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36 results for “Optical Networks”
Data for "Comparing ultrastable lasers at 7×10-17 fractional frequency instability through a 2220 km optical fibre network"
<p>Here we share the relevant data of the manuscript “Comparing ultrastable lasers at 7×10<sup>-17</sup> fractional frequency instability through a 2,220 km optical fibre network”.</p> <p>Raw data was acquired using multiple synchronised, dead-time free frequency counters in Lambda-mode [1]. The integration time for each data point was 1 s. The data provided here have been processed to reflect the fractional frequency difference between the ultrastable lasers at NPL and PTB, scaled to 1542 nm. Specifically,<br> <span class="math-tex">\(y=(\nu_{\text{NPL(ULE)}}\frac{777327}{1126090}-\frac{767233}{767235}\nu_{\text{PTB(Si)}})/194.4 \ \text{THz}\)</span></p> <p>where <span class="math-tex">\(y\)</span> is the value recorded in the data files, <span class="math-tex">\(\nu_{\text{NPL(ULE)}}\)</span> and <span class="math-tex">\(\nu_{\text{PTB(Si)}}\)</span> are the optical frequencies of the ultrastable lasers at NPL (referenced to a ULE cavity) and PTB (referenced to Si cavity), respectively. The numerators and the denominators of the scaling factors correspond to mode numbers of the optical frequency comb at NPL and PTB, respectively. The expression for <span class="math-tex">\(y\)</span> corresponds to the fractional transfer beat [2] between the NPL and PTB ultrastable lasers.</p> <p>The file</p> <ul> <li>“833000_s_874000_s_data_for_fig_2.txt”</li> </ul> <p> contains the timeseries data used to compute the modified Allan deviation reported in <strong>Fig. 2a</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The file</p> <ul> <li>“432000_s_912077_s_data_for_fig_3.txt”</li> </ul> <p>contains the timeseries data used in <strong>Fig. 3.</strong> The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. These data have additionally been high pass filtered with a cut off frequency of 1 mHz to decouple the short-term instability of the optical fibre link from the drift of the ultrastable lasers (with a characteristic time >1000 s), as described in the manuscript.</p> <p>The files</p> <ul> <li>“222000_s_232000_s_data_for_supp_fig_1.txt”,</li> <li>“270000_s_288000_s_data_for_supp_fig_1.txt”,</li> <li>“754000_s_765000_s_data_for_supp_fig_1.txt”,</li> <li>“832000_s_890000_s_data_for_supp_fig_1.txt”,</li> </ul> <p>contain the timeseries data used to compute the modified Allan deviation reported in <strong>Supplementary Fig. 1</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The temporal starting point is displayed in seconds in the title of the files relative to 00:00 UTC of 2019/07/06.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Dawkins, S. T., McFerran, J. J. & Luiten, A. N. Considerations on the Measurement of the Stability of Oscillators with Frequency Counters. <em>IEEE Transactions on ultrasonics, ferroelectrics, and frequency control</em> <strong>54</strong>, 918-925 (2007).</p> <p>[2] Telle, H.R., Lipphardt, B. & Stenger, J. Kerr-lens, mode-locked lasers as transfer oscillators for optical frequency measurements. <em>Appl. Phys. B</em> <strong>74</strong>, 1-6 (2002).</p> <p> </p>
Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels
<p>These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied.</p> <p>[1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in <em>Journal of Lightwave Technology</em>, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI: 10.1109/JLT.2013.2295208.</p> <p>[2] P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.</p>
A quantum network node with crossed optical fibre cavities
<p>Data published in "<em> A quantum network node with crossed optical fibre cavities </em>"</p> <p>Nature Physics (2020)</p>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks
<p>Research data of <em>Modelling and Simulation in Engineering </em>journal article “Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks”.</p> <p>Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine and raised sine apodizations are studied to achieve the main requirements, which are - maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm), FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.</p> <p> </p>
Image sensing with multilayer, nonlinear optical neural networks
<p>This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”.</p> <p>This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper:</p> <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
Dataset for the Manuscript: Demonstration of optically-driven plasmonic nanomotors designed by deep learning networks
<p>This repository contains the data corresponding to the manuscript "Demonstration of the optically-driven plasmonic nanomotor designed by deep learning networks." It consists of 5 parts: Machine learning, Numerical analysis, Rotation measurement, Scattering measurement, and Supplementary information. The code for the machine learning algorithm is available at "https://github.com/mintaechung/Nanomotor_Predictor_Generator."</p> <p> </p> <ul> <li><strong>'Machine_learning.zip'</strong>: Correlation between optical torques calculated by SIE and predicted by trained CNN, Objective loss functions at the 1st iteration, and the torque distribution of the initial randomset and the output of the nanorotor generator after the 3rd iteration.</li> <li><strong>'Numerical_analysis.zip'</strong>: MATLAB codes to retrieve 'Moments', 'Field intensity distribution', 'Poynting vectors', and 'Torques'. </li> <li><strong>'Rotation_measurement.zip'</strong>: Raw videos, Intensity profiles of ROI, Rotation measurement results.</li> <li><strong>'Scattering_measurement.zip'</strong>: Scattering intensity measurement with reference light.</li> <li><strong>'Supplementary_Info.zip'</strong>: Random geometry generation, Optical torques of 6 blades, Expanded structure, Shrinkage, Polarization independence, Angular momentum, and Machine learning progress.</li> </ul>
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
<p>TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.</p> <p> </p> <p>If you use any of the data provided, please cite our paper:</p> <p> </p> <pre>@article{matzner2024topology, title={Topology Bench: systematic graph-based benchmarking for core optical networks}, author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina}, journal={Journal of Optical Communications and Networking}, volume={17}, number={1}, pages={7--27}, year={2024}, publisher={IEEE} }<br><br></pre>
