Search results for "A* algorithm"
showing 10 items of 2538 documents
From Random Walker to Vehicular Traffic: Motion on a Circle
2014
Driving of cars on a highway is a complex process which can be described by deterministic and stochastic forces. It leads to equations of motion with asymmetric interaction and dissipation as well as to new energy flow law already presented at previous TRAFFIC AND GRANULAR FLOW meetings. Here we consider a model, where motion of an asymmetric random walker on a ring with periodic boundary conditions takes place. It is related to driven systems with active particles, energy input and depot. This simple model can be further developed towards more complicated ones, describing vehicular or pedestrian traffic. Three particular cases are considered, starting with discrete coordinate and time, the…
TU-E-116-01: Clinical Implementation for Advanced Brachytherapy Dose Calculation Algorithms Beyond the TG-43 Formalism
2013
With the recent introduction of heterogeneity correction algorithms for brachytherapy, the AAPM community is still unclear on how to commission and implement these into clinical practice. The recently‐published AAPM TG‐186 report discusses important issues for clinical implementation of these algorithms. In this practical medical physics course, specific examples on how to perform the commissioning process are presented, as well as descriptions of the clinical impact from recent literature reporting comparisons of TG‐43 and heterogeneity‐based dosimetry. A proposed commissioning flowchart will be discussed, guiding the audience through the clinical process. Further, QA tests specific to the…
Life History Trade-Offs and Relaxed Selection Can Decrease Bacterial Virulence in Environmental Reservoirs
2012
Pathogen virulence is usually thought to evolve in reciprocal selection with the host. While this might be true for obligate pathogens, the life histories of opportunistic pathogens typically alternate between within-host and outside-host environments during the infection-transmission cycle. As a result, opportunistic pathogens are likely to experience conflicting selection pressures across different environments, and this could affect their virulence through life-history trait correlations. We studied these correlations experimentally by exposing an opportunistic bacterial pathogen Serratia marcescens to its natural protist predator Tetrahymena thermophila for 13 weeks, after which we meas…
Case-studies on average-case analysis for an elementary course on algorithms
1999
Average-case algorithm analysis is usually viewed as a tough subject by students in the first courses in computer science. Traditionally, these topics are fully developed in advanced courses with a clear mathematical orientation. The work presented here is not an alternative to this, rather, it presents the analysis of algorithms (and average-case in particular) adapted to the mathematical background of students in an elementary course on algorithms or programming by using two selected case-studies.
The iconic interface for the PIctorial C language
2003
Iconic environments intend to provide expressive tools to implement, to debug and to execute programs. Moreover its pictorial constructs guide the user to design algorithms in an interactive fashion. Visual interfaces are especially required whenever programs run on an heterogeneous and reconfigurable multiprocessor system oriented to image analysis. Pictorial tools help the user to control the scope of variables, and the distribution of the tasks into the processors. In this paper, the general design, the visual-syntax, and the implementation of the first prototype of an iconic user interface for the PIctorial C Language (PICL) are described. >
An integrated fuzzy cells-classifier
2007
This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.
A genetic integrated fuzzy classifier
2005
This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
Combining one class fuzzy KNN’s
2007
This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …
Distance-constrained data clustering by combined k-means algorithms and opinion dynamics filters
2014
Data clustering algorithms represent mechanisms for partitioning huge arrays of multidimensional data into groups with small in–group and large out–group distances. Most of the existing algorithms fail when a lower bound for the distance among cluster centroids is specified, while this type of constraint can be of help in obtaining a better clustering. Traditional approaches require that the desired number of clusters are specified a priori, which requires either a subjective decision or global meta–information knowledge that is not easily obtainable. In this paper, an extension of the standard data clustering problem is addressed, including additional constraints on the cluster centroid di…
Scalable Clustering by Iterative Partitioning and Point Attractor Representation
2016
Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon the concept of synchronization. Inherited from the powerful concept of synchronization, the proposed algorithm, CIPA (Clustering by Iterative Partitioning and Point Attractor Representations), is capable of handling very large datasets by iteratively partitioning them into thousands of subsets and clustering each subset separately. Using dynamic clustering by synchronization, each subset is then represented by a set of point attractors and outliers. Finally, CIPA identifies the…