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14,538 results on '"Caprio A"'

1. Conformalized Credal Regions for Classification with Ambiguous Ground Truth

Author: Caprio, Michele, Stutz, David, Li, Shuo, and Doucet, Arnaud
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: An open question in \emph{Imprecise Probabilistic Machine Learning} is how to empirically derive a credal region (i.e., a closed and convex family of probabilities on the output space) from the available data, without any prior knowledge or assumption. In classification problems, credal regions are a tool that is able to provide provable guarantees under realistic assumptions by characterizing the uncertainty about the distribution of the labels. Building on previous work, we show that credal regions can be directly constructed using conformal methods. This allows us to provide a novel extension of classical conformal prediction to problems with ambiguous ground truth, that is, when the exact labels for given inputs are not exactly known. The resulting construction enjoys desirable practical and theoretical properties: (i) conformal coverage guarantees, (ii) smaller prediction sets (compared to classical conformal prediction regions) and (iii) disentanglement of uncertainty sources (epistemic, aleatoric). We empirically verify our findings on both synthetic and real datasets.
Published: 2024

2. Optimal Transport for $\epsilon$-Contaminated Credal Sets

Author: Caprio, Michele
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Probability, 49Q22, 68T37, 60A99
Abstract: We provide a version for lower probabilities of Monge's and Kantorovich's optimal transport problems. We show that, when the lower probabilities are the lower envelopes of $\epsilon$-contaminated sets, then our version of Monge's, and a restricted version of our Kantorovich's problems, coincide with their respective classical versions. We also give sufficient conditions for the existence of our version of Kantorovich's optimal plan, and for the two problems to be equivalent. As a byproduct, we show that for $\epsilon$-contaminations the lower probability versions of Monge's and Kantorovich's optimal transport problems need not coincide. The applications of our results to Machine Learning and Artificial Intelligence are also discussed.
Published: 2024

3. Robust ab initio predictions for dimensionless ratios of E2 and radius observables. I. Electric quadrupole moments and deformation

Author: Caprio, Mark A. and Maris, Pieter
Subjects: Nuclear Theory
Abstract: Converged results for E2 observables are notoriously challenging to obtain in ab initio no-core configuration interaction (NCCI) approaches. Matrix elements of the E2 operator are sensitive to the large-distance tails of the nuclear wave function, which converge slowly in an oscillator basis expansion. Similar convergence challenges beset ab initio prediction of the nuclear charge radius. However, we exploit systematic correlations between the calculated E2 and radius observables to yield meaningful predictions for relations among these observables. In particular, we examine ab initio predictions for dimensionless ratios of the form Q/r^2, for nuclei throughout the $p$ shell. Meaningful predictions for electric quadrupole moments may then be made by calibrating to the ground-state charge radius, if experimentally known, or vice versa. Moreover, these dimensionless ratios provide ab initio insight into the nuclear quadrupole deformation., Comment: 18 pages, 11 figures
Published: 2024

4. Shadowing and Stability of non invertible $p$-adic Dynamics

Author: Caprio, D. A., Lenarduzzi, F., Messaoudi, A., and Tsokanos, I.
Subjects: Mathematics - Number Theory, Mathematics - Dynamical Systems, Primary 54H20, Secondary 37C50, 11S82
Abstract: In this paper, we study the shadowing and the stability properties for non-invertible $p$-adic dynamics. In particular, the spaces considered are those of the $p$-adic integers $\mathbb{Z}_p$ and the $p$-adic numbers $\mathbb{Q}_p$, where $p \geq 2$ is a prime number., Comment: 22 pages
Published: 2024

5. Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality

Author: Caprio, Rocco and Johansen, Adam M
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control, Mathematics - Statistics Theory, Statistics - Computation
Abstract: By utilizing recently developed tools for constructing gradient flows on Wasserstein spaces, we extend an analysis technique commonly employed to understand alternating minimization algorithms on Euclidean space to the Expectation Maximization (EM) algorithm via its representation as coordinate-wise minimization on the product of a Euclidean space and a space of probability distributions due to Neal and Hinton (1998). In so doing we obtain finite sample error bounds and exponential convergence of the EM algorithm under a natural generalisation of a log-Sobolev inequality. We further demonstrate that the analysis technique is sufficiently flexible to allow also the analysis of several variants of the EM algorithm.
Published: 2024

6. SHARP -- A near-IR multi-mode spectrograph conceived for MORFEO@ELT

Author: Saracco, P., Conconi, P., Arcidiacono, C., Portaluri, E., Mahmoodzadeh, H., D'Orazi, V., Fedele, D., Gargiulo, A., Vanzella, E., Franzetti, P., Arosio, I., Barbalini, L., Lops, G., Molinari, E., Cascone, E., Cianniello, V., D'Auria, D., De Caprio, V., Di Antonio, I., Di Francesco, B., Di Rico, G., Eredia, C., Fumana, M., Greggio, D., Rodeghiero, G., Scalera, M., Alcala', J. M., Bisogni, S., Bonito, R., Bono, G., Garatti, A. Caratti o, Bonta', E. Dalla, Dall'Ora, M., Fiorentino, G., Gallazzi, A. R., Guarcello, M., Izzo, L., La Barbera, F., Lardo, C., Longhetti, M., Longobardo, A., Magrini, L., Mancini, C., Mura, A., Piconcelli, E., Pizzella, A., Podio, L., Polletta, M., Prisinzano, L., Ricci, F., Ripepi, V., Roccatagliata, V., and Vietri, G.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: The Extremely Large Telescopes (ELTs), thanks to their large apertures and cutting-edge Multi-Conjugate Adaptive Optics (MCAO) systems, promise to deliver sharper and deeper data even than the JWST. SHARP is a concept study for a near-IR (0.95-2.45 $\mu$m) spectrograph conceived to fully exploit the collecting area and the angular resolution of the upcoming generation of ELTs. In particular, SHARP is designed for the 2nd port of MORFEO@ELT. Composed of a Multi-Object Spectrograph, NEXUS, and a multi-Integral Field Unit, VESPER, MORFEO-SHARP will deliver high angular ($\sim$30 mas) and spectral (R$\simeq$300, 2000, 6000, 17000) resolution, outperforming NIRSpec@JWST (100 mas). SHARP will enable studies of the nearby Universe and the early Universe in unprecedented detail. NEXUS is fed by a configurable slit system deploying up to 30 slits with $\sim$2.4 arcsec length and adjustable width, over a field of about 1.2"$\times$1.2" (35 mas/pix). Each slit is fed by an inversion prism able to rotate by an arbitrary angle the field that can be seen by the slit. VESPER is composed of 12 probes of 1.7"$\times$1.5" each (spaxel 31 mas) probing a field 24"$\times$70". SHARP is conceived to exploit the ELTs apertures reaching the faintest flux and the sharpest angular resolution by joining the sensitivity of NEXUS and the high spatial sampling of VESPER to MORFEO capabilities. This article provides an overview of the scientific design drivers, their solutions, and the resulting optical design of the instrument achieving the required optical performance., Comment: Proceedings of the SPIE Astronomical Telescopes and Instrumentation 2024, Volume 13096, Paper No. 130965I, 11 pp, 11 figs
Published: 2024
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7. Direct ab initio calculation of the $^{4}$He nuclear electric dipole polarizability

Author: Yin, Peng, Shirokov, Andrey M., Maris, Pieter, Fasano, Patrick J., Caprio, Mark A., Li, He, Zuo, Wei, and Vary, James P.
Subjects: Nuclear Theory
Abstract: The calculation of nuclear electromagnetic sum rules by directly diagonalizing the nuclear Hamiltonian in a large basis is numerically challenging and has not been performed for $A>2$ nuclei. With the significant progress of high performance computing, we show that calculating sum rules using numerous discretized continuum states obtained by directly diagonalizing the ab initio no-core shell model Hamiltonian is achievable numerically. Specifically, we calculate the $^{4}$He electric dipole ($E1$) polarizability, that is an inverse energy weighted sum rule, employing the Daejeon16 $NN$ interaction. We demonstrate that the calculations are numerically tractable as the dimension of the basis increases and are convergent. Our results for the $^{4}$He electric dipole polarizability are consistent with the most recent experimental data and are compared with those of other theoretical studies employing different techniques and various interactions.
Published: 2024
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8. Imprecise Markov Semigroups and their Ergodicity

Author: Caprio, Michele
Subjects: Mathematics - Probability, Mathematics - Statistics Theory, Statistics - Machine Learning, Primary: 60J60, Secondary: 58J65, 62A01
Abstract: We introduce the concept of an imprecise Markov semigroup $\mathbf{Q}$. It is a tool that allows to represent ambiguity around both the initial and the transition probabilities of a Markov process via a compact collection of plausible Markov semigroups, each associated with a (different, plausible) Markov process. We use techniques from geometry, functional analysis, and (high dimensional) probability to study the ergodic behavior of $\mathbf{Q}$. We show that, if the initial distribution of the Markov processes associated with the elements of $\mathbf{Q}$ is known and invariant, under some conditions that also involve the geometry of the state space, eventually the ambiguity around their transition probability fades. We call this property ergodicity of the imprecise Markov semigroup, and we relate it to the classical notion of ergodicity. We prove ergodicity both when the state space is Euclidean or a Riemannian manifold, and when it is an arbitrary measurable space. The importance of our findings for the fields of machine learning and computer vision is also discussed.
Published: 2024

9. Dark Matter Line Searches with the Cherenkov Telescope Array

Author: Abe, S., Abhir, J., Abhishek, A., Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Alfaro, R., Alvarez-Crespo, N., Batista, R. Alves, Amans, J. -P., Amato, E., Ambrosi, G., Angel, L., Aramo, C., Arcaro, C., Arnesen, T. T. H., Arrabito, L., Asano, K., Ascasibar, Y., Aschersleben, J., Ashkar, H., Backes, M., Baktash, A., Balazs, C., Balbo, M., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Batković, I., Batzofin, R., Baxter, J., González, J. Becerra, Beck, G., Benbow, W., Berge, D., Bernardini, E., Bernete, J., Bernlöhr, K., Berti, A., Bertucci, B., Bhattacharjee, P., Bhattacharyya, S., Bigongiari, C., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Bocchino, F., Boisson, C., Bolmont, J., Bonnoli, G., Bonollo, A., Bordas, P., Bosnjak, Z., Bottacini, E., Böttcher, M., Bringmann, T., Bronzini, E., Brose, R., Brown, A. M., Brunelli, G., Bulgarelli, A., Bulik, T., Burelli, I., Burmistrov, L., Burton, M., Buscemi, M., Bylund, T., Cailleux, J., Campoy-Ordaz, A., Cantlay, B. K., Capasso, G., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Caroff, S., Carosi, A., Carosi, R., Carquin, E., Carrasco, M. -S., Cassol, F., Castaldini, L., Castrejon, N., Castro-Tirado, A. J., Cerasole, D., Cerruti, M., Chadwick, P. M., Chaty, S., Chen, A. W., Chernyakova, M., Chiavassa, A., Chudoba, J., Chytka, L., Cicciari, G. M., Cifuentes, A., Araujo, C. H. Coimbra, Colapietro, M., Conforti, V., Conte, F., Contreras, J. L., Costa, A., Costantini, H., Cotter, G., Cristofari, P., Cuevas, O., Curtis-Ginsberg, Z., D'Amico, G., D'Ammando, F., Dai, S., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Caprio, V., Pino, E. M. de Gouveia Dal, De Lotto, B., De Lucia, M., de Menezes, R., de Naurois, M., de Souza, V., del Peral, L., del Valle, M. V., Giler, A. G. Delgado, Mengual, J. Delgado, Delgado, C., Dell'aiera, M., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Diebold, S., Dinesh, A., Djuvsland, J., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Dörner, J., Doro, M., Dournaux, J. -L., Duangchan, C., Dubos, C., Ducci, L., Dwarkadas, V. V., Ebr, J., Eckner, C., Egberts, K., Einecke, S., Elsässer, D., Emery, G., Errando, M., Escanuela, C., Escarate, P., Godoy, M. Escobar, Escudero, J., Esposito, P., Ettori, S., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Feng, Q., Ferrand, G., Ferrarotto, F., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Guiteras, L. Font, Fontaine, G., Fröse, S., Fukazawa, Y., Fukui, Y., Furniss, A., Galanti, G., Galaz, G., Galelli, C., Gallozzi, S., Gammaldi, V., Garczarczyk, M., Gasbarra, C., Gasparrini, D., Ghalumyan, A., Gianotti, F., Giarrusso, M., Paiva, J. G. Giesbrecht Formiga, Giglietto, N., Giordano, F., Giuffrida, R., Glicenstein, J. -F., Glombitza, J., Goldoni, P., González, J. M., González, M. M., Coelho, J. Goulart, Gradetzke, T., Granot, J., Grasso, D., Grau, R., Gréaux, L., Green, D., Green, J. G., Grolleron, G., Guedes, L. M. V., Gueta, O., Hackfeld, J., Hadasch, D., Hamal, P., Hanlon, W., Hara, S., Harvey, V. M., Hassan, T., Hayashi, K., Heß, B., Heckmann, L., Heller, M., Cadena, S. Hernández, Hervet, O., Hinton, J., Hiroshima, N., Hnatyk, B., Hnatyk, R., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hovatta, T., Hrabovsky, M., Hrupec, D., Iarlori, M., Inada, T., Incardona, F., Inoue, S., Inoue, Y., Iocco, F., Iori, M., Ishio, K., Jamrozy, M., Janecek, P., Jankowsky, F., Jean, P., Quiles, J. Jimenez, Jin, W., Juramy-Gilles, C., Jurysek, J., Kagaya, M., Kalekin, O., Karas, V., Katagiri, H., Kataoka, J., Kaufmann, S., Kazanas, D., Kerszberg, D., Kieda, D. B., Kleiner, T., Kluge, G., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kosack, K., Kowal, G., Kubo, H., Kushida, J., La Barbera, A., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Laporte, P., Lazarević, S., Lazendic-Galloway, J., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leonora, E., Leto, G., Lindfors, E., Linhoff, M., Liodakis, I., Lipniacka, A., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Bahilo, J. Lozano, Luque-Escamilla, P. L., Macias, O., Majumdar, P., Mallamaci, M., Malyshev, D., Mandat, D., Manicò, G., Mariotti, M., Márquez, I., Marquez, P., Marsella, G., Martí, J., Martínez, G. A., Martínez, M., Martinez, O., Marty, C., Mas-Aguilar, A., Mastropietro, M., Mazin, D., Menchiari, S., Mestre, E., Meunier, J. -L., Meyer, D. M. -A., Meyer, M., Miceli, D., Miceli, M., Michailidis, M., Michałowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mizote, M., Mizuno, T., Moderski, R., Molero, M., Molfese, C., Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moulin, E., Zamanillo, V. Moya, Munari, K., Murach, T., Muraczewski, A., Muraishi, H., Nakamori, T., Nayak, A., Nemmen, R., Neto, J. P., Nickel, L., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikołajuk, M., Nikolić, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Okumura, A., Olive, J. -F., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Orlando, S., Ostrowski, M., Otero-Santos, J., Oya, I., Pagano, I., Pagliaro, A., Palatiello, M., Panebianco, G., Paneque, D., Pantaleo, F. R., Paredes, J. M., Parmiggiani, N., Patricelli, B., Pe'er, A., Pech, M., Pecimotika, M., Pensec, U., Peresano, M., Pérez-Romero, J., Persic, M., Peters, K. P., Petruk, O., Piano, G., Pierre, E., Pietropaolo, E., Pihet, M., Pinchbeck, L., Pirola, G., Pittori, C., Plard, C., Podobnik, F., Pohl, M., Pollet, V., Ponti, G., Prandini, E., Principe, G., Priyadarshi, C., Produit, N., Prouza, M., Pueschel, E., Pühlhofer, G., Pumo, M. L., Queiroz, F., Quirrenbach, A., Rainò, S., Rando, R., Razzaque, S., Regeard, M., Reimer, A., Reimer, O., Reisenegger, A., Rhode, W., Ribeiro, D., Ribó, M., Ricci, C., Richtler, T., Rico, J., Rieger, F., Riitano, L., Rizi, V., Roache, E., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Rodríguez-Vázquez, J. J., Romano, P., Romeo, G., Rosado, J., de Leon, A. Rosales, Rowell, G., Rudak, B., Ruiter, A. J., Rulten, C. B., Sadeh, I., Saha, L., Saito, T., Salzmann, H., Sánchez-Conde, M., Sandaker, H., Sangiorgi, P., Sano, H., Santander, M., Santos-Lima, R., Sapienza, V., Šarić, T., Sarkar, A., Sarkar, S., Saturni, F. G., Savarese, S., Scherer, A., Schiavone, F., Schipani, P., Schleicher, B., Schovanek, P., Schubert, J. L., Schwanke, U., Arroyo, M. Seglar, Seitenzahl, I. R., Sergijenko, O., Servillat, M., Siegert, T., Siejkowski, H., Siqueira, C., Sliusar, V., Slowikowska, A., Sol, H., Spencer, S. T., Spiga, D., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stawarz, Ł., Steppa, C., Hatlen, E. Sæther, Stolarczyk, T., Strišković, J., Suda, Y., Świerk, P., Tajima, H., Tak, D., Takahashi, M., Takeishi, R., Tavernier, T., Tejedor, L. A., Terauchi, K., Teshima, M., Testa, V., Tian, W. W., Tibaldo, L., Tibolla, O., Peixoto, C. J. Todero, Torradeflot, F., Torres, D. F., Tosti, G., Tothill, N., Toussenel, F., Tramacere, A., Travnicek, P., Tripodo, G., Trois, A., Truzzi, S., Tutone, A., Vaclavek, L., Vacula, M., Vallania, P., Vallés, R., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Vassiliev, V., Acosta, M. Vázquez, Vecchi, M., Ventura, S., Vercellone, S., Verna, G., Viana, A., Viaux, N., Vigliano, A., Vignatti, J., Vigorito, C. F., Villanueva, J., Visentin, E., Vitale, V., Vodeb, V., Voisin, V., Voitsekhovskyi, V., Vorobiov, S., Voutsinas, G., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., White, M., White, R., Wierzcholska, A., Will, M., Williams, D. A., Wohlleben, F., Wolter, A., Yamamoto, T., Yang, L., Yoshida, T., Yoshikoshi, T., Zaharijas, G., Zampieri, L., Sanchez, R. Zanmar, Zavrtanik, D., Zavrtanik, M., Zdziarski, A. A., Zech, A., Zhang, W., Zhdanov, V. I., Ziętara, K., Živec, M., and Zuriaga-Puig, J.
Subjects: High Energy Physics - Phenomenology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version
Published: 2024
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10. Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities

Author: Caprio, Rocco, Kuntz, Juan, Power, Samuel, and Johansen, Adam M.
Subjects: Computer Science - Machine Learning, Mathematics - Functional Analysis, Mathematics - Optimization and Control, Statistics - Computation, Statistics - Machine Learning
Abstract: We prove non-asymptotic error bounds for particle gradient descent (PGD)~(Kuntz et al., 2023), a recently introduced algorithm for maximum likelihood estimation of large latent variable models obtained by discretizing a gradient flow of the free energy. We begin by showing that, for models satisfying a condition generalizing both the log-Sobolev and the Polyak--{\L}ojasiewicz inequalities (LSI and P{\L}I, respectively), the flow converges exponentially fast to the set of minimizers of the free energy. We achieve this by extending a result well-known in the optimal transport literature (that the LSI implies the Talagrand inequality) and its counterpart in the optimization literature (that the P{\L}I implies the so-called quadratic growth condition), and applying it to our new setting. We also generalize the Bakry--\'Emery Theorem and show that the LSI/P{\L}I generalization holds for models with strongly concave log-likelihoods. For such models, we further control PGD's discretization error, obtaining non-asymptotic error bounds. While we are motivated by the study of PGD, we believe that the inequalities and results we extend may be of independent interest.
Published: 2024

11. Cultivar-Specific Defense Responses in Wild and Cultivated Squash Induced by Belowground and Aboveground Herbivory

Author: Ye, Wenfeng, Di Caprio, Leandro, Bruno, Pamela, Jaccard, Charlyne, Bustos-Segura, Carlos, Arce, Carla C. M., and Benrey, Betty
Published: 2024
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12. A New Nomenclature for the Very Low-Calorie Ketogenic Diet (VLCKD): Very Low-Energy Ketogenic Therapy (VLEKT). Ketodiets and Nutraceuticals Expert Panels: “KetoNut”, Italian Society of Nutraceuticals (SINut) and the Italian Association of Dietetics and Clinical Nutrition (ADI)

Author: Barrea, Luigi, Caprio, Massimiliano, Grassi, Davide, Cicero, Arrigo Francesco Giuseppe, Bagnato, Carmela, Paolini, Barbara, and Muscogiuri, Giovanna
Published: 2024
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13. Information and Communication Technologies and Labor Productivity: A Dynamic Slacks-Based Data Envelopment Analysis

Author: Santos-Arteaga, Francisco J., Di Caprio, Debora, and Tavana, Madjid
Published: 2024
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14. Intruder band mixing in an ab initio description of 12Be

Author: McCoy, Anna E., Caprio, Mark A., Maris, Pieter, and Fasano, Patrick J.
Subjects: Nuclear Theory
Abstract: The spectrum of 12Be exhibits exotic features, e.g., an intruder ground state and shape coexistence, normally associated with the breakdown of a shell closure. While previous phenomenological treatments indicated the ground state has substantial contributions from intruder configurations, it is only with advances in computational abilities and improved interactions that this intruder mixing is observed in ab initio no-core shell model (NCSM) predictions. In this work, we extract electromagnetic observables and symmetry decompositions from the NCSM wave functions to demonstrate that the low-lying positive parity spectrum can be explained in terms of mixing of rotational bands with very different intrinsic structure coexisting within the low-lying spectrum. These observed bands exhibit an approximate SU(3) symmetry and are qualitatively consistent with Elliott model predictions., Comment: 10 pages, 7 figures
Published: 2024

15. Credal Learning Theory

Author: Caprio, Michele, Sultana, Maryam, Elia, Eleni, and Cuzzolin, Fabio
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract: Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptation/generalization issues. In this paper we lay the foundations for a `credal' theory of learning, using convex sets of probabilities (credal sets) to model the variability in the data-generating distribution. Such credal sets, we argue, may be inferred from a finite sample of training sets. Bounds are derived for the case of finite hypotheses spaces (both assuming realizability or not), as well as infinite model spaces, which directly generalize classical results., Comment: 30 pages, 2 figures
Published: 2024

16. Second-Order Uncertainty Quantification: A Distance-Based Approach

Author: Sale, Yusuf, Bengs, Viktor, Caprio, Michele, and Hüllermeier, Eyke
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order probability distributions, i.e., predictions in the form of distributions on probability distributions. A completely conclusive solution has not yet been found, however, as shown by recent criticisms of commonly used uncertainty measures associated with second-order distributions, identifying undesirable theoretical properties of these measures. In light of these criticisms, we propose a set of formal criteria that meaningful uncertainty measures for predictive uncertainty based on second-order distributions should obey. Moreover, we provide a general framework for developing uncertainty measures to account for these criteria, and offer an instantiation based on the Wasserstein distance, for which we prove that all criteria are satisfied., Comment: 16 pages, 2 figures
Published: 2023

17. The Birth of a Base State: Japanese Experiences in the Korean War by Nam Kichŏng (review)

Author: Caprio, Mark E. and Jiesun, Kim
Published: 2024

18. Nutritional assessment and medical dietary therapy for management of obesity in patients with non-dialysis chronic kidney disease: a practical guide for endocrinologist, nutritionists and nephrologists. A consensus statement from the Italian society of endocrinology (SIE), working group of the club nutrition–hormones and metabolism; the Italian society of nutraceuticals (SINut), club ketodiets and nutraceuticals “KetoNut-SINut”; and the Italian society of nephrology (SIN)

Author: Annunziata, G., Caprio, M., Verde, L., Carella, A. M., Camajani, E., Benvenuto, A., Paolini, B., De Nicola, L., Aucella, F., Bellizzi, V., Barberi, S., Grassi, D., Fogacci, F., Colao, A., Cicero, A. F. G., Prodam, F., Aimaretti, G., Muscogiuri, G., and Barrea, L.
Published: 2024
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19. Lights in the dark: a new UV photoluminescence-based sampling method for the protected moth Proserpinus proserpina

Author: Anselmo, Luca, Caprio, Enrico, and Bonelli, Simona
Published: 2024
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20. The Role of Physical Exercise as a Therapeutic Tool to Improve Lipedema: A Consensus Statement from the Italian Society of Motor and Sports Sciences (Società Italiana di Scienze Motorie e Sportive, SISMeS) and the Italian Society of Phlebology (Società Italiana di Flebologia, SIF)

Author: Annunziata, Giuseppe, Paoli, Antonio, Manzi, Vincenzo, Camajani, Elisabetta, Laterza, Francesco, Verde, Ludovica, Capó, Xavier, Padua, Elvira, Bianco, Antonino, Carraro, Attilio, Di Baldassarre, Angela, Guidetti, Laura, Marcora, Samuele Maria, Orrù, Stefania, Tessitore, Antonio, Di Mitri, Roberto, Auletta, Lucia, Piantadosi, Angela, Bellisi, Mario, Palmeri, Edmondo, Savastano, Silvia, Colao, Annamaria, Caprio, Massimiliano, Muscogiuri, Giovanna, and Barrea, Luigi
Published: 2024
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21. Sensor Selection and Defect Classification via Machine Learning During the Laser Welding of Busbar Connections for High-Performance Battery Pack Production

Author: Caprio, Leonardo, Previtali, Barbara, and Demir, Ali Gökhan
Published: 2024
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22. Methodological comparison of laser stripping solutions with contemporary pulsed lasers for e-drive copper hairpins

Author: D’Arcangelo, Simone, Caprio, Leonardo, Chesi, Davide, Nocciolini, Daniele, Corbinelli, Rubino, Previtali, Barbara, and Demir, Ali Gökhan
Published: 2024
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23. Quantum tailoring of electronic properties in covalently functionalized graphene: application to ammonia gas detection

Author: Dammak, A., Raouafi, F., Cavanna, A., Rudolf, P., di Caprio, D., Sallet, V., Madouri, A., and Jancu, J. M.
Subjects: Condensed Matter - Materials Science
Abstract: Functionalized graphene offers great potential in the field of rapid detection of gases at room temperature. We performed first-principles calculations to study the suitability of 4-sulfobenzenediazonium salts (4SBD) as bandgap modifier in graphene. The signature of unpaired spins is evidenced near the Fermi level owing to the symmetry breaking of graphene sublattices. 4SBD-chemisorbed on graphene is found to be electronically sensitive to the presence of ammonia NH3 with increasing gas concentration.
Published: 2023
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24. On the contact conditions for the density and charge profiles in the theory of electrical double layer: From planar to spherical and cylindrical geometry

Author: Holovko, Myroslav, Vlachy, Vojko, and di Caprio, Dung
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: In this paper, starting from the Bogoliubov-Born-Green-Yvon equations of the liquid-state theory, we formulate two equivalent approaches for the calculation of the total density profile and of the charge density profile of ionic fluids near nonplanar charged surfaces. In the framework of these approaches, we establish exact conditions, that a particular point of these profiles should satisfy, in the form of contact theorems. These contact theorems for the total density profile and the charge density profile are obtained by direct integration of a system of equations derived from the Bogoliubov-Born-Green-Yvon equations. The contact theorems for both profiles have nonlocal character. It is shown that the contact value of the total density profile for uncharged surfaces is characterized by the bulk pressure and the surface tension. The contact theorems are applied to the cases of spherical and cylindrical surfaces. It is shown that the contact theorem for the total density profile coincides with the recent results obtained by W. Silvester-Alcantara, D. Henderson and L.B. Bhuiyan Mol. Phys., 113, 3403, 2015
Published: 2023
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25. MORFEO enters final design phase

Author: Busoni, Lorenzo, Agapito, Guido, Ballone, Alessandro, Puglisi, Alfio, Goncharov, Alexander, Petrella, Amedeo, Di Cianno, Amico, Balestra, Andrea, Baruffolo, Andrea, Bianco, Andrea, Di Dato, Andrea, Valentini, Angelo, Di Francesco, Benedetta, Sassolas, Benoit, Salasnich, Bernardo, Arcidiacono, Carmelo, Plantet, Cedric, Eredia, Christian, Fantinel, Daniela, Selvestrel, Danilo, Malone, Deborah, Magrin, Demetrio, D'Auria, Domenico, Redaelli, Edoardo, Carolo, Elena, Costa, Elia, Portaluri, Elisa, Cascone, Enrico, Giro, Enrico, Battaini, Federico, Annibali, Francesca, Laudisio, Fulvio, Rodeghiero, Gabriele, Umbriaco, Gabriele, Chauvin, Gael, Di Rico, Gianluca, Pariani, Giorgio, Carlà, Giulia, Capasso, Giulio, Cosentino, Giuseppe, Correia, Jean Jacques, Foppiani, Italo, Di Antonio, Ivan, Farinato, Jacopo, Radhakrishnan, Kalyan Kumar, Gluck, Laurence, Pinard, Laurent, Marafatto, Luca, Scalera, Marcello Agostino, Gullieuszik, Marco, Bonaglia, Marco, Riva, Marco, Xompero, Marco, Bergomi, Maria, Aliverti, Matteo, Genoni, Matteo, Munari, Matteo, Dolci, Mauro, Christophe, Michel, Cantiello, Michele, Colapietro, Mirko, Devaney, Nicholas, Azzaroli, Nicolò, Grani, Paolo, Ciliegi, Paolo, Rabou, Patrick, Feautrier, Philippe, Schipani, Pietro, Ragazzoni, Roberto, Sordo, Rosanna, Briguglio, Runa, Lampitelli, Salvatore, Savarese, Salvatore, Benedetti, Simone, Di Filippo, Simone, Esposito, Simone, Chinellato, Simonetta, Oberti, Sylvain, Rochat, Sylvain, Lapucci, Tommaso, Di Giammatteo, Ugo, Cianniello, Vincenzo, De Caprio, Vincenzo, and Hubert, Zoltan
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: MORFEO (Multi-conjugate adaptive Optics Relay For ELT Observations, formerly MAORY), the MCAO system for the ELT, will provide diffraction-limited optical quality to the large field camera MICADO. MORFEO has officially passed the Preliminary Design Review and it is entering the final design phase. We present the current status of the project, with a focus on the adaptive optics system aspects and expected milestones during the next project phase.
Published: 2023
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26. Chasing Gravitational Waves with the Cherenkov Telescope Array

Author: Green, Jarred Gershon, Carosi, Alessandro, Nava, Lara, Patricelli, Barbara, Schüssler, Fabian, Seglar-Arroyo, Monica, Consortium, Cta, Abe, Kazuki, Abe, Shotaro, Acharyya, Atreya, Adam, Remi, Aguasca-Cabot, Arnau, Agudo, Ivan, Alfaro, Jorge, Alvarez-Crespo, Nuria, Batista, Rafael Alves, Amans, Jean-Philippe, Amato, Elena, Ambrosino, Filippo, Angüner, Ekrem Oguzhan, Antonelli, Lucio Angelo, Aramo, Carla, Arcaro, Cornelia, Arrabito, Luisa, Asano, Katsuaki, Aschersleben, Jann, Ashkar, Halim, Stuani, Luiz Augusto, Baack, Dominik, Backes, Michael, Balazs, Csaba, Balbo, Matteo, Larriva, Andres Baquero, Martins, Victor Barbosa, de Almeida, Ulisses Barres, Barrio, Juan Abel, Bastieri, Denis, Batista, Pedro Ivo, Batković, Ivana, Batzofin, Rowan William, Baxter, Joshua Ryo, Beck, Geoffrey, Tjus, Julia Becker, Beiske, Lukas, Belardinelli, Daniele, Benbow, Wystan, Bernardini, Elisa, Medrano, Juan Bernete, Bernlöhr, Konrad, Berti, Alessio, Beshley, Vasyl, Bhattacharjee, Pooja, Bhattacharyya, Saptashwa, Bi, Baiyang, Biederbeck, Noah, Biland, Adrian, Bissaldi, Elisabetta, Blanch, Oscar, Blazek, Jiri, Boisson, Catherine, Bolmont, Julien, Bonnoli, Giacomo, Bordas, Pol, Bošnjak, Željka, Bradascio, Federica, Braiding, Catherine, Bronzini, Ettore, Brose, Robert, Brown, Anthony M., Brun, Francois, Brunelli, Giulia, Bulgarelli, Andrea, Burelli, Irene, Burmistrov, Leonid, Burton, Michael, Bylund, Tomas, Calisse, Paolo Gherardo, Campoy-Ordaz, Anna, Cantlay, Brandon Khan, Capalbi, Milvia, Caproni, Anderson, Capuzzo-Dolcetta, Roberto, Carlile, Colin, Caroff, Sami, Carosi, Roberto, Carrasco, Marie-Sophie, Cascone, Enrico, Cassol, Franca, Castrejon, Noelia, Catalani, Fernando, Cerasole, Davide, Cerruti, Matteo, Chaty, Sylvain, Chen, Andrew W, Chernyakova, Maria, Chiavassa, Andrea, Chudoba, Jiří, Araujo, Carlos Henrique Coimbra, Conforti, Vito, Conte, Francesco, Contreras, Jose Luis, Cossou, Christophe, Costa, Alessandro, Costantini, Heide, Cristofari, Pierre, Cuevas, Omar, Curtis-Ginsberg, Zachary, D'Amico, Giacomo, D'Ammando, Filippo, Dadina, Mauro, Dalchenko, Mykhailo, David, Ludovic, Davids, Isak Delberth, Dazzi, Francesco, De Angelis, Alessandro, de Lavergne, Mathieu de Bony, De Caprio, Vincenzo, De Cesare, Giovanni, Pino, Elisabete M. de Gouveia Dal, De Lotto, Barbara, de Lucia, Mario, de Menezes, Raniere, de Naurois, Mathieu, Wilhelmi, Emma de Oña, De Simone, Nicola, de Souza, Vitor, Del Peral, Luis, del Valle, Maria Victoria, Delagnes, Eric, Giler, Andres Gabriel Delgado, Delgado, Carlos, Dell'aiera, Michael, Della Ceca, Roberto, Della Valle, Massimo, Della Volpe, Domenico, Depaoli, Davide, Dettlaff, Antonios, Di Girolamo, Tristano, Di Piano, Ambra, Di Pierro, Federico, Di Tria, Riccardo, Di Venere, Leonardo, Díaz-Bahamondes, Christian, Dib, Claudio, Diebold, Sebastian, Dima, Razvan, Dinesh, Adithiya, Djannati-Ataï, Arache, Djuvsland, Julia Isabel, Dominguez, Alberto, Dominik, Rune Michael, Donini, Alice, Dorner, Daniela, Dörner, Julien, Doro, Michele, Anjos, Rita de Cassia dos, Dournaux, Jean-Laurent, Dravins, Dainis, Duangchan, Chaimongkol, Dubos, Coline, Ducci, Lozenzo, Dwarkadas, Vikram V., Ebr, Jan, Eckner, Christopher, Egberts, Kathrin, Einecke, Sabrina, Elsässer, Dominik, Emery, Gabriel, Godoy, Miguel Escobar, Escudero, Juan, Esposito, Paolo, Falceta-Gonçalves, Diego, Ramazani, Vandad Fallah, Faure, Alice, Fedorova, Elena, Fegan, Stephen, Feijen, Kirsty, Feng, Qi, Ferrand, Gilles, Ferrarotto, Fabio, Fiandrini, Emanuele, Fiasson, Armand, Fioretti, Valentina, Foffano, Luca, Guiteras, Lluis Font, Fontaine, Gerard, Fröse, Stefan, Fukami, Satoshi, Fukui, Yasuo, Funk, Stefan, Gaggero, Daniele, Galanti, Giorgio, Galaz, Gaspar, Gallant, Yves A., Gallozzi, Stefano, Gammaldi, Viviana, Gasbarra, Claudio, Gaug, Markus, Ghalumyan, Arsen, Gianotti, Fulvio, Giarrusso, Marina, Giglietto, Nicola, Giordano, Francesco, Giuliani, Andrea, Glicenstein, Jean-Francois, Glombitza, Jonas, Goldoni, Paolo, González, José Mauricio, González, Maria Magdalena, Coelho, Jaziel Goulart, Granot, Jonathan, Grasso, Dario, Haro, Roger Grau, Green, David, Greenshaw, Tim, Grolleron, Guillaume, Grube, Jeff, Gueta, Orel, Gunji, Shuichi, Hadasch, Daniela, Hamal, Petr, Hanlon, William, Hara, Satoshi, Harvey, Violet M., Hashiyama, Kazuaki, Hassan, Tarek, Heller, Matthieu, Cadena, Sergio Hernández, Hie, Jonathan, Hiroshima, Nagisa, Hnatyk, Bohdan, Hnatyk, Roman, Hoffmann, Dirk, Hofmann, Werner, Holler, Markus, Horan, Deirdre, Horvath, Pavel, Hovatta, Talvikki, Hrupec, Dario, Hussain, Saqib, Iarlori, Marco, Inada, Tomohiro, Incardona, Federico, Inome, Yusuke, Inoue, Susumu, Iocco, Fabio, Ishio, Kazuma, Jamrozy, Marek, Janecek, Petr, Jankowsky, Felix, Jarnot, Christian, Jean, Pierre, Martínez, Irene Jiménez, Jin, Weidong, Jocou, Laurent, Juramy-Gilles, Claire, Jurysek, Jakub, KALEKIN, Oleg, Kantzas, Dimitrios, Karas, Vladimir, Kaufmann, Sarah, Kerszberg, Daniel, Khelifi, Bruno, Kieda, David B, Kleiner, Tobias Kai, Kluźniak, Włodzimierz, Kobayashi, Yukiho, Kohri, Kazunori, Komin, Nukri, Kornecki, Paula, Kosack, Karl, Kubo, Hidetoshi, Kushida, Junko, La Barbera, Antonino, La Palombara, Nicola, Lainez, Maria, Lamastra, Alessandra, Lapington, Jon S, Lazarevic, Sanja, Lazendic-Galloway, Jasmina, Leach, Steven, Lemoine-Goumard, Marianne, Lenain, Jean-Philippe, Leto, Giuseppe, Leuschner, Fabian, Lindfors, Elina, Linhoff, Maximilian, Liodakis, Ioannis, Loïc, Lozach, Lombardi, Saverio, Longo, Francesco, López-Coto, Rubén, López-Moya, Marcos, López-Oramas, Alicia, Loporchio, Serena, Bahilo, Julio Lozano, Luque-Escamilla, Pedro L., Macias, Oscar, Maier, Gernot, Majumdar, Pratik, Malyshev, Denys, Malyshev, Dmitry, Mandat, Dusan, Manicò, Giulio, Marinos, Peter David, Markoff, Sera, Márquez, Isabel, Marquez, Patricia, Marsella, Giovanni, Martí, Josep, Martin, Pierrick, Martínez, Gustavo Augusto, Martínez, Manel, Martinez, Oibar, Marty, Christophe, Mas-Aguilar, Alvaro, Mastropietro, Michele, Maurin, Gilles, Max-Moerbeck, Walter, Mazin, Daniel, Melkumyan, David, Menchiari, Stefano, Mestre, Enrique, Meunier, Jean-Luc, Meyer, Dominique M. -A., Miceli, Davide, Michailidis, Miltiadis, Michałowski, Jerzy, Miener, Tjark, Miranda, Jose Miguel, Mitchell, Alison, Mizote, Masaya, Mizuno, Tsunefumi, Moderski, Rafal, Mohrmann, Lars, Molero, Miguel, Molfese, Cesare, Molina, Edgar, Montaruli, Teresa, Moralejo, Abelardo, Morcuende, Daniel, Morik, Katharina, Morselli, Aldo, Moulin, Emmanuel, Zamanillo, Victor Moya, Mukherjee, Reshmi, Munari, Kevin, Muraczewski, Adam, Muraishi, Hiroshi, Nakamori, Takeshi, Nayak, Amrit, Nemmen, Rodrigo, Nickel, Lukas, Niemiec, Jacek, Nieto, Daniel, Rosillo, Mireia Nievas, Nikołajuk, Marek, Nishijima, Kyoshi, Noda, Koji, Nosek, Dalibor, Novosyadlyj, Bohdan, Novotný, Vladimír, Nozaki, Seiya, O'Brien, Paul, Ohishi, Michiko, Ohtani, Yoshiki, Okumura, Akira, Olive, Jean-François, Olmi, Barbara, Ong, Rene A., Orienti, Monica, Orito, Reiko, Orlandini, Mauro, Orlando, Elena, Ostrowski, Michal, Otte, Nepomuk, Oya, Igor, Pagano, Isabella, Pagliaro, Antonio, Palatiello, Michele, Panebianco, Gabriele, Paredes, Josep M., Parmiggiani, Nicolò, Patel, Sonal Ramesh, Pavlović, Dijana, Pe'er, Asaf, Pech, Miroslav, Pecimotika, Mario, Peresano, Michele, Pérez-Romero, Judit, Peron, Giada, Persic, Massimo, Petrucci, Pierre-Olivier, Petruk, Oleh, Pfeifle, Felix, Pintore, Fabio, Pirola, Giorgio, Pittori, Carlotta, Plard, Cyann, Podobnik, Franjo, Pohl, Martin, Pons, Estelle, Prandini, Elisa, Prast, Julie, Principe, Giacomo, Priyadarshi, Chaitanya, Produit, Nicolas, Prokhorov, Dmitry, Pueschel, Elisa, Pühlhofer, Gerd, Pumo, Maria Letizia, Punch, Michael, Quirrenbach, Andreas, Raino, Silvia, Randazzo, Nunzio, Rando, Riccardo, Ravel, Thierry, Razzaque, Soebur, Regeard, Maxime, Reichherzer, Patrick, Reimer, Anita, Reimer, Olaf, Reisenegger, Andreas, Reposeur, Thierry, Reville, Brian, Rhode, Wolfgang, Ribó, Marc, Richtler, Tom, Rieger, Frank, Roache, Emmet, Fernandez, Gonzalo Rodriguez, Frías, Maria Dolores Rodríguez, Rodríguez-Vázquez, Juan José, Romano, Patrizia, Romeo, Giuseppe, Rosado, Jaime, Rowell, Gavin P, Rudak, Bronislaw, Ruiter, Ashley J., Rulten, Cameron Boyd, Russo, Federico, Sadeh, Iftach, Saha, Lab, Saito, Takayuki, Sakurai, Shunsuke, Salzmann, Heiko, Sanchez, David, Sanchez-Conde, Miguel, Sangiorgi, Pierluca, Sano, Hidetoshi, Santander, Marcos, Santangelo, Andrea, Santos-Lima, Reinaldo, Sanuy, Andreu, Šarić, Toni, Sarkar, Arkadipta, Sarkar, Subir, Saturni, Francesco Gabriele, Savchenko, Volodymyr, Scherer, Andres, Schipani, Pietro, Schleicher, Bernd, Schovanek, Petr, Schubert, Jan Lukas, Schwanke, Ullrich, Schwefer, Georg, Scuderi, Salvatore, Arroyo, Monica Seglar, Seitenzahl, Ivo, Sergijenko, Olga, Sguera, Vito, Shang, Ruo-Yu, Sharma, Pooja, Sidibe, Guereguin Der Sylvestre, Sidoli, Lara, Siejkowski, Hubert, Siqueira, Clarissa, Sizun, Patrick, Sliusar, Vitalii, Slowikowska, Agnieszka, Sol, Helene, Specovius, Andreas, Spencer, Samuel Timothy, Spiga, Daniele, Stamerra, Antonio, Stanič, Samo, Starecki, Tomasz, Starling, Rhaana, Steppa, Constantin, Stolarczyk, Thierry, Strišković, Jelena, Strzys, Marcel C., Suda, Yusuke, Suomijarvi, Tiina, Tak, Donggeun, Takahashi, Mitsunari, Takeishi, Ryuji, Tam, Pak-Hin Thomas, Tanaka, Shuta J, Tanaka, Takaaki, Terauchi, Kenta, Testa, Vincenzo, Tibaldo, Luigi, Tibolla, Omar, Torradeflot, Francesc, Torres, Diego F., Torresi, Eleonora, Tothill, Nick, Toussenel, Francois, Touzard, Victoria, Tramacere, Andrea, Travnicek, Petr, Tripodo, Giovanni, Truzzi, Stefano, Tsiahina, Adellain, Tutone, Antonio, Vacula, Martin, Vallage, Bertrand, Vallania, Piero, Vallés, Ramon, van Eldik, Christopher, van Scherpenberg, Juliane, Vandenbroucke, Justin, Vassiliev, Vladimir, Venault, Philippe, Ventura, Sofia, Vercellone, Stefano, Verna, Gaia, Viana, Aion, Viaux, Nicolás, Vigliano, Alessandro, Vignatti, Jonatan, Vigorito, Carlo francesco, Vitale, Vincenzo, Vodeb, Veronika, Voisin, Vincent, Vorobiov, Serguei, Voutsinas, Georgios Gerasimos, Vovk, Ievgen, Waegebaert, Vincent, Wagner, Stefan Joachim, Walter, Roland, Ward, Martin, Wechakama, Maneenate, White, Richard, Wierzcholska, Alicja, Will, Martin, Williams, David A., Wohlleben, Frederik, Wolter, Anna, Yamamoto, Tokonatsu, Yamazaki, Ryo, Yang, Lili, Yoshida, Tatsuo, Yoshikoshi, Takanori, Zacharias, Michael, Sanchez, Ricardo Zanmar, Zavrtanik, Danilo, Zavrtanik, Marko, Zdziarski, Andrzej A., Zech, Andreas, Zhdanov, Valery I., Ziȩtara, Krzysztof, Živec, Miha, and Zuriaga-Puig, Jaume
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)
Published: 2023

27. A calculus for Markov chain Monte Carlo: studying approximations in algorithms

Author: Caprio, Rocco and Johansen, Adam M.
Subjects: Mathematics - Probability, Mathematics - Statistics Theory, Statistics - Computation
Abstract: Markov chain Monte Carlo (MCMC) algorithms are based on the construction of a Markov chain with transition probabilities leaving invariant a probability distribution of interest. In this work, we look at these transition probabilities as functions of their invariant distributions, and we develop a notion of derivative in the invariant distribution of a MCMC kernel. We build around this concept a set of tools that we refer to as Markov chain Monte Carlo Calculus. This allows us to compare Markov chains with different invariant distributions within a suitable class via what we refer to as mean value inequalities. We explain how MCMC Calculus provides a natural framework to study algorithms using an approximation of an invariant distribution, and we illustrate this by using the tools developed to prove convergence of interacting and sequential MCMC algorithms. Finally, we discuss how similar ideas can be used in other frameworks., Comment: Revised commentary and some auxiliary results
Published: 2023

28. IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning

Author: Lu, Pengyuan, Caprio, Michele, Eaton, Eric, and Lee, Insup
Subjects: Computer Science - Machine Learning
Abstract: Like generic multi-task learning, continual learning has the nature of multi-objective optimization, and therefore faces a trade-off between the performance of different tasks. That is, to optimize for the current task distribution, it may need to compromise performance on some previous tasks. This means that there exist multiple models that are Pareto-optimal at different times, each addressing a distinct task performance trade-off. Researchers have discussed how to train particular models to address specific trade-off preferences. However, existing algorithms require training overheads proportional to the number of preferences -- a large burden when there are multiple, possibly infinitely many, preferences. As a response, we propose Imprecise Bayesian Continual Learning (IBCL). Upon a new task, IBCL (1) updates a knowledge base in the form of a convex hull of model parameter distributions and (2) obtains particular models to address task trade-off preferences with zero-shot. That is, IBCL does not require any additional training overhead to generate preference-addressing models from its knowledge base. We show that models obtained by IBCL have guarantees in identifying the Pareto optimal parameters. Moreover, experiments on standard image classification and NLP tasks support this guarantee. Statistically, IBCL improves average per-task accuracy by at most 23\% and peak per-task accuracy by at most 15\% with respect to the baseline methods, with steadily near-zero or positive backward transfer. Most importantly, IBCL significantly reduces the training overhead from training 1 model per preference to at most 3 models for all preferences., Comment: This should be a replacement for arXiv:2305.14782. I falsely submitted a new paper
Published: 2023

29. Prospects for $\gamma$-ray observations of the Perseus galaxy cluster with the Cherenkov Telescope Array

Author: Consortium, The Cherenkov Telescope Array, Abe, K., Abe, S., Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Alfaro, R., Alvarez-Crespo, N., Batista, R. Alves, Amans, J. -P., Amato, E., Angüner, E. O., Antonelli, L. A., Aramo, C., Araya, M., Arcaro, C., Arrabito, L., Asano, K., Ascasíbar, Y., Aschersleben, J., Ashkar, H., Stuani, L. Augusto, Baack, D., Backes, M., Baktash, A., Balazs, C., Balbo, M., Ballester, O., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Batista, P. I., Batkovic, I., Batzofin, R., Baxter, J., González, J. Becerra, Beck, G., Tjus, J. Becker, Benbow, W., Medrano, J. Bernete, Bernlöhr, K., Berti, A., Bertucci, B., Beshley, V., Bhattacharjee, P., Bhattacharyya, S., Bi, B., Biederbeck, N., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Boisson, C., Bolmont, J., Bordas, P., Bosnjak, Z., Bottacini, E., Bradascio, F., Braiding, C., Bronzini, E., Brose, R., Brown, A. M., Brun, F., Brunetti, G., Bucciantini, N., Bulgarelli, A., Burelli, I., Burmistrov, L., Burton, M., Bylund, T., Calisse, P. G., Campoy-Ordaz, A., Cantlay, B. K., Capalbi, M., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Caroff, S., Carosi, R., Carquin, E., Carrasco, M. -S., Cascone, E., Cassol, F., Castro-Tirado, A. J., Cerasole, D., Cerruti, M., Chadwick, P., Chaty, S., Chen, A. W., Chernyakova, M., Chiavassa, A., Chudoba, J., Chytka, L., Cifuentes, A., Araujo, C. H. Coimbra, Conforti, V., Conte, F., Contreras, J. L., Cortina, J., Costa, A., Costantini, H., Cotter, G., Cristofari, P., Cuevas, O., Curtis-Ginsberg, Z., D'Amico, G., D'Ammando, F., Dalchenko, M., Dazzi, F., de Lavergne, M. de Bony, De Caprio, V., Laadim, F. De Frondat, Pino, E. M. de Gouveia Dal, De Lotto, B., De Lucia, M., De Martino, D., de Menezes, R., de Naurois, M., De Simone, N., de Souza, V., del Valle, M. V., Delagnes, E., Giler, A. G. Delgado, Delgado, C., Dell'aiera, M., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Diebold, S., Djannati-Ataï, A., Djuvsland, J., Dominik, R. M., Donini, A., Dorner, D., Dörner, J., Doro, M., Anjos, R. D. C. dos, Dournaux, J. -L., Duangchan, C., Dubos, C., Dumora, D., Dwarkadas, V. V., Ebr, J., Eckner, C., Egberts, K., Einecke, S., Elsässer, D., Emery, G., Godoy, M. Escobar, Escudero, J., Esposito, P., Ettori, S., Evoli, C., Falceta-Goncalves, D., Ramazani, V. Fallah, Fattorini, A., Faure, A., Fedorova, E., Fegan, S., Feijen, K., Feng, Q., Ferrand, G., Ferrarotto, F., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Foffano, L., Guiteras, L. Font, Fontaine, G., Fröse, S., Fukazawa, Y., Fukui, Y., Gaggero, D., Galanti, G., Gallozzi, S., Gammaldi, V., Garczarczyk, M., Gasbarra, C., Gasparrini, D., Gaug, M., Ghalumyan, A., Gianotti, F., Giarrusso, M., Giesbrecht, J., Giglietto, N., Giordano, F., Glicenstein, J. -F., Göksu, H., Goldoni, P., González, J. M., González, M. M., Coelho, J. Goulart, Granot, J., Grau, R., Gréaux, L., Green, D., Green, J. G., Grenier, I., Grolleron, G., Grube, J., Gueta, O., Hackfeld, J., Hadasch, D., Hamal, P., Hanlon, W., Hara, S., Harvey, V. M., Hassan, T., Heckmann, L., Heller, M., Cadena, S. Hernández, Hervet, O., Hie, J., Hiroshima, N., Hnatyk, B., Hnatyk, R., Hoang, J., Hoffmann, D., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hrupec122, D., Hütten, M., Iarlori, M., Inada, T., Incardona, F., Inoue, S., Iocco, F., Iori, M., Jamrozy, M., Janecek, P., Jankowsky, F., Jarnot, C., Jean, P., Martínez, I. Jiménez, Jin, W., Juramy-Gilles, C., Jurysek, J., Kagaya, M., Kantzas, D., Karas, V., Katagiri, H., Kataoka, J., Kaufmann, S., Kerszberg, D., Khélifi, B., Kissmann, R., Kleiner, T., Kluge, G., Kluźniak, W., Knödlseder, J., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kosack, K., Kowal, G., Kubo, H., Kushida, J., La Barbera, A., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Laporte, P., Lazarević, S., Leitgeb, F., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leto, G., Leuschner, F., Lindfors, E., Linhoff, M., Liodakis, I., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Luque-Escamilla, P. L., Macias, O., Mackey, J., Majumdar, P., Malyshev, D., Mandat, D., Manganaro, M., Manicò, G., Mariotti, M., Markoff, S., Márquez, I., Marquez, P., Marsella, G., Martínez, G. A., Martínez, M., Martinez, O., Marty, C., Mas-Aguilar, A., Mastropietro, M., Maurin, G., Mazin, D., Melkumyan, D., Mello, A. J. T. S., Meunier, J. -L., Meyer, D. M. -A., Meyer, M., Miceli, D., Michailidis, M., Michałowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mizote, M., Mizuno, T., Moderski, R., Molero, M., Molfese, C., Molina, E., Montaruli, T., Morcuende, D., Morik, K., Morlino, G., Morselli, A., Moulin, E., Zamanillo, V. Moya, Munari, K., Murach, T., Muraczewski, A., Muraishi, H., Nagataki, S., Nakamori, T., Nemmen, R., Neyroud, N., Nickel, L., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikołajuk, M., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., O'Brien, P., Ohishi, M., Ohtani, Y., Okumura, A., Olive, J. -F., Olmi, B., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Ostrowski, M., Oya, I., Pagliaro, A., Palatiello, M., Panebianco, G., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Parmiggiani, N., Patel, S. R., Patricelli, B., Pavlović, D., Pech, M., Pecimotika, M., Pensec, U., Peresano, M., Pérez-Romero, J., Peron, G., Persic, M., Petrucci, P. -O., Petruk, O., Piano, G., Pierre, E., Pietropaolo, E., Pintore, F., Pirola, G., Pita, S., Plard, C., Podobnik, F., Pohl, M., Polo, M., Pons, E., Ponti, G., Prandini, E., Prast, J., Principe, G., Priyadarshi, C., Produit, N., Pueschel, E., Pühlhofer, G., Pumo, M. L., Punch, M., Queiroz, F., Quirrenbach, A., Rainò, S., Rando, R., Razzaque, S., Recchia, S., Regeard, M., Reichherzer, P., Reimer, A., Reimer, O., Reisenegger, A., Rhode, W., Ribeiro, D., Ribó, M., Richtler, T., Rico, J., Rieger, F., Righi, C., Riitano, L., Rizi, V., Roache, E., Fernandez, G. Rodriguez, Rodríguez-Vázquez, J. J., Romano, P., Romeo, G., Rosado, J., de Leon, A. Rosales, Rowell, G., Rudak, B., Rulten, C. B., Russo, F., Sadeh, I., Saha, L., Saito, T., Salzmann, H., Sanchez, D., Sánchez-Conde, M., Sangiorgi, P., Sano, H., Santander, M., Santangelo, A., Santos-Lima, R., Sanuy, A., Šarić, T., Sarkar, A., Sarkar, S., Satalecka, K., Saturni, F. G., Savchenko, V., Scherer, A., Schipani, P., Schleicher, B., Schubert, J. L., Schussler, F., Schwanke, U., Schwefer, G., Arroyo, M. Seglar, Seiji, S., Semikoz, D., Sergijenko, O., Servillat, M., Sguera, V., Shang, R. Y., Sharma, P., Siejkowski, H., Sinha, A., Siqueira, C., Sliusar, V., Slowikowska, A., Sol, H., Specovius, A., Spencer, S. T., Spiga, D., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stawarz, Ł., Steppa, C., Stolarczyk, T., Strišković, J., Suda, Y., Suomijärvi, T., Tajima, H., Tak, D., Takahashi, M., Takeishi, R., Tanaka, S. J., Tavernier, T., Tejedor, L. A., Terauchi, K., Terrier, R., Teshima, M., Tian, W. W., Tibaldo, L., Tibolla, O., Torradeflot, F., Torres, D. F., Torresi, E., Tosti, G., Tosti, L., Tothill, N., Toussenel, F., Touzard, V., Tramacere, A., Travnicek, P., Tripodo, G., Truzzi, S., Tsiahina, A., Tutone, A., Vacula, M., Vallage, B., Vallania, P., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Vassiliev, V., Acosta, M. Vázquez, Vecchi, M., Ventura, S., Vercellone, S., Verna, G., Viana, A., Viaux, N., Vigliano, A., Vigorito, C. F., Vitale, V., Vodeb, V., Voisin, V., Vorobiov, S., Voutsinas, G., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., Wechakama, M., White, R., Wierzcholska, A., Will, M., Williams, D. A., Wohlleben, F., Wolter, A., Yamamoto, T., Yamazaki, R., Yoshida, T., Yoshikoshi, T., Zacharias, M., Zaharijas, G., Zavrtanik, D., Zavrtanik, M., Zdziarski, A. A., Zech, A., Zhdanov, V. I., Živec, M., Zuriaga-Puig, J., and Luque, P. De la Torre
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract: Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with $\tau_{\chi}>10^{27}$s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario., Comment: 93 pages (including author list, appendix and references), 143 figures. Submitted to JCAP
Published: 2023
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30. High-fidelity pose estimation for real-time extended reality (XR) visualization for cardiac catheterization

Author: Mohsen Annabestani, Sandhya Sriram, Alexandre Caprio, Sepehr Janghorbani, S. Chiu Wong, Alexandros Sigaras, and Bobak Mosadegh
Subjects: Percutaneous cardiac intervention, Extended reality, Mixed reality, Computer vision, 3D visualization, Medicine, Science
Abstract: Abstract Extended reality (XR) technologies are emerging as promising platforms for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a high-fidelity methodology to perform real-time 3D catheter tracking and visualization during simulated cardiac interventions. A custom 3D-printed setup with mounted cameras enables biplane video capture of a catheter. A computer vision algorithm processes the biplane images in real-time to reconstruct the 3D catheter trajectory represented by any designated number of points along its length. This method accurately localizes the catheter tip within 1 mm and can reconstruct any arbitrary catheter configuration. The tracked catheter data is integrated into an interactive Unity-based scene rendered on the Meta Quest 3 headset. The visualization seamlessly combines a reconstructed 3D patient-specific heart model with the dynamically tracked catheter, creating an immersive extended reality training environment. Our experimental study, involving six participants, demonstrated that the 3D visualization provided by the proposed XR system significantly outperformed 2D visualization in terms of speed and user experience. This suggests that the XR system has the potential to enhance catheterization training by improving spatial comprehension and procedural skills. The proposed system demonstrates the potential of XR technologies to transform percutaneous cardiac interventions through improved visualization and interactivity.
Published: 2024
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31. Distributionally Robust Statistical Verification with Imprecise Neural Networks

Author: Dutta, Souradeep, Caprio, Michele, Lin, Vivian, Cleaveland, Matthew, Jang, Kuk Jin, Ruchkin, Ivan, Sokolsky, Oleg, and Lee, Insup
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Robotics
Abstract: A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability analysis fail to scale, and purely statistical approaches are constrained by the distributional assumptions about the sampling process. Instead, we pose a distributionally robust version of the statistical verification problem for black-box systems, where our performance guarantees hold over a large family of distributions. This paper proposes a novel approach based on a combination of active learning, uncertainty quantification, and neural network verification. A central piece of our approach is an ensemble technique called Imprecise Neural Networks, which provides the uncertainty to guide active learning. The active learning uses an exhaustive neural-network verification tool Sherlock to collect samples. An evaluation on multiple physical simulators in the openAI gym Mujoco environments with reinforcement-learned controllers demonstrates that our approach can provide useful and scalable guarantees for high-dimensional systems.
Published: 2023

32. A Novel Bayes' Theorem for Upper Probabilities

Author: Caprio, Michele, Sale, Yusuf, Hüllermeier, Eyke, and Lee, Insup
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, 68T37
Abstract: In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper bound for the posterior probability when both the prior and the likelihood belong to a set of probabilities. Furthermore, we give a sufficient condition for this upper bound to become an equality. This result is interesting on its own, and has the potential of being applied to various fields of engineering (e.g. model predictive control), machine learning, and artificial intelligence., Comment: arXiv admin note: text overlap with arXiv:2302.09656
Published: 2023

33. Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?

Author: Sale, Yusuf, Caprio, Michele, and Hüllermeier, Eyke
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract: Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence. As an alternative to representing uncertainty via one single probability measure, we consider credal sets (convex sets of probability measures). The geometric representation of credal sets as $d$-dimensional polytopes implies a geometric intuition about (epistemic) uncertainty. In this paper, we show that the volume of the geometric representation of a credal set is a meaningful measure of epistemic uncertainty in the case of binary classification, but less so for multi-class classification. Our theoretical findings highlight the crucial role of specifying and employing uncertainty measures in machine learning in an appropriate way, and for being aware of possible pitfalls.
Published: 2023

34. High-fidelity pose estimation for real-time extended reality (XR) visualization for cardiac catheterization

Author: Annabestani, Mohsen, Sriram, Sandhya, Caprio, Alexandre, Janghorbani, Sepehr, Wong, S. Chiu, Sigaras, Alexandros, and Mosadegh, Bobak
Published: 2024
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35. Effects of recent wildfires on giant sequoia groves were anomalous at millennial timescales: a response to Hanson et al.

Author: Stephenson, Nathan L., Soderberg, David N., Flickinger, Joshua A., Caprio, Anthony C., and Das, Adrian J.
Published: 2024
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36. Yeasts, arthropods, and environmental matrix: a triad to disentangle the multi-level definition of biodiversity

Author: Valentini, Beatrice, Penna, Margherita, Viazzo, Massimiliano, Caprio, Enrico, Casacci, Luca Pietro, Barbero, Francesca, and Stefanini, Irene
Published: 2024
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37. Paradoxical effect of dopamine-agonists on IGF-1 in patients with prolactinoma: the role of weight

Author: Caprio, S., Pilli, T., Cantara, S., Sestini, F., Fioravanti, C., Ciuoli, C., Dalmiglio, C., Corbo, A., and Castagna, M. G.
Published: 2024
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38. Effectiveness of test-and-treat model with direct-acting antiviral for hepatitis C virus infection in migrants: a prospective interventional study in Italy

Author: Coppola, Nicola, Alessio, Loredana, De Pascalis, Stefania, Macera, Margherita, Di Caprio, Giovanni, Messina, Vincenzo, Onorato, Lorenzo, Minichini, Carmine, Stanzione, Maria, Stornaiuolo, Gianfranca, Starace, Mario, Monari, Caterina, Calò, Federica, Sagnelli, Caterina, and Pisaturo, Mariantonietta
Published: 2024
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39. Microinterfaces in biopolymer-based bicontinuous hydrogels guide rapid 3D cell migration

Author: Xu, Karen L., Di Caprio, Nikolas, Fallahi, Hooman, Dehghany, Mohammad, Davidson, Matthew D., Laforest, Lorielle, Cheung, Brian C. H., Zhang, Yuqi, Wu, Mingming, Shenoy, Vivek, Han, Lin, Mauck, Robert L., and Burdick, Jason A.
Published: 2024
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40. Author Correction: Neoadjuvant durvalumab plus radiation versus durvalumab alone in stages I–III non-small cell lung cancer: survival outcomes and molecular correlates of a randomized phase II trial

Author: Altorki, Nasser K., Walsh, Zachary H., Melms, Johannes C., Port, Jeffery L., Lee, Benjamin E., Nasar, Abu, Spinelli, Cathy, Caprio, Lindsay, Rogava, Meri, Ho, Patricia, Christos, Paul J., Saxena, Ashish, Elemento, Olivier, Bhinder, Bhavneet, Ager, Casey, Amin, Amit Dipak, Sanfilippo, Nicholas J., Mittal, Vivek, Borczuk, Alain C., Formenti, Silvia C., Izar, Benjamin, and McGraw, Timothy E.
Published: 2024
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41. Loss of Pip4k2c confers liver-metastatic organotropism through insulin-dependent PI3K-AKT pathway activation

Author: Rogava, Meri, Aprati, Tyler J., Chi, Wei-Yu, Melms, Johannes C., Hug, Clemens, Davis, Stephanie H., Earlie, Ethan M., Chung, Charlie, Deshmukh, Sachin K., Wu, Sharon, Sledge, George, Tang, Stephen, Ho, Patricia, Amin, Amit Dipak, Caprio, Lindsay, Gurjao, Carino, Tagore, Somnath, Ngo, Bryan, Lee, Michael J., Zanetti, Giorgia, Wang, Yiping, Chen, Sean, Ge, William, Melo, Luiza Martins Nascentes, Allies, Gabriele, Rösler, Jonas, Gibney, Goeffrey T., Schmitz, Oliver J., Sykes, Megan, Creusot, Rémi J., Tüting, Thomas, Schadendorf, Dirk, Röcken, Martin, Eigentler, Thomas K., Molotkov, Andrei, Mintz, Akiva, Bakhoum, Samuel F., Beyaz, Semir, Cantley, Lewis C., Sorger, Peter K., Meckelmann, Sven W., Tasdogan, Alpaslan, Liu, David, Laughney, Ashley M., and Izar, Benjamin
Published: 2024
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42. Mixotrophic and heterotrophic growth of microalgae using acetate from different production processes

Author: Proietti Tocca, Giacomo, Agostino, Valeria, Menin, Barbara, Tommasi, Tonia, Fino, Debora, and Di Caprio, Fabrizio
Published: 2024
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43. Temporal and Spatial Beam Shaping in LPBF for Fine and Porous Ti-Alloy Structures for Regenerative Fuel Cell Applications

Author: Sanchez, Salomé, Zafari, Ahmad, Caprio, Leonardo, Demir, Ali Gökhan, and Jafari, Davoud
Published: 2024
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44. Ketogenic nutritional therapy (KeNuT)—a multi-step dietary model with meal replacements for the management of obesity and its related metabolic disorders: a consensus statement from the working group of the Club of the Italian Society of Endocrinology (SIE)—diet therapies in endocrinology and metabolism

Author: Barrea, L., Caprio, M., Camajani, E., Verde, L., Perrini, S., Cignarelli, A., Prodam, F., Gambineri, A., Isidori, A. M., Colao, A., Giorgino, F., Aimaretti, G., and Muscogiuri, G.
Published: 2024
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45. The Influence of Beam Shape on the Single-Track Formation of Pure Zn Towards the Additive Manufacturing of Battery Electrodes

Author: Baldi, Chiara, Caprio, Leonardo, Milroy, Craig, Previtali, Barbara, and Demir, Ali Gökhan
Published: 2024
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46. IBCL: Zero-shot Model Generation under Stability-Plasticity Trade-offs

Author: Lu, Pengyuan, Caprio, Michele, Eaton, Eric, and Lee, Insup
Subjects: Computer Science - Machine Learning
Abstract: Algorithms that balance the stability-plasticity trade-off are well-studied in the continual learning literature. However, only a few of them focus on obtaining models for specified trade-off preferences. When solving the problem of continual learning under specific trade-offs (CLuST), state-of-the-art techniques leverage rehearsal-based learning, which requires retraining when a model corresponding to a new trade-off preference is requested. This is inefficient since there exist infinitely many different trade-offs, and a large number of models may be requested. As a response, we propose Imprecise Bayesian Continual Learning (IBCL), an algorithm that tackles CLuST efficiently. IBCL replaces retraining with constant-time convex combination. Given a new task, IBCL (1) updates the knowledge base in the form of a convex hull of model parameter distributions and (2) generates one Pareto-optimal model per given trade-off via convex combination without any additional training. That is, obtaining models corresponding to specified trade-offs via IBCL is zero-shot. Experiments whose baselines are current CLuST algorithms show that IBCL improves by at most 45% on average per task accuracy and by 43% on peak per task accuracy, while maintaining a near-zero to positive backward transfer. Moreover, its training overhead, measured by number of batch updates, remains constant at every task, regardless of the number of preferences requested., Comment: Preprint of our latest version (as in NeurIPS 2024)
Published: 2023

47. Magnetic moments of $A = 3$ nuclei with chiral effective field theory operators

Author: Pal, Soham, Sarker, Shiplu, Fasano, Patrick J., Maris, Pieter, Vary, James P., Caprio, Mark A., and Basili, Robert A. M.
Subjects: Nuclear Theory, Quantum Physics
Abstract: Chiral effective field theory ($\chi$EFT) provides a framework for obtaining internucleon interactions in a systematically improvable fashion from first principles, while also providing for the derivation of consistent electroweak current operators. In this work, we apply consistently derived interactions and currents towards calculating the magnetic dipole moments of the $A=3$ systems Triton and Helium-3. We focus here on LENPIC interactions obtained using semilocal coordinate-space (SCS) regularization. Starting from the momentum-space representation of the LENPIC $\chi$EFT vector current, we derive the SCS-regularized magnetic dipole operator up through N2LO. We then carry out no-core shell model calculations for Triton and Helium-3 systems, using the SCS LENPIC interaction at N2LO in $\chi$EFT, and evaluate the magnetic dipole moments obtained using the consistently derived one-nucleon and two-nucleon electromagnetic currents. As anticipated by prior results with $\chi$EFT currents, the current corrections through N2LO provide improved, but not yet complete, agreement with experiment for the Triton and Helium-3 magnetic dipole moments., Comment: 16 pages, 2 figures
Published: 2023
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48. Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova Remnants

