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5 results for “spectral sequence”
The $E_2$ page of the 2-primary Adams spectral sequence in a large range
<p>This dataset contains a description of the cohomology of the 2-primary Steenrod algebra in a large range. Broadly, it contains</p> <ul> <li>complete additive data up to stem 256,</li> <li>complete multiplicative and secondary data up to stem 200 (including the Adams <span class="math-tex">\(d_2\)</span> differentials).</li> </ul> <p>This data was generated using `<a href="https://github.com/SpectralSequences/sseq/tree/master/ext">ext-rs</a>` running on the Wayne State Grid. The Grid is a high-performance computing cluster operated by Wayne State University Computing & Information Technology. See the accompanying document for a more detailed breakdown of the timeline and the resource usage.</p> <p>This repository is comprised of several files.</p> <ul> <li>`rank.csv`: The rank of the cohomology in each bidegree. This data is complete up to and including stem 256 and homological degree 132. The bidegrees with rank zero are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`dimension`: The rank of the cohomology at the given stem and homological degree.</li> </ul> </li> </ul> <p>We use `rank.csv` to define a basis for the cohomology. The symbol <span class="math-tex">\(x_{n,s,i}\)</span> refers to the <span class="math-tex">\(i\)</span>th basis element in bidegree <span class="math-tex">\((n,s)\)</span>, where <span class="math-tex">\(n\)</span> is the stem and <span class="math-tex">\(s\)</span> is the homological degree.</p> <ul> <li>`d2.csv`: The Adams <span class="math-tex">\(d_2\)</span> differentials. This data is complete up to and including stem 200 and homological degree 128. If the target bidegree is zero-dimensional, the differentials are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis of the bidegree <span class="math-tex">\((n,s)\)</span> given by `rank.csv`.</li> <li>`target`: The value of <span class="math-tex">\(d_2(x_{n,s,i})\)</span>.</li> </ul> </li> <li>`products.csv`: The multiplication table of the product structure with respect to our basis. This data is complete for all pairs of elements whose product is in stem at most 200 and homological degree at most 128. The pairs of elements that multiply to zero are omitted. The columns are as follows: <ul> <li>`stem_1`, `Adams_filtration_1`, `index_1`: The stem <span class="math-tex">\(n_1\)</span>, homological degree <span class="math-tex">\(s_1\)</span>, and index <span class="math-tex">\(i_1\)</span> of the first element.</li> <li>`stem_2`, `Adams_filtration_2`, `index_2`: The stem <span class="math-tex">\(n_2\)</span>, homological degree <span class="math-tex">\(s_2\)</span>, and index <span class="math-tex">\(i_2\)</span> of the second element.</li> <li>`product`: The vector representing the product <span class="math-tex">\(x_{n_1, s_1, i_1} \cdot x_{n_2, s_2, i_2}\)</span> in bidegree <span class="math-tex">\((n_1 + n_2, s_1 + s_2)\)</span>.</li> </ul> </li> <li>`differentials.txt.zst`: The differentials in our minimal resolution, in a human-readable plain text format. The output was generated using <a href="https://github.com/SpectralSequences/sseq/blob/v0.0.4/ext/examples/differentials.rs">this</a> example code. It is compressed using Zstandard for convenience, and decompresses to a 64 GiB file.</li> <li>`change_of_basis.csv`: The correspondence between our basis and the Bruner-Rognes (BR) basis defined in <a href="https://arxiv.org/abs/2109.13117">arXiv:2109.13117</a>. Each row expresses one of our basis elements in terms of the BR basis. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis given by `rank.csv` in bidegree <span class="math-tex">\((n,s)\)</span>.</li> <li>`BR`: The vector in the BR basis corresponding to our element <span class="math-tex">\(x_{n,s,i}\)</span>.</li> </ul> </li> </ul> <p>All CSV files are in UTF-8 encoded plain text. Unless otherwise noted, the entries are ordered by stem, then homological degree, then index.</p> <p>v1.0.1: We fixed an error in the `products.csv` file. Due to a software error, some products involving elements in bidegrees <span class="math-tex">\((0,9)\)</span>, <span class="math-tex">\((9,5)\)</span>, <span class="math-tex">\((11,5)\)</span>, <span class="math-tex">\((11,6)\)</span>, <span class="math-tex">\((20,5)\)</span>, <span class="math-tex">\((25,9)\)</span>, <span class="math-tex">\((30,9)\)</span>, <span class="math-tex">\((31,9)\)</span>, and <span class="math-tex">\((33,5)\)</span> were missing. We emphasize that none of the data that was already present in the original file was incorrect.</p> <p>Moreover, we have checked that our multiplication table is compatible with Bruner and Rognes' data (<a href="https://doi.org/10.11582/2022.00015">doi:10.11582/2022.00015</a>), and satisfies the relations described by Lin in (<a href="https://doi.org/10.11582/2022.00015">doi:10.5281/zenodo.7786290</a>).</p>
Spectral models for binary products: Unifying subdwarfs and Wolf-Rayet stars as a sequence of stripped-envelope stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018A&A...615A..78G/abstract">Götberg et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201732274">10.1051/0004-6361/201732274</a></p>
Data for 'NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning'
<p>The uploaded files include two archives for <a href="https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00300" target="_blank" rel="noopener">NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning</a>. The '<em>mgf_data</em>' archive contains all MGF files used in the study, while the '<em>sample_data</em>' archive includes sequencing data, clustering data generated using <code>MSCluster</code>, and XCorr calculation data computed with <code>CometX</code>, all of which were used in the research.</p>
Modeling Sequences of Earthquakes and Aseismic Slip (SEAS) in Elasto-Plastic Fault Zones With a Hybrid Finite Element Spectral Boundary Integral Scheme
<p>This repository contains the results of 2D simulations of the earthquake cycles accounting for off-fault plasticity.</p> <p>Folder Case1 contains the slip rate and time history for all simulations with cohesion c = 47 MPa</p> <p>Folder Case2 contains the slip rate and time history for all simulations with cohesion c = 25 MPa</p> <p>the mat files contain equivalent plastic strain of each element stored at the start and end of each event with element connectivity and node coordinates.</p>
SPECtre: a spectral coherence-based classifier of actively translated transcripts from ribosome profiling sequence data
GEO Series GSE75947. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
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