Implementazione di algoritmi genetici multiobiettivo distribuiti in ambiente Matlab®

Parallelizzare gli algoritmi genetici multiobiettivo è un sistema molto efficacie per aumentare la potenza di calcolo e rimediare all’elevata complessità di questi strumenti. Il calcolo delle soluzioni non-dominate rappresenta però un ostacolo in quanto l’operazione deve disporre dell’intera popolazione. Per risolvere questo inconveniente è stato proposto nel 2004 un algoritmo chiamato cone separation, che sfrutta alcune proprietà del fronte di Pareto per poter distribuire il calcolo della non-dominanza tra più processori. In questa tesi viene riesaminata questa tecnica in modo approfondito, evidenziando pregi e difetti. Dall’analisi di quest’ultimi, vengono proposte una serie di varianti per migliorare la ricerca del fronte di Pareto e successivamente, vengono riassunte in un unico algoritmo chiamato ranking separation. Le simulazioni presentate nell’ultima parte della tesi mostrano che entrambi gli algoritmi sono capaci di trovare le soluzioni ottime del problema. Questi risultati sono ottenuti però con un diverso utilizzo delle risorse, in particolare, con un differente numero di dati trasmetti tra i processori. Sotto questo aspetto l’algoritmo ranking separation dimostra di funzionare correttamente con un numero limitato e controllato di individui che migrano

A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

Short-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative

Becoming JILDA

The difficulty in finding use-ful dialogic data to train a conversationalagent is an open issue even nowadays,when chatbots and spoken dialogue sys-tems are widely used. For this reason wedecided to build JILDA, a novel data col-lection of chat-based dialogues, producedby Italian native speakers and related to thejob-offer domain. JILDA is the first dia-logue collection related to this domain forthe Italian language. Because of its collec-tion modalities, we believe that JILDA canbe a useful resource not only for the Italianresearch community, but also for the inter-national one

3d plasmonic nanoantennas integrated with mea biosensors

Plasmonic 3D nanoantennas are integrated on multielectrode arrays. These biosensors can record extracellular activity and enhance Raman signals from living neurons

The Role of PIXE and XRF in Heritage Science: The INFN-CHNet LABEC Experience

Analytical techniques play a fundamental role in heritage science. Among them, Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence (XRF) techniques are widely used in many laboratories for elemental composition analysis. Although they are well-established, a strong effort is put on their upgrade, making them suitable for more and more applications. Over the years, at the INFN-LABEC (the laboratory of nuclear techniques for the environment and cultural heritage of the Italian National Institute of Nuclear Physics), the INFN-CHNet group, the network devoted to cultural heritage, has carried out many technological improvements to the PIXE and XRF set-ups for the analysis of works of art and archaeological finds. Among the many, we recall here the scanning external microbeam facility at the TANDEM accelerator and the MA-XRF scanner. The two instruments have shown complementary features: the former permits quantitative analysis of elements heavier than sodium, which is not possible with the latter in most of the case studies. On the contrary, the scanner has the undeniable advantage of portability, allowing it to work in situ. In this framework of technological developments in heritage science, INFN, CERN, and OPD are jointly carrying on the MACHINA (Movable Accelerator for Cultural Heritage In-situ Non-destructive Analysis) project for on-site Ion Beam Analysis (IBA) studies on cultural heritage