The impact of investor horizon on say-on-pay voting

Shareholder investment horizons have a significant impact on say-on-pay voting patterns. Short-term investors are more likely to avoid expressing opinion on executive pay proposals by casting an abstaining vote. They vote against board proposals on pay only in cases where the CEO already receives excessive pay levels. In contrast, long-term investors typically cast favourable votes. According to our findings, this is due to effective monitoring rather than collusion with the management. Overall, investor heterogeneity in terms of investment horizons helps explain say-on-pay voting, in particular the low levels of say-on-pay dissent, which have recently raised questions over the efficiency of this corporate governance mechanism

Framing of Corporate Social Responsibility by Agribusiness in the USA and Europe : A study of whether the Corporate Social Responsibility disclosure of the agribusiness firms in the USA and Europe align to the stakeholders’ expectations, specifically to the NGOs and the external constituents

Master thesis Business Administration BE501 - University of Agder 2016This thesis examines whether the CSR reports by agricultural biotechnology and agrochemical companies in the USA and Europe align to the stakeholders’ (NGOs and the external constituents) expectations. The focus is on issues of key importance to these firms and the stakeholders, including GMOs, chemicals, and the corporate control over seeds. Framing creates expectations, as framing theory indicates framing is to focus on some of the many facets through which an issue can be seen, and highlight them using salient words and phrases to render them significant. The analysis revealed that 16 companies prepare CSR reports: 8 European and 8 US. In the European region: 4 CSR reports discussed about these issues, and 4 did not. In the USA region: 3 CSR reports discussed about these issues and 5 did not. These 7 CSR reports discussed about the facets of the issues that were of interest for the stakeholders, but from a different angle, creating different framing approaches amongst the actors, and misalignment to the stakeholders’ expectations. Framing explains different actors’ approach towards issues of discourse. Comprehension of the framing is vital for companies, since CSR reporting is about communication, and framing is present to any kind of communication, deliberate or inadvertent. Framing of an issue might take place on a mutual accepted and common frame, or on diverse frames. In the second case each actor creates a unique frame towards an issue which produce a parallel monologue leading to conflict. My thesis recommendation for companies publishing CSR reports is to improve their disclosures by aligning framing approaches. Keywords: CSR reporting, Framing, Agribusiness, GMOs, chemical

Data aware sparse non-negative signal processing

Greedy techniques are a well established framework aiming to reconstruct signals which are sparse in some domain of representations. They are renowned for their relatively low computational cost, that makes them appealing from the perspective of real time applications. Within the current work we focus on the explicit case of sparse non–negative signals that finds applications in several aspects of daily life e.g., food analysis, hazardous materials detection etc. The conventional approach to deploy this type of algorithms does not employ benefits from properties that characterise natural data, such as lower dimensional representations, underlying structures. Motivated by these properties of data we are aiming to incorporate methodologies within the domain of greedy techniques that will boost their performance in terms of: 1) computational efficiency and 2) signal recovery improvement (for the remainder of the thesis we will use the term acceleration when referring to the first goal and robustness when we are referring to the second goal). These benefits can be exploited via data aware methodologies that arise, from the Machine Learning and Deep Learning community. Within the current work we are aiming to establish a link among conventional sparse non–negative signal decomposition frameworks that rely on greedy techniques and data aware methodologies. We have explained the connection among data aware methodologies and the challenges associated with the sparse non–negative signal decompositions: 1) acceleration and 2) robustness. We have also introduced the standard data aware methodologies, which are relevant to our problem, and the theoretical properties they have. The practical implementations of the proposed frameworks are provided here. The main findings of the current work can be summarised as follows: • We introduce novel algorithms, theory for the Nearest Neighbor problem. • We accelerate a greedy algorithm for sparse non–negative signal decomposition by incorporating our algorithms within its structure. • We introduce a novel reformulation of greedy techniques from the perspective of a Deep Neural Network that boosts the robustness of greedy techniques. • We introduce the theoretical framework that fingerprints the conditions that lay down the soil for the exact recovery of the signal

DeepMP for Non-Negative Sparse Decomposition

Non-negative signals form an important class of sparse signals. Many algorithms have already beenproposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. The greedy techniques are low computational cost algorithms, which have also been modified to incorporate the non-negativity of the representations. One such modification has been proposed for Matching Pursuit (MP) based algorithms, which first chooses positive coefficients and uses a non-negative optimisation technique that guarantees the non-negativity of the coefficients. The performance of greedy algorithms, like all non-exhaustive search methods, suffer from high coherence with the linear generative model, called the dictionary. We here first reformulate the non-negative matching pursuit algorithm in the form of a deep neural network. We then show that the proposed model after training yields a significant improvement in terms of exact recovery performance, compared to other non-trained greedy algorithms, while keeping the complexity low

Unlocking the deployment of spectrum sharing with a policy enforcement framework

Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players”respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior

Dynamic Licensed Shared Access - a New Architecture and Spectrum Allocation Techniques

This paper proposes a new system architecture for Licensed Shared Access (LSA) wireless networks, as well as novel band management techniques for fair and ranking-based spectrum allocation. The proposed architecture builds upon recently standardized and regulatory-accepted LSA systems and stems from the work done in the EU-funded project ADEL. Two new resource allocation algorithms are introduced and their behaviour is validated via system-level simulations

A geophysical insight of the lithostratigraphic subsurface of Rodafnidia area (Lesbos Isl., Greece)

The study area of Rodafnidia on the island of Lesbos (Greece) is considered of archaeological interest, as Paleolithic stone tools have been recovered through excavation and collected from the ground surface in recent years. Geologically, the area is mostly covered by Quaternary post-alpine deposits and volcanic rocks. This paper presents the application of a local geophysical survey to determine the volume of the upper Quaternary deposits in which the Paleolithic artefacts can be found and the identification of their ignimbrite substrates. For this reason, the geoelectrical method was selected as the most appropriate for determining the lithostratigraphic subsurface layers. More specifically, a grid of twenty-one (21) Vertical Electrical Soundings (VES) along with an Electrical Resistivity Tomography (ERT) was carried out. The interpretation of the results of these surveys, in conjunction with the results of older excavation trenches, revealed that the Quaternary deposits have been investigated at depths ranging from 0.5 up to 28.5 meters. Furthermore, the lithological boundary of these post-alpine deposits and their underlying pyroclastic ignimbrite flow (with resistivity 24.0–58.0 Ohm.m) seem to dip to the north. The volume of the Quaternary layer is proposed as the maximum depth for archaeological investigation with high chances to recover more Paleolithic material