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7,963 results on '"Sandhu, P."'

1. Radial Diffusion Driven by Spatially Localized ULF Waves in the Earth's Magnetosphere

Author

Osmane, Adnane, Sandhu, Jasmine, Elsden, Tom, Allanson, Oliver, and Turc, Lucile

Subjects
Physics - Space Physics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Physics - Plasma Physics
Abstract

Ultra-Low Frequency (ULF) waves are critical drivers of particle acceleration and loss in the Earth's magnetosphere. While statistical models of ULF-induced radial transport have traditionally assumed that the waves are uniformly distributed across magnetic local time (MLT), decades of observational evidence show significant MLT localization of ULF waves in the Earth's magnetosphere. This study presents, for the first time, a quasi-linear radial diffusion coefficient accounting for localized ULF waves. We demonstrate that even though quasi-linear radial diffusion is averaged over drift orbits, MLT localization significantly alters the efficiency of particle transport. Our results reveal that when ULF waves cover more than 30\% of the MLT, the radial diffusion efficiency is comparable to that of uniform wave distributions. However, when ULF waves are confined within 10\% of the drift orbit, the diffusion coefficient is enhanced by 10 to 25\%, indicating that narrowly localized ULF waves are efficient drivers of radial transport., Comment: 16 pages, 4 figures. Currently under review. Feedback and suggestions are welcome

Published
2024

2. Enhanced strong-field ionization and fragmentation of methanol using non-commensurate fields

Author

Zamudio, Eladio Prieto, Das, Rituparna, Katturi, Naga Krishnakanth, Stamm, Jacob, Sandhu, Jesse, Kwon, Sung, Minasian, Matthew, and Dantus, Marcos

Subjects
Physics - Chemical Physics
Abstract

Electron-initiated chemistry with chemically relevant electron energies (10-200 eV) is at the heart of several high-energy processes and phenomena. To probe these dissociation and fragmentation reactions with femtosecond resolution requires the use of femtosecond lasers to induce ionization of the polyatomic molecules via electron rescattering. Here, we combine non-commensurate fields with intensity-difference spectra using methanol as a model system. Experimentally, we find orders of magnitude enhancement in several product ions of methanol when comparing coherent vs incoherent combinations of non-commensurate fields. This approach not only mitigates multi-photon ionization and multi-cycle effects during ionization but also enhances tunnel ionization and electron rescattering energy., Comment: 21 pages 8 figures

Published
2024

3. Potential Hessian Ascent: The Sherrington-Kirkpatrick Model

Author

Jekel, David, Sandhu, Juspreet Singh, and Shi, Jonathan

Subjects
Mathematics - Probability, Computer Science - Data Structures and Algorithms, Mathematical Physics, 82B44 (Primary) 35Q82, 60B20, 68Q87, 82M60 (Secondary)
Abstract

We present the first iterative spectral algorithm to find near-optimal solutions for a random quadratic objective over the discrete hypercube, resolving a conjecture of Subag [Subag, Communications on Pure and Applied Mathematics, 74(5), 2021]. The algorithm is a randomized Hessian ascent in the solid cube, with the objective modified by subtracting an instance-independent potential function [Chen et al., Communications on Pure and Applied Mathematics, 76(7), 2023]. Using tools from free probability theory, we construct an approximate projector into the top eigenspaces of the Hessian, which serves as the covariance matrix for the random increments. With high probability, the iterates' empirical distribution approximates the solution to the primal version of the Auffinger-Chen SDE [Auffinger et al., Communications in Mathematical Physics, 335, 2015]. The per-iterate change in the modified objective is bounded via a Taylor expansion, where the derivatives are controlled through Gaussian concentration bounds and smoothness properties of a semiconcave regularization of the Fenchel-Legendre dual to the Parisi PDE. These results lay the groundwork for (possibly) demonstrating low-degree sum-of-squares certificates over high-entropy step distributions for a relaxed version of the Parisi formula [Open Question 1.8, arXiv:2401.14383]., Comment: 102 pages, 1 table

Published
2024

4. Ultrafast Dynamics of the Rydberg States of CO$_{2}$: Autoionization and Dissociation Lifetimes

Author

Biswas, D., Wood, J. K., Shalaby, I., and Sandhu, A.

Subjects
Physics - Atomic Physics, Physics - Chemical Physics
Abstract

We report the measurement of ultrafast relaxation dynamics of excited states of carbon dioxide molecule using time-resolved pump-probe photoelectron spectroscopy. Neutral ground state carbon dioxide is excited to $nd\sigma_g$ Henning sharp Rydberg states with an attosecond extreme ultraviolet pulse train. A time delayed near infrared probe pulse is used to photoionize these states to their corresponding ionization limit $B^2\Sigma_u^+$. We obtain differential kinetic energy spectrograms and angular distributions for photoionization and autoionization channels. We model the competition between predissociation and autoionization in the Rydberg state dynamics and analyze differential photoelectron yield as a function of the time delay to extract previously unknown autoionization and predissociation lifetimes for three Henning sharp states (n = 4, 5, 6)., Comment: 8 pages, 6 figures

Published
2024

5. Noise And Artifacts Elimination In ECG Signals Using Wavelet, Variational Mode Decomposition And Nonlocal Means Algorithm

Author

Jabeur, Taoufik Ben, Bashier, Eihab, Sandhu, Qudsia, Bwalya, Kelvin Joseph, and Joshua, Adason

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Electrocardiogram (ECG) signals can frequently be affected by the introduction of noise and artifacts. Since these types of signal corruptions disrupt the accurate interpretation of ECG signals, noise and artifacts must be eliminated during the preprocessing phase. In this paper, we introduced a comprehensive pre-processing phase that eliminates motion artifacts and noise prior to detecting and extracting entirely corrupted ECG signal segments. The first method, denoted as the WLNH method, is constructed using wavelet multiresolution analysis (MRA), the Lillifors test, NLM, and a high-pass filter. The second method entails substituting the wavelet MRA decomposition with the variational mode decomposition (VMD) while retaining all other stages from the first method. This technique is denoted as the VLWNH. The two proposed methods differ from some existing methods in that they first employ the Lilliefors test to identify whether a component is white Gaussian noise and then utilize the High Pass Filter to eliminate motion anomalies. The simulation results show that the offered solutions are effective, particularly when dealing with white Gaussian noise and base-line wander (BW) noise.

Published
2024

6. Submersible voltammetric sensing probe for rapid and extended remote monitoring of opioids in community water systems.

Author

Zhou, Jiachi, Ding, Shichao, Sandhu, Samar, Chang, An-Yi, Taechamahaphan, Anubhap, Gudekar, Shipra, and Wang, Joseph

Subjects
Electrochemical sensors, Fentanyl, Opioids, Remote sensing, Square wave voltammetry, Submersible probes, Wastewater-based epidemiology, Water Pollutants, Chemical, Electrochemical Techniques, Fentanyl, Analgesics, Opioid, Metal-Organic Frameworks, Electrodes, Wastewater, Environmental Monitoring, Limit of Detection, Carbon, Nanoparticles, Remote Sensing Technology
Abstract

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Published
2024

7. [18F]Flotaza for Aβ Plaque Diagnostic Imaging: Evaluation in Postmortem Human Alzheimer’s Disease Brain Hippocampus and PET/CT Imaging in 5xFAD Transgenic Mice

Author

Sandhu, Yasmin K, Bath, Harman S, Shergill, Jasmine, Liang, Christopher, Syed, Amina U, Ngo, Allyson, Karim, Fariha, Serrano, Geidy E, Beach, Thomas G, and Mukherjee, Jogeshwar

Subjects
Biochemistry and Cell Biology, Biological Sciences, Medicinal and Biomolecular Chemistry, Chemical Sciences, Microbiology, Neurodegenerative, Aging, Alzheimer's Disease, Neurosciences, Dementia, Brain Disorders, Bioengineering, Biomedical Imaging, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Brain, Hippocampus, Animals, Mice, Transgenic, Humans, Mice, Alzheimer Disease, Disease Models, Animal, Fluorine Radioisotopes, Pyridines, Pyrrolidinones, Radiopharmaceuticals, Autopsy, Aged, Aged, 80 and over, Female, Male, Plaque, Amyloid, Positron Emission Tomography Computed Tomography, 5xFAD transgenic mice, Alzheimer’s disease, PET imaging, [18F]flotaza, hippocampus, human Aβ plaques, Other Chemical Sciences, Genetics, Other Biological Sciences, Chemical Physics, Biochemistry and cell biology, Medicinal and biomolecular chemistry
Abstract

