Página 1 dos resultados de 15310 itens digitais encontrados em 0.005 segundos
Resultados filtrados por Publicador: Universidade Cornell

A compression algorithm for the combination of PDF sets

Carrazza, Stefano; Latorre, Jose I.; Rojo, Juan; Watt, Graeme
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
256.05934%
The current PDF4LHC recommendation to estimate uncertainties due to parton distribution functions (PDFs) in theoretical predictions for LHC processes involves the combination of separate predictions computed using PDF sets from different groups, each of which comprises a relatively large number of either Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and parton shower programs allow the evaluation of PDF uncertainties for a single PDF set at no additional CPU cost, this feature is not universal, and moreover the a posteriori combination of the predictions using at least three different PDF sets is still required. In this work, we present a strategy for the statistical combination of individual PDF sets, based on the MC representation of Hessian sets, followed by a compression algorithm for the reduction of the number of MC replicas. We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The resulting Compressed Monte Carlo PDF (CMC-PDF) sets are validated at the level of parton luminosities and LHC inclusive cross-sections and differential distributions. We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set...

Watermarking PDF Documents using Various Representations of Self-inverting Permutations

Chroni, Maria; Nikolopoulos, Stavros D.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/01/2015 Português
Relevância na Pesquisa
256.05934%
This work provides to web users copyright protection of their Portable Document Format (PDF) documents by proposing efficient and easily implementable techniques for PDF watermarking; our techniques are based on the ideas of our recently proposed watermarking techniques for software, image, and audio, expanding thus the digital objects that can be efficiently watermarked through the use of self-inverting permutations. In particular, we present various representations of a self-inverting permutation $\pi^*$ namely 1D-representation, 2D-representation, and RPG-representation, and show that theses representations can be efficiently applied to PDF watermarking. Indeed, we first present an audio-based technique for marking a PDF document $T$ by exploiting the 1D-representation of a permutation $\pi^*$, and then, since pages of a PDF document $T$ are 2D objects, we present an image-based algorithm for encoding $\pi^*$ into $T$ by first mapping the elements of $\pi^*$ into a matrix $A^*$ and then using the information stored in $A^*$ to mark invisibly specific areas of PDF document $T$. Finally, we describe a graph-based watermarking algorithm for embedding a self-inverting permutation $\pi^*$ into the document structure of a PDF file $T$ by exploiting the RPG-representation of $\pi^*$ and the structure of a PDF document. We have evaluated the embedding and extracting algorithms by testing them on various and different in characteristics PDF documents.; Comment: 17 pages...