11,398 research outputs found
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial
Life (ECAL) 2013, Taormina, Ital
History of art paintings through the lens of entropy and complexity
Art is the ultimate expression of human creativity that is deeply influenced
by the philosophy and culture of the corresponding historical epoch. The
quantitative analysis of art is therefore essential for better understanding
human cultural evolution. Here we present a large-scale quantitative analysis
of almost 140 thousand paintings, spanning nearly a millennium of art history.
Based on the local spatial patterns in the images of these paintings, we
estimate the permutation entropy and the statistical complexity of each
painting. These measures map the degree of visual order of artworks into a
scale of order-disorder and simplicity-complexity that locally reflects
qualitative categories proposed by art historians. The dynamical behavior of
these measures reveals a clear temporal evolution of art, marked by transitions
that agree with the main historical periods of art. Our research shows that
different artistic styles have a distinct average degree of entropy and
complexity, thus allowing a hierarchical organization and clustering of styles
according to these metrics. We have further verified that the identified groups
correspond well with the textual content used to qualitatively describe the
styles, and that the employed complexity-entropy measures can be used for an
effective classification of artworks.Comment: 10 two-column pages, 5 figures; accepted for publication in PNAS
[supplementary information available at
http://www.pnas.org/highwire/filestream/824089/field_highwire_adjunct_files/0/pnas.1800083115.sapp.pdf
How algorithmic popularity bias hinders or promotes quality
Algorithms that favor popular items are used to help us select among many
choices, from engaging articles on a social media news feed to songs and books
that others have purchased, and from top-raked search engine results to
highly-cited scientific papers. The goal of these algorithms is to identify
high-quality items such as reliable news, beautiful movies, prestigious
information sources, and important discoveries --- in short, high-quality
content should rank at the top. Prior work has shown that choosing what is
popular may amplify random fluctuations and ultimately lead to sub-optimal
rankings. Nonetheless, it is often assumed that recommending what is popular
will help high-quality content "bubble up" in practice. Here we identify the
conditions in which popularity may be a viable proxy for quality content by
studying a simple model of cultural market endowed with an intrinsic notion of
quality. A parameter representing the cognitive cost of exploration controls
the critical trade-off between quality and popularity. We find a regime of
intermediate exploration cost where an optimal balance exists, such that
choosing what is popular actually promotes high-quality items to the top.
Outside of these limits, however, popularity bias is more likely to hinder
quality. These findings clarify the effects of algorithmic popularity bias on
quality outcomes, and may inform the design of more principled mechanisms for
techno-social cultural markets
An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings
Structural Health Monitoring (SHM) of historical building is an emerging field of research aimed at the development of strategies for on-line assessment of structural condition and identification of damage in the earliest stage. Built heritage is weak against operational and environmental condition and preservation must guarantee minimum repair and non-intrusiveness. SHM provides a cost-effective management and maintenance allowing prevention and prioritization of the interventions. Recently, in computer science, mimicking nature to address complex problems is becoming more frequent. Nature-inspired approaches turn out to be extremely efficient in facing optimization, commonly used to analyze engineering processes in SHM, providing interesting advantages when compared with classic methods. This paper begins with an introduction to Natural Computing. Then, focusing on its applications to SHM, possible improvements in built heritage conservation are shown and discussed suggesting a general framework for safety assessment and damage identification of existing structures.This work was financed by FEDER funds through the Competitiveness Factors Operational Programme COMPETE and by national funds through FCT - Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio
Probing the limits to microRNA-mediated control of gene expression
According to the `ceRNA hypothesis', microRNAs (miRNAs) may act as mediators
of an effective positive interaction between long coding or non-coding RNA
molecules, carrying significant potential implications for a variety of
biological processes. Here, inspired by recent work providing a quantitative
description of small regulatory elements as information-conveying channels, we
characterize the effectiveness of miRNA-mediated regulation in terms of the
optimal information flow achievable between modulator (transcription factors)
and target nodes (long RNAs). Our findings show that, while a sufficiently
large degree of target derepression is needed to activate miRNA-mediated
transmission, (a) in case of differential mechanisms of complex processing
and/or transcriptional capabilities, regulation by a post-transcriptional
miRNA-channel can outperform that achieved through direct transcriptional
control; moreover, (b) in the presence of large populations of weakly
interacting miRNA molecules the extra noise coming from titration disappears,
allowing the miRNA-channel to process information as effectively as the direct
channel. These observations establish the limits of miRNA-mediated
post-transcriptional cross-talk and suggest that, besides providing a degree of
noise buffering, this type of control may be effectively employed in cells both
as a failsafe mechanism and as a preferential fine tuner of gene expression,
pointing to the specific situations in which each of these functionalities is
maximized.Comment: 16 page
- …