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3,581 results on '"Paine, P."'

1. BICEP/Keck XIX: Extremely Thin Composite Polymer Vacuum Windows for BICEP and Other High Throughput Millimeter Wave Telescopes

Author

Collaboration, BICEP/Keck, Ade, P. A. R., Ahmed, Z., Amiri, M., Barkats, D., Thakur, R. Basu, Bischoff, C. A., Beck, D., Bock, J. J., Boenish, H., Buza, V., Carter, K., Cheshire IV, J. R., Connors, J., Cornelison, J., Corrigan, L., Crumrine, M., Crystian, S., Cukierman, A. J., Denison, E., Duband, L., Echter, M., Eiben, M., Elwood, B. D., Fatigoni, S., Filippini, J. P., Fortes, A., Gao, M., Giannakopoulos, C., Goeckner-Wald, N., Goldfinger, D. C., Grayson, J. A., Greathouse, A., Grimes, P. K., Hall, G., Halal, G., Halpern, M., Hand, E., Harrison, S. A., Henderson, S., Hubmayr, J., Hui, H., Irwin, K. D., Kang, J. H., Karkare, K. S., Kefeli, S., Kovac, J. M., Kuo, C., Lau, K., Lautzenhiser, M., Lennox, A., Liu, T., Megerian, K. G., Miller, M., Minutolo, L., Moncelsi, L., Nakato, Y., Nguyen, H. T., O'brient, R., Paine, S., Patel, A., Petroff, M. A., Polish, A. R., Prouve, T., Pryke, C., Reintsema, C. D., Romand, T., Santalucia, D., Schillaci, A., Schmitt, B., Sheffield, E., Singari, B., Sjoberg, K., Soliman, A., Germaine, T. St, Steiger, A., Steinbach, B., Sudiwala, R., Thompson, K. L., Tsai, C., Tucker, C., Turner, A. D., Vergès, C., Vieregg, A. G., Wandui, A., Weber, A. C., Willmert, J., Wu, W. L. K., Yang, H., Yu, C., Zeng, L., Zhang, C., and Zhang, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Optics
Abstract

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

Published
2024

2. Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Author

da Silva, Rafael Ferreira, Bard, Deborah, Chard, Kyle, de Witt, Shaun, Foster, Ian T., Gibbs, Tom, Goble, Carole, Godoy, William, Gustafsson, Johan, Haus, Utz-Uwe, Hudson, Stephen, Jha, Shantenu, Los, Laila, Paine, Drew, Suter, Frédéric, Ward, Logan, Wilkinson, Sean, Amaris, Marcos, Babuji, Yadu, Bader, Jonathan, Balin, Riccardo, Balouek, Daniel, Beecroft, Sarah, Belhajjame, Khalid, Bhattarai, Rajat, Brewer, Wes, Brunk, Paul, Caino-Lores, Silvina, Casanova, Henri, Cassol, Daniela, Coleman, Jared, Coleman, Taina, Colonnelli, Iacopo, Da Silva, Anderson Andrei, de Oliveira, Daniel, Elahi, Pascal, Elfaramawy, Nour, Elwasif, Wael, Etz, Brian, Fahringer, Thomas, Ferreira, Wesley, Filgueira, Rosa, Tande, Jacob Fosso, Gadelha, Luiz, Gallo, Andy, Garijo, Daniel, Georgiou, Yiannis, Gritsch, Philipp, Grubel, Patricia, Gueroudji, Amal, Guilloteau, Quentin, Hamalainen, Carlo, Enriquez, Rolando Hong, Huet, Lauren, Kesling, Kevin Hunter, Iborra, Paula, Jahangiri, Shiva, Janssen, Jan, Jordan, Joe, Kanwal, Sehrish, Kunstmann, Liliane, Lehmann, Fabian, Leser, Ulf, Li, Chen, Liu, Peini, Luettgau, Jakob, Lupat, Richard, Fernandez, Jose M., Maheshwari, Ketan, Malik, Tanu, Marquez, Jack, Matsuda, Motohiko, Medic, Doriana, Mohammadi, Somayeh, Mulone, Alberto, Navarro, John-Luke, Ng, Kin Wai, Noelp, Klaus, Kinoshita, Bruno P., Prout, Ryan, Crusoe, Michael R., Ristov, Sashko, Robila, Stefan, Rosendo, Daniel, Rowell, Billy, Rybicki, Jedrzej, Sanchez, Hector, Saurabh, Nishant, Saurav, Sumit Kumar, Scogland, Tom, Senanayake, Dinindu, Shin, Woong, Sirvent, Raul, Skluzacek, Tyler, Sly-Delgado, Barry, Soiland-Reyes, Stian, Souza, Abel, Souza, Renan, Talia, Domenico, Tallent, Nathan, Thamsen, Lauritz, Titov, Mikhail, Tovar, Benjamin, Vahi, Karan, Vardar-Irrgang, Eric, Vartina, Edite, Wang, Yuandou, Wouters, Merridee, Yu, Qi, Bkhetan, Ziad Al, and Zulfiqar, Mahnoor

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

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Published
2024
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3. Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis

Author

Mahesh, Karan, Paine, Tyler M., Greene, Max L., Rober, Nicholas, Lee, Steven, Monteiro, Sildomar T., Annaswamy, Anuradha, Benjamin, Michael R., and How, Jonathan P.

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV's MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a PID baseline, showing a 45%-81% reduction in RMSE position error. Additionally, the reachability module provided real-time safety certification, ensuring the USV's safety. We further validated our pipeline's effectiveness in underway replenishment and canal scenarios, simulating relevant marine tasks., Comment: 35 pages, 23 figures, 6 tables

Published
2024

4. Adaptive bias for dissensus in nonlinear opinion dynamics with application to evolutionary division of labor games

Author

Paine, Tyler M., Bizyaeva, Anastasia, and Benjamin, Michael R.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Multiagent Systems, Computer Science - Robotics
Abstract

This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game., Comment: v1) To appear at the 2024 IEEE Conference on Decision and Control (CDC) in Milan, Italy. 8 Pages, 5 Figures. v2) Fixed typo

Published
2024

5. 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

6. Upgrading the Submillimeter Array: wSMA and beyond

Author

Grimes, Paul K., Keating, Garrett K., Blundell, Raymond, Christensen, Robert D., Gurwell, Mark, Kovacs, Attila, Norton, Timothy, Paine, Scott N., Rao, Ramprasad, Tong, Edward C. -Y., Weintroub, Jonathan, Wilner, David, Wilson, Robert W., Zeng, Lingzhen, and Zhang, Qizhou

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Submillimeter Array (SMA) is an array of 8 antennas operating at millimeter and submillimeter wavelengths on Maunakea, Hawaii, operated by the Smithsonian Astrophysical Observatory and Academia Sinica Institute of Astronomy and Astrophysics, Taiwan. Over the past several years, we have been preparing a major upgrade to the SMA that will replace the aging original receiver cryostats and receiver cartridges with all new cryostats and new 230 and 345 GHz receiver designs. This wideband upgrade (wSMA) will also include significantly increased instantaneous bandwidth, improved sensitivity, and greater capabilities for dual frequency observations. In this paper, we will describe the wSMA receiver upgrade and status, as well as the future upgrades that will be enabled by the deployment of the wSMA receivers., Comment: To be published in the proceedings of SPIE Astronomical Telescopes + Instrumentation 2024, paper number 13096-122

Published
2024

7. The Precursor Small Aperture Telescope (PreSAT) CMB polarimeter

Author

Petroff, Matthew A., Ahmed, Zeeshan, Bock, James J., Dierickx, Marion, Fatigoni, Sofia, Goldfinger, David C., Grimes, Paul K., Henderson, Shawn W., Karkare, Kirit S., Kovac, John M., Nguyen, Hien T., Paine, Scott N., Polish, Anna R., Pryke, Clement, Romand, Thibault, Schmitt, Benjamin L., and Vieregg, Abigail G.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The search for the polarized imprint of primordial gravitational waves in the cosmic microwave background (CMB) as direct evidence of cosmic inflation requires exquisite sensitivity and control over systematics. The next-generation CMB-S4 project intends to improve upon current-generation experiments by deploying a significantly greater number of highly-sensitive detectors, combined with refined instrument components based on designs from field-proven instruments. The Precursor Small Aperture Telescope (PreSAT) is envisioned as an early step to this next generation, which will test prototype CMB-S4 components and technologies within an existing BICEP Array receiver, with the aim of enabling full-stack laboratory testing and early risk retirement, along with direct correlation of laboratory component-level performance measurements with deployed system performance. The instrument will utilize new 95/155GHz dichroic dual-linear-polarization prototype detectors developed for CMB-S4, cooled to 100mK via the installation of an adiabatic demagnetization refrigerator, along with a prototype readout chain and prototype optics manufactured with wide-band anti-reflection coatings. The experience gained by integrating, deploying, and calibrating PreSAT will also help inform planning for CMB-S4 small aperture telescope commissioning, calibration, and operations well in advance of the fabrication of CMB-S4 production hardware., Comment: 12 pages, 4 figures, submitted to Proc. SPIE

