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1. Novel Application of Neutrinos to Evaluate U.S. Nuclear Weapons Performance

Author

Distel, J. R., Dunton, E. C., Durham, J. M., Hayes, A. C., Louis, W. C., Martin, J. D., Misch, G. W., Mumpower, M. R., Tang, Z., Thornton, R. T., Turner, B. T., Van De Water, R. G., and Wilburn, W. S.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract

There is a growing realization that neutrinos can be used as a diagnostic tool to better understand the inner workings of a nuclear weapon. Robust estimates demonstrate that an Inverse Beta Decay (IBD) neutrino scintillation detector built at the Nevada Test Site of 1000-ton active target mass at a standoff distance of 500 m would detect thousands of neutrino events per kTe of nuclear yield. This would provide less than 4% statistical error on measured neutrino rate and 5% error on neutrino energy. Extrapolating this to an error on the test device explosive yield requires knowledge from evaluated nuclear databases, non-equilibrium fission rates, and assumptions on internal neutron fluxes. Initial calculations demonstrate that prompt neutrino rates from a short pulse of Pu-239 fission is about a factor of two less than that from a steady state assumption. As well, there are significant energy spectral differences as a function of time after the pulse that needs to be considered. In the absence of nuclear weapons testing, many of the technical and theoretical challenges of a full nuclear test could be mitigated with a low cost smaller scale 20 ton fiducial mass IBD demonstration detector placed near a TRIGA pulsed reactor. The short duty cycle and repeatability of pulses would provide critical real environment testing and the measured neutrino rate as a function of time data would provide unique constraints on fission databases and equilibrium assumptions.

Published
2024

2. More variable circadian rhythms in epilepsy: a retrospective cross-sectional study using long-term heart rate recordings from wearable sensors

Author

Smith, Billy C., Thornton, Christopher, Stirling, Rachel E., Besne, Guillermo M., Evans, Nathan, Taylor, Peter N., Karoly, Philippa J., and Wang, Yujiang

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Background: The circadian rhythm aligns physiology and behaviour with the 24-hour light-dark cycle, and its disruption is linked to neurological disorders such as epilepsy. However, how to best quantify circadian disruption remains unclear, as it can manifest across various properties and timescales. A promising but under-explored approach is to assess the intra-individual variability in circadian rhythms over timescales of weeks to years. This is yet to be studied in epilepsy. Methods: We retrospectively used wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 controls. For each participant, we extracted the circadian oscillation underlying their heart rate time series and analysed the intra-individual variability of three circadian properties: period, acrophase, and amplitude. Findings: We found increased intra-individual variability in period (77 min vs. 62 min, z=3.32, p<0.001) and acrophase (68 min vs. 54 min, z=2.97, p=0.003) for PWE compared to controls, but not in amplitude (1.98 bpm vs. 2.05 bpm, z=-0.66, p=0.51). For PWE, we did not find any correlations between seizure frequency and intra-individual variability in circadian properties, or any difference between weeks with and without seizures. Interpretation: This finding indicates that the circadian rhythm of heart rate is more variable for people with epilepsy and that this can be detected using a wearable device. However, we were unable to find any associations with seizure frequency or occurrence, suggesting intra-individual variability could be another manifestation of epilepsy aetiology. Future work should investigate the combined role of anti-seizure medications, demographics, co-morbidities, and health behaviours in driving the increased intra-individual variability of circadian properties in epilepsy.

Published
2024

3. Limits of sparse hypergraphs

Author

Thornton, Riley

Subjects
Mathematics - Combinatorics, Mathematics - Logic, 05C65 (primary), 37A15, 03C20 (secondary)
Abstract

We generalize ultraproducts and local-global limits of graphs to hypergraphs and other structures. We show that the local statistics of an ultraproduct of a sequence of hypergraphs are the ultralimits of the local statistics of the hypergraphs. Using some standard results from model theory, we conclude that the space of (equivalence classes of) pmp hypergraphs with the topology of local-global convergence is compact, and that any countable set of local statistics for a pmp hypergraph can be realized as the statistics of a set of labellings (rather than just approximated) in a local-global equivalent hypergraph. We give two applications. First, we characterize those structures where any solution to the corresponding CSP can be turned into a measurable solution. These turn out to be the width-1 structures. We can also use the limit machinery to extract from this theorem a purely finitary characterizations of width-1 structures involving asymptotic solutions. Second, we prove two measurable versions of the Frankl--R\"odl matching theorem using measurable nibble and differential equation arguments. The measurable proofs are much softer than the purely finitary results. And, we can recover the finitary theorems using the limit machinery.

Published
2024

4. Seizure Onset Zone Localisation Algorithms with Intracranial EEG: Evaluating Methodological Variations and Targeted Features

Author

Gascoigne, Sarah J., Vila-Vidal, Manel, Evans, Nathan, Da Silva, Anderson Brito, Thomas, Rhys H., Thornton, Christopher, Wilson, Kevin, Taylor, Peter N., Tauste, Adrià, and Wang, Yujiang

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity - known as the Seizure Onset Zone (SOZ) - is identified. This is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated SOZ localisation algorithms provide objective identification of the SOZ by highlighting changes in signal features associated with seizure onset. In this work we investigate how methodological differences in such algorithms can result in different SOZ localisations. We analysed ictal intracranial EEG (icEEG) recordings in 16 subjects (100 seizures) with drug-resistant epilepsy from the SWEZ-ETHZ public database. Through our analysis, we identified a series of key methodological differences that must be considered when designing or selecting a SOZ localisation algorithm. These differences were demonstrated using three distinct algorithms that capture different, but complementary, seizure onset features: Imprint, Epileptogenicity Index (EI), and Low Entropy Map (LEM). We assessed methodological differences at each Decision Point, comparing the resultant SOZ localisations. Our independent application of all three algorithms to the same ictal icEEG dataset revealed low agreement between algorithms: 27-60% of seizure onsets were as minimally or non-overlapping (<25% overlap). Therefore, we investigated the effect of three key methodological differences (or Decision Points). Changes at each Decision Point were found to result in significantly different SOZ localisations (p<0.05). Our results demonstrate how seemingly small methodological changes can result in large differences in SOZ localisations. We propose that key Decision Points must be considered when using or designing a SOZ localisation algorithm.

Published
2024

5. Simple ReFlow: Improved Techniques for Fast Flow Models

Author

Kim, Beomsu, Hsieh, Yu-Guan, Klein, Michal, Cuturi, Marco, Ye, Jong Chul, Kawar, Bahjat, and Thornton, James

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

Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical tasks. The ReFlow procedure can accelerate sampling by straightening generation trajectories. However, ReFlow is an iterative procedure, typically requiring training on simulated data, and results in reduced sample quality. To mitigate sample deterioration, we examine the design space of ReFlow and highlight potential pitfalls in prior heuristic practices. We then propose seven improvements for training dynamics, learning and inference, which are verified with thorough ablation studies on CIFAR10 $32 \times 32$, AFHQv2 $64 \times 64$, and FFHQ $64 \times 64$. Combining all our techniques, we achieve state-of-the-art FID scores (without / with guidance, resp.) for fast generation via neural ODEs: $2.23$ / $1.98$ on CIFAR10, $2.30$ / $1.91$ on AFHQv2, $2.84$ / $2.67$ on FFHQ, and $3.49$ / $1.74$ on ImageNet-64, all with merely $9$ neural function evaluations.

