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227. Emergency Department and Urgent Care Medical Malpractice Claims 2001–15

Author

Kelly E. Wong, P. Divya Parikh, Kwon C. Miller, and Mark R. Zonfrillo

Subjects
Medicine, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Introduction: This study reviews malpractice, also called medical professional liability (MPL), claims involving adult patients cared for in emergency departments (ED) and urgent care settings. Methods: We conducted a retrospective review of closed MPL claims of adults over 18 years, from the Medical Professional Liability Association’s Data Sharing Project database from 2001–2015, identifying 6,779 closed claims. Data included the total amount, origin, top medical specialties named, chief medical factors, top medical conditions, severity of injury, resolution, average indemnity, and defense costs of closed claims. Results: Of 6,779 closed claims, 65.9% were dropped, withdrawn, or dismissed. Another 22.8% of claims settled for an average indemnity of $297,709. Of the 515 (7.6%) cases that went to trial, juries returned verdicts for the defendant in 92.6% of cases (477/515). The remaining 7.4% of cases (38/515) were jury verdicts for the plaintiff, with an average indemnity of $816,909. The most common resulting medical condition cited in paid claims was cardiac or cardiorespiratory arrest (10.4%). Error in diagnosis was the most common chief medical error cited in closed claims. Death was the most common level of severity listed in closed (38.5%) and paid (42.8%) claims. Claims reporting major permanent injury had the highest paid-to-closed ratio, and those reporting grave injury had the highest average indemnity of $686,239. Conclusion: This retrospective review updates the body of knowledge surrounding medical professional liability and represents the most recent analysis of claims in emergency medicine. As the majority of emergency providers will be named in a MPL claim during their career, it is essential to have a better understanding of the most common factors resulting in MPL claims.

Published
2020
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229. A.I. In All The Wrong Places

Author

Böhlen, Marc, Chen, Ruolin, Dong, Xiaoxu, Gopaladinne, Srikar, Gorla, Hemanth, Kandukuri, Divya, and Mansfield, Sean

Subjects
Computer Science - Computers and Society
Abstract

This text describes experiences gained across a two-year test period during which two generations of Generative Artificial Intelligence (A.I.) systems were incorporated into an interdisciplinary, university level course on A.I. for art and design practices. The text uses the results from the courses to reflect on new opportunities for generative systems in art and design, while considering traps and limits., Comment: 20 pages, 3 tables, 4 images

Published
2024

230. Exploring filament galaxies using {\em AstroSat}/ UVIT

Author

Pandey, Divya, Saha, Kanak, and Pradhan, Ananta C

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present results from our deep Far-ultraviolet (FUV) survey using {\em AstroSat}/UVIT of a filamentary structure at $z$ $\sim$ $0.072$. A total of four filaments comprising 58 galaxies were probed in our study. We detect 18 filament galaxies in our FUV observation. All filament galaxies are further classified based on their photometric color, nuclear activity, and morphology. The filaments contain galaxies with mixed stellar population types and structures. We do not detect galaxies in our UVIT survey up to a distance of 0.4~Mpc $h^{-1}$ from the filament axis, implying a lack of recent star formation in the inner region of filaments. The FUV star formation rate (SFR) for star-forming galaxies agrees well with the SFR$_{\rm 144 MHz}$ calculated using Low-Frequency Array (LOFAR) radio-continuum observations. We witness an increase in FUV specific-SFR (sSFR) of filament galaxies with increasing distance from the filament spine (D$_{\rm fil}$). The intermediate-to-high stellar mass filament galaxies were more star-forming than cluster galaxies in a fixed stellar mass bin. The FUV morphology of some filament galaxies detected in the filament outskirts (D$_{\rm fil}$ $\gtrsim$ 0.7~Mpc $h^{-1}$) is comparable to or slightly extended than their optical counterpart. The mass assembly of galaxies examined by estimating $(FUV-r)$ color gradients show that more `red-cored' galaxies reside in the outer region of the filaments. Our results prove that the likelihood of merger interaction and gas starvation increases when approaching the filament spine. We report a definitive and in-homogeneous impact of filaments on the galaxies residing inside them., Comment: Accepted for publication in ApJ

Published
2024

231. I-SplitEE: Image classification in Split Computing DNNs with Early Exits

Author

Bajpai, Divya Jyoti, Jaiswal, Aastha, and Hanawal, Manjesh Kumar

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networks on resource-constrained devices like edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud computation offloading (split computing) to integrating early exits within DNN layers. Our work presents an innovative unified approach merging early exits and split computing. We determine the 'splitting layer', the optimal depth in the DNN for edge device computations, and whether to infer on edge device or be offloaded to the cloud for inference considering accuracy, computational efficiency, and communication costs. Also, Image classification faces diverse environmental distortions, influenced by factors like time of day, lighting, and weather. To adapt to these distortions, we introduce I-SplitEE, an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data. Experimental validation using Caltech-256 and Cifar-10 datasets subjected to varied distortions showcases I-SplitEE's ability to reduce costs by a minimum of 55% with marginal performance degradation of at most 5%., Comment: To appear in proceedings of IEEE International Conference on Communications 2024

Published
2024

232. Experimental verification of field-enhanced molecular vibrational scattering at single infrared antennas

Author

Virmani, Divya, Maciel-Escudero, Carlos, Hillenbrand, Rainer, and Schnell, Martin

Subjects
Physics - Optics
Abstract

Surface-enhanced infrared absorption (SEIRA) spectroscopy exploits the field enhancement near nanophotonic structures for highly sensitive characterization of (bio)molecules. The vibrational signature observed in SEIRA spectra is typically interpreted as field-enhanced molecular absorption. Here we study molecular vibrations in the near field of single antennas and show that the vibrational signature can be equally well explained by field-enhanced molecular scattering. Although the infrared scattering cross section of molecules is negligible compared to their absorption cross section, the interference between the molecular-scattered field and the incident field enhances the spectral signature caused by molecular vibrational scattering by 10 orders of magnitude, thus becoming as large as that of field-enhanced molecular absorption. We provide experimental evidence that field-enhanced molecular scattering can be measured, scales in intensity with the fourth power of the local field enhancement and fully explains the vibrational signature in SEIRA spectra in both magnitude and line shape. Our work may open new paths for developing highly sensitive SEIRA sensors that exploit the presented scattering concept., Comment: 38 pages, 5 figures, 6 extended data figures

Published
2024

233. Origin of discrete electrical switching in chemically heterogeneous vanadium oxide crystals

Author

Naik, B. Raju, Chandran, Yadu, Rohini, Kakunuri, Verma, Divya, Ramanathan, Shriram, and Balakrishnan, Viswanath

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

Electrically driven insulator-metal transitions in prototypical quantum materials such as VO2 offer a foundational platform for designing novel solid-state devices. Tuning the V: O stoichiometry offers a vast electronic phase space with non-trivial collective properties. Here, we report the discovery of discrete threshold switching voltages with constant threshold voltage difference between cycles in vanadium oxide crystals. The observed threshold fields over 10000 cycles are ~100X lower than that noted for stoichiometric VO2 and show unique discrete behaviour. We correlate the observed discrete memristor behaviour with the valence change mechanism and fluctuations in the chemical composition of spatially distributed VO2-VnO2n-1 complex oxide phases. Design of chemical heterogeneity in Mott insulators, therefore, offers an intriguing path to realizing low-energy neuromorphic devices., Comment: 21 pages, 4 figures

Published
2024

234. Elastic fields generated by multiple small inclusions with high mass density at nearly resonant frequencies

Author

Challa, Durga Prasad, Gangadaraiah, Divya, and Sini, Mourad

Subjects
Mathematics - Analysis of PDEs, 35C15, 35C20, 35Q60
Abstract

We derive the elastic field generated by multiple small-scaled inclusions distributed in a bounded set of $\mathbb{R}^3$. These inclusions are modeled with moderate values of the Lam\'e coefficients while they have a large relative mass density. These properties allow them to enjoy a sequences of resonant frequencies that can be computed via the eigenvalues of the volume integral operator having the Navier fundamental matrix as a kernel, i.e. the Navier volume operator. The dominant field, i.e. the Foldy-Lax field, models the multiple interactions between the inclusions with scattering coefficients that are inversely proportional to the difference between the used incident frequency and the already mentioned resonances. We show, in particular, that to reconstruct remotely the scattered field generated after $N$ interactions between the inclusions, one needs to use an incident frequency appropriately close to the proper resonance of the inclusions. We provide an explicit link between the order $N$ of interactions and the distance from the incident frequency to the resonance. Finally, if the cluster of the inclusions is densely distributed in a given bounded domain, then the expression of the induced dominant field suggests that the equivalent homogenized mass density can change sign depending if the used incident frequencies is smaller or larger than a certain threshold (which is explicitly given in terms of the resonant frequencies of the inclusions).

Published
2024
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235. A Broadband Conversion Loss Measurement Technique for Terahertz Harmonic Mixers

Author

Jayasankar, Divya, Reck, Theodore, Durant, Steven, Stake, Jan, and Hesler, Jeffrey

Subjects
Physics - Instrumentation and Detectors, Physics - Optics
Abstract

This letter presents an experimental characterization technique for assessing the performance of terahertz harmonic mixers across a wide frequency range. The total signal transfer loss of three mixers was measured in both up- and down-conversion configurations, and the conversion loss was determined through the solution of a linear system of equations. The proposed method uses LO signals with a frequency offset to ensure single sideband measurements, thereby eliminating the need for image-reject filters. The three-mixer method was verified by measurements of millimeter-wave mixers, which matched the traditional characterization method using a calibrated source and power meter. Given this successful millimeter-wave demonstration, we characterized three WM-86 Schottky diode x4-harmonic mixers from 2.2 to 3 THz. This technique presents a notable advantage for conducting broadband mixer characterizations, particularly in the terahertz frequency regime which lacks tunable, wide-band sources.

Published
2024
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236. Virtual Learning Decreases the Carbon Footprint of Medical Education.

Author

Sharma, Divya, Rizzo, Julianne, Nong, Yvonne, Murase, Lilia, Fong, Sydney, Lo, Kenny, Rosenbach, Misha, Murase, Jenny, and Sivamani, Raja

Subjects
Carbon dioxide emissions, Conference, Emissions, Greenhouse gas, In-person conferences, Travel, Virtual learning
Abstract

INTRODUCTION: The environmental impact of holding in-person academic conferences and continuing medical education (CME) programs can be significant. In-person conferences provide a unique social and professional platform to engage in networking and foster professional development; however, there is an opportunity for hybrid and virtual platforms to provide CME for broader audiences looking to improve their clinical skills and strengthen their knowledge base. This study seeks to describe the reduction in carbon emissions associated with a webinar hosted by an online dermatology-focused medical education platform. METHODS: This cross-sectional study used the location of deidentified virtual attendees of a webinar to predict the carbon emissions produced if attendees had instead traveled to the location of the most recent Integrative Dermatology Symposium (Sacramento, CA). Following collection of each virtual attendees location, the mode of transportation was predicted on the basis of each participants distance to the conference. RESULTS: The estimated carbon emissions were calculated for 576 participants. The total estimated, unadjusted carbon emissions for both attendees predicted to fly or drive was 370,100 kg CO2. The emissions produced per participant from those expected to fly to an in-person CME after adjusting for all additional passengers on every flight were 4.5 kg CO2. The emissions produced per participant from those expected to drive were 42.7 kg CO2. CONCLUSION: The use of a virtual CME webinar led to a significant reduction in travel-related carbon dioxide emissions when compared to running the same program in-person event. When accounting for all passengers traveling via plane on any flight, driving to an event produced more emissions per participant than flying.

Published
2024

237. Capacity for the management of kidney failure in the International Society of Nephrology South Asia region: report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA).

