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Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification, (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm, and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth&ndash, damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. Recent advances in sequencing technologies and analyses enable us to compare hundreds of samples simultaneously; such large-scale analysis has potential to reveal the high-dimensional interrelationship level for regulatory elements and annotate novel functional genomic regions de novo. Because many experimental considerations are relevant to the choice of a method in a ChIP-seq analysis, the overall design and quality management of the experiment are of critical importance. This review offers guiding principles of computation and sample preparation for ChIP-seq analyses, highlighting the validity and limitations of the state-of-the-art procedures at each step. We also discuss the latest challenges of single-cell analysis that will encourage a new era in this field.

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information on musical genre preferences for more than 120,000 listeners and links to the LFM-1b dataset. We created the dataset by exploiting social tags, indexing them using two genre term sets, and aggregating the resulting annotated listening events on the user level. We foresee several applications of the dataset in music retrieval and recommendation tasks, among others to build and evaluate decent user models, to alleviate cold-start situations in music recommender systems, and to increase their performance using the additional abstraction layer of genre. We further present results of statistical analyses of the dataset, regarding genre preferences and their consistencies. We do so for the entire user population and for user groups defined by demographic similarities. Moreover, we report interesting insights about correlations between musical preferences on the genre level.

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks., Comment: 34 pages, 33 figures

This paper describes the parallel finite element analysis of large scale 3-D structural dynamic problems based on the Domain Decomposition Method (DDM) with preconditioner using Balancing Domain Decomposition (BDD) for massively parallel processors. This domain decomposition has been implemented within a finite element code for linear implicit transient dynamic analysis. Time integration is performed using Newmark-βconstant average acceleration method. The parallel finite element code uses an MPI-based message passing approach to provide portable parallel execution on shared, distributed and distributed shared memory computers. 3-D structural dynamic problems have been conducted on supercomputer to evaluate the performance of the implicit parallel linear finite element code. Results indicate that the proposed methods are highly adaptive for parallel computing and superior in performance.

With the constant migration of applications from the desktop to the web, power users have found ways of enhancing web applications, at the client-side, according to their needs. In this paper, we investigate this phenomenon by focusing on the popular Greasemonkey extension which enables users to write scripts that arbitrarily change the content of any page, allowing them to remove unwanted features from web applications, or add additional, desired features to them. The creation of script markets, on which these scripts are often shared, extends the standard web security model with two new actors, introducing novel vulnerabilities. We describe the architecture of Greasemonkey and perform a large-scale analysis of the most popular, community-driven, script market for Greasemonkey. Through our analysis, we discover not only dozens of malicious scripts waiting to be installed by users, but thousands of benign scripts with vulnerabilities that could be abused by attackers. In 58 cases, the vulnerabilities are so severe, that they can be used to bypass the Same-Origin Policy of the user's browser and steal sensitive user-data from all sites. We verify the practicality of our attacks, by developing a proof-of-concept exploit against a vulnerable user script with an installation base of 1.2 million users, equivalent to a ``Man-in-the-browser'' attack. online only ispartof: pages:525-530 ispartof: ASIA CCS 2014 - Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security pages:525-530 ispartof: ASIACCS location:Kyoto, Japan date:2 Jun - 4 Jun 2014 status: published

Date of Conference: July 24 - 28, 2011 Conference name: SIGIR '11 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval We provide a first large-scale analysis of the evolution of query results obtained from a real search engine at two distant points in time, namely, in 2007 and 2010, for a set of 630,000 real queries.