Relevant bibliographies by topics / Similarities

Academic literature on the topic 'Similarities'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 May 2024

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Journal articles on the topic "Similarities"

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Robson, David. "Virtual similarities." Nature 455, no. 7209 (September 2008): 32–33. http://dx.doi.org/10.1038/455032a.

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Chalfie, Martin, Monica Driscoll, and Mingxia Huang. "Degenerin similarities." Nature 361, no. 6412 (February 1993): 504. http://dx.doi.org/10.1038/361504a0.

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Ibanez, Mariana, and Simon Kim. "self-similarities." Thresholds 35 (January 2009): 38–41. http://dx.doi.org/10.1162/thld_a_00207.

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Cove, D. J. "Genetic similarities." Nature 314, no. 6006 (March 1985): 44. http://dx.doi.org/10.1038/314044b0.

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RUPES˘, IVAN. "Cytoskeletal similarities." Nature 345, no. 6271 (May 1990): 119. http://dx.doi.org/10.1038/345119b0.

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Kurilić, Miloš S. "Different similarities." Archive for Mathematical Logic 54, no. 7-8 (September 8, 2015): 839–59. http://dx.doi.org/10.1007/s00153-015-0443-x.

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Swindells, Mark B. "Loopy similarities." Nature Structural & Molecular Biology 1, no. 7 (July 1994): 421–22. http://dx.doi.org/10.1038/nsb0794-421.

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Baldini, Enrico. "Cloning similarities." Nature Biotechnology 20, no. 5 (May 2002): 432. http://dx.doi.org/10.1038/nbt0502-432.

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Goodman, Robert P. "Medication Similarities." JAMA: The Journal of the American Medical Association 253, no. 5 (February 1, 1985): 632. http://dx.doi.org/10.1001/jama.1985.03350290034009.

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Offermann, Dirk, Jens Oberheide, Michael Jarisch, Klaus-Ulrich Grossmann, and Oleg Gusev. "Similarities in middle atmosphere structures." Meteorologische Zeitschrift 15, no. 3 (July 10, 2006): 333–42. http://dx.doi.org/10.1127/0941-2948/2006/0135.

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Dissertations / Theses on the topic "Similarities"

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Gültekin, Emrah, Dursun Karaduman, and Ines Weiser. "Describing differences and similarities." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6594/.

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1. Theoretical background 2. Impressions 3. Teaching group (differences and similarities) 4. Questionnaire 5. Use of similarities and differences 6. The way of teaching similarities and differences 7. Understanding of differences and similarities 8. Problems 9. Methods and strategies 10. Conclusion
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Malchova, Lenka, Zdenko Majer, and Pieter Laban. "Describing differences (and recognising similarities)." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6600/.

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1. A general look at describing differences and recognising similarities 2. Teaching strategies used describing differences and recognising similarities 3. Teaching practice 4. Student teachers’ evaluations 5. Conclusion
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Chen, Beichen. "Stylometric Embeddings for Book Similarities." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303125.

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Stylometry is the field of research aimed at defining features for quantifying writing style, and the most studied question in stylometry has been authorship attribution, where given a set of texts with known authorship, we are asked to determine the author of a new unseen document. In this study a number of lexical and syntactic stylometric feature sets were extracted for two datasets, a smaller one containing 27 books from 25 authors, and a larger one containing 11,063 books from 316 authors. Neural networks were used to transform the features into embeddings after which the nearest neighbor method was used to attribute texts to their closest neighbor. The smaller dataset achieved an accuracy of 91.25% using frequencies of 50 most common functional words, dependency relations, and Part-of-speech (POS) tags as features, and the larger dataset achieved 69.18% accuracy using a similar feature set with 100 most common functional words. In addition to performing author attribution, a user test showed the potentials of the model in generating author similarities and hence being useful in an applied setting for recommending books to readers based on author style.
Stilometri eller stilistisk statistik är ett forskningsområde som arbetar med att definiera särdrag för att kvantitativt studera stilistisk variation hos författare. Stilometri har mest fokuserat på författarbestämning, där uppgiften är att avgöra vem som skrivit en viss text där författaren är okänd, givet tidigare texter med kända författare. I denna stude valdes ett antal lexikala och syntaktiska stilistiska särdrag vilka användes för att bestämma författare. Experimentella resultat redovisas för två samlingar litterära verk: en mindre med 27 böcker skrivna av 25 författare och en större med 11 063 böcker skrivna av 316 författare. Neurala nätverk användes för att koda de valda särdragen som vektorer varefter de närmaste grannarna för de okända texterna i vektorrummet användes för att bestämma författarna. För den mindre samlingen uppnåddes en träffsäkerhet på 91,25% genom att använda de 50 vanligaste funktionsorden, syntaktiska dependensrelationer och ordklassinformation. För den större samlingen uppnåddes en träffsäkerhet på 69,18% med liknande särdrag. Ett användartest visar att modellen utöver att bestämma författare har potential att representera likhet mellan författares stil. Detta skulle kunna tillämpas för att rekommendera böcker till läsare baserat på stil.
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Mequanint, Eyasu Zemene <1984&gt. "Multiobjective Games and Multiple Similarities." Master's Degree Thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/3376.