The diagnostic value of imaging Aβ plaques in Alzheimer's disease (AD) has accelerated the development of fluorine-18 labeled radiotracers with a longer half-life for easier translation to clinical use. We have developed [18F]flotaza, which shows high binding to Aβ plaques in postmortem human AD brain slices with low white matter binding. We report the binding of [18F]flotaza in postmortem AD hippocampus compared to cognitively normal (CN) brains and the evaluation of [18F]flotaza in transgenic 5xFAD mice expressing Aβ plaques. [18F]Flotaza binding was assessed in well-characterized human postmortem brain tissue sections consisting of HP CA1-subiculum (HP CA1-SUB) regions in AD (n = 28; 13 male and 15 female) and CN subjects (n = 32; 16 male and 16 female). Adjacent slices were immunostained with anti-Aβ and analyzed using QuPath. In vitro and in vivo [18F]flotaza PET/CT studies were carried out in 5xFAD mice. Post-mortem human brain slices from all AD subjects were positively IHC stained with anti-Aβ. High [18F]flotaza binding was measured in the HP CA1-SUB grey matter (GM) regions compared to white matter (WM) of AD subjects with GM/WM > 100 in some subjects. The majority of CN subjects had no decipherable binding. Male AD exhibited greater WM than AD females (AD WM♂/WM♀ > 5; p < 0.001) but no difference amongst CN WM. In vitro studies in 5xFAD mice brain slices exhibited high binding [18F]flotaza ratios (>50 versus cerebellum) in the cortex, HP, and thalamus. In vivo, PET [18F]flotaza exhibited binding to Aβ plaques in 5xFAD mice with SUVR~1.4. [18F]Flotaza is a new Aβ plaque PET imaging agent that exhibited high binding to Aβ plaques in postmortem human AD. Along with the promising results in 5xFAD mice, the translation of [18F]flotaza to human PET studies may be worthwhile.

Published
2024

8. Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography.

Author

Nurmohamed, Nick, van Rosendael, Alexander, Danad, Ibrahim, Ngo-Metzger, Quyen, Taub, Pam, Ray, Kausik, Figtree, Gemma, Bonaca, Marc, Hsia, Judith, Rodriguez, Fatima, Sandhu, Alexander, Nieman, Koen, Earls, James, Hoffmann, Udo, Bax, Jeroen, Min, James, Maron, David, and Bhatt, Deepak

Subjects
Atherosclerotic cardiovascular disease, Coronary artery disease, Coronary computed tomography angiography, Major adverse cardiovascular events, Prevention, Humans, Computed Tomography Angiography, Coronary Artery Disease, Risk Assessment, Coronary Angiography, Plaque, Atherosclerotic, Heart Disease Risk Factors, Prognosis, Coronary Stenosis
Abstract

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individuals complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.

Published
2024

9. AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids.

Author

Srivastava, Siddharth, Sandhu, Nehmat, Liu, Jun, and Xie, Ya-Hong

Subjects
Raman spectroscopy, SERS, biosensing, neural networks, plasmonics, proteomics
Abstract

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the models predictions and the proteins known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.

Published
2024

10. High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

Author

Mentzer, Alexander, Dilthey, Alexander, Pollard, Martin, Gurdasani, Deepti, Karakoc, Emre, Carstensen, Tommy, Muhwezi, Allan, Cutland, Clare, Diarra, Amidou, da Silva Antunes, Ricardo, Paul, Sinu, Smits, Gaby, Wareing, Susan, Kim, HwaRan, Pomilla, Cristina, Chong, Amanda, Brandt, Debora, Neaves, Samuel, Timpson, Nicolas, Crinklaw, Austin, Lindestam Arlehamn, Cecilia, Rautanen, Anna, Kizito, Dennison, Parks, Tom, Auckland, Kathryn, Elliott, Kate, Mills, Tara, Ewer, Katie, Edwards, Nick, Fatumo, Segun, Webb, Emily, Peacock, Sarah, Jeffery, Katie, van der Klis, Fiona, Kaleebu, Pontiano, Vijayanand, Pandurangan, Peters, Bjorn, Sette, Alessandro, Cereb, Nezih, Sirima, Sodiomon, Madhi, Shabir, Elliott, Alison, McVean, Gil, Hill, Adrian, Sandhu, Manjinder, and Nielsen, Rasmus

