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17 results for “multifractality”
Shapefiles reporting the boundaries of glaciers analyzed by applying a multifractal approach
<p>We recently investigated the multifractal properties of the perimeters of the Lombardy glaciers in the Italian Alps. We characterized the area and perimeter distributions of the population of glaciers and we showed that the distribution of perimeters exhibits a marked peak, not present in the distribution of areas. We investigated the multifractal spectra of perimeters and we showed that their features are strongly correlated with the area of the glaciers.</p> <p>Here are reported the shapefiles of the boundaries of glaciers of the Lombardia region measured in 2003, 2007, and 2012 that were used for this work.</p>
Data for "Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model"
<p>The data files arranged here correspond to the data used in the paper: “Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model”, submitted to <em>Water</em>.</p> <p>The data organized as follows:</p> <ul> <li>Data_type_20150912_time_steps.mat: the rainfall data for 3 different products of the X-band radar (FIR filter, a=200, b=1.6; FIR filter, a=150, b=1.3; simple filter, a=150, b=1.3) for the event of 12-13 September 2015, over an area of 64 km x 64 km.</li> <li>Data_type_Event_time_steps.mat: X-band radar data (FIR filter, a=150, b=1.3) for the events of 16 September 2015 and 5-6 October 2015, over an area of 64 km x 64 km.</li> <li>Sub-catchment_name_Data_type_Event.txt: the rainfall series for each of 26 sub-catchments of the model, for 3 different types of rainfall data (C-band, X-band and rain gauges) for the events of 12-13 September 2015, 16 September 2015 and 5-6 October 2015.</li> <li>X-band_Pixels_Event.txt: the rainfall series for all 6 X-band radar pixels corresponding to the 6 rain gauges for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015).</li> <li>X-Band_Optim 20150916_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with X-band data for the 16 September 2015 event, with the implementation of the tool mimicking the regulation optimization.</li> <li>Data_type_Event_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with 3 different types of rainfall data (C-band, X-band and rain gauges) for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015), without the implementation of the tool mimicking the regulation optimization.</li> </ul> <p>The original C-band radar data remains property of Météo-France and was provided to the authors for this research study, without any possibility of data disclosure.</p> <p>The details on how the rainfall series were generated over each sub-catchment could be found in the paper.</p> <p>The authors greatly acknowledge partial financial supports of the Chair “Hydrology for resilient cities” endowed by Veolia, and of the Department of Science and Technology of the Brazilian Army. The authors are thankful to M Bernard Urban (Météo-France) for providing access to the C-band radar data and documentation in the framework of the INTERREG NWE RainGain project.</p>
Multifractality approach of a generalized Shannon index in financial time series
<p>Multifractality is a concept that extends locally the usual ideas of fractality in a system. Nevertheless, the multifractal approaches used lack a multifractal dimension tied to an entropy index like the Shannon index. This paper introduces a generalized Shannon index (GSI) and demonstrates its application in understanding system fluctuations. To this end, traditional multifractality approaches are explained. Then, using the temporal Theil scaling and the diffusive trajectory algorithm, the GSI and its partition function are defined. Next, the multifractal exponent of the GSI is derived from the partition function, establishing a connection between the temporal Theil scaling exponent and the generalized Hurst exponent. Finally, this relationship is verified in a fractional Brownian motion and applied to financial time series. In fact, this leads us to propose an approximation called local fractional Brownian motion approximation, where multifractal systems are viewed as a local superposition of distinct fractional Brownian motions with varying monofractal exponents. Also, we furnish an algorithm for identifying the optimal q-th moment of the probability distribution associated with an empirical time series to enhance the accuracy of generalized Hurst exponent estimation.</p>
Impact of prolonged chronic social isolation stress on behavior and multifractal complexity of metabolic rate in Octodon degus
<p>We investigated the effects of prolonged chronic social isolation stress on behavioral, cognitive, and physiological performance in the social, long-lived rodent <em>Octodon degus</em>. Degu pups were separated into two social stress treatments: control (CTRL) and chronically isolated (CI) individuals from post-natal and post-weaning until adulthood. We quantified anxiety-like behavior and cognitive performance with behavioral tests. Additionally, we measured their basal metabolic rate (BMR). Data include behavioral tests and physiological traits (body mass and Basal Metabolic Rate).</p>
Multifractality approach of a generalized Shannon index in financial time series
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Data for "Entanglement phases, localization and multifractality of monitored free fermions in two dimensions".
<p>This folder contains the numerical data to reproduce all figures in the paper "Entanglement phases, localization and multifractality of monitored free fermions in two dimensions". All data are in csv format and were read and plotted on Jupyter notebooks. </p> <p>Each subfigure has an own subfolder where data is stored. For figures with insets there is a separate subfolder for the main plot and the inset, except the cases where inset and main use the same data (e.g. Fig. 3b). All data is obtained directly from simulations as outlined in the main text except fit data (e.g. Fig. 5a) or data obtained from a scaling collapse (inset of Fig. 3b).</p>
Multifractal structure and Gutenberg–Richter parameter associated with volcanic emissions of high energy in Colima, Mexico (years 2013–2015)
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Comparable Investigation of One Fraction Radiotherapy and Multifraction Radiotherapy in Patients With Multiple Myeloma.
