53,795 results on '"Rush A"'
Search Results
202. I Could've Asked That: Reformulating Unanswerable Questions.
- Author
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Wenting Zhao, Ge Gao, Claire Cardie, and Alexander M. Rush
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- 2024
203. Diffusion Models Without Attention.
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Jing Nathan Yan, Jiatao Gu, and Alexander M. Rush
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- 2024
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204. A Sub-THz CMOS Transceiver IC and System for Medium-Reach Guided Wave and Short-Reach Wireless Communication Links.
- Author
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Swaminathan Sankaran, Gerd Schuppener, Juan Herbsommer, Bradley Kramer, Hassan Ali, Robert Payne, Carole Rush, Baher Haroun, and Nirmal Warke
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- 2024
- Full Text
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205. Technology-Related Safety Event Analysis in Community Clinical Informatics: A Case Study.
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Chantelle Recsky, Megan Stowe, Kathy L. Rush, Maura MacPhee, Lorraine Blackburn, Allison Muniak, and Leanne M. Currie
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- 2024
- Full Text
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206. Entity Disambiguation via Fusion Entity Decoding.
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Junxiong Wang, Ali Mousavi 0003, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander M. Rush, Umar Farooq Minhas, and Yunyao Li 0001
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- 2024
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207. Predicting Text Preference Via Structured Comparative Reasoning.
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Jing Nathan Yan, Tianqi Liu 0002, Justin T. Chiu, Jiaming Shen, Zhen Qin 0001, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander M. Rush, Jialu Liu, and Michael Bendersky
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- 2024
- Full Text
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208. Intelligent Anomaly Detection System Based on Ensemble and Deep Learning.
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Babu Kaji Baniya and Thomas Rush
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- 2024
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209. Pathologic Features of Primary Pancreatic Malignancies
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Abi-Saab, Tarek, Cunningham, Ashley M., Rush, Patrick S., Matkowskyj, Kristina A., Rosen, Steven T., Series Editor, Bentrem, David, editor, and Benson, Al B., editor
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- 2024
- Full Text
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210. Four Musicians and the Fates : A Fairy Tale
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Rush, Kayla, Reed-Danahay, Deborah, Series Editor, Wulff, Helena, Series Editor, van Roekel, Eva, editor, and Murphy, Fiona, editor
- Published
- 2024
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211. Radiation, Ruins and the Post-Apocalyptic Stories: The Chornobyl Landscape in S.T.A.L.K.E.R.
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Rush-Cooper, Nick, Holloway, Philippa, editor, and Jordan-Baker, Craig, editor
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- 2024
- Full Text
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212. Metaverse Application Forensics: Unravelling the Virtual Truth
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Hannan Bin Azhar, M. A., Rush-Gadsby, Oliver, Masys, Anthony J., Editor-in-Chief, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, and Jahankhani, Hamid, editor
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- 2024
- Full Text
- View/download PDF
213. Rejecting the Accusation of a Violated STAR*D Protocol
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Rush, John, Trivedi, Madhukar, Fava, Maurizio, Thase, Michael E., and Wisniewski, Stephen
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Health ,Psychology and mental health - Abstract
At the heart of this matter is the eye-popping and contentious difference in the results of 2 teams analyzing ostensibly the same data set, with one team reporting an estimated [...]
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- 2024
214. Is it easier to count communities than find them?
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Rush, Cynthia, Skerman, Fiona, Wein, Alexander S., and Yang, Dana
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Mathematics - Statistics Theory ,Computer Science - Computational Complexity ,Computer Science - Data Structures and Algorithms ,Mathematics - Combinatorics ,Statistics - Machine Learning ,05C80, 62F03, 68Q25 ,F.2 ,G.2 - Abstract
Random graph models with community structure have been studied extensively in the literature. For both the problems of detecting and recovering community structure, an interesting landscape of statistical and computational phase transitions has emerged. A natural unanswered question is: might it be possible to infer properties of the community structure (for instance, the number and sizes of communities) even in situations where actually finding those communities is believed to be computationally hard? We show the answer is no. In particular, we consider certain hypothesis testing problems between models with different community structures, and we show (in the low-degree polynomial framework) that testing between two options is as hard as finding the communities. In addition, our methods give the first computational lower bounds for testing between two different `planted' distributions, whereas previous results have considered testing between a planted distribution and an i.i.d. `null' distribution., Comment: Accepted to Innovations in Theoretical Computer Science (ITCS) 2023
- Published
- 2022
215. Pretraining Without Attention
- Author
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Wang, Junxiong, Yan, Jing Nathan, Gu, Albert, and Rush, Alexander M.
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Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Transformers have been essential to pretraining success in NLP. While other architectures have been used, downstream accuracy is either significantly worse, or requires attention layers to match standard benchmarks such as GLUE. This work explores pretraining without attention by using recent advances in sequence routing based on state-space models (SSMs). Our proposed model, Bidirectional Gated SSM (BiGS), combines SSM layers with a multiplicative gating architecture that has been effective in simplified sequence modeling architectures. The model learns static layers that do not consider pair-wise interactions. Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation. Analysis shows that while the models have similar average accuracy, the approach has different inductive biases than BERT in terms of interactions and syntactic representations. All models from this work are available at https://github.com/jxiw/BiGS.
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- 2022
216. Abstract Visual Reasoning with Tangram Shapes
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Ji, Anya, Kojima, Noriyuki, Rush, Noah, Suhr, Alane, Vong, Wai Keen, Hawkins, Robert D., and Artzi, Yoav
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly annotated dataset that, with >1k distinct stimuli, is orders of magnitude larger and more diverse than prior resources. It is both visually and linguistically richer, moving beyond whole shape descriptions to include segmentation maps and part labels. We use this resource to evaluate the abstract visual reasoning capacities of recent multi-modal models. We observe that pre-trained weights demonstrate limited abstract reasoning, which dramatically improves with fine-tuning. We also observe that explicitly describing parts aids abstract reasoning for both humans and models, especially when jointly encoding the linguistic and visual inputs. KiloGram is available at https://lil.nlp.cornell.edu/kilogram ., Comment: EMNLP 2022 long paper
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- 2022
217. The out-of-sample prediction error of the square-root-LASSO and related estimators
- Author
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Olea, José Luis Montiel, Rush, Cynthia, Velez, Amilcar, and Wiesel, Johannes
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Mathematics - Statistics Theory ,Mathematics - Optimization and Control - Abstract
We study the classical problem of predicting an outcome variable, $Y$, using a linear combination of a $d$-dimensional covariate vector, $\mathbf{X}$. We are interested in linear predictors whose coefficients solve: % \begin{align*} \inf_{\boldsymbol{\beta} \in \mathbb{R}^d} \left( \mathbb{E}_{\mathbb{P}_n} \left[ \left(Y-\mathbf{X}^{\top}\beta \right)^r \right] \right)^{1/r} +\delta \, \rho\left(\boldsymbol{\beta}\right), \end{align*} where $\delta>0$ is a regularization parameter, $\rho:\mathbb{R}^d\to \mathbb{R}_+$ is a convex penalty function, $\mathbb{P}_n$ is the empirical distribution of the data, and $r\geq 1$. We present three sets of new results. First, we provide conditions under which linear predictors based on these estimators % solve a \emph{distributionally robust optimization} problem: they minimize the worst-case prediction error over distributions that are close to each other in a type of \emph{max-sliced Wasserstein metric}. Second, we provide a detailed finite-sample and asymptotic analysis of the statistical properties of the balls of distributions over which the worst-case prediction error is analyzed. Third, we use the distributionally robust optimality and our statistical analysis to present i) an oracle recommendation for the choice of regularization parameter, $\delta$, that guarantees good out-of-sample prediction error; and ii) a test-statistic to rank the out-of-sample performance of two different linear estimators. None of our results rely on sparsity assumptions about the true data generating process; thus, they broaden the scope of use of the square-root lasso and related estimators in prediction problems.
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- 2022
218. Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
- Author
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Choquette-Choo, Christopher A., McMahan, H. Brendan, Rush, Keith, and Thakurta, Abhradeep
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Computer Science - Machine Learning ,Computer Science - Cryptography and Security ,Computer Science - Data Structures and Algorithms ,Statistics - Machine Learning - Abstract
We introduce new differentially private (DP) mechanisms for gradient-based machine learning (ML) with multiple passes (epochs) over a dataset, substantially improving the achievable privacy-utility-computation tradeoffs. We formalize the problem of DP mechanisms for adaptive streams with multiple participations and introduce a non-trivial extension of online matrix factorization DP mechanisms to our setting. This includes establishing the necessary theory for sensitivity calculations and efficient computation of optimal matrices. For some applications like $>\!\! 10,000$ SGD steps, applying these optimal techniques becomes computationally expensive. We thus design an efficient Fourier-transform-based mechanism with only a minor utility loss. Extensive empirical evaluation on both example-level DP for image classification and user-level DP for language modeling demonstrate substantial improvements over all previous methods, including the widely-used DP-SGD . Though our primary application is to ML, our main DP results are applicable to arbitrary linear queries and hence may have much broader applicability., Comment: 9 pages main-text, 3 figures. 40 pages with 13 figures total
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- 2022
219. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
- Author
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Workshop, BigScience, Scao, Teven Le, Fan, Angela, Akiki, Christopher, Pavlick, Ellie, Ilić, Suzana, Hesslow, Daniel, Castagné, Roman, Luccioni, Alexandra Sasha, Yvon, François, Gallé, Matthias, Tow, Jonathan, Rush, Alexander M., Biderman, Stella, Webson, Albert, Ammanamanchi, Pawan Sasanka, Wang, Thomas, Sagot, Benoît, Muennighoff, Niklas, del Moral, Albert Villanova, Ruwase, Olatunji, Bawden, Rachel, Bekman, Stas, McMillan-Major, Angelina, Beltagy, Iz, Nguyen, Huu, Saulnier, Lucile, Tan, Samson, Suarez, Pedro Ortiz, Sanh, Victor, Laurençon, Hugo, Jernite, Yacine, Launay, Julien, Mitchell, Margaret, Raffel, Colin, Gokaslan, Aaron, Simhi, Adi, Soroa, Aitor, Aji, Alham Fikri, Alfassy, Amit, Rogers, Anna, Nitzav, Ariel Kreisberg, Xu, Canwen, Mou, Chenghao, Emezue, Chris, Klamm, Christopher, Leong, Colin, van Strien, Daniel, Adelani, David Ifeoluwa, Radev, Dragomir, Ponferrada, Eduardo