Author: "Hoover A" / Search Limiters: Full Text - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Hoover A"' showing total 31,468 results

Start Over Author "Hoover A" Search Limiters Full Text

31,468 results on '"Hoover A"'

1. MODIFIED MAGNETITE NANOPARTICLES WITH THE JUICE OF PLANTAIN PSEUDOSTEM: AN OPTION FOR WATER DECONTAMINATION

Author: Camilo Eduardo García Henao, Guillermo Salamanca Grosso, Hoover Albeiro Valencia Sánchez, and Anderson Guarnizo Franco
Subjects: green chemistry, biofunctionalization, bioremediation, phytofunctionalization, natural products, nanotechnology, Chemistry, QD1-999
Abstract: In recent years, various methods for surface functionalization of magnetite nanoparticles with emerging modifications have been explored and reported. Green nano-chemistry aims to synthesize nanomaterials using bio-based resources, providing new materials for biological and environmental applications. Surface modification of magnetite nanoparticles using phytochemicals extracted from plants is a fundamental principle of green synthesis routes. Agricultural waste from different crops, such as plantain pseudostem, is of particular interest as a renewable resource due to its abundant availability, low toxicity, and economic feasibility. In this study, the juice extracted from plantain pseudostem was utilized for the phytofunctionalization of magnetite nanoparticles. Characterization techniques including transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and fourier-transform infrared spectroscopy (FTIR) confirmed the nanometric dimensions of the composite (size range of 12.6 ± 3.17 nm) and established its organic-inorganic nature. The superparamagnetic properties of the composite were also demonstrated. Moreover, the potential application of this nanohybrid in bioremediation for the immobilization of cadmium(II) and lead(II) showed promising results, highlighting its efficacy in managing water pollutants.
Published: 2024
Full Text: View/download PDF

2. Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

Author: Alejandro Gómez-García, Daniel A. Acuña Jiménez, William J. Zamora, Haruna L. Barazorda-Ccahuana, Miguel Á. Chávez-Fumagalli, Marilia Valli, Adriano D. Andricopulo, Vanderlan da S. Bolzani, Dionisio A. Olmedo, Pablo N. Solís, Marvin J. Núñez, Johny R. Rodríguez Pérez, Hoover A. Valencia Sánchez, Héctor F. Cortés Hernández, and José L. Medina-Franco
Subjects: chemical multiverse, chemical space, chemoinformatics, databases, diversity, drug discovery, Medicine, Pharmacy and materia medica, RS1-441
Abstract: The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.
Published: 2023
Full Text: View/download PDF

3. Dense Associative Memory Through the Lens of Random Features

Author: Hoover, Benjamin, Chau, Duen Horng, Strobelt, Hendrik, Ram, Parikshit, and Krotov, Dmitry
Subjects: Computer Science - Machine Learning
Abstract: Dense Associative Memories are high storage capacity variants of the Hopfield networks that are capable of storing a large number of memory patterns in the weights of the network of a given size. Their common formulations typically require storing each pattern in a separate set of synaptic weights, which leads to the increase of the number of synaptic weights when new patterns are introduced. In this work we propose an alternative formulation of this class of models using random features, commonly used in kernel methods. In this formulation the number of network's parameters remains fixed. At the same time, new memories can be added to the network by modifying existing weights. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories and shares their desirable computational properties., Comment: Accepted to NeurIPS 2024
Published: 2024

4. GPT-4o System Card

Author: OpenAI, Hurst, Aaron, Lerer, Adam, Goucher, Adam P., Perelman, Adam, Ramesh, Aditya, Clark, Aidan, Ostrow, AJ, Welihinda, Akila, Hayes, Alan, Radford, Alec, Mądry, Aleksander, Baker-Whitcomb, Alex, Beutel, Alex, Borzunov, Alex, Carney, Alex, Chow, Alex, Kirillov, Alex, Nichol, Alex, Paino, Alex, Renzin, Alex, Passos, Alex Tachard, Kirillov, Alexander, Christakis, Alexi, Conneau, Alexis, Kamali, Ali, Jabri, Allan, Moyer, Allison, Tam, Allison, Crookes, Amadou, Tootoochian, Amin, Tootoonchian, Amin, Kumar, Ananya, Vallone, Andrea, Karpathy, Andrej, Braunstein, Andrew, Cann, Andrew, Codispoti, Andrew, Galu, Andrew, Kondrich, Andrew, Tulloch, Andrew, Mishchenko, Andrey, Baek, Angela, Jiang, Angela, Pelisse, Antoine, Woodford, Antonia, Gosalia, Anuj, Dhar, Arka, Pantuliano, Ashley, Nayak, Avi, Oliver, Avital, Zoph, Barret, Ghorbani, Behrooz, Leimberger, Ben, Rossen, Ben, Sokolowsky, Ben, Wang, Ben, Zweig, Benjamin, Hoover, Beth, Samic, Blake, McGrew, Bob, Spero, Bobby, Giertler, Bogo, Cheng, Bowen, Lightcap, Brad, Walkin, Brandon, Quinn, Brendan, Guarraci, Brian, Hsu, Brian, Kellogg, Bright, Eastman, Brydon, Lugaresi, Camillo, Wainwright, Carroll, Bassin, Cary, Hudson, Cary, Chu, Casey, Nelson, Chad, Li, Chak, Shern, Chan Jun, Conger, Channing, Barette, Charlotte, Voss, Chelsea, Ding, Chen, Lu, Cheng, Zhang, Chong, Beaumont, Chris, Hallacy, Chris, Koch, Chris, Gibson, Christian, Kim, Christina, Choi, Christine, McLeavey, Christine, Hesse, Christopher, Fischer, Claudia, Winter, Clemens, Czarnecki, Coley, Jarvis, Colin, Wei, Colin, Koumouzelis, Constantin, Sherburn, Dane, Kappler, Daniel, Levin, Daniel, Levy, Daniel, Carr, David, Farhi, David, Mely, David, Robinson, David, Sasaki, David, Jin, Denny, Valladares, Dev, Tsipras, Dimitris, Li, Doug, Nguyen, Duc Phong, Findlay, Duncan, Oiwoh, Edede, Wong, Edmund, Asdar, Ehsan, Proehl, Elizabeth, Yang, Elizabeth, Antonow, Eric, Kramer, Eric, Peterson, Eric, Sigler, Eric, Wallace, Eric, Brevdo, Eugene, Mays, Evan, Khorasani, Farzad, Such, Felipe Petroski, Raso, Filippo, Zhang, Francis, von Lohmann, Fred, Sulit, Freddie, Goh, Gabriel, Oden, Gene, Salmon, Geoff, Starace, Giulio, Brockman, Greg, Salman, Hadi, Bao, Haiming, Hu, Haitang, Wong, Hannah, Wang, Haoyu, Schmidt, Heather, Whitney, Heather, Jun, Heewoo, Kirchner, Hendrik, Pinto, Henrique Ponde de Oliveira, Ren, Hongyu, Chang, Huiwen, Chung, Hyung Won, Kivlichan, Ian, O'Connell, Ian, Osband, Ian, Silber, Ian, Sohl, Ian, Okuyucu, Ibrahim, Lan, Ikai, Kostrikov, Ilya, Sutskever, Ilya, Kanitscheider, Ingmar, Gulrajani, Ishaan, Coxon, Jacob, Menick, Jacob, Pachocki, Jakub, Aung, James, Betker, James, Crooks, James, Lennon, James, Kiros, Jamie, Leike, Jan, Park, Jane, Kwon, Jason, Phang, Jason, Teplitz, Jason, Wei, Jason, Wolfe, Jason, Chen, Jay, Harris, Jeff, Varavva, Jenia, Lee, Jessica Gan, Shieh, Jessica, Lin, Ji, Yu, Jiahui, Weng, Jiayi, Tang, Jie, Yu, Jieqi, Jang, Joanne, Candela, Joaquin Quinonero, Beutler, Joe, Landers, Joe, Parish, Joel, Heidecke, Johannes, Schulman, John, Lachman, Jonathan, McKay, Jonathan, Uesato, Jonathan, Ward, Jonathan, Kim, Jong Wook, Huizinga, Joost, Sitkin, Jordan, Kraaijeveld, Jos, Gross, Josh, Kaplan, Josh, Snyder, Josh, Achiam, Joshua, Jiao, Joy, Lee, Joyce, Zhuang, Juntang, Harriman, Justyn, Fricke, Kai, Hayashi, Kai, Singhal, Karan, Shi, Katy, Karthik, Kavin, Wood, Kayla, Rimbach, Kendra, Hsu, Kenny, Nguyen, Kenny, Gu-Lemberg, Keren, Button, Kevin, Liu, Kevin, Howe, Kiel, Muthukumar, Krithika, Luther, Kyle, Ahmad, Lama, Kai, Larry, Itow, Lauren, Workman, Lauren, Pathak, Leher, Chen, Leo, Jing, Li, Guy, Lia, Fedus, Liam, Zhou, Liang, Mamitsuka, Lien, Weng, Lilian, McCallum, Lindsay, Held, Lindsey, Ouyang, Long, Feuvrier, Louis, Zhang, Lu, Kondraciuk, Lukas, Kaiser, Lukasz, Hewitt, Luke, Metz, Luke, Doshi, Lyric, Aflak, Mada, Simens, Maddie, Boyd, Madelaine, Thompson, Madeleine, Dukhan, Marat, Chen, Mark, Gray, Mark, Hudnall, Mark, Zhang, Marvin, Aljubeh, Marwan, Litwin, Mateusz, Zeng, Matthew, Johnson, Max, Shetty, Maya, Gupta, Mayank, Shah, Meghan, Yatbaz, Mehmet, Yang, Meng Jia, Zhong, Mengchao, Glaese, Mia, Chen, Mianna, Janner, Michael, Lampe, Michael, Petrov, Michael, Wu, Michael, Wang, Michele, Fradin, Michelle, Pokrass, Michelle, Castro, Miguel, de Castro, Miguel Oom Temudo, Pavlov, Mikhail, Brundage, Miles, Wang, Miles, Khan, Minal, Murati, Mira, Bavarian, Mo, Lin, Molly, Yesildal, Murat, Soto, Nacho, Gimelshein, Natalia, Cone, Natalie, Staudacher, Natalie, Summers, Natalie, LaFontaine, Natan, Chowdhury, Neil, Ryder, Nick, Stathas, Nick, Turley, Nick, Tezak, Nik, Felix, Niko, Kudige, Nithanth, Keskar, Nitish, Deutsch, Noah, Bundick, Noel, Puckett, Nora, Nachum, Ofir, Okelola, Ola, Boiko, Oleg, Murk, Oleg, Jaffe, Oliver, Watkins, Olivia, Godement, Olivier, Campbell-Moore, Owen, Chao, Patrick, McMillan, Paul, Belov, Pavel, Su, Peng, Bak, Peter, Bakkum, Peter, Deng, Peter, Dolan, Peter, Hoeschele, Peter, Welinder, Peter, Tillet, Phil, Pronin, Philip, Tillet, Philippe, Dhariwal, Prafulla, Yuan, Qiming, Dias, Rachel, Lim, Rachel, Arora, Rahul, Troll, Rajan, Lin, Randall, Lopes, Rapha Gontijo, Puri, Raul, Miyara, Reah, Leike, Reimar, Gaubert, Renaud, Zamani, Reza, Wang, Ricky, Donnelly, Rob, Honsby, Rob, Smith, Rocky, Sahai, Rohan, Ramchandani, Rohit, Huet, Romain, Carmichael, Rory, Zellers, Rowan, Chen, Roy, Chen, Ruby, Nigmatullin, Ruslan, Cheu, Ryan, Jain, Saachi, Altman, Sam, Schoenholz, Sam, Toizer, Sam, Miserendino, Samuel, Agarwal, Sandhini, Culver, Sara, Ethersmith, Scott, Gray, Scott, Grove, Sean, Metzger, Sean, Hermani, Shamez, Jain, Shantanu, Zhao, Shengjia, Wu, Sherwin, Jomoto, Shino, Wu, Shirong, Shuaiqi, Xia, Phene, Sonia, Papay, Spencer, Narayanan, Srinivas, Coffey, Steve, Lee, Steve, Hall, Stewart, Balaji, Suchir, Broda, Tal, Stramer, Tal, Xu, Tao, Gogineni, Tarun, Christianson, Taya, Sanders, Ted, Patwardhan, Tejal, Cunninghman, Thomas, Degry, Thomas, Dimson, Thomas, Raoux, Thomas, Shadwell, Thomas, Zheng, Tianhao, Underwood, Todd, Markov, Todor, Sherbakov, Toki, Rubin, Tom, Stasi, Tom, Kaftan, Tomer, Heywood, Tristan, Peterson, Troy, Walters, Tyce, Eloundou, Tyna, Qi, Valerie, Moeller, Veit, Monaco, Vinnie, Kuo, Vishal, Fomenko, Vlad, Chang, Wayne, Zheng, Weiyi, Zhou, Wenda, Manassra, Wesam, Sheu, Will, Zaremba, Wojciech, Patil, Yash, Qian, Yilei, Kim, Yongjik, Cheng, Youlong, Zhang, Yu, He, Yuchen, Zhang, Yuchen, Jin, Yujia, Dai, Yunxing, and Malkov, Yury
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
Published: 2024

5. Edge-based Modeling for Disease Transmission on Random Graphs: An Application to Mitigate a Syphilis Outbreak

Author: Zhao, S., Saeed, S., Carter, M., Stoner, B., Hoover, M., Guan, H., and Magpantay, F. M. G.
Subjects: Quantitative Biology - Populations and Evolution, Physics - Physics and Society, 00A71, 37N25, 92D25, 92D30
Abstract: Edge-based network models, especially those based on bond percolation methods, can be used to model disease transmission on complex networks and accommodate social heterogeneity while keeping tractability. Here we present an application of an edge-based network model to the spread of syphilis in the Kingston, Frontenac and Lennox & Addington (KFL&A) region of Southeastern Ontario, Canada. We compared the results of using a network-based susceptible-infectious-recovered (SIR) model to those generated from using a traditional mass action SIR model. We found that the network model yields very different predictions, including a much lower estimate of the final epidemic size. We also used the network model to estimate the potential impact of introducing a rapid syphilis point of care test (POCT) and treatment intervention strategy that has recently been implemented by the public health unit to mitigate syphilis transmission.
Published: 2024

6. Sparse Degree Optimization for BATS Codes

Author: Yin, Hoover H. F. and Wang, Jie
Subjects: Computer Science - Information Theory
Abstract: Batched sparse (BATS) code is a class of batched network code that can achieve a close-to-optimal rate when an optimal degree distribution is provided. We observed that most probability masses in this optimal distribution are very small, i.e., the distribution "looks" sparse. In this paper, we investigate the sparsity optimization of degree distribution for BATS codes that produces sparse degree distributions. There are many advantages to use a sparse degree distribution, say, it is robust to precision errors when sampling the degree distribution during encoding and decoding in practice. We discuss a few heuristics and also a way to obtain an exact sparsity solution. These approaches give a trade-off between computational time and achievable rate, thus give us the flexibility to adopt BATS codes in various scenarios, e.g., device with limited computational power, stable channel condition, etc., Comment: Full version of the conference version in ITW'24
Published: 2024

7. Criteria for identifying and evaluating locations that could potentially host the Cosmic Explorer observatories

Author: Daniel, Kathryne J., Smith, Joshua R., Ballmer, Stefan, Bristol, Warren, Driggers, Jennifer C., Effler, Anamaria, Evans, Matthew, Hoover, Joseph, Kuns, Kevin, Landry, Michael, Lovelace, Geoffrey, Lukinbeal, Chris, Mandic, Vuk, Pham, Kiet, Read, Jocelyn, Russell, Joshua B., Schiettekatte, Francois, Schofield, Robert M. S., Scholz, Christopher A., Shoemaker, David H., Sledge, Piper, and Strunk, Amber
Subjects: Physics - Instrumentation and Detectors, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Cosmic Explorer (CE) is a next-generation ground-based gravitational-wave observatory that is being designed in the 2020s and is envisioned to begin operations in the 2030s together with the Einstein Telescope in Europe. The CE concept currently consists of two widely separated L-shaped observatories in the United States, one with 40 km-long arms and the other with 20 km-long arms. This order of magnitude increase in scale with respect to the LIGO-Virgo-KAGRA observatories will, together with technological improvements, deliver an order of magnitude greater astronomical reach, allowing access to gravitational waves from remnants of the first stars and opening a wide discovery aperture to the novel and unknown. In addition to pushing the reach of gravitational-wave astronomy, CE endeavors to approach the lifecycle of large scientific facilities in a way that prioritizes mutually beneficial relationships with local and Indigenous communities. This article describes the (scientific, cost and access, and social) criteria that will be used to identify and evaluate locations that could potentially host the CE observatories., Comment: 12 pages, 1 figure
Published: 2024

