296 research outputs found

    In situ growth regime characterization of cubic GaN using reflection high energy electron diffraction

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    Cubic GaN layers were grown by plasma-assisted molecular beam epitaxy on 3C-SiC (001)substrates. In situ reflection high energy electron diffraction was used to quantitatively determine the Ga coverage of the GaN surface during growth. Using the intensity of the electron beam as a probe,optimum growth conditions of c-GaN were found when a 1 ML Ga coverage is formed at the surface. 1 micrometer thick c-GaN layers had a minimum surface roughness of 2.5 nm when a Ga coverage of 1 ML was established during growth. These samples revealed also a minimum full width at half maximum of the (002)rocking curve.Comment: 3pages with 4 figure

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

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    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. Springer Verlag (Germany). 6609:277-288. https://doi.org/10.1007/978-3-642-19437-5_23S2772886609Wikimedia Foundation: Wikipedia (2010) [Online; accessed December 29, 2010]Wikimedia Foundation: Wikistats (2010) [Online; accessed December 29, 2010]Potthast, M.: Crowdsourcing a Wikipedia Vandalism Corpus. In: Proc. of the 33rd Intl. ACM SIGIR Conf. (SIGIR 2010). ACM Press, New York (July 2010)Gralla, P.: U.S. senator: It’s time to ban Wikipedia in schools, libraries, http://blogs.computerworld.com/4598/u_s_senator_its_time_to_ban_wikipedia_in_schools_libraries [Online; accessed November 15, 2010]Olanoff, L.: School officials unite in banning Wikipedia. Seattle Times (November 2007)Mola-Velasco, S.M.: Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals. In: Braschler, M., Harman, D. (eds.) 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

    Retention and generalizability of balance recovery response adaptations from trip-perturbations across the adult lifespan

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    For human locomotion, varying environments require adjustments of the motor system. We asked whether age affects gait balance recovery adaptation, its retention over months and the transfer of adaptation to an untrained reactive balance task. Healthy adults (26 young, 27 middle-aged and 25 older; average ages 24, 52 and 72 years respectively) completed two tasks. The primary task involved treadmill walking: either unperturbed (control; n=39) or subject to unexpected trip perturbations (training; n=39). A single trip perturbation was repeated after a 14-week retention period. The secondary transfer task, before and after treadmill walking, involved sudden loss of balance in a lean-and-release protocol. For both tasks the anteroposterior margin of stability (MoS) was calculated at foot touchdown. For the first (i.e. novel) trip, older adults required one more recovery step ( P=0.03) to regain positive MoS compared to younger, but not middle-aged, adults. However, over several trip perturbations, all age groups increased their MoS for the first recovery step to a similar extent (up to 70%), and retained improvements over 14 weeks, though a decay over time was found for older adults ( P=0.002; middle-aged showing a tendency for decay: P=0.076). Thus, although adaptability in reactive gait stability control remains effective across the adult lifespan, retention of adaptations over time appears diminished with aging. Despite these robust adaptations, the perturbation training group did not show superior improvements in the transfer task compared to aged-matched controls (no differences in MoS changes), suggesting that generalizability of acquired fall-resisting skills from gait-perturbation training may be limited

    THE SUCCESS OF A SOCCER KICK DEPENDS ON RUN UP DECELERATION

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    The purpose of the study was to relate the motion of the centre of mass (CoM) during run ups in soccer full insteps kicks with the obtained ball speeds. Nineteen experienced play-ers performed kicks onto the goal and their full body kinematics as well as ball motion were analysed in three dimensions using two high speed video cameras. Higher decel-erations of the CoM with the last step are associated with higher ball velocities and higher thigh angular impulses. Those data suggest, that an intensive breaking of the CoM veloc-ity provides a prerequisite to transfer a portion of the CoM impulse into angular impulse of the thigh. High angular impulse of the thigh however can be beneficial for fast instep kicks

    Cellulose / Drying of a cellulose II gel: effect of physical modification and redispersibility in water

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    nicht verf\ufcgbarThe agglomeration of cellulosic materials upon drying, often called hornification, causes a reduction of water retention, among other undesired effects. It is one of the main issues in industrial cellulose processing, especially with regard to nanocelluloses. As a consequence, high transportation and storage costs arise since nanocelluloses need to remain in aqueous suspensions unless trade-offs in reactivity, redispersibility and surface properties are accepted. In this study, different drying strategies for TENCEL\uae gel, a nanostructured gel derived from the Lyocell process consisting of spherical particles, are compared and evaluated. First, freeze-drying with consideration of the influence of freezing temperature and the use of tert-butanol as cryo-protectant, and second, simple oven-drying at 60 \ub0C. Surprisingly, oven-dried xerogels showed higher water retention values and also better colloidal stability than the cryogels. This is in stark contrast to cellulose nanofibrils for which freeze-drying has been shown to be significantly superior to oven drying in terms of redispersibility. For the TENCEL\uae gel, oven-drying was thus selected and the influence of additives on the redispersibility of the cellulose II gel was studied by means of the common water retention value, particle size, colloidal stability, appearance of the redispersed gel and viscosity. The addition of the polysaccharides carboxymethyl cellulose or xanthan showed the most promising results with regard to redispersibility. Also sucrose and ammonium bicarbonate provided higher colloidal stabilities than that of the untreated TENCEL\uae gel. The redispersibility of the cellulose II xerogels could thus be significantly improved by simple and cost-efficient mixing with additives prior to drying

    Monte Carlo Procedure for Protein Design

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    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

    Predicting the Next Best View for 3D Mesh Refinement

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    3D reconstruction is a core task in many applications such as robot navigation or sites inspections. Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View. Recently, many volumetric methods have been proposed; they choose the Next Best View by reasoning over a 3D voxelized space and by finding which pose minimizes the uncertainty decoded into the voxels. Such methods are effective, but they do not scale well since the underlaying representation requires a huge amount of memory. In this paper we propose a novel mesh-based approach which focuses on the worst reconstructed region of the environment mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and an energy function over the worst regions of the mesh which takes into account the mutual parallax with respect to the previous cameras, the angle of incidence of the viewing ray to the surface and the visibility of the region. We test our approach over a well known dataset and achieve state-of-the-art results.Comment: 13 pages, 5 figures, to be published in IAS-1
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