Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach

Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach.     

A threshold insensitive method for locating the forest canopy top with waveform lidar

Lidars have the unique ability to make direct, physical measurements of forest height and vertical structure in much denser canopies than is possible with passive optical or short wavelength radars. However the literature reports a consistent underestimate of tree height when using physically based methods, necessitating empirical corrections. This bias is a result of overestimating the range to the canopy top due to background noise and failing to correctly identify the ground.This paper introduces a method, referred to as “noise tracking”, to avoid biases when determining the range to the canopy top. Simulated waveforms, created with Monte-Carlo ray tracing over geometrically explicit forest models, are used to test noise tracking against simple thresholding over a range of forest and system characteristics. It was found that noise tracking almost completely removed the bias in all situations except for very high noise levels and very low (< 10%) canopy covers. In all cases noise tracking gave lower errors than simple thresholding and had a lower sensitivity to the initial noise threshold.Finite laser pulses spread out the measured signal, potentially overriding the benefit of noise tracking. In the past laser pulse length has been corrected by adding half that length to the signal start range. This investigation suggests that this is not always appropriate for simple thresholding and that the results for noise tracking were more directly related to pulse length than for simple thresholding. That this effect has not been commented on before may be due to the possible confounding impacts of instrument and survey characteristics inherent in field data. This method should help improve the accuracy of waveform lidar measurements of forests, whether using airborne or spaceborne instruments.     

A Vector-based Representation for Image Warping

A method for image analysis, representation and re-synthesis is introduced. Unlike other schemes it is not pixel based but rather represents a picture as vector data, from which an altered version of the original image can be rendered. Representing an image as vector data allows performing operations such as zooming, retouching or colourising, avoiding common problems associated with pixel image manipulation. This paper brings together methods from the areas of computer vision, image compositing and image based rendering to prove that this type of image representation is a step towards accurate and efficient image manipulation.     

A survey of approaches to automatic schema matching

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component. Received: 5 February 2001 / Accepted: 6 September 2001 Published online: 21 November 2001     

The Philosophy of Critical Realism—An Opportunity for Information Systems Research

It is argued that recent research in the information systems field has tended to either emphasise the structural/collective dimension or the agency/individual dimension, not both. Structuration theory is a more recent attempt to address both agency and structure, however there are a number of issues with the use of structuration theory in information systems research, not the least of which is its lack of recognition of the temporal and longitudinal nature of information systems development. A relatively new philosophy, critical realism, provides the potential for a new approach to social investigations in its provision of an ontology for the analytical separation of structure and agency. The philosophy is introduced and its implications for sociological investigation are discussed.     

Effective on-line algorithms for reliable due date quotation and large-scale scheduling

We consider the sequencing of a series of jobs that arrive at a single processor over time. At each job’s arrival time, a due date must be quoted for the job, and the job must complete processing before its quoted due date. The objective is to minimize the sum (or average) of quoted due dates, or equivalently, the average quoted lead time. In this paper, we propose on-line heuristics for this problem and characterize the conditions under which these heuristics are asymptotically optimal. Computational testing further demonstrates the relative effectiveness of these heuristics under various conditions. Both authors made equal contributions to this paper and are listed in alphabetical order.     

Correlating burst events on streaming stock market data

We address the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. We follow a two-step methodology: first we identify the burst sections in our data and subsequently we store them for easy retrieval in an efficient in-memory index. The burst detection scheme imposes a variable threshold on the examined data and takes advantage of the skewed distribution that is typically encountered in many applications. The detected bursts are compacted into burst intervals and stored in an interval index. The index facilitates the identification of correlated bursts by performing very efficient overlap operations on the stored burst regions. We present the merits of the proposed indexing scheme through a thorough analysis of its complexity. We also manifest the real-time response of our burst indexing technique, and demonstrate the usefulness of the approach for correlating surprising volume trading events using historical stock data of the NY stock exchange. While the focus of this work is on financial data, the proposed methods and data-structures can find applications for anomaly or novelty detection in telecommunication, network traffic and medical data.     

Dynamic graph cuts for efficient inference in Markov Random Fields

Abstract-In this paper we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP solutions for certain dynamically changing MRF models in computer vision such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, the dynamic algorithm efficiently computes the maximum flow in a modified version of the graph. The time taken by it is roughly proportional to the total amount of change in the edge weights of the graph. Our experiments show that, when the number of changes in the graph is small, the dynamic algorithm is significantly faster than the best known static graph cut algorithm. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video. It should be noted that the application of our algorithm is not limited to the above problem, the algorithm is generic and can be used to yield similar improvements in many other cases that involve dynamic change.     

Modeling sensorimotor learning with linear dynamical systems

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation-the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a substantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.     

Graduate school origins of Journal of Counseling Psychology authors: Volumes 15-28.

Examined the graduate school origins of authors of Journal of Counseling Psychology (JCP) articles in Volumes 15–28 to extend a previous analysis of Volumes 1–24 by the 3rd author et al (1969). Comparisons of the 2 14-yr periods reveal that some universities retained the same or similar ranks across both periods, some declined in importance as sources of authors, and others emerged as major sources of JCP authors. (6 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)