Identification and characterization of nursery areas of red mullet Mullus barbatus in the Central Tyrrhenian Sea

Red Mullet Mullus barbatus is an important target of fishing activities in the central Tyrrhenian Sea, so it is essential to identify its critical habitats in order to manage this resource efficiently. Our research specifically focused on the identification and characterization of nursery areas. The use of spatial interpolation techniques enabled us to identify five nurseries that were highly persistent through time. Moreover, the estimate of juvenile density confirmed the strong aggregation effect of these nursery grounds, as a great portion of young individuals were concentrated in a relatively small surface of the study area. The environmental characterization of these areas showed that juveniles were mainly distributed on bottoms with a relatively high percentage of sand (>70%; P <0.05). Shannon biodiversity index analysis indicated that the southern nurseries reached the highest values of habitat quality (P < 0.0001). Multivariate analysis showed that nursery grounds were divided into three main groups, and analysis of spatial dynamics showed that two different strategies characterized Red Mullet juveniles when density changes over time. In particular, in some areas young individuals selected habitats in a density-dependent way following the basin model scheme, while in other zones they selected habitats in a density-independent way according to the proportional density model. Results also showed that juveniles followed the proportional density model strategy into nursery areas with the highest Shannon biodiversity index values

Conditional Invertible Generative Models for Supervised Problems

Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data

The DECIDE Project: Designing and Implementing a Prototype Service for Supporting Early Diagnosis of Alzheimer's Disease

This paper will present the design and implementation challenges of the innovative DECIDE service, to support research and early diagnosis of Alzheimer’s and other neurodegenerative diseases. DECIDE service, which is based on a Grid eInfrastructure, offers a set of tools providing quantitative measurements, to help researchers and clinicians make more informed diagnosis. As the service specifically targets the clinical community, it differs significantly from other initiatives since it needs to comply with the requirements imposed by the clinical routine in terms of accuracy, robustness, ease of use, data handling policies, adherence to clinical praxis. Moreover, sustainability aspects will also be discussed, since DECIDE aims to propose such service as a reference at European level, possibly extending it to other pathologies. We will then summarize the main results obtained to date, and the possible future developments

Learning the dynamics of articulated tracked vehicles

In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV

Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

Image Quality Assessment by Saliency Maps

Image Quality Assessment (IQA) is an interesting challenge for image processing applications. The goal of IQA is to replace human judgement of perceived image quality with a machine evaluation. A large number of methods have been proposed to evaluate the quality of an image which may be corrupted by noise, distorted during acquisition, transmission, compression, etc. Many methods, in some cases, do not agree with human judgment because they are not correlated with human visual perception. In the last years the most modern IQA models and metrics considered visual saliency as a fundamental issue. The aim of visual saliency is to produce a saliency map that replicates the human visual system (HVS) behaviour in visual attention process. In this paper we show the relationship between different kind of visual saliency maps and IQA measures. We particularly perform a lot of comparisons between Saliency-Based IQA Measures and traditional Objective IQA Measure. In Saliency scientific literature there are many different approaches for saliency maps, we want to investigate which is best one for IQA metrics

Saliency Map for Visual Perception

Human and other primates move their eyes to select visual information from the scene, psycho-visual experiments (Constantinidis, 2005) suggest that attention is directed to visually salient locations in the image. This allows human beings to bring the fovea onto the relevant parts of the image, to interpret complex scenes in real time. In visual perception, an important result was the discovery of a limited set of visual properties (called pre attentive), detected in the first 200-300 milliseconds of observation of a scene, by the low-level visual system. In last decades many progresses have been made into research of visual perception by analyzing both bottom up (stimulus driven) and top down (task dependent) processes involved in human attention. Visual Saliency deals with identifying fixation points that a human viewer would focus on the first seconds of the observation of a scene