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Random Statuee
Academic work Source: Objaverse 1.0 / Sketchfab
Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments
<p>This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [<em>NSE</em>] > 0.5), whereas SWAT generally yielded unsatisfactory results (<em>NSE</em> < 0.5) and tended to overestimate daily streamflow by up to 27% (<em>PBIAS</em>). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.</p> <p>This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results.</p> <p>The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.</p>
Simulated performance data: MILC, LAMMPS, and uniform random traffic patterns on 72-ndoe dragonfly network
<p>Data generated from an old, private fork of the CODES simulation toolkit: https://github.com/codes-org/codes</p> <p>Data dictionary: https://github.com/kevinabrown/codes/wiki/Dragonfly-Dally-DEBUG-Metrics</p>
Figure 2 in Hyperparasitism among larval stages of Digenea in snail hosts: sophisticated life strategy or pure randomness? The scenario of Cotylurus sp.
Figure 2. The mean intensity of tetracotyle metacercariae in snail hosts infected or not infected with sporocysts/rediae.
Figure 3 in Hyperparasitism among larval stages of Digenea in snail hosts: sophisticated life strategy or pure randomness? The scenario of Cotylurus sp.
Figure 3. The mean intensity of tetracotyle metacercariae in relationship to presence or absence of hyperparasitism in the snail host.
Replication package for: Convicting Corrupt Officials: Evidence from Randomly Assigned Cases
<p>This package contains replication files for "Convicting Corrupt Officials: Evidence from Randomly Assigned Cases", by Sebastian Axbard, to be published in the <em>Review of Economic Studies</em>. The package contains all neccesary preparation and analysis code as well as analysis data files. The README file describes how to get access to the part of the raw data that is proprietary. </p>
Potential drug targets for benign prostatic hyperplasia: A proteome-wide Mendelian randomization study
<p>Additional file 3</p>
Software artifacts corresponding to the paper "Pragmatic Random Sampling of the Linux Kernel: Enhancing the Randomness and Correctness of the conf Tool"
<div><strong>Software artifacts corresponding to the paper "Pragmatic Random Sampling of the Linux Kernel: Enhancing the Randomness and Correctness of the conf Tool"</strong></div> <div> </div> <div> <div> <div>This repository is organized in two main folders:</div> <br> <div>1. <strong>randconfig+</strong> includes the source code of our tool randconfig+, which improves the randomness and correctness of the conf tool (a built-in Linux kernel tool for generating random samples of kernel configurations). It has two subfolders:</div> - <strong>source</strong>: includes the source code of randconfig+. <div> - <strong>bin</strong>: includes the compiled version of randconfig+.</div> <br> <div>2. <strong>experimental_validation</strong> includes the experimental validation of randconfig+ reported in the paper.</div> - <strong>systems</strong>: includes the 10 Linux Kernel versions analyzed in the paper. <div> - <strong>scripts_for_sample_generation</strong>: Bash shell scripts to generate the data.</div> <div> - <strong>data</strong>: includes the following data generated by the experimental validation:</div> <div> + Generated samples (subfolder configuration_samples). The samples are available in two formats: CSV and <a href="https://r4ds.hadley.nz/arrow">Apache Arrow</a>.</div> <div> + Entropies and number of distinct values per configuration option.</div> <div> + Data regarding the correctness of the samples and the time spent to generate them (subfolder correctness_and_runtime).</div> <div> - <strong>statistical_analysis</strong>: R scripts to perform the statistical analysis of the data (subfolder statistical_analysis).</div> <div> - <strong>plots</strong>: graphs produced from the data statistical analysis.</div> </div> </div>
Nonhuman Primate Center-Out and Random-Walk Reaching with Multichannel Motor Cortex Electrophysiology
<p><strong>General Description</strong></p> <p>A rhesus macaque was implanted with a 96-channel microelectrode array (Blackrock Neurotech, Inc.) in the arm area of primary motor cortex (M1). The monkey performed each reaching task with the arm contralateral to the array. We collected broadband data from each electrode at 30 kHz using a 128-channel acquisition system (Cerebus, Blackrock Neurotech, Inc.). To extract spikes, we high pass filtered the broadband data (1st order causal filter, 300 Hz cutoff) and thresholded it using a threshold manually set for each channel (average threshold = 5.2 standard deviations above mean waveform potential). To extract LFPs, we first bandpass filtered the broadband data from 0.3-500 Hz (1st order causal filter), then resampled the signal at 2 kHz, and finally notch filtered it at harmonics of 60 Hz for powerline noise removal. </p> <p>The first task we analyzed was an eight-target center-out reaching task. On each trial, the monkey began by holding at the center of a 10 cm-radius circle of targets for 0.5-0.6 s. Then, one of eight 2 cm square targets spaced at 45° intervals around the circle was illuminated. The monkey had to reach the outer target within 1.5 s and hold for a random time between 0.2-0.4s to obtain a liquid reward. The second task was a random target reaching task. On each trial, the monkey had to acquire a series of 6 randomly positioned targets appearing one-at-a-time, holding each for 0.1 s, to obtain the reward. The targets spanned the majority of the 20-by-20 pixel workspace. </p> <p><strong>Possible Uses</strong></p> <p>These data may be used for testing new BCI decoders or otherwise investigating the motor cortical electrophysiological signals (spikes, local field potentials) during reaching.</p> <p><strong>Variable Names</strong></p> <p>Inside each .mat file is a structure, traditionally called ‘bdf’, or in some cases, ‘out_struct’. We delineate important fields below: </p> <ul> <li> <p><em>bdf.units</em> contains spike timestamps </p> </li> <ul> <li> <p><em>bdf.units.id </em>is a two-element vector [channel, unit]</p> </li> <li> <p><em>bdf.units.ts </em>is an array of spike timestamps for each unit </p> </li> <li> <p>A note on resorting: The<em> bdf.units</em> structure has elements for every sorted unit, plus one element per channel for the unsorted waveforms. In the above example, that amounts to 198 elements. Generally, all these elements will have a unit ID array and some timestamps. However, the waveform data for all recorded spike waveforms in a channel will be found in only one element for that channel (the 255 unit). To illustrate, here is the bdf.units information about channel 16 in the file used to make the above structure map.</p> </li> </ul> </ul> <div> <table> <tbody> <tr> <td> <p>id </p> </td> <td> <p>ts </p> </td> <td> <p>tsAll </p> </td> <td> <p>wv </p> </td> </tr> <tr> <td> <p>[16,0] </p> </td> <td> <p>38x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,1] </p> </td> <td> <p>3438x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,255] </p> </td> <td> <p>4199x1 double </p> </td> <td> <p>7675x1 double </p> </td> <td> <p>48x7675 double </p> </td> </tr> </tbody> </table> </div> <ul> <li> <p>There are two sorted units 0 and 1, and a 255 element for unsorted waveforms. Notes: </p> </li> <ul> <li> <p><em>tsAll</em> and <em>wv</em> are only filled out for unit 255 of channel 16. </p> </li> <li> <p>The <em>tsAll</em> field, as its name implies, contains all timestamps recorded on that channel, including those for units 0 and 1, and the unsorted waveforms. A sanity check is that 38+3438+4199 = 7675.</p> </li> <li> <p>The <em>wv</em> field contains the waveforms corresponding to tsAll. Here, there are 48 points per waveform.</p> </li> <li> <p>In general, <em>tsAll</em> will accompany the highest numbered unit.</p> </li> </ul> </ul> <ul> <li> <p><em>bdf.analog.data</em> contains the local field potential (LFP) data</p> </li> <li> <p><em>bdf.analog.ts</em> contains the timestamps for the LFP data as well as the kinematic data </p> </li> <li> <p><em>bdf.pos</em> contains manipulandum position </p> </li> <li> <p><em>bdf.vel </em>contains manipulandum velocity </p> </li> <li> <p><em>bdf.acc</em> contains manipulandum acceleration</p> </li> <li> <p><em>bdf.words</em> contains information about how the run occurred, such as times of trial onset, whether the trial was a success or failure, etc </p> </li> <ul> <li> <p>For <span>center-out</span> data </p> </li> <ul> <li> <p>The <em>bdf.words</em> field can be processed by running the function: tt = co_trial_table(bdf); </p> </li> <li> <p>The numeric array tt will then have Nx10 columns, where each row is a trial and each column gives the following information (this is replicated in the documentation of the function file): </p> </li> <ul> <li> <p>1: Start time </p> </li> <li> <p>2: Bump direction -- -1 for none </p> </li> <li> <p>3: Bump phase -- H (hold), D (delay), or M (movement) </p> </li> <li> <p>4: Bump time </p> </li> <li> <p>5: Target -- -1 for none (e.g., a neutral bump) </p> </li> <li> <p>6: OT on time </p> </li> <li> <p>7: Go cue </p> </li> <li> <p>8: Movement start time </p> </li> <li> <p>9: Trial End time </p> </li> <li> <p>10: Trial result -- R (reward), A (aborted), I (incomplete), or N (no-result) </p> </li> </ul> </ul> <li> <p>For <span>random-walk</span> data </p> </li> <ul> <li> <p>In RW hand-control data, 1 trial consisted of N distinct movements, where N was usually 6 or 8. In other words, N targets were presented successively, and all required to be hit, prior to delivery of a reward. For each target presentation, there is a time-stamp in the field bdf.words. There is also a time-stamp for the event of the cursor first entering the target. This information is presented in two columns, where the first column is the timestamp and the second column is an event code, which indicates:</p> </li> <ul> <li> <p>18: trial-start event flag </p> </li> <li> <p>49: target presentation </p> </li> <li> <p>160: cursor enters target </p> </li> <li> <p>32: success </p> </li> <li> <p>33/35: failure</p> </li> </ul> </ul> </ul> </ul>
Data for "Phase transition in Random Circuit Sampling"
<p><strong>Purpose</strong></p> <p>This dataset defines the Random Quantum Circuits (RQCs) used in our paper "Phase transition in Random Circuit Sampling" and lists the bitstrings observed in the experimental executions of the circuits on the Sycamore processor. See [1] for more details about the experiment. This data upload is modeled after that of [5].</p> <p><strong>Background</strong></p> <p><strong>Circuit parameters</strong></p> <p>RQCs posted here are uniquely identified<br>using the following parameters:</p> <ul> <li> `n`: number of qubits (69, 70),</li> <li> `m`: number of cycles (04, 06, 08, ..., 28, 30),</li> <li> `s`: seed for the pseudo-random number generator (000, 001, ..., 595),</li> <li> `patches`: the number of patches,</li> <li> `p`: sequence of coupler activation patterns (`ABCD`, `ABCDCDAB`),</li> <li> `num_sq`: the number of distinct single-qubit gates (3, 8),</li> <li> the date on which the data was collected, included in yymmdd format in the filename, and</li> <li> `phase_match`: included in the file name if phase matching was performed.</li> </ul> <p>See Figure S25 in [2] and Figure 3 of [1] for the coupler activation patterns and Figure 4 of [1] for illustrations of the patches. Also see code snippets below for visualizing the patches and activation patterns from the provided circuits.<br>When `num_sq` is 8, the single-qubit gates are chosen randomly from \(Z^p X^{1/2} Z^{-p}\), with \(p \in \{-1, -3/4, -1/2, -1/4, 0, 1/4, 1/2, 3/4 \}\), whereas when `num_sq` is 3, they are chosen randomly from among \(\sqrt X\), \(\sqrt Y\), and \(\sqrt W\). Phase matching is described in Appendix C.1 of [1]. Note that circuits which share the same seed `s` share the same initial gate sequence.</p> <p><strong>Content description</strong></p> <p>For each RQC there are four files in the dataset:<br> * original RQC specification in QSIM format, named `circuit_\*.qsim`,<br> * derived RQC specificaton as python code using cirq, named `circuit_\*.py`,<br> * derived RQC specification in QASM format, named `circuit_\*.qasm`,<br> * bitstrings observed in experiments, named `measurements_\*.txt`.</p> <p>The asterisk \* in the names above stands for a string specifying the parameters identifying a RQC. For example,<br>`circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.qasm` contains the definition of the 70-qubit, 24-cycle RQC with PRNG seed 0, 3 patches, a simplifiable sequence of coupler activation patterns (i.e. ABCD), and 8 distinct single-qubit gates, taken on October 14, 2022, with phase matching, in the QASM format.</p> <p>Files are grouped by parameters `n`, `m`, and `patches` and into compressed tarballs. For example, tarball `n69_m04_patches2.tar.gz` contains all RQCs and measurement files for circuits with 69 qubits, 4 cycles, and 2 patches.</p> <p><strong>Circuit file formats</strong></p> <p><strong>QSIM format</strong></p> <p>First line specifies the number of qubits n. Each subsequent line specifies a single gate and consists of moment number, gate name and one or two qubits as a number in 0..n-1 optionally followed by gate parameters.</p> <p>The circuits use the following gates:<br> * `x_1_2`: parameter-free, single-qubit pi/2 rotation around the X axis of the Bloch sphere, see equation (45) in section VII of [2],<br> * `y_1_2`: parameter-free, single-qubit pi/2 rotation around the Y axis of the Bloch sphere, see equation (46) in section VII of [2],<br> * `hz_1_2`: parameter-free, single-qubit pi/2 rotation around the X+Y axis of the Bloch sphere, see equation (47) in section VII of [2],<br> * `rz`: single-qubit rotation around the Z axis of the Bloch sphere through the angle specified in radians by the gate's sole parameter,<br> * `fsim`: two-qubit gate corresponding to the composition of the iSWAP and CPHASE gates and taking two parameters in radians: theta (the negative iSWAP angle) and phi (the CPHASE angle), see equation (48) in section VII of [2].</p> <p>Note that the two-qubit gates executed in our experiments on Sycamore belong to the five-parameter family of two-qubit gates that preserve the number of 0 and 1 states of the qubits. Each such gate can be decomposed into one fsim gate and four rz gates. Therefore, one cycle consisting of one application of<br>single-qubit gates and one application of two-qubit gates is represented in the file using four moments. The first moment contains `x_1_2`, `y_1_2` and `hz_1_2` gates. The other three moments use `rz` and `fsim` gates to describe the two-qubit gates used in the experiments. See section VII in [2] for more details about the Sycamore gates and their decomposition.</p> <p>Qubits are specified as numbers in 0..n-1 and hence do not directly indicate qubit location on the device.</p> <p><strong>Python/cirq format</strong></p> <p>Each python file defines two variables: QUBIT_ORDER and CIRCUIT. The former is a python list object containing `cirq.GridQubit` objects initialized with the<br>row and column of each qubit on the device. The latter is a `cirq.Circuit` object initialized with all gate operations contained in the circuit. The files have been tested using cirq version 1.2.0.dev20230613162638.</p> <p>The following code snippet illustrates how one can use cirq and our circuit definitions to compute output state amplitudes:</p> <pre><code>$ python -i circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.py >>> cirq.final_wavefunction(CIRCUIT, qubit_order=QUBIT_ORDER) array([ 0.00263724+0.00337646j, 0.0009332 +0.00111853j, -0.0007809 +0.00386362j, ..., 0.00574739-0.00027827j, -0.00254766+0.00299345j, 0.00396056+0.00312335j], dtype=complex64)</code></pre> <p> </p> <p>See [3] for more details about cirq.