38,018 research outputs found
Industry diversity, competition and firm relatedness: The impact on employment before and after the 2008 global financial crisis
Industry diversity, competition and firm relatedness: the impact on employment before and after the 2008 global financial crisis. Regional Studies. This study investigates the extent to which indicators of external-scale economies impacted employment growth in Canada over the period 2004–11. It focuses on knowledge spillovers between firms while accounting for Marshallian specialization, Jacobs’ diversity and competition by industry, as well as related and unrelated firm varieties in terms of employment and sales. It is found that the employment growth effects of local competition and diversity are positive, while the effect of Marshallian specialization is negative. Diversification is found to be particularly important for employment growth during the global financial crisis and immediately thereafter
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
Information as Distinctions: New Foundations for Information Theory
The logical basis for information theory is the newly developed logic of
partitions that is dual to the usual Boolean logic of subsets. The key concept
is a "distinction" of a partition, an ordered pair of elements in distinct
blocks of the partition. The logical concept of entropy based on partition
logic is the normalized counting measure of the set of distinctions of a
partition on a finite set--just as the usual logical notion of probability
based on the Boolean logic of subsets is the normalized counting measure of the
subsets (events). Thus logical entropy is a measure on the set of ordered
pairs, and all the compound notions of entropy (join entropy, conditional
entropy, and mutual information) arise in the usual way from the measure (e.g.,
the inclusion-exclusion principle)--just like the corresponding notions of
probability. The usual Shannon entropy of a partition is developed by replacing
the normalized count of distinctions (dits) by the average number of binary
partitions (bits) necessary to make all the distinctions of the partition
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
We present a canonical way to turn any smooth parametric family of
probability distributions on an arbitrary search space into a
continuous-time black-box optimization method on , the
\emph{information-geometric optimization} (IGO) method. Invariance as a design
principle minimizes the number of arbitrary choices. The resulting \emph{IGO
flow} conducts the natural gradient ascent of an adaptive, time-dependent,
quantile-based transformation of the objective function. It makes no
assumptions on the objective function to be optimized.
The IGO method produces explicit IGO algorithms through time discretization.
It naturally recovers versions of known algorithms and offers a systematic way
to derive new ones. The cross-entropy method is recovered in a particular case,
and can be extended into a smoothed, parametrization-independent maximum
likelihood update (IGO-ML). For Gaussian distributions on , IGO
is related to natural evolution strategies (NES) and recovers a version of the
CMA-ES algorithm. For Bernoulli distributions on , we recover the
PBIL algorithm. From restricted Boltzmann machines, we obtain a novel algorithm
for optimization on . All these algorithms are unified under a
single information-geometric optimization framework.
Thanks to its intrinsic formulation, the IGO method achieves invariance under
reparametrization of the search space , under a change of parameters of the
probability distributions, and under increasing transformations of the
objective function.
Theory strongly suggests that IGO algorithms have minimal loss in diversity
during optimization, provided the initial diversity is high. First experiments
using restricted Boltzmann machines confirm this insight. Thus IGO seems to
provide, from information theory, an elegant way to spontaneously explore
several valleys of a fitness landscape in a single run.Comment: Final published versio
Learning to select data for transfer learning with Bayesian Optimization
Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.Comment: EMNLP 2017. Code available at:
https://github.com/sebastianruder/learn-to-select-dat
Stochastic Dominance, Entropy and Biodiversity Management
In this paper we develop a model of population dynamics using the Shannon entropy index, a measure of diversity that allows for global and specific population shocks. We model the effects of increasing the number of parcels on biodiversity, varying the number of spatially diverse parcels to capture risk diversification. We discuss the concepts of stochastic dominance as a means of project selection, in order to model biodiversity returns and risks. Using a Monte Carlo simulation we find that stochastic dominance may be a useful theoretical construct for project selections but it is unable to rank every case.
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