15,678 research outputs found
Estimating High Dimensional Covariance Matrices and its Applications
Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater than the number of observations. The limitations of the sample covariance matrix are discussed. Several new approaches are presented, including the shrinkage method, the observable and latent factor method, the Bayesian approach, and the random matrix theory approach. For each method, the construction of covariance matrices is given. The relationships among these methods are discussed.Factor analysis, Principal components, Singular value decomposition, Random matrix theory, Empirical Bayes, Shrinkage method, Optimal portfolios, CAPM, APT, GMM
Direct measurement of giant electrocaloric effect in BaTiO3 multilayer thick film structure beyond theoretical prediction
The electrocaloric effect of BaTiO3 multilayer thick film structure was
investigated by direct measurement and theoretical calculation. The samples
were prepared by the tape-casting method, which had 180 dielectric layers with
an average thickness of 1.4\mu m. The thermodynamic calculation based on the
polarization- temperature curves predicted a peak heat adsorption of 0.32J/g at
80^\circC under 176kV/cm electric field. The direct measurement via
differential scanning calorimeter showed a much higher electrocaloric effect of
0.91J/g at 80^\circC under same electric field. The difference could result
from the different trends of changes of electric polarization and lattice
elastic energy under ultrahigh electric field.Comment: 11 pages, 4 figure
Detecting Oriented Text in Natural Images by Linking Segments
Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment is an oriented box covering a part of a word or text line; A link
connects two adjacent segments, indicating that they belong to the same word or
text line. Both elements are detected densely at multiple scales by an
end-to-end trained, fully-convolutional neural network. Final detections are
produced by combining segments connected by links. Compared with previous
methods, SegLink improves along the dimensions of accuracy, speed, and ease of
training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
Incidental (Challenge 4) benchmark, outperforming the previous best by a large
margin. It runs at over 20 FPS on 512x512 images. Moreover, without
modification, SegLink is able to detect long lines of non-Latin text, such as
Chinese.Comment: To Appear in CVPR 201
Root optimization of polynomials in the number field sieve
The general number field sieve (GNFS) is the most efficient algorithm known
for factoring large integers. It consists of several stages, the first one
being polynomial selection. The quality of the chosen polynomials in polynomial
selection can be modelled in terms of size and root properties. In this paper,
we describe some algorithms for selecting polynomials with very good root
properties.Comment: 16 pages, 18 reference
Sharpening and generalizations of Shafer's inequality for the arc tangent function
In this paper, we sharpen and generalize Shafer's inequality for the arc
tangent function. From this, some known results are refined
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