8. Packet Aggregation May Harm Batched Network Coding

Author: Yin, Hoover H. F.
Subjects: Computer Science - Networking and Internet Architecture, Computer Science - Information Theory
Abstract: Batched network coding (BNC) is a solution to multi-hop transmission on networks with packet loss. To be compatible with the existing infrastructure, BNC is usually implemented over UDP. A single error bit will probably result in discarding the packet. UDP-Lite is a variant of UDP that supports partial checksums. As long as the data covered by the checksum is correct, damaged payload will be delivered. With UDP-Lite, we can cope with other techniques such as payload aggregation of BNC packets to reduce the protocol overhead, and forward error correction to combat against bit errors. Unlike traditional transmissions, BNC has a loss resilience feature and there are dependencies between BNC packets. In this paper, we conduct a preliminary investigation on BNC over UDP-Lite. We show that aggregating as much as we can is not always the best strategy, and a hop-by-hop distributed efficiency optimization approach may lead to a worse throughput compared with the scheme without aggregation in a long network. These unnatural results caution that a casual integration of techniques with BNC can be harmful, and give us hints on future research directions., Comment: Full version of the conference version in TENCON'24
Published: 2024

9. Time Efficiency of BATS Coding on Wireless Relay Network With Overhearing

Author: Yin, Hoover H. F.
Subjects: Computer Science - Information Theory
Abstract: Wireless relay network is a solution to extend the reach of a wireless connection by installing a relay node between the source node and the sink node. Due to the broadcast nature of wireless transmission, the sink node has a chance to receive part of the data sent by the source node. In this paper, we apply a network coding scheme called BATS codes on a wireless relay network where the relay node has a stable power supply, so that we can aim for the best decoding time instead of minimizing the number of transmissions for saving energy. We optimize the time efficiency that maximize the average decoding rate per unit time by some heuristics, and bring out a message that it is not optimal to set an average number of recoded packets per batch at the relay node equals the number of packets per batch sent by the source node., Comment: Full version of the conference version in TENCON'24
Published: 2024

10. N-dimensional maximum-entropy tomography via particle sampling

Author: Hoover, Austin
Subjects: Physics - Accelerator Physics, Physics - Data Analysis, Statistics and Probability
Abstract: This paper proposes an alternative implementation of the MENT algorithm, an exact maximum-entropy algorithm used to infer a phase space distribution from its projections. A key step in the MENT algorithm is to compute the distribution's projections via numerical integration. In this approach, the run time scales quickly with the phase space dimension and measurement resolution. The proposed MENT implementation computes the projections via particle sampling, rather than numerical integration, eliminating the dependence on the measurement resolution. Furthermore, with the appropriate sampling algorithm, the particle-based approach scales to $N$-dimensions without computer memory limitations. Using synthetic data, we demonstrate MENT convergence in four dimensions using a grid-based sampling method and in six dimensions using Markov Chain Monte Carlo (MCMC) sampling., Comment: 7 pages, 2 figures, comments welcome
Published: 2024

11. Four-dimensional phase space tomography from one-dimensional measurements of a hadron beam

Author: Hoover, Austin
Subjects: Physics - Accelerator Physics
Abstract: In this paper, we use one-dimensional measurements to infer the four-dimensional phase space density of an accumulated proton beam in the Spallation Neutron Source (SNS) accelerator. The reconstruction was performed by maximizing the distribution's entropy subject to the measurement constraints, and thus represents the most conservative inference from the data. The reconstructed distribution reproduces the measured profiles down to the noise level, and simulations indicate that the problem is reasonably well-constrained. Similar measurements could serve as benchmarks for beam dynamics simulations in the SNS or hadron accelerators., Comment: 9 pages, 9 figures, submitted to PRAB
Published: 2024

12. Nano-ablative immunotherapy for cancer treatment

Author: Hoover Ashley R., Liu Kaili, Valerio Trisha I., Li Min, Mukherjee Priyabrata, and Chen Wei R.
Subjects: photonics, nanomedicine, cancer immunotherpay, immune checkpoint therapy, nano-ablative immunotherapy, tumor microenvironment, Physics, QC1-999
Abstract: Immunotherapy has provided a new avenue to treat metastatic cancers, which result in ∼90% of cancer related deaths. However, current immunotherapies, such as immune checkpoint therapy (ICT), have met with limited success, primarily due to tumor intrinsic and extrinsic factors that inhibit antitumor immune responses. To overcome the immune suppression of the tumor microenvironment (TME) and enhance the tumoricidal activity of ICT, phototherapy, particularly photothermal therapy (PTT), combined with nanomedicine has become a viable option. PTT disrupts target tumor homeostasis, releasing tumor associated antigens (TAAs), tumor specific antigens (TSAs), danger associated molecular patterns (DAMPs), and scarce nutrients required to “feed” activated antitumor immune cells. While nanoparticles localize and specify the phototherapeutic effect, they can also be loaded with immune stimulants, TME modulators, and/or chemotherapeutic agents to greatly enhance immune stimulation and tumor killing. Combining these three technologies, which we term nano-ablative immunotherapy (NAIT), with ICT can greatly enhance their therapeutic effects. In this review, we will discuss the successes and limitations of NAIT + ICT. Specifically, we will discuss how the TME limits tumoricidal activity and what should be considered to overcome these limitations.
Published: 2021
Full Text: View/download PDF

13. Balance and modulation of immunoediting for cancer treatment using synergistic nano-photo-immuno effects

Author: Hoover Ashley R., Liu Kaili, and Chen Wei R.
Subjects: immunoediting, immunotherapy, metastatic cancers, nanotechnology, photonics, tumor microenvironment, Physics, QC1-999
Abstract: Nanotechnology, photonics, and immunotherapy are far-reaching technologies with the potential to revolutionize the field of cancer diagnostics and therapeutics. While each technology has limitations in cancer treatment, they can be synergized to exert profound impact on the balance and modulation of immunoediting in tumor microenvironment (TME) and in the entire host immune system. We provide our perspectives on how nano-photo-immuno interactions can be used as an effective therapy, particularly when combined with other treatment modalities, such as checkpoint immune therapy, chemotherapy, and TME modulation, to provide a long-term, tumor-specific immunity against tumor metastasis and tumor recurrence.
Published: 2021
Full Text: View/download PDF

14. The East Bay Diesel Exposure Project: a biomonitoring study of parents and their children in heavily impacted communities.

Author: Sultana, Daniel, Kauffman, Duyen, Castorina, Rosemary, Paulsen, Michael, Bartlett, Russell, Ranjbar, Kelsey, Gunier, Robert, Aguirre, Victor, Rowen, Marina, Garban, Natalia, DeGuzman, Josephine, She, Jianwen, Patterson, Regan, Simpson, Christopher, Bradman, Asa, and Hoover, Sara
Subjects: 1-nitropyrene, Biomonitoring, Children, Diesel exhaust, Human exposure, Urinary metabolites, Humans, Biological Monitoring, Female, Male, Child, Pyrenes, Adult, Vehicle Emissions, Air Pollution, Indoor, Environmental Exposure, San Francisco, Parents, Air Pollutants, Dust, Child, Preschool, Middle Aged, Seasons, Environmental Monitoring, Adolescent, Bays
Abstract: BACKGROUND: Diesel exhaust (DE) exposures pose concerns for serious health effects, including asthma and lung cancer, in California communities burdened by multiple stressors. OBJECTIVE: To evaluate DE exposures in disproportionately impacted communities using biomonitoring and compare results for adults and children within and between families. METHODS: We recruited 40 families in the San Francisco East Bay area. Two metabolites of 1-nitropyrene (1-NP), a marker for DE exposures, were measured in urine samples from parent-child pairs. For 25 families, we collected single-day spot urine samples during two sampling rounds separated by an average of four months. For the 15 other families, we collected daily spot urine samples over four consecutive days during the two sampling rounds. We also measured 1-NP in household dust and indoor air. Associations between urinary metabolite levels and participant demographics, season, and 1-NP levels in dust and air were evaluated. RESULTS: At least one 1-NP metabolite was present in 96.6% of the urine samples. Detection frequencies for 1-NP in dust and indoor air were 97% and 74%, respectively. Results from random effect models indicated that levels of the 1-NP metabolite 6-hydroxy-1-nitropyrene (6-OHNP) were significantly higher in parents compared with their children (p-value = 0.005). Urinary 1-NP metabolite levels were generally higher during the fall and winter months. Within-subject variability was higher than between-subject variability (~60% of total variance versus ~40%, respectively), indicating high short-term temporal variability. IMPACT: Biomonitoring, coupled with air monitoring, improves understanding of hyperlocal air pollution impacts. Results from these studies will inform the design of effective exposure mitigation strategies in disproportionately affected communities.
Published: 2024

15. Transformer Explainer: Interactive Learning of Text-Generative Models

Author: Cho, Aeree, Kim, Grace C., Karpekov, Alexander, Helbling, Alec, Wang, Zijie J., Lee, Seongmin, Hoover, Benjamin, and Chau, Duen Horng
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction
Abstract: Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and enabling smooth transitions across abstraction levels of mathematical operations and model structures. It runs a live GPT-2 instance locally in the user's browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. Our tool requires no installation or special hardware, broadening the public's education access to modern generative AI techniques. Our open-sourced tool is available at https://poloclub.github.io/transformer-explainer/. A video demo is available at https://youtu.be/ECR4oAwocjs., Comment: To be presented at IEEE VIS 2024
Published: 2024

16. Canonical Temperature Control by Molecular Dynamics

Author: Hoover, William Graham and Hoover, Carol Griswold
Subjects: Condensed Matter - Statistical Mechanics
Abstract: "Pedagogical derivations for Nos\'e's dynamics can be developed in two different ways, (i) by starting with a temperature-dependent Hamiltonian in which the variable $s$ scales the time or the mass, or (ii) by requiring that the equations of motion generate the canonical distribution including a Gaussian distribution in the friction coefficient $\zeta$. Nos\'e's papers follow the former approach. Because the latter approach is not only constructive and simple, but also can be generalized to other forms of the equations of motion, we illustrate it here. We begin by considering the probability density $f(q,p,\zeta)$ in an extended phase space which includes $\zeta$ as well as all pairs of phase variables $q$ and $p$. This density $f(q,p,\zeta)$ satisfies the conservation of probability (Liouville's Continuity Equation)" $$(\partial f/\partial t) + \sum (\partial (\dot q f)/\partial q) + \sum (\partial (\dot p f)/\partial p) + \sum (\partial (\dot \zeta f)/\partial \zeta) = 0 \ . $$ The multi-authored ``review''\cite{b1} motivated our quoting the history of Nos\'e and Nos\'e-Hoover mechanics, aptly described on page 31 of Bill's 1986 {\it Molecular Dynamics} book, reproduced above\cite{b2}., Comment: Nine pages with three figures written for Computational Methods in Science and Technology
Published: 2024
Full Text: View/download PDF

17. High-dimensional maximum-entropy phase space tomography using normalizing flows

Author: Hoover, Austin and Wong, Jonathan C.
Subjects: Physics - Accelerator Physics
Abstract: Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this paper, we highlight that such problems can be severely underdetermined and that entropy maximization is the most conservative solution strategy. We leverage normalizing flows -- invertible generative models -- to extend maximum-entropy tomography to six-dimensional phase space and perform numerical experiments to validate the model's performance. Our numerical experiments demonstrate consistency with exact two-dimensional maximum-entropy solutions and the ability to fit complicated six-dimensional distributions to large measurement sets in reasonable time., Comment: 13 pages, 7 figures, submitted to PRResearch
Published: 2024

18. Watermarking Language Models for Many Adaptive Users

Author: Cohen, Aloni, Hoover, Alexander, and Schoenbach, Gabe
Subjects: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: We study watermarking schemes for language models with provable guarantees. As we show, prior works offer no robustness guarantees against adaptive prompting: when a user queries a language model more than once, as even benign users do. And with just a single exception (Christ and Gunn, 2024), prior works are restricted to zero-bit watermarking: machine-generated text can be detected as such, but no additional information can be extracted from the watermark. Unfortunately, merely detecting AI-generated text may not prevent future abuses. We introduce multi-user watermarks, which allow tracing model-generated text to individual users or to groups of colluding users, even in the face of adaptive prompting. We construct multi-user watermarking schemes from undetectable, adaptively robust, zero-bit watermarking schemes (and prove that the undetectable zero-bit scheme of Christ, Gunn, and Zamir (2024) is adaptively robust). Importantly, our scheme provides both zero-bit and multi-user assurances at the same time. It detects shorter snippets just as well as the original scheme, and traces longer excerpts to individuals. The main technical component is a construction of message-embedding watermarks from zero-bit watermarks. Ours is the first generic reduction between watermarking schemes for language models. A challenge for such reductions is the lack of a unified abstraction for robustness -- that marked text is detectable even after edits. We introduce a new unifying abstraction called AEB-robustness. AEB-robustness provides that the watermark is detectable whenever the edited text "approximates enough blocks" of model-generated output., Comment: 39 pages
Published: 2024

19. Distributionally Robust Degree Optimization for BATS Codes

Author: Yin, Hoover H. F., Wang, Jie, and Chow, Sherman S. M.
Subjects: Computer Science - Information Theory
Abstract: Batched sparse (BATS) code is a network coding solution for multi-hop wireless networks with packet loss. Achieving a close-to-optimal rate relies on an optimal degree distribution. Technical challenges arise from the sensitivity of this distribution to the often empirically obtained rank distribution at the destination node. Specifically, if the empirical distribution overestimates the channel, BATS codes experience a significant rate degradation, leading to unstable rates across different runs and hence unpredictable transmission costs. Confronting this unresolved obstacle, we introduce a formulation for distributionally robust optimization in degree optimization. Deploying the resulting degree distribution resolves the instability of empirical rank distributions, ensuring a close-to-optimal rate, and unleashing the potential of applying BATS codes in real-world scenarios., Comment: 8 pages, accepted by 2024 IEEE International Symposium on Information Theory
Published: 2024

20. Interactive Visual Learning for Stable Diffusion

Author: Lee, Seongmin, Hoover, Benjamin, Strobelt, Hendrik, Wang, Zijie J., Peng, ShengYun, Wright, Austin, Li, Kevin, Park, Haekyu, Yang, Haoyang, and Chau, Polo
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract: Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion's hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users' web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7,200 users spanning 113 countries have used our open-sourced tool at https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/MbkIADZjPnA., Comment: 4 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2305.03509
Published: 2024

21. Reliability and Preventive Maintenance of Ducted Wind Turbines

Author: Sharar, Shafat and Hoover, Carl D.
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: This paper presents a reliability life analysis and preventive maintenance schedule for ducted wind turbines. Ducted wind turbines (DWT) are an emerging segment of the renewable energy industry with innovations that promise reliable, efficient, low-cost energy for consumer and small business markets. Many attempts have been made to build viable ducted turbines over the last century, but until recently none have succeeded commercially. Optimal shroud and blade designs are the focus of most engineering research to improve performance and efficiency, however, we hypothesize that an equally important key to the long-term success of small wind innovations is reliability analysis. For consumers and companies who want to efficiently maximize the lifespan of DWTs, this has significant ramifications. Operating beyond service life can result in catastrophic component failure and high replacement costs, making the technology economically infeasible. Our approach is focused on the analysis of 3.5 kW D3 turbines manufactured by Ducted Wind Turbines, Inc. We develop a component-level reliability analysis using ASTM E3159 and a consumer-level preventative maintenance schedule including failure modes and life estimates. Future research can use these findings to guide options for DWT life extension as well as localized maintenance solutions meant to reduce operational costs while preserving energy output.
Published: 2024

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

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

23. Bijections between colored compositions, Dyck paths, and polygon partitions

Author: Gil, Juan B., Hoover, Emma G., and Shearer, Jessica A.
Subjects: Mathematics - Combinatorics, 05A19
Abstract: In this paper, we give part-preserving bijections between three fundamental families of objects that serve as natural framework for many problems in enumerative combinatorics. Specifically, we consider compositions, Dyck paths, and partitions of a convex polygon, and identify suitable building blocks that are then appropriately decorated to achieve matching cardinalities. Our bijections are constructive and apply for the general case where the building blocks are allowed to come in different colors., Comment: 10 pages. Submitted for publication
Published: 2024

24. SECONDARY PRODUCTION OF THE CENTRAL RANGELAND REGION OF THE UNITED STATES : A STUDY OF YEARLING CATTLE PRODUCTION

Author: Raynor, Edward J., Derner, Justin D., Hartman, Melannie D., Dorich, Christopher D., Parton, William J., Hendrickson, John R., Harmoney, Keith R., Brennan, Jameson R., Owensby, Clenton E., Kaplan, Nicole E., Lutz, Susan M., Hoover, David L., and Augustine, David J.
Published: 2024

25. Temporal Characterization of Prion Shedding in Secreta of White-Tailed Deer in Longitudinal Study of Chronic Wasting Disease, United States

Author: Denkers, Nathaniel D., McNulty, Erin E., Kraft, Caitlyn N., Nalls, Amy V., Westrich, Joseph A., Hoover, Edward A., and Mathiason, Candace K.
Subjects: Prions -- Health aspects, White-tailed deer -- Health aspects, Health
Abstract: Chronic wasting disease (CWD) has been identified in North America, Asia, and Scandinavia (2-4) and is the only known prion disease in wildlife. CWD is often considered the most infectious [...]
Published: 2024
Full Text: View/download PDF

26. The Secure Rural Schools and Community Self-Determination Act: Background and Issues. CRS Report R41303, Version 41. Updated

Author: Library of Congress, Congressional Research Service (CRS) and Hoover, Katie
Abstract: This report provides background information on Forest Service (FS) and Bureau of Land Management (BLM) revenue-based payments and a brief overview of a related payment program--the Payments in Lieu of Taxes (PILT) program. Because the revenue-based, Secure Rural Schools and Community Self-Determination Act of 2000 (SRS), and PILT payments interact with one another in varying ways, proposals to amend the revenue-based programs or SRS have often included modifications to the PILT program as well. This report then provides on overview of the SRS payments and a discussion of some of the legislative issues facing Congress when considering these payment programs.
Published: 2023

27. Mindfulness, Self-Compassion, and Perfectionism in Physician Assistant Students: Implications for Wellness Curricula

Author: Butaney, Bhupin, Hoover, Eve B., Bernard, Kari, Coplan, Bettie, Barnett, Jacqueline, LeLacheur, Susan, Straker, Howard, Blesse-Hampton, Laura, Naidu, Amee, Blazar, Melinda, and Bolch, Charlotte
Published: 2024
Full Text: View/download PDF

28. On Defeating Graph Analysis of Anonymous Transactions

Author: Egger, Christoph, Lai, Russell W. F., Ronge, Viktoria, Woo, Ivy K. Y., and Yin, Hoover H. F.
Subjects: Computer Science - Cryptography and Security
Abstract: In a ring-signature-based anonymous cryptocurrency, signers of a transaction are hidden among a set of potential signers, called a ring, whose size is much smaller than the number of all users. The ring-membership relations specified by the sets of transactions thus induce bipartite transaction graphs, whose distribution is in turn induced by the ring sampler underlying the cryptocurrency. Since efficient graph analysis could be performed on transaction graphs to potentially deanonymise signers, it is crucial to understand the resistance of (the transaction graphs induced by) a ring sampler against graph analysis. Of particular interest is the class of partitioning ring samplers. Although previous works showed that they provide almost optimal local anonymity, their resistance against global, e.g. graph-based, attacks were unclear. In this work, we analyse transaction graphs induced by partitioning ring samplers. Specifically, we show (partly analytically and partly empirically) that, somewhat surprisingly, by setting the ring size to be at least logarithmic in the number of users, a graph-analysing adversary is no better than the one that performs random guessing in deanonymisation up to constant factor of 2.
Published: 2024
Full Text: View/download PDF

29. FAST: Factorizable Attention for Speeding up Transformers

Author: Gerami, Armin, Hoover, Monte, Dulepet, Pranav S., and Duraiswami, Ramani
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Numerical Analysis
Abstract: Motivated by the factorization inherent in the original fast multipole method and the improved fast Gauss transform we introduce a factorable form of attention that operates efficiently in high dimensions. This approach reduces the computational and memory complexity of the attention mechanism in transformers from $O(N^2)$ to $O(N)$. In comparison to previous attempts, our work presents a linearly scaled attention mechanism that maintains the full representation of the attention matrix without compromising on sparsification and incorporates the all-to-all relationship between tokens. We explore the properties of our new attention metric and conduct tests in various standard settings. Results indicate that our attention mechanism has a robust performance and holds significant promise for diverse applications where self-attention is used.
Published: 2024

30. Extracellular vesicles from GABAergic but not glutamatergic neurons protect against neurological dysfunction following cranial irradiation.

Author: Smith, Sarah, Ranjan, Kashvi, Hoover, Brianna, Drayson, Olivia, Acharya, Munjal, Kramár, Eniko, Baulch, Janet, and Limoli, Charles
Subjects: Animals, Extracellular Vesicles, Rats, Cranial Irradiation, GABAergic Neurons, Male, Hippocampus, Brain-Derived Neurotrophic Factor, Neurons, Glutamic Acid, Neuronal Plasticity, Glial Cell Line-Derived Neurotrophic Factor, Behavior, Animal
Abstract: Cranial irradiation used to control brain malignancies invariably leads to progressive and debilitating declines in cognition. Clinical efforts implementing hippocampal avoidance and NMDAR antagonism, have sought to minimize dose to radiosensitive neurogenic regions while normalizing excitatory/inhibitory (E/I) tone. Results of these trials have yielded only marginal benefits to cognition, prompting current studies to evaluate the potential of systemic extracellular vesicle (EV) therapy to restore neurocognitive functionality in the irradiated brain. Here we tested the hypothesis that EVs derived from inhibitory but not excitatory neuronal cultures would prove beneficial to cognition and associated pathology. Rats subjected to a clinically relevant, fractionated cranial irradiation paradigm were given multiple injections of either GABAergic- or glutamatergic-derived EV and subjected to behavioral testing. Rats treated with GABAergic but not glutamatergic EVs showed significant improvements on hippocampal- and cortical-dependent behavioral tasks. While each treatment enhanced levels of the neurotrophic factors BDNF and GDNF, only GABAergic EVs preserved granule cell neuron dendritic spine density. Additional studies conducted with GABAergic EVs, confirmed significant benefits on amygdala-dependent behavior and modest changes in synaptic plasticity as measured by long-term potentiation. These data point to a potentially more efficacious approach for resolving radiation-induced neurological deficits, possibly through a mechanism able to restore homeostatic E/I balance.
Published: 2024

31. Telehealth Utilization in Oral Medicine and Oral and Maxillofacial Surgery

Author: Miranda-Hoover, Annachristine, He, Puhan, Chau, Trinh, Cimba, Michael J, Francois, Kevin, Day, Saxon, Shanti, Rabie, Foote, Joseph, Wilder-Smith, Petra, Messadi, Diana V, and Le, Anh D
Subjects: Health Services and Systems, Public Health, Health Sciences, Dental/Oral and Craniofacial Disease, Health Services, Clinical Research, Networking and Information Technology R&D (NITRD), Good Health and Well Being, Humans, Retrospective Studies, Telemedicine, Delivery of Health Care, Surgery, Oral, Pandemics, health care access, oral and maxillofacial surgery, oral medicine, specialty care, teledentistry, telehealth, Library and Information Studies, Biomedical Engineering, Public Health and Health Services, Medical Informatics, Health services and systems, Public health
Abstract: Objectives: The objectives of this retrospective study were to analyze telehealth utilization for two specialty care practices: oral medicine (OM) and oral and maxillofacial surgery (OMFS) during the first 2 years of the pandemic, its impact as a new treatment modality and on participating providers, as well as identify the type of patient visit that most readily adopted telehealth. Methods: Retrospective study of patients who sought specialty services, OM and OMFS, at an outpatient clinic in a university health system setting between March 1, 2019, and February 28, 2022. Source data were obtained from Epic, an electronic medical record application. Data were graphed using Tableau and Microsoft Excel software. Statistical analysis was performed utilizing chi-squared test and analysis of variance (ANOVA). Results: OMFS utilized telehealth 12% of the time, and OM 8% of the time. The majority (87%) of telehealth visits were for return patients (RPs). Compared with the first year of the pandemic, there was a decrease in the number of telehealth visits in the second year (p = 0.0001). As of August 2022, new patient (NP) telehealth encounters have largely returned to prepandemic levels (0-1.5%), whereas RP telehealth visits remained at an average level of 11.4% (9.4-12.4%). Surveyed providers consider telehealth as an effective complement to in-person care and will continue its use (4.2/5 Likert scale). Conclusions: Telehealth has become a viable pathway of care for OM and OMFS who previously did not utilize the remote platform to deliver healthcare. As a new treatment modality, telehealth is perceived as impactful in increasing access to specialty care by participating providers. NP visits are now almost completely in person, but telehealth continues for RPs. Ongoing demand for telehealth highlights urgency to develop appropriate standards and effective remote diagnostic/monitoring tools to maximize telehealth's capability to leverage finite health care resources and increase access to specialty care.
Published: 2024

32. 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

33. Resource-constrained knowledge diffusion processes inspired by human peer learning

Author: Beikihassan, Ehsan, Hoover, Amy K., Koutis, Ioannis, Parviz, Ali, and Aghaieabiane, Niloofar
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By `natural', we mean processes that reflect human peer learning where the students' internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels.
Published: 2023

34. Tensor discriminant analysis on grassmann manifold with application to video based human action recognition

Author: Ozdemir, Cagri, Hoover, Randy C., Caudle, Kyle, and Braman, Karen
Published: 2024
Full Text: View/download PDF

35. Shifting forward: Urban ecology in perspective

Author: Pickett, Steward T. A., Frantzeskaki, Niki, Andersson, Erik, Barau, Aliyu Salisu, Childers, Daniel L., Hoover, Fushcia-Ann, Lugo, Ariel E., McPhearson, Timon, Nagendra, Harini, Schepers, Selina, and Sharifi, Ayyoob
Published: 2024
Full Text: View/download PDF

36. Forging just ecologies: 25 years of urban long-term ecological research collaboration

Author: Grove, Morgan, Pickett, Steward, Boone, Christopher G., Buckley, Geoffrey L., Anderson, Pippin, Hoover, Fushcia-Ann, Lugo, Ariel E., Meléndez-Ackerman, Elvia, Muñoz-Erickson, Tischa A., Nagendra, Harini, and Selles, L. Kidany
Published: 2024
Full Text: View/download PDF

37. The relational shift in urban ecology: From place and structures to multiple modes of coproduction for positive urban futures

Author: Pickett, Steward T. A., Simone, AbdouMaliq T., Anderson, Pippin, Sharifi, Ayyoob, Barau, Aliyu, Hoover, Fushcia-Ann, Childers, Daniel L., McPhearson, Timon, Muñoz-Erickson, Tischa A., Pacteau, Chantal, Grove, Morgan, Frantzeskaki, Niki, Nagendra, Harini, and Ginsberg, Joshua
Published: 2024
Full Text: View/download PDF

38. A transformative shift in urban ecology toward a more active and relevant future for the field and for cities

Author: Frantzeskaki, Niki, Childers, Daniel L., Pickett, Steward, Hoover, Fushcia-Ann, Anderson, Pippin, Barau, Aliyu, Ginsberg, Joshua, Grove, Morgan, Lodder, Marleen, Lugo, Ariel E., McPhearson, Timon, Muñoz-Erickson, Tischa A., Quartier, Mien, Schepers, Selina, Sharifi, Ayyoob, and van de Sijpe, Katrien
Published: 2024
Full Text: View/download PDF

39. System for Analysis of Wind Collocations (SAWC): A Novel Archive and Collocation Software Application for the Intercomparison of Winds from Multiple Observing Platforms

Author: Lukens, Katherine E., Garrett, Kevin, Ide, Kayo, Santek, David, Hoover, Brett, Huber, David, Hoffman, Ross N., and Liu, Hui
Subjects: Physics - Atmospheric and Oceanic Physics
Abstract: Accurate atmospheric 3D wind observations are a high priority in the science community. To address this requirement and to support researchers' needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019-August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. Recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC's capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture, Comment: 20 pages, 13 figures, 6 tables
Published: 2023

40. The impact of high-dimensional phase space correlations on the beam dynamics in a linear accelerator

Author: Hoover, A., Ruisard, K., Aleksandrov, A., Zhukov, A., Cousineau, S., and Shishlo, A.
Subjects: Physics - Accelerator Physics
Abstract: Hadron beams develop intensity-dependent transverse-longitudinal correlations within radio-frequency quadrupole (RFQ) accelerating structures. These correlations are only visible in six-dimensional phase space and are destroyed by reconstructions from low-dimensional projections. In this work, we estimate the effect of artificial decorrelation on the beam dynamics in the Spallation Neutron Source (SNS) linac and Beam Test Facility (BTF). We show that the evolution of a realistic initial distribution and its decorrelated twin converge during the early acceleration stages; thus, low-dimensional projections are probably sufficient for detailed predictions in high-power linacs., Comment: 5 pages, 6 figures, 68th ICFA Advanced Beam Dynamics Workshop on High-Intensity and High-Brightness Hadron Beams
Published: 2023

41. Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models

Author: Hoover, Benjamin, Strobelt, Hendrik, Krotov, Dmitry, Hoffman, Judy, Kira, Zsolt, and Chau, Duen Horng
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Dynamical Systems
Abstract: The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks. Though the generative process is traditionally understood as an "iterative denoiser", there is no universally accepted language to describe it. We introduce a novel perspective to describe DMs using the mathematical language of memory retrieval from the field of energy-based Associative Memories (AMs), making efforts to keep our presentation approachable to newcomers to both of these fields. Unifying these two fields provides insight that DMs can be seen as a particular kind of AM where Lyapunov stability guarantees are bypassed by intelligently engineering the dynamics (i.e., the noise and step size schedules) of the denoising process. Finally, we present a growing body of evidence that records DMs exhibiting empirical behavior we would expect from AMs, and conclude by discussing research opportunities that are revealed by understanding DMs as a form of energy-based memory., Comment: 15 pages, 4 figures
Published: 2023

42. Urine proteomic signatures of histological class, activity, chronicity, and treatment response in lupus nephritis.

Author: Fava, Andrea, Buyon, Jill, Magder, Laurence, Hodgin, Jeff, Rosenberg, Avi, Demeke, Dawit, Rao, Deepak, Arazi, Arnon, Celia, Alessandra, Putterman, Chaim, Anolik, Jennifer, Barnas, Jennifer, DallEra, Maria, Wofsy, David, Furie, Richard, Kamen, Diane, Kalunian, Kenneth, James, Judith, Guthridge, Joel, Atta, Mohamed, Monroy Trujillo, Jose, Fine, Derek, Clancy, Robert, Belmont, H, Izmirly, Peter, Apruzzese, William, Goldman, Daniel, Berthier, Celine, Hoover, Paul, Hacohen, Nir, Raychaudhuri, Soumya, Davidson, Anne, Diamond, Betty, and Petri, Michelle
Subjects: Autoimmunity, Diagnostics, Lupus, Nephrology, Proteomics, Humans, Lupus Nephritis, Proteomics, Proteinuria, Inflammation, Aggression
Abstract: Lupus nephritis (LN) is a pathologically heterogenous autoimmune disease linked to end-stage kidney disease and mortality. Better therapeutic strategies are needed as only 30%-40% of patients completely respond to treatment. Noninvasive biomarkers of intrarenal inflammation may guide more precise approaches. Because urine collects the byproducts of kidney inflammation, we studied the urine proteomic profiles of 225 patients with LN (573 samples) in the longitudinal Accelerating Medicines Partnership in RA/SLE cohort. Urinary biomarkers of monocyte/neutrophil degranulation (i.e., PR3, S100A8, azurocidin, catalase, cathepsins, MMP8), macrophage activation (i.e., CD163, CD206, galectin-1), wound healing/matrix degradation (i.e., nidogen-1, decorin), and IL-16 characterized the aggressive proliferative LN classes and significantly correlated with histological activity. A decline of these biomarkers after 3 months of treatment predicted the 1-year response more robustly than proteinuria, the standard of care (AUC: CD206 0.91, EGFR 0.9, CD163 0.89, proteinuria 0.8). Candidate biomarkers were validated and provide potentially treatable targets. We propose these biomarkers of intrarenal immunological activity as noninvasive tools to diagnose LN and guide treatment and as surrogate endpoints for clinical trials. These findings provide insights into the processes involved in LN activity. This data set is a public resource to generate and test hypotheses and validate biomarkers.
Published: 2024

43. Approach to Validate ISPM-15 Compliance for Commercial Treatment Certification of Dielectric Standard Heating of Bulk Solid Wood Packing Materials using Radio Frequency

Author: Szymona, Karolina K., Janowiak, John J., Mack, Ron, Hamelin, Mark, Gagnon, Mark, and Hoover, Kelli
Subjects: Regulatory compliance -- Methods, Dielectric heating -- Methods, Company business management, Business, Forest products industry
Abstract: To substantially reduce the risk of alien invasive species moving to new geographic areas, phytosanitary treatment of wood packaging materials (WPM) in compliance with the International Standard of Phytosanitary Measures No. 15 (ISPM-15) is required by trading partners. Approved treatments include conventional heating, methyl bromide and sulfuryl fluoride fumigation, and dielectric heating (DH). The DH standard was officially adopted in 2013 but has not been practiced commercially due primarily to insufficient operational validation at commercial scale. In 2022, we converted our 50-kW radio frequency (RF) unit with a 1,200-board foot capacity from an oscillator electromagnetic field power generator to a solid-state power supply, which allows for selective power input adjustments during treatment; we also switched from a five-plate to a three-plate winged electrode system to improve heating uniformity. Each loading cycle can treat sufficient material to build ~94 standard Grocery Manufacturers Association pallets. Our research team characterized the dielectric heating pattern and options for monitoring wood temperatures over a wide-ranging test matrix of WPM that varied by wood species, dimension, moisture content, and loading configuration. We found that this upgraded RF system markedly reduced treatment times and improved heating uniformity, allowing us to develop methods that can be used to verify compliance with ISPM-15 for improved technology transfer to industry. We also discuss the operational cost of RF treatment and make general cost comparisons to conventional heat treatment for WPM., Invasive species are one of the major causes of global ecological and economic impacts on importing countries through inadvertent introduction during international trade (Olson 2006). In the United States alone, [...]
Published: 2024
Full Text: View/download PDF

44. Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurements

Author: Gallastegi, Unay Dorken, Rueda-Chacon, Hoover, Stevens, Martin J., and Goyal, Vivek K
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing
Abstract: Passive hyperspectral long-wave infrared measurements are remarkably informative about the surroundings, such as remote object material composition, temperature, and range; and air temperature and gas concentrations. Remote object material and temperature determine the spectrum of thermal radiance, and range, air temperature, and gas concentrations determine how this spectrum is modified by propagation to the sensor. We computationally separate these phenomena, introducing a novel passive range imaging method based on atmospheric absorption of ambient thermal radiance. Previously demonstrated passive absorption-based ranging methods assume hot and highly emitting objects. However, the temperature variation in natural scenes is usually low, making range imaging challenging. Our method benefits from explicit consideration of air emission and parametric modeling of atmospheric absorption. To mitigate noise in low-contrast scenarios, we jointly estimate range and intrinsic object properties by exploiting a variety of absorption lines spread over the infrared spectrum. Along with Monte Carlo simulations that demonstrate the importance of regularization, temperature differentials, and availability of many spectral bands, we apply this method to long-wave infrared (8--13 $\mu$m) hyperspectral image data acquired from natural scenes with no active illumination. Range features from 15m to 150m are recovered, with good qualitative match to unaligned lidar data., Comment: 15 pages, 14 figures
Published: 2023

45. Interstellar Comets from Post-Main Sequence Systems as Tracers of Extrasolar Oort Clouds

Author: Levine, W. Garrett, Taylor, Aster G., Seligman, Darryl Z., Hoover, Devin J., Jedicke, Robert, Bergner, Jennifer B., and Laughlin, Gregory P.
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Interstellar small bodies are unique probes into the histories of exoplanetary systems. One hypothesized class of interlopers are "Jurads," exo-comets released into the Milky Way during the post-main sequence as the thermally-pulsing asymptotic giant branch (AGB) host stars lose mass. In this study, we assess the prospects for the Legacy Survey of Space and Time (LSST) to detect a Jurad and examine whether such an interloper would be observationally distinguishable from exo-comets ejected during the (pre-)main sequence. Using analytic and numerical methods, we estimate the fraction of exo-Oort Cloud objects that are released from 1-8 solar mass stars during post-main sequence evolution. We quantify the extent to which small bodies are altered by the increased luminosity and stellar outflows during the AGB, finding that some Jurads may lack hypervolatiles and that stellar winds could deposit dust that covers the entire exo-comet surface. Next, we construct models of the interstellar small body reservoir for various size-frequency distribution slopes, characteristic sizes, and the total mass sequestered in the minor planets of exo-Oort Clouds. Even with the LSST's increased search volume compared to contemporary surveys, we find that detecting a Jurad is unlikely but not infeasible given the current understanding of (exo)planet formation., Comment: 28 pages, 13 figures; accepted to PSJ
Published: 2023

46. Safety and efficacy of inhaled interferon-β1a (SNG001) in adults with mild-to-moderate COVID-19: a randomized, controlled, phase II trial

Author: Jagannathan, Prasanna, Chew, Kara W, Giganti, Mark J, Hughes, Michael D, Moser, Carlee, Main, Mark J, Monk, Phillip D, Javan, Arzhang Cyrus, Li, Jonathan Z, Fletcher, Courtney V, McCarthy, Caitlyn, Wohl, David A, Daar, Eric S, Eron, Joseph J, Currier, Judith S, Singh, Upinder, Smith, Davey M, Fischer, William, Team, ACTIV-2 A5401 Study, Chew, Kara, Smith, David, Daar, Eric, Wohl, David, Currier, Judith, Eron, Joseph, Hughes, Michael, Giganti, Mark, Ritz, Justin, Hosey, Lara, Roa, Jhoanna, Patel, Nilam, Colsh, Kelly, Rwakazina, Irene, Beck, Justine, Sieg, Scott, Li, Jonathan, Fletcher, Courtney, Evering, Teresa, Ignacio, Rachel Bender, Cardoso, Sandra, Corado, Katya, Jilg, Nikolaus, Perelson, Alan, Pillay, Sandy, Riviere, Cynthia, Taiwo, Babafemi, Gottesman, Joan, Newell, Matthew, Pedersen, Susan, Dragavon, Joan, Jennings, Cheryl, Greenfelder, Brian, Murtaugh, William, Kosmyna, Jan, Gapara, Morgan, Shahkolahi, Akbar, Pierone, Gerald, Elliott, Juliana, Jacobson, Jeffrey, Hojat, Leila, Pasternak, Julie, Berardi, Jonathan, Arar, Celine, Bukhman, Yevgeniy, Jain, Manish, Bukhman, Eugene, Shaik, Sadia, Hatlen, Timothy, Dooley, Kelly, Becker, Becky, Wilkins, Adaliah, Pérez, Jose, Roman, Eloy, Fernández, Heriberto, Hoover, Keila, Renfroe, James, Weldon, Mauney, Bougher, Genei, Malvestutto, Carlos, Harber, Heather, Cicarella, Robyn, Neytman, Gene, Herman, Jack, Herman, Craig, Aziz, Mariam, Swiatek, Joan, Pathak, Divya, Choudhary, Madhu, Sullivano, Jennifer, Osiyemi, Olayemi, Izquierdo, Myriam, Torna, Odelsey, Khodabakhshian, Aleen, and Fortier, Samantha
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Clinical Research, Coronaviruses, Infectious Diseases, Coronaviruses Therapeutics and Interventions, Emerging Infectious Diseases, Patient Safety, Prevention, Lung, 6.1 Pharmaceuticals, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, Inhaled interferon, SARS-CoV-2, COVID-19, ACTIV-2, Randomized clinical trial, ACTIV-2/A5401 Study Team, Clinical sciences, Health services and systems, Public health
Abstract: BackgroundWith the emergence of SARS-CoV-2 variants resistant to monoclonal antibody therapies and limited global access to therapeutics, the evaluation of novel therapeutics to prevent progression to severe COVID-19 remains a critical need.MethodsSafety, clinical and antiviral efficacy of inhaled interferon-β1a (SNG001) were evaluated in a phase II randomized controlled trial on the ACTIV-2/A5401 platform (ClinicalTrials.govNCT04518410). Adult outpatients with confirmed SARS-CoV-2 infection within 10 days of symptom onset were randomized and initiated either orally inhaled nebulized SNG001 given once daily for 14 days (n = 110) or blinded pooled placebo (n = 110) between February 10 and August 18, 2021.FindingsThe proportion of participants reporting premature treatment discontinuation was 9% among SNG001 and 13% among placebo participants. There were no differences between participants who received SNG001 or placebo in the primary outcomes of treatment emergent Grade 3 or higher adverse events (3.6% and 8.2%, respectively), time to symptom improvement (median 13 and 9 days, respectively), or proportion with unquantifiable nasopharyngeal SARS-CoV-2 RNA at days 3 (28% [26/93] vs. 39% [37/94], respectively), 7 (65% [60/93] vs. 66% [62/94]) and 14 (91% [86/95] vs. 91% [83/81]). There were fewer hospitalizations with SNG001 (n = 1; 1%) compared with placebo (n = 7; 6%), representing an 86% relative risk reduction (p = 0.07). There were no deaths in either arm.InterpretationIn this trial, SNG001 was safe and associated with a non-statistically significant decrease in hospitalization for COVID-19 pneumonia.FundingThe ACTIV-2 platform study is funded by the NIH. Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number UM1 AI068634, UM1 AI068636 and UM1 AI106701. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Published: 2023

47. One Week of Oral Camostat Versus Placebo in Nonhospitalized Adults With Mild-to-Moderate Coronavirus Disease 2019: A Randomized Controlled Phase 2 Trial

