Patterns of Transcendence : Classical Myth in Marina Tsvetaeva's Poetry of the 1920s

This study examines the position and meaning of Classical mythological plots, themes and characters in the oeuvre of the Russian Modernist poet Marina Tsvetaeva (1892-1941). The material consists of lyric poems from the collection Posle Rossii (1928) and two longer lyrical tragedies, Ariadna (1924) and Fedra (1927). These works are examined in the context of Russian Modernism and Tsvetaeva s own poetic development, also taking into account the author s biography, namely, her correspondence with Boris Pasternak. Tsvetaeva s appropriations of the myths enter into a dialogue with the Classical tradition and with the earlier Russian and Western literary manifestations of the source material. Her Classical texts are inextricably linked with her own authorial myth, they are used to project both her ideas about poetry as well as the authored self of her poems. An important context for Tsvetaeva s application of the Classical myths is the concept of the Platonic ladder of Eros. This plot evokes the process of transcendence of the mortal subject into the immaterial realm and is applied by the author as an extended metaphor of the poet s birth. Emphasizing the dialectical movement between the earthly and the divine, Tsvetaeva s Classical personae foreground various positions of the individual between these two realms. By means of kaleidoscopic reformulations of similar metaphors and concepts, Tsvetaeva s mythological poems illustrate the poet s position between the material and the immaterial and the various consequences of this dichotomy on the creative mission. At the heart of Tsvetaeva s appropriation of the Sibyl, Phaedra, Eurydice and Ariadne is the tension between the body and disembodiment. The two lyrical tragedies develop the dichotomous worldview further, nevertheless emphasizing the dual perspective of the divine and the earthly realms: immaterial existence is often evaluated from a material perspective and vice versa. The Platonic subtext is central for Ariadna, focussing on Theseus development from an earthly hero to a spiritual one. Fedra concentrates on Phaedra s divinely induced physical passion, which is nevertheless evoked in a creative light.

Groteski suomalaisessa kirjallisuudessa : Neljä tapaustutkimusta

The grotesque in Finnish literature. Four case studies The topic of the dissertation is the grotesque in Finnish literature. The dissertation is twofold. Firstly, it focuses on the genre tradition of the grotesque, especially its other main branch, which has been named, following in Bakhtin s footsteps, subjective ( chamber ) grotesque, to be distinguished from carnivalistic ( public square ) grotesque. Secondly, the dissertation analyses and interprets four fictional literary works within the context of the grotesque genre, constructed on the basis of previous research and literature. These works are the novel Rakastunut rampa (1922) by Joel Lehtonen, the novel Prins Efflam (1953, transl. into Finnish as Kalastajakylän prinssi) by Sally Salminen, the short story Orjien kasvattaja (1965) by Juhani Peltonen, and the novel Veljeni Sebastian (1985) by Annika Idström. What connects these stirring novels, representing early or full modernism, is the supposition that they belong to the tradition of the subjective grotesque, not only due to occasional details, but also in a more comprehensive manner. The premises are that genre is a significant part of the work and that reading a novel in the context of the genre tradition adds something essential to the interpretation of individual texts and reveals meanings that might otherwise go unnoticed. The main characteristic of the grotesque is breaking the norm. This is accomplished through different means: degradation, distortion, inversion, combination, exaggeration and multiplication. The most significant strategy for breaking the norm is incongruence: the grotesque combines conflicting or mutually exclusive categories and elements on different levels. Simultaneously, the grotesque unravels categorisations and questions our way of perceiving the world. The grotesque not only poses a threat to one s identity, but can also pose a threat to the cognitive process. An analysis of the fictional works is presented as case studies of each chosen work as a whole. The analysis is based on the method of close reading, which draws on both classical and postclassical narratology, and the analysis and interpretation are expanded within the genre tradition of the grotesque. The grotesque is also analysed in terms of its relationship to the neighbouring categories and genre traditions, such as the tragic, the sublime, the horror story and the coming-of-age story. This dissertation shows how the grotesque is constructed repeatedly on deviations from the norm as well as on incongruence, also in the works analysed, and how it stratifies in these novels on and between different levels, such as the story, text, narration, composition and the world of the novels. In all the works analysed, the grotesque reduces and subverts. Again and again it reveals different sides of humanity stripped of idealisation and glorification. The dissertation reveals that Finnish literature is not a solitary island, even regarding the grotesque, for it continues and offers variations of the common tradition of grotesque literature, and likewise draws on grotesque visual arts. This dissertation is the first monograph in Finnish literature research focusing on the subjective grotesque.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.