337 research outputs found
New Geometric Algorithms for Fully Connected Staged Self-Assembly
We consider staged self-assembly systems, in which square-shaped tiles can be
added to bins in several stages. Within these bins, the tiles may connect to
each other, depending on the glue types of their edges. Previous work by
Demaine et al. showed that a relatively small number of tile types suffices to
produce arbitrary shapes in this model. However, these constructions were only
based on a spanning tree of the geometric shape, so they did not produce full
connectivity of the underlying grid graph in the case of shapes with holes;
designing fully connected assemblies with a polylogarithmic number of stages
was left as a major open problem. We resolve this challenge by presenting new
systems for staged assembly that produce fully connected polyominoes in O(log^2
n) stages, for various scale factors and temperature {\tau} = 2 as well as
{\tau} = 1. Our constructions work even for shapes with holes and uses only a
constant number of glues and tiles. Moreover, the underlying approach is more
geometric in nature, implying that it promised to be more feasible for shapes
with compact geometric description.Comment: 21 pages, 14 figures; full version of conference paper in DNA2
An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks
Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later
are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Games on graphs with a public signal monitoring
We study pure Nash equilibria in games on graphs with an imperfect monitoring
based on a public signal. In such games, deviations and players responsible for
those deviations can be hard to detect and track. We propose a generic
epistemic game abstraction, which conveniently allows to represent the
knowledge of the players about these deviations, and give a characterization of
Nash equilibria in terms of winning strategies in the abstraction. We then use
the abstraction to develop algorithms for some payoff functions.Comment: 28 page
Grammatical evolution decision trees for detecting gene-gene interactions
<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p
A Cooperative Emergency Navigation Framework using Mobile Cloud Computing
The use of wireless sensor networks (WSNs) for emergency navigation systems
suffer disadvantages such as limited computing capacity, restricted battery
power and high likelihood of malfunction due to the harsh physical environment.
By making use of the powerful sensing ability of smart phones, this paper
presents a cloud-enabled emergency navigation framework to guide evacuees in a
coordinated manner and improve the reliability and resilience in both
communication and localization. By using social potential fields (SPF),
evacuees form clusters during an evacuation process and are directed to
egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm.
Rather than just rely on the conventional telecommunications infrastructures,
we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong
the life time of smart phones, that adaptively searches optimal communication
routes between portable devices and the egress node that provides access to a
cloud server with respect to the remaining battery power of smart phones and
the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by
ISCIS 2014 (29th International Symposium on Computer and Information
Sciences
Solving the subset-sum problem with a light-based device
We propose a special computational device which uses light rays for solving
the subset-sum problem. The device has a graph-like representation and the
light is traversing it by following the routes given by the connections between
nodes. The nodes are connected by arcs in a special way which lets us to
generate all possible subsets of the given set. To each arc we assign either a
number from the given set or a predefined constant. When the light is passing
through an arc it is delayed by the amount of time indicated by the number
placed in that arc. At the destination node we will check if there is a ray
whose total delay is equal to the target value of the subset sum problem (plus
some constants).Comment: 14 pages, 6 figures, Natural Computing, 200
Quantifying Robotic Swarm Coverage
In the field of swarm robotics, the design and implementation of spatial
density control laws has received much attention, with less emphasis being
placed on performance evaluation. This work fills that gap by introducing an
error metric that provides a quantitative measure of coverage for use with any
control scheme. The proposed error metric is continuously sensitive to changes
in the swarm distribution, unlike commonly used discretization methods. We
analyze the theoretical and computational properties of the error metric and
propose two benchmarks to which error metric values can be compared. The first
uses the realizable extrema of the error metric to compute the relative error
of an observed swarm distribution. We also show that the error metric extrema
can be used to help choose the swarm size and effective radius of each robot
required to achieve a desired level of coverage. The second benchmark compares
the observed distribution of error metric values to the probability density
function of the error metric when robot positions are randomly sampled from the
target distribution. We demonstrate the utility of this benchmark in assessing
the performance of stochastic control algorithms. We prove that the error
metric obeys a central limit theorem, develop a streamlined method for
performing computations, and place the standard statistical tests used here on
a firm theoretical footing. We provide rigorous theoretical development,
computational methodologies, numerical examples, and MATLAB code for both
benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering
(LNEE). This book contribution is an extension of our ICINCO 2018 conference
paper arXiv:1806.02488. 27 pages, 8 figures, 2 table
Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions
The field of algorithmic self-assembly is concerned with the design and
analysis of self-assembly systems from a computational perspective, that is,
from the perspective of mathematical problems whose study may give insight into
the natural processes through which elementary objects self-assemble into more
complex ones. One of the main problems of algorithmic self-assembly is the
minimum tile set problem (MTSP), which asks for a collection of types of
elementary objects (called tiles) to be found for the self-assembly of an
object having a pre-established shape. Such a collection is to be as concise as
possible, thus minimizing supply diversity, while satisfying a set of stringent
constraints having to do with the termination and other properties of the
self-assembly process from its tile types. We present a study of what we think
is the first practical approach to MTSP. Our study starts with the introduction
of an evolutionary heuristic to tackle MTSP and includes results from extensive
experimentation with the heuristic on the self-assembly of simple objects in
two and three dimensions. The heuristic we introduce combines classic elements
from the field of evolutionary computation with a problem-specific variant of
Pareto dominance into a multi-objective approach to MTSP.Comment: Minor typos correcte
Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies
Neural networks for modeling gene-gene interactions in association studies
<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p
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