scispace - formally typeset
Search or ask a question
Institution

University of Sannio

EducationBenevento, Italy
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.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, a model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints, and the trade off between the vehicle speed and the required preview on the desired path is highlighted.
Abstract: In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.

385 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Matthew Abernathy1  +977 moreInstitutions (106)
TL;DR: In this paper, the results of a matched-filter search using relativistic models of compact-object binaries that recovered GW150914 as the most significant event during the coincident observations between the two LIGO detectors were reported.
Abstract: On September 14, 2015, at 09∶50:45 UTC the two detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) simultaneously observed the binary black hole merger GW150914. We report the results of a matched-filter search using relativistic models of compact-object binaries that recovered GW150914 as the most significant event during the coincident observations between the two LIGO detectors from September 12 to October 20, 2015 GW150914 was observed with a matched-filter signal-to-noise ratio of 24 and a false alarm rate estimated to be less than 1 event per 203000 years, equivalent to a significance greater than 5.1 σ.

384 citations

Journal ArticleDOI
Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3, Fausto Acernese4  +1692 moreInstitutions (195)
TL;DR: In this article, the authors reported the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.
Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

374 citations

Journal ArticleDOI
TL;DR: Nongenomic actions expand the repertoire of cellular events controlled by thyroid hormone and can modulate TR-dependent nuclear events.
Abstract: The nongenomic actions of thyroid hormone begin at receptors in the plasma membrane, mitochondria or cytoplasm. These receptors can share structural homologies with nuclear thyroid hormone receptors (TRs) that mediate transcriptional actions of T3, or have no homologies with TR, such as the plasma membrane receptor on integrin αvβ3. Nongenomic actions initiated at the plasma membrane by T4 via integrin αvβ3 can induce gene expression that affects angiogenesis and cell proliferation, therefore, both nongenomic and genomic effects can overlap in the nucleus. In the cytoplasm, a truncated TRα isoform mediates T4-dependent regulation of intracellular microfilament organization, contributing to cell and tissue structure. p30 TRα1 is another shortened TR isoform found at the plasma membrane that binds T3 and mediates nongenomic hormonal effects in bone cells. T3 and 3,5-diiodo-L-thyronine are important to the complex nongenomic regulation of cellular respiration in mitochondria. Thus, nongenomic actions expand the repertoire of cellular events controlled by thyroid hormone and can modulate TR-dependent nuclear events. Here, we review the experimental approaches required to define nongenomic actions of the hormone, enumerate the known nongenomic effects of the hormone and their molecular basis, and discuss the possible physiological or pathophysiological consequences of these actions.

374 citations

Proceedings ArticleDOI
01 Dec 2015
TL;DR: This paper study analytically and experimentally how under sampling affects the posterior probability of a machine learning model, and uses Bayes Minimum Risk theory to find the correct classification threshold and show how to adjust it after under sampling.
Abstract: Under sampling is a popular technique for unbalanced datasets to reduce the skew in class distributions. However, it is well-known that under sampling one class modifies the priors of the training set and consequently biases the posterior probabilities of a classifier. In this paper, we study analytically and experimentally how under sampling affects the posterior probability of a machine learning model. We formalize the problem of under sampling and explore the relationship between conditional probability in the presence and absence of under sampling. Although the bias due to under sampling does not affect the ranking order returned by the posterior probability, it significantly impacts the classification accuracy and probability calibration. We use Bayes Minimum Risk theory to find the correct classification threshold and show how to adjust it after under sampling. Experiments on several real-world unbalanced datasets validate our results.

369 citations


Authors

Showing all 1300 results

NameH-indexPapersCitations
Alberto Vecchio11557279416
Andrea Alù109113847717
Vijay P. Singh106169955831
Kenneth A. Strain10548570966
N. A. Robertson10538469504
G. D. Hammond10035267549
B. Sorazu9834765989
I. W. Martin9735264772
Maria Ilaria Del Principe9339862000
Innocenzo M. Pinto8937856567
Karl Henrik Johansson88108933751
Vincenzo Pierro8326342535
R. DeSalvo8322551227
Paolo Addesso7120245552
Francesco Borrelli6632717254
Network Information
Related Institutions (5)
University of Pisa
73.1K papers, 2.1M citations

88% related

University of Bologna
115.1K papers, 3.4M citations

88% related

University of Naples Federico II
68.8K papers, 1.9M citations

88% related

University of Padua
114.8K papers, 3.6M citations

88% related

Royal Institute of Technology
68.4K papers, 1.9M citations

88% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
202254
2021404
2020401
2019389
2018376