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29 results for “Mobility Prediction”
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Explaining human mobility predictions through a pattern matching algorithm
<p>The name of the file indicate information:<br> {type of sequence}_{type of measure}_{sequence properites}_{additional information}.csv</p> <p>{type of sequence} - 'synth' for synthetic or 'london' for real mobility data from London, UK.<br> {type of measure} - 'r2' for R-squared measure or 'corr' for Spearman's correlation<br> {sequence properties} - for synthetic data there are three types of sequences, described in the research article (random, markovian, nonstationary). For real mobility data this part includes information about data processing parameters: (...)_london_{type of mobility sequence}_{DBSCAN epsilon value}_{DBSCAN min_pts value}. {type of mobility sequence} is 'seq' for next-place sequences and '30min' or '1H' for the next time-bin sequences and indicate the size of the time-bin.<br> Files with 'predictability' at the end of the file contain R-squared and Spearman's correlation of measures calculated in relation to the predictability measure.</p> <p>R2 files include values of R-squared for all types of modelled regression functions.<br> 'line' indicates {y = ax + b} for single variable and {y = ax + by + c} for two variables.<br> 'expo' indicates {y = a*x^b + c} for single variable and {y = a*x^b + c*y^d + e} for two variables<br> 'log' indicates {y = a*log(x*b) + c} for single variable and {y = a * x + c * log(y) + e + d*x * log(y)} for two variables.<br> 'logf' indicates {y = a*log(x) + c * log(y) + e + b*log(x) * log(y)} for two variables</p>
Predicting harvest impact and establishment success when translocating highly mobile and endangered species
<p>Harvesting individuals for translocations can negatively impact source populations, a critical challenge for species reduced to small populations. Consequently, translocation cohorts often remain small, reducing the establishment probability at the destination. Balancing the potential benefits and risks of such translocations is further complicated by philopatry and natural metapopulation dynamics if the target species is highly mobile. These challenges highlight the importance of translocation feasibility assessments, but such assessments often remain qualitative to date. The critically endangered Kuaka (Whenua Hou Diving Petrel; Pelecanoides whenuahouensis) is a philopatric, highly mobile seabird that could benefit from conservation translocations, but only one small population remains. Through expert elicitations with a user-friendly Shiny app, we developed a novel metapopulation extension to an integrated population model fitted to long-term data, allowing us to simultaneously project harvest impact on the source and establishment of destination populations under alternative translocation scenarios, while accounting for philopatry and metapopulation dynamics. Establishment of a destination population without excessive impact on the source was possible, but subject to uncertainty about philopatry and metapopulation dynamics. Accounting for juveniles returning to the source post-translocations reduced impact on the source, but also decreased establishment at the destination. Natural movements of adults and juveniles between source and destination populations were predicted to modulate effects of different harvest intensities. Synthesis and application: Using state-of-the art integrated population models and expert elicitations, we illustrate how translocation feasibility can be evaluated transparently and quantitatively, even when targeting endangered, philopatric, and highly mobile species. Our approach is a considerable improvement on current qualitative feasibility assessments. However, we also illustrate that, ultimately, the favoured translocation strategy depends on balancing biological and other fundamental objectives inherent to translocations. Therefore, the ideal strategy cannot be determined solely mathematically, and feasibility assessments should incorporate explicit value statements. Our methodology is applicable to any future translocation scenario.</p>
Predicting mobility and research performance of the faculty members in the economics departments at Turkish public universities
<p>The data used for the regression analysis is in Table 5 on page 17 of the paper. </p>
Predicting sex bias in mobility from functional traits in flying insects
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Predicting harvest impact and establishment success when translocating highly mobile and endangered species
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Prediction and realisation of high mobility and degenerate p-type conductivity in CaCuP thin films Dataset
<p>Experimental and computational datasets for this publication, including README files. To unzip, in the command line paste the following command:</p> <blockquote> <p>tar -xzvf cacup_data_repository_v2.tar.gz</p> </blockquote> <p>And repeat the command:</p> <blockquote> <p>tar -xzvf <file.tar.gz></p> </blockquote> <p>for each necessary tar file.</p> <p>For any issues with accessibility, please email joe.willis.15@ucl.ac.uk.</p>
Distributed predictive QoS in presence of network- and mobility-related drifts
<p>The datasets represent a dynamic environment, where several client-vehicles are moving in an urban area. Each client runs a streaming cloud service constantly receiving data packets. Network simulation is performed using Simu5G, a library that emulates a 5G cellular environment in OMNeT++. The simulator's radio parameters are set according to the Macro-cell model proposed by International Telecommunication Union. The map comprises of an urban 600x600 square meters area located in a suburb of a European capital. Inside this area four 5G base-stations (gNodeBs) have been installed by the national network operator, enabling four 5G cells. This area, divided into several blocks by the actual road network is integrated in our simulation by an OpenStreetMap (OSM) instance. The total number of included vehicles is set to 25, according to vehicle density statistics in the corresponding country. The road network's traffic is simulated by SUMO that creates a digitized version of the (real-world) OSM map and produces the route files for the vehicles. Route files are loaded in the Simu5G simulator, where a network-vehicular mobility co-simulation takes place. For each vehicle's route we assume SUMO's default parameters for urban environment: exponential speed model (with maximum speed restriction as defined by the OSM traffic rules) and the probability matrix at intersections for {lane keeping, turn left and right} as {0.5, 0.25 and 0.25}, respectively. The following information is collected for each vehicle using OMNeT++'s monitoring service: timestamp, channel quality indicator, packet delay, measured signal to noise ratio (SNR), client position (x,y,z), client velocity (x,y,z), received SNR, radio link control throughput, serving cell, client throughput. These features are sampled at 1 Hz and comprise the values of our synthetic time-series QoS dataset. </p> <p>We have created two drift datasets that correspond to complementary cases of major long-term changes in the considered environment: 1) a network infrastructure-driven scenario (Sc1) and 2) a human behavior-driven scenario (Sc2). In Sc1 we assume that two out of four gNodeBs are switched off under a cost-reduction on/off policy or an infrastructure-share strategy (adopted by MNOs) that would imply such changes. For Sc2 we modify the users' mobility pattern; we assume that a "hotspot" e.g., a metro station is created in the lower-right edge of the map resulting in a traffic increase to that area. This is achieved by increasing the probabilities of the routes leading to the "hotspot" in SUMO's route planning. All generated datasets have a total duration of 20 hrs (simulation time) and the respective drift event is introduced at t=10 hrs. </p>
Datasets and Trained Models for "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"
<p>Datasets and Trained models for ICSE 2020 "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"</p>
Multi-center Validation of a Hypothermia Prediction Mobile Application (APP)
ClinicalTrials.gov study NCT05333120. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Efficacy of the Mobile Application for Prediction and Prevention of Mood Episode Recurrence Based on Machine Learning
ClinicalTrials.gov study NCT05400785. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of a Sarcopenia Prediction Model and Development and Effectiveness of a Nursing Guidance Mobile App
ClinicalTrials.gov study NCT05363033. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Pressure Injury Prediction and Education Model Using a Mobile Application System
ClinicalTrials.gov study NCT05357391. IPD Sharing: NO. Countries: 1. Publications: 6.