Subjects
Humans, HLA-DRB1 Chains, Infant, Black People, Hepatitis B Vaccines, Quantitative Trait Loci, Male, Female, Uganda, Antibody Formation, Pertussis Vaccine, Vaccination, Whooping Cough
Abstract

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

Published
2024

11. Looking Together $\neq$ Seeing the Same Thing: Understanding Surgeons' Visual Needs During Intra-operative Coordination and Instruction

Author

Popov, Vitaliy, Chen, Xinyue, Wang, Jingying, Kemp, Michael, Sandhu, Gurjit, Kantor, Taylor, Mateju, Natalie, and Wang, Xu

Subjects
Computer Science - Human-Computer Interaction
Abstract

Shared gaze visualizations have been found to enhance collaboration and communication outcomes in diverse HCI scenarios including computer supported collaborative work and learning contexts. Given the importance of gaze in surgery operations, especially when a surgeon trainer and trainee need to coordinate their actions, research on the use of gaze to facilitate intra-operative coordination and instruction has been limited and shows mixed implications. We performed a field observation of 8 surgeries and an interview study with 14 surgeons to understand their visual needs during operations, informing ways to leverage and augment gaze to enhance intra-operative coordination and instruction. We found that trainees have varying needs in receiving visual guidance which are often unfulfilled by the trainers' instructions. It is critical for surgeons to control the timing of the gaze-based visualizations and effectively interpret gaze data. We suggest overlay technologies, e.g., gaze-based summaries and depth sensing, to augment raw gaze in support of surgical coordination and instruction.

Published
2024
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12. Specification and Enforcement of Activity Dependency Policies using XACML

Author

Mawla, Tanjila, Gupta, Maanak, and Sandhu, Ravi

Subjects
Computer Science - Cryptography and Security
Abstract

The evolving smart and interconnected systems are designed to operate with minimal human intervention. Devices within these smart systems often engage in prolonged operations based on sensor data and contextual factors. Recently, an Activity-Centric Access Control (ACAC) model has been introduced to regulate these prolonged operations, referred to as activities, which undergo state changes over extended duration of time. Dependencies among different activities can influence and restrict the execution of one another, necessitating active and real-time monitoring of the dependencies between activities to prevent security violation. In the ACAC model, the activity dependencies, denoted as "D", is considered as a decision parameter for controlling a requested activity. These dependencies must be evaluated throughout all phases of an activity's life cycle. To ensure the consistency of access control rules across diverse domains and applications, a standard policy language is essential. We propose a policy framework adapting the widely-used eXtensible Access Control Markup Language (XACML) , referred to as $\mathrm{XACML_{AD}}$, to specify the activity dependency policies. This work involves extending the syntax and semantics of XACML by introducing new elements to check dependent activities' states and handle state updates on dependent activities. In addition to the language extension, we present the enforcement architecture and data flow model of evaluating policies for activity dependencies. The integration of the proposed $\mathrm{XACML_{AD}}$ policy framework and the enforcement of the policies supports dependency evaluation, necessary updates and continuous enforcement of policies to control an activity throughout its life cycle. We implement the enforcement architecture exploiting the $\mathrm{XACML_{AD}}$ policy framework and discuss the performance evaluation results., Comment: 10 pages, Accepted by ISSSR 2024 (The 10th International Symposium on System Security, Safety, and Reliability) sponsored by the IEEE Reliability Society

