26,435 research outputs found
Rough approximation quality revisited
AbstractIn rough set theory, the approximation quality Îł is the traditional measure to evaluate the classification success of attributes in terms of a numerical evaluation of the dependency properties generated by these attributes. In this paper we re-interpret the classical γ in terms of a classic measure based on sets, the MarczewskiâSteinhaus metric, and also in terms of âproportional reduction of errorsâ (PRE) measures. We also exhibit infinitely many possibilities to define Îł-like statistics which are meaningful in situations different from the classical one, and provide tools to ascertain the statistical significance of the proposed measures, which are valid for any kind of sample
Ultrasound wave propagation through rough interfaces: Iterative methods
Two iterative methods for the calculation of acoustic transmission through a rough interface\ud
between two media are compared. The methods employ a continuous version of the conjugate\ud
gradient technique. One method is based on plane-wave expansions and the other on boundary\ud
integral equations and Greenâs functions. A preconditioner is presented which improves the\ud
convergence for spectra that include evanescent modes. The methods are compared with regard to\ud
computational efficiency, rate of convergence, and residual error. The sound field differences are\ud
determined for a focused ultrasound beam distorted by surfaces having a Gaussian roughness\ud
spectrum. The differences are evaluated from the root-mean-square differences on the rough surface\ud
and in the focal plane
Phenomenology on the QCD dipole picture revisited
We perform an adjust to the most recent structure function data, considering
the QCD dipole picture applied to ep scattering. The structure function F2 at
small x and intermediate Q2 can be described by the model containing an
economical number of free-parameters, which encodes the hard Pomeron physics.
The longitudinal structure function and the gluon distribution are predicted
without further adjustments. The data description is effective, whereas a
resummed next-to-leading level analysis is deserved.Comment: 18 pages, 6 figures. Version to be published in Eur. Phys. J.
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets
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