Author: Consortium, The Cherenkov Telescope Array, Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Aloisio, R., Crespo, N. Álvarez, Batista, R. Alves, Amati, L., Amato, E., Ambrosi, G., Angüner, E. O., Aramo, C., Arcaro, C., Armstrong, T., Asano, K., Ascasibar, Y., Aschersleben, J., Backes, M., Baktash, A., Balazs, C., Balbo, M., Ballet, J., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Bastieri, D., Baxter, J. R., Tjus, J. Becker, Benbow, W., Bernardos-Martín, M. I., Bernete, J., Berti, A., Bertucci, B., Beshley, V., Bhattacharjee, P., Bhattacharyya, S., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Bordas, P., Bottacini, E., Bregeon, J., Brose, R., Bucciantini, N., Bulgarelli, A., Capasso, M., Dolcetta, R. A. Capuzzo, Caraveo, P., Cardillo, M., Carosi, R., Casanova, S., Cascone, E., Cassol, F., Catalani, F., Cerruti, M., Chadwick, P., Chaty, S., Chen, A., Chernyakova, M., Chiavassa, A., Chudoba, J., Coimbra-Araujo, C., Conforti, V., Contreras, J. L., Costa, A., Costantini, H., Cristofari, P., Crocker, R., D'Amico, G., D'Ammando, F., De Angelis, A., De Caprio, V., Pino, E. M. de Gouveia Dal, Wilhelmi, E. de Ona, de Souza, V., Delgado, C., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Pierro, F., Di Tria, R., Di Venere, L., Diebold, S., Djuvsland, J. I., Donini, A., Doro, M., Anjos, R. d. C. Dos, Dwarkadas, V. V., Einecke, S., Elsässer, D., Emery, G., Evoli, C., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Ferrand, G., Fiandrini, E., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Fontaine, G., Fukami, S., Galanti, G., Galaz, G., Gammaldi, V., Gasbarra, C., Ghalumyan, A., Ghirlanda, G., Giarrusso, M., Giavitto, G., Giglietto, N., Giordano, F., Giroletti, M., Giuliani, A., Giunti, L., Godinovic, N., Coelho, J. Goulart, Gréaux, L., Green, D., Grondin, M-H., Gueta, O., Gunji, S., Hassan, T., Heller, M., Hernández-Cadena, S., Hinton, J., Hnatyk, B., Hnatyk, R., Hoffmann, D., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hrabovsky, M., Hrupec, D., Inada, T., Incardona, F., Inoue, S., Ishio, K., Jamrozy, M., Janecek, P., Martínez, I. Jiménez, Jin, W., Jung-Richardt, I., Jurysek, J., Kaaret, P., Karas, V., Katz, U., Kerszberg, D., Khélifi, B., Kieda, D. B., Kissmann, R., Kleiner, T., Kluge, G., Kluzniak, W., Knödlseder, J., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kubo, H., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leto, G., Leuschner, F., Lindfors, E., Liodakis, I., Lohse, T., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Luque-Escamilla, P. L., Macias, O., Mackey, J., Majumdar, P., Mandat, D., Manganaro, M., Manicò, G., Marconi, M., Martí, J., Martínez, G., Martinez, M., Martinez, O., Mello, A. J. T. S., Menchiari, S., Meyer, D. M. -A., Micanovic, S., Miceli, D., Miceli, M., Michalowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mode, B., Moderski, R., Mohrmann, L., Molina, E., Montaruli, T., Morcuende, D., Morlino, G., Morselli, A., Mosè, M., Moulin, E., Mukherjee, R., Munari, K., Murach, T., Nagai, A., Nagataki, S., Nemmen, R., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikolajuk, M., Nishijima, K., Noda, K., Novosyadlyj, B., Nozaki, S., Ohishi, M., Ohm, S., Ohtani, Y., Okumura, A., Olmi, B., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Orlando, S., Ostrowski, M., Oya, I., Pantaleo, F. R., Paredes, J. M., Patricelli, B., Pecimotika, M., Peresano, M., Pérez-Romero, J., Persic, M., Petruk, O., Piano, G., Pietropaolo, E., Pirola, G., Pittori, C., Pohl, M., Ponti, G., Prandini, E., Principe, G., Priyadarshi, C., Pueschel, E., Pühlhofer, G., Pumo, M. L., Quirrenbach, A., Rando, R., Razzaque, S., Reichherzer, P., Reimer, A., Reimer, O., Renaud, M., Reposeur, T., Ribó, M., Richtler, T., Rico, J., Rieger, F., Rigoselli, M., Riitano, L., Rizi, V., Roache, E., Romano, P., Romeo, G., Rosado, J., Rowell, G., Rudak, B., Sadeh, I., Safi-Harb, S., Saha, L., Sailer, S., Sánchez-Conde, M., Sarkar, S., Satalecka, K., Saturni, F. G., Scherer, A., Schovánek, P., Schussler, F., Schwanke, U., Scuderi, S., Seglar-Arroyo, M., Sergijenko, O., Servillat, M., Shang, R-Y., Sharma, P., Siejkowski, H., Sliusar, V., Słowikowska, A., Sol, H., Specovius, A., Spencer, S. T., Spengler, G., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stolarczyk, T., Pereira, L. A. Stuani, Suda, Y., Suomijarvi, T., Sushch, I., Tajima, H., Tam, P-H. T., Tanaka, S. J., Tavecchio, F., Testa, V., Tian, W., Tibaldo, L., Torres, D. F., Tothill, N., Vallage, B., Vallania, P., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Acosta, M. Vazquez, Vecchi, M., Vercellone, S., Verna, G., Viana, A., Vignatti, J., Vitale, V., Vodeb, V., Vorobiov, S., Vuillaume, T., Wagner, S. J., Walter, R., White, M., Wierzcholska, A., Will, M., Williams, D., Yang, L., Yoshida, T., Yoshikoshi, T., Zaharijas, G., Zampieri, L., Zavrtanik, D., Zavrtanik, M., Zhdanov, V. I., and Živec, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3~PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy $\gamma$-ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte--Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a $\gamma$-ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 hours of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with $\mathcal{O}(100)$ hours of exposure per source., Comment: 34 pages, 16 figures, Accepted for publication in Astroparticle Physics
Published: 2023
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49. Yeasts, arthropods, and environmental matrix: a triad to disentangle the multi-level definition of biodiversity

Author: Beatrice Valentini, Margherita Penna, Massimiliano Viazzo, Enrico Caprio, Luca Pietro Casacci, Francesca Barbero, and Irene Stefanini
Subjects: Interspecific associations, Vectors, Systematic interactions, Mycobiota, Ecology, Medicine, Science
Abstract: Abstract Our understanding of the spread of yeasts in natural ecosystems remains somewhat limited. The recent momentum of yeast ecology research has unveiled novel habitats and vectors that, alongside human activities, impact yeast communities in their natural environments. Yeasts, as non-airborne microorganisms, rely on animal vectors, predominantly insects. However, the overlooked actor in this interplay is the environmental matrix, a player potentially influencing yeast populations and their vectors. This study aims to delve deeper into the intricate, multi-layered connections between yeast populations and ecosystems, focusing on the interactions between the attributes of the environmental matrix, arthropod diversity, and the mycobiota within a renowned yeast-inhabited framework: the vineyard. To investigate these relationships, we sampled both invertebrate and yeast diversity in six organic and conventional vineyards described in terms of management and landscape composition. We identified 80 different invertebrate taxa and isolated 170 yeast strains belonging to 18 species. Notably, new species-specific yeast-insect associations were observed, including the exclusive association between Candida orthopsilosis and Hymenoptera and between Metschnikowia pulcherrima and Coleoptera. These newly identified potential associations provide valuable insights into insect and yeast physiology, hence holding the promise of enhancing our understanding of yeast and arthropod ecology and their collective impact on overall ecosystem health.
Published: 2024
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50. Paradoxical effect of dopamine-agonists on IGF-1 in patients with prolactinoma: the role of weight

Author: S. Caprio, T. Pilli, S. Cantara, F. Sestini, C. Fioravanti, C. Ciuoli, C. Dalmiglio, A. Corbo, and M. G. Castagna
Subjects: Prolactinoma, Dopamine-agonists, IGF-1, Body mass index, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Abstract Purpose An increase of IGF-1 has been reported during therapy with dopamine agonists (DA) for prolactinomas; in such cases a correct diagnosis is pivotal to avoid an unnecessary reduction or withdrawal of DA, which are needed to maintain normal prolactin levels. This study was aimed to measure IGF-1 levels, at baseline and during follow-up, in a cohort of patients with prolactinoma, treated with cabergoline, stratified by body mass index. Methods We retrospectively enrolled 35 patients (15 F/20 M; age m ± SD, years: 43.4 ± 13.7) with prolactinoma (21 microadenomas and 14 macroadenomas) who were followed-up at the Endocrinology Unit, in Siena, and with available pituitary hormone assessment at baseline and during follow-up (m ± SD, years: 2.74 ± 0.55). Results IGF-1 increased in the whole cohort, but remaining within normal range, except two patients, in whom acromegaly was ruled out with oral glucose tolerance test. After dividing patients by weight, this trend was confirmed only in subjects with overweight and obesity (OV/OB) (p = 0.04). Interestingly, the reduction of prolactin levels was significantly greater in the OV/OB compared to normal-weight patients (median decrease of 97.5% versus 88.2%, p = 0.04). Conclusions Since DA and normalization of prolactin are known to improve insulin sensitivity, we speculated they have favored the increase of IGF-1 in OV/OB. Our results should be confirmed and the hypothesis proven by further studies.
Published: 2024
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