Published
2024

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

Author

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

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

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

Published
2024

14. Ruledger: Ensuring Execution Integrity in Trigger-Action IoT Platforms

Author

Fan, Jingwen, He, Yi, Tang, Bo, Li, Qi, and Sandhu, Ravi

Subjects
Computer Science - Cryptography and Security
Abstract

Smart home IoT systems utilize trigger-action platforms, e.g., IFTTT, to manage devices from various vendors. However, they may be abused by triggering malicious rule execution with forged IoT devices or events violating the execution integrity and the intentions of the users. To address this issue, we propose a ledger based IoT platform called Ruledger, which ensures the correct execution of rules by verifying the authenticity of the corresponding information. Ruledger utilizes smart contracts to enforce verifying the information associated with rule executions, e.g., the user and configuration information from users, device events, and triggers in the trigger-action platforms. In particular, we develop three algorithms to enable ledger-wallet based applications for Ruledger and guarantee that the records used for verification are stateful and correct. Thus, the execution integrity of rules is ensured even if devices and platforms in the smart home systems are compromised. We prototype Ruledger in a real IoT platform, i.e., IFTTT, and evaluate the performance with various settings. The experimental results demonstrate Ruledger incurs an average of 12.53% delay, which is acceptable for smart home systems.

Published
2024
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15. GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

Author

Naseer, Ehtasham, Sandhu, Ali Imran, Siddique, Muhammad Adnan, Ahmed, Waqas W., Farhat, Mohamed, and Wu, Ying

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and efficiently reconstruct randomly-shaped two-dimensional dielectric objects from amplitudes of multi-frequency scattered electric fields. An adversarial autoencoder (AAE) is trained to learn to generate the scatterer's geometry from a lower-dimensional latent representation constrained to adhere to the Gaussian distribution. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers, the already-trained generator as well as a separately trained forward neural network. The images reconstructed at the output of the inverse network are validated through comparison with outputs from the forward neural network, addressing the non-uniqueness challenge inherent to electromagnetic (EM) imaging problems. The trained INN demonstrates an enhanced robustness, evidenced by a mean binary cross-entropy (BCE) loss of $0.13$ and a structure similarity index (SSI) of $0.90$. The study not only demonstrates a significant reduction in computational load, but also marks a substantial improvement over traditional objective-function-based methods. It contributes both to the fields of machine learning and EM imaging by offering a real-time quantitative imaging approach. The results obtained with the simulated data, for both training and testing, yield promising results and may open new avenues for radio-frequency inverse imaging.

Published
2024

30. A fingertip-wearable microgrid system for autonomous energy management and metabolic monitoring

Author

Ding, Shichao, Saha, Tamoghna, Yin, Lu, Liu, Ruixiao, Khan, Muhammad Inam, Chang, An-Yi, Lee, Hyungjin, Zhao, Han, Liu, Yuanzhe, Nazemi, Ariane Sina, Zhou, Jiachi, Chen, Chuanrui, Li, Zhengxing, Zhang, Chenyang, Earney, Sara, Tang, Selene, Djassemi, Omeed, Chen, Xiangjun, Lin, Muyang, Sandhu, Samar S., Moon, Jong-Min, Moonla, Chochanon, Nandhakumar, Ponnusamy, Park, Youngmin, Mahato, Kuldeep, Xu, Sheng, and Wang, Joseph

Published
2024
Full Text
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47. Multi-Objective Aerodynamic Optimization of External Compression Supersonic Intake using Kriging/MOGA

Author

Sandhu, JPS, Bhardwaj, M, Ananthkrishnan, N, Sharma, A, and Park, IS

Subjects
Physics - Fluid Dynamics
Abstract

A formal optimization procedure is reported for an external-compression supersonic intake with the twin objectives of maximizing the total pressure recovery while simultaneously minimizing the intake pressure drag. Prior to that, two key design modifications are introduced to the supersonic intake. First, a small cowl offset distance is applied, deliberately violating the shock-on-lip condition. This ensures an attached external shock at the cowl, which is beneficial, in return for a small, controlled flow spillage. Second, in a novel development, an internal wedge angle is introduced at the cowl lip, which forces the terminal shock to be a strong oblique shock. This also helps anchor the terminal shock at the cowl lip for a range of intake back-pressure values, and reduces the cowl external angle with respect to the free stream. The optimization problem is formulated based on axiomatic design theory with the two design parameters innovatively selected as the Oswatitsch ramp angle triplets and the cowl internal wedge angle, respectively. A multi-objective genetic algorithm (MOGA), assisted by a Kriging meta-model with infilling, is run in tandem with a RANS solver, to iteratively compute the Pareto front. The optimal design yields ramp angles that are somewhat smaller than the theoretical Oswatitsch values, yet returns a total pressure recovery very close to the Oswatitsch optimum while reducing the intake pressure drag significantly., Comment: 25 pages, 12 figures

Published
2024

48. Sum-of-Squares & Gaussian Processes I: Certification

Author

Sandhu, J. S. and Shi, J.

