Deep learning for automatic violence detection: tests on the AIRTLab dataset

Following the growing availability of video surveillance cameras and the need for techniques to automatically identify events in video footages, there is an increasing interest towards automatic violence detection in videos. Deep learning-based architectures, such as 3D Convolutional Neural Networks, demonstrated their capability of extracting spatio-temporal features from videos, being effective in violence detection. However, friendly behaviours or fast moves such as hugs, small hits, claps, high fives, etc., can still cause false positives, interpreting a harmless action as violent. To this end, we present three deep-learning based models for violence detection and test them on the AIRTLab dataset, a novel dataset designed to check the robustness of algorithms against false positives. The objective is twofold: on one hand, we compute accuracy metrics on the three proposed models (two are based on transfer learning and one is trained from scratch), building a baseline of metrics for the AIRTLab dataset; on the other hand, we validate the capability of the proposed dataset of challenging the robustness to false positives. The results of the proposed models are in line with the scientific literature, in terms of accuracy, with transfer learning-based networks exhibiting better generalization capabilities than the trained from scratch network. Moreover, the tests highlighted that most of the classification errors concern the identification of non-violent clips, validating the design of the proposed dataset. Finally, to demonstrate the significance of the proposed models, the paper presents a comparison with the related literature, as well as with models based on well-established pre-trained 2D Convolutional Neural Networks 2D CNNs. Such comparison highlights that 3D models get better accuracy performance than time distributed 2D CNNs (merged with a recurrent model) in processing the spatio-temporal features of video clips. The source code of the experiments and the AIRTLab dataset are available in public repositories

Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey

Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks

Premorbid intelligence of inpatients with different psychiatric diagnoses does not differ

The diagnostic specificity of poor premorbid intelligence is controversial. We explored premorbid intelligence level in psychiatric patients with personality disorders, depressive disorders, bipolar disorders and schizophrenic disorders. 273 consecutively admitted patients and 81 controls were included in the study and tested with the ‘Test di Intelligenza Breve’, an Italian adaptation of the National Adult Reading Test. Significant differences between the clinical samples and the control subjects were found but not among the 4 clinical groups. The observation of premorbid IQ deficits in subjects with diagnoses other than schizophrenia suggests a common vulnerability diathesis, which is most likely to have a neurodevelopmental basis

An Exact Renormalization Group analysis of 3-d Well Developed turbulence

We take advantage of peculiar properties of three dimensional incompressible turbulence to introduce a nonstandard Exact Renormalization Group method. A Galilean invariance preserving regularizing procedure is utilized and a field truncation is adopted to test the method. Results are encouraging: the energy spectrum E(k) in the inertial range scales with exponent -1.666+/- 0.001 and the Kolmogorov constant C_K, computed for several (realistic) shapes of the stirring force correlator, agrees with experimental data.Comment: 12 pg, 2figures, LaTex, To be published on Physics Letters

The laser-matter interaction meets the high energy physics: Laser-plasma accelerators and bright X/gamma-ray sources

Laser matter interaction in the regime of super-intense and ultra-short laser pulses is discovering common interests and goals for plasma and elementary particles physics. Among them, the electron laser wakefield acceleration and the X/γ tunable sources, based on the Thomson scattering (TS) of optical photons on accelerated electrons, represent the most challenging applications. The activity of the Intense Laser Irradiation Laboratory in this field will be presented

OsTIUM – Ostia’s Transformations: Investigating an Urban Model. Presentazione del progetto e dei risultati della prima campagna di documentazione della Domus del Portico di Tufo (2019)

L'intervento si inserisce nell'ambito di una giornata di studio organizzata presso l'Academia Belgica di Roma da Marco Cavalieri, Martina Marano, Julian Richard e Paolo Tomassini. Obiettivo della comunicazione è la presentazione delle ricerche effettuate ad Ostia dall'UCLouvain e dall'UNamur a partire dal 2019, con particolare riferimento alle campagne di studio e documentazione realizzate dalle due università presso la Domus del Portico di Tufo (parcella IV, VI, 1). Durante la giornata, il dott. Alessandro D'Alessio (Direttore del Parco Archeologico di Ostia Antica) e la dott.ssa Claudia Tempesta (Funzionario Archeologo presso il Parco Archeologico di Ostia Antica) presenteranno i risultati del progetto di ricerca, scavo e restauro delle c.d. Terme di Buticoso. Al termine dei lavori sarà presentato il volume di Ilaria Romeo, Scavi di Ostia. XVII. I ritratti. Parte 3: ritratti romani dal 250 circa al VI secolo d.C., Roma 2019 (ed. All’Insegna del Giglio)