Pattern classes and priority queues

When a set of permutations comprising a pattern class C is submitted as input to a priority queue the resulting output is again a pattern class C'. The basis of C' is determined for pattern classes C whose basis elements have length 3, and is finite in these cases. An example is given of a class C with basis 2431 for which C is not finitely based

Isomorphisms between pattern classes

Isomorphisms p between pattern classes A and B are considered. It is shown that, if p is not a symmetry of the entire set of permutations, then, to within symmetry, A is a subset of one a small set of pattern classes whose structure, including their enumeration, is determined.Comment: 11 page

Reframing Literacy in Adult ESL Programs: Making the case for the inclusion of identity

Adult  ESL  programs  in  the  Australian  context  are  heavily  influenced  by  neo-liberal notions  of  functional  literacy  and  numeracy.  This paper argues that such notions, designed to enable the learner to function within the workplace or community can fail to acknowledge the complexity of ESL program participation for adult learners. This may be considered especially so for pre-literate learners from refugee backgrounds who have low or minimal levels of literacy in their own language and are hence negotiating a new skill set, a new culture and arguably a new sense of identity. This paper is based on research which points to the need to position the learning of literacy and numeracy in the ESL context as a social and educational journey made meaningful by a learner's sense of (emerging) identity.  In this context a holistic, socially orientated  understanding  of  their  learning  and  their progress  is  preferable  to  an approach  which  views  and  evaluates  learners  against  preconceived  functional  literacy skills. The participants in this study were people of refugee background from Africa with minimal literacy skills

Subclasses of the separable permutations

We prove that all subclasses of the separable permutations not containing Av(231) or a symmetry of this class have rational generating functions. Our principal tools are partial well-order, atomicity, and the theory of strongly rational permutation classes introduced here for the first time

Typesafety for Explicitly-Coded Probabilistic Inference Procedures

Researchers have recently proposed several systems that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this paper, we present Shuffle, a programming language for developing manual inference algorithms that enforces 1) the basic rules of probability theory and 2) statistical dependencies of the algorithm's corresponding probabilistic model. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver performant implementations of these algorithms with the added benefit of Shuffle's correctness guarantees

Inflations of Geometric Grid Classes: Three Case Studies

We enumerate three specific permutation classes defined by two forbidden patterns of length four. The techniques involve inflations of geometric grid classes

Novel shape indices for vector landscape pattern analysis

The formation of an anisotropic landscape is influenced by natural and/or human processes, which can then be inferred on the basis of geometric indices. In this study, two minimal bounding rectangles in consideration of the principles of mechanics (i.e. minimal width bounding (MWB) box and moment bounding (MB) box) were introduced. Based on these boxes, four novel shape indices, namely MBLW (the length-to-width ratio of MB box), PAMBA (area ratio between patch and MB box), PPMBP (perimeter ratio between patch and MB box) and ODI (orientation difference index between MB and MWB boxes), were introduced to capture multiple aspects of landscape features including patch elongation, patch compactness, patch roughness and patch symmetry. Landscape pattern was, thus, quantified by considering both patch directionality and patch shape simultaneously, which is especially suitable for anisotropic landscape analysis. The effectiveness of the new indices were tested with real landscape data consisting of three kinds of saline soil patches (i.e. the elongated shaped slightly saline soil class, the circular or half-moon shaped moderately saline soil, and the large and complex severely saline soil patches). The resulting classification was found to be more accurate and robust than that based on traditional shape complexity indices

Spatiotemporal subpixel mapping of time-series images

Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs