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Weyl nodes in Ce3Bi4Pd3 revealed by dynamical mean-field theory
No full text<p>This repository contains code and data corresponding to the Publication:</p>
<p>"Weyl nodes in Ce3Bi4Pd3 revealed by dynamical mean-field theory" by M. Braß, J. M. Tomczak and Karsten Held published in Physical Review Research (2024) (preprint: https://arxiv.org/abs/2404.17425)</p>
<p>All data is licensed under Creative Commons Attribution Share Alike 4.0 International, all software is licensed under the MIT License.</p>
<p>Please refer to the README.txt files and contact us in case of questions.</p>
<p>The files allow you to</p>
<ul>
<li>perform the DFT bandstructure calculation of Ce3Bi4Pd3</li>
<li>construct the Tight-binding Hamiltonian</li>
<li>search for Weyl points in the DFT bands</li>
<li>perform the DMFT calculations</li>
<li>analytically continue DMFT Green's function and self-energy</li>
<li>obtain the k-dependent DMFT spectral function</li>
<li>construct the quasi particle Hamiltonian and find its Weyl points</li>
</ul>
<p>Software requirements:</p>
<ul>
<li>FPLO : https://www.fplo.de/</li>
<li>w2dynamics : https://github.com/w2dynamics/w2dynamics</li>
<li>OmegaMaxEnt : https://github.com/amstremblay/OmegaMaxEnt</li>
<li>TightBindingToolBox : https://github.com/martinbrass/TightBindingToolBox</li>
</ul>
RAAV - Results of the PT-STA accessibility analysis for five public transport scenarios based on automated vehicles in Sooss, Lower Austria
No full text<h3>Dataset description</h3>
<p>As part of the project <strong>"RAAV - Rural Accessibility and Automated Vehicles"</strong> between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of the application of the PT-STA method for separate public transport scenarios in a comprehensible manner and to make them publicly available.</p>
<h3>Context and methodology</h3>
<p>An adaption of a classical STA accessibility analysis was applied on a sample of over 100 individuals in Sooss, Lower Austria. Five different public transport scenarios based on a possible implication of automated vehicle technology were compared regarding their potential impact on accessibility for the local population.</p>
<p>To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research.</p>
<h3>Technical details</h3>
<p>The dataset contains one Microsoft Excel file containing multiple data sheets. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.</p>
<p>Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.</p>
Numerical data for "Origin of the anomalous Hall effect in Cr-doped RuO2"
No full text<p>This dataset contains the numerical data accompanying the "Origin of the anomalous Hall effect in Cr-doped RuO2" <a href="https://doi.org/10.1103/PhysRevB.111.064406" target="_blank" rel="noopener">paper</a>.<br>Computations were performed using <a href="https://www.vasp.at" target="_blank" rel="noopener">VASP</a> 6.3.0, and the postprocessing necessary to obtain the band structures was done using the <a href="https://vaspkit.com" target="_blank" rel="noopener">vaspkit</a> package.<br><br>The <code>Data</code> directory contains the necessary input files to reproduce the computations and the output files produced by the computations.<br>The <code>Plots</code> directory contains the data and gnuplot scripts used to plot figures presented in the paper:</p>
<ul>
<li>Fig. 1: energies and weighted average of local magnetic moments of various magnetic phases of Cr-doped RuO2 in the VCA approximation</li>
<li>Fig. 2: the absolute value of total magnetic moment with corresponding energy and local magnetic moment for a set of supercells of RuO2 with 20% Cr doping</li>
<li>Fig. 3: magnetic moment distribution for the two lowest-energy structures</li>
<li>Fig. 4: effective Curie-Weiss moments from Ref. [14] compared to a Cr-only model and anomalous Hall conductivity measured in Ref. [14] and its derivative with respect to the magnetic moment</li>
<li>Fig. 5: band-structure plots for the lowest-energy structure among the set of studied supercells</li>
</ul>
<p>To produce the figure one simply runs <code>plot.sh</code> script in the respective directory: the script will use <code>gnuplot</code> and <code>pdflatex</code> to produce the plots.<br>The data necessary to produce plots can be collected using the <code>get.sh</code> or <code>get_data.sh</code> scripts.<br>In the case of VASP computations, the total energy is reported in the <code>OUTCAR</code> file at the line containing the <code>free energy TOTEN =</code> token, the local magnetization in the block after the <code>magnetization</code> token, and the total magnetic moment in the <code>OSZICAR</code> file at the line containing the <code>mag=</code>token. The input parameters are specified in the <code>INCAR</code> file and the k-grid mesh in <code>KPOINTS</code> file. The pseudopotentials should be obtained from the VASP distribution and saved as a single file, named <code>POTCAR</code>.</p>
