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Author Affiliation: Universität Stuttgart License: Custom Terms

1 to 10 of 62 Results

Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Sep 2, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing" Reichmann, Luca; Hägele, David; Weiskopf, Daniel, 2024, "Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions", https://doi.org/10.18419/darus-4441, DaRUS, V1, UNF:6:WoQ4MNffz92VcvZ/qCGL5w== [fileUNF] This dataset contains the supplemental material for "Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions". The contents and usage of this dataset are described in the README.md files.
Replication Data for: Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model Aug 23, 2024 - Usability and Sustainability of Simulation Software Homs-Pons, Carme; Schneider, David; Simonis, Frédéric; Schulte, Miriam; Uekermann, Benjamin, 2024, "Replication Data for: Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model", https://doi.org/10.18419/darus-4228, DaRUS, V1 This dataset contains software (preCICE v3.1.2, OpenDiHu, deal.II-based solver and ASTE) as well as setup files and instructions to reproduce the experiments "Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model". The actual results of the expe...
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Aug 8, 2024 - PN 6-3 Holzmüller, David; Grinsztajn, Léo; Steinwart, Ingo, 2024, "Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", https://doi.org/10.18419/darus-4255, DaRUS, V1 This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data". The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provided on Gi...
Code for Faithful Embeddings for EL++ Knowledge Bases Jul 5, 2024 - KnowGraphs (EU) 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...
Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction Jul 5, 2024 - KnowGraphs (EU) 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 ex...
Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24) 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 objec...
Code for Ultrahyperbolic Knowledge Graph Embeddings 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 dataset...
Code for Pseudo-Riemannian Graph Convolutional Networks 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 instru...
EHTM Code and Manual Jun 26, 2024 - Extended Hill-Type Muscle Material Model (EHTM) Nölle, Lennart Vincent; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V3 This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...
Supporting data for 'Collective Hall current in chiral active fluids: Coupling of phase and mass transport through traveling bands' Jun 18, 2024 - Institute for Theoretical Physics IV Speck, Thomas; Siebers, Frank; Bebon, Robin; Jayaram, Ashreya, 2024, "Supporting data for 'Collective Hall current in chiral active fluids: Coupling of phase and mass transport through traveling bands'", https://doi.org/10.18419/darus-3771, DaRUS, V1 Supporting Brownian dynamics simulation data. It includes the simulated trajectories that were processed to produce the figures in the main text of the accompanying paper. The code that was used to generate this data can be found in the provided Github repository.

Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Sep 2, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"

Reichmann, Luca; Hägele, David; Weiskopf, Daniel, 2024, "Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions", https://doi.org/10.18419/darus-4441, DaRUS, V1, UNF:6:WoQ4MNffz92VcvZ/qCGL5w== [fileUNF]

This dataset contains the supplemental material for "Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions". The contents and usage of this dataset are described in the README.md files.

Replication Data for: Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model

Aug 23, 2024 - Usability and Sustainability of Simulation Software

Homs-Pons, Carme; Schneider, David; Simonis, Frédéric; Schulte, Miriam; Uekermann, Benjamin, 2024, "Replication Data for: Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model", https://doi.org/10.18419/darus-4228, DaRUS, V1

This dataset contains software (preCICE v3.1.2, OpenDiHu, deal.II-based solver and ASTE) as well as setup files and instructions to reproduce the experiments "Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model". The actual results of the expe...

Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

Aug 8, 2024 - PN 6-3

Holzmüller, David; Grinsztajn, Léo; Steinwart, Ingo, 2024, "Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", https://doi.org/10.18419/darus-4255, DaRUS, V1

This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data". The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provided on Gi...

Code for Faithful Embeddings for EL++ Knowledge Bases

Jul 5, 2024 - KnowGraphs (EU)

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...

Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction

Jul 5, 2024 - KnowGraphs (EU)

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 ex...

Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)

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 objec...

Code for Ultrahyperbolic Knowledge Graph Embeddings

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 dataset...

Code for Pseudo-Riemannian Graph Convolutional Networks

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 instru...

EHTM Code and Manual

Jun 26, 2024 - Extended Hill-Type Muscle Material Model (EHTM)

Nölle, Lennart Vincent; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V3

This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...

Supporting data for 'Collective Hall current in chiral active fluids: Coupling of phase and mass transport through traveling bands'

Jun 18, 2024 - Institute for Theoretical Physics IV

Speck, Thomas; Siebers, Frank; Bebon, Robin; Jayaram, Ashreya, 2024, "Supporting data for 'Collective Hall current in chiral active fluids: Coupling of phase and mass transport through traveling bands'", https://doi.org/10.18419/darus-3771, DaRUS, V1

Supporting Brownian dynamics simulation data. It includes the simulated trajectories that were processed to produce the figures in the main text of the accompanying paper. The code that was used to generate this data can be found in the provided Github repository.

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