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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. 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

6. 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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7. 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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8. 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

12. 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

13. 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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14. 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

15. Immune profiling of SARS-CoV-2 epitopes in asymptomatic and symptomatic pediatric and adult patients

Author

Tornesello, Anna Lucia, Botti, Chiara, Micillo, Alberto, Labonia, Francesco, Arpino, Sergio, Isgrò, Maria Antonietta, Meola, Serena, Russo, Luigi, Cavalcanti, Ernesta, Sale, Silvia, Nicastro, Carmine, Atripaldi, Luigi, Starita, Noemy, Cerasuolo, Andrea, Reimer, Ulf, Holenya, Pavlo, Buonaguro, Luigi, Buonaguro, Franco M., and Tornesello, Maria Lina

Published
2023
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16. 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

17. 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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18. Developments and results in the context of the JEM-EUSO program obtained with the ESAF simulation and analysis framework

Author

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

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

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 ESAF in 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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20. 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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21. Effects of a Dietary Supplement Composed of Baicalin, Bromelain and Escin for Venous Chronic Insufficiency Treatment: Insights from a Retrospective Observational Study

Author

Selene Francesca Anna Drago, Michelangelo Rottura, Antonino Molonia, Viviana Maria Gianguzzo, Giovanni Pallio, Natasha Irrera, Luana Orlando, Marianna Gigliotti De Fazio, Marilena Isgrò, Natalia Zirilli, Vincenzo Arcoraci, and Egidio Imbalzano

Subjects
chronic venous insufficiency, dietary supplement, baicalin, bromelain, escin, graduated compression stockings, Medicine, Pharmacy and materia medica, RS1-441
Abstract

Chronic venous insufficiency (CVI) represents a risk factor for cardiovascular events. The first-line treatment includes the use of compression stockings and lifestyle changes. Natural products, such as flavonoids, could be used to improve the effects of compression therapy due to their anti-inflammatory and anti-oxidant properties. This study aims to evaluate the effects of a dietary supplement containing baicalin, bromeline and escin in CVI patients. A retrospective cohort study was performed by using the medical records of CVI affected outpatients. Patients treated with the dietary supplement were defined as “users”. A modified Venous Clinical Severity Score (VCSS) was calculated, including pain, inflammation, vessels induration and skin pigmentation. All clinical variables were evaluated at baseline (T0), after 30 (T1) and 90(T2) days in “users” and “non-users”. Out of 62 patients, 30 (48.4%) were “users”. No difference was observed between groups at baseline. A lower VCSS value was recorded in “users” than that observed in “non-users” at T2 (7.0 (4.0–9.0) vs. 9.0 (5.0–10.0); p = 0.025). Vessels’ induration and pain significantly reduced in 53.3% and 43.3% of “users” and in 18.8% and 9.4% of “non-users”. Only “users” (33.3%) showed a reduction of the inflammatory signs as well as a decrease in malleolar circumference, from 29.0 (26.5–30.0) to 27.5 (26.0–28.5) (p < 000.1). A reduction of C-reactive Protein levels was found in “users” compared to “non-users” at T2 (1.0 (0.9–1.2) vs. 1.3 (1.0–1.5); p = 0.006). These findings suggest that implementation of a dietary supplement could improve the clinical outcomes of CVI patients.

Published
2024
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28. 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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30. 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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32. 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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33. 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

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

Subjects
External validity, Immune-mediated inflammatory disease, Pivotal randomized clinical trial, Representativeness, Generalizability, Biological drugs, Therapeutics. Pharmacology, RM1-950
Abstract

To date, no population-based studies have specifically explored the external validity of pivotal randomized clinical trials (RCTs) of biologics simultaneously for a broad spectrum of immuno-mediated inflammatory diseases (IMIDs). The aims of this study were, firstly, to compare the patients’ characteristics and median treatment duration of biologics approved for IMIDs between RCTs’ and real-world setting (RW); secondly, to assess the extent of biologic users treated for IMIDs in the real-world setting that would not have been eligible for inclusion into pivotal RCT for each indication of use. Using the Italian VALORE distributed database (66,639 incident biologic users), adult patients with IMIDs treated with biologics in the Italian real-world setting were substantially older (mean age ± SD: 50 ± 15 years) compared to those enrolled in pivotal RCTs (45 ± 15 years). In the real-world setting, certolizumab pegol was more commonly used by adult women with psoriasis/ankylosing spondylitis (F/M ratio: 1.8–1.9) compared to RCTs (F/M ratio: 0.5–0.6). The median treatment duration (weeks) of incident biologic users in RW was significantly higher than the duration of pivotal RCTs in almost all indications for use and most biologics (4–100 vs. 6–167). Furthermore, almost half (46.4%) of biologic users from RW settings would have been ineligible for inclusion in the respective indication-specific pivotal RCTs. The main reasons were: advanced age, recent history of cancer and presence of other concomitant IMIDs. These findings suggest that post-marketing surveillance of biologics should be prioritized for those patients.

Published
2024
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34. 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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35. 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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36. 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

38. Testing of Coding Algorithms for Inflammatory Bowel Disease Identification, as Indication for Use of Biological Drugs, Using a Claims Database from Southern Italy

Author

Ingrasciotta Y, Isgrò V, Foti SS, Ientile V, Fontana A, L'Abbate L, Benoni R, Fiore ES, Tari M, Alibrandi A, and Trifirò G

Subjects
biological drugs, algorithm, healthcare database, ulcerative colitis, chron’s disease, Infectious and parasitic diseases, RC109-216
Abstract

Ylenia Ingrasciotta,1,2 Valentina Isgrò,1 Saveria Serena Foti,2 Valentina Ientile,3 Andrea Fontana,4 Luca L’Abbate,3 Roberto Benoni,1 Elena Sofia Fiore,1 Michele Tari,5 Angela Alibrandi,6 Gianluca Trifirò1,2 1Department of Diagnostics and Public Health, University of Verona, Verona, Italy; 2Academic Spin-off “INSPIRE – Innovative Solutions for Medical Prediction and Big Data Integration in Real World Setting” – Azienda Ospedaliera Universitaria “G. Martino”, Messina, Italy; 3Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; 4Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni, Rotondo, Italy; 5Caserta-1 Local Health Service, Caserta, Italy; 6Department of Economics, University of Messina, Messina, ItalyCorrespondence: Ylenia Ingrasciotta, Department of Diagnostics and Public Health, University of Verona, P. le L.A. Scuro 10, Verona, 37134, Italy, Tel +39 045 802 7679, Email ylenia.ingrasciotta@univr.itBackground: Inflammatory bowel diseases (IBDs), Crohn’s disease (CD) and ulcerative colitis (UC), are chronic diseases that have been increasingly treated with biological drugs in recent years. Newly developed coding algorithms for IBD identification using claims databases are needed to improve post-marketing surveillance of biological drugs.Objective: To test algorithms to identify CD and UC, as indication for use of biological drugs approved for IBD treatment, using a claims database.Methods: Data were extracted from the Caserta Local Health Unit database between 2015 and 2018. CD/UC diagnoses reported by specialists in electronic therapeutic plans (ETPs) were considered as gold standard. Five algorithms were developed based on ICD-9-CM codes as primary cause of hospital admissions, exemption from healthcare service co-payment codes and drugs dispensing with only indication for CD/UC. The accuracy was assessed by sensitivity (Se), specificity (Sp), positive (PPV) and negative predicted values (NPV) along with computation of the Youden Index and F-score.Results: In the study period, 1205 subjects received at least one biological drug dispensing approved for IBD and 134 (11.1%) received ≥ 1 ETP with IBD as use indication. Patients with CD and CU were 83 (61.9%) and 51 (38.1%), respectively. Sensitivity of the different algorithms ranged from 71.1% (95% CI: 60.1– 80.5) to 98.8 (95% CI: 93.5– 100.0) for CD and from 64.7% (95% CI: 50.1– 77.6) to 94.1 (95% CI: 83.8– 98.8) for UC, while specificity was always higher than 91%. The best CD algorithm was “Algorithm 3”, based on hospital CD diagnosis code OR CD exemption code OR [IBD exemption code AND dispensing of non-biological drugs with only CD indication] (Se: 98.8%; Sp: 97.2%; PPV: 84.5%, NPV: 99.8%), achieving the highest diagnostic accuracy (Youden Index=0.960). The best UC algorithm was “Algorithm 3”, based on specific hospital UC diagnosis code OR UC exemption code OR [IBD exemption code AND golimumab dispensing] OR dispensing of non-biological drugs with only UC indication (Se: 94.1%; Sp: 91.6%; PPV: 50.0%; NPV: 99.4%), and achieving the highest diagnostic accuracy (Youden Index=0.857).Conclusion: In a population-based claims database, newly coding algorithms including diagnostic and exemption codes plus specific drug dispensing yielded highly accurate identification of CD and UC as distinct indication for biological drug use.Keywords: biological drugs, algorithm, healthcare database, ulcerative colitis, Crohn’s disease

Published
2023

39. 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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40. Impact of modifiable lifestyle risk factors for prostate cancer prevention: a review of the literature

Author

Francesco Ziglioli, Annalisa Patera, Gianmarco Isgrò, Davide Campobasso, Giulio Guarino, and Umberto Maestroni

Subjects
prostate cancer, risk factors, prevention, lifestyle, prevention policy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionAlthough prostate cancer (PCa) is one of the most common cancers among men, the impact of modifiable risk and protective factors is still being debated. This review aims to qualitatively summarize the most recent studies related to potential healthy lifestyle factors affecting the development of PCa.Methods for data acquisitionThe literature focusing on modifiable risk factors for prostate cancer was reviewed. Medline and Embase via the Ovid database were searched, and all relevant and inherent articles were analyzed. Non-eligible publications, such as review articles, editorials, comments, guidelines, or case reports, were excluded.Synthesis of the evidence obtained from data analysisThis review confirms that there is strong evidence that being overweight or obese increases the risk of advanced prostate cancer (assessed by body mass index (BMI), waist circumference, and waist–hip ratio), particularly considering central adiposity and ethnicity as contributing factors. The possible contribution of smoking still seems not entirely clear, while alcohol seems to affect PCa prevention in patients taking 5α-reductase inhibitors (5-ARIs). Dietary fibers may have anti-inflammatory properties and improve insulin sensitivity by reducing IGF bioactivity. In particular, dietary fiber intake like insoluble and legume fibers may be inversely associated with prostate cancer risk. Also, hyperglycemia and hyperinsulinemia, with untreated diabetic fasting blood glucose levels, could be modifiable prostate cancer risk factors. In two studies, aspirin was associated with a lower risk of lethal PCa and overall mortality. Concerning the role of vitamins, despite conflicting and divergent results, serum retinol concentration seems to be associated with an increased risk of prostate cancer and high-grade prostate cancer. Some studies resulted in disagreement about the role of selenium and vitamin E. We found inconsistent and controversial findings about the association between vitamin D and prostate cancer risk.ConclusionFar from being conclusive evidence, our findings confirm some strong evidence already found in the previous literature and highlight the need to clarify the role of some risk factors whose role is not yet completely known. This effort would facilitate the cultural and social change that may allow the shift from the treatment of prostate cancer when diagnosed to the real efforts needed for systematic prevention.

Published
2023
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42. 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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43. 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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44. Immune profiling of SARS-CoV-2 epitopes in asymptomatic and symptomatic pediatric and adult patients

Author

Anna Lucia Tornesello, Chiara Botti, Alberto Micillo, Francesco Labonia, Sergio Arpino, Maria Antonietta Isgrò, Serena Meola, Luigi Russo, Ernesta Cavalcanti, Silvia Sale, Carmine Nicastro, Luigi Atripaldi, Noemy Starita, Andrea Cerasuolo, Ulf Reimer, Pavlo Holenya, Luigi Buonaguro, Franco M. Buonaguro, and Maria Lina Tornesello

Subjects
Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), COVID-19, Peptide microarray, Neutralizing antibodies, Peptide biomarkers, Children SARS-CoV-2 infection, Medicine
Abstract

Abstract Background The infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has unpredictable manifestations of coronavirus disease (COVID-19) and variable clinical course with some patients being asymptomatic whereas others experiencing severe respiratory distress, or even death. We aimed to evaluate the immunoglobulin G (IgG) response towards linear peptides on a peptide array containing sequences from SARS-CoV-2, Middle East respiratory syndrome-related coronavirus (MERS) and common-cold coronaviruses 229E, OC43, NL63 and HKU1 antigens, in order to identify immunological indicators of disease outcome in SARS-CoV-2 infected patients. Methods We included in the study 79 subjects, comprising 19 pediatric and 30 adult SARS-CoV-2 infected patients with increasing disease severity, from mild to critical illness, and 30 uninfected subjects who were vaccinated with one dose of SARS-CoV-2 spike mRNA BNT162b2 vaccine. Serum samples were analyzed by a peptide microarray containing 5828 overlapping 15-mer synthetic peptides corresponding to the full SARS-CoV-2 proteome and selected linear epitopes of spike (S), envelope (E) and membrane (M) glycoproteins as well as nucleoprotein (N) of MERS, SARS and coronaviruses 229E, OC43, NL63 and HKU1 (isolates 1, 2 and 5). Results All patients exhibited high IgG reactivity against the central region and C-terminus peptides of both SARS-CoV-2 N and S proteins. Setting the threshold value for serum reactivity above 25,000 units, 100% and 81% of patients with severe disease, 36% and 29% of subjects with mild symptoms, and 8% and 17% of children younger than 8-years reacted against N and S proteins, respectively. Overall, the total number of peptides in the SARS-CoV-2 proteome targeted by serum samples was much higher in children compared to adults. Notably, we revealed a differential antibody response to SARS-CoV-2 peptides of M protein between adults, mainly reacting against the C-terminus epitopes, and children, who were highly responsive to the N-terminus of M protein. In addition, IgG signals against NS7B, NS8 and ORF10 peptides were found elevated mainly among adults with mild (63%) symptoms. Antibodies towards S and N proteins of other coronaviruses (MERS, 229E, OC43, NL63 and HKU1) were detected in all groups without a significant correlation with SARS-CoV-2 antibody levels. Conclusions Overall, our results showed that antibodies elicited by specific linear epitopes of SARS-CoV-2 proteome are age dependent and related to COVID-19 clinical severity. Cross-reaction of antibodies to epitopes of other human coronaviruses was evident in all patients with distinct profiles between children and adult patients. Several SARS-CoV-2 peptides identified in this study are of particular interest for the development of vaccines and diagnostic tests to predict the clinical outcome of SARS-CoV-2 infection.

Published
2023
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45. The Role of Emotional Intelligence and Metacognition in Teachers’ Stress during Pandemic Remote Working: A Moderated Mediation Model

Author

Calogero Iacolino, Brenda Cervellione, Rachele Isgrò, Ester Maria Concetta Lombardo, Giuseppina Ferracane, Massimiliano Barattucci, and Tiziana Ramaci

Subjects
teachers, remote working, COVID-19, emotional intelligence, metacognition, burnout, Public aspects of medicine, RA1-1270, Psychology, BF1-990
Abstract

During the COVID-19 pandemic, in adapting to social and work changes and new technological methods for remote teaching, teachers were subjected to increased work pressure, which affected their well-being and led to increased negative stress and burnout. This study was designed to test whether dysfunctional outcomes resulting from adapting to new ways of teaching via technological tools can be mitigated by the protective factors of emotional intelligence and metacognition. The study involved 604 teachers in Sicily filling out a questionnaire consisting of four different scales: (1) the Schutte Self-Report Emotional Intelligence Test (SREIT); (2) the Metacognitive Functions Screening Scale (MFSS-30); (3) the Link Burnout Questionnaire (LBQ); and (4) the Secondary Traumatic Stress Scale STSS-I. The results show that emotional intelligence mediates the relationship between certain remote work risk factors, as well as stress and burnout. In addition, metacognition was found to be a significant moderating factor in the relationship between risk factors and emotional intelligence. With regard to the United Nations’ Agenda 2030 Goals, our results emphasize the importance of teachers’ emotional and metacognitive skills in promoting quality of life and psychological well-being.

Published
2023
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