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1. Multi-task Learning Approach for Intracranial Hemorrhage Prognosis

Author

Cobo, Miriam, del Barrio, Amaia Pérez, Fernández-Miranda, Pablo Menéndez, Bellón, Pablo Sanz, Iglesias, Lara Lloret, and Silva, Wilson

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, I.2
Abstract

Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git., Comment: 16 pages. Accepted at Machine Learning in Medical Imaging Workshop @ MICCAI 2024 (MLMI2024). This is the submitted manuscript with added link to github repo, funding acknowledgements and authors' names and affiliations. No further post submission improvements or corrections were integrated. Final version not published yet

Published
2024

2. Properties of sunspot light bridges on a geometric height scale

Author

Pozuelo, S. Esteban, Ramos, A. Asensio, Baso, C. J. Díaz, and Cobo, B. Ruiz

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Investigating light bridges (LBs) helps us comprehend key aspects of sunspots. However, few studies have analyzed the properties of LBs in terms of the geometric height, which is a more realistic perspective given the corrugation of the solar atmosphere. We aim to shed light on LBs by studying the variation in their physical properties with geometric height. We used the SICON code to infer the physical quantities in terms of the optical depth and the Wilson depression values of three LBs hosted by a sunspot observed with Hinode/SP in the Fe I 630 nm pair lines. We also used SIR inversions to cross-check the height variation of the field inclination in the LBs. In both output sets, we performed linear interpolation to convert the physical parameters from optical depth into a geometric height scale in each pixel. We classified each LB as filamentary, grainy, or umbral. They appear as ridges that reach different maximum heights, with the umbral LB being the deepest. While the filamentary LB hosts a plasma inflow from the penumbra, the results for the grainy LB are compatible with an injection of hot plasma through convective cells of reduced field strength. Only a few positions reveal hints suggesting a cusp-like magnetic canopy. Moreover, strong gradients in the magnetic field strength and inclination usually exhibit enhanced electric currents, with the filamentary LB having remarkably strong currents that appear to be related to chromospheric events. The height stratification in filamentary and grainy LBs differ, indicating diverse mechanisms at work. Our results are in general incompatible with a magnetic canopy scenario, and further analysis is needed to confirm whether it exists along the entire LB or only at specific locations. Furthermore, this work assesses the usefulness of SICON when determining the height stratification of solar structures., Comment: 13 pages, 12 figures. Accepted for publication in A&A. Abstract has been abridged

Published
2024

3. 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

4. Quantitative Group Testing and Pooled Data in the Linear Regime with Sublinear Tests

Author

Tan, Nelvin, Cobo, Pablo Pascual, and Venkataramanan, Ramji

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items in the pool belonging to each category. A prominent special case is quantitative group testing (QGT), which is the case of pooled data with two categories. We consider these problems in the non-adaptive and linear regime, where the fraction of items in each category is of constant order. We propose a scheme with a spatially coupled Bernoulli test matrix and an efficient approximate message passing (AMP) algorithm for recovery. We rigorously characterize its asymptotic performance in both the noiseless and noisy settings, and prove that in the noiseless case, the AMP algorithm achieves almost-exact recovery with a number of tests sublinear in the number of items. For both QGT and pooled data, this is the first efficient scheme that provably achieves recovery in the linear regime with a sublinear number of tests, with performance degrading gracefully in the presence of noise. Numerical simulations illustrate the benefits of the spatially coupled scheme at finite dimensions, showing that it outperforms i.i.d. test designs as well as other recovery algorithms based on convex programming., Comment: 49 pages, 6 figures

Published
2024

5. Observation of quantum effects on radiation reaction in strong fields

Author

Los, E. E., Gerstmayr, E., Arran, C., Streeter, M. J. V., Colgan, C., Cobo, C. C., Kettle, B., Blackburn, T. G., Bourgeois, N., Calvin, L., Carderelli, J., Cavanagh, N., Di Piazza, S. J. D. Dann A., Fitzgarrald, R., Ilderton, A., Keitel, C. H., Marklund, M., McKenna, P., Murphy, C. D., Najmudin, Z., Parsons, P., Rajeev, P. P., Symes, D. R., Tamburini, M., Thomas, A. G. R., Wood, J. C., Zepf, M., Sarri, G., Ridgers, C. P., and Mangles, S. P. D

Subjects
High Energy Physics - Phenomenology, Physics - Plasma Physics
Abstract

Radiation reaction describes the effective force experienced by an accelerated charge due to radiation emission. Quantum effects dominate charge dynamics and radiation production[1][2] for charges accelerated by fields with strengths approaching the Schwinger field, $\mathbf{E_{sch}=}$\textbf{\SI[detect-weight]{1.3e18}{\volt\per\metre}[3]. Such fields exist in extreme astrophysical environments such as pulsar magnetospheres[4], may be accessed by high-power laser systems[5-7], dense particle beams interacting with plasma[8], crystals[9], and at the interaction point of next generation particle colliders[10]. Classical radiation reaction theories do not limit the frequency of radiation emitted by accelerating charges and omit stochastic effects inherent in photon emission[11], thus demanding a quantum treatment. Two quantum radiation reaction models, the quantum-continuous[12] and quantum-stochastic[13] models, correct the former issue, while only the quantum-stochastic model incorporates stochasticity[12]. Such models are of fundamental importance, providing insight into the effect of the electron self-force on its dynamics in electromagnetic fields. The difficulty of accessing conditions where quantum effects dominate inhibited previous efforts to observe quantum radiation reaction in charged particle dynamics with high significance. We report the first direct, high significance $(>5{\sigma})$ observation of strong-field radiation reaction on charged particles. Furthermore, we obtain strong evidence favouring the quantum radiation reaction models, which perform equivalently, over the classical model. Robust model comparison was facilitated by a novel Bayesian framework which inferred collision parameters. This framework has widespread utility for experiments where parameters governing lepton-laser collisions cannot be directly measured, including those using conventional accelerators., Comment: 31 pages, 14 figures

Published
2024

6. Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams

Author

Schommartz, Jasper O., Klein, Dominik K., Cobo, Juan C. Alzate, and Weeger, Oliver

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

We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mechanical conditions by construction. Strains and curvatures of the beam are used as input for feed-forward neural networks that represent the effective hyperelastic beam potential. Forces and moments are then received as the gradients of the beam potential, ensuring thermodynamic consistency. Furthermore, normalization conditions are considered via additional projection terms. To include the symmetry of beams with point-symmetric cross-sections, a flip symmetry constraint is introduced. Additionally, parameterized models are proposed that can represent the beam's constitutive behavior for varying cross-sectional geometries. The physically motivated parameterization takes into account the influence of the beam radius on the beam potential. Formulating the beam potential as a neural network provides a highly flexible model. This enables efficient constitutive surrogate modeling for geometrically exact beams with nonlinear material behavior and cross-sectional deformation, which otherwise would require computationally much more expensive methods. The models are calibrated to data generated for beams with circular, deformable cross-sections and varying radii, showing excellent accuracy and generalization. The applicability of the proposed model is further demonstrated by applying it in beam simulations. In all studied cases, the proposed model shows excellent performance.

