Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.

"And Fostered Me and Sent Me Out":Muldoon Reading Heaney

A short essay on Paul Muldoon's reading aloud of his own and Seamus Heaney's poems during the Seamus Heaney commemorative conference at QUB in April 2014.

Dignity and the Challenge to Liberty :Reading Andras Sajo's Constitutional Sentiments

This paper considers debates about the anti-liberal tendencies of the concept of “human dignity”, in particular those conceptions that are “expressivist”. My aim is to examine how far conceptions of dignity are expressivist, and if so what problems the concept of dignity understood in this way poses for liberty. I consider concerns about dignity’s potential illiberality, in particular the potential illiberality of respect-based conceptions of dignity, in the context of Professor András Sajó’s recent writing, illustrating the discussion with examples drawn from recent judicial decisions of the European Court of Human Rights regarding freedom of speech.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

‘Reading Aran knitting: stitches, text andinterpretation’

The densely textured surfaces of Aran knitting seem to invite interpretation. They have been ‘read’ as identity documents, family trees, references to natural and spiritual phenomena, or even maps. This paper traces the search for meaning in Aran knitting, examining how these stitch patterns have been ‘read’ in the contexts of tourism, fine art and fashion. As Jo Turney (2013:55) argues, the idea of knitted textiles as communicative media in non-literate societies ‘consigns the garments to a preindustrial era of more rural and simple times’, situating them in an imagined state of ‘stasis’. Thus the ways in which Aran stitches are ‘read’ sometimes obscure the processes through which they are ‘written’, whether in terms of individual authorship and creativity, or in terms of their manufacture. Regardless of the historical veracity of claims that particular Aran stitch patterns index features of the social, natural or spiritual worlds, analysing the ways they have been ‘read’ in the context of comparable textile traditions, other crafts which have taken on ‘heritage’ souvenir status, and Irish national identity, reveals how Aran knitting has performed broader communicative functions (see Sonja Andrew 2008), which continue to be subverted and elaborated by fine artists, and translated into couture and mass market fashion products.

The Gentle Island (Staged Reading): by Brian Friel

Staged Reading on Arranmore Island, West Donegal for the Lughnasa International Friel Festival

Learning to be Boys: Reading the Lessons of Shakespeare’s Love’s Labour’s Lost and Marston’s What You Will

This essay focuses on the lessons of Love’s Labour’s Lost’s pageboy-schoolboy-boy actor, Moth, to examine the production of boyhood in early modern culture. It reads Shakespeare’s boy character alongside John Marston’s schoolboy, Holofernes Pippo, in What You Will to investigate the ways in which school lessons might be deployed to produce aged and gendered identities that complicate traditional understandings of early modern masculinity. Reading the comic staging of lessons in these plays, it will suggest that while the educational system aimed to produce gendered subjects, early modern masculine identities exist as a range of categories on a developmental scale. It will propose that although Moth and Pippo comically expose the limits of many pedagogical methods to produce ‘men’, they demonstrate the ways in which these characters learn to be boys. Finally, it will consider the extent to which this production of early modern age and gender identity in the plays is paralleled by the historical boy actors performing these roles.

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.