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1. On the value of 'God' and thus of nature: Ethicality, meaningfulness, and usefulness of monetary valuation of ecosystem services and natural capital

Author

Fortunato A. Ascioti and Francesca Moraci

Subjects
Economic value of religions, Religious tourism, Ecological economics, Conservation, Ecosystem services, Natural capital, Environmental sciences, GE1-350
Abstract

The Economic Monetary Valuation (EMV) of Ecosystem Services (ESs) and Natural Capital (NC) has been proposed and pursued by several scholars as an effective way to make the vital role of Nature evident and appropriately accounted for in our complex relationship with Her. Other scholars have negatively reacted to this approach, considering it an unethical commodification of Nature that can only lead to a “shopping mall” view of Her, meaningless and even dangerous. It has been said that by following this same line of thought, the next step might be estimating even God's value. In this sense, the EMV of ESs&NC would resemble some “laical blasphemy.” We argue that “God,” when viewed as synonymous with the “willingness to pay” to perpetuate religious faith and as an attractor of religious tourism and pilgrimages, is a terrific economic asset whose value can be estimated in monetary terms. We assessed “God” ‘s EMV (in the previously specified sense), showing that this more mundane appraisal is neither necessarily unethical nor diminishes the sacred and immaterial relevance of religions and beliefs. Thus, we consider by analogy that although the value of Nature, like that of “God,” can never be entirely accounted for by monetary estimation, the EMV of ESs&NC is ethically acceptable. Moreover, the successful application of EMV to NC&ESs supports its usefulness and effectiveness in showing, beyond any reasonable doubt, that Nature counts by making us healthier, wealthier, and, last but not least, happier through both material and immaterial goods, services, and experiences.

Published
2024
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2. ANALOG-1 ISS – The first part of an analogue mission to guide ESA’s robotic moon exploration efforts

Author

Wormnes Kjetil, Carey William, Krueger Thomas, Cencetti Leonardo, Exter Emiel den, Ennis Stephen, Ferreira Edmundo, Fortunato Antonio, Gerdes Levin, Hann Lukas, Lombardi Chiara, Luzzi Erica, Martin Sebastian, Massironi Matteo, Payler Samuel, Pereira Aaron, Rossi Angelo Pio, Pozzobon Riccardo, Sauro Francesco, Schoonejans Philippe, van der Hulst Frank, and Grenouilleau Jessica

Subjects
robotics, teleoperation, haptics, geology, lunar, moon, Astronomy, QB1-991
Abstract

The European Space Agency’s ANALOG-1 experiment is the culmination of 12 distinct METERON experiments carried out since 2011. These all address aspects of teleoperating a robotic asset from an orbital platform, i.e., technical implementation, user interfaces, autonomy and operations. The ANALOG-1 technology demonstration and operations concept experiment is based upon the surface mission scenario segment of the notional EL3 sample return mission. This segment focuses on the control of a lunar surface robotic asset from the Earth and from the Lunar Gateway. The experiment is taking place in two parts, with the first successfully completed from the ISS in November 2019. It assessed the effectiveness of a state-of-the-art robotic control interface to control a complex mobile robot from orbit, as well as evaluating the scientific interactions, during robotic-assisted geology exploration, between crew in orbit and scientists on the ground. Luca Parmitano operated the robot while he was on the ISS. For this experiment, a complex control station had been installed on the ISS. The experiment demonstrated the advantage of having an immersive control station and high level of robotic dexterity, with Luca finishing all his assigned and secondary geology targets ahead of time.

Published
2022
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3. Parallel Algorithms for Median Consensus Clustering in Complex Networks

Author

Hussain, Md Taufique, Halappanavar, Mahantesh, Chatterjee, Samrat, Radicchi, Filippo, Fortunato, Santo, and Azad, Ariful

Subjects
Computer Science - Information Retrieval, Computer Science - Computers and Society, Computer Science - Data Structures and Algorithms, Computer Science - Social and Information Networks
Abstract

We develop an algorithm that finds the consensus of many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median set partitions, our algorithm takes graph structure into account and finds a comparable quality solution much faster than the other approaches. For graphs with known communities, our consensus partition captures the actual community structure more accurately than alternative approaches. To make it applicable to large graphs, we remove sequential dependencies from our algorithm and design a parallel algorithm. Our parallel algorithm achieves 35x speedup when utilizing 64 processing cores for large real-world graphs from single-cell experiments., Comment: 12 pages

Published
2024

4. Fast Radio Bursts and Artificial Neural Networks: a cosmological-model-independent estimation of the Hubble Constant

Author

Fortunato, Jéferson A. S., Bacon, David J., Hipólito-Ricaldi, Wiliam S., and Wands, David

