271 to 280 of 1,830 Results
Traa, Yvonne; Tari, Faeze, 2024, "Replication Data for "Investigating the Long-Term Kinetics of Pd Nanoparticles Prepared from Microemulsions and the Lindlar Catalyst for Selective Hydrogenation of 3-Hexyn-1-ol"", https://doi.org/10.18419/DARUS-4136, DaRUS, V1
All data files related to the publications, reaction conditions, and characterization equipment are discussed in detail in the publication. Kinetic behavior related to the Pd agglomerates, sintered Pd particles, Lindlar and BASF LF200 catalysts for the catalytic hydrogenation of 3-hexyn-1-ol can be found in the data sets. It includes the correspond... |
Jul 8, 2024 - Hard Negative Captions
Tilli, Pascal, 2024, "Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities", https://doi.org/10.18419/DARUS-4341, DaRUS, V1
Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image–text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard N... |
Grunenberg, Lars; Kessler, Christopher; Teh, Tiong Wei; Schuldt, Robin; Heck, Fabian; Kästner, Johannes; Gross, Joachim; Hansen, Niels; Lotsch, Bettina V., 2024, "Supplementary material for 'Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment'", https://doi.org/10.18419/DARUS-3269, DaRUS, V1, UNF:6:vHsUiLwzEUUMiOsYM8xu+Q== [fileUNF]
This dataset contains input files and results from Grand Canonical Monte Carlo (GCMC) adsorption simulation and Molecular Dynamics (MD) Simulation. All data is presented in a jupyter notebook and for a fast overview without executing the notebook also as PDF-file. Furthermore the dataset contains the modified cif files of COF PI-3, including partia... |
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... |
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. |
