Thesis (M. Med. Sc.)--University of Hong Kong, 2006.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Universiẗat, Diss., 2004--Tübingen.

Universiẗat, Diss., 1997--Saarbrücken.

Universiẗat, Diss., 2004--Kiel.

University, Diss., 2004--Heidelberg.

FernUniversity, Diss., 2005--Hagen.

Freie Universiẗat, Diss., 2005--Berlin. / Dateiformat: zip, Dateien im PDF-Format. Erscheinungsjahr an der Haupttitelstelle: 2005.

The Norwegian government has submitted a report to the Norwegian parliament, concluding that raw oil export can no longer sustain economic growth. Norway is therefore seeking other industries to minimize the economic loss this is causing. Norway is already the largest exporter of salmon and trout in the world. The report states that the government's goal is to increase production to five times today's production by 2050. The ability and desire to grow has its backing from a handful of large companies dominating the aquaculture business and a newly recognized right-wing government eager to make the playroom larger for the aquaculture industry. This paper analyzes the report, using an ethical matrix, looking for ethical arguments from the government as well as defining the stakeholders affected by the government's goal. This thesis' aim is to analyze and discuss the broad range of ethical concerns, based on an ethical matrix, so that a variety of ethical perspectives can be included in an evaluative discussion.

Often, data organized in matrix form contains missing entries. Further, such data has been observed to exhibit effective low-rank, and has led to interest in the particular problem of low-rank matrix-completion: Given a partially-observed matrix, estimate the missing entries such that the output completion is low-rank. The goal of this thesis is to improve matrix-completion algorithms by explicitly analyzing two sources of information in the observed entries: their locations and their values. First, we provide a categorization of a new approach to matrix-completion, which we call structural. Structural methods quantify the possibility of completion using tests applied only to the locations of known entries. By framing each test as the class of partially-observed matrices that pass the test, we provide the first organizing framework for analyzing the relationship among structural completion methods. Building on the structural approach, we then develop a new algorithm for active matrix-completion that is combinatorial in nature. The algorithm uses just the locations of known entries to suggest a small number of queries to be made on the missing entries that allow it to produce a full and accurate completion. If a budget is placed on the number of queries, the algorithm outputs a partial completion, indicating which entries it can and cannot accurately estimate given the observations at hand. Finally, we propose a local approach to matrix-completion that analyzes the values of the observed entries to discover a structure that is more fine-grained than the traditional low-rank assumption. Motivated by the Singular Value Decomposition, we develop an algorithm that finds low-rank submatrices using only the first few singular vectors of a matrix. By completing low-rank submatrices separately from the rest of the matrix, the local approach to matrix-completion produces more accurate reconstructions than traditional algorithms.