Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

Absolute Dimensions and Apsidal Motion of the Young Detached System LT Canis Majoris

New high resolution spectra of the short period (P~1.76 days) young detached binary LT CMa are reported for the first time. By combining the results from the analysis of new radial velocity curves and published light curves, we determine values for the masses, radii and temperatures as follows: M_1= 5.59 (0.20) M_o, R_1=3.56 (0.07) R_o and T_eff1= 17000 (500) K for the primary and M_2=3.36 (0.14) M_o, R_2= 2.04 (0.05) R_o and T_eff2= 13140 (800) K for the secondary. Static absorbtion features apart from those coming from the close binary components are detected in the several spectral regions. If these absorbtion features are from a third star, as the light curve solutions support, its radial velocity is measured to be RV_3=70(8) km s^-1. The orbit of the binary system is proved to be eccentric (e=0.059) and thus the apsidal motion exists. The estimated linear advance in longitude of periastron corresponds to an apsidal motion of U=694+/-5 yr for the system. The average internal structure constant log k_2,obs=-2.53 of LT CMa is found smaller than its theoretical value of log k_2,theo=-2.22 suggesting the stars would have more central concentration in mass. The photometric distance of LT CMa (d=535+/-45 pc) is found to be much smaller than the distance of CMa OB1 association (1150 pc) which rules out membership. A comparison with current stellar evolution models for solar metallicity indicates that LT CMa (35 Myr) is much older than the CMa OB1 association (3 Myr), confirming that LT CMa is not a member of CMa OB1. The kinematical and dynamical analysis indicate LT CMa is orbiting the Galaxy in a circular orbit and belongs to the young thin-disk population.Comment: 19 pages, 6 figures and 6 tables, accepted for publication in Publication of the Astronomical Society of Japa

A Study of the Story of Sadāprarudita in the Aṣṭasāhasrikā Prajñāpāramitā Sūtra

This dissertation focuses on the story of the Bodhisattva Sadāprarudita found in various Buddhist prajñāpāramitā sūtras. The richness of the story’s contents, the complexity of its multiple extant versions, and its association with prajñāpāramitā make it a piece worthy of investigation. Looking at the origins of the story, previous studies have assumed a linear relationship among the two main versions of the story. Yet a closer analysis conducted in this study reveals two branches of a family tree that appears to stem from an earlier (now lost) parent. The historical analysis of the evolution of the story also provides fresh and reliable evidence concerning the editorial processes of Buddhist texts. Jan Nattier (2003: 49–63) proposed several rules for identifying interpolations in a text. Application of these rules to the Sadāprarudita narrative has led to the formulation of several supplementary rules. Where Nattier’s rules help to identify stratification in the later parallels of the text, these supplementary rules allow for the identification of interpolations in the earlier parallels of the text and between the two main versions as well. Apart from revealing the historical development of the text this thesis makes important contributions to our understanding of the story’s employment across time and space, revealing the importance given to this narrative by many of the great Buddhist masters from India, Tibet and China, and spanning thousands of years. In addition, the unique episode which lists many states of samādhi with vivid names is explored to determine whether these samādhis could have had a practical basis or are merely as fanciful as their names suggest. The samādhi on viewing all tathāgatas is further investigated to see what implications this may have for the beginnings of Mahāyāna teachings

Iterated learning and grounding: from holistic to compositional languages

This paper presents a new computational model for studying the origins and evolution of compositional languages grounded through the interaction between agents and their environment. The model is based on previous work on adaptive grounding of lexicons and the iterated learning model. Although the model is still in a developmental phase, the first results show that a compositional language can emerge in which the structure reflects regularities present in the population's environment

The Evolution of Organic Agriculture in Denmark

In this working paper it is the intention to outline the evolution of organic agriculture in Denmark. The paper do not claim to be a total presentation of the history but is aiming to present important milestones, actors involved, intentions and reflections of the actors, and especially to illustrate interaction between the sector labelled as “organic agriculture” and the social surroundings. Based on the findings of the OASE-project a cut off of epoches is used to structure the presentation: Grassroot pioneering, rallying by means of separation (the more excluded an focus narrowed to farming), inclusion (organic agriculture certified by government etc.), absorption (organic farming as an integrated part of established agri systems) while the present epoche is suggested as Funky Business

Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation

Feature models are used to specify variability of user-configurable systems as appearing, e.g., in software product lines. Software product lines are supposed to be long-living and, therefore, have to continuously evolve over time to meet ever-changing requirements. Evolution imposes changes to feature models in terms of edit operations. Ensuring consistency of concurrent edits requires appropriate conflict detection techniques. However, recent approaches fail to handle crucial subtleties of extended feature models, namely constraints mixing feature-tree patterns with first-order logic formulas over non-Boolean feature attributes with potentially infinite value domains. In this paper, we propose a novel conflict detection approach based on symbolic graph transformation to facilitate concurrent edits on extended feature models. We describe extended feature models formally with symbolic graphs and edit operations with symbolic graph transformation rules combining graph patterns with first-order logic formulas. The approach is implemented by combining eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm