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1,249 results on '"Isgro A."'

1. SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder

Author

Pollastro, Andrea, Isgrò, Francesco, and Prevete, Roberto

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

Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately the 1 \% of the population. These patients face significant risks, underscoring the need for reliable, continuous seizure monitoring in daily life. Most of the techniques discussed in the literature rely on supervised Machine Learning (ML) methods. However, the challenge of accurately labeling variations in epileptic EEG waveforms complicates the use of these approaches. Additionally, the rarity of ictal events introduces an high imbalancing within the data, which could lead to poor prediction performance in supervised learning approaches. Instead, a semi-supervised approach allows to train the model only on data not containing seizures, thus avoiding the issues related to the data imbalancing. This work proposes a semi-supervised approach for detecting epileptic seizures from EEG data, utilizing a novel Deep Learning-based method called SincVAE. This proposal incorporates the learning of an ad-hoc array of bandpass filter as a first layer of a Variational Autoencoder (VAE), potentially eliminating the preprocessing stage where informative band frequencies are identified and isolated. Results indicate that SincVAE improves seizure detection in EEG data and is capable of identifying early seizures during the preictal stage as well as monitoring patients throughout the postictal stage.

Published
2024

2. Towards a general framework for improving the performance of classifiers using XAI methods

Author

Apicella, Andrea, Giugliano, Salvatore, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning
Abstract

Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial Intelligence (XAI) inspects internal mechanisms of AI models providing explanations about their decisions. While current XAI research predominantly concentrates on explaining AI systems, there is a growing interest in using XAI techniques to automatically improve the performance of AI systems themselves. This paper proposes a general framework for automatically improving the performance of pre-trained DL classifiers using XAI methods, avoiding the computational overhead associated with retraining complex models from scratch. In particular, we outline the possibility of two different learning strategies for implementing this architecture, which we will call auto-encoder-based and encoder-decoder-based, and discuss their key aspects.

Published
2024

3. Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning

Author

Apicella, Andrea, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, utilizing user-friendly interfaces without a thorough understanding of underlying algorithms. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. This paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Users, due to a lack of understanding, may inadvertently overlook crucial steps, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning, and compares standard inductive ML with transductive ML frameworks. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications., Comment: under review

Published
2024

4. EUSO-SPB1 Mission and Science

Author

Collaboration, JEM-EUSO, Abdellaoui, G., Abe, S., Adams. Jr., J. H., Allard, D., Alonso, G., Anchordoqui, L., Anzalone, A., Arnone, E., Asano, K., Attallah, R., Attoui, H., Pernas, M. Ave, Bachmann, R., Bacholle, S., Bagheri, M., Bakiri, M., Baláz, J., Barghini, D., Bartocci, S., Battisti, M., Bayer, J., Beldjilali, B., Belenguer, T., Belkhalfa, N., Bellotti, R., Belov, A. A., Benmessai, K., Bertaina, M., Bertone, P. F., Biermann, P. L., Bisconti, F., Blaksley, C., Blanc, N., Blin-Bondil, S., Bobik, P., Bogomilov, M., Bolmgren, K., Bozzo, E., Briz, S., Bruno, A., Caballero, K. S., Cafagna, F., Cambié, G., Campana, D., Capdevielle, J. N., Capel, F., Caramete, A., Caramete, L., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Catalano, O., Cellino, A., Černý, K., Chikawa, M., Chiritoi, G., Christl, M. J., Colalillo, R., Conti, L., Cotto, G., Crawford, H. J., Cremonini, R., Creusot, A., Cummings, A., Gónzalez, A. de Castro, de la Taille, C., del Peral, L., Desiato, J., Damian, A. Diaz, Diesing, R., Dinaucourt, P., Djakonow, A., Djemil, T., Ebersoldt, A., Ebisuzaki, T., Eser, J., Fenu, F., Fernández-González, S., Ferrarese, S., Filippatos, G., Finch, W., Fornaro, C., Fouka, M., Franceschi, A., Franchini, S., Fuglesang, C., Fujii, T., Fukushima, M., Galeotti, P., García-Ortega, E., Gardiol, D., Garipov, G. K., Gascón, E., Gazda, E., Genci, J., Golzio, A., Gorodetzky, P., Gregg, R., Green, A., Guarino, F., Guépin, C., Guzmán, A., Hachisu, Y., Haungs, A., Heigbes, T., Carretero, J. Hernández, Hulett, L., Ikeda, D., Inoue, N., Inoue, S., Isgrò, F., Itow, Y., Jammer, T., Jeong, S., Jochum, J., Joven, E., Judd, E. G., Jung, A., Kajino, F., Kajino, T., Kalli, S., Kaneko, I., Kasztelan, M., Katahira, K., Kawai, K., Kawasaki, Y., Kedadra, A., Khales, H., Khrenov, B. A., Kim, Jeong-Sook, Kim, Soon-Wook, Kleifges, M., Klimov, P. A., Kreykenbohm, I., Krizmanic, J. F., Królik, K., Kungel, V., Kurihara, Y., Kusenko, A., Kuznetsov, E., Lahmar, H., Lakhdari, F., Licandro, J., Campano, L. López, Martínez, F. López, Mackovjak, S., Mahdi, M., Mandát, D., Manfrin, M., Marcelli, L., Marcos, J. L., Marszał, W., Martín, Y., Martinez, O., Mase, K., Mastafa, M., Matthews, J. N., Mebarki, N., Medina-Tanco, G., Menshikov, A., Merino, A., Mese, M., Meseguer, J., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Ríos, J. A. Morales de los, Nachtman, J. M., Nagataki, S., Naitamor, S., Napolitano, T., Neronov, A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Onel, Y., Osteria, G., Otte, A. N., Pagliaro, A., Painter, W., Panasyuk, M. I., Panico, B., Parizot, E., Park, I. H., Pastircak, B., Paul, T., Pech, M., Pérez-Grande, I., Perfetto, F., Peter, T., Picozza, P., Pindado, S., Piotrowski, L. W., Piraino, S., Plebaniak, Z., Pollini, A., Popescu, E. M., Prevete, R., Prévôt, G., Prieto, H., Przybylak, M., Puehlhofer, G., Putis, M., Reardon, P., Reno, M. H., Reyes, M., Ricci, M., Frías, M. D. Rodríguez, Matamala, O. F. Romero, Ronga, F., Sabau, M. D., Saccá, G., Sagawa, H., Sahnoune, Z., Saito, A., Sakaki, N., Salazar, H., Sánchez, J. L., Balanzar, J. C. Sanchez, Santangelo, A., Sanz-Andrés, A., Saprykin, O. A., Sarazin, F., Sato, M., Scagliola, A., Schanz, T., Schieler, H., Schovánek, P., Scotti, V., Serra, M., Sharakin, S. A., Shimizu, H. M., Shinozaki, K., Soriano, J. F., Sotgiu, A., Stan, I., Strharský, I., Sugiyama, N., Supanitsky, D., Suzuki, M., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takeda, M., Takizawa, Y., Talai, M. C., Tameda, Y., Tenzer, C., Thomas, S. B., Tibolla, O., Tkachev, L. G., Tomida, T., Tone, N., Toscano, S., Traïche, M., Tsunesada, Y., Tsuno, K., Turriziani, S., Uchihori, Y., Valdés-Galicia, J. F., Vallania, P., Valore, L., Vankova-Kirilova, G., Venters, T. M., Vigorito, C., Villaseñor, L., Vlcek, B., von Ballmoos, P., Vrabel, M., Wada, S., Watanabe, J., Watts. Jr., J., Muñoz, R. Weigand, Weindl, A., Wiencke, L., Wille, M., Wilms, J., Yamamoto, T., Yang, J., Yano, H., Yashin, I. V., Yonetoku, D., Yoshida, S., Young, R., Zgura, I. S., Zotov, M. Yu., and Marchi, A. Zuccaro

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

The Extreme Universe Space Observatory on a Super Pressure Balloon 1 (EUSO-SPB1) was launched in 2017 April from Wanaka, New Zealand. The plan of this mission of opportunity on a NASA super pressure balloon test flight was to circle the southern hemisphere. The primary scientific goal was to make the first observations of ultra-high-energy cosmic-ray extensive air showers (EASs) by looking down on the atmosphere with an ultraviolet (UV) fluorescence telescope from suborbital altitude (33~km). After 12~days and 4~hours aloft, the flight was terminated prematurely in the Pacific Ocean. Before the flight, the instrument was tested extensively in the West Desert of Utah, USA, with UV point sources and lasers. The test results indicated that the instrument had sensitivity to EASs of approximately 3 EeV. Simulations of the telescope system, telescope on time, and realized flight trajectory predicted an observation of about 1 event assuming clear sky conditions. The effects of high clouds were estimated to reduce this value by approximately a factor of 2. A manual search and a machine-learning-based search did not find any EAS signals in these data. Here we review the EUSO-SPB1 instrument and flight and the EAS search., Comment: 18 pages, 19 figures

Published
2024
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5. Outcomes of allogeneic haematopoietic cell transplantation for myelofibrosis in children and adolescents: the retrospective study of the EBMT Paediatric Diseases WP

Author

Wachowiak, Jacek, Galimard, Jacques-Emmanuel, Dalissier, Arnaud, Rihani, Rawad, AlSaedi, Hawazen, Wynn, Robert F., Dalle, Jean-Hugues, Peffault de Latour, Régis, Sedlacek, Petr, Balduzzi, Adriana, Schroeder, Thomas, Bodova, Ivana, Gonzalez Vicent, Marta, Gruhn, Bernd, Hamladji, Rose-Marie, Krivan, Gergely, Patrick, Katharine, Sobkowiak-Sobierajska, Agnieszka, Stepensky, Polina, Unal, Ali, Amrolia, Persis, Perez Martinez, Antonio, Rialland, Fanny, Aljurf, Mahmoud, Isgro, Antonella, Toren, Amos, Bierings, Marc, Corbacioglu, Selim, and Kałwak, Krzysztof

Published
2024
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6. JEM-EUSO Collaboration contributions to the 38th International Cosmic Ray Conference

Author

Abe, S., Adams Jr., J. H., Allard, D., Alldredge, P., Aloisio, R., Anchordoqui, L., Anzalone, A., Arnone, E., Bagheri, M., Baret, B., Barghini, D., Battisti, M., Bellotti, R., Belov, A. A., Bertaina, M., Bertone, P. F., Bianciotto, M., Bisconti, F., Blaksley, C., Blin-Bondil, S., Bolmgren, K., Briz, S., Burton, J., Cafagna, F., Cambiè, G., Campana, D., Capel, F., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Černý, K., Christl, M. J., Colalillo, R., Conti, L., Cotto, G., Crawford, H. J., Cremonini, R., Creusot, A., Cummings, A., Gónzalez, A. de Castro, de la Taille, C., Diesing, R., Dinaucourt, P., Di Nola, A., Ebisuzaki, T., Eser, J., Fenu, F., Ferrarese, S., Filippatos, G., Finch, W. W., Flaminio, F., Fornaro, C., Fuehne, D., Fuglesang, C., Fukushima, M., Gadamsetty, S., Gardiol, D., Garipov, G. K., Gazda, E., Golzio, A., Guarino, F., Guépin, C., Haungs, A., Heibges, T., Isgrò, F., Judd, E. G., Kajino, F., Kaneko, I., Kim, S. -W., Klimov, P. A., Krizmanic, J. F., Kungel, V., Kuznetsov, E., Martínez, F. López, Mandát, D., Manfrin, M., Marcelli, A., Marcelli, L., Marszał, W., Matthews, J. N., Mese, M., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Nagataki, S., Nachtman, J. M., Naumov, D., Neronov, A., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Onel, Y., Osteria, G., Otte, A. N., Pagliaro, A., Panico, B., Parizot, E., Park, I. H., Paul, T., Pech, M., Perfetto, F., Picozza, P., Piotrowski, L. W., Plebaniak, Z., Posligua, J., Potts, M., Prevete, R., Prévôt, G., Przybylak, M., Reali, E., Reardon, P., Reno, M. H., Ricci, M., Matamala, O. F. Romero, Romoli, G., Sagawa, H., Sakaki, N., Saprykin, O. A., Sarazin, F., Sato, M., Schovánek, P., Scotti, V., Selmane, S., Sharakin, S. A., Shinozaki, K., Stepanoff, S., Soriano, J. F., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takeda, M., Takizawa, Y., Thomas, S. B., Tkachev, L. G., Tomida, T., Toscano, S., Traïche, M., Trofimov, D., Tsuno, K., Vallania, P., Valore, L., Venters, T. M., Vigorito, C., Vrábel, M., Wada, S., Watts Jr., J., Wiencke, L., Winn, D., Wistrand, H., Yashin, I. V., Young, R., and Zotov, M. Yu.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

This is a collection of papers presented by the JEM-EUSO Collaboration at the 38th International Cosmic Ray Conference (Nagoya, Japan, July 26-August 3, 2023)

Published
2023

7. Developments and results in the context of the JEM-EUSO program obtained with the ESAF Simulation and Analysis Framework

Author

Abe, S., Adams Jr., J. H., Allard, D., Alldredge, P., Anchordoqui, L., Anzalone, A., Arnone, E., Baret, B., Barghini, D., Battisti, M., Bayer, J., Bellotti, R., Belov, A. A., Bertaina, M., Bertone, P. F., Bianciotto, M., Biermann, P. L., Bisconti, F., Blaksley, C., Blin-Bondil, S., Bobik, P., Bolmgren, K., Briz, S., Burton, J., Cafagna, F., Cambié, G., Campana, D., Capel, F., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Černý, K., Christl, M. J., Colalillo, R., Conti, L., Cotto, G., Crawford, H. J., Cremonini, R., Creusot, A., Cummings, A., Gónzalez, A. de Castro, de la Taille, C., del Peral, L., Diesing, R., Dinaucourt, P., Di Nola, A., Ebersoldt, A., Ebisuzaki, T., Eser, J., Fenu, F., Ferrarese, S., Filippatos, G., Finch, W. W., Flaminio, F., Fornaro, C., Fuehne, D., Fuglesang, C., Fukushima, M., Gardiol, D., Garipov, G. K., Golzio, A., Gorodetzky, P., Guarino, F., Guépin, C., Guzmán, A., Haungs, A., Heibges, T., Hernández-Carretero, J., Isgrò, F., Judd, E. G., Kajino, F., Kaneko, I., Kawasaki, Y., Kleifges, M., Klimov, P. A., Kreykenbohm, I., Krizmanic, J. F., Kungel, V., Kuznetsov, E., Martínez, F. López, Mackovjak, S., Mandát, D., Manfrin, M., Marcelli, A., Marcelli, L., Marszał, W., Matthews, J. N., Menshikov, A., Mernik, T., Mese, M., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Ríos, J. A Morales de los, Nagataki, S., Nachtman, J. M., Naumov, D., Neronov, A., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Onel, Y., Osteria, G., Pagliaro, A., Panico, B., Parizot, E., Park, I. H., Pastircak, B., Paul, T., Pech, M., Perfetto, F., Picozza, P., Piotrowski, L. W., Plebaniak, Z., Posligua, J., Prevete, R., Prévôt, G., Prieto, H., Przybylak, M., Putis, M., Reali, E., Reardon, P., Reno, M. H., Ricci, M., Frías, M. Rodríguez, Romoli, G., Cano, G. Sáez, Sagawa, H., Sakaki, N., Santangelo, A., Saprykin, O. A., Sarazin, F., Sato, M., Schieler, H., Schovánek, P., Scotti, V., Selmane, S., Sharakin, S. A., Shinozaki, K., Soriano, J. F., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takeda, M., Takizawa, Y., Tenzer, C., Thomas, S. B., Tkachev, L. G., Tomida, T., Toscano, S., Traïche, M., Trofimov, D., Tsuno, K., Vallania, P., Valore, L., Venters, T. M., Vigorito, C., von Ballmoos, P., Vrabel, M., Wada, S., Watts Jr., J., Weindl, A., Wiencke, L., Wilms, J., Winn, D., Wistrand, H., Yashin, I. V., Young, R., and Zotov, M. Yu.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment
Abstract

JEM--EUSO is an international program for the development of space-based Ultra-High Energy Cosmic Ray observatories. The program consists of a series of missions which are either under development or in the data analysis phase. All instruments are based on a wide-field-of-view telescope, which operates in the near-UV range, designed to detect the fluorescence light emitted by extensive air showers in the atmosphere. We describe the simulation software ESAFin the framework of the JEM--EUSO program and explain the physical assumptions used. We present here the implementation of the JEM--EUSO, POEMMA, K--EUSO, TUS, Mini--EUSO, EUSO--SPB1 and EUSO--TA configurations in ESAF. For the first time ESAF simulation outputs are compared with experimental data.

Published
2023
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8. Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers

Author

Apicella, Andrea, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic idea is that each hidden neural layer accomplishes a data transformation which is expected to make the data representation "somewhat more linearly separable" than the previous one to obtain a final data representation which is as linearly separable as possible. However, determining the appropriate neural network parameters that can perform these transformations is a critical problem. In this paper, we investigate the impact on deep network classifier performances of a training approach favouring solutions where data representations at the hidden layers have a higher degree of linear separability between the classes with respect to standard methods. To this aim, we propose a neural network architecture which induces an error function involving the outputs of all the network layers. Although similar approaches have already been partially discussed in the past literature, here we propose a new architecture with a novel error function and an extensive experimental analysis. This experimental analysis was made in the context of image classification tasks considering four widely used datasets. The results show that our approach improves the accuracy on the test set in all the considered cases., Comment: Paper accepted on Pattern Recognition Letters journal in Open Access with doi https://www.sciencedirect.com/science/article/pii/S0167865523003264 . Please refer to the published version

Published
2023
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9. Strategies to exploit XAI to improve classification systems

Author

Apicella, Andrea, Di Lorenzo, Luca, Isgrò, Francesco, Pollastro, Andrea, and Prevete, Roberto

Subjects
Computer Science - Artificial Intelligence
Abstract

Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models by providing explanations for their decision-making processes. However, most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system. In this work, a set of well-known XAI methods typically used with Machine Learning (ML) classification tasks are investigated to verify if they can be exploited, not just to provide explanations but also to improve the performance of the model itself. To this aim, two strategies to use the explanation to improve a classification system are reported and empirically evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest that explanations built by Integrated Gradients highlight input features that can be effectively used to improve classification performance., Comment: This work has been accepted to be presented to The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28, 2023 - Lisboa, Portugal

Published
2023

10. Toward the application of XAI methods in EEG-based systems

Author

Apicella, Andrea, Isgrò, Francesco, Pollastro, Andrea, and Prevete, Roberto

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

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself., Comment: Accepted at XAI.it 2022 - Italian Workshop on Explainable Artificial Intelligence (https://ceur-ws.org/Vol-3277/paper1.pdf)

Published
2022

11. On The Effects Of Data Normalisation For Domain Adaptation On EEG Data

Author

Apicella, Andrea, Isgrò, Francesco, Pollastro, Andrea, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalisation performances. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. In fact, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several proposed solutions are based on recent transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods. In particular, using \textit{SEED}, \textit{DEAP}, and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods, comparing the obtained performances. It results that the choice of the normalisation strategy plays a key role on the classifier performances in DA scenarios, and interestingly, in several cases, the use of only an appropriate normalisation schema outperforms the DA technique., Comment: Published in its final version on Engineering Applications of Artificial Intelligence (EAAI) https://doi.org/10.1016/j.engappai.2023.106205

Published
2022
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12. JEM-EUSO Collaboration contributions to the 37th International Cosmic Ray Conference

Author

Abdellaoui, G., Abe, S., Adams Jr., J. H., Allard, D., Alonso, G., Anchordoqui, L., Anzalone, A., Arnone, E., Asano, K., Attallah, R., Attoui, H., Pernas, M. Ave, Bagheri, M., Baláz, J., Bakiri, M., Barghini, D., Bartocci, S., Battisti, M., Bayer, J., Beldjilali, B., Belenguer, T., Belkhalfa, N., Bellotti, R., Belov, A. A., Benmessai, K., Bertaina, M., Bertone, P. F., Biermann, P. L., Bisconti, F., Blaksley, C., Blanc, N., Blin-Bondil, S., Bobik, P., Bogomilov, M., Bolmgren, K., Bozzo, E., Briz, S., Bruno, A., Caballero, K. S., Cafagna, F., Cambié, G., Campana, D., Capdevielle, J-N., Capel, F., Caramete, A., Caramete, L., Carlson, P., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Catalano, O., Cellino, A., Černý, K., Chikawa, M., Chiritoi, G., Christl, M. J., Colalillo, R., Conti, L., Cotto, G., Crawford, H. J., Cremonini, R., Creusot, A., Gónzalez, A. de Castro, de la Taille, C., del Peral, L., Damian, A. Diaz, Diesing, R., Dinaucourt, P., Djakonow, A., Djemil, T., Ebersoldt, A., Ebisuzaki, T., Eser, J., Fenu, F., Fernández-González, S., Ferrarese, S., Filippatos, G., Fornaro, W. I. Finch C., Fouka, M., Franceschi, A., Franchini, S., Fuglesang, C., Fujii, T., Fukushima, M., Galeotti, P., García-Ortega, E., Gardiol, D., Garipov, G. K., Gascón, E., Gazda, E., Genci, J., Golzio, A., Alvarado, C. González, Gorodetzky, P., Green, A., Guarino, F., Guépin, C., Guzmán, A., Hachisu, Y., Haungs, A., Carretero, J. Hernández, Hulett, L., Ikeda, D., Inoue, N., Inoue, S., Isgrò, F., Itow, Y., Jammer, T., Jeong, S., Joven, E., Judd, E. G., Jochum, J., Kajino, F., Kajino, T., Kalli, S., Kaneko, I., Karadzhov, Y., Kasztelan, M., Katahira, K., Kawai, K., Kawasaki, Y., Kedadra, A., Khales, H., Khrenov, B. A., Kim, Jeong-Sook, Kim, Soon-Wook, Kleifges, M., Klimov, P. A., Kolev, D., Kreykenbohm, I., Krizmanic, J. F., Królik, K., Kungel, V., Kurihara, Y., Kusenko, A., Kuznetsov, E., Lahmar, H., Lakhdari, F., Licandro, J., Campano, L. López, Martínez, F. López, Mackovjak, S., Mahdi, M., Mandát, D., Manfrin, M., Marcelli, L., Marcos, J. L., Marszał, W., Martín, Y., Martinez, O., Mase, K., Matev, R., Matthews, J. N., Mebarki, N., Medina-Tanco, G., Menshikov, A., Merino, A., Mese, M., Meseguer, J., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Ríos, J. A. Morales de los, Mastafa, M., Nagataki, S., Naitamor, S., Napolitano, T., Neronov, J. M. Nachtman A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Osteria, Y. Onel G., Otte, A. N., Pagliaro, A., Painter, W., Panasyuk, M. I., Panico, B., Parizot, E., Park, I. H., Pastircak, B., Paul, T., Pech, M., Pérez-Grande, I., Perfetto, F., Peter, T., Picozza, P., Pindado, S., Piotrowski, L. W., Piraino, S., Plebaniak, Z., Pollini, A., Popescu, E. M., Prevete, R., Prévôt, G., Prieto, H., Przybylak, M., Puehlhofer, G., Putis, M., Reardon, P., Reno, M. H., Reyes, M., Ricci, M., Frías, M. D. Rodríguez, Matamala, O. F. Romero, Ronga, F., Sabau, M. D., Saccá, G., Cano, G. Sáez, Sagawa, H., Sahnoune, Z., Saito, A., Sakaki, N., Salazar, H., Balanzar, J. C. Sanchez, Sánchez, J. L., Santangelo, A., Sanz-Andrés, A., Palomino, M. Sanz, Saprykin, O. A., Sarazin, F., Sato, M., Scagliola, A., Schanz, T., Schieler, H., Schovánek, P., Scotti, V., Serra, M., Sharakin, S. A., Shimizu, H. M., Shinozaki, K., Soriano, J. F., Sotgiu, A., Stan, I., Strharský, I., Sugiyama, N., Supanitsky, D., Suzuki, M., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takeda, M., Takizawa, Y., Talai, M. C., Tameda, Y., Tenzer, C., Thomas, S. B., Tibolla, O., Tkachev, L. G., Tomida, T., Tone, N., Toscano, S., Traïche, M., Tsunesada, Y., Tsuno, K., Turriziani, S., Uchihori, Y., Vaduvescu, O., Valdés-Galicia, J. F., Vallania, P., Valore, L., Vankova-Kirilova, G., Venters, T. M., Vigorito, C., Villaseñor, L., Vlcek, B., von Ballmoos, P., Vrabel, M., Wada, S., Watanabe, J., Watts Jr., J., Muñoz, R. Weigand, Weindl, A., Wiencke, L., Wille, M., Wilms, J., Yamamoto, D. Winn T., Yang, J., Yano, H., Yashin, I. V., Yonetoku, D., Yoshida, S., Young, R., Zgura, I. S., Zotov, M. Yu., and Marchi, A. Zuccaro

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Compilation of papers presented by the JEM-EUSO Collaboration at the 37th International Cosmic Ray Conference (ICRC), held on July 12-23, 2021 (online) in Berlin, Germany., Comment: html page with links to the JEM-EUSO Collaboration papers presented at ICRC-2021, Berlin, Germany

Published
2022

13. Measurement of the charge asymmetry of electrons from the decays of $W$ bosons produced in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV

Author

CDF Collaboration, Aaltonen, T., Amerio, S., Amidei, D., Anastassov, A., Annovi, A., Antos, J., Apollinari, G., Appel, J. A., Arisawa, T., Artikov, A., Asaadi, J., Ashmanskas, W., Auerbach, B., Aurisano, A., Azfar, F., Badgett, W., Bae, T., Barbaro-Galtieri, A., Barnes, V. E., Barnett, B. A., Barria, P., Bartos, P., Bauce, M., Bedeschi, F., Behari, S., Bellettini, G., Bellinger, J., Benjamin, D., Beretvas, A., Bhatti, A., Bland, K. R., Blumenfeld, B., Bocci, A., Bodek, A., Bortoletto, D., Boudreau, J., Boveia, A., Brigliadori, L., Bromberg, C., Brucken, E., Budagov, J., Budd, H. S., Burkett, K., Busetto, G., Bussey, P., Butti, P., Buzatu, A., Calamba, A., Camarda, S., Campanelli, M., Canelli, F., Carls, B., Carlsmith, D., Carosi, R., Carrillo, S., Casal, B., Casarsa, M., Castro, A., Catastini, P., Cauz, D., Cavaliere, V., Cerri, A., Cerrito, L., Chen, Y. C., Chertok, M., Chiarelli, G., Chlachidze, G., Cho, K., Chokheli, D., Clark, A., Clarke, C., Convery, M. E., Conway, J., Corbo, M., Cordelli, M., Cox, C. A., Cox, D. J., Cremonesi, M., Cruz, D., Cuevas, J., Culbertson, R., d'Ascenzo, N., Datta, M., de Barbaro, P., Demortier, L., Deninno, M., D'Errico, M., Devoto, F., Di Canto, A., Di Ruzza, B., Dittmann, J. R., Donati, S., D'Onofrio, M., Dorigo, M., Driutti, A., Ebina, K., Edgar, R., Elagin, A., Erbacher, R., Errede, S., Esham, B., Farrington, S., Ramos, J. P. Fernández, Field, R., Flanagan, G., Forrest, R., Franklin, M., Freeman, J. C., Frisch, H., Funakoshi, Y., Galloni, C., Garfinkel, A. F., Garosi, P., Gerberich, H., Gerchtein, E., Giagu, S., Giakoumopoulou, V., Gibson, K., Ginsburg, C. M., Giokaris, N., Giromini, P., Glagolev, V., Glenzinski, D., Gold, M., Goldin, D., Golossanov, A., Gomez, G., Gomez-Ceballos, G., Goncharov, M., López, O. González, Gorelov, I., Goshaw, A. T., Goulianos, K., Gramellini, E., Grosso-Pilcher, C., da Costa, J. Guimaraes, Hahn, S. R., Han, J. Y., Happacher, F., Hara, K., Hare, M., Harr, R. F., Harrington-Taber, T., Hatakeyama, K., Hays, C., Heinrich, J., Herndon, M., Hocker, A., Hong, Z., Hopkins, W., Hou, S., Hughes, R. E., Husemann, U., Hussein, M., Huston, J., Introzzi, G., Iori, M., Isgrò, A., Ivanov, A., James, E., Jang, D., Jayatilaka, B., Jeon, E. J., Jindariani, S., Jones, M., Joo, K. K., Jun, S. Y., Junk, T. R., Kambeitz, M., Kamon, T., Karchin, P. E., Kasmi, A., Kato, Y., Ketchum, W., Keung, J., Kilminster, B., Kim, D. H., Kim, H. S., Kim, J. E., Kim, M. J., Kim, S. H., Kim, S. B., Kim, Y. J., Kim, Y. K., Kimura, N., Kirby, M., Kondo, K., Kong, D. J., Konigsberg, J., Kotwal, A. V., Kreps, M., Kroll, J., Kruse, M., Kuhr, T., Kurata, M., Laasanen, A. T., Lammel, S., Lancaster, M., Lannon, K., Latino, G., Lee, H. S., Lee, J. S., Leo, S., Leone, S., Lewis, J. D., Limosani, A., Lipeles, E., Lister, A., Liu, Q., Liu, T., Lockwitz, S., Loginov, A., Lucchesi, D., Lucà, A., Lueck, J., Lujan, P., Lukens, P., Lungu, G., Lys, J., Lysak, R., Madrak, R., Maestro, P., Malik, S., Manca, G., Manousakis-Katsikakis, A., Marchese, L., Margaroli, F., Marino, P., Matera, K., Mattson, M. E., Mazzacane, A., Mazzanti, P., McNulty, R., Mehta, A., Mehtala, P., Mesropian, C., Miao, T., Michielin, E., Mietlicki, D., Mitra, A., Miyake, H., Moed, S., Moggi, N., Moon, C. S., Moore, R., Morello, M. J., Mukherjee, A., Muller, Th., Murat, P., Mussini, M., Nachtman, J., Nagai, Y., Naganoma, J., Nakano, I., Napier, A., Nett, J., Nigmanov, T., Nodulman, L., Noh, S. Y., Norniella, O., Oakes, L., Oh, S. H., Oh, Y. D., Okusawa, T., Orava, R., Ortolan, L., Pagliarone, C., Palencia, E., Palni, P., Papadimitriou, V., Parker, W., Pauletta, G., Paulini, M., Paus, C., Phillips, T. J., Piacentino, G., Pianori, E., Pilot, J., Pitts, K., Plager, C., Pondrom, L., Poprocki, S., Potamianos, K., Pranko, A., Prokoshin, F., Ptohos, F., Punzi, G., Fernández, I. Redondo, Renton, P., Rescigno, M., Rimondi, F., Ristori, L., Robson, A., Rodriguez, T., Rolli, S., Ronzani, M., Roser, R., Rosner, J. L., Ruffini, F., Ruiz, A., Russ, J., Rusu, V., Sakumoto, W. K., Sakurai, Y., Santi, L., Sato, K., Saveliev, V., Savoy-Navarro, A., Schlabach, P., Schmidt, E. E., Schwarz, T., Scodellaro, L., Scuri, F., Seidel, S., Seiya, Y., Semenov, A., Sforza, F., Shalhout, S. Z., Shears, T., Shepard, P. F., Shimojima, M., Shochet, M., Shreyber-Tecker, I., Simonenko, A., Sliwa, K., Smith, J. R., Snider, F. D., Song, H., Sorin, V., Denis, R. St., Stancari, M., Stentz, D., Strologas, J., Sudo, Y., Sukhanov, A., Suslov, I., Takemasa, K., Takeuchi, Y., Tang, J., Tecchio, M., Teng, P. K., Thom, J., Thomson, E., Thukral, V., Toback, D., Tokar, S., Tollefson, K., Tomura, T., Tonelli, D., Torre, S., Torretta, D., Totaro, P., Trovato, M., Ukegawa, F., Uozumi, S., Vecchio, V., Velev, G., Vellidis, C., Vernieri, C., Vidal, M., Vilar, R., Vizán, J., Vogel, M., Volpi, G., Vázquez, F., Wagner, P., Wallny, R., Wang, S. M., Waters, D., Wester III, W. C., Whiteson, D., Wicklund, A. B., Wilbur, S., Williams, H. H., Wilson, J. S., Wilson, P., Winer, B. L., Wittich, P., Wolbers, S., Wolfmeister, H., Wright, T., Wu, X., Wu, Z., Yamamoto, K., Yamato, D., Yang, T., Yang, U. K., Yang, Y. C., Yao, W. -M., Yeh, G. P., Yi, K., Yoh, J., Yorita, K., Yoshida, T., Yu, G. B., Yu, I., Zanetti, A. M., Zeng, Y., Zhou, C., and Zucchelli, S.

Subjects
High Energy Physics - Experiment
Abstract

At the Fermilab Tevatron proton-antiproton ($p\bar{p}$) collider, high-mass electron-neutrino ($e\nu$) pairs are produced predominantly in the process $p \bar{p} \rightarrow W(\rightarrow e\nu) + X$. The asymmetry of the electron and positron yield as a function of their pseudorapidity constrain the slope of the ratio of the $u$- to $d$-quark parton distributions versus the fraction of the proton momentum carried by the quarks. This paper reports on the measurement of the electron-charge asymmetry using the full data set recorded by the Collider Detector at Fermilab in 2001--2011 and corresponding to 9.1~fb$^{-1}$ of integrated luminosity. The measurement significantly improves the precision of the Tevatron constraints on the parton-distribution functions of the proton. Numerical tables of the measurement are provided., Comment: 27 pages, 25 figures. To be published in PRD

Published
2021

15. Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems

Author

Apicella, Andrea, Giugliano, Salvatore, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine Learning (ML) non-interpretable models after the training. Recently, it is becoming more and more evident that new directions to create better explanations should take into account what a good explanation is to a human user. This paper suggests taking advantage of developing an XAI framework that allows producing multiple explanations for the response of image a classification system in terms of potentially different middle-level input features. To this end, we propose an XAI framework able to construct explanations in terms of input features extracted by auto-encoders. We start from the hypothesis that some autoencoders, relying on standard data representation approaches, could extract more salient and understandable input properties, which we call here \textit{Middle-Level input Features} (MLFs), for a user with respect to raw low-level features. Furthermore, extracting different types of MLFs through different type of autoencoders, different types of explanations for the same ML system behaviour can be returned. We experimentally tested our method on two different image datasets and using three different types of MLFs. The results are encouraging. Although our novel approach was tested in the context of image classification, it can potentially be used on other data types to the extent that auto-encoders to extract humanly understandable representations can be applied., Comment: published on Knowledge-Based Systems

Published
2021
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16. Adaptive Filters in Graph Convolutional Neural Networks

Author

Apicella, Andrea, Isgrò, Francesco, Pollastro, Andrea, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in exploring the possibilities in performing convolution on graphs using an extension of the GNN architecture, generally referred to as Graph Convolutional Neural Networks (ConvGNN). Convolution on graphs has been achieved mainly in two forms: spectral and spatial convolutions. Due to the higher flexibility in exploring and exploiting the graph structure of data, there is recently an increasing interest in investigating the possibilities that the spatial approach can offer. The idea of finding a way to adapt the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. This paper presents a novel method to adapt the behaviour of a ConvGNN to the input proposing a method to perform spatial convolution on graphs using input-specific filters, which are dynamically generated from nodes feature vectors. The experimental assessment confirms the capabilities of the proposed approach, which achieves satisfying results using a low number of filters., Comment: This paper has been published in its final version on \textit{Pattern Recognition} journal with DOI https://doi.org/10.1016/j.patcog.2023.109867 in Open Access mode. Please consider it as final and peer-reviewed version

Published
2021
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18. Dynamic Local Filters in Graph Convolutional Neural Networks

Author

Apicella, Andrea, Isgrò, Francesco, Pollastro, Andrea, Prevete, Roberto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2023
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20. Comparing clinical trial population representativeness to real-world users of 17 biologics approved for immune-mediated inflammatory diseases: An external validity analysis of 66,639 biologic users from the Italian VALORE project

Author

Ingrasciotta, Ylenia, Spini, Andrea, L'Abbate, Luca, Fiore, Elena Sofia, Carollo, Massimo, Ientile, Valentina, Isgrò, Valentina, Cavazzana, Anna, Biasi, Valeria, Rossi, Paola, Ejlli, Lucian, Belleudi, Valeria, Poggi, Francesca, Sapigni, Ester, Puccini, Aurora, Ancona, Domenica, Stella, Paolo, Pollina Addario, Sebastiano, Allotta, Alessandra, Leoni, Olivia, Zanforlini, Martina, Tuccori, Marco, Gini, Rosa, and Trifirò, Gianluca

Published
2024
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22. EUSO-SPB1 mission and science

Author

Abdellaoui, G., Abe, S., Adams, J.H., Jr., Allard, D., Alonso, G., Anchordoqui, L., Anzalone, A., Arnone, E., Asano, K., Attallah, R., Attoui, H., Pernas, M. Ave, Bachmann, R., Bacholle, S., Bagheri, M., Bakiri, M., Baláz, J., Barghini, D., Bartocci, S., Battisti, M., Bayer, J., Beldjilali, B., Belenguer, T., Belkhalfa, N., Bellotti, R., Belov, A.A., Benmessai, K., Bertaina, M., Bertone, P.F., Biermann, P.L., Bisconti, F., Blaksley, C., Blanc, N., Blin-Bondil, S., Bobik, P., Bogomilov, M., Bolmgren, K., Bozzo, E., Briz, S., Bruno, A., Caballero, K.S., Cafagna, F., Cambié, G., Campana, D., Capdevielle, J.N., Capel, F., Caramete, A., Caramete, L., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Catalano, O., Cellino, A., Černý, K., Chikawa, M., Chiritoi, G., Christl, M.J., Colalillo, R., Conti, L., Cotto, G., Crawford, H.J., Cremonini, R., Creusot, A., Cummings, A., de Castro Gónzalez, A., de la Taille, C., del Peral, L., Desiato, J., Damian, A. Diaz, Diesing, R., Dinaucourt, P., Djakonow, A., Djemil, T., Ebersoldt, A., Ebisuzaki, T., Eser, J., Fenu, F., Fernández-González, S., Ferrarese, S., Filippatos, G., Finch, W., Fornaro, C., Fouka, M., Franceschi, A., Franchini, S., Fuglesang, C., Fujii, T., Fukushima, M., Galeotti, P., García-Ortega, E., Gardiol, D., Garipov, G.K., Gascón, E., Gazda, E., Genci, J., Golzio, A., Gorodetzky, P., Gregg, R., Green, A., Guarino, F., Guépin, C., Guzmán, A., Hachisu, Y., Haungs, A., Heigbes, T., Carretero, J. Hernández, Hulett, L., Ikeda, D., Inoue, N., Inoue, S., Isgrò, F., Itow, Y., Jammer, T., Jeong, S., Jochum, J., Joven, E., Judd, E.G., Jung, A., Kajino, F., Kajino, T., Kalli, S., Kaneko, I., Kasztelan, M., Katahira, K., Kawai, K., Kawasaki, Y., Kedadra, A., Khales, H., Khrenov, B.A., Kim, Jeong-Sook, Kim, Soon-Wook, Kleifges, M., Klimov, P.A., Kreykenbohm, I., Krizmanic, J.F., Królik, K., Kungel, V., Kurihara, Y., Kusenko, A., Kuznetsov, E., Lahmar, H., Lakhdari, F., Licandro, J., Campano, L. López, Martínez, F. López, Mackovjak, S., Mahdi, M., Mandát, D., Manfrin, M., Marcelli, L., Marcos, J.L., Marszał, W., Martín, Y., Martinez, O., Mase, K., Mastafa, M., Matthews, J.N., Mebarki, N., Medina-Tanco, G., Menshikov, A., Merino, A., Mese, M., Meseguer, J., Meyer, S.S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., de los Ríos, J.A. Morales, Nachtman, J.M., Nagataki, S., Naitamor, S., Napolitano, T., Neronov, A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A.V., Onel, Y., Osteria, G., Otte, A.N., Pagliaro, A., Painter, W., Panasyuk, M.I., Panico, B., Parizot, E., Park, I.H., Pastircak, B., Paul, T., Pech, M., Pérez-Grande, I., Perfetto, F., Peter, T., Picozza, P., Pindado, S., Piotrowski, L.W., Piraino, S., Plebaniak, Z., Pollini, A., Popescu, E.M., Prevete, R., Prévôt, G., Prieto, H., Przybylak, M., Puehlhofer, G., Putis, M., Reardon, P., Reno, M.H., Reyes, M., Ricci, M., Frías, M.D. Rodríguez, Matamala, O.F. Romero, Ronga, F., Sabau, M.D., Saccá, G., Sagawa, H., Sahnoune, Z., Saito, A., Sakaki, N., Salazar, H., Sánchez, J.L., Balanzar, J.C. Sanchez, Santangelo, A., Sanz-Andrés, A., Saprykin, O.A., Sarazin, F., Sato, M., Scagliola, A., Schanz, T., Schieler, H., Schovánek, P., Scotti, V., Serra, M., Sharakin, S.A., Shimizu, H.M., Shinozaki, K., Soriano, J.F., Sotgiu, A., Stan, I., Strharský, I., Sugiyama, N., Supanitsky, D., Suzuki, M., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takeda, M., Takizawa, Y., Talai, M.C., Tameda, Y., Tenzer, C., Thomas, S.B., Tibolla, O., Tkachev, L.G., Tomida, T., Tone, N., Toscano, S., Traïche, M., Tsunesada, Y., Tsuno, K., Turriziani, S., Uchihori, Y., Valdés-Galicia, J.F., Vallania, P., Valore, L., Vankova-Kirilova, G., Venters, T.M., Vigorito, C., Villaseñor, L., Vlcek, B., von Ballmoos, P., Vrabel, M., Wada, S., Watanabe, J., Watts, J., Jr., Muñoz, R. Weigand, Weindl, A., Wiencke, L., Wille, M., Wilms, J., Yamamoto, T., Yang, J., Yano, H., Yashin, I.V., Yonetoku, D., Yoshida, S., Young, R., Zgura, I.S., Zotov, M.Yu., and Marchi, A. Zuccaro

Published
2024
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23. Inclusion of Saccharomyces cerevisiae var. boulardii RC009 and Pediococcus pentosaceus RC007 as a Probiotic Additive in Pigs’ Postweaning Diets and Its Effect on Meat Composition, Carcass Characteristics, and Fatty Acids Profile after Slaughter

Author

J. Parada, A. Magnoli, V. Alonso, L. Diaz Vergara, M. Corti Isgro, J. J. T. Posse, M. A. Montenegro, and L. Cavaglieri

Subjects
Veterinary medicine, SF600-1100
Abstract

The postweaning is recognized as one of the most challenging stages of pig growth that can affect their lifetime productivity. The aim of the study was to evaluate the inclusion of Saccharomyces cerevisiae var. boulardii RC009 and Pediococcus pentosaceus RC007 as a probiotic additive in pigs’ postweaning diets and its effect on meat composition, carcass characteristics, and fatty acids profile after slaughter. The following three treatments (550 animals each) were included: T1: control balanced diet (CD), T2: CD with S. boulardii RC009 (1 × 109 CFU/kg feed), and T3: CD with P. pentosaceus RC007 (1 × 109 CFU/kg feed). The additive was administered throughout the postweaning phase (49 d), and then the pigs were moved to a fattening house where they no longer received probiotics in the feed. At 115 ± 5 kg, the animals were transferred to the slaughterhouse. Analyses of carcass composition, muscle depth, protein content, total fat and ash, drip and cooking water loss, and fatty acids were performed. Pigs consuming the probiotic additives had improvements in some of the production parameters. According to our results, we could observe that some modifications in meat composition after slaughter were observed in the group of pigs that consumed the probiotic additives during the postweaning, which could be considered as an improvement in meat quality. Pigs consuming probiotics had higher percentages of essential omega-3 fatty acids such as linolenic acid, and pigs consuming S. cerevisiae var. boulardii RC009 increased lean percentage and reduced the eicosanoic contents in meat. Also, an improvement in water retention capacity was observed in both groups treated with probiotics. While these results are promising, further studies are needed to evaluate the possible effect of these additives closer to slaughter, as well as their combined use.

Published
2024
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24. A general approach to compute the relevance of middle-level input features

Author

Apicella, Andrea, Giugliano, Salvatore, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features. One can isolate two different ways to provide explanations in the context of XAI: low and middle-level explanations. Middle-level explanations have been introduced for alleviating some deficiencies of low-level explanations such as, in the context of image classification, the fact that human users are left with a significant interpretive burden: starting from low-level explanations, one has to identify properties of the overall input that are perceptually salient for the human visual system. However, a general approach to correctly evaluate the elements of middle-level explanations with respect ML model responses has never been proposed in the literature., Comment: Presented on the Explainable Deep Learning/AI (EDL/AI) Workshop during the 25th International Conference on Pattern Recognition (ICPR2020)

Published
2020
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25. A survey on modern trainable activation functions

Author

Apicella, Andrea, Donnarumma, Francesco, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers., Comment: Published in "Neural Networks" journal (Elsevier)

Published
2020
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26. In-feed nutritional additive probiotic Saccharomyces boulardii RC009 can substitute for prophylactic antibiotics and improve the production and health of weaning pigs

Author

Julián Parada, Alejandra Magnoli, Maite Corti Isgro, Valeria Poloni, Analía Fochesato, María Pía Martínez, Alicia Carranza, and Lilia Cavaglieri

Subjects
nutritional feed additive, post-weaning pig, prophylactic antibiotics, saccharomyces cerevisiae var. boulardii rc009, Animal culture, SF1-1100, Veterinary medicine, SF600-1100
Abstract

Background and Aim: Non-therapeutic antibiotic use is associated with the current decrease in antibiotic therapeutic efficiency and the emergence of a wide range of resistant strains, which constitutes a public health risk. This study aimed to evaluate the use of Saccharomyces cerevisiae var. boulardii RC009 as a nutritional feed additive to substitute the prophylactic use of antibiotics and improve the productive performance and health of post-weaning piglets. Materials and Methods: Four regular nutritional phases were prepared. Post-weaning pigs (21-70 days old) received one of two dietary treatments: T1-basal diet (BD-control group) with in-feed antibiotics as a prophylactic medication (one pulse of Tiamulin in P3 and one pulse of Amoxicillin in P4); and T2-BD without in-feed antibiotics but with Saccharomyces boulardii RC009 (1 × 1012 colony forming unit/T feed). The feed conversion ratio (FCR), total weight gain (TWG-kg), and daily weight gain (DWG-kg) were determined. A post-weaning growth index (GI) was calculated and animals (160 days old) from each treatment were analyzed at the abattoir after sacrifice for carcass weight and respiratory tract lesions. Results: Pigs consuming probiotics had higher TWG and DWG than the control group. The group of animals with low body weight obtained the same results. Saccharomyces boulardii administration decreased diarrhea, and FCR reduction was related to a GI improvement. A significant increase in carcass weight and muscle thickness reduction was observed in animals received the probiotic post-weaning. Conclusion: Saccharomyces boulardii RC009, a probiotic additive, was found to improve the production parameters of pigs post-weaning and enhance their health status, indicating that it may be a promising alternative to prophylactic antibiotics.

Published
2023
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28. Thombosis, major bleeding, and survival in COVID-19 supported by veno-venous extracorporeal membrane oxygenation in the first vs second wave: a multicenter observational study in the United Kingdom

Author

Arachchillage, Deepa J., Weatherill, Anna, Rajakaruna, Indika, Gaspar, Mihaela, Odho, Zain, Isgro, Graziella, Cagova, Lenka, Fleming, Lucy, Ledot, Stephane, Laffan, Mike, Szydlo, Richard, Jooste, Rachel, Scott, Ian, Vuylsteke, Alain, and Yusuff, Hakeem

Published
2023
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29. Contributions to the 36th International Cosmic Ray Conference (ICRC 2019) of the JEM-EUSO Collaboration

Author

Abdellaoui, G., Abe, S., Adams Jr., J. H., Ahriche, A., Allard, D., Allen, L., Alonso, G., Anchordoqui, L., Anzalone, A., Arai, Y., Asano, K., Attallah, R., Attoui, H., Pernas, M. Ave, Bacholle, S., Bakiri, M., Baragatti, P., Barrillon, P., Bartocci, S., Bayer, J., Beldjilali, B., Belenguer, T., Belkhalfa, N., Bellotti, R., Belov, A., Belov, K., Benmessai, K., Bertaina, M., Biermann, P. L., Biktemerova, S., Bisconti, F., Blaksley, C., Blanc, N., Błęcki, J., Blin-Bondil, S., Bobik, P., Bogomilov, M., Bozzo, E., Pacheco, S. Briz, Bruno, A., Caballero, K. S., Cafagna, F., Campana, D., Capdevielle, J-N., Capel, F., Caramete, A., Caramete, L., Carlson, P., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Catalano, O., Cellino, A., Chikawa, M., Chiritoi, G., Christl, M. J., Connaughton, V., Conti, L., Cotto, G., Crawford, H. J., Cremonini, R., Creusot, A., Csorna, S., Cummings, A., Dagoret-Campagne, S., Gónzalez, A. de Castro, De Donato, C., de la Taille, C., De Santis, C., del Peral, L., Di Martino, M., Damian, A. Diaz, Djemil, T., Dutan, I., Ebersoldt, A., Ebisuzaki, T., Engel, R., Eser, J., Fenu, F., Fernández-González, S., Ferrarese, S., Flamini, M., Fornaro, C., Fouka, M., Franceschi, A., Franchini, S., Fuglesang, C., Fujii, T., Fujimoto, J., Fukushima, M., Galeotti, P., García-Ortega, E., Garipov, G., Gascón, E., Genci, J., Giraudo, G., Alvarado, C. González, Gorodetzky, P., Greg, R., Guarino, F., Guzmán, A., Hachisu, Y., Haiduc, M., Harlov, B., Haungs, A., Carretero, J. Hernández, Cruz, W. Hidber, Ikeda, D., Inoue, N., Inoue, S., Isgrò, F., Itow, Y., Jammer, T., Jeong, S., Joven, E., Judd, E. G., Jung, A., Jochum, J., Kajino, F., Kajino, T., Kalli, S., Kaneko, I., Karadzhov, Y., Karczmarczyk, J., Katahira, K., Kawai, K., Kawasaki, Y., Kedadra, A., Khales, H., Khrenov, B. A., Kim, Jeong-Sook, Kim, Soon-Wook, Kleifges, M., Klimov, P. A., Kolev, D., Krantz, H., Kreykenbohm, I., Krizmanic, J. F., Kudela, K., Kurihara, Y., Kusenko, A., Kuznetsov, E., La Barbera, A., Lahmar, H., Lakhdari, F., Larsson, O., Lee, J., Licandro, J., Campano, L. López, Martínez, F. López, Maccarone, M. C., Mackovjak, S., Mahdi, M., Maravilla, D., Marcelli, L., Marcos, J. L., Marini, A., Marszał, W., Martens, K., Martín, Y., Martinez, O., Martucci, M., Masciantonio, G., Mase, K., Matev, R., Matthews, J. N., Mebarki, N., Medina-Tanco, G., Menshikov, A., Merino, A., Meseguer, J., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Ríos, J. A. Morales de los, Mustafa, M., Nagataki, S., Naitamor, S., Napolitano, T., Nava, R., Neronov, A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Orleaánski, P., Osteria, G., Pagliaro, A., Painter, W., Panasyuk, M. I., Panico, B., Parizot, E., Park, I. H., Pastircak, B., Patzak, T., Paul, T., Pérez-Grande, I., Perfetto, F., Peter, T., Picozza, P., Pindado, S., Piotrowski, L. W., Piraino, S., Placidi, L., Plebaniak, Z., Pliego, S., Pollini, A., Polonski, Z., Popescu, E. M., Prat, P., Prévôt, G., Prieto, H., Puehlhofer, G., Putis, M., Rabanal, J., Radu, A. A., Reyes, M., Rezazadeh, M., Ricci, M., Frías, M. D. Rodríguez, Ronga, F., Roudil, G., Rusinov, I., Rybczyński, M., Sabau, M. D., Cano, G. Sáaez, Sagawa, H., Sahnoune, Z., Saito, A., Sakaki, N., Salazar, H., Balanzar, J. C. Sanchez, Sánchez, J. L., Santangelo, A., Sanz-Andrés, A., Palomino, M. Sanz, Saprykin, O., Sarazin, F., Sato, M., Schanz, T., Schieler, H., Scotti, V., Selmane, S., Semikoz, D., Serra, M., Sharakin, S., Shimizu, H. M., Shinozaki, K., Shirahama, T., Spataro, B., Stan, I., Sugiyama, T., Supanitsky, D., Suzuki, M., Szabelska, B., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takami, H., Takeda, M., Takizawa, Y., Talai, M. C., Tenzer, C., Thomas, S. B., Tibolla, O., Tkachev, L., Tokuno, H., Tomida, T., Tone, N., Toscano, S., Traïche, M., Tsenov, R., Tsunesada, Y., Tsuno, K., Tubbs, J., Turriziani, S., Uchihori, Y., Vaduvescu, O., Valdés-Galicia, J. F., Vallania, P., Vankova, G., Vigorito, C., Villaseñor, L., Vlcek, B., von Ballmoos, P., Vrabel, M., Wada, S., Watanabe, J., Watts Jr., J., Weber, M., Muñoz, R. Weigand, Weindl, A., Wiencke, L., Wille, M., Wilms, J., Włodarczyk, Z., Yamamoto, T., Yang, J., Yano, H., Yashin, I. V., Yonetoku, D., Yoshida, S., Young, R., Zgura, I. S, Zotov, M. Yu., and Marchi, A. Zuccaro

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Compilation of papers presented by the JEM-EUSO Collaboration at the 36th International Cosmic Ray Conference (ICRC), held July 24 through August 1, 2019 in Madison, Wisconsin., Comment: links to the 24 papers published in arXiv

Published
2019

31. Next-to-leading power corrections to $V+1$ jet production in $N$-jettiness subtraction

Author

Boughezal, Radja, Isgrò, Andrea, and Petriello, Frank

Subjects
High Energy Physics - Phenomenology
Abstract

We discuss the subleading power corrections to one-jet production processes in $N$-jettiness subtraction using vector-boson plus jet production as an example. We analytically derive the next-to-leading power leading logarithmic corrections (NLP-LL) through ${\cal O}(\alpha_S)$ in perturbative QCD, and outline the calculation of the next-to-leading logarithmic corrections (NLP-NLL). Our result is differential in the jet transverse momentum and rapidity, and in the vector boson momentum squared and rapidity. We present simple formulae that separate the NLP corrections into universal factors valid for any one-jet cross section and process-dependent matrix-element corrections. We discuss in detail features of the NLP corrections such as the process independence of the leading-logarithmic result that occurs due to the factorization of matrix elements in the subleading soft limit, the occurrence of poles in the non-hemisphere soft function at NLP and the cancellation of potential $\sqrt{\mathcal{T}_1/Q}$ corrections to the $N$-jettiness factorization theorem. We validate our analytic result by comparing them to numerically-fitted coefficients, finding good agreement for both the inclusive and the differential cross sections., Comment: 43 pages, 12 figures, added references. Matches the published version Phys. Rev. D 101, 016005 (2020)

Published
2019
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32. A simple and efficient architecture for trainable activation functions

Author

Apicella, Andrea, Isgrò, Francesco, and Prevete, Roberto

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to determine a method for learning an activation function that is at the same time theoretically simple and easy to implement. Moreover, most of the methods proposed so far introduce new parameters or adopt different learning techniques. In this work we propose a simple method to obtain trained activation function which adds to the neural network local subnetworks with a small amount of neurons. Experiments show that this approach could lead to better result with respect to using a pre-defined activation function, without introducing a large amount of extra parameters that need to be learned.

Published
2019
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33. Busulfan–fludarabine- or treosulfan–fludarabine-based conditioning before allogeneic HSCT from matched sibling donors in paediatric patients with sickle cell disease: A study on behalf of the EBMT Paediatric Diseases and Inborn Errors Working Parties

Author

Cseh, A, Galimard, J, de la Fuente, J, Isgro, A, Zecca, M, Garwer, B, Biffi, A, Aljurf, M, Sundin, M, Belendez, C, Locatelli, F, Balduzzi, A, Lawson, S, Sengeloev, H, Ifversen, M, Saccardi, R, Wynn, R, Lankester, A, Corbacioglu, S, Peters, C, Cseh A., Galimard J. -E., de la Fuente J., Isgro A., Zecca M., Garwer B., Biffi A., Aljurf M., Sundin M., Belendez C., Locatelli F., Balduzzi A., Lawson S., Sengeloev H., Ifversen M., Saccardi R., Wynn R., Lankester A. C., Corbacioglu S., Peters C., Cseh, A, Galimard, J, de la Fuente, J, Isgro, A, Zecca, M, Garwer, B, Biffi, A, Aljurf, M, Sundin, M, Belendez, C, Locatelli, F, Balduzzi, A, Lawson, S, Sengeloev, H, Ifversen, M, Saccardi, R, Wynn, R, Lankester, A, Corbacioglu, S, Peters, C, Cseh A., Galimard J. -E., de la Fuente J., Isgro A., Zecca M., Garwer B., Biffi A., Aljurf M., Sundin M., Belendez C., Locatelli F., Balduzzi A., Lawson S., Sengeloev H., Ifversen M., Saccardi R., Wynn R., Lankester A. C., Corbacioglu S., and Peters C.

Published
2024

34. Outcomes of allogeneic haematopoietic cell transplantation for myelofibrosis in children and adolescents: the retrospective study of the EBMT Paediatric Diseases WP

Author

Wachowiak, J, Galimard, J, Dalissier, A, Rihani, R, Alsaedi, H, Wynn, R, Dalle, J, Peffault de Latour, R, Sedlacek, P, Balduzzi, A, Schroeder, T, Bodova, I, Gonzalez Vicent, M, Gruhn, B, Hamladji, R, Krivan, G, Patrick, K, Sobkowiak-Sobierajska, A, Stepensky, P, Unal, A, Amrolia, P, Perez Martinez, A, Rialland, F, Aljurf, M, Isgro, A, Toren, A, Bierings, M, Corbacioglu, S, Kalwak, K, Wachowiak J., Galimard J. -E., Dalissier A., Rihani R., AlSaedi H., Wynn R. F., Dalle J. -H., Peffault de Latour R., Sedlacek P., Balduzzi A., Schroeder T., Bodova I., Gonzalez Vicent M., Gruhn B., Hamladji R. -M., Krivan G., Patrick K., Sobkowiak-Sobierajska A., Stepensky P., Unal A., Amrolia P., Perez Martinez A., Rialland F., Aljurf M., Isgro A., Toren A., Bierings M., Corbacioglu S., Kalwak K., Wachowiak, J, Galimard, J, Dalissier, A, Rihani, R, Alsaedi, H, Wynn, R, Dalle, J, Peffault de Latour, R, Sedlacek, P, Balduzzi, A, Schroeder, T, Bodova, I, Gonzalez Vicent, M, Gruhn, B, Hamladji, R, Krivan, G, Patrick, K, Sobkowiak-Sobierajska, A, Stepensky, P, Unal, A, Amrolia, P, Perez Martinez, A, Rialland, F, Aljurf, M, Isgro, A, Toren, A, Bierings, M, Corbacioglu, S, Kalwak, K, Wachowiak J., Galimard J. -E., Dalissier A., Rihani R., AlSaedi H., Wynn R. F., Dalle J. -H., Peffault de Latour R., Sedlacek P., Balduzzi A., Schroeder T., Bodova I., Gonzalez Vicent M., Gruhn B., Hamladji R. -M., Krivan G., Patrick K., Sobkowiak-Sobierajska A., Stepensky P., Unal A., Amrolia P., Perez Martinez A., Rialland F., Aljurf M., Isgro A., Toren A., Bierings M., Corbacioglu S., and Kalwak K.

Abstract

This retrospective study evaluated 35 children (median age 5.2 years; range 0.4–18) with myelofibrosis (MF), including 33 with primary myelofibrosis and 2 with secondary myelofibrosis transplanted from matched sibling donor (MSD) (n = 17) or non-MSD (n = 18) between 2000 and 2022. Conditioning was usually chemotherapy-based (n = 33) and myeloablative (n = 32). Fifteen patients received bone marrow (BM), 14 haematopoietic cells (HC) from peripheral blood (PB), and 6 from cord blood (CB). Day +100 acute GvHD II–IV incidence was significantly lower after MSD-haematopoietic cell transplantation (MSD-HCT) than after non-MSD-HCT [18.8% (4.3–41.1) vs 58.8% (31–78.6); p = 0.01]. Six-year non-relapse mortality (NRM) was 18% (7.1–32.8), relapse incidence was 15.9% (5.6–30.9), progression-free survival (PFS) was 66.1% (47–79.7), GvHD-free relapse-free survival was 50% (30.6–66.7), and overall survival (OS) was 71.1% (51.4–84). Six-year PFS and OS were significantly higher after BM transplantation compared to HCT from other sources [85.1% (52.3–96.1) vs 50.8% (26.3–71), p = 0.03, and 90.9% (50.8–98.7) vs 54% (28.1–74.2), p = 0.01, respectively], whereas NRM was significantly lower [0% vs 32% (12.3–53.9); p = 0.02]. This first multicentre study on outcomes of allogeneic HCT in children with myelofibrosis proves feasibility and curative effect of transplantation in these children, suggests that bone marrow transplantation is associated with better outcomes, and indicates the need for further studies.

Published
2024

38. First observations of speed of light tracks by a fluorescence detector looking down on the atmosphere

Author

Abdellaoui, G., Abe, S., Adams Jr., J. H., Ahriche, A., Allard, D., Allen, L., Alonso, G., Anchordoqui, L., Anzalone, A., Arai, Y., Asano, K., Attallah, R., Attoui, H., Pernas, M. Ave, Bacholle, S., Bakiri, M., Baragatti, P., Barrillon, P., Bartocci, S., Bayer, J., Beldjilali, B., Belenguer, T., Belkhalfa, N., Bellotti, R., Belov, A., Belov, K., Benmessai, K., Bertaina, M., Biermann, P. L., Biktemerova, S., Bisconti, F., Blanc, N., Błȩcki, J., Blin-Bondil, S., Bobik, P., Bogomilov, M., Bozzo, E., Bruno, A., Caballero, K. S., Cafagna, F., Campana, D., Capdevielle, J-N., Capel, F., Caramete, A., Caramete, L., Carlson, P., Caruso, R., Casolino, M., Cassardo, C., Castellina, A., Catalano, C. Catalano O., Cellino, A., Chikawa, M., Chiritoi, G., Christl, M. J., Connaughton, V., Conti, L., Cordero, G., Cotto, G., Crawford, H. J., Cremonini, R., Csorna, S., Cummings, A., Dagoret-Campagne, S., De Donato, C., de la Taille, C., De Santis, C., del Peral, L., Di Martino, M., Damian, A. Diaz, Djemil, T., Dutan, I., Ebersoldt, A., Ebisuzaki, T., Engel, R., Eser, J., Fenu, F., Fernández-González, S., Fernández-Soriano, J., Ferrarese, S., Flamini, M., Fornaro, C., Fouka, M., Franceschi, A., Franchini, S., Fuglesang, C., Fujii, T., Fujimoto, J., Fukushima, M., Galeotti, P., García-Ortega, E., Garipov, G., Gascón, E., Genci, J., Giraudo, G., Alvarado, C. González, Gorodetzky, P., Greg, R., Guarino, F., Guzmán, A., Hachisu, Y., Haiduc, M., Harlov, B., Haungs, A., Carretero, J. Hernández, Cruz, W. Hidber, Ikeda, D., Inoue, N., Inoue, S., Isgrò, F., Itow, Y., Jammer, T., Jeong, S., Joven, E., Judd, E. G., Jung, A., Jochum, J., Kajino, F., Kajino, T., Kalli, S., Kaneko, I., Karadzhov, Y., Karczmarczyk, J., Katahira, K., Kawai, K., Kawasaki, Y., Kedadra, A., Khales, H., Khrenov, B. A., Kim, Jeong-Sook, Kim, Soon-Wook, Kleifges, M., Klimov, P. A., Kolev, D., Krantz, H., Kreykenbohm, I., Kudela, K., Kurihara, Y., Kusenko, A., Kuznetsov, E., La Barbera, A., Lachaud, C., Lahmar, H., Lakhdari, F., Larson, R., Larsson, O., Lee, J., Licandro, J., Campano, L. López, Maccarone, M. C., Mackovjak, S., Mahdi, M., Maravilla, D., Marcelli, L., Marcos, J. L., Marini, A., Marszał, W., Martens, K., Martín, Y., Martinez, O., Martucci, M., Masciantonio, G., Mase, K., Mastafa, M., Matev, R., Matthews, J. N., Mebarki, N., Medina-Tanco, G., Mendoza, M. A., Menshikov, A., Merino, A., Meseguer, J., Meyer, S. S., Mimouni, J., Miyamoto, H., Mizumoto, Y., Monaco, A., Ríos, J. A. Morales de los, Nagataki, C. Moretto S., Naitamor, S., Napolitano, T., Naslund, W., Nava, R., Neronov, A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Orleański, P., Osteria, G., Pagliaro, A., Painter, W., Panasyuk, M. I., Panico, B., Pasqualino, G., Parizot, E., Park, I. H., Pastircak, B., Patzak, T., Paul, T., Pérez-Grande, I., Perfetto, F., Peter, T., Picozza, P., Pindado, S., Piotrowski, L. W., Piraino, S., Placidi, L., Plebaniak, Z., Pliego, S., Pollini, A., Polonski, Z., Popescu, E. M., Prat, P., Prévôt, G., Prieto, H., Puehlhofer, G., Putis, M., Rabanal, J., Radu, A. A., Reyes, M., Rezazadeh, M., Ricci, M., Frías, M. D. Rodríguez, Rodencal, M., Ronga, F., Roudil, G., Rusinov, I., Rybczyński, M., Sabau, M. D., Cano, G. Sáez, Sagawa, H., Sahnoune, Z., Saito, A., Sakaki, N., Salazar, H., Balanzar, J. C. Sanchez, Sánchez, J. L., Santangelo, A., Sanz-Andrés, A., Palomino, M. Sanz, Saprykin, O., Sarazin, F., Sato, M., Schanz, T., Schieler, H., Scotti, V., Selmane, S., Semikoz, D., Serra, M., Sharakin, S., Shimizu, H. M., Shinozaki, K., Shirahama, T., Spataro, B., Stan, I., Sugiyama, T., Supanitsky, D., Suzuki, M., Szabelska, B., Szabelski, J., Tajima, N., Tajima, T., Takahashi, Y., Takami, H., Takeda, M., Takizawa, Y., Talai, M. C., Tenzer, C., Thomas, S. B., Tibolla, O., Tkachev, L., Tokuno, H., Tomida, T., Tone, N., Toscano, S., Traïche, M., Tsenov, R., Tsunesada, Y., Tsuno, K., Tubbs, J., Turriziani, S., Uchihori, Y., Vaduvescu, O., Valdés-Galicia, J. F., Vallania, P., Vankova, G., Vigorito, C., Villaseñor, L., Vlcek, B., von Ballmoos, P., Vrabel, M., Wada, S., Watanabe, J., Watts Jr., J., Weber, M., Muñoz, R. Weigand, Weindl, A., Wiencke, L., Wille, M., Wilms, J., Włodarczyk, Z., Yamamoto, T., Yang, J., Yano, H., Yashin, I. V., Yonetoku, D., Yoshida, S., Young, R., Zgura, I. S, Zotov, M. Yu., and Marchi, A. Zuccaro

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

EUSO-Balloon is a pathfinder mission for the Extreme Universe Space Observatory onboard the Japanese Experiment Module (JEM-EUSO). It was launched on the moonless night of the 25$^{th}$ of August 2014 from Timmins, Canada. The flight ended successfully after maintaining the target altitude of 38 km for five hours. One part of the mission was a 2.5 hour underflight using a helicopter equipped with three UV light sources (LED, xenon flasher and laser) to perform an inflight calibration and examine the detectors capability to measure tracks moving at the speed of light. We describe the helicopter laser system and details of the underflight as well as how the laser tracks were recorded and found in the data. These are the first recorded laser tracks measured from a fluorescence detector looking down on the atmosphere. Finally, we present a first reconstruction of the direction of the laser tracks relative to the detector., Comment: 18 pages, 14 figures

Published
2018
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39. Next-to-leading-logarithmic power corrections for $N$-jettiness subtraction in color-singlet production

Author

Boughezal, Radja, Isgrò, Andrea, and Petriello, Frank

Subjects
High Energy Physics - Phenomenology
Abstract

We present a detailed derivation of the power corrections to the factorization theorem for the 0-jettiness event shape variable $\mathcal{T}$. Our calculation is performed directly in QCD without using the formalism of effective field theory. We analytically calculate the next-to-leading logarithmic power corrections for small $\mathcal{T}$ at next-to-leading order in the strong coupling constant, extending previous computations which obtained only the leading-logarithmic power corrections. We address a discrepancy in the literature between results for the leading-logarithmic power corrections to a particular definition of 0-jettiness. We present a numerical study of the power corrections in the context of their application to the $N$-jettiness subtraction method for higher-order calculations, using gluon-fusion Higgs production as an example. The inclusion of the next-to-leading-logarithmic power corrections further improves the numerical efficiency of the approach beyond the improvement obtained from the leading-logarithmic power corrections., Comment: 30 pages, 12 figures. Improved plots and added clarifications as well as minor corrections

Published
2018
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43. Pediococcus pentosaceus RC007 and Saccharomyces boulardii RC009 as antibiotic alternatives for gut health in post-weaning pigs.

Author

Parada, Julián, Magnoli, Alejandra, Poloni, Valeria, Corti Isgro, Maite, Rosales Cavaglieri, Lorenzo, Luna, María Julieta, Carranza, Alicia, and Cavaglieri, Lilia

Abstract

Aims The aim of the present study was to evaluate a novel probiotic Pediococcus pentosaceus RC007 used alone and convined with Saccharomyces cerevisiae var. boulardii RC009, as in-feed additives to substitute the nontherapeutic use of antibiotics, and evaluate the different structural characteristics of intestinal bacterial populations between groups, correlated with pig production performance. Methods and results The in vivo study was conducted on post-weaning pigs, from 21 to 56 days old. Three dietary treatments were included: T1—basal diet (BD—control group); T2—BD with P. pentosaceus RC007; and T3—BD with a mix of P. pentosaceus RC007 and S. boulardii RC009. The weight gain increase of pigs consuming non-therapeutic antibiotics was similar to those that did not consume antibiotics during the study (P  = 0.0234), but had better health indicators. The use of a probiotic combination increased carcass weight and significantly reduced the lumbar fat thickness. In terms of taxonomic composition, there was a tendency to modify the abundance of Proteobacteria, Cyanobacteria, Enterobacteriaceae, and Lactobacillaceae in pigs that consumed the additives. The genus Butyricicoccus, Collinsella , and Ruminococcus tended to be more abundant in the microbiota of pigs at T3. Conclusions For the first time, the results of the present study indicate that P. pentosaceus RC007 and S. boulardii RC009, a probiotic combination, could be a good substitute for antibiotics in improving pig production performance, while also contributing to a healthier gut microbiota, especially with the reduced abundance of Proteobacteria and Cyanobacteria. [ABSTRACT FROM AUTHOR]

Published
2024
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44. A General Approach to Compute the Relevance of Middle-Level Input Features

Author

Apicella, Andrea, Giugliano, Salvatore, Isgrò, Francesco, Prevete, Roberto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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46. A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

Author

Andrea Apicella, Pasquale Arpaia, Francesco Isgro, Giovanna Mastrati, and Nicola Moccaldi

Subjects
Emotion, EEG, channel selection, machine learning, neurophysiology of emotions, wearable devices, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.

Published
2022
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47. Predictors of reduced smoking quantity among recovering alcohol dependent men in a smoking cessation trial

Author

Worley, Matthew J, Isgro, Melodie, Heffner, Jaimee L, Lee, Soo Yong, Daniel, Belinda E, and Anthenelli, Robert M

Subjects
Biological Psychology, Psychology, Mental Health, Tobacco Smoke and Health, Tobacco, Prevention, Clinical Research, Behavioral and Social Science, Substance Misuse, Alcoholism, Alcohol Use and Health, Brain Disorders, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Stroke, Cancer, Good Health and Well Being, Adult, Adult Survivors of Child Abuse, Alcohol Abstinence, Alcoholism, Cognition, Depression, Humans, Impulsive Behavior, Male, Middle Aged, Motivation, Prognosis, Randomized Controlled Trials as Topic, Smoking, Smoking Cessation, Smoking Reduction, Tobacco Use Disorder, Topiramate, Smoking cessation, Nicotine, Alcohol dependence, Public Health and Health Services, Substance Abuse, Public health, Biological psychology, Clinical and health psychology
Abstract

IntroductionAdults with alcohol dependence (AD) have exceptionally high smoking rates and poor smoking cessation outcomes. Discovery of factors that predict reduced smoking among AD smokers may help improve treatment. This study examined baseline predictors of smoking quantity among AD smokers in a pharmacotherapy trial for smoking cessation.MethodsThe sample includes male, AD smokers (N = 129) with 1-32 months of alcohol abstinence who participated in a 12-week trial of medication (topiramate vs. placebo) and adjunct counseling with 6 months of follow-up. Baseline measures of nicotine dependence, AD severity, psychopathology, motivation to quit smoking, and smoking-related cognitions were used to predict smoking quantity (cigarettes per day) at post-treatment and follow-up.ResultsOverall, the sample had statistically significant reductions in smoking quantity. Greater nicotine dependence (Incidence rate ratios (IRRs) = 0.82-0.90), motivation to quit (IRRs = 0.65-0.85), and intrinsic reasons for quitting (IRRs = 0.96-0.98) predicted fewer cigarettes/day. Conversely, greater lifetime AD severity (IRR = 1.02), depression severity (IRRs = 1.05-1.07), impulsivity (IRRs = 1.01-1.03), weight-control expectancies (IRRs = 1.10-1.15), and childhood sexual abuse (IRRs = 1.03-1.07) predicted more cigarettes/day.ConclusionsSmokers with AD can achieve large reductions in smoking quantity during treatment, and factors that predict smoking outcomes in the general population also predict greater smoking reductions in AD smokers. Treatment providers can use severity of nicotine dependence and AD, motivation to quit, smoking-related cognitions, and severity of depression to guide treatment and improve outcomes among AD smokers.

Published
2018

48. Prognostic Factors and Clinical Outcomes of Surgical Treatment of Major Thoracic Trauma

Author

Sibilia, Maria Chiara, primary, Danuzzo, Federica, additional, Spinelli, Francesca, additional, Cassina, Enrico Mario, additional, Libretti, Lidia, additional, Pirondini, Emanuele, additional, Raveglia, Federico, additional, Tuoro, Antonio, additional, Bertolaccini, Luca, additional, Isgro’, Stefano, additional, Perrone, Stefano, additional, Rizzo, Stefania, additional, and Petrella, Francesco, additional

Published
2024
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49. Virtual Reality System for Ship Handling Simulations: A Case Study on Nautical Personnel Performance, Observed Behaviour, Sense of Presence and Sickness

Author

Bassano, Chiara, Chessa, Manuela, Fengone, Luca, Isgrò, Luca, Solari, Fabio, Spallarossa, Giovanni, Tozzi, Davide, Zini, Aldo, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Cláudio, Ana Paula, editor, Bouatouch, Kadi, editor, Chessa, Manuela, editor, Paljic, Alexis, editor, Kerren, Andreas, editor, Hurter, Christophe, editor, Tremeau, Alain, editor, and Farinella, Giovanni Maria, editor

Published
2020
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