Assessing the legality of coercive restructuring tactics in uk exchange offers

This article discusses bondholder exchange offers, a useful private debtrestructuring technique. In a typical offer, an under-performing issuer will seek to exchange its old bonds for new bonds with economically less favourable terms to bondholders, thus deleveraging the issuer without the difficulties of a formal insolvency process. Some issuers seek to incentivise their bondholders to accept these new, less favourable bonds by using coercive tactics, such as ‘exit consents’ and ‘covenant strips’. While lawful in the US, the English courts have only recently considered them for the first time in relation to English Law bonds. The Assénagon case declared an egregious coercive tactic invalid on the basis of an old company law principle, casting doubt on the validity of other coercive tactics. This principle (the’abuse principle’) originally restricted the abuse of minority shareholders by the majority, but is now also applicable to debt security voting arrangements. This article examines the abuse principle through the cases and discusses its potential application to other forms of coercive tactics in exchange offers. The article argues that where a coercive tactic is used purely to compel bondholders to exchange their bonds, this will contravene the abuse principle. The use of coercive tactics may however still be consistent with the abuse principle and Assénagon. An issuer will need to show that ‘reasonable men’ could see the tactic as beneficial for the class of bondholders, even though its use might adversely affect non-exchanging bondholders. A potential permissible example is a covenant strip that removes a restriction on asset disposals in order to facilitate a disposal pursuant to a restructuring

HTML Macros -- Easing the Construction and Maintenance of Web Texts

Authoring and maintaining large collections of Web texts is a cumbersome, error-prone and time-consuming business. Ongoing development of courseware for the High Performance Computing Consortium (HPCC) TLTP has only helped to emphasise these problems. Courseware requires the application of a coherent document layout (templates) for each page, and also the use of standard icons with a consistent functionality, in order to create a constant look and feel throughout the material. This provides the user with an environment where he or she can access new pages, and instantly recognise the format used, making the extraction of the information on the page much quicker, and less immediately confusing. This paper describes a system that was developed at UKC to provide a solution to the above problems via the introduction of HTML macros. These macros can be used to provide a standard document layout with a consistent look and feel, as well as tools to ease user navigation. The software is written in Perl, and achieves macro expansion and replacement using the Common Gateway Interface (CGI) and filtering the HTML source. Using macros in your HTML results in your document source code being shorter, more robust, and more powerful. Webs of documents can be built extremely fast and maintenance is made much simpler. Keywords: Authoring, Automation Tools, Perl filters for HTML, Teaching and learning on the We

Active Discovery of Network Roles for Predicting the Classes of Network Nodes

Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way that nodes link in a network exhibits a different, more complex relationship to their attributes. Here, we consider networks in which we know how the nodes are connected, but we do not know the class labels of the nodes or how class labels relate to the network links. We wish to identify the best subset of nodes to label in order to learn this relationship between node attributes and network links. We can then use this discovered relationship to accurately predict the class labels of the rest of the network nodes. We present a model that identifies groups of nodes with similar link patterns, which we call network roles, using a generative blockmodel. The model then predicts labels by learning the mapping from network roles to class labels using a maximum margin classifier. We choose a subset of nodes to label according to an iterative margin-based active learning strategy. By integrating the discovery of network roles with the classifier optimisation, the active learning process can adapt the network roles to better represent the network for node classification. We demonstrate the model by exploring a selection of real world networks, including a marine food web and a network of English words. We show that, in contrast to other network classifiers, this model achieves good classification accuracy for a range of networks with different relationships between class labels and network links

Supervised Blockmodelling

Collective classification models attempt to improve classification performance by taking into account the class labels of related instances. However, they tend not to learn patterns of interactions between classes and/or make the assumption that instances of the same class link to each other (assortativity assumption). Blockmodels provide a solution to these issues, being capable of modelling assortative and disassortative interactions, and learning the pattern of interactions in the form of a summary network. The Supervised Blockmodel provides good classification performance using link structure alone, whilst simultaneously providing an interpretable summary of network interactions to allow a better understanding of the data. This work explores three variants of supervised blockmodels of varying complexity and tests them on four structurally different real world networks.Comment: Workshop on Collective Learning and Inference on Structured Data 201

Topological Feature Based Classification

There has been a lot of interest in developing algorithms to extract clusters or communities from networks. This work proposes a method, based on blockmodelling, for leveraging communities and other topological features for use in a predictive classification task. Motivated by the issues faced by the field of community detection and inspired by recent advances in Bayesian topic modelling, the presented model automatically discovers topological features relevant to a given classification task. In this way, rather than attempting to identify some universal best set of clusters for an undefined goal, the aim is to find the best set of clusters for a particular purpose. Using this method, topological features can be validated and assessed within a given context by their predictive performance. The proposed model differs from other relational and semi-supervised learning models as it identifies topological features to explain the classification decision. In a demonstration on a number of real networks the predictive capability of the topological features are shown to rival the performance of content based relational learners. Additionally, the model is shown to outperform graph-based semi-supervised methods on directed and approximately bipartite networks.Comment: Awarded 3rd Best Student Paper at 14th International Conference on Information Fusion 201

A Beginner\u27s Guide to Applied Educational Research using Thematic Analysis

Interest in applied educational research methodology is growing as educators and researchers strive to seek empirical evidence about what is effective teaching within distinctive contexts. However, for beginner researchers who are interested in conducting case studies within educational settings and are looking for an appropriate starting point, there is limited literature that shapes comprehensively the theory and application of a rigorous research design. This article outlines the theoretical foundation, philosophical assumptions and application of a research design suitable for implementation in educational settings. For researchers and educators pursuing a case study approach within a specific context, an inquiry framework provides the roadmap to navigate the journey. The main components of this systematic inquiry framework include the interconnected practices for: identifying the issue; collecting the data; preparing and engaging with the data; analysing thematically the data; interpreting the data analysis; and composing the research findings and generalisations. Throughout the discussion, examples are drawn from a case study to illustrate how the innovative design and the sixstage qualitative data collection and thematic analysis were implemented to investigate the prevalent roles that teachers play in generating environments for self-regulated learning. Finally, research design considerations are discussed to reflect high standards of ethical practice for reporting research findings and interpretations that can be trusted and contribute practically, theoretically and methodologically to educational research

Everyday classroom teaching practices for self-regulated learning

This study investigated everyday classroom teaching that provides opportunities for young adolescent students to self-regulate their learning. Evidence drawn from literature in the field of self-regulated learning (SRL) underpins this investigation that was focused on the transition years from primary school to secondary school. Research was conducted in Australia as dual case studies, with data collected through semi-structured interviews and classroom observations from eight teacher participants. The data were analysed through the lens of a conceptual framework that aligns the findings with the fundamentals for SRL. The four themes generated are best understood as teaching approaches that describe how teachers within social learning environments connect the goal orientated learning with purposeful engagement, facilitate the activation of thinking strategies through instructional support, and diversify learning opportunities that enable an expectation of success. The findings are illustrated by classroom examples of the core practices that influence students' self-regulatory capacity. An outcome of this research is the SRL model that offers a vision for pedagogy to support teacher professional dialogue and learning, and a practical decision-making tool intended to guide teachers to reflect, analyse and tailor practices for their everyday classroom teaching. The paper concludes with some suggestions that provide scope for future research

Detecting change points in the large-scale structure of evolving networks

Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions changes fundamentally and quantify how large and what kind of change occurred. Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it. This method combines a generalized hierarchical random graph model with a Bayesian hypothesis test to quantitatively determine if, when, and precisely how a change point has occurred. We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identifies a sequence of change points that align with known external "shocks" to these networks