Published
2024

6. Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models

Author

Kirchhof, Michael, Thornton, James, Ablin, Pierre, Béthune, Louis, Ndiaye, Eugene, and Cuturi, Marco

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

The increased adoption of diffusion models in text-to-image generation has triggered concerns on their reliability. Such models are now closely scrutinized under the lens of various metrics, notably calibration, fairness, or compute efficiency. We focus in this work on two issues that arise when deploying these models: a lack of diversity when prompting images, and a tendency to recreate images from the training set. To solve both problems, we propose a method that coaxes the sampled trajectories of pretrained diffusion models to land on images that fall outside of a reference set. We achieve this by adding repellency terms to the diffusion SDE throughout the generation trajectory, which are triggered whenever the path is expected to land too closely to an image in the shielded reference set. Our method is sparse in the sense that these repellency terms are zero and inactive most of the time, and even more so towards the end of the generation trajectory. Our method, named SPELL for sparse repellency, can be used either with a static reference set that contains protected images, or dynamically, by updating the set at each timestep with the expected images concurrently generated within a batch. We show that adding SPELL to popular diffusion models improves their diversity while impacting their FID only marginally, and performs comparatively better than other recent training-free diversity methods. We also demonstrate how SPELL can ensure a shielded generation away from a very large set of protected images by considering all 1.2M images from ImageNet as the protected set.

Published
2024

7. Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture

Author

Brown, Samuel B., Young, Stephen, Wagenknecht, Adam, Jakubisin, Daniel, Thornton, Charles E., Orndorff, Aaron, and Headley, William C.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this challenge is primarily due to the need to reconstruct the modulated data stream which is generally highly stochastic in nature. In this work, we take advantage of this limitation by using the denoising autoencoder to instead remove interfering radio frequency communication signals while reconstructing highly structured FMCW radar signals. More specifically, in this work we show that a CNN-layer only autoencoder architecture can be utilized to improve the accuracy of a radar altimeter's ranging estimate even in severe interference environments consisting of a multitude of interference signals. This is demonstrated through comprehensive performance analysis of an end-to-end FMCW radar altimeter simulation with and without the convolutional layer-only autoencoder. The proposed approach significantly improves interference mitigation in the presence of both narrow-band tone interference as well as wideband QPSK interference in terms of range RMS error, number of false altitude reports, and the peak-to-sidelobe ratio of the resulting range profile. FMCW radar signals of up to 40,000 IQ samples can be reliably reconstructed., Comment: To be presented at MILCOM 2024, Washington DC

Published
2024

8. Mechanical Model for a Full Fusion Tokamak Enabled by Supercomputing

Author

Ellis, W. M. E., Reali, L., Davis, A., Brooks, H. M., Katramados, I., Thornton, A. J., Akers, R. J., and Dudarev, S. L.

Subjects
Physics - Applied Physics
Abstract

Determining stress and strain in a component of a fusion power plant involves defining boundary conditions for the mechanical equilibrium equations, implying the availability of a full reactor model for defining those conditions. To address this fundamental challenge of reactor design, a finite element method (FEM) model for the Mega-Ampere Spherical Tokamak Upgrade (MAST-U) fusion tokamak, operating at the Culham Campus of UKAEA, has been developed and applied to assess mechanical deformations, strain, and stress in the full tokamak structure, taken as a proxy for a fusion power plant. The model, handling 127 million finite elements using about 800 processors in parallel, illustrates the level of fidelity of structural simulations of a complex nuclear device made possible by the modern supercomputing systems. The model predicts gravitational and atmospheric pressure-induced deformations in broad agreement with observations, and enables computing the spectrum of acoustic vibrations of a tokamak, arising from mechanical disturbances like an earthquake or a plasma disruption. We introduce the notion of the density of stress to characterise the distribution of stress in the entire solid body of the tokamak, and to predict the magnitude and locations of stress concentrations. The model enables defining computational requirements for simulating a whole operating fusion power plant, and provides a digital foundation for the assessment of reactor performance as well as for specifying the relevant materials testing programme.

Published
2024

9. Linear Jamming Bandits: Learning to Jam 5G-based Coded Communications Systems

Author

Schutz, Zachary, Jakubisin, Daniel J., Thornton, Charles E., and Buehrer, R. Michael

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

We study jamming of an OFDM-modulated signal which employs forward error correction coding. We extend this to leverage reinforcement learning with a contextual bandit to jam a 5G-based system implementing some aspects of the 5G protocol. This model introduces unreliable reward feedback in the form of ACK/NACK observations to the jammer to understand the effect of how imperfect observations of errors can affect the jammer's ability to learn. We gain insights into the convergence time of the jammer and its ability to jam a victim 5G waveform, as well as insights into the vulnerabilities of wireless communications for reinforcement learning-based jamming., Comment: Accepted to IEEE MILCOM, Washington DC, 2024

Published
2024

10. Rigidity transitions in anisotropic networks happen in multiple steps

Author

Wang, William Y., Thornton, Stephen J., Chakraborty, Bulbul, Barth, Anna, Singh, Navneet, Omonira, Japheth, Michel, Jonathan A., Das, Moumita, Sethna, James P., and Cohen, Itai

Subjects
Condensed Matter - Soft Condensed Matter, Condensed Matter - Statistical Mechanics
Abstract

We study how the rigidity transition in a triangular lattice changes as a function of anisotropy by preferentially filling bonds on the lattice in one direction. We discover that the onset of rigidity in anisotropic spring networks arises in at least two steps, reminiscent of the two-step melting transition in two dimensional crystals. In particular, our simulations demonstrate that the percolation of stress-supporting bonds happens at different critical volume fractions along different directions. By examining each independent component of the elasticity tensor, we determine universal exponents and develop universal scaling functions to analyze isotropic rigidity percolation as a multicritical point. We expect that these results will be important for elucidating the underlying mechanical phase transitions governing the properties of biological materials ranging from the cytoskeletons of cells to the extracellular networks of tissues such as tendon where the networks are often preferentially aligned., Comment: 13 pages, 17 figures

Published
2024

11. Evolution of radiation profiles in a strongly baffled divertor on MAST Upgrade

Author

Federici, Fabio, Reinke, Matthew L., Lipschultz, Bruce, Lovell, Jack J., Verhaegh, Kevin, Cowley, Cyd, Kryjak, Mike, Ryan, Peter, Thornton, Andrew J., Harrison, James R., Peterson, Byron J., Lomanowski, Bartosz, Lore, Jeremy D., and Damizia, Yacopo