Published
2024

7. Applying Recommendation System for Developing Programming Competencies in Children from a Non-WEIRD Context

Author

Jesennia Cárdenas-Cobo, Cristian Vidal-Silva, Lisett Arévalo, and Magali Torres

Abstract

The information society is part of current life, and algorithmic thinking and programming are relevant for everybody regardless of educational background. Today's world needs professionals with computing competencies from WEIRD (Western, Educated, Industrialized, Rich, and Democratic Societies) and non-WEIRD contexts. Traditional programming languages include syntax barriers that complicate their overall adoption and usefulness for people from a non-WEIRD context. To solve it, block-based programming languages like Scratch permit the development of programming competencies without syntax restrictions in online environments. This article presents empirical evidence of the positive impact of Scratch with the CARAMBA recommendation system for lessons and exercises proposals based on collaborative filtering of personalized learning from students' experiences. Previous experiences demonstrated that students require assistance in successfully defining sub-competencies and exercises to develop programming competencies by applying Scratch. This work shows the application of Scratch and CARAMBA in a non-WEIRD school context for developing programming competencies. Obtained results show that developing exercises with Scratch and CARAMBA motivated students' autonomy, and as well, the programming learning application increased exam scores in all the analyzed grades. Those results encourage us to continue using Scratch and CARAMBA for developing programming competencies in similar non-WEIRD contexts.

Published
2024
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8. Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Author

Jurenka, Irina, Kunesch, Markus, McKee, Kevin R., Gillick, Daniel, Zhu, Shaojian, Wiltberger, Sara, Phal, Shubham Milind, Hermann, Katherine, Kasenberg, Daniel, Bhoopchand, Avishkar, Anand, Ankit, Pîslar, Miruna, Chan, Stephanie, Wang, Lisa, She, Jennifer, Mahmoudieh, Parsa, Rysbek, Aliya, Ko, Wei-Jen, Huber, Andrea, Wiltshire, Brett, Elidan, Gal, Rabin, Roni, Rubinovitz, Jasmin, Pitaru, Amit, McAllister, Mac, Wilkowski, Julia, Choi, David, Engelberg, Roee, Hackmon, Lidan, Levin, Adva, Griffin, Rachel, Sears, Michael, Bar, Filip, Mesar, Mia, Jabbour, Mana, Chaudhry, Arslan, Cohan, James, Thiagarajan, Sridhar, Levine, Nir, Brown, Ben, Gorur, Dilan, Grant, Svetlana, Hashimshoni, Rachel, Weidinger, Laura, Hu, Jieru, Chen, Dawn, Dolecki, Kuba, Akbulut, Canfer, Bileschi, Maxwell, Culp, Laura, Dong, Wen-Xin, Marchal, Nahema, Van Deman, Kelsie, Misra, Hema Bajaj, Duah, Michael, Ambar, Moran, Caciularu, Avi, Lefdal, Sandra, Summerfield, Chris, An, James, Kamienny, Pierre-Alexandre, Mohdi, Abhinit, Strinopoulous, Theofilos, Hale, Annie, Anderson, Wayne, Cobo, Luis C., Efron, Niv, Ananda, Muktha, Mohamed, Shakir, Heymans, Maureen, Ghahramani, Zoubin, Matias, Yossi, Gomes, Ben, and Ibrahim, Lila

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

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

Published
2024

9. Combining magneto-hydrostatic constraints with Stokes profile inversions. IV. Imposing $\nabla\cdot{\bf B}=0$ condition

Author

Borrero, Juan M., Yabar, Adur Pastor, and Cobo, Basilio Ruiz

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Inferences of the magnetic field in the solar atmosphere by means of spectropolarimetric inversions (i.e., Stokes inversion codes) yield magnetic fields that are non-solenoidal($\nabla\cdot{\bf B} \ne 0$). Because of this, results obtained by such methods are sometimes put into question. We aim to develop and implement a new technique that can retrieve magnetic fields that are simultaneously consistent with observed polarization signals and with the null divergence condition. The method used in this work strictly imposes $\nabla\cdot{\bf B}=0$ by determining the vertical component of the magnetic field ($B_{\rm z}$) from the horizontal ones ($B_{\rm x},B_{\rm y}$). We implement this solenoidal inversion into the FIRTEZ Stokes inversion code and apply it to spectropolarimetric observations of a sunspot observed with the Hinode/SP instrument. We show that the solenoidal inversion retrieves a vertical component of the magnetic field that is consistent with the vertical component of the magnetic field inferred from the non-solenoidal one. We demonstrate that the solenoidal inversion is capable of a better overall fitting to the observed Stokes vector than the non-solenoidal inversion. In fact, the solenoidal magnetic field fits Stokes $V$ worse, but this is compensated by a better fit to Stokes $I$. We find a direct correlation between the worsening in the fit to the circular polarization profiles by the solenoidal inversion and the deviations in the inferred $B_{\rm z}$ with respect to the non-solenoidal inversion. These results support the idea that common Stokes inversion techniques fail to reproduce $\nabla\cdot{\bf B}=0$ mainly as a consequence of the uncertainties in the determination of the individual components of the magnetic field., Comment: 10 pages, 10 figures, accepted for publication in Astronomy & Astrophysics

Published
2024

10. The first cut is the cheapest: optimizing Athena/X-IFU-like TES detectors resolution by filter truncation

Author

Ceballos, M. Teresa, Cardiel, Nicolás, Cobo, Beatriz, Smith, Stephen J., Witthoeft, Michael C., Peille, Philippe, and Durkin, Malcolm S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The X-ray Integral Field Unit (X-IFU) instrument on the future ESA mission Athena X-ray Observatory is a cryogenic micro-calorimeter array of Transition Edge Sensor (TES) detectors designed to provide spatially-resolved high-resolution spectroscopy. The onboard reconstruction software provides energy, spatial location and arrival time of incoming X-ray photons hitting the detector. A new processing algorithm based on a truncation of the classical optimal filter and called 0-padding, has been recently proposed aiming to reduce the computational cost without compromising energy resolution. Initial tests with simple synthetic data displayed promising results. This study explores the slightly better performance of the 0-padding filter and assess its final application to real data. The goal is to examine the larger sensitivity to instrumental conditions that was previously observed during the analysis of the simulations. This 0-padding technique is thoroughly tested using more realistic simulations and real data acquired from NASA and NIST laboratories employing X-IFU-like TES detectors. Different fitting methods are applied to the data, and a comparative analysis is performed to assess the energy resolution values obtained from these fittings. The 0-padding filter achieves energy resolutions as good as those obtained with standard filters, even with those of larger lengths, across different line complexes and instrumental conditions. This method proves to be useful for energy reconstruction of X-ray photons detected by the TES detectors provided proper corrections for baseline drift and jitter effects are applied. The finding is highly promising especially for onboard processing, offering efficiency in computational resources and facilitating the analysis of sources with higher count rates at high resolution., Comment: 35 pages, 26 figures, published version

Published
2024
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11. 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

13. CTLA4 blockade abrogates KEAP1/STK11-related resistance to PD-(L)1 inhibitors

Author

Skoulidis, Ferdinandos, Araujo, Haniel A., Do, Minh Truong, Qian, Yu, Sun, Xin, Cobo, Ana Galan, Le, John T., Montesion, Meagan, Palmer, Rachael, Jahchan, Nadine, Juan, Joseph M., Min, Chengyin, Yu, Yi, Pan, Xuewen, Arbour, Kathryn C., Vokes, Natalie, Schmidt, Stephanie T., Molkentine, David, Owen, Dwight H., Memmott, Regan, Patil, Pradnya D., Marmarelis, Melina E., Awad, Mark M., Murray, Joseph C., Hellyer, Jessica A., Gainor, Justin F., Dimou, Anastasios, Bestvina, Christine M., Shu, Catherine A., Riess, Jonathan W., Blakely, Collin M., Pecot, Chad V., Mezquita, Laura, Tabbó, Fabrizio, Scheffler, Matthias, Digumarthy, Subba, Mooradian, Meghan J., Sacher, Adrian G., Lau, Sally C. M., Saltos, Andreas N., Rotow, Julia, Johnson, Rocio Perez, Liu, Corinne, Stewart, Tyler, Goldberg, Sarah B., Killam, Jonathan, Walther, Zenta, Schalper, Kurt, Davies, Kurtis D., Woodcock, Mark G., Anagnostou, Valsamo, Marrone, Kristen A., Forde, Patrick M., Ricciuti, Biagio, Venkatraman, Deepti, Van Allen, Eliezer M., Cummings, Amy L., Goldman, Jonathan W., Shaish, Hiram, Kier, Melanie, Katz, Sharyn, Aggarwal, Charu, Ni, Ying, Azok, Joseph T., Segal, Jeremy, Ritterhouse, Lauren, Neal, Joel W., Lacroix, Ludovic, Elamin, Yasir Y., Negrao, Marcelo V., Le, Xiuning, Lam, Vincent K., Lewis, Whitney E., Kemp, Haley N., Carter, Brett, Roth, Jack A., Swisher, Stephen, Lee, Richard, Zhou, Teng, Poteete, Alissa, Kong, Yifan, Takehara, Tomohiro, Paula, Alvaro Guimaraes, Parra Cuentas, Edwin R., Behrens, Carmen, Wistuba, Ignacio I., Zhang, Jianjun, Blumenschein, George R., Gay, Carl, Byers, Lauren A., Gibbons, Don L., Tsao, Anne, Lee, J. Jack, Bivona, Trever G., Camidge, D. Ross, Gray, Jhannelle E., Lieghl, Natasha, Levy, Benjamin, Brahmer, Julie R., Garassino, Marina C., Gandara, David R., Garon, Edward B., Rizvi, Naiyer A., Scagliotti, Giorgio Vittorio, Wolf, Jürgen, Planchard, David, Besse, Benjamin, Herbst, Roy S., Wakelee, Heather A., Pennell, Nathan A., Shaw, Alice T., Jänne, Pasi A., Carbone, David P., Hellmann, Matthew D., Rudin, Charles M., Albacker, Lee, Mann, Helen, Zhu, Zhou, Lai, Zhongwu, Stewart, Ross, Peters, Solange, Johnson, Melissa L., Wong, Kwok K., Huang, Alan, Winslow, Monte M., Rosen, Michael J., Winters, Ian P., Papadimitrakopoulou, Vassiliki A., Cascone, Tina, Jewsbury, Philip, and Heymach, John V.

Published
2024
Full Text
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15. Efficacy and safety of colistin plus beta-lactams for bone and joint infection caused by fluoroquinolone-resistant gram-negative bacilli: a prospective multicenter study

Author

Mancheño-Losa, Mikel, Murillo, Oscar, Benavent, Eva, Sorlí, Luisa, Riera, Melchor, Cobo, Javier, Benito, Natividad, Morata, Laura, Ribera, Alba, Sobrino, Beatriz, Fernández-Sampedro, Marta, Múñez, Elena, Bahamonde, Alberto, Barbero, José María, del Toro, Mª Dolores, Villa, Jenifer, Rigo-Bonnin, Raül, Luque, Sonia, García-Luque, Isabel, Oliver, Antonio, Esteban, Jaime, and Lora-Tamayo, Jaime

Published
2024
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16. Blurred combinatorics in resolution of singularities: (a little) beyond the characteristic polytope

Author

Cobo, Helena, Soto, M. J., and Tornero, José M.

Subjects
Mathematics - Algebraic Geometry, Mathematics - Combinatorics, 14H20, 32S25
Abstract

We introduce a variation of the well-known Newton-Hironaka polytope for algebroid hypersurfaces. This combinatorial object is a perturbed version of the original one, parametrized by a real number. For well-chosen values of the parameter, the objects obtained are very close to the original, while at the same time presenting more (hopefully interesting) information in a way which does not depend on the choice of parameter.

Published
2024

17. Variable selection for Na\'ive Bayes classification

Author

Blanquero, Rafael, Carrizosa, Emilio, Ramírez-Cobo, Pepa, and Sillero-Denamiel, M. Remedios

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

The Na\"ive Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Na\"ive Bayes' assumption of conditional independence, and may deteriorate the method's performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method's execution. In this paper we propose a sparse version of the Na\"ive Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Na\"ive Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.

Published
2024
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18. A cost-sensitive constrained Lasso

Author

Blanquero, Rafael, Carrizosa, Emilio, Ramírez-Cobo, Pepa, and Sillero-Denamiel, M. Remedios

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance constraints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization problem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered.