Subjects
Computer Science - Computational Complexity, Mathematical Physics, Mathematics - Classical Analysis and ODEs, Mathematics - Optimization and Control, Mathematics - Probability
Abstract

We propose a new SoS hierarchy for a broad class of average-case problems. The new high-entropy steps (HES) SoS hierarchy certifies properties of input instances only over a certain family of well behaved distributions of possible solutions. This allows the hierarchy to circumvent the oft-cited impossibility of providing a low-degree SoS proof of concentration of measure \cite[Lec.~1.2, Marley Paradigm]{barak2016proofs} by vastly reducing the set of solution concepts they need to be proven over. We evaluate the HES SoS hierarchy on a well-studied Gaussian process: the spherical spin-glass, which is a particular ensemble of random low-degree polynomials (LDPs). We prove that, given an instance sampled from the problem distribution, with high probability there exists a HES distribution that achieves value arbitrarily close to the conjectured optimal algorithmic threshold and, at the same time, there are low-degree SoS certificates that no HES distribution can achieve value above a constant factor times this threshold, improving over standard SoS whose certificates are loose by a factor of $n^{\lfloor p/2 - 1\rfloor/2}$ for a degree-$p$ random polynomial \cite{bhattiprolu2017sum, hopkins2017power}. We also give examples of anisotropic modifications to the spherical spin glass problem where there is proof (or strong evidence) that the HES SoS hierarchy does better than the only other known algorithm \cite{subag2021following}. We conjecture that the HES SoS hierarchy can be applied to any LDP approximate Gaussian process to obtain value that is optimal among polynomial-time algorithms. The rounding algorithm is introduced and analyzed in a companion paper., Comment: 141 pages, 0 figures. Updates to Section-4 & Appendix-E; The updated bound of Theorem-1.1, which is off from ALG by a constant factor, is due to lemma-5.26 giving a suboptimal bound, and was incorrectly stated in the earlier version. The proof is strengthened with a tight bound in the upcoming companion paper which introduces the rounding algorithm

Published
2024

49. Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.

Author

Stein, Nathan, Duffy, Grant, Sandhu, Roopinder, Chugh, Sumeet, Chen, Peng-Sheng, Rosenberg, Carine, Albert, Christine, Cheng, Susan, Siegel, Robert, Ouyang, David, and Yuan, Neal

Abstract

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Published
2024

50. Modelling energy-harvesting processes in primitive cells: Proton transport across bilayers driven by the oxidation of sulfite

Author

Paula, Stefan, Acosta, Andres, Judge, Naiki, Ramirez, Stephanie, Sandhu, Amaan, and Deamer, David

Subjects
Biological Sciences, Engineering, Generic health relevance, Affordable and Clean Energy, Protons, Liposomes, Lipid Bilayers, Oxidation-Reduction, Sulfites, Bioinformatics, Biological sciences
Abstract

A frequently debated topic related to the origin of life centers around the question of how complex forms of life on today's Earth may have evolved over time from simpler predecessors. For example, the question of how proton concentration gradients across cellular membranes developed in ancestral protocells remains unanswered. This process, which is indispensable for the generation of chemical energy in modern organisms, is driven by energy derived from redox processes in the respiratory chain. Since it is highly unlikely that the complex machinery of the respiratory chain was available on early Earth, we provide an example of how proton gradients can be established in less complex systems. Utilizing liposomes as models of primitive cells, we were able to generate proton gradients of about two pH units across the liposome bilayers using redox reactions as the driving force. Electrons were transferred from sodium sulfite present on the outside of the liposomes to ferricyanide, which was trapped on the inside. A lipid-soluble phenazine derivative served as a shuttle that transferred both electrons and protons across the lipid bilayer. Because sulfite would have been an abundant reduced solute available to the earliest cells, we propose that it may have been a primary source of redox energy for primitive chemiosmotic energy transduction.

Published
2024

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