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51. Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks

Author

Schwinn, Leo, Nguyen, An, Raab, René, Zanca, Dario, Eskofier, Bjoern, Tenbrinck, Daniel, and Burger, Martin

Subjects
Computer Science - Machine Learning
Abstract

The vulnerability of deep neural networks to small and even imperceptible perturbations has become a central topic in deep learning research. Although several sophisticated defense mechanisms have been introduced, most were later shown to be ineffective. However, a reliable evaluation of model robustness is mandatory for deployment in safety-critical scenarios. To overcome this problem we propose a simple yet effective modification to the gradient calculation of state-of-the-art first-order adversarial attacks. Normally, the gradient update of an attack is directly calculated for the given data point. This approach is sensitive to noise and small local optima of the loss function. Inspired by gradient sampling techniques from non-convex optimization, we propose Dynamically Sampled Nonlocal Gradient Descent (DSNGD). DSNGD calculates the gradient direction of the adversarial attack as the weighted average over past gradients of the optimization history. Moreover, distribution hyperparameters that define the sampling operation are automatically learned during the optimization scheme. We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces. In addition, we show that DSNGD-based attacks are on average 35% faster while achieving 0.9% to 27.1% higher success rates compared to their gradient descent-based counterparts.

Published
2020
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52. Gravitational Models Explain Shifts on Human Visual Attention

Author

Zanca, Dario, Gori, Marco, Melacci, Stefano, and Rufa, Alessandra

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

Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It proceeds in two phases. One in which visual feature maps are acquired and processed in parallel. Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning. Its computational description is challenging, especially if the temporal dynamics of the process are taken into account. Numerous methods to estimate saliency have been proposed in the last three decades. They achieve almost perfect performance in estimating saliency at the pixel level, but the way they generate shifts in visual attention fully depends on winner-take-all (WTA) circuitry. WTA is implemented} by the biological hardware in order to select a location with maximum saliency, towards which to direct overt attention. In this paper we propose a gravitational model (GRAV) to describe the attentional shifts. Every single feature acts as an attractor and {the shifts are the result of the joint effects of the attractors. In the current framework, the assumption of a single, centralized saliency map is no longer necessary, though still plausible. Quantitative results on two large image datasets show that this model predicts shifts more accurately than winner-take-all.

Published
2020
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53. Wave Propagation of Visual Stimuli in Focus of Attention

Author

Faggi, Lapo, Betti, Alessandro, Zanca, Dario, Melacci, Stefano, and Gori, Marco

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition
Abstract

Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they still struggle approximating the effectiveness and efficiency exhibited by foveated animals. In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of "inhibition of return" so as not to get stuck in potential holes. An accurate experimentation of the model shows that it achieves top level performance in scanpath prediction tasks. This can easily be understood at the light of a theoretical result that we establish in the paper, where we prove that as the velocity of wave propagation goes to infinity, the proposed model reduces to recently proposed state of the art gravitational models of focus of attention.

Published
2020
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54. Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach

Author

Zanca, Dario, Melacci, Stefano, and Gori, Marco

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

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

Published
2020

55. 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions

Author

Zanca, Dario and Rufa, Alessandra

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

It is well known that a systematic analysis of the pupil size variations, recorded by means of an eye-tracker, is a rich source of information about a subject's arousal and cognitive state. Current methods for pupil analysis are limited to descriptive statistics, struggle in handling the wide inter-subjects variability and must be coupled with a long series of pre-processing signal operations. In this we present a data-driven approach in which 1-D Convolutional Neural Networks are applied directly to the raw pupil size data. To test its effectiveness, we apply our method in a binary classification task with two different groups of subjects: a group of elderly patients with Parkinson disease (PDs), a condition in which pupil abnormalities have been extensively reported, and a group of healthy adults subjects (HCs). Long-range registration (10 minutes) of the pupil size were collected in scotopic conditions (complete darkness, 0 lux). 1-D convolutional neural network models are trained for classification of short-range sequences (10 to 60 seconds of registration). The model provides prediction with high average accuracy on a hold out test set. Dataset and codes are released for reproducibility and benchmarking purposes.

Published
2020

56. End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data

Author

Rossi, Alessandro, Ermini, Sara, Bernabini, Dario, Zanca, Dario, Todisco, Marino, Genovese, Alessandro, and Rizzo, Antonio

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events. Eye movements are among the most direct markers of the individual attentive load and may serve as an important proxy of information. In this work we propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events between the two systems through the collection and processing of data related to eye movements. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. Moreover, we show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy and to investigate more in depth the gaze dynamics., Comment: 11 pages, 2 figures, 1 tables

Published
2020

59. Evaluation of the effectiveness of the decision support algorithm for physicians in retinal dystrophy using machine learning methods

Author

A.E. Zhdanov, A.Yu. Dolganov, D. Zanca, V.I. Borisov, E. Luchian, and L.G. Dorosinsky

Subjects
electroretinography, electroretinogram, erg, electrophysiological study, eps, retinal dystrophy, wavelet analysis, wavelet scalogram, decision trees, decision support algorithm, Information theory, Q350-390, Optics. Light, QC350-467
Abstract

Electroretinography is a method of electrophysiological testing, which allows diagnosing diseases associated with disorders of the vascular structures of the retina. The classical analysis of the electroretinogram is based on assessing four parameters of the amplitude-time representation and often needs to be specified further using alternative diagnostic methods. This study proposes the use of an original physician decision support algorithm for diagnosing retinal dystrophy. The proposed algorithm is based on machine learning methods and uses parameters extracted from the wavelet scalogram of pediatric and adult electroretinogram signals. The study also uses a labeled database of pediatric and adult electroretinogram signals recorded using a computerized electrophysiological workstation EP-1000 (Tomey GmbH) at the IRTC Eye Microsurgery Ekaterinburg Center. The scientific novelty of this study consists in the development of special mathematical and algorithmic software for analyzing a procedure for extracting wavelet scalogram parameters of the electroretinogram signal using the cwt function of the PyWT. The basis function is a Gaussian wavelet of order 8. Also, the scientific novelty includes the development of an algorithm for analyzing electroretinogram signals that implements the classification of adult (pediatric) electroretinogram signals 19 (20) percent more accurately than classical analysis.

Published
2023
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61. El antifascismo católico: coincidencias, tensiones y enfrentamientos

Author

José Zanca and Diego Mauro

Subjects
Catolicismo, Antifascismo católico, Iglesia católica, Liberalismo, Totalitarismo, America, E11-143, Latin America. Spanish America, F1201-3799
Abstract

En las últimas décadas, los cambios en el estudio del catolicismo se vieron alentados por el propio proceso de profesionalización disciplinar, que se desarrolló en el país desde los años ochenta, en el que influyó el impacto de la coyuntura política, fuertemente marcada por las consecuencias del terrorismo de Estado. Más recientemente, los historiadores comenzaron a prestar más atención a grupos y tendencias hasta entonces poco investigados. Surgieron nuevos estudios que alumbraron recodos inexplorados del catolicismo argentino. En esta línea, diversos trabajos reconstruyeron los círculos de seguidores de Jacques Maritain. Por otro lado, comenzaron a estudiarse también los sectores democristianos del Partido Popular de Buenos Aires. Dicho grupo encabezó, en el plano local, iniciativas internacionales alentadas por Luigi Sturzo como el Comité por la Paz Civil y Religiosa en España y People and Freedom Group (P&F). En este artículo, proponemos un recorrido panorámico por algunas de las principales vertientes del antifascismo católico con el propósito de analizar los puntos de convergencia, así como las principales divergencias que, entre otros factores, les impidieron compartir un mismo espacio intelectual y político durante las décadas de 1930 y 1940.

Published
2023

62. Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

Author

Pelegrina, Beatriz, Paytubi, Sonia, Marin, Fátima, Martínez, José Manuel, Carmona, Álvaro, Frias-Gomez, Jon, Peremiquel-Trillas, Paula, Dorca, Eduard, Zanca, Alba, López-Querol, Marta, Onieva, Irene, Benavente, Yolanda, Barahona, Marc, Fernandez-Gonzalez, Sergi, De Francisco, Javier, Caño, Víctor, Vidal, August, Pijuan, Lara, Canet-Hermida, Júlia, Dueñas, Núria, Brunet, Joan, Pineda, Marta, Matias-Guiu, Xavier, Ponce, Jordi, Bosch, Francesc Xavier, De Sanjosé, Silvia, Alemany, Laia, and Costas, Laura

Published
2023
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63. End-to-End Models for the Analysis of System 1 and System 2 Interactions basedon Eye-Tracking Data

Author

Rossi, Alessandro, Ermini, Sara, Bernabini, Dario, Zanca, Dario, Todisco, Marino, Genovese, Alessandro, and Rizzo, Antonio

Abstract

The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of onestimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are anindicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collecteye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitiveinterference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, wetrained different Machine Learning models to recognize tasks performed in the different conditions (i.e. with interference,without interference). The models achieved good classification performances when distinguishing between similar tasksperformed with or without cognitive interference. This suggests the presence of characterizing patterns common amongsubjects, despite of the individual variability of visual behavior. The results open up interesting investigations.

Published
2020

64. Aequorea’s secrets revealed: New fluorescent proteins with unique properties for bioimaging and biosensing

Author

Lambert, Gerard G, Depernet, Hadrien, Gotthard, Guillaume, Schultz, Darrin T, Navizet, Isabelle, Lambert, Talley, Adams, Stephen R, Torreblanca-Zanca, Albertina, Chu, Meihua, Bindels, Daphne S, Levesque, Vincent, Moffatt, Jennifer Nero, Salih, Anya, Royant, Antoine, and Shaner, Nathan C

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, Generic health relevance, Animals, Biosensing Techniques, Color, Crystallography, X-Ray, Green Fluorescent Proteins, Hydrozoa, Luminescent Proteins, Models, Molecular, Optical Imaging, Phylogeny, Static Electricity, Agricultural and Veterinary Sciences, Medical and Health Sciences, Developmental Biology, Agricultural, veterinary and food sciences, Biological sciences, Biomedical and clinical sciences
Abstract

Using mRNA sequencing and de novo transcriptome assembly, we identified, cloned, and characterized 9 previously undiscovered fluorescent protein (FP) homologs from Aequorea victoria and a related Aequorea species, with most sequences highly divergent from A. victoria green fluorescent protein (avGFP). Among these FPs are the brightest green fluorescent protein (GFP) homolog yet characterized and a reversibly photochromic FP that responds to UV and blue light. Beyond green emitters, Aequorea species express purple- and blue-pigmented chromoproteins (CPs) with absorbances ranging from green to far-red, including 2 that are photoconvertible. X-ray crystallography revealed that Aequorea CPs contain a chemically novel chromophore with an unexpected crosslink to the main polypeptide chain. Because of the unique attributes of several of these newly discovered FPs, we expect that Aequorea will, once again, give rise to an entirely new generation of useful probes for bioimaging and biosensing.

Published
2020

68. Diagnostic Applications of Nuclear Medicine: Multiple Myeloma

Author

Aghakhanyan, Gayane, Sollini, Martina, Galimberti, Sara, Zanca, Roberta, Boni, Roberto, Esposito, Enrica, Bartoli, Francesco, Erba, Paola A., Volterrani, Duccio, editor, Erba, Paola A., editor, Strauss, H. William, editor, Mariani, Giuliano, editor, and Larson, Steven M., editor

Published
2022
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69. Prosthetic Valve Endocarditis

Author

Sollini, Martina, Bartoli, Francesco, Zanca, Roberta, Esposito, Enrica, Lazzeri, Elena, Slart, Riemer H. J. A., Erba, Paola Anna, Pelletier-Galarneau, Matthieu, editor, and Martineau, Patrick, editor

Published
2022
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73. Characterization of the spectrum of trivalent VAV1‐mutation‐driven tumours using a gene‐edited mouse model

Author

Javier Robles‐Valero, Lucía Fernández‐Nevado, Myriam Cuadrado, Luis Francisco Lorenzo‐Martín, Isabel Fernández‐Pisonero, Antonio Abad, Esther Redín, Luis Montuenga, Dionisio Martín‐Zanca, Anna Bigas, Moisés Mallo, Mercedes Dosil, and Xosé R. Bustelo

Subjects
angioimmunoblastic T cell lymphoma, follicular helper T cells, nonsmall‐cell lung cancer, peripheral T cell lymphoma, RAC1, TP53, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Mutations in the VAV1 guanine nucleotide exchange factor 1 have been recently found in peripheral T cell lymphoma and nonsmall‐cell lung cancer (NSCLC). To understand their pathogenic potential, we generated a gene‐edited mouse model that expresses a VAV1 mutant protein that recapitulates the signalling alterations present in the VAV1 mutant subclass most frequently found in tumours. We could not detect any overt tumourigenic process in those mice. However, the concurrent elimination of the Trp53 tumour suppressor gene in them drives T cell lymphomagenesis. This process represents an exacerbation of the normal functions that wild‐type VAV1 plays in follicular helper T cells. We also found that, in combination with the Kras oncogene, the VAV1 mutant version favours progression of NSCLC. These data indicate that VAV1 mutations play critical, although highly cell‐type‐specific, roles in tumourigenesis. They also indicate that such functions are contingent on the mutational landscape of the tumours involved.

Published
2022
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74. Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancerResearch in context

Author

Beatriz Pelegrina, Sonia Paytubi, Fátima Marin, José Manuel Martínez, Álvaro Carmona, Jon Frias-Gomez, Paula Peremiquel-Trillas, Eduard Dorca, Alba Zanca, Marta López-Querol, Irene Onieva, Yolanda Benavente, Marc Barahona, Sergi Fernandez-Gonzalez, Javier De Francisco, Víctor Caño, August Vidal, Lara Pijuan, Júlia Canet-Hermida, Núria Dueñas, Joan Brunet, Marta Pineda, Xavier Matias-Guiu, Jordi Ponce, Francesc Xavier Bosch, Silvia De Sanjosé, Laia Alemany, and Laura Costas

Subjects
Endometrial neoplasms, Early detection of cancer, Papanicolaou test, Mutation, Biomarkers, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: The incidence of endometrial cancer is increasing worldwide. While delays in diagnosis reduce survival, case molecular misclassification might be associated with under- and over-treatment. The objective of this study was to evaluate genetic alterations to detect and molecularly classify cases of endometrial cancer using non-invasive samples. Methods: Consecutive patients with incident endometrial cancer (N = 139) and controls (N = 107) from a recent Spanish case–control study were included in this analysis. Overall, 339 cervicovaginal samples (out of which 228 were clinician-collected and 111 were self-collected) were analysed using a test based on next-generation sequencing (NGS), which targets 47 genes. Immunohistochemical markers were evaluated in 133 tumour samples. A total of 159 samples were used to train the detection algorithm and 180 samples were used for validation. Findings: Overall, 73% (N = 94 out of 129 clinician-collected samples, and N = 66 out of 90 self-collected samples) of endometrial cancer cases had detectable mutations in clinician-collected and self-collected samples, while the specificity was 80% (79/99) for clinician-collected samples and 90% (19/21) for self-collected samples. The molecular classifications obtained using tumour samples and non-invasive gynaecologic samples in our study showed moderate-to-good agreement. The molecular classification of cases of endometrial cancer into four groups using NGS of both clinician-collected and self-collected cervicovaginal samples yielded significant differences in disease-free survival. The cases with mutations in POLE had an excellent prognosis, whereas the cases with TP53 mutations had the poorest clinical outcome, which is consistent with the data on tumour samples. Interpretation: This study classified endometrial cancer cases into four molecular groups based on the analysis of cervicovaginal samples that showed significant differences in disease-free survival. The molecular classification of endometrial cancer in non-invasive samples may improve patient care and survival by indicating the early need for aggressive surgery, as well as reducing referrals to highly specialized hospitals in cancers with good prognosis. Validation in independent sets will confirm the potential for molecular classification in non-invasive samples. Funding: This study was funded by a competitive grant from Instituto de Salud Carlos III through the projects PI19/01835, PI23/00790, and FI20/00031, CIBERESP CB06/02/0073 and CIBERONC CB16/12/00231, CB16/12/00234 (Co-funded by European Regional Development Fund. ERDF: A way to build Europe). Samples and data were provided by Biobank HUB-ICO-IDIBELL, integrated into the Spanish Biobank Network, and funded by the Instituto de Salud Carlos III (PT20/00171) and by Xarxa de Bancs de Tumors de Catalunya (XBTC) sponsored by Pla Director d’Oncologia de Catalunya. This work was supported in part by the AECC, Grupos estables (GCTRA18014MATI). It also counts with the support of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112.

Published
2023
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75. Reseñas iberoamericanas

Author

Alexander Altevoigt, M.ª Carmen Lechosa Maldonado, Pablo Rojas, Virginie Giuliana, Jaime Cárdenas Isasi, José Luis Bellón Aguilera, Adriana Rodríguez Alfonso, Maravillas Moreno Amor, Miguel González-Abellás, María Belén Riveiro, Joserra Ortiz, Leyre Arrieta Alberdi, Diego Caro Cancela, Patricia Suárez Cano, Carlos Larrinaga, Víctor M. Heredia Flores, Isusko Vivas Ziarrusta, Vivian Basto Estrada, Izaskun Álvarez Cuartero, Stefan Rinke, José Zanca, Sebastián Álvarez, Manuel Cardozo Ruidiaz, and Theresa Bachmann

Subjects
History of Portugal, DP501-900.22, History of Spain, DP1-402, Latin America. Spanish America, F1201-3799, French literature - Italian literature - Spanish literature - Portuguese literature, PQ1-3999, Social Sciences
Abstract

Reseñas iberoamericanas

Published
2023
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76. Overview of the EUROfusion Tokamak Exploitation programme in support of ITER and DEMO

Author

E. Joffrin, M. Wischmeier, M. Baruzzo, A. Hakola, A. Kappatou, D. Keeling, B. Labit, E. Tsitrone, N. Vianello, the ASDEX Upgrade Team, JET Contributors, the MAST-U Team, the TCV Team, the WEST Team, the EUROfusion Tokamak Exploitation Team:, D. Abate, J. Adamek, M. Agostini, C. Albert, F.C.P. Albert Devasagayam, S. Aleiferis, E. Alessi, J. Alhage, S. Allan, J. Allcock, M. Alonzo, G. Anastasiou, E. Andersson Sunden, C. Angioni, Y. Anquetin, L. Appel, G.M. Apruzzese, M. Ariola, C. Arnas, J.F. Artaud, W. Arter, O. Asztalos, L. Aucone, M.H. Aumeunier, F. Auriemma, J. Ayllon, E. Aymerich, A. Baciero, F. Bagnato, L. Bähner, F. Bairaktaris, P. Balázs, L. Balbinot, I. Balboa, M. Balden, A. Balestri, M. Baquero Ruiz, T. Barberis, C. Barcellona, O. Bardsley, S. Benkadda, T. Bensadon, E. Bernard, M. Bernert, H. Betar, R. Bianchetti Morales, J. Bielecki, R. Bilato, P. Bilkova, W. Bin, G. Birkenmeier, R. Bisson, P. Blanchard, A. Bleasdale, V. Bobkov, A. Boboc, A. Bock, K. Bogar, P. Bohm, T. Bolzonella, F. Bombarda, N. Bonanomi, L. Boncagni, D. Bonfiglio, R. Bonifetto, M. Bonotto, D. Borodin, I. Borodkina, T.O.S.J. Bosman, C. Bourdelle, C. Bowman, S. Brezinsek, D. Brida, F. Brochard, R. Brunet, D. Brunetti, V. Bruno, R. Buchholz, J. Buermans, H. Bufferand, P. Buratti, A. Burckhart, J. Cai, R. Calado, J. Caloud, S. Cancelli, F. Cani, B. Cannas, M. Cappelli, S. Carcangiu, A. Cardinali, S. Carli, D. Carnevale, M. Carole, M. Carpita, D. Carralero, F. Caruggi, I.S. Carvalho, I. Casiraghi, A. Casolari, F.J. Casson, C. Castaldo, A. Cathey, F. Causa, J. Cavalier, M. Cavedon, J. Cazabonne, M. Cecconello, L. Ceelen, A. Celora, J. Cerovsky, C.D. Challis, R. Chandra, A. Chankin, B. Chapman, H. Chen, M. Chernyshova, A.G. Chiariello, P. Chmielewski, A. Chomiczewska, C. Cianfarani, G. Ciraolo, J. Citrin, F. Clairet, S. Coda, R. Coelho, J.W. Coenen, I.H. Coffey, C. Colandrea, L. Colas, S. Conroy, C. Contre, N.J. Conway, L. Cordaro, Y. Corre, D. Costa, S. Costea, D. Coster, X. Courtois, C. Cowley, T. Craciunescu, G. Croci, A.M. Croitoru, K. Crombe, D.J. Cruz Zabala, G. Cseh, T. Czarski, A. Da Ros, A. Dal Molin, M. Dalla Rosa, Y. Damizia, O. D’Arcangelo, P. David, M. De Angeli, E. De la Cal, E. De La Luna, G. De Tommasi, J. Decker, R. Dejarnac, D. Del Sarto, G. Derks, C. Desgranges, P. Devynck, S. Di Genova, L.E. di Grazia, A. Di Siena, M. Dicorato, M. Diez, M. Dimitrova, T. Dittmar, L. Dittrich, J.J. Domínguez Palacios Durán, P. Donnel, D. Douai, S. Dowson, S. Doyle, M. Dreval, P. Drews, L. Dubus, R. Dumont, D. Dunai, M. Dunne, A. Durif, F. Durodie, G. Durr Legoupil Nicoud, B. Duval, R. Dux, T. Eich, A. Ekedahl, S. Elmore, G. Ericsson, J. Eriksson, B. Eriksson, F. Eriksson, S. Ertmer, A. Escarguel, B. Esposito, T. Estrada, E. Fable, M. Faitsch, N. Fakhrayi Mofrad, A. Fanni, T. Farley, M. Farník, N. Fedorczak, F. Felici, X. Feng, J. Ferreira, D. Ferreira, N. Ferron, O. Fevrier, O. Ficker, A.R. Field, A. Figueiredo, N. Fil, D. Fiorucci, M. Firdaouss, R. Fischer, M. Fitzgerald, M. Flebbe, M. Fontana, J. Fontdecaba Climent, A. Frank, E. Fransson, L. Frassinetti, D. Frigione, S. Futatani, R. Futtersack, S. Gabriellini, D. Gadariya, D. Galassi, K. Galazka, J. Galdon, S. Galeani, D. Gallart, A. Gallo, C. Galperti, M. Gambrioli, S. Garavaglia, J. Garcia, M. Garcia Munoz, J. Gardarein, L. Garzotti, J. Gaspar, R. Gatto, P. Gaudio, M. Gelfusa, J. Gerardin, S.N. Gerasimov, R. Gerru Miguelanez, G. Gervasini, Z. Ghani, F.M. Ghezzi, G. Ghillardi, L. Giannone, S. Gibson, L. Gil, A. Gillgren, E. Giovannozzi, C. Giroud, G. Giruzzi, T. Gleiter, M. Gobbin, V. Goloborodko, A. González Ganzábal, T. Goodman, V. Gopakumar, G. Gorini, T. Görler, S. Gorno, G. Granucci, D. Greenhouse, G. Grenfell, M. Griener, W. Gromelski, M. Groth, O. Grover, M. Gruca, A. Gude, C. Guillemaut, R. Guirlet, J. Gunn, T. Gyergyek, L. Hagg, J. Hall, C.J. Ham, M. Hamed, T. Happel, G. Harrer, J. Harrison, D. Harting, N.C. Hawkes, P. Heinrich, S. Henderson, P. Hennequin, R. Henriques, S. Heuraux, J. Hidalgo Salaverri, J. Hillairet, J.C. Hillesheim, A. Hjalmarsson, A. Ho, J. Hobirk, E. Hodille, M. Hölzl, M. Hoppe, J. Horacek, N. Horsten, L. Horvath, M. Houry, K. Hromasova, J. Huang, Z. Huang, A. Huber, E. Huett, P. Huynh, A. Iantchenko, M. Imrisek, P. Innocente, C. Ionita Schrittwieser, H. Isliker, P. Ivanova, I. Ivanova Stanik, M. Jablczynska, S. Jachmich, A.S. Jacobsen, P. Jacquet, A. Jansen van Vuuren, A. Jardin, H. Järleblad, A. Järvinen, F. Jaulmes, T. Jensen, I. Jepu, S. Jessica, T. Johnson, A. Juven, J. Kalis, J. Karhunen, R. Karimov, A.N. Karpushov, S. Kasilov, Y. Kazakov, P.V. Kazantzidis, W. Kernbichler, HT. Kim, D.B. King, V.G. Kiptily, A. Kirjasuo, K.K. Kirov, A. Kirschner, A. Kit, T. Kiviniemi, F. Kjær, E. Klinkby, A. Knieps, U. Knoche, M. Kochan, F. Köchl, G. Kocsis, J.T.W. Koenders, L. Kogan, Y. Kolesnichenko, Y. Kominis, M. Komm, M. Kong, B. Kool, S.B. Korsholm, D. Kos, M. Koubiti, J. Kovacic, Y. Kovtun, E. Kowalska Strzeciwilk, K. 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Tholerus, B. Thomas, E. Thoren, A. Thornton, A. Thrysoe, Q. TICHIT, W. Tierens, A. Titarenko, P. Tolias, E. Tomasina, M. Tomes, E. Tonello, A. Tookey, M. Toscano Jiménez, C. Tsironis, C. Tsui, A. Tykhyy, M. Ugoletti, M. Usoltseva, D.F. Valcarcel, A. Valentini, M. Valisa, M. Vallar, M. Valovic, SI. Valvis, M. van Berkel, D. Van Eester, S. Van Mulders, M. van Rossem, R. Vann, B. Vanovac, J. Varela Rodriguez, J. Varje, S. Vartanian, M. Vecsei, L. Velarde Gallardo, M. Veranda, T. Verdier, G. Verdoolaege, K. Verhaegh, L. Vermare, G. Verona Rinati, J. Vicente, E. Viezzer, L. Vignitchouk, F. Villone, B. Vincent, P. Vincenzi, M.O. Vlad, G. Vogel, I. Voitsekhovitch, I. Voldiner, P. Vondracek, N.M.T. VU, T. Vuoriheimo, C. Wade, E. Wang, T. Wauters, M. Weiland, H. Weisen, N. Wendler, D. Weston, A. Widdowson, S. Wiesen, M. Wiesenberger, T. Wijkamp, M. Willensdorfer, T. Wilson, A. Wojenski, C. Wuethrich, I. Wyss, L. Xiang, S. Xu, D. Yadykin, Y. Yakovenko, H. Yang, V. Yanovskiy, R. Yi, B. Zaar, G. Zadvitskiy, L. Zakharov, P. Zanca, D. Zarzoso, Y. Zayachuk, J. Zebrowski, M. Zerbini, P. Zestanakis, C. F. B. Zimmermann, M. Zlobinski, A. Zohar, V.K. Zotta, X. Zou, M. Zuin, M. Zurita, and I. Zychor

Subjects
JET, ASDEX Upgrade, MAST-U, TCV, WEST, Tokamak Exploitation Task Force, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

Published
2024
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77. Overview of T and D–T results in JET with ITER-like wall

Author

C.F. Maggi, D. Abate, N. Abid, P. Abreu, O. Adabonyan, M. Afzal, I. Ahmad, M. Akhtar, R. Albanese, S. Aleiferis, E. Alessi, P. Aleynikov, J. Alguacil, J. Alhage, M. Ali, H. Allen, M. Allinson, M. Alonzo, E. Alves, R. Ambrosino, E. Andersson Sundén, P. Andrew, M. Angelone, C. Angioni, I. Antoniou, L. Appel, C. Appelbee, C. Aramunde, M. Ariola, G. Arnoux, G. Artaserse, J.-F. Artaud, W. Arter, V. Artigues, F.J. Artola, A. Ash, O. Asztalos, D. Auld, F. Auriemma, Y. Austin, L. Avotina, J. Ayllón, E. Aymerich, A. Baciero, L. Bähner, F. Bairaktaris, I. Balboa, M. Balden, N. Balshaw, V.K. Bandaru, J. Banks, A. Banon Navarro, C. Barcellona, O. Bardsley, M. Barnes, R. Barnsley, M. Baruzzo, M. Bassan, A. Batista, P. Batistoni, L. Baumane, B. Bauvir, L. Baylor, C. Bearcroft, P. Beaumont, D. Beckett, A. Begolli, M. Beidler, N. Bekris, M. Beldishevski, E. Belli, F. Belli, S. Benkadda, J. Bentley, E. Bernard, J. Bernardo, M. Bernert, M. Berry, L. Bertalot, H. Betar, M. Beurskens, P.G. Bhat, S. 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Lewin, J. Lewis, J. Likonen, Ch. Linsmeier, X. Litaudon, E. Litherland-Smith, F. Liu, T. Loarer, A. Loarte, R. Lobel, B. Lomanowski, P.J. Lomas, J. Lombardo, R. Lorenzini, S. Loreti, V.P. Loschiavo, M. Loughlin, T. Lowe, C. Lowry, T. Luce, R. Lucock, T. Luda Di Cortemiglia, M. Lungaroni, C.P. Lungu, T. Lunt, V. Lutsenko, B. Lyons, J. Macdonald, E. Macusova, R. Mäenpää, H. Maier, J. Mailloux, S. Makarov, P. Manas, A. Manning, P. Mantica, M.J. Mantsinen, J. Manyer, A. Manzanares, Ph. Maquet, M. Maraschek, G. Marceca, G. Marcer, C. Marchetto, O. Marchuk, A. Mariani, G. Mariano, M. Marin, A. Marin Roldan, M. Marinelli, T. Markovič, L. Marot, C. Marren, S. Marsden, S. Marsen, J. Marsh, R. Marshall, L. Martellucci, A.J. Martin, C. Martin, R. Martone, S. Maruyama, M. Maslov, M. Mattei, G.F. Matthews, D. Matveev, E. Matveeva, A. Mauriya, F. Maviglia, M. Mayer, M.-L. Mayoral, S. Mazzi, C. Mazzotta, R. McAdams, P.J. McCarthy, P. McCullen, R. McDermott, D.C. McDonald, D. McGuckin, V. McKay, L. McNamee, A. McShee, D. Mederick, M. Medland, S. Medley, K. Meghani, A.G. Meigs, S. Meitner, S. Menmuir, K. Mergia, S. Mianowski, P. Middleton, J. Mietelski, K. Mikszuta-Michalik, D. Milanesio, E. Milani, E. Militello-Asp, F. Militello, J. Milnes, A. Milocco, S. Minucci, I. Miron, J. Mitchell, J. Mlynář, V. Moiseenko, P. Monaghan, I. Monakhov, A. Montisci, S. Moon, R. Mooney, S. Moradi, R.B. Morales, L. Morgan, F. Moro, J. Morris, T. Mrowetz, L. Msero, S. Munot, A. Muñoz-Perez, M. Muraglia, A. Murari, A. Muraro, B. N’Konga, Y.S. Na, F. Nabais, R. Naish, F. Napoli, E. Nardon, V. Naulin, M.F.F. Nave, R. Neu, S. Ng, M. Nicassio, D. Nicolai, A.H. Nielsen, S.K. Nielsen, D. Nina, C. Noble, C.R. Nobs, M. Nocente, H. Nordman, S. Nowak, H. Nyström, J. O’Callaghan, M. O’Mullane, C. O’Neill, C. Olde, H.J.C. Oliver, R. Olney, J. Ongena, G.P. Orsitto, A. Osipov, R. Otin, N. Pace, L.W. Packer, E. Pajuste, D. Palade, J. Palgrave, O. Pan, N. Panadero, T. Pandya, E. Panontin, A. Papadopoulos, G. Papadopoulos, G. Papp, V.V. Parail, A. Parsloe, K. Paschalidis, M. Passeri, A. Patel, A. Pau, G. Pautasso, R. Pavlichenko, A. Pavone, E. Pawelec, C. Paz-Soldan, A. Peacock, M. Pearce, I.J. Pearson, E. Peluso, C. Penot, K. Pepperell, A. Perdas, T. Pereira, E. Perelli Cippo, C. Perez von Thun, D. Perry, P. Petersson, G. Petravich, N. Petrella, M. Peyman, L. Pigatto, M. Pillon, S. Pinches, G. Pintsuk, C. Piron, A. Pironti, F. Pisano, R. Pitts, U. Planck, N. Platt, V. Plyusnin, M. Podesta, G. Pokol, F.M. Poli, O.G. Pompilian, M. Poradzinski, M. Porkolab, C. Porosnicu, G. Poulipoulis, A.S. Poulsen, I. Predebon, A. Previti, D. Primetzhofer, G. Provatas, G. Pucella, P. Puglia, K. Purahoo, O. Putignano, T. Pütterich, A. Quercia, G. Radulescu, V. Radulovic, R. Ragona, M. Rainford, P. Raj, M. Rasinski, D. Rasmussen, J. Rasmussen, J.J. Rasmussen, A. Raso, G. Rattá, S. Ratynskaia, R. Rayaprolu, M. Rebai, A. Redl, D. Rees, D. Réfy, R. Reichle, H. Reimerdes, B.C.G. Reman, C. Reux, S. Reynolds, D. Rigamonti, E. Righi, F.G. Rimini, J. Risner, J.F. Rivero-Rodriguez, C.M. Roach, J. Roberts, R. Robins, S. Robinson, D. Robson, S. Rode, P. Rodrigues, P. Rodriguez-Fernandez, S. Romanelli, J. Romazanov, E. Rose, C. Rose-Innes, R. Rossi, S. Rowe, D. Rowlands, C. Rowley, M. Rubel, G. Rubinacci, G. Rubino, M. Rud, J. Ruiz Ruiz, F. Ryter, S. Saarelma, A. Sahlberg, M. Salewski, A. Salmi, R. Salmon, F. Salzedas, F. Sanchez, I. Sanders, D. Sandiford, F. Sanni, O. Sauter, P. Sauvan, G. Schettini, A. Shevelev, A.A. Schekochihin, K. Schmid, B.S. Schmidt, S. Schmuck, M. Schneider, P.A. Schneider, N. Schoonheere, R. Schramm, D. Scoon, S. Scully, M. Segato, J. Seidl, L. Senni, J. Seo, G. Sergienko, M. Sertoli, S.E. Sharapov, R. Sharma, A. Shaw, R. Shaw, H. Sheikh, U. Sheikh, N. Shi, P. Shigin, D. Shiraki, G. Sias, M. Siccinio, B. Sieglin, S.A. Silburn, A. Silva, C. Silva, J. Silva, D. Silvagni, D. Simfukwe, J. Simpson, P. Sirén, A. Sirinelli, H. Sjöstrand, N. Skinner, J. Slater, T. Smart, R.D. Smirnov, N. Smith, P. Smith, T. Smith, J. Snell, L. Snoj, E.R. Solano, V. Solokha, C. Sommariva, K. Soni, M. Sos, J. Sousa, C. Sozzi, T. Spelzini, F. Spineanu, L. Spolladore, D. Spong, C. Srinivasan, G. Staebler, A. Stagni, I. Stamatelatos, M.F. Stamp, Ž. Štancar, P.A. Staniec, G. Stankūnas, M. Stead, B. Stein-Lubrano, A. Stephen, J. Stephens, P. Stevenson, C. Steventon, M. Stojanov, D.A. St-Onge, P. Strand, S. Strikwerda, C.I. Stuart, S. Sturgeon, H.J. Sun, S. Surendran, W. Suttrop, J. Svensson, J. Svoboda, R. Sweeney, G. Szepesi, M. Szoke, T. Tadić, B. Tal, T. Tala, P. Tamain, K. Tanaka, W. Tang, G. Tardini, M. Tardocchi, D. Taylor, A.S. Teimane, G. Telesca, A. Teplukhina, A. Terra, D. Terranova, N. Terranova, D. Testa, B. Thomas, V.K. Thompson, A. Thorman, A.S. Thrysoe, W. Tierens, R.A. Tinguely, A. Tipton, H. Todd, M. Tomeš, A. Tookey, P. Tsavalas, D. Tskhakaya, L.-P. Turică, A. Turner, I. Turner, M. Turner, M.M. Turner, G. Tvalashvili, A. Tykhyy, S. Tyrrell, A. Uccello, V. Udintsev, A. Vadgama, D.F. Valcarcel, A. Valentini, M. Valisa, M. Vallar, M. Valovic, M. Van Berkel, K.L. van de Plassche, M. van Rossem, D. Van Eester, J. Varela, J. Varje, T. Vasilopoulou, G. Vayakis, M. Vecsei, J. Vega, M. Veis, P. Veis, S. Ventre, M. Veranda, G. Verdoolaege, C. Verona, G. Verona Rinati, E. Veshchev, N. Vianello, E. Viezzer, L. Vignitchouk, R. Vila, R. Villari, F. Villone, P. Vincenzi, A. Vitins, Z. Vizvary, M. Vlad, I. Voldiner, U. Von Toussaint, P. Vondráček, B. Wakeling, M. Walker, R. Walker, M. Walsh, R. Walton, E. Wang, F. Warren, R. Warren, J. Waterhouse, C. Watts, T. Webster, M. Weiland, H. Weisen, M. Weiszflog, N. Wendler, A. West, M. Wheatley, S. Whetham, A. Whitehead, D. Whittaker, A. Widdowson, S. Wiesen, M. Willensdorfer, J. Williams, I. Wilson, T. Wilson, M. Wischmeier, A. Withycombe, D. Witts, A. Wojcik-Gargula, E. Wolfrum, R. Wood, R. Woodley, R. Worrall, I. Wyss, T. Xu, D. Yadykin, Y. Yakovenko, Y. Yang, V. Yanovskiy, R. Yi, I. Young, R. Young, B. Zaar, R.J. Zabolockis, L. Zakharov, P. Zanca, A. Zarins, D. Zarzoso Fernandez, K.-D. Zastrow, Y. Zayachuk, M. Zerbini, W. Zhang, B. Zimmermann, M. Zlobinski, A. Zocco, V.K. Zotta, M. Zuin, W. Zwingmann, and I. Zychor

Subjects
magnetic fusion, JET-ILW, D–T, tritium, alpha particles, fusion prediction, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

In 2021 JET exploited its unique capabilities to operate with T and D–T fuel with an ITER-like Be/W wall (JET-ILW). This second major JET D–T campaign (DTE2), after DTE1 in 1997, represented the culmination of a series of JET enhancements—new fusion diagnostics, new T injection capabilities, refurbishment of the T plant, increased auxiliary heating, in-vessel calibration of 14 MeV neutron yield monitors—as well as significant advances in plasma theory and modelling in the fusion community. DTE2 was complemented by a sequence of isotope physics campaigns encompassing operation in pure tritium at high T-NBI power. Carefully conducted for safe operation with tritium, the new T and D–T experiments used 1 kg of T (vs 100 g in DTE1), yielding the most fusion reactor relevant D–T plasmas to date and expanding our understanding of isotopes and D–T mixture physics. Furthermore, since the JET T and DTE2 campaigns occurred almost 25 years after the last major D–T tokamak experiment, it was also a strategic goal of the European fusion programme to refresh operational experience of a nuclear tokamak to prepare staff for ITER operation. The key physics results of the JET T and DTE2 experiments, carried out within the EUROfusion JET1 work package, are reported in this paper. Progress in the technological exploitation of JET D–T operations, development and validation of nuclear codes, neutronic tools and techniques for ITER operations carried out by EUROfusion (started within the Horizon 2020 Framework Programme and continuing under the Horizon Europe FP) are reported in (Litaudon et al Nucl. Fusion accepted), while JET experience on T and D–T operations is presented in (King et al Nucl. Fusion submitted).

Published
2024
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78. Error field detection and correction studies towards ITER operation

Author

L. Piron, C. Paz-Soldan, L. Pigatto, P. Zanca, O. Sauter, T. Putterich, P. Bettini, M. Bonotto, G. Cunningham, G. De Tommasi, N. Ferron, M. Gambrioli, G. Graham, P. De Vries, Y. Gribov, Q. Hu, K. Kirov, N.C. Logan, M. Lennholm, M. Mattei, M. Maraschek, T. Markovic, G. Manduchi, P. Martin, A. Pironti, A.R. Polevoi, T. Ravensbergen, D. Ryan, B. Sieglin, W. Suttrop, D. Terranova, W. Teschke, D.F. Valcarcel, C. Vincent, JET Contributors, the EUROfusion Tokamak Exploitation Team, the ASDEX Upgrade Team, and MAST-U Team

Subjects
error fields, plasma control, ITER, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

In magnetic fusion devices, error field (EF) sources, spurious magnetic field perturbations, need to be identified and corrected for safe and stable (disruption-free) tokamak operation. Within Work Package Tokamak Exploitation RT04, a series of studies have been carried out to test the portability of the novel non-disruptive method, designed and tested in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion 62 126007), and to perform an assessment of model-based EF control strategies towards their applicability in ITER. In this paper, the lessons learned, the physical mechanism behind the magnetic island healing, which relies on enhanced viscous torque that acts against the static electro-magnetic torque, and the main control achievements are reported, together with the first design of the asynchronous EF correction current/density controller for ITER.

Published
2024
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79. RFX-mod2 as a flexible device for reversed-field-pinch and low-field tokamak research

Author

D. Terranova, M. Agostini, F. Auriemma, M. Gobbin, G. Marchiori, L. Pigatto, P. Porcu, I. Predebon, G. Spizzo, N. Vianello, P. Zanca, D. Abate, T. Bolzonella, D. Bonfiglio, M. Bonotto, S. Cappello, L. Carraro, R. Cavazzana, P. Franz, R. Lorenzini, L. Marrelli, R. Milazzo, S. Peruzzo, M.E. Puiatti, P. Scarin, M. Spolaore, E. Tomasina, M. Valisa, M. Veranda, B. Zaniol, and M. Zuin

Subjects
RFP, tokamak, transport, MHD, plasma control, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The RFX-mod2 installation is planned to be completed by 2024 and the start of operations is expected in 2025. The high flexibility of the machine (already tested in the previous RFX-mod experiment) allows operation in Reversed Field Pinch and tokamak configuration as well as ultra-low q pulses. In this work we present predictive analysis on transport, performances and plasma control in RFX-mod2 in view of the first experimental campaigns.

Published
2024
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82. Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks

Author

Marra, Giuseppe, Zanca, Dario, Betti, Alessandro, and Gori, Marco

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

The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectifier and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discusses an optimal selection of the neuron non-linearity in a functional framework that is inspired from classic regularization arguments. It is shown that the best activation function is represented by a kernel expansion in the training set, that can be effectively approximated over an opportune set of points modeling 1-D clusters. The idea can be naturally extended to recurrent networks, where the expressiveness of kernel-based activation functions turns out to be a crucial ingredient to capture long-term dependencies. We give experimental evidence of this property by a set of challenging experiments, where we compare the results with neural architectures based on state of the art LSTM cells.

Published
2018

83. Visual Attention driven by Convolutional Features

Author

Zanca, Dario and Gori, Marco

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The understanding of where humans look in a scene is a problem of great interest in visual perception and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations. In this paper we give two contribution. First, we show a model of visual attention that is simply based on deep convolutional neural networks trained for object classification tasks. A method for visualizing saliency maps is defined which is evaluated in a saliency prediction task. Second, we integrate the information of these maps with a bottom-up differential model of eye-movements to simulate visual attention scanpaths. Results on saliency prediction and scores of similarity with human scanpaths demonstrate the effectiveness of this model.

Published
2018

84. FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity

Author

Zanca, Dario, Serchi, Valeria, Piu, Pietro, Rosini, Francesca, and Rufa, Alessandra

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

In the last three decades, human visual attention has been a topic of great interest in various disciplines. In computer vision, many models have been proposed to predict the distribution of human fixations on a visual stimulus. Recently, thanks to the creation of large collections of data, machine learning algorithms have obtained state-of-the-art performance on the task of saliency map estimation. On the other hand, computational models of scanpath are much less studied. Works are often only descriptive or task specific. This is due to the fact that the scanpath is harder to model because it must include the description of a dynamic. General purpose computational models are present in the literature, but are then evaluated in tasks of saliency prediction, losing therefore information about the dynamics and the behaviour. In addition, two technical reasons have limited the research. The first reason is the lack of robust and uniformly used set of metrics to compare the similarity between scanpath. The second reason is the lack of sufficiently large and varied scanpath datasets. In this report we want to help in both directions. We present FixaTons, a large collection of datasets human scanpaths (temporally ordered sequences of fixations) and saliency maps. It comes along with a software library for easy data usage, statistics calculation and implementation of metrics for scanpath and saliency prediction evaluation.

Published
2018

85. Oncogene Amplification in Growth Factor Signaling Pathways Renders Cancers Dependent on Membrane Lipid Remodeling

Author

Bi, Junfeng, Ichu, Taka-Aki, Zanca, Ciro, Yang, Huijun, Zhang, Wei, Gu, Yuchao, Chowdhry, Sudhir, Reed, Alex, Ikegami, Shiro, Turner, Kristen M, Zhang, Wenjing, Villa, Genaro R, Wu, Sihan, Quehenberger, Oswald, Yong, William H, Kornblum, Harley I, Rich, Jeremy N, Cloughesy, Timothy F, Cavenee, Webster K, Furnari, Frank B, Cravatt, Benjamin F, and Mischel, Paul S

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, Cancer, Rare Diseases, Genetics, 2.1 Biological and endogenous factors, Aetiology, 1-Acylglycerophosphocholine O-Acyltransferase, A549 Cells, Animals, Cell Survival, ErbB Receptors, Female, Gene Amplification, Gene Expression Regulation, Neoplastic, Genotype, Heterografts, Humans, Mice, Mice, Nude, Neoplasms, Oncogenes, PC-3 Cells, Phospholipids, Signal Transduction, Transfection, cancer dependency, cancer metabolism, gene amplification, growth factor signaling, membrane lipid remodeling, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

Advances in DNA sequencing technologies have reshaped our understanding of the molecular basis of cancer, providing a precise genomic view of tumors. Complementary biochemical and biophysical perspectives of cancer point toward profound shifts in nutrient uptake and utilization that propel tumor growth and major changes in the structure of the plasma membrane of tumor cells. The molecular mechanisms that bridge these fundamental aspects of tumor biology remain poorly understood. Here, we show that the lysophosphatidylcholine acyltransferase LPCAT1 functionally links specific genetic alterations in cancer with aberrant metabolism and plasma membrane remodeling to drive tumor growth. Growth factor receptor-driven cancers are found to depend on LPCAT1 to shape plasma membrane composition through enhanced saturated phosphatidylcholine content that is, in turn, required for the transduction of oncogenic signals. These results point to a genotype-informed strategy that prioritizes lipid remodeling pathways as therapeutic targets for diverse cancers.

Published
2019

86. NAD metabolic dependency in cancer is shaped by gene amplification and enhancer remodelling.

Author

Chowdhry, Sudhir, Zanca, Ciro, Rajkumar, Utkrisht, Koga, Tomoyuki, Diao, Yarui, Raviram, Ramya, Liu, Feng, Turner, Kristen, Yang, Huijun, Brunk, Elizabeth, Bi, Junfeng, Furnari, Frank, Bafna, Vineet, Ren, Bing, and Mischel, Paul

Subjects
Animals, Carbon-Nitrogen Ligases with Glutamine as Amide-N-Donor, Cell Death, Cell Line, Tumor, Cytokines, Enhancer Elements, Genetic, Epigenesis, Genetic, Female, Gene Amplification, Gene Expression Regulation, Neoplastic, Humans, Mice, NAD, Neoplasms, Nicotinamide Phosphoribosyltransferase, Pentosyltransferases, Phosphotransferases (Alcohol Group Acceptor)
Abstract

Precision oncology hinges on linking tumour genotype with molecularly targeted drugs1; however, targeting the frequently dysregulated metabolic landscape of cancer has proven to be a major challenge2. Here we show that tissue context is the major determinant of dependence on the nicotinamide adenine dinucleotide (NAD) metabolic pathway in cancer. By analysing more than 7,000 tumours and 2,600 matched normal samples of 19 tissue types, coupled with mathematical modelling and extensive in vitro and in vivo analyses, we identify a simple and actionable set of rules. If the rate-limiting enzyme of de novo NAD synthesis, NAPRT, is highly expressed in a normal tissue type, cancers that arise from that tissue will have a high frequency of NAPRT amplification and be completely and irreversibly dependent on NAPRT for survival. By contrast, tumours that arise from normal tissues that do not express NAPRT highly are entirely dependent on the NAD salvage pathway for survival. We identify the previously unknown enhancer that underlies this dependence. Amplification of NAPRT is shown to generate a pharmacologically actionable tumour cell dependence for survival. Dependence on another rate-limiting enzyme of the NAD synthesis pathway, NAMPT, as a result of enhancer remodelling is subject to resistance by NMRK1-dependent synthesis of NAD. These results identify a central role for tissue context in determining the choice of NAD biosynthetic pathway, explain the failure of NAMPT inhibitors, and pave the way for more effective treatments.

Published
2019

87. Inhibition of Nuclear PTEN Tyrosine Phosphorylation Enhances Glioma Radiation Sensitivity through Attenuated DNA Repair

Author

Ma, Jianhui, Benitez, Jorge A, Li, Jie, Miki, Shunichiro, de Albuquerque, Claudio Ponte, Galatro, Thais, Orellana, Laura, Zanca, Ciro, Reed, Rachel, Boyer, Antonia, Koga, Tomoyuki, Varki, Nissi M, Fenton, Tim R, Marie, Suely Kazue Nagahashi, Lindahl, Erik, Gahman, Timothy C, Shiau, Andrew K, Zhou, Huilin, DeGroot, John, Sulman, Erik P, Cavenee, Webster K, Kolodner, Richard D, Chen, Clark C, and Furnari, Frank B

Subjects
Genetics, Rare Diseases, Brain Disorders, Cancer, Neurosciences, Brain Cancer, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Animals, Brain Neoplasms, Cell Nucleus, DNA Repair, Female, Glioma, Humans, Male, Mice, PTEN Phosphohydrolase, Phosphorylation, Pyrimidines, Rad51 Recombinase, Radiation Tolerance, Receptor, Fibroblast Growth Factor, Type 2, Tyrosine, Xenograft Model Antitumor Assays, DNA damage, FGFR2, GBM, PTEN, ionizing radiation, tyrosine phosphorylation, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Ionizing radiation (IR) and chemotherapy are standard-of-care treatments for glioblastoma (GBM) patients and both result in DNA damage, however, the clinical efficacy is limited due to therapeutic resistance. We identified a mechanism of such resistance mediated by phosphorylation of PTEN on tyrosine 240 (pY240-PTEN) by FGFR2. pY240-PTEN is rapidly elevated and bound to chromatin through interaction with Ki-67 in response to IR treatment and facilitates the recruitment of RAD51 to promote DNA repair. Blocking Y240 phosphorylation confers radiation sensitivity to tumors and extends survival in GBM preclinical models. Y240F-Pten knockin mice showed radiation sensitivity. These results suggest that FGFR-mediated pY240-PTEN is a key mechanism of radiation resistance and is an actionable target for improving radiotherapy efficacy.

Published
2019

88. Shoulder-specific rehabilitation combined with aerobic exercises versus solely shoulder-specific rehabilitation in patients with type 2 diabetes mellitus: study protocol for a randomized controlled superiority trial

Author

Fernanda A. P. Habechian, Mauricio E. Flores Quezada, Ann M. Cools, Birgitte Hougs Kjaer, Rodrigo I. Cuevas Cid, and Gisele G. Zanca

Subjects
Shoulder pain, Diabetes mellitus type 2, Musculoskeletal rehabilitation, Randomized controlled trial, Medicine (General), R5-920
Abstract

Abstract Background Musculoskeletal disorders are very common in patients with diabetes mellitus (DM). The upper limb is one of the regions that is most frequently affected generally presenting limited joint mobility, pain, and a decreased muscle strength. Most clinical trials with a focus on shoulder musculoskeletal rehabilitation are carried out in patients who do not present DM. Thus, the purpose of the present study is to compare the effects of two distinct treatment protocols (conventional shoulder musculoskeletal rehabilitation combined with aerobic exercises versus solely conventional shoulder musculoskeletal rehabilitation) on shoulder pain, function, strength, kinematics, and supraspinatus tendon thickness in patients with type 2 DM after 12 weeks of intervention and a subsequent follow-up at week 20. Methods A randomized controlled superiority trial will be conducted. Participants with a clinical diagnosis of type 2 DM of both sexes, age between 40 and 70 years, presenting shoulder pain will be randomly assigned to one of the following groups: (1) conventional shoulder musculoskeletal rehabilitation combined with aerobic exercises; (2) solely conventional shoulder musculoskeletal rehabilitation. All individuals will be evaluated before starting the treatment protocol (baseline) and at the end of treatment (post 12 weeks) and as a follow-up at 20 weeks. The shoulder function assessed by the SPADI (Shoulder Pain and Disability Index) questionnaire will be considered as primary outcome; the secondary outcome will be shoulder pain, measured with NPRS scales. Other outcomes will include range of motion, measured using a digital inclinometer; isometric shoulder muscle strength, measured using a manual muscle dynamometer; shoulder kinematics, measured using three-dimensional inertial units measurement; supraspinatus tendon thickness, measured using an ultrasound; AGE accumulation, using a skin autofluorescence measurement; and HbA1c (hemoglobin a1c), fasting glucose and lipid profile measured by a simple blood test. Discussion DM is a highly prevalent disease and a public health problem worldwide, and the upper extremity musculoskeletal disorders in DM are barely recognized and largely underestimated. In this way, it would be interesting to analyze if the combination of aerobic exercises with conventional musculoskeletal rehabilitation protocols could generate better results in the functionality, pain, mobility and an improvement in the biochemical aspects related to the hyperglycemia of these patients compared to solely the conventional musculoskeletal rehabilitation. Trial registration ClinicalTrials.gov NCT04817514. Registered on March 26, 2021.

Published
2022
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89. SVC-onGoing: Signature verification competition

Author

Tolosana, Ruben, Vera-Rodriguez, Ruben, Gonzalez-Garcia, Carlos, Fierrez, Julian, Morales, Aythami, Ortega-Garcia, Javier, Carlos Ruiz-Garcia, Juan, Romero-Tapiador, Sergio, Rengifo, Santiago, Caruana, Miguel, Jiang, Jiajia, Lai, Songxuan, Jin, Lianwen, Zhu, Yecheng, Galbally, Javier, Diaz, Moises, Angel Ferrer, Miguel, Gomez-Barrero, Marta, Hodashinsky, Ilya, Sarin, Konstantin, Slezkin, Artem, Bardamova, Marina, Svetlakov, Mikhail, Saleem, Mohammad, Lia Szcs, Cintia, Kovari, Bence, Pulsmeyer, Falk, Wehbi, Mohamad, Zanca, Dario, Ahmad, Sumaiya, Mishra, Sarthak, and Jabin, Suraiya

Published
2022
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91. Gender

Author

Zanca, Federico, additional, Caporusso, Edoardo, additional, Giordano, Giulia Maria, additional, and Galderisi, Silvana, additional

Published
2022
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92. Quantum Lubricity

Author

Zanca, Tommaso, Pellegrini, Franco, Santoro, Giuseppe E., and Tosatti, Erio

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The quantum motion of nuclei, generally ignored in sliding friction, can become important for an atom, ion, or light molecule sliding in an optical lattice. The density-matrix-calculated evolution of a quantum Prandtl-Tomlinson model, describing the frictional dragging by an external force of a quantum particle, shows that classical predictions can be very wrong. The strongest quantum effect occurs not for weak, but for strong periodic potentials, where barriers are high but energy levels in each well are discrete, and resonant tunnelling to excited states in the nearest well can preempt classical stick-slip with great efficiency. The resulting permeation of otherwise impassable barriers is predicted to cause quantum lubricity

Published
2017

93. Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

Author

Giannini, Francesco, Laveglia, Vincenzo, Rossi, Alessandro, Zanca, Dario, and Zugarini, Andrea

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

This report provides an introduction to some Machine Learning tools within the most common development environments. It mainly focuses on practical problems, skipping any theoretical introduction. It is oriented to both students trying to approach Machine Learning and experts looking for new frameworks.

Published
2017

94. Controlling nanocluster growth through nanoconfinement: the effect of the number and nature of metal–organic framework functionalities.

Author

King, James, Zhipeng Lin, Zanca, Federica, Hui Luo, Zhang, Linda, Cullen, Patrick, Danaie, Mohsen, Hirscher, Michael, Meloni, Simone, Elena, Alin M., and Szilágyi, Petra Á.

Abstract

Controlled nanocluster growth via nanoconfinement is an attractive approach as it allows for geometry control and potential surface-chemistry modification simultaneously. However, it is still not a straight-forward method and much of its success depends on the nature and possibly concentration of functionalities on the cavity walls that surround the clusters. To independently probe the effect of the nature and number of functional groups on the controlled Pd nanocluster growth within the pores of the metal–organic frameworks, Pd-laden UiO-66 analogues with mono- and bi-functionalised linkers of amino and methyl groups were successfully prepared and studied in a combined experimental–computational approach. The nature of the functional groups determines the strength of host–guest interactions, while the number of functional groups affects the extent of Pd loading. The interplay of these two effects means that for a successful Pd embedding, mono-functionalised host matrices are more favourable. Interestingly, in the context of the present and previous research, we find that host frameworks with functional groups displaying higher Lewis basicity are more successful at controlled Pd NC growth via nanoconfinement in MOFs. [ABSTRACT FROM AUTHOR]

Published
2024
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95. Composite Prediction Score to Interpret Bone Focal Uptake in Hormone-Sensitive Prostate Cancer Patients Imaged with [18F]PSMA-1007 PET/CT.

Author

Bauckneht, Matteo, D'Amico, Francesca, Albano, Domenico, Balma, Michele, Cabrini, Camilla, Dondi, Francesco, Di Raimondo, Tania, Liberini, Virginia, Sofia, Luca, Peano, Simona, Riondato, Mattia, Fornarini, Giuseppe, Laudicella, Riccardo, Carmisciano, Luca, Lopci, Egesta, Zanca, Roberta, Rodari, Marcello, Raffa, Stefano, Donegani, Maria Isabella, and Dubois, Daniela

Published
2024
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96. Data-Centric Benchmarking of Neural Network Architectures for the Univariate Time Series Forecasting Task.

Author

Schlieper, Philipp, Dombrowski, Mischa, Nguyen, An, Zanca, Dario, and Eskofier, Bjoern

Subjects
CONVOLUTIONAL neural networks, DEEP learning, TIME series analysis, TRANSFORMER models, MACHINE learning
Abstract

Time series forecasting has witnessed a rapid proliferation of novel neural network approaches in recent times. However, performances in terms of benchmarking results are generally not consistent, and it is complicated to determine in which cases one approach fits better than another. Therefore, we propose adopting a data-centric perspective for benchmarking neural network architectures on time series forecasting by generating ad hoc synthetic datasets. In particular, we combine sinusoidal functions to synthesize univariate time series data for multi-input-multi-output prediction tasks. We compare the most popular architectures for time series, namely long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformers, and directly connect their performance with different controlled data characteristics, such as the sequence length, noise and frequency, and delay length. Our findings suggest that transformers are the best architecture for dealing with different delay lengths. In contrast, for different noise and frequency levels and different sequence lengths, LSTM is the best-performing architecture by a significant amount. Based on our insights, we derive recommendations which allow machine learning (ML) practitioners to decide which architecture to apply, given the dataset's characteristics. [ABSTRACT FROM AUTHOR]

Published
2024
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98. ICDAR 2021 Competition on On-Line Signature Verification.

Author

Ruben Tolosana, Rubén Vera-Rodríguez, Carlos Gonzalez-Garcia, Julian Fiérrez, Santiago Rengifo, Aythami Morales, Javier Ortega-Garcia, Juan-Carlos Ruiz-Garcia, Sergio Romero-Tapiador, Jiajia Jiang, Songxuan Lai, Lianwen Jin, Yecheng Zhu, Javier Galbally, Moisés Díaz, Miguel ángel Ferrer, Marta Gomez-Barrero, Ilya A. Hodashinsky, Konstantin S. Sarin, Artem Slezkin, Marina Bardamova, Mikhail Svetlakov, Mohammad Saleem 0001, Cintia Lia Szücs, Bence Kovári, Falk Pulsmeyer, Mohamad Wehbi, Dario Zanca, Sumaiya Ahmad, Sarthak Mishra, and Suraiya Jabin

Published
2021
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