Author

Wijewickrama, Eranga, Alam, Muhammad, Bajpai, Divya, Divyaveer, Smita, Iyengar, Arpana, Kumar, Vivek, Qayyum, Ahad, Yadav, Shankar, Yadla, Manjusha, Arruebo, Silvia, Bello, Aminu, Caskey, Fergus, Damster, Sandrine, Donner, Jo-Ann, Jha, Vivekanand, Johnson, David, Levin, Adeera, Malik, Charu, Nangaku, Masaomi, Okpechi, Ikechi, Tonelli, Marcello, Ye, Feng, Singh Shah, Dibya, and Prasad, Narayan

Subjects
Global Kidney Health Atlas, International Society of Nephrology, South Asia, epidemiology, kidney failure, kidney replacement therapy
Abstract

The South Asia region is facing a high burden of chronic kidney disease (CKD) with limited health resources and low expenditure on health care. In addition to the burden of CKD and kidney failure from traditional risk factors, CKD of unknown etiologies from India and Sri Lanka compounds the challenges of optimal management of CKD in the region. From the third edition of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA), we present the status of CKD burden, infrastructure, funding, resources, and health care personnel using the World Health Organizations building blocks for health systems in the ISN South Asia region. The poor status of the public health care system and low health care expenditure resulted in high out-of-pocket expenditures for people with kidney disease, which further compounded the situation. There is insufficient country capacity across the region to provide kidney replacement therapies to cover the burden. The infrastructure was also not uniformly distributed among the countries in the region. There were no chronic hemodialysis centers in Afghanistan, and peritoneal dialysis services were only available in Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Kidney transplantation was not available in Afghanistan, Bhutan, and Maldives. Conservative kidney management was reported as available in 63% (n = 5) of the countries, yet no country reported availability of the core CKM care components. There was a high hospitalization rate and early mortality because of inadequate kidney care. The lack of national registries and actual disease burden estimates reported in the region prevent policymakers attention to CKD as an important cause of morbidity and mortality. Data from the 2023 ISN-GKHA, although with some limitations, may be used for advocacy and improving CKD care in the region.

Published
2024

238. Inadequate Use of Newer Treatments and Glycemic Control by Cardiovascular Risk and Sociodemographic Groups in US Adults with Diabetes in the NIH Precision Medicine Initiative All of Us Research Program.

Author

Devineni, Divya, Akbarpour, Meleeka, Gong, Yufan, and Wong, Nathan

Subjects
Cardiovascular risk, Diabetes, GLP-1 receptor agonists, SGLT2-inhibitors, Adult, Male, Humans, Glycemic Control, Precision Medicine, Cardiovascular Diseases, Sodium-Glucose Transporter 2, Risk Factors, Population Health, Diabetes Mellitus, Heart Disease Risk Factors, Atherosclerosis, Glucagon-Like Peptide 1, Glucose, Diabetes Mellitus, Type 2, Glucagon-Like Peptide-1 Receptor, Hypoglycemic Agents
Abstract

PURPOSE: Data are limited on sodium glucose co-transport 2 inhibitors (SGLT2-is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) among real-world cohorts of underrepresented patients. We examined these therapies and glycemic control in US adults with diabetes mellitus (DM) by atherosclerotic cardiovascular disease (ASCVD) risk and sociodemographic factors. METHODS: In the NIH Precision Medicine Initiative All of Us Research Program, we categorized DM as (1) moderate risk, (2) high risk, and (3) with ASCVD. We examined proportions on DM therapies, including SGLT2-i or GLP-1 RA, and at glycemic control by sociodemographic factors and CVD risk groups. RESULTS: Our 81,332 adults aged ≥ 18 years with DM across 340 US sites included 22.3% non-Hispanic Black, 17.2% Hispanic, and 1.8% Asian participants; 31.1%, 30.3%, and 38.6% were at moderate risk, high risk, or with ASCVD, respectively. Those with DM and ASCVD were most likely on SGLT2-i (8.6%) or GLP-1 RA (11.9%). SGLT2-i use was

Published
2024

239. Health Care Cost Reductions with Machine Learning–Directed Evaluations during Radiation Therapy — An Economic Analysis of a Randomized Controlled Study

