DR(eye)VE: a Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving

Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations

Future Urban Scenes Generation Through Vehicles Synthesis

In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset

Penetration of Sodium Hypochlorite Modified with Surfactants into Root Canal Dentin

Abstract The aim of this study was to evaluate the effect of concentration, exposure time and temperature of sodium hypochlorite (NaOCl) added with surfactants on its penetration into dentinal tubules. Sixty-five extracted human permanent maxillary anterior teeth with single canals were prepared by ProTaper SX hand-operated instruments. The teeth were then sectioned perpendicular to the long axis. The crowns and apical thirds of all the teeth were removed. The remaining roots were processed into 4-mm-long blocks and stained overnight in crystal violet. One hundred and thirty stained blocks were further split into halves and treated by nine different types of NaOCl-based solutions. Three solutions were added with surfactants (Hypoclean, H6, Chlor-Xtra) and the others were regular hypochlorites at increasing concentrations (1%, 2%, 4%, 5.25%, <6%, 6% NaOCl) from different brands. The dentin blocks were exposed to the solutions for 2, 5, and 20 min at 20 °C, 37 °C and 45 °C, respectively. The depth of NaOCl penetration was determined by bleaching of the stain and measured by light microscopy at 20 and 40. Statistical comparisons were made by using a generalized linear model with Bonferroni's post-hoc correction. The shortest penetration (81±6.6 μm) was obtained after incubation in 1% NaOCl for 2 min at 20 °C; the highest penetration (376.3±3.8 μm) was obtained with Chlor-Xtra for 20 min at 45 °C. Varying NaOCl concentration produced a minimal effect while temperature and exposure time had a significant direct relationship with NaOCl penetration into dentinal tubules, especially those with lowered surface tension. The exposure time and temperature of sodium hypochlorite as well as the addition of surfactants may influence the penetration depth of irrigants into dentinal tubules

Cyclotron lines in X-ray pulsars as a probe of relativistic plasmas in superstrong magnetic fields

The systematic search for the presence of cyclotron lines in the spectra of accreting X-ray pulsars is being carried on with the BeppoSAX satellite since the beginning of the mission. These highly successful observations allowed the detection of cyclotron lines in many of the accreting X-ray pulsars observed. Some correlations between the different measured parameters were found. We present these correlations and discuss them in the framework of the current theoretical scenario for the X-ray emission from these sources.Comment: 5 pages, 2 figures, uses aipproc.sty, to appear in Proceeding of Fifth Compton Symposiu

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry