36,432 research outputs found
Temporal video transcoding from H.264/AVC-to-SVC for digital TV broadcasting
Mobile digital TV environments demand flexible video compression like scalable video coding (SVC) because of varying bandwidths and devices. Since existing infrastructures highly rely on H.264/AVC video compression, network providers could adapt the current H.264/AVC encoded video to SVC. This adaptation needs to be done efficiently to reduce processing power and operational cost. This paper proposes two techniques to convert an H.264/AVC bitstream in Baseline (P-pictures based) and Main Profile (B-pictures based) without scalability to a scalable bitstream with temporal scalability as part of a framework for low-complexity video adaptation for digital TV broadcasting. Our approaches are based on accelerating the interprediction, focusing on reducing the coding complexity of mode decision and motion estimation tasks of the encoder stage by using information available after the H. 264/AVC decoding stage. The results show that when our techniques are applied, the complexity is reduced by 98 % while maintaining coding efficiency
Congruence properties of depths in some random trees
Consider a random recusive tree with n vertices. We show that the number of
vertices with even depth is asymptotically normal as n tends to infinty. The
same is true for the number of vertices of depth divisible by m for m=3, 4 or
5; in all four cases the variance grows linearly. On the other hand, for m at
least 7, the number is not asymptotically normal, and the variance grows faster
than linear in n. The case m=6 is intermediate: the number is asymptotically
normal but the variance is of order n log n.
This is a simple and striking example of a type of phase transition that has
been observed by other authors in several cases. We prove, and perhaps explain,
this non-intuitive behavious using a translation to a generalized Polya urn.
Similar results hold for a random binary search tree; now the number of
vertices of depth divisible by m is asymptotically normal for m at most 8 but
not for m at least 9, and the variance grows linearly in the first case both
faster in the second. (There is no intermediate case.)
In contrast, we show that for conditioned Galton-Watson trees, including
random labelled trees and random binary trees, there is no such phase
transition: the number is asymptotically normal for every m.Comment: 23 page
Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
We propose a robust principal component analysis (PCA) framework for the
exploitation of multi-band photometric measurements in large surveys. Period
search results are improved using the time series of the first principal
component due to its optimized signal-to-noise ratio.The presence of correlated
excess variations in the multivariate time series enables the detection of
weaker variability. Furthermore, the direction of the largest variance differs
for certain types of variable stars. This can be used as an efficient attribute
for classification. The application of the method to a subsample of Sloan
Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti
variables. We found also 129 new RR Lyrae variables, complementary to the
catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR
Lyrae stars towards the Galactic bulge. The sample comprises also 25
multiperiodic or Blazhko RR Lyrae stars.Comment: 23 pages, 17 figure
Fast and scalable inference of multi-sample cancer lineages.
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee
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