Vasopressin-dependent flank marking in golden hamsters is suppressed by drugs used in the treatment of obsessive-compulsive disorder

BACKGROUND: Alterations in arginine vasopressin regulation and secretion have been proposed as one possible biochemical abnormality in patients with obsessive-compulsive disorder. In golden hamsters, arginine vasopressin microinjections into the anterior hypothalamus trigger robust grooming and flank marking, a stereotyped scent marking behaviors. The intensity and repetition of the behaviors induced by arginine vasopressin is somewhat reminiscent of Obsessive Compulsive Disorder in humans. The present experiments were carried out to test whether pharmacological agents used to alleviate obsessive compulsive disorder could inhibit arginine vasopressin-induced flank marking and grooming. RESULTS: Male golden hamsters were treated daily for two weeks with either vehicle, fluoxetine, clomipramine, or desipramine (an ineffective drug), before being tested for arginine vasopressin-induced flank marking and grooming. Flank marking was significantly inhibited in animals treated with fluoxetine or clomipramine but unaffected by treatment with desipramine. Grooming behavior was not affected by any treatment. CONCLUSION: These data suggest that arginine vasopressin-induced flank marking may serve as an animal model for screening drugs used in the control of Obsessive Compulsive Disorder

Signatures of chaos in animal search patterns

One key objective of the emerging discipline of movement ecology is to link animal movement patternsto underlying biological processes, including those operating at the neurobiological level. Nonetheless,little is known about the physiological basis of animal movement patterns, and the underlying searchbehaviour. Here we demonstrate the hallmarks of chaotic dynamics in the movement patterns ofmud snails (Hydrobia ulvae) moving in controlled experimental conditions, observed in the temporaldynamics of turning behaviour. Chaotic temporal dynamics are known to occur in pacemaker neuronsin molluscs, but there have been no studies reporting on whether chaotic properties are manifest in themovement patterns of molluscs. Our results suggest that complex search patterns, like the Lévy walksmade by mud snails, can have their mechanistic origins in chaotic neuronal processes. This possibilitycalls for new research on the coupling between neurobiology and motor properties

Spatial heterogeneity and irreversible vegetation change in semi-arid grazing systems

Recent theoretical studies have shown that spatial redistribution of surface water may explain the occurrence of patterns of alternating vegetated and degraded patches in semiarid grasslands. These results implied, however, that spatial redistribution processes cannot explain the collapse of production on coarser scales observed in these systems. We present a spatially explicit vegetation model to investigate possible mechanisms explaining irreversible vegetation collapse on coarse spatial scales. The model results indicate that the dynamics of vegetation on coarse scales are determined by the interaction of two spatial feedback processes. Loss of plant cover in a certain area results in increased availability of water in remaining vegetated patches through run-on of surface water, promoting within-patch plant production. Hence, spatial redistribution of surface water creates negative feedback between reduced plant cover and increased plant growth in remaining vegetation. Reduced plant cover, however, results in focusing of herbivore grazing in the remaining vegetation. Hence, redistribution of herbivores creates positive feedback between reduced plant cover and increased losses due to grazing in remaining vegetated patches, leading to collapse of the entire vegetation. This may explain irreversible vegetation shifts in semiarid grasslands on coarse spatial scales

Algorithmic statistics: forty years later

Algorithmic statistics has two different (and almost orthogonal) motivations. From the philosophical point of view, it tries to formalize how the statistics works and why some statistical models are better than others. After this notion of a "good model" is introduced, a natural question arises: it is possible that for some piece of data there is no good model? If yes, how often these bad ("non-stochastic") data appear "in real life"? Another, more technical motivation comes from algorithmic information theory. In this theory a notion of complexity of a finite object (=amount of information in this object) is introduced; it assigns to every object some number, called its algorithmic complexity (or Kolmogorov complexity). Algorithmic statistic provides a more fine-grained classification: for each finite object some curve is defined that characterizes its behavior. It turns out that several different definitions give (approximately) the same curve. In this survey we try to provide an exposition of the main results in the field (including full proofs for the most important ones), as well as some historical comments. We assume that the reader is familiar with the main notions of algorithmic information (Kolmogorov complexity) theory.Comment: Missing proofs adde

Overview of PAN'17: Author Identification, Author Profiling, and Author Obfuscation

[EN] The PAN 2017 shared tasks on digital text forensics were held in conjunction with the annual CLEF conference. This paper gives a high-level overview of each of the three shared tasks organized this year, namely author identification, author profiling, and author obfuscation. For each task, we give a brief summary of the evaluation data, performance measures, and results obtained. Altogether, 29 participants submitted a total of 33 pieces of software for evaluation, whereas 4 participants submitted to more than one task. All submitted software has been deployed to the TIRA evaluation platform, where it remains hosted for reproducibility purposes.The work at the Universitat Politècnica de València was funded by the MINECO research project SomEMBED (TIN2015-71147-C2-1-P).Potthast, M.; Rangel-Pardo, FM.; Tschuggnall, M.; Stamatatos, E.; Rosso, P.; Stein, B. (2017). Overview of PAN'17: Author Identification, Author Profiling, and Author Obfuscation. Lecture Notes in Computer Science. 10456:275-290. https://doi.org/10.1007/978-3-319-65813-1_25S27529010456Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retrieval 12(4), 461–486 (2009)Bagnall, D.: Authorship clustering using multi-headed recurrent neural networks—notebook for PAN at CLEF 2016. In: Balog et al. [3] (2016). http://ceur-ws.org/Vol-1609/Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.): CLEF 2016 Evaluation Labs and Workshop – Working Notes Papers, 5–8 September, Évora, Portugal. CEUR Workshop Proceedings. CEUR-WS.org (2016). http://www.clef-initiative.eu/publication/working-notesClarke, C.L., Craswell, N., Soboroff, I., Voorhees, E.M.: Overview of the TREC 2009 web track. Technical report, DTIC Document (2009)García, Y., Castro, D., Lavielle, V., Noz, R.M.: Discovering author groups using a $$\beta$$ β -compact graph-based clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Glavaš, G., Nanni, F., Ponzetto, S.P.: Unsupervised text segmentation using semantic relatedness graphs. In: Association for Computational Linguistics (2016)Gollub, T., Stein, B., Burrows, S.: Ousting ivory tower research: towards a web framework for providing experiments as a service. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M. (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), pp. 1125–1126. ACM, August 2012Gómez-Adorno, H., Aleman, Y., no, D.V., Sanchez-Perez, M.A., Pinto, D., Sidorov, G.: Author clustering using hierarchical clustering analysis. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2017: safety evaluation revisited. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Halvani, O., Graner, L.: Author clustering based on compression-based dissimilarity scores. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Comput. Linguist. 23(1), 33–64 (1997)Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Advances in Neural Information Processing Systems (NIPS), pp. 3294–3302 (2015)Kocher, M., Savoy, J.: UniNE at CLEF 2017: author clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Koppel, M., Akiva, N., Dershowitz, I., Dershowitz, N.: Unsupervised decomposition of a document into authorial components. In: Lin, D., Matsumoto, Y., Mihalcea, R. (eds.) Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1356–1364 (2011)Misra, H., Yvon, F., Jose, J.M., Cappe, O.: Text segmentation via topic modeling: an analytical study. In: Proceedings of CIKM 2009, pp. 1553–1556. ACM (2009)Pevzner, L., Hearst, M.A.: A critique and improvement of an evaluation metric for text segmentation. Comput. Linguis. 28(1), 19–36 (2002)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Notebook Papers of the 5th Evaluation Lab on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN), Amsterdam, The Netherlands, September 2011Potthast, M., Gollub, T., Rangel, F., Rosso, P., Stamatatos, E., Stein, B.: Improving the reproducibility of PAN’s shared tasks: plagiarism detection, author identification, and author profiling. In: Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., Toms, E. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 268–299. Springer, Cham (2014). doi: 10.1007/978-3-319-11382-1_22Potthast, M., Hagen, M., Stein, B.: Author obfuscation: attacking the state of the art in authorship verification. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing interaction logs to understand text reuse from the web. In: Fung, P., Poesio, M. (eds.) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 13), pp. 1212–1221. Association for Computational Linguistics (2013). http://www.aclweb.org/anthology/p13-1119Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop – Working Notes Papers, 8–11 September, Toulouse, France. CEUR Workshop Proceedings, CEUR-WS.org, September 2015Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkmann, M., Stein, B., Verhoeven, B., Daelemans, W.: Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Evaluation Labs and Workshop – Working Notes Papers, 15–18 September, Sheffield, UK. CEUR Workshop Proceedings, CEUR-WS.org, September 2014Rangel, F., Rosso, P., Franco-Salvador, M.: A low dimensionality representation for language variety identification. In: 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing. LNCS. Springer (2016). arXiv:1705.10754Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) CLEF 2013 Evaluation Labs and Workshop – Working Notes Papers, 23–26 September, Valencia, Spain (2013)Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Potthast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog et al. [3]Riedl, M., Biemann, C.: TopicTiling: a text segmentation algorithm based on LDA. In: Proceedings of ACL 2012 Student Research Workshop, pp. 37–42. Association for Computational Linguistics (2012)Scaiano, M., Inkpen, D.: Getting more from segmentation evaluation. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 362–366. Association for Computational Linguistics (2012)Stamatatos, E., Tschuggnall, M., Verhoeven, B., Daelemans, W., Specht, G., Stein, B., Potthast, M.: Clustering by authorship within and across documents. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org. http://ceur-ws.org/Vol-1609/Stamatatos, E., Tschuggnall, M., Verhoeven, B., Daelemans, W., Specht, G., Stein, B., Potthast, M.: Clustering by authorship within and across documents. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016Tschuggnall, M., Stamatatos, E., Verhoeven, B., Daelemans, W., Specht, G., Stein, B., Potthast, M.: Overview of the author identification task at PAN-2017: style breach detection and author clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 201

