang, Yunda, Fong, Youyi, Kauffman, Carol, Linder, Kathleen, Nofz, Kimberly, McConnell, Andrew, Buynak, Robert J., Webb, Angella, Petty, Taryn, Andree, Stephanie, Sanchez, Erica, Mackey, Nolan, Baudelaire, Clarisse, Dzigiel, Sarah, Marquez, Adrienna, Quillin, Kim, King, Michelle, Abad, Vanessa, Knowles, Jennifer, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Tovar, Dalia, Shaw, Marian E., Turner, Mark A., Huffine, Cory J., Huffine, Esther S., Ake, Julie A., Secord, Elizabeth, McGrath, Eric, Levy, Phillip, Stewart, Brittany, Cromer, Charnell, Walters, Ayanna, Ellsworth, Grant, Greene, Caroline, Galloway, Sarah, Kapadia, Shashi, DeHaan, Elliot, Wilson, Clint, Milligan, Jason, Raley, Danielle, Bocchini, Joseph, McClenathan, Bruce, Hussain, Mary, Lomasney, Evelyn, Hall, Evelyn, Lamberth, Sherry, Schmeck, Christy, Leathers, Vickie, Theodore, Deborah A., Branche, Angela R., Graciaa, Daniel S., Hatlen, Timothy J., Miller, Jacqueline, Sadoff, Jerald, Falsey, Ann R., Sobieszczyk, Magdalena E., Rick, Anne-Marie, Laurens, Matthew B., Huang, Ying, Yu, Chenchen, Martin, Thomas C.S., Rodriguez, Carina A., Rostad, Christina A., Maboa, Rebone M., Baden, Lindsey R., El Sahly, Hana M., Grinsztejn, Beatriz, Gray, Glenda E., Gay, Cynthia L., Gilbert, Peter B., Janes, Holly E., Kublin, James G., Leav, Brett, Hirsch, Ian, Struyf, Frank, Dunkle, Lisa M., Neuzil, Kathleen M., Corey, Lawrence, Goepfert, Paul A., Follmann, Dean, and Kotloff, Karen L.

Published
2023
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27. Genome-wide association study of pediatric obsessive-compulsive traits: shared genetic risk between traits and disorder.

Author

Burton, Christie L, Lemire, Mathieu, Xiao, Bowei, Corfield, Elizabeth C, Erdman, Lauren, Bralten, Janita, Poelmans, Geert, Yu, Dongmei, Shaheen, S-M, Goodale, Tara, Sinopoli, Vanessa M, OCD Working Group of the Psychiatric Genomics Consortium, Soreni, Noam, Hanna, Gregory L, Fitzgerald, Kate D, Rosenberg, David, Nestadt, Gerald, Paterson, Andrew D, Strug, Lisa J, Schachar, Russell J, Crosbie, Jennifer, and Arnold, Paul D

Subjects
OCD Working Group of the Psychiatric Genomics Consortium, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10-8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p's

Published
2021

28. The Lost Poet's Teacher

Author

Rosenberg, David

Subjects
Bible as literature, Poets, Teachers, Bible and literature, Arts, visual and performing
Abstract

I didn't have full command of Spanish as an undergrad translating poems of Jorge Guillen. Still, I was surprised my versions 'sounded' authoritative when published without the originals. What is [...]

Published
2024

33. Three‐dimensional printing in radiation oncology: A systematic review of the literature

Author

Rooney, Michael K, Rosenberg, David M, Braunstein, Steve, Cunha, Adam, Damato, Antonio L, Ehler, Eric, Pawlicki, Todd, Robar, James, Tatebe, Ken, and Golden, Daniel W

Subjects
Medical and Biological Physics, Biomedical and Clinical Sciences, Clinical Sciences, Physical Sciences, Oncology and Carcinogenesis, Cancer, Animals, Brachytherapy, Phantoms, Imaging, Printing, Three-Dimensional, Radiation Oncology, 3D printing, additive manufacturing, clinical application, radiation oncology, Other Physical Sciences, Medical Physiology, Nuclear Medicine & Medical Imaging, Medical physiology, Medical and biological physics
Abstract

Purpose/objectivesThree-dimensional (3D) printing is recognized as an effective clinical and educational tool in procedurally intensive specialties. However, it has a nascent role in radiation oncology. The goal of this investigation is to clarify the extent to which 3D printing applications are currently being used in radiation oncology through a systematic review of the literature.Materials/methodsA search protocol was defined according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Included articles were evaluated using parameters of interest including: year and country of publication, experimental design, sample size for clinical studies, radiation oncology topic, reported outcomes, and implementation barriers or safety concerns.ResultsOne hundred and three publications from 2012 to 2019 met inclusion criteria. The most commonly described 3D printing applications included quality assurance phantoms (26%), brachytherapy applicators (20%), bolus (17%), preclinical animal irradiation (10%), compensators (7%), and immobilization devices (5%). Most studies were preclinical feasibility studies (63%), with few clinical investigations such as case reports or series (13%) or cohort studies (11%). The most common applications evaluated within clinical settings included brachytherapy applicators (44%) and bolus (28%). Sample sizes for clinical investigations were small (median 10, range 1-42). A minority of articles described basic or translational research (11%) and workflow or cost evaluation studies (3%). The number of articles increased over time (P

Published
2020

34. Improving Grey-Box Fuzzing by Modeling Program Behavior

Author

Karamcheti, Siddharth, Mann, Gideon, and Rosenberg, David

Subjects
Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Grey-box fuzzers such as American Fuzzy Lop (AFL) are popular tools for finding bugs and potential vulnerabilities in programs. While these fuzzers have been able to find vulnerabilities in many widely used programs, they are not efficient; of the millions of inputs executed by AFL in a typical fuzzing run, only a handful discover unseen behavior or trigger a crash. The remaining inputs are redundant, exhibiting behavior that has already been observed. Here, we present an approach to increase the efficiency of fuzzers like AFL by applying machine learning to directly model how programs behave. We learn a forward prediction model that maps program inputs to execution traces, training on the thousands of inputs collected during standard fuzzing. This learned model guides exploration by focusing on fuzzing inputs on which our model is the most uncertain (measured via the entropy of the predicted execution trace distribution). By focusing on executing inputs our learned model is unsure about, and ignoring any input whose behavior our model is certain about, we show that we can significantly limit wasteful execution. Through testing our approach on a set of binaries released as part of the DARPA Cyber Grand Challenge, we show that our approach is able to find a set of inputs that result in more code coverage and discovered crashes than baseline fuzzers with significantly fewer executions., Comment: 5 pages, 3 figures

Published
2018

37. Igniting the Learning Engine: How School Systems Accelerate Teacher Effectiveness and Student Growth through Connected Professional Learning

Author

Education Resource Strategies, District of Columbia Public Schools, Miles, Karen Hawley, Rosenberg, David, and Green, Genevieve Quist

Abstract

The introduction of college- and career-ready standards (CCRS) profoundly raises the bar for teaching and learning in American schools--and for professional development. Some school systems are rising to the challenge and significantly improving instruction and seeing student learning growth. With the support of the Bill & Melinda Gates Foundation, Education Resource Strategies sought to understand not just what is happening in these systems, but how leaders have reorganized resources--including people, time, and money--to make it happen. The authors identified four systems where instruction and student performance are improving even under more rigorous academic standards, where teachers are serving a relatively high-needs student population (e.g., at least 64 percent of students receive federal free or reduced-price lunch benefits), and where system leaders highlighted "redesigned professional learning" as a key driver of growth. These systems--District of Columbia Public Schools, Duval County Public Schools, Sanger Unified School District, and the charter management organization Achievement First--represent a range of sizes, regions, funding levels, and system types, enabling the authors to identify insights that they hope can be applied across the country. Each system took a different path, but they found that they all relied on three elements: (1) Rigorous, comprehensive curricula and assessments; (2) Content-focused, expert-led collaboration; and (3) Frequent, growth-oriented feedback. These school systems made these common, research-backed practices powerful by integrating the elements in one cohesive strategy, tightly connected to the work teachers do every day. The authors call this "Connected Professional Learning." It requires significant shifts in how school systems organize resources--moving away from one-size-fits-all workshops and pay for advanced degrees--toward time and instructional leaders to help teachers engage with the curriculum and adjust to student results. This report explores what these strategic practices look like, how to organize resources, and where to get started. [This work was produced with Duval County Public Schools, Sanger Unified School District, and Achievement First.]

Published
2017

40. Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning

Author

Zhao, Ran, Deng, Yuntian, Dredze, Mark, Verma, Arun, Rosenberg, David, and Stent, Amanda

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Technical and fundamental analysis are traditional tools used to analyze individual stocks; however, the finance literature has shown that the price movement of each individual stock correlates heavily with other stocks, especially those within the same sector. In this paper we propose a general purpose market representation that incorporates fundamental and technical indicators and relationships between individual stocks. We treat the daily stock market as a "market image" where rows (grouped by market sector) represent individual stocks and columns represent indicators. We apply a convolutional neural network over this market image to build market features in a hierarchical way. We use a recurrent neural network, with an attention mechanism over the market feature maps, to model temporal dynamics in the market. We show that our proposed model outperforms strong baselines in both short-term and long-term stock return prediction tasks. We also show another use for our market image: to construct concise and dense market embeddings suitable for downstream prediction tasks., Comment: Accepted as full paper in the 32nd International FLAIRS Conference

Published
2018

41. Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

Author

Karamcheti, Siddharth, Mann, Gideon, and Rosenberg, David

Subjects
Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws. Grey-box fuzzing, the most popular fuzzing strategy, combines light program instrumentation with a data driven process to generate new program inputs. In this work, we present a machine learning approach that builds on AFL, the preeminent grey-box fuzzer, by adaptively learning a probability distribution over its mutation operators on a program-specific basis. These operators, which are selected uniformly at random in AFL and mutational fuzzers in general, dictate how new inputs are generated, a core part of the fuzzer's efficacy. Our main contributions are two-fold: First, we show that a sampling distribution over mutation operators estimated from training programs can significantly improve performance of AFL. Second, we introduce a Thompson Sampling, bandit-based optimization approach that fine-tunes the mutator distribution adaptively, during the course of fuzzing an individual program. A set of experiments across complex programs demonstrates that tuning the mutational operator distribution generates sets of inputs that yield significantly higher code coverage and finds more crashes faster and more reliably than both baseline versions of AFL as well as other AFL-based learning approaches., Comment: Published as a workshop paper in the 11th ACM Workshop on Artificial Intelligence and Security (AISec '18) with the 25th ACM Conference on Computer and Communications Security (CCS '18)

Published
2018
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44. Five reasons BoC rate cuts won't reignite a boom in Canadian home prices

Author

Rosenberg, David

Subjects
Bank of Canada -- Powers and duties, Inflation (Finance) -- Forecasts and trends -- Canada, Dwellings -- Prices and rates, Housing -- Prices and rates, Interest rates -- Forecasts and trends, Residential real estate -- Forecasts and trends, Market trend/market analysis, Company pricing policy, General interest, News, opinion and commentary
Abstract

Byline: DAVID ROSENBERG Founder of Rosenberg Research, and author of the daily economic report, Breakfast with Dave I'm fielding a ton of questions about whether the Bank of Canada will [...]

Published
2024

46. Scatteract: Automated extraction of data from scatter plots

Author

Cliche, Mathieu, Rosenberg, David, Madeka, Dhruv, and Yee, Connie

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Statistics - Machine Learning
Abstract

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set., Comment: Submitted to ECML PKDD 2017 proceedings, 16 pages

Published
2017
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47. Journal of Water Resources Planning and Management's Reproducibility Review Program: Accomplishments, Lessons, and Next Steps.

Author

Stagge, James H., Rosenberg, David E., Castronova, Anthony M., Ostfeld, Avi, and Jones, Amber Spackman

Subjects
*WATER management, *CLIMATE change adaptation, *FREEWARE (Computer software), *WATER distribution
Abstract

The Journal of Water Resources Planning and Management (JWRPM) launched a Reproducibility Review Program in 2020 to address the lack of reproducibility in water-related journals. The program encourages authors to publish data, models, code, and directions with their articles so that independent reviewers can replicate their work. The program has five objectives, including promoting reproducibility, facilitating the use of reproducible work, and recognizing authors who make their work more reproducible. The program has already awarded Silver and Bronze badges to several manuscripts and plans to expand to other journals. Challenges include incorporating reproducibility reviews into the submission process and educating authors about the program. The program aims to improve reproducibility in the water resources field and shift towards reproducibility as the default in research. [Extracted from the article]

Published
2024
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48. Teamwork in Tulsa: District Reshapes Learning to Make the Best Use of People, Time, and Money

Author

Rosenberg, David and Green, Genevieve Quist

Abstract

Tulsa Public Schools Superintendent Deborah Gist and her team, along with school-based leaders, have worked with the district's school leaders to implement a comprehensive strategy to improve instructional quality for all students in Tulsa. Grounded in a clear vision for instruction and starting with the introduction of new curricula aligned to college- and career-ready standards, the Tulsa strategy recognizes that to move the needle on "student" learning, schools must change the structural conditions that influence "teachers'" professional learning. Education Resource Strategies (ERS), along with their partners from Leading Educators, have worked closely with leaders and educators in Tulsa Public Schools to implement a new job-embedded professional learning approach grounded in high-quality curriculum. With deliberate and strategic resource shifts, the district is setting up teachers to generate the most impact on student learning.

Published
2020

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