Sourcery VSIPL++ HPEC Benchmark Performance

2006 ◽  
Author(s):  
Jules Bergmann ◽  
Don McCoy
Keyword(s):  
Benchmark Performance

scholarly journals Do physiological measures predict selected CrossFit® benchmark performance?

2015 ◽  
pp. 241 ◽  
Author(s):  
Scotty Butcher ◽  
Tyler Neyedly ◽  
Karla Horvey ◽  
Chad Benko
Keyword(s):  
Physiological Measures ◽  
Benchmark Performance

Benchmark Performance Indicators for Utility Water and Wastewater Pipelines Infrastructure

2018 ◽  
Vol 144 (3) ◽  
pp. 04018003 ◽  
Author(s):  
A. Ganjidoost ◽  
M. A. Knight ◽  
A. J. A. Unger ◽  
C. T. Haas
Keyword(s):  
Performance Indicators ◽  
Water And Wastewater ◽  
Benchmark Performance

scholarly journals DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data

2011 ◽  
pp. 454-469 ◽  
Author(s):  
Mohamed Morsey ◽  
Jens Lehmann ◽  
Sören Auer ◽  
Axel-Cyrille Ngonga Ngomo
Keyword(s):  
Performance Assessment ◽  
Real Data ◽  
Benchmark Performance

scholarly journals Search Tree Pruning for Progressive Neural Architecture Search (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13783-13784
Author(s):  
Deanna Flynn ◽  
P. Michael Furlong ◽  
Brian Coltin
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Search Tree ◽  
Maximum Depth ◽  
Network Architectures ◽  
Transition Graph ◽  
Neural Architecture ◽  
Neural Network Architectures ◽  
Benchmark Performance ◽  
Tree Pruning

Our neural architecture search algorithm progressively searches a tree of neural network architectures. Child nodes are created by inserting new layers determined by a transition graph into a parent network up to a maximum depth and pruned when performance is worse than its parent. This increases efficiency but makes the algorithm greedy. Simpler networks are successfully found before more complex ones that can achieve benchmark performance similar to other top-performing networks.

scholarly journals Benchmark datasets of immune receptor-epitope structural complexes

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Swapnil Mahajan ◽  
Zhen Yan ◽  
Martin Closter Jespersen ◽  
Kamilla Kjærgaard Jensen ◽  
Paolo Marcatili ◽  
...  
Keyword(s):  
Epitope Prediction ◽  
Structural Quality ◽  
Sequence Information ◽  
High Quality ◽  
Immune Receptor ◽  
Prediction Tools ◽  
Specific Epitope ◽  
Benchmark Datasets ◽  
Benchmark Performance ◽  
New Generation

Abstract Background The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.

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