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84 results for “feedback control”
Dataset for "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos"
<p>Raw datasets used in the paper "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos". Use with the GitHub repository to reproduce results and figures from the referenced paper (https://github.com/clema12/CliffordBernoulli)</p>
Supplementary Material for "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control"
<h2>Supplementary material for the paper "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control" </h2><p>The paper is submitted to "International Conference on Dependable Systems and Networks, 2024". Author names withheld for double-blind reviewing.</p><ul><li>The file <strong>proofs_and_hyperparameters.pdf </strong>contains proofs of Theorem 1--2 and Corollary 1 in the paper. It also includes formulas for computing the belief state (Eq. 4) and for computing the curves in Fig. 6. It also includes a complete list of hyperparameters used for all experiments detailed in the paper.</li><li>The file <strong>ids_alerts_statistics.json</strong> contains the statistics used to produce Fig. 10 in the paper and to define the parameter Z for the experiments in section VIII.<ul><li>The JSON file contains a single object with the following keys: 'conditionals_counts', 'conditionals_kl_divergences', 'conditionals_probs', 'conditions', 'descr', 'emulation_name', 'id', 'initial_distributions_counts', 'initial_distributions_probs', 'initial_maxs', 'initial_means', 'initial_mins', 'initial_stds', 'maxs', 'means', 'metrics', 'mins', 'num_conditions', 'num_measurements', 'num_metrics', 'stds'. </li><li>The key "conditionals_counts" leads to another object with the following keys: 'A:CVE-2010-0426 exploit_D:Continue_M:[]', 'A:CVE-2015-3306 exploit_D:Continue_M:[]', 'A:CVE-2015-5602 exploit_D:Continue_M:[]', 'A:CVE-2016-10033 exploit_D:Continue_M:[]', 'A:Continue_D:Continue_M:[]', 'A:DVWA SQL Injection Exploit_D:Continue_M:[]', 'A:FTP dictionary attack for username=pw_D:Continue_M:[]', 'A:Ping Scan_D:Continue_M:[]', 'A:SSH dictionary attack for username=pw_D:Continue_M:[]', 'A:Sambacry Explolit_D:Continue_M:[]', 'A:ShellShock Explolit_D:Continue_M:[]', 'A:TCP SYN (Stealth) Scan_D:Continue_M:[]', 'A:Telnet dictionary attack for username=pw_D:Continue_M:[]', 'intrusion', 'no_intrusion'</li><li>The above keys correspond to different types of intrusions, see Table 6 in the paper.</li><li>Each of the keys listed above leads to a new object with 1551 keys which correspond to different types of metrics collected from the infrastructure. The metric used for produce Fig. 10 in the paper is called "alerts_weighted_by_priority". This key leads to another object where the keys correspond to the number of alerts weighted by priority and the values correspond to the measurements from the system.</li></ul></li><li>The file <strong>intrusion_traces.zip</strong> contains 6400 intrusion traces. Each trace contains a list of attacker actions and the corresponding measurements from the system. When unzipped, it is a directory with 64 files which take up 1500GB. Each file contains 100 traces in JSON format.</li><li>The file <strong>source_code_and_docker_files.zip </strong>contains the source code and the docker containers used for the experiments. It is a system we have developed for 3 years. It includes 225,000 lines of Python, 40,000 lines of JavaScript, 3000 lines of Dockerfiles, 2500 lines of Makefile, and 1800 lines of Bash. When unzipped one can find documentation about the source code in a file called "documentation.pdf" and in the README file.</li></ul>
Dataset for study: Adaptive Hip Exoskeleton Control using Heart Rate Feedback Reduces Oxygen Cost during Ecological Locomotion
<p>This record contains the dataset for a manuscript currently under preparation and submission. See the description PDF file for more details. The information here will be updated according to the progress in the peer review and publication procedure.</p>
Enhancing two-dimensional control via single-channel haptic feedback: A multi-dimensional encoding strategy
Open the record for dataset details and reuse information.
Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot
<p>This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors.</p> <p>The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.</p>
Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control
<p>Data, computational software, and supplemental movies generated in the study: "Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control."</p>
A cerebellar internal model calibrates a feedback controller involved in sensorimotor control
<p>This upload contains datasets from the manuscript by Markov et al., 2021 "A cerebellar internal model calibrates a feedback controller involved in sensorimotor control" (preprint <a href="https://www.biorxiv.org/content/10.1101/2020.02.12.945956v1">here</a>; the code used to acquire and process these data can be found <a href="http://doi.org/10.5281/zenodo.5044042">here</a>). The datasets are organized in the following zip folders:</p> <ul> <li><strong>behavioral_data.zip</strong> contains data from acute reaction and long-term adaptation experiments for wild-type control, treatment control, and Purkinje-cell-ablated zebrafish larvae</li> <li><strong>feedback_control_model.zip</strong> contains parameters of the model after fitting it to the data acquired during the acute reaction experiment.</li> <li><strong>PC_ablation_quantification.zip</strong> contains confocal images of the cerebella of zebrafish larvae before and after pharmaco-genetic ablation of Purkinje cells.</li> <li><strong>reference_brain_stacks.zip</strong> contains z-stacks used for anatomical registration of the functional imaging data</li> <li><strong>whole_brain_imaging_inetgrators.zip</strong> contains whole-brain light-sheet data acquired while zebrafish larvae were performing optomotor response to forward moving grating. This dataset was used to identify sensory integrators in the larval zebrafish brain</li> <li><strong>PC_imaging_long_term_adaptation.zip</strong> contains light-sheet recordings of Purkinje cell activity acquired during long-term adaptation to lagged visual feedback. This dataset was used to identify signatures of internal models in the Purkinje cell activity.</li> <li><strong>whole_brain_imaging_long_term_adaptation.zip</strong> contains whole-brain light-sheet recordings during long-term adaptation to lagged visual feedback. This dataset was used to study correlates of the long-term adaptation in the entire brain (such as change in sensory integration time constants).</li> </ul> <p>For any question about the data, please write at <a href="mailto:ruben.portugues@tum.de">ruben.portugues@tum.de</a> or <a href="mailto:ruben.portugues@tum.de">daniil.markov@charite.de</a>.</p>
Data from: Active-feedback quantum control of an integrated, low-frequency mechanical resonator
<p>Source data for Figures.</p>
Cardiovascular Rehabilitation Early After Stroke Using Feedback-controlled Robotics-assisted Treadmill Exercise
ClinicalTrials.gov study NCT01679600. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data from: Tuning of feedforward control enables stable muscle force-length dynamics after loss of autogenic proprioceptive feedback
Open the record for dataset details and reuse information.
Kinematics and EMG to show integration of proprioceptive and visual feedback during online control of reaching
<p>Visual and proprioceptive feedback both contribute to optimal perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model (Experiment 1). In a control experiment we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign (Experiment 2). These results highlight a complex process for multi-sensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb. The dataset contains hand kinematics and EMG data recorded during each experiment, and information of visual/mechanical disturbances that were applied during the experiments.</p>
Disrupting abnormal neuronal oscillations with adaptive delayed feedback control
