A work domain analysis framework for modelling intensive care unit patients

Work domain analysis (WDA) has been applied to a range of complex work domains, but few WDAs have been undertaken in medical contexts. One pioneering effort suggested that clinical abstraction is not based on means-ends relations, whereas another effort downplayed the role of bio-regulatory mechanisms. In this paper it is argued that bio-regulatory mechanisms that govern physiological behaviour must be part of WDA models of patients as the systems at the core of intensive care units. Furthermore it is argued that because the inner functioning of patients is not completely known, clinical abstraction is based on hypothetico-deductive abstract reasoning. This paper presents an alternative modelling framework that conforms to the broader aspirations of WDA. A modified version of the viable systems model is used to represent the patient system as a nested dissipative structure while aspects of the recognition primed decision model are used to represent the information resources available to clinicians in ways that support lsquoif...thenrsquo conceptual relations. These two frameworks come together to form the recursive diagnostic framework, which may provide a more appropriate foundation for information display design in the intensive care unit.

On application of structural decomposition for process model abstraction

Real world business process models may consist of hundreds of elements and have sophisticated structure. Although there are tasks where such models are valuable and appreciated, in general complexity has a negative influence on model comprehension and analysis. Thus, means for managing the complexity of process models are needed. One approach is abstraction of business process models-creation of a process model which preserves the main features of the initial elaborate process model, but leaves out insignificant details. In this paper we study the structural aspects of process model abstraction and introduce an abstraction approach based on process structure trees (PST). The developed approach assures that the abstracted process model preserves the ordering constraints of the initial model. It surpasses pattern-based process model abstraction approaches, allowing to handle graph-structured process models of arbitrary structure. We also provide an evaluation of the proposed approach.

Cellular decomposition of quaternionic spherical space forms

We obtain an explicit cellular decomposition of the quaternionic spherical space forms, manifolds of positive constant curvature that are factors of an odd sphere by a free orthogonal action of a generalized quaternionic group. The cellular structure gives and explicit description of the associated cellular chain complex of modules over the integral group ring of the fundamental group. As an application we compute the Whitehead torsion of these spaces for any representation of the fundamental group. © 2012 Springer Science+Business Media B.V.

Knowledge-based spatiotemporal linear abstraction

We present a theoretical framework and a case study for reusing the same conceptual and computational methodology for both temporal abstraction and linear (unidimensional) space abstraction, in a domain (evaluation of traffic-control actions) significantly different from the one (clinical medicine) in which the method was originally used. The method, known as knowledge-based temporal abstraction, abstracts high-level concepts and patterns from time-stamped raw data using a formal theory of domain-specific temporal-abstraction knowledge. We applied this method, originally used to interpret time-oriented clinical data, to the domain of traffic control, in which the monitoring task requires linear pattern matching along both space and time. First, we reused the method for creation of unidimensional spatial abstractions over highways, given sensor measurements along each highway measured at the same time point. Second, we reused the method to create temporal abstractions of the traffic behavior, for the same space segments, but during consecutive time points. We defined the corresponding temporal-abstraction and spatial-abstraction domain-specific knowledge. Our results suggest that (1) the knowledge-based temporal-abstraction method is reusable over time and unidimensional space as well as over significantly different domains; (2) the method can be generalized into a knowledge-based linear-abstraction method, which solves tasks requiring abstraction of data along any linear distance measure; and (3) a spatiotemporal-abstraction method can be assembled from two copies of the generalized method and a spatial-decomposition mechanism, and is applicable to tasks requiring abstraction of time-oriented data into meaningful spatiotemporal patterns over a linear, decomposable space, such as traffic over a set of highways.

Decomposition of Banach Space into a Direct Sum of Separable and Reflexive Subspaces and Borel Maps

* This paper was supported in part by the Bulgarian Ministry of Education, Science and Technologies under contract MM-506/95.

Sobolev Type Decomposition of Paley-Wiener-Schwartz Space with Application to Sampling Theory

2000 Mathematics Subject Classification: 94A12, 94A20, 30D20, 41A05.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

Imaging affect : abstraction and the echo of the unknowable

As an artist my primary interest is in the abstract, that is in images of the imageless. I am curious about the emergence of pictorial significance and content from this unknowable space. To speak of the significance of an imageless image is also to speak of its affect. I aim to explore this both theoretically and practically. Theoretically I will explore affect through the late work of Lyotard and his notion of the affect-phrase. This is an under-examined aspect of Lyotard and demarcates a valuable way to look at the origins, impact and ramifications of affect for art. Practically I will apply these understandings to the development of my own creative work which includes both painting and digital work. My studio practice moves towards exploring the unfamiliar through the powerful and restless silence of affect.In this intense space each work or body of work 'leaks' into the next occasioning a sense of borderlessness, or of uncertainty. This interpenetration and co-mingling of conceptual and material terrains combines to present temporal and spatial slippages evident within the works themselves and their making, but it is also evident in bodies of work across the chronology of their making. Through a mapping of my own painting and digital arts practice and the utilisation of Lyotard’s notion of the affect -phrase I aim to describe the action of this ‘charged emptiness’ on creativity and explore and explain its significance on that we call image and its animation of what we call critical discourse.

Abstraction as noise : enhancing the poetic

Abstraction in its resistance to evident meaning has the capacity to interrupt or at least provide tools with which to question an overly compliant reception of the information to which we are subject. It does so by highlighting a latency or potentiality inherent in materiality that points to the possibility of a critical resistance to this ceaseless flow of sound/image/data. This resistance has been remarked on in differing ways by a number of commentators such as Lyotard, in his exploration of the avant-garde and the sublime for example. This joint paper will initially map the collaborative project by Daniel Mafe and Andrew Brown, Affecting Interference which conjoins painting with digital sound and animations into a single, large scale, immersive exhibition/installation. The work acts as an interstitial point between contrasting approaches to abstraction: the visual and aural, the digital and analogue. The paper will then explore the ramifications of this through the examination of abstraction as ‘noise’, that is as that raw inassimilable materiality, within which lays the creative possibility to forge and embrace the as-yet-unthought and almost-forgotten. It does so by establishing a space for a more poetic and slower paced critical engagement for the viewing and receiving information or data. This slowing of perception through the suspension of easy recognition runs counter to our current ‘high performance’ culture, and it’s requisite demand for speedy assimilation of content, representing instead the poetic encounter with a potentiality or latency inherent in the nameless particularity of that which is.