European urban spaces in crisis - the mapping of affective pracitices with Living with Difference: introduction

The introduction outlines the notion of urban space and crisis in Europe while taking into account the more recent protests and riots in different cities, in and beyond Europe. It is argued that the phenomen of protest is happening alongside the economic crisis underscoring an alternative political public civic spirit expressing to a certain degree the renaissance and timely making of, what might be called in the digital age, #œuvre. Its forces and emotional properties capture a political realm that unfolds as a globalized urban transnational public space, still progressing. Further, it introduces the collection of papers for the special themed feature. Five papers look at affective practices through a Continental European lens, which places the meaning of race, migration and intersecting identity angles at the centre of debates of individual encounters in public spaces. The final and sixth paper, written by Brenda Yeoh, looks through a Singapore/East Asia lens, and comments on the common European threats as well as on the historical specificity and implications of distinctive geo-political spaces for affective practices.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

Building professionalism and employability skills: embedding employer engagement within first-year computing modules

This paper outlines a means of improving the employability skills of first-year university students through a closely integrated model of employer engagement within computer science modules. The outlined approach illustrates how employability skills, including communication, teamwork and time management skills, can be contextualised in a manner that directly relates to student learning but can still be linked forward into employment. The paper tests the premise that developing employability skills early within the curriculum will result in improved student engagement and learning within later modules. The paper concludes that embedding employer participation within first-year models can help relate a distant notion of employability into something of more immediate relevance in terms of how students can best approach learning. Further, by enhancing employability skills early within the curriculum, it becomes possible to improve academic attainment within later modules.

Bright Molecules for Sensing, Computing and Imaging: A Tale of Two Once-troubled Cities

The circumstances in Colombo, Sri Lanka, and in Belfast, Northern Ireland, which led to a) the generalization of luminescent PET (photoinduced electron transfer) sensing/switching as a design tool, b) the construction of a market-leading blood electrolyte analyzer and c) the invention of molecular logic-based computation as an experimental field, are delineated. Efforts to extend the philosophy of these approaches into issues of small object identification, nanometric mapping, animal visual perception and visual art are also outlined.

Partially Ordered Preferences in Decision Trees: Computing Strategies with Imprecision in Probabilities

Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.

Strength of visual percept generated by famous faces perceived without awareness: effects of affective valence, response latency, and visual field

Participants who were unable to detect familiarity from masked 17 ms faces ([Stone and Valentine, 2004] and [Stone and Valentine, in press-b]) did report a vague, partial visual percept. Two experiments investigated the relative strength of the visual percept generated by famous and unfamiliar faces, using masked 17 ms exposure. Each trial presented simultaneously a famous and an unfamiliar face, one face in LVF and the other in RVF. In one task, participants responded according to which of the faces generated the stronger visual percept, and in the other task, they attempted an explicit familiarity decision. The relative strength of the visual percept of the famous face compared to the unfamiliar face was moderated by response latency and participants’ attitude towards the famous person. There was also an interaction of visual field with response latency, suggesting that the right hemisphere can generate a visual percept differentiating famous from unfamiliar faces more rapidly than the left hemisphere. Participants were at chance in the explicit familiarity decision, confirming the absence of awareness of facial familiarity.

Various Joyful Encounters with the Dystopias of Affective Capitalism

This article contends that what appear to be the dystopic conditions of affective capitalism are just as likely to be felt in various joyful encounters as they are in atmospheres of fear associated with post 9/11 securitization. Moreover, rather than grasping these joyful encounters with capitalism as an ideological trick working directly on cognitive systems of belief, they are approached here by way of a repressive affective relation a population establishes between politicized sensory environments and what Deleuze and Guattari (1994) call a brain-becoming-subject. This is a radical relationality (Protevi, 2010) understood in this context as a mostly nonconscious brain-somatic process of subjectification occurring in contagious sensory environments populations become politically situated in. The joyful encounter is not therefore merely an ideological manipulation of belief, but following Gabriel Tarde (as developed in Sampson, 2012), belief is always the object of desire. The discussion starts by comparing recent efforts by Facebook to manipulate mass emotional contagion to a Huxleyesque control through appeals to joy. Attention is then turned toward further manifestations of affective capitalism; beginning with the so-called emotional turn in the neurosciences, which has greatly influenced marketing strategies intended to unconsciously influence consumer mood (and choice), and ending with a further comparison between encounters with Nazi joy in the 1930s (Protevi, 2010) and the recent spreading of right wing populism similarly loaded with political affect. Indeed, the dystopian presence of a repressive political affect in all of these examples prompts an initial question concerning what can be done to a brain so that it involuntarily conforms to the joyful encounter. That is to say, what can affect theory say about an apparent brain-somatic vulnerability to affective suggestibility and a tendency toward mass repression? However, the paper goes on to frame a second (and perhaps more significant) question concerning what can a brain do. Through the work of John Protevi (in Hauptmann and Neidich (eds.), 2010: 168-183), Catherine Malabou (2009) and Christian Borch (2005), the article discusses how affect theory can conceive of a brain-somatic relation to sensory environments that might be freed from its coincidence with capitalism. This second question not only leads to a different kind of illusion to that understood as a product of an ideological trick, but also abnegates a model of the brain which limits subjectivity in the making to a phenomenological inner self or Being in the world.