6,552 research outputs found
Super-diffusion versus competitive advection: a simulation
Magnetic element tracking is often used to study the transport and diffusion
of the magnetic field on the solar photosphere. From the analysis of the
displacement spectrum of these tracers, it has been recently agreed that a
regime of super-diffusivity dominates the solar surface. Quite habitually this
result is discussed in the framework of fully developed turbulence. But the
debate whether the super-diffusivity is generated by a turbulent dispersion
process, by the advection due to the convective pattern, or by even another
process, is still open, as is the question about the amount of diffusivity at
the scales relevant to the local dynamo process. To understand how such
peculiar diffusion in the solar atmosphere takes places, we compared the
results from two different data-sets (ground-based and space-borne) and
developed a simulation of passive tracers advection by the deformation of a
Voronoi network. The displacement spectra of the magnetic elements obtained by
the data-sets are consistent in retrieving a super-diffusive regime for the
solar photosphere, but the simulation also shows a super-diffusive displacement
spectrum: its competitive advection process can reproduce the signature of
super-diffusion. Therefore, it is not necessary to hypothesize a totally
developed turbulence regime to explain the motion of the magnetic elements on
the solar surface
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used
in the machine learning and dynamical systems literature to represent complex
dynamical or sequential relationships between variables. More recently, as deep
learning models have become more common, RNNs have been used to forecast
increasingly complicated systems. Dynamical spatio-temporal processes represent
a class of complex systems that can potentially benefit from these types of
models. Although the RNN literature is expansive and highly developed,
uncertainty quantification is often ignored. Even when considered, the
uncertainty is generally quantified without the use of a rigorous framework,
such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a
more formal framework while maintaining the forecast accuracy that makes these
models appealing, by presenting a Bayesian RNN model for nonlinear
spatio-temporal forecasting. Additionally, we make simple modifications to the
basic RNN to help accommodate the unique nature of nonlinear spatio-temporal
data. The proposed model is applied to a Lorenz simulation and two real-world
nonlinear spatio-temporal forecasting applications
Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Perez-Jorge, S., Tobena, M., Prieto, R., Vandeperre, F., Calmettes, B., Lehodey, P., & Silva, M. A. Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean. Diversity and Distributions, 00, (2020): 1-16, doi:10.1111/ddi.13038.Aim
Understanding the environmental drivers of movement and habitat use of highly migratory marine species is crucial to implement appropriate management and conservation measures. However, this requires quantitative information on their spatial and temporal presence, which is limited in the high seas. Here, we aimed to gain insights of the essential habitats of three baleen whale species around the midâNorth Atlantic (NA) region, linking their largeâscale movements with information on oceanographic and biological processes.
Location
MidâNA Ocean.
Methods
We present the first study combining data from 31 satellite tracks of baleen whales (15, 10 and 6 from fin, blue and sei whales, respectively) from March to July (2008â2016) with data on remotely sensed oceanography and midâ and lower trophic level biomass derived from the spatial ecosystem and population dynamics model (SEAPODYM). A Bayesian switching stateâspace model was applied to obtain regular tracks and correct for location errors, and pseudoâabsences were created through simulated positions using a correlated random walk model. Based on the tracks and pseudoâabsences, we applied generalized additive mixed models (GAMMs) to determine the probability of occurrence and predict monthly distributions.
Results
This study provides the most detailed research on the spatioâtemporal distribution of baleen whales in the midâNA, showing how dynamic biophysical processes determine their habitat preference. Movement patterns were mainly influenced by the interaction of temperature and the lower trophic level biomass; however, this relationship differed substantially among species. Bestâfit models suggest that movements of whales migrating towards more productive areas in northern latitudes were constrained by depth and eddy kinetic energy.
Main conclusions
These novel insights highlight the importance of integrating telemetry data with spatially explicit prey models to understand which factors shape the movement patterns of highly migratory species across large geographical scales. In addition, our outcomes could contribute to inform management of anthropogenic threats to baleen whales in sparsely surveyed region.We are very grateful to ClĂĄudia Oliveira, Irma CascĂŁo, Maria JoĂŁo Cruz, Miriam Romagosa and many volunteers, skilled skippers, crew and spotters that participated in the tagging fieldwork. This work was supported by Fundação para a CiĂȘncia e Tecnologia (FCT), Azores 2020 Operational Programme and Fundo Regional da CiĂȘncia e Tecnologia (FRCT) through research projects FCTâExploratory project (IF/00943/2013/CP1199/CT0001), TRACE (PTDC/MAR/74071/2006) and MAPCET (M2.1.2/F/012/2011) coâfunded by FEDER, COMPETE, QREN, POPH, ESF, ERDF, Portuguese Ministry for Science and Education, and Proconvergencia Açores/EU Program. We also acknowledge funds provided by FCT to MARE, through the strategic project UID/MAR/04292/2013. SPJ was supported by a postdoctoral grant (REF.GREENUP/001â2016), MT by a DRCT doctoral grant (M3.1.a/F/028/2015), MAS by an FCTâInvestigator contract (IF/00943/2013), FV by an FCT Investigator contract (CEECIND/03469/2017) and RP by an FCT postdoctoral grant (SFRH/BPD/108007/2015). LMTL modelling work has been supported by the CMEMS Service Evolution GREENUP project, funded by Mercator Ocean. We are grateful to Elliott Hazen for offering guidance and advice, and to two anonymous referees whose comments greatly improved this work
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades
with several ground-breaking methods introduced. A particularly interesting --
and prevalent in several fields of study -- problem is that of inferring a
function defined over the nodes of a network. This work presents a versatile
kernel-based framework for tackling this inference problem that naturally
subsumes and generalizes the reconstruction approaches put forth recently by
the signal processing on graphs community. Both the static and the dynamic
settings are considered along with effective modeling approaches for addressing
real-world problems. The herein analytical discussion is complemented by a set
of numerical examples, which showcase the effectiveness of the presented
techniques, as well as their merits related to state-of-the-art methods.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018). This chapter surveys recent work on kernel-based inference
of functions over graphs including arXiv:1612.03615 and arXiv:1605.07174 and
arXiv:1711.0930
Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand
In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999â2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999â2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
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