41 to 50 of 130 Results
Oct 7, 2024 - Architectures and Middleware @IAAS
Pesl, Robin D.; Mombrey, Carolin; Klein, Kevin; Georgievski, Ilche; Becker, Steffen; Herzwurm, Georg; Aiello, Marco, 2024, "Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing", https://doi.org/10.18419/DARUS-4497, DaRUS, V1
A classic, central Service-Oriented Computing (SOC) challenge is the service composition problem. It concerns solving a user-defined task by selecting a suitable set of services, possibly found at runtime, determining an invocation order, and handling request and response parameters. The solutions proposed in the past two decades mostly resort to a... |
Sep 30, 2024 - Quantum Nitric Oxide Sensing Experiment (QNOSE)
Munkes, Fabian; Rayment, Matthew H.; Trachtmann, Alexander; Anschütz, Florian; Eder, Ettore; Hengel, Philipp; Schellander, Yannick; Fruehauf, Norbert; Anders, Jens; Loew, Robert; Pfau, Tilman; Hogan, Stephen; Kübler, Harald, 2024, "Replication Data for: High-resolution cw laser spectroscopy of long-lived Rydberg states in NO", https://doi.org/10.18419/DARUS-4301, DaRUS, V1
This dataset holds all measurement data, as well as the evaluation and plotting scripts to replicate all figures of the paper. Please refer to the README.md file for more information. |
Sep 6, 2024 - Institute of Applied Analysis and Numerical Simulation
Bordignon, Andrea; Cances, Eric; Dusson, Geneviève; Kemlin, Gaspard; Lainez Reyes, Rafael; Stamm, Benjamin, 2024, "Replication Data for: Fully guaranteed and computable error bounds on the energy for periodic Kohn-Sham equations with convex density functionals", https://doi.org/10.18419/DARUS-4469, DaRUS, V1
Data for reproducibility of the numerical simulations of the research paper "Fully guaranteed and computable error bounds on the energy for periodic Kohn-Sham equations with convex density functionals". The .zip file contains everything needed to generate the plots shown in the paper, as well as the code to run your own simulations with the bounds... |
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. |
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 GitHub. Here, we additionally provide the data that is generated by the... |
Jul 29, 2024 - CHOLife_TUHH_LSP_tracking
Rautenbach, Ryan; Buntkiel, Lukas; Schäfer, Jan; Hofmann, Sebastian, 2024, "Processes data and code for Dynamics of Lagrangian Sensor Particles", https://doi.org/10.18419/DARUS-4238, DaRUS, V1
This repository entails the data and Pythoncode for the publication "Dynamics of Lagrangian Sensor Particles: The Effect of Non-Homogeneous Mass Distribution" in the journal "Processes". In the following a brief introduction and guide based on the folders in the repository is laid out. More code specific instructions can be found in the respective... |
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... |
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... |
