11 to 20 of 26 Results
Feb 26, 2025 - InMotion (EXC)
Frank, Daniel, 2025, "Damped pendulum for nonlinear system identification - inputs are sampled from a multivariate-normal distribution - synthetically generated", https://doi.org/10.18419/DARUS-4770, DaRUS, V1
Overview This dataset contains input-output data of a damped nonlinear pendulum that is actuated at the mounting point. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system actuated pendulum. The configuration .json files for the corresponding datasets (in-distribution and out-of-distri... |
Feb 26, 2025 - InMotion (EXC)
Frank, Daniel, 2025, "Coupled mass-spring-damper system for nonlinear system identification - actuated with random static inputs - synthetically generated", https://doi.org/10.18419/DARUS-4768, DaRUS, V1
Overview This dataset contains input-output data of a coupled mass-spring-damper system with a nonlinear force profile. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system coupled-msd. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution... |
Jan 8, 2025 - KR-Building (EXC IntCDC)
Glaser, Gabriel Timon, 2025, "Knowledge Graph Generator", https://doi.org/10.18419/DARUS-4436, DaRUS, V1
Code and experiment results for a synthetic knowledge graph generator. The generator receives a set of rules, with an expected body support and support, and returns a knowledge graph that approximately matches the rules according to the body support and confidence. This code was developed during the Bachelor thesis by Gabriel Glaser, Generating Ran... |
Nov 25, 2024
Gaziev, Georgii, 2024, "Dynamics of Head Pointing Using Static and Dynamic Gains", https://doi.org/10.18419/DARUS-4577, DaRUS, V1, UNF:6:3DnoTrzOzr95W/g7bxpHAg== [fileUNF]
Data was acquired from experiments of the master's thesis "Exploring the dynamics of the head pointing." There were two studies: preliminary and the main study. The task of both was a two-dimensional pointing, as described in ISO 9241-41. Pointing was performed by tracking the head rotation using Tobii 4C Eyetracker, capable of detecting head rotat... |
Xiong, Bo; Potyka, Nico; Tran, Trung-Kien; Nayyeri, Mojtaba; Staab, Steffen, 2024, "Code for Faithful Embeddings for EL++ Knowledge Bases", https://doi.org/10.18419/DARUS-3989, DaRUS, V1
This is the official pytorch implementation of the paper "Faithful embeddings for EL++ Knowledge Bases" published in ISWC 2022. The code was implemented based on el-embeddings. The code can be used to reproduce the experiments on subsumption reasoning. To execute the code, follow the instructions in the README.md file. For more info, please check t... |
Jul 5, 2024 - KnowGraphs (EU)
Xiong, Bo; Nayyeri, Mojtaba; Pan, Shirui; Staab, Steffen, 2024, "Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs", https://doi.org/10.18419/DARUS-3979, DaRUS, V1
This is a Pytorch implementation of the paper Shrinking Embeddings for Hyper-relational Knowledge Graphs published in ACL'23. This code is used to reproduce the experiments of the method ShrinkE, a geometric embedding approach for hyper-relational knowledge graphs. The code is implemented with Python 3 and pytorch. The code is tested on public data... |
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... |
Jul 5, 2024 - KnowGraphs (EU)
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... |
