A programmer's environment for music analysis

A model for representing music scores in a form suitable for general processing by a music-analyst-programmer is proposed and implemented. Typical input to the model consists of one or more pieces of music which are encoded in a file-based score representation. File-based representations are in a form unsuited for general processing, as they do not provide a suitable level of abstraction for a programmer-analyst. Instead, a representation is created giving a programmer's view of the score. This frees the analyst-programmer from implementation details, that otherwise would form a substantial barrier to progress. The score representation uses an object-oriented approach to create a natural and robust software environment for the musicologist. The system is used to explore ways in which it could benefit musicologists. Methodologies for analysing music corpora are presented in a series of analytic examples which illustrate some of the potential of this model. Proving hypotheses or performing analysis on corpora involves the construction of algorithms. Some unique aspects of using this score model for corpus-based musicology are: - Algorithms impose a discipline which arises from the necessity for formalism. - Automatic analysis enables musicologists to complete tasks that otherwise would be infeasible because of limitations of their energy, attentiveness, accuracy and time.

Properties of molecules in tunnel junctions

Molecular tunnel junctions involve studying the behaviour of a single molecule sandwiched between metal leads. When a molecule makes contact with electrodes, it becomes open to the environment which can heavily influence its properties, such as electronegativity and electron transport. While the most common computational approaches remain to be single particle approximations, in this thesis it is shown that a more explicit treatment of electron interactions can be required. By studying an open atomic chain junction, it is found that including electron correlations corrects the strong lead-molecule interaction seen by the ΔSCF approximation, and has an impact on junction I − V properties. The need for an accurate description of electronegativity is highlighted by studying a correlated model of hexatriene-di-thiol with a systematically varied correlation parameter and comparing the results to various electronic structure treatments. The results indicating an overestimation of the band gap and underestimation of charge transfer in the Hartree-Fock regime is equivalent to not treating electron-electron correlations. While in the opposite limit, over-compensating for electron-electron interaction leads to underestimated band gap and too high an electron current as seen in DFT/LDA treatment. It is emphasised in this thesis that correcting electronegativity is equivalent to maximising the overlap of the approximate density matrix to the exact reduced density matrix found at the exact many-body solution. In this work, the complex absorbing potential (CAP) formalism which allows for the inclusion metal electrodes into explicit wavefunction many-body formalisms is further developed. The CAP methodology is applied to study the electron state lifetimes and shifts as the junction is made open.

Simulation of multigate SOI transistors with silicon, germanium and III-V channels

In this work by employing numerical three-dimensional simulations we study the electrical performance and short channel behavior of several multi-gate transistors based on advanced SOI technology. These include FinFETs, triple-gate and gate-all-around nanowire FETs with different channel material, namely Si, Ge, and III-V compound semiconductors, all most promising candidates for future nanoscale CMOS technologies. Also, a new type of transistor called “junctionless nanowire transistor” is presented and extensive simulations are carried out to study its electrical characteristics and compare with the conventional inversion- and accumulation-mode transistors. We study the influence of device properties such as different channel material and orientation, dimensions, and doping concentration as well as quantum effects on the performance of multi-gate SOI transistors. For the modeled n-channel nanowire devices we found that at very small cross sections the nanowires with silicon channel are more immune to short channel effects. Interestingly, the mobility of the channel material is not as significant in determining the device performance in ultrashort channels as other material properties such as the dielectric constant and the effective mass. Better electrostatic control is achieved in materials with smaller dielectric constant and smaller source-to-drain tunneling currents are observed in channels with higher transport effective mass. This explains our results on Si-based devices. In addition to using the commercial TCAD software (Silvaco and Synopsys TCAD), we have developed a three-dimensional Schrödinger-Poisson solver based on the non-equilibrium Green’s functions formalism and in the framework of effective mass approximation. This allows studying the influence of quantum effects on electrical performance of ultra-scaled devices. We have implemented different mode-space methodologies in our 3D quantum-mechanical simulator and moreover introduced a new method to deal with discontinuities in the device structures which is much faster than the coupled-mode-space approach.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.