24,149 research outputs found
How Scale Affects Structure in Java Programs
Many internal software metrics and external quality attributes of Java
programs correlate strongly with program size. This knowledge has been used
pervasively in quantitative studies of software through practices such as
normalization on size metrics. This paper reports size-related super- and
sublinear effects that have not been known before. Findings obtained on a very
large collection of Java programs -- 30,911 projects hosted at Google Code as
of Summer 2011 -- unveils how certain characteristics of programs vary
disproportionately with program size, sometimes even non-monotonically. Many of
the specific parameters of nonlinear relations are reported. This result gives
further insights for the differences of "programming in the small" vs.
"programming in the large." The reported findings carry important consequences
for OO software metrics, and software research in general: metrics that have
been known to correlate with size can now be properly normalized so that all
the information that is left in them is size-independent.Comment: ACM Conference on Object-Oriented Programming, Systems, Languages and
Applications (OOPSLA), October 2015. (Preprint
Distinct dynamical behavior in Erd\H{o}s-R\'enyi networks, regular random networks, ring lattices, and all-to-all neuronal networks
Neuronal network dynamics depends on network structure. In this paper we
study how network topology underpins the emergence of different dynamical
behaviors in neuronal networks. In particular, we consider neuronal network
dynamics on Erd\H{o}s-R\'enyi (ER) networks, regular random (RR) networks, ring
lattices, and all-to-all networks. We solve analytically a neuronal network
model with stochastic binary-state neurons in all the network topologies,
except ring lattices. Given that apart from network structure, all four models
are equivalent, this allows us to understand the role of network structure in
neuronal network dynamics. Whilst ER and RR networks are characterized by
similar phase diagrams, we find strikingly different phase diagrams in the
all-to-all network. Neuronal network dynamics is not only different within
certain parameter ranges, but it also undergoes different bifurcations (with a
richer repertoire of bifurcations in ER and RR compared to all-to-all
networks). This suggests that local heterogeneity in the ratio between
excitation and inhibition plays a crucial role on emergent dynamics.
Furthermore, we also observe one subtle discrepancy between ER and RR networks,
namely ER networks undergo a neuronal activity jump at lower noise levels
compared to RR networks, presumably due to the degree heterogeneity in ER
networks that is absent in RR networks. Finally, a comparison between network
oscillations in RR networks and ring lattices shows the importance of
small-world properties in sustaining stable network oscillations.Comment: 9 pages, 4 figure
A cyclic time-dependent Markov process to model daily patterns in wind turbine power production
Wind energy is becoming a top contributor to the renewable energy mix, which
raises potential reliability issues for the grid due to the fluctuating nature
of its source. To achieve adequate reserve commitment and to promote market
participation, it is necessary to provide models that can capture daily
patterns in wind power production. This paper presents a cyclic inhomogeneous
Markov process, which is based on a three-dimensional state-space (wind power,
speed and direction). Each time-dependent transition probability is expressed
as a Bernstein polynomial. The model parameters are estimated by solving a
constrained optimization problem: The objective function combines two maximum
likelihood estimators, one to ensure that the Markov process long-term behavior
reproduces the data accurately and another to capture daily fluctuations. A
convex formulation for the overall optimization problem is presented and its
applicability demonstrated through the analysis of a case-study. The proposed
model is capable of reproducing the diurnal patterns of a three-year dataset
collected from a wind turbine located in a mountainous region in Portugal. In
addition, it is shown how to compute persistence statistics directly from the
Markov process transition matrices. Based on the case-study, the power
production persistence through the daily cycle is analysed and discussed
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