Search results for " elaborazione"

Gl-learning

2016

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application s…

Topographic maps for clustering and fast identification of bacteria using 16s housekeeping gene

2012

In microbial identification the standard method to attribute a specific name to a bacterial isolate relays on the comparison of morphologic and phenotypic characters to those described for type or typical strains. In the last years a new standard for identifying bacteria using genotypic information began to be developed. In this new approach phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so called "housekeeping genes". The most commonly used gene for taxonomic purposes for bacteria is the 16S rRNA. The goal of this chapter is to show that genotypic features can be used to build a topographic map for clustering of a lar…

Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

2018

Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …

A Notation for Modeling Jason-Like BDI Agents

2012

The design and development of a large Multi Agent System (MAS) is a complex and difficult activity where a proper modeling notation may offer a significant contribution to the formulation of the best solution. The support provided by a specific CASE tool can significantly contribute to make the chosen approach technically valid and it is also a fundamental element of a feasible development strategy. The present work reports a UML profile and the related graphical notation for describing a MAS based on the Jason meta model. Moreover a specific CASE tool has been developed for supporting MASs design and automatic code generation. The proposed notation is shown in details using a classical exa…

Pervasive access to MRI bias artifact suppression service on a grid.

2009

Bias artifact corrupts magnetic resonance images in such a way that the image is afflicted by illumination variations. Some of the authors proposed the Exponential Entropy Driven - Homomorphic Unsharp Masking (E2D-HUM) algorithm that corrects this artifact without any a priori hypothesis about the tissues or the Magnetic Resonance image modality. Moreover, E2D-HUM does not care about the body part under examination and does not require any particular training task. People who want to use this algorithm, which is Matlab-based, have to set their own computers in order to execute it. Furthermore, they have to be Matlab-skilled to exploit all the features of the algorithm. In our work we propos…

Developing Self-Awareness in Robots via Inner Speech

2019

The experience of inner speech is a common one. Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness. Here, the widespread psychological models of inner speech are reviewed, and a cognitive architecture for a robot implementing such a capability is…

Maxwell-Dirac Theory and Occam's Razor: Unified Field, Elementary Particles, and Nuclear Interactions

2019

We introduce and use the space-time Clifford algebra, showing that only one fundamental physical entity is sufficient to describe the origin of electromagnetic fields, charges and currents: the electromagnetic four-potential. This simplified electromagnetic model turns out to be an improved understanding of electromagnetism. The obtained electromagnetic Lagrangian is the simplest possible relativistic Lagrangian formulation. Quantum mechanical relations follow naturally from this model, and we derive the electromagnetic formulation of the Dirac equation. The spinor field is shown to correspond to electromagnetic energy-momentum, and the complex-valued probability density is shown to corresp…

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

2020

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

Content based indexing of MPEG-4 video on relational DBMS

2001

Convolutional architectures for virtual screening

2020

Abstract Background A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. Results A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% …