Overview of PAN 2018. Author identification, author profiling, and author obfuscation

[EN] PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics.More specifically, this edition of PAN introduces a shared task in cross-domain authorship attribution, where texts of known and unknown authorship belong to distinct domains, and another task in style change detection that distinguishes between single author and multi-author texts. In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender. Finally, the author obfuscation task studies how a text by a certain author can be paraphrased so that existing author identification tools are confused and cannot recognize the similarity with other texts of the same author. New corpora have been built to support these shared tasks. A relatively large number of software submissions (41 in total) was received and evaluated. Best paradigms are highlighted while baselines indicate the pros and cons of submitted approaches.The work at the Universitat Polit`ecnica de Val`encia was funded by the MINECO research project SomEMBED (TIN2015-71147-C2-1-P)Stamatatos, E.; Rangel-Pardo, FM.; Tschuggnall, M.; Stein, B.; Kestemont, M.; Rosso, P.; Potthast, M. (2018). Overview of PAN 2018. Author identification, author profiling, and author obfuscation. Lecture Notes in Computer Science. 11018:267-285. https://doi.org/10.1007/978-3-319-98932-7_25S26728511018Argamon, S., Juola, P.: Overview of the international authorship identification competition at PAN-2011. In: Petras, V., Forner, P., Clough, P. (eds.) Notebook Papers of CLEF 2011 Labs and Workshops, 19–22 September 2011, Amsterdam, Netherlands, September 2011. http://www.clef-initiative.eu/publication/working-notesBird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)Bogdanova, D., Lazaridou, A.: Cross-language authorship attribution. In: Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, pp. 2015–2020 (2014)Choi, F.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference (NAACL), pp. 26–33. Association for Computational Linguistics, Seattle, April 2000Custódio, J.E., Paraboni, I.: EACH-USP ensemble cross-domain authorship attribution. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaneshvar, S.: Gender identification in Twitter using n-grams and LSA. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaniel Karaś, M.S., Sobecki, P.: OPI-JSA at CLEF 2017: author clustering and style breach detection. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings. CLEF and CEUR-WS.org, September 2017Giannella, C.: An improved algorithm for unsupervised decomposition of a multi-author document. The MITRE Corporation. Technical Papers, February 2014Glover, A., Hirst, G.: Detecting stylistic inconsistencies in collaborative writing. In: Sharples, M., van der Geest, T. (eds.) The New Writing Environment, pp. 147–168. Springer, London (1996). https://doi.org/10.1007/978-1-4471-1482-6_12Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2017: safety evaluation revisited. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2018. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org (2018)Hellekson, K., Busse, K. (eds.): The Fan Fiction Studies Reader. University of Iowa Press, Iowa City (2014)Juola, P.: An overview of the traditional authorship attribution subtask. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop - Working Notes Papers, 17–20 September 2012, Rome, Italy, September 2012. http://www.clef-initiative.eu/publication/working-notesJuola, P.: The rowling case: a proposed standard analytic protocol for authorship questions. Digital Sch. Humanit. 30(suppl–1), i100–i113 (2015)Kestemont, M., Luyckx, K., Daelemans, W., Crombez, T.: Cross-genre authorship verification using unmasking. Engl. Stud. 93(3), 340–356 (2012)Kestemont, M., et al.: Overview of the author identification task at PAN-2018: cross-domain authorship attribution and style change detection. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org (2018)Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring differentiability: unmasking pseudonymous authors. J. Mach. Learn. Res. 8, 1261–1276 (2007)Overdorf, R., Greenstadt, R.: Blogs, Twitter feeds, and reddit comments: cross-domain authorship attribution. Proc. Priv. Enhanc. Technol. 2016(3), 155–171 (2016)Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Notebook Papers of the 5th Evaluation Lab on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN), Amsterdam, The Netherlands, September 2011Potthast, M., Hagen, M., Stein, B.: Author obfuscation: attacking the state of the art in authorship verification. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing interaction logs to understand text reuse from the web. In: Fung, P., Poesio, M. (eds.) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 1212–1221. Association for Computational Linguistics, August 2013. http://www.aclweb.org/anthology/P13-1119Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France, pp. 8–11. CEUR-WS.org, September 2015Rangel, F., et al.: Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Evaluation Labs and Workshop - Working Notes Papers, Sheffield, UK, pp. 15–18. CEUR-WS.org, September 2014Rangel, F., Rosso, P., G’omez, M.M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org (2017)Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) CLEF 2013 Evaluation Labs and Workshop - Working Notes Papers, 23–26 September 2013, Valencia, Spain, September 2013Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Potthast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, September 2016Safin, K., Kuznetsova, R.: Style breach detection with neural sentence embeddings. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Sapkota, U., Bethard, S., Montes, M., Solorio, T.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Sapkota, U., Solorio, T., Montes, M., Bethard, S., Rosso, P.: Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of the 25th International Conference on Computational Linguistics. Technical Papers, pp. 1228–1237 (2014)Stamatatos, E.: Intrinsic plagiarism detection using character $$n$$n-gram Profiles. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E. (eds.) SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 38–46. Universidad Politécnica de Valencia and CEUR-WS.org, September 2009. http://ceur-ws.org/Vol-502Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. J. Law Policy 21, 421–439 (2013)Stamatatos, E.: Authorship attribution using text distortion. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1138–1149. Association for Computational Linguistics (2017)Stamatatos, E., et al.: Overview of the author identification task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, 8–11 September 2015, Toulouse, France. CEUR-WS.org, September 2015Stamatatos, E., et al.: Clustering by authorship within and across documents. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Takahashi, T., Tahara, T., Nagatani, K., Miura, Y., Taniguchi, T., Ohkuma, T.: Text and image synergy with feature cross technique for gender identification. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTellez, E.S., Miranda-Jiménez, S., Moctezuma, D., Graff, M., Salgado, V., Ortiz-Bejar, J.: Gender identification through multi-modal tweet analysis using microtc and bag of visual words. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTschuggnall, M., Specht, G.: Automatic decomposition of multi-author documents using grammar analysis. In: Proceedings of the 26th GI-Workshop on Grundlagen von Datenbanken. CEUR-WS, Bozen, October 2014Tschuggnall, M., et al.: Overview of the author identification task at PAN-2017: style breach detection and author clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, vol. 1866. CLEF and CEUR-WS.org, September 2017. http://ceur-ws.org/Vol-1866

Vasopressin/serotonin interactions in the anterior hypothalamus control aggressive behavior in golden hamsters

Studies in several species of rodents show that arginine vasopressin (AVP) acting through a V1A receptor facilitates offensive aggression, i.e., the initiation of attacks and bites, whereas serotonin (5-HT) acting through a 5-HT1B receptor inhibits aggressive responding. One area of the CNS that seems critical for the organization of aggressive behavior is the basolateral hypothalamus, particularly the anterior hypothalamic region. The present studies examine the neuroanatomical and neurochemical interaction between AVP and 5-HT at the level of the anterior hypothalamus (AH) in the control of offensive aggression in Syrian golden hamsters. First, specific V1A and 5-HT1B binding sites in the AH are shown by in vitro receptor autoradiography. The binding for each neurotransmitter colocalizes with a dense field of immunoreactive AVP and 5-HT fibers and putative terminals. Putative 5-HT synapses on AVP neurons in the area of the AH are identified by double-staining immunocytochemistry and laser scanning confocal microscopy. These morphological data predispose a functional interaction between AVP and 5-HT at the level of the AH. When tested for offensive aggression in a resident/intruder paradigm, resident hamsters treated with fluoxetine, a selective 5-HT reuptake inhibitor, have significantly longer latencies to bite and bite fewer times than vehicle-treated controls. Conversely, AVP microinjections into the AH significantly shorten the latency to bite and increase biting attacks. The action of microinjected AVP to increase offensive aggression is blocked by the pretreatment of hamsters with fluoxetine. These data suggest that 5-HT inhibits fighting, in part, by antagonizing the aggression-promoting action of the AVP system

Overview of the PAN/CLEF 2015 Evaluation Lab

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24027-5_49This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of text mining research focusing on the identification of personal traits of authors left behind in texts unintentionally. PAN 2015 comprises three tasks: plagiarism detection, author identification and author profiling studying important variations of these problems. In plagiarism detection, community-driven corpus construction is introduced as a new way of developing evaluation resources with diversity. In author identification, cross-topic and cross-genre author verification (where the texts of known and unknown authorship do not match in topic and/or genre) is introduced. A new corpus was built for this challenging, yet realistic, task covering four languages. In author profiling, in addition to usual author demographics, such as gender and age, five personality traits are introduced (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and a new corpus of Twitter messages covering four languages was developed. In total, 53 teams participated in all three tasks of PAN 2015 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.Stamatatos, E.; Potthast, M.; Rangel, F.; Rosso, P.; Stein, B. (2015). Overview of the PAN/CLEF 2015 Evaluation Lab. En Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings. 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Experimental evidence for inherent Lévy search behaviour in foraging animals

Recently, Lévy walks have been put forward as a new paradigm for animal search and many cases have been made for its presence in nature. However, it remains debated whether Lévy walks are an inherent behavioural strategy or emerge from the animal reacting to its habitat. Here, we demonstrate signatures of Lévy behaviour in the search movement of mud snails (Hydrobia ulvae) based on a novel, direct assessment of movement properties in an experimental set-up using different food distributions. Our experimental data uncovered clusters of small movement steps alternating with long moves independent of food encounter and landscape complexity. Moreover, size distributions of these clusters followed truncated power laws. These two findings are characteristic signatures of mechanisms underlying inherent Lévy-like movement. Thus, our study provides clear experimental evidence that such multi-scale movement is an inherent behaviour rather than resulting from the animal interacting with its environmen