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1. Efficient Track Anything

Author: Xiong, Yunyang, Zhou, Chong, Xiang, Xiaoyu, Wu, Lemeng, Zhu, Chenchen, Liu, Zechun, Suri, Saksham, Varadarajan, Balakrishnan, Akula, Ramya, Iandola, Forrest, Krishnamoorthi, Raghuraman, Soran, Bilge, and Chandra, Vikas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for frame feature extraction and a memory mechanism that stores memory contexts from past frames to help current frame segmentation. The high computation complexity of multistage image encoder and memory module has limited its applications in real-world tasks, e.g., video object segmentation on mobile devices. To address this limitation, we propose EfficientTAMs, lightweight track anything models that produce high-quality results with low latency and model size. Our idea is based on revisiting the plain, nonhierarchical Vision Transformer (ViT) as an image encoder for video object segmentation, and introducing an efficient memory module, which reduces the complexity for both frame feature extraction and memory computation for current frame segmentation. We take vanilla lightweight ViTs and efficient memory module to build EfficientTAMs, and train the models on SA-1B and SA-V datasets for video object segmentation and track anything tasks. We evaluate on multiple video segmentation benchmarks including semi-supervised VOS and promptable video segmentation, and find that our proposed EfficientTAM with vanilla ViT perform comparably to SAM 2 model (HieraB+SAM 2) with ~2x speedup on A100 and ~2.4x parameter reduction. On segment anything image tasks, our EfficientTAMs also perform favorably over original SAM with ~20x speedup on A100 and ~20x parameter reduction. On mobile devices such as iPhone 15 Pro Max, our EfficientTAMs can run at ~10 FPS for performing video object segmentation with reasonable quality, highlighting the capability of small models for on-device video object segmentation applications.
Published: 2024

2. Explaining GPT-4's Schema of Depression Using Machine Behavior Analysis

Author: Ganesan, Adithya V, Varadarajan, Vasudha, Lal, Yash Kumar, Eijsbroek, Veerle C., Kjell, Katarina, Kjell, Oscar N. E., Dhanasekaran, Tanuja, Stade, Elizabeth C., Eichstaedt, Johannes C., Boyd, Ryan L., Schwartz, H. Andrew, and Flek, Lucie
Subjects: Computer Science - Computation and Language
Abstract: Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = .71 with self-report on 955 samples, and r = .81 with experts judgments on 209 samples); (b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78 ) that largely aligned with literature and self-report; except that GPT-4 (c) underemphasized suicidality's -- and overemphasized psychomotor's -- relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood)., Comment: 21 pages, 3 tables, 6 figures, 1 supplementary table, 83 references
Published: 2024

3. A Comparative Study of Distributed Feedback Optimizing Control Architectures

Author: Dirza, Risvan, Varadarajan, Hari Prasad, Aas, Vegard, Skogestad, Sigurd, and Krishnamoorthy, Dinesh
Subjects: Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract: This paper considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared resource. Such problems are often studied under the context of distributed optimization, where decisions are made locally in each subsystem, and are coordinated to optimize the overall performance. Here, we use distributed feedback-optimizing control framework, where the local systems and the coordinator problems are converted into feedback control problems. This is a powerful scheme that allows us to design feedback control loops, and estimate parameters locally, as well as provide local fast response, allowing different closed-loop time constants for each local subsystem. This paper provides a comparative study of different distributed feedback optimizing control architectures using two case studies. The first case study considers the problem of demand response in a residential energy hub powered by a common renewable energy source, and compares the different feedback optimizing control approaches using simulations. The second case study experimentally validates and compares the different approaches using a lab-scale experimental rig that emulates a subsea oil production network, where the common resource is the gas lift that must be optimally allocated among the wells. %The pros and cons of the different approaches are discussed., Comment: Accepted to IEEE Transactions on Control Systems Technology
Published: 2024

4. Wound Infiltration with Levobupivacaine, Ketorolac, and Adrenaline for Postoperative Pain Control after Spinal Fusion Surgery

Author: Valerio Pace, Arif Gul, Varadarajan Prakash, Chang Park, Giacomo Placella, and Geoffrey Raine
Subjects: wound infiltration, spinal surgery, spinal fusion, pain control, postoperative management, pain scores, Medicine
Abstract: Study Design This study enrolled patients in from a single center who underwent primary spinal fusion procedure and divided them into two groups (group-control study). Purpose Good local infiltration can reduce postoperative analgesic requirements and enable expedited discharge. Administration of a combination of levobupivacaine (200 mg/100 mL, 0.9% normal saline), ketorolac (30 mg), and adrenaline (0.5 mg) as a wound infiltrate is recommended at an optimum combination. Overview of Literature There is currently no consensus on the optimum intraoperative local infiltration of spinal surgery patients undergoing operative fusion. Methods Patients who were enrolled in two spinal centers (over 24 months) undergoing primary spinal fusion procedures were allocated into two groups, comparing the type of local infiltration used at the time of the procedure. Group 1 received the combination of levobupivacaine (200 mg), ketorolac (30 mg), and adrenaline (0.5 mg), while group 2 received other types of local anesthetics. Primary outcome measures include patient-controlled analgesia (PCA) use, morphine consumption, and length of hospital stay. Secondary outcome measure are as follows: days of physiotherapy, pain score, side effects, and complications. Results There are a total of 140 patients enrolled. Seventy-five patients enrolled were allocated to group 1, receiving the study combination, and 65 patients were assigned in group 2, receiving other local infiltrations. All primary outcome measures (consumption of morphine, use of PCA, and length of stay) were significantly higher in group 2 than the study combination (p
Published: 2021
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5. The effects of mosaicism on biological and clinical markers of Alzheimer's disease in adults with Down syndrome

