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6,044 results on '"P. Alberti"'

1. The learned range test method for the inverse inclusion problem

Author

Sun, Shiwei and Alberti, Giovanni S.

Subjects
Mathematics - Numerical Analysis, Computer Science - Machine Learning, 35R30, 65N21, 68T07
Abstract

We consider the inverse problem consisting of the reconstruction of an inclusion $B$ contained in a bounded domain $\Omega\subset\mathbb{R}^d$ from a single pair of Cauchy data $(u|_{\partial\Omega},\partial_\nu u|_{\partial\Omega})$, where $\Delta u=0$ in $\Omega\setminus\overline B$ and $u=0$ on $\partial B$. We show that the reconstruction algorithm based on the range test, a domain sampling method, can be written as a neural network with a specific architecture. We propose to learn the weights of this network in the framework of supervised learning, and to combine it with a pre-trained classifier, with the purpose of distinguishing the inclusions based on their distance from the boundary. The numerical simulations show that this learned range test method provides accurate and stable reconstructions of polygonal inclusions. Furthermore, the results are superior to those obtained with the standard range test method (without learning) and with an end-to-end fully connected deep neural network, a purely data-driven method., Comment: 25 pages, 12 figures

Published
2024

2. On low frequency inference for diffusions without the hot spots conjecture

Author

Alberti, Giovanni S., Barnes, Douglas, Jambhale, Aditya, and Nickl, Richard

Subjects
Mathematics - Statistics Theory, Mathematics - Analysis of PDEs, Mathematics - Numerical Analysis
Abstract

We remove the dependence on the `hot-spots' conjecture in two of the main theorems of the recent paper of Nickl (2024, Annals of Statistics). Specifically, we characterise the minimax convergence rates for estimation of the transition operator $P_{f}$ arising from the Neumann Laplacian with diffusion coefficient $f$ on arbitrary convex domains with smooth boundary, and further show that a general Lipschitz stability estimate holds for the inverse map $P_f\mapsto f$ from $H^2\to H^2$ to $L^1$.

Published
2024

3. On $L^\infty$ stability for wave propagation and for linear inverse problems

Author

Alaifari, Rima, Alberti, Giovanni S., and Gauksson, Tandri

Subjects
Mathematics - Analysis of PDEs, Mathematics - Numerical Analysis, 35L05, 47A52, 65J20
Abstract

Stability is a key property of both forward models and inverse problems, and depends on the norms considered in the relevant function spaces. For instance, stability estimates for hyperbolic partial differential equations are often based on energy conservation principles, and are therefore expressed in terms of $L^2$ norms. The focus of this paper is on stability with respect to the $L^\infty$ norm, which is more relevant to detect localized phenomena. The linear wave equation is not stable in $L^\infty$, and we design an alternative solution method based on the regularization of Fourier multipliers, which is stable in $L^\infty$. Furthermore, we show how these ideas can be extended to inverse problems, and design a regularization method for the inversion of compact operators that is stable in $L^\infty$. We also discuss the connection with the stability of deep neural networks modeled by hyperbolic PDEs., Comment: 29 pages, 8 figures

Published
2024

4. Motion-Insensitive Time-Optimal Control of Optical Qubits

Author

Van Damme, Léo, Zhang, Zhao, Devra, Amit, Glaser, Steffen J., and Alberti, Andrea

Subjects
Quantum Physics, Physics - Atomic Physics, 81Q93
Abstract

In trapped-atom quantum computers, high-fidelity control of optical qubits is challenging due to the motion of atoms in the trap. If not corrected, the atom motion gets entangled with the qubit degrees of freedom through two fundamental mechanisms, (i) photon recoil and (ii) thermal motion, both leading to a reduction of the gate fidelity. We develop motion-insensitive pulses that suppress both sources of infidelity by modulating the phase of the driving laser field in time. To eliminate photon recoil, we use bang-bang pulses$-$derived using time-optimal control$-$which shorten the gate duration by about 20 times compared to conventional pulses. However, even when photon recoil is eliminated, we find that the gate error does not vanish, but is rather limited by a bound arising from thermal motion-induced entanglement. Remarkably, this bound is independent of the Rabi frequency, meaning that, unlike for photon recoil, operating in the resolved sideband regime does not mitigate this source of infidelity. To overcome this bound, we derive smooth-phase pulses, which allow for a further reduction of the gate error by more than an order of magnitude for typical thermal atoms. Motion-insensitive pulses can be refined to compensate for laser inhomogeneities, enhancing the gate performance in practical situations. Our results are validated through simulations of one-qubit gates operating on the optical clock transition of ${}^{88}$Sr atoms trapped in an optical tweezers array., Comment: 22 pages, 10 figures

Published
2024

5. Genius and Joy through Student-Driven Civic Action

Author

Julia Shields Chrestay, Kyra Atterbury, Contributor, Janet Chance, Contributor, Stecy Mbemba, Contributor, Helena Miller, Contributor, Joe Alberti, Contributor, Sara Caselle, Contributor, Suzanne Cilli, Contributor, Katherine Collier, Contributor, Erica Darken, Contributor, Emily Goedde, Contributor, Rochelle Krauss, Contributor, Bernadine Magee, Contributor, Noga Newberg, Contributor, An Nguyen, Contributor, and Candice Smith, Contributor

Abstract

Need in Deed's work with Philadelphia public school teachers and their students centers empathy, informed investigation, and civic action. Bolstered by these values and with the support of the My Voice framework and dedicated Need in Deed staff, educators are equipped to co-create authentic classroom communities with their students. In Need in Deed classrooms, students feel a sense of belonging, are supported in taking academic risks, and understand their agency as change makers. In this piece, Need in Deed staff and teachers give an overview of the My Voice framework and share stories from Need in Deed classrooms that highlight Dr. Gholdy Muhammad's five pursuits of culturally and historically responsive teaching - identity, skills, intellectualism, criticality, and joy.

Published
2024

6. Supportive care 2030 movement: towards unifying ambitions for global excellence in supportive cancer care-an international Delphi study.

Author

Chan, Raymond, Knowles, Reegan, Ashbury, Fredrick, Bowen, Joanne, Chan, Alexandre, Chin, Melissa, Olver, Ian, Taylor, Carolyn, Tinianov, Stacey, Alberti, Paola, Bossi, Paolo, Brito-Dellan, Norman, Cooksley, Tim, Crawford, Gregory, Dixit, Niharika, Fitch, Margaret, Freedman, Jason, Ginex, Pamela, Hart, Nicolas, Hertz, Daniel, Jefford, Michael, Koczwara, Bogda, Naito, Tateaki, Orsey, Andrea, Ruhlmann, Christina, Tsoukalas, Nikolaos, van den Hurk, Corina, Van Sebille, Ysabella, Wardill, Hannah, Scotte, Florian, and Lustberg, Maryam

Subjects
Optimal care, Palliative care, Supportive care, Supportive oncology, Toxicity
Abstract

BACKGROUND: Supportive care to ensure optimal quality of life is an essential component of cancer care and symptom control across the lifespan. Ongoing advances in cancer treatment, increasing toxicity from many novel treatment regimes, and variations in access to care and cancer outcomes across the globe and resource settings present significant challenges for supportive care delivery. To date, no overarching framework has been developed to guide supportive care development worldwide. As an initial step of the Multinational Association of Supportive Care in Cancer (MASCC) Supportive Care 2030 Movement, we developed a targeted, unifying set of ambition statements to envision the future of supportive cancer care. METHODS: From September 2022 until June 2023, we used a modified Delphi methodology to develop and attain consensus about ambition statements related to supportive cancer care. Leaders of MASCC Study Groups were invited to participate in an Expert Panel for the first two Delphi rounds (and a preliminary round to suggest potential ambition statements). Patient Advocates then examined and provided input regarding the ambition statements. FINDINGS: Twenty-seven Expert Panelists and 11 Patient Advocates participated. Consensus was attained on 13 ambition statements, with two sub-statements. The ambition statements addressed global standards for guideline development and implementation, coordinated and individualized care, dedicated supportive oncology services, self-management, needs for screening and actions, patient education, behavioral support, financial impact minimization, comprehensive survivorship care, and timely palliative care, reflecting collaboration, coordination and team-based approach across all levels. INTERPRETATION: This study is the first to develop shared ambitions for the future of supportive cancer care on a global level. These ambition statements can facilitate a coordinated, resource-stratified, and person-centered approach and inform research, education, clinical services, and policy efforts. FUNDING: This project received funding support from Prof Raymond Chans NHMRC Investigator Grant (APP1194051).

Published
2024

7. The global distribution and drivers of wood density and their impact on forest carbon stocks.

Author

Mo, Lidong, Crowther, Thomas W, Maynard, Daniel S, van den Hoogen, Johan, Ma, Haozhi, Bialic-Murphy, Lalasia, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B, Phillips, Oliver L, Abegg, Meinrad, Adou Yao, Yves C, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F, Amaral, Iêda, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A, Baker, Timothy R, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G, Bastian, Meredith L, Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Boonman, Coline CF, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brearley, Francis Q, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chen, Han YH, Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D, Coomes, David A, Valverde, Fernando Cornejo, Corral-Rivas, José J, Crim, Philip M, Cumming, Jonathan R, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J, Eyre, Teresa J, Fandohan, Adandé Belarmain, Fayle, Tom M, Feldpausch, Ted R, Ferreira, Leandro V, Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier GP, Gianelle, Damiano, Glick, Henry B, Harris, David J, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L, Herold, Martin, Hietz, Peter, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Ibanez, Thomas, Imai, Nobuo, Jagodziński, Andrzej M, Jaroszewicz, Bogdan, and Johannsen, Vivian Kvist

Subjects
Ecology, Evolutionary biology, Environmental management
Abstract

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Published
2024

8. Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing

Author

Winklmann, Jonas, Alberti, Andrea, and Schulz, Martin

Subjects
Quantum Physics, Computer Science - Software Engineering
Abstract

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. To also rule out the possibility of better algorithms existing, we calculated the Cram\'er-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one. Our comparison shows that of our tested algorithms, a global non-linear least-squares solver that uses the optical system's PSF to return a each sites' number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its huge computational complexity and, thus, required calculation time. We manage to somewhat reduce this problem, suggesting that its use may be viable. However, our study also shows that for cases where utmost speed is required, simple algorithms may be preferable., Comment: 10 pages, 11 figures

Published
2024
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9. Value-Enriched Population Synthesis: Integrating a Motivational Layer

Author

Aguilera, Alba, Albertí, Miquel, Osman, Nardine, and Curto, Georgina

Subjects
Computer Science - Multiagent Systems
Abstract

In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.

Published
2024

10. Recoil-free Quantum Gates with Optical Qubits

Author

Zhang, Zhao, Van Damme, Léo, Rossignolo, Marco, Festa, Lorenzo, Melchner, Max, Eberhard, Robin, Tsevas, Dimitrios, Mours, Kevin, Reches, Eran, Zeiher, Johannes, Blatt, Sebastian, Bloch, Immanuel, Glaser, Steffen J., and Alberti, Andrea

Subjects
Quantum Physics, Physics - Atomic Physics
Abstract

We propose a scheme to perform optical pulses that suppress the effect of photon recoil by three orders of magnitude compared to ordinary pulses in the Lamb-Dicke regime. We derive analytical insight about the fundamental limits to the fidelity of optical qubits for trapped atoms and ions. This paves the way towards applications in quantum computing for realizing $>1000$ of gates with an overall fidelity above 99\%., Comment: 5 pages plus appendices and bibliography, 6 figures

Published
2024

11. ANDES, the high resolution spectrograph for the ELT: science goals, project overview and future developments

Author

Marconi, A., Abreu, M., Adibekyan, V., Alberti, V., Albrecht, S., Alcaniz, J., Aliverti, M., Prieto, C. Allende, Gómez, J. D. Alvarado, Alves, C. S., Amado, P. J., Amate, M., Andersen, M. I., Antoniucci, S., Artigau, E., Bailet, C., Baker, C., Baldini, V., Balestra, A., Barnes, S. A., Baron, F., Barros, S. C. C., Bauer, S. M., Beaulieu, M., Bellido-Tirado, O., Benneke, B., Bensby, T., Bergin, E. A., Berio, P., Biazzo, K., Bigot, L., Bik, A., Birkby, J. L., Blind, N., Boebion, O., Boisse, I., Bolmont, E., Bolton, J. S., Bonaglia, M., Bonfils, X., Bonhomme, L., Borsa, F., Bouret, J. -C., Brandeker, A., Brandner, W., Broeg, C. H., Brogi, M., Brousseau, D., Brucalassi, A., Brynnel, J., Buchhave, L. A., Buscher, D. F., Cabona, L., Cabral, A., Calderone, G., Calvo-Ortega, R., Cantalloube, F., Martins, B. L. Canto, Carbonaro, L., Caujolle, Y., Chauvin, G., Chazelas, B., Cheffot, A. -L., Cheng, Y. S., Chiavassa, A., Christensen, L., Cirami, R., Cirasuolo, M., Cook, N. J., Cooke, R. J., Coretti, I., Covino, S., Cowan, N., Cresci, G., Cristiani, S., Parro, V. Cunha, Cupani, G., D'Odorico, V., Dadi, K., Leão, I. de Castro, De Cia, A., De Medeiros, J. R., Debras, F., Debus, M., Delorme, A., Demangeon, O., Derie, F., Dessauges-Zavadsky, M., Di Marcantonio, P., Di Stefano, S., Dionies, F., de Souza, A. Domiciano, Doyon, R., Dunn, J., Egner, S., Ehrenreich, D., Faria, J. P., Ferruzzi, D., Feruglio, C., Fisher, M., Fontana, A., Frank, B. S., Fuesslein, C., Fumagalli, M., Fusco, T., Fynbo, J., Gabella, O., Gaessler, W., Gallo, E., Gao, X., Genolet, L., Genoni, M., Giacobbe, P., Giro, E., Goncalves, R. S., Gonzalez, O. A., Hernández, J. I. González, Gouvret, C., Temich, F. Gracia, Haehnelt, M. G., Haniff, C., Hatzes, A., Helled, R., Hoeijmakers, H. J., Hughes, I., Huke, P., Ivanisenko, Y., Järvinen, A. S., Järvinen, S. P., Kaminski, A., Kern, J., Knoche, J., Kordt, A., Korhonen, H., Korn, A. J., Kouach, D., Kowzan, G., Kreidberg, L., Landoni, M., Lanotte, A. A., Lavail, A., Lavie, B., Lee, D., Lehmitz, M., Li, J., Li, W., Liske, J., Lovis, C., Lucatello, S., Lunney, D., MacIntosh, M. J., Madhusudhan, N., Magrini, L., Maiolino, R., Maldonado, J., Malo, L., Man, A. W. S., Marquart, T., Marques, C. M. J., Marques, E. L., Martinez, P., Martins, A., Martins, C. J. A. P., Martins, J. H. C., Maslowski, P., Mason, C. A., Mason, E., McCracken, R. A., Sousa, M. A. F. Melo e, Mergo, P., Micela, G., Milaković, D., Molliere, P., Monteiro, M. A., Montgomery, D., Mordasini, C., Morin, J., Mucciarelli, A., Murphy, M. T., N'Diaye, M., Nardetto, N., Neichel, B., Neri, N., Niedzielski, A. T., Niemczura, E., Nisini, B., Nortmann, L., Noterdaeme, P., Nunes, N. J., Oggioni, L., Olchewsky, F., Oliva, E., Onel, H., Origlia, L., Ostlin, G., Ouellette, N. N. -Q., Palle, E., Papaderos, P., Pariani, G., Pasquini, L., Castro, J. Peñate, Pepe, F., Peroux, C., Levasseur, L. Perreault, Perruchot, S., Petit, P., Pfuhl, O., Pino, L., Piqueras, J., Piskunov, N., Pollo, A., Poppenhaeger, K., Porru, M., Puschnig, J., Quirrenbach, A., Rauscher, E., Rebolo, R., Redaelli, E. M. A., Reffert, S., Reid, D. T., Reiners, A., Richter, P., Riva, M., Rivoire, S., Rodriguez-López, C., Roederer, I. U., Romano, D., Roth, M., Rousseau, S., Rowe, J., Saccardi, A., Salvadori, S., Sanna, N., Santos, N. C., Diaz, P. Santos, Sanz-Forcada, J., Sarajlic, M., Sauvage, J. -F., Savio, D., Scaudo, A, Schäfer, S., Schiavon, R. P., Schmidt, T. M., Selmi, C., Simoes, R., Simonnin, A., Sivanandam, S., Sordet, M., Sordo, R., Sortino, F., Sosnowska, D., Sousa, S. G., Spang, A., Spiga, R., Stempels, E., Stevenson, J. R. Y., Strassmeier, K. G., Mascareño, A. Suárez, Sulich, A., Sun, X., Tanvir, N. R., Tenegi-Sangines, F., Thibault, S., Thompson, S. J., Tisserand, P., Tozzi, A., Turbet, M., Veran, J. -P., Vallee, P., Vanni, I., Varas, R., Vega-Moreno, A., Venn, K. A., Verma, A., Vernet, J., Viel, M., Wade, G., Waring, C., Weber, M., Weder, J., Wehbe, B., Weingrill, J., Woche, M., Xompero, M., Zackrisson, E., Zanutta, A., Osorio, M. R. Zapatero, Zechmeister, M., and Zimara, J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

Published
2024

12. Numerical simulation of a helium Plasma-Material Interaction experiment in GyM linear device through SOLPS-ITER and ERO2.0 codes

Author

Mombelli, F., Alberti, G., Tonello, E., Tuccari, C., Uccello, A., Baumann, C., Bonnin, X., Romazanov, J., and Passoni, M.

Subjects
Physics - Plasma Physics
Abstract

Learning how to safely handle Plasma-Material Interaction (PMI) is a key challenge towards the commercialisation of energy from nuclear fusion. In this respect, linear plasma devices are ideal experimental testbeds, and numerical codes play a crucial complementary role. In this paper, a numerical investigation of PMI-relevant helium plasma experimental discharges in GyM linear device is presented, in which SOLPS-ITER and ERO2.0 codes are coupled for plasma background generation and material erosion investigation respectively, with the aim to support the interpretation and complement the available experimental dataset. On the plasma side, simulated profiles are validated against experimental data to provide a realistic plasma background, and the role of He metastable states is assessed for the first time in SOLPS simulations. On the material side, the erosion and deposition effects due to the introduction of the sample-holder in the simulation volume are investigated, now considering also the real stainless steel composition as wall material.

Published
2024

13. Transient-absorption spectroscopy of dendrimers via nonadiabatic excited-state dynamics simulations.

Author

Perez-Castillo, Royle, Freixas, Victor, Mukamel, Shaul, Martinez-Mesa, Aliezer, Uranga-Piña, Llinersy, Tretiak, Sergei, Gelin, Maxim, and Fernandez-Alberti, Sebastian

Abstract

The efficiency of light-harvesting and energy transfer in multi-chromophore ensembles underpins natural photosynthesis. Dendrimers are highly branched synthetic multi-chromophoric conjugated supra-molecules that mimic these natural processes. After photoexcitation, their repeated units participate in a number of intramolecular electronic energy relaxation and redistribution pathways that ultimately funnel to a sink. Here, a model four-branched dendrimer with a pyrene core is theoretically studied using nonadiabatic molecular dynamics simulations. We evaluate excited-state photoinduced dynamics of the dendrimer, and demonstrate on-the-fly simulations of its transient absorption pump-probe (TA-PP) spectra. We show how the evolutions of the simulated TA-PP spectra monitor in real time photoinduced energy relaxation and redistribution, and provide a detailed microscopic picture of the relevant energy-transfer pathways. To the best of our knowledge, this is the first of this kind of on-the-fly atomistic simulation of TA-PP signals reported for a large molecular system.

Published
2024

14. Design and First Tests of the Trapped Electrons Experiment T-REX

Author

Romano, Francesco, Bars, Guillaume Le, Loizu, Joaquim, Nöel, Marc, Hogge, Jean-Philippe, Alberti, Stefano, Genoud, Jeremy, Antonioni, Severino, Naux, Lorraine, Giroud-Garampon, Pierrick, Couturier, Steve, Leresche, Thierry, and Fasel, Damien

Subjects
Physics - Plasma Physics
Abstract

Gyrotrons are essential for electron cyclotron resonance heating (ECRH) in fusion reactors, making efficient operation crucial for advancing fusion energy. Past experiments revealed instability issues due to trapped electrons in the magnetron injection gun (MIG) region, causing undesired currents and operational failures. To address this, tight manufacturing tolerances are required for the MIG geometry [1]. We present initial findings of the TRapped Electrons eXperiment (T-REX) developed at the Swiss Plasma Center, designed to understand the physics of electron clouds in gyrotron MIGs. T-REX replicates MIG geometries, as well as their typical electric and magnetic fields, and it is supported by 2D Particle-in-Cell (PIC) simulations with the FENNECS code [2, 3]. The setup includes two coaxial electrodes in a vacuum chamber atop a superconducting magnet, with a central electrode biased to negative DC voltages and an outer one at ground, creating a radial electric field (1 to 2 MV/M) and an axial magnetic field (B < 0.4 T). This setup mimics Penning-Malmberg traps. We present the experimental device and first findings on current distribution and also qualitative comparison with FENNECS simulations [4]. Planned diagnostics include optical emission spectroscopy, phosphor screen imaging, Streak camera imaging, and potentially electric field distribution via the Stark effect. This research aims to enhance gyrotron performance and reliability in fusion energy systems.

Published
2024

15. On the block size spectrum of the multiplicative Beta coalescent

Author

Alberti, Frederic, Boenkost, Florin, and Cordero, Fernando

Subjects
Mathematics - Probability
Abstract

In this work we introduce the dynamic $\Lambda$-random graph and the associated multiplicative $\Lambda$-coalescent. The dynamic $\Lambda$-random graph is an exchangeable and consistent random graph process, inspired by the classical $\Lambda$-coalescent studied in mathematical population genetics. Our main concern is the multiplicative $\Lambda$-coalescent, which accounts for the connected components (blocks) of the graph. This process is exchangeable but not consistent. We focus on the case where $\Lambda$ is the beta measure and prove a dynamic law of large numbers for the block size spectrum, which tracks the numbers of blocks containing $1,...,d$ elements. On top of that, we provide a functional limit theorem for the fluctuations around its deterministic trajectory. The limit process satisfies a stochastic differential equation of Ornstein-Uhlenbeck type., Comment: 20 pages, 2 figures

Published
2024

16. DOLOMITES: Domain-Specific Long-Form Methodical Tasks

Author

Malaviya, Chaitanya, Agrawal, Priyanka, Ganchev, Kuzman, Srinivasan, Pranesh, Huot, Fantine, Berant, Jonathan, Yatskar, Mark, Das, Dipanjan, Lapata, Mirella, and Alberti, Chris

Subjects
Computer Science - Computation and Language
Abstract

Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for students, these tasks are pervasive, requiring to methodically generate structured long-form output for a given input. We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output, and introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields. Our benchmark further contains specific instantiations of methodical tasks with concrete input and output examples (1,857 in total) which we obtain by collecting expert revisions of up to 10 model-generated examples of each task. We use these examples to evaluate contemporary language models highlighting that automating methodical tasks is a challenging long-form generation problem, as it requires performing complex inferences, while drawing upon the given context as well as domain knowledge., Comment: Accepted to TACL; to be presented at EMNLP 2024. Dataset available at https://dolomites-benchmark.github.io

Published
2024

17. Oncological results and cancer control definition in focal therapy for Prostate Cancer: a systematic review

Author

Nicoletti, Rossella, Alberti, Andrea, Castellani, Daniele, Yee, Chi Hang, Zhang, Kai, Poon, Darren M. C., Chiu, Peter Ka-Fung, Campi, Riccardo, Resta, Giulio Raffaele, Dibilio, Edoardo, Pirola, Giacomo Maria, Chiacchio, Giuseppe, Fuligni, Demetra, Brocca, Carlo, Giulioni, Carlo, De Stefano, Virgilio, Serni, Sergio, Gauhar, Vineet, NG, Chi Fai, Gacci, Mauro, and Teoh, Jeremy Yuen Chun

Published
2024
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18. Functional outcomes and safety of focal therapy for prostate cancer: a systematic review on results and patient-reported outcome measures (PROMs)

Author

Nicoletti, Rossella, Alberti, Andrea, Castellani, Daniele, Yee, Chi Hang, Zhang, Kai, Poon, Darren M. C., Chiu, Peter Ka-Fung, Campi, Riccardo, Resta, Giulio Raffaele, Dibilio, Edoardo, Pirola, Giacomo Maria, Chiacchio, Giuseppe, Fuligni, Demetra, Brocca, Carlo, Giulioni, Carlo, De Stefano, Virgilio, Serni, Sergio, Gauhar, Vineet, Ng, Chi Fai, Gacci, Mauro, and Teoh, Jeremy Yuen Chun

Published
2024
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24. Widening global variability in grassland biomass since the 1980s

Author

MacDougall, Andrew S., Esch, Ellen, Chen, Qingqing, Carroll, Oliver, Bonner, Colin, Ohlert, Timothy, Siewert, Matthias, Sulik, John, Schweiger, Anna K., Borer, Elizabeth T., Naidu, Dilip, Bagchi, Sumanta, Hautier, Yann, Wilfahrt, Peter, Larson, Keith, Olofsson, Johan, Cleland, Elsa, Muthukrishnan, Ranjan, O’Halloran, Lydia, Alberti, Juan, Anderson, T. Michael, Arnillas, Carlos A., Bakker, Jonathan D., Barrio, Isabel C., Biederman, Lori, Boughton, Elizabeth H., Brudvig, Lars A., Bruschetti, Martin, Buckley, Yvonne, Bugalho, Miguel N., Cadotte, Marc W., Caldeira, Maria C., Catford, Jane A., D’Antonio, Carla, Davies, Kendi, Daleo, Pedro, Dickman, Christopher R., Donohue, Ian, DuPre, Mary Ellyn, Elgersma, Kenneth, Eisenhauer, Nico, Eskelinen, Anu, Estrada, Catalina, Fay, Philip A., Feng, Yanhao, Gruner, Daniel S., Hagenah, Nicole, Haider, Sylvia, Harpole, W. Stanley, Hersch-Green, Erika, Jentsch, Anke, Kirkman, Kevin, Knops, Johannes M. H., Laanisto, Lauri, Lannes, Lucíola S., Laungani, Ramesh, Lkhagva, Ariuntsetseg, Macek, Petr, Martina, Jason P., McCulley, Rebecca L., Melbourne, Brett, Mitchell, Rachel, Moore, Joslin L., Morgan, John W., Muraina, Taofeek O., Niu, Yujie, Pärtel, Meelis, Peri, Pablo L., Power, Sally A., Price, Jodi N., Prober, Suzanne M., Ren, Zhengwei, Risch, Anita C., Smith, Nicholas G., Sonnier, Grégory, Standish, Rachel J., Stevens, Carly J., Tedder, Michelle, Tognetti, Pedro, Veen, G. F. (Ciska), Virtanen, Risto, Wardle, Glenda M., Waring, Elizabeth, Wolf, Amelia A., Yahdjian, Laura, and Seabloom, Eric W.

Published
2024
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26. Reperfusion therapies in patients with acute ischaemic stroke and atrial fibrillation: data on safety and effectiveness from a multi-centre cohort study

Author

Cancelloni, Virginia, Buratti, Mariachiara, Tsivgoulis, Georgios, Furie, Karen L., Tadi, Prasanna, Caso, Valeria, Becattini, Cecilia, Agnelli, Giancarlo, Zedde, Marialuisa, Abdul-Rahim, Azmil H., Alberti, Andrea, Venti, Michele, de Magistris, Ilaria Leone, Acciarresi, Monica, D’Amore, Cataldo, Mosconi, Maria G., Cimini, Ludovica A., Cappellari, Manuel, Putaala, Jukka, Tomppo, Liisa, Tatlisumak, Turgut, Bandini, Fabio, Marcheselli, Simona, Pezzini, Alessandro, Sohn, Sung-I. I., Lorenzini, Gianni, Tassi, Rossana, Guideri, Francesca, Acampa, Maurizio, Ntaios, George, Karagkiozi, Efstathia, Athanasakis, George, Makaritsis, Kostantinos, Sagris, Dimitrios, Adamou, Anastasia, Vadikolias, Kostantinos, Palaiodimou, Lina, Chondrogianni, Maria, Mumoli, Nicola, Galati, Franco, Sacco, Simona, Tiseo, Cindy, Corea, Francesco, Ageno, Walter, Bellesini, Marta, Silvestrelli, Giorgio, Ciccone, Alfonso, Mancuso, Michelangelo, Orlandi, Giovanni, Pascarella, Rosario, Tassinari, Tiziana, Rueckert, Christina, Baldi, Antonio, Toni, Danilo, Lettieri, Federica, Giuntini, Martina, Lotti, Enrico M., Flomin, Yuriy, Pieroni, Alessio, Kargiotis, Odysseas, Karapanayiotides, Theodore, Halvatsiotis, Panagiotis, Monaco, Serena, Baronello, Mario M., Csiba, Laszlò, Szabò, Lilla, Chiti, Alberto, Giorli, Elisa, Del Sette, Massimo, Imberti, Davide, Zabzuni, Dorjan, Doronin, Boris, Volodina, Vera, Michel, Patrik, Vanacker, Peter, Barlinn, Kristian, Pallesen, Lars P., Kepplinger, Jessica, Deleu, Dirk, Gourbali, Vanessa, Yaghi, Shadi, and Paciaroni, Maurizio

Published
2024
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27. 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

28. Simple linear attention language models balance the recall-throughput tradeoff

Author

Arora, Simran, Eyuboglu, Sabri, Zhang, Michael, Timalsina, Aman, Alberti, Silas, Zinsley, Dylan, Zou, James, Rudra, Atri, and Ré, Christopher

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we can improve language model efficiency (e.g. by reducing memory consumption) without compromising on recall. By applying experiments and theory to a broad set of architectures, we identify a key tradeoff between a model's state size and recall ability. We show that efficient alternatives to attention (e.g. H3, Mamba, RWKV) maintain a fixed-size recurrent state, but struggle at recall. We propose BASED a simple architecture combining linear and sliding window attention. By varying BASED window size and linear attention feature dimension, we can dial the state size and traverse the pareto frontier of the recall-memory tradeoff curve, recovering the full quality of attention on one end and the small state size of attention-alternatives on the other. We train language models up to 1.3b parameters and show that BASED matches the strongest sub-quadratic models (e.g. Mamba) in perplexity and outperforms them on real-world recall-intensive tasks by 6.22 accuracy points. Implementations of linear attention are often less efficient than optimized standard attention implementations. To make BASED competitive, we develop IO-aware algorithms that enable 24x higher throughput on language generation than FlashAttention-2, when generating 1024 tokens using 1.3b parameter models. Code for this work is provided at: https://github.com/HazyResearch/based.

Published
2024

29. Ontology Engineering to Model the European Cultural Heritage: The Case of Cultural Gems

Author

Alberti, Valentina, Cocco, Cinzia, Consoli, Sergio, Montalto, Valentina, and Panella, Francesco

Subjects
Computer Science - Computers and Society, Computer Science - Digital Libraries
Abstract

Cultural gems is a web application conceived by the European Commission's Joint Research Centre (DG JRC), which aims at engaging people and organisations across Europe to create a unique repository of cultural and creative places. The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cultural realm. Cultural gems maps more than 130,000 physical places in over 300 European cities and towns, and since 2020 it also lists online cultural initiatives. The new release aims, among other, to increase the interoperability of the application. At this purpose, we provide an overview on the current development of an ontology for Cultural gems used to map cultural heritage in European cities by using Linked Open Data (LOD) standards, and making the data FAIR, that is Findable, Accessible, Interoperable, and Reusable. We provide an overview of the methodology, presenting the structure of the ontology, and the services and tools we are currently building on top., Comment: 11 pages, 3 figures, Eighth International Congress on Information and Communication Technology (ICICT 2023)

Published
2024
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30. Cultural gems linked open data: Mapping culture and intangible heritage in European cities

Author

Consoli, Sergio, Alberti, Valentina, Cocco, Cinzia, Panella, Francesco, and Montalto, Valentina

Subjects
Computer Science - Digital Libraries
Abstract

The recovery and resilience of the cultural and creative sectors after the COVID-19 pandemic is a current topic with priority for the European Commission. Cultural gems is a crowdsourced web platform managed by the Joint Research Centre of the European Commission aimed at creating community-led maps as well as a common repository for cultural and creative places across European cities and towns. More than 130,000 physical locations and online cultural activities in more than 300 European cities and towns are currently tracked by the application. The main objective of Cultural gems consists in raising a holistic vision of European culture, reinforcing a sense of belonging to a common European cultural space. This data article describes the ontology developed for Cultural gems, adopted to represent the domain of knowledge of the application by means of FAIR (Findable, Accessible, Interoperable, Reusable) principles and following the paradigms of Linked Open Data (LOD). We provide an overview of this dataset, and describe the ontology model, along with the services used to access and consume the data., Comment: 10 pages, 3 figures

Published
2024
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31. Localization of point scatterers via sparse optimization on measures

Author

Alberti, Giovanni S., Petit, Romain, and Santacesaria, Matteo

Subjects
Mathematics - Numerical Analysis
Abstract

We consider the inverse scattering problem for time-harmonic acoustic waves in a medium with pointwise inhomogeneities. In the Foldy-Lax model, the estimation of the scatterers' locations and intensities from far field measurements can be recast as the recovery of a discrete measure from nonlinear observations. We propose a "linearize and locally optimize" approach to perform this reconstruction. We first solve a convex program in the space of measures (known as the Beurling LASSO), which involves a linearization of the forward operator (the far field pattern in the Born approximation). Then, we locally minimize a second functional involving the nonlinear forward map, using the output of the first step as initialization. We provide guarantees that the output of the first step is close to the sought-after measure when the scatterers have small intensities and are sufficiently separated. We also provide numerical evidence that the second step still allows for accurate recovery in settings that are more involved.

Published
2024
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32. Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems

Author

Alberti, Giovanni S., Ratti, Luca, Santacesaria, Matteo, and Sciutto, Silvia

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution. This approach is grounded on the a priori assumption that the unknown can be appropriately represented in a basis with a limited number of significant components, while most coefficients are close to zero. This occurrence is frequently observed in real-world scenarios, such as with piecewise smooth signals. In this study, we propose a probabilistic sparsity prior formulated as a mixture of degenerate Gaussians, capable of modeling sparsity with respect to a generic basis. Under this premise, we design a neural network that can be interpreted as the Bayes estimator for linear inverse problems. Additionally, we put forth both a supervised and an unsupervised training strategy to estimate the parameters of this network. To evaluate the effectiveness of our approach, we conduct a numerical comparison with commonly employed sparsity-promoting regularization techniques, namely LASSO, group LASSO, iterative hard thresholding, and sparse coding/dictionary learning. Notably, our reconstructions consistently exhibit lower mean square error values across all $1$D datasets utilized for the comparisons, even in cases where the datasets significantly deviate from a Gaussian mixture model.

Published
2024

33. Black-Box Access is Insufficient for Rigorous AI Audits

Author

Casper, Stephen, Ezell, Carson, Siegmann, Charlotte, Kolt, Noam, Curtis, Taylor Lynn, Bucknall, Benjamin, Haupt, Andreas, Wei, Kevin, Scheurer, Jérémy, Hobbhahn, Marius, Sharkey, Lee, Krishna, Satyapriya, Von Hagen, Marvin, Alberti, Silas, Chan, Alan, Sun, Qinyi, Gerovitch, Michael, Bau, David, Tegmark, Max, Krueger, David, and Hadfield-Menell, Dylan

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system's inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone., Comment: FAccT 2024

Published
2024
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34. Modelling of power exhaust in TCV positive and negative triangularity L-mode plasmas

Author

Tonello, E., Mombelli, F., Février, O., Alberti, G., Bolzonella, T., Durr-Legoupil-Nicoud, G., Gorno, S., Reimerdes, H., Theiler, C., Vianello, N., Passoni, M., team, the TCV, and team, the WPTE

Subjects
Physics - Plasma Physics
Abstract

L-mode negative triangularity (NT) operation is a promising alternative to the positive triangularity (PT) H-mode as a high-confinement ELM-free operational regime. In this work, two TCV L-mode lower single null Ohmic discharges with opposite triangularity $\delta \simeq \pm 0.3$ are investigated using SOLPS-ITER modelling. The main focus is the exploration of the reasons behind the experimentally observed feature of NT plasmas being more difficult to detach than similar PT experiments. SOLPS-ITER simulations are performed assuming the same anomalous diffusivity for particles $D_n^{AN}$ and energy $\kappa_{e/i}^{AN}$ in PT and NT. Nonetheless, the results clearly show dissimilar transport and accumulation of neutral particles in the scrape-off layer (SOL) of the two configurations, which consequently gives rise to different ionization sources for the plasma and produces different poloidal and cross-field fluxes. Simulations also recover the experimental feature of the outer target being hotter in the NT scenario (with $T_{e, NT} \gtrsim 5 \, \mathrm{eV}$) than in the PT counterpart., Comment: 18 pages, 8 figures

Published
2024
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35. Eulerian, Lagrangian and broad continuous solutions to a balance law with non convex flux II

Author

Alberti, Giovanni, Bianchini, Stefano, and Caravenna, Laura

Subjects
Mathematics - Analysis of PDEs, (2020):35L60 (primary), 37C10, 58C20, 76N10 (secondary)
Abstract

We consider a *continuous* solution $u$ of the balance law \[ \partial_{\mathit t} u + \partial_{\mathit x} (f(u)) = g\] in one space dimension, where the flux function $f$ is of class $C^2$ and the source term $g$ is bounded. This equation admits an Eulerian intepretation (namely the distributional one) and a Lagrangian intepretation (which can be further specified). Since $u$ is only continuous, these interpretations do not necessessarily agree; moreover each interpretation naturally entails a different equivalence class for the source term $g$. In this paper we complete the comparison between these notions of solutions started in the companion paper [Alberti-Bianchini-Caravenna I], and analize in detail the relations between the corresponding notions of source term.

Published
2024

43. 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

44. Three-dimensional imaging of single atoms in an optical lattice via helical point-spread-function engineering

Author

Legrand, Tangi, Winkelmann, Falk-Richard, Alt, Wolfgang, Meschede, Dieter, Alberti, Andrea, and Weidner, Carrie A.

Subjects
Quantum Physics
Abstract

We demonstrate a method for determining the three-dimensional location of single atoms in a quantum gas microscopy system using a phase-only spatial light modulator to modify the point-spread function of the high-resolution imaging system. Here, the typical diffracted spot generated by a single atom as a point source is modified to a double spot that rotates as a function of the atom's distance from the focal plane of the imaging system. We present and numerically validate a simple model linking the rotation angle of the point-spread function with the distance to the focal plane. We show that, when aberrations in the system are carefully calibrated and compensated for, this method can be used to determine an atom's position to within a single lattice site in a single experimental image, extending quantum simulation with microscopy systems further into the regime of three dimensions., Comment: Accepted version

Published
2023
Full Text
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45. Disentangling the solar activity-solar wind predictive causality at Space Climate scales

Author

Reda, Raffaele, Stumpo, Mirko, Giovannelli, Luca, Alberti, Tommaso, and Consolini, Giuseppe

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

The variability in the magnetic activity of the Sun is the main source of the observed changes in the plasma and electromagnetic environments within the heliosphere. The primary way in which solar activity affects the Earth's environment is via the solar wind and its transients. However, the relationship between solar activity and solar wind is not the same at the Space Weather and Space Climate time scales. In this work, we investigate this relationship exploiting five solar cycles data of Ca II K index and solar wind parameters, by taking advantage of the Hilbert-Huang Transform, which allows to separate the contribution at the different time scales. By filtering out the high frequency components and looking at decennial time scales, we confirm the presence of a delayed response of solar wind to Ca II K index variations, with a time lag of ~ 3.1-year for the speed and ~ 3.4-year for the dynamic pressure. To assess the results in a stronger framework, we make use of a Transfer Entropy approach to investigate the information flow between the quantities and to test the causality of the relation. The time lag results from the latter are consistent with the cross-correlation ones, pointing out the presence of a statistical significant information flow from Ca II K index to solar wind dynamic pressure that peaks at time lag of 3.6-year. Such a result could be of relevance to build up a predictive model in a Space Climate context.

Published
2023
Full Text
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46. On the closability of differential operators

Author

Alberti, Giovanni, Bate, David, and Marchese, Andrea

Subjects
Mathematics - Classical Analysis and ODEs, Mathematics - Analysis of PDEs, Mathematics - Functional Analysis
Abstract

We discuss the closability of directional derivative operators with respect to a general Radon measure $\mu$ on $\mathbb{R}^d$; our main theorem completely characterizes the vectorfields for which the corresponding operator is closable from the space of Lipschitz functions ${\mathrm{Lip}}(\mathbb{R^d})$ to $L^p(\mu)$, for $1\leq p\leq\infty$. We also consider certain classes of multilinear differential operators. We then discuss the closability of the same operators from $L^q(\mu)$ to $L^p(\mu)$; we give necessary conditions and sufficient conditions for closability, but we do not have an exact characterization. As a corollary we obtain that classical differential operators such as gradient, divergence and jacobian determinant are closable from $L^q(\mu)$ to $L^p(\mu)$ only if $\mu$ is absolutely continuous with respect to the Lebesgue measure. Lastly, we rephrase our results on multilinear operators in terms of metric currents.

Published
2023

47. On the structure of flat chains with finite mass

Author

Alberti, Giovanni and Marchese, Andrea

Subjects
Mathematics - Classical Analysis and ODEs, Mathematics - Analysis of PDEs, Mathematics - Functional Analysis
Abstract

We prove that every flat chain with finite mass in $\mathbb{R}^d$ with coefficients in a normed abelian group $G$ is the restriction of a normal $G$-current to a Borel set. We deduce a characterization of real flat chains with finite mass in terms of a pointwise relation between the associated measure and vector field. We also deduce that any codimension-one real flat chain with finite mass can be written as an integral of multiplicity-one rectifiable currents, without loss of mass. Given a Lipschitz homomorphism $\phi:\tilde G\to G$ between two groups, we then study the associated map $\pi$ between flat chains in $\mathbb{R}^d$ with coefficients in $\tilde G$ and $G$ respectively. In the case $\tilde G=\mathbb{R}$ and $G=\mathbb{S}^1$, we prove that if $\phi$ is surjective, so is the restriction of $\pi$ to the set of flat chains with finite mass of dimension $0$, $1$, $d-1$, $d$.