Author

Natesan, Divya, Eisenstein, Eric L, Thomas, Samantha M, Eclov, Neville CW, Dalal, Nicole H, Stephens, Sarah J, Malicki, Mary, Shields, Stacey, Cobb, Alyssa, Mowery, Yvonne M, Niedzwiecki, Donna, Tenenbaum, Jessica D, Palta, Manisha, and Hong, Julian C

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Machine Learning, Good Health and Well Being
Published
2024

241. High-Strength Organic-Inorganic Composites with Superior Thermal Insulation and Acoustic Attenuation.

Author

Iyer, Divya, Galadari, Mohammad, Wirawan, Fernaldy, Huaco, Vanessa, Martinez, Ricardo, Gallagher, Michael, Pilon, Laurent, Ono, Kanji, Simonetti, Dante, Sant, Gaurav, and Srivastava, Samanvaya

Abstract

We demonstrate facile fabrication of highly filled, lightweight organic-inorganic composites comprising polyurethanes covalently linked with naturally occurring clinoptilolite microparticles. These polyurethane/clinoptilolite (PUC) composites are shown to mitigate particle aggregation usually observed in composites with high particle loadings and possess enhanced thermal insulation and acoustic attenuation compared with conventionally employed materials (e.g., drywall and gypsum). In addition to these functional properties, the PUC composites also possess flexural strengths and strain capacities comparable to and higher than ordinary Portland cement (OPC), respectively, while being ∼1.5× lighter than OPC. The porosity, density, and mechanical and functional properties of these composites are tuned by systematically varying their composition (diisocyanate, polyurethane, and inorganic contents) and the nature of the organic (reactivity and source of polyol) components. The fabrication process involves mild curing conditions and uses commonly available reagents (naturally occurring aluminosilicate particles, polyols, and diisocyanate), thereby making the process scalable. Finally, the composite properties are shown to be independent of the polyol source (virgin or recycled), underlining the generality of this approach for the scalable utilization of recycled polyols.

Published
2024

242. The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities.

Author

Venner, Eric, Patterson, Karynne, Kalra, Divya, Wheeler, Marsha, Chen, Yi-Ju, Kalla, Sara, Yuan, Bo, Karnes, Jason, Walker, Kimberly, Smith, Joshua, McGee, Sean, Radhakrishnan, Aparna, Haddad, Andrew, Empey, Philip, Wang, Qiaoyan, Lichtenstein, Lee, Toledo, Diana, Jarvik, Gail, Musick, Anjene, and Gibbs, Richard

Subjects
All of Us Research Program Investigators, Humans, Genetic Predisposition to Disease, Genomics, United States, Population Health, Hispanic or Latino, Black or African American, Black People, African People, European People
Abstract

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants with diverse ancestry and access to healthcare, representing one of the largest biomedical research repositories of its kind. Here, we examine pathogenic and likely pathogenic variants that were identified in the All of Us cohort. The European ancestry subgroup showed the highest overall rate of pathogenic variation, with 2.26% of participants having a pathogenic variant. Other ancestry groups had lower rates of pathogenic variation, including 1.62% for the African ancestry group and 1.32% in the Latino/Admixed American ancestry group. Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer or Hypercholesterolemia. Variant frequencies in many genes were consistent with the data from the public gnomAD database, with some notable exceptions resolved using gnomAD subsets. Differences in pathogenic variant frequency observed between ancestral groups generally indicate biases of ascertainment of knowledge about those variants, but some deviations may be indicative of differences in disease prevalence. This work will allow targeted precision medicine efforts at revealed disparities.

Published
2024

243. Evaluation of the Xpert MTB Host Response assay for the triage of patients with presumed pulmonary tuberculosis: a prospective diagnostic accuracy study in Viet Nam, India, the Philippines, Uganda, and South Africa

Author

Gupta-Wright, Ankur, Ha, Huy, Abdulgadar, Shima, Crowder, Rebecca, Emmanuel, Jerusha, Mukwatamundu, Job, Marcelo, Danaida, Phillips, Patrick PJ, Christopher, Devasahayam Jesudas, Nhung, Nguyen Viet, Theron, Grant, Yu, Charles, Nahid, Payam, Cattamanchi, Adithya, Worodria, William, Denkinger, Claudia M, Network, R2D2 TB, Thangakunam, Balamugesh, Shankar, Deepa, Ernest, Vinita, John, Flavita, Karthikeyan, Bharath, Sekar, Reena, Mangal, Divya, Vijayasree, Sai, Sankar, Swetha, Shibiya, Mary, Gajendran, Priyadarshini, Elango, Shanmugasundaram, Sekar, Rajasekar, Almonte, Jared, Basilio, Ramon, Cariaga, Asella Ruvijean, Destura, Raul, Dalay, Victoria, Dayawon, Karlo, Gelina, Darecil, Goleña, Joseph Aldwin, Golla, Maria Marissa, Ilagan, Gidalthi Jonathan, Lim, Dodge, Pabruada, Angelita, Reyes, Annalyn, Reyes, Roeus Vincent Arjay G, Tonquin, Maricef, Derendinger, Brigitta, Hendrikse, Megan, Okunola, Anna, Palmer, Zaida, Andama, Alfred, Kisakye, Esther, Mwebe, Sandra, Nakaye, Martha, Nyawere, Justine, Bukirwa, Alice, Mangeni, Wilson, Ssonko, John Baptist, Nakaweesa, Annet, Nassuna, Irene, Nekesa, Irene, Katumba, David, Asege, Lucy, Nalugwa, Talemwa, Dang, Hai, Dinh, Luong, Doan, Thien, Do, Hang, Do, Tam, Le, Hien, Le, Nguyet, Nguyen, Anh, Nguyen, Dong, Nguyen, Hanh, Nguyen, Hoang, Nguyen, Thanh, Pham, Nam, Pham, Thuong, Phan, Ha, Trinh, Trang, Castro, Robert, Cook, Catherine, Mochizuki, Tessa, Kato-Maeda, Midori, Nathavitharana, Ruvandhi, Nolan, Kevin, Olaru, Ioana-Diana, del Mar Castro Noriega, Maria, and Yerlikaya, Seda

Subjects
Epidemiology, Health Services and Systems, Public Health, Health Sciences, Tuberculosis, Infectious Diseases, Clinical Research, HIV/AIDS, Rare Diseases, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Infection, Good Health and Well Being, Adult, Female, Humans, Male, Cough, HIV Seropositivity, India, Mycobacterium tuberculosis, Philippines, Prospective Studies, Sensitivity and Specificity, South Africa, Sputum, Triage, Tuberculosis, Pulmonary, Uganda, Vietnam, R2D2 TB Network, Microbiology, Public Health and Health Services, Health services and systems, Public health
Abstract

BackgroundNon-sputum-based triage tests for tuberculosis are a priority for ending tuberculosis. We aimed to evaluate the diagnostic accuracy of the late-prototype Xpert MTB Host Response (Xpert HR) blood-based assay.MethodsWe conducted a prospective diagnostic accuracy study among outpatients with presumed tuberculosis in outpatient clinics in Viet Nam, India, the Philippines, Uganda, and South Africa. Eligible participants were aged 18 years or older and reported cough lasting at least 2 weeks. We excluded those receiving tuberculosis treatment in the preceding 12 months and those who were unwilling to consent. Xpert HR was performed on capillary or venous blood. Reference standard testing included sputum Xpert MTB/RIF Ultra and mycobacterial culture. We performed receiver operating characteristic (ROC) analysis to identify the optimal cutoff value for the Xpert HR to achieve the target sensitivity of 90% or more while maximising specificity, then calculated diagnostic accuracy using this cutoff value. This study was prospectively registered with ClinicalTrials.gov, NCT04923958.FindingsBetween July 13, 2021, and Aug 15, 2022, 2046 adults with at least 2 weeks of cough were identified, of whom 1499 adults (686 [45·8%] females and 813 [54·2%] males) had valid Xpert HR and reference standard results. 329 (21·9%) had microbiologically confirmed tuberculosis. Xpert HR had an area under the ROC curve of 0·89 (95% CI 0·86-0·91). The optimal cutoff value was less than or equal to -1·25, giving a sensitivity of 90·3% (95% CI 86·5-93·3; 297 of 329) and a specificity of 62·6% (95% CI 59·7-65·3; 732 of 1170). Sensitivity was similar across countries, by sex, and by subgroups, although specificity was lower in people living with HIV (45·1%, 95% CI 37·8-52·6) than in those not living with HIV (65·9%, 62·8-68·8; difference of 20·8%, 95% CI 13·0-28·6; p

Published
2024

244. Cancer-related cells and oncosomes in the liquid biopsy of pancreatic cancer patients undergoing surgery

Author

Shishido, Stephanie N, Lin, Emmeline, Nissen, Nicholas, Courcoubetis, George, Suresh, Divya, Mason, Jeremy, Osipov, Arsen, Hendifar, Andrew E, Lewis, Michael, Gaddam, Srinivas, Pandol, Stephen, Kuhn, Peter, and Lo, Simon K

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Digestive Diseases, Clinical Research, Cancer, Rare Diseases, Pancreatic Cancer, Patient Safety, Evaluation of treatments and therapeutic interventions, 6.4 Surgery, Immunology, Oncology and carcinogenesis
Abstract

Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 10% due to its late diagnosis, rapid metastasis, and chemotherapeutic resistance. For a small proportion (10-20%) of early-stage patients however, surgical resection of the pancreatic tumor offers the best chance for survival but the effect of surgery on disease dissemination is unknown. The primary objective of this study was to characterize cellular and acellular blood-based analytes in portal and peripheral blood before pancreatic manipulation, during tumor dissection and immediately after surgical resection to determine the effects of the surgery. This study used the non-enriching third generation High-Definition Single Cell Assay (HDSCA3.0) workflow to investigate heterogeneous circulating rare cell population in the blood. Blood from both sites taken before surgical manipulation of the pancreas had significantly greater incidence of total rare cellular and acellular analytes than normal donor samples. Post-surgery portal and peripheral blood had significantly greater incidence of specific cellular and acellular subtypes compared to the matched pre- and during-surgery samples. Our results reveal that in patients with PDAC liquid biopsy analytes are increased in both the portal and peripheral blood; portal blood contains a higher frequency of analytes than in the peripheral blood; total analytes in the portal and peripheral blood samples were significantly associated with the tumor volume and pathological T stage; and the surgical procedure increased the blood levels of circulating cellular and acellular analytes, but not Epi.CTCs or Mes.CTCs. This study demonstrates liquid biopsy's utility in monitoring patients with PDAC with surgically resectable disease.

Published
2024

245. Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters

Author

Loya, Manikanta, Sinha, Divya Anand, and Futrell, Richard

Subjects
Computer Science - Computation and Language
Abstract

The advancement of Large Language Models (LLMs) has led to their widespread use across a broad spectrum of tasks including decision making. Prior studies have compared the decision making abilities of LLMs with those of humans from a psychological perspective. However, these studies have not always properly accounted for the sensitivity of LLMs' behavior to hyperparameters and variations in the prompt. In this study, we examine LLMs' performance on the Horizon decision making task studied by Binz and Schulz (2023) analyzing how LLMs respond to variations in prompts and hyperparameters. By experimenting on three OpenAI language models possessing different capabilities, we observe that the decision making abilities fluctuate based on the input prompts and temperature settings. Contrary to previous findings language models display a human-like exploration exploitation tradeoff after simple adjustments to the prompt., Comment: EMNLP 2023

Published
2023
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246. Markovian embedding of nonlocal equations using spectral representation

Author

Jaganathan, Divya and Valani, Rahil N.

