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1,855 results for “Autonomous”
Data from: Recurrent myocardial infarction: mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control
The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as 'free-floating' in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease.
Data from: Optimized flocking of autonomous drones in confined environments
We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
FIGURE 1 in A new species of Cyrtodactylus (Reptilia: Squamata: Geckkonidae) from Xizang Autonomous Region, China
FIGURE 1. Photo of holotype, Cyrtodactylus zhaoermii, showing color and pattern.
FIGURE 5 in Two new species of freshwater crabs of the genus Heterochelamon Türkay & Dai, 1997 (Crustacea: Decapoda: Brachyura: Potamidae) from Guangxi Zhuang Autonomous Region, southern China
FIGURE 5. Holotype of Heterochelamon castanea n. sp.: a, thorax and abdomen; b, major chela.
FIGURE 1 in Fauna of Cladocera and Copepoda from Xinjiang Uyghur Autonomous Region (China)
FIGURE 1. Location of the studied water bodies from Xinjiang Uygur Autonomous Region.
Supplementary data: Single Cardiac Ventricular Myosins are Autonomous Motors
<p>The files in this record contain partial data for the paper ‘Single Cardiac Ventricular Myosins are Autonomous Motors’. It includes one movie for unloaded Qdot data of WT, A57G and E143K, one movie for data at 3ug/ml of alpha-actinin, and related files.</p>
Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces
<p><strong>General Description. </strong>TThis dataset comprises recordings from four BMI (Brain-Machine Interface) experiments conducted on two adult macaques. Three of the experiments involved joystick-controlled tasks, while the fourth was a center-out reaching task. In the center-out task, the macaque was trained to maneuver a joystick-controlled cursor from a central position on a computer screen to one of eight square-shaped target locations. The macaques were able to use a wireless integrated system to control a robotic platform (on which they were seated) enabling independent mobility driven by neuronal activity in their motor cortices. Neural activity was recorded from populations of single neurons via multiple electrode arrays implanted in the arm region of the primary motor cortex. A general overview is provided below:</p> <ol> <li>A titanium head post (Crist Instruments, MD, USA) was surgically affixed before implanting the microelectrode arrays. In NHP-A, four microelectrode arrays with 16 electrodes each were implanted, while NHP-B was implanted with one array containing 100 electrodes in the hand/arm region of the left primary motor cortex.</li> <li>Spike signals were recorded using an in-house 100-channel wireless neural recording system, sampled at 13 kHz. The wide-band signals were band-pass filtered between 300 and 3000 Hz to eliminate low-frequency components. Spike detection thresholds were determined using the formula: Thr = 5σ, where σ = median(|x| / 0.6745), <em>x</em> is the filtered signal, and <em>σ</em> estimates the standard deviation of background noise.</li> </ol> <p>In Experiments 1, 2, and 3, the behavioral task involved controlling the motion of a robotic wheelchair using a three-directional, spring-loaded joystick. These tasks included: a) turning 90° right, b) moving forward by 2 meters, c) turning 90° left, and d) remaining stationary for 5 seconds (stop task). The success rate varied across experiments. Experiment 4 also involved joystick control, but followed a classical center-out reaching paradigm. </p> <p><strong>Dataset Description. </strong>The dataset is organized into folders (labeled as experiment 1, 2, 3, and 4) containing data from both NHP-A and NHP-B. Each folder contains data from separate dates labeled as YYMMDD (at the end of the filename). For experiment 1, data from the following dates are present: 15-10-08, 15-10-12, 15-10-19, 15-10-26, 15-11-02, 15-11-16, 15-11-23, and 15-12-10. For experiment 2, following dates are: 18-02-20, 18-03-06, 18-03-08, 18-03-20, 18-03-26, 18-04-13, 18-04-16, 18-04-23. For experiment 3: 14-08-14, 14-08-18, 14-08-20, 15-10-14. For experiment 4: 18-12-03, 18-12-13, 19-01-07, 19-02-20. File naming conventions across all experiments are as follows</p> <ol> <li>targTest: This corresponds to the direction of the joystick recorded for each trial. (decoded using the decoder)</li> <li>targTrain: Ground truth label, corresponding to the actual direction of the joystick.</li> <li>testSet: Number of spike counts from each channel (used for testing corresponding to all the sessions)</li> <li>trainSet: Number of spike counts from each channel (used for calibration, mostly) </li> </ol> <p><strong>Additional Information.</strong> This dataset is a simplified and curated version designed to reproduce the results presented in the associated paper. Note that Experiments 1 and 3 have partial datasets already publicly available at: <a href="https://osf.io/dce96/" target="_new" rel="noopener">https://osf.io/dce96/</a>. However, those versions are raw and can be processed using the variable descriptions below to extract spike counts with a specified bin width. Each file includes the following fields:</p> <ol> <li> joystick_adfreq: The frequency of operation of the joystick.</li> <li>X_Voltage: The voltage reading corresponding to the x-coordinate (while joystick operation).</li> <li>Y_Voltage: The voltage reading corresponding to the y-coordinate (while joystick operation).</li> <li>Spike_data(Channel Number): The Channel Number corresponding to which the neuronal data is recorded.</li> <li>Spike_data(Cluster): Descripting the cluster on which the channels are placed.</li> <li>Spike_data(Spike Times): The timestamp corresponding to the detection of a spike.</li> <li>Spike_data(Spike Number): The total number of spikes calculated for a particular trial from a particular channel.</li> <li>Spike_data(Mean Spike Waveform): The mean neuronal data (for that trial from a particular channel) describing a spike.</li> <li>Spike_data(Spike Amplitude): The mean spike amplitude of that particular channel.</li> <li>IMETrainingData(SentSignals): The truth labels corresponding to a particular trial.</li> <li>IMETrainingData(Timestamps): Time stamps corresponding to each sent signal (data).</li> <li>IMETrainingData(ReasonFail): String data; Reason if the trial failed.</li> <li>IMETrainingData(TrialOutcomes): Trial results corresponding to successful or unsuccessful!</li> <li>IMETrainingData(StartTime): corresponding to the beginning of each trial.</li> <li>IMETrainingData(EndTime): corresponding to the end of each trial.</li> </ol> <p><strong>Possible use cases. </strong>This dataset is well-suited for designing, training, and evaluating iBMI decoders. It provides a valuable resource for researchers aiming to model sensorimotor cortical spiking, benchmark iBMI decoders under consistent conditions, or explore neuromorphic and reinforcement learning-based approaches to decoder design.</p> <p><strong>Contact Information. </strong>We would be delighted to hear from you if you find this dataset useful—especially if it contributes to a publication. Contact: A. Basu <arinbasu@cityu.edu.hk>; A. Ghosh <aghosh14@illinois.edu>.</p> <p><strong>Citation. </strong>A. Ghosh, S. Shaikh, B. Zhou, P. S. V. Sun, C. Libedinsky, R. So, A. Basu, "Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces," Neuroelectronics 2025(2):0006, <a href="https://doi.org/10.55092/neuroelectronics20250006">https://doi.org/10.55092/neuroelectronics20250006</a></p>
Fig. 2 in A Diminutive New Speciesof DicronocephalusHope (Coleoptera: Scarabaeidae: Cetoniinae) from Xizang Zizhiqu (Tibet Autonomous Region), China, with a Distributional Analysis of the Genus
Fig. 2. Parameres of Dicronocephalus dabryi (left) and Dicronocephalus diminuata (right).
Autonomous Execution of Highly Reactive Chemical Transformations in the Schlenkputer
Open the record for dataset details and reuse information.
Supported data for manuscript "Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot"
<p>The repository data corresponds partially to the manuscript titled <strong>"Can LLM-Augmented Autonomous Agents Cooperate? An Evaluation of Their Cooperative Capabilities through Melting Pot,"</strong> submitted to <em>IEEE Transactions on Artificial Intelligence</em>. The dataset comprises experiments conducted with <strong>Large Language Model-Augmented Autonomous Agents (LAAs)</strong>, as implemented in the ["Cooperative Agents" repository](https://github.com/Cooperative-IA/CooperativeGPT/tree/main), using substrates from the Melting Pot framework.</p> <h3>Dataset Scope</h3> <p>This dataset is divided into two main experiment categories:</p> <ol> <li> <p><strong>Personality__experiments</strong>:</p> <ul> <li>These focus on a single scenario (<strong>Commons Harvest</strong>) to assess various agent personalities and their cooperative dynamics.