1,514 research outputs found
Describing synchronization and topological excitations in arrays of magnetic spin torque oscillators through the Kuramoto model
The collective dynamics in populations of magnetic spin torque oscillators
(STO) is an intensely studied topic in modern magnetism. Here, we show that
arrays of STO coupled via dipolar fields can be modeled using a variant of the
Kuramoto model, a well-known mathematical model in non-linear dynamics. By
investigating the collective dynamics in arrays of STO we find that the
synchronization in such systems is a finite size effect and show that the
critical coupling-for a complete synchronized state-scales with the number of
oscillators. Using realistic values of the dipolar coupling strength between
STO we show that this imposes an upper limit for the maximum number of
oscillators that can be synchronized. Further, we show that the lack of long
range order is associated with the formation of topological defects in the
phase field similar to the two-dimensional XY model of ferromagnetism. Our
results shed new light on the synchronization of STO, where controlling the
mutual synchronization of several oscillators is considered crucial for
applications.Comment: Accepted for publication in Scientific Reports. Corrected typo in
Eq.(9) from previous versio
Artificial intelligence for modeling real estate price using call detail records and hybrid machine learning approach
Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices
On the Inability of Markov Models to Capture Criticality in Human Mobility
We examine the non-Markovian nature of human mobility by exposing the
inability of Markov models to capture criticality in human mobility. In
particular, the assumed Markovian nature of mobility was used to establish a
theoretical upper bound on the predictability of human mobility (expressed as a
minimum error probability limit), based on temporally correlated entropy. Since
its inception, this bound has been widely used and empirically validated using
Markov chains. We show that recurrent-neural architectures can achieve
significantly higher predictability, surpassing this widely used upper bound.
In order to explain this anomaly, we shed light on several underlying
assumptions in previous research works that has resulted in this bias. By
evaluating the mobility predictability on real-world datasets, we show that
human mobility exhibits scale-invariant long-range correlations, bearing
similarity to a power-law decay. This is in contrast to the initial assumption
that human mobility follows an exponential decay. This assumption of
exponential decay coupled with Lempel-Ziv compression in computing Fano's
inequality has led to an inaccurate estimation of the predictability upper
bound. We show that this approach inflates the entropy, consequently lowering
the upper bound on human mobility predictability. We finally highlight that
this approach tends to overlook long-range correlations in human mobility. This
explains why recurrent-neural architectures that are designed to handle
long-range structural correlations surpass the previously computed upper bound
on mobility predictability
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