Ricerc@Sapienza

In the last few years, computer-assisted diagnosis systems have obtained a growing interest from researchers thanks to the use of deep learning techniques. We propose a deep neural network based on a multi-input architecture that allows to use all the information available to physicians during the diagnosis. The results obtained show an interesting improvement in performance in terms of predictive skill compared to the results in the literature.

Abstract: In recent years, interest has grown in addressing the problem of encoding categorical variables, especially in deep learning applied to big-data. However, the current proposals are not entirely satisfactory. The aim of this work is to show the logic and advantages of a new encoding method that takes its cue from the recent word embedding proposals and which we have called Categorical Embedding. Both a supervised and an unsupervised approach will be considered.

In recent years, computer-assisted diagnostic systems increasingly gained
interest through the use of deep learning techniques. Surely, the medical field could
be one of the best environments in which the power of the AI algorithms can be
tangible for everyone. Deep learning models can be useful to help radiologists elaborate
fast and even more accurate diagnosis or accelerate the triage systems in hospitals.
However, differently from other fields of works, the collaboration and co-work

To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, subjective sleep quality and the degree of sleep disruption in large community-based cohort studies of older people.

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and

Reverse Engineering (RE) may help tolerance inspection during production by digitalization of analyzed components and their comparison with design requirements. RE techniques are already applied for geometrical and tolerance shape control. Plastic injection molding is one of the fields where it may be applied, in particular for die set-up of multi-cavities, since no severe accuracy is required for the acquisition system. In this field, RE techniques integrated with Computer-Aided tools for tolerancing and inspection may contribute to the so-called “Smart Manufacturing”.

The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc. In this paper we describe SAFE, a novel architecture for function representation based on a self-attentive neural network.

In this paper, we propose a Deep Recurrent Neural Network (DRNN) approach based on Long-Short Term Memory (LSTM) units for the classification of audio signals recorded in construction sites. Five classes of multiple vehicles and tools, normally used in construction sites, have been considered. The input provided to the DRNN consists in the concatenation of several spectral features, like MFCCs, mel-scaled spectrogram, chroma and spectral contrast. The proposed architecture and the feature extraction have been described.

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19.

The large-scale penetration of renewable energy sources is forcing the transition towards
the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new
methodologies for the dynamic energy management of distributed energy resources and foster to form
partnerships and overcome integration barriers. The prediction of energy production of renewable energy
sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool