MOTIVATION: With advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de novo assemblies from short-reads remains challenging, primarily because of the complex repeat structures found in the genomes of most higher organisms. The telomeric regions of many genomes are particularly difficult to assemble, though much could be gained from the study of these regions, as their evolution has not been fully characterized and they have been linked to aging. RESULTS: In this article, we tackle the problem of assembling highly repetitive regions by developing a novel algorithm that iteratively extends long paths through a series of read-overlap graphs and evaluates them based on a statistical framework. Our algorithm, Telescoper, uses short- and long-insert libraries in an integrated way throughout the assembly process. Results on real and simulated data demonstrate that our approach can effectively resolve much of the complex repeat structures found in the telomeres of yeast genomes, especially when longer long-insert libraries are used. AVAILABILITY: Telescoper is publicly available for download at sourceforge.net/p/telescoper. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper proposes a novel approach to check the authenticity of hardware based on the inevitable performance gap between real hardware and simulations or emulations that impersonate it. More specifically, we demonstrate that each processor design can be authenticated by requiring a checksum incorporating internals of complex micro-architectural mechanisms to be computed within a time limit; this checksum is different for each processor model and only authentic secure hardware can obtain the checksum fast enough. This new authentication approach provides attractive solutions to privacy, scaling, and security issues of traditional approaches that otherwise rely only on certificates. Architectural simulations and an RTL implementation show that the proposed approach is viable with very low hardware overheads.

In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i. e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.