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.

Agroforestry systems for sustainable livelihoods and improved land management in the East Usambara Mountains, Tanzania

Forest destruction for agriculture continues to be a major threat to the rich biological diversity in the East Usambara Mountains in the north-eastern corner of Tanzania. The highest ratio of endemic plant and animal species found on 100 km2 anywhere in the world is depending on the remaining natural forests. Forests are vitally important for the local population in many different ways, and nationally they are an important source of water and hydroelectricity. The soils, of low fertility and mostly acidic Ferrasols, mainly have the nutrients in the topsoil. After clear-cutting, the soils soon become poor when the topsoil is eroded. High-value cardamom is nowadays unsustainably cultivated in the natural forests of the East Usambaras. The general aim was to study the possibilities to develop new profitable and sustainable agroforestry systems for the benefit of the local people that could contribute to relieving the pressure on the remaining natural forests in the East Usambara Mountains. Results from a spice crop agroforestry trial, established in cooperation with a local farmer, showed a clear advantage of intercropping cardamom (Elettaria cardamomum) and black pepper (Piper nigrum) with trees, especially with Grevillea robusta. The nitrogen fixing tree species Gliricidia sepium also improved the nitrogen and organic matter content of the soil over levels found in the natural forest. With improved agroforestry methods for spice production the households generated as much as13 times the net income obtained with traditional forest cultivation practices. There are thus sustainable and profitable ways to cultivate spices as cash crops in well-managed homegardens. However, the farmers need stable markets, access to credit and comprehensive extension services. The soil fertility depletion should be reversed with organic manure application and an enabling policy environment for the smallholder-farming sector. Strong farmers organisations and equal rights to resources and decision-making are needed. Organic spices have an increasing demand, and their export would be profitable for these farmers. What is, however, most needed for a change is a political will of a government that understands the importance of agricultural and forestry development for poverty reduction.

Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

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.

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.