Published
2024

8. Evaluating Collaborative Autonomy in Opposed Environments using Maritime Capture-the-Flag Competitions

Author

Beason, Jordan, Novitzky, Michael, Kliem, John, Errico, Tyler, Serlin, Zachary, Becker, Kevin, Paine, Tyler, Benjamin, Michael, Dasgupta, Prithviraj, Crowley, Peter, O'Donnell, Charles, and James, John

Subjects
Computer Science - Robotics
Abstract

The objective of this work is to evaluate multi-agent artificial intelligence methods when deployed on teams of unmanned surface vehicles (USV) in an adversarial environment. Autonomous agents were evaluated in real-world scenarios using the Aquaticus test-bed, which is a Capture-the-Flag (CTF) style competition involving teams of USV systems. Cooperative teaming algorithms of various foundations in behavior-based optimization and deep reinforcement learning (RL) were deployed on these USV systems in two versus two teams and tested against each other during a competition period in the fall of 2023. Deep reinforcement learning applied to USV agents was achieved via the Pyquaticus test bed, a lightweight gymnasium environment that allows simulated CTF training in a low-level environment. The results of the experiment demonstrate that rule-based cooperation for behavior-based agents outperformed those trained in Deep-reinforcement learning paradigms as implemented in these competitions. Further integration of the Pyquaticus gymnasium environment for RL with MOOS-IvP in terms of configuration and control schema will allow for more competitive CTF games in future studies. As the development of experimental deep RL methods continues, the authors expect that the competitive gap between behavior-based autonomy and deep RL will be reduced. As such, this report outlines the overall competition, methods, and results with an emphasis on future works such as reward shaping and sim-to-real methodologies and extending rule-based cooperation among agents to react to safety and security events in accordance with human experts intent/rules for executing safety and security processes., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Published
2024

9. Atmospheric limitations for high-frequency ground-based VLBI

Author

Pesce, Dominic W., Blackburn, Lindy, Chaves, Ryan, Doeleman, Sheperd S., Freeman, Mark, Issaoun, Sara, Johnson, Michael D., Lindahl, Greg, Natarajan, Iniyan, Paine, Scott N., Palumbo, Daniel C. M., Roelofs, Freek, and Tiede, Paul

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Very long baseline interferometry (VLBI) provides the highest-resolution images in astronomy. The sharpest resolution is nominally achieved at the highest frequencies, but as the observing frequency increases so too does the atmospheric contribution to the system noise, degrading the sensitivity of the array and hampering detection. In this paper, we explore the limits of high-frequency VLBI observations using ngehtsim, a new tool for generating realistic synthetic data. ngehtsim uses detailed historical atmospheric models to simulate observing conditions, and it employs heuristic visibility detection criteria that emulate single- and multi-frequency VLBI calibration strategies. We demonstrate the fidelity of ngehtsim's predictions using a comparison with existing 230 GHz data taken by the Event Horizon Telescope (EHT), and we simulate the expected performance of EHT observations at 345 GHz. Though the EHT achieves a nearly 100% detection rate at 230 GHz, our simulations indicate that it should expect substantially poorer performance at 345 GHz; in particular, observations of M87 at 345 GHz are predicted to achieve detection rates of $\lesssim$20% that may preclude imaging. Increasing the array sensitivity through wider bandwidths and/or longer integration times -- as enabled through, e.g., the simultaneous multi-frequency upgrades envisioned for the next-generation EHT -- can improve the 345 GHz prospects and yield detection levels that are comparable to those at 230 GHz. M87 and Sgr A* observations carried out in the atmospheric window around 460 GHz could expect to regularly achieve multiple detections on long baselines, but analogous observations at 690 and 875 GHz consistently obtain almost no detections at all., Comment: 29 pages, 12 figures, accepted for publication in ApJ

Published
2024

10. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol

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

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

14. A search for millisecond radio bursts with Messier 82

Author

Paine, S., Hawkins, T., Lorimer, D. R., Stanley, J., Kania, J., Crawford, F., and Fairfield, N.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Fast radio bursts (FRBs) are short-duration radio pulses of cosmological origin. Among the most common sources predicted to explain this phenomenon are bright pulses from a class of extremely highly magnetized neutron stars known as magnetars. Motivated by the discovery of an FRB-like pulse from the Galactic magnetar SGR 1935+2154, we searched for similar events in Messier 82 (M82). With a star formation rate 40 times that of the Milky Way, one might expect that the implied rate of events similar to that seen from SGR 1935+2154 from M82 should be 40 times higher than that of the Milky Way. We observed M82 at 1.4 GHz with the 20-m telescope at the Green Bank Observatory for 34.8 days. While we found many candidate events, none had a signal-to-noise ratio greater than 8. We also show that there are insufficient numbers of repeating low-significance events at similar dispersion measures to constitute a statistically significant detection. From these results, we place an upper bound for the rate of radio pulses from M82 to be 30 per year above a fluence limit of 8.5 Jy ms. While this is less than 9 times the rate of radio bursts from magnetars in the Milky Way inferred from the previous radio detections of SGR 1935+2154, it is possible that propagation effects from interstellar scattering are currently limiting our ability to detect sources in M82. Further searches of M82 and other nearby galaxies are encouraged to probe this putative FRB population.

Published
2024

15. Towards Non-contact Muscle Activity Estimation using FMCW Radar

Author

Tsengwa, Kukhokuhle, Paine, Stephen, Nicolls, Fred, Albertus, Yumna, and Patel, Amir

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin above the target muscle and the received signal can be used to infer the state of the muscle - active, inactive or fatigued - which serves as vital information during neurological and orthopaedic rehabilitation. Additionally, the sEMG signal can also be used for the control of prostheses. sEMG requires contact with the participant's skin and is thus a potentially uncomfortable method for the measurement of muscle activity. Moreover, the setup procedure has been termed time-consuming by sEMG experts and is listed as one of the main barriers to the clinical employment of the technique. Previous studies have shown that architectural changes, particularly muscle deformation, can provide information about the activity of the muscle, providing an alternative to sEMG. In all these studies, the muscle deformation signal is acquired using ultrasound imaging, an approach known as sonomyography (SMG). Despite its advantages, such as improved spatial resolution, SMG is still a contact based approach. In this paper, we propose a non-contact muscle activity monitoring approach that measures the muscle deformation signal using a Frequency Modulated Continuous Wave (FMCW) mmWave radar which we call radiomyography (RMG). In future, this system will enable muscle activation to be measured in an unconstrained and less cumbersome manner for both the person conducting the test and the individual being tested., Comment: 13 pages, 11 figures

Published
2023

16. 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

17. A Model for Multi-Agent Autonomy That Uses Opinion Dynamics and Multi-Objective Behavior Optimization

Author

Paine, Tyler M. and Benjamin, Michael R.

Subjects
Computer Science - Robotics, Computer Science - Multiagent Systems
Abstract

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical `explore-exploit-migrate' scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts., Comment: v1) 7 pages, 7 figures. v2) To appear at the 2024 IEEE International Conference on Robotics and Automation (ICRA) in Yokohama, Japan. v3) Fixed typos and added publication info

Published
2023
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18. Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances

Author

Rober, Nicholas, Mahesh, Karan, Paine, Tyler M., Greene, Max L., Lee, Steven, Monteiro, Sildomar T., Benjamin, Michael R., and How, Jonathan P.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach's ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures., Comment: 6 pages, 7 figures

Published
2023

19. Active Anomaly Detection in Confined Spaces Using Ergodic Traversal of Directed Region Graphs

Author

Wong, Benjamin, Paine, Tyler M., Devasia, Santosh, and Banerjee, Ashis G.

