Data Management and Mining in Astrophysical Databases

We analyse the issues involved in the management and mining of astrophysical data. The traditional approach to data management in the astrophysical field is not able to keep up with the increasing size of the data gathered by modern detectors. An essential role in the astrophysical research will be assumed by automatic tools for information extraction from large datasets, i.e. data mining techniques, such as clustering and classification algorithms. This asks for an approach to data management based on data warehousing, emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Clustering and classification techniques, on large datasets, pose additional requirements: computational and memory scalability with respect to the data size, interpretability and objectivity of clustering or classification results. In this study we address some possible solutions.Comment: 10 pages, Late

A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Human motion modeling and simulation by anatomical approach

To instantly generate desired infinite realistic human motion is still a great challenge in virtual human simulation. In this paper, the novel emotion effected motion classification and anatomical motion classification are presented, as well as motion capture and parameterization methods. The framework for a novel anatomical approach to model human motion in a HTR (Hierarchical Translations and Rotations) file format is also described. This novel anatomical approach in human motion modelling has the potential to generate desired infinite human motion from a compact motion database. An architecture for the real-time generation of new motions is also propose

SITE-SPECIFIC AND LANDSCAPE FEATURES ASSOCIATED WITH SHRUBLAND BIRD OCCURENCE IN ANTHROPOGENIC SHRUBLANDS IN THE NORTHEASTERN UNITED STATES

Habitats dominated by low-growing trees and shrubs are becoming increasingly uncommon in the northeastern U.S. Human development, altered natural-disturbance regimes, and forest succession have reduced the quantity and quality of these shrublands. As a result, over half of the shrubland-dependent songbirds in the region have experienced long-term population declines. Anthropogenic shrublands, including regenerating clearcuts, sand and gravel mines, old fields, and transmission line rights-of-way may provide nesting habitat for most shrubland birds; but differences in size, site-specific features, and landscape composition may affect bird use. To assess the features that may influence shrubland bird occurrence in anthropogenic shrublands, I conducted presence/absence surveys of 8 species [alder flycatcher (Empidonax alnorum), brown thrasher (Toxostoma rufum), blue-winged warbler (Vermivora cyanoptera), chestnut-sided warbler (Setophaga pensylvanica), eastern towhee (Pipilo erythrophthalmus), field sparrow (Spizella pusilla), indigo bunting (Passerina cyanea), and prairie warbler (Setophaga discolor)] in 101 sites in southeastern New Hampshire during the 2015 and 2016 nesting seasons. For each shrubland, I measured area, site-specific features (e.g., vegetation height, density, and coverage), and characteristics of surrounding landscape features within different buffer zones. Overall, 67% of the variables in the best models predicting bird occurrence were landscape features and 33% were site-specific features. Bird occurrence at a site was positively associated with the proportion of shrublands in the surrounding landscape, particularly within a 500 m buffer. Occurrence of all species except blue-winged warblers and indigo buntings was negatively associated with the proportion of urban development in the surrounding landscape. Shrubland bird species richness increased with vegetation density until vegetation density became too dense for brown thrashers, field sparrows, and prairie warblers. Occurrence of all species except blue-winged warblers increased with shrubland size. These results provide opportunities to enhance existing anthropogenic habitats to benefit populations of declining shrubland birds

Multidimensional Data Analysis for Enhancing In-Depth Knowledge on the Characteristics of Science and Technology Parks

The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality datasets. This work uses existing terminologies, definitions, and core features of STPs to conduct a multidimensional data analysis that explores and evaluates the 21 core features which describe the key internal factors of an STP. The core features are gathered from a reliable and updatable dataset of Spanish STPs. The methodological framework can be replicated for other STP contexts and is based on descriptive techniques and machine-learning tools. The results of the study provide an overview of the general situation of STPs in Spain, validate the existence and characteristics of three types of STPs, and identify the typical features of STPs. Moreover, the prototype STP can be used as a benchmark so that other STPs can identify the features that need to be improved. Finally, this work makes it possible to carry out classifications of STPs, in addition to prediction and decision making for innovation ecosystems.This research work has been partially funded by the Generalitat Valenciana through the project NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation with grant reference (CIPROM/2021/21); the Ministry of Science and Innovation, PID2021-123956OB-I00, CORTEX; PID2021-122263OB-C22 COOLANG; and the R&D project CLEARTEXT TED2021-130707B-I00

Research in sub-saharan African food systems must address post-sustainability challenges and increase developmental returns

The article argue that the livelihood approach is relevant for Research in sub-saharan African food systems, which must address post-sustainability challenges and increase developmental return