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1. Imagen 3

Author

Imagen-Team-Google, Baldridge, Jason, Bauer, Jakob, Bhutani, Mukul, Brichtova, Nicole, Bunner, Andrew, Chan, Kelvin, Chen, Yichang, Dieleman, Sander, Du, Yuqing, Eaton-Rosen, Zach, Fei, Hongliang, de Freitas, Nando, Gao, Yilin, Gladchenko, Evgeny, Colmenarejo, Sergio Gómez, Guo, Mandy, Haig, Alex, Hawkins, Will, Hu, Hexiang, Huang, Huilian, Igwe, Tobenna Peter, Kaplanis, Christos, Khodadadeh, Siavash, Kim, Yelin, Konyushkova, Ksenia, Langner, Karol, Lau, Eric, Luo, Shixin, Mokrá, Soňa, Nandwani, Henna, Onoe, Yasumasa, Oord, Aäron van den, Parekh, Zarana, Pont-Tuset, Jordi, Qi, Hang, Qian, Rui, Ramachandran, Deepak, Rane, Poorva, Rashwan, Abdullah, Razavi, Ali, Riachi, Robert, Srinivasan, Hansa, Srinivasan, Srivatsan, Strudel, Robin, Uria, Benigno, Wang, Oliver, Wang, Su, Waters, Austin, Wolff, Chris, Wright, Auriel, Xiao, Zhisheng, Xiong, Hao, Xu, Keyang, van Zee, Marc, Zhang, Junlin, Zhang, Katie, Zhou, Wenlei, Zolna, Konrad, Aboubakar, Ola, Akbulut, Canfer, Akerlund, Oscar, Albuquerque, Isabela, Anderson, Nina, Andreetto, Marco, Aroyo, Lora, Bariach, Ben, Barker, David, Ben, Sherry, Berman, Dana, Biles, Courtney, Blok, Irina, Botadra, Pankil, Brennan, Jenny, Brown, Karla, Buckley, John, Bunel, Rudy, Bursztein, Elie, Butterfield, Christina, Caine, Ben, Carpenter, Viral, Casagrande, Norman, Chang, Ming-Wei, Chang, Solomon, Chaudhuri, Shamik, Chen, Tony, Choi, John, Churbanau, Dmitry, Clement, Nathan, Cohen, Matan, Cole, Forrester, Dektiarev, Mikhail, Du, Vincent, Dutta, Praneet, Eccles, Tom, Elue, Ndidi, Feden, Ashley, Fruchter, Shlomi, Garcia, Frankie, Garg, Roopal, Ge, Weina, Ghazy, Ahmed, Gipson, Bryant, Goodman, Andrew, Górny, Dawid, Gowal, Sven, Gupta, Khyatti, Halpern, Yoni, Han, Yena, Hao, Susan, Hayes, Jamie, Hertz, Amir, Hirst, Ed, Hou, Tingbo, Howard, Heidi, Ibrahim, Mohamed, Ike-Njoku, Dirichi, Iljazi, Joana, Ionescu, Vlad, Isaac, William, Jana, Reena, Jennings, Gemma, Jenson, Donovon, Jia, Xuhui, Jones, Kerry, Ju, Xiaoen, Kajic, Ivana, Ayan, Burcu Karagol, Kelly, Jacob, Kothawade, Suraj, Kouridi, Christina, Ktena, Ira, Kumakaw, Jolanda, Kurniawan, Dana, Lagun, Dmitry, Lavitas, Lily, Lee, Jason, Li, Tao, Liang, Marco, Li-Calis, Maggie, Liu, Yuchi, Alberca, Javier Lopez, Lu, Peggy, Lum, Kristian, Ma, Yukun, Malik, Chase, Mellor, John, Mosseri, Inbar, Murray, Tom, Nematzadeh, Aida, Nicholas, Paul, Oliveira, João Gabriel, Ortiz-Jimenez, Guillermo, Paganini, Michela, Paine, Tom Le, Paiss, Roni, Parrish, Alicia, Peckham, Anne, Peswani, Vikas, Petrovski, Igor, Pfaff, Tobias, Pirozhenko, Alex, Poplin, Ryan, Prabhu, Utsav, Qi, Yuan, Rahtz, Matthew, Rashtchian, Cyrus, Rastogi, Charvi, Raul, Amit, Rebuffi, Sylvestre-Alvise, Ricco, Susanna, Riedel, Felix, Robinson, Dirk, Rohatgi, Pankaj, Rosgen, Bill, Rumbley, Sarah, Ryu, Moonkyung, Salgado, Anthony, Singla, Sahil, Schroff, Florian, Schumann, Candice, Shah, Tanmay, Shillingford, Brendan, Shivakumar, Kaushik, Shtatnov, Dennis, Singer, Zach, Sluzhaev, Evgeny, Sokolov, Valerii, Sottiaux, Thibault, Stimberg, Florian, Stone, Brad, Stutz, David, Su, Yu-Chuan, Tabellion, Eric, Tang, Shuai, Tao, David, Thomas, Kurt, Thornton, Gregory, Toor, Andeep, Udrescu, Cristian, Upadhyay, Aayush, Vasconcelos, Cristina, Vasiloff, Alex, Voynov, Andrey, Walker, Amanda, Wang, Luyu, Wang, Miaosen, Wang, Simon, Wang, Stanley, Wang, Qifei, Wang, Yuxiao, Weisz, Ágoston, Wiles, Olivia, Wu, Chenxia, Xu, Xingyu Federico, Xue, Andrew, Yang, Jianbo, Yu, Luo, Yurtoglu, Mete, Zand, Ali, Zhang, Han, Zhang, Jiageng, Zhao, Catherine, Zhaxybay, Adilet, Zhou, Miao, Zhu, Shengqi, Zhu, Zhenkai, Bloxwich, Dawn, Bordbar, Mahyar, Cobo, Luis C., Collins, Eli, Dai, Shengyang, Doshi, Tulsee, Dragan, Anca, Eck, Douglas, Hassabis, Demis, Hsiao, Sissie, Hume, Tom, Kavukcuoglu, Koray, King, Helen, Krawczyk, Jack, Li, Yeqing, Meier-Hellstern, Kathy, Orban, Andras, Pinsky, Yury, Subramanya, Amar, Vinyals, Oriol, Yu, Ting, and Zwols, Yori

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

Published
2024

2. Into the Unknown: Generating Geospatial Descriptions for New Environments

Author

Paz-Argaman, Tzuf, Palowitch, John, Kulkarni, Sayali, Tsarfaty, Reut, and Baldridge, Jason

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

Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires reasoning over allocentric spatial relationships (independent of the observer's viewpoint) using non-sequential navigation instructions and maps. However, performance substantially drops in new environments with no training data. Using opensource descriptions paired with coordinates (e.g., Wikipedia) provides training data but suffers from limited spatially-oriented text resulting in low geolocation resolution. We propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. Our method constructs a grounded knowledge-graph, capturing entity relationships. Sampled entities and relations (`shop north of school') generate navigation instructions via (i) generating numerous templates using context-free grammar (CFG) to embed specific entities and relations; (ii) feeding the entities and relation into a large language model (LLM) for instruction generation. A comprehensive evaluation on RVS, showed that our approach improves the 100-meter accuracy by 45.83% on unseen environments. Furthermore, we demonstrate that models trained with CFG-based augmentation achieve superior performance compared with those trained with LLM-based augmentation, both in unseen and seen environments. These findings suggest that the potential advantages of explicitly structuring spatial information for text-based geospatial reasoning in previously unknown, can unlock data-scarce scenarios.

Published
2024

3. Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models

Author

Vasconcelos, Cristina N., Rashwan, Abdullah, Waters, Austin, Walker, Trevor, Xu, Keyang, Yan, Jimmy, Qian, Rui, Luo, Shixin, Parekh, Zarana, Bunner, Andrew, Fei, Hongliang, Garg, Roopal, Guo, Mandy, Kajic, Ivana, Li, Yeqing, Nandwani, Henna, Pont-Tuset, Jordi, Onoe, Yasumasa, Rosston, Sarah, Wang, Su, Zhou, Wenlei, Swersky, Kevin, Fleet, David J., Baldridge, Jason M., and Wang, Oliver

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

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {\it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {\it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.

Published
2024

4. ImageInWords: Unlocking Hyper-Detailed Image Descriptions

Author

Garg, Roopal, Burns, Andrea, Ayan, Burcu Karagol, Bitton, Yonatan, Montgomery, Ceslee, Onoe, Yasumasa, Bunner, Andrew, Krishna, Ranjay, Baldridge, Jason, and Soricut, Radu

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

Despite the longstanding adage "an image is worth a thousand words," generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with ImageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66%) and GPT4V (+48%) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31% against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6% on ARO, SVO-Probes, and Winoground datasets. We release the IIW Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model., Comment: Webpage (https://google.github.io/imageinwords), GitHub (https://github.com/google/imageinwords), HuggingFace (https://huggingface.co/datasets/google/imageinwords)

Published
2024

5. DOCCI: Descriptions of Connected and Contrasting Images

Author

Onoe, Yasumasa, Rane, Sunayana, Berger, Zachary, Bitton, Yonatan, Cho, Jaemin, Garg, Roopal, Ku, Alexander, Parekh, Zarana, Pont-Tuset, Jordi, Tanzer, Garrett, Wang, Su, and Baldridge, Jason

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

Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would allow for richer associations to be learned by models. To fill the gap, we introduce Descriptions of Connected and Contrasting Images (DOCCI), a dataset with long, human-annotated English descriptions for 15k images that were taken, curated and donated by a single researcher intent on capturing key challenges such as spatial relations, counting, text rendering, world knowledge, and more. We instruct human annotators to create comprehensive descriptions for each image; these average 136 words in length and are crafted to clearly distinguish each image from those that are related or similar. Each description is highly compositional and typically encompasses multiple challenges. Through both quantitative and qualitative analyses, we demonstrate that DOCCI serves as an effective training resource for image-to-text generation -- a PaLI 5B model finetuned on DOCCI shows equal or superior results compared to highly-performant larger models like LLaVA-1.5 7B and InstructBLIP 7B. Furthermore, we show that DOCCI is a useful testbed for text-to-image generation, highlighting the limitations of current text-to-image models in capturing long descriptions and fine details.

Published
2024

7. Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions

Author

Paz-Argaman, Tzuf, Kulkarni, Sayali, Palowitch, John, Baldridge, Jason, and Tsarfaty, Reut

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Multimedia
Abstract

When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquired knowledge on textual descriptions. Current navigation studies concentrate on egocentric local descriptions (e.g., `it will be on your right') that require reasoning over the agent's local perception. These instructions are typically given as a sequence of steps, with each action-step explicitly mentioning and being followed by a landmark that the agent can use to verify they are on the right path (e.g., `turn right and then you will see...'). In contrast, descriptions based on knowledge acquired through a map provide a complete view of the environment and capture its overall structure. These instructions (e.g., `it is south of Central Park and a block north of a police station') are typically non-sequential, contain allocentric relations, with multiple spatial relations and implicit actions, without any explicit verification. This paper introduces the Rendezvous (RVS) task and dataset, which includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. Our analysis reveals that RVS exhibits a richer use of spatial allocentric relations, and requires resolving more spatial relations simultaneously compared to previous text-based navigation benchmarks.

Published
2024

8. Clinical variants paired with phenotype: A rich resource for brain gene curation.

Author

Chopra, Maya, Savatt, Juliann, Bingaman, Taylor, Good, Molly, Morgan, Alexis, Cooney, Caitlin, Rossel, Allison, VanHoute, Bryanna, Cordova, Ineke, Mahida, Sonal, Lanzotti, Virginia, Baldridge, Dustin, Gurnett, Christina, Piven, Joseph, Hazlett, Heather, Pomeroy, Scott, Sahin, Mustafa, Payne, Philip, Riggs, Erin, and Constantino, John

Subjects
Autism, Gene curation, Intellectual disability, Neurodevelopmental disorders, Variant of uncertain significance, Humans, Genetic Variation, Databases, Genetic, Genetic Testing, Phenotype, Brain
Abstract

PURPOSE: Clinically ascertained variants are under-utilized in neurodevelopmental disorder research. We established the Brain Gene Registry (BGR) to coregister clinically identified variants in putative brain genes with participant phenotypes. Here, we report 179 genetic variants in the first 179 BGR registrants and analyze the proportion that were novel to ClinVar at the time of entry and those that were absent in other disease databases. METHODS: From 10 academically affiliated institutions, 179 individuals with 179 variants were enrolled into the BGR. Variants were cross-referenced for previous presence in ClinVar and for presence in 6 other genetic databases. RESULTS: Of 179 variants in 76 genes, 76 (42.5%) were novel to ClinVar, and 62 (34.6%) were absent from all databases analyzed. Of the 103 variants present in ClinVar, 37 (35.9%) were uncertain (ClinVar aggregate classification of variant of uncertain significance or conflicting classifications). For 5 variants, the aggregate ClinVar classification was inconsistent with the interpretation from the BGR site-provided classification. CONCLUSION: A significant proportion of clinical variants that are novel or uncertain are not shared, limiting the evidence base for new gene-disease relationships. Registration of paired clinical genetic test results with phenotype has the potential to advance knowledge of the relationships between genes and neurodevelopmental disorders.

Published
2024

9. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

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

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

10. Out-of-School Time Programs in the United States in an Era of Racial Reckoning: Insights on Equity from Practitioners, Scholars, Policy Influencers, and Young People

Author

Bianca J. Baldridge, Daniela K. DiGiacomo, Ben Kirshner, Sam Mejias, and Deepa S. Vasudevan

Abstract

The out-of-school time (OST) field in the United States has a complex history. The push to offer programming reflects a legacy rooted in moral panics about racially minoritized youth. However, this field is populated by community spaces that act as multipurpose sites of culturally sustaining educational practices supporting positive youth development. We report findings from interviews with OST leaders, youth workers, policy influencers, and youth about how to create, sustain, and protect more liberatory and humanizing practices, demonstrating that racism and deficit-based thinking continue to inform programmatic practices and youth experiences. Furthermore, this study reveals that programs with expansive ideas of youth voice, healing justice, and whole-child approaches to youth development create better opportunities for connection and belonging.

Published
2024
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11. DOCCI: Descriptions of Connected and Contrasting Images

Author

Onoe, Yasumasa, Rane, Sunayana, Berger, Zachary, Bitton, Yonatan, Cho, Jaemin, Garg, Roopal, Ku, Alexander, Parekh, Zarana, Pont-Tuset, Jordi, Tanzer, Garrett, Wang, Su, Baldridge, Jason, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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13. Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation

Author

Cho, Jaemin, Hu, Yushi, Garg, Roopal, Anderson, Peter, Krishna, Ranjay, Baldridge, Jason, Bansal, Mohit, Pont-Tuset, Jordi, and Wang, Su

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

Evaluating text-to-image models is notoriously difficult. A strong recent approach for assessing text-image faithfulness is based on QG/A (question generation and answering), which uses pre-trained foundational models to automatically generate a set of questions and answers from the prompt, and output images are scored based on whether these answers extracted with a visual question answering model are consistent with the prompt-based answers. This kind of evaluation is naturally dependent on the quality of the underlying QG and VQA models. We identify and address several reliability challenges in existing QG/A work: (a) QG questions should respect the prompt (avoiding hallucinations, duplications, and omissions) and (b) VQA answers should be consistent (not asserting that there is no motorcycle in an image while also claiming the motorcycle is blue). We address these issues with Davidsonian Scene Graph (DSG), an empirically grounded evaluation framework inspired by formal semantics, which is adaptable to any QG/A frameworks. DSG produces atomic and unique questions organized in dependency graphs, which (i) ensure appropriate semantic coverage and (ii) sidestep inconsistent answers. With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above. Finally, we present DSG-1k, an open-sourced evaluation benchmark that includes 1,060 prompts, covering a wide range of fine-grained semantic categories with a balanced distribution. We release the DSG-1k prompts and the corresponding DSG questions., Comment: ICLR 2024; Project website: https://google.github.io/dsg

Published
2023

14. Can Generative Multimodal Models Count to Ten?

Author

Rane, Sunayana, Ku, Alexander, Baldridge, Jason, Tenney, Ian, Griffiths, Tom, and Kim, Been

Subjects
Artificial Intelligence, Computer Science, Psychology
Abstract

The creation of sophisticated AI systems that are able to process and produce images and text creates new challenges in assessing the capabilities of those systems.We adapt a behavioral paradigm from developmental psychology to characterize the counting ability of a model that generates images from text. We show that three model scales of the Parti model (350m, 3B, and 20B parameters respectively) each have some counting ability, with a significant jump in performance between the 350m and 3B model scales. We also demonstrate that it is possible to interfere with these models' counting ability simply by incorporating unusual descriptive adjectives for the objects being counted into the text prompt. We analyze our results in the context of the knower-level theory of child number learning. Our results show that we can gain experimental intuition for how to probe model behavior by drawing from a rich literature of behavioral experiments on humans, and, perhaps most importantly, by adapting human developmental benchmarking paradigms to AI models, we can characterize and understand their behavior with respect to our own.

Published
2024

15. CRISPR/Cas9 screens identify key host factors that enhance rotavirus reverse genetics efficacy and vaccine production

Author

Yinxing Zhu, Meagan E. Sullender, Danielle E. Campbell, Leran Wang, Sanghyun Lee, Takahiro Kawagishi, Gaopeng Hou, Alen Dizdarevic, Philippe H. Jais, Megan T. Baldridge, and Siyuan Ding

Subjects
Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Rotaviruses pose a significant threat to young children. To identify novel pro- and anti-rotavirus host factors, we performed genome-wide CRISPR/Cas9 screens using rhesus rotavirus and African green monkey cells. Genetic deletion of either SERPINB1 or TMEM236, the top two antiviral factors, in MA104 cells increased virus titers in a rotavirus strain independent manner. Using this information, we optimized the existing rotavirus reverse genetics systems by combining SERPINB1 knockout MA104 cells with a C3P3-G3 helper plasmid. We improved the recovery efficiency and rescued several low-titer rotavirus reporter and mutant strains that prove difficult to rescue otherwise. Furthermore, we demonstrate that TMEM236 knockout in Vero cells supported higher yields of two live-attenuated rotavirus vaccine strains than the parental cell line and represents a more robust vaccine-producing cell substrate. Collectively, we developed a third-generation optimized rotavirus reverse genetics system and generated gene-edited Vero cells as a new substrate for improving rotavirus vaccine production.

Published
2024
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16. Enhancing hypertension education of community health extension workers in Nigeria’s federal capital territory: the impact of the extension for community healthcare outcomes model on primary care, a quasi-experimental study

Author

Abigail S. Baldridge, Ikechukwu A. Orji, Gabriel L. Shedul, Guhan Iyer, Erica L. Jamro, Jiancheng Ye, Blessing O. Akor, Emmanuel Okpetu, Samuel Osagie, Adaora Odukwe, Haulat Olabisi Dabiri, L. Nneka Mobisson, Namratha R. Kandula, Lisa R. Hirschhorn, Mark D. Huffman, and Dike B. Ojji

Subjects
Hypertension, Implementation, Qualitative, Primary care, Education, Medicine (General), R5-920
Abstract

Abstract Background Healthcare workers (HCWs) including community health extension workers (CHEWs) in the Federal Capital Territory, Nigeria participated in a hypertension training series following the Extension for Community Healthcare Outcomes (ECHO) model which leverages technology and a practical peer-to-peer learning framework to virtually train healthcare practitioners. We sought to evaluate the patient-level effects of the hypertension ECHO series. Methods HCWs from 12 of 33 eligible primary healthcare centers (PHCs) in the Hypertension Treatment in Nigeria Program (NCT04158154) were selected to participate in a seven-part hypertension ECHO series from August 2022 to April 2023. Concurrent Hypertension Treatment in Nigeria Program patient data were used to evaluate changes in hypertension treatment and control rates, and adherence to Nigeria’s hypertension treatment protocol. Outcomes were compared between the 12 PHCs in the ECHO program and the 21 which were not. Results Between July 2022 and June 2023, 16,691 PHC visits were documented among 4340 individuals (ECHO: n = 1428 [33%], non-ECHO: n = 2912 [67%]). Patients were on average (SD) 51.5 (12.0) years old, and one-third were male (n = 1372, 32%) with no differences between cohorts in either characteristic (p ≥ 0.05 for both). Blood pressures at enrollment were higher in the ECHO cohort compared to the non-ECHO cohort (systolic p

Published
2024
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17. A state sum for the total face color polynomial

Author

Baldridge, Scott, Kauffman, Louis H., and McCarty, Ben

Subjects
Mathematics - Geometric Topology, Mathematics - Combinatorics, 57R56, 05C10, 05C15, 05C31, 05C70, 57M15, 57K16
Abstract

The total face color polynomial is based upon the Poincar\'{e} polynomials of a family of filtered $n$-color homologies. It counts the number of $n$-face colorings of ribbon graphs for each positive integer $n$. As such, it may be seen as a successor of the Penrose polynomial, which at $n=3$ counts $3$-edge colorings (and consequently $4$-face colorings) of planar trivalent graphs. In this paper we describe a state sum formula for the polynomial. This formula unites two different perspectives about graph coloring: one based upon topological quantum field theory and the other on diagrammatic tensors., Comment: 19 pages, numerous figures

Published
2023

19. Enhancing hypertension education of community health extension workers in Nigeria’s federal capital territory: the impact of the extension for community healthcare outcomes model on primary care, a quasi-experimental study

Author

Baldridge, Abigail S., Orji, Ikechukwu A., Shedul, Gabriel L., Iyer, Guhan, Jamro, Erica L., Ye, Jiancheng, Akor, Blessing O., Okpetu, Emmanuel, Osagie, Samuel, Odukwe, Adaora, Dabiri, Haulat Olabisi, Mobisson, L. Nneka, Kandula, Namratha R., Hirschhorn, Lisa R., Huffman, Mark D., and Ojji, Dike B.

Published
2024
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20. The neonatal Fc receptor (FcRn) is a pan-arterivirus receptor

Author

Shaw, Teressa M., Huey, Devra, Mousa-Makky, Makky, Compaleo, Jared, Nennig, Kylie, Shah, Aadit P., Jiang, Fei, Qiu, Xueer, Klipsic, Devon, Rowland, Raymond R. R., Slukvin, Igor I., Sullender, Meagan E., Baldridge, Megan T., Li, Haichang, Warren, Cody J., and Bailey, Adam L.

Published
2024
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22. The Brain Gene Registry: a data snapshot

Author

Baldridge, Dustin, Kaster, Levi, Sancimino, Catherine, Srivastava, Siddharth, Molholm, Sophie, Gupta, Aditi, Oh, Inez, Lanzotti, Virginia, Grewal, Daleep, Riggs, Erin Rooney, Savatt, Juliann M., Hauck, Rachel, Sveden, Abigail, Constantino, John N., Piven, Joseph, Gurnett, Christina A., Chopra, Maya, Hazlett, Heather, and Payne, Philip R. O.

Published
2024
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25. Gaussian Process Probes (GPP) for Uncertainty-Aware Probing

Author

Wang, Zi, Ku, Alexander, Baldridge, Jason, Griffiths, Thomas L., and Kim, Been

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

Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented by models. As a Bayesian extension of linear probing methods, GPP asks what kind of distribution over classifiers (of concepts) is induced by the model. This distribution can be used to measure both what the model represents and how confident the probe is about what the model represents. GPP can be applied to any pre-trained model with vector representations of inputs (e.g., activations). It does not require access to training data, gradients, or the architecture. We validate GPP on datasets containing both synthetic and real images. Our experiments show it can (1) probe a model's representations of concepts even with a very small number of examples, (2) accurately measure both epistemic uncertainty (how confident the probe is) and aleatory uncertainty (how fuzzy the concepts are to the model), and (3) detect out of distribution data using those uncertainty measures as well as classic methods do. By using Gaussian processes to expand what probing can offer, GPP provides a data-efficient, versatile and uncertainty-aware tool for understanding and evaluating the capabilities of machine learning models.

Published
2023

26. CoBIT: A Contrastive Bi-directional Image-Text Generation Model

Author

You, Haoxuan, Guo, Mandy, Wang, Zhecan, Chang, Kai-Wei, Baldridge, Jason, and Yu, Jiahui

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

The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like CLIP, image-to-text generative objective like PaLI, or text-to-image generative objective like Parti. However, the three objectives can be pre-trained on the same data, image-text pairs, and intuitively they complement each other as contrasting provides global alignment capacity and generation grants fine-grained understanding. In this work, we present a Contrastive Bi-directional Image-Text generation model (CoBIT), which attempts to unify the three pre-training objectives in one framework. Specifically, CoBIT employs a novel unicoder-decoder structure, consisting of an image unicoder, a text unicoder and a cross-modal decoder. The image/text unicoders can switch between encoding and decoding in different tasks, enabling flexibility and shared knowledge that benefits both image-to-text and text-to-image generations. CoBIT achieves superior performance in image understanding, image-text understanding (Retrieval, Captioning, VQA, SNLI-VE) and text-based content creation, particularly in zero-shot scenarios. For instance, 82.7% in zero-shot ImageNet classification, 9.37 FID score in zero-shot text-to-image generation and 44.8 CIDEr in zero-shot captioning., Comment: 14 pages, 5 figures

Published
2023

27. A topological quantum field theory approach to graph coloring

Author

Baldridge, Scott and McCarty, Ben

Subjects
Mathematics - Geometric Topology, Mathematics - Combinatorics, 57R56 (Primary) 05C10, 05C15, 05C31, 05C70, 57M15 57K16 (Secondary)
Abstract

In this paper, we use a topological quantum field theory (TQFT) to define families of new homology theories of a $2$-dimensional CW complex of a smooth closed surface. The dimensions of these homology groups can be used to count the number of ways that each face of the CW complex can be colored with one of $n$ colors so that no two adjacent faces have the same color. We use these homologies to define new invariants of graphs, give new characterizations of well-known polynomial invariants of graphs, and rephrase and offer new approaches to famous conjectures about graph coloring. In particular, we show that the TQFT has the potential to generate $4$-face colorings of a bridgeless planar graph, leading to a constructive approach to the four color theorem. The TQFT has ramifications for the study of smooth surfaces and provides examples of new types of Frobenius algebras., Comment: 88 pages, numerous figures

Published
2023

28. The neonatal Fc receptor (FcRn) is a pan-arterivirus receptor

Author

Teressa M. Shaw, Devra Huey, Makky Mousa-Makky, Jared Compaleo, Kylie Nennig, Aadit P. Shah, Fei Jiang, Xueer Qiu, Devon Klipsic, Raymond R. R. Rowland, Igor I. Slukvin, Meagan E. Sullender, Megan T. Baldridge, Haichang Li, Cody J. Warren, and Adam L. Bailey

Subjects
Science
Abstract

Abstract Arteriviruses infect a variety of mammalian hosts, but the receptors used by these viruses to enter cells are poorly understood. We identified the neonatal Fc receptor (FcRn) as an important pro-viral host factor via comparative genome-wide CRISPR-knockout screens with multiple arteriviruses. Using a panel of cell lines and divergent arteriviruses, we demonstrate that FcRn is required for the entry step of arterivirus infection and serves as a molecular barrier to arterivirus cross-species infection. We also show that FcRn synergizes with another known arterivirus entry factor, CD163, to mediate arterivirus entry. Overexpression of FcRn and CD163 sensitizes non-permissive cells to infection and enables the culture of fastidious arteriviruses. Treatment of multiple cell lines with a pre-clinical anti-FcRn monoclonal antibody blocked infection and rescued cells from arterivirus-induced death. Altogether, this study identifies FcRn as a novel pan-arterivirus receptor, with implications for arterivirus emergence, cross-species infection, and host-directed pan-arterivirus countermeasure development.

Published
2024
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29. Evaluating diabetes care in primary healthcare centers in Abuja, Nigeria: a cross-sectional formative assessment

Author

Ikechukwu A. Orji, Abigail S. Baldridge, Mercy U. Ikechukwu-Orji, Bolanle Banigbe, Nelson C. Eze, Aashima Chopra, Kasarachi Omitiran, Guhan Iyer, Deborah Odoh, Morenike Alex-Okoh, Rifkatu Reng, Lisa R. Hirschhorn, Mark D. Huffman, and Dike B. Ojji

Subjects
Service availability, Service readiness, Primary healthcare centers, Diabetes care, Abuja, Nigeria, Medicine (General), R5-920
Abstract

Abstract Introduction Noncommunicable diseases (NCDs) are associated with high and rising burden of morbidity and mortality in sub-Saharan Africa, including Nigeria. Diabetes mellitus (DM) is among the leading causes of NCD-related deaths worldwide and is a foremost public health problem in Nigeria. As part of National policy, Nigeria has committed to implement the World Health Organization (WHO) Package of Essential Non-communicable Disease interventions for primary care. Implementing the intervention requires the availability of essential elements, including guidelines, trained staff, health management information systems (HMIS), equipment, and medications, in primary healthcare centers (PHCs). This study assessed the availability of the DM component of the WHO package, and the readiness of the health workers in these PHCs to implement a DM screening, evaluation, and management program to inform future adoption and implementation. Methods This cross-sectional formative assessment adapted the WHO Service Availability and Readiness Assessment (SARA) tool to survey 30 PHCs selected by multistage sampling for readiness to deliver DM diagnosis and care in Abuja, Nigeria, between August and October 2021. The SARA tool was adapted to focus on DM services and the availability and readiness indicator scores were calculated based on the proportion of PHCs with available DM care services, minimum staff requirement, diagnostic tests, equipment, medications, and national guidelines/protocols for DM care within the defined SARA domain. Results All 30 PHCs reported the availability of at least two full-time staff (median [interquartile range] = 5 [4–9]), which were mostly community health extension workers (median [interquartile range]) = 3 [1–4]. At least one staff member was recently trained in DM care in 11 PHCs (36%). The study also reported high availability of paper-based HMIS (100%), and DM screening services using a glucometer (87%), but low availability of DM job aids (27%), treatment (23%), and national guidelines/protocols (0%). Conclusion This formative assessment of PHCs’ readiness to implement a DM screening, evaluation, and management program in Abuja demonstrated readiness to integrate DM care into PHCs regarding equipment, paper-based HMIS, and nonphysician health workers' availability. However, strategies are needed to promote DM health workforce training, provide DM management guidelines, and supply essential DM medications.

Published
2024
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31. Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting

Author

Wang, Su, Saharia, Chitwan, Montgomery, Ceslee, Pont-Tuset, Jordi, Noy, Shai, Pellegrini, Stefano, Onoe, Yasumasa, Laszlo, Sarah, Fleet, David J., Soricut, Radu, Baldridge, Jason, Norouzi, Mohammad, Anderson, Peter, and Chan, William

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

Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. In addition, Imagen Editor captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes., Comment: CVPR 2023 Camera Ready

Published
2022

32. Antibiotic‐associated neutropenia is marked by the depletion of intestinal Lachnospiraceae and associated metabolites in pediatric patients

Author

Josaura Fernandez‐Sanchez, Rachel Rodgers, Arushana A. Maknojia, Nusrat Shaikh, Hannah Yan, Marlyd E. Mejia, Hope Hendricks, Robert R. Jenq, Pavan Reddy, Ritu Banerjee, Jeremy M. Schraw, Megan T. Baldridge, and Katherine Y. King

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Abstract Prolonged antibiotic exposure causes dangerous hematologic side effects, including neutropenia, in up to 34% of patients. Murine studies established a link between the intestinal microbiota and hematopoiesis. To identify factors that predispose to neutropenia in pediatric patients, we evaluated changes in microbiota‐derived metabolites and intestinal microbiota composition after prolonged courses of antibiotics. In this multi‐center study, patients with infections requiring anticipated antibiotic treatment of two or more weeks were enrolled. Stool samples were obtained at the start and completion of antibiotics or at neutropenia onset (prospective arm). Some patients were enrolled in a retrospective arm in which a stool sample was collected at the time of neutropenia during antibiotic therapy and 2–4 weeks after completion of antibiotics with recovery of blood counts. We identified 10 patients who developed neutropenia on antibiotics and 29 controls matched for age, sex, race, and ethnicity. Clinical data demonstrated no association between neutropenia and the type of infection or antibiotic used; however, patients with neutropenia were admitted to the intensive care unit more often and received longer courses of antibiotics. Reduced intestinal microbiome richness and, specifically, decreased abundance of Lachnospiraceae family members correlated with neutropenia. Untargeted stool metabolomic profiling revealed several metabolites that were depleted exclusively in patients with neutropenia, including members of the urea cycle pathway, pyrimidine metabolism, and fatty acid metabolism that are known to be produced by Lachnospiraceae. Our study shows a relationship between intestinal microbiota disruption and abnormal hematopoiesis and identifies taxa and metabolites likely to contribute to microbiota‐sustained hematopoiesis.

Published
2024
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33. Identification of ERAD-dependent degrons for the endoplasmic reticulum lumen

Author

Rachel Sharninghausen, Jiwon Hwang, Devon D Dennison, and Ryan D Baldridge

Subjects
degrons, protein degradation, endoplasmic reticulum associated degradation, ERAD, Medicine, Science, Biology (General), QH301-705.5
Abstract

Degrons are minimal protein features that are sufficient to target proteins for degradation. In most cases, degrons allow recognition by components of the cytosolic ubiquitin proteasome system. Currently, all of the identified degrons only function within the cytosol. Using Saccharomyces cerevisiae, we identified the first short linear sequences that function as degrons from the endoplasmic reticulum (ER) lumen. We show that when these degrons are transferred to proteins, they facilitate proteasomal degradation through the endoplasmic reticulum associated degradation (ERAD) system. These degrons enable degradation of both luminal and integral membrane ER proteins, expanding the types of proteins that can be targeted for degradation in budding yeast and mammalian tissue culture. This discovery provides a framework to target proteins for degradation from the previously unreachable ER lumen and builds toward therapeutic approaches that exploit the highly conserved ERAD system.

Published
2024
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36. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

37. Underspecification in Scene Description-to-Depiction Tasks

Author

Hutchinson, Ben, Baldridge, Jason, and Prabhakaran, Vinodkumar

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

Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have received little attention to date. This position paper maps out a conceptual framework to address this gap, focusing on systems which generate images depicting scenes from scene descriptions. In doing so, we account for how texts and images convey meaning differently. We outline a set of core challenges concerning textual and visual ambiguity, as well as risks that may be amplified by ambiguous and underspecified elements. We propose and discuss strategies for addressing these challenges, including generating visually ambiguous images, and generating a set of diverse images.

