University of Sannio

About: University of Sannio is a education organization based out in Benevento, Italy. It is known for research contribution in the topics: Gravitational wave & LIGO. The organization has 1278 authors who have published 6125 publications receiving 167577 citations. The organization is also known as: Università degli Studi del Sannio & Universita degli Studi del Sannio.

The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914

Abstract: A transient gravitational-wave signal, GW150914, was identified in the twin Advanced LIGO detectors on September 14, 2015 at 09:50:45 UTC. To assess the implications of this discovery, the detectors remained in operation with unchanged configurations over a period of 39 d around the time of the signal. At the detection statistic threshold corresponding to that observed for GW150914, our search of the 16 days of simultaneous two-detector observational data is estimated to have a false alarm rate (FAR) of < 4.9 × 10^(−6) yr^(−1), yielding a p-value for GW150914 of < 2 × 10^(−7). Parameter estimation followup on this trigger identifies its source as a binary black hole (BBH) merger with component masses (m_1, m_2) = (36^(+5)_(−4), 29^(+4)_(−4)) M_⊙ at redshift z = 0.09^(+0.03)_(−0.04) (median and 90\% credible range). Here we report on the constraints these observations place on the rate of BBH coalescences. Considering only GW150914, assuming that all BBHs in the Universe have the same masses and spins as this event, imposing a search FAR threshold of 1 per 100 years, and assuming that the BBH merger rate is constant in the comoving frame, we infer a 90% credible range of merger rates between 2--53 Gpc^(−3) yr^(−1) (comoving frame). Incorporating all search triggers that pass a much lower threshold while accounting for the uncertainty in the astrophysical origin of each trigger, we estimate a higher rate, ranging from 13--600 Gpc^(−3) yr^(−1) depending on assumptions about the BBH mass distribution. All together, our various rate estimates fall in the conservative range 2--600 Gpc^(−3) yr^(−1).

How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms

Abstract: Information Retrieval (IR) methods, and in particular topic models, have recently been used to support essential software engineering (SE) tasks, by enabling software textual retrieval and analysis. In all these approaches, topic models have been used on software artifacts in a similar manner as they were used on natural language documents (e.g., using the same settings and parameters) because the underlying assumption was that source code and natural language documents are similar. However, applying topic models on software data using the same settings as for natural language text did not always produce the expected results. Recent research investigated this assumption and showed that source code is much more repetitive and predictable as compared to the natural language text. Our paper builds on this new fundamental finding and proposes a novel solution to adapt, configure and effectively use a topic modeling technique, namely Latent Dirichlet Allocation (LDA), to achieve better (acceptable) performance across various SE tasks. Our paper introduces a novel solution called LDA-GA, which uses Genetic Algorithms (GA) to determine a near-optimal configuration for LDA in the context of three different SE tasks: (1) traceability link recovery, (2) feature location, and (3) software artifact labeling. The results of our empirical studies demonstrate that LDA-GA is ableto identify robust LDA configurations, which lead to a higher accuracy on all the datasets for these SE tasks as compared to previously published results, heuristics, and the results of a combinatorial search.