Alternative Detection Methods for Highest Energy Neutrinos

Several experimental techniques are currently under development, to measure the expected tiny fluxes of highest energy neutrinos above 10**18 eV. Projects in different stages of realisation are discussed here, which are based on optical and radio as well as acoustic detectors. For the detection of neutrino events in this energy range a combination of different detector concepts in one experiment seems to be most promising.Comment: 8 pages, 8 figures, to be published in Nuclear Physics B (Proceedings Supplement): Proceedings of the XXIst International Conference on Neutrino Physics and Astrophysics, Paris, June 14-19, 200

Shiga Toxin Detection Methods : A Short Review

The Shiga toxins comprise a family of related protein toxins secreted by certain types of bacteria. Shigella dysenteriae, some strain of Escherichia coli and other bacterias can express toxins which caused serious complication during the infection. Shiga toxin and the closely related Shiga-like toxins represent a group of very similar cytotoxins that may play an important role in diarrheal disease and hemolytic-uremic syndrome. The outbreaks caused by this toxin raised serious public health crisis and caused economic losses. These toxins have the same biologic activities and according to recent studies also share the same binding receptor, globotriosyl ceramide (Gb3). Rapid detection of food contamination is therefore relevant for the containment of food-borne pathogens. The conventional methods to detect pathogens, such as microbiological and biochemical identification are time-consuming and laborious. The immunological or nucleic acid-based techniques require extensive sample preparation and are not amenable to miniaturization for on-site detection. In the present are necessary of techniques of rapid identification, simple and sensitive which can be employed in the countryside with minimally-sophisticated instrumentation. Biosensors have shown tremendous promise to overcome these limitations and are being aggressively studied to provide rapid, reliable and sensitive detection platforms for such applications.Comment: 16 pages, 2 figure

Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comparing anomaly detection methods in computer networks

This work in progress outlines a comparison of anomaly detection methods that we are undertaking. We are comparing different types of anomaly detection methods with the purpose of achieving results covering a broad spectrum of anomalies. We also outline the datasets that we will be using and the metrics that we will use for our evaluation

Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

Deep Investigation of Cross-Language Plagiarism Detection Methods

This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.Comment: Accepted to BUCC (10th Workshop on Building and Using Comparable Corpora) colocated with ACL 201