Published
2022

38. A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning

Author

Kamath, Aishwarya, Anderson, Peter, Wang, Su, Koh, Jing Yu, Ku, Alexander, Waters, Austin, Yang, Yinfei, Baldridge, Jason, and Parekh, Zarana

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pretraining on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky, a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale imitation learning and the development of synthetic instruction generation capabilities., Comment: CVPR 2023

Published
2022

41. The Brain Gene Registry: a data snapshot

Author

Dustin Baldridge, Levi Kaster, Catherine Sancimino, Siddharth Srivastava, Sophie Molholm, Aditi Gupta, Inez Oh, Virginia Lanzotti, Daleep Grewal, Erin Rooney Riggs, Juliann M. Savatt, Rachel Hauck, Abigail Sveden, the Brain Gene Registry Consortium, John N. Constantino, Joseph Piven, Christina A. Gurnett, Maya Chopra, Heather Hazlett, and Philip R. O. Payne

Subjects
Brain gene registry, Neurodevelopmental disorders, Electronic health records, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource’s (ClinGen’s) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen’s BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders.

Published
2024
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42. Ivermectin increases striatal cholinergic activity to facilitate dopamine terminal function

Author

Hillary A. Wadsworth, Alicia M. P. Warnecke, Joshua C. Barlow, J. Kayden Robinson, Emma Steimle, Joakim W. Ronström, Pacen E. Williams, Christopher J. Galbraith, Jared Baldridge, Michael W. Jakowec, Daryl L. Davies, and Jordan T. Yorgason

Subjects
Receptors, Purinergic P2X4 receptors, FSCV, Ivermectin, Parkinson’s, Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5, Biochemistry, QD415-436
Abstract

Abstract Ivermectin (IVM) is a commonly prescribed antiparasitic treatment with pharmacological effects on invertebrate glutamate ion channels resulting in paralysis and death of invertebrates. However, it can also act as a modulator of some vertebrate ion channels and has shown promise in facilitating L-DOPA treatment in preclinical models of Parkinson’s disease. The pharmacological effects of IVM on dopamine terminal function were tested, focusing on the role of two of IVM’s potential targets: purinergic P2X4 and nicotinic acetylcholine receptors. Ivermectin enhanced electrochemical detection of dorsal striatum dopamine release. Although striatal P2X4 receptors were observed, IVM effects on dopamine release were not blocked by P2X4 receptor inactivation. In contrast, IVM attenuated nicotine effects on dopamine release, and antagonizing nicotinic receptors prevented IVM effects on dopamine release. IVM also enhanced striatal cholinergic interneuron firing. L-DOPA enhances dopamine release by increasing vesicular content. L-DOPA and IVM co-application further enhanced release but resulted in a reduction in the ratio between high and low frequency stimulations, suggesting that IVM is enhancing release largely through changes in terminal excitability and not vesicular content. Thus, IVM is increasing striatal dopamine release through enhanced cholinergic activity on dopamine terminals.

Published
2024
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43. Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Author

Yu, Jiahui, Xu, Yuanzhong, Koh, Jing Yu, Luong, Thang, Baid, Gunjan, Wang, Zirui, Vasudevan, Vijay, Ku, Alexander, Yang, Yinfei, Ayan, Burcu Karagol, Hutchinson, Ben, Han, Wei, Parekh, Zarana, Li, Xin, Zhang, Han, Baldridge, Jason, and Wu, Yonghui

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

We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty aspects. We also explore and highlight limitations of our models in order to define and exemplify key areas of focus for further improvements. See https://parti.research.google/ for high-resolution images., Comment: Preprint

Published
2022

44. Simple and Effective Synthesis of Indoor 3D Scenes

Author

Koh, Jing Yu, Agrawal, Harsh, Batra, Dhruv, Tucker, Richard, Waters, Austin, Lee, Honglak, Yang, Yinfei, Baldridge, Jason, and Anderson, Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A vision-and-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the R2R benchmark. Our code will be made available to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks., Comment: AAAI 2023

Published
2022

45. Endocannabinoid dysfunction in neurological disease: neuro-ocular DAGLA-related syndrome (NODRS)

Author

Bainbridge, Matthew N, Mazumder, Aloran, Ogasawara, Daisuke, Jamra, Rami Abou, Bernard, Geneviève, Bertini, Enrico, Burglen, Lydie, Cope, Heidi, Crawford, Ali, Derksen, Alexa, Dure, Leon, Gantz, Emily, Koch-Hogrebe, Margarete, Hurst, Anna CE, Mahida, Sonal, Marshall, Paige, Micalizzi, Alessia, Novelli, Antonio, Peng, Hongfan, Medicine, Rady Children's Institute for Genomic, Rodriguez, Diana, Robbins, Shira L, Rutledge, S Lane, Scalise, Roberta, Schließke, Sophia, Shashi, Vandana, Srivastava, Siddharth, Thiffault, Isabella, Topol, Sarah, Acosta, Maria T, Adam, Margaret, Adams, David R, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G, Deardorff, Matthew, Dell’Angelica, Esteban C, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L, Douine, Emilie D, Duncan, Laura, and Earl, Dawn

Subjects
Biotechnology, Brain Disorders, Genetics, Neurosciences, Clinical Research, Rare Diseases, Eye Disease and Disorders of Vision, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Neurological, Humans, Child, Endocannabinoids, Phenotype, Nervous System Diseases, Heterozygote, Syndrome, Mutant Proteins, endocannabinoid, episodic ataxia, paroxysmal tonic upgaze, nystagmus, developmental delay, Rady Children's Institute for Genomic Medicine, Undiagnosed Disease Network, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

