SEARCH · Engineering Papers
Results for “algorithm”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Fast algorithms for integral formulations of steady-state radiative transfer equation
Not provided.
LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine
Not Available
K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data
Not provided.
RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes
The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.
A neural network clustering algorithm for the ATLAS silicon pixel detector
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
Schwinger-Keldysh formalism on the lattice: A faster algorithm and its application to field theory
Not Available
Computationally efficient algorithms for Brownian dynamics simulation of long flexible macromolecules modeled as bead-rod chains
Not Available
Unveiling the Interplay Between Global Link Arrangements and Network Management Algorithms on Dragonfly Networks.
Abstract not provided.
Resistive Memory Device Requirements for a Neural Algorithm Accelerator.
Abstract not provided.
Spiking Neural Algorithms for Markov Process Random Walk.
Abstract not provided.