Search results for "methodologies"
showing 10 items of 2106 documents
Measurement of the lifetime of tau-lepton
1996
The tau lepton lifetime is measured with the L3 detector at LEP using the complete data taken at centre-of-mass energies around the Z pole resulting in tau_tau = 293.2 +/- 2.0 (stat) +/- 1.5 (syst) fs. The comparison of this result with the muon lifetime supports lepton universality of the weak charged current at the level of six per mille. Assuming lepton universality, the value of the strong coupling constant, alpha_s is found to be alpha_s(m_tau^2) = 0.319 +/- 0.015(exp.) +/- 0.014 (theory). The tau lepton lifetime is measured with the L3 detector at LEP using the complete data taken at centre-of-mass energies around the Z pole resulting in τ τ =293.2 ± 2.0 (stat) ± 1.5 (syst) fs . The c…
Stronger proprioceptive BOLD-responses in the somatosensory cortices reflect worse sensorimotor function in adolescents with and without cerebral pal…
2020
Graphical abstract
A Fast GPU-Based Motion Estimation Algorithm for H.264/AVC
2012
H.264/AVC is the most recent predictive video compression standard to outperform other existing video coding standards by means of higher computational complexity. In recent years, heterogeneous computing has emerged as a cost-efficient solution for high-performance computing. In the literature, several algorithms have been proposed to accelerate video compression, but so far there have not been many solutions that deal with video codecs using heterogeneous systems. This paper proposes an algorithm to perform H.264/AVC inter prediction. The proposed algorithm performs the motion estimation, both with full-pixel and sub-pixel accuracy, using CUDA to assist the CPU, obtaining remarkable time …
Fuzzy subgroup mining for gene associations
2004
When studying the therapeutic efficacy of potential new drugs, it would be much more efficient to use predictors in order to assess their toxicity before going into clinical trials. One promising line of research has focused on the discovery of sets of candidate gene profiles to be used as toxicity indicators in future drug development. In particular genomic microarrays may be used to analyze the causality relationship between the administration of the drugs and the so-called gene expression, a parameter typically used by biologists to measure its influence at gene level. This kind of experiments involves a high throughput analysis of noisy and particularly unreliable data, which makes the …
Unmanned aerial system imagery and photogrammetric canopy height data in area-based estimation of forest variables
2015
In this paper we examine the feasibility of data from unmanned aerial vehicle (UAV)-borne aerial imagery in stand-level forest inventory. As airborne sensor platforms, UAVs offer advantages cost and flexibility over traditional manned aircraft in forest remote sensing applications in small areas, but they lack range and endurance in larger areas. On the other hand, advances in the processing of digital stereo photography make it possible to produce three-dimensional (3D) forest canopy data on the basis of images acquired using simple lightweight digital camera sensors. In this study, an aerial image orthomosaic and 3D photogrammetric canopy height data were derived from the images acquired …
GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
2018
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results).…
Rethinking the sGLOH Descriptor
2018
sGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is a…
Output Field-Quadrature Measurements and Squeezing in Ultrastrong Cavity-QED
2015
We study the squeezing of output quadratures of an electro-magnetic field escaping from a resonator coupled to a general quantum system with arbitrary interaction strengths. The generalized theoretical analysis of output squeezing proposed here is valid for all the interaction regimes of cavity-quantum electrodynamics: from the weak to the strong, ultrastrong, and deep coupling regimes. For coupling rates comparable or larger then the cavity resonance frequency, the standard input–output theory for optical cavities fails to calculate the variance of output field-quadratures and predicts a non-negligible amount of output squeezing, even if the system is in its ground state. Here we show that…
Adding symbolic information to picture models: definitions and properties
2005
AbstractIn the paper we propose extensions of some picture models, such as colored, drawn and pixel pictures. Such extensions are conceived by observing that a picture may embed more information than the shape, such as colors, labels, etc., which can be represented by a symbol from an alphabet and can be associated to segments, points or pixels. New interesting issues derived from the introduction of symbols will be investigated together with some complexity and decidability questions for the proposed extensions.
Modeling Multi-label Recurrence in Data Streams
2019
Most of the existing data stream algorithms assume a single label as the target variable. However, in many applications, each observation is assigned to several labels with latent dependencies among them, which their target function may change over time. Classification of such non-stationary multi-label streaming data with the consideration of dependencies among labels and potential drifts is a challenging task. The few existing studies mostly cope with drifts implicitly, and all learn models on the original label space, which requires a lot of time and memory. None of them consider recurrent drifts in multi-label streams and particularly drifts and recurrences visible in a latent label spa…