Institution
University of Milano-Bicocca
Education•Milan, Italy•
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.
Topics: Population, Blood pressure, Large Hadron Collider, Branching fraction, Ambulatory blood pressure
Papers published on a yearly basis
Papers
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TL;DR: In this paper, an implementation of the vector boson pair production processes ZZ, Wυγγγαγαβαβββγβαγ βαβ β βαγββ βα βββα β β ββ ββααβγ ββγγ β βγβγα βγ βγγβ βγ αβα αββ αβγ is presented.
Abstract: We present an implementation of the vector boson pair production processes ZZ, W
+
W
− and WZ within the POWHEG framework, which is a method that allows the interfacing of NLO calculations to shower Monte Carlo programs. The implementation is built within the POWHEG BOX package. The Z/γ
* interference, as well as singly resonant contributions, are properly included. We also considered interference terms arising from identical leptons in the final state. As a result, all contributions leading to the desired four-lepton system have been included in the calculation, with the sole exception of the interference between ZZ and W
+
W
− in the production of a pair of same-flavour, oppositely charged fermions and a pair of neutrinos, which we show to be fully negligible. Anomalous trilinear couplings can be also set in the program, and we give some examples of their effect at the LHC. We have made the relevant code available at the POWHEG BOX web site.
289 citations
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TL;DR: In this article, a high-yield, low-cost synthesis route to colloidal colloidal Cu1-xInS2 nanocrystals with a tunable amount of Cu vacancies in the crystal lattice was reported.
Abstract: We report a high-yield, low cost synthesis route to colloidal Cu1-xInS2 nanocrystals with a tunable amount of Cu vacancies in the crystal lattice These are then converted into quaternary Cu–In–Zn–S (CIZS) nanocrystals by partial exchange of Cu+ and In3+ cations with Zn2+ cations The photoluminescence quantum yield of these CIZS nanocrystals could be tuned up to a record 80%, depending on the amount of copper vacancies
288 citations
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TL;DR: The complexity and the severity of the clinical presentation at birth and the high neonatal and infant mortality make the perinatal and neonatal management of babies with trisomy 18 particularly challenging, controversial, and unique among multiple congenital anomaly syndromes.
Abstract: The trisomy 18 syndrome, also known as Edwards syndrome, is a common chromosomal disorder due to the presence of an extra chromosome 18, either full, mosaic trisomy, or partial trisomy 18q. The condition is the second most common autosomal trisomy syndrome after trisomy 21. The live born prevalence is estimated as 1/6,000-1/8,000, but the overall prevalence is higher (1/2500-1/2600) due to the high frequency of fetal loss and pregnancy termination after prenatal diagnosis. The prevalence of trisomy 18 rises with the increasing maternal age. The recurrence risk for a family with a child with full trisomy 18 is about 1%. Currently most cases of trisomy 18 are prenatally diagnosed, based on screening by maternal age, maternal serum marker screening, or detection of sonographic abnormalities (e.g., increased nuchal translucency thickness, growth retardation, choroid plexus cyst, overlapping of fingers, and congenital heart defects ). The recognizable syndrome pattern consists of major and minor anomalies, prenatal and postnatal growth deficiency, an increased risk of neonatal and infant mortality, and marked psychomotor and cognitive disability. Typical minor anomalies include characteristic craniofacial features, clenched fist with overriding fingers, small fingernails, underdeveloped thumbs, and short sternum. The presence of major malformations is common, and the most frequent are heart and kidney anomalies. Feeding problems occur consistently and may require enteral nutrition. Despite the well known infant mortality, approximately 50% of babies with trisomy 18 live longer than 1 week and about 5-10% of children beyond the first year. The major causes of death include central apnea, cardiac failure due to cardiac malformations, respiratory insufficiency due to hypoventilation, aspiration, or upper airway obstruction and, likely, the combination of these and other factors (including decisions regarding aggressive care). Upper airway obstruction is likely more common than previously realized and should be investigated when full care is opted by the family and medical team. The complexity and the severity of the clinical presentation at birth and the high neonatal and infant mortality make the perinatal and neonatal management of babies with trisomy 18 particularly challenging, controversial, and unique among multiple congenital anomaly syndromes. Health supervision should be diligent, especially in the first 12 months of life, and can require multiple pediatric and specialist evaluations.
288 citations
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31 Jul 2014
288 citations
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TL;DR: The largest experiment of applying machine learning algorithms to code smells to the best of the authors' knowledge concludes that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.
Abstract: Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.
288 citations
Authors
Showing all 9226 results
Name | H-index | Papers | Citations |
---|---|---|---|
Carlo Rovelli | 146 | 1502 | 103550 |
Giuseppe Mancia | 145 | 1369 | 139692 |
Marco Bersanelli | 142 | 526 | 105135 |
Teruki Kamon | 142 | 2034 | 115633 |
Marco Colonna | 139 | 512 | 71166 |
M. I. Martínez | 134 | 1251 | 79885 |
A. Mennella | 132 | 463 | 93236 |
Roberto Salerno | 132 | 1197 | 83409 |
Federico Ferri | 132 | 1376 | 89337 |
Marco Paganoni | 132 | 1438 | 88482 |
Arabella Martelli | 131 | 1318 | 84029 |
Sandra Malvezzi | 129 | 1326 | 84401 |
Andrea Massironi | 129 | 1115 | 78457 |
Marco Pieri | 129 | 1285 | 82914 |
Cristina Riccardi | 129 | 1627 | 91452 |