González, Levkovizh, Efrat, Kim, Ethan, Natan, Eyal Bar, De Toni, Francesco, Dupont, Gérard, Kruszewski, Germán, Pistilli, Giada, Elsahar, Hady, Benyamina, Hamza, Tran, Hieu, Yu, Ian, Abdulmumin, Idris, Johnson, Isaac, Gonzalez-Dios, Itziar, de la Rosa, Javier, Chim, Jenny, Dodge, Jesse, Zhu, Jian, Chang, Jonathan, Frohberg, Jörg, Tobing, Joseph, Bhattacharjee, Joydeep, Almubarak, Khalid, Chen, Kimbo, Lo, Kyle, Von Werra, Leandro, Weber, Leon, Phan, Long, allal, Loubna Ben, Tanguy, Ludovic, Dey, Manan, Muñoz, Manuel Romero, Masoud, Maraim, Grandury, María, Šaško, Mario, Huang, Max, Coavoux, Maximin, Singh, Mayank, Jiang, Mike Tian-Jian, Vu, Minh Chien, Jauhar, Mohammad A., Ghaleb, Mustafa, Subramani, Nishant, Kassner, Nora, Khamis, Nurulaqilla, Nguyen, Olivier, Espejel, Omar, de Gibert, Ona, Villegas, Paulo, Henderson, Peter, Colombo, Pierre, Amuok, Priscilla, Lhoest, Quentin, Harliman, Rheza, Bommasani, Rishi, López, Roberto Luis, Ribeiro, Rui, Osei, Salomey, Pyysalo, Sampo, Nagel, Sebastian, Bose, Shamik, Muhammad, Shamsuddeen Hassan, Sharma, Shanya, Longpre, Shayne, Nikpoor, Somaieh, Silberberg, Stanislav, Pai, Suhas, Zink, Sydney, Torrent, Tiago Timponi, Schick, Timo, Thrush, Tristan, Danchev, Valentin, Nikoulina, Vassilina, Laippala, Veronika, Lepercq, Violette, Prabhu, Vrinda, Alyafeai, Zaid, Talat, Zeerak, Raja, Arun, Heinzerling, Benjamin, Si, Chenglei, Taşar, Davut Emre, Salesky, Elizabeth, Mielke, Sabrina J., Lee, Wilson Y., Sharma, Abheesht, Santilli, Andrea, Chaffin, Antoine, Stiegler, Arnaud, Datta, Debajyoti, Szczechla, Eliza, Chhablani, Gunjan, Wang, Han, Pandey, Harshit, Strobelt, Hendrik, Fries, Jason Alan, Rozen, Jos, Gao, Leo, Sutawika, Lintang, Bari, M Saiful, Al-shaibani, Maged S., Manica, Matteo, Nayak, Nihal, Teehan, Ryan, Albanie, Samuel, Shen, Sheng, Ben-David, Srulik, Bach, Stephen H., Kim, Taewoon, Bers, Tali, Fevry, Thibault, Neeraj, Trishala, Thakker, Urmish, Raunak, Vikas, Tang, Xiangru, Yong, Zheng-Xin, Sun, Zhiqing, Brody, Shaked, Uri, Yallow, Tojarieh, Hadar, Roberts, Adam, Chung, Hyung Won, Tae, Jaesung, Phang, Jason, Press, Ofir, Li, Conglong, Narayanan, Deepak, Bourfoune, Hatim, Casper, Jared, Rasley, Jeff, Ryabinin, Max, Mishra, Mayank, Zhang, Minjia, Shoeybi, Mohammad, Peyrounette, Myriam, Patry, Nicolas, Tazi, Nouamane, Sanseviero, Omar, von Platen, Patrick, Cornette, Pierre, Lavallée, Pierre François, Lacroix, Rémi, Rajbhandari, Samyam, Gandhi, Sanchit, Smith, Shaden, Requena, Stéphane, Patil, Suraj, Dettmers, Tim, Baruwa, Ahmed, Singh, Amanpreet, Cheveleva, Anastasia, Ligozat, Anne-Laure, Subramonian, Arjun, Névéol, Aurélie, Lovering, Charles, Garrette, Dan, Tunuguntla, Deepak, Reiter, Ehud, Taktasheva, Ekaterina, Voloshina, Ekaterina, Bogdanov, Eli, Winata, Genta Indra, Schoelkopf, Hailey, Kalo, Jan-Christoph, Novikova, Jekaterina, Forde, Jessica Zosa, Clive, Jordan, Kasai, Jungo, Kawamura, Ken, Hazan, Liam, Carpuat, Marine, Clinciu, Miruna, Kim, Najoung, Cheng, Newton, Serikov, Oleg, Antverg, Omer, van der Wal, Oskar, Zhang, Rui, Zhang, Ruochen, Gehrmann, Sebastian, Mirkin, Shachar, Pais, Shani, Shavrina, Tatiana, Scialom, Thomas, Yun, Tian, Limisiewicz, Tomasz, Rieser, Verena, Protasov, Vitaly, Mikhailov, Vladislav, Pruksachatkun, Yada, Belinkov, Yonatan, Bamberger, Zachary, Kasner, Zdeněk, Rueda, Alice, Pestana, Amanda, Feizpour, Amir, Khan, Ammar, Faranak, Amy, Santos, Ana, Hevia, Anthony, Unldreaj, Antigona, Aghagol, Arash, Abdollahi, Arezoo, Tammour, Aycha, HajiHosseini, Azadeh, Behroozi, Bahareh, Ajibade, Benjamin, Saxena, Bharat, Ferrandis, Carlos Muñoz, McDuff, Daniel, Contractor, Danish, Lansky, David, David, Davis, Kiela, Douwe, Nguyen, Duong A., Tan, Edward, Baylor, Emi, Ozoani, Ezinwanne, Mirza, Fatima, Ononiwu, Frankline, Rezanejad, Habib, Jones, Hessie, Bhattacharya, Indrani, Solaiman, Irene, Sedenko, Irina, Nejadgholi, Isar, Passmore, Jesse, Seltzer, Josh, Sanz, Julio Bonis, Dutra, Livia, Samagaio, Mairon, Elbadri, Maraim, Mieskes, Margot, Gerchick, Marissa, Akinlolu, Martha, McKenna, Michael, Qiu, Mike, Ghauri, Muhammed, Burynok, Mykola, Abrar, Nafis, Rajani, Nazneen, Elkott, Nour, Fahmy, Nour, Samuel, Olanrewaju, An, Ran, Kromann, Rasmus, Hao, Ryan, Alizadeh, Samira, Shubber, Sarmad, Wang, Silas, Roy, Sourav, Viguier, Sylvain, Le, Thanh, Oyebade, Tobi, Le, Trieu, Yang, Yoyo, Nguyen, Zach, Kashyap, Abhinav Ramesh, Palasciano, Alfredo, Callahan, Alison, Shukla, Anima, Miranda-Escalada, Antonio, Singh, Ayush, Beilharz, Benjamin, Wang, Bo, Brito, Caio, Zhou, Chenxi, Jain, Chirag, Xu, Chuxin, Fourrier, Clémentine, Periñán, Daniel León, Molano, Daniel, Yu, Dian, Manjavacas, Enrique, Barth, Fabio, Fuhrimann, Florian, Altay, Gabriel, Bayrak, Giyaseddin, Burns, Gully, Vrabec, Helena U., Bello, Imane, Dash, Ishani, Kang, Jihyun, Giorgi, John, Golde, Jonas, Posada, Jose David, Sivaraman, Karthik Rangasai, Bulchandani, Lokesh, Liu, Lu, Shinzato, Luisa, de Bykhovetz, Madeleine Hahn, Takeuchi, Maiko, Pàmies, Marc, Castillo, Maria A, Nezhurina, Marianna, Sänger, Mario, Samwald, Matthias, Cullan, Michael, Weinberg, Michael, De Wolf, Michiel, Mihaljcic, Mina, Liu, Minna, Freidank, Moritz, Kang, Myungsun, Seelam, Natasha, Dahlberg, Nathan, Broad, Nicholas Michio, Muellner, Nikolaus, Fung, Pascale, Haller, Patrick, Chandrasekhar, Ramya, Eisenberg, Renata, Martin, Robert, Canalli, Rodrigo, Su, Rosaline, Su, Ruisi, Cahyawijaya, Samuel, Garda, Samuele, Deshmukh, Shlok S, Mishra, Shubhanshu, Kiblawi, Sid, Ott, Simon, Sang-aroonsiri, Sinee, Kumar, Srishti, Schweter, Stefan, Bharati, Sushil, Laud, Tanmay, Gigant, Théo, Kainuma, Tomoya, Kusa, Wojciech, Labrak, Yanis, Bajaj, Yash Shailesh, Venkatraman, Yash, Xu, Yifan, Xu, Yingxin, Xu, Yu, Tan, Zhe, Xie, Zhongli, Ye, Zifan, Bras, Mathilde, Belkada, Younes, and Wolf, Thomas
- Subjects
Computer Science - Computation and Language - Abstract
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
- Published
- 2022
220. Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
- Author
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Xu, Zhiying, Yan, Francis Y., Singh, Rachee, Chiu, Justin T., Rush, Alexander M., and Yu, Minlan
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Computer Science - Networking and Internet Architecture ,Computer Science - Machine Learning - Abstract
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance. We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links. We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625x speedups.
- Published
- 2022
221. ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition
- Author
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Gandhi, Sanchit, von Platen, Patrick, and Rush, Alexander M.
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Computer Science - Computation and Language ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Speech recognition applications cover a range of different audio and text distributions, with different speaking styles, background noise, transcription punctuation and character casing. However, many speech recognition systems require dataset-specific tuning (audio filtering, punctuation removal and normalisation of casing), therefore assuming a-priori knowledge of both the audio and text distributions. This tuning requirement can lead to systems failing to generalise to other datasets and domains. To promote the development of multi-domain speech systems, we introduce the End-to-end Speech Benchmark (ESB) for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. Benchmarked systems must use the same data pre- and post-processing algorithm across datasets - assuming the audio and text data distributions are a-priori unknown. We compare a series of state-of-the-art (SoTA) end-to-end (E2E) systems on this benchmark, demonstrating how a single speech system can be applied and evaluated on a wide range of data distributions. We find E2E systems to be effective across datasets: in a fair comparison, E2E systems achieve within 2.6% of SoTA systems tuned to a specific dataset. Our analysis reveals that transcription artefacts, such as punctuation and casing, pose difficulties for ASR systems and should be included in evaluation. We believe E2E benchmarking over a range of datasets promotes the research of multi-domain speech recognition systems. ESB is available at https://huggingface.co/esb., Comment: 25 pages, 1 figure, submitted to ICLR 2023
- Published
- 2022
222. Unsupervised Text Deidentification
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Morris, John X., Chiu, Justin T., Zabih, Ramin, and Rush, Alexander M.
- Subjects
Computer Science - Computation and Language - Abstract
Deidentification seeks to anonymize textual data prior to distribution. Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points. We propose an unsupervised deidentification method that masks words that leak personally-identifying information. The approach utilizes a specially trained reidentification model to identify individuals from redacted personal documents. Motivated by K-anonymity based privacy, we generate redactions that ensure a minimum reidentification rank for the correct profile of the document. To evaluate this approach, we consider the task of deidentifying Wikipedia Biographies, and evaluate using an adversarial reidentification metric. Compared to a set of unsupervised baselines, our approach deidentifies documents more completely while removing fewer words. Qualitatively, we see that the approach eliminates many identifying aspects that would fall outside of the common named entity based approach., Comment: Findings of EMNLP 2022
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- 2022
223. Model Criticism for Long-Form Text Generation
- Author
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Deng, Yuntian, Kuleshov, Volodymyr, and Rush, Alexander M.
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Language models have demonstrated the ability to generate highly fluent text; however, it remains unclear whether their output retains coherent high-level structure (e.g., story progression). Here, we propose to apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of the generated text. Model criticism compares the distributions between real and generated data in a latent space obtained according to an assumptive generative process. Different generative processes identify specific failure modes of the underlying model. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality -- and find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference., Comment: EMNLP 2022
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- 2022
224. Markup-to-Image Diffusion Models with Scheduled Sampling
- Author
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Deng, Yuntian, Kojima, Noriyuki, and Rush, Alexander M.
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Computer Science - Machine Learning ,Computer Science - Computation and Language ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Building on recent advances in image generation, we present a fully data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations on top of a Gaussian noise distribution. We view the diffusion denoising process as a sequential decision making process, and show that it exhibits compounding errors similar to exposure bias issues in imitation learning problems. To mitigate these issues, we adapt the scheduled sampling algorithm to diffusion training. We conduct experiments on four markup datasets: mathematical formulas (LaTeX), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES). These experiments each verify the effectiveness of the diffusion process and the use of scheduled sampling to fix generation issues. These results also show that the markup-to-image task presents a useful controlled compositional setting for diagnosing and analyzing generative image models.
- Published
- 2022
225. Explaining Patterns in Data with Language Models via Interpretable Autoprompting
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Singh, Chandan, Morris, John X., Aneja, Jyoti, Rush, Alexander M., and Gao, Jianfeng
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Quantitative Biology - Neurons and Cognition ,Statistics - Machine Learning - Abstract
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns in data. Specifically, given a pre-trained LLM and data examples, we introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt iteratively alternates between generating explanations with an LLM and reranking them based on their performance when used as a prompt. Experiments on a wide range of datasets, from synthetic mathematics to natural-language understanding, show that iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Moreover, the prompts produced by iPrompt are simultaneously human-interpretable and highly effective for generalization: on real-world sentiment classification datasets, iPrompt produces prompts that match or even improve upon human-written prompts for GPT-3. Finally, experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery. All code for using the methods and data here is made available on Github., Comment: The two first authors contributed equally
- Published
- 2022
226. Roxadustat and Oral Iron Absorption in Chinese Patients with Anemia of Chronic Kidney Disease: A Randomized, Open-Label, Phase 4 Study (ALTAI)
- Author
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Wu, Haiting, Cheng, Hong, Wang, Caili, Yao, Li, Qin, Shuguang, Zuo, Li, Hu, Zhao, Zhang, Chun, Wu, Yiqing, Hofherr, Alexis, Mohan, Katie, Rush, Stephen, and Li, Xuemei
- Published
- 2024
- Full Text
- View/download PDF
227. Adherence to direct or vitamin K antagonist oral anticoagulants in patients with atrial fibrillation: a long-term observational study
- Author
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Salmasi, Shahrzad, Safari, Abdollah, De Vera, Mary A., Högg, Tanja, Lynd, Larry D., Koehoorn, Mieke, Barry, Arden R., Andrade, Jason G., Deyell, Marc W., Rush, Kathy L., Zhao, Yinshan, and Loewen, Peter
- Published
- 2024
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- View/download PDF
228. Conspecific nest destruction by black vulture (Coragyps atratus)
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Rush, Scott A. and Naveda-Rodríguez, Adrián
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- 2024
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229. The challenge of hypophosphatasia diagnosis in adults: results from the HPP International Working Group Literature Surveillance
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Brandi, Maria Luisa, Khan, Aliya A., Rush, Eric T., Ali, Dalal S., Al-Alwani, Hatim, Almonaei, Khulod, Alsarraf, Farah, Bacrot, Severine, Dahir, Kathryn M., Dandurand, Karel, Deal, Chad, Ferrari, Serge Livio, Giusti, Francesca, Guyatt, Gordon, Hatcher, Erin, Ing, Steven W., Javaid, Muhammad Kassim, Khan, Sarah, Kocijan, Roland, Lewiecki, E. Michael, Linglart, Agnes, M’Hiri, Iman, Marini, Francesca, Nunes, Mark E., Rockman-Greenberg, Cheryl, Seefried, Lothar, Simmons, Jill H., Starling, Susan R., Ward, Leanne M., Yao, Liang, Brignardello-Petersen, Romina, and Roux, Christian
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- 2024
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230. Hypophosphatasia diagnosis: current state of the art and proposed diagnostic criteria for children and adults
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Khan, Aliya A., Brandi, Maria Luisa, Rush, Eric T., Ali, Dalal S., Al-Alwani, Hatim, Almonaei, Khulod, Alsarraf, Farah, Bacrot, Severine, Dahir, Kathryn M., Dandurand, Karel, Deal, Chad, Ferrari, Serge Livio, Giusti, Francesca, Guyatt, Gordon, Hatcher, Erin, Ing, Steven W., Javaid, Muhammad Kassim, Khan, Sarah, Kocijan, Roland, Linglart, Agnes, M’Hiri, Iman, Marini, Francesca, Nunes, Mark E., Rockman-Greenberg, Cheryl, Roux, Christian, Seefried, Lothar, Simmons, Jill H., Starling, Susan R., Ward, Leanne M., Yao, Liang, Brignardello-Petersen, Romina, and Lewiecki, E. Michael
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- 2024
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231. Corticomuscular cross-recurrence analysis reveals between-limb differences in motor control among individuals with ACL reconstruction
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Riehm, Christopher D., Bonnette, Scott, Rush, Justin L., Diekfuss, Jed A., Koohestani, Moein, Myer, Gregory D., Norte, Grant E., and Sherman, David A.
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- 2024
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232. Assessing the Perception of Family and Caregivers’ Experience with Mental Health and Substance Use Services
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Smith, Tayla, Wells, Leslie, Jones, Kelsey, Jaouich, Alexia, and Rush, Brian
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- 2024
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233. Proposed diagnostic criteria for the diagnosis of hypophosphatasia in children and adolescents: results from the HPP International Working Group
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Rush, Eric, Brandi, Maria Luisa, Khan, Aliya, Ali, Dalal S., Al-Alwani, Hatim, Almonaei, Khulod, Alsarraf, Farah, Bacrot, Severine, Dahir, Kathryn M., Dandurand, Karel, Deal, Chad, Ferrari, Serge Livio, Giusti, Francesca, Guyatt, Gordon, Hatcher, Erin, Ing, Steven W., Javaid, Muhammad Kassim, Khan, Sarah, Kocijan, Roland, Lewiecki, E. Michael, Linglart, Agnes, M’Hiri, Iman, Marini, Francesca, Nunes, Mark E., Rockman-Greenberg, Cheryl, Roux, Christian, Seefried, Lothar, Starling, Susan R., Ward, Leanne, Yao, Liang, Brignardello-Petersen, Romina, and Simmons, Jill H.
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- 2024
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234. Using Flexible Grouping Instruction to Create Culturally Relevant PK-12 Learning Communities for Culturally and Linguistically Diverse Learners with Exceptionalities
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Hunter, William C., Barnes, Keishana L., Taylor, Aylcia, Rush, Charmion, and Banks, Tachelle
- Abstract
In this article, two instructional practices, "Numbered Heads Together" (NHT) and "Carousel Brainstorming" (CB), are discussed to guide instructors through the intentional practice of incorporating flexible groupings (CEC, HLP 17) in their daily instruction for the purpose of creating Culturally Relevant PK-12 Learning Communities for CLD Learners with Exceptionalities. Although NHT and CB are not the only approaches for implementing cooperative learning groups as an effective instructional tool, it is the authors' premise that both practices successfully promote academic achievement and provide a positive, culturally relevant design for diverse learners, as well as a practitioner-friendly framework that is easily implemented. NHT and CB also serve as a means to provide students with a voice for their learning and to promote positive student behaviors. Regardless of the setting or identified disability, when facilitated with foresight and careful planning, evidence-based instructional best practices are supported, and inclusive course content is attained through the use of NHT and CB. Example lesson plans to intentionally incorporate both strategies are included within the article.
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- 2023
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235. Are antimicrobial stewardship and sepsis awareness competing goals?
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Rush, Lynne
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QR Microbiology ,RA0421 Public health. Hygiene. Preventive Medicine - Abstract
Antimicrobial resistance (AMR) has emerged as one of the most significant threats to population health of recent times. It is estimated that its associated mortality could reach 10 million by 2050. Availability of effective antimicrobial prophylaxis is essential to allow many routine surgical and obstetric procedures to be performed safely. Reducing unnecessary antimicrobial use is integral to controlling the spread of AMR. As awareness of the need for judicious antimicrobial prescribing has grown, so has recognition of the importance of early diagnosis and management of sepsis, with high profile media reporting of selected cases, often involving children. Early administration of antibiotics to improve outcomes from sepsis conflicts, in part, with a drive to reduce antimicrobial prescribing. Previous research has suggested that AMR is often perceived as a distant and theoretical threat that has little personal impact, which may in part be related to how it is framed in news media. There is no evidence about how reporting of sepsis in children impacts on public understandings about antibiotic use. This PhD aims to better understand how the risks of antimicrobial resistance and sepsis are constructed in the popular news and how these impact on the attitudes and behaviour of the public, as parents and carers. Content analysis of 616 articles from 6 national newspapers published between 1988 and 2018 demonstrated key differences in the way AMR and sepsis are framed. AMR is framed predominantly according to its potential impact on future global health. Its causes and solutions are presented as complex and dependent on co-ordinated actions between policymakers and the healthcare, farming and pharmaceutical industries. In contrast, sepsis is framed as an issue whose drivers lie predominantly within the healthcare sector and whose main solution is better awareness. The use of personalised narratives about individuals affected by sepsis increases its relevance and accessibility for the public. Thematic analysis of a subset of articles demonstrated that failings in the health service were portrayed as the cause of avoidable deaths in children, often through failure to prescribe timely antibiotics, with parents positioned as advocates for their children. Exploration of these themes in 20 focus groups with 84 parents, carers and individuals with lived experience of sepsis demonstrated that decisions about when to seek health advice had to be balanced against a perceived moral duty to avoid placing excessive demands on healthcare resources. Health professionals were frequently perceived to be ambivalent about the need for antibiotics, with parent preference often influencing decisions. Few participants had direct experience of AMR, which was widely perceived to be a risk confined to individuals who use antibiotics inappropriately. There is a need to align messages about the complex interplay between AMR, sepsis and antimicrobial use. Personal narratives about individuals affected by AMR, similar to those used in sepsis awareness campaigns, may increase accessibility of public health messaging about preserving the efficacy of antibiotics.
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- 2023
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236. Theological reflections on the first assembly of the plenary council
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Lennan, Richard, Rush, Ormond, Kelly, Gerard, and McEvoy, James
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- 2022
237. Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
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von Werra, Leandro, Tunstall, Lewis, Thakur, Abhishek, Luccioni, Alexandra Sasha, Thrush, Tristan, Piktus, Aleksandra, Marty, Felix, Rajani, Nazneen, Mustar, Victor, Ngo, Helen, Sanseviero, Omar, Šaško, Mario, Villanova, Albert, Lhoest, Quentin, Chaumond, Julien, Mitchell, Margaret, Rush, Alexander M., Wolf, Thomas, and Kiela, Douwe
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Computer Science - Machine Learning - Abstract
Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML. Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models. Its goal is to support reproducibility of evaluation, centralize and document the evaluation process, and broaden evaluation to cover more facets of model performance. It includes over 50 efficient canonical implementations for a variety of domains and scenarios, interactive documentation, and the ability to easily share implementations and outcomes. The library is available at https://github.com/huggingface/evaluate. In addition, we introduce Evaluation on the Hub, a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button. Evaluation on the Hub is available at https://huggingface.co/autoevaluate.
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- 2022
238. Monoidal Pull-Push II: Local Systems
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Rush, Angus Hadrian
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Mathematics - Algebraic Topology - Abstract
Our aim in this work is to provide an explicit, simple construction of pull-push of local systems as a lax monoidal functor. To this end, we show that one can solve horn filling problems Cat_\infty using left Kan extensions, and use this to provide an explicit construction of a left Kan extension functor. We use this result to show that pull-push of local systems induces a functor from Span(S), the infinity category of spans of spaces, into Cat_\infty. We then develop a machinery of monoidal Beck-Chevalley fibrations, and use this to show that the pull-push functor above admits a lax monoidal structure., Comment: Comments welcome!
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- 2022
239. Monoidal Pull-Push I: Cocartesian Fibrations and Categories of Spans
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Rush, Angus Hadrian
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Mathematics - Algebraic Topology - Abstract
We develop a basic theory of cocartesian fibrations between Segal spaces (in line with that of arxiv:2102.05190), and use it to provide a proof of a theorem of Barwick (the main result of arxiv:1404.0108). Note: This work was originally the author's master's thesis, submitted 21.09.2020., Comment: Comments welcome!
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- 2022
240. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
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Strobelt, Hendrik, Webson, Albert, Sanh, Victor, Hoover, Benjamin, Beyer, Johanna, Pfister, Hanspeter, and Rush, Alexander M.
