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Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study
No full textThe source code and benchmark scripts related to "Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study".
This paper conducts a case study of the multidimensional Fast Fourier Transform to identify which applications will benefit from the asynchronous many-task model. Our basis is the popular FFTW library. We use the asynchronous many-task model HPX and a one-dimensional FFTW backend to implement multiple versions using different HPX features and highlight overheads and pitfalls during migration. Furthermore, we add an HPX threading backend to FFTW.
The case study analyzes shared memory scaling properties between our HPX-based parallelization and FFTW with its pthreads, OpenMP, and HPX backends. The case study also compares FFTW's MPI+X backend to a purely HPX-based distributed implementation.
Find more information how to run the code and scripts in the README.md.</p
Replication Data for: Wetting behavior in the inertial phase of droplet impacts onto sub-millimeter microstructured surfaces
No full textReplication Data for: Wetting behavior in the inertial phase of droplet impacts onto sub-millimeter microstructured surfaces, published in the Journal of Colloid and Interface Science.
The data consists of experimental data, which are the images obtained for all four perspectives on a single measurement day and numerical data, which is a single simulation which has been reported in detail in the study
Processes data and code for Dynamics of Lagrangian Sensor Particles
No full textThis 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 codes.
01 --> The tracking always begins with the same 01_milti[...] folder in which the python code with OpenCV algorithm is located. For tracking the tracking to work certain directories are required in which the raw images are to be stored (separate from anything else) as well as a directory in which the results are to be save (not the same directory as the raw data).
After tracking is completed for all respective experiments and the results directories are adequately labelled and stored any of the other code files can be used for respective analyses. The order of folders beyond the first 01 directory has no relevance to the order of evaluation however can ease the understanding of evaluated data if followed.
02 --> Evaluation of amount of circulations and respective circulation time in experimental vat. (code can be extended to calculate the circulation time based on the various plains that are artificially set)
03 --> Code for the calculation of the amount of contacts with the vat floor. Code requires certain visual evaluations based on the LP trajectories, as the plain/barrier for the contact evaluation has to be manually set.
04 --> Contains two codes that can be applied to results data to combine individual results into larger more processable arrays within python
05 --> Contains the code to plot the trajectory of single experiments of Lagrangian particles based on their positional results and velocity at respective position, highlighting the trajectory over the experiment.
06 --> Condes to create 1D histograms based on the probability density distribution and velocity distributions in cumulative experiments.
07 --> Codes for plotting the 2D probability density distribution (2D Histograms) of Lagrangian Particles based on the cumulative experiments. Code provides values for the 2D grid, plotting is conducted in Origin Lab or similar graphing tools, graphing can also be conducted in python whereby the seaborn (matplotlib) library is suggested.
08 --> Contain the code for the dimensionless evaluation of the results based on the respective Stokes number approaches and weighted averages. 2D histograms are also vital to this evaluation, whereby the plotting is again conducted in Origin Lab as values are only calculated in code.
09 --> Directory does not contain any python codes but instead contains the respective Origin Lab files for the graphing, plotting and evaluation of results calculated via python is given. Respective tables, histograms and heat maps are hereby given to be used as templates if necessary.
The project used the Origin 2023 (64-bit) version, if no Origin license is available then Origin Lab provides a free Origin Viewer with which the projects can be opened and viewed. (https://www.originlab.com/viewer/)</p
Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities
No full textImage-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 Negative Captions (HNC): an automatically created dataset containing foiled hard negative captions for ITM training towards achieving fine-grained cross-modal comprehension in VL. Additionally, we provide a challenging manually-created test set for benchmarking models on a fine-grained cross-modal mismatch with varying levels of compositional complexity. Our results show the effectiveness of training on HNC by improving the models’ zero-shot capabilities in detecting mismatches on diagnostic tasks and performing robustly under noisy visual input scenarios. Also, we demonstrate that HNC models yield a comparable or better initialization for fine-tuning.
The dataset consists of image-caption pairs stored in the JSON data format. The captions describe fine-grained aspects of the corresponding images and each positive caption is associated with exactly one hard negative caption
Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
No full textThis 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
Publication data for: "Probing the Interactions of Immobilized Ruthenium Dihydride Complexes with Metal Oxide Surfaces by MAS NMR: Effects on CO2 Hydrogenation" - data from Estes group
No full textThis file contains all the characterization and reactivity data for our publication given in the title. This includes all catalyst characterization data (NMR, IR, and XAS data) and reactivity data (NMR spectra and excel tables containing the results)
D1244 sensor data (August 2023)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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exaFOAM Industrial Benchmark B9 - DrivAer ExtAero derivative
No full textThis case has been developed as part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. This derivative of geometry, provided by the exaFOAM Stakeholder Audi, is a production ready case in geometry detail. The vehicle contains production-ready design-level details in respect of external surface resolution, under-hood and underbody detail, brake and suspension, wheels with spokes and treaded tyres. We specifically reserve this production-ready design-level configuration to the execution, profiling and performance of any new solvers developed. Detailed information and case setup can be found in the README-B9.pdf file or the README.md file contained in the case setup file
Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)
No full textThis 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, President), succeed_by, (DonaldTrump, holds_position, President)).
