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1 to 10 of 21 Results

Data repository for "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting" Dec 15, 2025 - Quantum Computing @IAAS Wundrack, Philipp; Barzen, Johanna; Bechtold, Marvin, 2025, "Data repository for "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting"", https://doi.org/10.18419/DARUS-5267, DaRUS, V1 This data repository contains the experiment data and code for the paper "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting". The code.zip file contains the git repositories of the code that was used in the experiments: fast-arrays library: executes vectorized calculations with AVX-512F operations mlflow-rs library: client for...
Data repository for "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects" Oct 28, 2025 - Quantum Computing @IAAS Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2025, "Data repository for "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects"", https://doi.org/10.18419/DARUS-5034, DaRUS, V2 This dataset provides reproduction code and experimental data for the publication "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects". The repository contains an exact snapshot of the code version used to generate all results in the paper, ensuring full reproducibility. The repository is organized into three zi...
Data repository for "Loss Behavior in Supervised Learning With Entangled States" Sep 12, 2025 - Quantum Computing @IAAS Mandl, Alexander; Barzen, Johanna; Bechtold, Marvin; Leymann, Frank; Stiliadou, Lavinia, 2025, "Data repository for "Loss Behavior in Supervised Learning With Entangled States"", https://doi.org/10.18419/DARUS-5174, DaRUS, V1 Replication code and experiment result data for training Parameterized Quantum Circuits (PQCs) with entangled data. The experiments evaluate the structure of the loss landscape during training based on the training sample that is used for training. The combined experiment and data extraction scripts are contained in experiments_and_data_extraction....
Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Dec 16, 2024 - Quantum Computing @IAAS Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander; Truger, Felix, 2024, "Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"", https://doi.org/10.18419/DARUS-4632, DaRUS, V1, UNF:6:oUWWwJbE35iP7+XujN7O1g== [fileUNF] This dataset contains the replication code for the publication titled "Joint Wire Cutting with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. The experiments investigate the influence of entangled states in a joint wire cut using di...
Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation Dec 9, 2024 - Architectures and Middleware @IAAS Pesl, Robin D.; Mathew, Jerin George; Mecella, Massimo; Aiello, Marco, 2024, "Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation", https://doi.org/10.18419/DARUS-4605, DaRUS, V1 Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle, e.g., services not yet existent at design time. A traditional approach is a registry that provides the API documentation of the systems’ endpoints. Large Language Mode...
Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Oct 8, 2024 - Quantum Computing @IAAS Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1 Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication. Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Sch...
Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing 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...
Data repository for: Cutting a Wire with Non-Maximally Entangled States Jan 29, 2024 - Quantum Computing @IAAS Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2024, "Data repository for: Cutting a Wire with Non-Maximally Entangled States", https://doi.org/10.18419/DARUS-3888, DaRUS, V1, UNF:6:79HIPgCvMDi51TZ2V7NUew== [fileUNF] This dataset contains the replication code for the publication titled "Cutting a Wire with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. For comprehensive instructions on using the provided data and code, please refer to the README...
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities Nov 27, 2023 - Architectures and Middleware @IAAS Pesl, Robin D.; Stötzner, Miles; Georgievski, Ilche; Aiello, Marco, 2023, "Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities", https://doi.org/10.18419/DARUS-3767, DaRUS, V1, UNF:6:GV+GzTPs7xXW9ITeS6uC7Q== [fileUNF] Experimental results for the ICSOC 2023 AI-PA position paper "Uncovering LLMs for Service-Composition: Challenges and Opportunities." Exemplars: List of scenarios found in the Google Scholar literature search. Experiment 1 Service Discovery: Chat history for experiment 1 asking ChatGPT for existing real services. Experiment 2 Service Composition: C...
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Sep 27, 2023 - Quantum Computing @IAAS Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/DARUS-3445, DaRUS, V1 Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp.py: Experiments for training QNNs using training data of varying Schm...

Data repository for "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting"

Dec 15, 2025 - Quantum Computing @IAAS

Wundrack, Philipp; Barzen, Johanna; Bechtold, Marvin, 2025, "Data repository for "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting"", https://doi.org/10.18419/DARUS-5267, DaRUS, V1

This data repository contains the experiment data and code for the paper "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting". The code.zip file contains the git repositories of the code that was used in the experiments: fast-arrays library: executes vectorized calculations with AVX-512F operations mlflow-rs library: client for...

Data repository for "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects"

Oct 28, 2025 - Quantum Computing @IAAS

Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2025, "Data repository for "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects"", https://doi.org/10.18419/DARUS-5034, DaRUS, V2

This dataset provides reproduction code and experimental data for the publication "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects". The repository contains an exact snapshot of the code version used to generate all results in the paper, ensuring full reproducibility. The repository is organized into three zi...

Data repository for "Loss Behavior in Supervised Learning With Entangled States"

Sep 12, 2025 - Quantum Computing @IAAS

Mandl, Alexander; Barzen, Johanna; Bechtold, Marvin; Leymann, Frank; Stiliadou, Lavinia, 2025, "Data repository for "Loss Behavior in Supervised Learning With Entangled States"", https://doi.org/10.18419/DARUS-5174, DaRUS, V1

Replication code and experiment result data for training Parameterized Quantum Circuits (PQCs) with entangled data. The experiments evaluate the structure of the loss landscape during training based on the training sample that is used for training. The combined experiment and data extraction scripts are contained in experiments_and_data_extraction....

Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Dec 16, 2024 - Quantum Computing @IAAS

Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander; Truger, Felix, 2024, "Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"", https://doi.org/10.18419/DARUS-4632, DaRUS, V1, UNF:6:oUWWwJbE35iP7+XujN7O1g== [fileUNF]

This dataset contains the replication code for the publication titled "Joint Wire Cutting with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. The experiments investigate the influence of entangled states in a joint wire cut using di...

Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation

Dec 9, 2024 - Architectures and Middleware @IAAS

Pesl, Robin D.; Mathew, Jerin George; Mecella, Massimo; Aiello, Marco, 2024, "Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation", https://doi.org/10.18419/DARUS-4605, DaRUS, V1

Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle, e.g., services not yet existent at design time. A traditional approach is a registry that provides the API documentation of the systems’ endpoints. Large Language Mode...

Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Oct 8, 2024 - Quantum Computing @IAAS

Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1

Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication. Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Sch...

Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

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...

Data repository for: Cutting a Wire with Non-Maximally Entangled States

Jan 29, 2024 - Quantum Computing @IAAS

Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2024, "Data repository for: Cutting a Wire with Non-Maximally Entangled States", https://doi.org/10.18419/DARUS-3888, DaRUS, V1, UNF:6:79HIPgCvMDi51TZ2V7NUew== [fileUNF]

This dataset contains the replication code for the publication titled "Cutting a Wire with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. For comprehensive instructions on using the provided data and code, please refer to the README...

Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

Nov 27, 2023 - Architectures and Middleware @IAAS

Pesl, Robin D.; Stötzner, Miles; Georgievski, Ilche; Aiello, Marco, 2023, "Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities", https://doi.org/10.18419/DARUS-3767, DaRUS, V1, UNF:6:GV+GzTPs7xXW9ITeS6uC7Q== [fileUNF]

Experimental results for the ICSOC 2023 AI-PA position paper "Uncovering LLMs for Service-Composition: Challenges and Opportunities." Exemplars: List of scenarios found in the Google Scholar literature search. Experiment 1 Service Discovery: Chat history for experiment 1 asking ChatGPT for existing real services. Experiment 2 Service Composition: C...

Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Sep 27, 2023 - Quantum Computing @IAAS

Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/DARUS-3445, DaRUS, V1

Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp.py: Experiments for training QNNs using training data of varying Schm...

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