Algorithms of Adaptation in Inductive Inference

We investigate the idea that human concept inference utilizes local incremental search within a compositional mental theory space. To explore this, we study judgments in a challenging task, where participants actively gather evidence about a symbolic rule governing the behavior of a simulated environment. Participants construct mini-experiments before making generalizations and explicit guesses about the hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants’ initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guesses. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants’ revisions better than a range of alternatives. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.

Incremental Search for Informative Gene Selection in Cancer Classification

Although numerous methods of using microarray data analysis for classification have been reported, there is space in the field of cancer classification for new inventions in terms of informative gene selection. This study introduces a new incremental search-based gene selection approach for cancer classification. The strength of wrappers in determining relevant genes in a gene pool can be increased as they evaluate each possible gene’s subset. Nevertheless, the searching algorithms play a major role in gene’s subset selection. Hence, there is the possibility of finding more informative genes with incremental application. Thus, we introduce an approach which utilizes two searching algorithms in gene’s subset selection. The approach was efficient enough to classify five out of six microarray datasets with 100% accuracy using only a few biomarkers while the rest classified with only one misclassification.

An efficient dynamic Bayesian network classifier structure learning algorithm: application to sport epidemiology

Exposing an athlete to intense physical exertion when their organism is not ready for the mobilization of such resources can lead to musculoskeletal injury. In turn, sport practitioners regularly monitor athlete readiness in hopes of mitigating these tragic events. Rapid developments in athlete monitoring technologies has thus resulted in sport practitioners aspiring to siphon meaningful insight from high-throughput datasets. However, revealing the temporal sequence of biological adaptation while yielding accurate probabilistic predictions of an event, demands computationally efficient and accurate algorithms. The purpose of the present study is to create a model in the form of the intuitively appealing dynamic Bayesian network (DBN). Existing DBN approaches can be split into two varieties: either computationally burdensome and thus unscalable, or place structural constraints to increase scalability. This article introduces a novel algorithm ‘rapid incremental search for time-varying associations’ $(Rista)$, to be time-efficient without imposing structural constraints. Furthermore, it offers such flexibility and computational efficiency without compromising prediction performance. The present algorithm displays comparable results to contemporary algorithms in classification accuracy while maintaining superior speed.

Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS

Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due to the tremendously increasing data size. This paper discusses the methods and techniques for solving the inter-related problems encountered when transmitting, processing, and serving metadata for heterogeneous Earth System Observation and Modeling (ESOM) data. A cyberinfrastructure-based solution is proposed to enable effective cataloging and two-step search on big climatic datasets by leveraging state-of-the-art web service technologies and crawling the existing data centers. To validate its feasibility, the big dataset served by UCAR THREDDS Data Server (TDS), which provides Petabyte-level ESOM data and updates hundreds of terabytes of data every day, is used as the case study dataset. A complete workflow is designed to analyze the metadata structure in TDS and create an index for data parameters. A simplified registration model which defines constant information, delimits secondary information, and exploits spatial and temporal coherence in metadata is constructed. The model derives a sampling strategy for a high-performance concurrent web crawler bot which is used to mirror the essential metadata of the big data archive without overwhelming network and computing resources. The metadata model, crawler, and standard-compliant catalog service form an incremental search cyberinfrastructure, allowing scientists to search the big climatic datasets in near real-time. The proposed approach has been tested on UCAR TDS and the results prove that it achieves its design goal by at least boosting the crawling speed by 10 times and reducing the redundant metadata from 1.85 gigabytes to 2.2 megabytes, which is a significant breakthrough for making the current most non-searchable climate data servers searchable.

Research on Minimum Time Interception Problem with a Tangent Impulse under Relative Motion Models

The minimum time interception problem with a tangent impulse whose direction is the same as the satellite’s velocity direction is studied based on the relative motion equations of elliptical orbits by the combination of analytical, numerical, and optimization methods. Firstly, the feasible domain of the true anomaly of the target under the fixed impulse point is given, and the interception solution is transformed into a univariate function only with respect to the target true anomaly by using the relative motion equation. On the basis of the above, the numerical solution of the function is obtained by the combination of incremental search and the false position method. Secondly, considering the initial drift when the impulse point is freely selected, the genetic algorithm-sequential quadratic programming (GA-SQP) combination optimization method is used to obtain the minimum time interception solution under the tangent impulse in a target motion cycle. Thirdly, under the high-precision orbit prediction (HPOP) model, the Nelder-Mead simplex method is used to optimize the impulse velocity and transfer time to obtain the accurate interception solution. Lastly, the effectiveness of the proposed method is verified by simulation examples.