141 to 150 of 295 Results
Xiong, Bo; Nayyeri, Mojtaba; Cochez, Michael; Staab, Steffen, 2024, "Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction", https://doi.org/10.18419/DARUS-3988, DaRUS, V1
This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For mor... |
Xiong, Bo; Nayyeri, Mojtaba; Luo, Linhao; Wang, Zihao; Pan, Shirui; Staab, Steffen, 2024, "Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)", https://doi.org/10.18419/DARUS-3978, DaRUS, V1
This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, Preside... |
Jul 5, 2024 - KnowGraphs (EU)
Xiong, Bo; Zhu, Shichao; Nayyeri, Mojtaba; Xu, Chengjin; Pan, Shirui; Staab, Steffen, 2024, "Code for Ultrahyperbolic Knowledge Graph Embeddings", https://doi.org/10.18419/DARUS-4342, DaRUS, V1
This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the... |
Jul 5, 2024 - Analytic Computing
Xiong, Bo; Zhu, Shichao; Potyka, Nico; Pan, Shirui; Zhou, Chuan; Staab, Steffen, 2024, "Code for Pseudo-Riemannian Graph Convolutional Networks", https://doi.org/10.18419/DARUS-4340, DaRUS, V1, UNF:6:XC5GdaJdFoY7V7SNqvdoiQ== [fileUNF]
This dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation. This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or... |
Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the droplet segmentation models", https://doi.org/10.18419/DARUS-4147, DaRUS, V1
This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md. |
Jun 28, 2024 - Scientific Computing
Pollinger, Theresa; Van Craen, Alexander; Offenhäuser, Philipp, 2024, "Replication Data for: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers - Two Superfacility Case Studies", https://doi.org/10.18419/DARUS-3707, DaRUS, V1
This data repository contains input and output files for large-scale experiments conducted on the three German national supercomputers HAWK, SuperMUC-NG, and JUWELS. More structure can be seen when switching to "Tree" view below. Then, the files are structured into three folders (`io-benchmark`, `two-systems, `three-systems`), and the outputs were... |
Jun 21, 2024 - POREMAPS: Code, Benchmarks, Applications
Krach, David; Ruf, Matthias; Steeb, Holger, 2024, "POREMAPS 1.0.0: Code, Benchmarks, Applications", https://doi.org/10.18419/DARUS-3676, DaRUS, V1
Initial release 1.0.0 for POREMAPS, PORous Media Anisotropic Permeability Solver for Stokes flow including benchmarks and applications according to Krach et al. (2024). POREMAPS is a Finite Difference Method (FDM) -based parallized Stokes flow solver using MPI, specifically designed to process large binarized 3D image datasets of porous media such... |
Jun 21, 2024 - SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"
Bienroth, Denis; Charitakis, Natalie; Jaeger-Honz, Sabrina; Garkov, Dimitar; Elliott, David; Porrello, Enzo R.; Klein, Karsten; Nim, Hieu T.; Schreiber, Falk; Ramialison, Mirana, 2024, "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)", https://doi.org/10.18419/DARUS-4254, DaRUS, V1
Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Abstract Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular s... |
Jun 20, 2024 - SFB 1244 "Adaptive skins and structures for the built environment of tomorrow"
Stiefelmaier, Jonas, 2024, "D1244 sensor data (November 2022 - May 2023)", https://doi.org/10.18419/DARUS-3453, DaRUS, V1, UNF:6:h59l2diKJntGVWhCWcKZNQ== [fileUNF]
General information: This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the buildin... |
Jun 20, 2024 - SFB 1244 "Adaptive skins and structures for the built environment of tomorrow"
Stiefelmaier, Jonas, 2024, "D1244 sensor data (June 2023)", https://doi.org/10.18419/DARUS-3545, DaRUS, V1, UNF:6:h59l2diKJntGVWhCWcKZNQ== [fileUNF]
General information: This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the buildin... |
