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199 results for “Adaptive Systems”
Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification
<p>This project combines the emerging field of Domain Adaptation with Uncertainty Quantification, working towards applying machine learning to real scientific datasets with limited labelled data. For this project, simulated images of strong gravitational lenses are used as source and target dataset, and the Einstein radius θ E and its uncertainty are determined through regression.</p> <p>Applying machine learning in science domains such as astronomy is difficult. With models trained on simulated data being applied to real data, models frequently underperform - simulations cannot perfectlty capture the true complexity of real data. Enter domain adaptation (DA). The DA techniques used in this work use Maximum Mean Discrepancy (MMD) Loss to train a network to being embeddings of labelled "source" data gravitational lenses in line with unlabeled "target" gravitational lenses. With source and target datasets made similar, training on source datasets can be used with greater fidelity on target datasets.</p> <p>Scientific analysis requires an estimate of uncertainty on measurements. We adopt an approach known as mean-variance estimation, which seeks to estimate the variance and control regression by minimizing the beta negative log-likelihood loss. To our knowledge, this is the first time that domain adaptation and uncertainty quantification are being combined, especially for regression on an astrophysical dataset.</p>
Implementation of an adaptive bias-aware extended Kalman filter for sea-ice data assimilation in the HARMONIE-AROME numerical weather prediction system: numerical experiments
<p>This data set provides the post-processed output of the numerical experiments performed to assess the possible effects of applying sea ice data assimilation within the surface analysis procedure of the HARMONIE-AROME NWP system. Results of five numerical experiments are provided:</p> <ul> <li>HA-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>HA-EKF – sensitivity experiment with sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>3DVAR-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF – sensitivity experiment with sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF-TS – sensitivity experiment with sea ice data assimilation, and with 3DVAR for the upper-air analysis using coupled surface and atmospheric data assimilation procedures</li> </ul> <p>For the HA-REF and HA-EKF experiments a subset of the gridded model output is provided; for the 3DVAR-REF, 3DVAR-EKF and 3DVAR-EKF-TS experiments a subset of the gridded model output and model data extracted at the positions of the SYNOP and TEMP stations within the model domain are provided. Additionally, in-situ observations, covering the same time period as the 3DVAR-REF, 3DVAR-EKF, 3DVAR-EKF-TS experiments, are provided.</p>
MAVIS adaptive optics system matrices dataset
<p>MAVIS adaptive optics system matrices dataset.</p>
Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation
<p>Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7</p> <p><strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong></p> <p>Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup></p> <p><strong>Abstract </strong></p> <p>Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks. </p>
Adaptation in Microservice-based Systems: A Systematic Literature Review
<p>Data extraction from the paper: Adaptation in Microservice-based Systems: A Systematic Literature Review.</p> <p>The attached file contains the result of data extraction from the primary studies following the defined extraction criteria, where: </p> <ul> <li><strong>1</strong>, corresponds to the property option that was identified in the study,</li> <li><strong>NS</strong> denotes that the property was specified in the study, and</li> <li><strong>Ad.</strong> denotes an additional option to the main one identified for that particular property.</li> </ul>
Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables
<p>The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems". </p> <p>Short description of the tables:</p> <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>
Adaptive System Identification Model
<p>This is a test.</p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
Novel Optical Surface Image Guidance System for Beam-Gated Online Adaptive SBRT Delivery in Mobile Lower Lung and Upper Abdominal Malignancies
ClinicalTrials.gov study NCT05030454. IPD Sharing: YES. Countries: 1. Publications: 1.
Adaptive Biobehavioral Control (ABC) in a Closed-Loop System
ClinicalTrials.gov study NCT05610111. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Application of piezoelectric intelligent materials in pipa adaptive tuning system and its influence on performance stability
Open the record for dataset details and reuse information.
Data from: Ontogenetic adaptations in the visual systems of deep-sea crustaceans
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Compounding heterochrony shapes the salamander visual system across adaptive zones
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Population genomic consequences of life history and mating system adaptation to a geothermal soil mosaic in yellow monkeyflowers (common garden phenotype data)
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Sequential maturation of stimulus-specific adaptation in the mouse lemniscal auditory system
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Datasets of "Whole genome sequencing of European autochthonous and commercial pig breeds provides selection signatures of adaptation of genetic resources to different breeding and production systems"
<p>Results of the F<sub>ST</sub> and H<sub>P</sub> analyses.</p>
Figure 14 in REVIEW Going underwater: multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
Figure 14. Summary of the main structures related with apneustic respiratory system. A–C, Berosus decolor Knisch, 1924, light microscope photograph: A, habitus, first-instar larva, dorsal view; B; terminal spiracle, third-instar larva, dorsal view; C; detail of the abdominal spiracular trachea and tracheal gill, dorsal view. D, Berosus pallipes Brullé, 1841, abdominal spiracle, third-instar larva, dorsal view. E–H, Berosus sp., third-instar larva, SEM micrograph: E, spiracular chamber, ventral view; F; first abdominal segment bearing tracheal gill, dorsal view; G, detail of tracheal gill surface; H, abdominal spiracle. I, J, Hemiosus bruchi Knisch, 1924, third-instar larva, SEM micrograph: I, last abdominal segments, dorsal view; J, abdominal spiracle. K, Hemiosus multimaculatus (Jensen-Haarup, 1910), spiracular chamber, third-instar larva, ventral view.
Figure 15 in REVIEW Going underwater: multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
Figure 15. Phylogeny of the Hydrophiloidea with mapped evolution of tracheal system (A) and mouthparts (B, C). Two alternative ancestral state reconstructions of mouthparts, considering mouthparts of the Pelthydrus-group as: B, piercingsucking; C, chewing (only tribe Laccobiini shown). D, number of species of aquatic genera of Hydrophilidae with known larvae. Colors of branches/bars/pie-charts indicate functional morphology of mouthparts (red = piercing-sucking, blue = chewing, green = filter-feeding) and development of the tracheal system (grey = open; orange = closed).
Figure 12 in REVIEW Going underwater: multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
Figure 12. Schematic drawing of the piercing-sucking feeding mechanism: 1, sucking channel; 2, epistomal-mandibular coupling system; 3, flexible area.
Figure 13 in REVIEW Going underwater: multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
Figure 13. Summary of the main structures related with metapneustic respiratory system. A, Tropisternus latus (Brullé, 1837), spiracular chamber, first-instar larva, light microscope photograph, dorsal view. B, Helochares ventricosus Bruch, 1915, spiracular chamber, first-instar larva, light microscope photograph, dorsal view. C, Tropisternus latus (Brullé, 1837), spiracular chamber, first-instar larva, light microscope photograph, dorsal view. D, Helochares ventricosus Bruch, 1915, abdominal spiracle, first-instar larva, light microscope photograph, dorsal view. E–H, Tropisternus setiger Germar, 1824, SEM micrograph: E, spiracular chamber, third-instar larva, ventral view; F, detail of the terminal spiracle with dust filter, third-instar larva, ventral view; G, abdominal spiracle, first-instar larva, dorsal view; H, detail of the closed abdominal spiracles, first-instar larva, dorsal view. I, J, Oocyclus iguazu (Oliva 1996) third-instar larva, SEM micrograph: I, spiracular chamber, dorsal view; J, biforous abdominal spiracle, dorsal view. K, Laccobius kunashiricus Shatrovskiy, 1984, spiracular chamber, third-instar larva, SEM micrograph, dorsal view.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.