Fault tree analysis for system modeling in case of intentional EMI

The complexity of modern systems on the one hand and the rising threat of intentional electromagnetic interference (IEMI) on the other hand increase the necessity for systematical risk analysis. Most of the problems can not be treated deterministically since slight changes in the configuration (source, position, polarization, ...) can dramatically change the outcome of an event. For that purpose, methods known from probabilistic risk analysis can be applied. One of the most common approaches is the fault tree analysis (FTA). The FTA is used to determine the system failure probability and also the main contributors to its failure. In this paper the fault tree analysis is introduced and a possible application of that method is shown using a small computer network as an example. The constraints of this methods are explained and conclusions for further research are drawn

Low-dose intra-arterial contrast-enhanced MR aortography in patients based on a theoretically derived injection protocol

Multiple intra-arterial contrast agent injections are necessary during MR-guided endovascular interventions. In respect to the approved limits of maximum daily gadolinium dose, a low-dose injection protocol is mandatory. The objective of this study was to derive and apply a low-dose injection protocol for intra-arterial 3D contrast-enhanced MR aortography in patients. Injection rate (Qinj), concentration of injected gadolinium [Gd]inj and aortal blood flow rate (Qblood) were included for the theoretical evaluation of signal intensity (SI) of the arterial lumen. SI simulations were carried out at Qinj=2 versus 4ml/s in the [Gd]inj range between 0-500mM. Qinj and [Gd]inj with SI above the 75% threshold of the maximal SI were regarded as optimal injection parameters. [Gd]inj=50mM and Qinj=4ml/s were considered as optimal and were administered in five patients for 3D MR aortography. All images revealed clear delineation of the abdominal aorta and its major branches. Mean±SD of contrast-to-noise ratios of the abdominal aorta, common iliac and renal artery were 70.2±15.2, 58.6±12.3 and 67.4±12.3. Approximately seven intra-aortal injections would be permissible in patients during MR-guided interventions without exceeding the maximal dose of gadoliniu

Monte Carlo Procedure for Protein Design

A new method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional probabilities rather than minimizing energy functions, is based upon a novel and very efficient multisequence Monte Carlo scheme. By construction, the method ensures that the designed sequences represent good folders thermodynamically. A bootstrap procedure for the sequence space search is devised making very large chains feasible. The algorithm is successfully explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.Comment: 7 pages LaTeX, 4 Postscript figures; minor change

Identification of Amino Acid Sequences with Good Folding Properties in an Off-Lattice Model

Folding properties of a two-dimensional toy protein model containing only two amino-acid types, hydrophobic and hydrophilic, respectively, are analyzed. An efficient Monte Carlo procedure is employed to ensure that the ground states are found. The thermodynamic properties are found to be strongly sequence dependent in contrast to the kinetic ones. Hence, criteria for good folders are defined entirely in terms of thermodynamic fluctuations. With these criteria sequence patterns that fold well are isolated. For 300 chains with 20 randomly chosen binary residues approximately 10% meet these criteria. Also, an analysis is performed by means of statistical and artificial neural network methods from which it is concluded that the folding properties can be predicted to a certain degree given the binary numbers characterizing the sequences.Comment: 15 pages, 8 Postscript figures. Minor change

Geometric representations for minimalist grammars

We reformulate minimalist grammars as partial functions on term algebras for strings and trees. Using filler/role bindings and tensor product representations, we construct homomorphisms for these data structures into geometric vector spaces. We prove that the structure-building functions as well as simple processors for minimalist languages can be realized by piecewise linear operators in representation space. We also propose harmony, i.e. the distance of an intermediate processing step from the final well-formed state in representation space, as a measure of processing complexity. Finally, we illustrate our findings by means of two particular arithmetic and fractal representations.Comment: 43 pages, 4 figure

How can existing ground-based profiling instruments improve European weather forecasts?

Observations of profiles of winds, aerosol, clouds, winds, temperature and humidity in the lowest few km of the atmosphere from networks of ceilometers, Doppler wind lidars and microwave radiometers are starting to flow in real time to forecasting centers in Europe. To realise the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few km are hardly accessible by satellite, especially in dynamically-active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real-time to forecast centers. The three classes of instruments are: (i) Automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog and volcanic ash, the last two being especially important for air traffic control; (ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind-gusts and low-level jets; and (iii) Microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. Twenty-two European countries and fifteen European National Weather Services are collaborating in the project, that involves the implementation of common operating procedures, instrument calibrations, data formats and retrieval algorithms. Currently, data from 220 ceilometers in 17 countries are being distributed in near real-time to national weather forecast centers; this should soon rise to many hundreds. The wind lidars should start delivering real time data in late 2018, and the plan is to incorporate the microwave radiometers in 2019. Initial data assimilation tests indicate a positive impact of the new data

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

Wikipedia vandalism detection: combining natural language, metadata, and reputation features

Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions.The authors from Universitat Politècnica de València thank also the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i). UPenn contributions were supported in part by ONR MURI N00014-07-1-0907. This research was partially supported by award 1R01GM089820-01A1 from the National Institute Of General Medical Sciences, and by ISSDM, a UCSC-LANL educational collaboration.Adler, BT.; Alfaro, LD.; Mola Velasco, SM.; Rosso, P.; West, AG. (2011). Wikipedia vandalism detection: combining natural language, metadata, and reputation features. En Computational Linguistics and Intelligent Text Processing. 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Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)Adler, B., de Alfaro, L., Pye, I.: Detecting Wikipedia Vandalism using WikiTrust. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)West, A.G., Kannan, S., Lee, I.: Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata. In: EUROSEC 2010: Proceedings of the Third European Workshop on System Security, pp. 22–28 (2010)West, A.G.: STiki: A Vandalism Detection Tool for Wikipedia (2010), http://en.wikipedia.org/wiki/Wikipedia:STikiWikipedia: User: AntiVandalBot – Wikipedia, http://en.wikipedia.org/wiki/User:AntiVandalBot (2010) [Online; accessed November 2, 2010]Wikipedia: User:MartinBot – Wikipedia (2010), http://en.wikipedia.org/wiki/User:MartinBot [Online; accessed November 2, 2010]Wikipedia: User:ClueBot – Wikipedia (2010), http://en.wikipedia.org/wiki/User:ClueBot [Online; accessed November 2, 2010]Carter, J.: ClueBot and Vandalism on Wikipedia (2008), http://www.acm.uiuc.edu/~carter11/ClueBot.pdf [Online; accessed November 2, 2010]Rodríguez Posada, E.J.: AVBOT: detección y corrección de vandalismos en Wikipedia. NovATIca (203), 51–53 (2010)Potthast, M., Stein, B., Gerling, R.: Automatic Vandalism Detection in Wikipedia. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 663–668. Springer, Heidelberg (2008)Smets, K., Goethals, B., Verdonk, B.: Automatic Vandalism Detection in Wikipedia: Towards a Machine Learning Approach. In: WikiAI 2008: Proceedings of the Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 43–48. AAAI Press, Menlo Park (2008)Druck, G., Miklau, G., McCallum, A.: Learning to Predict the Quality of Contributions to Wikipedia. In: WikiAI 2008: Proceedings of the Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 7–12. AAAI Press, Menlo Park (2008)Itakura, K.Y., Clarke, C.L.: Using Dynamic Markov Compression to Detect Vandalism in the Wikipedia. In: SIGIR 2009: Proc. of the 32nd Intl. ACM Conference on Research and Development in Information Retrieval, pp. 822–823 (2009)Chin, S.C., Street, W.N., Srinivasan, P., Eichmann, D.: Detecting Wikipedia Vandalism with Active Learning and Statistical Language Models. In: WICOW 2010: Proc. of the 4th Workshop on Information Credibility on the Web (April 2010)Zeng, H., Alhoussaini, M., Ding, L., Fikes, R., McGuinness, D.: Computing Trust from Revision History. In: Intl. Conf. on Privacy, Security and Trust (2006)McGuinness, D., Zeng, H., da Silva, P., Ding, L., Narayanan, D., Bhaowal, M.: Investigation into Trust for Collaborative Information Repositories: A Wikipedia Case Study. In: Proc. of the Workshop on Models of Trust for the Web (2006)Adler, B., de Alfaro, L.: A Content-Driven Reputation System for the Wikipedia. In: WWW 2007: Proceedings of the 16th International World Wide Web Conference. ACM Press, New York (2007)Belani, A.: Vandalism Detection in Wikipedia: a Bag-of-Words Classifier Approach. Computing Research Repository (CoRR) abs/1001.0700 (2010)Potthast, M., Stein, B., Holfeld, T.: Overview of the 1st International Competition on Wikipedia Vandalism Detection. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: ICML 2006: Proc. of the 23rd Intl. Conf. on Machine Learning (2006

On the representation error in data assimilation

Representation, representativity, representativeness error, forward interpolation error, forward model error, observation operator error, aggregation error and sampling error are all terms used to refer to components of observation error in the context of data assimilation. This paper is an attempt to consolidate the terminology that has been used in the earth sciences literature and was suggested at a European Space Agency workshop held in Reading in April 2014. We review the state-of-the-art, and through examples, motivate the terminology. In addition to a theoretical framework, examples from application areas of satellite data assimilation, ocean reanalysis and atmospheric chemistry data assimilation are provided. Diagnosing representation error statistics as well as their use in state-of-the-art data assimilation systems is discussed within a consistent framework