<p><br>The following POTCARs were used for VASP calculations:<br><code>md5sum name and location</code><br><code>b5c924befef4a180481bb6f65e4e516d potpaw_LDA.54/Ru_pv/POTCAR</code><br><code>6bfe8c9fc881319367b05c52d7f764ba potpaw_LDA.54/Cr_pv/POTCAR</code><br><code>dd29215744b63de40827d7952527f753 potpaw_LDA.54/POTCAR</code></p>
<h2>Licenses</h2>
<p>The data is licensed under CC-BY, the code (scripts) are licensed under MIT.</p>
LongEval 2024 Train Collection
No full text<p>The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. All the data were collected over January 2023. In total, the collection contains 599 train queries, with corresponding 9,785 relevance assessments coming from the Qwant click model. The set of documents consist of 2,049,729 downloaded, cleaned and filtered Web Pages. Apart from their original French versions, the collection also contains translations of the webpages and queries into English. The collection serves as the official training collection for the 2024 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF.</p><p> </p><p>The data is released under the <a href="https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License">Qwant LongEval Attribution-NonCommercial-ShareAlike License.</a></p>
Benchmarking Transparent Objects Geometry Estimation (B-TOGE)
No full text<h2>Challenges of Depth Estimation for Transparent Objects</h2>
<h3>Context and methodology</h3>
<p>This dataset was created to investigate the limitations of current method targeting depth estimation of transparent objects. The aim is to highlight the advantages and disadvantages of the different types of approaches and to quantitatively evaluate the expected error. We collected diverse data to empirically investigate the reliability of different methods that provide depth for transparent objects. By using glass and plastic objects, filled with liquid and empty, properties like opacity and index of refraction are varied. The selected objects also vary significantly in shape and size, with a mix of transparent and non-transparent materials. Additionally, scene properties, including viewing angle, object arrangements, support plane texture, and lighting are varied to create diverse evaluation scenarios.</p>
<h3>Technical details</h3>
<p>The dataset is collected by moving a camera attached to a robot arm around a scene. The same viewpoints are collected for every scene, and the camera poses are obtained through inverse kinematics of the robot arm. We use 3D-DAT (<a href="https://github.com/markus-suchi/3D-DAT">https://github.com/markus-suchi/3D-DAT</a>) for annotation, placing object models in the virtual 3D scene, and manually correcting their poses based on their reprojection error in the different RGB views.</p>
<p>To obtain 3D object models, the physical objects are coated using a mat spray paint after collecting the different scenes. A high-quality depth sensor (Photoneo MotionCam-3D scanner, <a href="https://www.photoneo.com/">https://www.photoneo.com/</a>) is used to reconstruct them. The set of 15 objects used in our experiments is illustrated in Figure 3, and includes plastics and glass objects, filled or empty with a variety of shapes, and a variety of sizes.</p>
<p>A total of 32 scenes is collected using a Intel Realsense D435 (<a href="https://www.intelrealsense.com/depth-camera-d435i/">https://www.intelrealsense.com/depth-camera-d435i/</a>), saving both the RGB image and the depth image at a resolution of 1280 × 720 pixels. The robotic arm performs a circular motion around the scene with the camera oriented toward the scene center, placing the camera at four different heights and corresponding polar angles (68°, 60°, 48° and 33°). For each circle, either 16 or 26 views are collecting resulting in a total of 64 or 104 views per scene. The light is uniform and comes from the top of the scene. For seven scenes, we add a strong light projector to the side of the scene, producing caustics and other refraction and reflection effects at the interface of transparent objects. Six scenes also have a textured background instead of an uniform one, and the number of distractors in the scene is varied.</p>
<p>For each scene in the "scenes/" folder, the structure is as follow:</p>
<ul>
<li>rgb/ contains the color images</li>
<li>depth/ contains the depth obtained with the Realsense D435 camera</li>
<li>groundtruth_handeye.txt contains the camera poses of each viewpoint (each line contains pose in TUM format: id, tx, ty, tz, rx, ry, rz, rw with id being the current view, tx,ty,tz the translation, rx, ry, rz, rw the rotation as quaternion).</li>
<li>poses.yaml contains the scene objects annotation in the same world reference frame as the camera poses</li>
</ul>
B-ring Hydroxylation in the Flavonoid Pathway: Dataset
No full text<p>Supporting and additional data to FWF project P 32901-B "B-ring hydroxylation in the flavonoid pathway".<br>More information can be found in the related journal articles (see the section "related works").</p>
<h2>Project context</h2>
<p>The project 'B-Ring hydroxylation in the flavonoid pathway' was designed as a follow up proposal to P29552-B29, which started the establishment of the first crystal structure of a cytochrome P450 dependent monooxygenase (CYP) of the flavonoid pathway using the specialized CYP75B member chalcone 3-hydroxylase (CH3H) as a model. CH3H is closely related to the prominent flavonoid 3'-hydroxylase (F3'H) and a key enzyme in the biosynthesis of anthochlor pigments, which provide yellow flower colour in a number of ornamental plants, form UV-honey guides in certain Asteraceae species and also show health-beneficial effects of chalcones in humans. The project aimed at enabling an understanding of the structure-function relationship of the CYPs determining the B-ring hydroxylation pattern of flavonoid structures (CH3H, F3'H, F3'5'H). The identification of the amino acids involved in substrate binding and their mode of action is of key interest as this allows targeted application in biotransformation, and breeding of plants with increased disease tolerance and/or improved aesthetic qualities. Procedures were established for the recombinant production of a soluble variant of CH3H and three cytochrome P450 reductases from different ornamental species with high yields, which allowed characterization of the purified enzyme and provided new insights into CH3H substrate specificity. Limited long-time stability of CH3H, however, so far impeded crystallization of the enzyme. Studies of F3'H from Malus sp. concentrated on the potential contribution to dihydrochalcone hydroxylation and its impact for watercore, an internal physiological disorder of apple. The results also contributed to the creation of the first genome edited poinsettias towards orange flower colour. For F3'5'H, amino acids essential for enzyme activity were identified for the first time. The project involved Heidi Halbwirth (expertise in flavonoid biosynthesis and hydroxylating enzymes), Oliver Spadiut (expertise in protein production and purification) and Christian Molitor (expertise in crystallization and modelling). This project led to seven publications in peer-reviewed journals.</p>
<h2>Files</h2>
<p>The data in the ZIP file is organized and structured by data type.<br>The software required to open and work with the files is:</p>
<ul>
<li>JPG: Any image viewer</li>
<li>TXT: Any text editor</li>
<li>XLS/XLSX: Microsoft Excel or other office suites</li>
</ul>
<h2>License</h2>
<p>All data are licensed under the CC BY 4.0 license.</p>
<h2>Funding</h2>
<p>FWF (Austrian Science Fund) – B-ring hydroxylation in the flavonoid pathway (P32901-B)</p>
Eggfit: An iterative algorithm to fit egg-shapes to object boundaries
No full text<h2>Fitting egg shapes to boundaries</h2>
<p>This is version 2.0.0 of a snapshot of the research code that produced results for the enhanced egg-fitting:</p>
<ul>
<li>J. Hladůvka and W. G. Kropatsch. Enhanced iterative egg-shape fitting to discretized object boundaries,</li>
</ul>
<p>submitted in September 2024 to the special issue of Journal of Mathematical Imaging and Vision.</p>
<p>It is also follow-up of the research code version 1.0.0 (DOI:<a href="https://doi.org/10.48436/d17s6-p5d44"><code>10.48436/d17s6-p5d44</code></a>) which is related to:</p>
<ul>
<li>J. Hladůvka and W. G. Kropatsch. Fitting egg-shapes to discretized object boundaries, in Discrete geometry and mathematical morphology, 2024, pp. 107–119, DOI:<a href="https://doi.org/10.1007/978-3-031-57793-2_9"><code>10.1007/978-3-031-57793-2_9</code></a>.</li>
</ul>
<p>To reproduce the experiments, please follow the instructions in the README of the zip archive.</p>
Road Network Mapping from Multispectral Satellite Imagery: Leveraging Deep Learning and Spectral Bands
No full text<h2>Road Network Mapping from Multispectral Satellite Imagery: Leveraging Deep Learning and Spectral Bands</h2><p>Submitted to AGILE24</p><h3>Abstract</h3><p>Updating road networks in rapidly changing urban landscapes is an important but difficult task, often challenged by the complexity and errors of manual mapping processes. Traditional methods that primarily use RGB satellite imagery struggle with obstacles in the environment and varying road structures, leading to limitations in global data processing. This paper presents an innovative approach that utilizes deep learning and multispectral satellite imagery to improve road network extraction and mapping. By exploring U-Net models with DenseNet backbones and integrating different spectral bands we apply semantic segmentation and extensive post-processing techniques to create georeferenced road networks. We trained two identical models to evaluate the impact of using images created from specially selected multispectral bands rather than conventional RGB images. Our experiments demonstrate the positive impact of using multispectral bands, by improving the results of the metrics Intersection over Union (IoU) by 6.5%, F1 by 5.4%, and the newly proposed relative graph edit distance (relGED) and topology metrics by 2.2% and 2.6% respectively.</p><h3>Data</h3><p>To use the code in this repository, download the required data from <a href="https://spacenet.ai/spacenet-roads-dataset/">SpaceNet Challenge 3</a> via AWS.</p><p>The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.</p><p>SpaceNet was accessed on 05.01.2023 from https://registry.opendata.aws/spacenet</p><h3>Software</h3><p>The analysis and results of this research were achieved with Python and several software packages such as:</p><p>- tensorflow</p><p>- networkx</p><p>- Pillow, cv2</p><p>- GDAL, rasterio, shapely</p><p>- APLS</p><p>For a fully reproducible environment and software versions refer to 'environment.yml'.</p><p>All data is licensed under CC BY 4.0, all software files are licensed under the MIT License.</p><h3>Reproducibility</h3><p>To execute the scripts and train your model, first refer to the 'Data' section of this file to download the data from the providers. A subsample of the whole dataset containing ten images from Las Vegas is included in the './code/data/' subdirectory. Refer to the file 'base_structure.txt' to learn more about the recommended file structure if you plan to implement these functions with more images. Execute the scripts in the following order:</p><p>- preprocessing.py</p><p>- train_model.py</p><p>- postprocessing.py</p><p>- evaluation.py</p><p>If you wish to conduct this experiment with multispectral instead of RGB images, please execute the script ms_channel_seperation.py first, and determine your selection of image bands in the 'channels' parameter of the function 'write_ms_image'. Please be cautious, and change the variables 'image_folder_name' and 'channel_selection' in the preprocessing.py script to accommodate your change between RGB or MS images. If multispectral images have been used, it is necessary to include the prefix 'MS' in your model name (it is highly recommended to use the prefix RGB otherwise).</p><p>Additionally, the post-processed results referenced in the publication are provided in the corresponding folders './results/UNetDense_MS_512/' and './results/UNetDense_RGB_512/'. These include the stitched and recombined images, without any post-processing applied to them, as well as the extracted and post-processed graphs as '.pickle' files. This provided data was used to calculate the metrics Intersection over Union (IoU), F1 score, relGED, and topology metric as presented in the publication.</p><p>If the whole dataset has been downloaded from Spacenet, the additional pre-processing steps of generating ground truth images have to be executed. These include the conversion of geojson road data into training images, the reduction of satellite images to an 8-bit format, and their conversion into '.png' files. These steps can be achieved by applying and, if necessary, modifying the APLS library which is publicly available under <a href="https://github.com/CosmiQ/apls">https://github.com/CosmiQ/apls</a>. An article thoroughly describing this process can be found at <a href="https://medium.com/the-downlinq/creating-training-datasets-for-the-spacenet-road-detection-and-routing-challenge-6f970d413e2f">https://medium.com/the-downlinq/creating-training-datasets-for-the-spacenet-road-detection-and-routing-challenge-6f970d413e2f</a>.</p><p>The figures included in the paper can be reproduced by saving images created during the preprocessing, training, and post-processing steps. To generate the plots of resulting graphs, refer to the corresponding functions and enable the boolean parameter 'plot'. Bounding boxes seen in the figures were drawn manually and only serve an explanatory purpose.</p><p>Please be advised that file paths and folder structure have to be adapted manually in the scripts to suit the user's folder structure. Be aware of selecting uniform file paths and storing the results in folders named after their model. While the code can be executed from the terminal, parameter adjustments have to be implemented in the code itself, running the individual scripts in an IDE is recommended. The used setup was a Windows PC with an NVIDIA graphic card and the corresponding CUDA version installed. It should be noted, that different GPU memory resources might impact the training process, possibly leading to a necessary batch size reduction to avoid a memory overload.</p>
PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments
No full text<h2>Description:</h2><p>Sample of 1,200 comments posted under articles of the Austrian Newspaper Der Standard collected between January 2019 and November 2021. This dataset is published in context with the paper "PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments" and serves the purpose of detecting populist statements in user comments under news articles in the German language. Details about the sampling and annotation process can be found in the paper as well as the accompanying GitHub repository (https://github.com/ahmadouw/COV-Populism-Standard)</p><p><strong>Abstract:</strong> Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore, our work provides a rich resource for future work on the classification of populist user comments in the German language.</p><h3>Structure</h3><ul><li>Each row contains an anonymized user comment and the binary labels given by each of the three annotators (per comment) for every motive</li><li>anti1, anti2, anti3 indicate whether or not anti-elitism was found in the given comment</li><li>cent1, cent2, cent3 indicate whether or not people-centrism was found in the given comment</li><li>sov1, sov2, sov3 indicate whether or not people-sovereignty was found in the given comment</li><li>none1, none2, none 3 indicate whether or not none of the motives was found in the given comment</li><li>Populism is the final label that is assigned by majority vote, if any of the motives is present in the given comment</li></ul><h3>Further Details</h3><ul><li>The data set is available for researchers upon request</li></ul>
Data related to article "Advanced quantification of receptor–ligand interaction lifetimes via single-molecule FRET microscopy"
No full text<h1>Simulated data files</h1>
<p>Simulated single-molecule tracks for characterizing the algorithm described in the article. <code>char_short_sim.h5</code>, <code>char_n_tracks_sim_1.0.h5</code>, and <code>char_long_sim.h5</code> were used to investigate the effect of changing recording intervals, <code>char_n_tracks_sim_0.5.h5</code>, <code>char_n_tracks_sim_1.0.h5</code>, <code>char_n_tracks_sim_2.0.h5</code> to examine the impact of the dataset size.</p>
<p>h5 files contain tables created using to <code>DataFrame.to_hdf</code> method from the <code>pandas</code> Python package. Each table is identfied by the key <code>/<interval>/<simulation run></code>, where <code><interval></code> is the simulated recording interval and <code><simulation run></code> is an integer identifying a particular simulation execution.</p>
<h1>Raw data files</h1>
<p>FRET microscopy image sequences of TCR–pMHC interactions of 5c.c7 and AND TCR-transgenic T cells as described in the article. Zip archives' <code>POPC</code> subfolders contain the recorded image sequences with recording delay (in ms) and number of donor excitation frames indicated in the file names. The <code>beads</code> subfolders contain images of fiducial markers for image registration.</p>
<h1>Analysis files</h1>
<p>Save files generated by the <em>smfret-bondtime</em> analysis software described in the article for 5c.c7 and AND T cell data. Note that these files were generated using a software version predating the version published as 1.0.0. They can nontheless be loaded with the newer version.</p>
<p>In order to load the experimental data,</p>
<ol>
<li><a href="https://github.com/schuetzgroup/smfret-bondtime" target="_blank" rel="noopener">install smfret-bondtime software</a></li>
<li>extract raw data</li>
<li>extract analysis files; the current folder should now contain <code>5cc7</code> and/or <code>AND</code> subfolders as well as <code>5cc7.yaml</code>, <code>5cc7.h5</code>, <code>AND.yaml</code>, and<code>AND.h5</code> files. If raw data is extracted to a different place, open the respective YAML files using a text editor and adjusts the <code>data_dir</code> entry accordingly.</li>
<li>start the <em>smfret-bondtime</em> software</li>
<li>open the YAML file in the software</li>
</ol>