Abstract SCHOOLING IN MODERNITY. THE POLITICS OF SPONSORED FILMS IN POSTWAR ITALY, PAOLA BONIFAZIO (2014) Toronto: University of Toronto Press, 304 pp., ISBN: 9781442615984, p/bk, $34.95 NEW VISIONS OF THE CHILD IN ITALIAN CINEMA, DANIELLE HIPKINS AND ROGER PITT (EDS) (2014) Bern: Peter Lang, 342 pp., ISBN: 9783034302692, p/bk, £40.00 UNFINISHED BUSINESS. SCREENING THE ITALIAN MAFIA IN THE NEW MILLENNIUM, DANA RENGA (2014) Toronto, Buffalo and London: University of Toronto Press, 264 pp., ISBN: 9781442615588, p/bk, £19.75 OSSERVATORIO TV, BARBARA MAIO (2014) Creative Commons Public License (http://www.osservatoriotv.it/Benvenuto.html), 228 pp. SGUARDO, CORPO, VIOLENZA. SADE E IL CINEMA, ALBERTO BRODESCO (2014) Milano-Udine: Mimesis, 366 pp., ISBN-13: 9788857523057, h/bk, €24 SO DEADLY, SO PERVERSE: 50 YEARS OF ITALIAN GIALLO FILMS, VOLUME 1: 1963–1973, TROY HOWARTH (2014) Baltimore, MD: Midnight Marquee Press Inc., 233 pp., ISBN: 9781936168507, p/bk, $49.95 STORIA DEI MEDIA DIGITALI: RIVOLUZIONI E CONTINUITÀ, GABRIELE BALBI AND PAOLO MAGAUDDA (2014) Rome-Bari: Editori Laterza, 182 pp., ISBN: 9788858116272, p/bk, €20 FILM SOUND IN ITALY: LISTENING TO THE SCREEN, ANTONELLA C. SISTO (2014) New York: Palgrave Macmillan, 224 pp., ISBN: 9781137387707, h/bk, $95 FINIRAI: I RETROSCENA DELLA RIFORMA E IL FUTURO DELLA TELEVISIONE, ROBERTO FAENZA (2015) N. P.: Privately published, 224 pp., ISBN: 9788891091321, e-book, €4.99, p/bk, €17 PALINSESTO: STORIA E TECNICA DELLA PROGRAMMAZIONE TELEVISIVA, LUCA BARRA (2015) Roma-Bari: Laterza, 202 pp., ISBN: 9788858117316, p/bk, €20,00 CINEMA, GENDER, AND EVERYDAY SPACE: COMEDY, ITALIAN STYLE, NATALIE FULLWOOD (2015) Basingstoke: Palgrave Macmillan, 272 pp., ISBN: 9781137403568, h/c/e-book, $95.00
Drawing from the REWIND Artists Video Archive and research project EWVA, a Polyphonic Essay on Memory is a performance, presentation and discussion on the theme of memory generated through artworks selected from the REWIND Artists Video Archive and research project EWVA (European Women’s Video Art in the 70s and 80s). Each curator will unveil their selected artworks to one another and the audience simultaneously.
This format agitates expected conventions and the mediated response of presenting artworks to an audience. Introducing a live, spontaneous element of performance in which the curators will respond in the moment. As such, this event proffers an alternative entry to reading artworks and is recommended to those interested in the expanded field of the curatorial. Live essaying will draw from individually gathered archives of material and will unfold, surround and contextualise the presented video artworks.
Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models.
Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid , gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with ≳90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.
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