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75,092 results on '"Carvajal, A."'

1. Identification of Complex Thinking Related Competencies: The Building Blocks of Reasoning for Complexity

Author

Juan Andrés Talamás-Carvajal, Héctor G. Ceballos, and María-Soledad Ramírez-Montoya

Abstract

Complex thinking competency enhances the high cognitive capacities necessary for the future of education. This study aimed to analyze these capacities through its sub-competencies (critical, systemic, and scientific thinking). We worked with the Cross Industry Standard Process for Data Mining methodology, with an original database of class data of 33,319 unique students, 46 different variables, and a random identification number. The variables were sociodemographic information, academic information, subject admission, competencies, and activities. Statistical analyses identified correlations between competency and sub-competencies. The findings show that (1) critical thinking is strategic in the development of complex thinking and its sub-competencies; (2) Development of Critical Thinking skills early in the curriculum can lead to a cascade effect, enhancing competence and sub-competence development; and (3) an overall performance encompasses the semester results. The study is of value to the academic, technological, and social communities to provide opportunities for the design and implementation of challenging scenarios for the future of education.

Published
2024

2. A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network

Author

Rico, Julian Carvajal, Alaeddini, Adel, Faruqui, Syed Hasib Akhter, Fisher-Hoch, Susan P, and Mccormick, Joseph B

Subjects
Computer Science - Machine Learning
Abstract

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.

Published
2024

3. Well-posedness for a higher order water wave model on modulation spaces

Author

Carvajal, Xavier and Panthee, Mahendra

Subjects
Mathematics - Analysis of PDEs
Abstract

Considered in this work is the initial value problem (IVP) associated to a higher order water wave model \begin{equation*} \begin{cases} \eta_t+\eta_x-\gamma_1 \eta_{xxt}+\gamma_2\eta_{xxx}+\delta_1 \eta_{xxxxt}+\delta_2\eta_{xxxxx}+\frac{3}{2}\eta \eta_x+\gamma (\eta^2)_{xxx}-\frac{7}{48}(\eta_x^2)_x-\frac{1}{8}(\eta^3)_x=0,\\ \eta(x,0) = \eta_0(x). \end{cases} \end{equation*} The main interest is in addressing the well-posedness issues of the IVP when the given initial data are considered in the modulation space $M_s^{2,p}(\mathbb{R})$ or the $L^p$-based Sobolev spaces $H^{s,p}(\mathbb{R})$, $1\leq p<\infty$. We derive some multilinear estimates in these spaces and prove that the above IVP is locally well-posed for data in $M_s^{2,p}(\mathbb{R})$ whenever $s>1$ and $p\geq 1$, and in $H^{s,p}(\mathbb{R})$ whenever $p\in [1,\infty)$ and $s\geq \max\left\{ \frac1{p}+\frac12, 1 \right\}$. We also use a combination of high-low frequency technique and an {\em a priori estimate}, and prove that the local solution with data in the modulation spaces $M_s^{2,p}(\mathbb{R})$ can be extended globally to the time interval $[0, T]$ for any given $T\gg1$ if $1\leq \frac32-\frac1p

Published
2024

4. OPENGRASP-LITE Version 1.0: A Tactile Artificial Hand with a Compliant Linkage Mechanism

Author

Groß, Sonja, Ratzel, Michael, Welte, Edgar, Hidalgo-Carvajal, Diego, Chen, Lingyun, Fortunić, Edmundo Pozo, Ganguly, Amartya, Swikir, Abdalla, and Haddadin, Sami

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

Recent research has seen notable progress in the development of linkage-based artificial hands. While previous designs have focused on adaptive grasping, dexterity and biomimetic artificial skin, only a few systems have proposed a lightweight, accessible solution integrating tactile sensing with a compliant linkage-based mechanism. This paper introduces OPENGRASP LITE, an open-source, highly integrated, tactile, and lightweight artificial hand. Leveraging compliant linkage systems and MEMS barometer-based tactile sensing, it offers versatile grasping capabilities with six degrees of actuation. By providing tactile sensors and enabling soft grasping, it serves as an accessible platform for further research in tactile artificial hands., Comment: Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 October 2024

Published
2024

5. The Long-lived Broadband Afterglow of Short Gamma-Ray Burst 231117A and the Growing Radio-Detected Short GRB Population

Author

Schroeder, Genevieve, Fong, Wen-fai, Kilpatrick, Charles D., Escorial, Alicia Rouco, Laskar, Tanmoy, Nugent, Anya E., Rastinejad, Jillian, Alexander, Kate D., Berger, Edo, Brink, Thomas G., Chornock, Ryan, de Bom, Clecio R., Dong, Yuxin, Eftekhari, Tarraneh, Filippenko, Alexei V., Fuentes-Carvajal, Celeste, Jacobson-Galan, Wynn V., Malkan, Matthew, Margutti, Raffaella, Pearson, Jeniveve, Rhodes, Lauren, Salinas, Ricardo, Sand, David J., Santana-Silva, Luidhy, Santos, Andre, Sears, Huei, Shrestha, Manisha, Smith, Nathan, Webb, Wayne, de Wet, Simon, and Yang, Yi

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present multiwavelength observations of the Swift short $\gamma$-ray burst GRB 231117A, localized to an underlying galaxy at redshift $z = 0.257$ at a small projected offset ($\sim 2~$kpc). We uncover long-lived X-ray (Chandra) and radio/millimeter (VLA, MeerKAT, and ALMA) afterglow emission, detected to $\sim 37~$days and $\sim 20~$days (rest frame), respectively. We measure a wide jet ($\sim 10.4^\circ$) and relatively high circumburst density ($\sim 0.07~{\rm cm}^{-3}$) compared to the short GRB population. Our data cannot be easily fit with a standard forward shock model, but they are generally well fit with the incorporation of a refreshed forward shock and a reverse shock at $< 1~$day. We incorporate GRB 231117A into a larger sample of 132 X-ray detected events, 71 of which were radio-observed (17 cm-band detections), for a systematic study of the distributions of redshifts, jet and afterglow properties, galactocentric offsets, and local environments of events with and without detected radio afterglows. Compared to the entire short GRB population, the majority of radio-detected GRBs are at relatively low redshifts ($z < 0.6$) and have high circumburst densities ($> 10^{-2}~{\rm cm}^{-3}$), consistent with their smaller ($< 8~$kpc) projected galactocentric offsets. We additionally find that 70% of short GRBs with opening angle measurements were radio-detected, indicating the importance of radio afterglows in jet measurements, especially in the cases of wide ($> 10^\circ$) jets where observational evidence of collimation may only be detectable at radio wavelengths. Owing to improved observing strategies and the emergence of sensitive radio facilities, the number of radio-detected short GRBs has quadrupled in the past decade., Comment: 30 pages, 11 figures, submitted to ApJ

Published
2024

6. Insights into the Mechanism, Selectivity, and Substituent Effects in the Diels-Alder Reaction of Azatrienes with Electron-rich Dienophiles: An MEDT Study

Author

Rafik, Amine, Salah, Abdeljabbar Jaddi Mohammed, Komiha, Najia, Carvajal, Miguel, and Marakchi, Khadija

Subjects
Physics - Chemical Physics
Abstract

The reactivity and mechanistic intricacies of azatrienes in Diels-Alder reactions have been relatively unexplored despite their intriguing potential applications. In this study, we employ Molecular Electron Density Theory to theoretically investigate the hetero-Diels-Alder reaction involving azatrienes with ethyl vinyl ether and allenyl methyl ether. Analysis of Conceptual Density Functional Theory, energetic profiles, and the topological characteristics is conducted to elucidate the reactions. The revealed mechanism manifests as a polar one-step two-stages process under kinetic control. We establish a clear relationship of between the periselectivity, regioselectivity, and stereoselectivity on one hand and the characteristics of the reactions mechanism on the other hand. The influence of weak interactions on reaction activation barriers and bonding evolution are discussed in detail. We demonstrate that substituents enhancing the reverse electron density flux facilitate the feasibility of the reactions. The results lay ground for a meticulous control of the reaction of azatriene in similar synthetic scenarios., Comment: accepted in Journal of Molecular Graphics and Modelling

Published
2024
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7. VVV catalog of ab-type RR Lyrae in the inner Galactic bulge

