32,155 research outputs found
Enumeration of self avoiding trails on a square lattice using a transfer matrix technique
We describe a new algebraic technique, utilising transfer matrices, for
enumerating self-avoiding lattice trails on the square lattice. We have
enumerated trails to 31 steps, and find increased evidence that trails are in
the self-avoiding walk universality class. Assuming that trails behave like , we find and .Comment: To be published in J. Phys. A:Math Gen. Pages: 16 Format: RevTe
Learning Points and Routes to Recommend Trajectories
The problem of recommending tours to travellers is an important and broadly
studied area. Suggested solutions include various approaches of
points-of-interest (POI) recommendation and route planning. We consider the
task of recommending a sequence of POIs, that simultaneously uses information
about POIs and routes. Our approach unifies the treatment of various sources of
information by representing them as features in machine learning algorithms,
enabling us to learn from past behaviour. Information about POIs are used to
learn a POI ranking model that accounts for the start and end points of tours.
Data about previous trajectories are used for learning transition patterns
between POIs that enable us to recommend probable routes. In addition, a
probabilistic model is proposed to combine the results of POI ranking and the
POI to POI transitions. We propose a new F score on pairs of POIs that
capture the order of visits. Empirical results show that our approach improves
on recent methods, and demonstrate that combining points and routes enables
better trajectory recommendations
An Evolutionary Algorithm to Generate Real Urban Traffic Flows
In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle ows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech. This research has been partially funded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava and Universidad de Málaga UMA/FEDER FC14-TIC36, programa de fortalecimiento de las capacidades de I+D+i en las universidades 2014-2015, de la ConsejerÃa de EconomÃa, Innovación, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER). Also, partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). The authors would like to thank the FEDER of European Union for financial support via project Movilidad Inteligente: Wi-Fi, Rutas y Contaminación (maxCT) of the "Programa Operativo FEDER de AndalucÃa 2014-2020. We also thank all Agency of Public Works of Andalusia Regional Government staff and researchers for their dedication and professionalism. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports
An Innovative Integration Methodology of Independent Data Sources to Improve the Quality of Freight Transport Surveys
AbstractPast experiences show that data of the official Austrian freight transport statistics are often underestimated. Therefore, a methodology was developed, merging existing independent road freight transport data to a consistent and valid road freight matrix. The methodology comprises four steps, using data of the Austrian and European freight transport statistics, data of roadside interviews of truck drivers, and data of counting stations and toll gantries. The methodology was applied to data from the year 2009. Results show the reliability and plausibility of the methodology, indicated by a high correlation with high quality roadside traffic counts
Bootstrap testing for cross-correlation under low firing activity
A new cross-correlation synchrony index for neural activity is proposed. The
index is based on the integration of the kernel estimation of the
cross-correlation function. It is used to test for the dynamic synchronization
levels of spontaneous neural activity under two induced brain states:
sleep-like and awake-like. Two bootstrap resampling plans are proposed to
approximate the distribution of the test statistics. The results of the first
bootstrap method indicate that it is useful to discern significant differences
in the synchronization dynamics of brain states characterized by a neural
activity with low firing rate. The second bootstrap method is useful to unveil
subtle differences in the synchronization levels of the awake-like state,
depending on the activation pathway.Comment: 22 pages, 7 figure
- …