Author: Xicota, Laura, Dang, Lam-Ha T, Lee, Alice, Krinsky-McHale, Sharon, Pang, Deborah, Melilli, Lisa, O'Bryant, Sid, Henson, Rachel L, Laymon, Charles, Lai, Florence, Rosas, H Diana, Ances, Beau, Lott, Ira, Hom, Christy, Christian, Bradley, Hartley, Sigan, Zaman, Shahid, Head, Elizabeth, Mapstone, Mark, Jin, Zhezhen, Silverman, Wayne, Schupf, Nicole, Handen, Benjamin, Lee, Joseph H, Syndrome, Alzheimer's Biomarker Consortium–Down, Aizenstein, Howard J, Ances, Beau M, Andrews, Howard F, Bell, Karen, Birn, Rasmus, Brickman, Adam M, Bulova, Peter, Cheema, Amrita, Chen, Kewei, Christian, Bradley T, Clare, Isabel, Clark, Lorraine, Cohen, Ann D, Constantino, John N, Doran, Eric W, Fagan, Anne, Feingold, Eleanor, Foroud, Tatiana M, Handen, Benjamin L, Harp, Jordan, Hartley, Sigan L, Henson, Rachel, Honig, Lawrence, Ikonomovic, Milos D, Johnson, Sterling C, Jordan, Courtney, Kamboh, M Ilyas, Keator, David, Klunk, William E, Kofler, Julia K, Kreisl, William Charles, Krinsky-McHale, Sharon J, Lao, Patrick, Lott, Ira T, Lupson, Victoria, Mathis, Chester A, Minhas, Davneet Singh, Nadkarni, Neelesh, O’Bryant, Sid, Parisi, Melisa, Pettersen, Melissa, Price, Julie C, Pulsifer, Margaret, Rafii, Michael S, Reiman, Eric, Rizvi, Batool, Ryan, Laurie, Schmitt, Frederick, Silverman, Wayne P, Tudorascu, Dana L, Tumuluru, Rameshwari, Tycko, Benjamin, Varadarajan, Badri, White, Desiree A, Yassa, Michael A, and Zhang, Fan
Subjects: Epidemiology, Health Sciences, Brain Disorders, Alzheimer's Disease, Aging, Acquired Cognitive Impairment, Down Syndrome, Prevention, Intellectual and Developmental Disabilities (IDD), Neurodegenerative, Neurosciences, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, 2.1 Biological and endogenous factors, 4.2 Evaluation of markers and technologies, Neurological, Congenital, Humans, Alzheimer Disease, Mosaicism, Biomarkers, Female, Male, Middle Aged, Amyloid beta-Peptides, Adult, tau Proteins, Aged, Down syndrome, Alzheimer's disease, Plasma biomarkers, CSF, PET, Alzheimer's Biomarker Consortium – Down Syndrome, mosaicism, Alzheimer&#x27, s disease, plasma biomarkers, Clinical Sciences, Public Health and Health Services, Clinical sciences
Abstract: BackgroundIndividuals with Down syndrome (DS) are at high risk of early-onset Alzheimer's disease (AD); yet, some 20 percent do not develop any signs of dementia until after 65 years or in their lifetime. Mosaicism could contribute to this phenotypic variation, where some disomic cells could lead to lower levels of gene products from chromosome 21.MethodsWe examined longitudinal neuropsychological and biomarker data from two large studies of DS: the Alzheimer Biomarker Consortium-Down syndrome study (ABC-DS) (n = 357); and a legacy study (n = 468). We assessed mosaicism using karyotyping or GWAS data. Participants had data on plasma AD biomarkers (Aβ40, Aβ42, tau, and NfL) and longitudinal cognitive measures. A subset had cerebrospinal fluid biomarkers (Aβ40, Aβ42, tau, ptau181, and NfL) and amyloid and tau PET data.FindingsFor both cohorts, the prevalence of mosaicism was
Published: 2024

6. LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding

Author: Shen, Xiaoqian, Xiong, Yunyang, Zhao, Changsheng, Wu, Lemeng, Chen, Jun, Zhu, Chenchen, Liu, Zechun, Xiao, Fanyi, Varadarajan, Balakrishnan, Bordes, Florian, Liu, Zhuang, Xu, Hu, Kim, Hyunwoo J., Soran, Bilge, Krishnamoorthi, Raghuraman, Elhoseiny, Mohamed, and Chandra, Vikas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effectively into a smaller size with state-of-the-art video understanding performance., Comment: Project page: https://vision-cair.github.io/LongVU
Published: 2024

7. A Census-Based Genetic Algorithm for Target Set Selection Problem in Social Networks

Author: Rahman, Md. Samiur, Ahsan, Mohammad Shamim, Chen, Tim, and Varadarajan, Vijayakumar
Subjects: Computer Science - Neural and Evolutionary Computing, Computer Science - Social and Information Networks
Abstract: This paper considers the Target Set Selection (TSS) Problem in social networks, a fundamental problem in viral marketing. In the TSS problem, a graph and a threshold value for each vertex of the graph are given. We need to find a minimum size vertex subset to "activate" such that all graph vertices are activated at the end of the propagation process. Specifically, we propose a novel approach called "a census-based genetic algorithm" for the TSS problem. In our algorithm, we use the idea of a census to gather and store information about each individual in a population and collect census data from the individuals constructed during the algorithm's execution so that we can achieve greater diversity and avoid premature convergence at locally optimal solutions. We use two distinct census information: (a) for each individual, the algorithm stores how many times it has been identified during the execution (b) for each network node, the algorithm counts how many times it has been included in a solution. The proposed algorithm can also self-adjust by using a parameter specifying the aggressiveness employed in each reproduction method. Additionally, the algorithm is designed to run in a parallelized environment to minimize the computational cost and check each individual's feasibility. Moreover, our algorithm finds the optimal solution in all cases while experimenting on random graphs. Furthermore, we execute the proposed algorithm on 14 large graphs of real-life social network instances from the literature, improving around 9.57 solution size (on average) and 134 vertices (in total) compared to the best solutions obtained in previous studies.
Published: 2024

8. Automating Semantic Analysis of System Assurance Cases using Goal-directed ASP

Author: Murugesan, Anitha, Wong, Isaac, Arias, Joaquín, Stroud, Robert, Varadarajan, Srivatsan, Salazar, Elmer, Gupta, Gopal, Bloomfield, Robin, and Rushby, John
Subjects: Computer Science - Logic in Computer Science, Computer Science - Software Engineering
Abstract: Assurance cases offer a structured way to present arguments and evidence for certification of systems where safety and security are critical. However, creating and evaluating these assurance cases can be complex and challenging, even for systems of moderate complexity. Therefore, there is a growing need to develop new automation methods for these tasks. While most existing assurance case tools focus on automating structural aspects, they lack the ability to fully assess the semantic coherence and correctness of the assurance arguments. In prior work, we introduced the Assurance 2.0 framework that prioritizes the reasoning process, evidence utilization, and explicit delineation of counter-claims (defeaters) and counter-evidence. In this paper, we present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities using common-sense reasoning and answer set programming solvers, specifically s(CASP). By employing these analysis techniques, we examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc. The application of these analyses provides both system developers and evaluators with increased confidence about the assurance case.
Published: 2024

9. Cerebrovascular disease is associated with Alzheimer’s plasma biomarker concentrations in adults with Down syndrome

Author: Edwards, Natalie C, Lao, Patrick J, Alshikho, Mohamad J, Ericsson, Olivia M, Rizvi, Batool, Petersen, Melissa E, O’Bryant, Sid, Aguilar, Lisi Flores, Simoes, Sabrina, Mapstone, Mark, Tudorascu, Dana L, Janelidze, Shorena, Hansson, Oskar, Handen, Benjamin L, Christian, Bradley T, Lee, Joseph H, Lai, Florence, Rosas, H Diana, Zaman, Shahid, Lott, Ira T, Yassa, Michael A, Aizenstein, Howard J, Ances, Beau M, Andrews, Howard F, Bell, Karen, Birn, Rasmus M, Brickman, Adam M, Bulova, Peter, Cheema, Amrita, Chen, Kewei, Clare, Isabel, Cohen, Ann D, Constantino, John N, Doran, Eric W, Fagan, Anne, Feingold, Eleanor, Foroud, Tatiana M, Harp, Jordan, Hartley, Sigan L, Head, Elizabeth, Henson, Rachel, Hom, Christy, Honig, Lawrence, Ikonomovic, Milos D, Johnson, Sterling C, Jordan, Courtney, Kamboh, M Ilyas, Keator, David, Klunk, William E, Kofler, Julia K, Kreisl, William Charles, Krinsky-McHale, Sharon J, Lao, Patrick, Laymon, Charles, Lupson, Victoria, Mathis, Chester A, Minhas, Davneet Singh, Nadkarni, Neelesh, Parisi, Melissa, Pang, Deborah, Petersen, Melissa, Price, Julie C, Pulsifer, Margaret, Rafii, Michael S, Reiman, Eric, Rosas, Herminia Diana, Ryan, Laurie, Schmitt, Frederick, Schupf, Nicole, Silverman, Wayne P, Tumuluru, Rameshwari, Tycko, Benjamin, Varadarajan, Badri, White, Desiree A, Zhang, Fan, Gutierrez, José, and Wilcock, Donna M
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Aging, Cerebrovascular, Dementia, Brain Disorders, Prevention, Clinical Research, Acquired Cognitive Impairment, Biomedical Imaging, Neurodegenerative, Alzheimer's Disease, Vascular Cognitive Impairment/Dementia, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Alzheimer's disease, Down syndrome, cerebrovascular disease, magnetic resonance imaging, biomarkers, Alzheimer’s Biomarkers Consortium–Down Syndrome (ABC-DS) Investigators, Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract: By age 40 years, over 90% of adults with Down syndrome have Alzheimer's disease pathology and most progress to dementia. Despite having few systemic vascular risk factors, individuals with Down syndrome have elevated cerebrovascular disease markers that track with the clinical progression of Alzheimer's disease, suggesting a role of cerebrovascular disease that is hypothesized to be mediated by inflammatory factors. This study examined the pathways through which small vessel cerebrovascular disease contributes to Alzheimer's disease-related pathophysiology and neurodegeneration in adults with Down syndrome. One hundred eighty-five participants from the Alzheimer's Biomarkers Consortium-Down Syndrome [mean (SD) age = 45.2 (9.3) years] with available MRI and plasma biomarker data were included in this study. White matter hyperintensity (WMH) volumes were derived from T2-weighted fluid-attenuated inversion recovery MRI scans, and plasma biomarker concentrations of amyloid beta 42/40, phosphorylated tau 217, astrocytosis (glial fibrillary acidic protein) and neurodegeneration (neurofilament light chain) were measured with ultrasensitive immunoassays. We examined the bivariate relationships of WMH, amyloid beta 42/40, phosphorylated tau 217 and glial fibrillary acidic protein with age-residualized neurofilament light chain across Alzheimer's disease diagnostic groups. A series of mediation and path analyses examined statistical pathways linking WMH and Alzheimer's disease pathophysiology to promote neurodegeneration in the total sample and groups stratified by clinical diagnosis. There was a direct and indirect bidirectional effect through the glial fibrillary acidic protein of WMH on phosphorylated tau 217 concentration, which was associated with neurofilament light chain concentration in the entire sample. Amongst cognitively stable participants, WMH was directly and indirectly, through glial fibrillary acidic protein, associated with phosphorylated tau 217 concentration, and in those with mild cognitive impairment, there was a direct effect of WMH on phosphorylated tau 217 and neurofilament light chain concentrations. There were no associations of WMH with biomarker concentrations among those diagnosed with dementia. The findings from this cross-sectional study suggest that among individuals with Down syndrome, cerebrovascular disease promotes neurodegeneration by increasing astrocytosis and tau pathophysiology in the presymptomatic phases of Alzheimer's disease, but future studies will need to confirm these associations with longitudinal data. This work joins an emerging literature that implicates cerebrovascular disease and its interface with neuroinflammation as a core pathological feature of Alzheimer's disease in adults with Down syndrome.
Published: 2024

10. Spherical collapse and black hole evaporation

Author: Varadarajan, Madhavan
Subjects: General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract: We consider spherically symmetric gravity coupled to a spherically symmetric scalar field with a specific coupling which depends on the Areal Radius. Classical collapse is described by the Vaidya solution. The semiclassical Einstein equations are well defined. Their solution describes an evaporating black hole. A balance law at future null infinity relates the rate of change of a back reaction-corrected Bondi mass to a manifestly positive flux. Its detailed form suggests a quantum extension of future null infinity along which all information including that in the collapsing matter is expected to emerge., Comment: 27 pages, 3 figures, typos corrected,references added
Published: 2024

11. Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting

Author: Srinivasan, Krishna Prasad Varadarajan, Gumpena, Prasanth, Yattapu, Madhusudhana, and Brahmbhatt, Vishal H.
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: In the domain of large language models (LLMs), arXiv:2305.16938 showed that few-shot full-model fine-tuning -- namely Vanilla Fine Tuning (FT) and Pattern-Based Fine Tuning (PBFT) --, and In-Context Learning (ICL) generalize similarly on Out-Of-Domain (OOD) datasets, but vary in terms of task adaptation. However, they both pose challenges, especially in term of memory requirements. In this paper, we further try to push the understanding of different fine-tuning strategies for LLM and aim to bring a myriad of these on the same pedestal for an elaborate comparison with full-model fine-tuning on two diverse datasets. To that end, we conducted a series of experiments, beginning with state-of-the-art methods like vanilla fine-tuning and Pattern-Based Fine-Tuning (PBFT) on pre-trained models across two datasets, COLA and MNLI. We then investigate adaptive fine-tuning and the efficiency of LoRA adapters in a few-shot setting. Finally, we also compare an alternative approach that has gained recent popularity -- context distillation -- with the vanilla FT and PBFT with and without few-shot setup. Our findings suggest that these alternative strategies that we explored can exhibit out-of-domain generalization comparable to that of vanilla FT and PBFT. PBFT under-performs Vanilla FT on out-of-domain (OOD) data, emphasizing the need for effective prompts. Further, our adaptive-fine tuning and LoRA experiments perform comparable or slightly worse than the standard fine-tunings as anticipated, since standard fine-tunings involve tuning the entire model. Finally, our context distillation experiments out-perform the standard fine-tuning methods. These findings underscore that eventually the choice of an appropriate fine-tuning method depends on the available resources (memory, compute, data) and task adaptability., Comment: 9 pages of main paper, 1 page of references, 6 appendix pages, 11 figures, 18 tables
Published: 2024

12. Backbone assignment of CcdB_G100T toxin from E.coli in complex with the toxin binding C-terminal domain of its cognate antitoxin CcdA

Author: Nanda, Bahnikana, Bhowmick, Jayantika, Varadarajan, Raghavan, and Sarma, Siddhartha P.
Published: 2024
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13. Interleukin-15-armoured GPC3 CAR T cells for patients with solid cancers

Author: Steffin, David, Ghatwai, Nisha, Montalbano, Antonino, Rathi, Purva, Courtney, Amy N., Arnett, Azlann B., Fleurence, Julien, Sweidan, Ramy, Wang, Tao, Zhang, Huimin, Masand, Prakash, Maris, John M., Martinez, Daniel, Pogoriler, Jennifer, Varadarajan, Navin, Thakkar, Sachin G., Lyon, Deborah, Lapteva, Natalia, Zhuyong, Mei, Patel, Kalyani, Lopez-Terrada, Dolores, Ramos, Carlos A., Lulla, Premal, Armaghany, Tannaz, Grilley, Bambi J., Gottschalk, Stephen, Dotti, Gianpietro, Metelitsa, Leonid S., Heslop, Helen E., Brenner, Malcolm K., Sumazin, Pavel, and Heczey, Andras
Published: 2024
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14. Bio-interface behaviour of graphene and semiconducting SWCNT:C(60) blend based nano photodiode for subretinal implant

Author: Moorthy Vijai Meyyappan, Varadarajan Parthasarathy, and Rathnasami Joseph Daniel
Subjects: fullerenes, photodiodes, prosthetics, cellular biophysics, polymer blends, graphene, biomedical electrodes, single-wall carbon nanotubes, nanomedicine, biomedical electronics, subretinal implant, bio-interface behaviour, hybrid interfaces, living cells, organic conjugated polymers, bioelectronics medicine, optical stimulation, bio-interface applications, neural probes, cellular scaffolds, drug release, nanophotodiode array device, quintessential graphene electrode, polymer blend, single-wall carbon nanotube, neural preparation, cell proliferation, pc-12 cells, c:c(60), Biotechnology, TP248.13-248.65, Biochemistry, QD415-436
Abstract: Hybrid interfaces between living cells and organic conjugated polymers play a pivotal role in bioelectronics medicine. Currently, conjugated polymers are widely utilised for optical stimulation of living cells and other bio-interface applications such as neural probes, cellular scaffolds and biosensors for drug release. In such a work, the authors fabricated and characterised the Nano Photodiode array device for the subretinal implant. However, the authors did not discuss in their report about the biocompatibility of this new device. In this work, the authors manifest that the quintessential graphene electrode and polymer blend of semiconducting single-wall carbon nanotube and C(60) fullerene (S-SWCNT:C(60)) sustains its optoelectronic properties all through the various strides for neural preparation. Further, the studies show that these materials can provide a favourable environment for cell proliferation and a high degree of biocompatibility. PC-12 cells used as a valuable model to validate the biocompatibility were grown successfully onto the S-SWCNT:C(60) active layer still preserving its optical and electrical properties. The improved electrical performance of nano photodiode made of graphene, S-SWCNT:C(60) established in the previous studies and the excellent bio-interface performance of this nano photodiode shows that the nano photodiode array proposed by the authors is a strong candidate for subretinal implants.
Published: 2020
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15. 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, Munkhdalai, Tsendsuren, 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, Perz, Bartek, 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, Jeff, 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

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

Author: Meric-Bernstam, Funda, Lloyd, Michael, Koc, Soner, Evrard, Yvonne, McShane, Lisa, Lewis, Michael, Evans, Kurt, Li, Dali, Rubinstein, Lawrence, Welm, Alana, Dean, Dennis, Srivastava, Anuj, Grover, Jeffrey, Ha, Min, Chen, Huiqin, Huang, Xuelin, Varadarajan, Kaushik, Wang, Jing, Roth, Jack, Welm, Bryan, Govinden, Ramaswamy, Ding, Li, Kaochar, Salma, Mitsiades, Nicholas, Carvajal-Carmona, Luis, Herylyn, Meenhard, Davies, Michael, Shapiro, Geoffrey, Fields, Ryan, Trevino, Jose, Harrell, Joshua, Doroshow, James, Chuang, Jeffrey, and Moscow, Jeffrey
Subjects: Humans, Animals, Xenograft Model Antitumor Assays, Neoplasms, National Cancer Institute (U.S.), United States, Mice, Antineoplastic Agents, Consensus
Abstract: Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.
Published: 2024

17. AAV gene therapy for hereditary spastic paraplegia type 50: a phase 1 trial in a single patient.

Author: Dowling, James, Pirovolakis, Terry, Devakandan, Keshini, Stosic, Ana, Pidsadny, Mia, Nigro, Elisa, Sahin, Mustafa, Ebrahimi-Fakhari, Darius, Messahel, Souad, Varadarajan, Ganapathy, Greenberg, Barry, Chen, Xin, Minassian, Berge, Cohn, Ronald, Bonnemann, Carsten, and Gray, Steven
Subjects: Humans, Dependovirus, Spastic Paraplegia, Hereditary, Genetic Therapy, Child, Preschool, Male, Genetic Vectors, Treatment Outcome
Abstract: There are more than 10,000 individual rare diseases and most are without therapy. Personalized genetic therapy represents one promising approach for their treatment. We present a road map for individualized treatment of an ultra-rare disease by establishing a gene replacement therapy developed for a single patient with hereditary spastic paraplegia type 50 (SPG50). Through a multicenter collaboration, an adeno-associated virus-based gene therapy product carrying the AP4M1 gene was created and successfully administered intrathecally to a 4-year-old patient within 3 years of diagnosis as part of a single-patient phase 1 trial. Primary endpoints were safety and tolerability, and secondary endpoints evaluated efficacy. At 12 months after dosing, the therapy was well tolerated. No serious adverse events were observed, with minor events, including transient neutropenia and Clostridioides difficile gastroenteritis, experienced but resolved. Preliminary efficacy measures suggest a stabilization of the disease course. Longer follow-up is needed to confirm the safety and provide additional insights on the efficacy of the therapy. Overall, this report supports the safety of gene therapy for SPG50 and provides insights into precision therapy development for rare diseases. Clinical trial registration: NCT06069687 .
Published: 2024

18. Comprehensive characterization and utilization of microcrystalline cellulose extracted from waste palm fruit: a biomass to biomaterial for cementitious composites

Author: Kokila, S., Varadarajan, Rajagopalan, and Venkatesan, G.
Published: 2024
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19. Clinical Profile of Children with Adenovirus Infection–A Hospital-based Observational Study

Author: Varadarajan, Poovazhagi, Subramanian, Ramesh, Srividya, Gomathy, Rangabashyam, Nisha, and Subramani, Seenivasan
Published: 2024
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20. Training Differentially Private Ad Prediction Models with Semi-Sensitive Features

Author: Chua, Lynn, Cui, Qiliang, Ghazi, Badih, Harrison, Charlie, Kamath, Pritish, Krichene, Walid, Kumar, Ravi, Manurangsi, Pasin, Narra, Krishna Giri, Sinha, Amer, Varadarajan, Avinash, and Zhang, Chiyuan
Subjects: Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Information Retrieval
Abstract: Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features. In this setting, a subset of the features is known to the attacker (and thus need not be protected) while the remaining features as well as the label are unknown to the attacker and should be protected by the DP guarantee. This task interpolates between training the model with full DP (where the label and all features should be protected) or with label DP (where all the features are considered known, and only the label should be protected). We present a new algorithm for training DP models with semi-sensitive features. Through an empirical evaluation on real ads datasets, we demonstrate that our algorithm surpasses in utility the baselines of (i) DP stochastic gradient descent (DP-SGD) run on all features (known and unknown), and (ii) a label DP algorithm run only on the known features (while discarding the unknown ones)., Comment: 7 pages, 4 figures
Published: 2024

21. Gemini: A Family of Highly Capable Multimodal Models

Author: Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
Published: 2023

22. SqueezeSAM: User friendly mobile interactive segmentation

Author: Varadarajan, Balakrishnan, Soran, Bilge, Iandola, Forrest, Xiang, Xiaoyu, Xiong, Yunyang, Wu, Lemeng, Zhu, Chenchen, Krishnamoorthi, Raghuraman, and Chandra, Vikas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and medical imaging. Despite its ability to process arbitrary user input and generate corresponding segmentation masks, SAM's 600 million parameter architecture, based on ViT-H, is not compatible with current mobile hardware due to its high computational demands and large model size. Our research aims to adapt SAM for use in mobile photography applications. To this end, we have developed a fully convolutional SqueezeSAM model architecture, which is 62.5 times faster and 31.6 times smaller than the original SAM, making it a viable solution for mobile applications. Furthermore, our tiny model achieves an mIOU within 1% of the original VIT-H architecture. Automated segmentation holds significant value in the creation flow for photography applications, as evidenced by its adoption by leading industry players like apple and capcut. To facilitate this automation, we employ salient object detection and simulate potential user clicks for foreground object selection, generating an initial segmentation mask that users can subsequently edit interactively. A common user expectation is that a click on a specific part of an object will result in the segmentation of the entire object. For example, a click on a person's t-shirt in a photo should ideally segment the entire person, not just the t-shirt. However, SAM typically only segments the clicked area. We address this limitation through a novel data augmentation scheme. Consequently, if a user clicks on a person holding a basketball, both the person and the basketball are segmented together, aligning with user expectations and enhancing the overall user experience.
Published: 2023

23. Growth of large-sized relaxor ferroelectric PZN-PT single crystals by modified flux growth method

Author: Vijayakumar, P., Manikandan, C., Sarguna, R. M., Amaladass, Edward Prabu, Ganesan, K., Roy, Varsha, Varadarajan, E., and Ganesamoorthy, S.
Subjects: Condensed Matter - Materials Science, Physics - Applied Physics
Abstract: A novel bottom-cooling high-temperature solution growth technique is developed for growing large-sized relaxor ferroelectric 0.91Pb(Zn1/3Nb2/3O3)-0.09PbTiO3 (PZN-PT) single crystals. During the growth, an inverse temperature gradient is maintained in the crucible base by flowing air at a controlled rate. This method restricts the number of spontaneously nucleated crystals at crucible bottom, reduces loss of volatile PbO component and favours the growth of large-sized PZN-PT single crystals. Large-sized PZN-PT single crystals of dimensions ~ 22x20x14 mm3 are reproducibly grown by the proposed method. The electrical characteristics of the PZN-PT wafers oriented along the <100>, <010> and <001> directions are investigated. PZN-PT wafers oriented along the <001> direction exhibited superior piezoelectric coefficient (d33) of ~ 2221 pm/V. The homogeneity of the physical parameters is analysed by preparing 10 elements with dimensions of ~5x2.5x2.5 mm3 which were cut from single wafer oriented along the <001> direction. The ferro-, piezo- and dielectric characteristics of these wafers were found to be highly uniform with small standard deviation. The observation of d33 value with less than 2 % deviation from mean value confirms the growth of high quality PZN-PT single crystals., Comment: 16 pages, 7 figures
Published: 2023
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24. Optimal Unbiased Randomizers for Regression with Label Differential Privacy

Author: Badanidiyuru, Ashwinkumar, Ghazi, Badih, Kamath, Pritish, Kumar, Ravi, Leeman, Ethan, Manurangsi, Pasin, Varadarajan, Avinash V, and Zhang, Chiyuan
Subjects: Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract: We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In particular, we leverage the trade-offs between bias and variance to construct better label randomizers depending on a privately estimated prior distribution over the labels. We demonstrate that these randomizers achieve state-of-the-art privacy-utility trade-offs on several datasets, highlighting the importance of reducing bias when training neural networks with label DP. We also provide theoretical results shedding light on the structural properties of the optimal unbiased randomizers., Comment: Proceedings version to appear at NeurIPS 2023
Published: 2023

25. EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Author: Xiong, Yunyang, Varadarajan, Bala, Wu, Lemeng, Xiang, Xiaoyu, Xiao, Fanyi, Zhu, Chenchen, Dai, Xiaoliang, Wang, Dilin, Sun, Fei, Iandola, Forrest, Krishnamoorthi, Raghuraman, and Chandra, Vikas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial, the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation, we propose EfficientSAMs, light-weight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further, we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs, and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification, object detection, instance segmentation, and semantic object detection, and find that our proposed pretraining method, SAMI, consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g., ~4 AP on COCO/LVIS) over other fast SAM models.
Published: 2023

26. Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API

Author: Aksu, Hidayet, Ghazi, Badih, Kamath, Pritish, Kumar, Ravi, Manurangsi, Pasin, Sealfon, Adam, and Varadarajan, Avinash V
Subjects: Computer Science - Cryptography and Security
Abstract: The Privacy Sandbox Attribution Reporting API has been recently deployed by Google Chrome to support the basic advertising functionality of attribution reporting (aka conversion measurement) after deprecation of third-party cookies. The API implements a collection of privacy-enhancing guardrails including contribution bounding and noise injection. It also offers flexibility for the analyst to allocate the contribution budget. In this work, we present methods for optimizing the allocation of the contribution budget for summary reports from the Attribution Reporting API. We evaluate them on real-world datasets as well as on a synthetic data model that we find to accurately capture real-world conversion data. Our results demonstrate that optimizing the parameters that can be set by the analyst can significantly improve the utility achieved by querying the API while satisfying the same privacy bounds.
Published: 2023

27. ALBA: Adaptive Language-based Assessments for Mental Health

Author: Varadarajan, Vasudha, Sikström, Sverker, Kjell, Oscar N. E., and Schwartz, H. Andrew
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Mental health issues differ widely among individuals, with varied signs and symptoms. Recently, language-based assessments have shown promise in capturing this diversity, but they require a substantial sample of words per person for accuracy. This work introduces the task of Adaptive Language-Based Assessment ALBA, which involves adaptively ordering questions while also scoring an individual's latent psychological trait using limited language responses to previous questions. To this end, we develop adaptive testing methods under two psychometric measurement theories: Classical Test Theory and Item Response Theory. We empirically evaluate ordering and scoring strategies, organizing into two new methods: a semi-supervised item response theory-based method ALIRT and a supervised Actor-Critic model. While we found both methods to improve over non-adaptive baselines, We found ALIRT to be the most accurate and scalable, achieving the highest accuracy with fewer questions (e.g., Pearson r ~ 0.93 after only 3 questions as compared to typically needing at least 7 questions). In general, adaptive language-based assessments of depression and anxiety were able to utilize a smaller sample of language without compromising validity or large computational costs.
Published: 2023

28. Role of Bi2O3 and PbO excess on the Di-/ferro-/piezoelectric and bi-polar strain properties in 0.39Bi(Ni0.5Ti0.5)O3–0.20PbZrO3–0.41PbTiO3 ternary ceramics near MPB

Author: Bharath, S. Guru, Babu, G. Anandha, Manikandan, C., and Varadarajan, E.
Published: 2025
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29. Understanding integrative approach of translational bioinformatics on cardiovascular disease: Myocardial Ischemia

Author: Ranganathan, Yeswanth, Nazar, Saayaa, Krishnan, Ravi Shankar, Dinakarkumar, Yuvaraj, Varadarajan, Vijayalakshmi, Sebastian, Lenita, and Rethinam, Brindha
Published: 2024
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30. Deciphering the role of VapBC13 and VapBC26 toxin antitoxin systems in the pathophysiology of Mycobacterium tuberculosis

Author: Sharma, Arun, Singh, Neelam, Bhasin, Munmun, Tiwari, Prabhakar, Chopra, Pankaj, Varadarajan, Raghavan, and Singh, Ramandeep
Published: 2024
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31. Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Author: Raju, Sekar Kidambi, Varadarajan, Ganesh Karthikeyan, Alharbi, Amal H., Kannan, Subhash, Khafaga, Doaa Sami, Sundaramoorthy, Raj Anand, Eid, Marwa M., and Towfek, S. K.
Published: 2024
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32. Author Correction: Influenza antibody breadth and effector functions are immune correlates from acquisition of pandemic infection of children

Author: Jia, Janice Z., Cohen, Carolyn A., Gu, Haogao, McLean, Milla R., Varadarajan, Raghavan, Bhandari, Nisha, Peiris, Malik, Leung, Gabriel M., Poon, Leo L. M., Tsang, Tim, Chung, Amy W., Cowling, Benjamin J., Leung, Nancy H. L., and Valkenburg, Sophie A.
Published: 2024
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33. Multi-antigen intranasal vaccine protects against challenge with sarbecoviruses and prevents transmission in hamsters

Author: Leekha, Ankita, Saeedi, Arash, Sefat, K M Samiur Rahman, Kumar, Monish, Martinez-Paniagua, Melisa, Damian, Adrian, Kulkarni, Rohan, Reichel, Kate, Rezvan, Ali, Masoumi, Shalaleh, Liu, Xinli, Cooper, Laurence J. N., Sebastian, Manu, Sands, Courtney M., Das, Vallabh E., Patel, Nimesh B., Hurst, Brett, and Varadarajan, Navin
Published: 2024
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34. A(H2N2) and A(H3N2) influenza pandemics elicited durable cross-reactive and protective antibodies against avian N2 neuraminidases

Author: Liang, Zaolan, Lin, Xia, Sun, Lihong, Edwards, Kimberly M., Song, Wenjun, Sun, Hailiang, Xie, Yanmin, Lin, Fangmei, Ling, Shiman, Liang, Tingting, Xiao, Biying, Wang, Jiaqi, Li, Min, Leung, Chin-Yu, Zhu, Huachen, Bhandari, Nisha, Varadarajan, Raghavan, Levine, Min Z., Peiris, Malik, Webster, Robert, Dhanasekaran, Vijaykrishna, Leung, Nancy H. L., Cowling, Benjamin J., Webby, Richard J., Ducatez, Mariette, Zanin, Mark, and Wong, Sook-San
Published: 2024
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35. Tumor biomechanical stiffness by magnetic resonance elastography predicts surgical outcomes and identifies biomarkers in vestibular schwannoma and meningioma

Author: Duhon, Bailey H., Thompson, Kristin, Fisher, Melanie, Kaul, Vivian F., Nguyen, Han TN., Harris, Michael S., Varadarajan, Varun, Adunka, Oliver F., Prevedello, Daniel M., Kolipaka, Arunark, and Ren, Yin
Published: 2024
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36. Influenza antibody breadth and effector functions are immune correlates from acquisition of pandemic infection of children

