Mapping the image

The Brainstorm feature introduced in Adobe After Effects CS3 (2007) allows users to automate parts of the process of generating design variations for the purposes of comparison and selection. The paper begins with a brief discussion of current discursive formations around software and software-based practice among digital design practitioners and educators. Next, the paper draws upon critical concepts drawn from multimodal discourse analysis, media theory and sociology to analyse Brainstorm in terms of the interplay of software structure and design agency. The key concepts used are modality, articulation and interpretation (Kress and van Leeuwen 1996 Kress, G. and van Leeuwen, T. 1996. Reading images: the grammar of visual design, London: Routledge.  [Google Scholar], 2001), the database as cultural form and the logic of selection (Manovich 2001 Manovich, L. 2001. The language of new media, Cambridge, MA: MIT Press.  [Google Scholar]), habitus and practical logic (Bourdieu 1977 Bourdieu, P. 1977. Outline of a theory of practice, New York: Cambridge University Press. [Crossref] , [Google Scholar]) and the radius of creativity (Toynbee 2000 Toynbee, J. 2000. Making popular music: musicians, creativity and institutions, London: Arnold.  [Google Scholar]). Throughout, the paper addresses specific structural features of the software, thus developing an overview of the affordances and constraints of Brainstorm as a creative tool.     

Nonlinear control of magnetically-geared drive-trains

The paper considers certain impedimental issues related to the use of magnetic gearbox and magnetic coupling technologies in high performance servo control systems. A prototype magnetic coupling is used as a basis for demonstrating that the underlying torque transfer characteristic is significantly nonlinear when transmitted torque approaches the maximum designed pull-out torque of the device. It is shown that linear controllers for speed control proportional plus integral （PI） and position control proportional plus derivative （PD） result in acceptable performance provided the magnetic coupling operates below 80 % of designed pull-out torque. To fully compensate for the inherent nonlinearity of the torque transfer characteristic, feedback linearizing control laws and state transformations are derived resulting in exactly linear input-output characteristic for position and speed control of magnetically-geared drive-trains. With the addition of state feedback, the closed-loop dynamics for both position and speed control of a magnetically-geared drive-train can be designed to satisfy the integral of time multiplied by absolute error （ITAE） optimized linear response for a step input. Outstanding results are demonstrated through simulation and experimental real-time implementation on a demonstrator magnetically-geared drive-train.     

A note comparing support vector machines and ordered choice models’ predictions of international banks’ ratings

We find that support vector machines can produce notably better predictions of international bank ratings than the standard method currently used for this purpose, ordered choice models. This appears due to the support vector machine's ability to estimate a large number of country dummies unrestrictedly, which was not possible with the ordered choice models due to the low sample size.     

Development of blood glucose control for extremely premature infants

Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.     

Modeling and analyzing social network dynamics using stochastic discrete graphical dynamical systems

Motivated by applications such as the spread of epidemics and the propagation of influence in social networks, we propose a formal model for analyzing the dynamics of such networks. Our model is a stochastic version of discrete graphical dynamical systems. Using this model, we formulate and study the computational complexity of two fundamental problems (called reachability and predecessor existence problems) which arise in the context of social networks. We also address other problems that deal with the time evolution of such stochastic dynamical systems. Further, we point out the implications of our results to problems for other computational models such as Hopfield networks, communicating finite state machines and systolic arrays. In particular, our polynomial time algorithms for the predecessor existence problem for stochastic dynamical systems imply similar results for one-dimensional finite cellular automata.     

Group awareness of social and cognitive performance in a CSCL environment: Effects of a peer feedback and reflection tool

A peer feedback tool (Radar) and a reflection tool (Reflector) were used to enhance group performance in a computer-supported collaborative learning environment. Radar allows group members to assess themselves and their fellow group members on six traits related to social and cognitive behavior. Reflector stimulates group members to reflect on their past, present and future group functioning, stimulating them to set goals and formulate plans to improve their social and cognitive performance. The underlying assumption was that group performance would be positively influenced by making group members aware of how they, their peers and the whole group perceive their social and cognitive behavior in the group. Participants were 108 fourth-year high school students working in dyads, triads and groups of four on a collaborative writing task, with or without the tools. Results demonstrate that awareness stimulated by the peer feedback and reflection tools enhances group-process satisfaction and social performance of CSCL-groups.     

Chern numbers of smooth varieties via homotopy continuation and intersection theory

Homotopy continuation provides a numerical tool for computing the equivalence of a smooth variety in an intersection product. Intersection theory provides a theoretical tool for relating the equivalence of a smooth variety in an intersection product to the degrees of the Chern classes of the variety. A combination of these tools leads to a numerical method for computing the degrees of Chern classes of smooth projective varieties in Pⁿ. We illustrate the approach through several worked examples.     

Inference Methods for CRFs with Co-occurrence Statistics

The Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is generally thought to be the only case that is computationally tractable. In this paper we consider a class of global potentials defined over all variables in the CRF. We show how they can be readily optimised using standard graph cut algorithms at little extra expense compared to a standard pairwise field. This result can be directly used for the problem of class based image segmentation which has seen increasing recent interest within computer vision. Here the aim is to assign a label to each pixel of a given image from a set of possible object classes. Typically these methods use random fields to model local interactions between pixels or super-pixels. One of the cues that helps recognition is global object co-occurrence statistics, a measure of which classes (such as chair or motorbike) are likely to occur in the same image together. There have been several approaches proposed to exploit this property, but all of them suffer from different limitations and typically carry a high computational cost, preventing their application on large images. We find that the new model we propose produces a significant improvement in the labelling compared to just using a pairwise model and that this improvement increases as the number of labels increases.     

Global Non-rigid Alignment of Surface Sequences

This paper presents a general approach based on the shape similarity tree for non-sequential alignment across databases of multiple unstructured mesh sequences from non-rigid surface capture. The optimal shape similarity tree for non-rigid alignment is defined as the minimum spanning tree in shape similarity space. Non-sequential alignment based on the shape similarity tree minimises the total non-rigid deformation required to register all frames in a database into a consistent mesh structure with surfaces in correspondence. This allows alignment across multiple sequences of different motions, reduces drift in sequential alignment and is robust to rapid non-rigid motion. Evaluation is performed on three benchmark databases of 3D mesh sequences with a variety of complex human and cloth motion. Comparison with sequential alignment demonstrates reduced errors due to drift and improved robustness to large non-rigid deformation, together with global alignment across multiple sequences which is not possible with previous sequential approaches.     

IS integration and knowledge sharing in multi-unit firms: the winner's curse

Knowledge sharing across business units (BUs) is paramount for enterprises that aim to exploit latent cross-BU synergies. This paper examines how information systems (ISs) integration and two forms of cross-BU knowledge complementarities (workflow interdependence and BU relatedness) affect an important dimension of a firm's absorptive capacity – cross-BU knowledge sharing. The analysis, performed on survey data from 75 enterprises, supports all of our hypotheses. First, we hypothesized that cross-BU workflow serves as a conduit for knowledge transfer as participants continually engage in spontaneous adaptations. Second, IS application integration and cross-BU relatedness should both directly contribute to knowledge sharing. Third, IS application integration should positively moderate the impacts of cross-BU relatedness. Finally, and most interestingly, we hypothesized a redistribution of the effects of IS integration by which it becomes a key contributor to knowledge sharing, but also causes a reduction in the impact of workflow interdependence. Thus, while the seamlessness of high IS application integration has obvious benefits, it paradoxically also reduces the occasions for cross-BU workflow adaptations, which is a key mechanism for knowledge transfer. That is the phenomenon we call the ‘winner's curse’.