416 research outputs found
Elementary Trigonometric Sums related to Quadratic Residues
Let p be a prime = 3 (mod 4). A number of elegant number-theoretical
properties of the sums T(p) = \sqrt{p}sum_{n=1}^{(p-1)/2} tan(n^2\pi/p) and
C(p) = \sqrt{p}sum_{n=1}^{(p-1)/2} cot(n^2\pi/p) are proved. For example, T(p)
equals p times the excess of the odd quadratic residues over the even ones in
the set {1,2,...,p-1}; this number is positive if p = 3 (mod 8) and negative if
p = 7 (mod 8). In this revised version the connection of these sums with the
class-number h(-p) is also discussed. For example, a very simple formula
expressing h(-p) by means of the aforementioned excess is proved. The
bibliography has been considerably enriched. This article is of an expository
nature.Comment: A number of misprints have been corrected and one or two improvements
have been done to the previous version of the paper with same title. The
paper will appear to Elem. der Mat
On the diophantine equation
We consider the diophantine equation
x^p-x=y^q-y
\tag"$(*)$"
in integers . We prove that for given and with 2\le p < q has only finitely many solutions. Assuming the abc-conjecture we can prove that and are bounded. In the special case and a prime power we are able to solve completely
Image Restoration Using Functional and Anatomical Information Fusion with Application to SPECT-MRI Images
Image restoration is usually viewed as an ill-posed problem in image processing, since there is no unique solution associated with it. The quality of restored image closely depends on the constraints imposed of the characteristics of the solution. In this paper, we propose an original extension of the NAS-RIF restoration technique by using information fusion as prior information with application in SPECT medical imaging. That extension allows the restoration process to be constrained by efficiently incorporating, within the NAS-RIF method, a regularization term which stabilizes the inverse solution. Our restoration method is constrained by anatomical information extracted from a high resolution anatomical procedure such as magnetic resonance imaging (MRI). This structural anatomy-based regularization term uses the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MRI and SPECT data volumes from each patient. This method was successfully tested on 30 pairs of brain MRI and SPECT acquisitions from different subjects and on Hoffman and Jaszczak SPECT phantoms. The experiments demonstrated that the method performs better, in terms of signal-to-noise ratio, than a classical supervised restoration approach using a Metz filter
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
The k-means clustering is one of the most popular clustering algorithms in
data mining. Recently a lot of research has been concentrated on the algorithm
when the dataset is divided into multiple parties or when the dataset is too
large to be handled by the data owner. In the latter case, usually some servers
are hired to perform the task of clustering. The dataset is divided by the data
owner among the servers who together perform the k-means and return the cluster
labels to the owner. The major challenge in this method is to prevent the
servers from gaining substantial information about the actual data of the
owner. Several algorithms have been designed in the past that provide
cryptographic solutions to perform privacy preserving k-means. We provide a new
method to perform k-means over a large set using multiple servers. Our
technique avoids heavy cryptographic computations and instead we use a simple
randomization technique to preserve the privacy of the data. The k-means
computed has exactly the same efficiency and accuracy as the k-means computed
over the original dataset without any randomization. We argue that our
algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems
Security. Springer, Cham, 201
The Ks-band Tully-Fisher Relation - A Determination of the Hubble Parameter from 218 ScI Galaxies and 16 Galaxy Clusters
The value of the Hubble Parameter (H0) is determined using the
morphologically type dependent Ks-band Tully-Fisher Relation (K-TFR). The slope
and zero point are determined using 36 calibrator galaxies with ScI morphology.
Calibration distances are adopted from direct Cepheid distances, and group or
companion distances derived with the Surface Brightness Fluctuation Method or
Type Ia Supernova. Distances are determined to 16 galaxy clusters and 218 ScI
galaxies with minimum distances of 40.0 Mpc. From the 16 galaxy clusters a
weighted mean Hubble Parameter of H0=84.2 +/-6 km s-1 Mpc-1 is found. From the
218 ScI galaxies a Hubble Parameter of H0=83.4 +/-8 km s-1 Mpc-1 is found. When
the zero point of the K-TFR is corrected to account for recent results that
find a Large Magellanic Cloud distance modulus of 18.39 +/-0.05 a Hubble
Parameter of 88.0 +/-6 km s-1 Mpc-1 is found. A comparison with the results of
the Hubble Key Project (Freedman et al 2001) is made and discrepancies between
the K-TFR distances and the HKP I-TFR distances are discussed. Implications for
Lamda-CDM cosmology are considered with H0=84 km s-1 Mpc-1. (Abridged)Comment: 37 pages including 12 tables and 7 figures. Final version accepted
for publication in the Journal of Astrophysics & Astronom
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