Subjects
Computer Science - Robotics
Abstract

We provide the first step toward developing a hierarchical control-estimation framework to actively plan robot trajectories for anomaly detection in confined spaces. The space is represented globally using a directed region graph, where a region is a landmark that needs to be visited (inspected). We devise a fast mixing Markov chain to find an ergodic route that traverses this graph so that the region visitation frequency is proportional to its anomaly detection uncertainty, while satisfying the edge directionality (region transition) constraint(s). Preliminary simulation results show fast convergence to the ergodic solution and confident estimation of the presence of anomalies in the inspected regions.

Published
2023

20. A Sociotechnical Typology of Scientific Software

Author

Paine, Drew, Poon, Sarah, Deshmukh, Rajshree, O'Donnell, Cody, Gunter, Daniel, and Ramakrishnan, Lavanya

Subjects
sociotechnical, STRUDEL, typology, scientific software development, user experience, software sustainability
Published
2024

21. Analyzing blank cutting edge efficiency associated with the adoption of microblade technology: A case study from Tolbor-17, Mongolia.

Author

Johnson, Corey, Bolorbat, Tsedendorj, Grote, Mark, Paine, Clea, Lkhundev, Guunii, Odsuren, Davaakhuu, Izuho, Masami, Gunchinsuren, Byambaa, and Zwyns, Nicolas

Subjects
Mongolia, Archaeology, Technology, Fossils, Humans, History, Ancient
Abstract

The phenomenon of lithic miniaturization during the Late Pleistocene at times coincided with increased artifact standardization and cutting edge efficiency-likely reflecting the use of small, sharp artifacts as interchangeable inserts for composite cutting tools and hunting weapons. During Marine Isotope Stage 2, Upper Paleolithic toolmakers in northern East Asia specifically used pressure techniques to make small, highly standardized lithic artifacts called microblades. However, little is currently known about how microblades affected the cutting edge efficiency of the toolkits they were a part of. We applied three methods of analyzing cutting edge efficiency to two Upper Paleolithic assemblages recently excavated from Tolbor-17, Mongolia, that document the periods before and after the introduction of microblade technology to the Tolbor Valley. A model incorporating allometric relationships between blank cutting edge length and mass suggests no difference in efficiency between the two periods, while two more conventional approaches both indicate a significant increase. The potential for improved cutting edge efficiency is only observed when the microblade sample is artificially inflated via simulation. Our results highlight challenges related to detecting and interpreting archaeological differences in cutting edge efficiency at the assemblage level.

Published
2024

22. Overexpression of TBX3 suppresses tumorigenesis in experimental and human cholangiocarcinoma

Author

Deng, Shanshan, Lu, Xinjun, Wang, Xue, Liang, Binyong, Xu, Hongwei, Yang, Doris, Cui, Guofei, Yonemura, Andrew, Paine, Honor, Zhou, Yi, Zhang, Yi, Simile, Maria Maddalena, Urigo, Francesco, Evert, Matthias, Calvisi, Diego F, Green, Benjamin L, and Chen, Xin

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Digestive Diseases, Rare Diseases, Human Genome, Genetics, Liver Disease, Liver Cancer, Digestive Diseases - (Gallbladder), 2.1 Biological and endogenous factors, Aetiology, Cholangiocarcinoma, T-Box Domain Proteins, Humans, Animals, Mice, Bile Duct Neoplasms, Carcinogenesis, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Cell Proliferation, Biochemistry and Cell Biology, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

TBX3 behaves as a tumor suppressor or oncoprotein across cancer. However, TBX3 function remains undetermined in intrahepatic cholangiocarcinoma (iCCA), a deadly primary liver malignancy with few systemic treatment options. This study sought to investigate the impact of TBX3 on iCCA. We found that overexpression of TBX3 strongly inhibited human iCCA cell growth. In the Akt/FBXW7ΔF mouse iCCA model, overexpression of Tbx3 reduced cholangiocarcinogenesis in vivo, while inducible genetic knockout of Tbx3 accelerated iCCA growth. RNA-seq identified MAD2L1 as a downregulated gene in TBX3-overexpressing cells, and ChIP confirmed that TBX3 binds to the MAD2L1 promoter. CRISPR-mediated knockdown of Mad2l1 significantly reduced the growth of two iCCA models in vivo. Finally, we found that TBX3 expression is upregulated in ~20% of human iCCA samples, and its high expression is associated with less proliferation and better survival. MAD2L1 expression is upregulated in most human iCCA samples and negatively correlated with TBX3 expression. Altogether, our findings suggest that overexpression of TBX3 suppresses CCA progression via repressing MAD2L1 expression.

Published
2024

23. ‘The Cloud is Not Not IT’: Ecological Change in Research Computing in the Cloud

Author

Sutherland, Will, Paine, Drew, and Lee, Charlotte P

Subjects
Distributed Computing and Systems Software, Information and Computing Sciences, Clinical Research, Generic health relevance, Artifact Ecologies, Coordinative Artifacts, Cloud Computing, Infrastructure, Research Computing, artifact ecologies, coordinative artifacts, cloud computing, infrastructure, research computing, Information Systems, Design Practice and Management, Cognitive Sciences, Human-centred computing
Abstract

Along with a number of other computing technologies, cloud computing services are increasingly being promoted as a way of enabling openness, reproducibility, and the acceleration of scientific work. While there have been a variety of studies of the cloud in terms of computing performance, there has been little empirical attention to the changes going on around cloud computing at the level of work and practice. Through a qualitative, ethnographic study, we follow a cosmology research group’s transition from a shared high performance computing cluster to a cloud computing service, and examine the cloud service as a coordinative artifact being integrated into a larger ecology of existing practices and artifacts. We find that the transition involves both change and continuity in the group’s coordinative work and maintenance work, and point out some of the effects this adoption has on the group’s larger set of practices. Finally, we discuss practical implications this has for the broader adoption of cloud computing in university-based scientific work.

Published
2024

25. Associations between maternal stressful life events and child health outcomes in indigenous and non-indigenous groups in New Zealand

Author

Sarah-Jane Paine, Rhiannon Walker, Arier Lee, Belinda Loring, and T. Leigh Signal

Subjects
Indigenous, maternal stress, stressful life events, child health, women’s health, inequities, Social Sciences
Abstract

Exposure to stressful life events (SLE) around the time of pregnancy is associated with adverse health outcomes for mothers and children. Previous New Zealand research found Indigenous Māori women are more likely to be exposed to SLE than non-Māori, and are exposed to a higher number of SLE. The consequences of this for ethnic inequities in child health outcomes are unknown. This paper examines the relationship between patterns of maternal SLE exposure with child health and development outcomes at age 3 years, for Indigenous and non-Indigenous children. We found most children had a stressful early life environment at least sometimes, but more than a quarter of Māori children had a mother experiencing multiple SLE on all occasions measured. We found a clear association between maternal experiences of SLE and disordered child sleep and development concerns. While not able to fully assess the contribution of maternal SLE to ethnic inequities in child health outcomes, we did clearly demonstrate that more Māori children have mothers exposed to multiple SLE, and that these maternal SLE are associated with poorer child outcomes. The impacts of chronic SLE exposure need to be better understood, especially given the large ethnic disparity in chronic SLE exposure.

Published
2024
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26. What can we learn from quantum convolutional neural networks?

Author

Umeano, Chukwudubem, Paine, Annie E., Elfving, Vincent E., and Kyriienko, Oleksandr

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Machine Learning
Abstract

We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognition can be attributed to generation of a very suitable basis set during the ground state embedding, where quantum criticality of spin models leads to basis functions with rapidly changing features; 3) pooling layers of QCNNs are responsible for picking those basis functions that can contribute to forming a high-performing decision boundary, and the learning process corresponds to adapting the measurement such that few-qubit operators are mapped to full-register observables; 4) generalization of QCNN models strongly depends on the embedding type, and that rotation-based feature maps with the Fourier basis require careful feature engineering; 5) accuracy and generalization of QCNNs with readout based on a limited number of shots favor the ground state embeddings and associated physics-informed models. We demonstrate these points in simulation, where our results shed light on classification for physical processes, relevant for applications in sensing. Finally, we show that QCNNs with properly chosen ground state embeddings can be used for fluid dynamics problems, expressing shock wave solutions with good generalization and proven trainability., Comment: 13 pages, 7 figures

Published
2023

27. Reinforced Self-Training (ReST) for Language Modeling

Author

Gulcehre, Caglar, Paine, Tom Le, Srinivasan, Srivatsan, Konyushkova, Ksenia, Weerts, Lotte, Sharma, Abhishek, Siddhant, Aditya, Ahern, Alex, Wang, Miaosen, Gu, Chenjie, Macherey, Wolfgang, Doucet, Arnaud, Firat, Orhan, and de Freitas, Nando

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

Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner., Comment: 23 pages, 16 figures

Published
2023

28. AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

Author

Mathieu, Michaël, Ozair, Sherjil, Srinivasan, Srivatsan, Gulcehre, Caglar, Zhang, Shangtong, Jiang, Ray, Paine, Tom Le, Powell, Richard, Żołna, Konrad, Schrittwieser, Julian, Choi, David, Georgiev, Petko, Toyama, Daniel, Huang, Aja, Ring, Roman, Babuschkin, Igor, Ewalds, Timo, Bordbar, Mahyar, Henderson, Sarah, Colmenarejo, Sergio Gómez, Oord, Aäron van den, Czarnecki, Wojciech Marian, de Freitas, Nando, and Vinyals, Oriol

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

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution. It also has an active professional competitive scene. StarCraft II is uniquely suited for advancing offline RL algorithms, both because of its challenging nature and because Blizzard has released a massive dataset of millions of StarCraft II games played by human players. This paper leverages that and establishes a benchmark, called AlphaStar Unplugged, introducing unprecedented challenges for offline reinforcement learning. We define a dataset (a subset of Blizzard's release), tools standardizing an API for machine learning methods, and an evaluation protocol. We also present baseline agents, including behavior cloning, offline variants of actor-critic and MuZero. We improve the state of the art of agents using only offline data, and we achieve 90% win rate against previously published AlphaStar behavior cloning agent., Comment: 32 pages, 13 figures, previous version published as a NeurIPS 2021 workshop: https://openreview.net/forum?id=Np8Pumfoty

Published
2023

29. An Updated Reference Frame for the Galactic Inner Parsec

Author

Darling, Jeremy, Paine, Jennie, Reid, Mark J., Menten, Karl M., Sakai, Shoko, and Ghez, Andrea

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

Infrared observations of stellar orbits about Sgr A* probe the mass distribution in the inner parsec of the Galaxy and provide definitive evidence for the existence of a massive black hole. However, the infrared astrometry is relative and is tied to the radio emission from Sgr A* using stellar SiO masers that coincide with infrared-bright stars. To support and improve this two-step astrometry, we present new astrometric observations of 15 stellar SiO masers within 2 pc of Sgr A*. Combined with legacy observations spanning 25.8 years, we re-analyze the relative offsets of these masers from Sgr A* and measure positions and proper motions that are significantly improved compared to the previously published reference frame. Maser positions are corrected for epoch-specific differential aberration, precession, nutation, and solar gravitational deflection. Omitting the supergiant IRS 7, the mean position uncertainties are 0.46 mas and 0.84 mas in RA and Dec., and the mean proper motion uncertainties are 0.07 mas yr$^{-1}$ and 0.12 mas yr$^{-1}$, respectively. At a distance of 8.2 kpc, these correspond to position uncertainties of 3.7 AU and 6.9 AU and proper motion uncertainties of 2.7 km s$^{-1}$ and 4.6 km s$^{-1}$. The reference frame stability, the uncertainty in the variance-weighted mean proper motion of the maser ensemble, is 8 $\mu$as yr$^{-1}$ (0.30 km s$^{-1}$) in RA and 11 $\mu$as yr$^{-1}$ (0.44 km s$^{-1}$) in Dec., which represents a 2.3-fold improvement over previous work and a new benchmark for the maser-based reference frame., Comment: 18 pages, 7 figures, 3 tables. Accepted by ApJ

Published
2023

30. Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Author

Paine, Annie E., Elfving, Vincent E., and Kyriienko, Oleksandr

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks
Abstract

We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional differential equations (DEs) in a quantum latent space. We suggest a strategy for building quantum models as state overlaps, where exponentially large sets of independent basis functions are used for implicitly representing solutions. By measuring the overlaps between states which are representations of DE terms, we construct a loss that does not require independent sequential function evaluations on grid points. In this sense, the solver evaluates the loss in an intrinsically parallel way, utilizing a global type of the model. When the loss is trained variationally, our approach can be related to the differentiable quantum circuit protocol, which does not scale with the training grid size. Specifically, using the proposed model definition and feature map encoding, we represent function- and derivative-based terms of a differential equation as corresponding quantum states. Importantly, we propose an efficient way for encoding nonlinearity, for some bases requiring only an additive linear increase of the system size $\mathcal{O}(N + p)$ in the degree of nonlinearity $p$. By utilizing basis mapping, we show how the proposed model can be evaluated explicitly. This allows to implement arbitrary functions of independent variables, treat problems with various initial and boundary conditions, and include data and regularization terms in the physics-informed machine learning setting. On the technical side, we present toolboxes for exponential Chebyshev and Fourier basis sets, developing tools for automatic differentiation and multiplication, implementing nonlinearity, and describing multivariate extensions. The approach is compatible with, and tested on, a range of problems including linear, nonlinear and multidimensional differential equations., Comment: 14+2 pages, 6+1 figures. Codename: "parallel DQC"

Published
2023

31. An ensemble of online estimation methods for one degree-of-freedom models of unmanned surface vehicles: applied theory and preliminary field results with eight vehicles

Author

Paine, Tyler M. and Benjamin, Michael R.

Subjects
Computer Science - Robotics
Abstract

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing., Comment: v1) 8 Pages, 5 Figures, To appear at 2023 RSJ/IEEE Conference on Intelligent Robotics and Systems (IROS) in Detroit, Michigan, USA, v2) corrected error in references

Published
2023

32. Precise characterization of nanometer-scale systems using interferometric scattering microscopy and Bayesian analysis

Author

de Wit, Xander M., Paine, Amelia W., Martin, Caroline, Goldfain, Aaron M., Garmann, Rees F., and Manoharan, Vinothan N.

Subjects
Physics - Optics
Abstract

Interferometric scattering microscopy (iSCAT) can image the dynamics of nanometer-scale systems. The typical approach to analyzing interferometric images involves intensive processing, which discards data and limits the precision of measurements. We demonstrate an alternative approach: modeling the interferometric point spread function (iPSF) and fitting this model to data within a Bayesian framework. This approach yields best-fit parameters, including the particle's three-dimensional position and polarizability, as well as uncertainties and correlations between these parameters. Building on recent work, we develop a model that is parameterized for rapid fitting. The model is designed to work with Hamiltonian Monte Carlo techniques that leverage automatic differentiation. We validate this approach by fitting the model to interferometric images of colloidal nanoparticles. We apply the method to track a diffusing particle in three dimensions, to directly infer the diffusion coefficient of a nanoparticle without calculating a mean-square displacement, and to quantify the ejection of DNA from an individual lambda phage virus, demonstrating that the approach can be used to infer both static and dynamic properties of nanoscale systems.

Published
2023
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33. Quantum Chebyshev Transform: Mapping, Embedding, Learning and Sampling Distributions

Author

Williams, Chelsea A., Paine, Annie E., Wu, Hsin-Yu, Elfving, Vincent E., and Kyriienko, Oleksandr

Subjects
Quantum Physics, Condensed Matter - Statistical Mechanics
Abstract

We develop a paradigm for building quantum models in the orthonormal space of Chebyshev polynomials. We show how to encode data into quantum states with amplitudes being Chebyshev polynomials with degree growing exponentially in the system size. Similar to the quantum Fourier transform which maps computational basis space into the phase (Fourier) basis, we describe the quantum circuit for the mapping between computational and Chebyshev spaces. We propose an embedding circuit for generating the orthonormal Chebyshev basis of exponential capacity, represented by a continuously-parameterized shallow isometry. This enables automatic quantum model differentiation, and opens a route to solving stochastic differential equations. We apply the developed paradigm to generative modeling from physically- and financially-motivated distributions, and use the quantum Chebyshev transform for efficient sampling of these distributions in extended computational basis., Comment: 6 pages (+ references), 3 figures

Published
2023

34. $\pi2\text{vec}$: Policy Representations with Successor Features

Author

Scarpellini, Gianluca, Konyushkova, Ksenia, Fantacci, Claudio, Paine, Tom Le, Chen, Yutian, and Denil, Misha

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

This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a task agnostic way, and can be trained from offline data, allowing them to be used in offline policy selection. This work provides a key piece of a recipe for fusing together three modern lines of research: Offline policy evaluation as a counterpart to offline RL, foundation models as generic and powerful state representations, and efficient policy selection in resource constrained environments., Comment: Accepted paper at ICLR2024

