Financial performance and Corporate social responsibility in the UK listed firms

The dissertation investigates the linkage between the social performance and financial performance of the UK listed companies. There was recently a move towards greater concern about the social and environmental issues among the corporations. Such behaviour from the theoretical point of view can be explained using quite a number of different theories. For example, the stakeholder theory, as well as positive accounting theory, the legitimacy theory, to name a few. Predictions from these theories are occasionally quite different from one another. Therefore, there is a need to empirically test the actual link between social and financial performance. For the purpose of such analysis, a sample of 30 firms from amongst the FTSE100 constituent firms was obtained, and data about the social and financial performance of these companies were acquired for the 2014 – 2018 years. Regression analysis was performed in order to evaluate potential linkages form social performance to financial performance. Three aspects of broader social performance were considered – social as such, environmental and governance. The relevance of governance performance relates to the agency costs theory and the ability of effective governance to alleviate the agency problem. The results showed that there is a general negative relationship between social and financial performance of the considered UK firms. The result, in fact, does only refer to the linkage from the environmental performance to the financial performance. The regulations about environmental reporting and performance are among the strictest as compared to other aspects of broader social performance. The impact of the social performance and the governance performance on ROE and ROA was not statistically significant. The findings are suggestive of the relevance of the legitimacy theory and that of the stakeholder view of the corporation, as the relevant theoretical frameworks that explain the behaviour of the considered UK corporations

Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-AnythingComment: 16 pages (including appendix and references), 3 figure

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework

Financial performance and Corporate social responsibility in the UK listed firms

The dissertation investigates the linkage between the social performance and financial performance of the UK listed companies. There was recently a move towards greater concern about the social and environmental issues among the corporations. Such behaviour from the theoretical point of view can be explained using quite a number of different theories. For example, the stakeholder theory, as well as positive accounting theory, the legitimacy theory, to name a few. Predictions from these theories are occasionally quite different from one another. Therefore, there is a need to empirically test the actual link between social and financial performance. For the purpose of such analysis, a sample of 30 firms from amongst the FTSE100 constituent firms was obtained, and data about the social and financial performance of these companies were acquired for the 2014 – 2018 years. Regression analysis was performed in order to evaluate potential linkages form social performance to financial performance. Three aspects of broader social performance were considered – social as such, environmental and governance. The relevance of governance performance relates to the agency costs theory and the ability of effective governance to alleviate the agency problem. The results showed that there is a general negative relationship between social and financial performance of the considered UK firms. The result, in fact, does only refer to the linkage from the environmental performance to the financial performance. The regulations about environmental reporting and performance are among the strictest as compared to other aspects of broader social performance. The impact of the social performance and the governance performance on ROE and ROA was not statistically significant. The findings are suggestive of the relevance of the legitimacy theory and that of the stakeholder view of the corporation, as the relevant theoretical frameworks that explain the behaviour of the considered UK corporations