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Straaten, Steve van. "Timing similarities among accreting neutron stars." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2004. http://dare.uva.nl/document/92823.

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Howarth, Peter Dennis. "Discovering images : features, similarities and subspaces." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445906.

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Franks, Matthew. "Similarities between eating disorders and obesity." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9888.

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This thesis examines the evidence base for psychological interventions for the treatment and prevention of eating disorders and obesity. The research process has three strands. Self-Contained Literature Review This paper considers the literature base for the treatment and prevention of eating disorders through critical appraisal of studies which employed a randomised controlled trial design. The results suggest that the psychopathology of eating disorders is complex and that presentations across diagnoses are variable. The review argues that treatment decisions should not be based on diagnosis alone but on the underlying psychological features of clients who present for treatment through initial screening. Research Report The second strand examines the hypothesis that some of the theories underlying eating disorders may also apply to individuals seeking treatment for obesity by comparing a sample of treatment-seeking obese adults to a sample with eating disorders, and to other group norms. Treatment seeking obese adults in this study were similar to eating disordered populations on measures of external shame, weight concern and shape concern, factors which are believed to be associated with an increased risk of developing an eating disorder. It is suggested that obese clients presenting for weight loss intervention should be screened for levels of shame and eating disordered thoughts and behaviours prior to embarking upon treatment. It is further suggested that for those individuals displaying high levels of shame and eating disordered beliefs/ behaviours, interventions targeting reduction of these constructs by increasing levels of self-compassion may enable some obese clients to lose weight whilst improving psychological wellbeing. It is hypothesised that in contrast to dieting, this may lead to more sustained weight loss and avenues for further investigation of these ideas are considered. Critical Appraisal The research process is considered and learning points discussed in this reflective piece.
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Glassford, Maureen Patricia. "Gender similarities and differences in managing conflicts." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq64768.pdf.

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Svanberg, Hakim. "Quine and Wittgenstein : Similarities and Common Misconceptions." Thesis, Uppsala universitet, Filosofiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-377156.

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Quine and late Wittgenstein have often found themselves being compared for similarities and dissimilarities, we can attribute the allure of this subject to both philosophers being similar advocates of holism and epistemological relativity despite taking on different subject matters. Pieranna Garavaso attempted an inquiry of this type, where she compares Quine’s view on holism, relying mainly on “Two Dogmas of Empiricism”, with Wittgenstein’s On Certainty. Garavaso shows in her essay that there is a high level of correspondence between On Certainty and “Two Dogmas of Empiricism” but ultimately concludes that despite any actual agreement between Quine and Wittgenstein, their views end up ultimately incompatible. I will argue that this is wrong; Quine’s conception of holism, and Wittgenstein’s conception of holism found in On Certainty are compatible, and even complementary. I will begin my text by giving a short presentation of Quine’s “Two Dogmas of Empiricism”. I will then raise the similarities, and dissimilarities between the two texts before giving my final argument against the supposed conflict between them.
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Silva, Renato Rodrigues Oliveira da. "Visualizing multidimensional data similarities: improvements and applications." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-15022017-162359/.

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Multidimensional datasetsare increasingly more prominent and important in data science and many application domains. Such datasets typically consist of a large set of observations, or data points, each which is described by several measurements, or dimensions. During the design of techniques and tools to process such datasets, a key component is to gather insights into their structure and patterns, a goal which is targeted by multidimensional visualization methods. Structures and patterns of high-dimensional data can be described, at a core level, by the notion of similarity of observations. Hence, to visualize such patterns, we need effective and efficient ways to depict similarity relations between a large number of observations, each having a potentially large number of dimensions. Within the realm of multidimensional visualization methods, two classes of techniques exist projections and similarity trees which effectively capture similarity patterns and also scale well to the number of observations and dimensions of the data. However, while such techniques show similarity patterns, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we proposemethodsthat make the computation of similarity treesefficient for large datasets, and also allow their visual explanation on a multiscale, or several levels of detail. We also propose ways to construct simplified representations of similarity trees, thereby extending their visual scalability even further. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of automatically detected groups of related observations which are also automatically annotated in terms of their similarity in the high-dimensional data space. We show next how these explanatory mechanismscan be adapted to handle both static and time-dependent multidimensional datasets. Our proposed techniques are designed to be easy to use, work nearly automatically, handle any typesof quantitativemultidimensional datasets and multidimensional projection techniques, and are demonstrated on a variety of real-world large datasets obtained from image collections, text archives, scientific measurements, and software engineeering.
Conjuntos de dados multidimensionais são cada vez mais proeminentes e importantes em data science e muitos domínios de aplicação. Esses conjuntos de dados são tipicamente constituídos de um grande número de observações, ou objetos, cada qual descrito por várias medidas, ou dimensões. Durante o projeto de técnicas e ferramentas para processar tais dados, um dos focos principais é prover meios para análise e levantamento de hipóteses a partir das principais estruturas e padrões. Esse objetivo é perseguido por métodos de visualização multidimensional. Estruturas e padrões em dados multidimensionais podem ser descritos, em linhas gerais, pela noção de similaridade das observações. Portanto, para visualizar esses padrões, precisamos de meios efetivos e eficientes para retratar relações de similaridade dentre um grande número de observações, que potencialmente possuem um grande número de dimensões cada. No contexto dos métodos de visualização multidimensional, existem duas categorias de técnicas projeções e árvores de similaridade que efetivamente capturam padrões de similaridade e oferecem boa escalabilidade, tanto para o número de observações e quanto de dimensões. No entanto, embora essas técnicas exibam padrões de similaridade, o entendimento e interpretação desses padrões, em termos das dimensões originais dos dados, ainda é difícil. O trabalho desenvolvido nessa tese visa o desenvolvimento de técnicas explicativas para a fácil interpretação de padrões de similaridade presentes em projeções multidimensionais e árvores de similaridade. Primeiro, propomos métodos que possibilitam a computação eficiente de árvores de similaridade para grandes conjuntos de dados, e também a sua explicação visual em multiescala, ou seja, em vários níveis de detalhe. Também propomos modos de construir representações simplificadas de árvores de similaridade, e desse modo estender ainda mais a sua escalabilidade visual. Segundo, propomos métodos para explicar visualmente projeções multidimensionais em termos de grupos de observações relacionadas, detectadas e anotadas automaticamente para explicitar aspectos de sua similaridade no espaço de alta dimensionalidade. Mostramos em seguida como esses mecanismos explicativos podem ser adaptados para lidar com dados de natureza estática e dependentes no tempo. Nossas técnicas sã construídas visando fácil utilização, funcionamento semi automático, aplicação em quaisquer tipos de dados multidimensionais quantitativos e quaisquer técnicas de projeção multidimensional. Demonstramos a sua utilização em uma variedade de conjuntos de dados reais, obtidos a partir de coleções de imagens, arquivos textuais, medições científicas e de engenharia de software.
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Books on the topic "Similarities"

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Della Riccia, Giacomo, Didier Dubois, Rudolf Kruse, and Hans-Joachim Lenz, eds. Preferences and Similarities. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7.

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Weiland, Claus. Mechanics of Flow Similarities. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42930-0.

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Celebrate our similarities: Primary. Huntington Beach, CA: Teacher Created Materials, 1995.

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Milan, Stras̆kraba, International Lake Environment Committee., and UNEP International Environmental Technology Centre., eds. Similarities, differences and importance. Shiga, Japan: United Nations Environment Programme-International Environmental Technology Centre, 2000.

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Khrō̜pkhrūa, Thailand Samnakngān Kitčhakān Sattrī læ Sathāban. Gender development: Similarities and differences. Bangkok: Office of Women's Affairs and Family Development, 2008.

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Gidvāṇī, Parso. Similarities in Sindhi and Dravidian languages. New Delhi: Sindhi Academi Delhi, 1996.

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Nielsen, Gudrun. Architecture and sculpture: Differences ans similarities. London: University of East London, 1993.

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United States. National Park Service, ed. [1st grade: Similarities and differences (biodiversity)]. [Washington, D.C.?: National Park Service, 1992.

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Mazumder, Bhashkar. Sibling similarities, differences and economic inequality. [Chicago, Ill.]: Federal Reserve Bank of Chicago, 2004.

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Kathryn, Dindia, and Canary Daniel J, eds. Sex differences and similarities in communication. 2nd ed. Mahwah, N.J: Lawrence Erlbaum Associates, 2006.

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Book chapters on the topic "Similarities"

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Shang, Yifan. "Similarities." In Ius Gentium: Comparative Perspectives on Law and Justice, 15–76. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-46467-6_3.

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le Calvé, G. "Similarities Functions." In Compstat, 341–47. Heidelberg: Physica-Verlag HD, 1988. http://dx.doi.org/10.1007/978-3-642-46900-8_47.

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Gebhardt, Friedrich, Angi Voß, Wolfgang Gräther, and Barbara Schmidt-Belz. "Case Similarities." In Reasoning with Complex Cases, 39–42. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6233-7_4.

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Ding, Cody S. "Configuration Similarities." In Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research, 121–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78172-3_9.

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Sukman, Daniel. "The Similarities." In American Football and the American Way of War, 135–46. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55345-5_12.

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De Baets, Bernard. "Similarity of Fuzzy Sets and Dominance of Random Variables: a Quest for Transitivity." In Preferences and Similarities, 1–22. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_1.

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Amgoud, Leila, and Henri Prade. "Comparing decisions on the basis of a bipolar typology of arguments." In Preferences and Similarities, 249–64. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_10.

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Domshlak, Carmel. "A Snapshot on Reasoning with Qualitative Preference Statements in AI." In Preferences and Similarities, 265–82. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_11.

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Hüllermeier, Eyke, and Johannes Fürnkranz. "Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants." In Preferences and Similarities, 283–304. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_12.

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Rossi, Francesca. "Constraints and Preferences: Modelling Frameworks and Multi-agent settings." In Preferences and Similarities, 305–20. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_13.

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Conference papers on the topic "Similarities"

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Rotondo, Devin M., Mark P. Wachowiak, Dean C. Hay, and Michel J. Johnson. "Uncovering similarities in biomedical signals." In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2014. http://dx.doi.org/10.1109/ccece.2014.6901075.

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Thiel, Kilian, and Michael R. Berthold. "Node Similarities from Spreading Activation." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.108.

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Chen, Yihua, Maya R. Gupta, and Benjamin Recht. "Learning kernels from indefinite similarities." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553393.

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Mair, M., R. Sitzenfrei, M. Möderl, and W. Rauch. "Identifying Multi Utility Network Similarities." In World Environmental And Water Resources Congress 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412312.315.

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Kawahara, Daisuke, and Sadao Kurohashi. "Coordination disambiguation without any similarities." In the 22nd International Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1599081.1599135.

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Welke, Kai, Erhan Oztop, Gordon Cheng, and Rudiger Dillmann. "Exploiting similarities for robot perception." In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iros.2007.4399215.

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Nanjo, Hiroaki, Yusuke Iyonaga, and Takehiko Yoshimi. "Spoken document retrieval for oral presentations integrating global document similarities into local document similarities." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-403.

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Doulaverakis, Charalampos, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Exploiting Visual Similarities for Ontology Alignment." In 7th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005588200290037.

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Zou, Zhaonian, and Jianzhong Li. "Structural-Context Similarities for Uncertain Graphs." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.22.

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Rousseau, Francois, Piotr A. Habas, and Colin Studholme. "Human brain labeling using image similarities." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995694.

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Reports on the topic "Similarities"

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Ori, Naomi, and Sarah Hake. Similarities and differences in KNOX function. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7696516.bard.

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Hypothalamic gonadotropinreleasing hormone (GnRH1) is the key hormone in the control of gametogenesis and gonadal growth in vertebrates. Developmentally, hypothalamic GnRHproducing neurons originate from the olfactory placode, migrate along olfactory axons into the forebrain, and continue to the preoptic area and hypothalamus where they function to stimulate gonadotropin secretion from the pituitary gland. An appropriate location of GnRH neurons within the hypothalamus is necessary for normal reproductive function in the adult; abnormal migration and targeting of GnRH neurons during embryogenesis results in hypogonadism and infertility. The developmental migration of GnRH neurons and axonal pathfinding in mammals are modulated by a plethora of factors, including receptors, secreted molecules, adhesion molecules, etc. Yet the exact mechanism that controls these developmental events is still unknown. We investigated these developmental events and the underlying mechanisms using a transgenic zebrafish model, Tg(gnrh1: EGFP), in which GnRH1 neurons and axons are fluorescently labeled. The role of factors that potentially affect the development of this system was investigated by testing the effect of their knockdown and mutation on the development of the GnRH1 system. In addition, their localization in relation to GnRH1 was described during development. These studies are expected to generate the scientific foundation that will lead to developing innovative technologies, based on the disruption of the early establishment of the GnRH system, for inducing sterility in farmed fish, which is highly desirable for economical and environmental reasons.
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Baldwin, Richard, and Philippe Martin. Two Waves of Globalisation: Superficial Similarities, Fundamental Differences. Cambridge, MA: National Bureau of Economic Research, January 1999. http://dx.doi.org/10.3386/w6904.

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Rose, Andrew. Surprising Similarities: Recent Monetary Regimes of Small Economies. Cambridge, MA: National Bureau of Economic Research, November 2013. http://dx.doi.org/10.3386/w19632.

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Gómez Ugarte Valerio, Ana C., Ugofilippo Basellini, Carlo G. Camarda, Fanny Janssen, and Emilio Zagheni. Reassessing socioeconomic inequalities in mortality via distributional similarities. Rostock: Max Planck Institute for Demographic Research, April 2024. http://dx.doi.org/10.4054/mpidr-wp-2024-007.

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Taylor, R. B., R. W. G. Carter, D. L. Forbes, and J. D. Orford. Beach Sedimentation in Ireland : Contrasts and Similarities With Atlantic Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/120350.

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Taylor, Teresa Z. Developing Army Leaders Across Components: Assessing Knowledge Similarities and Differences. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada469719.

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Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. Similarities and Differences in the Adoption of General Purpose Technologies. Cambridge, MA: National Bureau of Economic Research, February 2023. http://dx.doi.org/10.3386/w30976.

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Almunia, Miguel, Agustín Bénétrix, Barry Eichengreen, Kevin O'Rourke, and Gisela Rua. From Great Depression to Great Credit Crisis: Similarities, Differences and Lessons. Cambridge, MA: National Bureau of Economic Research, November 2009. http://dx.doi.org/10.3386/w15524.

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Sauermann, Henry, and Paula Stephan. Twins or Strangers? Differences and Similarities between Industrial and Academic Science. Cambridge, MA: National Bureau of Economic Research, June 2010. http://dx.doi.org/10.3386/w16113.

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Szołtysek, Mikołaj. Family systems and welfare provision in Poland-Lithuania: discrepancies and similarities. Rostock: Max Planck Institute for Demographic Research, April 2012. http://dx.doi.org/10.4054/mpidr-wp-2012-016.

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You might also be interested in the extended bibliographies on the topic 'Similarities' for particular source types:
Journal articles Dissertations / Theses Books
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