Author

Zoccali, M., Quezada, C., Ramos, R. Contreras, Valenti, E., Valenzuela-Navarro, A., Carvajal, J. Olivares, Arriagada, A. Rojas, Minniti, J. H., Gran, F., and De Leo, M.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Context. Observational evidence has accumulated in the past years, showing that the Galactic bulge includes two populations, a metal poor and a metal rich one that, in addition to a different metallicity, show different alpha over iron abundances, spatial distribution, and kinematics. While the metal rich, barred component has been fairly well characterized, the metal poor, spheroidal component has been more elusive and harder to describe. RR Lyrae variables are clean tracers of the old bulge component, and they are, on average, more metal poor than red clump stars. Aims. In the present paper, we provide a new catalog of 16488 ab type RR Lyrae variables in the bulge region within -10

Published
2024
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8. Intergrader Agreement in Grading Optical Coherence Tomography Morphologic Features in Eyes With Intermediate Nonexudative Age-Related Macular Degeneration.

Author

Carvajal, Nicole, Yang, Daphne, Nava, Kiana, Kedia, Anjani, Keenan, Jeremy, Yiu, Glenn, and Stewart, Jay

Subjects
Humans, Tomography, Optical Coherence, Aged, Female, Male, Reproducibility of Results, Macular Degeneration, Observer Variation, Middle Aged, Aged, 80 and over, Retinal Pigment Epithelium, Retinal Drusen, Severity of Illness Index
Abstract

PURPOSE: To determine the reliability of a nine-point summary scale for grading intermediate age-related macular degeneration (AMD) image morphologic features based on the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. METHODS: Two trained graders independently divided spectral domain-optical coherence tomography (SD-OCT) scans into nine subfields and then graded each subfield for the presence of intraretinal hyperreflective foci (HRF), reticular pseudodrusen (RPD), and incomplete or complete retinal pigment epithelium and outer retinal atrophy (iRORA or cRORA). Grading results were assessed by summing the subfield grades into a nine-point summary score and also by using an eye-level binary grade for presence of the finding in any subfield. Gwets first-order agreement coefficient (AC1) was calculated to assess intergrader agreement. RESULTS: Images of 79 eyes from 52 patients were evaluated. Intergrader agreement was higher when the OCT grades were summarized with a nine-point summary score (Gwets AC1 0.92, 0.89, 0.99, and 0.99 for HRF, RPD, iRORA, and cRORA, respectively) compared with the eye-level binary grade (Gwets AC1 0.75, 0.76, 0.97, and 0.96 for HRF, RPD, iRORA, and cRORA, respectively), with significant differences detected for HRF and RPD. CONCLUSIONS: The use of a nine-point summary score showed higher reliability in grading when compared to the binary subfield- and eye-level data, and thus may offer more precise estimation of AMD disease staging. TRANSLATIONAL RELEVANCE: These findings suggest that a nine-point summary score could be a useful means of disease staging by using findings on OCT in clinical studies of AMD.

Published
2024

9. Development and application of genetic ancestry reconstruction methods to study diversity of patient-derived models in the NCI PDXNet Consortium

Author

Lott, Paul C, Chiu, Katherine, Quino, Juanita Elizabeth, Vang, April Pangia, Lloyd, Michael W, Srivastava, Anuj, Chuang, Jeffrey H, Consortium, for the PDXNet, and Carvajal-Carmona, Luis G

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Health Services and Systems, Health Sciences, Human Genome, Social Determinants of Health, Health Disparities, Precision Medicine, Cancer Genomics, Cancer, Minority Health, Good Health and Well Being, Humans, United States, Neoplasms, Animals, National Cancer Institute (U.S.), Genomics, Mice, Xenograft Model Antitumor Assays, PDXNet Consortium
Abstract

Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), are developed and for whom treatments are optimized. In this study, we developed a genetic ancestry pipeline for the Cancer Genomics Cloud, which we used to assess the diversity of models currently available in the National Cancer Institute-supported PDX Development and Trial Centers Research Network (PDXNet). We showed that there is an under-representation of models derived from patients of non-European ancestry, consistent with other cancer model resources. We discussed these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US, as well as power analyses for biomarker discovery, to highlight the immediate need for developing models from minority populations to address cancer health equity in precision medicine. Our analyses identified key priority disparity-associated cancer types for which new models should be developed.SignificanceUnderstanding whether and how tumor genetic factors drive differences in outcomes among U.S. minority groups is critical to addressing cancer health disparities. Our findings suggest that many additional models will be necessary to understand the genome-driven sources of these disparities.

