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22,913 results on '"Luciano, P."'

1. Lagrangian formulation of the Tsallis entropy

Author

D'Agostino, Rocco and Luciano, Giuseppe Gaetano

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Theory
Abstract

We investigate the gravitational origin of the Tsallis entropy, characterized by the nonadditive index $\delta$. Utilizing Wald's formalism within the framework of $f(R)$ modified theories of gravity, we evaluate the entropy on the black hole horizon for constant curvature solutions to the spherically symmetric vacuum field equations. In so doing, we demonstrate that the Tsallis entropy can be effectively derived from a generalization of the Lagrangian $\mathcal{L}\propto R^{1+\epsilon}$, where $\epsilon=\delta-1$ quantifies small deviations from general relativity. In conclusion, we examine the physical implications of our findings in light of cosmological observations and elaborate on the possibility of solving the thermodynamic instability of Schwarzschild black holes., Comment: 8 pages. Accepted for publication in Physics Letters B

Published
2024
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2. Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis

Author

Ayres, Luciano Carvalho, de Almeida, Sérgio José Melo, Bermudez, José Carlos Moreira, and Borsoi, Ricardo Augusto

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

Published
2024
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3. The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

Author

Mokander, Jakob, Sheth, Margi, Watson, David, and Floridi, Luciano

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

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems found in previous literature use one of three mental model. The Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics. The Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose. And the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, data input, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice.

Published
2024
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4. The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?

Author

Mokander, Jakob, Juneja, Prathm, Watson, David, and Floridi, Luciano

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

On the whole, the U.S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA)., Comment: Minds & Machines (2022)

Published
2024
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5. Modeling Frequency Shifts in Small-Scale Beams with Multiple Eccentric Masses

Author

Darban, Hossein, Luciano, Raimondo, and Basista, Michał

Subjects
Physics - Instrumentation and Detectors
Abstract

Studying the dynamics of small-scale beams with attached particles is crucial for sensing applications in various fields, such as bioscience, material science, energy storage devices, and environmental monitoring. Here, a stress-driven nonlocal model is presented for the free transverse vibration of small-scale beams carrying multiple masses taking into account the eccentricity of the masses relative to the beam axis. The results show excellent agreement with the experimental and numerical data in the literature. New insights into the frequency shifts and mode shapes of the first four vibrational modes of stress-driven nonlocal beams with up to three attached particles are presented. The study investigates the inverse problem of detecting the location and mass of an attached particle based on natural frequency shifts. The knowledge acquired from the present study provides valuable guidance for the design and analysis of ultrasensitive mechanical mass sensors., Comment: 43 pages, 17 figures, Published in Thin-Walled Structures, 112005, 2024

Published
2024
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6. Approaching the conformal limit of quark matter with different chemical potentials

Author

Brown, Connor, Dexheimer, Veronica, Jacobsen, Rafael Bán, and Farias, Ricardo Luciano Sonego

Subjects
High Energy Physics - Phenomenology, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, High Energy Physics - Theory, Nuclear Theory
Abstract

We study in detail the influence of different chemical potentials (baryon, charged, strange, and neutrino) on how and how fast a free gas of quarks in the zero-temperature limit reaches the conformal limit. We discuss the influence of non-zero masses, the inclusion of leptons, and different constraints, such as charge neutrality, zero-net strangeness, and fixed lepton fraction. We also investigate for the first time how the symmetry energy of the system under some of these conditions approaches the conformal limit. Finally, we briefly discuss what kind of corrections are expected from perturbative QCD as one goes away from the conformal limit., Comment: 14 pages, 5 figures

Published
2024
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7. Game-Based Learning Enhances Students' Understanding of Endocrine Physiology in Veterinary Medicine

Author

Marcos Vinícius Ramos Afonso, Rodrigo Braga Lopes, Eric Francelino Andrade, and Luciano José Pereira

Abstract

Endocrine physiology is a complex subject for students. Game-based learning (GBL) and case-based learning (CBL) are active methodologies that are widely used because of their potential for motivation and greater proximity to the reality of modern students. We evaluated the effectiveness of GBL and CBL among veterinary medicine students compared with a control group using peer tutoring. Students (n = 106) from two institutions volunteered to participate in this study. The participants were submitted to a pretest questionnaire and subsequently were divided into three paired groups by their performance on the pretest exam: (1) traditional class + peer tutoring, (2) traditional class + GBL, and (3) traditional class + CBL. After the students completed the activities, their performance was once again evaluated by applying a new test with the same initial 10 questions and another set of 10 different questions. The students' perceptions and satisfaction with the methodologies and learning strategies were assessed. Anxiety was assessed with the State-Trait Anxiety Inventory before and after the conventional class and after the active methodologies. The GBL group significantly improved their correct answers compared with the baseline (P < 0.05), with no significant difference from CBL and peer tutoring. Anxiety levels did not differ regardless of the time of evaluation or the teaching methodology applied. GBL promoted a greater perception of the stimulus for self-study and problem-solving ability and contributed to the development of group dynamics compared with the group who received CBL (P < 0.05). In conclusion, GBL showed better results than peer tutoring and CBL.

Published
2024
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8. The Effect of Collective Sight-Singing before Melodic Dictation: A Pilot Study

Author

Caroline Caregnato, Ronaldo da Silva, Cristiane Hatsue Vital Otutumi, and Luciano Jeyson Santos da Rocha

Abstract

Sight-singing and musical dictation are considered as complementary activities by different Ear Training pedagogues but, surprisingly, studies conducted with participants working individually were not able to find benefits of singing associated with dictation taking. This pilot study aims at observing the effect of a sight-singing, performed collectively before melodic dictation, on dictation results. We carried out an experimental study involving 54 students from three universities, who were tested in situations emulating Ear Training classes. The experimental group performed a collective sight-singing before the dictation, and the control group remained silent during the activity. Statistical analyses demonstrated that the experimental group had a significantly better performance on dictation than the control group, showing new data in relation to previous researches, that did not observe contributions of sight-singing related to dictation taking. We believe that collective sight-singing promotes cooperation between students, leading to better performance on reading than individual activities, thus improving dictation results. Although our pilot study counted on a small number of participants, remaining the necessity of future research expanding this one, it points to the potential benefits that collective activities could bring to the often-individualized instruction in Ear Training classes.

Published
2024
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9. Characterizing unstructured data with the nearest neighbor permutation entropy

Author

Voltarelli, Leonardo G. J. M., Pessa, Arthur A. B., Zunino, Luciano, Zola, Rafael S., Lenzi, Ervin K., Perc, Matjaz, and Ribeiro, Haroldo V.

Subjects
Physics - Data Analysis, Statistics and Probability
Abstract

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data, but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data., Comment: 14 two-column pages, 6 figures

Published
2024
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10. A Systemic Analysis of the Impacts of the COVID-19 Pandemic on the Studies of Brazilian Graduate Students: An Exploratory Study

Author

Arantes do Amaral, João Alberto, Meister, Izabel Patricia, and Gamez, Luciano

Abstract

This article presents the findings related to an exploratory study of the systemic impacts of the COVID-19 pandemic on the studies of graduate students of Brazil. It was sent a survey to 38 students who attended a University of São Paulo graduate course during the first semester of 2021. An exploratory sequential mixed method approach was followed. Quantitative and qualitative data was collected using questionnaires. The quantitative data was analyzed through descriptive statistics. The qualitative data was examined following a language processing method. The insights gained from both kinds of data were connected using a causal loop diagram. It was found that home confinement was the factor that created the main barriers to learning. In addition, digital fatigue, inadequate teaching strategies and the limitation of video conferencing tools also impacted negatively on learning. On the other hand, the pandemic also brought learning opportunities: the students took advantage of courses, webinars and conferences offered during the pandemic. In addition, the students developed new IT and communication skills.

Published
2023

13. Readdressing democracy and social justice: coping with inequalities in physical education

Author

Sanches Neto, Luiz, Venâncio, Luciana, Ovens, Alan, Dania, Aspasia, Ulasowicz, Carla, Garbett, Dawn, Freire, Elisabete dos Santos, Gerdin, Göran, Silva, Jorlany Pereira da, Corsino, Luciano Nascimento, Gonçalves, Luiza Lana, Souza, Maurício Teodoro de, Philpot, Rod, Barreto, Samara Moura, Lynch, Shrehan, Okimura-Kerr, Tiemi, and Conceição, Willian Lazaretti da

Published
2024
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30. An updated model for predicting side-specific extraprostatic extension in the era of MRI-targeted biopsy

Author

Martini, Alberto, Wever, Lieke, Soeterik, Timo F. W., Rakauskas, Arnas, Fankhauser, Christian Daniel, Grogg, Josias Bastian, Checcucci, Enrico, Amparore, Daniele, Haiquel, Luciano, Rodriguez-sanchez, Lara, Ploussard, Guillaume, Qiang, Peng, Affentranger, Andres, Marquis, Alessandro, Marra, Giancarlo, Ettala, Otto, Zattoni, Fabio, Falagario, Ugo Giovanni, De Angelis, Mario, Kesch, Claudia, Apfelbeck, Maria, Al-Hammouri, Tarek, Kretschmer, Alexander, Kasivisvanathan, Veeru, Preisser, Felix, Lefebvre, Emilie, Olivier, Jonathan, Radtke, Jan Philipp, Carrieri, Giuseppe, Moro, Fabrizio Dal, Boström, Peter, Jambor, Ivan, Gontero, Paolo, Chiu, Peter K., John, Hubert, Macek, Petr, Porpiglia, Francesco, Hermanns, Thomas, van den Bergh, Roderick C. N., van Basten, Jean-Paul A., Gandaglia, Giorgio, and Valerio, Massimo

Published
2024
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37. Sorbitol-plasticized jackfruit starch-based films: investigation of the effect of the starch and plasticizer concentration on the film properties

Author

Santana, Renata F., Lima, Clara Mariana G., Alves, Annie N., Maniglia, Bianca C., dos Anjos, Laíza, Pires, Ana Clarissa S., Santos, Leandro Soares, Rodrigues, Luciano B., Fontan, Rafael C. I., Gonçalves, Jaqueline Teixeira T., Guiné, Raquel P. F., Coutinho, Henrique Douglas Melo, and Bonomo, Renata Cristina F.

Published
2024
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42. The Italian experience on liver transplantation for unresectable peri-hilar cholangiocarcinoma: a national survey and future perspectives

Author

Gringeri, Enrico, Furlanetto, Alessandro, Billato, Ilaria, Cescon, Matteo, De Carlis, Luciano, Mazzaferro, Vincenzo, Romagnoli, Renato, De Simone, Paolo, Vivarelli, Marco, Di Benedetto, Fabrizio, Ravaioli, Matteo, Lauterio, Andrea, Sposito, Carlo, Patrono, Damiano, Ghinolfi, Davide, Moccheggiani, Federico, Di Sandro, Stefano, D’Amico, Francesco Enrico, Lanari, Jacopo, Gambato, Martina, Trapani, Silvia, Bergamo, Francesca, Cardillo, Massimo, Burra, Patrizia, and Cillo, Umberto

Published
2024
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44. The Magnetic Microbolometer: A Proposal for QUBIC Next Gen

Author

Hampel, Matías, Almela, Alejandro, Bonaparte, Juan, Neira, Jesús Bonilla, Ferreyro, Luciano, Fuster, Alan, Redondo, Manuel García, Gartmann, Robert, Geria, Juan, Müller, Nahuel, Muscheid, Timo, Salum, Juan, Platino, Manuel, Ardila, Luis, Sander, Oliver, Wegner, Mathias, Kempf, Sebastian, Weber, Marc, and Etchegoyen, Alberto

Published
2024
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