Institution
University of Trento
Education•Trento, Italy•
About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the first study of isolated photon + jet correlations in relativistic heavy ion collisions is reported using data from PbPb collisions at a centre-of-mass energy of 2.76 TeV2.
188 citations
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TL;DR: A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications.
Abstract: This paper proposes a new class of estimators of an unknown entropy of random vector Its asymptotic unbiasedness and consistency are proved Further, this class of estimators is used to build both goodness-of-fit and independence tests based on sample entropy A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications
188 citations
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Icahn School of Medicine at Mount Sinai1, Sage Bionetworks2, Duke University3, University of Pittsburgh4, National Institutes of Health5, Carnegie Mellon University6, High Point University7, Cedars-Sinai Medical Center8, Rush University Medical Center9, Broad Institute10, University of North Carolina at Chapel Hill11, Takeda Pharmaceutical Company12, University of Pennsylvania13, Veterans Health Administration14, University of Trento15
TL;DR: Co-expression analyses identify a gene module that shows enrichment for genetic associations and is thus relevant for schizophrenia, paving the way for mechanistic interpretations of genetic liability for schizophrenia and other brain diseases.
Abstract: Over 100 genetic loci harbor schizophrenia associated variants, yet how these common variants confer risk is uncertain. The CommonMind Consortium has sequenced dorsolateral prefrontal cortex RNA from schizophrenia cases (n=258) and control subjects (n=279), creating the largest publicly available resource to date of gene expression and its genetic regulation; ~5 times larger than the latest release of GTEx. Using this resource, we find that ~20% of the schizophrenia risk loci have common variants that could explain regulation of brain gene expression. In five loci, these variants modulate expression of a single gene: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Experimentally altered expression of three of them, FURIN, TSNARE1, and CNTN4, perturbs the proliferation and apoptotic index of neural progenitors and leads to neuroanatomical deficits in zebrafish. Furthermore, shRNA mediated knock-down of FURIN in neural progenitor cells derived from human induced pluripotent stem cells produces abnormal neural migration. Although 4.2% of genes (N = 693) display significant differential expression between cases and controls, 44% show some evidence for differential expression. All fold changes are ≤ 1.33, and an independent cohort yields similar differential expression for these 693 genes (r = 0.58). These findings are consistent with schizophrenia being highly polygenic, as has been reported in investigations of common and rare genetic variation. Co-expression analyses identify a gene module that shows enrichment for genetic associations and is thus relevant for schizophrenia. Taken together, these results pave the way for mechanistic interpretations of genetic liability for schizophrenia and other brain diseases.
187 citations
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TL;DR: The findings indicate that the bacterial adhesion is influenced by the chemical properties of the polymeric surface, and may be interpreted taking into account a mechanism in which the acid/base (Lewis) interaction plays an important role.
187 citations
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01 Jan 2019TL;DR: Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data, are proposed, which outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.
Abstract: Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality detection), where D has been trained without the need of manually-annotated abnormal data. Moreover, in order to prevent G learn a trivial identity function, we use a cross-channel approach, forcing G to transform raw-pixel data in motion information and vice versa. The quantitative results on standard benchmarks show that our method outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.
187 citations
Authors
Showing all 10758 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Jie Zhang | 178 | 4857 | 221720 |
Richard B. Lipton | 176 | 2110 | 140776 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
J. N. Butler | 172 | 2525 | 175561 |
Andrea Bocci | 172 | 2402 | 176461 |
P. Chang | 170 | 2154 | 151783 |
Bradley Cox | 169 | 2150 | 156200 |
Marc Weber | 167 | 2716 | 153502 |
Guenakh Mitselmakher | 165 | 1951 | 164435 |
Brian L Winer | 162 | 1832 | 128850 |
J. S. Lange | 160 | 2083 | 145919 |
Ralph A. DeFronzo | 160 | 759 | 132993 |
Darien Wood | 160 | 2174 | 136596 |
Robert Stone | 160 | 1756 | 167901 |