Archaeology of wings : Birds and people in the Baltic Sea region during the Stone Age

The prominent roles of birds, often mentioned in historical sources, are not well reflected in archaeological research. Absence or scarcity of bird bones in archaeological assemblages has been often seen as indication of a minor role of birds in the prehistoric economy or ideology, or explained by taphonomic loss. Few studies exist where birds form the basis for extensive archaeological interpretation. In this doctoral dissertation bird bone material from various Stone Age sites in the Baltic Sea region is investigated. The study period is approximately 7000-3400 BP, comprising mainly Neolithic cultures. The settlement material comes from Finland, Åland, Gotland, Saaremaa and Hiiumaa. Osteological materials are used for studying the economic and cultural importance of birds, fowling methods and principal fowling seasons. The bones were identified and earlier identifications partially checked with help of a reference material of modern skeletons. Fracture analysis was used in order to study the deposition history of bones at Ajvide settlement site. Birds in burials at two large cemeteries, Ajvide on Gotland and Zvejnieki in northern Latvia were investigated in order to study the roles of birds in burial practices. My study reveals that the economic importance of birds is at least seasonally often more prominent than usually thought, and varies greatly in different areas. Fowling has been most important in coastal areas, and especially during the breeding season. Waterbirds and grouse species were generally the most important groups in Finnish Stone Age economy. The identified species composition shows much resemblance to contemporary hunting with species such as the mallard and capercaillie commonly found. Burial materials and additional archaeological evidence from Gotland, Latvia and some other parts of northern Europe indicate that birds –e.g., jay, whooper swan, ducks – have been socially and ideologically important for the studied groups (indicating a place in the belief system, e.g. clan totemism). The burial finds indicate that some common ideas about waterbirds (perhaps as messengers or spirit helpers) might have existed in the northern European Stone Age.

Minimalistinen yhdyssanateoria ja käsitteiden yhdistely

The thesis aims to link the biolinguistic research program and the results of studies in comceptual combination from cognitive psychology. The thesis derives a theory of syntactic structure of noun and adjectival compounds from the Empty Lexicon Hypothesis. Two compound-forming operations are described: root-compounding and word-compounding. The aptness of theory is tested with finnish and greek compounds. From the syntactic theory semantic requirements for conceptual system are derived, especially requirements for handling morphosyntactic features. These requirements are compared to three formidable theories of conceptual combination: relation theory CARIN, Dual-Process theory and C3-theory. The claims of explanatory power of relational distributions of modifier in CARIN-theory ared discarded, as the method for sampling and building relational distributions is not reliable and the algorithmic instantiation of theory does not compute what it claims to compute. From relational theory there still remains results supporting existence of 'easy' relations for certain concepts. Dual-Process theory is found to provide results that cannot in theory be affected by linguistic system, but the basic idea of property compounds is kept. C3-theory is found to be not computationally realistic, but the basic results of diagnosticity and local properties (domains) of conceptual system are solid. The three conceptual combination models are rethought as a problem of finding the shortest route between the two concepts. The new basis for modeling is suggested to be bare conceptual landscape with morphosyntactiic or semantic features working as guidance and structural features of landscape basically unknown, but such as they react to features from linguistic system. Minimalistic principles to conceptual modeling are suggested.

Identifying Meaningful Places

Place identification refers to the process of analyzing sensor data in order to detect places, i.e., spatial areas that are linked with activities and associated with meanings. Place information can be used, e.g., to provide awareness cues in applications that support social interactions, to provide personalized and location-sensitive information to the user, and to support mobile user studies by providing cues about the situations the study participant has encountered. Regularities in human movement patterns make it possible to detect personally meaningful places by analyzing location traces of a user. This thesis focuses on providing system level support for place identification, as well as on algorithmic issues related to the place identification process. The move from location to place requires interactions between location sensing technologies (e.g., GPS or GSM positioning), algorithms that identify places from location data and applications and services that utilize place information. These interactions can be facilitated using a mobile platform, i.e., an application or framework that runs on a mobile phone. For the purposes of this thesis, mobile platforms automate data capture and processing and provide means for disseminating data to applications and other system components. The first contribution of the thesis is BeTelGeuse, a freely available, open source mobile platform that supports multiple runtime environments. The actual place identification process can be understood as a data analysis task where the goal is to analyze (location) measurements and to identify areas that are meaningful to the user. The second contribution of the thesis is the Dirichlet Process Clustering (DPCluster) algorithm, a novel place identification algorithm. The performance of the DPCluster algorithm is evaluated using twelve different datasets that have been collected by different users, at different locations and over different periods of time. As part of the evaluation we compare the DPCluster algorithm against other state-of-the-art place identification algorithms. The results indicate that the DPCluster algorithm provides improved generalization performance against spatial and temporal variations in location measurements.

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.

Traditional patterns and textures as values for meaningful automatization in music

This contribution focuses on the accelerated loss of traditional sound patterning in music, parallel to the exponential loss of linguistic and cultural variety in a world increasingly 'globalized' by market policies and economic liberalization, in which scientific or technical justification plays a crucial role. As a suggestion to an alternative trend, composers and music theorists are invited to explore the world of design and patterning by grammar rules from non-dominant cultures, and to make an effort to understand their contextual usage and its transformation, in order to appreciate their symbolism and aesthetic depth. Practical examples are provided.