The endocannabinoid system is a highly conserved and ubiquitous signalling pathway with broad-ranging effects. Despite critical pathway functions, gene variants have not previously been conclusively linked to human disease. We identified nine children from eight families with heterozygous, de novo truncating variants in the last exon of DAGLA with a neuro-ocular phenotype characterized by developmental delay, ataxia and complex oculomotor abnormality. All children displayed paroxysms of nystagmus or eye deviation accompanied by compensatory head posture and worsened incoordination most frequently after waking. RNA sequencing showed clear expression of the truncated transcript and no differences were found between mutant and wild-type DAGLA activity. Immunofluorescence staining of patient-derived fibroblasts and HEK cells expressing the mutant protein showed distinct perinuclear aggregation not detected in control samples. This report establishes truncating variants in the last DAGLA exon as the cause of a unique paediatric syndrome. Because enzymatic activity was preserved, the observed mislocalization of the truncated protein may account for the observed phenotype. Potential mechanisms include DAGLA haploinsufficiency at the plasma membrane or dominant negative effect. To our knowledge, this is the first report directly linking an endocannabinoid system component with human genetic disease and sets the stage for potential future therapeutic avenues.

Published
2022

46. Cohort Expansion and Genotype-Phenotype Analysis of RAB11A-Associated Neurodevelopmental Disorder

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cooper, Cynthia M., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Duncan, Laura, Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Kennedy, Jennifer, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Scott, C. Ron, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solem, Emily, Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Borroto, Maria Carla, Patel, Heena, Srivastava, Siddharth, Swanson, Lindsay C., Keren, Boris, Whalen, Sandra, Mignot, Cyril, Wang, Xiaodong, Chen, Qian, McLean, Scott, Littlejohn, Rebecca O., Emrick, Lisa, Attali, Ruben, Lesca, Gaetan, Acquaviva-Bourdain, Cecile, Sarret, Catherine, Seaver, Laurie H., Platzer, Konrad, Bartolomaeus, Tobias, Wünsch, Cornelia, Fischer, Susann, Rodriguez Barreto, Ana Maria, Granadillo, Jorge L., Schreiner, Elisabeth, Brunet, Theresa, Schatz, Ulrich A., Thiffault, Isabelle, Mullegama, Sureni V., Michaud, Jacques L., Hamdan, Fadi F., Rossignol, Elsa, and Campeau, Philippe M.

Published
2024
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47. Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly

Author

Acosta, Maria T., Adams, David R., Raquel, Alvarez, L., Alvey, Justin, Allworth, Aimee, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Carvhalo Neto, George D., Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Grajewski, Alana, Hadley, Don, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Kaitryn, Emerald, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, Audrey Stephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Might, Matthew, Miller, Danny, Mirzaa, Ghayda, Moore, Ryan M., Morava, Eva, Moretti, Paolo, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Huang, Yue, Jay, Kristy L., Yen-Wen Huang, Alden, Jangam, Sharayu V., Chorin, Odelia, Rothschild, Annick, Barel, Ortal, Mariani, Milena, Iascone, Maria, Xue, Han, Huang, Jing, Mignot, Cyril, Keren, Boris, Saillour, Virginie, Mah-Som, Annelise Y., Sacharow, Stephanie, Rajabi, Farrah, Costin, Carrie, Kanca, Oguz, and Martinez-Agosto, Julian A.

Published
2024
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49. The Undiagnosed Diseases Network: Characteristics of solvable applicants and diagnostic suggestions for nonaccepted ones

Author

Acosta, Maria T., Adams, David R., Afzali, Ben, Al-Beshri, Ali, Allworth, Aimee, Alvarez, Raquel L., Alvey, Justin, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Borja, Nicholas, Botto, Lorenzo, Briere, Lauren C., Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Callaway, Kaitlin, Carey, John, Carvalho, George, Cassini, Thomas, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Corner, Brian, Corona, Rosario I., Craigen, William J., Crouse, Andrew B., Cuddapah, Vishnu, Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Delgado, Margaret, Dell’Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douglas, Jessica, Douine, Emilie D., Earl, Dawn, Emrick, Lisa T., Eng, Christine M., Ezell, Kimberly, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Fu, Jiayu, Gahl, William A., Ganetzky, Rebecca, Glanton, Emily, Glass, Ian, Goddard, Page C., Gonzalez, Joanna M., Gropman, Andrea, Halley, Meghan C., Hamid, Rizwan, Hanchard, Neal, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yan, Hurst, Anna, Introne, Wendy, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Marie, Orpa Jean, Jobanputra, Vaidehi, Kaitryn, Emerald, Kanca, Oguz, Karasozen, Yigit, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korf, Bruce, Korrick, Susan, Krakow, Deborah, Kravets, Elijah, Lalani, Seema R., Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., Latchman, Kumarie, LeBlanc, Kimberly, Lee, Brendan H., Lewis, Richard A., Liu, Pengfei, Longo, Nicola, Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, Audrey Stephannie, Mahoney, Rachel, Malicdan, May Christine V., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McConkie-Rosell, Allyn, McCray, Alexa T., Might, Matthew, Mikati, Mohamad, Miller, Danny, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Morimoto, Marie, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Neumann, Serena, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Parker, Neil H., Peart, LéShon, Petcharet, Leoyklang, Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rajagopalan, Ramakrishnan, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Rebelo, Adriana, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rodriguez, Martin, Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sabaii, Marla, Sampson, Jacinda B., Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sisco, Kathy, Skelton, Tammi, Skraban, Cara, Smith, Carson A., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Taylor, Herman, Tekin, Mustafa, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Wood, Heidi, Worley, Kim, Yamamoto, Shinya, Zhang, Zhe, Zuchner, Stephan, Findley, Laura, Ni, Weihong, Sinsheimer, Janet S., Cole, F. Session, Esteves, Cecilia, Newman, John H., and Mokry, Jill R.

Published
2024
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