Subjects
Physics - Plasma Physics
Abstract

Plasma detachment involves interactions of the plasma with impurities and neutral particles, leading to significant losses of plasma power, momentum, and particles. Accurate mapping of plasma emissivity in the divertor and X-point region is essential for assessing the relationship between particle flux and radiative detachment. The recently validated InfraRed Video Bolometer (IRVB) diagnostic, in MAST-U enables this mapping with higher spatial resolution than more established methods like resistive bolometers. In previous preliminary work, the evolution of radiative detachment was characterised in L-mode (power entering the scrape-off layer, PSOL ~0.4MW). With a conventional divertor the inner leg consistently detached ahead of the outer leg, and radiative detachment preceded particle flux detachment. This work presents results also from the third MAST-U experimental campaign, fuelled from the low field side instead of the high field side, including Ohmic and beam heated L-mode shots (with a power exiting the core up to PSOL ~1-1.5MW). The radiation peak moves upstream from the target at lower upstream densities than the ion target flux roll-over (typically considered the detachment onset), while the inner leg detaches before the outer one. The movement of the radiation is in partial agreement with the expectations from the DLS model, predicting a sudden shift from the target to the X-point. The energy confinement is found to be related to detachment, but there seems to be some margin between the radiation on the inner leg reaching the X-point and confinement being affected, a beneficial characteristic if it could be extrapolated to future reactors. For increasing PSOL the particle flux roll over is almost unaffected, while radiative detachment occurs at higher density in both legs, but much higher on the outer, suggesting an uneven distribution of the power exiting the core., Comment: Submitted to Nuclear Materials and Energy

Published
2024

12. Anti-seizure medication load is not correlated with early termination of seizure spread

Author

Evans, Nathan, Gascoigne, Sarah J., Besne, Guillermo M., Thornton, Chris, Schroeder, Gabrielle M., Chowdhury, Fahmida A, Diehl, Beate, Duncan, John S, McEvoy, Andrew W, Miserocchi, Anna, de Tisi, Jane, Taylor, Peter N., and Wang, Yujiang

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Anti-seizure medications (ASMs) are the mainstay of treatment for epilepsy, yet their effect on seizure spread is not fully understood. Higher ASM doses have been associated with shorter and less severe seizures. Our objective was to test if this effect was due to limiting seizure spread through early termination of otherwise unchanged seizures. We retrospectively examined intracranial EEG (iEEG) recordings in 15 subjects that underwent ASM tapering during pre-surgical monitoring. We estimated ASM plasma concentrations based on pharmaco-kinetic modelling. In each subject, we identified seizures that followed the same onset and initial spread patterns, but some seizures terminated early (truncated seizures), and other seizures continued to spread (continuing seizures). We compared ASM concentrations at the times of truncated seizures and continuing seizures. We found no substantial difference between ASM concentrations when truncated vs. continuing seizures occurred (Mean difference = 4%, sd = 29%, p=0.6). Our results indicate that ASM did not appear to halt established seizures in this cohort. Further research is needed to understand how ASM may modulate seizure duration and severity.

Published
2024

13. The Enactment of Distributed Leadership in Secondary Schools in Aotearoa New Zealand

Author

Greg Sharland and Kate Thornton

Abstract

School leaders have a significant impact on student outcomes; however, there has been a shift from viewing school leadership as the actions of an individual, to seeing leadership as a collaborative endeavour, including in Aotearoa New Zealand. The traditional heroic model of leadership is making way for a new leadership paradigm which considers leadership as the actions of many rather than the few and promotes the construct of distributed leadership. This research study, using an interpretive qualitative design, focused on the experiences of secondary school principals in their attempts to distribute leadership. Moreover, the study aimed to connect principal's interpretations of distributed leadership with their practice of the construct. The findings highlight the challenges in defining and enacting distributed leadership. Nine recommendations for principals who are considering distributed leadership as a tool for effectively distributing leadership and improving student outcomes are presented. These are designed to provide principals with a starting point when considering distributed leadership and to stimulate further discussion on the topic.

Published
2024

14. A Systematic Review and Meta-Analysis of School-Based Preventive Interventions Targeting E-Cigarette Use among Adolescents

Author

Lauren A. Gardner, Amy-Leigh Rowe, Nicola C. Newton, Lyra Egan, Emily Hunter, Emma K. Devine, Tess Aitken, Louise Thornton, Maree Teesson, Emily Stockings, and Katrina E. Champion

Abstract

This study aimed to examine the efficacy of school-based e-cigarette preventive interventions via a systematic review and meta-analysis. We searched Medline, Embase, PsycINFO, Scopus, CINAHL, Cochrane, and clinical trial registries for studies published between January 2000 and June 2023 using keywords for e-cigarettes, adolescents, and school. Of 1566 double-screened records, 11 met the criteria of targeting adolescents, evaluating an e-cigarette preventive intervention, being conducted in a secondary school, using a randomized controlled trial (RCT), cluster RCT, or quasi-experimental design, and comparing an intervention to a control. Pre-specified data pertaining to the study design, outcomes, and quality were extracted by one reviewer and confirmed by a second, and where necessary, a third reviewer. Meta-analyses found no evidence that school-based interventions prevented e-cigarette use at the longest follow-up, which ranged between 6 and 36 months post-intervention (OR = 0.43, 95% CI = 0.16, 1.12; p = 0.09). However, subgroup analyses identified significant effects at post-test and when studies with < 12-month follow-up were omitted. No effect was found for tobacco use at the longest follow-up (OR = 1.01, 95% CI = 0.65, 1.59, p = 0.95); however, reductions in past 30-day tobacco use (OR = 0.59, 95% CI = 0.39, 0.89, p = 0.01) which encompassed e-cigarettes in some studies were identified. Narrative synthesis supported these mixed results and found some school-based interventions prevented or reduced e-cigarette and/or tobacco use; however, some increased use. School-based interventions were also associated with improved knowledge (SMD = - 0.38, 95% CI = - 0.68, - 0.08, p = 0.01), intentions (SMD = - 0.15, 95% CI = - 0.22, - 0.07, p = 0.0001), and attitudes (SMD = - 0.14, 95% CI = - 0.22, - 0.06; p = 0.0007) in the short term. Overall, the quality of evidence was low-to-moderate. School-based interventions hold the potential for addressing e-cigarette use, however, can have null or iatrogenic effects. More high-quality research is needed to develop efficacious interventions, and schools must be supported to adopt evidence-based programs. This is the first systematic review and meta-analysis to examine the efficacy of school-based preventive interventions for e-cigarette use. It provides crucial new knowledge about the efficacy of such interventions in preventing e-cigarette use and improving other outcomes (e.g., tobacco use, knowledge, intentions, attitudes, and mental health) among adolescents and the key characteristics associated with efficacious interventions. Our findings have important practical implications, highlighting future research directions for the development and evaluation of e-cigarette preventive interventions, along with the need to provide support to schools to help them identify and adopt evidence-based programs.