Published
2024

10. Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations.

Author

Meric-Bernstam, Funda, Lloyd, Michael, Koc, Soner, Evrard, Yvonne, McShane, Lisa, Lewis, Michael, Evans, Kurt, Li, Dali, Rubinstein, Lawrence, Welm, Alana, Dean, Dennis, Srivastava, Anuj, Grover, Jeffrey, Ha, Min, Chen, Huiqin, Huang, Xuelin, Varadarajan, Kaushik, Wang, Jing, Roth, Jack, Welm, Bryan, Govinden, Ramaswamy, Ding, Li, Kaochar, Salma, Mitsiades, Nicholas, Carvajal-Carmona, Luis, Herylyn, Meenhard, Davies, Michael, Shapiro, Geoffrey, Fields, Ryan, Trevino, Jose, Harrell, Joshua, Doroshow, James, Chuang, Jeffrey, and Moscow, Jeffrey

Subjects
Humans, Animals, Xenograft Model Antitumor Assays, Neoplasms, National Cancer Institute (U.S.), United States, Mice, Antineoplastic Agents, Consensus
Abstract

Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Published
2024

11. Identifying type II quasars at intermediate redshift with few-shot learning photometric classification

Author

Cunha, P. A. C., Humphrey, A., Brinchmann, J., Morais, S. G., Carvajal, R., Gomes, J. M., Matute, I., and Paulino-Afonso, A.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We aim to identify QSO2 candidates in the redshift desert using optical and infrared photometry. At this intermediate redshift range, most of the prominent optical emission lines in QSO2 sources (e.g. CIV1549; [OIII]4959,5008) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a few-shot learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set. We validated the performance of AMELIA by addressing the problem of identifying QSO2s at 1 $\leq$ z $\leq$ 2 using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then used AMELIA to select new QSO2 candidates in the redshift desert and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of [NeV]3426 emitters at z $\sim$ 1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio cross-matching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting., Comment: 20 pages, 9 figures, Accepted for publication in A&A

Published
2024
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12. Comparing bulge RR Lyrae stars with bulge giants -- Insight from 3D kinematics

Author

Carvajal, J. Olivares, Zoccali, M., De Leo, M., Ramos, R. Contreras, Quezada, C., Rojas-Arriagada, Á., Valenti, E., Albarracín, R., and Navarro, Á. Valenzuela

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The structure and kinematics of the old component of the Galactic bulge are still a matter of debate. The bulk of the bulge as traced by red clump stars includes two main components, which are usually identified as the metal-rich and metal-poor components. They have different shapes, kinematics, mean metallicities, and alpha-element abundances. It is our current understanding that they are associated with a bar and a spheroid, respectively. On the other hand, RR Lyrae variables trace the oldest population of the bulge. While it would be natural to think that they follow the structure and kinematics of the metal-poor component, the data analysed in the literature show conflicting results. We aim to derive a rotation curve for bulge RR Lyrae stars in order to determine that the old component traced by these stars is distinct from the two main components observed in the Galactic bulge. This paper combines APOGEE-2S spectra with OGLE-IV light curves, near-IR photometry, and proper motions from the VISTA Variables in the V\'ia L\'actea survey for 4197 RR Lyrae stars. Six-dimensional phase-space coordinates were used to calculate orbits within an updated Galactic potential and to isolate the stars. The stars that stay confined within the bulge represent 57% of our sample. Our results show that bulge RR Lyrae variables rotate more slowly than metal-rich red clump stars and have a lower velocity dispersion. Their kinematics is compatible with them being the low-metallicity tail of the metal-poor component. We confirm that a rather large fraction of halo RR Lyrae stars pass by the bulge within their orbits, increasing the velocity dispersion. A proper orbital analysis is therefore critical to isolate bona fide bulge variables. Finally, bulge RR Lyrae seem to trace a spheroidal component, although the current data do now allow us to reach a firm conclusion about the spatial distribution., Comment: 14 pages, 13 figures, accepted by A&A