Subjects
Mathematics - Numerical Analysis, Physics - Computational Physics, Physics - Fluid Dynamics
Abstract

Nonlocal evolutionary equations containing memory terms model a variety of non-Markovian processes. We present a Markovian embedding procedure for a class of nonlocal equations by utilising the spectral representation of the nonlinear memory kernel. This allows us to transform the nonlocal system to a local-in-time system in an abstract extended space. We demonstrate our embedding procedure and its efficacy for two different physical models, namely the (i) 1D walking droplet and (ii) the 1D single-phase Stefan problem.

Published
2023

247. Use large language models to promote equity

Author

Pierson, Emma, Shanmugam, Divya, Movva, Rajiv, Kleinberg, Jon, Agrawal, Monica, Dredze, Mark, Ferryman, Kadija, Gichoya, Judy Wawira, Jurafsky, Dan, Koh, Pang Wei, Levy, Karen, Mullainathan, Sendhil, Obermeyer, Ziad, Suresh, Harini, and Vafa, Keyon

Subjects
Computer Science - Computers and Society
Abstract

Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been cautionary or negative, focusing on questions like "how might LLMs be biased and how would we mitigate those biases?" This is a vital discussion: the ways in which AI generally, and LLMs specifically, can entrench biases have been well-documented. But equally vital, and much less discussed, is the more opportunity-focused counterpoint: "what promising applications do LLMs enable that could promote equity?" If LLMs are to enable a more equitable world, it is not enough just to play defense against their biases and failure modes. We must also go on offense, applying them positively to equity-enhancing use cases to increase opportunities for underserved groups and reduce societal discrimination. There are many choices which determine the impact of AI, and a fundamental choice very early in the pipeline is the problems we choose to apply it to. If we focus only later in the pipeline -- making LLMs marginally more fair as they facilitate use cases which intrinsically entrench power -- we will miss an important opportunity to guide them to equitable impacts. Here, we highlight the emerging potential of LLMs to promote equity by presenting four newly possible, promising research directions, while keeping risks and cautionary points in clear view.

Published
2023

248. Vision-Based Automatic Groceries Tracking System -- Smart Homes

Author

Mereddy, Divya

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

With advanced AI, while every industry is growing at rocket speed, the smart home industry has not reached the next generation. There is still a huge leap of innovation that needs to happen before we call a home a Smart home. A Smart home should predict residents' needs and fulfill them in a timely manner. One of the important tasks of maintaining a home is timely grocery tracking and supply maintenance. Grocery tracking models are very famous in the retail industry but they are nonexistent in the common household. Groceries detection in household refrigerators or storage closets is very complicated compared to retail shelving data. In this paper, home grocery tracking problem is resolved by combining retail shelving data and fruits dataset with real-time 360 view data points collected from home groceries storage. By integrating this vision-based object detection system along with supply chain and user food interest prediction systems, complete automation of groceries ordering can be achieved., Comment: 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

Published
2023

249. Effects of cavity-mediated processes on the polarization entanglement of photon pairs emitted from quantum dots

Author

Samal, Mukesh Kumar, Mishra, Divya, and Kumar, Parvendra

Subjects
Quantum Physics
Abstract

Semiconductor quantum dots are among the best sources of on-demand entangled photon pairs. The degree of entanglement, however, is generally limited by the fine structure splitting of exciton states. In this paper, we theoretically investigate the generation of polarisation-entangled photon pairs under two-photon excitation and cavity-assisted two-photon emission, both in the weak and strong cavity coupling regimes. We demonstrate and clarify that cavity coupling together with an excitation pulse reduces the degree of entanglement in three different ways. Firstly, in a strong coupling regime, cavity introduces the unequal ac-Stark shift of horizontally and vertically polarised exciton states, which results in the effective splitting of exciton states. Secondly, it induces the cross-coupling between the exciton states even in the weak coupling regime, causing the creation of unfavorable two-photon states. Finally, higher excited states of the cavity modes also contribute to the reduction of entanglement. Therefore, in the setting considered here, cavity coupling, which is generally required for the efficient collection of emitted photons, degrades the entanglement both in weak and strong coupling regimes., Comment: 14 pages, 9 figures

Published
2023

250. 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 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Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

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