Published
2023

42. A narrative review of little league shoulder: proximal humeral physis widening is only one piece of the puzzle, it is time to consider posterior glenoid dysplasia

Author

Natalie L. Myers, PhD, ATC, PES, Sean M. Kennedy, PT, DPT, PhD(c), SCS, CSCS, Amanda J. Arnold, PT, DPT, PhD, Zachary A. Gehring, Kaylin J. Kruseman, PT, DPT, John E. Conway, MD, Russ M. Paine, PT, Lane B. Bailey, PT, PhD, and J Craig Garrison, PT, PhD, ATC, SCS

Subjects
Level V, Narrative Review, Orthopedic surgery, RD701-811, Diseases of the musculoskeletal system, RC925-935
Abstract

Baseball athletes across all levels of play are at an increased risk for upper extremity injury due to the supraphysiologic demands on the shoulder and elbow during overhead throwing. Little league baseball players present with a unique subset of injuries that can affect the growth plate, commonly at the shoulder or the elbow. Ascertaining a diagnosis and plan of care for little league shoulder (LLS) historically focuses on the proximal humeral physis in skeletally immature throwing athletes presenting with shoulder pain. However, while not a current standard of care, posterior glenoid dysplasia is often present in youth baseball athletes presenting with LLS, warranting a shift in the way clinicians evaluate for and treat the youth baseball athlete’s pathologic shoulder. Therefore, purpose of this narrative review is 2-fold: first, to describe the current standard of care as it relates to a diagnosis of LLS, and second, to critically describe a comprehensive evaluation process for youth throwing athletes with shoulder pain that includes screening for evidence of posterior glenoid dysplasia. This paper summarizes the current state of the available evidence for anatomic considerations of LLS in the baseball athletes throwing shoulder. Additionally, we provide a framework for clinical evaluation using a multidisciplinary approach to evaluate the entire kinetic chain of the youth baseball athlete presenting with LLS and posterior glenoid dysplasia. A case study is presented to describe common presentations, clinical and objective examinations, and a plan of care from time of evaluation to return to throwing.

Published
2024
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43. Overexpression of TBX3 suppresses tumorigenesis in experimental and human cholangiocarcinoma

Author

Shanshan Deng, Xinjun Lu, Xue Wang, Binyong Liang, Hongwei Xu, Doris Yang, Guofei Cui, Andrew Yonemura, Honor Paine, Yi Zhou, Yi Zhang, Maria Maddalena Simile, Francesco Urigo, Matthias Evert, Diego F. Calvisi, Benjamin L. Green, and Xin Chen

Subjects
Cytology, QH573-671
Abstract

Abstract TBX3 behaves as a tumor suppressor or oncoprotein across cancer. However, TBX3 function remains undetermined in intrahepatic cholangiocarcinoma (iCCA), a deadly primary liver malignancy with few systemic treatment options. This study sought to investigate the impact of TBX3 on iCCA. We found that overexpression of TBX3 strongly inhibited human iCCA cell growth. In the Akt/FBXW7ΔF mouse iCCA model, overexpression of Tbx3 reduced cholangiocarcinogenesis in vivo, while inducible genetic knockout of Tbx3 accelerated iCCA growth. RNA-seq identified MAD2L1 as a downregulated gene in TBX3-overexpressing cells, and ChIP confirmed that TBX3 binds to the MAD2L1 promoter. CRISPR-mediated knockdown of Mad2l1 significantly reduced the growth of two iCCA models in vivo. Finally, we found that TBX3 expression is upregulated in ~20% of human iCCA samples, and its high expression is associated with less proliferation and better survival. MAD2L1 expression is upregulated in most human iCCA samples and negatively correlated with TBX3 expression. Altogether, our findings suggest that overexpression of TBX3 suppresses CCA progression via repressing MAD2L1 expression.

Published
2024
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44. Real‐world study of the use of azacitidine in myelodysplasia in Australia

Author

Anoop Enjeti, Asma Ashraf, Vincent Caillet, Arif Alam, Jonathan Silar, Harold Keer, Francesco Castaldi, and Taleisha Paine

Subjects
azacitidine, hypomethylating agents, myelodysplastic syndromes, real‐world, survival, Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Abstract Hypomethylating agents are the most widely used upfront therapy for patients with myelodysplastic syndrome (MDS) who are not suitable for hematopoietic stem cell transplantation. In Australia, azacitidine was, until recently, the only approved and subsidized treatment for patients with intermediate‐2 and high‐risk MDS, chronic myelomonocytic leukemia, and low blast acute myeloid leukemia. We analyzed prescription data to evaluate the real‐world persistence and overall survival (OS) of patients prescribed azacitidine for the first time in Australia. A retrospective cohort analysis of patients who had been prescribed Pharmaceutical Benefits Scheme (PBS)‐listed azacitidine for the first time, between January 2016 and April 2021, was conducted using the PBS 10% dataset. Treatment persistence and OS were estimated using Kaplan–Meier methods. The impact of the number of treatment cycles and treatment adherence on OS was also estimated. There were 351 patients in the PBS 10% dataset who initiated treatment with azacitidine. The average age (standard deviation [SD]) at azacitidine initiation was 71.9 (11.1) years and the average number (SD) of azacitidine prescriptions was 5.6 (0.2). The median persistence on azacitidine was 15.6 months, and the OS was 13.4 months. The median OS for patients who had six or more cycles of azacitidine treatment was greater compared to patients who had five or less cycles of treatment. The data from this real‐world study illustrate the unmet medical needs of patients with MDS treated with azacitidine in Australia. The majority of patients are not treated with the optimal number of cycles of azacitidine, which is negatively correlated with patient outcomes.

Published
2024
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45. Development of the treatment preference in myelodysplasia questionnaire for clinicians, carers, and patients

Author

Robert Morlock, Chun Fong, Francesco Castaldi, Taliesha Paine, Donna Collett, and Anoop Enjeti

Subjects
hypomethylating agents, myelodysplastic syndromes, questionnaires, treatment preference, Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Abstract This study reports the development activities for the Treatment Preference Myelodysplasia Questionnaires (TPMQ) for clinicians (mTPMQ), carers (cTPMQ), and patients (pTPMQ). These tools are intended to evaluate treatment preferences for patients with myelodysplastic syndromes (MDS). This was a non‐interventional, cross‐sectional qualitative interview study consisting of interviews with clinicians, patients, and those caring for patients with MDS. All participants were located in Australia and data were collected from qualitative mixed‐method interviews composed of concept elicitation and cognitive debriefing related to initial drafts of the questionnaires. Fifteen individuals participated in interviews (five from each group). Based on the concept elicitation portion of interviews, concepts of importance were classified and reasons for treatment preference were documented. From cognitive debriefing, the questionnaires were generally deemed to be clear and easy to understand. Participant input from both concept elicitation and cognitive debriefing portions was used to revise initial drafts of the questionnaires. The mTPMQ, cTPMQ, and pTPMQ were developed with direct input from clinicians, patients, and caregivers to assess the key concepts of interest related to the preference for the treatment of MDS and are ready to be used and evaluated further in clinical trials.

Published
2024
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46. Changes in Lung Volumes with Spirometric Disease Progression in COPD.

Author

Barr, R, Bleecker, Eugene, Buhr, Russell, Criner, Gerard, Comellas, Alejandro, Couper, David, Curtis, Jeffrey, Dransfield, Mark, Fortis, Spyridon, Han, MeiLan, Hansel, Nadia, Hoffman, Eric, Hokanson, John, Kaner, Robert, Kanner, Richard, Krishnan, Jerry, Labaki, Wassim, Lynch, David, Ortega, Victor, Peters, Stephen, Woodruff, Prescott, Cooper, Christopher, Bowler, Russell, Paine, Robert, Rennard, Stephen, Tashkin, Donald, Arjomandi, Mehrdad, Zeng, Siyang, Chen, Jianhong, Bhatt, Surya, Abtin, Fereidoun, and Barjaktarevic, Igor

Subjects
COPD, air trapping, computed tomography, early disease, lung volumes
Abstract

BACKGROUND: Abnormal lung volumes representing air trapping identify the subset of smokers with preserved spirometry who develop spirometric chronic obstructive pulmonary disease (COPD) and adverse outcomes. However, how lung volumes evolve in early COPD as airflow obstruction develops remains unclear. METHODS: To establish how lung volumes change with the development of spirometric COPD, we examined lung volumes from the pulmonary function data (seated posture) available in the U.S. Department of Veterans Affairs electronic health records (n=71,356) and lung volumes measured by computed tomography (supine posture) available from the COPD Genetic Epidemiology (COPDGene®) study (n=7969) and the SubPopulations and InterMediate Outcome Measures In COPD Study (SPIROMICS) (n=2552) cohorts, and studied their cross-sectional distributions and longitudinal changes across the airflow obstruction spectrum. Patients with preserved ratio-impaired spirometry (PRISm) were excluded from this analysis. RESULTS: Lung volumes from all 3 cohorts showed similar patterns of distributions and longitudinal changes with worsening airflow obstruction. The distributions for total lung capacity (TLC), vital capacity (VC), and inspiratory capacity (IC) and their patterns of change were nonlinear and included different phases. When stratified by airflow obstruction using Global initiative for chronic Obstructive Lung Disease (GOLD) stages, patients with GOLD 1 (mild) COPD had larger lung volumes (TLC, VC, IC) compared to patients with GOLD 0 (smokers with preserved spirometry) or GOLD 2 (moderate) disease. In longitudinal follow-up of baseline GOLD 0 patients who progressed to spirometric COPD, those with an initially higher TLC and VC developed mild obstruction (GOLD 1) while those with an initially lower TLC and VC developed moderate obstruction (GOLD 2). CONCLUSIONS: In COPD, TLC, and VC have biphasic distributions, change in nonlinear fashions as obstruction worsens, and could differentiate those GOLD 0 patients at risk for more rapid spirometric disease progression.

Published
2023

47. Impact of Bronchiectasis on COPD Severity and Alpha-1 Antitrypsin Deficiency as a Risk Factor in Individuals with a Heavy Smoking History.

Author

Izquierdo, Manuel, Marion, Chad, Genese, Frank, Newell, John, ONeal, Wanda, Li, Xingnan, Hawkins, Gregory, Barjaktarevic, Igor, Barr, R, Christenson, Stephanie, Cooper, Christopher, Couper, David, Curtis, Jeffrey, Han, Meilan, Hansel, Nadia, Kanner, Richard, Martinez, Fernando, Paine, Robert, Tejwani, Vickram, Woodruff, Prescott, Zein, Joe, Hoffman, Eric, Peters, Stephen, Meyers, Deborah, Bleecker, Eugene, and Ortega, Victor

Subjects
COPD, alpha-1 antitrypsin, bronchiectasis, lung function
Abstract

RATIONALE: Bronchiectasis is common among those with heavy smoking histories, but risk factors for bronchiectasis, including alpha-1 antitrypsin deficiency, and its implications for COPD severity are uncharacterized in such individuals. OBJECTIVES: To characterize the impact of bronchiectasis on COPD and explore alpha-1antitrypsin as a risk factor for bronchiectasis. METHODS: SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) participants (N=914; ages 40-80 years; ≥20-pack-year smoking) had high-resolution computed tomography (CT) scans interpreted visually for bronchiectasis, based on airway dilation without fibrosis or cicatrization. We performed regression-based models of bronchiectasis with clinical outcomes and quantitative CT measures. We deeply sequenced the gene encoding -alpha-1 antitrypsin, SERPINA1, in 835 participants to test for rare variants, focusing on the PiZ genotype (Glu366Lys, rs28929474). MEASUREMENTS AND MAIN RESULTS: We identified bronchiectasis in 365 (40%) participants, more frequently in women (45% versus 36%, p=0.0045), older participants (mean age=66[standard deviation (SD)=8.3] versus 64[SD=9.1] years, p=0.0083), and those with lower lung function (forced expiratory volume in 1 second [FEV1 ] percentage predicted=66%[SD=27] versus 77%[SD=25], p

Published
2023

48. Symbolic innovation at the onset of the Upper Paleolithic in Eurasia shown by the personal ornaments from Tolbor-21 (Mongolia).

Author

Rigaud, Solange, Rybin, Evgeny P, Khatsenovich, Arina M, Queffelec, Alain, Paine, Clea H, Gunchinsuren, Byambaa, Talamo, Sahra, Marchenko, Daria V, Bolorbat, Tsedendorj, Odsuren, Davaakhuu, Gillam, J Christopher, Izuho, Masami, Fedorchenko, Alexander Yu, Odgerel, Dashdorjgochoo, Shelepaev, Roman, Hublin, Jean-Jacques, and Zwyns, Nicolas

Subjects
Humans, Archaeology, Infant, Newborn, Africa, Mongolia, Europe
Abstract

Figurative depictions in art first occur ca. 50,000 years ago in Europe, Africa, and Southeast Asia. Considered by most as an advanced form of symbolic behavior, they are restricted to our species. Here, we report a piece of ornament interpreted as a phallus-like representation. It was found in a 42,000 ca.-year-old Upper Paleolithic archaeological layer at the open-air archaeological site of Tolbor-21, in Mongolia. Mineralogical, microscopic, and rugosimetric analyses points toward the allochthonous origin of the pendant and a complex functional history. Three-dimensional phallic pendants are unknown in the Paleolithic record, and this discovery predates the earliest known sexed anthropomorphic representation. It attests that hunter-gatherer communities used sex anatomical attributes as symbols at a very early stage of their dispersal in the region. The pendant was produced during a period that overlaps with age estimates for early introgression events between Homo sapiens and Denisovans, and in a region where such encounters are plausible.

Published
2023

49. Consequences of how third sector organisations are commissioned in the NHS and local authorities in England: a mixed-methods study

Author

Rod Sheaff, Angela Ellis Paine, Mark Exworthy, Alex Gibson, Joanna Stuart, Véronique Jochum, Pauline Allen, Jonathan Clark, Russell Mannion, and Sheena Asthana

Subjects
covid-19, cross-sectional studies, policy, patient and public involvement, persuasive communication, socialised medicine, state medicine, voluntary organisations, Medicine (General), R5-920, Public aspects of medicine, RA1-1270
Abstract

Background As a matter of policy, voluntary, community and social enterprises contribute substantially to the English health and care system. Few studies explain how the National Health Service and local authorities commission them, what outputs result, what contexts influence these outcomes and what differentiates this kind of commissioning. Objectives To explain how voluntary, community and social enterprises are commissioned, the consequences, what barriers both parties face and what absorptive capacities they need. Design Observational mixed-methods realist analysis: exploratory scoping, cross-sectional analysis of National Health Service Clinical Commissioning Group spending on voluntary, community and social enterprises, systematic comparison of case studies, action learning. Social prescribing, learning disability support and end-of-life care were tracers. Setting Maximum-variety sample of six English local health and care economies, 2019–23. Participants Commissioning staff; voluntary, community and social enterprise members. Interventions None; observational study. Main outcome measures How the consequences of commissioning compared with the original aims of the commissioners and the voluntary, community and social enterprises: predominantly qualitative (non-measurable) outcomes. Data sources Data sources were: 189 interviews, 58 policy and position papers, 37 items of rapportage, 692,659 Clinical Commissioning Group invoices, 102 Freedom of Information enquiries, 131 survey responses, 18 local project group meetings, 4 national action learning set meetings. Data collected in England during 2019–23. Results Two modes of commissioning operated in parallel. Commodified commissioning relied on creating a principal–agent relationship between commissioner and the voluntary, community and social enterprises, on formal competitive selection (‘procurement’) of providers. Collaborative commissioning relied on ‘embedded’ interorganisational relationships, mutual recognition of resource dependencies, a negotiated division of labour between organisations, and control through persuasion. Commissioners and voluntary, community and social enterprises often worked around the procurement regulations. Both modes were present everywhere but the balance depended inter alia on the number and size of voluntary, community and social enterprises in each locality, their past commissioning experience, the character of the tracer activity, and the level of deprivation and the geographic dispersal of the populations served. The COVID-19 pandemic produced a shift towards collaborative commissioning. Voluntary, community and social enterprises were not always funded at the full cost of their activity. Integrated Care System formation temporarily disrupted local co-commissioning networks but offered a longer-term prospect of greater voluntary, community and social enterprise influence on co-commissioning. To develop absorptive capacity, commissioners needed stronger managerial and communication capabilities, and voluntary, community and social enterprises needed greater capability to evidence what outcomes their proposals would deliver. Limitations Published data quality limited the spending profile accuracy, which did not include local authority commissioning. Case studies did not cover London, and focused on three tracer activities. Absorptive capacity survey was not a random sample. Conclusions The two modes of commissioning sometimes conflicted. Workarounds arose from organisations’ embeddedness and collaboration, which the procurement regulations often disrupted. Commissioning activity at below its full cost appears unsustainable. Future work Spending profiles of local authority commissioning; analysis of commissioning in London and of activities besides the present tracers. Analysis of absorptive capacity and its consequences, adjusting the concept for application to voluntary, community and social enterprises. Comparison with other health systems’ commissioning of voluntary, community and social enterprises. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR128107) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 39. See the NIHR Funding and Awards website for further award information. Plain language summary National Health Service commissioners and local councils often buy health and care services from voluntary, community and social enterprises. This study aimed to explore how commissioners and voluntary, community and social enterprises worked together and where improvements could be made. We talked to commissioners and voluntary, community and social enterprises in six areas across England and focused on services for learning disabilities, social prescribing and end-of-life care. We analysed National Health Service financial accounts to see how much the National Health Service was spending on services provided by voluntary, community and social enterprises. We surveyed how commissioners and voluntary, community and social enterprises were using information and knowledge to make decisions. We organised events bringing together commissioners and voluntary, community and social enterprises to share knowledge and experience. We found there were two ways commissioners bought services from voluntary, community and social enterprises. One was commodified (a buying-and-selling model), the other collaborative (based on working together). Both were happening at the same time in all the areas of the study, but usually one of them was more present than the other. We saw a general move towards collaboration, but some areas were further along with this than others. Various things helped commissioners and voluntary, community and social enterprises collaborate, such as: paying voluntary, community and social enterprises enough for their services; having people and networks that encouraged others to work with voluntary, community and social enterprises; and including voluntary, community and social enterprises in making decisions about health and care. Commissioners and voluntary, community and social enterprises might therefore lean further towards working together to see how collaborative commissioning can be further developed; how to make contract prices cover voluntary, community and social enterprises’ costs; how to enable longer-term contracts; and how to enable less hurried, more considered ways of renewing contracts (e.g. by putting draft contract specifications out to pre-tender consultation). The new Integrated Care Systems in local areas could encourage all this to happen, but pressures elsewhere in the health and care sector might make it more difficult. Scientific summary Background This study examines how the NHS and local authorities commissioned voluntary, community and social enterprises (VCSEs); some outcomes for commissioners, VCSEs and the health system; and which contexts affected these outcomes. Existing studies describe how the public funding of independent providers occurs through a commissioning cycle of service specification, provider selection, contract-letting and monitoring of the activities actually delivered. The original policy (and theoretical) formulations of this cycle presupposed a clear separation of commissioners from providers. In practice, however, the cycle is partly implemented, and attenuated, by co-commissioning activities, collaborations in which potential providers, citizens and the public contribute at each stage. Commissioning has been explained as, inter alia, a means by which governments continued to exercise governance (not direct control) over independent providers of public-funded services through six main media of power: managerial techniques, negotiated order, discursive control, resource dependencies, provider competition, and juridical control. Each particular combination constitutes a ‘mode of commissioning’. Previous studies have examined the modes of commissioning applied elsewhere, but not to VCSEs, and then usually from the standpoint of policy-makers’ aims and intended service outcomes of commissioning. Fewer studies have explored commissioning from the VCSE standpoint. Some that did reported largely negative consequences for the commissioned VCSEs: a loss of freedom to criticise policy and a ‘degeneration’ of the VCSEs’ democratic internal regimes. Still fewer studies have closely examined the interactions between commissioners and VCSEs as they engage in the commissioning cycle and in co-commissioning. The practical import of these questions is whether the commissioning of health-related VCSEs enables them to supplement the reach of NHS activities and strengthen users’ voice in the health system, or whether it undermines the characteristics of VCSEs which first motivated the commissioning of them. Because commissioning involves two main groups of agents, their interactions involve not just one but two context–mechanism–outcome configurations (CMOCs). In one, the commissioners are the focal actor (as in most realist evaluations); in the other, VCSEs are. The two configurations intersect at the commissioning mechanisms, where the parties interact. Earlier studies suggested that the parties’ absorptive capacity (ACAP) to acquire, assimilate, transform and exploit externally sourced knowledge was an important context affecting what interactions occurred, and to what effect. Objectives This study aimed to produce knowledge about which factors strengthen (or weaken) collaboration between healthcare commissioners and VSCEs, and make commissioning relationships between the commissioners and VCSEs more productive for all. Research questions were: RQ1. How do healthcare commissioners address the task of commissioning VCSEs as service providers, and what barriers do they face? RQ2. What are the consequences for VCSEs of the public bodies commissioning services from them? RQ3. How are VCSEs involved in Clinical Commissioning Group (CCG), local authority and other [e.g. Integrated Care System (ICS), NHS England] commissioning decisions? RQ4. What ACAPs do healthcare commissioners and VCSEs respectively need for enabling VCSEs to be commissioned, and for co-commissioning? Methods This study was a mixed-methods realist analysis of the mechanisms by which English health-related VCSEs are commissioned. To investigate the intersecting CMOCs, we used five main methods: Preliminary scoping work with national-level NHS and VCSE organisations to identify important current developments in this domain and likely data sources, based on interviews and content analysis of policy documents. A cross-sectional profile of CCG spending on VCSEs, which provided data about patterns of VCSE commissioning and was a sampling frame for the three following work packages. We content-analysed 226,138 CCG invoices (for sums over £25k) from 2018 to 2019 to discover the distribution of CCG spending on VCSEs and how it compared with CCG spending on non-VCSE providers. Using findings from the preceding work packages, we drew a sample of six places contrasted by their proportion of spending on VCSEs. We systematically compared case studies of VCSE–commissioner collaboration in formulating local commissioning strategies (‘co-commissioning’) in them (2020–2). Commissioners’ and VCSEs’ aims when engaging in commissioning were examined separately for either side, as were the outcomes relevant to each, but not the structures and activities through which they interacted. A systematic comparison of case studies of the commissioning of VCSEs, using the same study sites, methods and unit of analysis as the preceding work package (late 2020 to summer 2022). Action learning activities (2020–3) in the same sites: Local project reference groups supported the project in each study site and combined as: national action learning workshops a preliminary, exploratory (not randomised sample) survey of ACAP in the study sites local co-researchers who conducted local research projects into the commissioning of VCSEs and whom the research team mentored As tracer studies we examined social prescribing, end-of-life care and support for learning disabilities, on the assumption that this selection gave variety in the scale and number of VCSEs involved, and type of activity (clinical vs. preventive). Framework analyses were used to synthesise the five sets of findings and map them onto the research questions. Results Two modes of commissioning VCSEs existed in parallel: A commodified mode centred on the commissioning cycle, financial dependencies, formalised procurement [a regulatory (i.e. juridically based) practice] and provider competition. Consulted VCSEs were often excluded at either service specification or provider selection stage in the cycle. The financial dependency of VCSEs was a central medium of power for commissioners. Juridical power was in the background but seldom used in practice. The paradigm form of this mechanism was a direct bilateral principal–agent relationship between one commissioner and one VCSE, with the latter wielding less power. This was a commodified mode of commissioning. To reduce their transaction work (costs), commissioners were introducing larger contracts (e.g. with one large ‘lead’ provider subcontracting many smaller VCSEs). Networks for negotiating what activities were commissioned and how they were implemented. Commissioners and VCSEs were both embedded in these networks, which constituted an interorganisational negotiated order among commissioners, among VCSEs, and between commissioners and VCSEs. Persuasion and legitimation were the main discursive media of power, supplemented by relationality (mutual trust) and mutual ‘real-side’ (as opposed to financial) resource dependency. Workarounds such as alliance and lead provider commissioning were important media of managerial power, adopted in order to diminish the expected, and in some cases observed, adverse consequences of commodified commissioning. VCSEs’ commissioning and co-commissioning activities overlapped considerably. The networking mechanisms supplemented and attenuated the quasi-market mechanisms, and indeed were partly intended to. This was a collaborative mode of commissioning. It was not that where one mode of commissioning existed, the other did not; both coexisted and interacted everywhere but the balance between them varied. Certain contexts affected how these mechanisms worked in practice: Local health and care system characteristics: Fiscal constraints upon commissioners constrained how much VCSE activity could be commissioned, compelled commissioners to prioritise VCSE activities that appeared to offer immediate cost savings elsewhere in the health system, and destabilised VCSE income. Diverse ownership mix of provider organisations (e.g. in learning disability services) motivated more cautious, commodified commissioning insofar as commissioners anticipated challenges to their decisions from unsuccessful bidders. Having a small number of stable VCSEs (e.g. in end-of-life care) enabled and necessitated collaborative commissioning. Geographical and historical characteristics: Population deprivation motivated commissioners and VCSEs to respond through collaborative commissioning. Spatial dispersion increased the practical difficulty and costs of, and the number of networks involved in, collaborative commissioning. Co-terminosity of commissioners minimised the number of interorganisational interfaces that the networks had to span. Local policies, ethos and history strongly influenced whether collaboration and trust between commissioners and VCSEs was long-established, and thus how collaborative commissioning could be. The presence and nature of networking spaces, in particular which VCSEs participated, and how much voice they had when they did participate, affected how collaborative co-commissioning could be. Organisational characteristics: ACAP affected commissioners’ ability to know what resources VCSEs could offer, and what VCSEs were aiming for, in becoming involved in commissioning, and vice versa. Commissioners relied on formal sources of information and evidence more than VCSEs tended to. Discursive (translation) gaps between VCSEs and commissioners were widespread. Organisational systems, culture and bureaucracy: the more narrowly procurement regulations were interpreted and implemented, the more commissioning was commodified rather than collaborative. Individuals’ role, discretion and influence: individuals who lobbied for VCSEs to engage, or be engaged, in commissioning and who were boundary-spanners with knowledge of both commissioning and VCSEs facilitated the development of collaborative commissioning, as did ‘maverick’ individuals who devised workarounds when procurement regulations appeared to obstruct the commissioning of VCSEs, especially collaborative commissioning. The nature of VCSE activity: Statutory or non-statutory status: statutory requirements for service provision exposed commissioners’ resource dependence on VCSEs, which led to more collaborative, flexible commissioning. Tracer group characteristics: tracer groups’ characteristics affected commissioning partly through the above contexts; that is, whether VCSEs were few and large (e.g. hospices) or the opposite (social prescribing); undertaking statutorily mandated activity (e.g. hospices, some learning disability support). The degree of specialisation of VCSE work affected the number and mix of providers. Stability of demand for a VCSE’s activity favoured more collaborative commissioning. A temporal context: the COVID-19 pandemic shifted the commissioning of VCSEs substantially towards a more collaborative mode, and this change had not fully reverted. The outcomes of these mechanisms included some that approximated to commissioners’ and VCSEs’ respective aims in using the above commissioning mechanisms. For commissioners the main outcomes were to obtain the use of VCSE resources, sometimes at below the full cost of provision. It was often claimed that VCSE activities had relieved, or would relieve, pressure on NHS services, but firm evidence was scarce. Commissioning VCSEs also offered a way for commissioners to pilot test possible innovations (e.g. for self-help in maintaining health) and a ready-made route to access patient, carer and public opinions. For VCSEs the main outcome was income, although the income flow was often unstable, which made it hard to retain paid staff, and below the cost of their activities. Some VCSEs also increased their voice in the local health system, but we also found a large periphery of VCSEs that did not participate in the networks described above and were not commissioned. We found a more nuanced picture than previous studies’ account of the internal ‘degeneration’ of VCSEs as a result of being commissioned. Most VCSEs reported little change to their aims or ‘mission’, and some had increased the professionalism of their management. We also found emergent, unforeseen outcomes. There was a general shift towards collaborative commissioning away from commodified commissioning, but it made the discursive gap between commissioners and VCSEs more apparent. The undercosting of some VCSE activities amounted to a hidden subsidy to commissioners from VCSEs. VCSE activities added a new category of public health activities, those of preventive self-care at a personal and family level, to the longer-established models of clinical prevention (vaccination, etc.) and the ‘new’ public health (legislative and policy changes, e.g. food labelling controls). ICS formation tended to disrupt co-commissioning networks in the short term, but offered a longer-term prospect of greater VCSE input into co-commissioning. Conclusions By research question: RQ1. Commissioners used two commissioning mechanisms in parallel. Each embodied a different kind of governance structure and a different mode of commissioning. Centred on the commissioning cycle, the quasi-market mechanisms implemented a heavily commodified mode of commissioning whose paradigm was a principal–agent relationship between commissioner and VCSE. The networking mechanisms of collaborative commissioning consisted of a triple negotiated order: networking among commissioners, networking among VCSEs and networking between commissioners and VCSEs. A barrier to using this hybrid mechanism was that at certain points in the commissioning cycle, especially provider selection, the mechanisms conflicted. Unfavourable contexts were other barriers: more specialised VCSE activities, dependence on a few individuals, non-coterminous commissioners, possible competition from corporate and public providers, population dispersal and fiscal austerity. RQ2. For VCSEs, consequences of being commissioned depended on the mode of commissioning but were mainly some extension of VCSE activities, including advocacy; marginal adjustments (not drift) in mission; and additional funding, but often unstable and below the full cost of these activities. RQ3. VCSE involvement in co-commissioning occurred through networking rather than quasi-market mechanisms. VCSEs with long-established working relationships with commissioners were often the ones involved, and were often larger VCSEs. Small local VCSEs were involved vicariously through their participation in VCSE infrastructure bodies or networks which were more directly involved, but a large periphery of small VCSEs remained outside these networks. RQ4. The ACAPs that commissioners needed were a combination of managerial and communication skills: to routinise ‘acquiring’ and ‘assimilating’ information from VCSEs; to specify calls for practical proposals or bids from VCSEs so as to elicit responses from suitable VCSEs; and then to apply (‘exploit’) information and ideas from VCSEs in ways that did not then penalise the VCSEs which provided them (e.g. by excluding them from tendering). VCSEs needed to develop the capacities to supply evidence, both hard and soft intelligence, about why they wished to supplement or amend commissioners’ assumptions and proposals, and to express what outcomes their proposals would offer, what metrics could be used to verify that, and more generally how the VCSE’s proposals would add social value. In ACAP terms, the commissioners appeared to need to develop their knowledge acquisition and exploitation capacities especially, while VCSEs needed to develop their knowledge acquisition and transformation capacities. Implications for health and care Below-cost funding for VCSE activity is not sustainable in the long term because VCSE activities require paid-for inputs, even when volunteer labour is used. The instability of VCSE income from commissioners contributed to VCSE staff turnover. If it continues, commissioners’ move towards longer contracts implies that a stratification and concentration of the VCSE side of the quasi-market may occur. Research recommendations are, in descending priority order, for research to: Quantify the impacts of VCSE activity on demand on NHS services. Extend our analysis of NHS commissioner spending on health-related VCSE activity to include local authority spending, and longitudinally. Test whether our findings apply to the commissioning of large (cross-England) VCSEs engaged in health-related activity. Assess the nature, effects and development of ACAP in commissioners and VCSEs by means that combine representative sample surveys (e.g. using the survey instrument developed for this study) and deeper analysis of the specific mechanisms by which ACAP affects commissioning practice. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR128107) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 39. See the NIHR Funding and Awards website for further award information.

Published
2024
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50. Collaborating across government agencies to combine linkage processes and algorithms to create an Australian National Linkage Spine (NLS) to facilitate cross-jurisdictional linkage

Author

Theresa Nunan, Elena Ougrinovski, Brett Frazer, Kelly Dahl, Bronwyn Wilson, Rian Jenkins, and Clinton Paine

Subjects
Demography. Population. Vital events, HB848-3697
Abstract

The Australian Bureau of Statistics (ABS) and the Australian Institute of Health and Welfare (AIHW) are co-technical lead agencies for developing the National Disability Data Asset (NDDA) which will bring together data across jurisdictions to build a more complete picture of the lives of people with disability. ABS and AIHW are collaboratively building a National Linkage Spine (NLS), a high-quality high-coverage population dataset to be shared under new legislation to facilitate cross-jurisdictional linkage and build the NDDA. ABS and AIHW each have extensive data integration experience, however our linkage methods have evolved differently in response to the needs of our respective researcher client bases and the datasets we work with. An ABS-AIHW cross-agency team was established to align terminology and share data processing methods, linkage approaches, and locally built linkage tools. The team collaborated on the design of an NLS that combines the best aspects of each of our approaches. The NLS build methodology delivers aligned processing of input data and applies a combination of ABS’s deterministic linkage algorithm and AIHW’s probabilistic linkage and machine learning algorithms. The NLS was peer reviewed by its future jurisdictional users and is considered fit for purpose for delivering the NDDA. The open sharing of knowledge and tools has significantly benefited both agencies and the broader NDDA project. Takeaways from this project extend beyond it, with improved data process approaches being considered by each agency in other work, and a growing common agreement of linkage best practice.

Published
2024
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