ClinicalTrials.gov study NCT02024815. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Multifractal evidence of nonlinear interactions stabilizing posture for phasmids in windy conditions: a reanalysis of insect postural-sway data
The present work is a reanalysis of prior work documenting postural sway in phasmids (i.e., "stick insects") [1]. The prior work pursued the possibility that postural sway was an evolutionary adaptation supporting motion camouflage to avoid the attention of predators. For instance, swaying along with leaves blown by the wind might reduce the likelihood of standing out to a predator. The present work addresses the alternative—but by no means conflicting and perhaps more explanatory—proposal that phasmid postural sway carries evidence of the tensegrity-like structures allowing postural stabilization under wind-like stimulation. Tensegrity structures are prestressed architectures embodying nonlinear interactions across scales of space and time that provide context-sensitive responses faster than neural tissue can support. Multifractal modeling of the postural-displacement series initially recorded in [1] offers a metric equally effective for quantifying complexity of phasmid postural sway under wind stimulation as for quantifying complexity of human postural sway [2-7]. Furthermore, multifractal modeling offers a means to demonstrate empirically the nonlinear interactions across space and time scales in body-wide coordination that tensegrity-based hypotheses predict. Specifically, multifractal modeling allows diagnosing the strength and direction of nonlinear interactions across time scale as the difference between multifractal estimates for the original postural-displacement series and for a sample of best-fitting linear models of the series. The reduction of postural sway directly following the application of wind stimulus appears as a significant decrease in the multifractal structure for original postural-displacement series as compared to best-fitting linear models of those series. This decrease indicates the capacity for nonlinear interactions across time scale to constrict variability, which is an aspect of nonlinear dynamics often overshadowed by the possibility that nonlinearity can produce more variability. This work offers the longer-range opportunity that multifractal modeling could provide a common language within which to coordinate behavioral sciences across a wide range of species.
Data from: Multifractal growth in periphyton communities
Periphyton is an aquatic community composed by algae, bacteria, fungi, and other microorganisms that can develop a complex architecture comparable to tropical forests. We analyzed the spatial pattern of a periphyton community along a succession developed in experimental tanks. Our aim was to identify regularities that may help us to explain the patchiness of this community. Therefore, we estimated the spatial pattern of periphyton biomass using a non-destructive image analysis technique to obtain a temporal series of the spatial distribution. These were analyzed using multifractal techniques. Multifractals are analogous to fractals but they look at the geometry of quantities instead of the geometry of pattern. To use these techniques the object of study must show scale invariance and then can be characterized by a spectra of fractal dimensions. Self-organization describes the evolution of complex structures that emerge spontaneously driven internally by variations of the system itself. The spatial distribution of biomass showed scale invariance at all stages of succession and as the periphyton developed in a homogeneous landscape, in a demonstration of self-organized behavior. Self-organization to a critical state (SOC) is presented in the complex systems literature as a general explanation for scale invariance in nature. SOC requires a mechanism where the history of past events in a place influence the actual dynamics, this was termed ecological memory. The scale invariance was found from the very beginning of the succession thus self-organized criticality is a very improbable explanation for the pattern because there would be not enough time for the build-up of ecological memory. Positive interactions between algae and bacteria, and the existence of different spatial scales of colonization and growth are the likely causes of this pattern. Our work is a demonstration of how large scale patterns emerge from local biotic interactions.
Data from: Multifractal growth in periphyton communities
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Data from: Bringing the nonlinearity of the movement system to gestural theories of language use: multifractal structure of spoken English supports the compensation for coarticulation in human speech perception
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Data from: Multifractal evidence of nonlinear interactions stabilizing posture for phasmids in windy conditions: a reanalysis of insect postural-sway data
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Data for ""Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data" by Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer, NPG 2020
<p>This data set corresponds to the python scripts and data that were used for the paper :<br> “Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data” by Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer which has been published in 2020 in Nonlin. Processes Geophys. (https://www.nonlinear-processes-in-geophysics.net/).</p> <p>Should this data be used, the above mentioned paper should be cited.</p>
Single Versus Multifraction Salvage Spine Stereotactic Radiosurgery for Previously Irradiated Spinal Metastases
ClinicalTrials.gov study NCT03028337. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Electrostimulation Therapies With Implementation of Multifractal Electromyographic Analysis
ClinicalTrials.gov study NCT04704778. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Decomposing the complexity of heart-rate variability by the multifractal–multiscale approach to detrended fluctuation analysis: an application to low-level spinal cord injury
<p>Raw data for reproducing (figure 2) and the main results of the paper: doi: 10.1088/1361-6579/ab2b4a</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.