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5,462 results for “Binaries”
TESS-validated Gaia DR3 Pulsating Variables of δ Scuti and γ Doradus: II. 360+ Eclipsing Binaries with δ Scuti and γ Doradus Components
<div> <div> <div> <p>I present serendipitous discoveries of 380 eclipsing binaries with δ Scuti and γ Doradus pulsators, 46 eclipsing binaries exhibiting rotational variability, and 8 new RR Lyrae stars, identified for the first time during a validation project of pulsating variables from Gaia Data Release 3. Gaia DR3 Part 4 Variability released 12.4 million variables, including 748,058 pulsating variable stars of `DSCT|GDOR|SXPHE' types among the variability classification results of all classifiers -- 9,976,881 objects (in the file vclassre.dat, https://cdsarc.cds.unistra.fr/viz-bin/cat/I/358}). Among 75,369 analyzed stars, I confirmed 12,145 δ Scuti stars (including 8,710 new) and 8,192 γ Doradus stars (including 7,531 new). This work has significantly expanded the bona fide DSCT and GDOR catalogs to include 98,968 and 19,466 stars, respectively, providing a valuable resource for future studies. The discovery of the remarkable number of pulsating binaries underscores the significance of this project in validating Gaia’s variable star catalog. </p> </div> </div> </div> <p>The attached CSV files report the current validation results. If you use any data from the catalogs in your research, I appreciate your citation to the paper: </p> <p><strong>Zhou, A.-Y., 2024, Research Notes of the AAS, Volume 8, Number 4, 110 (ADS bibcode: 2024RNAAS...8..110Z) </strong></p> <ul> <li>CSV file GaiaDR3_vari_DSCTgDorSXPhe_Validated_R2_NewEB_Pul.csv for the newly identified Eclipsing Binaries with Pulsating components;</li> <li>CSV file GaiaDR3_vari_DSCTgDorSXPhe_Validated_R2.csv for the entire validated and newly identified results from 75,369 analyzed samples.</li> </ul> <p>This is a developing story. Check back for updates.</p>
Variability Census of Legacy Catalogs: New Pulsating Variables and Eclipsing Binaries
<div> <p>I present the preliminary results of a comprehensive variability census of the four legacy catalogs: BD (+CD+SD), HD, SAO, and PPM. This dedicated survey project aims to identify bright A-F type variable stars using high-precision space photometry from the Transiting Exoplanet Survey Satellite (TESS ) and Gaia. Phases I through V of this survey, encompassing analyses of 193,940 A-F stars selected from the aforementioned catalogs, have been completed. To date, the project has yielded a substantial number of new variable star discoveries, including: over 14,510 new δ Scuti stars, more than 18,382 new γ Doradus stars, and over 2,354 new eclipsing binary systems, with approximately 360 binaries exhibiting pulsations in the primary components. Furthermore, thousands of rotational variables and dozens of Heartbeat stars and RR Lyrae stars have been identified, demonstrating the power of this systematic approach in revealing the rich diversity of stellar variability. Due to potential blending and contamination in TESS photometry, the binarity of a few pulsating stars or those eclipsing binaries exhibiting superimposed intrinsic pulsations of <em>δ</em> Scuti and <em>γ</em> Doradus types should be rechecked. Follow-up studies of individual systems are necessary to resolve contamination issues and accurately identify the true source of variability.</p> <p> </p> </div> <div> <p>The full list of newly identified variable stars is presented in machine-readable format in the attached file `PPM_AFstars_NewVar_R5.csv', and an atlas of selected representative light curves is provided as a separate PDF.</p> </div> <p> </p> <p><strong>Citation:</strong> Zhou, Ai-Ying: 2023, <em>Res. Notes AAS,</em> Vol.<strong>7, </strong> 210 (I)</p> <p> Zhou, Ai-Ying: 2024, <em>Res. Notes AAS,</em> Vol.<strong>8, </strong> 81 (II)</p> <p> Zhou, Ai-Ying: 2024, <em>Res. Notes AAS,</em> Vol.<strong>8, </strong> 190 (III)</p> <p> Zhou, Ai-Ying: 2025, <em>Res. Notes AAS,</em> Vol.<strong>9, </strong> 56 (IV)</p> <p> Zhou, Ai-Ying: 2025, <em>Res. Notes AAS,</em> Vol.<strong>9, </strong> 1?? (V)</p>
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries
<p># About this artifact<br> This artifact contains the implementation and the results of the evaluation of a<br> static slicer for WebAssembly described in the ICSE 2022 paper titled "Static<br> Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries".</p> <p>The artifact contains a docker image (`wassail-eval.tar.xz`) that contains<br> everything necessary to reproduce our evaluation, and the actual data resulting<br> from our evaluation:<br> 1. The implementation of our slicer (presented in Section 4.1) is included in<br> the docker machine, and is available publicly here:<br> https://github.com/acieroid/wassail/tree/icse2022<br> 2. Test cases used for our evaluation of RQ1 are included in the docker machine<br> and in the `rq1.tar.xz` archive.<br> 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine.<br> 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the<br> docker machine.<br> 5. The scripts used to generate the statistics and graphs that are included in<br> the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the<br> `*.py` files in this artifact.<br> 6. The data of RQ5 that has been used in our manual investigation is included in<br> the docker machine and in the `rq5.tar.xz` archive, along with<br> `rq5-manual.txt` detailing our manual analysis findings.</p> <p># How to obtain it<br> Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007</p> <p># Setting up the Docker image<br> ## Downloading The Artifact<br> The artifact is available at the following URL: https://zenodo.org/record/5821007</p> <p>## Loading The Docker Image<br> Once the artifact is downloaded in the file `icse2022slicing.tar.xz`, it can be extracted and loaded into Docker as follows (this takes a few minutes):<br> ```<br> docker import icse2022slicing.tar.xz<br> ```<br> To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the `docker import` command resulted in the hash `54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2`, you can run:<br> ```<br> docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval<br> ```</p> <p>Once the Docker image has been loaded, you can run the following commands to<br> obtain a shell in the appropriate environment:<br> ```<br> docker volume create result<br> docker run -it -v result:/tmp/out/ wassail-eval bash<br> su - opam<br> ```</p> <p># Reproducing results of RQ1<br> Our manual translations of the "classical" examples are included in the `rq1/`<br> directory (available in the docker image and in `rq1.tar.xz`). We<br> include the slices computed by our implementation in the `rq1/out/` directory.</p> <p>A slice can be produced for each example in the docker image as follows, where<br> the first argument is the name of the program being sliced, the second the<br> function index being sliced, the third the slicing criterion (indicated as the<br> instruction index, where instructions start at 1), and the last argument is the<br> output file for the slice:</p> <p>```<br> cd rq1/<br> wassail slice scam-mug.wat 5 8 scam-mug-slice.wat<br> wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat<br> wassail slice word-count.wat 1 41 word-count-slice1.wat<br> wassail slice word-count.wat 1 43 word-count-slice2.wat<br> wassail slice word-count.wat 1 39 word-count-slice3.wat<br> wassail slice word-count.wat 1 45 word-count-slice4.wat<br> wassail slice word-count.wat 1 37 word-count-slice5.wat<br> wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat<br> wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat<br> ```</p> <p>The slice results can then be inspected manually, and compared with the original<br> version of the .wat program to see which instructions have been removed, or with<br> the expected solutions in the `out/` directory, e.g. by running:<br> ```<br> diff word-count-slice1.wat out/word-count-slice1.wat<br> ```<br> (No output is expected if the slice is correct)</p> <p># Reproducing results of RQ2, RQ3, and RQ4<br> For these RQ, we include the data resulting from our evaluation, but we also<br> allow reviewers to rerun the full evaluation if needed. However, such an<br> evaluation requires a heavy machine and takes quite some time (4-5 days to run<br> to completion with a 4 hours timeout). In our case, we used a machine with 256<br> GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to<br> run 128 slicing jobs in parallel.</p> <p>## Runnig the Evaluation<br> We explain how to run the full evaluation, or only a partial evaluation below.<br> One can directly skip to the next section and reuse our raw evaluation results,<br> provided alongside this artifact.</p> <p>### Running the Full Evaluation<br> In order to reproduce our evaluation, you can run the following commands in the<br> docker image. It is recommended to run them in a tmux session if one wants to<br> inspect other elements in parallel (tmux is installed in the docker image). The<br> timeout (set to 4 hours per binary, like in the paper) can be decreased by<br> editing the `evaluate.sh` script (vim is installed in the docker image).</p> <p>This is expected to take 2-3 days of time, on a machine with 128 cores.<br> In order to produce only partial results, see the next section.</p> <p>```<br> cd filtered<br> cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The results are outputted in the `/tmp/out/` directory.</p> <p>### Running a Partial Evaluation<br> If one does not have access to a high-end machine with 128 cores nor the time to<br> run the full evaluation, it is possible to produce partial results. To do so,<br> the following commands can be run. This will run the evaluation on the full<br> dataset in a random order, which can be stopped early to represent a partial<br> view of our evaluation, on a random subset of the data. In order to gather more<br> datapoints, it is also advised to decrease the timeout in the `evaluate.sh`<br> file, for example to 20 minutes by setting `TIMEOUT=20m` with `nano<br> evaluate.sh`. The number of slicing jobs running in parallel can also be<br> decreased to match the number of processors on the machine running the<br> experiments (the `-j 128` argument in the following command runs 128 parallel<br> jobs)</p> <p>```<br> sudo chown opam:opam /tmp/out/<br> cd filtered<br> shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The evaluation results will be stored in the `/tmp/out/` directory.</p> <p>### Skipping the Evaluation Run<br> Instead of rerunning the evaluation, one can rely on our full results included<br> in the `data.txt.xz` and `error.txt.xz` archives. These can simply be downloaded<br> from within the Docker machine and extracted in `/tmp/out/`:</p> <p>```<br> cd /tmp/out/<br> wget https://zenodo.org/record/5821007/files/data.txt.xz<br> wget https://zenodo.org/record/5821007/files/error.txt.xz<br> unxz data.txt.7z<br> unxz error.txt.7z<br> ```</p> <p>## Processing the data</p> <p>In order to process this data, we included multiple python script.<br> These require around 100GB of RAM to load the full dataset in memory.<br> The scripts should be run with Python 3.<br> When running this in the docker image, first run `cd /tmp/out/ && cp /home/opam/*.py ./`<br> - To count the number of functions sliced, run `cut -d, -f 1,2 data.txt | sort<br> -u | wc -l`. This takes around 6 minutes to run on the full dataset.<br> - To count the total number of slices encountered, run `wc -l data.txt<br> error.txt`. This takes around 15 seconds to run.<br> - To count the number of errors encountered, run `wc -l error.txt`. This takes<br> around 1 second to run.<br> - To produce data and graphs regarding the sizes and timing, run `python3<br> statistics-and-plots.py`. This will output the statistics presented in the<br> paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This<br> script takes around 35 minutes to run.<br> - To find the executable slices that are larger than the original programs, run<br> `python3 larger-slices.py > larger.txt`. This script takes around 2h30 to<br> run. It will list the slice using the notation `filename function-sliced<br> slicing-criterion` in the larger.txt file, from which the slice can be<br> recomputed by running `wassail slice function-sliced slicing-criterion<br> output.wat` in the docker image. It will also output statistics regarding<br> these slices, which you can easily inspect by running `tail larger.txt`.<br> - To investigate slices that could not be computed, run:<br> ```<br> sed -i error.txt -e 's/annotation,/annotation./'<br> python3 errors.py<br> ```<br> This will take a few seconds to run and will print a summary of the errors<br> encountered during the slicing process, and requires some manual sorting to map<br> to the categories we discuss in the paper. Here is a summary of the errors<br> encountered and their root cause:</p> <p>### Root Cause: Unsupported Usage of br_table<br> Error: (Failure"Invalid vstack when popping 2 values")<br> Error: (Failure"Spec_inference.drop: not enough elements in stack")<br> Error: (Failure"Spec_inference.take: not enough element in var list")<br> Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)")<br> ### Root Cause: Unreachable Code<br> Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.")<br> Error: (Failure"bottom annotation")<br> Error: (Failure"bottom annotation. this an unreachable instruction")</p> <p># RQ5: Comparison to Slicing C Programs<br> For this RQ, we include the following data in the `rq5.7z` archive, and in the `rq5/` directory in the docker image:<br> - The slicing subjects in their C and textual wasm form in `rq5/subjects/`<br> - The CodeSurfer slices in their C and textual wasm form in `rq5/codesurfer/`<br> - Our slices in their wasm form in `rq5/wasm-slices/`</p> <p>As this RQ requires heavy manual comparison, we do not expect the reviewers to<br> reproduce all of our results. We include a summary of our manual investigation<br> in `rq5-manual.txt`. In order to validate these manual findings, one can for<br> example inspect a specific slice. For example, the following line in<br> `rq5-manual.txt`:</p> <p>```<br> adpcm_apl1_565_expr.c.wat INTERPROCEDURAL<br> ```</p> <p>can be validated as follows:<br> ```<br> cd ~/<br> # This generates a trimmed down version of the CodeSurfer slice, only containing the function of interest<br> wassail count-in-slice rq5/codesurfer/adpcm_slices/adpcm_apl1_565_expr.c.wat slice.wat<br> # This compares the CodeSurfer slice with our slice<br> diff --side-by-side slice.wat rq5/adpcm_apl1_565_expr.c.wat<br> ```</p> <p>In this case, most extraneous instructions are present in the CodeSurfer slices,<br> at the end of the function. This indicates that these are present in order to<br> preserve interprocedural behavior, which corresponds to the `INTERPROCEDURAL`<br> tag in the `rq5-manual.txt`</p> <p> </p>
The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release
<p>Data associated with Figures, Tables, and population parameter samples associated with <br><strong>The population of merging compact binaries inferred using gravitational waves through GWTC-3 , </strong><br><strong><a href="https://dcc.ligo.org/LIGO-P2100239/public">LIGO DCC</a>, <a href="https://arxiv.org/abs/2111.03634">arXiv</a>, <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.011048">PRX</a>. </strong><br>This is v3, superseding v2. Please see the README.md for more information.</p>
Supplementary material for the paper "DS Andromedae, A Detached Eclipsing Double-Lined Spectroscopic Binary in the Galactic Cluster NGC 752
<p>Supplementary material supporting the paper "DS Andromedae: A Detached Eclipsing Double-Lined Spectroscopic</p> <p>Binary in the Galactic Cluster NGC 752" by E. F. Milone, S. J. Schiller, Th. Mellergaard Amby, and S. Frandsen.</p> <p>It includes:</p> <p>A Read-me file in three formats (docx, rtf, pdf); Unabridged Section 3 with extended modeling details (pdf);</p> <p>Extended spreadsheet version of Table 3 of adjusted parameters (pdf); Extended spreadsheet version of Table 8 of absolute</p> <p>parameters (pdf); and Complete Table 15 of photometric data (txt): and a sample DC input file (for Model 41, used in the</p> <p>DS And modeling) in dat format.</p>
CMS DoubleMuParked dataset from 2012 in simple little endian binary format
<p>The Muon.bin file contains in a binary data little-endian format the dataset from Ref [1]. This dataset contains about 60 millon data events from the CMS detector taken in 2012 during Run B and C.</p> <p>The file format is described in the companion file Muon.txt.</p> <p>[1] Wunsch, Stefan; (2019). DoubleMuParked dataset from 2012 in NanoAOD format reduced on muons. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.LVG5.QT81">10.7483/OPENDATA.CMS.LVG5.QT81</a></p>
Catalogs of Eclipsing Binaries with Pulsating Components, δ Scuti stars and γ Doradus stars
<p>I present a comprehensive, up-to-date catalog of <strong>3324</strong> <strong>eclipsing binary star systems containing pulsating components </strong>(not updated in this version). The initial compilation builds upon existing lists of `oscillating Algol-type eclipsing binaries' (oEA) harboring δ Scuti stars. However, the catalog expands upon this foundation to encompass a broader range of pulsating binary systems identified in recent years. This new catalog is valuable for researchers studying binary stars' evolution and pulsating stars. It incorporates various pulsating variable types across the Hertzsprung-Russell diagram, including δ Scuti stars, γ Doradus stars, β Cephei stars, Cepheids, and red giants exhibiting solar-like oscillations. However, this catalog is NOT an exhaustive list of eclipsing binaries with pulsating components of the above types and it is subject to updates. </p> <p>Many stars in this catalog are potentially interesting for further studies. Due to potential blending and contamination in TESS photometry, the binarity of a few pulsating stars could be attributed to neighboring eclipsing binaries. Follow-up studies of individual systems are necessary to resolve contamination issues and accurately identify the true source of variability. If you use part of the catalog in your research, please cite: Zhou, A.-Y., 2010, arXiv e-prints (DOI: 10.48550/arXiv.1002.2729) (ADS: https://ui.adsabs.harvard.edu/abs/2010arXiv1002.2729Z/abstract)</p> <p>In addition, I have also included the up-to-date catalogs of <strong>118,410 δ Scuti stars</strong> and <strong>41,622 γ Doradus stars</strong>, featuring thousands of unpublished discoveries. For these two catalogs, please cite: </p> <p>Zhou, Ai-Ying, 2024, New Astronomy, Volume 105, 102081 (Published: January 2024)</p> <p>Paper in ADS: https://ui.adsabs.harvard.edu/abs/2024NewA..10502081Z/abstract</p> <p>Paper in Publisher web: https://www.sciencedirect.com/science/article/pii/S1384107623000829</p> <p>Thanks for your reading. Your comments are more than welcome!</p>
Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"
<p>Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum"</p> <p>Included are:</p> <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> <p>Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance</p> <p>Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE</p>
Data set for (binary) text classification, involving spoken utterances and written text
<p>This data set contains sentences belonging to either of two classes: Transcripts of spoken<br> (informal) text (Class 0), and written, formal text (Class 1). Sentences in Class 0 were<br> obtained from publicly available transcripts of radio shows (e.g. NPR),<br> whereas Sentences in Class 1 were obtained from Wikipedia.</p> <p>The data set is divided into three subsets: Training, validation, and test (specified by the file names).<br> Each set contains a large number of sentences, belonging to either of the two classes:</p> <p>In total, there are 13,640,458 sentences, of which 6,374,487 in Class 0 and 7,265,971.<br> The training set contains 9.743,188 sentences (of which 4,553,205 in Class 0 and 5,189,983 in Class 1), <br> the validation set contains 1,948,639 sentences (of which 910,641 in Class 0 and 1,037,998 in Class 1), and the <br> test set contains 1,948,631 sentences (of which 910,641 in Class0 and 1,037,990 in Class1). </p> <p>The data sets are in plain text format. Every row contains (i) the class label (0 or 1) and<br> (ii) the text of the sentence, separated from the class label by a tab character.</p> <p>Note that the sentences contain 5 tokens or more (including punctuation marks). </p>
15000 Ellipsoidal Binary Candidates in TESS: Associated Tables
<p>A catalogue of 15779 candidate ellipsoidal binary systems, identified from the first two years of TESS full-frame images.</p> <p>Table 2 contains the 'BEER' score applied to approximately 8,000,000 input TESS targets.</p> <p>Table 3 contains the details of the 15779 selected candidates.</p> <p>Full details of both tables, and the selection process, can be found in the associated paper.</p> <p>https://arxiv.org/abs/2211.06194</p>
Data for: Attractive solution of binary Bose mixtures: Liquid-vapor coexistence and critical point
<p>Path-integral Monte-Carlo results for a balanced Bose mixture with attractive interspecies interaction. In the files named "press_*" we provide the pressure data, presented in figures 1, 2, and S1, and then used to extract the coexistence regions. In the files named "coexistence_region_*" we provide the data extracted using the Maxwell construction (as well as the condensate fraction for a12=-1.2a) and presented in figures 3, 4, 5 and S2.</p>
SEVN parameter file from the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023
<p>The repository contains the runtime parameters used in the SEVN simulations analysed in the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023.</p> <p><strong>Repository content: </strong></p> <p>- <em>used_params_Sgalletta2023.txt<br> </em>The file contains all the runtime parameters used in the SEVN simulations. The parameters that have been varied in different runs are indicated with **** and the explored values are reported in the comment. See the SEVN userguide (<a href="https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf">https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf</a>) for the description of each parameter </p> <p> </p>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Evolution of binary stars on the HR diagram
<p>We studied the evolution of different classes of binary objects that can be observed in a typical stellar cluster, using a grid of detailed massive binary evolution models (Wang et al. 2020) with an initial metallicity of that of the Small Magellanic Cloud (SMC). To compute the models, we use the 1D stellar evolution code MESA (Modules for Experiments in Stellar Astrophysics, Paxton et al. 2011, 2013, 2015, 2018, version 8845).</p> <p>Our grid consists of 2078 binary models with initial primary masses greater than 5 MSun. This translates to a total cluster mass of ∼10^5 MSun in stars between 0.1 to 100 MSun (assuming a binary fraction of 1. The grid covers an initial mass ratio (mass of secondary over the mass of primary, hence always less than 1) range of 0.3-0.95 and orbital periods of 1 day to 8.6 yrs. In this range of masses, mass ratios, and orbital periods, a Monte Carlo method was used to sample initial binary model parameters assuming a Saltpeter initial mass function (IMF) (Salpeter 1955), a flat distribution of mass ratios and logarithm of initial orbital periods.</p> <p>Translucent grey circles indicate pre-interaction binaries - binaries that have not yet undergone a mass transfer phase via Roche Lobe overflow. Hence, the grey line traced on the HRD by the collection of pre-interaction binaries together essentially denotes the Single Star Isochrone (SSI). Grey squares indicate the merger product when we expect a binary to merge during the Case A mass transfer phase. We note that we only model and follow the evolution of Main Sequence mergers and not the mergers coming from the Case B channel. As such, the number of mergers in each frame is likely to be the lower limit to the number of merger products. Single star tracks at SMC metallicity are also plotted in the background from 5-100 MSun. When any component of a binary system completes core carbon burning at a certain cluster age (or helium-burning for the most massive stars), we mark the occurrence of a supernova by putting an ‘*’ symbol in the HRD, that fades over three time steps in the animation.</p> <p>Mass donors and accretors are shown with triangles and diamonds respectively. The binaries that are interacting or have interacted during their Main Sequence lifetime (i.e. the Case A models) are shown in colour, with the colour coding describing the rotation of the component stars (v rot /v crit ) of the binary. All donors and accretors of binaries that have interacted via Case B/C are shown in greyscale. A black frame around the triangles for the mass donor indicates that the surface Hydrogen mass fraction is less than 0.1. Similarly, a black frame around the diamonds for the mass accretors indicates that the surface Helium mass fraction is greater than 0.3. The current age of the cluster is displayed in the center bottom with a time bar that fills up as the animation moves forward in time.</p> <p>In the table above the legend, (from top) we indicate the number of Algol systems i.e. in the nuclear timescale slow Case A mass transfer phase, the number of Main Sequence merger products that are still burning hydrogen at the core, and the number of cool red supergiants (log T e f f < 3.7) at the respective time frames. Moreover, in the next two rows, we denote the number of OB stars that has a neutron star or black hole companion, arising from Case A and Case B evolution channels, at that cluster age. In the next row, we indicate the number of supernovae that have already happened until the current cluster age of the animation. We report the numbers of supernovae occurring from Case A and Case B donors separately from the other progenitors as we expect that the donor stars that have interacted via the Case A or Case B channels will be highly stripped of their envelopes and will likely be progenitors to stripped-envelope supernova (of type Ib and IIb) while the remaining will be progenitors to type IIp/n. The last line gives the number of pre-interacting binaries having luminosity lesser than the brightest non-interacted binary component by up to 1.5 dex.</p> <p>The individual components of the binaries that are in the semi-detached configuration and are interacting via the nuclear timescale slow Case A phase are joined together with solid black lines with an arrow indicating the direction of mass transfer (donor to the accretor). On the other hand, the individual components that have interacted in the past via Case A or B mass transfer are connected to each other with grey dotted lines. These are usually the systems where the donor star is a post-Main Sequence helium star and the accretor is a rejuvenated star still burning hydrogen at the core. The black and white dots over the Case A and B accretors denote that the donors of those binaries have imploded/exploded to form a black hole or neutron star respectively.</p>
Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis
<p>NOTE: the official repository of bcov is: <a href="https://github.com/abenkhadra/bcov">https://github.com/abenkhadra/bcov</a></p> <p>This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows:</p> <p> - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov.</p> <p> - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format.</p> <p> - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`.</p> <p> - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies.</p> <p> - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script.</p> <p> - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br> </p>
Simulation of GW150914 binary black hole merger using the Einstein Toolkit
<p>On February 11, 2016, the LIGO collaboration announced that they had achieved the first ever direct detection of gravitational waves. The gravitational waves – which were detected by both LIGO detectors on September 14, 2015 at 09:51 UTC – were generated over a billion years ago by the merger of a binary black hole system. The announcement came along with the simultaneous publication of a peer-reviewed paper [Phys. Rev. Lett. 116, 061102]; several other papers giving technical details; and a full release of the data from the detection, which has been given the name GW150914.</p> <p>The LIGO analysis found that the merger consisted of a 36 + 29 solar mass binary black hole system, the remnant was a 62 solar mass black hole, and the remaining 3 solar masses were radiated as gravitational waves. This dataset represents a subset of the data from a simulation in which the Einstein Toolkit was used to evolve the last 6 orbits and merger of a binary black hole system with parameters that match the GW150914 event.</p> <p>More details on the simulation, including instructions for how to run it and how to analyse the data can be found in the Einstein Toolkit gallery at http://einsteintoolkit.org/about/gallery/gw150914/.</p>
SEM images of SiO2 and juniper charcoal powders (pure samples and intimate binary mixtures).
<p><strong>Summary:</strong><br>These images are those from SiO2 and juniper charcoal (JChc) powder samples observed with a SEM. The powders were obtained from commercial sources and these samples were prepared at the Bern University (Switzerland) as part of the D-A-CH/CoPhyLab project (https://www.cophylab.space/index.php?id=home)<br><br><strong>Details:</strong><br>¤ SEM images from the sample of pure SiO2:<br> 001-St5607t0_00.tif<br> 002-St5607t0_01.tif<br> 003-St5607t0_03.tif:<br><br>¤ SEM images from the sample of pure juniper charcoal powder:<br> 004-St5607t6_00.tif<br> 005-St5607t6_01.tif<br> 006-St5607t6_04.tif<br> 007-St5607t6_05.tif<br> 008-St5607t6_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 90% SiO2 - 10% JChc by mass:<br> 009-St5606t1_00.tif<br> 010-St5607t1_01.tif:</p><p>¤ SEM images from the sample of the intimate mixture with 70% SiO2 - 30% JChc by mass:<br> 011-St5607t2_00.tif<br> 012-St5607t2_02.tif<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample at lignin fragment peppered with smaller JChc and SiO2 particles and agglomerates:<br> 013-St5607t2_03.tif:<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample with apparent large fragment of lignin structure:<br> 014-St5607t2_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 50% SiO2 - 50% JChc by mass:<br> 015-St5607t3_00_St5606t3_00.tif<br> 016-St5607t3_03.tif<br><br>¤ SEM images from the sample of the intimate mixture with 30% SiO2 - 70% JChc by mass:<br> 017-St5607t4_00.tif<br> 018-St5607t4_01.tif, <br><br>¤ SEM images from the sample of the intimate mixture with 10% SiO2 - 90% JChc by mass:<br> 019-St5607t5_00.tif<br><br><strong>Addendum:</strong><br>These SEM images are associated with the spectroscopic and photometric data available at the following addresses:<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200723_000<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200813_000</p>
Low-entropy Packed Binary Detection using Hardware Performance Counters
<p><span>Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method using<br>Hardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detecting<br>packers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,<br>offering a promising avenue for further exploration and refinement of techniques in malware analysis<br><br><br></span></p> <p><span>The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened, Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.</span></p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
ScienceDex guides
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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)
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.