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Dataset for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography
<p>Official dataset repository for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography. The repository contains both the experimental dataset (allTrainingDat_30-Sep-2021.mat) as well as data that is a dependency for running our code (5_29_21_src-det_10x10_scene_4cm.zip).</p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Reproducibility Package for "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks"
<p>This is a reproducibility package for the VMCAI'25 paper "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks".</p> <p>Constantly growing demands on higher bandwidth and quality of service in modern communication networks motivate the introduction of fully optical network technologies that can eliminate the bottlenecks of optical to digital signal conversions. Recent advances in elastic optical networks enable fine-grained resource allocation technologies for traffic demands, which introduces the Routing and Spectrum Allocation (RSA) problem. In order to improve network resilience for multiple link failures while avoiding double light-spectrum allocation, we present Ex- pectAll---a novel approach and a tool for resilience and path/spectrum allocation based on binary decision diagrams (BDDs). Our method efficiently computes and stores all solutions to the RSA problem in the BDD data structure, facilitating optimal and fast failover protection for failure scenarios even with multiple failing links. ExpectAll surpasses the state- of-the-art methods in both the speed of finding a single optimal solution for a currently occurring failure scenario as well as in the preparation time required to precompute all optimal route and spectrum assignments.</p> <p> </p> <p> </p>
TEFNET24: Reference Packet Optical Network Topology for Edge to Core Transport [Invited]
<p>In this paper, we introduce TEFNET24, a reference multi-layer hierarchical network topology that spans from access to core networks, specifically designed to meet the demands of beyond-5G and and prepared for next-generation 6G communication systems. This topology, inspired by the actual network deployments of Telefónica in medium-sized countries in Europe and America, integrates both IP and optical (DWDM) layers to provide a comprehensive framework for network design, optimization, and analysis. Our primary contribution is the development of an open-source benchmarking network, accessible to both researchers and industry professionals. This resource aims to facilitate the study and advancement of integrated IP and optical networks, allowing researchers to address key challenges such as traffic aggregation, latency reduction, cost efficiency, and support for advanced applications. We provide guidelines for utilizing this benchmark network, enabling users to evaluate and enhance their solutions for AI-driven network management, ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. By sharing this detailed and practical benchmarking network, we seek to foster innovation and collaboration within the optical network community, driving forward the capabilities and performance of future communication networks. </p>
Nanosecond Optical Switching and Control System for Data Center Networks(data)
<p>This file contains the data and codes for the paper "Nanosecond Optical Switching and Control System for Data Center Networks" </p>
Single-Shot Optical Neural Network
<p>Data from classification of MNIST [1], Fashion-MNIST [2] and QuickDraw [3] images reported in "Single-Shot Optical Neural Network" by L. Bernstein et al. The networks of 784 -> N (-> N) -> 10 activations performed inference on the test sets through consecutive matrix products implemented on the optical hardware, with ReLU applied electronically between each layer (see main text for more details). Folders for each tested network contain the following text files:</p> <ul> <li>Inputs: 2D matrices of size B x 784 containing training (B = 50,000 or 100,000), validation (B = 10,000) and test (B = 10,000) sets. Each row is an input vector that can be reshaped into an input image of size 28 x 28.</li> <li>True labels: B-length vectors containing the true label of each input in the training, validation and test sets.</li> <li>Neural network weights: 2D matrices of size K x N used in inference experiments to classify the test sets. Weights were pre-trained on the training set using a digital electronic computer as described in Materials and Methods. Weight values were normalized such that all values fall between -255 and 255. In the optical neural network, the weighting SLM displays the absolute values of the weights (rounded to the nearest integer), and the negative weight signs are applied in post-processing. The 32-bit weight values were used for inference performed on the digital electronic computer for the ground truth comparison.</li> <li>Outputs (normalized): 2D output matrices of size 10,000 x 10 from the networks processed on a digital electronic computer (ground truth) and the optical neural network. Each row is an output vector where the position of the maximum value indicates the predicted label of the input in the same row of the test set.</li> <li>Predicted labels: 10,000-element vectors that represent the labels predicted by the networks processed on a digital electronic computer (ground truth) and the optical neural network. These predicted labels were used to generate the confusion matrices and calculate the classification accuracies (versus the true labels).</li> </ul> <p>The classes for the Fashion-MNIST dataset are the following:</p> <ul> <li>0: T-shirt</li> <li>1: Trouser</li> <li>2: Pullover</li> <li>3: Dress</li> <li>4: Coat</li> <li>5: Sandal</li> <li>6: Shirt</li> <li>7: Sneaker</li> <li>8: Bag</li> <li>9: Ankle boot</li> </ul> <p>And the randomly selected classes for QuickDraw are:</p> <ul> <li>0: Hourglass</li> <li>1: Saw</li> <li>2: Golf club</li> <li>3: See saw</li> <li>4: Spoon</li> <li>5: Horse</li> <li>6: Onion</li> <li>7: Light bulb</li> <li>8: Harp</li> <li>9: Flip flops</li> </ul> <p>[1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. <em>Proceedings of the IEEE</em> <strong>86</strong>, 2278–2324 (1998).</p> <p>[2] H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017).</p> <p>[3] J. Jongejan, H. Rowley, T. Kawashima, J. Kim, N. Fox-Gieg, The Quick, Draw! AI experiment, https://quickdraw.withgoogle.com/ (2016).</p>
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.