Published
2024
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19. A bivariate two-state Markov modulated Poisson process for failure modelling

Author

Yera, Yoel G., Lillo, Rosa E., Nielsen, Bo F., Ramírez-Cobo, Pepa, and Ruggeri, Fabrizio

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

Motivated by a real failure dataset in a two-dimensional context, this paper presents an extension of the Markov modulated Poisson process (MMPP) to two dimensions. The one-dimensional MMPP has been proposed for the modeling of dependent and non-exponential inter-failure times (in contexts as queuing, risk or reliability, among others). The novel two-dimensional MMPP allows for dependence between the two sequences of inter-failure times, while at the same time preserves the MMPP properties, marginally. The generalization is based on the Marshall-Olkin exponential distribution. Inference is undertaken for the new model through a method combining a matching moments approach with an Approximate Bayesian Computation (ABC) algorithm. The performance of the method is shown on simulated and real datasets representing times and distances covered between consecutive failures in a public transport company. For the real dataset, some quantities of importance associated with the reliability of the system are estimated as the probabilities and expected number of failures at different times and distances covered by trains until the occurrence of a failure.

Published
2024
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20. Fitting procedure for the two-state Batch Markov modulated Poisson process

Author

Yera, Yoel G., Lillo, Rosa E., and Ramírez-Cobo, Pepa

Subjects
Statistics - Computation
Abstract

The Batch Markov Modulated Poisson Process (BMMPP) is a subclass of the versatile Batch Markovian Arrival process (BMAP) which has been proposed for the modeling of dependent events occurring in batches (as group arrivals, failures or risk events). This paper focuses on exploring the possibilities of the BMMPP for the modeling of real phenomena involving point processes with group arrivals. The first result in this sense is the characterization of the two-state BMMPP with maximum batch size equal to K, the BMMPP2(K), by a set of moments related to the inter-event time and batch size distributions. This characterization leads to a sequential fitting approach via a moments matching method. The performance of the novel fitting approach is illustrated on both simulated and a real teletraffic data set, and compared to that of the EM algorithm. In addition, as an extension of the inference approach, the queue length distributions at departures in the queueing system BMMPP/M/1 is also estimated.

Published
2024
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21. Analysis of an aggregate loss model in a Markov renewal regime

Author

Ramírez-Cobo, Pepa, Carrizosa, Emilio, and Lillo, Rosa Elvira

Subjects
Quantitative Finance - Risk Management, Statistics - Applications
Abstract

In this article we consider an aggregate loss model with dependent losses. The losses occurrence process is governed by a two-state Markovian arrival process (MAP2), a Markov renewal process process that allows for (1) correlated inter-losses times, (2) non-exponentially distributed inter-losses times and, (3) overdisperse losses counts. Some quantities of interest to measure persistence in the loss occurrence process are obtained. Given a real operational risk database, the aggregate loss model is estimated by fitting separately the inter-losses times and severities. The MAP2 is estimated via direct maximization of the likelihood function, and severities are modeled by the heavy-tailed, double-Pareto Lognormal distribution. In comparison with the fit provided by the Poisson process, the results point out that taking into account the dependence and overdispersion in the inter-losses times distribution leads to higher capital charges.

Published
2024

22. On the representation and methodology for wide and short range head pose estimation

Author

Cobo, Alejandro, Valle, Roberto, Buenaposada, José M., and Baumela, Luis

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Head pose estimation (HPE) is a problem of interest in computer vision to improve the performance of face processing tasks in semi-frontal or profile settings. Recent applications require the analysis of faces in the full 360{\deg} rotation range. Traditional approaches to solve the semi-frontal and profile cases are not directly amenable for the full rotation case. In this paper we analyze the methodology for short- and wide-range HPE and discuss which representations and metrics are adequate for each case. We show that the popular Euler angles representation is a good choice for short-range HPE, but not at extreme rotations. However, the Euler angles' gimbal lock problem prevents them from being used as a valid metric in any setting. We also revisit the current cross-data set evaluation methodology and note that the lack of alignment between the reference systems of the training and test data sets negatively biases the results of all articles in the literature. We introduce a procedure to quantify this misalignment and a new methodology for cross-data set HPE that establishes new, more accurate, SOTA for the 300W-LP|Biwi benchmark. We also propose a generalization of the geodesic angular distance metric that enables the construction of a loss that controls the contribution of each training sample to the optimization of the model. Finally, we introduce a wide range HPE benchmark based on the CMU Panoptic data set.

Published
2024
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23. On support vector machines under a multiple-cost scenario

Author

Benítez-Peña, Sandra, Blanquero, Rafael, Carrizosa, Emilio, and Ramírez-Cobo, Pepa

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

Support Vector Machine (SVM) is a powerful tool in binary classification, known to attain excellent misclassification rates. On the other hand, many realworld classification problems, such as those found in medical diagnosis, churn or fraud prediction, involve misclassification costs which may be different in the different classes. However, it may be hard for the user to provide precise values for such misclassification costs, whereas it may be much easier to identify acceptable misclassification rates values. In this paper we propose a novel SVM model in which misclassification costs are considered by incorporating performance constraints in the problem formulation. Specifically, our aim is to seek the hyperplane with maximal margin yielding misclassification rates below given threshold values. Such maximal margin hyperplane is obtained by solving a quadratic convex problem with linear constraints and integer variables. The reported numerical experience shows that our model gives the user control on the misclassification rates in one class (possibly at the expense of an increase in misclassification rates for the other class) and is feasible in terms of running times.

Published
2023
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24. 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

25. Advanced non-squamous NSCLC with no actionable oncogenic driver in Spain: a cross-sectional descriptive analysis of data from the Thoracic Tumor Registry

Author

Carcereny, Enric, Rodriguez-Abreu, Delvys, Lopez, Rafael, Franco, Fabio, Guirado, Maria, Massutí, Bartomeu, Cobo, Manuel, Blasco, Ana, Suay, Guillermo, del Barco, Edel, Ortega, Ana Laura, Sala, Maria Angeles, Cordeiro, Patricia, Bernabé, Reyes, González Larriba, José Luís, Bosch-Barrera, Joaquim, Calzas, Julia, Casal, Joaquín, Padilla, Airam, Sánchez-Hernandez, Alfredo, and Provencio, Mariano

Published
2024
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27. Multi-task Learning Approach for Intracranial Hemorrhage Prognosis

Author

Cobo, Miriam, Pérez del Barrio, Amaia, Menéndez Fernández-Miranda, Pablo, Sanz Bellón, Pablo, Lloret Iglesias, Lara, Silva, Wilson, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xu, Xuanang, editor, Cui, Zhiming, editor, Rekik, Islem, editor, Ouyang, Xi, editor, and Sun, Kaicong, editor

Published
2025
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31. Influence of the medical treatment schedule in new diagnoses patients with heart failure and reduced ejection fraction