<p>This dataset contains the spike data of the experiments performed for the paper:</p><p><strong>Domingos Leite de Castro, Miguel Aroso, A. Pedro Aguiar, David B. Grayden, Paulo Aguiar, "Disrupting abnormal neuronal oscillations with adaptive delayed feedback control", </strong>currently under review</p><p>You can access the pre-print at: https://doi.org/10.1101/2022.07.05.498735</p><p>The data was recorded with the 2100MEA-System from Multichannel Systems using their software Multichannel Experimenter. Each h5 file contains the spike detection results for all electrodes, the electrode labels, and stimulation times. The experiments were performed with microelectrode arrays that contained multiple independent culture wells. The h5 files contain data recorded from all the wells, even though the experiments are only performed in one well at a time. To access the data of a given experiment, choose the electrodes associated with the well identified in the name of the folder, or consult the table in DataTable.xlsx.</p><p>The experiments followed both the European legislation regarding the use of animals for scientific purposes and the protocols approved by the ethical committee of i3S. The Animal Facility of i3S follows the FELASA guidelines and recommendations concerning laboratory animal welfare, complies with the European Guidelines (Directive 2010/63/EU) transposed to Portuguese legislation by Decreto-Lei no 113/2013 and is licensed by the Portuguese official veterinary department (DGAV, Ref 004461). Embryonic (E18) rat hippocampal neurons were cultured on 6-well or 9-well chamber MEA (256-6well MEA200/30iR-ITO-rcr and 256-9wellMEA300/30iR-ITO, respectively) (Multichannel System MCS, Germany) with a density of 5 × 104 cells/well and 3 x 104 cells/well, respectively. Each well of the 6-well MEA has 42 TiN electrodes (array of 7 × 6), and the 9-well contain arrays of 26 TiN electrodes (6 x 5 without corners). The experiments were performed between 13 and 55 days in vitro. </p>
Normative feedback on performance in a closed motor skill task: A randomized controlled study
<p><span>Augmented feedback can alter motor performance. This study examines whether, in the short term, positive normative feedback influences the execution of a closed motor task differently compared to negative normative feedback and exact augmented feedback. Using a double-blind experimental design, 68 students (73.5% female, <span><em>M<sub>age</sub></em><sub> </sub></span><span></span><span>= </span><span>21.63</span>, <span><em>SD</em> = 2.88) </span>were randomized into three groups: G1 - Exact Augmented Feedback Group (</span><span>𝑛</span><span> = 21), G2 - Positive Normative Feedback Group (</span><span>𝑛</span><span> </span><span>= 24), and G3 - Negative Normative Feedback Group (</span><span>𝑛</span><span> </span><span>= 23). The dependent variable was the score obtained in a dart-throwing task. Results showed that participants receiving positive normative feedback achieved higher scores than those receiving negative normative feedback or exact augmented feedback. These differences persisted in retention and transfer tests conducted 24 hours after the practice phase but only between the positive NF group and the exact AF group. Meanwhile, the exact augmented feedback group performed similarly to the negative normative feedback group. These findings have practical implications for training and execution in motor tasks, potentially contributing to enhanced athletic performance.</span></p>
Data for Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks
<p>This folder contains PlioMIP2 ensemble data and CESM2 simulations used in Feng et al., Past terrestrial hydroclimate sensitivity controlled by Earth System Feedbacks (2022). Here is some useful information:</p> <p>1. Experiment IDs of CESM2 experiments are described in the Method section of the manuscript. The first dimension of variables in ensemble files reflects model IDs : "CCSM4", "CESM1", "CESM2", "COSMOS", "IPSL-CM6", "MIROC4m", "NorESM1-F", "HadCM3", "EC-Earth3.3", "IPSL-CM5", "IPSL-CM5A2", "HadGEM3", "GISS-E2-1G".</p> <p>2. The naming convention of variables in ensemble files follows CMIP6 convention. The naming convention of CESM2 variables follows the convention of the model.</p> <p>3. The script pe_budget_season_new.ncl produces moisture budget decomposition. The results are shown in Fig. 4 of the manuscript.</p>
Effectiveness of a multicomponent intervention consisting of education and feedback on reducing benzodiazepine prescriptions by general practitioners: BENZORED hybrid type I cluster randomized controlled trial.
<p>Complete dataset variables:</p> <p> </p> <p>GP_ID<br> Health_District<br> Health_District_name<br> PHC_ID<br> PHC_ID_name<br> Arm<br> DHD_Baseline<br> DHD_12m<br> PercentageBZD_baseline<br> PercentageBZD_12m<br> PercentageBZD_baseline_age65<br> PercentageBZD_12m_age65</p>
Data From: ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial
<p>Dataset Info</p> <p><strong>1) Immediate Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants’ iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in immediate test. </p> <p><strong>2) Delayed Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in delayed test. </p> <p><strong>3) Pre-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the responses of the participants to survey questions before the intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p> <p><strong>4) Post-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the evaluation of the participants to survey questions after intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p>
Koster et al. - Balance training in older adults enhances feedback control after perturbations - data & code
<p>Data & code for: </p> <p><strong><u>Balance training in older adults enhances feedback control after perturbations</u></strong></p> <p><em><u>R A J Koster, L Alizadehsaravi, W Muijres, S M Bruijn, N Dominici, J H van Dieen</u></em></p> <p> </p> <p>This folder contains two subfolders: <em>Code</em> & <em>Data</em>. The folder <em>Code</em> contains the MATLAB scripts used to analyse the data contained within the folder <em>Data</em>.</p> <p><strong>Scripts</strong>/</p> <p>· <strong>RK_Analysis_Kinematics.m</strong>: This is the main script used to analyse the kinematics. Running it will compute the kinematic parameters investigated in the paper and contrast them between conditions. Visualisations of these as well as the statistical results will be stored in the newly created folder <em>/Data/Processed/Figures/Kinematics/</em></p> <p>· <strong>RK_Analysis_Synergies.m</strong>: This is the main script used to analyse the EMG activity. Running it will compute the synergies from the EMGs and contrast them between conditions. Visualisations of these as well as the statistical results will be stored in the newly created folder <em>/Data/Processed/Figures/Synergies/</em></p> <p>· <strong>Subfunctions/</strong>: This folder contains all scripts used within the two main scripts. These scripts are subdivided into 3 folders (<em>General/, Kinematics analysis/, Synergy analysis/</em>) based on which part of the analysis they belong to.</p> <p>o <strong>General/spmi1d/</strong>: The external toolbox used to perform the statistics.</p> <p>o <strong>Kinematics analysis/=VU 3D model=/</strong>: the VU 3D model used to compute kinematic parameters from the trajectories of individual body segments.</p> <p><strong>Data/</strong></p> <p>· <strong>EMG/</strong>: Contains the raw EMG data for each participant & recording.</p> <p>· <strong>Excel sheets/</strong>: Contains participant & recording information sheets. This is used to determine on which leg the participant was standing.</p> <p>· <strong>PT/</strong>: Contains information about the rotating platform the participants were standing on for every recording. This is used to determine perturbation onset and direction.</p> <p>· <strong>Trajectory/</strong>: Contains the trajectories of the participant’s body segments during the recordings.</p> <p>· <strong>Processed/</strong>: (This folder is not there initially, but it is added by the 2 main codes) Will contain the kinematic parameters and synergy weights & activation patterns for every condition and participant after they are computed. This folder is also where the results from the analysis will be stored.</p> <p>o <strong>Figures/</strong>: For kinematics & synergies separately, will contain figures displaying the parameters and the statistical results. The ‘<em>Stats – X – Y.tiff</em>’ figure files display the ANOVA and post hoc test results. If statistical differences are found, their p-values are reported in the title of the individual plots (Note: for subthreshold results p-values are undefined in such analyses). Additional individual muscle analysis is included in the synergy analysis.</p>
Data for the figures of Ando et al. entitled "Hysteresis of the glacial Atlantic Meridional Overturning Circulation controlled by thermal feedbacks."
<p>Data for the figures of Ando et al. entitled "Hysteresis of the glacial Atlantic Meridional Overturning Circulation controlled by thermal feedbacks."</p>
Combination of Force Control Training and Mirror Visual Feedback Device on Stroke Patients on Brain Activation and Hand Function
ClinicalTrials.gov study NCT07150325. IPD Sharing: Not stated. Countries: 1. Publications: 89.
Randomized Controlled Trial of Multi-Source Feedback to Pediatric Residents
ClinicalTrials.gov study NCT00302783. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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