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Fast Radio Bursts (FRBs) have emerged as powerful cosmological probes in recent years offering valuable insights into cosmic expansion. These predominantly extragalactic transients encode information on the expansion of the Universe through their dispersion measure, reflecting interactions with the intervening medium along the line of sight. In this study, we introduce a novel method for reconstructing the late-time cosmic expansion rate and estimating the Hubble constant, solely derived from FRBs measurements coupled with their redshift information while employing Artificial Neural Networks. Our approach yields a Hubble constant estimate of $H_0 = 67.3\pm6.6\rm \ km \ s^{-1} \ Mpc^{-1}$. With a dataset comprising 23 localised data points, we demonstrate a precision of $\sim10\%$. However, our forecasts using simulated datasets indicate that in the future it could be possible to achieve precision comparable to the SH0ES collaboration or the Planck satellite. Our findings underscore the potential of FRBs as alternative, independent tools for probing cosmic dynamics.

Published
2024

5. The Use of Zirconia for Implant-Supported Fixed Complete Dental Prostheses: A Narrative Review

Author

Chiara Cinquini, Fortunato Alfonsi, Vincenzo Marchio, Francesco Gallo, Francesco Zingari, Alessandro Remigio Bolzoni, Stefano Romeggio, and Antonio Barone

Subjects
zirconia, full-arch rehabilitation, dental implants, prosthesis, Dentistry, RK1-715
Abstract

The success of implant-supported fixed complete dental prostheses (ISFCDPs) depends on multiple factors: some are related to the fixtures, such as fixture material, surface characteristics, positioning, and type of connection to prosthetic components; others are related to the prostheses, such as design and materials used. Zirconia is a material widely used in fixed prosthodontics, whether on natural teeth or on implants, with excellent results over time. Regarding the use of zirconia for ISFCDPs, the 2018 ITI Consensus Report stated that “implant-supported monolithic zirconia prostheses may be a future option with more supporting evidence”. Since CAD/CAM technology and zirconia are being continuously innovated to achieve better results and performances over time, a narrative review of the literature seems necessary to focus research efforts towards effective and durable solutions for implant-supported, full-arch rehabilitations. The objective of the present narrative review was to search the literature for studies regarding the clinical performance of zirconia-based ISFCDPs. According to the results of this review, the use of zirconia for ISFCDPs showed good clinical outcomes, with high survival rates ranging from 88% to 100% and prosthetic complications that were restorable by the clinicians in most cases.

Published
2023
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6. Effects on Right Ventricular Function One Year after COVID-19-Related Pulmonary Embolism

Author

Federica Ilardi, Mario Crisci, Cecilia Calabrese, Anna Scognamiglio, Fortunato Arenga, Rachele Manzo, Domenica F. Mariniello, Valentino Allocca, Anna Annunziata, Antonello D’Andrea, Raffaele Merenda, Vittorio Monda, Giovanni Esposito, and Giuseppe Fiorentino

Subjects
COVID-19, pulmonary embolism, right-ventricle global longitudinal strain, right-ventricle free wall longitudinal strain, right-ventricle dysfunction, Medicine
Abstract

The aim of this study was to investigate the presence of subclinical cardiac dysfunction in recovered coronavirus disease 2019 (COVID-19) patients, who were stratified according to a previous diagnosis of pulmonary embolism (PE) as a complication of COVID-19 pneumonia. Out of 68 patients with SARS-CoV-2 pneumonia followed up for one year, 44 patients (mean age 58.4 ± 13.3, 70% males) without known cardiopulmonary disease were divided in two groups (PE+ and PE−, each comprising 22 patients) and underwent clinical and transthoracic echocardiographic examination, including right-ventricle global longitudinal strain (RV-GLS), and RV free wall longitudinal strain (RV-FWLS). While no significant differences were found in the left- or right-heart chambers’ dimensions between the two study groups, the PE+ patients showed a significant reduction in RV-GLS (−16.4 ± 2.9 vs. −21.6 ± 4.3%, p < 0.001) and RV-FWLS (−18.9 ± 4 vs. −24.6 ± 5.12%, p < 0.001) values compared to the PE- patients. According to the ROC-curve analysis, RV-FWLS < 21% was the best cut-off with which to predict PE diagnosis in patients after SARS-CoV-2 pneumonia (sensitivity 74%, specificity 89%, area under the curve = 0.819, p < 0.001). According to the multivariate logistic regression model, RV-FWLS < 21% was independently associated with PE (HR 34.96, 95% CI:3.24–377.09, p = 0.003) and obesity (HR 10.34, 95% CI:1.05–101.68, p = 0.045). In conclusion, in recovered COVID-19 patients with a history of PE+, there is a persistence of subclinical RV dysfunction one year after the acute phase of the disease, detectable by a significant impairment in RV-GLS and RV-FWLS. A reduction in RV-FWLS of lower than 21% is independently associated with COVID-related PE.

Published
2023
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7. Antibody characterization is critical to enhance reproducibility in biomedical research.

Author

Kahn, Richard, Virk, Harvinder, Laflamme, Carl, Houston, Douglas, Polinski, Nicole, Meijers, Rob, Levey, Allan, Saper, Clifford, Errington, Timothy, Turn, Rachel, Bandrowski, Anita, Trimmer, James, Rego, Meghan, Freedman, Leonard, Ferrara, Fortunato, Bradbury, Andrew, Cable, Hannah, and Longworth, Skye

Subjects
RRID, YCharOS, antibody characterization, antibody validation, cell biology, immunology, inflammation, none, renewables, reproducibility, Biomedical Research, Reproducibility of Results, Humans, Antibodies, Animals
Abstract

Antibodies are used in many areas of biomedical and clinical research, but many of these antibodies have not been adequately characterized, which casts doubt on the results reported in many scientific papers. This problem is compounded by a lack of suitable control experiments in many studies. In this article we review the history of the antibody characterization crisis, and we document efforts and initiatives to address the problem, notably for antibodies that target human proteins. We also present recommendations for a range of stakeholders - researchers, universities, journals, antibody vendors and repositories, scientific societies and funders - to increase the reproducibility of studies that rely on antibodies.

Published
2024

8. Cardiovascular toxicities of immune therapies for cancer – a scientific statement of the Heart Failure Association (HFA) of the ESC and the ESC Council of Cardio‐Oncology

Author

Tocchetti, Carlo Gabriele, Farmakis, Dimitrios, Koop, Yvonne, Andres, Maria Sol, Couch, Liam S, Formisano, Luigi, Ciardiello, Fortunato, Pane, Fabrizio, Au, Lewis, Emmerich, Max, Plummer, Chris, Gulati, Geeta, Ramalingam, Sivatharshini, Cardinale, Daniela, Brezden‐Masley, Christine, Iakobishvili, Zaza, Thavendiranathan, Paaladinesh, Santoro, Ciro, Bergler‐Klein, Jutta, Keramida, Kalliopi, de Boer, Rudolf A, Maack, Christoph, Lutgens, Esther, Rassaf, Tienush, Fradley, Michael G, Moslehi, Javid, Yang, Eric H, De Keulenaer, Gilles, Ameri, Pietro, Bax, Jeroen, Neilan, Tomas G, Herrmann, Joerg, Mbakwem, Amam C, Mirabel, Mariana, Skouri, Hadi, Hirsch, Emilio, Cohen‐Solal, Alain, Sverdlov, Aaron L, van der Meer, Peter, Asteggiano, Riccardo, Barac, Ana, Ky, Bonnie, Lenihan, Daniel, Dent, Susan, Seferovic, Petar, Coats, Andrew JS, Metra, Marco, Rosano, Giuseppe, Suter, Thomas, Lopez‐Fernandez, Teresa, and Lyon, Alexander R

Subjects
Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

ABSTRACT: The advent of immunological therapies has revolutionized the treatment of solid and haematological cancers over the last decade. Licensed therapies which activate the immune system to target cancer cells can be broadly divided into two classes. The first class are antibodies that inhibit immune checkpoint signalling, known as immune checkpoint inhibitors (ICIs). The second class are cell‐based immune therapies including chimeric antigen receptor T lymphocyte (CAR‐T) cell therapies, natural killer (NK) cell therapies, and tumour infiltrating lymphocyte (TIL) therapies. The clinical efficacy of all these treatments generally outweighs the risks, but there is a high rate of immune‐related adverse events (irAEs), which are often unpredictable in timing with clinical sequalae ranging from mild (e.g. rash) to severe or even fatal (e.g. myocarditis, cytokine release syndrome) and reversible to permanent (e.g. endocrinopathies).The mechanisms underpinning irAE pathology vary across different irAE complications and syndromes, reflecting the broad clinical phenotypes observed and the variability of different individual immune responses, and are poorly understood overall. Immune‐related cardiovascular toxicities have emerged, and our understanding has evolved from focussing initially on rare but fatal ICI‐related myocarditis with cardiogenic shock to more common complications including less severe ICI‐related myocarditis, pericarditis, arrhythmias, including conduction system disease and heart block, non‐inflammatory heart failure, takotsubo syndrome and coronary artery disease. In this scientific statement on the cardiovascular toxicities of immune therapies for cancer, we summarize the pathophysiology, epidemiology, diagnosis, and management of ICI, CAR‐T, NK, and TIL therapies. We also highlight gaps in the literature and where future research should focus.

