Multiscale histograms: Summarizing topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this paper, we present several novel techniques to eectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important topological relations: contains, contained, overlap, and disjoint. We rst present a novel framework to construct a multiscale histogram composed of multiple Euler histograms with the guarantee of the exact summarization results for aligned windows in constant time. Then we present an approximate algorithm, with the approximate ratio 19/12, to minimize the storage spaces of such multiscale Euler histograms, although the problem is generally NP-hard. To conform to a limited storage space where only k Euler histograms are allowed, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy. Finally, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. Our extensive experiments against both synthetic and real world datasets demonstrated that the approximate mul- tiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost effciency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for the real datasets.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

Monitoring growth in rapidly urbanizing areas using remotely sensed data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.

Ecosystem process models at multiple scales for mapping tropical forest productivity

Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

Dendritic branching patterns of pyramidal cells in the visual cortex of the new world marmoset monkey, with comparative notes on the old world macaque monkey

The basal dendritic arbors of 442 supragranular pyramidal cells in visual cortex of the marmoset monkey were compared by fractal analyses. As detailed in a previous study,(1) individual cells were injected with Lucifer Yellow and processed for a DAB reaction product. The basal dendritic arbors were drawn, in the tangential plane, and the fractal dimension (D) determined by the dilation method. The fractal dimensions were compared between cells in ten cortical areas containing cells involved in visual processing, including the primary visual area (Vi), the second visual area (V2), the dorsoanterior area (DA), the dorsomedial area (DM), the dorsolateral. area (DL), the middle temporal area (MT), the posterior parietal area (PP), the fundus of the superior temporal area (FST) and the caudal and rostral subdivisions of inferotemporal cortex (ITc and ITr, respectively). Of 45 pairwise interareal comparisons of the fractal dimension of neurones, 20 were significantly different. Moreover, comparison of data according to previously published visual processing pathways revealed a trend for cells with greater fractal dimensions in higher cortical areas. Comparison of the present results with those in homologous cortical areas in the macaque monkey(2) revealed some similarities between the two species. The similarity in the trends of D values of cells in both species may reflect developmental features which, result in different functional attributes.

Pyramidal neurones in macaque visual cortex: Interareal phenotypic variation of dendritic branching patterns

The basal dendritic arbors of over 500-layer III pyramidal neurones of the macaque cortex were compared by fractal analyses, which provides a measure of the space filling (or branching pattern) of dendritic arbors. Fractal values (D) of individual cells were compared between the cytochrome oxidase (CO)-rich blobs and CO-poor interblobs, of middle and upper layer III, and between sublaminae, in the primary visual area (Vi). These data were compared with those in the CO compartments in the second visual area (V2), and seven other extrastriate cortical areas. (V4, MT, LIP, 7a, TEO, TE and STP). There were significant differences in the fractal dimensions, and therefore the dendritic branching patterns, of cells in striate and extrastriate areas. Of the 55 possible pairwise comparisons of fractal dimension of neurones in different cortical areas (or CO compartments), 39 proved to be significantly different. The markedly different morphologies of pyramidal cells in the different cortical areas may be one of the features that determine the functional signatures of these cells by influencing the number of inputs received by, and propagation of potentials through, their dendritic arbors.

Characterisation of pore structure of carbon adsorbents using regularisation procedure

The constrained regularisation procedure was applied to compute the pore size distributions (PSDs, f(x)) for a variety of activated carbons using overall adsorption equation based on the combination of the Kelvin equation and the statistical adsorbed film thickness. The impact of the boundary values of relative nitrogen pressure p/p(0) was analysed on the basis of the corresponding alterations in the PSDs. Changes in microporosity and mesoporosity of activated carbons can be described adequately only when the range of p/p(0) is as wide as possible, as at a high initial p/p(0) value, the f(x) curves can be broadened with shifted maxima especially for micropores and narrow mesopores. Comparative analysis of the PSDs and the adsorption potential, adsorption energy and fractal dimension distributions gives useful information on the complete description of the adsorbent characteristics. (C) 2001 Elsevier Science B.V. All rights reserved.

Novel molecular sieve silica (MSS) membranes: characterisation and permeation of single-step and two-step sol-gel membranes

High quality MSS membranes were synthesised by a single-step and two-step catalysed hydrolyses employing tetraethylorthosilicate (TEOS), absolute ethanol (EtOH), I M nitric acid (HNO3) and distilled water (H2O). The Si-29 NMR results showed that the two-step xerogels consistently had more contribution of silanol groups (Q(3) and Q(2)) than the single-step xerogel. According to the fractal theory, high contribution of Q(2) and Q(3) species are responsible for the formation of weakly branched systems leading to low pore volume of microporous dimension. The transport of diffusing gases in these membranes is shown to be activated as the permeance increased with temperature. Albeit the permeance of He for both single-step and two-step membranes are very similar, the two-step membranes permselectivity (ideal separation factor) for He/CO2 (69-319) and He/CH4 (585-958) are one to two orders of magnitude higher than the single-step membranes results of 2-7 and 69, respectively. The two-step membranes have high activation energy for He and H-2 permeance, in excess of 16 kJ mol(-1). The mobility energy for He permeance is three to six-fold higher for the two-step than the single-step membranes. As the mobility energy is higher for small pores than large pores and coupled with the permselectivity results, the two-step catalysed hydrolysis sol-gel process resulted in the formation of pore sizes in the region of 3 Angstrom while the single-step process tended to produce slightly larger pores. (C) 2002 Elsevier Science B.V. All rights reserved.

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.