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9,152 results for “Networking”
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
<p>Emitting conspicuous signals into the environment to attract mates comes with the increased risk of interception by eavesdropping enemies. As a defence, a commonly described strategy is for signallers to group together in leks, diluting each individual's risk. Lekking systems are often highly social settings in which competing males dynamically alter their signalling behaviour to attract mates. Thus, signalling at the lek requires navigating fluctuations in risk, competition, and reproductive opportunities. Here, we investigate how behavioural defence strategies directed at an eavesdropping enemy have cascading effects across the communication network. We investigated these behaviours in the túngara frog (<em>Engystomops pustulosus</em>), examining how a calling male's swatting defence directed at frog-biting midges indirectly affects the calling behaviour of his rival. We found that the rival responds to swat-induced water ripples by increasing his call rate and complexity. Then, performing phonotaxis experiments, we found that eavesdropping fringe-lipped bats (<em>Trachops cirrhosus</em>) do not exhibit a preference for a swatting male compared to his rival, but females strongly prefer the rival male. Defences to minimize attacks from eavesdroppers thus shift the mate competition landscape in favour of rival males. By modulating the attractiveness of signalling prey to female receivers, we posit that eavesdropping micropredators likely have an unappreciated impact on the ecology and evolution of sexual communication systems.</p>
Online Appendix for Networks of Inequality: Access to Water in Roman Pompeii
<p><strong>Online Appendix for Networks of Inequality: Access to Water in Roman Pompeii (Samuli Simelius)</strong></p><p>in Journal of Computer Applications in Archaeology "A Bridge too Far – Historical, Archaeological and Criminal Network Research"(eds. M. De Bernardin, M. Lorenzon, L. Tambs & A. Traviglia)<br><br>Figure 1: The locations of the dwelling entrances used in the study, as well as the entrances to the unexcavated area.<br>Figure 2: The polyline layer representing the street network.</p><p>Table 1: The dwellings which I have interpreted differently from Matthew Notarian's interpretation – including their new water collection values.</p><p>Table 2: The population estimations of the dwellings and their new water collection values (seconds).<br>Table 3: The distances (minutes and meters) from dwellings to baths. </p>
Plant interaction networks reveal the limits of our understanding of diversity maintenance
<p>Species interactions are key drivers of biodiversity and ecosystem stability. Current theoretical frameworks for understanding the role of interactions make many assumptions which, unfortunately, do not always hold in natural, diverse communities. This mismatch extends to annual plants, a common model system for studying coexistence, where interactions are typically averaged across environmental conditions and transitive competitive hierarchies are assumed to dominate. We quantify interaction networks for a community of annual wildflowers in Western Australia across a natural shade gradient at local scales. Whilst competition dominated, intraspecific and interspecific facilitation were widespread in all shade categories. Interaction strengths and directions varied substantially despite close spatial proximity and similar levels of local species richness, with most species interacting in different ways under different environmental conditions. Contrary to expectations, all networks were predominantly intransitive. These findings encourage us to rethink how we conceive of and categorise the mechanisms driving biodiversity in plant systems.</p>
RAN and NWDAF combined data from Cellular Network
<p><span>Dataset containing various RAN and UEs metrics collected from 4 BSs deployed at Piazza D'Uomo, Catania. Metrics can be used for machine learning-based studies for physical resource block (PRBs) allocation in the context of O-RAN to maximize various KPMs, such as throughput or delay.</span></p>
TWIN2PIPSA - Twinning for strategic networking and impactful research and innovation in Biomedicine and Biotechnology
<p>Presentation by Prof. Cláudio M. Gomes at the 5<sup>th</sup> edition of Ciências Research & Innovation Day.</p>
Greenhouse gas dynamics in river networks fragmented by drying and damming
<p>River fragmentation by drying and damming is occurring more frequently in the Anthropocene, yet there is limited research about its effects on greenhouse gas (GHG) fluxes. These fragmented rivers have the potential to be important sources of GHGs to the atmosphere through both similar and dissimilar mechanisms. Our objective was to review the literature of the individual and the interactive effects of fragmentation by drying and damming on GHG fluxes in river networks, identifying the magnitudes and drivers of CO<sub>2</sub>, methane (CH<sub>4</sub>), and N<sub>2</sub>O flux rates. We conducted a systematic search of studies addressing the separate and interactive effects of drying and damming on GHG fluxes from running waters. The search was primarily conducted using Web of Science for studies published from 1900 to January 2021. For the 42 studies about rivers impacted by drying, 54 about damming, and 6 about their interactive effects, we collected a suite of qualitative and quantitative information. The major proximal drivers of GHG emissions in river networks impacted by drying were sediment moisture, sediment temperature, sediment organic matter content and sediment texture. In networks impacted by damming, the major proximal drivers were water temperature, dissolved oxygen, and chlorophyll ɑ. We found research lacking in non-arid climates for drying, and on small water retention structures for damming. We propose a conceptual model where the spatial distribution of fragmentation is the principle driver of GHG fluxes at the network scale. </p>
IW-NET sample data-set: River Weser IW network
<p>This data-set provides a sample of the data that was utilized for the analysis performed in the context of the IW-NET research project. The data have been collected via publicly available sources and are offered in this package as a sample. The use case the data refer to is River Weser, in northern Germany. The following table explains the contents of each of the uploaded files.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserWaterway.json/content" target="_blank" rel="noopener noreferrer">RiverWeserWaterway.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserRelationsWaysNodes.json/content" target="_blank" rel="noopener noreferrer">RiverWeserRelationsWaysNodes.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/IWTWeather.json/content" target="_blank" rel="noopener noreferrer">IWTWeather.json</a></div> </td> <td>Weather reports from 6 stations in the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/unCitiesDE.json/content" target="_blank" rel="noopener noreferrer">unCitiesDE.json</a></td> <td>UN/LOCODE data in json format.</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/MMSI.xlsx/content" target="_blank" rel="noopener">MMSI.xlsx</a></div> </td> <td>Correspondance of MMSI codes to Vessel registration country</td> </tr> </tbody> </table> <p>These resources were combined with AIS data logs from vessels active in the area - which, for legal reasons, cannot be made publicly available to provide powerful insights into the logistics operations and their intricacies.</p>
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Data for: Brain control of bimanual movement enabled by recurrent neural networks
<p>Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. In this study, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders.</p> <p>This dataset represents all neural activity recorded during these experiments. This includes the neural activity corresponding to unimanual and bimanual hand movements during (1) instructed delay experiments and (2) real-time BCI control of two cursors. </p> <p>Code associated with the data can be found here: <a href="https://github.com/d-r-deo/bimanualBCI" target="_blank" rel="noopener">https://github.com/d-r-deo/bimanualBCI</a></p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Motion Policy Networks
<p>This is the pretrained model, training data set, evaluation problem sets, and sample real robot data accompanying Motion Policy Networks, an end-to-end neural model that can be used to generate collision-free, smooth motion from just a single depth camera observation. The training data set consists of over 3 million motion planning problems for a Franka Panda arm in over 500,000 environments. </p>
Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset
<p>In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a></p> <p>A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>.</p> <p>Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.</p> <p>We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a></p> <p>To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.</p> <p>To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.</p>
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the trained model checkpoints for all tasks of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains 300 temporal convolutional networks (TCNs) and 50 LSTM models trained on 16 tasks as well as the untrained initialization. </p> <p>The overall structure of the data is:</p> <p>└── models<br> ├── deepdraw_models - Contains networks hyperparameters<br> │ ├── template_models - Contains the default parameters<br> │ ├── torque - Contains network hyperparameters for the torque task<br> │ └── ... <br> ├── experiment_*** - Contains checkpoint of trained and untrained models <br> ├── ... <br> └── ... </p> <p>--------------------------------</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction - task transfer (AR): 10020, 10035, 10050<br>- Redundancy reduction - task transfer (HP): 10021, 10036, 10051<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p>The code to load, evaluate and train the models is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training">https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Nonlinear mechanosensation in fiber networks
<p>Dataset corresponding to the underlying numerical and experimental data of the research article "Nonlinear mechanosensation in fiber networks". </p> <p>This repository contains four folders containing the data used to produced the figures shown in the article:<br>- EXPERIMENTS.zip : microrheology measurements in biopolymer networks<br>- MACRO.zip : macroscopic loading of disordered fiber networks (simulations results)<br>- MICRO.zip : local probing of disordered fiber networks (simulations results)<br>- Spatial-NL.zip : local probing of disordered fiber networks, with records of the networks spatial deformations (simulations results)<br>The README.txt document provides a detailed description of the content, including files naming convention and data description.</p> <div> <div> <div> <p>We would like to acknowledge that this project has received funding (E.B. and C.P.B.) from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 891217 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project ID 201269156 - SFB 1032 (Project B12). P.R. is supported by France 2030, the French National Research Agency (ANR-16-CONV-0001) and the Excellence Initiative of Aix-Marseille University - A*MIDEX. M.G. and H.Y. acknowledge support from NIH Grant No. 1R01G140108.</p> </div> </div> </div>
Evaluation Data of a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "Trusting Decentralized Web Data in a Solid-based Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>calculations.csv:</strong> All calculated numbers based on the raw data of the conducted empircal user study.</li> <li><strong>questions_translation.csv:</strong> A translation of all German questions asked in the survey to English, including a mapping of the question codes to the questions.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> </ul>
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>
CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines
<p><strong>Please refer to the original data article for further data description: </strong>Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2023.108888">https://doi.org/10.1016/j.dib.2023.108888</a>. </p><p><strong>We recommend using the</strong> <strong>CESNET DataZoo python library, which facilitates the work with large network traffic datasets. </strong>More information about the DataZoo project can be found in the GitHub repository <a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a>.</p><p>The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies.</p><p><strong>Data capture</strong> The data was captured in the flow monitoring infrastructure of the <a href="https://www.cesnet.cz">CESNET2</a> network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:</p><ul><li><strong>W-2022-44</strong><ul><li>Uncompressed Size: 19 GB</li><li>Capture Period: 31.10.2022 - 6.11.2022</li><li>Number of flows: 32.6M</li></ul></li><li><strong>W-2022-45</strong><ul><li>Uncompressed Size: 25 GB</li><li>Capture Period: 7.11.2022 - 13.11.2022</li><li>Number of flows: 42.6M</li></ul></li><li><strong>W-2022-46</strong><ul><li>Uncompressed Size: 20 GB</li><li>Capture Period: 14.11.2022 - 20.11.2022</li><li>Number of flows: 33.7M</li></ul></li><li><strong>W-2022-47</strong><ul><li>Uncompressed Size: 25 GB</li><li>Capture Period: 21.11.2022 - 27.11.2022</li><li>Number of flows: 44.1M</li></ul></li><li><strong>CESNET-QUIC22 </strong><ul><li>Uncompressed Size: 89 GB</li><li>Capture Period: 31.10.2022 - 27.11.2022</li><li>Number of flows: 153M</li></ul></li></ul><p> </p><p><strong>Data description</strong> The dataset consists of network flows describing encrypted QUIC communications. Flows were created using <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a> flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications.</p><p><strong>Packet Sequences</strong> Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, <i>+1</i> meaning a packet sent from client to server, and <i>-1</i> a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip.</p><p><strong>Flow statistics</strong> Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the <a href="https://github.com/CESNET/ipfixprobe/tree/master#phists">PHISTS plugin documentation</a> There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official <a href="https://www.iana.org/assignments/ipfix/ipfix.xhtml#ipfix-flow-end-reason">IANA IPFIX-specified values</a>. The <i>FLOW_ENDREASON_OTHER</i> field represents the <i>forced end</i> and <i>lack of resources</i> reasons. The <i>end of flow detected</i> reason is not considered because it is not relevant for UDP connections.</p><p><strong>Dataset structure</strong> The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the <i>stats-week.json</i> file aggregating flow counts of a whole week and the <i>stats-dataset.json</i> file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the <i>dataset-statistics</i> folder. The mapping between services and service providers is provided in the <i>servicemap.csv</i> file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:</p><ul><li><strong>ID:</strong> Unique identifier</li><li><strong>SRC_IP:</strong> Source IP address</li><li><strong>DST_IP:</strong> Destination IP address</li><li><strong>DST_ASN:</strong> Destination Autonomous System number</li><li><strong>SRC_PORT:</strong> Source port</li><li><strong>DST_PORT:</strong> Destination port</li><li><strong>PROTOCOL:</strong> Transport protocol</li><li><strong>QUIC_VERSION QUIC:</strong> protocol version</li><li><strong>QUIC_SNI:</strong> Server Name Indication domain</li><li><strong>QUIC_USER_AGENT:</strong> User agent string, if available in the QUIC Initial Packet</li><li><strong>TIME_FIRST:</strong> Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li><li><strong>TIME_LAST:</strong> Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li><li><strong>DURATION:</strong> Duration of the flow in seconds</li><li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li><li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li><li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li><li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li><li><strong>PPI:</strong> Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]]</li><li><strong>PPI_LEN:</strong> Number of packets in the PPI sequence</li><li><strong>PPI_DURATION:</strong> Duration of the PPI sequence in seconds</li><li><strong>PPI_ROUNDTRIPS:</strong> Number of roundtrips in the PPI sequence</li><li><strong>PHIST_SRC_SIZES:</strong> Histogram of packet sizes from client to server</li><li><strong>PHIST_DST_SIZES: </strong>Histogram of packet sizes from server to client</li><li><strong>PHIST_SRC_IPT: </strong>Histogram of inter-packet times from client to server</li><li><strong>PHIST_DST_IPT:</strong> Histogram of inter-packet times from server to client</li><li><strong>APP:</strong> Web service label</li><li><strong>CATEGORY:</strong> Service category</li><li><strong>FLOW_ENDREASON_IDLE:</strong> Flow was terminated because it was idle</li><li><strong>FLOW_ENDREASON_ACTIVE:</strong> Flow was terminated because it reached the active timeout</li><li><strong>FLOW_ENDREASON_OTHER:</strong> Flow was terminated for other reasons</li></ul><p> </p><p><strong>Link to other CESNET datasets</strong></p><ul><li><a href="https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/">https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/</a></li><li><a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a></li></ul><p><strong>Please cite the original data article:</strong></p><blockquote><p>@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }</p></blockquote>
ScienceDex guides
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