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19 results for “channel coding”
Numerical code and data for: Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon
<p>The numerical code and data accompanying the analysis of Figs. 4 and 5 of Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon, Phys. Rev. Lett. (2020)</p>
MALAYALAM LANGUAGE (MIX CODE) Recipe channels Youtube Comments
<p>The dataset used for performing Text Classification is a combination of two different datasets scrapped from the comment section of two Youtube channels namely "Veen's Curryworld" and "Lekshmi Nair". The data is extracted using the YouTube API and contains two atrributes namely “text” and “label” where the former contains the comments and later contains the corresponding label. The comments are classified into 7 labels:</p> <p> </p> <p>- Label 1: Gratitude</p> <p>- Label 2: About the recipe</p> <p>- Label 3: About the video</p> <p>- Label 4: Praising</p> <p>- Label 5: Hybrid</p> <p>- Label 6: Undefined</p> <p>- Label 7: Suggestions and Queries</p> <p> </p> <p>The number of instances in each category is listed below:</p> <p> </p> <p>- Label 1: 484</p> <p>- Label 2: 396</p> <p>- Label 3: 300</p> <p>- Label 4: 362</p> <p>- Label 5: 249</p> <p>- Label 6: 2062</p> <p>- Label 7: 438</p>
Data and MATLAB Code for the paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow"
<p>This link includes the data and MATLAB code files for the research paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow" by Zhou, J.W.; Bro, W.M.; Tick*, G.R.; Mofatakari, H.; Li, Y.; and Cheng, L., which has been submitted to the Journal of Fluids Engineering. These files are edited under the GB18030 character set standard.</p>
To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)
<p>The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories.</p> <p>The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories:</p> <ol> <li><strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows:</li> </ol> <ul> <li><strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong></li> <li><strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong></li> <li><strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong></li> <li><strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong></li> <li><strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong></li> </ul> <p><strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel.</p> <p>The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). </p> <p>The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV).</p> <p> 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files.</p> <p>The Python code is placed in two files:</p> <ol> <li><strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook.</li> <li><strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.</li> </ol> <p> </p> <p> </p> <p> </p>
Dataset for: A hemispheric two-channel code accounts for binaural unmasking in humans
<p><strong>Dataset for the paper: A hemispheric two-channel code accounts for binaural unmasking in humans.</strong></p> <p>The model code to generate this data has been published here: <a href="https://doi.org/10.5281/zenodo.5643429">https://doi.org/10.5281/zenodo.5643429</a></p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
<p><em>Significance</em></p> <p>Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field-of-view and spatial resolution are similarly important.</p> <p><em>Aim</em></p> <p>Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field-of-view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff.</p> <p><em>Approach</em></p> <p>In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer.</p> <p><em>Results</em></p> <p>We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making.</p> <p><em>Conclusions</em></p> <p>These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.</p>
QR-Code Optical Covert Channel Benchmark
<p>Benchmark results of the QR-Code Optical Covert Channel existing in the reference implementation of a open-source secure data infrastructure and processes.</p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
Open the record for dataset details and reuse information.
Parent dataset and code from: Atomistic Mechanisms of the regulation of small conductance Ca 2+ -activated K + channel (SK2) by PIP2
Open the record for dataset details and reuse information.
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Replication data and theory code for: Observation of a Majorana zero mode in a topologically protected edge channel
<p>Replication Data for: Observation of a Majorana zero mode in a topologically protected edge channel</p>
Data & Code for Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening
<p>Data & Code for the article: "Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening"</p> <p>- Input-files used to run the models</p> <p>- Scripts used for the figures</p> <p>- Excel-based tool to calculate tidal response to channel deepening with system-specific parameters</p> <p>- ...</p>
Natural river data and analysis code for the correlation of channel threads of the Brahmaputra-Jamuna River (v2)
<p>This is the archive of the data of processed water masks and channel-thread centerlines, code used for analyzing the coherent motion of channel threads, results of channel thread migration and numerical modeling for the braided Brahmaputra-Jamuna River, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Coherent motion of channel threads in the braided Brahmaputra-Jamuna River.</p> <p>Running the analyze code needs a MATLAB® software environment. The MATLAB script 'demo_dtw.m' in folder 'braided_dataRepo/Code' recreates the correlation for the braided channel threads in Figure 5 of the manuscript.</p>
A Weighting Function Model for Unsteady Open Channel Friction: Raw code and output
<p>Matlab source code and Raw Origin Lab file (.opj) for a weighting function model simulating unsteady open channel friction slopes. This material is provided as is. Methods (formulas) and Figures are explained further in the related article "A Weighting Function Model for Unsteady Open Channel Friction" by Junwei Zhou; Weimin Bao; Geoffrey R. Tick; Qing Cao; and Fanghong Ye.</p>
Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, dataset+code
<p>Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, updated dataset+code submitted for publication in The Cryosphere. Dataset includes all data produced in this study, including velocity maps derived from speckle tracking of Sentinel 1 images, maps of rates of ice shelf change and the annual mosaic digital surface models from which they were derived, maps of the basal conditions at Thwaites glacier, shapefiles and masks of the annual hydrostatic boundary (grounding line proxy position), and shapefiles of all hydrostatic boundary features, ice shelf basal channels and surface depressions, intermediate polygons used to filter the features, and digital surface model strips with registration data included as attributes. </p>
Code and Data for "Intrinsic interface adsorption drives selectivity in atomically smooth nanofluidic channels"
<p>Code and data for reproducing the results in "Intrinsic interface adsorption drives selectivity in atomically smooth nanofluidic channels" by P. Helms, A. Poggioli, & D. T. Limmer</p>
Dataset and code: Compact module for complementary-channel THz pulse slicing
<p>Dataset and processing files for the data presented in manuscript "Compact module for complementary-channel THz pulse slicing"</p>
Multibeam bathymetry data, multi-channel seismic reflection profiles and pore water modeling code
<p>Supplementary material for "Complex architecture of mud volcano systems: new insights on flow pathways unravel intricate fluid circulation"</p>
Multi-channel seismic reflection profiles MP06b and INS-Line1 (INSIGHT cruises) and diffraction imaging code
<p>This archive contains sections of multi-channel seismic reflection profiles MP06b and INS-Line1 (INSIGHT cruises) (pre-stack gathers, unmigrated water velocity stacks and migration velocities in SEG-Y format) and an archive containing code and processing horizons needed to generate conventional and diffraction images. Requires Madagascar 3.0, Python 3.7+, pdflatex. Please contact the author (Jonathan Ford, jford@inogs.it) if assistance is required to run the code.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.