Author: Jia, Janice Z., Cohen, Carolyn A., Gu, Haogao, McLean, Milla R., Varadarajan, Raghavan, Bhandari, Nisha, Peiris, Malik, Leung, Gabriel M., Poon, Leo L. M., Tsang, Tim, Chung, Amy W., Cowling, Benjamin J., Leung, Nancy H. L., and Valkenburg, Sophie A.
Published: 2024
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37. Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs

Author: Magnéli, Martin, Borjali, Alireza, Takahashi, Eiji, Axenhus, Michael, Malchau, Henrik, Moratoglu, Orhun K., and Varadarajan, Kartik M.
Published: 2024
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38. Ex Vivo Delivery of mRNA to Immune Cells via a Nonendosomal Route Obviates the Need for Nucleoside Modification

Author: Bartika Ghoshal, Debajyoti Chakraborty, Manish Nag, Raghavan Varadarajan, and Siddharth Jhunjhunwala
Subjects: Biology (General), QH301-705.5, Biochemistry, QD415-436
Published: 2024
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39. Developing a Preservation Metadata Standard for Languages

Author: Varadarajan, Udaya and Bharti, Sneha
Subjects: Computer Science - Digital Libraries
Abstract: We have so many languages to communicate with others as humans. There are approximately 7000 languages in the world, and many are becoming extinct for a variety of reasons. In order to preserve and prevent the extinction of these languages, we need to preserve them. One way of preservation is to have a preservation metadata for languages. Metadata is data about data. Metadata is required for item description, preservation, and retrieval. There are various types of metadata, e.g., descriptive, administrative, structural, preservation, etc. After the literature study, the authors observed that there is a lack of study on the preservation metadata for language. Consequently, the purpose of this paper is to demonstrate the need for language preservation metadata. We found some archaeological metadata standards for this purpose, and after applying inclusion and exclusion criteria, we chose three archaeological metadata standards, namely: Archaeon-core, CARARE, and LIDO (Lightweight Information Describing Objects) for mapping metadata.
Published: 2023

40. Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research

Author: Sufian, Md Abu and Varadarajan, Jayasree
Subjects: Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: This research investigates road traffic accident severity in the UK, using a combination of machine learning, econometric, and statistical methods on historical data. We employed various techniques, including correlation analysis, regression models, GMM for error term issues, and time-series forecasting with VAR and ARIMA models. Our approach outperforms naive forecasting with an MASE of 0.800 and ME of -73.80. We also built a random forest classifier with 73% precision, 78% recall, and a 73% F1-score. Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for Explainable AI, highlighting influential factors like Driver_Home_Area_Type and Road_Type. Our study enhances understanding of accident severity and offers insights for evidence-based road safety policies., Comment: 36
Published: 2023

41. Magnetically threaded accretion disks in resistive magnetohydrodynamic simulations and asymptotic expansion

Author: Čemeljić, Miljenko, Kluźniak, Włodek, and Parthasarathy, Varadarajan
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Aims. A realistic model of magnetic linkage between a central object and its accretion disk is a prerequisite for understanding the spin history of stars and stellar remnants. To this end, we aim to provide an analytic model in agreement with magnetohydrodynamic (MHD) simulations. Methods. For the first time, we wrote a full set of stationary asymptotic expansion equations of a thin magnetic accretion disk, including the induction and energy equations. We also performed a resistive MHD simulation of an accretion disk around a star endowed with a magnetic dipole, using the publicly available code PLUTO. We compared the analytical results with the numerical solutions, and discussed the results in the context of previous solutions of the induction equation describing the star-disk magnetospheric interaction. Results. We found that the magnetic field threading the disk is suppressed by orders of magnitude inside thin disks, so the presence of the stellar magnetic field does not strongly affect the velocity field, nor the density profile inside the disk. Density and velocity fields found in the MHD simulations match the radial and vertical profiles of the analytic solution. Qualitatively, the MHD simulations result in an internal magnetic field similar to the solutions previously obtained by solving the induction equation in the disk alone. However, the magnetic field configuration is quantitatively affected by magnetic field inflation outside the disk; this is reflected in the net torque. The torque on the star is an order of magnitude larger in the magnetic than in the non-magnetic case. Spin-up of the star occurs on a timescale comparable to the accretion timescale in the MHD case, and is an order of magnitude slower in the absence of a stellar magnetic field., Comment: 17 pages, accepted in Astronomy and Astrophysics. arXiv admin note: text overlap with arXiv:1907.12592
Published: 2023
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42. A Parallel Ensemble of Metaheuristic Solvers for the Traveling Salesman Problem

Author: Varadarajan, Swetha and Whitley, Darrell
Subjects: Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract: The travelling salesman problem (TSP) is one of the well-studied NP-hard problems in the literature. The state-of-the art inexact TSP solvers are the Lin-Kernighan-Helsgaun (LKH) heuristic and Edge Assembly crossover (EAX). A recent study suggests that EAX with restart mechanisms perform well on a wide range of TSP instances. However, this study is limited to 2,000 city problems. We study for problems ranging from 2,000 to 85,900. We see that the performance of the solver varies with the type of the problem. However, combining these solvers in an ensemble setup, we are able to outperform the individual solver's performance. We see the ensemble setup as an efficient way to make use of the abundance of compute resources. In addition to EAX and LKH, we use several versions of the hybrid of EAX and Mixing Genetic Algorithm (MGA). A hybrid of MGA and EAX is known to solve some hard problems. We see that the ensemble of the hybrid version outperforms the state-of-the-art solvers on problems larger than 10,000 cities., Comment: Plan not to publish the article
Published: 2023

43. Identification of a clinically efficacious CAR T cell subset in diffuse large B cell lymphoma by dynamic multidimensional single-cell profiling

Author: Rezvan, Ali, Romain, Gabrielle, Fathi, Mohsen, Heeke, Darren, Martinez-Paniagua, Melisa, An, Xingyue, Bandey, Irfan N., Montalvo, Melisa J., Adolacion, Jay R. T., Saeedi, Arash, Sadeghi, Fatemeh, Fousek, Kristen, Puebla-Osorio, Nahum, Cooper, Laurence J. N., Bernatchez, Chantale, Singh, Harjeet, Ahmed, Nabil, Mattie, Mike, Bot, Adrian, Neelapu, Sattva, and Varadarajan, Navin
Published: 2024
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44. Development and Characterization of Guinea Pig Anti-Insulin Polyclonal Antibody

Author: Varadarajan, Sathiya, Muruganandam, Arumugam, and Kumar, V. Ramesh
Published: 2024
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45. Sepsis mimics among presumed sepsis patients at intensive care admission: a retrospective observational study