Author

Esteban-Fernández, Alberto, Gómez-Otero, Inés, López-Fernández, Silvia, Santamarta, Miguel Rodríguez, Pastor-Pérez, Francisco J., Fluvià-Brugués, Paula, Pérez-Rivera, José-Ángel, López López, Andrea, García-Pinilla, José Manuel, Palomas, Juan Luis Bonilla, Bonet, Luis Almenar, Cobo-Marcos, Marta, Mateo, Virgilio Martínez, Llergo, Javier Torres, Fernández, Vanesa Alonso, Vives, Cristina Goena, de Juan Bagudá, Javier, Benedicto, Alba Maestro, de Polavieja, José Ignacio Morgado, Solla-Ruiz, Itziar, Solé-González, Eduard, Cardona, Montserrat, Olaetxea, Javier Rekondo, Cortés, Carolina Ortiz, Dosantos, Victor Martínez, López, Antonio Gámez, Amao, Elvis, Sánchez, Borja Casas, Torres, Edgardo Alania, Carrillo, Víctor Gómez, García-Fuertes, Daniel, and Ridocci-Soriano, Francisco

Published
2024
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35. Multidisciplinary approach for locally advanced non-small cell lung cancer (NSCLC): 2023 expert consensus of the Spanish Lung Cancer Group GECP

Author

Ospina, Aylen Vanessa, Bolufer Nadal, Sergio, Campo-Cañaveral de la Cruz, José Luis, González Larriba, Jose Luis, Macía Vidueira, Ivan, Massutí Sureda, Bartomeu, Nadal, Ernest, Trancho, Florentino Hernando, Álvarez Kindelán, Antonio, Del Barco Morillo, Edel, Bernabé Caro, Reyes, Bosch Barrera, Joaquim, Calvo de Juan, Virginia, Casal Rubio, Joaquin, de Castro, Javier, Cilleruelo Ramos, Ángel, Cobo Dols, Manuel, Dómine Gómez, Manuel, Figueroa Almánzar, Santiago, Garcia Campelo, Rosario, Insa Mollá, Amelia, Jarabo Sarceda, José Ramón, Jiménez Maestre, Unai, López Castro, Rafael, Majem, Margarita, Martinez-Marti, Alex, Martínez Téllez, Elisabeth, Sánchez Lorente, David, and Provencio, Mariano

Published
2024
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40. Cost-sensitive probabilistic predictions for support vector machines

Author

Benítez-Peña, Sandra, Blanquero, Rafael, Carrizosa, Emilio, and Ramírez-Cobo, Pepa

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

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature. On the other hand, the tuning of the regularization parameters in SVM is known to imply a high computational effort and generates pieces of information that are not fully exploited, not being used to build a probabilistic classification rule. In this paper we propose a novel approach to generate probabilistic outputs for the SVM. The new method has the following three properties. First, it is designed to be cost-sensitive, and thus the different importance of sensitivity (or true positive rate, TPR) and specificity (true negative rate, TNR) is readily accommodated in the model. As a result, the model can deal with imbalanced datasets which are common in operational business problems as churn prediction or credit scoring. Second, the SVM is embedded in an ensemble method to improve its performance, making use of the valuable information generated in the parameters tuning process. Finally, the probabilities estimation is done via bootstrap estimates, avoiding the use of parametric models as competing approaches. Numerical tests on a wide range of datasets show the advantages of our approach over benchmark procedures., Comment: European Journal of Operational Research (2023)

Published
2023
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41. Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing

Author

Tan, Nelvin, Cobo, Pablo Pascual, Scarlett, Jonathan, and Venkataramanan, Ramji

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items of each category within the pool. We study an approximate message passing (AMP) algorithm for estimating the categories and rigorously characterize its performance, in both the noiseless and noisy settings. For the noiseless setting, we show that the AMP algorithm is equivalent to one recently proposed by El Alaoui et al. Our results provide a rigorous version of their performance guarantees, previously obtained via non-rigorous techniques. For the case of pooled data with two categories, known as quantitative group testing (QGT), we use the AMP guarantees to compute precise limiting values of the false positive rate and the false negative rate. Though the pooled data problem and QGT are both instances of estimation in a linear model, existing AMP theory cannot be directly applied since the design matrices are binary valued. The key technical ingredient in our analysis is a rigorous asymptotic characterization of AMP for generalized linear models defined via generalized white noise design matrices. This result, established using a recent universality result of Wang et al., is of independent interest. Our theoretical results are validated by numerical simulations. For comparison, we propose estimators based on convex relaxation and iterative thresholding, without providing theoretical guarantees. The simulations indicate that AMP consistently outperforms these estimators., Comment: 62 pages, 11 figures, appeared in SIAM Journal on Mathematics of Data Science. The simulation results here use a slightly different metric from the journal version; see Remark 4.2

Published
2023
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42. Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling

Author

Cobo, Pablo Pascual, Hsieh, Kuan, and Venkataramanan, Ramji

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

We consider the problem of signal estimation in a generalized linear model (GLM). GLMs include many canonical problems in statistical estimation, such as linear regression, phase retrieval, and 1-bit compressed sensing. Recent work has precisely characterized the asymptotic minimum mean-squared error (MMSE) for GLMs with i.i.d. Gaussian sensing matrices. However, in many models there is a significant gap between the MMSE and the performance of the best known feasible estimators. In this work, we address this issue by considering GLMs defined via spatially coupled sensing matrices. We propose an efficient approximate message passing (AMP) algorithm for estimation and prove that with a simple choice of spatially coupled design, the MSE of a carefully tuned AMP estimator approaches the asymptotic MMSE in the high-dimensional limit. To prove the result, we first rigorously characterize the asymptotic performance of AMP for a GLM with a generic spatially coupled design. This characterization is in terms of a deterministic recursion (`state evolution') that depends on the parameters defining the spatial coupling. Then, using a simple spatially coupled design and a judicious choice of functions for the AMP algorithm, we analyze the fixed points of the resulting state evolution and show that it achieves the asymptotic MMSE. Numerical results for phase retrieval and rectified linear regression show that spatially coupled designs can yield substantially lower MSE than i.i.d. Gaussian designs at finite dimensions when used with AMP algorithms., Comment: 41 pages, 4 figures. Appeared in the IEEE Transactions on Information Theory

Published
2023
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43. Genome evolution and transcriptome plasticity is associated with adaptation to monocot and dicot plants in Colletotrichum fungi

Author

Baroncelli, Riccardo, Cobo-Díaz, José F, Benocci, Tiziano, Peng, Mao, Battaglia, Evy, Haridas, Sajeet, Andreopoulos, William, LaButti, Kurt, Pangilinan, Jasmyn, Lipzen, Anna, Koriabine, Maxim, Bauer, Diane, Le Floch, Gaetan, Mäkelä, Miia R, Drula, Elodie, Henrissat, Bernard, Grigoriev, Igor V, Crouch, Jo Anne, de Vries, Ronald P, Sukno, Serenella A, and Thon, Michael R