</li> </ul> </li> <li> <p><strong>Comparison_baselines__experiments</strong>:</p> <ul> <li>These experiments include three distinct scenarios designed by Melting Pot: <ul> <li><strong>Commons Harvest Open</strong></li> <li><strong>Externally Mushrooms</strong></li> <li><strong>Coins</strong></li> </ul> </li> </ul> </li> </ol> <p>These scenarios evaluate different cooperative and competitive behaviors among agents and are used to compare <strong>decision-making architectures</strong> of LAAs against reinforcement learning (RL) baselines. Unlike the <strong>Personality__experiments</strong>, these comparisons do not involve bots but exclusively analyze RL and LAA architectures.</p> <h3>Scenarios and Metrics</h3> <p>The metrics and indicators extracted from the experiments depend on the scenario being evaluated:</p> <ol> <li> <p><strong>Commons Harvest Open</strong>:</p> <ul> <li>Focus: <strong>Resource consumption</strong> and <strong>environmental impact</strong>.</li> <li>Metrics include: <ul> <li>Number of apples consumed.</li> <li>Devastation of trees (i.e., depletion of resources).</li> </ul> </li> </ul> </li> <li> <p><strong>Externally Mushrooms</strong>:</p> <ul> <li>Focus: <strong>Self-interest vs. collective benefit</strong>.</li> <li>Agents consume mushrooms with different outcomes: <ul> <li>Mushrooms that benefit the individual.</li> <li>Mushrooms that benefit everyone.</li> <li>Mushrooms that benefit only others.</li> <li>Mushrooms that benefit the individual but penalize others.</li> </ul> </li> <li>Metrics evaluate trade-offs between individual gain and collective welfare.</li> </ul> </li> <li> <p><strong>Coins</strong>:</p> <ul> <li>Focus: <strong>Reciprocity and fairness</strong>.</li> <li>Agents collect coins with two options: <ul> <li>Collect their own color coin for a reward.</li> <li>Collect a different color coin, which grants a reward to the agent but penalizes the other.</li> </ul> </li> <li>Metrics include reciprocity rates and the balance of mutual benefits.</li> </ul> </li> </ol> <h3>Objectives of Comparison Experiments</h3> <p>The <strong>Comparison_baselines__experiments</strong> aim to:</p> <ol> <li>Assess how LAAs compare to RL baselines in cooperative and competitive tasks across diverse scenarios.</li> <li>Compare <strong>decision-making architectures</strong> within LAAs, including chain-of-thought and generative approaches.</li> </ol> <p>These experiments help evaluate the robustness of LAAs in scenarios with varying complexity and social dilemmas, providing insights into their potential applications in real-world cooperative systems.</p> <h3>Simulation Details (Applicable to All Experiments)</h3> <p>In each simulation:</p> <ol> <li> <p><strong>Participants</strong>:</p> <ul> <li>Experiments involve predefined numbers of LAAs or RL agents.</li> <li>No bots are included in <strong>Comparison_baselines__experiments</strong>.</li> </ul> </li> <li> <p><strong>Action Dynamics</strong>:</p> <ul> <li>Each agent performs high-level actions sequentially.</li> <li>Simulations conclude either after reaching a <strong>preset maximum number of rounds</strong> (typically 100) or prematurely if the scenario's resources are fully depleted.</li> </ul> </li> <li> <p><strong>Metrics and Indicators</strong>:</p> <ul> <li>Extracted metrics depend on the scenario and include measures of individual performance, collective outcomes, and agent reciprocity.</li> </ul> </li> </ol> <p>This repository enables reproducibility and serves as a benchmark for advancing research into cooperative and competitive behaviors in LLM-based agents.</p>
Replication data of Autonomous flow routing for near real-time quality of service assurance
<p>The dataset contains replication data of the journal paper titled "Autonomous Flow Routing for Near Real-Time Quality of Service Assurance", published in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2023.3339201.</p>
Figure 1 from: Fedorova LI, Kaygorodova IA (2022) First data on the Hirudinea fauna of lotic ecosystems of the Khanty-Mansi Autonomous Area (Russia). ZooKeys 1082: 73-85. https://doi.org/10.3897/zookeys.1082.71859
Figure 1 Schematic map of geographic location of the Khanty-Mansi Autonomous Area and studied lotic systems. River basins: I = Severnaya Sosva, II = Konda-Irtysh, III = Ob, and IV = Bolshoi Yugan.
Action Planning Makes Physical Activity More Automatic, Only If it Is Autonomously Regulated: A Moderated Mediation Analysis
<p>Dataset used for analysis. </p>
FIGURE 12 in A new species of the genus Pseudohampsonella Solovyev & Saldaitis (Lepidoptera: Limacodidae) from Xizang Autonomous Region, China
FIGURE 12 Molecular relationships of the Pseudohampsonella species based on COI gene.