Author: Lengquist, Maria, Varadarajan, Anjali, Alestam, Shiva, Friberg, Hans, Frigyesi, Attila, and Mellhammar, Lisa
Published: 2024
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46. Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

Author: Varadarajan, Vasudha, Juhng, Swanie, Mahwish, Syeda, Liu, Xiaoran, Luby, Jonah, Luhmann, Christian, and Schwartz, H. Andrew
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.
Published: 2023

47. Deciphering the role of VapBC13 and VapBC26 toxin antitoxin systems in the pathophysiology of Mycobacterium tuberculosis

Author: Arun Sharma, Neelam Singh, Munmun Bhasin, Prabhakar Tiwari, Pankaj Chopra, Raghavan Varadarajan, and Ramandeep Singh
Subjects: Biology (General), QH301-705.5
Abstract: Abstract The expansion of VapBC TA systems in M. tuberculosis has been linked with its fitness and survival upon exposure to stress conditions. Here, we have functionally characterized VapBC13 and VapBC26 TA modules of M. tuberculosis. We report that overexpression of VapC13 and VapC26 toxins in M. tuberculosis results in growth inhibition and transcriptional reprogramming. We have also identified various regulatory proteins as hub nodes in the top response network of VapC13 and VapC26 overexpression strains. Further, analysis of RNA protection ratios revealed potential tRNA targets for VapC13 and VapC26. Using in vitro ribonuclease assays, we demonstrate that VapC13 and VapC26 degrade serT and leuW tRNA, respectively. However, no significant changes in rRNA cleavage profiles were observed upon overexpression of VapC13 and VapC26 in M. tuberculosis. In order to delineate the role of these TA systems in M. tuberculosis physiology, various mutant strains were constructed. We show that in comparison to the parental strain, ΔvapBC13 and ΔvapBC26 strains were mildly susceptible to oxidative stress. Surprisingly, the growth patterns of parental and mutant strains were comparable in aerosol-infected guinea pigs. These observations imply that significant functional redundancy exists for some TA systems from M. tuberculosis.
Published: 2024
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48. Purpura Fulminans from Capnocytophaga canimorsus in an Immunocompromised Host

Author: Indumathy Varadarajan, Natalie Pham, and Karen Ballen
Subjects: purpura fulminans, capnocytophaga canimorsus, stem cell transplant, chronic graft-versus-host disease, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Introduction: Purpura fulminans is a rare but fatal manifestation of Capnocytophaga canimorsus bacteremia that can present in immunocompromised hosts. This can have a profound impact on patients, including recipients of allogeneic hematopoietic stem cell transplant. Despite aggressive therapy, mortality can be as high as 60% and most patients require amputation of multiple extremities. Case Presentation: We present a 31-year-old woman s/p myeloablative allogeneic transplant, presenting with purpura fulminans and septic shock. She had been on Immunosuppressive therapy with rituximab and tacrolimus. Despite aggressive antibiotic coverage and supportive therapy, although her shock was resuscitated, she had to undergo bilateral below-knee amputations. Discussion: We attempt to highlight the clinical presentation, pathophysiology and potential therapeutic options for immunocompromised patients presenting with septic shock from C. canimorsus. The importance of pre-transplant counselling on handling and adoption of new pets to prevent zoonotic infections is also discussed. Early recognition and initiation of antibiotics play a crucial role to reduce mortality in patients receiving HSCT.
Published: 2024
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49. Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Author: Sekar Kidambi Raju, Ganesh Karthikeyan Varadarajan, Amal H. Alharbi, Subhash Kannan, Doaa Sami Khafaga, Raj Anand Sundaramoorthy, Marwa M. Eid, and S. K. Towfek
Subjects: Nanomaterial selection mechanism, Energy harvesting schemes, Reinforcement learning approaches, Deep Q-network, Fuzzy PROMETHEE, Road traffic conditions, Medicine, Science
Abstract: Abstract Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.
Published: 2024
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50. Cardiovascular Interactions of Renin-Angiotensin-Aldosterone System Assessed by Cardiac Magnetic Resonance: The Multi-Ethnic Study of Atherosclerosis.

Author: Varadarajan, Vinithra, Marques, Mateus D, Venkatesh, Bharath Ambale, Allison, Matthew, Ostovaneh, Mohammad R, Yoneyama, Kihei, Donekal, Sirisha, Shah, Ravi V, Murthy, Venkatesh L, Wu, Colin O, Tracy, Russell P, Ouyang, Pamela, Rochitte, Carlos E, Bluemke, David A, and Lima, Joao AC
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Atherosclerosis, Hypertension, Heart Disease, Aging, Cardiovascular, 6.1 Pharmaceuticals, Female, Humans, Middle Aged, Aged, Male, Renin-Angiotensin System, Renin, Aldosterone, Cardiovascular System, Magnetic Resonance Spectroscopy, blood pressure, cardiac magnetic resonance imaging, hypertension, left atrium dysfunction, left ventricle remodeling, renin–angiotensin–aldosterone system, vascular remodeling, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract: BackgroundThe effects of the renin-angiotensin-aldosterone system in cardiovascular system have been described based on small studies. The aim of this study was to evaluate the relationship between aldosterone and plasma renin activity (PRA) and cardiovascular structure and function.MethodsWe studied a random sample of Multi-Ethnic Study of Atherosclerosis participants who had aldosterone and PRA blood assays at 2003-2005 and underwent cardiac magnetic resonance at 2010. Participants taking angiotensin-converting enzyme inhibitors or angiotensin receptor blockers were excluded.ResultsThe aldosterone group was composed by 615 participants, mean age 61.6 ± 8.9 years, while the renin group was 580 participants, mean age 61.5 ± 8.8 years and both groups had roughly 50% females. In multivariable analysis, 1 SD increment of log-transformed aldosterone level was associated with 0.07 g/m2 higher left ventricle (LV) mass index (P = 0.04) and 0.11 ml/m2 higher left atrium (LA) minimal volume index (P < 0.01). Additionally, higher log-transformed aldosterone was associated with lower LA maximum strain and LA emptying fraction (P < 0.01). Aldosterone levels were not significantly associated with aortic measures. Log-transformed PRA was associated with lower LV end diastolic volume index (β standardized = 0.08, P = 0.05). PRA levels were not significantly associated with LA and aortic structural or functional differences.ConclusionsHigher levels of aldosterone and PRA are associated with concentric LV remodeling changes. Moreover, aldosterone was related to deleterious LA remodeling changes.
Published: 2023

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