Published
2023

48. SuperHF: Supervised Iterative Learning from Human Feedback

Author

Mukobi, Gabriel, Chatain, Peter, Fong, Su, Windesheim, Robert, Kutyniok, Gitta, Bhatia, Kush, and Alberti, Silas

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

While large language models demonstrate remarkable capabilities, they often present challenges in terms of safety, alignment with human values, and stability during training. Here, we focus on two prevalent methods used to align these models, Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). SFT is simple and robust, powering a host of open-source models, while RLHF is a more sophisticated method used in top-tier models like ChatGPT but also suffers from instability and susceptibility to reward hacking. We propose a novel approach, Supervised Iterative Learning from Human Feedback (SuperHF), which seeks to leverage the strengths of both methods. Our hypothesis is two-fold: that the reward model used in RLHF is critical for efficient data use and model generalization and that the use of Proximal Policy Optimization (PPO) in RLHF may not be necessary and could contribute to instability issues. SuperHF replaces PPO with a simple supervised loss and a Kullback-Leibler (KL) divergence prior. It creates its own training data by repeatedly sampling a batch of model outputs and filtering them through the reward model in an online learning regime. We then break down the reward optimization problem into three components: robustly optimizing the training rewards themselves, preventing reward hacking-exploitation of the reward model that degrades model performance-as measured by a novel METEOR similarity metric, and maintaining good performance on downstream evaluations. Our experimental results show SuperHF exceeds PPO-based RLHF on the training objective, easily and favorably trades off high reward with low reward hacking, improves downstream calibration, and performs the same on our GPT-4 based qualitative evaluation scheme all the while being significantly simpler to implement, highlighting SuperHF's potential as a competitive language model alignment technique., Comment: Accepted to the Socially Responsible Language Modelling Research (SoLaR) workshop at NeurIPS 2023

Published
2023

49. Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Author

Maestre, Fernando, Power, Sally, Yu, Qiang, Felton, Andrew, Munson, Seth, Luo, Yiqi, Abdoli, Hamed, Abedi, Mehdi, Alados, Concepción, Alberti, Juan, Alon, Moshe, An, Hui, Anacker, Brian, Anderson, Maggie, Auge, Harald, Bachle, Seton, Bahalkeh, Khadijeh, Bahn, Michael, Batbaatar, Amgaa, Bauerle, Taryn, Beard, Karen, Behn, Kai, Beil, Ilka, Biancari, Lucio, Blindow, Irmgard, Bondaruk, Viviana, Borer, Elizabeth, Bork, Edward, Bruschetti, Carlos, Byrne, Kerry, Cahill, James, Calvo, Dianela, Carbognani, Michele, Cardoni, Augusto, Carlyle, Cameron, Castillo-Garcia, Miguel, Chang, Scott, Chieppa, Jeff, Cianciaruso, Marcus, Cohen, Ofer, Cordeiro, Amanda, Cusack, Daniela, Dahlke, Sven, Daleo, Pedro, Dietterich, Lee, S Doherty, Tim, Dubbert, Maren, Ebeling, Anne, Eisenhauer, Nico, Fischer, Felícia, Forte, Tai, Gebauer, Tobias, Gozalo, Beatriz, Greenville, Aaron, Guidoni-Martins, Karlo, Hannusch, Heather, Vatsø Haugum, Siri, Hautier, Yann, Hefting, Mariet, Henry, Hugh, Hoss, Daniela, Ingrisch, Johannes, Iribarne, Oscar, Isbell, Forest, Johnson, Yari, Jordan, Samuel, Kelly, Eugene, Kimmel, Kaitlin, Kreyling, Juergen, Kröel-Dulay, György, Kröpfl, Alicia, Kübert, Angelika, Kulmatiski, Andrew, Lamb, Eric, Larsen, Klaus, Larson, Julie, Lawson, Jason, Leder, Cintia, Linstädter, Anja, Liu, Jielin, Liu, Shirong, Lodge, Alexandra, Longo, Grisel, Loydi, Alejandro, Luan, Junwei, Curtis Lubbe, Frederick, Macfarlane, Craig, Mackie-Haas, Kathleen, Malyshev, Andrey, Maturano-Ruiz, Adrián, Merchant, Thomas, Metcalfe, Daniel, Mori, Akira, Mudongo, Edwin, Newman, Gregory, Nielsen, Uffe, Nimmo, Dale, Niu, Yujie, Nobre, Paola, and OConnor, Rory

Subjects
Drought-Net, International Drought Experiment, climate extreme, productivity, Droughts, Ecosystem, Grassland, Carbon Cycle, Climate Change, Receptor Protein-Tyrosine Kinases
Abstract

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

Published
2024

50. Dominance and rarity in tree communities across the globe: Patterns, predictors and threats

Author

Hordijk, Iris, Bialic‐Murphy, Lalasia, Lauber, Thomas, Routh, Devin, Poorter, Lourens, Rivers, Malin C, Steege, Hans ter, Liang, Jingjing, Reich, Peter B, de‐Miguel, Sergio, Nabuurs, Gert‐Jan, Gamarra, Javier GP, Chen, Han YH, Zhou, Mo, Wiser, Susan K, Pretzsch, Hans, Paquette, Alain, Picard, Nicolas, Hérault, Bruno, Bastin, Jean‐Francois, Alberti, Giorgio, Abegg, Meinrad, Yao, Yves C Adou, Zambrano, Angelica M Almeyda, Alvarado, Braulio V, Alvarez‐Davila, Esteban, Alvarez‐Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón‐Fernández, Clara, Araujo‐Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Corredor, Gerardo A Aymard, Baker, Timothy, Banki, Olaf, Barroso, Jorcely, Bastian, Meredith L, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, Cesar, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chisholm, Chelsea, Cienciala, Emil, Clark, Connie J, Clar, David B, Colletta, Gabriel, Coomes, David, Valverde, Fernando Cornejo, Corral‐Rivas, Jose J, Crim, Philip, Cumming, Jonathan, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Iêda, Amaral, Dourdain, Aurélie, Dolezal, Jiri, Obiang, Nestor Laurier Engone, Enquist, Brian, Eyre, Teresa, Fandohan, Adandé Belarmain, Fayle, Tom M, Ferreira, Leandro V, Feldpausch, Ted R, Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gianelle, Damiano, Glick, Henry B, Harris, David, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Herbohn, John, Hillers, Annika, Coronado, Eurídice N Honorio, Hui, Cang, Cho, Hyunkook, Ibanez, Thomas, Jung, Ilbin, Imai, Nobuo, Jagodzinski, Andrzej M, and Jaroszewicz, Bogdan

Subjects
Environmental Sciences, Ecological Applications, Ecology, Biological Sciences, Machine Learning and Artificial Intelligence, Life on Land, Life Below Water
Abstract

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?. Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.

Published
2024

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