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, a method was developed that yields the residual stress, the orientation distribution coefficients, the average crystallite dimension, the microstrain, and the crystal structure parameters from x-ray diffraction data in a single step procedure.
Abstract: A method is developed that yields the residual stress, the orientation distribution coefficients, the average crystallite dimension, the microstrain, and the crystal structure parameters from x‐ray diffraction data in a single‐step procedure. To this end, a general approach is introduced that combines the equations of micromechanics with the harmonic description of texture. All relationships are cast into a Rietveld‐like format, which incorporates a microstructure model derived from line‐broadening methods. In this manner, data collected over the whole x‐ray‐diffraction pattern at different tilting of the sample can be fitted directly. The associated fitting parameters are the crystal structure and microstructure, the texture coefficients, and the micromechanical properties and fields.
310 citations
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TL;DR: A novel unsupervised context-sensitive framework—deep change vector analysis (DCVA)—for CD in multitemporal VHR images that exploit convolutional neural network (CNN) features is proposed and experimental results on mult itemporal data sets of Worldview-2, Pleiades, and Quickbird images confirm the effectiveness of the proposed method.
Abstract: Change detection (CD) in multitemporal images is an important application of remote sensing. Recent technological evolution provided very high spatial resolution (VHR) multitemporal optical satellite images showing high spatial correlation among pixels and requiring an effective modeling of spatial context to accurately capture change information. Here, we propose a novel unsupervised context-sensitive framework—deep change vector analysis (DCVA)—for CD in multitemporal VHR images that exploit convolutional neural network (CNN) features. To have an unsupervised system, DCVA starts from a suboptimal pretrained multilayered CNN for obtaining deep features that can model spatial relationship among neighboring pixels and thus complex objects. An automatic feature selection strategy is employed layerwise to select features emphasizing both high and low prior probability change information. Selected features from multiple layers are combined into a deep feature hypervector providing a multiscale scene representation. The use of the same pretrained CNN for semantic segmentation of single images enables us to obtain coherent multitemporal deep feature hypervectors that can be compared pixelwise to obtain deep change vectors that also model spatial context information. Deep change vectors are analyzed based on their magnitude to identify changed pixels. Then, deep change vectors corresponding to identified changed pixels are binarized to obtain a compressed binary deep change vectors that preserve information about the direction (kind) of change. Changed pixels are analyzed for multiple CD based on the binary features, thus implicitly using the spatial information. Experimental results on multitemporal data sets of Worldview-2, Pleiades, and Quickbird images confirm the effectiveness of the proposed method.
310 citations
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TL;DR: It is shown by angle-resolved photoemission spectroscopy that a tunable gap in quasi-free-standing monolayer graphene on Au can be induced by hydrogenation, and thereby provides a model system to study hydrogen storage in carbon materials.
Abstract: We show by angle-resolved photoemission spectroscopy that a tunable gap in quasi-free-standing monolayer graphene on Au can be induced by hydrogenation. The size of the gap can be controlled via hydrogen loading and reaches ∼1.0 eV for a hydrogen coverage of 8%. The local rehybridization from sp2 to sp3 in the chemical bonding is observed by X-ray photoelectron spectroscopy and X-ray absorption and allows for a determination of the amount of chemisorbed hydrogen. The hydrogen induced gap formation is completely reversible by annealing without damaging the graphene. Calculations of the hydrogen loading dependent core level binding energies and the spectral function of graphene are in excellent agreement with photoemission experiments. Hydrogenation of graphene gives access to tunable electronic and optical properties and thereby provides a model system to study hydrogen storage in carbon materials.
310 citations
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TL;DR: Several randomized trials of charged-particle therapies that are ongoing are reviewed, with results that will enable selective delivery to patients who are most likely to benefit from them and aspects related to radiobiology, including the immune response and hypoxia, which will need to be taken into consideration in future randomized trials to fully exploit the potential of charged particles.
Abstract: Radiotherapy with high-energy charged particles has become an attractive therapeutic option for patients with several tumour types because this approach better spares healthy tissue from radiation than conventional photon therapy. The cost associated with the delivery of charged particles, however, is higher than that of even the most elaborate photon-delivery technologies. Reliable evidence of the relative cost-effectiveness of both modalities can only come from the results of randomized clinical trials. Thus, the hurdles that currently limit direct comparisons of these two approaches in clinical trials, especially those related to insurance coverage, should be removed. Herein, we review several randomized trials of charged-particle therapies that are ongoing, with results that will enable selective delivery to patients who are most likely to benefit from them. We also discuss aspects related to radiobiology, including the immune response and hypoxia, which will need to be taken into consideration in future randomized trials to fully exploit the potential of charged particles.
309 citations
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TL;DR: The transient noise backgrounds used to determine the significance of the event (designated GW150914) are described and the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of theevent are presented.
Abstract: On 14 September 2015, a gravitational wave signal from a coalescing black hole binary system was observed by the Advanced LIGO detectors. This paper describes the transient noise backgrounds used to determine the significance of the event (designated GW150914) and presents the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of the event. The detectors were operating nominally at the time of GW150914. We have ruled out environmental influences and non-Gaussian instrument noise at either LIGO detector as the cause of the observed gravitational wave signal.
308 citations
Authors
Showing all 10758 results
Name | H-index | Papers | Citations |
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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 |