Published
2024
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15. AP STEM Access Impact Evaluation

Author

Sarah K. Mason, Matt Hancock, and Izzy Thornton

Abstract

Rural Mississippi schools often face challenges in providing equitable access to Advanced Placement (AP) courses, particularly in STEM fields. This lack of access limits opportunities for high-achieving students to pursue rigorous STEM coursework and related careers. The AP STEM program aims to improve access to AP STEM courses for high-achieving students in rural Mississippi. Students in the program experience AP STEM material through a blended learning program wherein on-campus teachers facilitate face-to-face learning, AP-certified teachers and university-based tutors provide online resources (e.g., tutoring, exercises), and students attend intermittent face-to-face meetings, social events, and workshops. STEM-major students from leading universities provide live tutoring and mentorship online and STEM experts participate in face-to-face events as well as online mentorship. In this report, we describe findings from one impact study, a cluster-level QED with schools serving as the unit of assignment and students as the unit of measurement, evaluating the impact of the AP STEM program on AP pass rates at high schools across rural Mississippi. The study sample consisted of 1,532 students in attendance at 25 treatment schools and 44 comparison schools. The final matched sample consists of 728 students: 364 from comparison schools, and 364 from treatment schools. For this study, the comparison condition was no intervention or business-as-usual conditions. The sample was restricted at the school level based on the percentage of minority students, academic performance -- measured as average ACT composite at the school level, the number of students enrolled in AP courses, and the overall enrollment at the school level. Overall, we see that, although the program did increase access to AP courses, it did not result in significantly higher pass rates for both AP Physics and AP Computer Science among students exposed to the treatment condition. This suggests that while the intervention may be a mechanism for increasing access, additional or different interventions may be needed to produce significantly improved academic outcomes in AP STEM courses. The study also found that students who scored higher on the ACT were more likely to pass AP exams, regardless of treatment status. As is consistent with prior research this suggests that prior achievement is a strong predictor of success in AP STEM courses. [This report was published by the Center for Research Evaluation, University of Mississippi.]

Published
2024

16. Phosphorylation-driven epichaperome assembly is a regulator of cellular adaptability and proliferation.

Author

Roychowdhury, Tanaya, McNutt, Seth, Pasala, Chiranjeevi, Nguyen, Hieu, Thornton, Daniel, Sharma, Sahil, Botticelli, Luke, Digwal, Chander, Joshi, Suhasini, Yang, Nan, Panchal, Palak, Chakrabarty, Souparna, Bay, Sadik, Markov, Vladimir, Kwong, Charlene, Lisanti, Jeanine, Chung, Sun, Ginsberg, Stephen, Yan, Pengrong, De Stanchina, Elisa, Corben, Adriana, Modi, Shanu, Alpaugh, Mary, Colombo, Giorgio, Erdjument-Bromage, Hediye, Neubert, Thomas, Chalkley, Robert, Baker, Peter, Burlingame, Alma, Rodina, Anna, Chiosis, Gabriela, and Chu, Feixia

Subjects
Humans, Cell Proliferation, Phosphorylation, HSP90 Heat-Shock Proteins, Molecular Chaperones, Protein Processing, Post-Translational, Animals, Protein Interaction Maps, Mice, Serine, Cell Line, Tumor
Abstract

The intricate network of protein-chaperone interactions is crucial for maintaining cellular function. Recent discoveries have unveiled the existence of specialized chaperone assemblies, known as epichaperomes, which serve as scaffolding platforms that orchestrate the reconfiguration of protein-protein interaction networks, thereby enhancing cellular adaptability and proliferation. This study explores the structural and regulatory aspects of epichaperomes, with a particular focus on the role of post-translational modifications (PTMs) in their formation and function. A key finding is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 within an intrinsically disordered region, as critical determinants of epichaperome assembly. Our data demonstrate that phosphorylation of these serine residues enhances HSP90s interactions with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Moreover, we establish a direct link between epichaperome function and cellular physiology, particularly in contexts where robust proliferation and adaptive behavior are essential, such as in cancer and pluripotent stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone assemblies in diseases characterized by epichaperome dysregulation, thereby bridging the gap between fundamental research and precision medicine.

Published
2024

17. Epidemiology and characteristics of coral snake bites reported to the National Poison Data System (2006‒2022).

Author

Velagapudi, Mary, Navarro, Juliana, Hill, Alexander, Darracq, Michael, and Thornton, Stephen

Subjects
antivenom, coral snake, elapidae
Abstract

OBJECTIVES: North American coral snake envenomations can result in life-threatening neurotoxicity. Their bites are relatively rare, making large studies difficult. Using the National Poison Data System (NPDS), we sought to investigate the epidemiological trends and clinical outcomes associated with North American coral snake bites over a 17-year period. METHODS: NPDS cases involving coral snakes from January 1, 2006, to December 31, 2022, were analyzed. Data collected included patient age, date, geographic location, clinical effects, treatments administered, and medical outcomes including incidence of dry bites (non-envenomation) and death. RESULTS: During the 17-year period, a total of 1374 cases were reported and analyzed. Cases included adults (≥ 20 years), accounting for 80% (n = 1107), and pediatric patients (≤19 years), accounting for 20% (n = 267) of total cases. Out of 50 US states and District of Columbia, 20 states reported cases. Florida and Texas accounted for 90.5% of all bites (n = 1243) with April being the month with the most reported cases (n = 184). The most bites (n = 96) were reported in 2008 and the fewest (n = 69) in 2016. Male patients predominated for both pediatric (75.7%, n = 202) and adult cases (75.3%, n = 834). Moderate to major clinical outcomes were documented in approximately 30% of total cases; with no reported deaths. Moderate effect is defined as the patient exhibited symptoms as a result of the exposure that were more pronounced, more prolonged, or more of a systemic nature than minor symptoms. Major effect was defined as the patient exhibited symptoms as a result of the exposure that were life threatening or resulted in significant residual disability or disfigurement. The three most reported clinical effects were wound/sting, dermal irritation/pain, and edema. Antivenom was administered in 21% (n = 286) of total cases and 37% (n = 511) of patients were admitted to a critical care unit. Dry bites occurred in 7% (n = 100) of total cases. CONCLUSION: Coral snake bites were rare, but consistently reported. While bites were associated with significant morbidity in adult and pediatric patients, there were no deaths reported. Antivenom use declined over the study period but was not associated with an increase in morbidity. An increased incidence of intubations was seen in association with decreased antivenom use.

Published
2024

18. Lightning declines over shipping lanes following regulation of fuel sulfur emissions

Author

Wright, Chris J., Thornton, Joel A., Jaeglé, Lyatt, Cao, Yang, Zhu, Yannian, Liu, Jihu, Jones II, Randall, Holzworth, Robert H, Rosenfeld, Daniel, Wood, Robert, Blossey, Peter, and Kim, Daehyun

Subjects
Physics - Atmospheric and Oceanic Physics, Physics - Geophysics
Abstract

Aerosol interactions with clouds represent a significant uncertainty in our understanding of the Earth system. Deep convective clouds may respond to aerosol perturbations in several ways that have proven difficult to elucidate with observations. Here, we leverage the two busiest maritime shipping lanes in the world, which emit aerosol particles and their precursors into an otherwise relatively clean tropical marine boundary layer, to make headway on the influence of aerosol on deep convective clouds. The recent seven-fold change in allowable fuel sulfur by the International Maritime Organization allows us to test the sensitivity of the lightning to changes in ship plume aerosol size distributions. We find that, across a range of atmospheric thermodynamic conditions, the previously documented enhancement of lightning over the shipping lanes has fallen by over 40%. The enhancement is therefore at least partially aerosol-mediated, a conclusion that is supported by observations of droplet number at cloud base, which show a similar decline over the shipping lane. These results have fundamental implications for our understanding of aerosol-cloud interactions, suggesting that deep convective clouds are impacted by the aerosol number distribution in the remote marine environment.

Published
2024

19. 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

20. Closed-loop underwater soft robotic foil shape control using flexible e-skin

Author

Micklem, Leo, Dong, Huazhi, Giorgio-Serchi, Francesco, Yang, Yunjie, Weymouth, Gabriel D., and Thornton, Blair

Subjects
Computer Science - Robotics
Abstract

The use of soft robotics for real-world underwater applications is limited, even more than in terrestrial applications, by the ability to accurately measure and control the deformation of the soft materials in real time without the need for feedback from an external sensor. Real-time underwater shape estimation would allow for accurate closed-loop control of soft propulsors, enabling high-performance swimming and manoeuvring. We propose and demonstrate a method for closed-loop underwater soft robotic foil control based on a flexible capacitive e-skin and machine learning which does not necessitate feedback from an external sensor. The underwater e-skin is applied to a highly flexible foil undergoing deformations from 2% to 9% of its camber by means of soft hydraulic actuators. Accurate set point regulation of the camber is successfully tracked during sinusoidal and triangle actuation routines with an amplitude of 5% peak-to-peak and 10-second period with a normalised RMS error of 0.11, and 2% peak-to-peak amplitude with a period of 5 seconds with a normalised RMS error of 0.03. The tail tip deflection can be measured across a 30 mm (0.15 chords) range. These results pave the way for using e-skin technology for underwater soft robotic closed-loop control applications., Comment: 6 pages, 10 figures

Published
2024

21. Lattice distortion leads to glassy thermal transport in crystalline Cs$_3$Bi$_2$I$_6$Cl$_3$

Author

Zeng, Zezhu, Fan, Zheyong, Chen, Chen, Liang, Ting, Chen, Yue, Thornton, Geoff, and Cheng, Bingqing

Subjects
Condensed Matter - Materials Science
Abstract

The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs$_3$Bi$_2$I$_6$Cl$_3$ is perplexing and lacking theoretical explanations. Here, we first experimentally measure such its thermal transport behavior from 20~K to 300~K, after synthesizing Cs$_3$Bi$_2$I$_6$Cl$_3$ single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs$_3$Bi$_2$I$_6$Cl$_3$ has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the regularized Wigner thermal transport theory, we reproduce the experimental thermal conductivities based on lattice-distorted structures. This study thus provides a framework for predicting and understanding glassy thermal transport in materials with strong lattice disorder.

Published
2024

22. Universal scaling solution for a rigidity transition: renormalization group flows near the upper critical dimension

Author

Thornton, Stephen J., Liarte, Danilo B., Cohen, Itai, and Sethna, James P.

Subjects
Condensed Matter - Soft Condensed Matter, Condensed Matter - Statistical Mechanics
Abstract

Rigidity transitions induced by the formation of system-spanning disordered rigid clusters, like the jamming transition, can be well-described in most physically relevant dimensions by mean-field theories. A dynamical mean-field theory commonly used to study these transitions, the coherent potential approximation (CPA), shows logarithmic corrections in $2$ dimensions. By solving the theory in arbitrary dimensions and extracting the universal scaling predictions, we show that these logarithmic corrections are a symptom of an upper critical dimension $d_{u}=2$, below which the critical exponents are modified. We recapitulate Ken Wilson's phenomenology of the $(4-\epsilon)$-dimensional Ising model, but with the upper critical dimension reduced to $2$. We interpret this using normal form theory as a transcritical bifurcation in the RG flows and extract the universal nonlinear coefficients to make explicit predictions for the behavior near $2$ dimensions. This bifurcation is driven by a variable that is dangerously irrelevant in all dimensions $d>2$ which incorporates the physics of long-wavelength phonons and low-frequency elastic dissipation. We derive universal scaling functions from the CPA sufficient to predict all linear response in randomly diluted isotropic elastic systems in all dimensions., Comment: 22 pages, 6 figures. Revised abstract, added references

Published
2024

23. The Simons Observatory: Dark Characterization of the Large Aperture Telescope

Author

Haridas, Saianeesh K., Ahmed, Zeeshan, Bhandarkar, Tanay, Devlin, Mark, Dicker, Simon, Duff, Shannon M., Dutcher, Daniel, Harrington, Kathleen, Henderson, Shawn W., Hubmayr, Johannes, Johnson, Bradley R., Kofman, Anna, Manduca, Alex, Niemack, Michael D., Randall, Michael J., Satterthwaite, Thomas P., Orlowski-Scherer, John, Schmitt, Benjamin L., Sierra, Carlos, Silva-Feaver, Max, Thornton, Robert J., Wang, Yuhan, and Zheng, Kaiwen

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Simons Observatory (SO) is a cosmic microwave background experiment composed of three 0.42 m Small Aperture Telescopes (SATs) and one 6 m Large Aperture Telescope (LAT) in the Atacama Desert of Chile. The Large Aperture Telescope Receiver (LATR) was integrated into the LAT in August 2023; however, because mirrors were not yet installed, the LATR optical chain was capped at the 4K stage. In this dark configuration we are able to characterize many elements of the instrument without contributions from atmospheric noise. Here we show this noise is below the required upper limit and its features are well described with a simple noise model. Maps produced using this noise model have properties that are in good agreement with the white noise levels of our dark data. Additionally, we show that our nominal scan strategy has a minimal effect on the noise when compared to the noise when the telescope is stationary

Published
2024
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24. Differentiable Cost-Parameterized Monge Map Estimators

Author

Howard, Samuel, Deligiannidis, George, Rebeschini, Patrick, and Thornton, James

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

Within the field of optimal transport (OT), the choice of ground cost is crucial to ensuring that the optimality of a transport map corresponds to usefulness in real-world applications. It is therefore desirable to use known information to tailor cost functions and hence learn OT maps which are adapted to the problem at hand. By considering a class of neural ground costs whose Monge maps have a known form, we construct a differentiable Monge map estimator which can be optimized to be consistent with known information about an OT map. In doing so, we simultaneously learn both an OT map estimator and a corresponding adapted cost function. Through suitable choices of loss function, our method provides a general approach for incorporating prior information about the Monge map itself when learning adapted OT maps and cost functions.

Published
2024

25. Progressive Entropic Optimal Transport Solvers

Author

Kassraie, Parnian, Pooladian, Aram-Alexandre, Klein, Michal, Thornton, James, Niles-Weed, Jonathan, and Cuturi, Marco

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

Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds of sizes $n$ and $m$ in $\mathbb{R}^d$, entropic OT (EOT) solvers have emerged as the most reliable tool to either solve the Kantorovich problem and output a $n\times m$ coupling matrix, or to solve the Monge problem and learn a vector-valued push-forward map. While the robustness of EOT couplings/maps makes them a go-to choice in practical applications, EOT solvers remain difficult to tune because of a small but influential set of hyperparameters, notably the omnipresent entropic regularization strength $\varepsilon$. Setting $\varepsilon$ can be difficult, as it simultaneously impacts various performance metrics, such as compute speed, statistical performance, generalization, and bias. In this work, we propose a new class of EOT solvers (ProgOT), that can estimate both plans and transport maps. We take advantage of several opportunities to optimize the computation of EOT solutions by dividing mass displacement using a time discretization, borrowing inspiration from dynamic OT formulations, and conquering each of these steps using EOT with properly scheduled parameters. We provide experimental evidence demonstrating that ProgOT is a faster and more robust alternative to standard solvers when computing couplings at large scales, even outperforming neural network-based approaches. We also prove statistical consistency of our approach for estimating optimal transport maps., Comment: In proceedings of the 38th conference on Advances in Neural Information Processing Systems (NeurIPs), 23 pages, 14 figures

Published
2024

26. 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

27. Using Social Justice Leadership Theory to Contextualize Detracking in the COVID-19 Era

Author

Margaret E. Thornton

Abstract

Ten years ago, Sunnydale High School leaders worked with teachers and community members to create an international baccalaureate (IB)-for-all model to prevent racially and socioeconomically identifiable class levels. For nearly a decade, the program has been successful with stakeholders largely supporting the model. Following pandemic-related school building closures, faculty at Sunnydale High School are having trouble continuing to support students who had vastly different online school experiences. Some faculty and community members have asked the leadership to reconsider the model because they feel some students need additional support that cannot be provided in pre-IB or IB classes while maintaining high expectations for other students. The author offers learning activities through the lens of social justice leadership.

Published
2024
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28. Rapid DNA unwinding accelerates genome editing by engineered CRISPR-Cas9

Author

Eggers, Amy R, Chen, Kai, Soczek, Katarzyna M, Tuck, Owen T, Doherty, Erin E, Xu, Bryant, Trinidad, Marena I, Thornton, Brittney W, Yoon, Peter H, and Doudna, Jennifer A

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, Biotechnology, Gene Therapy, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Generic health relevance, Cancer, Humans, Bacterial Proteins, CRISPR-Associated Protein 9, CRISPR-Cas Systems, Cryoelectron Microscopy, DNA, Gene Editing, Geobacillus stearothermophilus, HEK293 Cells, Protein Domains, Genome, Human, Models, Molecular, Protein Structure, Tertiary, Nucleic Acid Conformation, Biocatalysis, Magnesium, CRISPR-Cas, Cas9 engineering, DNA unwinding, GeoCas9, R-loop formation, WED domain, cryo-EM, genome editing, iGeoCas9, magnesium, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Thermostable clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas9) enzymes could improve genome-editing efficiency and delivery due to extended protein lifetimes. However, initial experimentation demonstrated Geobacillus stearothermophilus Cas9 (GeoCas9) to be virtually inactive when used in cultured human cells. Laboratory-evolved variants of GeoCas9 overcome this natural limitation by acquiring mutations in the wedge (WED) domain that produce >100-fold-higher genome-editing levels. Cryoelectron microscopy (cryo-EM) structures of the wild-type and improved GeoCas9 (iGeoCas9) enzymes reveal extended contacts between the WED domain of iGeoCas9 and DNA substrates. Biochemical analysis shows that iGeoCas9 accelerates DNA unwinding to capture substrates under the magnesium-restricted conditions typical of mammalian but not bacterial cells. These findings enabled rational engineering of other Cas9 orthologs to enhance genome-editing levels, pointing to a general strategy for editing enzyme improvement. Together, these results uncover a new role for the Cas9 WED domain in DNA unwinding and demonstrate how accelerated target unwinding dramatically improves Cas9-induced genome-editing activity.

Published
2024

29. Contrasting Multiple Representations with the Multi-Marginal Matching Gap

Author

Piran, Zoe, Klein, Michal, Thornton, James, and Cuturi, Marco

Subjects
Computer Science - Machine Learning
Abstract

Learning meaningful representations of complex objects that can be seen through multiple ($k\geq 3$) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to $k$ views, either by instantiating $\tfrac12k(k-1)$ loss-pairs, or by using reduced embeddings, following a \textit{one vs. average-of-rest} strategy. We propose the multi-marginal matching gap (M3G), a loss that borrows tools from multi-marginal optimal transport (MM-OT) theory to simultaneously incorporate all $k$ views. Given a batch of $n$ points, each seen as a $k$-tuple of views subsequently transformed into $k$ embeddings, our loss contrasts the cost of matching these $n$ ground-truth $k$-tuples with the MM-OT polymatching cost, which seeks $n$ optimally arranged $k$-tuples chosen within these $n\times k$ vectors. While the exponential complexity $O(n^k$) of the MM-OT problem may seem daunting, we show in experiments that a suitable generalization of the Sinkhorn algorithm for that problem can scale to, e.g., $k=3\sim 6$ views using mini-batches of size $64~\sim128$. Our experiments demonstrate improved performance over multiview extensions of pairwise losses, for both self-supervised and multimodal tasks., Comment: To be presented at ICML 2024

Published
2024

30. Expected Gamma-Ray Burst Detection Rates and Redshift Distributions for the BlackCAT CubeSat Mission

Author

Colosimo, Joseph M., Fox, Derek B., Falcone, Abraham D., Palmer, David M., Hancock, Frederic, Betts, Michael, Bevidas Jr., William A., Buffington, Jacob C., Burrows, David N., Catlin, Zachary E., Emeigh, Timothy, Forstmeier, Thomas, Nizam, Kadri M., Reichard, Collin, Scigliani, Ana C., Stone, Lukas R., Thornton, Ian, Wages, Mitchell, Washington, Daniel, and Zugger, Michael E.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We report the results of an extensive set of simulations exploring the sensitivity of the BlackCAT CubeSat to long-duration gamma-ray bursts (GRBs). BlackCAT is a NASA APRA-funded CubeSat mission for the detection and real-time sub-arcminute localization of high-redshift ($z\gtrsim 3.5$) GRBs. Thanks to their luminous and long-lived afterglow emissions, GRBs are uniquely valuable probes of high-redshift star-forming galaxies and the intergalactic medium. In addition, each detected GRB with a known redshift serves to localize a region of high-redshift star formation in three dimensions, enabling deep follow-on searches for host galaxies and associated local and large-scale structures. We explore two distinct models for the GRB redshift distribution and luminosity function, both consistent with Swift observations. We find that, for either model, BlackCAT is expected to detect a mean of 42 bursts per year on-orbit, with 6.7% to 10% of these at $z>3.5$. BlackCAT bursts will be localized to $r_{90} \lesssim 55^{\prime\prime}$ precision and reported to the community within seconds. Due to the mission orbit and pointing scheme, bursts will be located in the night sky and well-placed for deep multiwavelength follow-up observations. BlackCAT is on schedule to achieve launch readiness in 2025., Comment: 10 pages, 4 figures, 3 tables; Published in ApJ

Published
2024
Full Text
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31. Simons Observatory: Pre-deployment Performance of a Large Aperture Telescope Optics Tube in the 90 and 150 GHz Spectral Bands

Author

Sierra, Carlos E., Harrington, Kathleen, Sutariya, Shreya, Alford, Thomas, Kofman, Anna M., Chesmore, Grace E., Austermann, Jason E., Bazarko, Andrew, Beall, James A., Bhandarkar, Tanay, Devlin, Mark J., Dicker, Simon R., Dow, Peter N., Duff, Shannon M., Dutcher, Daniel, Galitzki, Nicholas, Golec, Joseph E., Groh, John C., Gudmundsson, Jon E., Haridas, Saianeesh K., Healy, Erin, Hubmayr, Johannes, Iuliano, Jeffrey, Johnson, Bradley R., Lessler, Claire S., Lew, Richard A., Link, Michael J., Lucas, Tammy J., McMahon, Jeffrey J., Moore, Jenna E., Nati, Federico, Niemack, Michael D., Schmitt, Benjamin L., Silva-Feaver, Max, Singh, Robinjeet, Sonka, Rita F., Thomas, Alex, Thornton, Robert J., Tsan, Tran, Ullom, Joel N., Van Lanen, Jeffrey L., Vavagiakis, Eve M., Vissers, Michael R., Wang, Yuhan, and Zheng, Kaiwen

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The Simons Observatory will map the temperature and polarization over half of the sky, at millimeter wavelengths in six spectral bands from the Atacama Desert in Chile. These data will provide new insights into the genesis, content, and history of our Universe; the astrophysics of galaxies and galaxy clusters; objects in our solar system; and time-varying astrophysical phenomena. This ambitious new instrument suite, initially comprising three 0.5 m small-aperture telescopes and one 6 m large aperture telescope, is designed using a common combination of new technologies and new implementations to realize an observatory significantly more capable than the previous generation. In this paper, we present the pre-deployment performance of the first mid-frequency "optics tube" which will be fielded on the large aperture telescope with sensitivity to the 90 and 150 GHz spectral bands. This optics tube contains lenses, filters, detectors, and readout components, all of which operate at cryogenic temperatures. It is one of seven that form the core of the large aperture telescope receiver in its initial deployment. We describe this optics tube, including details of comprehensive testing methods, new techniques for beam and passband characterization, and its measured performance. The performance metrics include beams, optical efficiency, passbands, and forecasts for the on-sky performance of the system. We forecast a sensitivity that exceeds the requirements of the large aperture telescope with greater than 30% margin in each spectral band, and predict that the instrument will realize diffraction-limited performance and the expected detector passbands.

Published
2024

32. Unsupervised Machine Learning Identifies Latent Ultradian States in Multi-Modal Wearable Sensor Signals

Author

Thornton, Christopher, Smith, Billy C., Besne, Guillermo M., Little, Bethany, and Wang, Yujiang

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Wearable sensors such as smartwatches have become ubiquitous in recent years, allowing the easy and continual measurement of physiological parameters such as heart rate, physical activity, body temperature, and blood glucose in an every-day setting. This multi-modal data offers the potential to identify latent states occurring across physiological measures, which may represent important bio-behavioural states that could not be observed in any single measure. Here we present an approach, utilising a hidden semi-Markov model, to identify such states in data collected using a smartwatch, electrocardiogram, and blood glucose monitor, over two weeks from a sample of 9 participants. We found 26 latent ultradian states across the sample, with many occurring at particular times of day. Here we describe some of these, as well as their association with subjective mood and time use diaries. These methods provide a novel avenue for developing insights into the physiology of everyday life.

Published
2024

33. Anti-seizure medication tapering correlates with daytime delta band power reduction in the cortex

Author

Besne, Guillermo M., Evans, Nathan, Panagiotopoulou, Mariella, Smith, Billy, Chowdhury, Fahmida A, Diehl, Beate, Duncan, John S, McEvoy, Andrew W, Miserocchi, Anna, de Tisi, Jane, Walker, Mathew, Taylor, Peter N., Thornton, Chris, and Wang, Yujiang

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Anti-seizure medications (ASMs) are the primary treatment for epilepsy, yet medication tapering effects have not been investigated in a dose, region, and time-dependent manner, despite their potential impact on research and clinical practice. We examined over 3000 hours of intracranial EEG recordings in 32 subjects during long-term monitoring, of which 22 underwent concurrent ASM tapering. We estimated ASM plasma levels based on known pharmaco-kinetics of all the major ASM types. We found an overall decrease in the power of delta band (${\delta}$) activity around the period of maximum medication withdrawal in most (80%) subjects, independent of their epilepsy type or medication combination. The degree of withdrawal correlated positively with the magnitude of ${\delta}$ power decrease. This dose-dependent effect was evident across all recorded cortical regions during daytime; but not in sub-cortical regions, or during night time. We found no evidence of a differential effect in seizure onset, spiking, or pathological brain regions. The finding of decreased ${\delta}$ band power during ASM tapering agrees with previous literature. Our observed dose-dependent effect indicates that monitoring ASM levels in cortical regions may be feasible for applications such as medication reminder systems, or closed-loop ASM delivery systems. ASMs are also used in other neurological and psychiatric conditions, making our findings relevant to a general neuroscience and neurology audience.

Published
2024

34. Automated Quantum Circuit Generation for Computing Inverse Hash Functions

Author

Henderson, Elena R., Henderson, Jessie M., Oxford, William V., and Thornton, Mitchell A.

Subjects
Quantum Physics, Computer Science - Cryptography and Security
Abstract

Several cryptographic systems depend upon the computational difficulty of reversing cryptographic hash functions. Robust hash functions transform inputs to outputs in such a way that the inputs cannot be later retrieved in a reasonable amount of time even if the outputs and the function that created them are known. Consequently, hash functions can be cryptographically secure, and they are employed in encryption, authentication, and other security methods. It has been suggested that such cryptographically-secure hash functions will play a critical role in the era of post-quantum cryptography (PQC), as they do in conventional systems. In this work, we introduce a procedure that leverages the principle of reversibility to generate circuits that invert hash functions. We provide a proof-of-concept implementation and describe methods that allow for scaling the hash function inversion approach. Specifically, we implement one manifestation of the algorithm as part of a more general automated quantum circuit synthesis, compilation, and optimization toolkit. We illustrate production of reversible circuits for crypto-hash functions that inherently provide the inverse of the function, and we describe data structures that increase the scalability of the hash function inversion approach., Comment: 12 pages, 9 figures, 1 table

Published
2024

35. Data Analysis Methods Preliminaries for a Photon-based Hardware Random Number Generator

Author

Beznosko, Dmitriy, Driscoll, Keith, Guadarrama, Fernando, Mai, Steven, and Thornton, Nikolas

Subjects
Computer Science - Cryptography and Security, Physics - Instrumentation and Detectors
Abstract

High quality random numbers are necessary in the modern world. Ranging from encryption keys in cyber security to models and simulations for scientific use: it's important that these random numbers are of high quality and quickly attainable. One common solution to the generation of random numbers is that of pseudo-random number generators, or PRNGs. PRNGs generate random numbers by first quantifying some unpredictable phenomena into a number or string and feeding it into an algorithm which yields numbers randomly based on that seed. Easy places to find seeds include the user's mouse movements or the machine's uptime. These are only pseudorandom, however, as if given the same seed twice, the PRNG would generate the same 'random' output. This is great for games like Minecraft, but not so great for cybersecurity encryption key generation. By using a hardware random number generator (HRNG), random numbers that are not susceptible to the flaws found in PRNGs can be attained at a high rate., Comment: Presented at College of STEM SYmposium, Clayton State University

Published
2024

36. The Next Generation of MeV Energy X-ray Sources for use in the Inspection of Additively Manufactured Parts for Industry

Author

Thornton, C., Karimi, S., Glenn, S., Brown, W. D., Draganic, N., Skeate, M., Ferrucci, M., Chen, Q., Jacob, R., Nakamura, K., Ostermayr, T., van Tilborg, J., Armstrong, C., Finlay, O. J., Turner, N., Glanvill, S., Martz, H., and Geddes, C.

Subjects
Physics - Accelerator Physics, Physics - Plasma Physics
Abstract

For the first time, we demonstrate the application of an inverse Compton scattering X-ray Source, driven by a laser-plasma accelerator, to image an additively manufactured component. X-rays with a mean energy of 380 keV were produced and used to image an additively manufactured part made of an Inconel (Nickel 718) alloy. Because inverse Compton scattering driven by laser-plasma acceleration produces high-energy X-rays while maintaining a focal spot size on the order of a micron, the source can provide several benefits over conventional X-ray production methods, particularly when imaging superalloy parts, with the potential to revolutionise what can be inspected.

Published
2024

37. Designing a Photonic Physically Unclonable Function Having Resilience to Machine Learning Attacks

Author

Henderson, Elena R., Henderson, Jessie M., Shahoei, Hiva, Oxford, William V., Larson, Eric C., MacFarlane, Duncan L., and Thornton, Mitchell A.

Subjects
Computer Science - Cryptography and Security, Physics - Optics
Abstract

Physically unclonable functions (PUFs) are designed to act as device 'fingerprints.' Given an input challenge, the PUF circuit should produce an unpredictable response for use in situations such as root-of-trust applications and other hardware-level cybersecurity applications. PUFs are typically subcircuits present within integrated circuits (ICs), and while conventional IC PUFs are well-understood, several implementations have proven vulnerable to malicious exploits, including those perpetrated by machine learning (ML)-based attacks. Such attacks can be difficult to prevent because they are often designed to work even when relatively few challenge-response pairs are known in advance. Hence the need for both more resilient PUF designs and analysis of ML-attack susceptibility. Previous work has developed a PUF for photonic integrated circuits (PICs). A PIC PUF not only produces unpredictable responses given manufacturing-introduced tolerances, but is also less prone to electromagnetic radiation eavesdropping attacks than a purely electronic IC PUF. In this work, we analyze the resilience of the proposed photonic PUF when subjected to ML-based attacks. Specifically, we describe a computational PUF model for producing the large datasets required for training ML attacks; we analyze the quality of the model; and we discuss the modeled PUF's susceptibility to ML-based attacks. We find that the modeled PUF generates distributions that resemble uniform white noise, explaining the exhibited resilience to neural-network-based attacks designed to exploit latent relationships between challenges and responses. Preliminary analysis suggests that the PUF exhibits similar resilience to generative adversarial networks, and continued development will show whether more-sophisticated ML approaches better compromise the PUF and -- if so -- how design modifications might improve resilience., Comment: 14 pages, 8 figures

Published
2024

38. A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks

Author

Henderson, Jessie M., Henderson, Elena R., Harper, Clayton A., Shahoei, Hiva, Oxford, William V., Larson, Eric C., MacFarlane, Duncan L., and Thornton, Mitchell A.

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Physically unclonable functions (PUFs) identify integrated circuits using nonlinearly-related challenge-response pairs (CRPs). Ideally, the relationship between challenges and corresponding responses is unpredictable, even if a subset of CRPs is known. Previous work developed a photonic PUF offering improved security compared to non-optical counterparts. Here, we investigate this PUF's susceptibility to Multiple-Valued-Logic-based machine learning attacks. We find that approximately 1,000 CRPs are necessary to train models that predict response bits better than random chance. Given the significant challenge of acquiring a vast number of CRPs from a photonic PUF, our results demonstrate photonic PUF resilience against such attacks., Comment: 6 pages, 4 figures

Published
2024

39. Optical modeling of systematic uncertainties in detector polarization angles for the Atacama Cosmology Telescope

Author

Murphy, Colin C., Choi, Steve K., Datta, Rahul, Devlin, Mark J., Hasselfield, Matthew, Koopman, Brian J., McMahon, Jeff, Naess, Sigurd, Niemack, Michael D., Page, Lyman A., Staggs, Suzanne T., Thornton, Robert, and Wollack, Edward J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present an estimate of the Atacama Cosmology Telescope (ACT) detector polarization angle systematic uncertainty from optics perturbation analysis using polarization-sensitive ray tracing in CODE V optical design software. Uncertainties in polarization angle calibration in CMB measurements can limit constraints on cosmic birefringence and other cosmological parameters sensitive to polarization leakage. Our framework estimates the angle calibration systematic uncertainties from possible displacements in lens positions and orientations, and anti-reflection coating (ARC) thicknesses and refractive indices. With millimeter displacements in lens positions and percent-level perturbations in ARC thicknesses and indices from design, we find the total systematic uncertainty for three ACT detector arrays operating between 90--220 GHz to be at the tenth of degree scale. Reduced lens position and orientation uncertainties from physical measurements could lead to a reduction in the systematic uncertainty estimated with the framework presented here. This optical modeling may inform polarization angle systematic uncertainties for current and future microwave polarimeters, such as the CCAT Observatory, Simons Observatory, and CMB-S4., Comment: 14 pages, 8 figures, version accepted for publication in Applied Optics

Published
2024
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40. 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, 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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

43. Genome-wide copy number variation association study in anorexia nervosa

Author

Walker, Alicia, Karlsson, Robert, Szatkiewicz, Jin P., Thornton, Laura M., Yilmaz, Zeynep, Leppä, Virpi M., Savva, Androula, Lin, Tian, Sidorenko, Julia, McRae, Allan, Kirov, George, Davies, Helena L., Fundín, Bengt T., Chawner, Samuel J. R. A., Song, Jie, Borg, Stina, Wen, Jia, Watson, Hunna J., Munn-Chernoff, Melissa A., Baker, Jessica H., Gordon, Scott, Berrettini, Wade H., Brandt, Harry, Crawford, Steven, Halmi, Katherine A., Kaplan, Allan S., Kaye, Walter H., Mitchell, James, Strober, Michael, Woodside, D. Blake, Pedersen, Nancy L., Parker, Richard, Jordan, Jennifer, Kennedy, Martin A., Birgegård, Andreas, Landén, Mikael, Martin, Nicholas G., Sullivan, Patrick F., Bulik, Cynthia M., and Wray, Naomi R.

Published
2024
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47. Yield of genetic association signals from genomes, exomes and imputation in the UK Biobank

Author

Gaynor, Sheila M., Joseph, Tyler, Bai, Xiaodong, Zou, Yuxin, Boutkov, Boris, Maxwell, Evan K., Delaneau, Olivier, Hofmeister, Robin J., Krasheninina, Olga, Balasubramanian, Suganthi, Marcketta, Anthony, Backman, Joshua, Reid, Jeffrey G., Overton, John D., Lotta, Luca A., Marchini, Jonathan, Salerno, William J., Baras, Aris, Abecasis, Goncalo R., and Thornton, Timothy A.

Published
2024
Full Text
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48. The pathobiology of human fungal infections

Author

Brown, Gordon D., Ballou, Elizabeth R., Bates, Steven, Bignell, Elaine M., Borman, Andrew M., Brand, Alexandra C., Brown, Alistair J. P., Coelho, Carolina, Cook, Peter C., Farrer, Rhys A., Govender, Nelesh P., Gow, Neil A. R., Hope, William, Hoving, J. Claire, Dangarembizi, Rachael, Harrison, Thomas S., Johnson, Elizabeth M., Mukaremera, Liliane, Ramsdale, Mark, Thornton, Christopher R., Usher, Jane, Warris, Adilia, and Wilson, Duncan

Published
2024
Full Text
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