An Integrated XRF/XRD Instrument for Mars Exobiology and Geology Experiments

By employing an integrated x-ray instrument on a future Mars mission, data obtained will greatly augment those returned by Viking; details characterizing the past and present environment on Mars and those relevant to the possibility of the origin and evolution of life will be acquired. A combined x-ray fluorescence/x-ray diffraction (XRF/XRD) instrument was breadboarded and demonstrated to accommodate important exobiology and geology experiment objectives outlined for MESUR and future Mars missions. Among others, primary objectives for the exploration of Mars include the intense study of local areas on Mars to establish the chemical, mineralogical, and petrological character of different components of the surface material; to determine the distribution, abundance, and sources and sinks of volatile materials, including an assessment of the biologic potential, now and during past epoches; and to establish the global chemical and physical characteristics of the Martian surface. The XRF/XRD breadboard instrument identifies and quantifies soil surface elemental, mineralogical, and petrological characteristics and acquires data necessary to address questions on volatile abundance and distribution. Additionally, the breadboard is able to characterize the biogenic element constituents of soil samples providing information on the biologic potential of the Mars environment. Preliminary breadboard experiments confirmed the fundamental instrument design approach and measurement performance

The enormous acidifying effect of the supersubstituent ᎐ ᎐ NSO 2 CF 3 on the acidity of derivatives of benzenesulfonamide and toluene-p- sulfonamide in the gas phase and in dimethyl sulfoxide

The effect of stepwise replacement of ᎐ ᎐ O oxygen atoms by ᎐ ᎐ NSO 2 CF 3 fragments in the sulfonyl group of toluenep-sulfonamide and benzenesulfonamide on their acidity has been studied in the gas phase and dimethyl sulfoxide (DMSO). Incorporation of the first ᎐ ᎐ NSO 2 CF 3 group into 4-MeC 6 H 4 SO 2 NH 2 increases its gas-phase acidity by 23.6 kcal mol Ϫ1 . Substituting the second ᎐ ᎐ O by the ᎐ ᎐ NSO 2 CF 3 group leads to an additional acidity increase of 10.7 kcal mol Ϫ1 ; the total acidity increase is thus 34.3 kcal mol Ϫ1 (25 powers of ten!). In DMSO solution the total acidity increase is 13 pK a units (17.7 kcal mol Ϫ1 ). These findings are also supported by computational studies using DFT B3LYP at the 6-31ϩG* level and the semiempirical PM3 method. The results of this work have potentially important implications for the design of new strongly acidic catalytic materials

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

The effects of fiber inclusion on pet food sensory characteristics and palatability

Citation: Koppel, K., Monti, M., Gibson, M., Alavi, S., Di Donfrancesco, B., & Carciofi, A. C. (2015). The effects of fiber inclusion on pet food sensory characteristics and palatability. Animals, 5(1), 110-125. doi:10.3390/ani5010110The objectives of this study were to determine (a) the influence of fiber on the sensory characteristics of dry dog foods; (b) differences of coated and uncoated kibbles for aroma and flavor characteristics; (c) palatability of these dry dog foods; and (d) potential associations between palatability and sensory attributes. A total of eight fiber treatments were manufactured: a control (no fiber addition), guava fiber (3%, 6%, and 12%), sugar cane fiber (9%; large and small particle size), and wheat bran fiber (32%; large and small particle size). The results indicated significant effects of fibers on both flavor and texture properties of the samples. Bitter taste and iron and stale aftertaste were examples of flavor attributes that differed with treatment, with highest intensity observed for 12% guava fiber and small particle size sugar cane fiber treatments. Fracturability and initial crispness attributes were lowest for the sugar cane fiber treatments. Flavor of all treatments changed after coating with a palatant, increasing in toasted, brothy, and grainy attributes. The coating also had a masking effect on aroma attributes such as stale, flavor attributes such as iron and bitter taste, and appearance attributes such as porosity. Palatability testing results indicated that the control treatment was preferred over the sugar cane or the wheat bran treatment. The treatment with large sugarcane fiber particles was preferred over the treatment with small particles, while both of the wheat bran treatments were eaten at a similar level. Descriptive sensory analysis data, especially textural attributes, were useful in pinpointing the underlying characteristics and were considered to be reasons that may influence palatability of dog foods manufactured with inclusion of different fibers. © 2015 by the authors; licensee MDPI, Basel, Switzerland

Algorithmic statistics revisited

The mission of statistics is to provide adequate statistical hypotheses (models) for observed data. But what is an "adequate" model? To answer this question, one needs to use the notions of algorithmic information theory. It turns out that for every data string $x$ one can naturally define "stochasticity profile", a curve that represents a trade-off between complexity of a model and its adequacy. This curve has four different equivalent definitions in terms of (1)~randomness deficiency, (2)~minimal description length, (3)~position in the lists of simple strings and (4)~Kolmogorov complexity with decompression time bounded by busy beaver function. We present a survey of the corresponding definitions and results relating them to each other

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

Demonstration of the feasibility of an integrated x ray laboratory for planetary exploration

The identification of minerals and elemental compositions is an important component in the geological and exobiological exploration of the solar system. X ray diffraction and fluorescence are common techniques for obtaining these data. The feasibility of combining these analytical techniques in an integrated x ray laboratory compatible with the volume, mass, and power constraints imposed by many planetary missions was demonstrated. Breadboard level hardware was developed to cover the range of diffraction lines produced by minerals, clays, and amorphous; and to detect the x ray fluorescence emissions of elements from carbon through uranium. These breadboard modules were fabricated and used to demonstrate the ability to detect elements and minerals. Additional effort is required to establish the detection limits of the breadboard modules and to integrate diffraction and fluorescence techniques into a single unit. It was concluded that this integrated x ray laboratory capability will be a valuable tool in the geological and exobiological exploration of the solar system

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

Analysis of Safety Concerns on Herbal Products with Assumed Phytoestrogenic Activity

Phytoestrogens (PEs) are plant-based compounds that can interact with estrogen receptors and are mainly used to treat menopausal complaints. However, the safety of products with assumed phytoestrogenic activity is not fully understood. This study aimed to identify plant species with assumed phytoestrogenic activity, review existing literature on their use and safety, and critically evaluate adverse reaction (AR) reports of single-herb, multi-herb, and mixed-multiple products, as submitted to the Netherlands Pharmacovigilance Centre Lareb and to VigiBase of the World Health Organization (WHO). In the Lareb database, the most commonly reported plant species to cause ARs (total of 67 reports) were Actaea racemosa L. (black cohosh) (47.8%), Humulus lupulus L. (hops) (32.8%), and Glycine max (L.) Merr. (soybean) (22.4%). In the VigiBase database (total of 21,944 reports), the top three consisted of Glycine max (L.) Merr. (71.4%), Actaea racemosa L. (11.6%), and Vitex agnus-castus L. (chaste tree) (6.4%). In the scoping review (total of 73 articles), Actaea racemosa L. (30.1%), Glycine max (L.) Merr. (28.8%), and Trifolium pratense L. (13.7%) were the most frequently mentioned plant species. ARs were most frequently reported in the system organ classes "gastrointestinal disorders", "skin and subcutaneous tissue disorders", "reproductive system and breast disorders", and "general disorders and administration site conditions". Furthermore, from the scoping review, it appeared that the use of products with assumed phytoestrogenic activity was associated with postmenopausal bleeding. It was concluded that, while the potential benefits of products with assumed phytoestrogenic activity have been extensively pursued, the potential occurrence of ARs after using these products is less well understood. This study highlights the need for further investigation and careful monitoring of these products to better understand their effects and ensure the safety and well-being of individuals using them. </p