We implement six variant models of NetsE based on different hypercomplex number systems. NestE_Q.py for NestE with quaternion. NestE_H.py for NestE with hyperbolic quaternion. NestE_D.py for NestE with split quaternion. NestE_B.py, NestE_HB.py, and NestE_DB.py are the respective version with a translation component.
This code is used to reproduce the experiments of the paper. To execute the code, follow the instructions in the README.md file.</p
Data for: Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty
No full textThis dataset includes the measurement data described in the paper "Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty" and additional supplementary data. All data in .csv files except 'Inj1 -- 5' are generated directly by the rheometer software from the rheometer measurements.
Microsoft Excel files:
settings.xlsx: Contains settings for all the measurements
Comma separated value files:
Benchmark-1 -- 4.csv: Oscillatory test at 3 mNm torque amplitude and 1 Hz frequency (Fig.S1)
Inj1 -- 5.csv: Injection test at 0.025 mL/s (Fig. 7a, 7b)
Rh1a.csv: Oscillatory deformation of 0.2\% strain amplitude and 1 Hz frequency for 30 minutes (Fig. 2a)
Rh1b.csv: Oscillatory deformation of 20\% strain amplitude and 1 Hz frequency for 30 minutes (Fig. 2b)
Rh2a.csv: Oscillatory deformation in three stages: (i) 0.1\% strain amplitude, 0.1 Hz (5 mins) (ii) 20\% strain amplitude, 1 Hz (3 mins) (iii) 0.1\% strain amplitude, 0.1 Hz (22 mins) (Fig. 2c)
Rh2b.csv: Oscillatory deformation in three stages: (i) 0.1\% strain amplitude, 0.1 Hz, 23 °C (5 mins) (ii) 20\% strain amplitude, 1 Hz, 23 °C (3 mins) (iii) 0.1\% strain amplitude, 0.1 Hz, 37 °C (22 mins) (Fig. 2d)
Rh3.csv: Rotational shear stress was applied in steps of (i) 0 Pa (ii) 100 Pa (iii) 0 Pa (iv) 500 Pa (v) 0 Pa (vi) 2000 Pa (vii) 0 Pa, of one minute each, to evaluate creep behaviour. (Fig. 3a)
Rh4-1.csv: Rotational shear strain was applied in steps of (i) 0\% (ii) 1\% (iii) 0\%, of two minutes each. (Fig. 3b)
Rh4-2.csv: Rotational shear strain was applied in steps of (i) 0\% (ii) 100\% (iii) 0\%, of two minutes each. (Fig. 3b)
Rh5-1 -- 3.csv: Amplitude sweep test, where the strain amplitude was gradually increased from 0.01\% to 1000\%. The test was done at 1 Hz frequency (Fig. 3c, 3d)
Rh6-1 -- 3.csv: Frequency sweep test, where the frequency was gradually increased from 0.1 Hz to 100 Hz at 0.01% strain amplitude (Fig. 3e, 3f)
Rh6-4 -- 6.csv: Frequency sweep test, where the frequency was gradually increased from 0.1 Hz to 100 Hz at 0.03% strain amplitude (Fig. 5)
Rh6-7 -- 9.csv: Frequency sweep test, where the frequency was gradually increased from 0.1 Hz to 100 Hz at 0.2% strain amplitude (Fig. 5)
Rh6-10.csv: Frequency sweep test, where the frequency was gradually increased from 0.1 Hz to 100 Hz at 200% strain amplitude (Fig. 5)
Rh7a-1.csv: Rotational shear rate sweep from 0.001 to 1000 1/s (Fig. 4a, 4b)
Rh7a-2.csv: Rotational shear rate sweep from 0.01 to 1 1/s (Fig. 4a, 4b)
Rh7a-3.csv: Rotational shear rate sweep from 0.001 to 0.01 1/s (Fig. 4a, 4b)
Rh7a-4.csv: Rotational shear rate sweep from 1 to 100 1/s (Fig. 4a, 4b)
Rh7a-5.csv: Rotational shear rate sweep from 10 to 1000 1/s (Fig. 4a, 4b)
Rh7b-1.csv: Rotational shear rate sweep from 0.1 to 100 1/s with sandpaper (Fig. 4a, 4b)
Rh7b-2.csv: Rotational shear rate sweep from 0.1 to 100 1/s with sandpaper (Fig. 4a, 4b)
Rh7b-3.csv: Rotational shear rate sweep from 0.1 to 100 1/s with sandpaper (Fig. 4a, 4b)
Rh8-1.csv: Constant rotational shear rate 0.0001 1/s for 20 minutes (Fig. 4c)
Rh8-2.csv: Constant rotational shear rate 0.001 1/s for 20 minutes (Fig. 4c)
Rh8-3.csv: Constant rotational shear rate 0.01 1/s for 20 minutes (Fig. 4c)
Rh8-4.csv: Constant rotational shear rate 0.1 1/s for 20 minutes (Fig. 4c)
Rh8-5.csv: Constant rotational shear rate 1 1/s for 20 minutes (Fig. 4c)
Rh8-6.csv: Constant rotational shear rate 100 1/s for 20 minutes (Fig. 4c)
Rh9.csv: Bone cement was prepared and simply left on the rheometer between the plates while maintaining 1.5 mm gap for 45 minutes without any action.
Rh10.csv: Bone cement was prepared and subjected to 15 minutes of rotational 100 1/s shear rate and then 30 minutes of no action on the rheometer.
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