University of Milano-Bicocca

About: University of Milano-Bicocca is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Blood pressure. The organization has 8972 authors who have published 22322 publications receiving 620484 citations. The organization is also known as: Università degli Studi di Milano-Bicocca & Universita degli Studi di Milano-Bicocca.

Searching for Neutrinoless Double-Beta Decay of 130Te with CUORE

Abstract: We report the results of a search for neutrinoless double-beta decay in a 9.8 kg yr exposure of (130)Te using a bolometric detector array, CUORE-0. The characteristic detector energy resolution and background level in the region of interest are 5.1±0.3 keV FWHM and 0.058±0.004(stat)±0.002(syst)counts/(keV kg yr), respectively. The median 90% C.L. lower-limit half-life sensitivity of the experiment is 2.9×10(24) yr and surpasses the sensitivity of previous searches. We find no evidence for neutrinoless double-beta decay of (130)Te and place a Bayesian lower bound on the decay half-life, T(1/2)(0ν)>2.7×10(24) yr at 90% C.L. Combining CUORE-0 data with the 19.75 kg yr exposure of (130)Te from the Cuoricino experiment we obtain T(1/2)(0ν)>4.0×10(24) yr at 90% C.L. (Bayesian), the most stringent limit to date on this half-life. Using a range of nuclear matrix element estimates we interpret this as a limit on the effective Majorana neutrino mass, m(ββ)<270-760 meV.

The cosmic merger rate of stellar black hole binaries from the Illustris simulation

Abstract: The cosmic merger rate density of black hole binaries (BHBs) can give us an essential clue to constraining the formation channels of BHBs, in light of current and forthcoming gravitational wave detections. Following a Monte Carlo approach, we couple new population-synthesis models of BHBs with the Illustris cosmological simulation, to study the cosmic history of BHB mergers. We explore six population-synthesis models, varying the prescriptions for supernovae, common envelope and natal kicks. In most considered models, the cosmic BHB merger rate follows the same trend as the cosmic star formation rate. The normalization of the cosmic BHB merger rate strongly depends on the treatment of common envelope and on the distribution of natal kicks. We find that most BHBs merging within LIGO's instrumental horizon come from relatively metal-poor progenitors (<0.2 Z_⊙). The total masses of merging BHBs span a large range of values, from ∼6 to ∼82 M_⊙. In our fiducial model, merging BHBs consistent with GW150914, GW151226 and GW170104 represent ∼6, 3 and 12 per cent of all BHBs merging within the LIGO horizon, respectively. The heavy systems, like GW150914, come from metal-poor progenitors (<0.15 Z_⊙). Most GW150914-like systems merging in the local Universe appear to have formed at high redshift, with a long delay time. In contrast, GW151226-like systems form and merge all the way through the cosmic history, from progenitors with a broad range of metallicities. Future detections will be crucial to put constraints on common envelope, on natal kicks, and on the BHB mass function.

Sentiment analysis

Abstract: The huge amount of textual data on the Web has grown in the last few years rapidly creating unique contents of massive dimension. In a decision making context, one of the most relevant tasks is polarity classification of a text source, which is usually performed through supervised learning methods. Most of the existing approaches select the best classification model leading to over-confident decisions that do not take into account the inherent uncertainty of the natural language. In this paper, we pursue the paradigm of ensemble learning to reduce the noise sensitivity related to language ambiguity and therefore to provide a more accurate prediction of polarity. The proposed ensemble method is based on Bayesian Model Averaging, where both uncertainty and reliability of each single model are taken into account. We address the classifier selection problem by proposing a greedy approach that evaluates the contribution of each model with respect to the ensemble. Experimental results on gold standard datasets show that the proposed approach outperforms both traditional classification and ensemble methods. A novel ensemble learning methodology is proposed for polarity classification task.A selection strategy is studied to reduce the search space of candidate ensembles.The proposed model has been shown to be effective and efficient in several domains.

Integrated FDG PET/CT in patients with persistent ovarian cancer: correlation with histologic findings.

Abstract: PURPOSE: To prospectively evaluate the accuracy of integrated positron emission tomography (PET) and computed tomography (CT) for depiction of persistent ovarian carcinoma after first-line treatment, with use of histologic findings as the reference standard. MATERIALS AND METHODS: Thirty-one women (mean age, 55.9 years) with ovarian carcinoma treated with primary cytoreductive surgery and followed up with platinum regimen chemotherapy were included. All 31 patients were scheduled for surgical second-look. Before surgical second-look, all patients underwent fluorodeoxyglucose (FDG) PET/CT. At PET/CT, three main categories of persistent disease were considered for data analysis: lymph nodal lesion, peritoneal lesion, and pelvic lesion. In all patients, imaging findings were compared with results of histologic examination after surgical second-look to determine the diagnostic accuracy of PET/CT in the evaluation of disease status. The κ statistic (Cohen κ) was used for statistical analysis. RESULTS: Seventee...

Long-term results of the Italian Association of Pediatric Hematology and Oncology (AIEOP) Studies 82, 87, 88, 91 and 95 for childhood acute lymphoblastic leukemia.

Abstract: We analyzed the long-term outcome of 4865 patients treated in Studies 82, 87, 88, 91 and 95 for childhood acute lymphoblastic leukemia (ALL) of the Italian Association of Pediatric Hematology and Oncology (AIEOP). Treatment was characterized by progressive intensification of systemic therapy and reduction of cranial radiotherapy. A progressive improvement of results with reduction of isolated central nervous system relapse rate was obtained. Ten-year event-free survival increased from 53% in Study 82 to 72% in Study 95, whereas survival improved from 64 to 82%. Since 1991, all patients were treated according to Berlin-Frankfurt-Muenster (BFM) ALL treatment strategy. In Study 91, reduced treatment intensity (25%) yielded inferior results, but intensification of maintenance with high-dose (HD)-L-asparaginase (randomized) allowed to compensate for this disadvantage; in high-risk patients (HR, 15%), substitution of intensive polychemotherapy blocks for conventional BFM backbone failed to improve results. A marked improvement of results was obtained in HR patients when conventional BFM therapy was intensified with three polychemotherapy blocks and double delayed intensification (Study 95). The introduction of minimal residual disease monitoring and evaluation of common randomized questions by AIEOP and BFM groups in the protocol AIEOP-BFM-ALL 2000 are expected to further ameliorate treatment of children with ALL.