Subjects
Biological Sciences, Evolutionary Biology, Genetics, Microbiology, Plant Biology, Human Genome, Biotechnology, Colletotrichum, Transcriptome, Genome, Fungal, Evolution, Molecular, Phylogeny, Adaptation, Physiological, Gene Expression Profiling, Plant Diseases, fungal genomics, comparative transcriptomics, fungal evolution, anthracnose, plant cell walls
Abstract

BackgroundColletotrichum fungi infect a wide diversity of monocot and dicot hosts, causing diseases on almost all economically important plants worldwide. Colletotrichum is also a suitable model for studying gene family evolution on a fine scale to uncover events in the genome associated with biological changes.ResultsHere we present the genome sequences of 30 Colletotrichum species covering the diversity within the genus. Evolutionary analyses revealed that the Colletotrichum ancestor diverged in the late Cretaceous in parallel with the diversification of flowering plants. We provide evidence of independent host jumps from dicots to monocots during the evolution of Colletotrichum, coinciding with a progressive shrinking of the plant cell wall degradative arsenal and expansions in lineage-specific gene families. Comparative transcriptomics of 4 species adapted to different hosts revealed similarity in gene content but high diversity in the modulation of their transcription profiles on different plant substrates. Combining genomics and transcriptomics, we identified a set of core genes such as specific transcription factors, putatively involved in plant cell wall degradation.ConclusionsThese results indicate that the ancestral Colletotrichum were associated with dicot plants and certain branches progressively adapted to different monocot hosts, reshaping the gene content and its regulation.

Published
2024

44. Genomic dynamics of high-risk carbapenem-resistant Klebsiella pneumoniae clones carrying hypervirulence determinants in Egyptian clinical settings

Author

Nehal Adel Abdelsalam, Shahira A. ElBanna, Shaimaa F. Mouftah, José F. Cobo-Díaz, Ahmed H. Shata, Sherine M. Shawky, Reham Atteya, and Mohamed Elhadidy

Subjects
Klebsiella pneumoniae, Convergence, ESBL, Carbapenem, Hypervirulence genetic determinants, Extensive-drug resistance, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Ongoing studies have revealed the global prevalence of severe infections caused by the hypervirulent strains of Klebsiella pneumoniae (K. pneumoniae). Meanwhile, the World Health Organization and the Centers for Disease Control declared carbapenem-resistant K. pneumoniae as an urgent public health threat, requiring swift and effective action to mitigate its spread. Low- and middle-income countries are severely impacted by such devastating infectious diseases owing to the ill implementation of antimicrobial practices and infection control policies. Having both hypervirulence and carbapenemase gene determinants, the emergence of convergent hypervirulent carbapenem-resistant K. pneumoniae is now being reported worldwide. Methods In this study, we sequenced 19 carbapenemase-producing K. pneumoniae strains recovered from various clinical specimens. Additionally, we evaluated the phenotypic antimicrobial susceptibility to multiple antimicrobial classes using the VITEK2 automated system. Utilizing the sequencing data, we characterized the sequence types, serotypes, pangenome, resistance profiles, virulence profiles, and mobile genetic elements of the examined isolates. We highlighted the emergence of high-risk clones carrying hypervirulence genetic determinants among the screened isolates. Results Our findings revealed that all carbapenem-resistant isolates exhibited either extensive- or pan-drug resistance and harbored multiple variants of resistance genes spanning nearly all the antimicrobial classes. The most prevalent carbapenemase genes detected within the isolates were bla NDM−5 and bla OXA−48 . We identified high-risk clones, such as ST383-K30, ST147-K64, ST11-K15, and ST14-K2, which may have evolved into putative convergent strains by acquiring the full set of hypervirulence-associated genetic determinants (iucABCD, rmpA and/ or rmpA2, putative transporter peg-344). Additionally, this study identified ST709-K9 as a high-risk clone for the first time and uncovered that capsule types K15 and K9 carried hypervirulence genetic determinants. The most frequent Inc types found in these isolates were Col440I, IncHI1B, and Inc FII(K). Conclusion This study highlights the emergence of high-risk, extensively carbapenem-resistant K. pneumoniae strains co-carrying hypervirulence determinants in Egyptian clinical settings. This poses an imminent threat not only to Egypt but also to the global community, underscoring the urgent need for enhanced surveillance and control strategies to combat this pathogen.

Published
2024
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45. In-depth characterization of food and environmental microbiomes across different meat processing plants

Author

Coral Barcenilla, José F. Cobo-Díaz, Alba Puente, Vincenzo Valentino, Francesca De Filippis, Danilo Ercolini, Niccolò Carlino, Federica Pinto, Nicola Segata, Miguel Prieto, Mercedes López, and Avelino Alvarez-Ordóñez

Subjects
Meat, Metagenomics, Microbiome, Resistome, Virulome, Resident microbiota, Microbial ecology, QR100-130
Abstract

Abstract Background Processing environments can be an important source of pathogenic and spoilage microorganisms that cross contaminate meat and meat products. The aim of this study was to characterize the microbiome of raw materials, processing environments and end products from 19 facilities producing different meat products. Results The taxonomic profiles of the microbial communities evolved along processing, from raw materials to end products, suggesting that food contact (FC) surfaces play an important role in modulating the microbiome of final products. Some species persisted with the highest relative abundance in raw materials, food processing environments and/or in the final product, including species from the genera Pseudomonas, Staphylococcus, Brochothrix, Acinetobacter and Psychrobacter. Processing environments showed a very diverse core microbiota, partially shared with the products. Pseudomonas fragi and Pseudomonas sp. Lz4W (in all sample and facility types) and Brochothrix thermosphacta, Psychrobacter sp. and Psychrobacter sp. P11F6 (in raw materials, FC surfaces and end products) were prominent members of the core microbiota for all facilities, while Latilactobacillus sakei was found as a dominant species exclusively in end products from the facilities producing fermented sausages. Processing environments showed a higher amount of antimicrobial resistance genes and virulence factors than raw materials and end products. One thousand four hundred twenty-one medium/high-quality metagenome-assembled genomes (MAGs) were reconstructed. Of these, 274 high-quality MAGs (completeness > 90%) corresponded to 210 putative new species, mostly found in processing environments. For two relevant taxa in meat curing and fermentation processes (S. equorum and L. sakei, respectively), phylogenetic variation was observed associated with the specific processing facility under study, which suggests that specific strains of these taxa may be selected in different meat processing plants, likely contributing to the peculiar sensorial traits of the end products produced in them. Conclusions Overall, our findings provide the most detailed metagenomics-based perspective up to now of the microbes that thrive in meat, meat products and associated environments and open avenues for future research activities to better understand the microbiome functionality and potential contribution to meat quality and safety. Video Abstract

Published
2024
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46. Cost-effectiveness of sodium zirconium cyclosilicate for the treatment of hyperkalemia in patients with chronic kidney disease or heart failure in Spain

Author

Roberto Alcázar-Arroyo, María G Crespo-Leiro, Jordi Bover, Juan Oliva, Miren Sequera-Mutiozabal, Simona Gradari, Anisia Martínez-López, Blanca López-Chicheri, Neus Vidal-Vilar, Susana Aceituno, and Marta Cobo

Subjects
Coste-efectividad, Ciclosilicato de sodio y zirconio, Hiperpotasemia, Enfermedad renal crónica, Insuficiencia cardíaca, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Background and objective: Hyperkalemia (HK) is an electrolyte disturbance in the concentration of potassium ions (K+), whose risk increases in patients with chronic kidney disease (CKD) or heart failure (HF) and/or in patients being treated with renin–angiotensin–aldosterone system inhibitors (RAASi). The new oral K+ chelators offer a safe and effective treatment to maintain normokalemia in these patients. The objective of the analysis is to estimate the cost-effectiveness of sodium zirconium cyclosilicate (SZC) for the treatment of chronic HK in patients with CKD or HF versus standard treatment (calcium polystyrene sulfonate and lifestyle modifications) from the perspective of the Spanish National Health System. Materials and methods: Two microsimulation models reflecting the natural history of CKD and HF were used. In both models, K+ levels were simulated individually. Based on efficacy (reduction of K+ levels), quality of life of patients (utilities according to health states, and disutilities of events derived from each pathology and adverse events [AEs] of treatment) and costs considered (cost of treatment for HK, of RAASi treatment and its modification, health states, management of events derived from each pathology, HK episodes, and AEs treatment) (, 2022), clinical benefit (quality-adjusted life years [QALYs]) and cost results were obtained. A time horizon of the patient’s lifetime was used and a discount rate of 3% was applied for costs and outcomes. Results: SZC is a more effective option in both pathologies, with a difference in QALYs of 0.476 in CKD and 0.978 in HF compared to standard treatment, and it represents an incremental cost of 3,616 and 14,749, respectively, obtaining an incremental cost-utility ratio of 7,605/QALY in CKD and 15,078/QALY in HF. Conclusions: SZC is a cost-effective alternative for the treatment of HK in patients with CKD or HF, taking into account the reference efficiency values commonly used in Spain. Resumen: Antecedentes y objetivo: La hiperpotasemia (HK) es una alteración electrolítica en la concentración de iones potasio (K+), cuyo riesgo aumenta en pacientes con enfermedad renal crónica (ERC) o insuficiencia cardiaca (IC) y/o en pacientes en tratamiento con inhibidores del sistema renina-angiotensina-aldosterona (iSRAA). Los nuevos quelantes orales de K+ ofrecen un tratamiento seguro y eficaz para mantener la normopotasemia en estos pacientes. El objetivo del análisis es estimar el coste-efectividad del ciclosilicato de sodio y zirconio (CSZ) para el tratamiento de la HK crónica en pacientes con ERC o IC frente al tratamiento estándar (poliestireno sulfonato cálcico y modificaciones del estilo de vida) desde la perspectiva del Sistema Nacional de Salud (SNS) español. Materiales y métodos: Se utilizaron dos modelos de microsimulación que reflejan la historia natural de la ERC y de la IC. En ambos modelos se realizó una simulación de forma individual de los niveles de K+. En base a la eficacia (reducción de los niveles de K+), la calidad de vida de los pacientes (utilidades según estado de salud, y disutilidades de los eventos derivados de cada patología y los eventos adversos [EA] del tratamiento) y a los costes contemplados (coste del tratamiento para la HK, del tratamiento con iSRAA y su modificación, de los estados de salud, del manejo de los eventos derivados de cada patología, de los episodios de HK, y de los EA del tratamiento) (, 2022), se obtuvieron resultados de beneficio clínico (años de vida ajustados por calidad [AVAC]) y costes. Se empleó un horizonte temporal de toda la vida del paciente y se aplicó una tasa de descuento del 3% para costes y resultados. Resultados: El CSZ resulta una opción más efectiva en ambas patologías, con una diferencia de AVAC de 0,476 en ERC, y de 0,978 en IC frente al tratamiento estándar, y supone un coste incremental de 3.616 y 14.749 , respectivamente, obteniéndose un ratio coste-utilidad incremental (RCUI) de 7.605 /AVAC en ERC y 15.078 /AVAC en IC. Conclusiones: El CSZ es una alternativa con una buena relación coste-efectividad para el tratamiento de la HK en pacientes con ERC o IC, teniendo en cuenta los valores de eficiencia de referencia empleados habitualmente en España.

Published
2024
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47. Coste-efectividad del ciclosilicato de sodio y zirconio para el tratamiento de la hiperpotasemia en pacientes con enfermedad renal crónica o insuficiencia cardiaca en España

Author

Roberto Alcázar-Arroyo, Maria G. Crespo-Leiro, Jordi Bover, Juan Oliva, Miren Sequera-Mutiozabal, Simona Gradari, Anisia Marinez-Lopez, Blanca Lopez-Chicheri, Neus Vidal-Vilar, Susana Aceituno, and Marta Cobo

Subjects
Cost-effectiveness, Sodium zirconium cyclosilicate, Hyperkalemia, Chronic kidney disease, Heart failure, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Resumen: Antecedentes y objetivo: La hiperpotasemia (HK) es una alteración electrolítica en la concentración de iones potasio (K+), cuyo riesgo aumenta en pacientes con enfermedad renal crónica (ERC) o insuficiencia cardiaca (IC) y/o en pacientes en tratamiento con inhibidores del sistema renina-angiotensina-aldosterona (iSRAA). Los nuevos quelantes orales de K+ ofrecen un tratamiento seguro y eficaz para mantener la normopotasemia en estos pacientes. El objetivo del análisis es estimar el coste-efectividad del ciclosilicato de sodio y zirconio (CSZ) para el tratamiento de la HK crónica en pacientes con ERC o IC vs. tratamiento estándar (poliestireno sulfonato cálcico y modificaciones del estilo de vida) desde la perspectiva del Sistema Nacional de Salud (SNS) español. Materiales y métodos: Se utilizaron dos modelos de microsimulación que reflejan la historia natural de la ERC y de la IC. En ambos modelos se realizó una simulación de forma individual de los niveles de K+. Con base en la eficacia (reducción de los niveles de K+), la calidad de vida de los pacientes (utilidades según estado de salud, y disutilidades de los eventos derivados de cada patología y los eventos adversos [EA] del tratamiento) y a los costes contemplados (coste del tratamiento para la HK, del tratamiento con iSRAA y su modificación, de los estados de salud, del manejo de los eventos derivados de cada patología, de los episodios de HK, y de los EA del tratamiento) (€, 2022), se obtuvieron resultados de beneficio clínico (años de vida ajustados por calidad [AVAC]) y costes. Se empleó un horizonte temporal de toda la vida del paciente y se aplicó una tasa de descuento del 3% para costes y resultados. Resultados: El CSZ resulta una opción más efectiva en ambas patologías, con una diferencia de AVAC de 0,476 en ERC, y de 0,978 en IC vs. tratamiento estándar, y supone un coste incremental de 3.616 € y 14.749 €, respectivamente, obteniéndose un ratio coste-utilidad incremental (RCUI) de 7.605 €/AVAC en ERC y 15.078 €/AVAC en IC. Conclusiones: El CSZ es una alternativa con una buena relación coste-efectividad para el tratamiento de la HK en pacientes con ERC o IC, teniendo en cuenta los valores de eficiencia de referencia empleados habitualmente en España. Abstract: Background and objective: Hyperkalemia (HK) is an electrolyte disturbance in the concentration of potassium ions (K+), whose risk increases in patients with chronic kidney disease (CKD) or heart failure (HF) and/or in patients being treated with renin-angiotensin-aldosterone system inhibitors (RAASi). The new oral K+ chelators offer a safe and effective treatment to maintain normokalemia in these patients. The objective of the analysis is to estimate the cost-effectiveness of sodium zirconium cyclosilicate (SZC) for the treatment of chronic HK in patients with CKD or HF versus standard treatment (calcium polystyrene sulfonate and lifestyle modifications) from the perspective of the Spanish National Health System. Materials and methods: Two microsimulation models reflecting the natural history of CKD and HF were used. In both models, K+ levels were simulated individually. Based on efficacy (reduction of K+ levels), quality of life of patients (utilities according to health states, and disutilities of events derived from each pathology and adverse events [AEs] of treatment) and costs considered (cost of treatment for HK, of RAASi treatment and its modification, health states, management of events derived from each pathology, HK episodes, and AEs treatment) (€, 2022), clinical benefit (quality-adjusted life years [QALYs]) and cost results were obtained. A time horizon of the patient's lifetime was used and a discount rate of 3% was applied for costs and outcomes. Results: SZC is a more effective option in both pathologies, with a difference in QALYs of 0.476 in CKD and 0.978 in HF compared to standard treatment, and it represents an incremental cost of € 3,616 and € 14,749, respectively, obtaining an incremental cost-utility ratio of € 7,605 /QALY in CKD and € 15,078 /QALY in HF. Conclusions: SZC is a cost-effective alternative for the treatment of HK in patients with CKD or HF, taking into account the reference efficiency values commonly used in Spain.

Published
2024
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48. A study of the capabilities for inferring atmospheric information from high-spatial-resolution simulations

Author

Noda, C. Quintero, Khomenko, E., Collados, M., Cobo, B. Ruiz, Gafeira, R., Vitas, N., Rempel, M., Campbell, R. J., Yabar, A. Pastor, Uitenbroek, H., and Suárez, D. Orozco

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In this work, we study the accuracy that can be achieved when inferring the atmospheric information from realistic numerical magneto-hydrodynamic simulations that reproduce the spatial resolution we will obtain with future observations made by the 4m class telescopes DKIST and EST. We first study multiple inversion configurations using the SIR code and the Fe I transitions at 630 nm until we obtain minor differences between the input and the inferred atmosphere in a wide range of heights. Also, we examine how the inversion accuracy depends on the noise level of the Stokes profiles. The results indicate that when the majority of the inverted pixels come from strongly magnetised areas, there are almost no restrictions in terms of the noise, obtaining good results for noise amplitudes up to 1$\times10^{-3}$ of $I_c$. At the same time, the situation is different for observations where the dominant magnetic structures are weak, and noise restraints are more demanding. Moreover, we find that the accuracy of the fits is almost the same as that obtained without noise when the noise levels are on the order of 1$\times10^{-4}$of $I_c$. We, therefore, advise aiming for noise values on the order of or lower than 5$\times10^{-4}$ of $I_c$ if observers seek reliable interpretations of the results for the magnetic field vector reliably. We expect those noise levels to be achievable by next-generation 4m class telescopes thanks to an optimised polarisation calibration and the large collecting area of the primary mirror., Comment: 14 pages, 13 figures

Published
2023
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49. RANK ligand converts the NCoR/HDAC3 co-repressor to a PGC1β- and RNA-dependent co-activator of osteoclast gene expression

Author

Abe, Yohei, Kofman, Eric R, Almeida, Maria, Ouyang, Zhengyu, Ponte, Filipa, Mueller, Jasmine R, Cruz-Becerra, Grisel, Sakai, Mashito, Prohaska, Thomas A, Spann, Nathanael J, Resende-Coelho, Ana, Seidman, Jason S, Stender, Joshua D, Taylor, Havilah, Fan, Weiwei, Link, Verena M, Cobo, Isidoro, Schlachetzki, Johannes CM, Hamakubo, Takao, Jepsen, Kristen, Sakai, Juro, Downes, Michael, Evans, Ronald M, Yeo, Gene W, Kadonaga, James T, Manolagas, Stavros C, Rosenfeld, Michael G, and Glass, Christopher K

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, 1.1 Normal biological development and functioning, Underpinning research, Humans, Mice, Animals, Co-Repressor Proteins, RNA, Osteoclasts, RANK Ligand, Nuclear Receptor Co-Repressor 1, Gene Expression, AP-1, H3K27ac, HDAC3, NCoR, NF-κB, PGC1β, RANK, co-activator, deacetylation, gene expression, non-coding RNA, osteoclast, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

The nuclear receptor co-repressor (NCoR) complex mediates transcriptional repression dependent on histone deacetylation by histone deacetylase 3 (HDAC3) as a component of the complex. Unexpectedly, we found that signaling by the receptor activator of nuclear factor κB (RANK) converts the NCoR/HDAC3 co-repressor complex to a co-activator of AP-1 and NF-κB target genes that are required for mouse osteoclast differentiation. Accordingly, the dominant function of NCoR/HDAC3 complexes in response to RANK signaling is to activate, rather than repress, gene expression. Mechanistically, RANK signaling promotes RNA-dependent interaction of the transcriptional co-activator PGC1β with the NCoR/HDAC3 complex, resulting in the activation of PGC1β and inhibition of HDAC3 activity for acetylated histone H3. Non-coding RNAs Dancr and Rnu12, which are associated with altered human bone homeostasis, promote NCoR/HDAC3 complex assembly and are necessary for RANKL-induced osteoclast differentiation in vitro. These findings may be prototypic for signal-dependent functions of NCoR in other biological contexts.

Published
2023

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