Author: Jilg, Nikolaus, Chew, Kara W, Giganti, Mark J, Daar, Eric S, Wohl, David A, Javan, Arzhang Cyrus, Kantor, Amy, Moser, Carlee, Coombs, Robert W, Neytman, Gene, Hoover, Keila, Jana, Atasi, Hart, Phil A, Greninger, Alexander L, Szurgot, Bob, Eron, Joseph J, Currier, Judith S, Hughes, Michael D, Smith, Davey M, Li, Jonathan Z, Chew, Kara, Smith, David, Daar, Eric, Wohl, David, Currier, Judith, Eron, Joseph, Hughes, Michael, Giganti, Mark, Ritz, Justin, Hosey, Lara, Roa, Jhoanna, Patel, Nilam, Colsh, Kelly, Rwakazina, Irene, Beck, Justine, Sieg, Scott, Li, Jonathan, Fletcher, Courtney, Fischer, William, Evering, Teresa, Coombs, Robert, Ignacio, Rachel Bender, Cardoso, Sandra, Corado, Katya, Jagannathan, Prasanna, Perelson, Alan, Pillay, Sandy, Riviere, Cynthia, Singh, Upinder, Taiwo, Babafemi, Gottesman, Joan, Newell, Matthew, Pedersen, Susan, Dragavon, Joan, Jennings, Cheryl, Greenfelder, Brian, Murtaugh, William, Kosmyna, Jan, Gapara, Morgan, and Shahkolahi, Akbar
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Emerging Infectious Diseases, Infectious Diseases, Coronaviruses Therapeutics and Interventions, Coronaviruses, Clinical Research, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Good Health and Well Being, Humans, Adult, COVID-19, SARS-CoV-2, RNA, Viral, Time Factors, Treatment Outcome, camostat, outpatient, phase 2, ACTIV-2/A5401 Study Team, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract: BackgroundCamostat inhibits severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in vitro. We studied the safety and efficacy of camostat in ACTIV-2/A5401, a phase 2/3 platform trial of therapeutics for COVID-19 in nonhospitalized adults.MethodsWe conducted a phase 2 study in adults with mild-to-moderate COVID-19 randomized to oral camostat for 7 days or a pooled placebo arm. Primary outcomes were time to improvement in COVID-19 symptoms through day 28, proportion of participants with SARS-CoV-2 RNA below the lower limit of quantification (LLoQ) from nasopharyngeal swabs through day 14, and grade ≥3 treatment-emergent adverse events (TEAEs) through day 28.ResultsOf 216 participants (109 randomized to camostat, 107 to placebo) who initiated study intervention, 45% reported ≤5 days of symptoms at study entry and 26% met the protocol definition of higher risk of progression to severe COVID-19. Median age was 37 years. Median time to symptom improvement was 9 days in both arms (P = .99). There were no significant differences in the proportion of participants with SARS-CoV-2 RNA ConclusionsIn a phase 2 study of nonhospitalized adults with mild-to-moderate COVID-19, oral camostat did not accelerate viral clearance or time to symptom improvement, or reduce hospitalizations or deaths. Clinical Trials Registration. ClinicalTrials.gov identifier: NCT04518410.
Published: 2023

48. Association Between Insomnia, Stress Events, and Other Psychosocial Factors and Incident Atrial Fibrillation in Postmenopausal Women: Insights From the Womens Health Initiative.

Author: Zhao, Susan, Tindle, Hilary, Larson, Joseph, Woods, Nancy, Hoover, Valerie, Salmoirago-Blotcher, Elena, Shadyab, Aladdin, Stefanick, Marcia, Perez, Marco, and Crawford, Michael
Subjects: atrial fibrillation, hierarchical cluster analysis, psychosocial clusters, strain, stress, womens health, Female, Humans, Middle Aged, Aged, Atrial Fibrillation, Sleep Initiation and Maintenance Disorders, Postmenopause, Retrospective Studies, Womens Health
Abstract: Background The association between psychosocial factors and atrial fibrillation (AF) is poorly understood. Methods and Results Postmenopausal women from the Womens Health Initiative were retrospectively analyzed to identify incident AF in relation to a panel of validated psychosocial exposure variables, as assessed by multivariable Cox proportional hazard regression and hierarchical cluster analysis. Among the 83 736 women included, the average age was 63.9±7.0 years. Over an average of 10.5±6.2 years follow-up, there were 23 954 cases of incident AF. Hierarchical cluster analysis generated 2 clusters of highly correlated psychosocial variables: the Stress Cluster included stressful life events, depressive symptoms, and insomnia, and the Strain Cluster included optimism, social support, social strain, cynical hostility, and emotional expressiveness. Incident AF was associated with higher values in the Stress Cluster (hazard ratio [HR], 1.07 per unit cluster score [95% CI, 1.05-1.09]) and the Strain Cluster (HR, 1.03 per unit cluster score [95% CI, 1.00-1.05]). Of the 8 individual psychosocial predictors that were tested, insomnia (HR, 1.04 [95% CI, 1.03-1.06]) and stressful life events (HR, 1.02 [95% CI, 1.01-1.04]) were most strongly associated with increased incidence of AF in Cox regression analysis after multivariate adjustment. Subgroup analyses showed that the Strain Cluster was more strongly associated with incident AF in those with lower traditional AF risks (P for interaction=0.02) as determined by the cohorts for heart and aging research in genomic epidemiology for atrial fibrillation score. Conclusions Among postmenopausal women, 2 clusters of psychosocial stressors were found to be significantly associated with incident AF. Further research is needed to validate these associations.
Published: 2023

49. Vagal Nerve Stimulation Reduces Ventricular Arrhythmias and Mitigates Adverse Neural Cardiac Remodeling Post-Myocardial Infarction.

Author: Hadaya, Joseph, Dajani, Al-Hassan, Cha, Steven, Hanna, Peter, Challita, Ronald, Hoover, Donald, Ajijola, Olujimi, Shivkumar, Kalyanam, and Ardell, Jeffrey
Subjects: myocardial infarction, neurocardiology, sympathetic nervous system, vagal nerve stimulation, ventricular tachycardia/ventricular fibrillation
Abstract: This study sought to evaluate the impact of chronic vagal nerve stimulation (cVNS) on cardiac and extracardiac neural structure/function after myocardial infarction (MI). Groups were control, MI, and MI + cVNS; cVNS was started 2 days post-MI. Terminal experiments were performed 6 weeks post-MI. MI impaired left ventricular mechanical function, evoked anisotropic electrical conduction, increased susceptibility to ventricular tachycardia and fibrillation, and altered neuronal and glial phenotypes in the stellate and dorsal root ganglia, including glial activation. cVNS improved cardiac mechanical function and reduced ventricular tachycardia/ventricular fibrillation post-MI, partly by stabilizing activation/repolarization in the border zone. MI-associated extracardiac neural remodeling, particularly glial activation, was mitigated with cVNS.
Published: 2023

50. 'Feeding' the Cloud: Reducing Carbon Emissions from Social Media and Streaming Services Through Private Information Disclosure.

Author: Hoover, Madison
Subjects: Streaming media -- Censorship -- Environmental aspects -- Laws, regulations and rules, Carbon taxes -- Laws, regulations and rules, Corporate governance -- Environmental aspects -- Social aspects -- Laws, regulations and rules, Harm principle (Ethics) -- Analysis, Corporate social responsibility -- Environmental aspects -- Laws, regulations and rules, Disclosure of information -- Laws, regulations and rules, Social media -- Privileges and immunities -- Environmental aspects -- Laws, regulations and rules, Pathological Internet Use -- Prevention -- Environmental aspects -- Laws, regulations and rules, Climatic changes -- Laws, regulations and rules, Government regulation, Streaming media technology, Telecommunications Act of 1996 (47 U.S.C. 230)
Abstract: TABLE OF CONTENTS I. THE EMERGENCE OF SOCIAL MEDIA AND STREAMING SERVICES 551 II. SOCIAL MEDIA AND STREAMING SERVICES' IMPACT ON ENVIRONMENTAL 554 ORGANIZATIONS III. THE MECHANICS OF SOCIAL MEDIA [...], Despite their well-known negative aspects, social media and streaming services have become integral for consumers' daily entertainment and social connection. Although most users have some idea of these services' harmful mental impacts, they are typically unaware of the carbon emissions that result from them. This dissonance is not an accident, but rather the direct result of incomplete data and the omission of individual user-level estimates for carbon emissions in private governance reports published by social media and streaming companies. Where these governance reports do include user-level figures, they are not provided in a meaningful way such that users could consequentially alter their behavior to minimize that harm. Though the impact of individual usage may initially seem minimal on a micro-level, minor changes in individual carbon contributions may have substantially positive impacts where numerous consumers implement them on a macro-level; these services benefit from billions of users, thus, seemingly small behavioral changes that reduce usage time can result in notable emissions reductions in the aggregate. While regulatory actions may retroactively address these consequences, stronger solutions reside in the private spehere through actions like reducing reliance on addictive algorithms, offering paid subscriptions that eliminate or decrease advertisements, and bolstering information disclosure to users through these companies' internal governance reports. While most adults in the United States are in agreement that environmental concerns are an important national focus, disagreement remains over adequate responses, and many environmentally harmful industries are still economically essential. As a result, attention must be afforded to these widespread energy uses for entertainment sectors and their resulting emissions.
Published: 2024

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

31,468 results on '"Hoover A"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources