Deconstruction of fractals and its implications for cartographic education

The research reported here was designed for two reasons: firstly, to involve anyone with an interest in cartographic visualization to participate in eliciting cartographic knowledge and to provide them with the opportunity to contribute their practical knowledge and opinions; and secondly, to inform the design of algorithms for line generalization. In the past, there has been some resistance to such mining and codification of expert knowledge. However, many cartographers now welcome highly interactive computer graphics, computer mapping, and virtual reality systems as providing them with new opportunities for launching cartography into a new creative age. Despite nearly thirty years of research on line generalization algorithms, the available algorithms are somewhat simplistic. This research, undertaken under the auspices of the BCS Design Group, explored the behavioural tendencies of cartographers engaged in line filtering. The results show that a carefully contrived, even if obviously artificial, exercise on the deconstruction of lines into meaningless forms can prompt cartographers to observe, record, and discuss their own cognitive processing

Sources of variability in cartographers' deconstruction of fractals : paper presented at the 18 November 1998 meeting of the British Cartographic Society's Design Group at the University of Glasgow

CISRG discussion paper ; 1

Cartographic Generalization in Digital Environment

Throughout the world numerous efforts to automate generalization are in progress. The results are yet to be satisfactory. Ample reasoning can be given to justify the lack of success, the most important being that generalization is an ambiguous process, highly subjective which lacks definitive rules, guidelines or systematization. This paper deals with the problem of generalization of vector data bases through the analysis of recent developments and research in the field. These developments tend to establish a promising framework which, with subsequent refinements and the utilization of state-of-the-art computer technology, may lead to successful results. What is needed is what lacks: Definitive rules in structuring the digital image of the world and development of expert systems which will intelligently manipulate this image

Requirements for Information Extraction for Knowledge Management

Knowledge Management (KM) systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction (IE) technology. However, IE was originally developed for database population and there is a mismatch between what is required to successfully perform KM and what current IE technology provides. In this paper we begin to address this issue by outlining requirements for IE based KM

Usage-based and emergentist approaches to language acquisition

It was long considered to be impossible to learn grammar based on linguistic experience alone. In the past decade, however, advances in usage-based linguistic theory, computational linguistics, and developmental psychology changed the view on this matter. So-called usage-based and emergentist approaches to language acquisition state that language can be learned from language use itself, by means of social skills like joint attention, and by means of powerful generalization mechanisms. This paper first summarizes the assumptions regarding the nature of linguistic representations and processing. Usage-based theories are nonmodular and nonreductionist, i.e., they emphasize the form-function relationships, and deal with all of language, not just selected levels of representations. Furthermore, storage and processing is considered to be analytic as well as holistic, such that there is a continuum between children's unanalyzed chunks and abstract units found in adult language. In the second part, the empirical evidence is reviewed. Children's linguistic competence is shown to be limited initially, and it is demonstrated how children can generalize knowledge based on direct and indirect positive evidence. It is argued that with these general learning mechanisms, the usage-based paradigm can be extended to multilingual language situations and to language acquisition under special circumstances

Detecting barriers to transport: A review of different techniques

We review and discuss some different techniques for describing local dispersion properties in fluids. A recent Lagrangian diagnostics, based on the Finite Scale Lyapunov Exponent (FSLE), is presented and compared to the Finite Time Lyapunov Exponent (FTLE), and to the Okubo-Weiss (OW) and Hua-Klein (HK) criteria. We show that the OW and HK are a limiting case of the FTLE, and that the FSLE is the most efficient method for detecting the presence of cross-stream barriers. We illustrate our findings by considering two examples of geophysical interest: a kinematic meandering jet model, and Lagrangian tracers advected by stratospheric circulation.Comment: 15 pages, 9 figures, submitted to Physica

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species