Published
2024

9. Cosmic slowing down of acceleration with the Chaplygin-Jacobi gas as a dark fluid

Author

Fortunato, J. A. S., Hipolito-Ricaldi, W. S., Videla, N., and Villanueva, J. R.

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

A particular generalization of the Chaplygin inflationary model, using the formalism of Hamilton-Jacobi and elliptic functions, results in a more general non-linear Chaplygin-type equation of state (Chaplygin-Jacobi model). We investigate the implementation of this model as a dark energy (DE) fluid to explain the recent acceleration of the universe. Unlike $\Lambda$CDM and other Chaplygin-like fluids, where the final fate of the universe is an eternal de Sitter (dS) phase, the dynamics of this model allow for the possibility of a decelerating phase in the future, following the current accelerating phase. In other words, a transient acceleration arises, accounting for the recently claimed slowing down phenomenon. This Chaplygin-Jacobi model shows important differences compared to the standard and generalized Chaplygin gas models. Additionally, we perform a Markov Chain Monte Carlo (MCMC) analysis using several datasets, including Type Ia Supernovae (SnIa), Cosmic Chronometers (CC), and Fast Radio Bursts (FRBs), to examine the observational viability of the model. Our results indicate that a transient phase of accelerated expansion is not excluded by current observations., Comment: 16 pages, 5 figures

Published
2024

10. A matter of performance & criticality: a review of rare-earth-based magnetocaloric intermetallic compounds for hydrogen liquefaction

Author

Liu, Wei, Gottschall, Tino, Scheibel, Franziska, Bykov, Eduard, Aubert, Alex, Fortunato, Nuno, Beckmann, Benedikt, Döring, Allan M., Zhang, Hongbin, Skokov, Konstantin, and Gutfleisch, Olivler

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

The low efficiency of conventional liquefaction technologies based on the Joule-Thomson expansion makes liquid hydrogen currently not attractive enough for large-scale energy-related technologies that are important for the transition to a carbon-neutral society. Magnetocaloric hydrogen liquefaction has great potential to achieve higher efficiency and is therefore a crucial enabler for affordable liquid hydrogen. Cost-effective magnetocaloric materials with large magnetic entropy and adiabatic temperature changes in the temperature range of 77 $\sim$ 20 K under commercially practicable magnetic fields are the foundation for the success of magnetocaloric hydrogen liquefaction. Heavy rare-earth-based magnetocaloric intermetallic compounds generally show excellent magnetocaloric performances, but the heavy rare-earth elements (Gd, Tb, Dy, Ho, Er, and Tm) are highly critical in resources. Yttrium and light rare-earth elements (La, Ce, Pr, and Nd) are relatively abundant, but their alloys generally show less excellent magnetocaloric properties. A dilemma appears: higher performance or lower criticality? In this review, we study how cryogenic temperature influences magnetocaloric performance by first reviewing heavy rare-earth-based intermetallic compounds. Next, we look at light rare-earth-based, "mixed" rare-earth-based, and Gd-based intermetallic compounds with the nature of the phase transition order taken into consideration, and summarize ways to resolve the dilemma.

Published
2024
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11. Design and Optimization of Reconfigurable Intelligent Surfaces Using the PEEC Method

Author

Pettanice, Giuseppe, Di Renzo, Marco, Valentini, Roberto, Jeong, Sumin, Di Marco, Piergiuseppe, Santucci, Fortunato, Romano, Daniele, and Antonini, Giulio

Subjects
Computer Science - Information Theory
Abstract

The design and optimization of Reconfigurable Intelligent Surfaces (RISs) are key challenges for future wireless communication systems. RISs are devices that can manipulate electromagnetic (EM) waves in a programmable way, thus enhancing the performance and efficiency of wireless links. To achieve this goal, it is essential to have reliable EM models that can capture the behavior of RISs in different scenarios. This work demonstrates that the Partial Elements Equivalent Circuit (PEEC) method is a powerful tool for EM analysis of RIS-aided wireless links. It might also be integrated with optimization algorithms in order to optimize wireless communication networks.

Published
2024

12. Multiport Network Modeling for Reconfigurable Intelligent Surfaces: Numerical Validation with a Full-Wave PEEC Simulator

Author

Pettanice, Giuseppe, Di Renzo, Marco, Jeong, Sumin, Valentini, Roberto, Di Marco, Piergiuseppe, Santucci, Fortunato, Romano, Daniele, and Antonini, Giulio

Subjects
Computer Science - Information Theory
Abstract

Reconfigurable Intelligent Surface (RIS) modeling and optimization are a crucial steps in developing the next generation of wireless communications. To this aim, the availability of accurate electromagnetic (EM) models is of paramount important for the design of RIS-assisted communication links. In this work, we validate a widely-used analytical multiport network for RISs by means of a well-established full-wave numerical method based on the Partial Elements Equivalent Circuit (PEEC) approach. Numerical results show good agreement between the two methods, thus demonstrating i) the considered multiport network model being effective and ii) the PEEC method being appropriate for EM modeling of RIS-assisted wireless links.

Published
2024

13. Histologic Evaluation of Soft Tissues around Dental Implant Abutments: A Narrative Review

Author

Chiara Cinquini, Vincenzo Marchio, Edouard Di Donna, Fortunato Alfonsi, Giacomo Derchi, Marco Nisi, and Antonio Barone

Subjects
dental implants, soft tissues, transmucosal surface, abutment, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
Abstract

The basis for dental implant success comes not only with the titanium implant osseointegration but also depends on other factors such as the development of a soft tissue barrier, which protects the peri-implant bone from the oral environment. The characteristics of surfaces in contact with peri-implant soft tissues may affect the capacity of peri-implant mucosal cells to create a tight seal around the implant, thus influencing long-term implant success. Many histological studies on animals have been conducted on different materials to better understand their influence on peri-implant soft tissues, with the limitation that results from animal studies cannot be fully translated in humans. The aim of this review paper was to analyze the literature focusing on histological clinical studies in humans which have examined different materials or different surface treatments and their effects on peri-implant soft tissues. The research was conducted according to the following PICO question: “Do different implant/abutment materials affect peri-implant soft tissues adhesion and health?”. Nine articles were analyzed in this review. The results of this review show the influence of different abutment materials on the peri-implant soft tissues, and the need of further research regarding the effect that abutment materials, surface treatments, and surface properties have on soft tissues.

Published
2022
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14. Comparative study of the kinetic properties of proton and alpha beams in the Alfv\'enic wind observed by SWA-PAS onboard Solar Orbiter

Author

Bruno, Roberto, DeMarco, Rossana, Amicis, Raffaella D, Perrone, Denise, Marcucci, Maria Federica, Telloni, Daniele, Marino, Raffaele, Valvo, Luca Sorriso, Fortunato, Vito, Mele, Gennaro, Monti, Francesco, Fedorov, Andrei, Louarn, Philippe, Owen, Chris, and Livi, Stefano

Subjects
Astrophysics - Solar and Stellar Astrophysics, Physics - Space Physics
Abstract

The problems of heating and acceleration of solar wind particles are of significant and enduring interest in astrophysics. The interactions between waves and particles are crucial in determining the distributions of proton and alpha particles, resulting in non-Maxwellian characteristics including temperature anisotropies and particle beams. These processes can be better understood as long as the beam can be separated from the core for the two major components of the solar wind. We utilized an alternative numerical approach that leverages the clustering technique employed in Machine Learning to differentiate the primary populations within the velocity distribution, rather than employing the conventional bi-Maxwellian fitting method. Separation of the core and beam revealed new features for protons and alphas. We estimated that the total temperature of the two beams was slightly higher than that of their respective cores, and the temperature anisotropy for the cores and beams was larger than 1. We concluded that the temperature ratio between alphas and protons largely over 4 is due to the presence of a massive alpha beam, which is approximately 50\% of the alpha core. We provided evidence that the alpha core and beam populations are sensitive to Alfv\'enic fluctuations and the surfing effect found in the literature can be recovered only when considering the core and beam as a single population. Several similarities between proton and alpha beams would suggest a common and local generation mechanism not shared with the alpha core, which may not have necessarily been accelerated and heated locally., Comment: paper accepted by APJ on May 4th 2024, 24 pages, 21 figures

Published
2024

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

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, 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

16. EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

Author

Leite, Daniel, Silva, Alisson, Casalino, Gabriella, Sharma, Arnab, Fortunato, Danielle, and Ngomo, Axel-Cyrille

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games -- a public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.0029 II interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem., Comment: 10 pages, IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024 (IEEE EAIS 2024)

Published
2024

17. Hydrostatic equilibrium configurations of neutron stars in the $f(R,\mathcal{L},T)$ gravity theory

Author

Fortunato, J. A. S., Moraes, P. H. R. S., Brito, E., Júnior, J. G. de Lima, and Guerini, T. S.

Subjects
General Relativity and Quantum Cosmology
Abstract

In the present work, we obtain the hydrostatic equilibrium configurations of neutron stars in the recently proposed $f(R,\mathcal{L},T)$ theory of gravity, for which $R$ is the Ricci scalar, $\mathcal{L}$ is the matter lagrangian density, $T$ is the trace of the energy-momentum tensor and $f$ is a function of the argument. This theory emerges in the present literature as a generalized geometry-matter coupling theory of gravity. We derive the Tolman-Oppenheimer-Volkoff-like equation for a particular functional form of the $f(R,\mathcal{L},T)$ function. Our solutions are obtained from realistic equations of state describing matter inside neutron stars. We obtain stable solutions for neutron stars and we show that for some values of the free parameter of the theory it is possible to be in agreement with both NICER and LIGO/Virgo observational data.

Published
2024

18. Modeling the amplification of epidemic spread by misinformed populations

Author

DeVerna, Matthew R., Pierri, Francesco, Ahn, Yong-Yeol, Fortunato, Santo, Flammini, Alessandro, and Menczer, Filippo

Subjects
Computer Science - Social and Information Networks, Computer Science - Computers and Society, Physics - Physics and Society
Abstract

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate and estimate various scenarios to understand the impact of misinformation on epidemic spreading. Using this model, we present a worst-case scenario in which a heavily misinformed population would result in an additional 14% of the U.S. population becoming infected over the course of the COVID-19 epidemic, compared to a best-case scenario.

Published
2024

19. Iterative embedding and reweighting of complex networks reveals community structure

Author

Kovács, Bianka, Kojaku, Sadamori, Palla, Gergely, and Fortunato, Santo

Subjects
Physics - Physics and Society
Abstract

Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforces intra-community links and weakens inter-community links, making the clusters of the initial network more visible and more easily detectable. The geometric separation between the communities can become so strong that even a very simple parsing of the links may recover the communities as isolated components with surprisingly high precision. Furthermore, when used as a pre-processing step, our embedding and reweighting procedure can improve the performance of traditional community detection algorithms., Comment: 41 pages, 20 figures

Published
2024
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23. Insecticidal and Repellent Activity of Essential Oils from Copaifera reticulata, Citrus paradisi, Lavandula hybrida and Salvia sclarea Against Immature and Adult Stages of Ctenocephalides felis felis

Author

Lima, Emily Andressa Santos, Campos, Diefrey Ribeiro, Soares, Eduardo Fellipe Melo Santos, Fortunato, Anna Beatriz Ribeiro, Silva, Taynara Monsores e, de Figueiredo Pereira, Nayana, Chaves, Douglas Siqueira de Almeida, Cid, Yara Peluso, and Coumendouros, Katherina

Published
2024
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26. Upper endoscopy in elderly patients: a multicentre, cross-sectional study

Author

Zullo, Angelo, De Francesco, Vincenzo, Amato, Arnaldo, Bergna, Irene, Bendia, Emanuele, Giorgini, Giorgia, Buscarini, Elisabetta, Manfredi, Guido, Cadoni, Sergio, Cannizzaro, Renato, Realdon, Stefano, Ciuffi, Mario, Ignomirelli, Orazio, Da Massa Carrara, Paola, Finucci, Giovanni, Di Somma, Antonietta, Frandina, Chiara, Loria, Mariafrancesca, Galeazzi, Francesca, Ferrara, Francesco, Gemme, Carlo, Bertetti, Noemi Sara, Gentili, Federica, Lotito, Antonio, Germanà, Bastianello, Russo, Nunzia, Grande, Giuseppe, Conigliaro, Rita, Cravero, Federico, Venezia, Giovanna, Marmo, Riccardo, Senneca, Piera, Milano, Angelo, Efthymakis, Konstantinos, Monica, Fabio, Montalto, Paolo, Lombardi, Mario, Morelli, Olivia, Castellani, Danilo, Nigro, Daniela, Festa, Roberto, Peralta, Sergio, Grasso, Maria, Privitera, Antonello, Di Stefano, Maria Emanuela, Scaccianoce, Giuseppe, Loiacono, Mariangela, Segato, Sergio, Balzarini, Marco, Satta, Paolo Usai, Lai, Mariantonia, Fortunato, Francesca, and Manta, Raffaele

Published
2024
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27. Future acceptance of automated insulin delivery systems in youths with type 1 diabetes: validation of the Italian artificial pancreas-acceptance measure

Author

Franceschi, Roberto, Pertile, Riccardo, Marigliano, Marco, Mozzillo, Enza, Maffeis, Claudio, Zaffani, Silvana, Dusini, Carlotta, Antonelli, Annalisa, Candia, Francesca Di, Maltoni, Giulio, Cantarelli, Erika, Minuto, Nicola, Bassi, Marta, Rabbone, Ivana, Savastio, Silvia, Passanisi, Stefano, Lombardo, Fortunato, Cherubini, Valentino, Saltarelli, Maria Alessandra, and Tumini, Stefano

Published
2024
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34. Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial

Author

Martinelli, Erika, Ciardiello, Davide, Martini, Giulia, Napolitano, Stefania, Del Tufo, Sara, D’Ambrosio, Luca, De Chiara, Marco, Famiglietti, Vincenzo, Nacca, Valeria, Cardone, Claudia, Avallone, Antonio, Cremolini, Chiara, Pietrantonio, Filippo, Maiello, Evaristo, Granata, Vincenza, Troiani, Teresa, Cappabianca, Salvatore, Ciardiello, Fortunato, Nardone, Valerio, and Reginelli, Alfonso

Published
2024
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35. The Impact of the COVID-19 Outbreak on Patients’ Adherence to PCSK9 Inhibitors Therapy

Author

Valentina Maria Caso, Simona Sperlongano, Biagio Liccardo, Emanuele Romeo, Sergio Padula, Fortunato Arenga, Antonello D’Andrea, Pio Caso, Paolo Golino, Gerardo Nigro, and Vincenzo Russo

Subjects
COVID-19, PCSK9 inhibitors, adherence, lockdown, Medicine
Abstract

PCSK9 inhibitors (PCSK9i) are monoclonal antibodies that have been shown to be effective in reducing both LDL cholesterol (LDL-C) values and major cardiovascular events in patients at high cardiovascular risk. Adherence to PCSK9i is critical for the success of the treatment. The aim of the present study is to evaluate patients’ adherence to PCSK9i during the COVID-19 pandemic. Patients referred to the Cardiac Diagnostic Unit of the University of Campania “Luigi Vanvitelli” Naples, taking PCSK9i, and who missed the cardiological follow-up visit during the first national COVID-19 lockdown (9 March–17 May 2020), were included. Each patient underwent medical teleconsultation to collect current clinical conditions, adherence to drug treatments, and lipid profile laboratory tests. Among 151 eligible patients, 20 were excluded for missing or untraceable telephone numbers and one for refusing to join the interview. The selected study population consisted of 130 patients (64 ± 9 years, 68% males), of whom 11 (8.5%) reported a temporary interruption of the PCSK-9 therapy for a mean period of 65 ± 1.5 days. The non-adherent patients showed a marked increase in LDL-C than in the pre-pandemic period (90.8 ± 6.0 vs. 54.4 ± 7.7 mg/dL, p < 0.0001), and 82% of patients moved out of the LDL-C therapeutic range. The non-adherent group was more likely to have a very high cardiovascular risk compared to the adherent group (81.8 vs. 33.6%, p < 0.001). Causes of interruption included drug prescription failure (63.6%) due to temporary interruption of the non-urgent outpatient visits and failure in drug withdrawal (36.4%) due to patients’ fear of becoming infected during the pandemic. The COVID-19 lockdown caused a remarkable lack of adherence to PCSK9i therapy, risking negative implications for the health status of patients at high cardiovascular risk. Facilitating patients’ access to PCSK9i and enhancing telemedicine seem to be effective strategies to ensure the continuity of care and appropriate management of these patients.

Published
2022
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36. Human perceptual decision making of nonequilibrium fluctuations

Author

Durmaz, Aybüke, Sarmiento, Yonathan, Fortunato, Gianfranco, Das, Debraj, Diamond, Mathew Ernst, Bueti, Domenica, and Roldán, Édgar

Subjects
Condensed Matter - Statistical Mechanics, Physics - Biological Physics
Abstract

Perceptual decision-making frequently requires making rapid, reliable choices upon encountering noisy sensory inputs. To better define the statistical processes underlying perceptual decision-making, here we characterize the choices of human participants visualizing a system of nonequilibrium stationary physical dynamics and compare such choices to the performance of an optimal agent computing Wald's sequential probability ratio test (SPRT). Participants viewed movies of a particle endowed with drifted Brownian dynamics and had to judge the motion as leftward or rightward. Overall, the results uncovered fundamental performance limits, consistent with recently established thermodynamic trade-offs involving speed, accuracy, and dissipation. Specifically, decision times are sensitive to entropy production rates. Moreover, to achieve a given level of observed accuracy, participants require more time than predicted by SPRT, indicating suboptimal integration of available information. In view of such suboptimality, we develop an alternative account based on evidence integration with a memory time constant. Setting the time constant proportionately to the deviation from equilibrium in the stimuli significantly improved trial-by-trial predictions of decision metrics with respect to SPRT. This study shows that perceptual psychophysics using stimuli rooted in nonequilibrium physical processes provides a robust platform for understanding how the brain takes decisions on stochastic information inputs., Comment: 18 pages, 13 figures

