18,725 research outputs found
Dynamic Matrix Factorization with Priors on Unknown Values
Advanced and effective collaborative filtering methods based on explicit
feedback assume that unknown ratings do not follow the same model as the
observed ones (\emph{not missing at random}). In this work, we build on this
assumption, and introduce a novel dynamic matrix factorization framework that
allows to set an explicit prior on unknown values. When new ratings, users, or
items enter the system, we can update the factorization in time independent of
the size of data (number of users, items and ratings). Hence, we can quickly
recommend items even to very recent users. We test our methods on three large
datasets, including two very sparse ones, in static and dynamic conditions. In
each case, we outrank state-of-the-art matrix factorization methods that do not
use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge
Discovery and Data Mining 201
Reduced Ambiguity Calibration for LOFAR
Interferometric calibration always yields non unique solutions. It is
therefore essential to remove these ambiguities before the solutions could be
used in any further modeling of the sky, the instrument or propagation effects
such as the ionosphere. We present a method for LOFAR calibration which does
not yield a unitary ambiguity, especially under ionospheric distortions. We
also present exact ambiguities we get in our solutions, in closed form. Casting
this as an optimization problem, we also present conditions for this approach
to work. The proposed method enables us to use the solutions obtained via
calibration for further modeling of instrumental and propagation effects. We
provide extensive simulation results on the performance of our method.
Moreover, we also give cases where due to degeneracy, this method fails to
perform as expected and in such cases, we suggest exploiting diversity in time,
space and frequency.Comment: Draft version. Final version published on 10 April 201
A Study of Feature Extraction Using Divergence Analysis of Texture Features
An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters
Polarization squeezing of light by single passage through an atomic vapor
We have studied relative-intensity fluctuations for a variable set of
orthogonal elliptic polarization components of a linearly polarized laser beam
traversing a resonant Rb vapor cell. Significant polarization squeezing
at the threshold level (-3dB) required for the implementation of several
continuous variables quantum protocols was observed. The extreme simplicity of
the setup, based on standard polarization components, makes it particularly
convenient for quantum information applications.Comment: Revised version. Minor changes. four pages, three figure
Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume
A local principal curve algorithm has been implemented in three dimensions
for automated track and shower reconstruction of neutrino interactions in a
liquid argon time projection chamber. We present details of the algorithm and
characterise its performance on simulated data sets.Comment: 14 pages, 17 figures; typing correction to Eq 5, the definition of
the local covariance matri
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