Epigenetic regulation of autonomous pathway mediates polymorphism of flowering time in Arabidopsis
<p><span>Plant flowering time is affected by endogenous and exogenous factors, but its polymorphism among different populations of a species has not been fully explained yet. Here, 27 <em>Arabidopsis thaliana</em> populations were used to </span><span>determine</span><span> how autonomous pathway gene methylation affects flowering differences </span><span>by</span><span> regulating gene expression patterns. DNA methylation analysis, qPCR and transgenic verification</span> <span>showed that polymorphism in the flowering time among the <em>Arabidopsis</em> populations ranged from 19 to 55 days was significantly correlated with methylation of the coding regions of six upstream genes in the autonomous pathway</span><span>,</span><span> <em>FVE</em>, <em>FY</em>, <em>FLD</em>, <em>PEP</em>, <em>HDA5</em> and <em>PPR</em> <em>39-1</em> and</span><span> their </span><span>relative expression levels</span> <span>(P < 0.05). Expression of selected <em>FVE</em> and <em>FVE(CS) </em>separately</span> <span>through codon degeneracy substitution of cytosine led to earlier flowering of transgenic plants by 8 days and 25 days, respectively. An accurate determination of </span><span>methylated sites</span><span> of <em>FVE</em> and <em>FVE(CS)</em> among those transgenic plants and recipient Col-0 verified the close relationship between their methylation number, expression and the flowering times. Our findings firstly suggest that the methylation variation of those six key upstream transcription factors regulates</span><span> the</span><span> gene expression level of </span><span>the</span><span> autonomous pathway, leading to phenotypic differences in flowering time of Arabidopsis. </span><span>The</span><span> <em>FVE(CS)</em></span><span> and <em>FVE</em> genes in transgenic plants tend to be hypermethylated, and this could be a protective mechanism for plants. However, modification of gene sequence through codon degeneracy substitution to reduce cytosine can avoid hypermethylated transferred-genes in transgenic plants. It may be possible to partially regulate the flowering of plants by modified trans-epigenetic technology.</span></p>
Dataset for Latella, I., Ben-Abdallah, P. Graphene-based autonomous pyroelectric system for near-field energy conversion. Sci Rep 11, 19489 (2021)
<p>This dataset contains data associated to plots published in the paper I. Latella and P. Ben-Abdallah, Graphene-based autonomous pyroelectric system for near-field energy conversion, Sci Rep 11, 19489 (2021), https://doi.org/10.1038/s41598-021-98656-8.</p> <p> </p>
Real-time oceanography captured by autonomous sailbuoy in Lofoten/Vesterålen 2018
<p>Real-time oceanography from sailbuoy <a href="http://sailbuoy.no/13-news/74-introducing-the-sb-echo">Echo</a>'s long-endurance data acquisition campaign for the <a href="https://prosjektbanken.forskningsradet.no/en/project/FORISS/269188">Glider project</a>.</p> <p>The study area ranged from 66–71N and 8–19E, focusing on the coastal shelf area surrounding the Lofoten and Vesterålen islands, Norway, from March to August 2018.</p> <p>The dataset contains 4684 oceanography data points, and ~10000 auxiliary data points, spread over a period of 154 days.</p> <p><strong>Oceanography variables</strong></p> <p>GPS</p> <ul> <li><code>Time</code> UTC</li> <li><code>Lat</code> latitude WGS84</li> <li><code>Long</code> longitude WGS84</li> </ul> <p>Conductivity and temperature, from <a href="https://nbosi.com">NBOSI</a> CT sensors</p> <ul> <li><code>CTTemp</code> Sea surface temperature <code>°C</code></li> <li><code>CTCond</code> Sea water conductivity <code>mS/cm</code></li> </ul> <p>From <a href="https://www.aanderaa.com/">Aanderaa</a> Oxygen Optode 4831</p> <ul> <li><code>O2Con</code> Oxygen O₂ concentration <code>µM</code></li> <li><code>O2Air</code> Oxygen O₂ air saturation <code>%</code></li> <li><code>O2Temp</code> Oxygen O₂ water temperature <code>°C</code></li> </ul> <p>The data is published as 2 TSV files from iridium real-time messages that were received, parsed, and exported from <a href="https://iridium2.azurewebsites.net/">Offshore Sensing's portal</a>.</p> <p>For more information on the <a href="https://www.akvaplan.niva.no/">Akvaplan-niva</a>-led Glider project, including its partners, technology, and results, see Camus et al.:<br> <a href="https://doi.org/10.3390/s21206752">Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project</a>. Sensors 2021, 21, 6752. <a href="https://doi.org/10.3390/s21206752">https://doi.org/10.3390/s21206752</a></p> <p>In 2022, Akvaplan-niva starts publishing the complete set of data from Glider (phase I) on their research archive on <a href="https://zenodo.org/communities/akvaplan-niva">Zenodo</a>.</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-07-01/2018-07-11
<p>_</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-03-08/2018-03-12
<p>2018-03-08/2018-03-12</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-03-18/2018-04-01
<p>2018-03-18/2018-04-01</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.