</p> <p><strong>QASM format</strong></p> <p>Each QASM file has been generated using cirq and specifies the RQC decomposed into CNOT and single-qubit gates. The files have been generated using cirq.</p> <p>See [4] for more details about the format.</p> <p><strong>Measurements file format</strong></p> <p>Each line contains the bitstring obtained in a single execution of the RQC on Sycamore. The first, left-most position corresponds to the qubit 0 in QSIM format and the first qubit in the `QUBIT_ORDER` list in the python/cirq files.</p> <p><strong>To visualize the patches</strong></p> <p>After loading `CIRCUIT` from the appropriate `.py` file, the following code snippet can be used to visualize the patches:</p> <pre><code>import cirq import matplotlib.pyplot as plt pairs = set() for moment in CIRCUIT: for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add(tuple(sorted(q))) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax)</code></pre> <p> </p> <p><strong>Visualize the activation patterns</strong></p> <p>The activation pattern sequence can also be visualized in a similar manner. After loading `CIRCUIT` from the appropriate `.py` pyle, the following code snippet can be used to visualize the activation patterns:</p> <pre><code>import cirq import matplotlib.pyplot as plt def has_two_qubit_gates(moment): has = False for op in moment.operations: if len(op.qubits) > 1: has = True break return has def plot_pairs(moment): pairs = set() for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add( tuple(sorted(q)) ) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax) return ax moments = [_ for _ in CIRCUIT if has_two_qubit_gates(_)] moment_to_visualize = moments[0] # iterate through this manually plot_pairs(moment_to_visualize)</code></pre> <p> </p> <p><strong>Content listing</strong></p> <p>The dataset includes the following tarball files:</p> <pre><code>n69_m04_patches2.tar.gz (80 files) n69_m04_patches3.tar.gz (80 files) n69_m06_patches2.tar.gz (80 files) n69_m06_patches3.tar.gz (80 files) n69_m08_patches2.tar.gz (80 files) n69_m08_patches3.tar.gz (80 files) n69_m10_patches2.tar.gz (80 files) n69_m10_patches3.tar.gz (80 files) n69_m12_patches2.tar.gz (80 files) n69_m12_patches3.tar.gz (80 files) n69_m14_patches2.tar.gz (80 files) n69_m14_patches3.tar.gz (80 files) n69_m16_patches2.tar.gz (80 files) n69_m16_patches3.tar.gz (80 files) n69_m18_patches2.tar.gz (80 files) n69_m18_patches3.tar.gz (80 files) n69_m20_patches2.tar.gz (80 files) n69_m20_patches3.tar.gz (80 files) n69_m22_patches2.tar.gz (80 files) n69_m22_patches3.tar.gz (80 files) n69_m24_patches1.tar.gz (4 files) n69_m24_patches2.tar.gz (80 files) n69_m24_patches3.tar.gz (80 files) n69_m26_patches2.tar.gz (80 files) n69_m26_patches3.tar.gz (80 files) n69_m28_patches2.tar.gz (80 files) n69_m28_patches3.tar.gz (80 files) n69_m30_patches2.tar.gz (80 files) n69_m30_patches3.tar.gz (80 files) n70_m16_patches2.tar.gz (960 files) n70_m16_patches3.tar.gz (1040 files) n70_m16_patches9.tar.gz (160 files) n70_m18_patches2.tar.gz (960 files) n70_m18_patches3.tar.gz (1040 files) n70_m18_patches9.tar.gz (160 files) n70_m20_patches2.tar.gz (960 files) n70_m20_patches3.tar.gz (1040 files) n70_m20_patches9.tar.gz (160 files) n70_m22_patches2.tar.gz (960 files) n70_m22_patches3.tar.gz (1040 files) n70_m22_patches9.tar.gz (160 files) n70_m24_patches1.tar.gz (56 files) n70_m24_patches2.tar.gz (960 files) n70_m24_patches3.tar.gz (1040 files) n70_m24_patches9.tar.gz (160 files) n70_m26_patches1.tar.gz (4 files) n70_m26_patches2.tar.gz (880 files) n70_m26_patches3.tar.gz (960 files) n70_m26_patches9.tar.gz (160 files)<br>67-qubit data (described below)</code></pre> <p> </p> <p>Additionally, the file <code>Figures 2 and 3 data.zip</code> contains processed data (9 files in Pickle format) that are shown in Figures 2 and 3 of the paper.</p> <p>The 67-qubit data files, <code>n67_full.zip</code>, <code>n67_patches2.zip</code>, and <code>n67_patches3.zip</code> are formatted differently. The circuits are savd in .json format and can be loaded with `cirq.read_json()`. The data files are in .npy format and can be opened with numpy.load(). The number of cycles (`m` elsewhere) is indicated after `d` in the filename. The contents of these zip files can be previewed with the "preview" button.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>[1] Google AI Quantum and collaborators, "Phase transition in Random Circuit Sampling". <a href="https://arxiv.org/abs/2304.11119">arXiv:2304.11119</a></p> <p>[2] Google AI Quantum and collaborators, Supplementary information for “Quantum supremacy using a programmable superconducting processor”. <a href="https://arxiv.org/abs/1910.11333">arXiv:1910.11333</a></p> <p>[3] Cirq: A Python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits, <a href="https://github.com/quantumlib/Cirq">https://github.com/quantumlib/Cirq</a>.</p> <p>[4] Cross, Andrew W.; Bishop, Lev S.; Smolin, John A.; Gambetta, Jay M. "Open Quantum Assembly Language", <a href="https://arxiv.org/abs/1707.03429">arXiv:1707.03429</a></p> <p>[5] Martinis, John M. et al. (2022), "Quantum supremacy using a programmable superconducting processor", Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.k6t1rj8">https://doi.org/10.5061/dryad.k6t1rj8</a></p>
Data for "Beyond theory driven discovery: hot random search and datum derived structures"
<p>Data associated with "Beyond theory driven discovery: hot random search and datum derived structures"</p> <p>contributed to: FD 2025: Data-driven discovery in the chemical sciences</p> <p><code>.</code><br><code>├── README</code><br><code>├── b</code><br><code>│ ├── collection</code><br><code>│ ├── eddp</code><br><code>│ ├── test-105-111</code><br><code>│ ├── test-192</code><br><code>│ └── test-56</code><br><code>├── c</code><br><code>│ ├── collection</code><br><code>│ ├── collection-0.5eV</code><br><code>│ ├── collection-1eV</code><br><code>│ ├── manifest-dia</code><br><code>│ ├── test-dia-25-48</code><br><code>│ ├── test-dia-8-24</code><br><code>│ └── test-dia-nosymm-8</code><br><code>└── pyrope</code><br><code> ├── airss</code><br><code> ├── collection-gamma-relax</code><br><code> ├── manifest</code><br><code> └── test</code></p> <p><br>C J Pickard, 2024<br>Cambridge</p>
Trained Random Forest model and scaler parameters on new physical and tsfel features from seismic data of 150s length.
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Random Hierarchy Model
<p>Code for the paper "How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model", preprint available here: <a href="https://arxiv.org/abs/2307.02129" rel="nofollow">arXiv:2307.02129</a>.</p>
Supplementary videos for the article "Simulation of Somatic Evolution Through the Introduction of Random Mutation to the Rules of Conway's Game of Life"
<p><strong>List of Supplementary Videos:</strong><strong> </strong></p> <p><strong>Supplementary Video 1:</strong> Animation of simulation run in the absence of mutation (mutation rate = 0).</p> <p><strong>Supplementary Video 2:</strong> Animation of simulation run with mutation rate = 0.01 and mutation magnitude = 1.0.</p> <p><strong>Supplementary Video 3:</strong> Animation of simulation run with mutation rate = 0.05 and mutation magnitude = 6.0.</p> <p><strong>Supplementary Video 4:</strong> Animation of simulation run with a mutation rate = 0.01 and a mutation magnitude = 5.0.</p> <p><strong>Supplementary Video 5:</strong> Animation of simulation run with a mutation rate = 0.05 and a mutation magnitude = 0.5.</p> <p><strong>Supplementary Video 6:</strong> Animation of simulation run with a mutation rate = 0.05 and a mutation magnitude = 1.0.</p> <p><strong>Supplementary Video 7:</strong> Animation of simulation run with a mutation rate =0.4 and a mutation magnitude = 1.5.</p> <p><strong>Supplementary Video 8:</strong> Animation of simulation run with a mutation rate =0.1 and a mutation magnitude = 0.5.</p>
Trained random forest models on 5000 traces per class based on updated features
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Breast cancer risk markedly lower with serum 25-hydroxyvitamin D concentrations ≥60 vs <20 ng/ml (150 vs 50 nmol/L): Pooled analysis of two randomized trials and a prospective cohort
<p>Background: While numerous epidemiologic studies have found an association between higher serum 25-hydroxyvitamin D [25(OH)D] concentrations and lower breast cancer risk, few have assessed this association for concentrations >40 ng/ml.</p> <p> </p> <p>Objective: To investigate the relationship between 25(OH)D concentration and breast cancer risk across a broad range of 25(OH)D concentrations among women aged 55 years and older.</p> <p> </p> <p>Methods: Analyses used pooled data from two randomized clinical trials (N=1129, N=2196) and a prospective cohort (N=1713) to examine a broad range of 25(OH)D concentrations. The outcome was diagnosis of breast cancer during the observation periods (median: 4.0 years). Three analyses were conducted: 1) Incidence rates were compared according to 25(OH)D concentration from <20 to ≥60 ng/ml (<50 to ≥150 nmol/L), 2) Kaplan-Meier plots were developed and 3) multivariate Cox regression was used to examine the association between 25(OH)D and breast cancer risk using multiple 25(OH)D measurements.</p> <p> </p> <p>Results: Within the pooled cohort (N=5038), 77 women were diagnosed with breast cancer (age-adjusted incidence: 512 cases per 100,000 person-years). Results were similar for the three analyses. First, comparing incidence rates, there was an 82% lower incidence rate of breast cancer for women with 25(OH)D concentrations ≥60 vs <20 ng/ml (Rate Ratio=0.18, <em>P</em>=0.006). Second, Kaplan-Meier curves for concentrations of <20, 20-39, 40-59 and ≥60 ng/ml were significantly different (<em>P</em>=0.02), with the highest proportion breast cancer-free in the ≥60 ng/ml group (99.3%) and the lowest proportion breast cancer-free in the <20 ng/ml group (96.8%). The proportion with breast cancer was 78% lower for ≥60 vs <20 ng/ml (<em>P</em>=0.02). Third, multivariate Cox regression revealed that women with 25(OH)D concentrations ≥60 ng/ml had an 80% lower risk of breast cancer than women with concentrations <20 ng/ml (HR=0.20, <em>P</em>=0.03), adjusting for age, BMI, smoking status, calcium supplement intake, and study of origin.</p> <p> </p> <p>Conclusions: Higher 25(OH)D concentrations were associated with a dose-response decrease in breast cancer risk with concentrations ≥60 ng/ml being most protective.</p>
Raw random data (DC TRNG)
<p>Raw random data obtained from the DC-TRNG on Cyclone IV and Cyclone V Intel FPGAs</p> <p>Cyclone IV (EP4CGX150DF31C7) & Cyclone V (5CEBA4F17C8) FPGAs</p>
Random symmetric interval matrices dataset
<p>A set of randomly created symmetric interval matrices used for experiments with a refinement algorithm for reducing the alpha-values in the alphaBB algorithm.</p>
Random data sets TRNG evaluation
<p>Random data sets that were obtain in the evaluation of True Random Number Generators in scope of the HECTOR project. Sets were both obtained under stress and in normal operation. Results of statistical tests (AIS31) are included.</p>
Changes in School-day Step Counts during a Physical Activity for Lent Intervention: A Cluster Randomized Crossover Trial of the Savior's Sandals
<p>Excel data file of 187 participants in a Physical Activity for Lent program conducted in spring 2017 at 4 Catholic middle schools in San Diego, CA, USA. Includes daily physical activity step counts and questionnaire data for religiosity, physical activity enjoyment, and situational interest motivation.</p>
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DANDI Archive for NWB datasets
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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
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