A sequential analysis of "nú" and "núna" in Icelandic conversation

This thesis is an empirical study of how two words in Icelandic, "nú" and "núna", are used in contemporary Icelandic conversation. My aims in this study are, first, to explain the differences between the temporal functions of "nú" and "núna", and, second, to describe the non-temporal functions of "nú". In the analysis, a focus is placed on comparing the sequential placement of the two words, on their syntactical distribution, and on their prosodic realization. The empirical data comprise 14 hours and 11 minutes of naturally occurring conversation recorded between 1996 and 2003. The selected conversations represent a wide range of interactional contexts including informal dinner parties, institutional and non-institutional telephone conversations, radio programs for teenagers, phone-in programs, and, finally, a political debate on television. The theoretical and methodological framework is interactional linguistics, which can be described as linguistically oriented conversation analysis (CA). A comparison of "nú" and "núna" shows that the two words have different syntactic distributions. "Nú" has a clear tendency to occur in the front field, before the finite verb, while "núna" typically occurs in the end field, after the object. It is argued that this syntactic difference reflects a functional difference between "nú" and "núna". A sequential analysis of "núna" shows that the word refers to an unspecified period of time which includes the utterance time as well as some time in the past and in the future. This temporal relation is referred to as reference time. "Nú", by contrast, is mainly used in three different environments: a) in temporal comparisons, 2) in transitions, and 3) when the speaker is taking an affective stance. The non-temporal functions of "nú" are divided into three categories: a) "nú" as a tone particle, 2) "nú" as an utterance particle, and 3) "nú" as a dialogue particle. "Nú" as a tone particle is syntactically integrated and can occur in two syntactic positions: pre-verbally and post-verbally. I argue that these instances are employed in utterances in which a speaker is foregrounding information or marking it as particularly important. The study shows that, although these instances are typically prosodically non-prominent and unstressed, they are in some cases delivered with stress and with a higher pitch than the surrounding talk. "Nú" as an utterance particle occurs turn-initially and is syntactically non-integrated. By using "nú", speakers show continuity between turns and link new turns to prior ones. These instances initiate either continuations by the same speaker or new turns after speaker shifts. "Nú" as a dialogue particle occurs as a turn of its own. The study shows that these instances register informings in prior turns as unexpected or as a departure from the normal state of affairs. "Nú" as a dialogue particle is often delivered with a prolonged vowel and a recognizable intonation contour. A comparative sequential and prosodic analysis shows that in these cases there is a correlation between the function of "nú" and the intonation contour by which it is delivered. Finally, I argue that despite the many functions of "nú", all the instances can be said to have a common denominator, which is to display attention towards the present moment and the utterances which are produced prior or after the production of "nú". Instead of anchoring the utterances in external time or reference time, these instances position the utterance in discourse internal time, or discourse time.

A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

Costs of purchasing new piglets and of feeding them until slaughter are the main variable expenditures in pig fattening. They both depend on slaughter intensity, the nature of feeding patterns and the technological constraints of pig fattening, such as genotype. Therefore, it is of interest to examine the effect of production technology and changes in input and output prices on feeding and slaughter decisions. This study examines the problem by using a dynamic programming model that links genetic characteristics of a pig to feeding decisions and the timing of slaughter and takes into account how these jointly affect the quality-adjusted value of a carcass. The model simulates the growth mechanism of a pig under optional feeding and slaughter patterns and then solves the optimal feeding and slaughter decisions recursively. The state of nature and the genotype of a pig are known in the analysis. The main contribution of this study is the dynamic approach that explicitly takes into account carcass quality while simultaneously optimising feeding and slaughter decisions. The method maximises the internal rate of return to the capacity unit. Hence, the results can have vital impact on competitiveness of pig production, which is known to be quite capital-intensive. The results suggest that producer can significantly benefit from improvements in the pig's genotype, because they improve efficiency of pig production. The annual benefits from obtaining pigs of improved genotype can be more than €20 per capacity unit. The annual net benefits of animal breeding to pig farms can also be considerable. Animals of improved genotype can reach optimal slaughter maturity quicker and produce leaner meat than animals of poor genotype. In order to fully utilise the benefits of animal breeding, the producer must adjust feeding and slaughter patterns on the basis of genotype. The results suggest that the producer can benefit from flexible feeding technology. The flexible feeding technology segregates pigs into groups according to their weight, carcass leanness, genotype and sex and thereafter optimises feeding and slaughter decisions separately for these groups. Typically, such a technology provides incentives to feed piglets with protein-rich feed such that the genetic potential to produce leaner meat is fully utilised. When the pig approaches slaughter maturity, the share of protein-rich feed in the diet gradually decreases and the amount of energy-rich feed increases. Generally, the optimal slaughter weight is within the weight range that pays the highest price per kilogram of pig meat. The optimal feeding pattern and the optimal timing of slaughter depend on price ratios. Particularly, an increase in the price of pig meat provides incentives to increase the growth rates up to the pig's biological maximum by increasing the amount of energy in the feed. Price changes and changes in slaughter premium can also have large income effects. Key words: barley, carcass composition, dynamic programming, feeding, genotypes, lean, pig fattening, precision agriculture, productivity, slaughter weight, soybeans

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Mining Sequential Data – in Search of Segmental Structures

Segmentation is a data mining technique yielding simplified representations of sequences of ordered points. A sequence is divided into some number of homogeneous blocks, and all points within a segment are described by a single value. The focus in this thesis is on piecewise-constant segments, where the most likely description for each segment and the most likely segmentation into some number of blocks can be computed efficiently. Representing sequences as segmentations is useful in, e.g., storage and indexing tasks in sequence databases, and segmentation can be used as a tool in learning about the structure of a given sequence. The discussion in this thesis begins with basic questions related to segmentation analysis, such as choosing the number of segments, and evaluating the obtained segmentations. Standard model selection techniques are shown to perform well for the sequence segmentation task. Segmentation evaluation is proposed with respect to a known segmentation structure. Applying segmentation on certain features of a sequence is shown to yield segmentations that are significantly close to the known underlying structure. Two extensions to the basic segmentation framework are introduced: unimodal segmentation and basis segmentation. The former is concerned with segmentations where the segment descriptions first increase and then decrease, and the latter with the interplay between different dimensions and segments in the sequence. These problems are formally defined and algorithms for solving them are provided and analyzed. Practical applications for segmentation techniques include time series and data stream analysis, text analysis, and biological sequence analysis. In this thesis segmentation applications are demonstrated in analyzing genomic sequences.