Agents of transparency: How sell-side financial analysts make corporate governance visible.

This thesis examines the phenomenon of sell-side financial analysts (analysts hereafter) "doing" corporate governance. The term "doing" is used in the current study to designate the various ways in which some analysts in the US and the UK, across the past decade or so, have made corporate governance visible. The thesis examines how this has occurred, and the mechanisms and devices that have made it possible. Analysts, it is suggested, can be viewed as "agents of transparency", in so far as they have taken the evaluation of companies beyond the financials, to include corporate governance issues. The thesis focuses primarily on the corporate governance reports produced by analysts, the official documents issued by various organisations and institutions, selected financial and business newspapers and magazines, together with other documents such as textbooks of corporate governance, as well as academic and practioner publications on corporate governance. Through an examination of these materials, the thesis investigates the pre-conditions that made possible the appearance and development of the corporate governance work pursued by analysts in the early twenty-first century. It examines the evaluations performed by analysts of the corporate governance procedures adopted by companies. In particular, it focuses on the ways in which analysts benchmarked the corporate governance procedures of companies against formal regulations, and how comparisons of the governance procedures adopted by different companies were undertaken and facilitated by analysts. Benchmarking, and the making of comparisons of corporate governance practices through a range of devices, are examined. The thesis also examines the linking of corporate governance to the financials (such as profitability, stock price performance, and equity valuation) in the investment analyses performed by analysts. It concentrates on the way in which analysts integrated corporate governance issues in the investment decision making process. Attention is paid to the ideas that shaped and articulated the integration, as well as to the tools and devices deployed by analysts. This thesis argues that greater attention is needed to the "doing" of corporate governance by analysts, and its implications for these "agents of transparency" that have broadened the parameters through which transparency is assessed

Numerical Modeling of Shallow Flows over Irregular Topography

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Sparsely Shared LoRA on Whisper for Child Speech Recognition

Whisper is a powerful automatic speech recognition (ASR) model. Nevertheless, its zero-shot performance on low-resource speech requires further improvement. Child speech, as a representative type of low-resource speech, is leveraged for adaptation. Recently, parameter-efficient fine-tuning (PEFT) in NLP was shown to be comparable and even better than full fine-tuning, while only needing to tune a small set of trainable parameters. However, current PEFT methods have not been well examined for their effectiveness on Whisper. In this paper, only parameter composition types of PEFT approaches such as LoRA and Bitfit are investigated as they do not bring extra inference costs. Different popular PEFT methods are examined. Particularly, we compare LoRA and AdaLoRA and figure out the learnable rank coefficient is a good design. Inspired by the sparse rank distribution allocated by AdaLoRA, a novel PEFT approach Sparsely Shared LoRA (S2-LoRA) is proposed. The two low-rank decomposed matrices are globally shared. Each weight matrix only has to maintain its specific rank coefficients that are constrained to be sparse. Experiments on low-resource Chinese child speech show that with much fewer trainable parameters, S2-LoRA can achieve comparable in-domain adaptation performance to AdaLoRA and exhibit better generalization ability on out-of-domain data. In addition, the rank distribution automatically learned by S2-LoRA is found to have similar patterns to AdaLoRA's allocation.Comment: Accepted by ICASSP 202

An Omnidirectional Approach to Touch-based Continuous Authentication

This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often focus on gathering data, tuning classifiers, and enhancing performance by evaluating touch interactions in a sequence rather than independently. However, such systems only work by providing data representing distinct behavioural traits. The typical approach separates behaviour into touch directions and creates multiple user profiles. This work presents an omnidirectional approach which outperforms the traditional method independent of the touch direction - depending on optimal behavioural features and a balanced training set. Thus, we evaluate five behavioural feature sets using the conventional approach against our direction-agnostic method while testing several classifiers, including an Extra-Tree and Gradient Boosting Classifier, which is often overlooked. Results show that in comparison with the traditional, an Extra-Trees classifier and the proposed approach are superior when combining strokes. However, the performance depends on the applied feature set. We find that the TouchAlytics feature set outperforms others when using our approach when combining three or more strokes. Finally, we highlight the importance of reporting the mean area under the curve and equal error rate for single-stroke performance and varying the sequence of strokes separately