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Computer Science - Computation and Language ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning - Abstract
State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task. The tool then allows easy deployment of the newly created ad-hoc models. We demonstrate the utility of PromptIDE (demo at http://prompt.vizhub.ai) and our workflow using several real-world use cases., Comment: 9 pages content, 2 pages references
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- 2022
241. CLUSTER Trial for Outbreak Detection and Response (CLUSTER)
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Hospital Corporation of America (HCA) Healthcare, Brigham and Women's Hospital, University of California, Irvine, Harvard School of Public Health (HSPH), Rush University, Duke University, University of Massachusetts, Amherst, University of California, San Francisco, Cook County Health & Hospitals System, Centers for Disease Control and Prevention, and Richard Platt, Professor and Department Chair
- Published
- 2023
242. Study of the Treatment and Outcomes in Critically Ill Patients With COVID-19 (STOP-COVID)
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Icahn School of Medicine at Mount Sinai, Montefiore Medical Center, Baylor College of Medicine, Baylor Health Care System, Beth Israel Deaconess Medical Center, University of Colorado, Denver, Cook County Hospital, The Cooper Health System, Duke University, Georgetown University, Hackensack mountainside hospital, Hackensack Meridian Health, Indiana University Health Methodist Hospital, Johns Hopkins University, Loma Linda University, Mayo Clinic, Medical College of Wisconsin, Northwestern, Weill Medical College of Cornell University, NYU Langone Health, Ochsner Health System, Oregon Health and Science University, Renown Health, Rush University Medical Center, New Jersey Medical School, Rutgers Robert Wood Johnson Medical School, Stanford University, Temple University, Tufts Medical Center, Tulane University, University of California, Davis, University of California, Los Angeles, University of California, San Diego, University of California, San Francisco, University of North Carolina, Chapel Hill, University Hospitals Cleveland Medical Center, University Medical Center of Southern Nevada, University of Alabama at Birmingham, University of Chicago, University of Florida, University of Illinois at Chicago, University of Kentucky, University of Miami, University of Michigan, University of Oklahoma, University of Pennsylvania, University of Pittsburgh Medical Center, University of Tennessee, University of Washington, University of Texas, Southwestern Medical Center at Dallas, Yale University, and David Leaf, Assistant Professor of Medicine, Harvard Medical School
- Published
- 2023
243. Randomized Trial of Immediate Endoscopic Necrosectomy vs. Step-up Endoscopic Interventions in Necrotizing Pancreatitis (DESTIN)
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Asian Institute of Gastroenterology, India, West Virginia University, University of Southern California, Marshall University, Mayo Clinic, University of Alabama at Birmingham, and Rush University
- Published
- 2023
244. DIFFIR - Geriatric Distal Femur Fixation Versus Replacement (DIFFIR)
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Mount Sinai Hospital, Canada, NYU Langone Health, Brigham and Women's Hospital, Queen Elizabeth II Health Sciences Centre, Hamilton Health Sciences Corporation, Yale New Haven Health System Center for Healthcare Solutions, Oregon Health and Science University, OrthoCincy Orthopaedics & Sports Medicine, University of California, Rush University Medical Center, Rothman Institute Orthopaedics, Hospital for Special Surgery, New York, Stanford University, Cedars-Sinai Medical Center, Thunder Bay Regional Health Sciences Centre, University of California, San Francisco, University of Arkansas, University of Calgary, and Ascension Health
- Published
- 2023
245. Nests and eggs of the chestnut-backed button-quail 'Turnix castanotus': Two new nests and a review of previous descriptions
- Author
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Webster, Patrick TD, Jackett, Nigel A, Mason, Ian J, Rush, Emily R, Leseberg, Nicholas P, and Watson, James EM
- Published
- 2022
246. Longitudinal change in daily stress across 20 years of adulthood: Results from the national study of daily experiences.
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Almeida, David, Rush, Jonathan, Mogle, Jacqueline, Piazza, Jennifer, Cerino, Eric, and Charles, Susan
- Subjects
Adult ,Humans ,Middle Aged ,Young Adult ,Stress ,Psychological ,Aging - Abstract
This study examined age-related patterns in exposure and affective reactivity to daily stressors across a 20-year time span among adults who were between 22 and 77 years old at their baseline interview. Longitudinal data from the National Study of Daily Experiences (NSDE) consisted of three bursts of eight consecutive nightly interviews of stress and affect. Analyses made use of all available data from a U.S. National sample of respondents who participated in any of the three NSDE bursts (N = 2,845; number of daily assessments = 33,688). Findings revealed increasing age-related benefits. Younger adults (< 30 years) reported the highest levels of stressor exposure and reactivity, but their stress profile improved with age. Over time, adults averaged an 11% reduction in the occurrence of stressor days, and the younger adults exhibited an even steeper decline (a 47% reduction) in their levels of stressor reactivity. For people in midlife and old age, stressor occurrence continued to decrease over time, yet among adults aged 54 years or older at baseline, stress reactivity remained stable across time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
- Published
- 2023
247. Spectrum of Clinical Presentations, Imaging Findings, and HLA Types in Immune Checkpoint Inhibitor–Induced Hypophysitis
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Quandt, Zoe, Kim, Stephanie, Villanueva-Meyer, Javier, Coupe, Catherine, Young, Arabella, Kang, Jee Hye, Yazdany, Jinoos, Schmajuk, Gabriela, Rush, Stephanie, Ziv, Elad, Perdigoto, Ana Luisa, Herold, Kevan, Lechner, Melissa G, Su, Maureen A, Tyrrell, J Blake, Bluestone, Jeffrey, Anderson, Mark, and Masharani, Umesh
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Immunology ,Clinical Research ,Biomedical Imaging ,2.1 Biological and endogenous factors ,Aetiology ,hypophysitis ,immune checkpoint inhibitors ,immunotherapy ,immune-related adverse events ,pan-hypopituitarism ,adrenal insufficiency ,Cardiovascular medicine and haematology - Abstract
ContextHypophysitis is a known immune-related adverse event (irAE) of immune checkpoint inhibitors (CPIs), commonly associated with CTLA-4 inhibitors and less often with PD-1/PD-L1 inhibitors.ObjectiveWe aimed to determine clinical, imaging, and HLA characteristics of CPI-induced hypophysitis (CPI-hypophysitis).MethodsWe examined the clinical and biochemical characteristics, magnetic resonance imaging (MRI) of the pituitary, and association with HLA type in patients with CPI-hypophysitis.ResultsForty-nine patients were identified. Mean age was 61.3 years, 61.2% were men, 81.6% were Caucasian, 38.8% had melanoma, and 44.5% received PD-1/PD-L1 inhibitor monotherapy while the remainder received CTLA-4 inhibitor monotherapy or CTLA-4/PD-1 inhibitor combination therapy. A comparison of CTLA-4 inhibitor exposure vs PD-1/PD-L1 inhibitor monotherapy revealed faster time to CPI-hypophysitis (median 84 vs 185 days, P < .01) and abnormal pituitary appearance on MRI (odds ratio 7.00, P = .03). We observed effect modification by sex in the association between CPI type and time to CPI-hypophysitis. In particular, anti-CTLA-4 exposed men had a shorter time to onset than women. MRI changes of the pituitary were most common at the time of hypophysitis diagnosis (55.6% enlarged, 37.0% normal, 7.4% empty or partially empty) but persisted in follow-up (23.8% enlarged, 57.1% normal, 19.1% empty or partially empty). HLA typing was done on 55 subjects; HLA type DQ0602 was over-represented in CPI-hypophysitis relative to the Caucasian American population (39.4% vs 21.5%, P = 0.01) and CPI population.ConclusionThe association of CPI-hypophysitis with HLA DQ0602 suggests a genetic risk for its development. The clinical phenotype of hypophysitis appears heterogenous, with differences in timing of onset, changes in thyroid function tests, MRI changes, and possibly sex related to CPI type. These factors may play an important role in our mechanistic understanding of CPI-hypophysitis.
- Published
- 2023
248. Causes of Death Among Individuals With Systemic Lupus Erythematosus by Race and Ethnicity: A Population‐Based Study
- Author
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Taylor, Tiffany, Anastasiou, Christine, Ja, Clairissa, Rush, Stephanie, Trupin, Laura, Dall'Era, Maria, Katz, Patricia, Barbour, Kamil E, Greenlund, Kurt J, Yazdany, Jinoos, and Gianfrancesco, Milena A
- Subjects
Biomedical and Clinical Sciences ,Public Health ,Health Sciences ,Autoimmune Disease ,Cardiovascular ,Heart Disease ,Lupus ,Clinical Research ,Good Health and Well Being ,Humans ,Middle Aged ,Aged ,Ethnicity ,Cause of Death ,Lupus Erythematosus ,Systemic ,Hispanic or Latino ,Cardiovascular Diseases ,Clinical Sciences ,Public Health and Health Services ,Psychology ,Clinical sciences ,Allied health and rehabilitation science - Abstract
ObjectiveNon-White populations are at higher risk of developing systemic lupus erythematosus (SLE) and have more severe outcomes, including mortality. The present study was undertaken to examine how specific causes of death vary by race and ethnicity, including Asian and Hispanic individuals.MethodsThe California Lupus Surveillance Project included SLE cases identified among residents of San Francisco County, CA during January 1, 2007 to December 31, 2009. Cases were matched to the National Death Index over a 10-year period. Logistic regression examined age-adjusted differences in causes of death by race, ethnicity, and sex. Age-standardized mortality ratios between individuals with SLE and the corresponding general population were calculated for the leading cause of death, and observed versus expected deaths were estimated.ResultsThe study included 812 individuals of White (38%), Asian (36%), Black (20%), and mixed/other/unknown (5%) race; 15% identified as Hispanic. One hundred thirty-five deaths were recorded, with a mean ± SD age at death of 62.2 ± 15.6 years. Cardiovascular disease (CVD) was the leading cause of death overall (33%), and across all racial and ethnic groups, followed by rheumatic disease (18%) and hematologic/oncologic conditions (18%). CVD as the underlying cause of death was 3.63 times higher among SLE cases than in the general population. CVD deaths for those with SLE were nearly 4 and 6 times higher for Asian and Hispanic individuals with SLE, respectively, compared to the general population.ConclusionIndividuals with SLE experience a disproportionate burden of CVD mortality compared to the general population, which is magnified for Asian and Hispanic groups.
- Published
- 2023
249. Paclitaxel-induced dorsal hand-foot syndrome
- Author
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Quan, Eugenie Y, Engel, Casey, Rush, Patrick S, and Eikenberg, Joshua D
- Subjects
acral erythema ,chemotherapy ,hand-foot syndrome ,paclitaxel ,palmoplantar erythrodysesthesia ,toxic erythema - Abstract
Hand-foot syndrome (HFS), also known as palmoplantar erythrodysesthesia or acral erythema, is a known adverse effect of chemotherapeutic agents that most commonly presents as palmoplantar dysesthesia and erythematous plaques localized to the palms and soles. Paclitaxel is an uncommon cause of HFS and is notable for its unique presentation on the dorsal hands and feet. We present an unusual case of paclitaxel-induced HFS localized to the dorsal hands of a 66-year-old man with metastatic angiosarcoma. Early identification and management of HFS is critical to allow for continuation of chemotherapy while improving patient quality of life.
- Published
- 2023
250. Reporting time toxicity in prospective cancer clinical trials: A scoping review
- Author
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Quinn, Patrick L., Saiyed, Shah, Hannon, Connor, Sarna, Angela, Waterman, Brittany L., Cloyd, Jordan M., Spriggs, Rodney, Rush, Laura J., McAlearney, Ann Scheck, and Ejaz, Aslam
- Published
- 2024
- Full Text
- View/download PDF
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