The Finance Acts 1998 and 2000: can the owners of agricultural land continue to gain from their capital disposals?

This paper seeks to analyse and discuss, from the perspective of the owners of agricultural land, the main changes to the Capital Gains Tax regime introduced in the Finance Act 1998 and subsequently amended in the Finance Act 2000. The replacement of indexation with a new Taper relief is examined, along with the phasing out of Retirement relief, and the interaction of Taper relief with Rollover relief. The opportunity for tax mitigation by the owners of agricultural land is critically examined.

Altitude acts as an environmental filter on phylogenetic composition, traits and diversity in bee communities.

Knowledge about the phylogeny and ecology of communities along environmental gradients helps to disentangle the role of competition-driven processes and environmental filtering for community assembly. In this study, we evaluated patterns in species richness, phylogenetic structure and life-history traits of bee communities along altitudinal gradients in the Alps, Germany. We found a linear decline in species richness and abundance but increasing phylogenetic clustering in communities with increasing altitude. The proportion of social- and ground-nesting species, as well as mean body size and altitudinal range of bee communities, increased with increasing altitude, whereas the mean geographical distribution decreased. Our results suggest that community assembly at high altitudes is dominated by environmental filtering effects, whereas the relative importance of competition increases at low altitudes. We conclude that inherent phylogenetic and ecological species attributes at high altitudes pose a threat for less competitive alpine specialists with ongoing climate change.

Acts of estrangement: reading the psoriatic-skin

I am continually surprised by the acts of estrangement in which my body engages. All bodies do this to some extent – lungs breathe, hearts beat and so on, but with psoriasis, which my article is concerned with, these processes are highly visible. As a condition of the skin, it is a pathology which is enacted both within and without, visible to the social world as well emerging from some little understood inflammatory source within the body. This article will undertake a phenomenological exploration of my own skin and how my experience of my body is affected by its lack of compliance with medicine, cosmetics etc. By some unknown causation it flakes, breaks, itches constantly drawing attention to bodily limits and the limits of medical knowledge. I want to think through the meanings we ascribe to such inflammatory conditions in Western society, how my skin materialises at the nexus of industrialisation, medicine, class and capital. This investigation will emerge at the limit point, the thin skin between the subjective and objective, observing and theorising my skin in ways which dissolve disciplinary boundaries, to comprehend the cultural, biological and environmental forces that work upon my body, estranging it from me.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Speech acts, responsibility and commitment in poetry

Philosophy has tended to regard poetry primarily in terms of truth and falsity, assuming that its business is to state or describe states of affairs. Speech act theory transforms philosophical debate by regarding poetry in terms of action, showing that its business is primarily to do things. The proposal can sharpen our understanding of types of poetry; examples of the ‘Chaucer-Type’ and its variants demonstrate this. Objections to the proposal can be divided into those that relate to the agent of actions associated with a poem, those that relate to the actions themselves, and those that relate to the things done. These objections can be answered. A significant consequence of the proposal is that it gives prominence to issues of responsibility and commitment. This prominence brings philosophical debate usefully into line with contemporary poetry, whose concern with such issues is manifest in characteristic forms of anxiety.

Towards expressive rule induction on IP network event streams

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.