Published
2024
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13. New SiS destruction and formation routes via neutral-neutral reactions and their fundamental role in interstellar clouds at low and high metallicity values

Author

Mendoza, Edgar, Costa, Samuel F. M., Carvajal, Miguel, Pilling, Sérgio, Alves, Márcio O., and Galvão, Breno R. L.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Among the silicon bearing species discovered in the interstellar medium, SiS and SiO stand out as key tracers due to their distinct chemistry and abundances in interstellar and circumstellar environments. Our objective is to enhance the network of Si- and S-bearing chemical reactions for a gas-grain model in molecular clouds, encompassing both low and high metallicities. We have calculated the energies and rate coefficients for 6 neutral atom-diatom reactions involved in the SiCS triatomic system, with a special focus on the C+SiS and S+SiC collisions. We employ the coupled cluster method with single and double substitutions and a perturbative treatment of triple substitutions (CCSD(T)) refined at the explicitly correlated CCSD(T)-F12 level. With these computational results in conjunction with data from the literature, we construct an extended network of neutral-neutral chemical reactions. We performed time-dependent models employing the Nautilus gas-grain code, setting the gas temperature to 10 K and the density to 2x10$^4$ cm$^{-3}$. The temperature dependence for the reactions involving SiS were modelled using $k(T)=\alpha \left( T/300 \right)^{\beta} \exp{(-\gamma/T)}$. The high-metallicity models significantly boost the SiS production. Higher initial abundances of C, S, and Si, roughly $\sim$2, 190, and 210 times higher, respectively, contribute to this. Around 10$^3$ yr, destruction mechanisms become relevant. The proposed production reaction S + SiC $\rightarrow$ C + SiS, mitigates these effects. By expanding the gas reaction network using a high metallicity model, we derived estimates for the abundances of interstellar molecules. The inclusion of neutral-neutral mechanisms, particularly via Si+HS and S+SiC channels, played a pivotal role in determining SiS abundance. These mechanisms carry a significance on a par with the well-known and fast ion-neutral reactions., Comment: 11 pages, 5 figures, accepted for publication in Astronomy and Astrophysics

Published
2024
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14. 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

15. RR Lyrae Stars Belonging to the Candidate Globular Cluster Patchick 99

Author

Butler, Evan, Kunder, Andrea, Prudil, Zdenek, Covey, Kevin R., Ball, Macy, Campos, Carlos, Gollnick, Kaylen, Carvajal, Julio Olivares, Hughes, Joanne, Devine, Kathryn, Johnson, Christian I., Vivas, A. Katherina, Rich, Michael R., Joyce, Meridith, Simon, Iulia T., Marchetti, Tommaso, Koch-Hansen, Andreas J., Clarkson, William I., and Kuss, Rebekah

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

Patchick 99 is a candidate globular cluster located in the direction of the Galactic bulge, with a proper motion almost identical to the field and extreme field star contamination. A recent analysis suggests it is a low-luminosity globular cluster with a population of RR Lyrae stars. We present new spectra of stars in and around Patchick 99, targeting specifically the 3 RR Lyrae stars associated with the cluster as well as the other RR Lyrae stars in the field. A sample of 53 giant stars selected from proper motions and a position on CMD are also observed. The three RR Lyrae stars associated with the cluster have similar radial velocities and distances, and two of the targeted giants also have radial velocities in this velocity regime and [Fe/H] metallicities that are slightly more metal-poor than the field. Therefore, if Patchick 99 is a bonafide globular cluster, it would have a radial velocity of -92+/-10 km s-1, a distance of 6.7+/-0.4 kpc (as determined from the RR Lyrae stars), and an orbit that confines it to the inner bulge., Comment: Accepted to The Astrophysical Journal Letters. Replaced due to a typo in the title

Published
2024

30. Status of truffle science and cultivation in North America

Author

Coleman, Mark D., Berch, Shannon, Bonito, Gregory, Allen, Brian, Andrews, Ellie, Arechiga Carvajal, Elva T., Cook, Stephen P., D’Amours, Cameron, Garibay-Orijel, Roberto, Guevara, Gonzalo, Hatzenbuehler, Patrick, Hawkins, Barbara, Heinse, Robert, Kernaghan, Gavin, Lefevre, Charles, Lemmond, Benjamin, Meadows, Inga M., Oneto, Scott, Sharma, Jyotsna, Strawn, Daniel G., Tanney, Joey B., Türkoğlu, Aziz, Uehling, Jessie, Winfree, Jason, and Smith, Matthew E.

Published
2024
Full Text
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32. Repository for Academic Documents, Algorithms and Data: A Utility in an Educational Context

Author

Ivan Jaramillo, Geovanny Brito, Anthony Pachay, and Duval Carvajal

Abstract

Data repositories currently constitute essential programs within institutions. In fact, universities are the primary institutions that promote the creation, management and storage for the safekeeping of a variety of documents, data and/or projects. This work is carried out within the framework of institutional need and the application of knowledge by the students and professors in the software degree program at a university in Ecuador. This project centers on a web application that makes it possible to manage, upload and download resources. This initial version focuses on the resources related to academic projects, data sets and algorithms. The scalability characteristics have been carefully introduced to ensure a progressive evolution of other types of resources and functionalities, in accordance with educational needs. Several aspects of the web interfaces are based on the characteristics of other existing repositories, and a series of tests related to functionality have been conducted in order to move beyond the introductory stage of use, to begin with, by the university community. This project is carried out according to a conception based on contributions, in which the agents, in principle, are members of the institution. The intensive and collaborative use of this utility will contribute to the growth of a data culture within the institution. Furthermore, the collection and organization of data for the purposes of study and research by the different university degree programs makes it possible to analyze and contrast them from different perspectives.

Published
2023

33. EFL Teacher Professional Development Needs: Voices from the Periphery

Author

Barrientos-Moncada, Marta Isabel, Carvajal-Castaño, Natalia Andrea, and Aristizabal-Cardona, Hernán Santiago

Abstract

EFL teachers' professional development in Colombia has been characterized by top-down policies that neglect the regions in terms of contextualized professional development programs and resources. This article is an effort to make EFL teachers' needs in the Eastern region of Antioquia, Colombia, visible for academic institutions and local authorities to propose coherent professional development programs. This report is based on a section of the findings of a larger study called "Diagnosis of the needs about plurilingualism in the regions of Antioquia". Findings show that EFL teachers in the Eastern region claim for ongoing and contextualized professional development programs, differentiated for primary and secondary-school teachers, and focused on instructional material design and adaptation according to the resources available in the local communities. Conclusions from this study outline implications for educational institutions to support teachers in pursuing their professional development within their contexts, methodological limitations for researchers, and possibilities for further research for stakeholders in the territory.

Published
2023

34. Correlation between microstructural deformation mechanisms and acoustic parameters on a cold-rolled Cu30Zn brass

Author

Sosa, Maria, Carvajal, Linton, Salinas, Vicente, Lund, Fernando, Aguilar, Claudio, and Castro, Felipe

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

The relationship between acoustic parameters and the microstructure of a Cu30Zn brass plate subjected to plastic deformation was evaluated. The plate, previously annealed at 550 {\deg}C for 30 minutes, was cold rolled to reductions in the 10-70\% range. Using the pulse-echo method, linear ultrasonic measurements were performed on each of the nine specimens, corresponding to the nine different reductions, recording the wave times of flight of longitudinal wave along the thickness axis. Subsequently, acoustic measurements were performed to determine the nonlinear parameter ($\beta$) through the second harmonic generation. X-ray diffraction analysis revealed a steady increase and subsequent saturation of deformation twins at 40\% thickness reduction. At higher deformations, the microstructure revealed the generation and proliferation of shear bands, which coincided with a decrease in the twinning structure and an increase in dislocation density rate. Longitudinal wave velocity exhibited a 0.9\% decrease at 20\% deformation, followed by a continuous increase of 2\% beyond this point. These results can be rationalized as a competition between a proliferation of dislocations, which tends to decrease the linear sound velocity, and a decrease in average grain size, which tends to increase it. These variations are in agreement with the values obtained with XRD, Vickers hardness and metallography measurements. The nonlinear parameter $\beta$ shows a significant maximum, at the factor of 8 level, at 40\% deformation. This maximum correlates well with a similar maximum, at a factor of ten level and also at 40\% deformation, of the twinning fault probability., Comment: 15 pages, 8 figures

Published
2023

35. Anthropomorphic Grasping with Neural Object Shape Completion

Author

Hidalgo-Carvajal, Diego, Chen, Hanzhi, Bettelani, Gemma C., Jung, Jaesug, Zavaglia, Melissa, Busse, Laura, Naceri, Abdeldjallil, Leutenegger, Stefan, and Haddadin, Sami

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handling objects. Such dexterity seems to derive from a robust understanding of object properties (such as weight, size, and shape), as well as a remarkable capacity to interact with them. Hand postures commonly demonstrate the influence of specific regions on objects that need to be grasped, especially when objects are partially visible. In this work, we leverage human-like object understanding by reconstructing and completing their full geometry from partial observations, and manipulating them using a 7-DoF anthropomorphic robot hand. Our approach has significantly improved the grasping success rates of baselines with only partial reconstruction by nearly 30% and achieved over 150 successful grasps with three different object categories. This demonstrates our approach's consistent ability to predict and execute grasping postures based on the completed object shapes from various directions and positions in real-world scenarios. Our work opens up new possibilities for enhancing robotic applications that require precise grasping and manipulation skills of real-world reconstructed objects., Comment: Accepted to RA-L 2023

Published
2023

36. Sharp global well-posedness for the higher order non-linear Schr\'odinger equation on modulations spaces

Author

Carvajal, X., Gamboa, P., and Santos, R.

Subjects
Mathematics - Analysis of PDEs
Abstract

We consider the initial value problem (IVP) associated to a higher order nonlinear Schr\"odinger (h-NLS) equation $ \partial_{t}u+ia \partial^{2}_{x}u+ b\partial^{3}_{x}u+ic_1|u|^{2}u+c_2 |u|^{2}\partial_{x}u=0, \quad x,t \in \mathbb{R}, $ for given data in the modulation space $M_s^{2,p}(\mathbb{R})$. Using ideias of Killip, Visan, Zhang, Oh, Wang, we prove that the IVP associated to the h-NLS equation is globally well-posed in the modulation spaces $M^{s,p}$ for $s\geq\frac14$ and $p\geq2$.

Published
2023

43. We Are Not All the Same: Implications of Heterogeneity Among Latine/e/x/o/a, Hispanic, and Spanish Origin People

Author

Carvajal, Diana N., Anaya, Yohualli B., McLean, Ivonne, Aragon, Miranda, Figueroa, Edgar, Plasencia, Gabriela, Martinez-Bianchi, Viviana, and Rodriguez, Jose E.

Subjects
Health care disparities -- Analysis, Hispanic Americans -- Health aspects -- Social aspects, Health, Science and technology
Abstract

There is great variation in the experiences of Latine/e/x/o/a, Hispanic, and/or Spanish origin (LHS) individuals in the United States, including differences in race, ancestry, colonization histories, and immigration experiences. This essay calls readers to consider the implications of the heterogeneity of lived experiences among LHS populations, including variations in country of origin, immigration histories, time in the United States, languages spoken, and colonization histories on patient care and academia. There is power in unity when advocating for community, social, and political change, especially as it pertains to equity, diversity, and inclusion (EDI; sometimes referred to as DEI) efforts in academic institutions. Yet, there is also a critical need to disaggregate the LHS diaspora and its conceptualization based on differing experiences so that we may improve our understanding of the sociopolitical attributes that impact health. We propose strategies to improve recognition of these differences and their potential health outcomes toward a goal of health equity. https://doi.org/10.1370/afm.3103, INTRODUCTION In this paper, Latine/e/x/o/a, Hispanic, and/or Spanish origin (LHS) family medicine faculty experts in equity, diversity, and inclusion (EDI; sometimes referred to as DEI) come together to unmask the [...]

Published
2024
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