Consumer mobility predicts impacts of herbivory across a wave stress gradient
<p>Environmental stress impedes predation and herbivory by limiting the ability of animals to search for and consume prey. We tested the contingency of this relationship on consumer traits, and specifically hypothesized that herbivore mobility relative to the return time of limiting environmental stress would predict consumer effects. We examined how wave-induced water motion affects marine communities via herbivory by highly mobile (fish) versus slow moving (pencil urchin) consumers at two wave-sheltered and two wave-exposed rocky subtidal locations in the Galapagos Islands. The exposed locations experienced 99th percentile flow speeds that were 2-5 times greater than sheltered locations, with mean flow speeds >33 cm/s vs <16 cm/s, 2-7 times higher standing macroalgal cover and 2-3 times lower cover of crustose coralline algae than the sheltered locations. As predicted by the Environmental Stress Hypothesis (ESH), there was a negative relationship between mean flow speed and urchin abundance and herbivory rates on Ulva spp. algal feeding assays. In contrast, the biomass of surgeonfishes (Acanthuridae) and parrotfishes (Labridae- Scarinae) was positively correlated with mean flow speed. Ulva assays were consumed at equal rates by fish at exposed and sheltered locations, indicating continued herbivory even when flow speeds surpassed maximum reported swimming speeds at a rate of 1-2 times per minute. Modeled variation in fish species richness revealed minimal effects of diversity on herbivory rates at flow speeds <40 cm/s, when all species were capable of foraging, and above 120 cm/s, when no species could forage, while increasing diversity maximized herbivory rates at flow speeds of 40 – 120 cm/s. Two-month herbivore exclusion experiments during warm and cool seasons revealed that macroalgal biomass was positively correlated with flow speed. Fish limited macroalgal development by 65-91% at one exposed location but not the second, and by 70% at the two sheltered locations. In contrast, pencil urchins did not affect algal communities at either exposed location, but reduced macroalgae by 87% relative to controls at both sheltered locations. We propose an extension of the ESH that is contingent upon mobility to explain species-specific changes in feeding rates and consumer effects on benthic communities across environmental gradients.</p>
Data from: Prediction limits of mobile phone activity modelling
Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.
AMD3100 (Plerixafor) in Multiple Myeloma (MM) or Non-Hodgkin's Lymphoma (NHL) Patients Predicted to be Unable to Mobilize With G-CSF Alone
ClinicalTrials.gov study NCT00395967. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Prediction limits of mobile phone activity modelling
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Consumer mobility predicts impacts of herbivory across a wave stress gradient
Open the record for dataset details and reuse information.
EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics
<h1>EchoGPT</h1> <p>This folder contains the supplementary data and code for the submission "EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics" to the NeurIPS 2024 conference.</p> <h2>Dependencies</h2> <p>Matlab 2024a or higher is needed with the following toolboxes:</p> <ul> <li>Image Processing Toolbox</li> <li>Parallel Computing Toolbox</li> <li>Deep Learning Toolbox</li> <li>Signal Processing Toolbox</li> <li>System Identification Toolbox</li> </ul> <h2>Data</h2> <h3>Simulation</h3> <p>This folder contains the saved sonar images (energyscapes) as well as the motion data for each frame from a particular simulation run (LongRun1). These are saved into batches and saved as compressed .mat files within the <em>\DataCalculated\SimulationData\LongRun1\Raw</em> folder.</p> <h3>Trained model</h3> <p>The model used in the experiments of this submission is saved in <em>\DataCalculated\Networks</em> as a Matlab dlnetwork object. There is a trained (with weights) and an untrained version available.</p> <h2>Code</h2> <h3>Data pre-processing</h3> <p>To extract the data from the batch .mat files into seperate frames a script <strong>preprocessEchoPT.m</strong> is available in the main folder. This will save these individual frames once again to individual .mat files into a folder <em>\DataCalculated\ESSequences</em> by default.</p> <h3>Training</h3> <p>To train the model a Matlab script <strong>trainEchoPT.m</strong> is provided.</p> <h3>Evaluation</h3> <p>To generate the figures of the submission as well as additional outputs such as GIFs three different evaluation scripts are available:</p> <ul> <li><strong>evaluateEchoPT.m</strong>: Evaluate the trained model on the dataset.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_ARLoop.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow with autoregressive prediction.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_NoAR.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow without autoregressive prediction.</li> </ul> <h3>Source</h3> <p>Within the folder <em>\Source</em> all additional Matlab functions and classes can be found.</p> <h2>Open-Source libraries included in this project</h2> <ul> <li>Progress bar by HyunGwang Cho <a href="https://www.mathworks.com/matlabcentral/fileexchange/121363-progress-bar-cli-gui-parfor?s_tid=srchtitle">(link)</a></li> </ul>
Clinical Validation of a Mobility Monitor to Measure and Predict Health Outcomes
ClinicalTrials.gov study NCT04911231. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.