Published
2023

37. Symmetry breaking in optimal transport networks

Author

Patwardhan, Siddharth, Barthelemy, Marc, Erkol, Sirag, Fortunato, Santo, and Radicchi, Filippo

Subjects
Physics - Physics and Society, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics
Abstract

Despite its importance for practical applications, not much is known about the optimal shape of a network that connects in an efficient way a set of points. This problem can be formulated in terms of a multiplex network with a fast layer embedded in a slow one. To connect a pair of points, one can then use either the fast or slow layer, or both, with a switching cost when going from one layer to the other. We consider here distributions of points in spaces of arbitrary dimension d and search for the fast-layer network of given size that minimizes the average time to reach a central node. We discuss the d = 1 case analytically and the d > 1 case numerically, and show the existence of transitions when we vary the network size, the switching cost and/or the relative speed of the two layers. Surprisingly, there is a transition characterized by a symmetry breaking indicating that it is sometimes better to avoid serving a whole area in order to save on switching costs, at the expense of using more the slow layer. Our findings underscore the importance of considering switching costs while studying optimal network structures, as small variations of the cost can lead to strikingly dissimilar results. Finally, we discuss real-world subways and their efficiency for the cities of Atlanta, Boston, and Toronto. We find that real subways are farther away from the optimal shapes as traffic congestion increases., Comment: 11 pages, 5 figures + Supplementary Material

Published
2023

38. The role of Debye temperature in achieving large adiabatic temperature changes at cryogenic temperatures: a case study on $Pr_2In$

Author

Liu, Wei, Scheibel, Franziska, Fortunato, Nuno, Dirba, Imants, Gottschall, Tino, Zhang, Hongbin, Skokov, Konstantin, and Gutfleisch, Oliver

Subjects
Condensed Matter - Materials Science
Abstract

The excellent magnetic entropy change ($\Delta S_T$) in the temperature range of 20 $\sim$ 77 K due to the first-order phase transition makes $Pr_2In$ an intriguing candidate for magnetocaloric hydrogen liquefaction. As an equally important magnetocaloric parameter, the adiabatic temperature change ($\Delta T_{ad}$) of $Pr_2In$ associated with the first-order phase transition has not yet been reported. In this work, the $\Delta T_{ad}$ of $Pr_2In$ is obtained from heat capacity measurements: 2 K in fields of 2 T and 4.3 K in fields of 5 T. While demonstrating a $\Delta T_{ad}$ that is not as impressive as its remarkable $\Delta S_T$, $Pr_2In$ exhibits an unusual low Debye temperature ($T_D$) of around 110 K. Based on these two observations, an approach that combines the mean-field and Debye models is developed to study the correlation between $\Delta T_{ad}$ and $T_D$. The role of $T_D$ in achieving large $\Delta T_{ad}$ is revealed: materials with higher $T_D$ tend to exhibit larger $\Delta T_{ad}$, particularly in the cryogenic temperature range. This discovery explains the absence of an outstanding $\Delta T_{ad}$ in $Pr_2In$ and can serve as a tool for designing or searching materials with both a large $\Delta S_T$ and a $\Delta T_{ad}$.

Published
2023
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39. The Lived Experiences of Culturally Responsive Educators

Author

Marta J. Fortunato

Abstract

Educators create lesson plans in order to guide instruction. This researcher will research and discuss how culturally responsive educators are influenced by mentoring and professional development. In order to understand how mentoring and professional development influence these lesson plans we must understand mentoring and professional development. Mentoring -- if it existed at all in the culture of a school- was initiated as an informal response to a new teacher seeking help or assistance offered by an experienced colleague willing to share his or her own experiences (Portner, 2008). Teacher mentoring programs are increasingly promoted as an effective mechanism for improving new teacher quality, new teacher retention, and student outcomes (Feiman-Nemser & Parker, 1992). Effective professional development requires considerable time, and that time must be well organized, carefully structured, purposefully directed, and focused on content or pedagogy or both (Guskey & Yoon, 2009). Those responsible for planning and implementing professional development must learn how to critically assess and evaluate the effectiveness of what they do (Guskey & Yoon, 2009). Culturally Responsive Professional Development in Education comprehensively teaches the whole teacher-learner through explicit recognition, valuing, and discussion of the diverse ways that cultural and personal identities mediate styles of cognitive engagement, and explicitly honoring these styles in the instructional design of professional development (Farmer et al., 2005). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2024

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