Deep Incremental Learning for Object Recognition

In recent years, deep learning techniques received great attention in the field of information technology. These techniques proved to be particularly useful and effective in domains like natural language processing, speech recognition and computer vision. In several real world applications deep learning approaches improved the state-of-the-art. In the field of machine learning, deep learning was a real revolution and a number of effective techniques have been proposed for both supervised and unsupervised learning and for representation learning. This thesis focuses on deep learning for object recognition, and in particular, it addresses incremental learning techniques. With incremental learning we denote approaches able to create an initial model from a small training set and to improve the model as new data are available. Using temporal coherent sequences proved to be useful for incremental learning since temporal coherence also allows to operate in unsupervised manners. A critical point of incremental learning is called forgetting which is the risk to forget previously learned patterns as new data are presented. In the first chapters of this work we introduce the basic theory on neural networks, Convolutional Neural Networks and incremental learning. CNN is today one of the most effective approaches for supervised object recognition; it is well accepted by the scientific community and largely used by ICT big players like Google and Facebook: relevant applications are Facebook face recognition and Google image search. The scientific community has several (large) datasets (e.g., ImageNet) for the development and evaluation of object recognition approaches. However very few temporally coherent datasets are available to study incremental approaches. For this reason we decided to collect a new dataset named TCD4R (Temporal Coherent Dataset For Robotics)

Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

I luoghi della collettività. Tiburtino III tra residenti e city users

Il presente contributo prende le mosse dai risultati di una lunga ricerca sul campo (circa 7 anni, dal 2005 al 2013) relativa alla borgata di Tiburtino III (Maggioli, Morri, 2006, 2009a, 2011; Morri, Maggioli et al., 2013), un parallelepipedo che, nelle periferia orientale di Roma, si sviluppa nel senso della lunghezza tra via Tiburtina e via Collatina

Bounding the mass of ultralight bosonic Dark Matter particles with the motion of the S2 star around Sgr A*

Dark matter is undoubtedly one of the fundamental, albeit unknown, components of the standard cosmological model. The failure to detect WIMPs, the most promising candidate particle for cold dark matter, actually opens the way for the exploration of viable alternatives, of which ultralight bosonic particles with masses $\sim 10^{-21}$ eV represent one of the most encouraging. Numerical simulations have shown that such particles form solitonic cores in the innermost parts of virialized galactic halos that are supported by internal quantum pressure on characteristic $\sim$kpc de Broglie scales. In the Galaxy, this halo region can be probed by means of S-stars orbiting the supermassive black hole Sagittarius A* to unveil the presence of such a solitonic core and, ultimately, to bound the boson mass $m_\psi$. Employing a Monte Carlo Markov Chain algorithm, we compare the predicted orbital motion of S2 with publicly available data and set an upper bound $m_\psi \lesssim 3.2\times 10^{-19}$ eV on the boson mass, at 95 \% confidence level. When combined with other galactic and cosmological probes, our constraints help to reduce the allowed range of the bosonic mass to $(2.0 \lesssim m_\psi \lesssim 32.2)\times 10^{-20}$ eV, at the 95 \% confidence level, which opens the way to precision measurements of the mass of the ultralight bosonic dark matter.Comment: 6 pages, 2 figures, 1 table. Accepted for publication on PRD. Additional plot and related code at http://produccioncientifica.usal.es/datos/6464bdb7a842f677be8feeb

Shutting the allowed mass range of the ultralight bosons with S2 star

It is well known that N-body simulations of ultralight bosons show the formation of a solitonic dark matter core in the innermost part of the halo. The scale length of such a soliton depends on the inverse of the mass of the boson. On the other hand, the orbital motion of stars in the Galactic Center depends on the distribution of matter whether be it baryonic or dark, providing an excellent probe for the gravitational field of the region. In this Letter we propose the S-stars in the Galactic Center as a new observational tool, complementary to other astrophysical systems, to narrow down the range of allowed values for an ultralight dark matter candidate boson mass. We built mock catalogs mirroring the forthcoming astrometric and spectroscopic observations of S2, and we used a MCMC analysis to predict the accuracy down to which the mass of an ultralight boson may be bounded, and we showed that, once complementary constraints are considered, this analysis will help to restrict the allowed range of the boson mass. Our analysis forecasts the bound on the mass of an ultralight boson to be $< 10^{-19}$ eV at the 95% of confidence level.Comment: 5 pages, 2 figures, 1 table, 5 appendices. Accepted for publication in A&A Letter

Fluids mobilization in Arabia Terra, Mars: depth of pressurized reservoir from mounds self-similar clustering

Arabia Terra is a region of Mars where signs of past-water occurrence are recorded in several landforms. Broad and local scale geomorphological, compositional and hydrological analyses point towards pervasive fluid circulation through time. In this work we focus on mound fields located in the interior of three casters larger than 40 km (Firsoff, Kotido and unnamed crater 20 km to the east) and showing strong morphological and textural resemblance to terrestrial mud volcanoes and spring-related features. We infer that these landforms likely testify the presence of a pressurized fluid reservoir at depth and past fluid upwelling. We have performed morphometric analyses to characterize the mound morphologies and consequently retrieve an accurate automated mapping of the mounds within the craters for spatial distribution and fractal clustering analysis. The outcome of the fractal clustering yields information about the possible extent of the percolating fracture network at depth below the craters. We have been able to constrain the depth of the pressurized fluid reservoir between ~2.5 and 3.2 km of depth and hence, we propose that mounds and mounds alignments are most likely associated to the presence of fissure ridges and fluid outflow. Their process of formation is genetically linked to the formation of large intra-crater bulges previously interpreted as large scale spring deposits. The overburden removal caused by the impact crater formation is the inferred triggering mechanism for fluid pressurization and upwelling, that through time led to the formation of the intra-crater bulges and, after compaction and sealing, to the widespread mound fields in their surroundings