The Development of a Robot-Based Learning Companion: A User-Centered Design Approach

ERIC Educational Resources Information Center

Hsieh, Yi-Zeng; Su, Mu-Chun; Chen, Sherry Y.; Chen, Gow-Dong

2015-01-01

A computer-vision-based method is widely employed to support the development of a variety of applications. In this vein, this study uses a computer-vision-based method to develop a playful learning system, which is a robot-based learning companion named RobotTell. Unlike existing playful learning systems, a user-centered design (UCD) approach is…

A novel iris localization algorithm using correlation filtering

NASA Astrophysics Data System (ADS)

Pohit, Mausumi; Sharma, Jitu

2015-06-01

Fast and efficient segmentation of iris from the eye images is a primary requirement for robust database independent iris recognition. In this paper we have presented a new algorithm for computing the inner and outer boundaries of the iris and locating the pupil centre. Pupil-iris boundary computation is based on correlation filtering approach, whereas iris-sclera boundary is determined through one dimensional intensity mapping. The proposed approach is computationally less extensive when compared with the existing algorithms like Hough transform.

Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.

PubMed

Xu, Min; Chai, Xiaoqi; Muthakana, Hariank; Liang, Xiaodan; Yang, Ge; Zeev-Ben-Mordehai, Tzviya; Xing, Eric P

2017-07-15

Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. Source code freely available at http://www.cs.cmu.edu/∼mxu1/software . mxu1@cs.cmu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.

PubMed

Ho, Hsiang; Milenković, Tijana; Memisević, Vesna; Aruri, Jayavani; Przulj, Natasa; Ganesan, Anand K

2010-06-15

RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

PubMed Central

2010-01-01

Background RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. Results In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. Conclusions We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches. PMID:20550706

Future Approach to tier-0 extension

NASA Astrophysics Data System (ADS)

Jones, B.; McCance, G.; Cordeiro, C.; Giordano, D.; Traylen, S.; Moreno García, D.

2017-10-01

The current tier-0 processing at CERN is done on two managed sites, the CERN computer centre and the Wigner computer centre. With the proliferation of public cloud resources at increasingly competitive prices, we have been investigating how to transparently increase our compute capacity to include these providers. The approach taken has been to integrate these resources using our existing deployment and computer management tools and to provide them in a way that exposes them to users as part of the same site. The paper will describe the architecture, the toolset and the current production experiences of this model.

What is Intrinsic Motivation? A Typology of Computational Approaches

PubMed Central

Oudeyer, Pierre-Yves; Kaplan, Frederic

2007-01-01

Intrinsic motivation, centrally involved in spontaneous exploration and curiosity, is a crucial concept in developmental psychology. It has been argued to be a crucial mechanism for open-ended cognitive development in humans, and as such has gathered a growing interest from developmental roboticists in the recent years. The goal of this paper is threefold. First, it provides a synthesis of the different approaches of intrinsic motivation in psychology. Second, by interpreting these approaches in a computational reinforcement learning framework, we argue that they are not operational and even sometimes inconsistent. Third, we set the ground for a systematic operational study of intrinsic motivation by presenting a formal typology of possible computational approaches. This typology is partly based on existing computational models, but also presents new ways of conceptualizing intrinsic motivation. We argue that this kind of computational typology might be useful for opening new avenues for research both in psychology and developmental robotics. PMID:18958277

A comparative study of approaches to compute the field distribution of deep brain stimulation in the Hemiparkinson rat model.

PubMed

Bohme, Andrea; van Rienen, Ursula

2016-08-01

Computational modeling of the stimulating field distribution during Deep Brain Stimulation provides an opportunity to advance our knowledge of this neurosurgical therapy for Parkinson's disease. There exist several approaches to model the target region for Deep Brain Stimulation in Hemi-parkinson Rats with volume conductor models. We have described and compared the normalized mapping approach as well as the modeling with three-dimensional structures, which include curvilinear coordinates to assure an anatomically realistic conductivity tensor orientation.

An overview of computer vision

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1982-01-01

An overview of computer vision is provided. Image understanding and scene analysis are emphasized, and pertinent aspects of pattern recognition are treated. The basic approach to computer vision systems, the techniques utilized, applications, the current existing systems and state-of-the-art issues and research requirements, who is doing it and who is funding it, and future trends and expectations are reviewed.

Novel scheme to compute chemical potentials of chain molecules on a lattice

NASA Astrophysics Data System (ADS)

Mooij, G. C. A. M.; Frenkel, D.

We present a novel method that allows efficient computation of the total number of allowed conformations of a chain molecule in a dense phase. Using this method, it is possible to estimate the chemical potential of such a chain molecule. We have tested the present method in simulations of a two-dimensional monolayer of chain molecules on a lattice (Whittington-Chapman model) and compared it with existing schemes to compute the chemical potential. We find that the present approach is two to three orders of magnitude faster than the most efficient of the existing methods.

Using Histories to Implement Atomic Objects

NASA Technical Reports Server (NTRS)

Ng, Pui

1987-01-01

In this paper we describe an approach of implementing atomicity. Atomicity requires that computations appear to be all-or-nothing and executed in a serialization order. The approach we describe has three characteristics. First, it utilizes the semantics of an application to improve concurrency. Second, it reduces the complexity of application-dependent synchronization code by analyzing the process of writing it. In fact, the process can be automated with logic programming. Third, our approach hides the protocol used to arrive at a serialization order from the applications. As a result, different protocols can be used without affecting the applications. Our approach uses a history tree abstraction. The history tree captures the ordering relationship among concurrent computations. By determining what types of computations exist in the history tree and their parameters, a computation can determine whether it can proceed.

Linear programming computational experience with onyx

DOE Office of Scientific and Technical Information (OSTI.GOV)

Atrek, E.

1994-12-31

ONYX is a linear programming software package based on an efficient variation of the gradient projection method. When fully configured, it is intended for application to industrial size problems. While the computational experience is limited at the time of this abstract, the technique is found to be robust and competitive with existing methodology in terms of both accuracy and speed. An overview of the approach is presented together with a description of program capabilities, followed by a discussion of up-to-date computational experience with the program. Conclusions include advantages of the approach and envisioned future developments.

On the Large-Scaling Issues of Cloud-based Applications for Earth Science Dat

NASA Astrophysics Data System (ADS)

Hua, H.

2016-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as NASA's SWOT and NISAR where its SAR data volumes and data throughput rates are order of magnitude larger than present day missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Experiences have shown that to embrace efficient cloud computing approaches for large-scale science data systems requires more than just moving existing code to cloud environments. At large cloud scales, we need to deal with scaling and cost issues. We present our experiences on deploying multiple instances of our hybrid-cloud computing science data system (HySDS) to support large-scale processing of Earth Science data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer 75%-90% costs savings but with an unpredictable computing environment based on market forces.

A toolbox and a record for scientific model development

NASA Technical Reports Server (NTRS)

Ellman, Thomas

1994-01-01

Scientific computation can benefit from software tools that facilitate construction of computational models, control the application of models, and aid in revising models to handle new situations. Existing environments for scientific programming provide only limited means of handling these tasks. This paper describes a two pronged approach for handling these tasks: (1) designing a 'Model Development Toolbox' that includes a basic set of model constructing operations; and (2) designing a 'Model Development Record' that is automatically generated during model construction. The record is subsequently exploited by tools that control the application of scientific models and revise models to handle new situations. Our two pronged approach is motivated by our belief that the model development toolbox and record should be highly interdependent. In particular, a suitable model development record can be constructed only when models are developed using a well defined set of operations. We expect this research to facilitate rapid development of new scientific computational models, to help ensure appropriate use of such models and to facilitate sharing of such models among working computational scientists. We are testing this approach by extending SIGMA, and existing knowledge-based scientific software design tool.

Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance

NASA Technical Reports Server (NTRS)

Eck, Thomas F.; Dye, Dennis G.

1991-01-01

A new satellite remote sensing method for estimating the amount of photosynthetically active radiation (PAR, 400-700 nm) incident at the earth's surface is described and tested. Potential incident PAR for clear sky conditions is computed from an existing spectral model. A major advantage of the UV approach over existing visible band approaches to estimating insolation is the improved ability to discriminate clouds from high-albedo background surfaces. UV spectral reflectance data from the Total Ozone Mapping Spectrometer (TOMS) were used to test the approach for three climatically distinct, midlatitude locations. Estimates of monthly total incident PAR from the satellite technique differed from values computed from ground-based pyranometer measurements by less than 6 percent. This UV remote sensing method can be applied to estimate PAR insolation over ocean and land surfaces which are free of ice and snow.

Toward a computational psycholinguistics of reference production.

PubMed

van Deemter, Kees; Gatt, Albert; van Gompel, Roger P G; Krahmer, Emiel

2012-04-01

This article introduces the topic ''Production of Referring Expressions: Bridging the Gap between Computational and Empirical Approaches to Reference'' of the journal Topics in Cognitive Science. We argue that computational and psycholinguistic approaches to reference production can benefit from closer interaction, and that this is likely to result in the construction of algorithms that differ markedly from the ones currently known in the computational literature. We focus particularly on determinism, the feature of existing algorithms that is perhaps most clearly at odds with psycholinguistic results, discussing how future algorithms might include non-determinism, and how new psycholinguistic experiments could inform the development of such algorithms. Copyright © 2012 Cognitive Science Society, Inc.

Applied Computational Chemistry for the Blind and Visually Impaired

ERIC Educational Resources Information Center

Wedler, Henry B.; Cohen, Sarah R.; Davis, Rebecca L.; Harrison, Jason G.; Siebert, Matthew R.; Willenbring, Dan; Hamann, Christian S.; Shaw, Jared T.; Tantillo, Dean J.

2012-01-01

We describe accommodations that we have made to our applied computational-theoretical chemistry laboratory to provide access for blind and visually impaired students interested in independent investigation of structure-function relationships. Our approach utilizes tactile drawings, molecular model kits, existing software, Bash and Perl scripts…

Link-Based Similarity Measures Using Reachability Vectors

PubMed Central

Yoon, Seok-Ho; Kim, Ji-Soo; Ryu, Minsoo; Choi, Ho-Jin

2014-01-01

We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the “Random Walk with Restart” strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures. PMID:24701188

Bioinformatics approaches to predict target genes from transcription factor binding data.

PubMed

Essebier, Alexandra; Lamprecht, Marnie; Piper, Michael; Bodén, Mikael

2017-12-01

Transcription factors regulate gene expression and play an essential role in development by maintaining proliferative states, driving cellular differentiation and determining cell fate. Transcription factors are capable of regulating multiple genes over potentially long distances making target gene identification challenging. Currently available experimental approaches to detect distal interactions have multiple weaknesses that have motivated the development of computational approaches. Although an improvement over experimental approaches, existing computational approaches are still limited in their application, with different weaknesses depending on the approach. Here, we review computational approaches with a focus on data dependency, cell type specificity and usability. With the aim of identifying transcription factor target genes, we apply available approaches to typical transcription factor experimental datasets. We show that approaches are not always capable of annotating all transcription factor binding sites; binding sites should be treated disparately; and a combination of approaches can increase the biological relevance of the set of genes identified as targets. Copyright © 2017 Elsevier Inc. All rights reserved.

A tale of three bio-inspired computational approaches

NASA Astrophysics Data System (ADS)

Schaffer, J. David

2014-05-01

I will provide a high level walk-through for three computational approaches derived from Nature. First, evolutionary computation implements what we may call the "mother of all adaptive processes." Some variants on the basic algorithms will be sketched and some lessons I have gleaned from three decades of working with EC will be covered. Then neural networks, computational approaches that have long been studied as possible ways to make "thinking machines", an old dream of man's, and based upon the only known existing example of intelligence. Then, a little overview of attempts to combine these two approaches that some hope will allow us to evolve machines we could never hand-craft. Finally, I will touch on artificial immune systems, Nature's highly sophisticated defense mechanism, that has emerged in two major stages, the innate and the adaptive immune systems. This technology is finding applications in the cyber security world.

An approach to computing direction relations between separated object groups

NASA Astrophysics Data System (ADS)

Yan, H.; Wang, Z.; Li, J.

2013-06-01

Direction relations between object groups play an important role in qualitative spatial reasoning, spatial computation and spatial recognition. However, none of existing models can be used to compute direction relations between object groups. To fill this gap, an approach to computing direction relations between separated object groups is proposed in this paper, which is theoretically based on Gestalt principles and the idea of multi-directions. The approach firstly triangulates the two object groups; and then it constructs the Voronoi Diagram between the two groups using the triangular network; after this, the normal of each Vornoi edge is calculated, and the quantitative expression of the direction relations is constructed; finally, the quantitative direction relations are transformed into qualitative ones. The psychological experiments show that the proposed approach can obtain direction relations both between two single objects and between two object groups, and the results are correct from the point of view of spatial cognition.

An approach to computing direction relations between separated object groups

NASA Astrophysics Data System (ADS)

Yan, H.; Wang, Z.; Li, J.

2013-09-01

Direction relations between object groups play an important role in qualitative spatial reasoning, spatial computation and spatial recognition. However, none of existing models can be used to compute direction relations between object groups. To fill this gap, an approach to computing direction relations between separated object groups is proposed in this paper, which is theoretically based on gestalt principles and the idea of multi-directions. The approach firstly triangulates the two object groups, and then it constructs the Voronoi diagram between the two groups using the triangular network. After this, the normal of each Voronoi edge is calculated, and the quantitative expression of the direction relations is constructed. Finally, the quantitative direction relations are transformed into qualitative ones. The psychological experiments show that the proposed approach can obtain direction relations both between two single objects and between two object groups, and the results are correct from the point of view of spatial cognition.

Cross-Sectional Time Series Designs: A General Transformation Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; McDonald, Roderick P.

1991-01-01

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

Search Path Mapping: A Versatile Approach for Visualizing Problem-Solving Behavior.

ERIC Educational Resources Information Center

Stevens, Ronald H.

1991-01-01

Computer-based problem-solving examinations in immunology generate graphic representations of students' search paths, allowing evaluation of how organized and focused their knowledge is, how well their organization relates to critical concepts in immunology, where major misconceptions exist, and whether proper knowledge links exist between content…

$10M Gift Supports "Data Recycling" at UCSF.

PubMed

2017-10-01

The University of California, San Francisco's Institute for Computational Health Sciences has received a $10 million gift to support "data recycling" investigations. The approach to medical research involves mining existing data to potentially uncover new uses for existing drugs and help improve clinical care. ©2017 American Association for Cancer Research.

Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

PubMed

Hu, Yuh-Jyh; Ku, Tien-Hsiung; Yang, Yu-Hung; Shen, Jia-Ying

2018-01-01

Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

Delamination detection using methods of computational intelligence

NASA Astrophysics Data System (ADS)

Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

2012-11-01

Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

An element search ant colony technique for solving virtual machine placement problem

NASA Astrophysics Data System (ADS)

Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

2017-09-01

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

The Influence of Large-Scale Computing on Aircraft Structural Design.

DTIC Science & Technology

1986-04-01

the customer in the most cost- effective manner. Computer facility organizations became computer resource power brokers. A good data processing...capabilities generated on other processors can be easily used. This approach is easily implementable and provides a good strategy for using existing...assistance to member nations for the purpose of increasing their scientific and technical potential; - Recommending effective ways for the member nations to

Fast parallel approach for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2009-12-01

Two-dimensional fast Gabor transform algorithms are useful for real-time applications due to the high computational complexity of the traditional 2-D complex-valued discrete Gabor transform (CDGT). This paper presents two block time-recursive algorithms for 2-D DHT-based real-valued discrete Gabor transform (RDGT) and its inverse transform and develops a fast parallel approach for the implementation of the two algorithms. The computational complexity of the proposed parallel approach is analyzed and compared with that of the existing 2-D CDGT algorithms. The results indicate that the proposed parallel approach is attractive for real time image processing.

Scale Space for Camera Invariant Features.

PubMed

Puig, Luis; Guerrero, José J; Daniilidis, Kostas

2014-09-01

In this paper we propose a new approach to compute the scale space of any central projection system, such as catadioptric, fisheye or conventional cameras. Since these systems can be explained using a unified model, the single parameter that defines each type of system is used to automatically compute the corresponding Riemannian metric. This metric, is combined with the partial differential equations framework on manifolds, allows us to compute the Laplace-Beltrami (LB) operator, enabling the computation of the scale space of any central projection system. Scale space is essential for the intrinsic scale selection and neighborhood description in features like SIFT. We perform experiments with synthetic and real images to validate the generalization of our approach to any central projection system. We compare our approach with the best-existing methods showing competitive results in all type of cameras: catadioptric, fisheye, and perspective.

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.

PubMed

Orchard, Garrick; Jayawant, Ajinkya; Cohen, Gregory K; Thakor, Nitish

2015-01-01

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, M.; Klimeck, G.; Hanks, D.

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

Accurate Bit Error Rate Calculation for Asynchronous Chaos-Based DS-CDMA over Multipath Channel

NASA Astrophysics Data System (ADS)

Kaddoum, Georges; Roviras, Daniel; Chargé, Pascal; Fournier-Prunaret, Daniele

2009-12-01

An accurate approach to compute the bit error rate expression for multiuser chaosbased DS-CDMA system is presented in this paper. For more realistic communication system a slow fading multipath channel is considered. A simple RAKE receiver structure is considered. Based on the bit energy distribution, this approach compared to others computation methods existing in literature gives accurate results with low computation charge. Perfect estimation of the channel coefficients with the associated delays and chaos synchronization is assumed. The bit error rate is derived in terms of the bit energy distribution, the number of paths, the noise variance, and the number of users. Results are illustrated by theoretical calculations and numerical simulations which point out the accuracy of our approach.

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sattari, Sulimon, E-mail: ssattari2@ucmerced.edu; Chen, Qianting, E-mail: qchen2@ucmerced.edu; Mitchell, Kevin A., E-mail: kmitchell@ucmerced.edu

Topological approaches to mixing are important tools to understand chaotic fluid flows, ranging from oceanic transport to the design of micro-mixers. Typically, topological entropy, the exponential growth rate of material lines, is used to quantify topological mixing. Computing topological entropy from the direct stretching rate is computationally expensive and sheds little light on the source of the mixing. Earlier approaches emphasized that topological entropy could be viewed as generated by the braiding of virtual, or “ghost,” rods stirring the fluid in a periodic manner. Here, we demonstrate that topological entropy can also be viewed as generated by the braiding ofmore » ghost rods following heteroclinic orbits instead. We use the machinery of homotopic lobe dynamics, which extracts symbolic dynamics from finite-length pieces of stable and unstable manifolds attached to fixed points of the fluid flow. As an example, we focus on the topological entropy of a bounded, chaotic, two-dimensional, double-vortex cavity flow. Over a certain parameter range, the topological entropy is primarily due to the braiding of a period-three orbit. However, this orbit does not explain the topological entropy for parameter values where it does not exist, nor does it explain the excess of topological entropy for the entire range of its existence. We show that braiding by heteroclinic orbits provides an accurate computation of topological entropy when the period-three orbit does not exist, and that it provides an explanation for some of the excess topological entropy when the period-three orbit does exist. Furthermore, the computation of symbolic dynamics using heteroclinic orbits has been automated and can be used to compute topological entropy for a general 2D fluid flow.« less

Using heteroclinic orbits to quantify topological entropy in fluid flows

NASA Astrophysics Data System (ADS)

Sattari, Sulimon; Chen, Qianting; Mitchell, Kevin A.

2016-03-01

Topological approaches to mixing are important tools to understand chaotic fluid flows, ranging from oceanic transport to the design of micro-mixers. Typically, topological entropy, the exponential growth rate of material lines, is used to quantify topological mixing. Computing topological entropy from the direct stretching rate is computationally expensive and sheds little light on the source of the mixing. Earlier approaches emphasized that topological entropy could be viewed as generated by the braiding of virtual, or "ghost," rods stirring the fluid in a periodic manner. Here, we demonstrate that topological entropy can also be viewed as generated by the braiding of ghost rods following heteroclinic orbits instead. We use the machinery of homotopic lobe dynamics, which extracts symbolic dynamics from finite-length pieces of stable and unstable manifolds attached to fixed points of the fluid flow. As an example, we focus on the topological entropy of a bounded, chaotic, two-dimensional, double-vortex cavity flow. Over a certain parameter range, the topological entropy is primarily due to the braiding of a period-three orbit. However, this orbit does not explain the topological entropy for parameter values where it does not exist, nor does it explain the excess of topological entropy for the entire range of its existence. We show that braiding by heteroclinic orbits provides an accurate computation of topological entropy when the period-three orbit does not exist, and that it provides an explanation for some of the excess topological entropy when the period-three orbit does exist. Furthermore, the computation of symbolic dynamics using heteroclinic orbits has been automated and can be used to compute topological entropy for a general 2D fluid flow.

Scene-based nonuniformity corrections for optical and SWIR pushbroom sensors.

PubMed

Leathers, Robert; Downes, Trijntje; Priest, Richard

2005-06-27

We propose and evaluate several scene-based methods for computing nonuniformity corrections for visible or near-infrared pushbroom sensors. These methods can be used to compute new nonuniformity correction values or to repair or refine existing radiometric calibrations. For a given data set, the preferred method depends on the quality of the data, the type of scenes being imaged, and the existence and quality of a laboratory calibration. We demonstrate our methods with data from several different sensor systems and provide a generalized approach to be taken for any new data set.

Study for incorporating time-synchronized approach control into the CH-47/VALT digital navigation system

NASA Technical Reports Server (NTRS)

Mcconnell, W. J., Jr.

1979-01-01

Techniques for obtaining time synchronized (4D) approach control in the VALT research helicopter is described. Various 4D concepts and their compatibility with the existing VALT digital computer navigation and guidance system hardware and software are examined. Modifications to various techniques were investigated in order to take advantage of the unique operating characteristics of the helicopter in the terminal area. A 4D system is proposed, combining the direct to maneuver with the existing VALT curved path generation capability.

Robust scalable stabilisability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: towards a distributed computing architecture

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2016-06-01

Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network size. Finally, a numerical example shows the applicability of the proposed method and its advantage in terms of computational complexity when compared with the existing approaches.

Mobile Cloud Computing with SOAP and REST Web Services

NASA Astrophysics Data System (ADS)

Ali, Mushtaq; Fadli Zolkipli, Mohamad; Mohamad Zain, Jasni; Anwar, Shahid

2018-05-01

Mobile computing in conjunction with Mobile web services drives a strong approach where the limitations of mobile devices may possibly be tackled. Mobile Web Services are based on two types of technologies; SOAP and REST, which works with the existing protocols to develop Web services. Both the approaches carry their own distinct features, yet to keep the constraint features of mobile devices in mind, the better in two is considered to be the one which minimize the computation and transmission overhead while offloading. The load transferring of mobile device to remote servers for execution called computational offloading. There are numerous approaches to implement computational offloading a viable solution for eradicating the resources constraints of mobile device, yet a dynamic method of computational offloading is always required for a smooth and simple migration of complex tasks. The intention of this work is to present a distinctive approach which may not engage the mobile resources for longer time. The concept of web services utilized in our work to delegate the computational intensive tasks for remote execution. We tested both SOAP Web services approach and REST Web Services for mobile computing. Two parameters considered in our lab experiments to test; Execution Time and Energy Consumption. The results show that RESTful Web services execution is far better than executing the same application by SOAP Web services approach, in terms of execution time and energy consumption. Conducting experiments with the developed prototype matrix multiplication app, REST execution time is about 200% better than SOAP execution approach. In case of energy consumption REST execution is about 250% better than SOAP execution approach.

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

PubMed Central

Kim, Joonhoon; Reed, Jennifer L.; Maravelias, Christos T.

2011-01-01

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering. PMID:21949695

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

PubMed

Kim, Joonhoon; Reed, Jennifer L; Maravelias, Christos T

2011-01-01

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.

Generic, Type-Safe and Object Oriented Computer Algebra Software

NASA Astrophysics Data System (ADS)

Kredel, Heinz; Jolly, Raphael

Advances in computer science, in particular object oriented programming, and software engineering have had little practical impact on computer algebra systems in the last 30 years. The software design of existing systems is still dominated by ad-hoc memory management, weakly typed algorithm libraries and proprietary domain specific interactive expression interpreters. We discuss a modular approach to computer algebra software: usage of state-of-the-art memory management and run-time systems (e.g. JVM) usage of strongly typed, generic, object oriented programming languages (e.g. Java) and usage of general purpose, dynamic interactive expression interpreters (e.g. Python) To illustrate the workability of this approach, we have implemented and studied computer algebra systems in Java and Scala. In this paper we report on the current state of this work by presenting new examples.

Existence and instability of steady states for a triangular cross-diffusion system: A computer-assisted proof

NASA Astrophysics Data System (ADS)

Breden, Maxime; Castelli, Roberto

2018-05-01

In this paper, we present and apply a computer-assisted method to study steady states of a triangular cross-diffusion system. Our approach consist in an a posteriori validation procedure, that is based on using a fixed point argument around a numerically computed solution, in the spirit of the Newton-Kantorovich theorem. It allows to prove the existence of various non homogeneous steady states for different parameter values. In some situations, we obtain as many as 13 coexisting steady states. We also apply the a posteriori validation procedure to study the linear stability of the obtained steady states, proving that many of them are in fact unstable.

A Parallel Processing Algorithm for Remote Sensing Classification

NASA Technical Reports Server (NTRS)

Gualtieri, J. Anthony

2005-01-01

A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

Combining existing numerical models with data assimilation using weighted least-squares finite element methods.

PubMed

Rajaraman, Prathish K; Manteuffel, T A; Belohlavek, M; Heys, Jeffrey J

2017-01-01

A new approach has been developed for combining and enhancing the results from an existing computational fluid dynamics model with experimental data using the weighted least-squares finite element method (WLSFEM). Development of the approach was motivated by the existence of both limited experimental blood velocity in the left ventricle and inexact numerical models of the same flow. Limitations of the experimental data include measurement noise and having data only along a two-dimensional plane. Most numerical modeling approaches do not provide the flexibility to assimilate noisy experimental data. We previously developed an approach that could assimilate experimental data into the process of numerically solving the Navier-Stokes equations, but the approach was limited because it required the use of specific finite element methods for solving all model equations and did not support alternative numerical approximation methods. The new approach presented here allows virtually any numerical method to be used for approximately solving the Navier-Stokes equations, and then the WLSFEM is used to combine the experimental data with the numerical solution of the model equations in a final step. The approach dynamically adjusts the influence of the experimental data on the numerical solution so that more accurate data are more closely matched by the final solution and less accurate data are not closely matched. The new approach is demonstrated on different test problems and provides significantly reduced computational costs compared with many previous methods for data assimilation. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Breast tumor malignancy modelling using evolutionary neural logic networks.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Panagi, Georgia; Panourgias, Evangelia

2006-01-01

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.

Parallel photonic information processing at gigabyte per second data rates using transient states

NASA Astrophysics Data System (ADS)

Brunner, Daniel; Soriano, Miguel C.; Mirasso, Claudio R.; Fischer, Ingo

2013-01-01

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Simulating Serious Games: A Discrete-Time Computational Model Based on Cognitive Flow Theory

ERIC Educational Resources Information Center

Westera, Wim

2018-01-01

This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing…

An Empirical Evaluation of Puzzle-Based Learning as an Interest Approach for Teaching Introductory Computer Science

ERIC Educational Resources Information Center

Merrick, K. E.

2010-01-01

This correspondence describes an adaptation of puzzle-based learning to teaching an introductory computer programming course. Students from two offerings of the course--with and without the puzzle-based learning--were surveyed over a two-year period. Empirical results show that the synthesis of puzzle-based learning concepts with existing course…

The Construction of 3-d Neutral Density for Arbitrary Data Sets

NASA Astrophysics Data System (ADS)

Riha, S.; McDougall, T. J.; Barker, P. M.

2014-12-01

The Neutral Density variable allows inference of water pathways from thermodynamic properties in the global ocean, and is therefore an essential component of global ocean circulation analysis. The widely used algorithm for the computation of Neutral Density yields accurate results for data sets which are close to the observed climatological ocean. Long-term numerical climate simulations, however, often generate a significant drift from present-day climate, which renders the existing algorithm inaccurate. To remedy this problem, new algorithms which operate on arbitrary data have been developed, which may potentially be used to compute Neutral Density during runtime of a numerical model.We review existing approaches for the construction of Neutral Density in arbitrary data sets, detail their algorithmic structure, and present an analysis of the computational cost for implementations on a single-CPU computer. We discuss possible strategies for the implementation in state-of-the-art numerical models, with a focus on distributed computing environments.

Design for a Crane Metallic Structure Based on Imperialist Competitive Algorithm and Inverse Reliability Strategy

NASA Astrophysics Data System (ADS)

Fan, Xiao-Ning; Zhi, Bo

2017-07-01

Uncertainties in parameters such as materials, loading, and geometry are inevitable in designing metallic structures for cranes. When considering these uncertainty factors, reliability-based design optimization (RBDO) offers a more reasonable design approach. However, existing RBDO methods for crane metallic structures are prone to low convergence speed and high computational cost. A unilevel RBDO method, combining a discrete imperialist competitive algorithm with an inverse reliability strategy based on the performance measure approach, is developed. Application of the imperialist competitive algorithm at the optimization level significantly improves the convergence speed of this RBDO method. At the reliability analysis level, the inverse reliability strategy is used to determine the feasibility of each probabilistic constraint at each design point by calculating its α-percentile performance, thereby avoiding convergence failure, calculation error, and disproportionate computational effort encountered using conventional moment and simulation methods. Application of the RBDO method to an actual crane structure shows that the developed RBDO realizes a design with the best tradeoff between economy and safety together with about one-third of the convergence speed and the computational cost of the existing method. This paper provides a scientific and effective design approach for the design of metallic structures of cranes.

From empirical data to time-inhomogeneous continuous Markov processes.

PubMed

Lencastre, Pedro; Raischel, Frank; Rogers, Tim; Lind, Pedro G

2016-03-01

We present an approach for testing for the existence of continuous generators of discrete stochastic transition matrices. Typically, existing methods to ascertain the existence of continuous Markov processes are based on the assumption that only time-homogeneous generators exist. Here a systematic extension to time inhomogeneity is presented, based on new mathematical propositions incorporating necessary and sufficient conditions, which are then implemented computationally and applied to numerical data. A discussion concerning the bridging between rigorous mathematical results on the existence of generators to its computational implementation is presented. Our detection algorithm shows to be effective in more than 60% of tested matrices, typically 80% to 90%, and for those an estimate of the (nonhomogeneous) generator matrix follows. We also solve the embedding problem analytically for the particular case of three-dimensional circulant matrices. Finally, a discussion of possible applications of our framework to problems in different fields is briefly addressed.

Multiprocessing on supercomputers for computational aerodynamics

NASA Technical Reports Server (NTRS)

Yarrow, Maurice; Mehta, Unmeel B.

1990-01-01

Very little use is made of multiple processors available on current supercomputers (computers with a theoretical peak performance capability equal to 100 MFLOPs or more) in computational aerodynamics to significantly improve turnaround time. The productivity of a computer user is directly related to this turnaround time. In a time-sharing environment, the improvement in this speed is achieved when multiple processors are used efficiently to execute an algorithm. The concept of multiple instructions and multiple data (MIMD) through multi-tasking is applied via a strategy which requires relatively minor modifications to an existing code for a single processor. Essentially, this approach maps the available memory to multiple processors, exploiting the C-FORTRAN-Unix interface. The existing single processor code is mapped without the need for developing a new algorithm. The procedure for building a code utilizing this approach is automated with the Unix stream editor. As a demonstration of this approach, a Multiple Processor Multiple Grid (MPMG) code is developed. It is capable of using nine processors, and can be easily extended to a larger number of processors. This code solves the three-dimensional, Reynolds averaged, thin-layer and slender-layer Navier-Stokes equations with an implicit, approximately factored and diagonalized method. The solver is applied to generic oblique-wing aircraft problem on a four processor Cray-2 computer. A tricubic interpolation scheme is developed to increase the accuracy of coupling of overlapped grids. For the oblique-wing aircraft problem, a speedup of two in elapsed (turnaround) time is observed in a saturated time-sharing environment.

Approaches for scalable modeling and emulation of cyber systems : LDRD final report.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Minnich, Ronald G.; Armstrong, Robert C.

2009-09-01

The goal of this research was to combine theoretical and computational approaches to better understand the potential emergent behaviors of large-scale cyber systems, such as networks of {approx} 10{sup 6} computers. The scale and sophistication of modern computer software, hardware, and deployed networked systems have significantly exceeded the computational research community's ability to understand, model, and predict current and future behaviors. This predictive understanding, however, is critical to the development of new approaches for proactively designing new systems or enhancing existing systems with robustness to current and future cyber threats, including distributed malware such as botnets. We have developed preliminarymore » theoretical and modeling capabilities that can ultimately answer questions such as: How would we reboot the Internet if it were taken down? Can we change network protocols to make them more secure without disrupting existing Internet connectivity and traffic flow? We have begun to address these issues by developing new capabilities for understanding and modeling Internet systems at scale. Specifically, we have addressed the need for scalable network simulation by carrying out emulations of a network with {approx} 10{sup 6} virtualized operating system instances on a high-performance computing cluster - a 'virtual Internet'. We have also explored mappings between previously studied emergent behaviors of complex systems and their potential cyber counterparts. Our results provide foundational capabilities for further research toward understanding the effects of complexity in cyber systems, to allow anticipating and thwarting hackers.« less

An integrated approach to model strain localization bands in magnesium alloys

NASA Astrophysics Data System (ADS)

Baxevanakis, K. P.; Mo, C.; Cabal, M.; Kontsos, A.

2018-02-01

Strain localization bands (SLBs) that appear at early stages of deformation of magnesium alloys have been recently associated with heterogeneous activation of deformation twinning. Experimental evidence has demonstrated that such "Lüders-type" band formations dominate the overall mechanical behavior of these alloys resulting in sigmoidal type stress-strain curves with a distinct plateau followed by pronounced anisotropic hardening. To evaluate the role of SLB formation on the local and global mechanical behavior of magnesium alloys, an integrated experimental/computational approach is presented. The computational part is developed based on custom subroutines implemented in a finite element method that combine a plasticity model with a stiffness degradation approach. Specific inputs from the characterization and testing measurements to the computational approach are discussed while the numerical results are validated against such available experimental information, confirming the existence of load drops and the intensification of strain accumulation at the time of SLB initiation.

Computational modeling of RNA 3D structures, with the aid of experimental restraints

PubMed Central

Magnus, Marcin; Matelska, Dorota; Łach, Grzegorz; Chojnowski, Grzegorz; Boniecki, Michal J; Purta, Elzbieta; Dawson, Wayne; Dunin-Horkawicz, Stanislaw; Bujnicki, Janusz M

2014-01-01

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation (“Greek science” approach) or utilize information derived from known structures of other RNA molecules (“Babylonian science” approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately. PMID:24785264

Chandrasekhar equations and computational algorithms for distributed parameter systems

NASA Technical Reports Server (NTRS)

Burns, J. A.; Ito, K.; Powers, R. K.

1984-01-01

The Chandrasekhar equations arising in optimal control problems for linear distributed parameter systems are considered. The equations are derived via approximation theory. This approach is used to obtain existence, uniqueness, and strong differentiability of the solutions and provides the basis for a convergent computation scheme for approximating feedback gain operators. A numerical example is presented to illustrate these ideas.

Computing the multifractal spectrum from time series: an algorithmic approach.

PubMed

Harikrishnan, K P; Misra, R; Ambika, G; Amritkar, R E

2009-12-01

We show that the existing methods for computing the f(alpha) spectrum from a time series can be improved by using a new algorithmic scheme. The scheme relies on the basic idea that the smooth convex profile of a typical f(alpha) spectrum can be fitted with an analytic function involving a set of four independent parameters. While the standard existing schemes [P. Grassberger et al., J. Stat. Phys. 51, 135 (1988); A. Chhabra and R. V. Jensen, Phys. Rev. Lett. 62, 1327 (1989)] generally compute only an incomplete f(alpha) spectrum (usually the top portion), we show that this can be overcome by an algorithmic approach, which is automated to compute the D(q) and f(alpha) spectra from a time series for any embedding dimension. The scheme is first tested with the logistic attractor with known f(alpha) curve and subsequently applied to higher-dimensional cases. We also show that the scheme can be effectively adapted for analyzing practical time series involving noise, with examples from two widely different real world systems. Moreover, some preliminary results indicating that the set of four independent parameters may be used as diagnostic measures are also included.

Sideloading - Ingestion of Large Point Clouds Into the Apache Spark Big Data Engine

NASA Astrophysics Data System (ADS)

Boehm, J.; Liu, K.; Alis, C.

2016-06-01

In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.

Modeling Biodegradation and Reactive Transport: Analytical and Numerical Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Y; Glascoe, L

The computational modeling of the biodegradation of contaminated groundwater systems accounting for biochemical reactions coupled to contaminant transport is a valuable tool for both the field engineer/planner with limited computational resources and the expert computational researcher less constrained by time and computer power. There exists several analytical and numerical computer models that have been and are being developed to cover the practical needs put forth by users to fulfill this spectrum of computational demands. Generally, analytical models provide rapid and convenient screening tools running on very limited computational power, while numerical models can provide more detailed information with consequent requirementsmore » of greater computational time and effort. While these analytical and numerical computer models can provide accurate and adequate information to produce defensible remediation strategies, decisions based on inadequate modeling output or on over-analysis can have costly and risky consequences. In this chapter we consider both analytical and numerical modeling approaches to biodegradation and reactive transport. Both approaches are discussed and analyzed in terms of achieving bioremediation goals, recognizing that there is always a tradeoff between computational cost and the resolution of simulated systems.« less

Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joe, Jeffrey Clark; Boring, Ronald Laurids; Herberger, Sarah Elizabeth Marie

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with othermore » experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations.« less

SCUBA divers as oceanographic samplers: The potential of dive computers to augment aquatic temperature monitoring

PubMed Central

Wright, Serena; Hull, Tom; Sivyer, David B.; Pearce, David; Pinnegar, John K.; Sayer, Martin D. J.; Mogg, Andrew O. M.; Azzopardi, Elaine; Gontarek, Steve; Hyder, Kieran

2016-01-01

Monitoring temperature of aquatic waters is of great importance, with modelled, satellite and in-situ data providing invaluable insights into long-term environmental change. However, there is often a lack of depth-resolved temperature measurements. Recreational dive computers routinely record temperature and depth, so could provide an alternate and highly novel source of oceanographic information to fill this data gap. In this study, a citizen science approach was used to obtain over 7,000 scuba diver temperature profiles. The accuracy, offset and lag of temperature records was assessed by comparing dive computers with scientific conductivity-temperature-depth instruments and existing surface temperature data. Our results show that, with processing, dive computers can provide a useful and novel tool with which to augment existing monitoring systems all over the globe, but especially in under-sampled or highly changeable coastal environments. PMID:27445104

Delayed flap approach procedures for noise abatement and fuel conservation

NASA Technical Reports Server (NTRS)

Edwards, F. G.; Bull, J. S.; Foster, J. D.; Hegarty, D. M.; Drinkwater, F. J., III

1976-01-01

The NASA/Ames Research Center is currently investigating the delayed flap approach during which pilot actions are determined and prescribed by an onboard digital computer. The onboard digital computer determines the proper timing for the deployment of the landing gear and flaps based on the existing winds and airplane gross weight. Advisory commands are displayed to the pilot. The approach is flown along the conventional ILS glide slope but is initiated at a higher airspeed and in a clean aircraft configuration that allows for low thrust and results in reduced noise and fuel consumption. Topics discussed include operational procedures, pilot acceptability of these procedures, and fuel/noise benefits resulting from flight tests and simulation.

Integrative Genomics and Computational Systems Medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Huang, Yufei; Zhang, Bing

The exponential growth in generation of large amounts of genomic data from biological samples has driven the emerging field of systems medicine. This field is promising because it improves our understanding of disease processes at the systems level. However, the field is still in its young stage. There exists a great need for novel computational methods and approaches to effectively utilize and integrate various omics data.

Computing Challenges in Coded Mask Imaging

NASA Technical Reports Server (NTRS)

Skinner, Gerald

2009-01-01

This slide presaentation reviews the complications and challenges in developing computer systems for Coded Mask Imaging telescopes. The coded mask technique is used when there is no other way to create the telescope, (i.e., when there are wide fields of view, high energies for focusing or low energies for the Compton/Tracker Techniques and very good angular resolution.) The coded mask telescope is described, and the mask is reviewed. The coded Masks for the INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL) instruments are shown, and a chart showing the types of position sensitive detectors used for the coded mask telescopes is also reviewed. Slides describe the mechanism of recovering an image from the masked pattern. The correlation with the mask pattern is described. The Matrix approach is reviewed, and other approaches to image reconstruction are described. Included in the presentation is a review of the Energetic X-ray Imaging Survey Telescope (EXIST) / High Energy Telescope (HET), with information about the mission, the operation of the telescope, comparison of the EXIST/HET with the SWIFT/BAT and details of the design of the EXIST/HET.

Review: Modelling chemical kinetics and convective heating in giant planet entries

NASA Astrophysics Data System (ADS)

Reynier, Philippe; D'Ammando, Giuliano; Bruno, Domenico

2018-01-01

A review of the existing chemical kinetics models for H2 / He mixtures and related transport and thermodynamic properties is presented as a pre-requisite towards the development of innovative models based on the state-to-state approach. A survey of the available results obtained during the mission preparation and post-flight analyses of the Galileo mission has been undertaken and a computational matrix has been derived. Different chemical kinetics schemes for hydrogen/helium mixtures have been applied to numerical simulations of the selected points along the entry trajectory. First, a reacting scheme, based on literature data, has been set up for computing the flow-field around the probe at high altitude and comparisons with existing numerical predictions are performed. Then, a macroscopic model derived from a state-to-state model has been constructed and incorporated into a CFD code. Comparisons with existing numerical results from the literature have been performed as well as cross-check comparisons between the predictions provided by the different models in order to evaluate the potential of innovative chemical kinetics models based on the state-to-state approach.

Computational studies of horizontal axis wind turbines in high wind speed condition using advanced turbulence models

NASA Astrophysics Data System (ADS)

Benjanirat, Sarun

Next generation horizontal-axis wind turbines (HAWTs) will operate at very high wind speeds. Existing engineering approaches for modeling the flow phenomena are based on blade element theory, and cannot adequately account for 3-D separated, unsteady flow effects. Therefore, researchers around the world are beginning to model these flows using first principles-based computational fluid dynamics (CFD) approaches. In this study, an existing first principles-based Navier-Stokes approach is being enhanced to model HAWTs at high wind speeds. The enhancements include improved grid topology, implicit time-marching algorithms, and advanced turbulence models. The advanced turbulence models include the Spalart-Allmaras one-equation model, k-epsilon, k-o and Shear Stress Transport (k-o-SST) models. These models are also integrated with detached eddy simulation (DES) models. Results are presented for a range of wind speeds, for a configuration termed National Renewable Energy Laboratory Phase VI rotor, tested at NASA Ames Research Center. Grid sensitivity studies are also presented. Additionally, effects of existing transition models on the predictions are assessed. Data presented include power/torque production, radial distribution of normal and tangential pressure forces, root bending moments, and surface pressure fields. Good agreement was obtained between the predictions and experiments for most of the conditions, particularly with the Spalart-Allmaras-DES model.

Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution

PubMed Central

Barrozo, Alexandre; Borstnar, Rok; Marloie, Gaël; Kamerlin, Shina Caroline Lynn

2012-01-01

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies. PMID:23202907

A Markov Chain-based quantitative study of angular distribution of photons through turbid slabs via isotropic light scattering

NASA Astrophysics Data System (ADS)

Li, Xuesong; Northrop, William F.

2016-04-01

This paper describes a quantitative approach to approximate multiple scattering through an isotropic turbid slab based on Markov Chain theorem. There is an increasing need to utilize multiple scattering for optical diagnostic purposes; however, existing methods are either inaccurate or computationally expensive. Here, we develop a novel Markov Chain approximation approach to solve multiple scattering angular distribution (AD) that can accurately calculate AD while significantly reducing computational cost compared to Monte Carlo simulation. We expect this work to stimulate ongoing multiple scattering research and deterministic reconstruction algorithm development with AD measurements.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

Prediction-Correction Algorithms for Time-Varying Constrained Optimization

DOE PAGES

Simonetto, Andrea; Dall'Anese, Emiliano

2017-07-26

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simonetto, Andrea; Dall'Anese, Emiliano

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

NASA Technical Reports Server (NTRS)

Ng, Hok Kwan; Sridhar, Banavar

2016-01-01

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.

Free Energy Calculations using a Swarm-Enhanced Sampling Molecular Dynamics Approach.

PubMed

Burusco, Kepa K; Bruce, Neil J; Alibay, Irfan; Bryce, Richard A

2015-10-26

Free energy simulations are an established computational tool in modelling chemical change in the condensed phase. However, sampling of kinetically distinct substates remains a challenge to these approaches. As a route to addressing this, we link the methods of thermodynamic integration (TI) and swarm-enhanced sampling molecular dynamics (sesMD), where simulation replicas interact cooperatively to aid transitions over energy barriers. We illustrate the approach by using alchemical alkane transformations in solution, comparing them with the multiple independent trajectory TI (IT-TI) method. Free energy changes for transitions computed by using IT-TI grew increasingly inaccurate as the intramolecular barrier was heightened. By contrast, swarm-enhanced sampling TI (sesTI) calculations showed clear improvements in sampling efficiency, leading to more accurate computed free energy differences, even in the case of the highest barrier height. The sesTI approach, therefore, has potential in addressing chemical change in systems where conformations exist in slow exchange. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Systematic Applications of Metabolomics in Metabolic Engineering

PubMed Central

Dromms, Robert A.; Styczynski, Mark P.

2012-01-01

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. PMID:24957776

Investigating a holobiont: Microbiota perturbations and transkingdom networks.

PubMed

Greer, Renee; Dong, Xiaoxi; Morgun, Andrey; Shulzhenko, Natalia

2016-01-01

The scientific community has recently come to appreciate that, rather than existing as independent organisms, multicellular hosts and their microbiota comprise a complex evolving superorganism or metaorganism, termed a holobiont. This point of view leads to a re-evaluation of our understanding of different physiological processes and diseases. In this paper we focus on experimental and computational approaches which, when combined in one study, allowed us to dissect mechanisms (traditionally named host-microbiota interactions) regulating holobiont physiology. Specifically, we discuss several approaches for microbiota perturbation, such as use of antibiotics and germ-free animals, including advantages and potential caveats of their usage. We briefly review computational approaches to characterize the microbiota and, more importantly, methods to infer specific components of microbiota (such as microbes or their genes) affecting host functions. One such approach called transkingdom network analysis has been recently developed and applied in our study. (1) Finally, we also discuss common methods used to validate the computational predictions of host-microbiota interactions using in vitro and in vivo experimental systems.

Numerical prediction of a draft tube flow taking into account uncertain inlet conditions

NASA Astrophysics Data System (ADS)

Brugiere, O.; Balarac, G.; Corre, C.; Metais, O.; Flores, E.; Pleroy

2012-11-01

The swirling turbulent flow in a hydroturbine draft tube is computed with a non-intrusive uncertainty quantification (UQ) method coupled to Reynolds-Averaged Navier-Stokes (RANS) modelling in order to take into account in the numerical prediction the physical uncertainties existing on the inlet flow conditions. The proposed approach yields not only mean velocity fields to be compared with measured profiles, as is customary in Computational Fluid Dynamics (CFD) practice, but also variance of these quantities from which error bars can be deduced on the computed profiles, thus making more significant the comparison between experiment and computation.

Cogeneration computer model assessment: Advanced cogeneration research study

NASA Technical Reports Server (NTRS)

Rosenberg, L.

1983-01-01

Cogeneration computer simulation models to recommend the most desirable models or their components for use by the Southern California Edison Company (SCE) in evaluating potential cogeneration projects was assessed. Existing cogeneration modeling capabilities are described, preferred models are identified, and an approach to the development of a code which will best satisfy SCE requirements is recommended. Five models (CELCAP, COGEN 2, CPA, DEUS, and OASIS) are recommended for further consideration.

Hydraulic parameters estimation by using an approach based on vertical electrical soundings (VES) in the semi-arid Khanasser valley region, Syria

NASA Astrophysics Data System (ADS)

Asfahani, Jamal

2016-05-01

A new alternative approach based on using Vertical electrical sounding (VES) technique is proposed for computing the hydraulic conductivity K of an aquifer. The approach takes only the salinity of the groundwater into consideration. VES measurements in the locations, where available water samples exist, are required in such an approach, in order to calibrate and establish empirical relationships between transverse resistance Dar-Zarrouck TR parameter and modified transverse resistance MTR, and between MTR and transmissivity T. Those relationships are thereafter used to extrapolate the transmissivity even in the VES points where no water samples exist. This approach is tested and practiced in the Khanasser valley, Northern Syria, where the hydraulic conductivity of the Quaternary aquifer is computed. An acceptable agreement is found between the hydraulic conductivity values obtained by the proposed approach and those obtained by the pumping test which range between 0.864 and 8.64 m/day (10-5 and 10-4 m/s). The Quaternary aquifer transmissivity of the Khanasser Valley, has been characterized by using this approach and by adapting the MTR parameter. The transmissivity varies between a minimum of 79 m2/day and a maximum of 814 m2/day, with an average of 283 m2/day and a standard deviation of 145 m2/day. The easy and inexpensive approach proposed in this paper can be applied in other semi arid regions.

Precision digital control systems

NASA Astrophysics Data System (ADS)

Vyskub, V. G.; Rozov, B. S.; Savelev, V. I.

This book is concerned with the characteristics of digital control systems of great accuracy. A classification of such systems is considered along with aspects of stabilization, programmable control applications, digital tracking systems and servomechanisms, and precision systems for the control of a scanning laser beam. Other topics explored are related to systems of proportional control, linear devices and methods for increasing precision, approaches for further decreasing the response time in the case of high-speed operation, possibilities for the implementation of a logical control law, and methods for the study of precision digital control systems. A description is presented of precision automatic control systems which make use of electronic computers, taking into account the existing possibilities for an employment of computers in automatic control systems, approaches and studies required for including a computer in such control systems, and an analysis of the structure of automatic control systems with computers. Attention is also given to functional blocks in the considered systems.

Head Pose Estimation on Eyeglasses Using Line Detection and Classification Approach

NASA Astrophysics Data System (ADS)

Setthawong, Pisal; Vannija, Vajirasak

This paper proposes a unique approach for head pose estimation of subjects with eyeglasses by using a combination of line detection and classification approaches. Head pose estimation is considered as an important non-verbal form of communication and could also be used in the area of Human-Computer Interface. A major improvement of the proposed approach is that it allows estimation of head poses at a high yaw/pitch angle when compared with existing geometric approaches, does not require expensive data preparation and training, and is generally fast when compared with other approaches.

Rigorous Numerics for ill-posed PDEs: Periodic Orbits in the Boussinesq Equation

NASA Astrophysics Data System (ADS)

Castelli, Roberto; Gameiro, Marcio; Lessard, Jean-Philippe

2018-04-01

In this paper, we develop computer-assisted techniques for the analysis of periodic orbits of ill-posed partial differential equations. As a case study, our proposed method is applied to the Boussinesq equation, which has been investigated extensively because of its role in the theory of shallow water waves. The idea is to use the symmetry of the solutions and a Newton-Kantorovich type argument (the radii polynomial approach) to obtain rigorous proofs of existence of the periodic orbits in a weighted ℓ1 Banach space of space-time Fourier coefficients with exponential decay. We present several computer-assisted proofs of the existence of periodic orbits at different parameter values.

A Hands-on Approach to the Teaching of Consumer Affairs.

ERIC Educational Resources Information Center

de Ruyter, Ko; Widdows, Richard

1992-01-01

In a course titled Computerized Consumer Responses and Information Systems, Purdue University students operate a consumer hotline for their school. They must promote its existence, answer calls, develop reports, produce training manuals, and set parameters for the computer system. (SK)

Dataflow computing approach in high-speed digital simulation

NASA Technical Reports Server (NTRS)

Ercegovac, M. D.; Karplus, W. J.

1984-01-01

New computational tools and methodologies for the digital simulation of continuous systems were explored. Programmability, and cost effective performance in multiprocessor organizations for real time simulation was investigated. Approach is based on functional style languages and data flow computing principles, which allow for the natural representation of parallelism in algorithms and provides a suitable basis for the design of cost effective high performance distributed systems. The objectives of this research are to: (1) perform comparative evaluation of several existing data flow languages and develop an experimental data flow language suitable for real time simulation using multiprocessor systems; (2) investigate the main issues that arise in the architecture and organization of data flow multiprocessors for real time simulation; and (3) develop and apply performance evaluation models in typical applications.

A computational approach for hypersonic nonequilibrium radiation utilizing space partition algorithm and Gauss quadrature

NASA Astrophysics Data System (ADS)

Shang, J. S.; Andrienko, D. A.; Huang, P. G.; Surzhikov, S. T.

2014-06-01

An efficient computational capability for nonequilibrium radiation simulation via the ray tracing technique has been accomplished. The radiative rate equation is iteratively coupled with the aerodynamic conservation laws including nonequilibrium chemical and chemical-physical kinetic models. The spectral properties along tracing rays are determined by a space partition algorithm of the nearest neighbor search process, and the numerical accuracy is further enhanced by a local resolution refinement using the Gauss-Lobatto polynomial. The interdisciplinary governing equations are solved by an implicit delta formulation through the diminishing residual approach. The axisymmetric radiating flow fields over the reentry RAM-CII probe have been simulated and verified with flight data and previous solutions by traditional methods. A computational efficiency gain nearly forty times is realized over that of the existing simulation procedures.

A singularity free analytical solution of artificial satellite motion with drag

NASA Technical Reports Server (NTRS)

Scheifele, G.; Mueller, A. C.; Starke, S. E.

1977-01-01

The connection between the existing Delaunay-Similar and Poincare-Similar satellite theories in the true anomaly version is outlined for the J(2) perturbation and the new drag approach. An overall description of the concept of the approach is given while the necessary expansions and the procedure to arrive at the computer program for the canonical forces is delineated. The procedure for the analytical integration of these developed equations is described. In addition, some numerical results are given. The computer program for the algebraic multiplication of the Fourier series which creates the FORTRAN coding in an automatic manner is described and documented.

Utilizing HDF4 File Content Maps for the Cloud

NASA Technical Reports Server (NTRS)

Lee, Hyokyung Joe

2016-01-01

We demonstrate a prototype study that HDF4 file content map can be used for efficiently organizing data in cloud object storage system to facilitate cloud computing. This approach can be extended to any binary data formats and to any existing big data analytics solution powered by cloud computing because HDF4 file content map project started as long term preservation of NASA data that doesn't require HDF4 APIs to access data.

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Aerodynamic optimization studies on advanced architecture computers

NASA Technical Reports Server (NTRS)

Chawla, Kalpana

1995-01-01

The approach to carrying out multi-discipline aerospace design studies in the future, especially in massively parallel computing environments, comprises of choosing (1) suitable solvers to compute solutions to equations characterizing a discipline, and (2) efficient optimization methods. In addition, for aerodynamic optimization problems, (3) smart methodologies must be selected to modify the surface shape. In this research effort, a 'direct' optimization method is implemented on the Cray C-90 to improve aerodynamic design. It is coupled with an existing implicit Navier-Stokes solver, OVERFLOW, to compute flow solutions. The optimization method is chosen such that it can accomodate multi-discipline optimization in future computations. In the work , however, only single discipline aerodynamic optimization will be included.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

PubMed

Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

2016-11-01

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

Structural design using equilibrium programming formulations

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.

1995-01-01

Solutions to increasingly larger structural optimization problems are desired. However, computational resources are strained to meet this need. New methods will be required to solve increasingly larger problems. The present approaches to solving large-scale problems involve approximations for the constraints of structural optimization problems and/or decomposition of the problem into multiple subproblems that can be solved in parallel. An area of game theory, equilibrium programming (also known as noncooperative game theory), can be used to unify these existing approaches from a theoretical point of view (considering the existence and optimality of solutions), and be used as a framework for the development of new methods for solving large-scale optimization problems. Equilibrium programming theory is described, and existing design techniques such as fully stressed design and constraint approximations are shown to fit within its framework. Two new structural design formulations are also derived. The first new formulation is another approximation technique which is a general updating scheme for the sensitivity derivatives of design constraints. The second new formulation uses a substructure-based decomposition of the structure for analysis and sensitivity calculations. Significant computational benefits of the new formulations compared with a conventional method are demonstrated.

Probabilistic segmentation and intensity estimation for microarray images.

PubMed

Gottardo, Raphael; Besag, Julian; Stephens, Matthew; Murua, Alejandro

2006-01-01

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

PubMed

Sudha, M

2017-09-27

As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

Computational biology for ageing

PubMed Central

Wieser, Daniela; Papatheodorou, Irene; Ziehm, Matthias; Thornton, Janet M.

2011-01-01

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions. PMID:21115530

Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

NASA Technical Reports Server (NTRS)

Aguilo, Miguel A.; Warner, James E.

2017-01-01

This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

Integrating interactive computational modeling in biology curricula.

PubMed

Helikar, Tomáš; Cutucache, Christine E; Dahlquist, Lauren M; Herek, Tyler A; Larson, Joshua J; Rogers, Jim A

2015-03-01

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.

Overview of the status of predictive computer models for skin sensitization (JRC Expert meeting on pre- and pro-haptens )

EPA Science Inventory

No abstract was prepared or requested. This is a short presentation aiming to present a status of what in silico models and approaches exists in the prediction of skin sensitization potential and/or potency.

A Multiphysics and Multiscale Software Environment for Modeling Astrophysical Systems

NASA Astrophysics Data System (ADS)

Portegies Zwart, Simon; McMillan, Steve; O'Nualláin, Breanndán; Heggie, Douglas; Lombardi, James; Hut, Piet; Banerjee, Sambaran; Belkus, Houria; Fragos, Tassos; Fregeau, John; Fuji, Michiko; Gaburov, Evghenii; Glebbeek, Evert; Groen, Derek; Harfst, Stefan; Izzard, Rob; Jurić, Mario; Justham, Stephen; Teuben, Peter; van Bever, Joris; Yaron, Ofer; Zemp, Marcel

We present MUSE, a software framework for tying together existing computational tools for different astrophysical domains into a single multiphysics, multiscale workload. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for a generalized stellar systems workload. MUSE has now reached a "Noah's Ark" milestone, with two available numerical solvers for each domain. MUSE can treat small stellar associations, galaxies and everything in between, including planetary systems, dense stellar clusters and galactic nuclei. Here we demonstrate an examples calculated with MUSE: the merger of two galaxies. In addition we demonstrate the working of MUSE on a distributed computer. The current MUSE code base is publicly available as open source at http://muse.li.

User interfaces for computational science: A domain specific language for OOMMF embedded in Python

NASA Astrophysics Data System (ADS)

Beg, Marijan; Pepper, Ryan A.; Fangohr, Hans

2017-05-01

Computer simulations are used widely across the engineering and science disciplines, including in the research and development of magnetic devices using computational micromagnetics. In this work, we identify and review different approaches to configuring simulation runs: (i) the re-compilation of source code, (ii) the use of configuration files, (iii) the graphical user interface, and (iv) embedding the simulation specification in an existing programming language to express the computational problem. We identify the advantages and disadvantages of different approaches and discuss their implications on effectiveness and reproducibility of computational studies and results. Following on from this, we design and describe a domain specific language for micromagnetics that is embedded in the Python language, and allows users to define the micromagnetic simulations they want to carry out in a flexible way. We have implemented this micromagnetic simulation description language together with a computational backend that executes the simulation task using the Object Oriented MicroMagnetic Framework (OOMMF). We illustrate the use of this Python interface for OOMMF by solving the micromagnetic standard problem 4. All the code is publicly available and is open source.

A neural network approach for the blind deconvolution of turbulent flows

NASA Astrophysics Data System (ADS)

Maulik, R.; San, O.

2017-11-01

We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a-priori testing of both two-dimensional Kraichnan and three-dimensional Kolmogorov turbulence and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier-Stokes equations.

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

PubMed Central

Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

A SINDA thermal model using CAD/CAE technologies

NASA Technical Reports Server (NTRS)

Rodriguez, Jose A.; Spencer, Steve

1992-01-01

The approach to thermal analysis described by this paper is a technique that incorporates Computer Aided Design (CAD) and Computer Aided Engineering (CAE) to develop a thermal model that has the advantages of Finite Element Methods (FEM) without abandoning the unique advantages of Finite Difference Methods (FDM) in the analysis of thermal systems. The incorporation of existing CAD geometry, the powerful use of a pre and post processor and the ability to do interdisciplinary analysis, will be described.

An overview of bioinformatics methods for modeling biological pathways in yeast

PubMed Central

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao

2016-01-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein–protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae. In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways in S. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. PMID:26476430

Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems With Switching [Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems

DOE PAGES

Zhang, Hong; Abhyankar, Shrirang; Constantinescu, Emil; ...

2017-01-24

Sensitivity analysis is an important tool for describing power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this paper, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating sensitivities of larger systems and is consistent, within machine precision, with the function whosemore » sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as dc exciters, by deriving and implementing the adjoint jump conditions that arise from state-dependent and time-dependent switchings. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach. In conclusion, this paper focuses primarily on the power system dynamics, but the approach is general and can be applied to hybrid dynamical systems in a broader range of fields.« less

Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems With Switching [Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Hong; Abhyankar, Shrirang; Constantinescu, Emil

Sensitivity analysis is an important tool for describing power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this paper, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating sensitivities of larger systems and is consistent, within machine precision, with the function whosemore » sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as dc exciters, by deriving and implementing the adjoint jump conditions that arise from state-dependent and time-dependent switchings. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach. In conclusion, this paper focuses primarily on the power system dynamics, but the approach is general and can be applied to hybrid dynamical systems in a broader range of fields.« less

A new approach to estimate time-to-cure from cancer registries data.

PubMed

Boussari, Olayidé; Romain, Gaëlle; Remontet, Laurent; Bossard, Nadine; Mounier, Morgane; Bouvier, Anne-Marie; Binquet, Christine; Colonna, Marc; Jooste, Valérie

2018-04-01

Cure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations. Cure models in net survival framework were briefly overviewed and a new definition of time-to-cure was introduced as the time TTC at which P(t), the estimated covariate-specific probability of being cured at a given time t after diagnosis, reaches 0.95. We applied flexible parametric cure models to data of four cancer sites provided by the French network of cancer registries (FRANCIM). Then estimates of the time-to-cure by TTC and by two existing methods were derived and compared. Cure fractions and probabilities P(t) were also computed. Depending on the age group, TTC ranged from to 8 to 10 years for colorectal and pancreatic cancer and was nearly 12 years for breast cancer. In thyroid cancer patients under 55 years at diagnosis, TTC was strikingly 0: the probability of being cured was >0.95 just after diagnosis. This is an interesting result regarding the health insurance premiums of these patients. The estimated values of time-to-cure from the three approaches were close for colorectal cancer only. We propose a new approach, based on estimated covariate-specific probability of being cured, to estimate time-to-cure. Compared to two existing methods, the new approach seems to be more intuitive and natural and less sensitive to the survival time distribution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computational Chemistry-based Acute Aquatic Toxicity Mode of Action Assignment

EPA Science Inventory

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertai...

Ab initio calculations of the concentration dependent band gap reduction in dilute nitrides

NASA Astrophysics Data System (ADS)

Rosenow, Phil; Bannow, Lars C.; Fischer, Eric W.; Stolz, Wolfgang; Volz, Kerstin; Koch, Stephan W.; Tonner, Ralf

2018-02-01

While being of persistent interest for the integration of lattice-matched laser devices with silicon circuits, the electronic structure of dilute nitride III/V-semiconductors has presented a challenge to ab initio computational approaches. The origin of the computational problems is the strong distortion exerted by the N atoms on most host materials. Here, these issues are resolved by combining density functional theory calculations based on the meta-GGA functional presented by Tran and Blaha (TB09) with a supercell approach for the dilute nitride Ga(NAs). Exploring the requirements posed to supercells, it is shown that the distortion field of a single N atom must be allowed to decrease so far that it does not overlap with its periodic images. This also prevents spurious electronic interactions between translational symmetric atoms, allowing us to compute band gaps in very good agreement with experimentally derived reference values. In addition to existing approaches, these results offer a promising ab initio avenue to the electronic structure of dilute nitride semiconductor compounds.

Integrative prescreening in analysis of multiple cancer genomic studies

PubMed Central

2012-01-01

Background In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost. Results An integrative prescreening approach is developed for the analysis of multiple cancer genomic datasets. Simulation shows that the proposed integrative prescreening has better performance than alternatives, particularly including prescreening with individual datasets, an intensity approach and meta-analysis. We also analyze multiple microarray gene profiling studies on liver and pancreatic cancers using the proposed approach. Conclusions The proposed integrative prescreening provides an effective way to reduce the dimensionality in cancer genomic studies. It can be coupled with existing analysis methods to identify cancer markers. PMID:22799431

Behavioral personal digital assistants: The seventh generation of computing

PubMed Central

Stephens, Kenneth R.; Hutchison, William R.

1992-01-01

Skinner (1985) described two divergent approaches to developing computer systems that would behave with some approximation to intelligence. The first approach, which corresponds to the mainstream of artificial intelligence and expert systems, models intelligence as a set of production rules that incorporate knowledge and a set of heuristics for inference and symbol manipulation. The alternative is a system that models the behavioral repertoire as a network of associations between antecedent stimuli and operants, and adapts when supplied with reinforcement. The latter approach is consistent with developments in the field of “neural networks.” The authors describe how an existing adaptive network software system, based on behavior analysis and developed since 1983, can be extended to provide a new generation of software systems capable of acquiring verbal behavior. This effort will require the collaboration of the academic and commercial sectors of the behavioral community, but the end result will enable a generational change in computer systems and support for behavior analytic concepts. PMID:22477053

Structured Overlapping Grid Simulations of Contra-rotating Open Rotor Noise

NASA Technical Reports Server (NTRS)

Housman, Jeffrey A.; Kiris, Cetin C.

2015-01-01

Computational simulations using structured overlapping grids with the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are presented for predicting tonal noise generated by a contra-rotating open rotor (CROR) propulsion system. A coupled Computational Fluid Dynamics (CFD) and Computational AeroAcoustics (CAA) numerical approach is applied. Three-dimensional time-accurate hybrid Reynolds Averaged Navier-Stokes/Large Eddy Simulation (RANS/LES) CFD simulations are performed in the inertial frame, including dynamic moving grids, using a higher-order accurate finite difference discretization on structured overlapping grids. A higher-order accurate free-stream preserving metric discretization with discrete enforcement of the Geometric Conservation Law (GCL) on moving curvilinear grids is used to create an accurate, efficient, and stable numerical scheme. The aeroacoustic analysis is based on a permeable surface Ffowcs Williams-Hawkings (FW-H) approach, evaluated in the frequency domain. A time-step sensitivity study was performed using only the forward row of blades to determine an adequate time-step. The numerical approach is validated against existing wind tunnel measurements.

Spatiotemporal video deinterlacing using control grid interpolation

NASA Astrophysics Data System (ADS)

Venkatesan, Ragav; Zwart, Christine M.; Frakes, David H.; Li, Baoxin

2015-03-01

With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.

PubMed

Wang, Ying; Hu, Haiyan; Li, Xiaoman

2016-08-01

Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/metagenomics/MBMC.html .

Towards an Approach of Semantic Access Control for Cloud Computing

NASA Astrophysics Data System (ADS)

Hu, Luokai; Ying, Shi; Jia, Xiangyang; Zhao, Kai

With the development of cloud computing, the mutual understandability among distributed Access Control Policies (ACPs) has become an important issue in the security field of cloud computing. Semantic Web technology provides the solution to semantic interoperability of heterogeneous applications. In this paper, we analysis existing access control methods and present a new Semantic Access Control Policy Language (SACPL) for describing ACPs in cloud computing environment. Access Control Oriented Ontology System (ACOOS) is designed as the semantic basis of SACPL. Ontology-based SACPL language can effectively solve the interoperability issue of distributed ACPs. This study enriches the research that the semantic web technology is applied in the field of security, and provides a new way of thinking of access control in cloud computing.

Window-based method for approximating the Hausdorff in three-dimensional range imagery

DOEpatents

Koch, Mark W [Albuquerque, NM

2009-06-02

One approach to pattern recognition is to use a template from a database of objects and match it to a probe image containing the unknown. Accordingly, the Hausdorff distance can be used to measure the similarity of two sets of points. In particular, the Hausdorff can measure the goodness of a match in the presence of occlusion, clutter, and noise. However, existing 3D algorithms for calculating the Hausdorff are computationally intensive, making them impractical for pattern recognition that requires scanning of large databases. The present invention is directed to a new method that can efficiently, in time and memory, compute the Hausdorff for 3D range imagery. The method uses a window-based approach.

Protecting clinical data on Web client computers: the PCASSO approach.

PubMed Central

Masys, D. R.; Baker, D. B.

1998-01-01

The ubiquity and ease of use of the Web have made it an increasingly popular medium for communication of health-related information. Web interfaces to commercially available clinical information systems are now available or under development by most major vendors. To the extent that such interfaces involve the use of unprotected operating systems, they are vulnerable to security limitations of Web client software environments. The Patient Centered Access to Secure Systems Online (PCASSO) project extends the protections for person-identifiable health data on Web client computers. PCASSO uses several approaches, including physical protection of authentication information, execution containment, graphical displays, and monitoring the client system for intrusions and co-existing programs that may compromise security. PMID:9929243

Ab Initio Infrared and Raman Spectra.

DTIC Science & Technology

1982-08-01

equilibrium and non -equilibrium systems. It b pointed out that a similar ab !ni- te QFC molecular dynamic approach could be used to compute other types of...applied to -2- equilibrium and non -equilibrium system. It is pointed out that a similar oh im- ib QFCT molecular dynamic approach could be used to...desire to be able to experimentally identify and understand transient species or states (such as those existing during the course of chemical

Computational pan-genomics: status, promises and challenges.

PubMed

2018-01-01

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains. © The Author 2016. Published by Oxford University Press.

Potential of Audiographic Computerized Telelearning for Distance Extension Education.

ERIC Educational Resources Information Center

Verma, Satish; And Others

In the last 10 years, an approach to electronic distance education called audiographic computerized telelearning using standard telephone lines has come to the fore. Telelearning is a cost-effective system which optimizes existing computer facilities and creates a teaching-learning environment that is interactive, efficient, and adaptable to a…

Elicitation Support Requirements of Multi-Expertise Teams

ERIC Educational Resources Information Center

Bitter-Rijpkema, Marlies; Martens, Rob; Jochems, Wim

2005-01-01

Tools to support knowledge elicitation are used more and more in situations where employees or students collaborate using the computer. Studies indicate that differences exist between experts and novices regarding their methods of work and reasoning. However, the commonly preferred approach tends to deal with team members as a single system with…

High-Resiliency and Auto-Scaling of Large-Scale Cloud Computing for OCO-2 L2 Full Physics Processing

NASA Astrophysics Data System (ADS)

Hua, H.; Manipon, G.; Starch, M.; Dang, L. B.; Southam, P.; Wilson, B. D.; Avis, C.; Chang, A.; Cheng, C.; Smyth, M.; McDuffie, J. L.; Ramirez, P.

2015-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as SWOT and NISAR where data volumes and data throughput rates are order of magnitude larger than present day missions. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. We present our experiences on deploying a hybrid-cloud computing science data system (HySDS) for the OCO-2 Science Computing Facility to support large-scale processing of their Level-2 full physics data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer ~10X costs savings but with an unpredictable computing environment based on market forces. We will present how we enabled high-tolerance computing in order to achieve large-scale computing as well as operational cost savings.

NOSTOS: a paper-based ubiquitous computing healthcare environment to support data capture and collaboration.

PubMed

Bång, Magnus; Larsson, Anders; Eriksson, Henrik

2003-01-01

In this paper, we present a new approach to clinical workplace computerization that departs from the window-based user interface paradigm. NOSTOS is an experimental computer-augmented work environment designed to support data capture and teamwork in an emergency room. NOSTOS combines multiple technologies, such as digital pens, walk-up displays, headsets, a smart desk, and sensors to enhance an existing paper-based practice with computer power. The physical interfaces allow clinicians to retain mobile paper-based collaborative routines and still benefit from computer technology. The requirements for the system were elicited from situated workplace studies. We discuss the advantages and disadvantages of augmenting a paper-based clinical work environment.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

PubMed Central

Nesvizhskii, Alexey I.

2010-01-01

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues. PMID:20816881

Representation-Independent Iteration of Sparse Data Arrays

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.

A fuzzy clustering algorithm to detect planar and quadric shapes

NASA Technical Reports Server (NTRS)

Krishnapuram, Raghu; Frigui, Hichem; Nasraoui, Olfa

1992-01-01

In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications.

LOCAL ORTHOGONAL CUTTING METHOD FOR COMPUTING MEDIAL CURVES AND ITS BIOMEDICAL APPLICATIONS

PubMed Central

Einstein, Daniel R.; Dyedov, Vladimir

2010-01-01

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method called local orthogonal cutting (LOC) for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stability and consistency tests. These concepts lend themselves to robust numerical techniques and result in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods. PMID:20628546

Computational attributes of the integral form of the equation of transfer

NASA Technical Reports Server (NTRS)

Frankel, J. I.

1991-01-01

Difficulties can arise in radiative and neutron transport calculations when a highly anisotropic scattering phase function is present. In the presence of anisotropy, currently used numerical solutions are based on the integro-differential form of the linearized Boltzmann transport equation. This paper, departs from classical thought and presents an alternative numerical approach based on application of the integral form of the transport equation. Use of the integral formalism facilitates the following steps: a reduction in dimensionality of the system prior to discretization, the use of symbolic manipulation to augment the computational procedure, and the direct determination of key physical quantities which are derivable through the various Legendre moments of the intensity. The approach is developed in the context of radiative heat transfer in a plane-parallel geometry, and results are presented and compared with existing benchmark solutions. Encouraging results are presented to illustrate the potential of the integral formalism for computation. The integral formalism appears to possess several computational attributes which are well-suited to radiative and neutron transport calculations.

Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection.

PubMed

Miyaji, Atsuko; Nakasho, Kazuhisa; Nishida, Shohei

2017-03-01

Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an intersection of multiple private datasets. This approach ensures that only designated parties can identify the intersection. In this paper, we propose a practical MPSI that satisfies the following requirements: The size of the datasets maintained by the different parties is independent of the others, and the computational complexity of the dataset held by each party is independent of the number of parties. Our MPSI is based on the use of an outsourcing provider, who has no knowledge of the data inputs or outputs. This reduces the computational complexity. The performance of the proposed MPSI is evaluated by implementing a prototype on a virtual private network to enable parallel computation in multiple threads. Our protocol is confirmed to be more efficient than comparable existing approaches.

Intercell scheduling: A negotiation approach using multi-agent coalitions

NASA Astrophysics Data System (ADS)

Tian, Yunna; Li, Dongni; Zheng, Dan; Jia, Yunde

2016-10-01

Intercell scheduling problems arise as a result of intercell transfers in cellular manufacturing systems. Flexible intercell routes are considered in this article, and a coalition-based scheduling (CBS) approach using distributed multi-agent negotiation is developed. Taking advantage of the extended vision of the coalition agents, the global optimization is improved and the communication cost is reduced. The objective of the addressed problem is to minimize mean tardiness. Computational results show that, compared with the widely used combinatorial rules, CBS provides better performance not only in minimizing the objective, i.e. mean tardiness, but also in minimizing auxiliary measures such as maximum completion time, mean flow time and the ratio of tardy parts. Moreover, CBS is better than the existing intercell scheduling approach for the same problem with respect to the solution quality and computational costs.

Lightweight computational steering of very large scale molecular dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beazley, D.M.; Lomdahl, P.S.

1996-09-01

We present a computational steering approach for controlling, analyzing, and visualizing very large scale molecular dynamics simulations involving tens to hundreds of millions of atoms. Our approach relies on extensible scripting languages and an easy to use tool for building extensions and modules. The system is extremely easy to modify, works with existing C code, is memory efficient, and can be used from inexpensive workstations and networks. We demonstrate how we have used this system to manipulate data from production MD simulations involving as many as 104 million atoms running on the CM-5 and Cray T3D. We also show howmore » this approach can be used to build systems that integrate common scripting languages (including Tcl/Tk, Perl, and Python), simulation code, user extensions, and commercial data analysis packages.« less

The NASA MERIT program - Developing new concepts for accurate flight planning

NASA Technical Reports Server (NTRS)

Steinberg, R.

1982-01-01

It is noted that the rising cost of aviation fuel has necessitated the development of a new approach to upper air forecasting for flight planning. It is shown that the spatial resolution of the present weather forecast models used in fully automated computer flight planning is an important accuracy-limiting factor, and it is proposed that man be put back into the system, although not in the way he has been used in the past. A new approach is proposed which uses the application of man-computer interactive display techniques to upper air forecasting to retain the fine scale features of the atmosphere inherent in the present data base in order to provide a more accurate and cost effective flight plan. It is pointed out that, as a result of NASA research, the hardware required for this approach already exists.

Computing many-body wave functions with guaranteed precision: the first-order Møller-Plesset wave function for the ground state of helium atom.

PubMed

Bischoff, Florian A; Harrison, Robert J; Valeev, Edward F

2012-09-14

We present an approach to compute accurate correlation energies for atoms and molecules using an adaptive discontinuous spectral-element multiresolution representation for the two-electron wave function. Because of the exponential storage complexity of the spectral-element representation with the number of dimensions, a brute-force computation of two-electron (six-dimensional) wave functions with high precision was not practical. To overcome the key storage bottlenecks we utilized (1) a low-rank tensor approximation (specifically, the singular value decomposition) to compress the wave function, and (2) explicitly correlated R12-type terms in the wave function to regularize the Coulomb electron-electron singularities of the Hamiltonian. All operations necessary to solve the Schrödinger equation were expressed so that the reconstruction of the full-rank form of the wave function is never necessary. Numerical performance of the method was highlighted by computing the first-order Møller-Plesset wave function of a helium atom. The computed second-order Møller-Plesset energy is precise to ~2 microhartrees, which is at the precision limit of the existing general atomic-orbital-based approaches. Our approach does not assume special geometric symmetries, hence application to molecules is straightforward.

Enhancements in medicine by integrating content based image retrieval in computer-aided diagnosis

NASA Astrophysics Data System (ADS)

Aggarwal, Preeti; Sardana, H. K.

2010-02-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. With cad, radiologists use the computer output as a "second opinion" and make the final decisions. Retrieving images is a useful tool to help radiologist to check medical image and diagnosis. The impact of contentbased access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. The challenge in medical informatics is to develop tools for analyzing the content of medical images and to represent them in a way that can be efficiently searched and compared by the physicians. CAD is a concept established by taking into account equally the roles of physicians and computers. To build a successful computer aided diagnostic system, all the relevant technologies, especially retrieval need to be integrated in such a manner that should provide effective and efficient pre-diagnosed cases with proven pathology for the current case at the right time. In this paper, it is suggested that integration of content-based image retrieval (CBIR) in cad can bring enormous results in medicine especially in diagnosis. This approach is also compared with other approaches by highlighting its advantages over those approaches.

Rational-operator-based depth-from-defocus approach to scene reconstruction.

PubMed

Li, Ang; Staunton, Richard; Tjahjadi, Tardi

2013-09-01

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods.

Location Estimation of Urban Images Based on Geographical Neighborhoods

NASA Astrophysics Data System (ADS)

Huang, Jie; Lo, Sio-Long

2018-04-01

Estimating the location of an image is a challenging computer vision problem, and the recent decade has witnessed increasing research efforts towards the solution of this problem. In this paper, we propose a new approach to the location estimation of images taken in urban environments. Experiments are conducted to quantitatively compare the estimation accuracy of our approach, against three representative approaches in the existing literature, using a recently published dataset of over 150 thousand Google Street View images and 259 user uploaded images as queries. According to the experimental results, our approach outperforms three baseline approaches and shows its robustness across different distance thresholds.

Bilingual parallel programming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foster, I.; Overbeek, R.

1990-01-01

Numerous experiments have demonstrated that computationally intensive algorithms support adequate parallelism to exploit the potential of large parallel machines. Yet successful parallel implementations of serious applications are rare. The limiting factor is clearly programming technology. None of the approaches to parallel programming that have been proposed to date -- whether parallelizing compilers, language extensions, or new concurrent languages -- seem to adequately address the central problems of portability, expressiveness, efficiency, and compatibility with existing software. In this paper, we advocate an alternative approach to parallel programming based on what we call bilingual programming. We present evidence that this approach providesmore » and effective solution to parallel programming problems. The key idea in bilingual programming is to construct the upper levels of applications in a high-level language while coding selected low-level components in low-level languages. This approach permits the advantages of a high-level notation (expressiveness, elegance, conciseness) to be obtained without the cost in performance normally associated with high-level approaches. In addition, it provides a natural framework for reusing existing code.« less

Exploring the Feasibility of a DNA Computer: Design of an ALU Using Sticker-Based DNA Model.

PubMed

Sarkar, Mayukh; Ghosal, Prasun; Mohanty, Saraju P

2017-09-01

Since its inception, DNA computing has advanced to offer an extremely powerful, energy-efficient emerging technology for solving hard computational problems with its inherent massive parallelism and extremely high data density. This would be much more powerful and general purpose when combined with other existing well-known algorithmic solutions that exist for conventional computing architectures using a suitable ALU. Thus, a specifically designed DNA Arithmetic and Logic Unit (ALU) that can address operations suitable for both domains can mitigate the gap between these two. An ALU must be able to perform all possible logic operations, including NOT, OR, AND, XOR, NOR, NAND, and XNOR; compare, shift etc., integer and floating point arithmetic operations (addition, subtraction, multiplication, and division). In this paper, design of an ALU has been proposed using sticker-based DNA model with experimental feasibility analysis. Novelties of this paper may be in manifold. First, the integer arithmetic operations performed here are 2s complement arithmetic, and the floating point operations follow the IEEE 754 floating point format, resembling closely to a conventional ALU. Also, the output of each operation can be reused for any next operation. So any algorithm or program logic that users can think of can be implemented directly on the DNA computer without any modification. Second, once the basic operations of sticker model can be automated, the implementations proposed in this paper become highly suitable to design a fully automated ALU. Third, proposed approaches are easy to implement. Finally, these approaches can work on sufficiently large binary numbers.

Navier-Stokes Computations With One-Equation Turbulence Model for Flows Along Concave Wall Surfaces

NASA Technical Reports Server (NTRS)

Wang, Chi R.

2005-01-01

This report presents the use of a time-marching three-dimensional compressible Navier-Stokes equation numerical solver with a one-equation turbulence model to simulate the flow fields developed along concave wall surfaces without and with a downstream extension flat wall surface. The 3-D Navier- Stokes numerical solver came from the NASA Glenn-HT code. The one-equation turbulence model was derived from the Spalart and Allmaras model. The computational approach was first calibrated with the computations of the velocity and Reynolds shear stress profiles of a steady flat plate boundary layer flow. The computational approach was then used to simulate developing boundary layer flows along concave wall surfaces without and with a downstream extension wall. The author investigated the computational results of surface friction factors, near surface velocity components, near wall temperatures, and a turbulent shear stress component in terms of turbulence modeling, computational mesh configurations, inlet turbulence level, and time iteration step. The computational results were compared with existing measurements of skin friction factors, velocity components, and shear stresses of the developing boundary layer flows. With a fine computational mesh and a one-equation model, the computational approach could predict accurately the skin friction factors, near surface velocity and temperature, and shear stress within the flows. The computed velocity components and shear stresses also showed the vortices effect on the velocity variations over a concave wall. The computed eddy viscosities at the near wall locations were also compared with the results from a two equation turbulence modeling technique. The inlet turbulence length scale was found to have little effect on the eddy viscosities at locations near the concave wall surface. The eddy viscosities, from the one-equation and two-equation modeling, were comparable at most stream-wise stations. The present one-equation turbulence model is an effective approach for turbulence modeling in the near solid wall surface region of flow over a concave wall.

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

PubMed

Ben Abdallah, Emna; Folschette, Maxime; Roux, Olivier; Magnin, Morgan

2017-01-01

This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

Spin density and orbital optimization in open shell systems: A rational and computationally efficient proposal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giner, Emmanuel, E-mail: gnrmnl@unife.it; Angeli, Celestino, E-mail: anc@unife.it

2016-03-14

The present work describes a new method to compute accurate spin densities for open shell systems. The proposed approach follows two steps: first, it provides molecular orbitals which correctly take into account the spin delocalization; second, a proper CI treatment allows to account for the spin polarization effect while keeping a restricted formalism and avoiding spin contamination. The main idea of the optimization procedure is based on the orbital relaxation of the various charge transfer determinants responsible for the spin delocalization. The algorithm is tested and compared to other existing methods on a series of organic and inorganic open shellmore » systems. The results reported here show that the new approach (almost black-box) provides accurate spin densities at a reasonable computational cost making it suitable for a systematic study of open shell systems.« less

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

PubMed

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

The Distributed Diagonal Force Decomposition Method for Parallelizing Molecular Dynamics Simulations

PubMed Central

Boršnik, Urban; Miller, Benjamin T.; Brooks, Bernard R.; Janežič, Dušanka

2011-01-01

Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed-diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load-balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used. PMID:21793007

EMILiO: a fast algorithm for genome-scale strain design.

PubMed

Yang, Laurence; Cluett, William R; Mahadevan, Radhakrishnan

2011-05-01

Systems-level design of cell metabolism is becoming increasingly important for renewable production of fuels, chemicals, and drugs. Computational models are improving in the accuracy and scope of predictions, but are also growing in complexity. Consequently, efficient and scalable algorithms are increasingly important for strain design. Previous algorithms helped to consolidate the utility of computational modeling in this field. To meet intensifying demands for high-performance strains, both the number and variety of genetic manipulations involved in strain construction are increasing. Existing algorithms have experienced combinatorial increases in computational complexity when applied toward the design of such complex strains. Here, we present EMILiO, a new algorithm that increases the scope of strain design to include reactions with individually optimized fluxes. Unlike existing approaches that would experience an explosion in complexity to solve this problem, we efficiently generated numerous alternate strain designs producing succinate, l-glutamate and l-serine. This was enabled by successive linear programming, a technique new to the area of computational strain design. Copyright © 2011 Elsevier Inc. All rights reserved.

Rational adaptation under task and processing constraints: implications for testing theories of cognition and action.

PubMed

Howes, Andrew; Lewis, Richard L; Vera, Alonso

2009-10-01

The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition-cognitively bounded rational analysis-that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

PubMed

Cui, Hui; Zhang, Menghuan; Yang, Qingmin; Li, Xiangyi; Liebman, Michael; Yu, Ying; Xie, Lu

2018-01-01

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

Fault management for data systems

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann

1993-01-01

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

Adaptive decision rules for the acquisition of nature reserves.

PubMed

Turner, Will R; Wilcove, David S

2006-04-01

Although reserve-design algorithms have shown promise for increasing the efficiency of conservation planning, recent work casts doubt on the usefulness of some of these approaches in practice. Using three data sets that vary widely in size and complexity, we compared various decision rules for acquiring reserve networks over multiyear periods. We explored three factors that are often important in real-world conservation efforts: uncertain availability of sites for acquisition, degradation of sites, and overall budget constraints. We evaluated the relative strengths and weaknesses of existing optimal and heuristic decision rules and developed a new set of adaptive decision rules that combine the strengths of existing optimal and heuristic approaches. All three of the new adaptive rules performed better than the existing rules we tested under virtually all scenarios of site availability, site degradation, and budget constraints. Moreover, the adaptive rules required no additional data beyond what was readily available and were relatively easy to compute.

Study of Unsteady Flows with Concave Wall Effect

NASA Technical Reports Server (NTRS)

Wang, Chi R.

2003-01-01

This paper presents computational fluid dynamic studies of the inlet turbulence and wall curvature effects on the flow steadiness at near wall surface locations in boundary layer flows. The time-stepping RANS numerical solver of the NASA Glenn-HT RANS code and a one-equation turbulence model, with a uniform inlet turbulence modeling level of the order of 10 percent of molecular viscosity, were used to perform the numerical computations. The approach was first calibrated for its predictabilities of friction factor, velocity, and temperature at near surface locations within a transitional boundary layer over concave wall. The approach was then used to predict the velocity and friction factor variations in a boundary layer recovering from concave curvature. As time iteration proceeded in the computations, the computed friction factors converged to their values from existing experiments. The computed friction factors, velocity, and static temperatures at near wall surface locations oscillated periodically in terms of time iteration steps and physical locations along the span-wise direction. At the upstream stations, the relationship among the normal and tangential velocities showed vortices effects on the velocity variations. Coherent vortices effect on the velocity components broke down at downstream stations. The computations also predicted the vortices effects on the velocity variations within a boundary layer flow developed along a concave wall surface with a downstream recovery flat wall surface. It was concluded that the computational approach might have the potential to analyze the flow steadiness in a turbine blade flow.

Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Dong; Zhang, Xinyu; Bian, Zhaoying, E-mail: zybian@smu.edu.cn, E-mail: jhma@smu.edu.cn

Purpose: Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition. Methods: Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivativesmore » of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach. Results: In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis. Conclusions: This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.« less

Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role.

PubMed

Kleftogiannis, Dimitrios; Korfiati, Aigli; Theofilatos, Konstantinos; Likothanassis, Spiros; Tsakalidis, Athanasios; Mavroudi, Seferina

2013-06-01

Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage, time consuming experiments and high cost reagents. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step. Copyright © 2013 Elsevier Inc. All rights reserved.

Computational Modeling Approaches to Multiscale Design of Icephobic Surfaces

NASA Technical Reports Server (NTRS)

Tallman, Aaron; Wang, Yan; Vargas, Mario

2017-01-01

To aid in the design of surfaces that prevent icing, a model and computational simulation of impact ice formation at the single droplet scale was implemented. The nucleation of a single supercooled droplet impacting on a substrate, in rime ice conditions, was simulated. Open source computational fluid dynamics (CFD) software was used for the simulation. To aid in the design of surfaces that prevent icing, a model of impact ice formation at the single droplet scale was proposed•No existing model simulates simultaneous impact and freezing of a single super-cooled water droplet•For the 10-week project, a low-fidelity feasibility study was the goal.

Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

ERIC Educational Resources Information Center

McDonald, Peter

2014-01-01

This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL). In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since…

Using Netnography to Explore the Culture of Online Language Teaching Communities

ERIC Educational Resources Information Center

Kulavuz-Onal, Derya

2015-01-01

Netnography (Kozinets, 2010) is an ethnographic approach to study communities that exist primarily online. Engaging in online participant observation, the netnographer connects to the online community through a computer screen, and the field is located inside the screen. Although it has been used in marketing research extensively, netnography is a…

Five C Framework: A Student-Centered Approach for Teaching Programming Courses to Students with Diverse Disciplinary Background

ERIC Educational Resources Information Center

Tom, Mary

2015-01-01

The already existing complexities of teaching and learning computer programming are increased where students are diverse in their disciplinary backgrounds, language skills, and culture. Learners experience emotional issues of anxiety, fear or boredom. Identifying opportunities for improvement and applying theoretical and empirical evidence found…

A New Approach to the Numerical Evaluation of the Inverse Radon Transform with Discrete, Noisy Data.

DTIC Science & Technology

1980-07-01

spline form. The resulting analytic expression for the inner integral in the inverse transform is then readily evaluated, and the outer (periodic...integral is replaced by a sum. The work involved to obtain the inverse transform appears to be within the capability of existing computing equipment for

The Effects of Integrating Social Learning Environment with Online Learning

ERIC Educational Resources Information Center

Raspopovic, Miroslava; Cvetanovic, Svetlana; Medan, Ivana; Ljubojevic, Danijela

2017-01-01

The aim of this paper is to present the learning and teaching styles using the Social Learning Environment (SLE), which was developed based on the computer supported collaborative learning approach. To avoid burdening learners with multiple platforms and tools, SLE was designed and developed in order to integrate existing systems, institutional…

Implementation of a Curriculum-Integrated Computer Game for Introducing Scientific Argumentation

ERIC Educational Resources Information Center

Wallon, Robert C.; Jasti, Chandana; Lauren, Hillary Z. G.; Hug, Barbara

2018-01-01

Argumentation has been emphasized in recent US science education reform efforts (NGSS Lead States 2013; NRC 2012), and while existing studies have investigated approaches to introducing and supporting argumentation (e.g., McNeill and Krajcik in "Journal of Research in Science Teaching," 45(1), 53-78, 2008; Kang et al. in "Science…

EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering.

PubMed

Lee, Soohyun; Seo, Chae Hwa; Alver, Burak Han; Lee, Sanghyuk; Park, Peter J

2015-09-03

RNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read is often ambiguous. Existing approaches for estimating expression levels from RNA-seq reads tend to compromise between accuracy and computational cost. We introduce a new approach for quantifying transcript abundance from RNA-seq data. EMSAR (Estimation by Mappability-based Segmentation And Reclustering) groups reads according to the set of transcripts to which they are mapped and finds maximum likelihood estimates using a joint Poisson model for each optimal set of segments of transcripts. The method uses nearly all mapped reads, including those mapped to multiple genes. With an efficient transcriptome indexing based on modified suffix arrays, EMSAR minimizes the use of CPU time and memory while achieving accuracy comparable to the best existing methods. EMSAR is a method for quantifying transcripts from RNA-seq data with high accuracy and low computational cost. EMSAR is available at https://github.com/parklab/emsar.

A novel patient-specific model to compute coronary fractional flow reserve.

PubMed

Kwon, Soon-Sung; Chung, Eui-Chul; Park, Jin-Seo; Kim, Gook-Tae; Kim, Jun-Woo; Kim, Keun-Hong; Shin, Eun-Seok; Shim, Eun Bo

2014-09-01

The fractional flow reserve (FFR) is a widely used clinical index to evaluate the functional severity of coronary stenosis. A computer simulation method based on patients' computed tomography (CT) data is a plausible non-invasive approach for computing the FFR. This method can provide a detailed solution for the stenosed coronary hemodynamics by coupling computational fluid dynamics (CFD) with the lumped parameter model (LPM) of the cardiovascular system. In this work, we have implemented a simple computational method to compute the FFR. As this method uses only coronary arteries for the CFD model and includes only the LPM of the coronary vascular system, it provides simpler boundary conditions for the coronary geometry and is computationally more efficient than existing approaches. To test the efficacy of this method, we simulated a three-dimensional straight vessel using CFD coupled with the LPM. The computed results were compared with those of the LPM. To validate this method in terms of clinically realistic geometry, a patient-specific model of stenosed coronary arteries was constructed from CT images, and the computed FFR was compared with clinically measured results. We evaluated the effect of a model aorta on the computed FFR and compared this with a model without the aorta. Computationally, the model without the aorta was more efficient than that with the aorta, reducing the CPU time required for computing a cardiac cycle to 43.4%. Copyright © 2014. Published by Elsevier Ltd.

Higher order approximation to the Hill problem dynamics about the libration points

NASA Astrophysics Data System (ADS)

Lara, Martin; Pérez, Iván L.; López, Rosario

2018-06-01

An analytical solution to the Hill problem Hamiltonian expanded about the libration points has been obtained by means of perturbation techniques. In order to compute the higher orders of the perturbation solution that are needed to capture all the relevant periodic orbits originated from the libration points within a reasonable accuracy, the normalization is approached in complex variables. The validity of the solution extends to energy values considerably far away from that of the libration points and, therefore, can be used in the computation of Halo orbits as an alternative to the classical Lindstedt-Poincaré approach. Furthermore, the theory correctly predicts the existence of the two-lane bridge of periodic orbits linking the families of planar and vertical Lyapunov orbits.

The transition of GTDS to the Unix workstation environment

NASA Technical Reports Server (NTRS)

Carter, D.; Metzinger, R.; Proulx, R.; Cefola, P.

1995-01-01

Future Flight Dynamics systems should take advantage of the possibilities provided by current and future generations of low-cost, high performance workstation computing environments with Graphical User Interface. The port of the existing mainframe Flight Dynamics systems to the workstation environment offers an economic approach for combining the tremendous engineering heritage that has been encapsulated in these systems with the advantages of the new computing environments. This paper will describe the successful transition of the Draper Laboratory R&D version of GTDS (Goddard Trajectory Determination System) from the IBM Mainframe to the Unix workstation environment. The approach will be a mix of historical timeline notes, descriptions of the technical problems overcome, and descriptions of associated SQA (software quality assurance) issues.

Advanced On-Board Processor (AOP). [for future spacecraft applications

NASA Technical Reports Server (NTRS)

1973-01-01

Advanced On-board Processor the (AOP) uses large scale integration throughout and is the most advanced space qualified computer of its class in existence today. It was designed to satisfy most spacecraft requirements which are anticipated over the next several years. The AOP design utilizes custom metallized multigate arrays (CMMA) which have been designed specifically for this computer. This approach provides the most efficient use of circuits, reduces volume, weight, assembly costs and provides for a significant increase in reliability by the significant reduction in conventional circuit interconnections. The required 69 CMMA packages are assembled on a single multilayer printed circuit board which together with associated connectors constitutes the complete AOP. This approach also reduces conventional interconnections thus further reducing weight, volume and assembly costs.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

PubMed Central

Cruz-Cano, Raul; Chew, David S.H.; Kwok-Pui, Choi; Ming-Ying, Leung

2010-01-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications. PMID:20729987

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

PubMed

Cruz-Cano, Raul; Chew, David S H; Kwok-Pui, Choi; Ming-Ying, Leung

2010-06-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.

An overview of bioinformatics methods for modeling biological pathways in yeast.

PubMed

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

2016-03-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A four stage approach for ontology-based health information system design.

PubMed

Kuziemsky, Craig E; Lau, Francis

2010-11-01

To describe and illustrate a four stage methodological approach to capture user knowledge in a biomedical domain area, use that knowledge to design an ontology, and then implement and evaluate the ontology as a health information system (HIS). A hybrid participatory design-grounded theory (GT-PD) method was used to obtain data and code them for ontology development. Prototyping was used to implement the ontology as a computer-based tool. Usability testing evaluated the computer-based tool. An empirically derived domain ontology and set of three problem-solving approaches were developed as a formalized model of the concepts and categories from the GT coding. The ontology and problem-solving approaches were used to design and implement a HIS that tested favorably in usability testing. The four stage approach illustrated in this paper is useful for designing and implementing an ontology as the basis for a HIS. The approach extends existing ontology development methodologies by providing an empirical basis for theory incorporated into ontology design. Copyright © 2010 Elsevier B.V. All rights reserved.

Remote Data Retrieval for Bioinformatics Applications: An Agent Migration Approach

PubMed Central

Gao, Lei; Dai, Hua; Zhang, Tong-Liang; Chou, Kuo-Chen

2011-01-01

Some of the approaches have been developed to retrieve data automatically from one or multiple remote biological data sources. However, most of them require researchers to remain online and wait for returned results. The latter not only requires highly available network connection, but also may cause the network overload. Moreover, so far none of the existing approaches has been designed to address the following problems when retrieving the remote data in a mobile network environment: (1) the resources of mobile devices are limited; (2) network connection is relatively of low quality; and (3) mobile users are not always online. To address the aforementioned problems, we integrate an agent migration approach with a multi-agent system to overcome the high latency or limited bandwidth problem by moving their computations to the required resources or services. More importantly, the approach is fit for the mobile computing environments. Presented in this paper are also the system architecture, the migration strategy, as well as the security authentication of agent migration. As a demonstration, the remote data retrieval from GenBank was used to illustrate the feasibility of the proposed approach. PMID:21701677

The post-genomic era of biological network alignment.

PubMed

Faisal, Fazle E; Meng, Lei; Crawford, Joseph; Milenković, Tijana

2015-12-01

Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches' biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

PubMed

Abduallah, Yasser; Turki, Turki; Byron, Kevin; Du, Zongxuan; Cervantes-Cervantes, Miguel; Wang, Jason T L

2017-01-01

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

PubMed

Woldegebriel, Michael; Derks, Eduard

2017-01-17

In this work, a novel probabilistic untargeted feature detection algorithm for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using artificial neural network (ANN) is presented. The feature detection process is approached as a pattern recognition problem, and thus, ANN was utilized as an efficient feature recognition tool. Unlike most existing feature detection algorithms, with this approach, any suspected chromatographic profile (i.e., shape of a peak) can easily be incorporated by training the network, avoiding the need to perform computationally expensive regression methods with specific mathematical models. In addition, with this method, we have shown that the high-resolution raw data can be fully utilized without applying any arbitrary thresholds or data reduction, therefore improving the sensitivity of the method for compound identification purposes. Furthermore, opposed to existing deterministic (binary) approaches, this method rather estimates the probability of a feature being present/absent at a given point of interest, thus giving chance for all data points to be propagated down the data analysis pipeline, weighed with their probability. The algorithm was tested with data sets generated from spiked samples in forensic and food safety context and has shown promising results by detecting features for all compounds in a computationally reasonable time.

A Flexible and Non-instrusive Approach for Computing Complex Structural Coverage Metrics

NASA Technical Reports Server (NTRS)

Whalen, Michael W.; Person, Suzette J.; Rungta, Neha; Staats, Matt; Grijincu, Daniela

2015-01-01

Software analysis tools and techniques often leverage structural code coverage information to reason about the dynamic behavior of software. Existing techniques instrument the code with the required structural obligations and then monitor the execution of the compiled code to report coverage. Instrumentation based approaches often incur considerable runtime overhead for complex structural coverage metrics such as Modified Condition/Decision (MC/DC). Code instrumentation, in general, has to be approached with great care to ensure it does not modify the behavior of the original code. Furthermore, instrumented code cannot be used in conjunction with other analyses that reason about the structure and semantics of the code under test. In this work, we introduce a non-intrusive preprocessing approach for computing structural coverage information. It uses a static partial evaluation of the decisions in the source code and a source-to-bytecode mapping to generate the information necessary to efficiently track structural coverage metrics during execution. Our technique is flexible; the results of the preprocessing can be used by a variety of coverage-driven software analysis tasks, including automated analyses that are not possible for instrumented code. Experimental results in the context of symbolic execution show the efficiency and flexibility of our nonintrusive approach for computing code coverage information

A multiphysics and multiscale software environment for modeling astrophysical systems

NASA Astrophysics Data System (ADS)

Portegies Zwart, Simon; McMillan, Steve; Harfst, Stefan; Groen, Derek; Fujii, Michiko; Nualláin, Breanndán Ó.; Glebbeek, Evert; Heggie, Douglas; Lombardi, James; Hut, Piet; Angelou, Vangelis; Banerjee, Sambaran; Belkus, Houria; Fragos, Tassos; Fregeau, John; Gaburov, Evghenii; Izzard, Rob; Jurić, Mario; Justham, Stephen; Sottoriva, Andrea; Teuben, Peter; van Bever, Joris; Yaron, Ofer; Zemp, Marcel

2009-05-01

We present MUSE, a software framework for combining existing computational tools for different astrophysical domains into a single multiphysics, multiscale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a "Noah's Ark" milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multiscale and multiphysics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe three examples calculated using MUSE: the merger of two galaxies, the merger of two evolving stars, and a hybrid N-body simulation. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.li.

Status and future of MUSE

NASA Astrophysics Data System (ADS)

Harfst, S.; Portegies Zwart, S.; McMillan, S.

2008-12-01

We present MUSE, a software framework for combining existing computational tools from different astrophysical domains into a single multi-physics, multi-scale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a ``Noah's Ark'' milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multi-scale and multi-physics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe two examples calculated using MUSE: the merger of two galaxies and an N-body simulation with live stellar evolution. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.li.

Computational approach for deriving cancer progression roadmaps from static sample data

PubMed Central

Yao, Jin; Yang, Le; Chen, Runpu; Nowak, Norma J.

2017-01-01

Abstract As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy. PMID:28108658

The NASA Science Internet: An integrated approach to networking

NASA Technical Reports Server (NTRS)

Rounds, Fred

1991-01-01

An integrated approach to building a networking infrastructure is an absolute necessity for meeting the multidisciplinary science networking requirements of the Office of Space Science and Applications (OSSA) science community. These networking requirements include communication connectivity between computational resources, databases, and library systems, as well as to other scientists and researchers around the world. A consolidated networking approach allows strategic use of the existing science networking within the Federal government, and it provides networking capability that takes into consideration national and international trends towards multivendor and multiprotocol service. It also offers a practical vehicle for optimizing costs and maximizing performance. Finally, and perhaps most important to the development of high speed computing is that an integrated network constitutes a focus for phasing to the National Research and Education Network (NREN). The NASA Science Internet (NSI) program, established in mid 1988, is structured to provide just such an integrated network. A description of the NSI is presented.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aimone, James Bradley; Bernard, Michael Lewis; Vineyard, Craig Michael

2014-10-01

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach bymore » which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.« less

Air freight demand models: An overview

NASA Technical Reports Server (NTRS)

Dajani, J. S.; Bernstein, G. W.

1978-01-01

A survey is presented of some of the approaches which have been considered in freight demand estimation. The few existing continuous time computer simulations of aviation systems are reviewed, with a view toward the assessment of this approach as a tool for structuring air freight studies and for relating the different components of the air freight system. The variety of available data types and sources, without which the calibration, validation and the testing of both modal split and simulation models would be impossible are also reviewed.

Promoter Sequences Prediction Using Relational Association Rule Mining

PubMed Central

Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

2012-01-01

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

Computation and brain processes, with special reference to neuroendocrine systems.

PubMed

Toni, Roberto; Spaletta, Giulia; Casa, Claudia Della; Ravera, Simone; Sandri, Giorgio

2007-01-01

The development of neural networks and brain automata has made neuroscientists aware that the performance limits of these brain-like devices lies, at least in part, in their computational power. The computational basis of a. standard cybernetic design, in fact, refers to that of a discrete and finite state machine or Turing Machine (TM). In contrast, it has been suggested that a number of human cerebral activites, from feedback controls up to mental processes, rely on a mixing of both finitary, digital-like and infinitary, continuous-like procedures. Therefore, the central nervous system (CNS) of man would exploit a form of computation going beyond that of a TM. This "non conventional" computation has been called hybrid computation. Some basic structures for hybrid brain computation are believed to be the brain computational maps, in which both Turing-like (digital) computation and continuous (analog) forms of calculus might occur. The cerebral cortex and brain stem appears primary candidate for this processing. However, also neuroendocrine structures like the hypothalamus are believed to exhibit hybrid computional processes, and might give rise to computational maps. Current theories on neural activity, including wiring and volume transmission, neuronal group selection and dynamic evolving models of brain automata, bring fuel to the existence of natural hybrid computation, stressing a cooperation between discrete and continuous forms of communication in the CNS. In addition, the recent advent of neuromorphic chips, like those to restore activity in damaged retina and visual cortex, suggests that assumption of a discrete-continuum polarity in designing biocompatible neural circuitries is crucial for their ensuing performance. In these bionic structures, in fact, a correspondence exists between the original anatomical architecture and synthetic wiring of the chip, resulting in a correspondence between natural and cybernetic neural activity. Thus, chip "form" provides a continuum essential to chip "function". We conclude that it is reasonable to predict the existence of hybrid computational processes in the course of many human, brain integrating activities, urging development of cybernetic approaches based on this modelling for adequate reproduction of a variety of cerebral performances.

Model Reduction of Computational Aerothermodynamics for Multi-Discipline Analysis in High Speed Flows

NASA Astrophysics Data System (ADS)

Crowell, Andrew Rippetoe

This dissertation describes model reduction techniques for the computation of aerodynamic heat flux and pressure loads for multi-disciplinary analysis of hypersonic vehicles. NASA and the Department of Defense have expressed renewed interest in the development of responsive, reusable hypersonic cruise vehicles capable of sustained high-speed flight and access to space. However, an extensive set of technical challenges have obstructed the development of such vehicles. These technical challenges are partially due to both the inability to accurately test scaled vehicles in wind tunnels and to the time intensive nature of high-fidelity computational modeling, particularly for the fluid using Computational Fluid Dynamics (CFD). The aim of this dissertation is to develop efficient and accurate models for the aerodynamic heat flux and pressure loads to replace the need for computationally expensive, high-fidelity CFD during coupled analysis. Furthermore, aerodynamic heating and pressure loads are systematically evaluated for a number of different operating conditions, including: simple two-dimensional flow over flat surfaces up to three-dimensional flows over deformed surfaces with shock-shock interaction and shock-boundary layer interaction. An additional focus of this dissertation is on the implementation and computation of results using the developed aerodynamic heating and pressure models in complex fluid-thermal-structural simulations. Model reduction is achieved using a two-pronged approach. One prong focuses on developing analytical corrections to isothermal, steady-state CFD flow solutions in order to capture flow effects associated with transient spatially-varying surface temperatures and surface pressures (e.g., surface deformation, surface vibration, shock impingements, etc.). The second prong is focused on minimizing the computational expense of computing the steady-state CFD solutions by developing an efficient surrogate CFD model. The developed two-pronged approach is found to exhibit balanced performance in terms of accuracy and computational expense, relative to several existing approaches. This approach enables CFD-based loads to be implemented into long duration fluid-thermal-structural simulations.

An overview of the information management component of RICIS

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1987-01-01

Information management is the RICIS (Research Institute for Computing and Information Systems) research area which covers four types of tasks initiated during the first year of research: (1) surveys - a description of the existing state of some area in computing and information systems; (2) forecasts - a description of the alternative future states of some area; (3) plans - an approach to accomplishing some objective in the future; and (4) demonstrations - working prototypes and field trials to study the feasibility and the benefits of a particular information system. The activity in these research areas is described.

TESS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dmitriy Morozov, Tom Peterka

2014-07-29

Computing a Voronoi or Delaunay tessellation from a set of points is a core part of the analysis of many simulated and measured datasets. As the scale of simulations and observations surpasses billions of particles, a distributed-memory scalable parallel algorithm is the only feasible approach. The primary contribution of this software is a distributed-memory parallel Delaunay and Voronoi tessellation algorithm based on existing serial computational geometry libraries that automatically determines which neighbor points need to be exchanged among the subdomains of a spatial decomposition. Other contributions include the addition of periodic and wall boundary conditions.

TarPmiR: a new approach for microRNA target site prediction.

PubMed

Ding, Jun; Li, Xiaoman; Hu, Haiyan

2016-09-15

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Efficient clustering aggregation based on data fragments.

PubMed

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

An intelligent decomposition approach for efficient design of non-hierarchic systems

NASA Technical Reports Server (NTRS)

Bloebaum, Christina L.

1992-01-01

The design process associated with large engineering systems requires an initial decomposition of the complex systems into subsystem modules which are coupled through transference of output data. The implementation of such a decomposition approach assumes the ability exists to determine what subsystems and interactions exist and what order of execution will be imposed during the analysis process. Unfortunately, this is quite often an extremely complex task which may be beyond human ability to efficiently achieve. Further, in optimizing such a coupled system, it is essential to be able to determine which interactions figure prominently enough to significantly affect the accuracy of the optimal solution. The ability to determine 'weak' versus 'strong' coupling strengths would aid the designer in deciding which couplings could be permanently removed from consideration or which could be temporarily suspended so as to achieve computational savings with minimal loss in solution accuracy. An approach that uses normalized sensitivities to quantify coupling strengths is presented. The approach is applied to a coupled system composed of analysis equations for verification purposes.

ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

PubMed Central

Meher, Prabina K.; Sahu, Tanmaya K.; Gahoi, Shachi; Rao, Atmakuri R.

2018-01-01

Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information is available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs, and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is made freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for prediction of HSPs, to the extent of their families and sub-types. PMID:29379521

On the existence of mosaic-skeleton approximations for discrete analogues of integral operators

NASA Astrophysics Data System (ADS)

Kashirin, A. A.; Taltykina, M. Yu.

2017-09-01

Exterior three-dimensional Dirichlet problems for the Laplace and Helmholtz equations are considered. By applying methods of potential theory, they are reduced to equivalent Fredholm boundary integral equations of the first kind, for which discrete analogues, i.e., systems of linear algebraic equations (SLAEs) are constructed. The existence of mosaic-skeleton approximations for the matrices of the indicated systems is proved. These approximations make it possible to reduce the computational complexity of an iterative solution of the SLAEs. Numerical experiments estimating the capabilities of the proposed approach are described.

Expressing clinical data sets with openEHR archetypes: a solid basis for ubiquitous computing.

PubMed

Garde, Sebastian; Hovenga, Evelyn; Buck, Jasmin; Knaup, Petra

2007-12-01

The purpose of this paper is to analyse the feasibility and usefulness of expressing clinical data sets (CDSs) as openEHR archetypes. For this, we present an approach to transform CDS into archetypes, and outline typical problems with CDS and analyse whether some of these problems can be overcome by the use of archetypes. Literature review and analysis of a selection of existing Australian, German, other European and international CDSs; transfer of a CDS for Paediatric Oncology into openEHR archetypes; implementation of CDSs in application systems. To explore the feasibility of expressing CDS as archetypes an approach to transform existing CDSs into archetypes is presented in this paper. In case of the Paediatric Oncology CDS (which consists of 260 data items) this lead to the definition of 48 openEHR archetypes. To analyse the usefulness of expressing CDS as archetypes, we identified nine problems with CDS that currently remain unsolved without a common model underpinning the CDS. Typical problems include incompatible basic data types and overlapping and incompatible definitions of clinical content. A solution to most of these problems based on openEHR archetypes is motivated. With regard to integrity constraints, further research is required. While openEHR cannot overcome all barriers to Ubiquitous Computing, it can provide the common basis for ubiquitous presence of meaningful and computer-processable knowledge and information, which we believe is a basic requirement for Ubiquitous Computing. Expressing CDSs as openEHR archetypes is feasible and advantageous as it fosters semantic interoperability, supports ubiquitous computing, and helps to develop archetypes that are arguably of better quality than the original CDS.

The rise of machine consciousness: studying consciousness with computational models.

PubMed

Reggia, James A

2013-08-01

Efforts to create computational models of consciousness have accelerated over the last two decades, creating a field that has become known as artificial consciousness. There have been two main motivations for this controversial work: to develop a better scientific understanding of the nature of human/animal consciousness and to produce machines that genuinely exhibit conscious awareness. This review begins by briefly explaining some of the concepts and terminology used by investigators working on machine consciousness, and summarizes key neurobiological correlates of human consciousness that are particularly relevant to past computational studies. Models of consciousness developed over the last twenty years are then surveyed. These models are largely found to fall into five categories based on the fundamental issue that their developers have selected as being most central to consciousness: a global workspace, information integration, an internal self-model, higher-level representations, or attention mechanisms. For each of these five categories, an overview of past work is given, a representative example is presented in some detail to illustrate the approach, and comments are provided on the contributions and limitations of the methodology. Three conclusions are offered about the state of the field based on this review: (1) computational modeling has become an effective and accepted methodology for the scientific study of consciousness, (2) existing computational models have successfully captured a number of neurobiological, cognitive, and behavioral correlates of conscious information processing as machine simulations, and (3) no existing approach to artificial consciousness has presented a compelling demonstration of phenomenal machine consciousness, or even clear evidence that artificial phenomenal consciousness will eventually be possible. The paper concludes by discussing the importance of continuing work in this area, considering the ethical issues it raises, and making predictions concerning future developments. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Simple and Robust Method for Partially Matched Samples Using the P-Values Pooling Approach

PubMed Central

Kuan, Pei Fen; Huang, Bo

2013-01-01

This paper focuses on statistical analyses in scenarios where some samples from the matched pairs design are missing, resulting in partially matched samples. Motivated by the idea of meta-analysis, we recast the partially matched samples as coming from two experimental designs, and propose a simple yet robust approach based on the weighted Z-test to integrate the p-values computed from these two designs. We show that the proposed approach achieves better operating characteristics in simulations and a case study, compared to existing methods for partially matched samples. PMID:23417968

Modeling Trait Anxiety: From Computational Processes to Personality

PubMed Central

Raymond, James G.; Steele, J. Douglas; Seriès, Peggy

2017-01-01

Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in “trait” anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed. PMID:28167920

Modeling Trait Anxiety: From Computational Processes to Personality.

PubMed

Raymond, James G; Steele, J Douglas; Seriès, Peggy

2017-01-01

Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in "trait" anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed.

Progress and challenges in bioinformatics approaches for enhancer identification

PubMed Central

Kleftogiannis, Dimitrios; Kalnis, Panos

2016-01-01

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

A Modular Approach to Teaching Mathematical Modeling in Biotechnology in the Undergraduate Curriculum

ERIC Educational Resources Information Center

Larripa, Kamila R.; Mazzag, Borbala

2016-01-01

Our paper describes a solution we found to a still existing need to develop mathematical modeling courses for undergraduate biology majors. Some challenges of such courses are: (i) relatively limited exposure of biology students to higher-level mathematical and computational concepts; (ii) availability of texts that can give a flavor of how…

Effects of Computer-Based Early-Reading Academic Learning Time on Early-Reading Achievement: A Dose-Response Approach

ERIC Educational Resources Information Center

Heuston, Edward Benjamin Hull

2010-01-01

Academic learning time (ALT) has long had the theoretical underpinnings sufficient to claim a causal relationship with academic achievement, but to this point empirical evidence has been lacking. This dearth of evidence has existed primarily due to difficulties associated with operationalizing ALT in traditional educational settings. Recent…

Game-Themed Programming Assignment Modules: A Pathway for Gradual Integration of Gaming Context into Existing Introductory Programming Courses

ERIC Educational Resources Information Center

Sung, K.; Hillyard, C.; Angotti, R. L.; Panitz, M. W.; Goldstein, D. S.; Nordlinger, J.

2011-01-01

Despite the proven success of using computer video games as a context for teaching introductory programming (CS1/2) courses, barriers including the lack of adoptable materials, required background expertise (in graphics/games), and institutional acceptance still prevent interested faculty members from experimenting with this approach. Game-themed…

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE PAGES

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; ...

2016-11-24

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Cohesive phase-field fracture and a PDE constrained optimization approach to fracture inverse problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tupek, Michael R.

2016-06-30

In recent years there has been a proliferation of modeling techniques for forward predictions of crack propagation in brittle materials, including: phase-field/gradient damage models, peridynamics, cohesive-zone models, and G/XFEM enrichment techniques. However, progress on the corresponding inverse problems has been relatively lacking. Taking advantage of key features of existing modeling approaches, we propose a parabolic regularization of Barenblatt cohesive models which borrows extensively from previous phase-field and gradient damage formulations. An efficient explicit time integration strategy for this type of nonlocal fracture model is then proposed and justified. In addition, we present a C++ computational framework for computing in- putmore » parameter sensitivities efficiently for explicit dynamic problems using the adjoint method. This capability allows for solving inverse problems involving crack propagation to answer interesting engineering questions such as: 1) what is the optimal design topology and material placement for a heterogeneous structure to maximize fracture resistance, 2) what loads must have been applied to a structure for it to have failed in an observed way, 3) what are the existing cracks in a structure given various experimental observations, etc. In this work, we focus on the first of these engineering questions and demonstrate a capability to automatically and efficiently compute optimal designs intended to minimize crack propagation in structures.« less

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

PubMed

Zhang, Guo-Qiang; Xing, Guangming; Cui, Licong

2018-04-01

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work. Copyright © 2018 Elsevier Inc. All rights reserved.

Mesoscale Models of Fluid Dynamics

NASA Astrophysics Data System (ADS)

Boghosian, Bruce M.; Hadjiconstantinou, Nicolas G.

During the last half century, enormous progress has been made in the field of computational materials modeling, to the extent that in many cases computational approaches are used in a predictive fashion. Despite this progress, modeling of general hydrodynamic behavior remains a challenging task. One of the main challenges stems from the fact that hydrodynamics manifests itself over a very wide range of length and time scales. On one end of the spectrum, one finds the fluid's "internal" scale characteristic of its molecular structure (in the absence of quantum effects, which we omit in this chapter). On the other end, the "outer" scale is set by the characteristic sizes of the problem's domain. The resulting scale separation or lack thereof as well as the existence of intermediate scales are key to determining the optimal approach. Successful treatments require a judicious choice of the level of description which is a delicate balancing act between the conflicting requirements of fidelity and manageable computational cost: a coarse description typically requires models for underlying processes occuring at smaller length and time scales; on the other hand, a fine-scale model will incur a significantly larger computational cost.

Local Orthogonal Cutting Method for Computing Medial Curves and Its Biomedical Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, Xiangmin; Einstein, Daniel R.; Dyedov, Volodymyr

2010-03-24

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stabilitymore » and consistency tests. These concepts lend themselves to robust numerical techniques including eigenvalue analysis, weighted least squares approximations, and numerical minimization, resulting in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods.« less

Computational Planning in Facial Surgery.

PubMed

Zachow, Stefan

2015-10-01

This article reflects the research of the last two decades in computational planning for cranio-maxillofacial surgery. Model-guided and computer-assisted surgery planning has tremendously developed due to ever increasing computational capabilities. Simulators for education, planning, and training of surgery are often compared with flight simulators, where maneuvers are also trained to reduce a possible risk of failure. Meanwhile, digital patient models can be derived from medical image data with astonishing accuracy and thus can serve for model surgery to derive a surgical template model that represents the envisaged result. Computerized surgical planning approaches, however, are often still explorative, meaning that a surgeon tries to find a therapeutic concept based on his or her expertise using computational tools that are mimicking real procedures. Future perspectives of an improved computerized planning may be that surgical objectives will be generated algorithmically by employing mathematical modeling, simulation, and optimization techniques. Planning systems thus act as intelligent decision support systems. However, surgeons can still use the existing tools to vary the proposed approach, but they mainly focus on how to transfer objectives into reality. Such a development may result in a paradigm shift for future surgery planning. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

NASA Astrophysics Data System (ADS)

Marojevic, Vuk; Gomez-Miguelez, Ismael; Gelonch, Antoni

2012-09-01

Software defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

Global computing for bioinformatics.

PubMed

Loewe, Laurence

2002-12-01

Global computing, the collaboration of idle PCs via the Internet in a SETI@home style, emerges as a new way of massive parallel multiprocessing with potentially enormous CPU power. Its relations to the broader, fast-moving field of Grid computing are discussed without attempting a review of the latter. This review (i) includes a short table of milestones in global computing history, (ii) lists opportunities global computing offers for bioinformatics, (iii) describes the structure of problems well suited for such an approach, (iv) analyses the anatomy of successful projects and (v) points to existing software frameworks. Finally, an evaluation of the various costs shows that global computing indeed has merit, if the problem to be solved is already coded appropriately and a suitable global computing framework can be found. Then, either significant amounts of computing power can be recruited from the general public, or--if employed in an enterprise-wide Intranet for security reasons--idle desktop PCs can substitute for an expensive dedicated cluster.

NOSTOS: A Paper–Based Ubiquitous Computing Healthcare Environment to Support Data Capture and Collaboration

PubMed Central

Bång, Magnus; Larsson, Anders; Eriksson, Henrik

2003-01-01

In this paper, we present a new approach to clinical workplace computerization that departs from the window–based user interface paradigm. NOSTOS is an experimental computer–augmented work environment designed to support data capture and teamwork in an emergency room. NOSTOS combines multiple technologies, such as digital pens, walk–up displays, headsets, a smart desk, and sensors to enhance an existing paper–based practice with computer power. The physical interfaces allow clinicians to retain mobile paper–based collaborative routines and still benefit from computer technology. The requirements for the system were elicited from situated workplace studies. We discuss the advantages and disadvantages of augmenting a paper–based clinical work environment. PMID:14728131

RESTOP: Retaining External Peripheral State in Intermittently-Powered Sensor Systems.

PubMed

Rodriguez Arreola, Alberto; Balsamo, Domenico; Merrett, Geoff V; Weddell, Alex S

2018-01-10

Energy harvesting sensor systems typically incorporate energy buffers (e.g., rechargeable batteries and supercapacitors) to accommodate fluctuations in supply. However, the presence of these elements limits the miniaturization of devices. In recent years, researchers have proposed a new paradigm, transient computing, where systems operate directly from the energy harvesting source and allow computation to span across power cycles, without adding energy buffers. Various transient computing approaches have addressed the challenge of power intermittency by retaining the processor's state using non-volatile memory. However, no generic approach has yet been proposed to retain the state of peripherals external to the processing element. This paper proposes RESTOP, flexible middleware which retains the state of multiple external peripherals that are connected to a computing element (i.e., a microcontroller) through protocols such as SPI or I 2 C. RESTOP acts as an interface between the main application and the peripheral, which keeps a record, at run-time, of the transmitted data in order to restore peripheral configuration after a power interruption. RESTOP is practically implemented and validated using three digitally interfaced peripherals, successfully restoring their configuration after power interruptions, imposing a maximum time overhead of 15% when configuring a peripheral. However, this represents an overhead of only 0.82% during complete execution of our typical sensing application, which is substantially lower than existing approaches.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

PubMed Central

Broadaway, K. Alaine; Cutler, David J.; Duncan, Richard; Moore, Jacob L.; Ware, Erin B.; Jhun, Min A.; Bielak, Lawrence F.; Zhao, Wei; Smith, Jennifer A.; Peyser, Patricia A.; Kardia, Sharon L.R.; Ghosh, Debashis; Epstein, Michael P.

2016-01-01

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. PMID:26942286

Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks

NASA Technical Reports Server (NTRS)

Das, Sajal K.; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.

The computation in diagnostics for tokamaks: systems, designs, approaches

NASA Astrophysics Data System (ADS)

Krawczyk, Rafał; Linczuk, Paweł; Czarski, Tomasz; Wojeński, Andrzej; Chernyshova, Maryna; Poźniak, Krzysztof; Kolasiński, Piotr; Kasprowicz, Grzegorz; Zabołotny, Wojciech; Kowalska-Strzeciwilk, Ewa; Malinowski, Karol; Gaska, Michał

2017-08-01

The requirements given for GEM (Gaseous Electron Multiplier) detector based acquisition system for plasma impurities diagnostics triggered a need for the development of a specialized software and hardware architecture. The amount of computations with latency and throughput restrictions cause that an advanced solution is sought for. In order to provide a mechanism fitting the designated tokamaks, an insight into existing solutions was necessary. In the article there is discussed architecture of systems used for plasma diagnostics and in related scientific fields. The developed solution is compared and contrasted with other diagnostic and control systems. Particular attention is payed to specific requirements for plasma impurities diagnostics in tokamak thermal fusion reactor. Subsequently, the details are presented that justified the choice of the system architecture and the discussion on various approaches is given.

Use of application containers and workflows for genomic data analysis.

PubMed

Schulz, Wade L; Durant, Thomas J S; Siddon, Alexa J; Torres, Richard

2016-01-01

The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.

Explosion safety in industrial electrostatics

NASA Astrophysics Data System (ADS)

Szabó, S. V.; Kiss, I.; Berta, I.

2011-01-01

Complicated industrial systems are often endangered by electrostatic hazards, both from atmospheric (lightning phenomenon, primary and secondary lightning protection) and industrial (technological problems caused by static charging and fire and explosion hazards.) According to the classical approach protective methods have to be used in order to remove electrostatic charging and to avoid damages, however no attempt to compute the risk before and after applying the protective method is made, relying instead on well-educated and practiced expertise. The Budapest School of Electrostatics - in close cooperation with industrial partners - develops new suitable solutions for probability based decision support (Static Control Up-to-date Technology, SCOUT) using soft computing methods. This new approach can be used to assess and audit existing systems and - using the predictive power of the models - to design and plan activities in industrial electrostatics.

Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach

NASA Astrophysics Data System (ADS)

Salleh, R. M.; Zawawi, N. I.; Gan, Z. F.; Nor, M. E.

2018-04-01

Existing of autocorrelation will bring a significant effect on the performance and accuracy of process control if the problem does not handle carefully. When dealing with autocorrelated process, Box-Jenkins method will be preferred because of the popularity. However, the computation of Box-Jenkins method is too complicated and challenging which cause of time-consuming. Therefore, an alternative method which known as Geometric Brownian Motion (GBM) is introduced to monitor the autocorrelated process. One real case of furnace temperature data is conducted to compare the performance of Box-Jenkins and GBM methods in monitoring autocorrelation process. Both methods give the same results in terms of model accuracy and monitoring process control. Yet, GBM is superior compared to Box-Jenkins method due to its simplicity and practically with shorter computational time.

Comparing the Pre- and Posttest Scores in Relations to the Emporium and the Hands-on Instructional Approaches of Teaching Science in Prekindergarten

NASA Astrophysics Data System (ADS)

Headen, Patricia Ann

This quantitative, quasi-experimental research investigated if two instructional approaches, the Emporium Computer-Based (Group 2) versus the hands-on approach (Group 1), resulted any difference in student achievement in science for four-year-old prekindergarten students at a private childcare facility in North Carolina. Three research questions hypothesized these relationships: (a) Group 2 versus Group 1 assessed student achievement as theoretically based on Piaget and Vygotsky's perspectives of child development, (b) the instructional approaches related to gender, and (c) the instructional approaches interrelated to ethnicity. Using a two-factor ANOVA and ANCOVA techniques, involved a convenience sample of 126 four-year-old prekindergarten students of which a convenience sample of 126 participated. The Assessment of Measurements for Pre-K (AMP-K), pretest and posttest scores of each group of 63 students measured student achievement. The t tests determined if a significant difference in student achievement existed (dependent variable) with the Emporium Computer-Based versus hands-on instructional approaches (independent variables). The posttest scores of Group 2 (p = 0.00), indicated a significant difference in student achievement. However, gender and ethnicity variables had no effect on student achievement, male (M = 36.14, SD = 19.61) and female (M = 42.91, SD = 18.99) with (p = 0.49), and ethnicity resulted, F (1,125) = 1.65, (p = 0.20). These results suggested that further research on the Emporium Computer-Based instructional approach could improve students' intellectual abilities through more innovative practices.

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration.

PubMed

Crouser, R J; Chang, R

2012-12-01

Visual Analytics is "the science of analytical reasoning facilitated by visual interactive interfaces". The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on human and machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

Accuracy of Time Integration Approaches for Stiff Magnetohydrodynamics Problems

NASA Astrophysics Data System (ADS)

Knoll, D. A.; Chacon, L.

2003-10-01

The simulation of complex physical processes with multiple time scales presents a continuing challenge to the computational plasma physisist due to the co-existence of fast and slow time scales. Within computational plasma physics, practitioners have developed and used linearized methods, semi-implicit methods, and time splitting in an attempt to tackle such problems. All of these methods are understood to generate numerical error. We are currently developing algorithms which remove such error for MHD problems [1,2]. These methods do not rely on linearization or time splitting. We are also attempting to analyze the errors introduced by existing ``implicit'' methods using modified equation analysis (MEA) [3]. In this presentation we will briefly cover the major findings in [3]. We will then extend this work further into MHD. This analysis will be augmented with numerical experiments with the hope of gaining insight, particularly into how these errors accumulate over many time steps. [1] L. Chacon,. D.A. Knoll, J.M. Finn, J. Comput. Phys., vol. 178, pp. 15-36 (2002) [2] L. Chacon and D.A. Knoll, J. Comput. Phys., vol. 188, pp. 573-592 (2003) [3] D.A. Knoll , L. Chacon, L.G. Margolin, V.A. Mousseau, J. Comput. Phys., vol. 185, pp. 583-611 (2003)

Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System

NASA Astrophysics Data System (ADS)

Skrzypek, Josef; Mesrobian, Edmond; Gungner, David J.

1989-03-01

The development of autonomous land vehicles (ALV) capable of operating in an unconstrained environment has proven to be a formidable research effort. The unpredictability of events in such an environment calls for the design of a robust perceptual system, an impossible task requiring the programming of a system bases on the expectation of future, unconstrained events. Hence, the need for a "general purpose" machine vision system that is capable of perceiving and understanding images in an unconstrained environment in real-time. The research undertaken at the UCLA Machine Perception Laboratory addresses this need by focusing on two specific issues: 1) the long term goals for machine vision research as a joint effort between the neurosciences and computer science; and 2) a framework for evaluating progress in machine vision. In the past, vision research has been carried out independently within different fields including neurosciences, psychology, computer science, and electrical engineering. Our interdisciplinary approach to vision research is based on the rigorous combination of computational neuroscience, as derived from neurophysiology and neuropsychology, with computer science and electrical engineering. The primary motivation behind our approach is that the human visual system is the only existing example of a "general purpose" vision system and using a neurally based computing substrate, it can complete all necessary visual tasks in real-time.

Transient Solid Dynamics Simulations on the Sandia/Intel Teraflop Computer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Attaway, S.; Brown, K.; Gardner, D.

1997-12-31

Transient solid dynamics simulations are among the most widely used engineering calculations. Industrial applications include vehicle crashworthiness studies, metal forging, and powder compaction prior to sintering. These calculations are also critical to defense applications including safety studies and weapons simulations. The practical importance of these calculations and their computational intensiveness make them natural candidates for parallelization. This has proved to be difficult, and existing implementations fail to scale to more than a few dozen processors. In this paper we describe our parallelization of PRONTO, Sandia`s transient solid dynamics code, via a novel algorithmic approach that utilizes multiple decompositions for differentmore » key segments of the computations, including the material contact calculation. This latter calculation is notoriously difficult to perform well in parallel, because it involves dynamically changing geometry, global searches for elements in contact, and unstructured communications among the compute nodes. Our approach scales to at least 3600 compute nodes of the Sandia/Intel Teraflop computer (the largest set of nodes to which we have had access to date) on problems involving millions of finite elements. On this machine we can simulate models using more than ten- million elements in a few tenths of a second per timestep, and solve problems more than 3000 times faster than a single processor Cray Jedi.« less

A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.

PubMed

Zhao, Yongan; Wang, Xiaofeng; Tang, Haixu

2018-05-09

The elastic and inexpensive computing resources such as clouds have been recognized as a useful solution to analyzing massive human genomic data (e.g., acquired by using next-generation sequencers) in biomedical researches. However, outsourcing human genome computation to public or commercial clouds was hindered due to privacy concerns: even a small number of human genome sequences contain sufficient information for identifying the donor of the genomic data. This issue cannot be directly addressed by existing security and cryptographic techniques (such as homomorphic encryption), because they are too heavyweight to carry out practical genome computation tasks on massive data. In this article, we present a secure algorithm to accomplish the read mapping, one of the most basic tasks in human genomic data analysis based on a hybrid cloud computing model. Comparing with the existing approaches, our algorithm delegates most computation to the public cloud, while only performing encryption and decryption on the private cloud, and thus makes the maximum use of the computing resource of the public cloud. Furthermore, our algorithm reports similar results as the nonsecure read mapping algorithms, including the alignment between reads and the reference genome, which can be directly used in the downstream analysis such as the inference of genomic variations. We implemented the algorithm in C++ and Python on a hybrid cloud system, in which the public cloud uses an Apache Spark system.

Cooperative inference: Features, objects, and collections.

PubMed

Searcy, Sophia Ray; Shafto, Patrick

2016-10-01

Cooperation plays a central role in theories of development, learning, cultural evolution, and education. We argue that existing models of learning from cooperative informants have fundamental limitations that prevent them from explaining how cooperation benefits learning. First, existing models are shown to be computationally intractable, suggesting that they cannot apply to realistic learning problems. Second, existing models assume a priori agreement about which concepts are favored in learning, which leads to a conundrum: Learning fails without precise agreement on bias yet there is no single rational choice. We introduce cooperative inference, a novel framework for cooperation in concept learning, which resolves these limitations. Cooperative inference generalizes the notion of cooperation used in previous models from omission of labeled objects to the omission values of features, labels for objects, and labels for collections of objects. The result is an approach that is computationally tractable, does not require a priori agreement about biases, applies to both Boolean and first-order concepts, and begins to approximate the richness of real-world concept learning problems. We conclude by discussing relations to and implications for existing theories of cognition, cognitive development, and cultural evolution. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Climate Modeling with a Million CPUs

NASA Astrophysics Data System (ADS)

Tobis, M.; Jackson, C. S.

2010-12-01

Michael Tobis, Ph.D. Research Scientist Associate University of Texas Institute for Geophysics Charles S. Jackson Research Scientist University of Texas Institute for Geophysics Meteorological, oceanographic, and climatological applications have been at the forefront of scientific computing since its inception. The trend toward ever larger and more capable computing installations is unabated. However, much of the increase in capacity is accompanied by an increase in parallelism and a concomitant increase in complexity. An increase of at least four additional orders of magnitude in the computational power of scientific platforms is anticipated. It is unclear how individual climate simulations can continue to make effective use of the largest platforms. Conversion of existing community codes to higher resolution, or to more complex phenomenology, or both, presents daunting design and validation challenges. Our alternative approach is to use the expected resources to run very large ensembles of simulations of modest size, rather than to await the emergence of very large simulations. We are already doing this in exploring the parameter space of existing models using the Multiple Very Fast Simulated Annealing algorithm, which was developed for seismic imaging. Our experiments have the dual intentions of tuning the model and identifying ranges of parameter uncertainty. Our approach is less strongly constrained by the dimensionality of the parameter space than are competing methods. Nevertheless, scaling up remains costly. Much could be achieved by increasing the dimensionality of the search and adding complexity to the search algorithms. Such ensemble approaches scale naturally to very large platforms. Extensions of the approach are anticipated. For example, structurally different models can be tuned to comparable effectiveness. This can provide an objective test for which there is no realistic precedent with smaller computations. We find ourselves inventing new code to manage our ensembles. Component computations involve tens to hundreds of CPUs and tens to hundreds of hours. The results of these moderately large parallel jobs influence the scheduling of subsequent jobs, and complex algorithms may be easily contemplated for this. The operating system concept of a "thread" re-emerges at a very coarse level, where each thread manages atomic computations of thousands of CPU-hours. That is, rather than multiple threads operating on a processor, at this level, multiple processors operate within a single thread. In collaboration with the Texas Advanced Computing Center, we are developing a software library at the system level, which should facilitate the development of computations involving complex strategies which invoke large numbers of moderately large multi-processor jobs. While this may have applications in other sciences, our key intent is to better characterize the coupled behavior of a very large set of climate model configurations.

A Review of Computational Methods for Finding Non-Coding RNA Genes

PubMed Central

Abbas, Qaisar; Raza, Syed Mansoor; Biyabani, Azizuddin Ahmed; Jaffar, Muhammad Arfan

2016-01-01

Finding non-coding RNA (ncRNA) genes has emerged over the past few years as a cutting-edge trend in bioinformatics. There are numerous computational intelligence (CI) challenges in the annotation and interpretation of ncRNAs because it requires a domain-related expert knowledge in CI techniques. Moreover, there are many classes predicted yet not experimentally verified by researchers. Recently, researchers have applied many CI methods to predict the classes of ncRNAs. However, the diverse CI approaches lack a definitive classification framework to take advantage of past studies. A few review papers have attempted to summarize CI approaches, but focused on the particular methodological viewpoints. Accordingly, in this article, we summarize in greater detail than previously available, the CI techniques for finding ncRNAs genes. We differentiate from the existing bodies of research and discuss concisely the technical merits of various techniques. Lastly, we review the limitations of ncRNA gene-finding CI methods with a point-of-view towards the development of new computational tools. PMID:27918472

Information diffusion, Facebook clusters, and the simplicial model of social aggregation: a computational simulation of simplicial diffusers for community health interventions.

PubMed

Kee, Kerk F; Sparks, Lisa; Struppa, Daniele C; Mannucci, Mirco A; Damiano, Alberto

2016-01-01

By integrating the simplicial model of social aggregation with existing research on opinion leadership and diffusion networks, this article introduces the constructs of simplicial diffusers (mathematically defined as nodes embedded in simplexes; a simplex is a socially bonded cluster) and simplicial diffusing sets (mathematically defined as minimal covers of a simplicial complex; a simplicial complex is a social aggregation in which socially bonded clusters are embedded) to propose a strategic approach for information diffusion of cancer screenings as a health intervention on Facebook for community cancer prevention and control. This approach is novel in its incorporation of interpersonally bonded clusters, culturally distinct subgroups, and different united social entities that coexist within a larger community into a computational simulation to select sets of simplicial diffusers with the highest degree of information diffusion for health intervention dissemination. The unique contributions of the article also include seven propositions and five algorithmic steps for computationally modeling the simplicial model with Facebook data.

Inelastic strain analogy for piecewise linear computation of creep residues in built-up structures

NASA Technical Reports Server (NTRS)

Jenkins, Jerald M.

1987-01-01

An analogy between inelastic strains caused by temperature and those caused by creep is presented in terms of isotropic elasticity. It is shown how the theoretical aspects can be blended with existing finite-element computer programs to exact a piecewise linear solution. The creep effect is determined by using the thermal stress computational approach, if appropriate alterations are made to the thermal expansion of the individual elements. The overall transient solution is achieved by consecutive piecewise linear iterations. The total residue caused by creep is obtained by accumulating creep residues for each iteration and then resubmitting the total residues for each element as an equivalent input. A typical creep law is tested for incremental time convergence. The results indicate that the approach is practical, with a valid indication of the extent of creep after approximately 20 hr of incremental time. The general analogy between body forces and inelastic strain gradients is discussed with respect to how an inelastic problem can be worked as an elastic problem.

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

PubMed Central

Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems. PMID:27187178

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

PubMed

Pârvu, Ovidiu; Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.

From photons to big-data applications: terminating terabits

PubMed Central

2016-01-01

Computer architectures have entered a watershed as the quantity of network data generated by user applications exceeds the data-processing capacity of any individual computer end-system. It will become impossible to scale existing computer systems while a gap grows between the quantity of networked data and the capacity for per system data processing. Despite this, the growth in demand in both task variety and task complexity continues unabated. Networked computer systems provide a fertile environment in which new applications develop. As networked computer systems become akin to infrastructure, any limitation upon the growth in capacity and capabilities becomes an important constraint of concern to all computer users. Considering a networked computer system capable of processing terabits per second, as a benchmark for scalability, we critique the state of the art in commodity computing, and propose a wholesale reconsideration in the design of computer architectures and their attendant ecosystem. Our proposal seeks to reduce costs, save power and increase performance in a multi-scale approach that has potential application from nanoscale to data-centre-scale computers. PMID:26809573

From photons to big-data applications: terminating terabits.

PubMed

Zilberman, Noa; Moore, Andrew W; Crowcroft, Jon A

2016-03-06

Computer architectures have entered a watershed as the quantity of network data generated by user applications exceeds the data-processing capacity of any individual computer end-system. It will become impossible to scale existing computer systems while a gap grows between the quantity of networked data and the capacity for per system data processing. Despite this, the growth in demand in both task variety and task complexity continues unabated. Networked computer systems provide a fertile environment in which new applications develop. As networked computer systems become akin to infrastructure, any limitation upon the growth in capacity and capabilities becomes an important constraint of concern to all computer users. Considering a networked computer system capable of processing terabits per second, as a benchmark for scalability, we critique the state of the art in commodity computing, and propose a wholesale reconsideration in the design of computer architectures and their attendant ecosystem. Our proposal seeks to reduce costs, save power and increase performance in a multi-scale approach that has potential application from nanoscale to data-centre-scale computers. © 2016 The Authors.

An effective wind speed for models of fire spread

Treesearch

Ralph M. Nelson

2002-01-01

In previous descriptions of wind-slope interaction and the spread rate of wildland fires it is assumed that the separate effects of wind and slope are independent and additive and that corrections for these effects may be applied to spread rates computed from existing rate of spread models. A different approach is explored in the present paper in which the upslope...

Total System Design (TSD) Methodology Assessment.

DTIC Science & Technology

1983-01-01

hardware implementation. Author: Martin - Marietta Aerospace Title: Total System Design Methodology Source: Martin - Marietta Technical Report MCR -79-646...systematic, rational approach to computer systems design is needed. Martin - Marietta has produced a Total System Design Methodology to support such design...gathering and ordering. The purpose of the paper is to document the existing TSD methoeology at Martin - Marietta , describe the supporting tools, and

Quantum Computers

DTIC Science & Technology

2010-03-04

and their sensitivity to charge and flux fluctuations. The first type of superconducting qubit , the charge qubit , omits the inductance . There is no...nanostructured NbN superconducting nanowire detectors have achieved high efficiency and photon number resolution16,17. One approach to a high-efficiency single...resemble classical high- speed integrated circuits and can be readily fabricated using existing technologies. The basic physics behind superconducting qubits

Assessing non-uniqueness: An algebraic approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasco, Don W.

Geophysical inverse problems are endowed with a rich mathematical structure. When discretized, most differential and integral equations of interest are algebraic (polynomial) in form. Techniques from algebraic geometry and computational algebra provide a means to address questions of existence and uniqueness for both linear and non-linear inverse problem. In a sense, the methods extend ideas which have proven fruitful in treating linear inverse problems.

A simplified approach to quasi-linear viscoelastic modeling

PubMed Central

Nekouzadeh, Ali; Pryse, Kenneth M.; Elson, Elliot L.; Genin, Guy M.

2007-01-01

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in one dimension is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of “ramp-and-hold” stretching tests were applied to rectangular collagen specimens. The relaxation force data from the “hold” was used to calibrate a new “adaptive QLV model” and several models from literature, and the force data from the “ramp” was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The “adaptive QLV model” based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation. PMID:17499254

Automatic approach to deriving fuzzy slope positions

NASA Astrophysics Data System (ADS)

Zhu, Liang-Jun; Zhu, A.-Xing; Qin, Cheng-Zhi; Liu, Jun-Zhi

2018-03-01

Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

Parallel implementation of geometrical shock dynamics for two dimensional converging shock waves

NASA Astrophysics Data System (ADS)

Qiu, Shi; Liu, Kuang; Eliasson, Veronica

2016-10-01

Geometrical shock dynamics (GSD) theory is an appealing method to predict the shock motion in the sense that it is more computationally efficient than solving the traditional Euler equations, especially for converging shock waves. However, to solve and optimize large scale configurations, the main bottleneck is the computational cost. Among the existing numerical GSD schemes, there is only one that has been implemented on parallel computers, with the purpose to analyze detonation waves. To extend the computational advantage of the GSD theory to more general applications such as converging shock waves, a numerical implementation using a spatial decomposition method has been coupled with a front tracking approach on parallel computers. In addition, an efficient tridiagonal system solver for massively parallel computers has been applied to resolve the most expensive function in this implementation, resulting in an efficiency of 0.93 while using 32 HPCC cores. Moreover, symmetric boundary conditions have been developed to further reduce the computational cost, achieving a speedup of 19.26 for a 12-sided polygonal converging shock.

The presence of mathematics and computer anxiety in nursing students and their effects on medication dosage calculations.

PubMed

Glaister, Karen

2007-05-01

To determine if the presence of mathematical and computer anxiety in nursing students affects learning of dosage calculations. The quasi-experimental study compared learning outcomes at differing levels of mathematical and computer anxiety when integrative and computer based learning approaches were used. Participants involved a cohort of second year nursing students (n=97). Mathematical anxiety exists in 20% (n=19) of the student nurse population, and 14% (n=13) experienced mathematical testing anxiety. Those students more anxious about mathematics and the testing of mathematics benefited from integrative learning to develop conditional knowledge (F(4,66)=2.52 at p<.05). Computer anxiety was present in 12% (n=11) of participants, with those reporting medium and high levels of computer anxiety performing less well than those with low levels (F(1,81)=3.98 at p<.05). Instructional strategies need to account for the presence of mathematical and computer anxiety when planning an educational program to develop competency in dosage calculations.

Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package.

PubMed

Kaus, Joseph W; Pierce, Levi T; Walker, Ross C; McCammont, J Andrew

2013-09-10

Alchemical transformations are widely used methods to calculate free energies. Amber has traditionally included support for alchemical transformations as part of the sander molecular dynamics (MD) engine. Here we describe the implementation of a more efficient approach to alchemical transformations in the Amber MD package. Specifically we have implemented this new approach within the more computational efficient and scalable pmemd MD engine that is included with the Amber MD package. The majority of the gain in efficiency comes from the improved design of the calculation, which includes better parallel scaling and reduction in the calculation of redundant terms. This new implementation is able to reproduce results from equivalent simulations run with the existing functionality, but at 2.5 times greater computational efficiency. This new implementation is also able to run softcore simulations at the λ end states making direct calculation of free energies more accurate, compared to the extrapolation required in the existing implementation. The updated alchemical transformation functionality will be included in the next major release of Amber (scheduled for release in Q1 2014) and will be available at http://ambermd.org, under the Amber license.

Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package

PubMed Central

Pierce, Levi T.; Walker, Ross C.; McCammont, J. Andrew

2013-01-01

Alchemical transformations are widely used methods to calculate free energies. Amber has traditionally included support for alchemical transformations as part of the sander molecular dynamics (MD) engine. Here we describe the implementation of a more efficient approach to alchemical transformations in the Amber MD package. Specifically we have implemented this new approach within the more computational efficient and scalable pmemd MD engine that is included with the Amber MD package. The majority of the gain in efficiency comes from the improved design of the calculation, which includes better parallel scaling and reduction in the calculation of redundant terms. This new implementation is able to reproduce results from equivalent simulations run with the existing functionality, but at 2.5 times greater computational efficiency. This new implementation is also able to run softcore simulations at the λ end states making direct calculation of free energies more accurate, compared to the extrapolation required in the existing implementation. The updated alchemical transformation functionality will be included in the next major release of Amber (scheduled for release in Q1 2014) and will be available at http://ambermd.org, under the Amber license. PMID:24185531

Radiation Detection Computational Benchmark Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaver, Mark W.; Casella, Andrew M.; Wittman, Richard S.

2013-09-24

Modeling forms an important component of radiation detection development, allowing for testing of new detector designs, evaluation of existing equipment against a wide variety of potential threat sources, and assessing operation performance of radiation detection systems. This can, however, result in large and complex scenarios which are time consuming to model. A variety of approaches to radiation transport modeling exist with complementary strengths and weaknesses for different problems. This variety of approaches, and the development of promising new tools (such as ORNL’s ADVANTG) which combine benefits of multiple approaches, illustrates the need for a means of evaluating or comparing differentmore » techniques for radiation detection problems. This report presents a set of 9 benchmark problems for comparing different types of radiation transport calculations, identifying appropriate tools for classes of problems, and testing and guiding the development of new methods. The benchmarks were drawn primarily from existing or previous calculations with a preference for scenarios which include experimental data, or otherwise have results with a high level of confidence, are non-sensitive, and represent problem sets of interest to NA-22. From a technical perspective, the benchmarks were chosen to span a range of difficulty and to include gamma transport, neutron transport, or both and represent different important physical processes and a range of sensitivity to angular or energy fidelity. Following benchmark identification, existing information about geometry, measurements, and previous calculations were assembled. Monte Carlo results (MCNP decks) were reviewed or created and re-run in order to attain accurate computational times and to verify agreement with experimental data, when present. Benchmark information was then conveyed to ORNL in order to guide testing and development of hybrid calculations. The results of those ADVANTG calculations were then sent to PNNL for compilation. This is a report describing the details of the selected Benchmarks and results from various transport codes.« less

A Computational Approach to Finding Novel Targets for Existing Drugs

PubMed Central

Li, Yvonne Y.; An, Jianghong; Jones, Steven J. M.

2011-01-01

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. PMID:21909252

A frequentist approach to computer model calibration

DOE PAGES

Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.

2016-05-05

The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less

Simulating effectiveness of helicopter evasive manoeuvres to RPG attack

NASA Astrophysics Data System (ADS)

Anderson, D.; Thomson, D. G.

2010-04-01

The survivability of helicopters under attack by ground troops using rocket propelled grenades has been amply illustrated over the past decade. Given that an RPG is unguided and it is infeasible to cover helicopters in thick armour, existing optical countermeasures are ineffective - the solution is to compute an evasive manoeuvre. In this paper, an RPG/helicopter engagement model is presented. Manoeuvre profiles are defined in the missile approach warning sensor camera image plane using a local maximum acceleration vector. Required control inputs are then computed using inverse simulation techniques. Assessments of platform survivability to several engagement scenarios are presented.

Gas-liquid coexistence in a system of dipolar soft spheres.

PubMed

Jia, Ran; Braun, Heiko; Hentschke, Reinhard

2010-12-01

The existence of gas-liquid coexistence in dipolar fluids with no other contribution to attractive interaction than dipole-dipole interaction is a basic and open question in the theory of fluids. Here we compute the gas-liquid critical point in a system of dipolar soft spheres subject to an external electric field using molecular dynamics computer simulation. Tracking the critical point as the field strength is approaching zero we find the following limiting values: T(c)=0.063 and ρ(c)=0.0033 (dipole moment μ=1). These values are confirmed by independent simulation at zero field strength.

Warfarin: history, tautomerism and activity

NASA Astrophysics Data System (ADS)

Porter, William R.

2010-06-01

The anticoagulant drug warfarin, normally administered as the racemate, can exist in solution in potentially as many as 40 topologically distinct tautomeric forms. Only 11 of these forms for each enantiomer can be distinguished by selected computational software commonly used to estimate octanol-water partition coefficients and/or ionization constants. The history of studies on warfarin tautomerism is reviewed, along with the implications of tautomerism to its biological properties (activity, protein binding and metabolism) and chemical properties (log P, log D, p K a). Experimental approaches to assessing warfarin tautomerism and computational results for different tautomeric forms are presented.

Computational methods for vortex dominated compressible flows

NASA Technical Reports Server (NTRS)

Murman, Earll M.

1987-01-01

The principal objectives were to: understand the mechanisms by which Euler equation computations model leading edge vortex flows; understand the vortical and shock wave structures that may exist for different wing shapes, angles of incidence, and Mach numbers; and compare calculations with experiments in order to ascertain the limitations and advantages of Euler equation models. The initial approach utilized the cell centered finite volume Jameson scheme. The final calculation utilized a cell vertex finite volume method on an unstructured grid. Both methods used Runge-Kutta four stage schemes for integrating the equations. The principal findings are briefly summarized.

Evaluating and minimizing noise impact due to aircraft flyover

NASA Technical Reports Server (NTRS)

Jacobson, I. D.; Cook, G.

1979-01-01

Existing techniques were used to assess the noise impact on a community due to aircraft operation and to optimize the flight paths of an approaching aircraft with respect to the annoyance produced. Major achievements are: (1) the development of a population model suitable for determining the noise impact, (2) generation of a numerical computer code which uses this population model along with the steepest descent algorithm to optimize approach/landing trajectories, (3) implementation of this optimization code in several fictitious cases as well as for the community surrounding Patrick Henry International Airport, Virginia.

Anatomical medial surfaces with efficient resolution of branches singularities.

PubMed

Gil, Debora; Vera, Sergio; Borràs, Agnés; Andaluz, Albert; González Ballester, Miguel A

2017-01-01

Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility of existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a confident application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an efficient GPU-CPU implementation using standard image processing tools. We show the method computational efficiency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. Copyright © 2016 Elsevier B.V. All rights reserved.

Graph-based similarity concepts in virtual screening.

PubMed

Hutter, Michael C

2011-03-01

Applying similarity for finding new promising compounds is a key issue in drug design. Conversely, quantifying similarity between molecules has remained a difficult task despite the numerous approaches. Here, some general aspects along with recent developments regarding similarity criteria are collected. For the purpose of virtual screening, the compounds have to be encoded into a computer-readable format that permits a comparison, according to given similarity criteria, comprising the use of the 3D structure, fingerprints, graph-based and alignment-based approaches. Whereas finding the most common substructures is the most obvious method, more recent approaches take into account chemical modifications that appear throughout existing drugs, from various therapeutic categories and targets.

A New Approach for Solving the Generalized Traveling Salesman Problem

NASA Astrophysics Data System (ADS)

Pop, P. C.; Matei, O.; Sabo, C.

The generalized traveling problem (GTSP) is an extension of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem. Based on this approach we describe a novel hybrid metaheuristic algorithm for solving the problem using genetic algorithms. Computational results are reported for Euclidean TSPlib instances and compared with the existing ones. The obtained results point out that our hybrid algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.

A Heuristic Probabilistic Approach to Estimating Size-Dependent Mobility of Nonuniform Sediment

NASA Astrophysics Data System (ADS)

Woldegiorgis, B. T.; Wu, F. C.; van Griensven, A.; Bauwens, W.

2017-12-01

Simulating the mechanism of bed sediment mobility is essential for modelling sediment dynamics. Despite the fact that many studies are carried out on this subject, they use complex mathematical formulations that are computationally expensive, and are often not easy for implementation. In order to present a simple and computationally efficient complement to detailed sediment mobility models, we developed a heuristic probabilistic approach to estimating the size-dependent mobilities of nonuniform sediment based on the pre- and post-entrainment particle size distributions (PSDs), assuming that the PSDs are lognormally distributed. The approach fits a lognormal probability density function (PDF) to the pre-entrainment PSD of bed sediment and uses the threshold particle size of incipient motion and the concept of sediment mixture to estimate the PSDs of the entrained sediment and post-entrainment bed sediment. The new approach is simple in physical sense and significantly reduces the complexity and computation time and resource required by detailed sediment mobility models. It is calibrated and validated with laboratory and field data by comparing to the size-dependent mobilities predicted with the existing empirical lognormal cumulative distribution function (CDF) approach. The novel features of the current approach are: (1) separating the entrained and non-entrained sediments by a threshold particle size, which is a modified critical particle size of incipient motion by accounting for the mixed-size effects, and (2) using the mixture-based pre- and post-entrainment PSDs to provide a continuous estimate of the size-dependent sediment mobility.

Fuzzy logic based robotic controller

NASA Technical Reports Server (NTRS)

Attia, F.; Upadhyaya, M.

1994-01-01

Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

Optimizing high performance computing workflow for protein functional annotation.

PubMed

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-09-10

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

Optimizing high performance computing workflow for protein functional annotation

PubMed Central

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-01-01

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296

Developments toward more accurate molecular modeling of liquids

NASA Astrophysics Data System (ADS)

Evans, Tom J.

2000-12-01

The general goal of this research has been to improve upon existing combined quantum mechanics/molecular mechanics (QM/MM) methodologies. Error weighting functions have been introduced into the perturbative Monte Carlo (PMC) method for use with QM/MM. The PMC approach, introduced earlier, provides a means to reduce the number of full self-consistent field (SCF) calculations in simulations using the QM/MM potential by evoking perturbation theory to calculate energy changes due to displacements of a MM molecule. This will allow the ab initio QM/MM approach to be applied to systems that require more advanced, computationally demanding treatments of the QM and/or MM regions. Efforts have also been made to improve the accuracy of the representation of the solvent molecules usually represented by MM force fields. Results from an investigation of the applicability of the embedded density functional theory (EDFT) for studying physical properties of solutions will be presented. In this approach, the solute wavefunction is solved self- consistently in the field of individually frozen electron-density solvent molecules. To test its accuracy, the potential curves for interactions between Li+, Cl- and H2O with a single frozen-density H 2O molecule in different orientations have been calculated. With the development of the more sophisticated effective fragment potential (EFP) representation of solvent molecules, a QM/EFP technique was created. This hybrid QM/EFP approach was used to investigate the solvation of Li + by small clusters of water, as a test case for larger ionic dusters. The EFP appears to provide an accurate representation of the strong interactions that exist between Li+ and H2O. With the QM/EFP methodology comes an increased computational expense, resulting in an even greater need to rely on the PMC approach. However, while including the PMC into the hybrid QM/EFP technique, it was discovered that the previous implementation of the PMC was done incorrectly, invalidating earlier test results. The PMC implementation was therefore reworked, and tests were performed to investigate the methods usefulness in reducing the computational load of these types of simulations. The results that were obtained while studying F-(H2O) and F-(H 2O)2 show that PMC can be used cautiously to increase computational efficiency.

Application of a hybrid MPI/OpenMP approach for parallel groundwater model calibration using multi-core computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Guoping; D'Azevedo, Ed F; Zhang, Fan

2010-01-01

Calibration of groundwater models involves hundreds to thousands of forward solutions, each of which may solve many transient coupled nonlinear partial differential equations, resulting in a computationally intensive problem. We describe a hybrid MPI/OpenMP approach to exploit two levels of parallelisms in software and hardware to reduce calibration time on multi-core computers. HydroGeoChem 5.0 (HGC5) is parallelized using OpenMP for direct solutions for a reactive transport model application, and a field-scale coupled flow and transport model application. In the reactive transport model, a single parallelizable loop is identified to account for over 97% of the total computational time using GPROF.more » Addition of a few lines of OpenMP compiler directives to the loop yields a speedup of about 10 on a 16-core compute node. For the field-scale model, parallelizable loops in 14 of 174 HGC5 subroutines that require 99% of the execution time are identified. As these loops are parallelized incrementally, the scalability is found to be limited by a loop where Cray PAT detects over 90% cache missing rates. With this loop rewritten, similar speedup as the first application is achieved. The OpenMP-parallelized code can be run efficiently on multiple workstations in a network or multiple compute nodes on a cluster as slaves using parallel PEST to speedup model calibration. To run calibration on clusters as a single task, the Levenberg Marquardt algorithm is added to HGC5 with the Jacobian calculation and lambda search parallelized using MPI. With this hybrid approach, 100 200 compute cores are used to reduce the calibration time from weeks to a few hours for these two applications. This approach is applicable to most of the existing groundwater model codes for many applications.« less

BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics.

PubMed

Ayres, Daniel L; Darling, Aaron; Zwickl, Derrick J; Beerli, Peter; Holder, Mark T; Lewis, Paul O; Huelsenbeck, John P; Ronquist, Fredrik; Swofford, David L; Cummings, Michael P; Rambaut, Andrew; Suchard, Marc A

2012-01-01

Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realistic or interesting models of evolution, these approaches remain computationally demanding. High-throughput sequencing can yield data for thousands of taxa, but scaling to such problems using serial computing often necessitates the use of nonstatistical or approximate approaches. The recent emergence of graphics processing units (GPUs) provides an opportunity to leverage their excellent floating-point computational performance to accelerate statistical phylogenetic inference. A specialized library for phylogenetic calculation would allow existing software packages to make more effective use of available computer hardware, including GPUs. Adoption of a common library would also make it easier for other emerging computing architectures, such as field programmable gate arrays, to be used in the future. We present BEAGLE, an application programming interface (API) and library for high-performance statistical phylogenetic inference. The API provides a uniform interface for performing phylogenetic likelihood calculations on a variety of compute hardware platforms. The library includes a set of efficient implementations and can currently exploit hardware including GPUs using NVIDIA CUDA, central processing units (CPUs) with Streaming SIMD Extensions and related processor supplementary instruction sets, and multicore CPUs via OpenMP. To demonstrate the advantages of a common API, we have incorporated the library into several popular phylogenetic software packages. The BEAGLE library is free open source software licensed under the Lesser GPL and available from http://beagle-lib.googlecode.com. An example client program is available as public domain software.

BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics

PubMed Central

Ayres, Daniel L.; Darling, Aaron; Zwickl, Derrick J.; Beerli, Peter; Holder, Mark T.; Lewis, Paul O.; Huelsenbeck, John P.; Ronquist, Fredrik; Swofford, David L.; Cummings, Michael P.; Rambaut, Andrew; Suchard, Marc A.

2012-01-01

Abstract Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realistic or interesting models of evolution, these approaches remain computationally demanding. High-throughput sequencing can yield data for thousands of taxa, but scaling to such problems using serial computing often necessitates the use of nonstatistical or approximate approaches. The recent emergence of graphics processing units (GPUs) provides an opportunity to leverage their excellent floating-point computational performance to accelerate statistical phylogenetic inference. A specialized library for phylogenetic calculation would allow existing software packages to make more effective use of available computer hardware, including GPUs. Adoption of a common library would also make it easier for other emerging computing architectures, such as field programmable gate arrays, to be used in the future. We present BEAGLE, an application programming interface (API) and library for high-performance statistical phylogenetic inference. The API provides a uniform interface for performing phylogenetic likelihood calculations on a variety of compute hardware platforms. The library includes a set of efficient implementations and can currently exploit hardware including GPUs using NVIDIA CUDA, central processing units (CPUs) with Streaming SIMD Extensions and related processor supplementary instruction sets, and multicore CPUs via OpenMP. To demonstrate the advantages of a common API, we have incorporated the library into several popular phylogenetic software packages. The BEAGLE library is free open source software licensed under the Lesser GPL and available from http://beagle-lib.googlecode.com. An example client program is available as public domain software. PMID:21963610

Mechanical properties of hydroxyapatite single crystals from nanoindentation data

PubMed Central

Zamiri, A.; De, S.

2011-01-01

In this paper we compute elasto-plastic properties of hydroxyapatite single crystals from nanindentation data using a two-step algorithm. In the first step the yield stress is obtained using hardness and Young’s modulus data, followed by the computation of the flow parameters. The computational approach is first validated with data from existing literature. It is observed that hydroxyapatite single crystals exhibit anisotropic mechanical response with a lower yield stress along the [1010] crystallographic direction compared to the [0001] direction. Both work hardening rate and work hardening exponent are found to be higher for indentation along the [0001] crystallographic direction. The stress-strain curves extracted here could be used for developing constitutive models for hydroxyapatite single crystals. PMID:21262492

Shared Memory Parallelization of an Implicit ADI-type CFD Code

NASA Technical Reports Server (NTRS)

Hauser, Th.; Huang, P. G.

1999-01-01

A parallelization study designed for ADI-type algorithms is presented using the OpenMP specification for shared-memory multiprocessor programming. Details of optimizations specifically addressed to cache-based computer architectures are described and performance measurements for the single and multiprocessor implementation are summarized. The paper demonstrates that optimization of memory access on a cache-based computer architecture controls the performance of the computational algorithm. A hybrid MPI/OpenMP approach is proposed for clusters of shared memory machines to further enhance the parallel performance. The method is applied to develop a new LES/DNS code, named LESTool. A preliminary DNS calculation of a fully developed channel flow at a Reynolds number of 180, Re(sub tau) = 180, has shown good agreement with existing data.

A Secure and Efficient Audit Mechanism for Dynamic Shared Data in Cloud Storage

PubMed Central

2014-01-01

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data. PMID:24959630

A 3D bioprinting exemplar of the consequences of the regulatory requirements on customized processes.

PubMed

Hourd, Paul; Medcalf, Nicholas; Segal, Joel; Williams, David J

2015-01-01

Computer-aided 3D printing approaches to the industrial production of customized 3D functional living constructs for restoration of tissue and organ function face significant regulatory challenges. Using the manufacture of a customized, 3D-bioprinted nasal implant as a well-informed but hypothetical exemplar, we examine how these products might be regulated. Existing EU and USA regulatory frameworks do not account for the differences between 3D printing and conventional manufacturing methods or the ability to create individual customized products using mechanized rather than craft approaches. Already subject to extensive regulatory control, issues related to control of the computer-aided design to manufacture process and the associated software system chain present additional scientific and regulatory challenges for manufacturers of these complex 3D-bioprinted advanced combination products.

Use of application containers and workflows for genomic data analysis

PubMed Central

Schulz, Wade L.; Durant, Thomas J. S.; Siddon, Alexa J.; Torres, Richard

2016-01-01

Background: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Methods: Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. Results: While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. Conclusions: With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia. PMID:28163975

A secure and efficient audit mechanism for dynamic shared data in cloud storage.

PubMed

Kwon, Ohmin; Koo, Dongyoung; Shin, Yongjoo; Yoon, Hyunsoo

2014-01-01

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data.

Computationally efficient algorithm for Gaussian Process regression in case of structured samples

NASA Astrophysics Data System (ADS)

Belyaev, M.; Burnaev, E.; Kapushev, Y.

2016-04-01

Surrogate modeling is widely used in many engineering problems. Data sets often have Cartesian product structure (for instance factorial design of experiments with missing points). In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation-Gaussian Process regression-can be hardly applied due to its computational complexity. In this paper a computationally efficient approach for constructing Gaussian Process regression in case of data sets with Cartesian product structure is presented. Efficiency is achieved by using a special structure of the data set and operations with tensors. Proposed algorithm has low computational as well as memory complexity compared to existing algorithms. In this work we also introduce a regularization procedure allowing to take into account anisotropy of the data set and avoid degeneracy of regression model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spycher, Nicolas; Peiffer, Loic; Finsterle, Stefan

GeoT implements the multicomponent geothermometry method developed by Reed and Spycher (1984, Geochim. Cosmichim. Acta 46 513–528) into a stand-alone computer program, to ease the application of this method and to improve the prediction of geothermal reservoir temperatures using full and integrated chemical analyses of geothermal fluids. Reservoir temperatures are estimated from statistical analyses of mineral saturation indices computed as a function of temperature. The reconstruction of the deep geothermal fluid compositions, and geothermometry computations, are all implemented into the same computer program, allowing unknown or poorly constrained input parameters to be estimated by numerical optimization using existing parameter estimationmore » software, such as iTOUGH2, PEST, or UCODE. This integrated geothermometry approach presents advantages over classical geothermometers for fluids that have not fully equilibrated with reservoir minerals and/or that have been subject to processes such as dilution and gas loss.« less

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

PubMed Central

Xu, Gaochao; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful. PMID:24385877

Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure.

PubMed

Al-Khatib, Ra'ed M; Rashid, Nur'Aini Abdul; Abdullah, Rosni

2011-08-01

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.

A novel artificial bee colony approach of live virtual machine migration policy using Bayes theorem.

PubMed

Xu, Gaochao; Ding, Yan; Zhao, Jia; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A discrete mechanics framework for real time virtual surgical simulations with application to virtual laparoscopic nephrectomy.

PubMed

Zhou, Xiangmin; Zhang, Nan; Sha, Desong; Shen, Yunhe; Tamma, Kumar K; Sweet, Robert

2009-01-01

The inability to render realistic soft-tissue behavior in real time has remained a barrier to face and content aspects of validity for many virtual reality surgical training systems. Biophysically based models are not only suitable for training purposes but also for patient-specific clinical applications, physiological modeling and surgical planning. When considering the existing approaches for modeling soft tissue for virtual reality surgical simulation, the computer graphics-based approach lacks predictive capability; the mass-spring model (MSM) based approach lacks biophysically realistic soft-tissue dynamic behavior; and the finite element method (FEM) approaches fail to meet the real-time requirement. The present development stems from physics fundamental thermodynamic first law; for a space discrete dynamic system directly formulates the space discrete but time continuous governing equation with embedded material constitutive relation and results in a discrete mechanics framework which possesses a unique balance between the computational efforts and the physically realistic soft-tissue dynamic behavior. We describe the development of the discrete mechanics framework with focused attention towards a virtual laparoscopic nephrectomy application.

Algorithmic aspects for the reconstruction of spatio-spectral data cubes in the perspective of the SKA

NASA Astrophysics Data System (ADS)

Mary, D.; Ferrari, A.; Ferrari, C.; Deguignet, J.; Vannier, M.

2016-12-01

With millions of receivers leading to TerraByte data cubes, the story of the giant SKA telescope is also that of collaborative efforts from radioastronomy, signal processing, optimization and computer sciences. Reconstructing SKA cubes poses two challenges. First, the majority of existing algorithms work in 2D and cannot be directly translated into 3D. Second, the reconstruction implies solving an inverse problem and it is not clear what ultimate limit we can expect on the error of this solution. This study addresses (of course partially) both challenges. We consider an extremely simple data acquisition model, and we focus on strategies making it possible to implement 3D reconstruction algorithms that use state-of-the-art image/spectral regularization. The proposed approach has two main features: (i) reduced memory storage with respect to a previous approach; (ii) efficient parallelization and ventilation of the computational load over the spectral bands. This work will allow to implement and compare various 3D reconstruction approaches in a large scale framework.

Model-based position correlation between breast images

NASA Astrophysics Data System (ADS)

Georgii, J.; Zöhrer, F.; Hahn, H. K.

2013-02-01

Nowadays, breast diagnosis is based on images of different projections and modalities, such that sensitivity and specificity of the diagnosis can be improved. However, this emburdens radiologists to find corresponding locations in these data sets, which is a time consuming task, especially since the resolution of the images increases and thus more and more data have to be considered in the diagnosis. Therefore, we aim at support radiologist by automatically synchronizing cursor positions between different views of the breast. Specifically, we present an automatic approach to compute the spatial correlation between MLO and CC mammogram or tomosynthesis projections of the breast. It is based on pre-computed finite element simulations of generic breast models, which are adapted to the patient-specific breast using a contour mapping approach. Our approach is designed to be fully automatic and efficient, such that it can be implemented directly into existing multimodal breast workstations. Additionally, it is extendable to support other breast modalities in future, too.

Bell-polynomial approach and Wronskian determinant solutions for three sets of differential-difference nonlinear evolution equations with symbolic computation

NASA Astrophysics Data System (ADS)

Qin, Bo; Tian, Bo; Wang, Yu-Feng; Shen, Yu-Jia; Wang, Ming

2017-10-01

Under investigation in this paper are the Belov-Chaltikian (BC), Leznov and Blaszak-Marciniak (BM) lattice equations, which are associated with the conformal field theory, UToda(m_1,m_2) system and r-matrix, respectively. With symbolic computation, the Bell-polynomial approach is developed to directly bilinearize those three sets of differential-difference nonlinear evolution equations (NLEEs). This Bell-polynomial approach does not rely on any dependent variable transformation, which constitutes the key step and main difficulty of the Hirota bilinear method, and thus has the advantage in the bilinearization of the differential-difference NLEEs. Based on the bilinear forms obtained, the N-soliton solutions are constructed in terms of the N × N Wronskian determinant. Graphic illustrations demonstrate that those solutions, more general than the existing results, permit some new properties, such as the solitonic propagation and interactions for the BC lattice equations, and the nonnegative dark solitons for the BM lattice equations.

Stabilization Approaches for Linear and Nonlinear Reduced Order Models

NASA Astrophysics Data System (ADS)

Rezaian, Elnaz; Wei, Mingjun

2017-11-01

It has been a major concern to establish reduced order models (ROMs) as reliable representatives of the dynamics inherent in high fidelity simulations, while fast computation is achieved. In practice it comes to stability and accuracy of ROMs. Given the inviscid nature of Euler equations it becomes more challenging to achieve stability, especially where moving discontinuities exist. Originally unstable linear and nonlinear ROMs are stabilized here by two approaches. First, a hybrid method is developed by integrating two different stabilization algorithms. At the same time, symmetry inner product is introduced in the generation of ROMs for its known robust behavior for compressible flows. Results have shown a notable improvement in computational efficiency and robustness compared to similar approaches. Second, a new stabilization algorithm is developed specifically for nonlinear ROMs. This method adopts Particle Swarm Optimization to enforce a bounded ROM response for minimum discrepancy between the high fidelity simulation and the ROM outputs. Promising results are obtained in its application on the nonlinear ROM of an inviscid fluid flow with discontinuities. Supported by ARL.

Fan Noise Prediction with Applications to Aircraft System Noise Assessment

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

2009-01-01

This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

Leveraging Crowdsourcing and Linked Open Data for Geoscience Data Sharing and Discovery

NASA Astrophysics Data System (ADS)

Narock, T. W.; Rozell, E. A.; Hitzler, P.; Arko, R. A.; Chandler, C. L.; Wilson, B. D.

2013-12-01

Data citation standards can form the basis for increased incentives, recognition, and rewards for scientists. Additionally, knowing which data were utilized in a particular publication can enhance discovery and reuse. Yet, a lack of data citation information in existing publications as well as ambiguities across datasets can limit the accuracy of automated linking approaches. We describe a crowdsourcing approach, based on Linked Open Data, in which AGU abstracts are linked to the data used in those presentations. We discuss our efforts to incentivize participants through promotion of their research, the role that the Semantic Web can play in this effort, and how this work differs from existing platforms such as Mendeley and ResearchGate. Further, we discuss the benefits and challenges of Linked Open Data as a technical solution including the role of provenance, trust, and computational reasoning.

Performance model-directed data sieving for high-performance I/O

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yong; Lu, Yin; Amritkar, Prathamesh

2014-09-10

Many scientific computing applications and engineering simulations exhibit noncontiguous I/O access patterns. Data sieving is an important technique to improve the performance of noncontiguous I/O accesses by combining small and noncontiguous requests into a large and contiguous request. It has been proven effective even though more data are potentially accessed than demanded. In this study, we propose a new data sieving approach namely performance model-directed data sieving, or PMD data sieving in short. It improves the existing data sieving approach from two aspects: (1) dynamically determines when it is beneficial to perform data sieving; and (2) dynamically determines how tomore » perform data sieving if beneficial. It improves the performance of the existing data sieving approach considerably and reduces the memory consumption as verified by both theoretical analysis and experimental results. Given the importance of supporting noncontiguous accesses effectively and reducing the memory pressure in a large-scale system, the proposed PMD data sieving approach in this research holds a great promise and will have an impact on high-performance I/O systems.« less

Pancreas and cyst segmentation

NASA Astrophysics Data System (ADS)

Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

2016-03-01

Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

Multi-dimensional rheology-based two-phase model for sediment transport and applications to sheet flow and pipeline scour

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Cheng-Hsien; Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan; Low, Ying Min, E-mail: ceelowym@nus.edu.sg

2016-05-15

Sediment transport is fundamentally a two-phase phenomenon involving fluid and sediments; however, many existing numerical models are one-phase approaches, which are unable to capture the complex fluid-particle and inter-particle interactions. In the last decade, two-phase models have gained traction; however, there are still many limitations in these models. For example, several existing two-phase models are confined to one-dimensional problems; in addition, the existing two-dimensional models simulate only the region outside the sand bed. This paper develops a new three-dimensional two-phase model for simulating sediment transport in the sheet flow condition, incorporating recently published rheological characteristics of sediments. The enduring-contact, inertial,more » and fluid viscosity effects are considered in determining sediment pressure and stresses, enabling the model to be applicable to a wide range of particle Reynolds number. A k − ε turbulence model is adopted to compute the Reynolds stresses. In addition, a novel numerical scheme is proposed, thus avoiding numerical instability caused by high sediment concentration and allowing the sediment dynamics to be computed both within and outside the sand bed. The present model is applied to two classical problems, namely, sheet flow and scour under a pipeline with favorable results. For sheet flow, the computed velocity is consistent with measured data reported in the literature. For pipeline scour, the computed scour rate beneath the pipeline agrees with previous experimental observations. However, the present model is unable to capture vortex shedding; consequently, the sediment deposition behind the pipeline is overestimated. Sensitivity analyses reveal that model parameters associated with turbulence have strong influence on the computed results.« less

Outcomes from a pilot study using computer-based rehabilitative tools in a military population.

PubMed

Sullivan, Katherine W; Quinn, Julia E; Pramuka, Michael; Sharkey, Laura A; French, Louis M

2012-01-01

Novel therapeutic approaches and outcome data are needed for cognitive rehabilitation for patients with a traumatic brain injury; computer-based programs may play a critical role in filling existing knowledge gaps. Brain-fitness computer programs can complement existing therapies, maximize neuroplasticity, provide treatment beyond the clinic, and deliver objective efficacy data. However, these approaches have not been extensively studied in the military and traumatic brain injury population. Walter Reed National Military Medical Center established its Brain Fitness Center (BFC) in 2008 as an adjunct to traditional cognitive therapies for wounded warriors. The BFC offers commercially available "brain-training" products for military Service Members to use in a supportive, structured environment. Over 250 Service Members have utilized this therapeutic intervention. Each patient receives subjective assessments pre and post BFC participation including the Mayo-Portland Adaptability Inventory-4 (MPAI-4), the Neurobehavioral Symptom Inventory (NBSI), and the Satisfaction with Life Scale (SWLS). A review of the first 29 BFC participants, who finished initial and repeat measures, was completed to determine the effectiveness of the BFC program. Two of the three questionnaires of self-reported symptom change completed before and after participation in the BFC revealed a statistically significant reduction in symptom severity based on MPAI and NBSI total scores (p < .05). There were no significant differences in the SWLS score. Despite the typical limitations of a retrospective chart review, such as variation in treatment procedures, preliminary results reveal a trend towards improved self-reported cognitive and functional symptoms.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning.

PubMed

Gorban, A N; Mirkes, E M; Zinovyev, A

2016-12-01

Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including high sensitivity to contaminating factors and dimensionality curse. Therefore, a lot of recent applications in machine learning exploited properties of non-quadratic error functionals based on L 1 norm or even sub-linear potentials corresponding to quasinorms L p (0

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm.

PubMed

Paliwal, Nikhil; Damiano, Robert J; Varble, Nicole A; Tutino, Vincent M; Dou, Zhongwang; Siddiqui, Adnan H; Meng, Hui

2017-12-01

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

A computational workflow for designing silicon donor qubits

DOE PAGES

Humble, Travis S.; Ericson, M. Nance; Jakowski, Jacek; ...

2016-09-19

Developing devices that can reliably and accurately demonstrate the principles of superposition and entanglement is an on-going challenge for the quantum computing community. Modeling and simulation offer attractive means of testing early device designs and establishing expectations for operational performance. However, the complex integrated material systems required by quantum device designs are not captured by any single existing computational modeling method. We examine the development and analysis of a multi-staged computational workflow that can be used to design and characterize silicon donor qubit systems with modeling and simulation. Our approach integrates quantum chemistry calculations with electrostatic field solvers to performmore » detailed simulations of a phosphorus dopant in silicon. We show how atomistic details can be synthesized into an operational model for the logical gates that define quantum computation in this particular technology. In conclusion, the resulting computational workflow realizes a design tool for silicon donor qubits that can help verify and validate current and near-term experimental devices.« less

A computer simulation approach to quantify the true area and true area compressibility modulus of biological membranes.

PubMed

Chacón, Enrique; Tarazona, Pedro; Bresme, Fernando

2015-07-21

We present a new computational approach to quantify the area per lipid and the area compressibility modulus of biological membranes. Our method relies on the analysis of the membrane fluctuations using our recently introduced coupled undulatory (CU) mode [Tarazona et al., J. Chem. Phys. 139, 094902 (2013)], which provides excellent estimates of the bending modulus of model membranes. Unlike the projected area, widely used in computer simulations of membranes, the CU area is thermodynamically consistent. This new area definition makes it possible to accurately estimate the area of the undulating bilayer, and the area per lipid, by excluding any contributions related to the phospholipid protrusions. We find that the area per phospholipid and the area compressibility modulus features a negligible dependence with system size, making possible their computation using truly small bilayers, involving a few hundred lipids. The area compressibility modulus obtained from the analysis of the CU area fluctuations is fully consistent with the Hooke's law route. Unlike existing methods, our approach relies on a single simulation, and no a priori knowledge of the bending modulus is required. We illustrate our method by analyzing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayers using the coarse grained MARTINI force-field. The area per lipid and area compressibility modulus obtained with our method and the MARTINI forcefield are consistent with previous studies of these bilayers.

Short-term effects of a randomized computer-based out-of-school smoking prevention trial aimed at elementary schoolchildren.

PubMed

Ausems, Marlein; Mesters, Ilse; van Breukelen, Gerard; De Vries, Hein

2002-06-01

Smoking prevention programs usually run during school hours. In our study, an out-of-school program was developed consisting of a computer-tailored intervention aimed at the age group before school transition (11- to 12-year-old elementary schoolchildren). The aim of this study is to evaluate the additional effect of out-of-school smoking prevention. One hundred fifty-six participating schools were randomly allocated to one of four research conditions: (a) the in-school condition, an existing seven-lesson program; (b) the out-of-school condition, three computer-tailored letters sent to the students' homes; (c) the in-school and out-of-school condition, a combined approach; (d) the control condition. Pretest and 6 months follow-up data on smoking initiation and continuation, and data on psychosocial variables were collected from 3,349 students. Control and out-of-school conditions differed regarding posttest smoking initiation (18.1 and 10.4%) and regarding posttest smoking continuation (23.5 and 13.1%). Multilevel logistic regression analyses showed positive effects regarding the out-of-school program. Significant effects were not found regarding the in-school program, nor did the combined approach show stronger effects than the single-method approaches. The findings of this study suggest that smoking prevention trials for elementary schoolchildren can be effective when using out-of-school computer-tailored interventions. Copyright 2002 Elsevier Science (USA).

An efficient and general numerical method to compute steady uniform vortices

NASA Astrophysics Data System (ADS)

Luzzatto-Fegiz, Paolo; Williamson, Charles H. K.

2011-07-01

Steady uniform vortices are widely used to represent high Reynolds number flows, yet their efficient computation still presents some challenges. Existing Newton iteration methods become inefficient as the vortices develop fine-scale features; in addition, these methods cannot, in general, find solutions with specified Casimir invariants. On the other hand, available relaxation approaches are computationally inexpensive, but can fail to converge to a solution. In this paper, we overcome these limitations by introducing a new discretization, based on an inverse-velocity map, which radically increases the efficiency of Newton iteration methods. In addition, we introduce a procedure to prescribe Casimirs and remove the degeneracies in the steady vorticity equation, thus ensuring convergence for general vortex configurations. We illustrate our methodology by considering several unbounded flows involving one or two vortices. Our method enables the computation, for the first time, of steady vortices that do not exhibit any geometric symmetry. In addition, we discover that, as the limiting vortex state for each flow is approached, each family of solutions traces a clockwise spiral in a bifurcation plot consisting of a velocity-impulse diagram. By the recently introduced "IVI diagram" stability approach [Phys. Rev. Lett. 104 (2010) 044504], each turn of this spiral is associated with a loss of stability for the steady flows. Such spiral structure is suggested to be a universal feature of steady, uniform-vorticity flows.

A computer simulation approach to quantify the true area and true area compressibility modulus of biological membranes

NASA Astrophysics Data System (ADS)

Chacón, Enrique; Tarazona, Pedro; Bresme, Fernando

2015-07-01

We present a new computational approach to quantify the area per lipid and the area compressibility modulus of biological membranes. Our method relies on the analysis of the membrane fluctuations using our recently introduced coupled undulatory (CU) mode [Tarazona et al., J. Chem. Phys. 139, 094902 (2013)], which provides excellent estimates of the bending modulus of model membranes. Unlike the projected area, widely used in computer simulations of membranes, the CU area is thermodynamically consistent. This new area definition makes it possible to accurately estimate the area of the undulating bilayer, and the area per lipid, by excluding any contributions related to the phospholipid protrusions. We find that the area per phospholipid and the area compressibility modulus features a negligible dependence with system size, making possible their computation using truly small bilayers, involving a few hundred lipids. The area compressibility modulus obtained from the analysis of the CU area fluctuations is fully consistent with the Hooke's law route. Unlike existing methods, our approach relies on a single simulation, and no a priori knowledge of the bending modulus is required. We illustrate our method by analyzing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayers using the coarse grained MARTINI force-field. The area per lipid and area compressibility modulus obtained with our method and the MARTINI forcefield are consistent with previous studies of these bilayers.

RESTOP: Retaining External Peripheral State in Intermittently-Powered Sensor Systems

PubMed Central

Rodriguez Arreola, Alberto; Balsamo, Domenico

2018-01-01

Energy harvesting sensor systems typically incorporate energy buffers (e.g., rechargeable batteries and supercapacitors) to accommodate fluctuations in supply. However, the presence of these elements limits the miniaturization of devices. In recent years, researchers have proposed a new paradigm, transient computing, where systems operate directly from the energy harvesting source and allow computation to span across power cycles, without adding energy buffers. Various transient computing approaches have addressed the challenge of power intermittency by retaining the processor’s state using non-volatile memory. However, no generic approach has yet been proposed to retain the state of peripherals external to the processing element. This paper proposes RESTOP, flexible middleware which retains the state of multiple external peripherals that are connected to a computing element (i.e., a microcontroller) through protocols such as SPI or I2C. RESTOP acts as an interface between the main application and the peripheral, which keeps a record, at run-time, of the transmitted data in order to restore peripheral configuration after a power interruption. RESTOP is practically implemented and validated using three digitally interfaced peripherals, successfully restoring their configuration after power interruptions, imposing a maximum time overhead of 15% when configuring a peripheral. However, this represents an overhead of only 0.82% during complete execution of our typical sensing application, which is substantially lower than existing approaches. PMID:29320441

A New Approach to Predicting the Thermal Environment in Buildings at the Early Design Stage. Building Research Establishment Current Paper 2/74.

ERIC Educational Resources Information Center

Milbank, N. O.

The paper argues that existing computer programs for thermal predictions do not produce suitable information for architects, particularly at the early stages of design. It reviews the important building features that determine the thermal environment and the need for heating and cooling plant. Graphical design aids are proposed, with examples to…

Approaching a parameter-free metadynamics.

PubMed

Dickson, Bradley M

2011-09-01

We present a unique derivation of metadynamics. This work leads to a more robust understanding of the error in the computed free energy than what has been obtained previously. Moreover, a formula for the exact free energy is introduced. The formula can be used to post-process any existing well-tempered metadynamics data, allowing one, in principle, to obtain an exact free energy regardless of the metadynamics parameters.

Approaching a parameter-free metadynamics

NASA Astrophysics Data System (ADS)

Dickson, Bradley M.

2011-09-01

We present a unique derivation of metadynamics. This work leads to a more robust understanding of the error in the computed free energy than what has been obtained previously. Moreover, a formula for the exact free energy is introduced. The formula can be used to post-process any existing well-tempered metadynamics data, allowing one, in principle, to obtain an exact free energy regardless of the metadynamics parameters.

Data Integration and Mining for Synthetic Biology Design.

PubMed

Mısırlı, Göksel; Hallinan, Jennifer; Pocock, Matthew; Lord, Phillip; McLaughlin, James Alastair; Sauro, Herbert; Wipat, Anil

2016-10-21

One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.

A personal computer-based nuclear magnetic resonance spectrometer

NASA Astrophysics Data System (ADS)

Job, Constantin; Pearson, Robert M.; Brown, Michael F.

1994-11-01

Nuclear magnetic resonance (NMR) spectroscopy using personal computer-based hardware has the potential of enabling the application of NMR methods to fields where conventional state of the art equipment is either impractical or too costly. With such a strategy for data acquisition and processing, disciplines including civil engineering, agriculture, geology, archaeology, and others have the possibility of utilizing magnetic resonance techniques within the laboratory or conducting applications directly in the field. Another aspect is the possibility of utilizing existing NMR magnets which may be in good condition but unused because of outdated or nonrepairable electronics. Moreover, NMR applications based on personal computer technology may open up teaching possibilities at the college or even secondary school level. The goal of developing such a personal computer (PC)-based NMR standard is facilitated by existing technologies including logic cell arrays, direct digital frequency synthesis, use of PC-based electrical engineering software tools to fabricate electronic circuits, and the use of permanent magnets based on neodymium-iron-boron alloy. Utilizing such an approach, we have been able to place essentially an entire NMR spectrometer console on two printed circuit boards, with the exception of the receiver and radio frequency power amplifier. Future upgrades to include the deuterium lock and the decoupler unit are readily envisioned. The continued development of such PC-based NMR spectrometers is expected to benefit from the fast growing, practical, and low cost personal computer market.

Computer-guided percutaneous interbody fixation and fusion of the L5-S1 disc: a 2-year prospective study.

PubMed

Mac Millan, Michael

2005-02-01

The clinical outcomes of lumbar fusion are diminished by the complications associated with the surgical approach. Posterior approaches cause segmental muscular necrosis and anterior approaches risk visceral and vascular injury. This report details a two-year prospective study of a percutaneous method which avoids the major problems associated with existing approaches. Seventeen patients underwent percutaneous, trans-sacral fusion and fixation of L5-S1 with the assistance of computer guidance. Each patient was followed for a minimum of two years post surgery. SF-36 questionnaires and radiographs were obtained preoperatively and at two years post-operatively. Fusion was assessed with post-operative radiographs and/or CT scan. Ninety-three percent of the people fused as judged by plain AP films, Ferguson's view radiographs, and/or CT scans at the two year follow-up. Prospective health and functional SF-36 scores showed significant improvement from the preoperative to the postoperative period. There were no significant complications related to the approach or to the placement of the implants. Percutaneous fusion of the lumbosacral spine appears safe and provides excellent clinical results with a minimal amount of associated tissue trauma.

Pre-existing Epithelial Diversity in Normal Human Livers: A Tissue-tethered Cytometric Analysis in Portal/Periportal Epithelial Cells

PubMed Central

Isse, Kumiko; Lesniak, Andrew; Grama, Kedar; Maier, John; Specht, Susan; Castillo-Rama, Marcela; Lunz, John; Roysam, Badrinath; Michalopoulos, George; Demetris, Anthony J.

2012-01-01

Routine light microscopy identifies two distinct epithelial cell populations in normal human livers: hepatocytes and biliary epithelial cells (BEC). Considerable epithelial diversity, however, arises during disease states when a variety of hepatocyte-BEC hybrid cells appear. This has been attributed to activation and differentiation of putative hepatic progenitor cells (HPC) residing in the Canals of Hering and/or metaplasia of pre-existing mature epithelial cells. A novel analytic approach consisting of multiplex labeling, high resolution whole slide imaging (WSI), and automated image analysis was used to determine if more complex epithelial cell phenotypes pre-existed in normal adult human livers, which might provide an alternative explanation for disease-induced epithelial diversity. “Virtually digested” WSI enabled quantitative cytometric analyses of individual cells displayed in a variety of formats (e.g. scatter plots) while still tethered to the WSI and tissue structure. We employed biomarkers specifically-associated with mature epithelial forms (HNF4α for hepatocytes, CK19 and HNF1β for BEC) and explored for the presence of cells with hybrid biomarker phenotypes. Results showed abundant hybrid cells in portal bile duct BEC, canals of Hering, and immediate periportal hepatocytes. These bi-potential cells likely serve as a reservoir for the epithelial diversity of ductular reactions, appearance of hepatocytes in bile ducts, and the rapid and fluid transition of BEC to hepatocytes, and vice versa. Conclusion Novel imaging and computational tools enable increased information extraction from tissue samples and quantify the considerable pre-existent hybrid epithelial diversity in normal human liver. This computationally-enabled tissue analysis approach offers much broader potential beyond the results presented here. PMID:23150208

Computational investigation of fluid flow and heat transfer of an economizer by porous medium approach

NASA Astrophysics Data System (ADS)

Babu, C. Rajesh; Kumar, P.; Rajamohan, G.

2017-07-01

Computation of fluid flow and heat transfer in an economizer is simulated by a porous medium approach, with plain tubes having a horizontal in-line arrangement and cross flow arrangement in a coal-fired thermal power plant. The economizer is a thermal mechanical device that captures waste heat from the thermal exhaust flue gasses through heat transfer surfaces to preheat boiler feed water. In order to evaluate the fluid flow and heat transfer on tubes, a numerical analysis on heat transfer performance is carried out on an 110 t/h MCR (Maximum continuous rating) boiler unit. In this study, thermal performance is investigated using the computational fluid dynamics (CFD) simulation using ANSYS FLUENT. The fouling factor ε and the overall heat transfer coefficient ψ are employed to evaluate the fluid flow and heat transfer. The model demands significant computational details for geometric modeling, grid generation, and numerical calculations to evaluate the thermal performance of an economizer. The simulation results show that the overall heat transfer coefficient 37.76 W/(m2K) and economizer coil side pressure drop of 0.2 (kg/cm2) are found to be conformity within the tolerable limits when compared with existing industrial economizer data.

Bifurcation Analysis Using Rigorous Branch and Bound Methods

NASA Technical Reports Server (NTRS)

Smith, Andrew P.; Crespo, Luis G.; Munoz, Cesar A.; Lowenberg, Mark H.

2014-01-01

For the study of nonlinear dynamic systems, it is important to locate the equilibria and bifurcations occurring within a specified computational domain. This paper proposes a new approach for solving these problems and compares it to the numerical continuation method. The new approach is based upon branch and bound and utilizes rigorous enclosure techniques to yield outer bounding sets of both the equilibrium and local bifurcation manifolds. These sets, which comprise the union of hyper-rectangles, can be made to be as tight as desired. Sufficient conditions for the existence of equilibrium and bifurcation points taking the form of algebraic inequality constraints in the state-parameter space are used to calculate their enclosures directly. The enclosures for the bifurcation sets can be computed independently of the equilibrium manifold, and are guaranteed to contain all solutions within the computational domain. A further advantage of this method is the ability to compute a near-maximally sized hyper-rectangle of high dimension centered at a fixed parameter-state point whose elements are guaranteed to exclude all bifurcation points. This hyper-rectangle, which requires a global description of the bifurcation manifold within the computational domain, cannot be obtained otherwise. A test case, based on the dynamics of a UAV subject to uncertain center of gravity location, is used to illustrate the efficacy of the method by comparing it with numerical continuation and to evaluate its computational complexity.

Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM.

PubMed

Petersson, K J F; Friberg, L E; Karlsson, M O

2010-10-01

Computer models of biological systems grow more complex as computing power increase. Often these models are defined as differential equations and no analytical solutions exist. Numerical integration is used to approximate the solution; this can be computationally intensive, time consuming and be a large proportion of the total computer runtime. The performance of different integration methods depend on the mathematical properties of the differential equations system at hand. In this paper we investigate the possibility of runtime gains by calculating parts of or the whole differential equations system at given time intervals, outside of the differential equations solver. This approach was tested on nine models defined as differential equations with the goal to reduce runtime while maintaining model fit, based on the objective function value. The software used was NONMEM. In four models the computational runtime was successfully reduced (by 59-96%). The differences in parameter estimates, compared to using only the differential equations solver were less than 12% for all fixed effects parameters. For the variance parameters, estimates were within 10% for the majority of the parameters. Population and individual predictions were similar and the differences in OFV were between 1 and -14 units. When computational runtime seriously affects the usefulness of a model we suggest evaluating this approach for repetitive elements of model building and evaluation such as covariate inclusions or bootstraps.

A modified approach to controller partitioning

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Veillette, Robert J.

1993-01-01

The idea of computing a decentralized control law for the integrated flight/propulsion control of an aircraft by partitioning a given centralized controller is investigated. An existing controller partitioning methodology is described, and a modified approach is proposed with the objective of simplifying the associated controller approximation problem. Under the existing approach, the decentralized control structure is a variable in the partitioning process; by contrast, the modified approach assumes that the structure is fixed a priori. Hence, the centralized controller design may take the decentralized control structure into account. Specifically, the centralized controller may be designed to include all the same inputs and outputs as the decentralized controller; then, the two controllers may be compared directly, simplifying the partitioning process considerably. Following the modified approach, a centralized controller is designed for an example aircraft mode. The design includes all the inputs and outputs to be used in a specified decentralized control structure. However, it is shown that the resulting centralized controller is not well suited for approximation by a decentralized controller of the given structure. The results indicate that it is not practical in general to cast the controller partitioning problem as a direct controller approximation problem.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Millis, Andrew

Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomesmore » included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.« less

A community proposal to integrate proteomics activities in ELIXIR.

PubMed

Vizcaíno, Juan Antonio; Walzer, Mathias; Jiménez, Rafael C; Bittremieux, Wout; Bouyssié, David; Carapito, Christine; Corrales, Fernando; Ferro, Myriam; Heck, Albert J R; Horvatovich, Peter; Hubalek, Martin; Lane, Lydie; Laukens, Kris; Levander, Fredrik; Lisacek, Frederique; Novak, Petr; Palmblad, Magnus; Piovesan, Damiano; Pühler, Alfred; Schwämmle, Veit; Valkenborg, Dirk; van Rijswijk, Merlijn; Vondrasek, Jiri; Eisenacher, Martin; Martens, Lennart; Kohlbacher, Oliver

2017-01-01

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on 'The Future of Proteomics in ELIXIR' that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR's existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

A community proposal to integrate proteomics activities in ELIXIR

PubMed Central

Vizcaíno, Juan Antonio; Walzer, Mathias; Jiménez, Rafael C.; Bittremieux, Wout; Bouyssié, David; Carapito, Christine; Corrales, Fernando; Ferro, Myriam; Heck, Albert J.R.; Horvatovich, Peter; Hubalek, Martin; Lane, Lydie; Laukens, Kris; Levander, Fredrik; Lisacek, Frederique; Novak, Petr; Palmblad, Magnus; Piovesan, Damiano; Pühler, Alfred; Schwämmle, Veit; Valkenborg, Dirk; van Rijswijk, Merlijn; Vondrasek, Jiri; Eisenacher, Martin; Martens, Lennart; Kohlbacher, Oliver

2017-01-01

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper. PMID:28713550

Topology optimization under stochastic stiffness

NASA Astrophysics Data System (ADS)

Asadpoure, Alireza

Topology optimization is a systematic computational tool for optimizing the layout of materials within a domain for engineering design problems. It allows variation of structural boundaries and connectivities. This freedom in the design space often enables discovery of new, high performance designs. However, solutions obtained by performing the optimization in a deterministic setting may be impractical or suboptimal when considering real-world engineering conditions with inherent variabilities including (for example) variabilities in fabrication processes and operating conditions. The aim of this work is to provide a computational methodology for topology optimization in the presence of uncertainties associated with structural stiffness, such as uncertain material properties and/or structural geometry. Existing methods for topology optimization under deterministic conditions are first reviewed. Modifications are then proposed to improve the numerical performance of the so-called Heaviside Projection Method (HPM) in continuum domains. Next, two approaches, perturbation and Polynomial Chaos Expansion (PCE), are proposed to account for uncertainties in the optimization procedure. These approaches are intrusive, allowing tight and efficient coupling of the uncertainty quantification with the optimization sensitivity analysis. The work herein develops a robust topology optimization framework aimed at reducing the sensitivity of optimized solutions to uncertainties. The perturbation-based approach combines deterministic topology optimization with a perturbation method for the quantification of uncertainties. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. The PCE approach combines the spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique. The resulting compact representations for the response quantities allow for efficient and accurate calculation of sensitivities of response statistics with respect to the design variables. The proposed methods are shown to be successful at generating robust optimal topologies. Examples from topology optimization in continuum and discrete domains (truss structures) under uncertainty are presented. It is also shown that proposed methods lead to significant computational savings when compared to Monte Carlo-based optimization which involve multiple formations and inversions of the global stiffness matrix and that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.

Solid liquid interfacial free energies of benzene

NASA Astrophysics Data System (ADS)

Azreg-Aı¨nou, M.

2007-02-01

In this work we determine for the range of melting temperatures 284.6⩽T⩽306.7 K, corresponding to equilibrium pressures 20.6⩽P⩽102.9 MPa, the benzene solid-liquid interfacial free energy by a cognitive approach including theoretical and experimental physics, mathematics, computer algebra (MATLAB), and some results from molecular dynamics computer simulations. From a theoretical and mathematical points of view, we deal with the elaboration of an analytical expression for the internal energy derived from a unified solid-liquid-vapor equation of state and with the elaboration of an existing statistical model for the entropy drop of the melt near the solid-liquid interface. From an experimental point of view, we will use our results obtained in collaboration with colleagues concerning the supercooled liquid benzene. Of particular interest for this work is the existing center-of-mass radial distribution function of benzene at 298 K obtained by computer simulation. Crystal-orientation-independent and minimum interfacial free energies are calculated and shown to increase slightly with the above temperatures. Both crystal-orientation-independent and minimum free energies agree with existing calculations and with rare existing experimental data. Taking into account the fact that the extent of supercooling is generally admitted as a constant, we determine the limits of supercooling by which we explore the behavior of the critical nucleus radius which is shown to decrease in terms of the above temperatures. The radius of the, and the number of molecules per, critical nucleus are shown to assume the average values of 20.2 A˚ and 175 with standard deviations of 0.16 Å and 4.5, respectively.

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

NASA Astrophysics Data System (ADS)

Qin, Cheng-Zhi; Zhan, Lijun

2012-06-01

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.

An innovative computationally efficient hydromechanical coupling approach for fault reactivation in geological subsurface utilization

NASA Astrophysics Data System (ADS)

Adams, M.; Kempka, T.; Chabab, E.; Ziegler, M.

2018-02-01

Estimating the efficiency and sustainability of geological subsurface utilization, i.e., Carbon Capture and Storage (CCS) requires an integrated risk assessment approach, considering the occurring coupled processes, beside others, the potential reactivation of existing faults. In this context, hydraulic and mechanical parameter uncertainties as well as different injection rates have to be considered and quantified to elaborate reliable environmental impact assessments. Consequently, the required sensitivity analyses consume significant computational time due to the high number of realizations that have to be carried out. Due to the high computational costs of two-way coupled simulations in large-scale 3D multiphase fluid flow systems, these are not applicable for the purpose of uncertainty and risk assessments. Hence, an innovative semi-analytical hydromechanical coupling approach for hydraulic fault reactivation will be introduced. This approach determines the void ratio evolution in representative fault elements using one preliminary base simulation, considering one model geometry and one set of hydromechanical parameters. The void ratio development is then approximated and related to one reference pressure at the base of the fault. The parametrization of the resulting functions is then directly implemented into a multiphase fluid flow simulator to carry out the semi-analytical coupling for the simulation of hydromechanical processes. Hereby, the iterative parameter exchange between the multiphase and mechanical simulators is omitted, since the update of porosity and permeability is controlled by one reference pore pressure at the fault base. The suggested procedure is capable to reduce the computational time required by coupled hydromechanical simulations of a multitude of injection rates by a factor of up to 15.

Time-Accurate Local Time Stepping and High-Order Time CESE Methods for Multi-Dimensional Flows Using Unstructured Meshes

NASA Technical Reports Server (NTRS)

Chang, Chau-Lyan; Venkatachari, Balaji Shankar; Cheng, Gary

2013-01-01

With the wide availability of affordable multiple-core parallel supercomputers, next generation numerical simulations of flow physics are being focused on unsteady computations for problems involving multiple time scales and multiple physics. These simulations require higher solution accuracy than most algorithms and computational fluid dynamics codes currently available. This paper focuses on the developmental effort for high-fidelity multi-dimensional, unstructured-mesh flow solvers using the space-time conservation element, solution element (CESE) framework. Two approaches have been investigated in this research in order to provide high-accuracy, cross-cutting numerical simulations for a variety of flow regimes: 1) time-accurate local time stepping and 2) highorder CESE method. The first approach utilizes consistent numerical formulations in the space-time flux integration to preserve temporal conservation across the cells with different marching time steps. Such approach relieves the stringent time step constraint associated with the smallest time step in the computational domain while preserving temporal accuracy for all the cells. For flows involving multiple scales, both numerical accuracy and efficiency can be significantly enhanced. The second approach extends the current CESE solver to higher-order accuracy. Unlike other existing explicit high-order methods for unstructured meshes, the CESE framework maintains a CFL condition of one for arbitrarily high-order formulations while retaining the same compact stencil as its second-order counterpart. For large-scale unsteady computations, this feature substantially enhances numerical efficiency. Numerical formulations and validations using benchmark problems are discussed in this paper along with realistic examples.

Crossing the chasm: how to develop weather and climate models for next generation computers?

NASA Astrophysics Data System (ADS)

Lawrence, Bryan N.; Rezny, Michael; Budich, Reinhard; Bauer, Peter; Behrens, Jörg; Carter, Mick; Deconinck, Willem; Ford, Rupert; Maynard, Christopher; Mullerworth, Steven; Osuna, Carlos; Porter, Andrew; Serradell, Kim; Valcke, Sophie; Wedi, Nils; Wilson, Simon

2018-05-01

Weather and climate models are complex pieces of software which include many individual components, each of which is evolving under pressure to exploit advances in computing to enhance some combination of a range of possible improvements (higher spatio-temporal resolution, increased fidelity in terms of resolved processes, more quantification of uncertainty, etc.). However, after many years of a relatively stable computing environment with little choice in processing architecture or programming paradigm (basically X86 processors using MPI for parallelism), the existing menu of processor choices includes significant diversity, and more is on the horizon. This computational diversity, coupled with ever increasing software complexity, leads to the very real possibility that weather and climate modelling will arrive at a chasm which will separate scientific aspiration from our ability to develop and/or rapidly adapt codes to the available hardware. In this paper we review the hardware and software trends which are leading us towards this chasm, before describing current progress in addressing some of the tools which we may be able to use to bridge the chasm. This brief introduction to current tools and plans is followed by a discussion outlining the scientific requirements for quality model codes which have satisfactory performance and portability, while simultaneously supporting productive scientific evolution. We assert that the existing method of incremental model improvements employing small steps which adjust to the changing hardware environment is likely to be inadequate for crossing the chasm between aspiration and hardware at a satisfactory pace, in part because institutions cannot have all the relevant expertise in house. Instead, we outline a methodology based on large community efforts in engineering and standardisation, which will depend on identifying a taxonomy of key activities - perhaps based on existing efforts to develop domain-specific languages, identify common patterns in weather and climate codes, and develop community approaches to commonly needed tools and libraries - and then collaboratively building up those key components. Such a collaborative approach will depend on institutions, projects, and individuals adopting new interdependencies and ways of working.

Quantum chemical approach to estimating the thermodynamics of metabolic reactions.

PubMed

Jinich, Adrian; Rappoport, Dmitrij; Dunn, Ian; Sanchez-Lengeling, Benjamin; Olivares-Amaya, Roberto; Noor, Elad; Even, Arren Bar; Aspuru-Guzik, Alán

2014-11-12

Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism.

An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming

PubMed Central

An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach. PMID:23861655

An approach to self-assembling swarm robots using multitree genetic programming.

PubMed

Lee, Jong-Hyun; Ahn, Chang Wook; An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.

Steering law design for redundant single-gimbal control moment gyroscopes. [for spacecraft attitude control

NASA Technical Reports Server (NTRS)

Bedrossian, Nazareth S.; Paradiso, Joseph; Bergmann, Edward V.; Rowell, Derek

1990-01-01

Two steering laws are presented for single-gimbal control moment gyroscopes. An approach using the Moore-Penrose pseudoinverse with a nondirectional null-motion algorithm is shown by example to avoid internal singularities for unidirectional torque commands, for which existing algorithms fail. Because this is still a tangent-based approach, however, singularity avoidance cannot be guaranteed. The singularity robust inverse is introduced as an alternative to the pseudoinverse for computing torque-producing gimbal rates near singular states. This approach, coupled with the nondirectional null algorithm, is shown by example to provide better steering law performance by allowing torque errors to be produced in the vicinity of singular states.

Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals

PubMed Central

Brown, Robert; Pasaniuc, Bogdan

2014-01-01

Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs. PMID:24743331

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

PubMed

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Applications of computational models to better understand microvascular remodelling: a focus on biomechanical integration across scales

PubMed Central

Murfee, Walter L.; Sweat, Richard S.; Tsubota, Ken-ichi; Gabhann, Feilim Mac; Khismatullin, Damir; Peirce, Shayn M.

2015-01-01

Microvascular network remodelling is a common denominator for multiple pathologies and involves both angiogenesis, defined as the sprouting of new capillaries, and network patterning associated with the organization and connectivity of existing vessels. Much of what we know about microvascular remodelling at the network, cellular and molecular scales has been derived from reductionist biological experiments, yet what happens when the experiments provide incomplete (or only qualitative) information? This review will emphasize the value of applying computational approaches to advance our understanding of the underlying mechanisms and effects of microvascular remodelling. Examples of individual computational models applied to each of the scales will highlight the potential of answering specific questions that cannot be answered using typical biological experimentation alone. Looking into the future, we will also identify the needs and challenges associated with integrating computational models across scales. PMID:25844149

All-Particle Multiscale Computation of Hypersonic Rarefied Flow

NASA Astrophysics Data System (ADS)

Jun, E.; Burt, J. M.; Boyd, I. D.

2011-05-01

This study examines a new hybrid particle scheme used as an alternative means of multiscale flow simulation. The hybrid particle scheme employs the direct simulation Monte Carlo (DSMC) method in rarefied flow regions and the low diffusion (LD) particle method in continuum flow regions. The numerical procedures of the low diffusion particle method are implemented within an existing DSMC algorithm. The performance of the LD-DSMC approach is assessed by studying Mach 10 nitrogen flow over a sphere with a global Knudsen number of 0.002. The hybrid scheme results show good overall agreement with results from standard DSMC and CFD computation. Subcell procedures are utilized to improve computational efficiency and reduce sensitivity to DSMC cell size in the hybrid scheme. This makes it possible to perform the LD-DSMC simulation on a much coarser mesh that leads to a significant reduction in computation time.

Embedded ubiquitous services on hospital information systems.

PubMed

Kuroda, Tomohiro; Sasaki, Hiroshi; Suenaga, Takatoshi; Masuda, Yasushi; Yasumuro, Yoshihiro; Hori, Kenta; Ohboshi, Naoki; Takemura, Tadamasa; Chihara, Kunihiro; Yoshihara, Hiroyuki

2012-11-01

A Hospital Information Systems (HIS) have turned a hospital into a gigantic computer with huge computational power, huge storage and wired/wireless local area network. On the other hand, a modern medical device, such as echograph, is a computer system with several functional units connected by an internal network named a bus. Therefore, we can embed such a medical device into the HIS by simply replacing the bus with the local area network. This paper designed and developed two embedded systems, a ubiquitous echograph system and a networked digital camera. Evaluations of the developed systems clearly show that the proposed approach, embedding existing clinical systems into HIS, drastically changes productivity in the clinical field. Once a clinical system becomes a pluggable unit for a gigantic computer system, HIS, the combination of multiple embedded systems with application software designed under deep consideration about clinical processes may lead to the emergence of disruptive innovation in the clinical field.

Computer vision-based classification of hand grip variations in neurorehabilitation.

PubMed

Zariffa, José; Steeves, John D

2011-01-01

The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation context. We describe a computer vision-based classifier that can be used to discriminate rehabilitation-relevant hand postures, and could be integrated into a virtual reality-based upper limb rehabilitation system. The proposed system was tested on a set of video recordings from able-bodied individuals performing cylindrical grasps, lateral key grips, and tip-to-tip pinches. The overall classification success rate was 91.2%, and was above 98% for 6 out of the 10 subjects. © 2011 IEEE

A Functional Description of a Digital Flight Test System for Navigation and Guidance Research in the Terminal Area

NASA Technical Reports Server (NTRS)

Hegarty, D. M.

1974-01-01

A guidance, navigation, and control system, the Simulated Shuttle Flight Test System (SS-FTS), when interfaced with existing aircraft systems, provides a research facility for studying concepts for landing the space shuttle orbiter and conventional jet aircraft. The SS-FTS, which includes a general-purpose computer, performs all computations for precisely following a prescribed approach trajectory while properly managing the vehicle energy to allow safe arrival at the runway and landing within prescribed dispersions. The system contains hardware and software provisions for navigation with several combinations of possible navigation aids that have been suggested for the shuttle. The SS-FTS can be reconfigured to study different guidance and navigation concepts by changing only the computer software, and adapted to receive different radio navigation information through minimum hardware changes. All control laws, logic, and mode interlocks reside solely in the computer software.

Electrically protected resonant exchange qubits in triple quantum dots.

PubMed

Taylor, J M; Srinivasa, V; Medford, J

2013-08-02

We present a modulated microwave approach for quantum computing with qubits comprising three spins in a triple quantum dot. This approach includes single- and two-qubit gates that are protected against low-frequency electrical noise, due to an operating point with a narrowband response to high frequency electric fields. Furthermore, existing double quantum dot advances, including robust preparation and measurement via spin-to-charge conversion, are immediately applicable to the new qubit. Finally, the electric dipole terms implicit in the high frequency coupling enable strong coupling with superconducting microwave resonators, leading to more robust two-qubit gates.

Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease

PubMed Central

Smith, Stephen L.; Lones, Michael A.; Bedder, Matthew; Alty, Jane E.; Cosgrove, Jeremy; Maguire, Richard J.; Pownall, Mary Elizabeth; Ivanoiu, Diana; Lyle, Camille; Cording, Amy; Elliott, Christopher J.H.

2015-01-01

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson’s disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson’s by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way. PMID:26577157

Large-scale Parallel Unstructured Mesh Computations for 3D High-lift Analysis

NASA Technical Reports Server (NTRS)

Mavriplis, Dimitri J.; Pirzadeh, S.

1999-01-01

A complete "geometry to drag-polar" analysis capability for the three-dimensional high-lift configurations is described. The approach is based on the use of unstructured meshes in order to enable rapid turnaround for complicated geometries that arise in high-lift configurations. Special attention is devoted to creating a capability for enabling analyses on highly resolved grids. Unstructured meshes of several million vertices are initially generated on a work-station, and subsequently refined on a supercomputer. The flow is solved on these refined meshes on large parallel computers using an unstructured agglomeration multigrid algorithm. Good prediction of lift and drag throughout the range of incidences is demonstrated on a transport take-off configuration using up to 24.7 million grid points. The feasibility of using this approach in a production environment on existing parallel machines is demonstrated, as well as the scalability of the solver on machines using up to 1450 processors.

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

PubMed

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-01-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Spreading dynamics on complex networks: a general stochastic approach.

PubMed

Noël, Pierre-André; Allard, Antoine; Hébert-Dufresne, Laurent; Marceau, Vincent; Dubé, Louis J

2014-12-01

Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible and susceptible-infectious-removed dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.

Modeling a Nursing Guideline with Standard Terminology and Unified Modeling Language for a Nursing Decision Support System: A Case Study.

PubMed

Choi, Jeeyae; Jansen, Kay; Coenen, Amy

In recent years, Decision Support Systems (DSSs) have been developed and used to achieve "meaningful use". One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users' perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS.

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography.

PubMed

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

2017-03-01

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

Computation of the properties of liquid neon, methane, and gas helium at low temperature by the Feynman-Hibbs approach.

PubMed

Tchouar, N; Ould-Kaddour, F; Levesque, D

2004-10-15

The properties of liquid methane, liquid neon, and gas helium are calculated at low temperatures over a large range of pressure from the classical molecular-dynamics simulations. The molecular interactions are represented by the Lennard-Jones pair potentials supplemented by quantum corrections following the Feynman-Hibbs approach. The equations of state, diffusion, and shear viscosity coefficients are determined for neon at 45 K, helium at 80 K, and methane at 110 K. A comparison is made with the existing experimental data and for thermodynamical quantities, with results computed from quantum numerical simulations when they are available. The theoretical variation of the viscosity coefficient with pressure is in good agreement with the experimental data when the quantum corrections are taken into account, thus reducing considerably the 60% discrepancy between the simulations and experiments in the absence of these corrections.

An Efficient Solution Method for Multibody Systems with Loops Using Multiple Processors

NASA Technical Reports Server (NTRS)

Ghosh, Tushar K.; Nguyen, Luong A.; Quiocho, Leslie J.

2015-01-01

This paper describes a multibody dynamics algorithm formulated for parallel implementation on multiprocessor computing platforms using the divide-and-conquer approach. The system of interest is a general topology of rigid and elastic articulated bodies with or without loops. The algorithm divides the multibody system into a number of smaller sets of bodies in chain or tree structures, called "branches" at convenient joints called "connection points", and uses an Order-N (O (N)) approach to formulate the dynamics of each branch in terms of the unknown spatial connection forces. The equations of motion for the branches, leaving the connection forces as unknowns, are implemented in separate processors in parallel for computational efficiency, and the equations for all the unknown connection forces are synthesized and solved in one or several processors. The performances of two implementations of this divide-and-conquer algorithm in multiple processors are compared with an existing method implemented on a single processor.

Modeling the Hydration Layer around Proteins: Applications to Small- and Wide-Angle X-Ray Scattering

PubMed Central

Virtanen, Jouko Juhani; Makowski, Lee; Sosnick, Tobin R.; Freed, Karl F.

2011-01-01

Small-/wide-angle x-ray scattering (SWAXS) experiments can aid in determining the structures of proteins and protein complexes, but success requires accurate computational treatment of solvation. We compare two methods by which to calculate SWAXS patterns. The first approach uses all-atom explicit-solvent molecular dynamics (MD) simulations. The second, far less computationally expensive method involves prediction of the hydration density around a protein using our new HyPred solvation model, which is applied without the need for additional MD simulations. The SWAXS patterns obtained from the HyPred model compare well to both experimental data and the patterns predicted by the MD simulations. Both approaches exhibit advantages over existing methods for analyzing SWAXS data. The close correspondence between calculated and observed SWAXS patterns provides strong experimental support for the description of hydration implicit in the HyPred model. PMID:22004761

Modeling a Nursing Guideline with Standard Terminology and Unified Modeling Language for a Nursing Decision Support System: A Case Study

PubMed Central

Choi, Jeeyae; Jansen, Kay; Coenen, Amy

2015-01-01

In recent years, Decision Support Systems (DSSs) have been developed and used to achieve “meaningful use”. One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users’ perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS. PMID:26958174

Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

NASA Astrophysics Data System (ADS)

Valderrama, Jesus A.; Díaz-Ramírez, Víctor H.; Kober, Vitaly; Hernandez, Enrique

2015-09-01

A real-time algorithm for single image dehazing is presented. The algorithm is based on calculation of local neighborhoods of a hazed image inside a moving window. The local neighborhoods are constructed by computing rank-order statistics. Next the dark-channel-prior approach is applied to the local neighborhoods to estimate the transmission function of the scene. By using the suggested approach there is no need for applying a refining algorithm to the estimated transmission such as the soft matting algorithm. To achieve high-rate signal processing the proposed algorithm is implemented exploiting massive parallelism on a graphics processing unit (GPU). Computer simulation results are carried out to test the performance of the proposed algorithm in terms of dehazing efficiency and speed of processing. These tests are performed using several synthetic and real images. The obtained results are analyzed and compared with those obtained with existing dehazing algorithms.

NLO renormalization in the Hamiltonian truncation

NASA Astrophysics Data System (ADS)

Elias-Miró, Joan; Rychkov, Slava; Vitale, Lorenzo G.

2017-09-01

Hamiltonian truncation (also known as "truncated spectrum approach") is a numerical technique for solving strongly coupled quantum field theories, in which the full Hilbert space is truncated to a finite-dimensional low-energy subspace. The accuracy of the method is limited only by the available computational resources. The renormalization program improves the accuracy by carefully integrating out the high-energy states, instead of truncating them away. In this paper, we develop the most accurate ever variant of Hamiltonian Truncation, which implements renormalization at the cubic order in the interaction strength. The novel idea is to interpret the renormalization procedure as a result of integrating out exactly a certain class of high-energy "tail states." We demonstrate the power of the method with high-accuracy computations in the strongly coupled two-dimensional quartic scalar theory and benchmark it against other existing approaches. Our work will also be useful for the future goal of extending Hamiltonian truncation to higher spacetime dimensions.

Neural-network quantum state tomography

NASA Astrophysics Data System (ADS)

Torlai, Giacomo; Mazzola, Guglielmo; Carrasquilla, Juan; Troyer, Matthias; Melko, Roger; Carleo, Giuseppe

2018-05-01

The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods to validate and fully exploit quantum resources. Quantum state tomography (QST) aims to reconstruct the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics1-3. However, exact brute-force approaches to QST place a high demand on computational resources, making them unfeasible for anything except small systems4,5. Here we show how machine learning techniques can be used to perform QST of highly entangled states with more than a hundred qubits, to a high degree of accuracy. We demonstrate that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements. This approach can benefit existing and future generations of devices ranging from quantum computers to ultracold-atom quantum simulators6-8.

New approach for optimal electricity planning and dispatching with hourly time-scale air quality and health considerations.

PubMed

Kerl, Paul Y; Zhang, Wenxian; Moreno-Cruz, Juan B; Nenes, Athanasios; Realff, Matthew J; Russell, Armistead G; Sokol, Joel; Thomas, Valerie M

2015-09-01

Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004-2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9 million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies.

Accelerating the Mining of Influential Nodes in Complex Networks through Community Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halappanavar, Mahantesh; Sathanur, Arun V.; Nandi, Apurba

Computing the set of influential nodes with a given size to ensure maximal spread of influence on a complex network is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that comes with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization withoutmore » using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x - 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.« less

New approach for optimal electricity planning and dispatching with hourly time-scale air quality and health considerations

PubMed Central

Kerl, Paul Y.; Zhang, Wenxian; Moreno-Cruz, Juan B.; Nenes, Athanasios; Realff, Matthew J.; Russell, Armistead G.; Sokol, Joel; Thomas, Valerie M.

2015-01-01

Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004–2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9 million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies. PMID:26283358

MCore: A High-Order Finite-Volume Dynamical Core for Atmospheric General Circulation Models

NASA Astrophysics Data System (ADS)

Ullrich, P.; Jablonowski, C.

2011-12-01

The desire for increasingly accurate predictions of the atmosphere has driven numerical models to smaller and smaller resolutions, while simultaneously exponentially driving up the cost of existing numerical models. Even with the modern rapid advancement of computational performance, it is estimated that it will take more than twenty years before existing models approach the scales needed to resolve atmospheric convection. However, smarter numerical methods may allow us to glimpse the types of results we would expect from these fine-scale simulations while only requiring a fraction of the computational cost. The next generation of atmospheric models will likely need to rely on both high-order accuracy and adaptive mesh refinement in order to properly capture features of interest. We present our ongoing research on developing a set of ``smart'' numerical methods for simulating the global non-hydrostatic fluid equations which govern atmospheric motions. We have harnessed a high-order finite-volume based approach in developing an atmospheric dynamical core on the cubed-sphere. This type of method is desirable for applications involving adaptive grids, since it has been shown that spuriously reflected wave modes are intrinsically damped out under this approach. The model further makes use of an implicit-explicit Runge-Kutta-Rosenbrock (IMEX-RKR) time integrator for accurate and efficient coupling of the horizontal and vertical model components. We survey the algorithmic development of the model and present results from idealized dynamical core test cases, as well as give a glimpse at future work with our model.

Generalized Ordinary Differential Equation Models 1

PubMed Central

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-01-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. PMID:25544787

Generalized Ordinary Differential Equation Models.

PubMed

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-10-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

A Graph-Embedding Approach to Hierarchical Visual Word Mergence.

PubMed

Wang, Lei; Liu, Lingqiao; Zhou, Luping

2017-02-01

Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various formulations. In this paper, we propose a unified hierarchical merging approach built upon the graph-embedding framework. Our approach is able to merge visual words for any scenario, where a preferred structure and an undesired structure are defined, and, therefore, can effectively attend to all kinds of requirements for the word-merging process. In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work and, thus, well maintain the state-of-the-art merging speed. To the best of our survey, the proposed approach is the first one that addresses the hierarchical visual word mergence in such a flexible and unified manner. As demonstrated, it can maintain excellent image classification performance even after a significant dimension reduction, and outperform all the existing comparable visual word-merging methods. In a broad sense, our work provides an open platform for applying, evaluating, and developing new criteria for hierarchical word-merging tasks.

A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study.

PubMed

Fu, Jicheng; Jones, Maria; Liu, Tao; Hao, Wei; Yan, Yuqing; Qian, Gang; Jan, Yih-Kuen

2016-01-01

The purpose of this pilot study was to provide a new approach for capturing and analyzing wheelchair maneuvering data, which are critical for evaluating wheelchair users' activity levels. We proposed a mobile-cloud (MC) system, which incorporated the emerging mobile and cloud computing technologies. The MC system employed smartphone sensors to collect wheelchair maneuvering data and transmit them to the cloud for storage and analysis. A k-nearest neighbor (KNN) machine-learning algorithm was developed to mitigate the impact of sensor noise and recognize wheelchair maneuvering patterns. We conducted 30 trials in an indoor setting, where each trial contained 10 bouts (i.e., periods of continuous wheelchair movement). We also verified our approach in a different building. Different from existing approaches that require sensors to be attached to wheelchairs' wheels, we placed the smartphone into a smartphone holder attached to the wheelchair. Experimental results illustrate that our approach correctly identified all 300 bouts. Compared to existing approaches, our approach was easier to use while achieving similar accuracy in analyzing the accumulated movement time and maximum period of continuous movement (p > 0.8). Overall, the MC system provided a feasible way to ease the data collection process and generated accurate analysis results for evaluating activity levels.

A Novel Mobile-Cloud System for Capturing and Analyzing Wheelchair Maneuvering Data: A Pilot Study

PubMed Central

Fu, Jicheng; Jones, Maria; Liu, Tao; Hao, Wei; Yan, Yuqing; Qian, Gang; Jan, Yih-Kuen

2016-01-01

The purpose of this pilot study was to provide a new approach for capturing and analyzing wheelchair maneuvering data, which are critical for evaluating wheelchair users’ activity levels. We proposed a mobile-cloud (MC) system, which incorporated the emerging mobile and cloud computing technologies. The MC system employed smartphone sensors to collect wheelchair maneuvering data and transmit them to the cloud for storage and analysis. A K-Nearest-Neighbor (KNN) machine-learning algorithm was developed to mitigate the impact of sensor noise and recognize wheelchair maneuvering patterns. We conducted 30 trials in an indoor setting, where each trial contained 10 bouts (i.e., periods of continuous wheelchair movement). We also verified our approach in a different building. Different from existing approaches that require sensors to be attached to wheelchairs’ wheels, we placed the smartphone into a smartphone holder attached to the wheelchair. Experimental results illustrate that our approach correctly identified all 300 bouts. Compared to existing approaches, our approach was easier to use while achieving similar accuracy in analyzing the accumulated movement time and maximum period of continuous movement (p > 0.8). Overall, the MC system provided a feasible way to ease the data collection process, and generated accurate analysis results for evaluating activity levels. PMID:26479684

Assessment methodology for computer-based instructional simulations.

PubMed

Koenig, Alan; Iseli, Markus; Wainess, Richard; Lee, John J

2013-10-01

Computer-based instructional simulations are becoming more and more ubiquitous, particularly in military and medical domains. As the technology that drives these simulations grows ever more sophisticated, the underlying pedagogical models for how instruction, assessment, and feedback are implemented within these systems must evolve accordingly. In this article, we review some of the existing educational approaches to medical simulations, and present pedagogical methodologies that have been used in the design and development of games and simulations at the University of California, Los Angeles, Center for Research on Evaluation, Standards, and Student Testing. In particular, we present a methodology for how automated assessments of computer-based simulations can be implemented using ontologies and Bayesian networks, and discuss their advantages and design considerations for pedagogical use. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Addressing Failures in Exascale Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snir, Marc; Wisniewski, Robert; Abraham, Jacob

2014-01-01

We present here a report produced by a workshop on Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach. The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, andmore » academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.« less

Object-oriented Approach to High-level Network Monitoring and Management

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi

2000-01-01

An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.

Neuroimaging studies in schizophrenia: an overview of research from Asia.

PubMed

Narayanaswamy, Janardhanan C; Venkatasubramanian, Ganesan; Gangadhar, Bangalore N

2012-10-01

Neuroimaging studies in schizophrenia help clarify the neural substrates underlying the pathogenesis of this neuropsychiatric disorder. Contemporary brain imaging in schizophrenia is predominated by magnetic resonance imaging (MRI)-based research approaches. This review focuses on the various imaging studies from India and their relevance to the understanding of brain abnormalities in schizophrenia. The existing studies are predominantly comprised of structural MRI reports involving region-of-interest and voxel-based morphometry approaches, magnetic resonance spectroscopy and single-photon emission computed tomography/positron emission tomography (SPECT/PET) studies. Most of these studies are significant in that they have evaluated antipsychotic-naïve schizophrenia patients--a relatively difficult population to obtain in contemporary research. Findings of these studies offer robust support to the existence of significant brain abnormalities at very early stages of the disorder. In addition, theoretically relevant relationships between these brain abnormalities and developmental aberrations suggest possible neurodevelopmental basis for these brain deficits.

On Target Localization Using Combined RSS and AoA Measurements

PubMed Central

Beko, Marko; Dinis, Rui

2018-01-01

This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified. PMID:29671832

Computational rationality: linking mechanism and behavior through bounded utility maximization.

PubMed

Lewis, Richard L; Howes, Andrew; Singh, Satinder

2014-04-01

We propose a framework for including information-processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing "levels" approaches to explanation, and to other optimality-based modeling approaches. Copyright © 2014 Cognitive Science Society, Inc.

Computer-Aided Drug Design Methods.

PubMed

Yu, Wenbo; MacKerell, Alexander D

2017-01-01

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

PubMed

Zhang, Cheng; Shahbaba, Babak; Zhao, Hongkai

2017-11-01

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.

An optimization approach for fitting canonical tensor decompositions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dunlavy, Daniel M.; Acar, Evrim; Kolda, Tamara Gibson

Tensor decompositions are higher-order analogues of matrix decompositions and have proven to be powerful tools for data analysis. In particular, we are interested in the canonical tensor decomposition, otherwise known as the CANDECOMP/PARAFAC decomposition (CPD), which expresses a tensor as the sum of component rank-one tensors and is used in a multitude of applications such as chemometrics, signal processing, neuroscience, and web analysis. The task of computing the CPD, however, can be difficult. The typical approach is based on alternating least squares (ALS) optimization, which can be remarkably fast but is not very accurate. Previously, nonlinear least squares (NLS) methodsmore » have also been recommended; existing NLS methods are accurate but slow. In this paper, we propose the use of gradient-based optimization methods. We discuss the mathematical calculation of the derivatives and further show that they can be computed efficiently, at the same cost as one iteration of ALS. Computational experiments demonstrate that the gradient-based optimization methods are much more accurate than ALS and orders of magnitude faster than NLS.« less

Is Model-Based Development a Favorable Approach for Complex and Safety-Critical Computer Systems on Commercial Aircraft?

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2014-01-01

A system is safety-critical if its failure can endanger human life or cause significant damage to property or the environment. State-of-the-art computer systems on commercial aircraft are highly complex, software-intensive, functionally integrated, and network-centric systems of systems. Ensuring that such systems are safe and comply with existing safety regulations is costly and time-consuming as the level of rigor in the development process, especially the validation and verification activities, is determined by considerations of system complexity and safety criticality. A significant degree of care and deep insight into the operational principles of these systems is required to ensure adequate coverage of all design implications relevant to system safety. Model-based development methodologies, methods, tools, and techniques facilitate collaboration and enable the use of common design artifacts among groups dealing with different aspects of the development of a system. This paper examines the application of model-based development to complex and safety-critical aircraft computer systems. Benefits and detriments are identified and an overall assessment of the approach is given.

Design and Evaluation of Fusion Approach for Combining Brain and Gaze Inputs for Target Selection

PubMed Central

Évain, Andéol; Argelaguet, Ferran; Casiez, Géry; Roussel, Nicolas; Lécuyer, Anatole

2016-01-01

Gaze-based interfaces and Brain-Computer Interfaces (BCIs) allow for hands-free human–computer interaction. In this paper, we investigate the combination of gaze and BCIs. We propose a novel selection technique for 2D target acquisition based on input fusion. This new approach combines the probabilistic models for each input, in order to better estimate the intent of the user. We evaluated its performance against the existing gaze and brain–computer interaction techniques. Twelve participants took part in our study, in which they had to search and select 2D targets with each of the evaluated techniques. Our fusion-based hybrid interaction technique was found to be more reliable than the previous gaze and BCI hybrid interaction techniques for 10 participants over 12, while being 29% faster on average. However, similarly to what has been observed in hybrid gaze-and-speech interaction, gaze-only interaction technique still provides the best performance. Our results should encourage the use of input fusion, as opposed to sequential interaction, in order to design better hybrid interfaces. PMID:27774048

Photomorphic analysis techniques: An interim spatial analysis using satellite remote sensor imagery and historical data

NASA Technical Reports Server (NTRS)

Keuper, H. R.; Peplies, R. W.; Gillooly, R. P.

1977-01-01

The use of machine scanning and/or computer-based techniques to provide greater objectivity in the photomorphic approach was investigated. Photomorphic analysis and its application in regional planning are discussed. Topics included: delineation of photomorphic regions; inadequacies of existing classification systems; tonal and textural characteristics and signature analysis techniques; pattern recognition and Fourier transform analysis; and optical experiments. A bibliography is included.

Automatic Generation of Mechanical Assembly Sequences

DTIC Science & Technology

1988-12-01

Planning Algorithm for General Robot Manipulators. In AAAI-86 Proceedings of the F~th National Conference on Artifcial Intelligence , pages 626-631...topic in artificial intelligence , and the Al approach has dominated much of the research in robot task planning using domain-independent methods. The...computed, using the data in the relational model: " The GEOMETRIC-FEASIBILITY predicate which is true if there exists a collision-free path to bring the two

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

Algorithm to solve a chance-constrained network capacity design problem with stochastic demands and finite support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schumacher, Kathryn M.; Chen, Richard Li-Yang; Cohn, Amy E. M.

2016-04-15

Here, we consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real-world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum-cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as amore » foundation, our primary focus is on a chance-constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user-defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut-sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches.« less

Computational Identification of Novel Genes: Current and Future Perspectives.

PubMed

Klasberg, Steffen; Bitard-Feildel, Tristan; Mallet, Ludovic

2016-01-01

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

New algorithms to represent complex pseudoknotted RNA structures in dot-bracket notation.

PubMed

Antczak, Maciej; Popenda, Mariusz; Zok, Tomasz; Zurkowski, Michal; Adamiak, Ryszard W; Szachniuk, Marta

2018-04-15

Understanding the formation, architecture and roles of pseudoknots in RNA structures are one of the most difficult challenges in RNA computational biology and structural bioinformatics. Methods predicting pseudoknots typically perform this with poor accuracy, often despite experimental data incorporation. Existing bioinformatic approaches differ in terms of pseudoknots' recognition and revealing their nature. A few ways of pseudoknot classification exist, most common ones refer to a genus or order. Following the latter one, we propose new algorithms that identify pseudoknots in RNA structure provided in BPSEQ format, determine their order and encode in dot-bracket-letter notation. The proposed encoding aims to illustrate the hierarchy of RNA folding. New algorithms are based on dynamic programming and hybrid (combining exhaustive search and random walk) approaches. They evolved from elementary algorithm implemented within the workflow of RNA FRABASE 1.0, our database of RNA structure fragments. They use different scoring functions to rank dissimilar dot-bracket representations of RNA structure. Computational experiments show an advantage of new methods over the others, especially for large RNA structures. Presented algorithms have been implemented as new functionality of RNApdbee webserver and are ready to use at http://rnapdbee.cs.put.poznan.pl. mszachniuk@cs.put.poznan.pl. Supplementary data are available at Bioinformatics online.

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

PubMed

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

New Ground Truth Capability from InSAR Time Series Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buckley, S; Vincent, P; Yang, D

2005-07-13

We demonstrate that next-generation interferometric synthetic aperture radar (InSAR) processing techniques applied to existing data provide rich InSAR ground truth content for exploitation in seismic source identification. InSAR time series analyses utilize tens of interferograms and can be implemented in different ways. In one such approach, conventional InSAR displacement maps are inverted in a final post-processing step. Alternatively, computationally intensive data reduction can be performed with specialized InSAR processing algorithms. The typical final result of these approaches is a synthesized set of cumulative displacement maps. Examples from our recent work demonstrate that these InSAR processing techniques can provide appealing newmore » ground truth capabilities. We construct movies showing the areal and temporal evolution of deformation associated with previous nuclear tests. In other analyses, we extract time histories of centimeter-scale surface displacement associated with tunneling. The potential exists to identify millimeter per year surface movements when sufficient data exists for InSAR techniques to isolate and remove phase signatures associated with digital elevation model errors and the atmosphere.« less

Estimating Small-area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L

The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age-sex proportion surfaces are separately estimated. Age-sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, andmore » a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age-sex proportion surfaces. To verify the model, age-sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup-count types of variables from information in existing administrative areas for custom-defined areas used as the spatial basis of support in other applications.« less

The software for automatic creation of the formal grammars used by speech recognition, computer vision, editable text conversion systems, and some new functions

NASA Astrophysics Data System (ADS)

Kardava, Irakli; Tadyszak, Krzysztof; Gulua, Nana; Jurga, Stefan

2017-02-01

For more flexibility of environmental perception by artificial intelligence it is needed to exist the supporting software modules, which will be able to automate the creation of specific language syntax and to make a further analysis for relevant decisions based on semantic functions. According of our proposed approach, of which implementation it is possible to create the couples of formal rules of given sentences (in case of natural languages) or statements (in case of special languages) by helping of computer vision, speech recognition or editable text conversion system for further automatic improvement. In other words, we have developed an approach, by which it can be achieved to significantly improve the training process automation of artificial intelligence, which as a result will give us a higher level of self-developing skills independently from us (from users). At the base of our approach we have developed a software demo version, which includes the algorithm and software code for the entire above mentioned component's implementation (computer vision, speech recognition and editable text conversion system). The program has the ability to work in a multi - stream mode and simultaneously create a syntax based on receiving information from several sources.

Research on the digital education resources of sharing pattern in independent colleges based on cloud computing environment

NASA Astrophysics Data System (ADS)

Xiong, Ting; He, Zhiwen

2017-06-01

Cloud computing was first proposed by Google Company in the United States, which was based on the Internet center, providing a standard and open network sharing service approach. With the rapid development of the higher education in China, the educational resources provided by colleges and universities had greatly gap in the actual needs of teaching resources. therefore, Cloud computing of using the Internet technology to provide shared methods liked the timely rain, which had become an important means of the Digital Education on sharing applications in the current higher education. Based on Cloud computing environment, the paper analyzed the existing problems about the sharing of digital educational resources in Jiangxi Province Independent Colleges. According to the sharing characteristics of mass storage, efficient operation and low input about Cloud computing, the author explored and studied the design of the sharing model about the digital educational resources of higher education in Independent College. Finally, the design of the shared model was put into the practical applications.

The snow system: A decentralized medical data processing system.

PubMed

Bellika, Johan Gustav; Henriksen, Torje Starbo; Yigzaw, Kassaye Yitbarek

2015-01-01

Systems for large-scale reuse of electronic health record data is claimed to have the potential to transform the current health care delivery system. In principle three alternative solutions for reuse exist: centralized, data warehouse, and decentralized solutions. This chapter focuses on the decentralized system alternative. Decentralized systems may be categorized into approaches that move data to enable computations or move computations to the where data is located to enable computations. We describe a system that moves computations to where the data is located. Only this kind of decentralized solution has the capabilities to become ideal systems for reuse as the decentralized alternative enables computation and reuse of electronic health record data without moving or exposing the information to outsiders. This chapter describes the Snow system, which is a decentralized medical data processing system, its components and how it has been used. It also describes the requirements this kind of systems need to support to become sustainable and successful in recruiting voluntary participation from health institutions.

Extrinsic Calibration of Camera Networks Based on Pedestrians

PubMed Central

Guan, Junzhi; Deboeverie, Francis; Slembrouck, Maarten; Van Haerenborgh, Dirk; Van Cauwelaert, Dimitri; Veelaert, Peter; Philips, Wilfried

2016-01-01

In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life. PMID:27171080

Defining Computational Thinking for Mathematics and Science Classrooms

NASA Astrophysics Data System (ADS)

Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri

2016-02-01

Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

PubMed Central

2014-01-01

Background Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers. Results We propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins. Conclusions The combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines. PMID:24843788

Computer vision based nacre thickness measurement of Tahitian pearls

NASA Astrophysics Data System (ADS)

Loesdau, Martin; Chabrier, Sébastien; Gabillon, Alban

2017-03-01

The Tahitian Pearl is the most valuable export product of French Polynesia contributing with over 61 million Euros to more than 50% of the total export income. To maintain its excellent reputation on the international market, an obligatory quality control for every pearl deemed for exportation has been established by the local government. One of the controlled quality parameters is the pearls nacre thickness. The evaluation is currently done manually by experts that are visually analyzing X-ray images of the pearls. In this article, a computer vision based approach to automate this procedure is presented. Even though computer vision based approaches for pearl nacre thickness measurement exist in the literature, the very specific features of the Tahitian pearl, namely the large shape variety and the occurrence of cavities, have so far not been considered. The presented work closes the. Our method consists of segmenting the pearl from X-ray images with a model-based approach, segmenting the pearls nucleus with an own developed heuristic circle detection and segmenting possible cavities with region growing. Out of the obtained boundaries, the 2-dimensional nacre thickness profile can be calculated. A certainty measurement to consider imaging and segmentation imprecisions is included in the procedure. The proposed algorithms are tested on 298 manually evaluated Tahitian pearls, showing that it is generally possible to automatically evaluate the nacre thickness of Tahitian pearls with computer vision. Furthermore the results show that the automatic measurement is more precise and faster than the manual one.

A computer simulation approach to quantify the true area and true area compressibility modulus of biological membranes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chacón, Enrique, E-mail: echacon@icmm.csic.es; Tarazona, Pedro, E-mail: pedro.tarazona@uam.es; Bresme, Fernando, E-mail: f.bresme@imperial.ac.uk

We present a new computational approach to quantify the area per lipid and the area compressibility modulus of biological membranes. Our method relies on the analysis of the membrane fluctuations using our recently introduced coupled undulatory (CU) mode [Tarazona et al., J. Chem. Phys. 139, 094902 (2013)], which provides excellent estimates of the bending modulus of model membranes. Unlike the projected area, widely used in computer simulations of membranes, the CU area is thermodynamically consistent. This new area definition makes it possible to accurately estimate the area of the undulating bilayer, and the area per lipid, by excluding any contributionsmore » related to the phospholipid protrusions. We find that the area per phospholipid and the area compressibility modulus features a negligible dependence with system size, making possible their computation using truly small bilayers, involving a few hundred lipids. The area compressibility modulus obtained from the analysis of the CU area fluctuations is fully consistent with the Hooke’s law route. Unlike existing methods, our approach relies on a single simulation, and no a priori knowledge of the bending modulus is required. We illustrate our method by analyzing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayers using the coarse grained MARTINI force-field. The area per lipid and area compressibility modulus obtained with our method and the MARTINI forcefield are consistent with previous studies of these bilayers.« less

Laser Spot Tracking Based on Modified Circular Hough Transform and Motion Pattern Analysis

PubMed Central

Krstinić, Damir; Skelin, Ana Kuzmanić; Milatić, Ivan

2014-01-01

Laser pointers are one of the most widely used interactive and pointing devices in different human-computer interaction systems. Existing approaches to vision-based laser spot tracking are designed for controlled indoor environments with the main assumption that the laser spot is very bright, if not the brightest, spot in images. In this work, we are interested in developing a method for an outdoor, open-space environment, which could be implemented on embedded devices with limited computational resources. Under these circumstances, none of the assumptions of existing methods for laser spot tracking can be applied, yet a novel and fast method with robust performance is required. Throughout the paper, we will propose and evaluate an efficient method based on modified circular Hough transform and Lucas–Kanade motion analysis. Encouraging results on a representative dataset demonstrate the potential of our method in an uncontrolled outdoor environment, while achieving maximal accuracy indoors. Our dataset and ground truth data are made publicly available for further development. PMID:25350502

Stability basin estimates fall risk from observed kinematics, demonstrated on the Sit-to-Stand task.

PubMed

Shia, Victor; Moore, Talia Yuki; Holmes, Patrick; Bajcsy, Ruzena; Vasudevan, Ram

2018-04-27

The ability to quantitatively measure stability is essential to ensuring the safety of locomoting systems. While the response to perturbation directly reflects the stability of a motion, this experimental method puts human subjects at risk. Unfortunately, existing indirect methods for estimating stability from unperturbed motion have been shown to have limited predictive power. This paper leverages recent advances in dynamical systems theory to accurately estimate the stability of human motion without requiring perturbation. This approach relies on kinematic observations of a nominal Sit-to-Stand motion to construct an individual-specific dynamic model, input bounds, and feedback control that are then used to compute the set of perturbations from which the model can recover. This set, referred to as the stability basin, was computed for 14 individuals, and was able to successfully differentiate between less and more stable Sit-to-Stand strategies for each individual with greater accuracy than existing methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Software Architecture for a Virtual Environment for Nano Scale Assembly (VENSA).

PubMed

Lee, Yong-Gu; Lyons, Kevin W; Feng, Shaw C

2004-01-01

A Virtual Environment (VE) uses multiple computer-generated media to let a user experience situations that are temporally and spatially prohibiting. The information flow between the user and the VE is bidirectional and the user can influence the environment. The software development of a VE requires orchestrating multiple peripherals and computers in a synchronized way in real time. Although a multitude of useful software components for VEs exists, many of these are packaged within a complex framework and can not be used separately. In this paper, an architecture is presented which is designed to let multiple frameworks work together while being shielded from the application program. This architecture, which is called the Virtual Environment for Nano Scale Assembly (VENSA), has been constructed for interfacing with an optical tweezers instrument for nanotechnology development. However, this approach can be generalized for most virtual environments. Through the use of VENSA, the programmer can rely on existing solutions and concentrate more on the application software design.

Brian hears: online auditory processing using vectorization over channels.

PubMed

Fontaine, Bertrand; Goodman, Dan F M; Benichoux, Victor; Brette, Romain

2011-01-01

The human cochlea includes about 3000 inner hair cells which filter sounds at frequencies between 20 Hz and 20 kHz. This massively parallel frequency analysis is reflected in models of auditory processing, which are often based on banks of filters. However, existing implementations do not exploit this parallelism. Here we propose algorithms to simulate these models by vectorizing computation over frequency channels, which are implemented in "Brian Hears," a library for the spiking neural network simulator package "Brian." This approach allows us to use high-level programming languages such as Python, because with vectorized operations, the computational cost of interpretation represents a small fraction of the total cost. This makes it possible to define and simulate complex models in a simple way, while all previous implementations were model-specific. In addition, we show that these algorithms can be naturally parallelized using graphics processing units, yielding substantial speed improvements. We demonstrate these algorithms with several state-of-the-art cochlear models, and show that they compare favorably with existing, less flexible, implementations.

Software Architecture for a Virtual Environment for Nano Scale Assembly (VENSA)

PubMed Central

Lee, Yong-Gu; Lyons, Kevin W.; Feng, Shaw C.

2004-01-01

A Virtual Environment (VE) uses multiple computer-generated media to let a user experience situations that are temporally and spatially prohibiting. The information flow between the user and the VE is bidirectional and the user can influence the environment. The software development of a VE requires orchestrating multiple peripherals and computers in a synchronized way in real time. Although a multitude of useful software components for VEs exists, many of these are packaged within a complex framework and can not be used separately. In this paper, an architecture is presented which is designed to let multiple frameworks work together while being shielded from the application program. This architecture, which is called the Virtual Environment for Nano Scale Assembly (VENSA), has been constructed for interfacing with an optical tweezers instrument for nanotechnology development. However, this approach can be generalized for most virtual environments. Through the use of VENSA, the programmer can rely on existing solutions and concentrate more on the application software design. PMID:27366610

Laser spot tracking based on modified circular Hough transform and motion pattern analysis.

PubMed

Krstinić, Damir; Skelin, Ana Kuzmanić; Milatić, Ivan

2014-10-27

Laser pointers are one of the most widely used interactive and pointing devices in different human-computer interaction systems. Existing approaches to vision-based laser spot tracking are designed for controlled indoor environments with the main assumption that the laser spot is very bright, if not the brightest, spot in images. In this work, we are interested in developing a method for an outdoor, open-space environment, which could be implemented on embedded devices with limited computational resources. Under these circumstances, none of the assumptions of existing methods for laser spot tracking can be applied, yet a novel and fast method with robust performance is required. Throughout the paper, we will propose and evaluate an efficient method based on modified circular Hough transform and Lucas-Kanade motion analysis. Encouraging results on a representative dataset demonstrate the potential of our method in an uncontrolled outdoor environment, while achieving maximal accuracy indoors. Our dataset and ground truth data are made publicly available for further development.

Incorporating gene-environment interaction in testing for association with rare genetic variants.

PubMed

Chen, Han; Meigs, James B; Dupuis, Josée

2014-01-01

The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants. In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions. We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study. Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient.

Evaluation and integration of existing methods for computational prediction of allergens

PubMed Central

2013-01-01

Background Allergy involves a series of complex reactions and factors that contribute to the development of the disease and triggering of the symptoms, including rhinitis, asthma, atopic eczema, skin sensitivity, even acute and fatal anaphylactic shock. Prediction and evaluation of the potential allergenicity is of importance for safety evaluation of foods and other environment factors. Although several computational approaches for assessing the potential allergenicity of proteins have been developed, their performance and relative merits and shortcomings have not been compared systematically. Results To evaluate and improve the existing methods for allergen prediction, we collected an up-to-date definitive dataset consisting of 989 known allergens and massive putative non-allergens. The three most widely used allergen computational prediction approaches including sequence-, motif- and SVM-based (Support Vector Machine) methods were systematically compared using the defined parameters and we found that SVM-based method outperformed the other two methods with higher accuracy and specificity. The sequence-based method with the criteria defined by FAO/WHO (FAO: Food and Agriculture Organization of the United Nations; WHO: World Health Organization) has higher sensitivity of over 98%, but having a low specificity. The advantage of motif-based method is the ability to visualize the key motif within the allergen. Notably, the performances of the sequence-based method defined by FAO/WHO and motif eliciting strategy could be improved by the optimization of parameters. To facilitate the allergen prediction, we integrated these three methods in a web-based application proAP, which provides the global search of the known allergens and a powerful tool for allergen predication. Flexible parameter setting and batch prediction were also implemented. The proAP can be accessed at http://gmobl.sjtu.edu.cn/proAP/main.html. Conclusions This study comprehensively evaluated sequence-, motif- and SVM-based computational prediction approaches for allergens and optimized their parameters to obtain better performance. These findings may provide helpful guidance for the researchers in allergen-prediction. Furthermore, we integrated these methods into a web application proAP, greatly facilitating users to do customizable allergen search and prediction. PMID:23514097

Evaluation and integration of existing methods for computational prediction of allergens.

PubMed

Wang, Jing; Yu, Yabin; Zhao, Yunan; Zhang, Dabing; Li, Jing

2013-01-01

Allergy involves a series of complex reactions and factors that contribute to the development of the disease and triggering of the symptoms, including rhinitis, asthma, atopic eczema, skin sensitivity, even acute and fatal anaphylactic shock. Prediction and evaluation of the potential allergenicity is of importance for safety evaluation of foods and other environment factors. Although several computational approaches for assessing the potential allergenicity of proteins have been developed, their performance and relative merits and shortcomings have not been compared systematically. To evaluate and improve the existing methods for allergen prediction, we collected an up-to-date definitive dataset consisting of 989 known allergens and massive putative non-allergens. The three most widely used allergen computational prediction approaches including sequence-, motif- and SVM-based (Support Vector Machine) methods were systematically compared using the defined parameters and we found that SVM-based method outperformed the other two methods with higher accuracy and specificity. The sequence-based method with the criteria defined by FAO/WHO (FAO: Food and Agriculture Organization of the United Nations; WHO: World Health Organization) has higher sensitivity of over 98%, but having a low specificity. The advantage of motif-based method is the ability to visualize the key motif within the allergen. Notably, the performances of the sequence-based method defined by FAO/WHO and motif eliciting strategy could be improved by the optimization of parameters. To facilitate the allergen prediction, we integrated these three methods in a web-based application proAP, which provides the global search of the known allergens and a powerful tool for allergen predication. Flexible parameter setting and batch prediction were also implemented. The proAP can be accessed at http://gmobl.sjtu.edu.cn/proAP/main.html. This study comprehensively evaluated sequence-, motif- and SVM-based computational prediction approaches for allergens and optimized their parameters to obtain better performance. These findings may provide helpful guidance for the researchers in allergen-prediction. Furthermore, we integrated these methods into a web application proAP, greatly facilitating users to do customizable allergen search and prediction.

Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes.

PubMed

Chen, Yang; Gao, Zhen; Wang, Bingcheng; Xu, Rong

2016-08-22

Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM. We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data. We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells. We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strout, Michelle

Programming parallel machines is fraught with difficulties: the obfuscation of algorithms due to implementation details such as communication and synchronization, the need for transparency between language constructs and performance, the difficulty of performing program analysis to enable automatic parallelization techniques, and the existence of important "dusty deck" codes. The SAIMI project developed abstractions that enable the orthogonal specification of algorithms and implementation details within the context of existing DOE applications. The main idea is to enable the injection of small programming models such as expressions involving transcendental functions, polyhedral iteration spaces with sparse constraints, and task graphs into full programsmore » through the use of pragmas. These smaller, more restricted programming models enable orthogonal specification of many implementation details such as how to map the computation on to parallel processors, how to schedule the computation, and how to allocation storage for the computation. At the same time, these small programming models enable the expression of the most computationally intense and communication heavy portions in many scientific simulations. The ability to orthogonally manipulate the implementation for such computations will significantly ease performance programming efforts and expose transformation possibilities and parameter to automated approaches such as autotuning. At Colorado State University, the SAIMI project was supported through DOE grant DE-SC3956 from April 2010 through August 2015. The SAIMI project has contributed a number of important results to programming abstractions that enable the orthogonal specification of implementation details in scientific codes. This final report summarizes the research that was funded by the SAIMI project.« less

Multiple neural network approaches to clinical expert systems

NASA Astrophysics Data System (ADS)

Stubbs, Derek F.

1990-08-01

We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

Flight Test Comparison of Synthetic Vision Display Concepts at Dallas/Fort Worth International Airport

NASA Technical Reports Server (NTRS)

Glaab, Louis J.; Kramer, Lynda J.; Arthur, Trey; Parrish, Russell V.; Barry, John S.

2003-01-01

Limited visibility is the single most critical factor affecting the safety and capacity of worldwide aviation operations. Synthetic Vision Systems (SVS) technology can solve this visibility problem with a visibility solution. These displays employ computer-generated terrain imagery to present 3D, perspective out-the-window scenes with sufficient information and realism to enable operations equivalent to those of a bright, clear day, regardless of weather conditions. To introduce SVS display technology into as many existing aircraft as possible, a retrofit approach was defined that employs existing HDD display capabilities for glass cockpits and HUD capabilities for the other aircraft. This retrofit approach was evaluated for typical nighttime airline operations at a major international airport. Overall, 6 evaluation pilots performed 75 research approaches, accumulating 18 hours flight time evaluating SVS display concepts that used the NASA LaRC's Boeing B-757-200 aircraft at Dallas/Fort Worth International Airport. Results from this flight test establish the SVS retrofit concept, regardless of display size, as viable for tested conditions. Future assessments need to extend evaluation of the approach to operations in an appropriate, terrain-challenged environment with daytime test conditions.

Measurement of motion detection of wireless capsule endoscope inside large intestine.

PubMed

Zhou, Mingda; Bao, Guanqun; Pahlavan, Kaveh

2014-01-01

Wireless Capsule Endoscope (WCE) provides a noninvasive way to inspect the entire Gastrointestinal (GI) tract, including large intestine, where intestinal diseases most likely occur. As a critical component of capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of detected intestinal diseases. Knowing how the capsule moves inside the large intestine would greatly complement the existing wireless localization systems by providing the motion information. Since the most recently released WCE can take up to 6 frames per second, it's possible to estimate the movement of the capsule by processing the successive image sequence. In this paper, a computer vision based approach without utilizing any external device is proposed to estimate the motion of WCE inside the large intestine. The proposed approach estimate the displacement and rotation of the capsule by calculating entropy and mutual information between frames using Fibonacci method. The obtained results of this approach show its stability and better performance over other existing approaches of motion measurements. Meanwhile, findings of this paper lay a foundation for motion pattern of WCEs inside the large intestine, which will benefit other medical applications.

Fluid-structure interaction including volumetric coupling with homogenised subdomains for modeling respiratory mechanics.

PubMed

Yoshihara, Lena; Roth, Christian J; Wall, Wolfgang A

2017-04-01

In this article, a novel approach is presented for combining standard fluid-structure interaction with additional volumetric constraints to model fluid flow into and from homogenised solid domains. The proposed algorithm is particularly interesting for investigations in the field of respiratory mechanics as it enables the mutual coupling of airflow in the conducting part and local tissue deformation in the respiratory part of the lung by means of a volume constraint. In combination with a classical monolithic fluid-structure interaction approach, a comprehensive model of the human lung can be established that will be useful to gain new insights into respiratory mechanics in health and disease. To illustrate the validity and versatility of the novel approach, three numerical examples including a patient-specific lung model are presented. The proposed algorithm proves its capability of computing clinically relevant airflow distribution and tissue strain data at a level of detail that is not yet achievable, neither with current imaging techniques nor with existing computational models. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of designed ligands by a multiple screening method: Application to glycogen phosphorylase inhibitors constructed with a variety of approaches

NASA Astrophysics Data System (ADS)

So, Sung-Sau; Karplus, Martin

2001-07-01

Glycogen phosphorylase (GP) is an important enzyme that regulates blood glucose level and a key therapeutic target for the treatment of type II diabetes. In this study, a number of potential GP inhibitors are designed with a variety of computational approaches. They include the applications of MCSS, LUDI and CoMFA to identify additional fragments that can be attached to existing lead molecules; the use of 2D and 3D similarity-based QSAR models (HQSAR and SMGNN) and of the LUDI program to identify novel molecules that may bind to the glucose binding site. The designed ligands are evaluated by a multiple screening method, which is a combination of commercial and in-house ligand-receptor binding affinity prediction programs used in a previous study (So and Karplus, J. Comp.-Aid. Mol. Des., 13 (1999), 243-258). Each method is used at an appropriate point in the screening, as determined by both the accuracy of the calculations and the computational cost. A comparison of the strengths and weaknesses of the ligand design approaches is made.

Comparison of measured temperatures, thermal stresses and creep residues with predictions on a built-up titanium structure

NASA Technical Reports Server (NTRS)

Jenkins, Jerald M.

1987-01-01

Temperature, thermal stresses, and residual creep stresses were studied by comparing laboratory values measured on a built-up titanium structure with values calculated from finite-element models. Several such models were used to examine the relationship between computational thermal stresses and thermal stresses measured on a built-up structure. Element suitability, element density, and computational temperature discrepancies were studied to determine their impact on measured and calculated thermal stress. The optimum number of elements is established from a balance between element density and suitable safety margins, such that the answer is acceptably safe yet is economical from a computational viewpoint. It is noted that situations exist where relatively small excursions of calculated temperatures from measured values result in far more than proportional increases in thermal stress values. Measured residual stresses due to creep significantly exceeded the values computed by the piecewise linear elastic strain analogy approach. The most important element in the computation is the correct definition of the creep law. Computational methodology advances in predicting residual stresses due to creep require significantly more viscoelastic material characterization.

DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lucas, Robert; Ang, James; Bergman, Keren

2014-02-10

Exascale computing systems are essential for the scientific fields that will transform the 21st century global economy, including energy, biotechnology, nanotechnology, and materials science. Progress in these fields is predicated on the ability to perform advanced scientific and engineering simulations, and analyze the deluge of data. On July 29, 2013, ASCAC was charged by Patricia Dehmer, the Acting Director of the Office of Science, to assemble a subcommittee to provide advice on exascale computing. This subcommittee was directed to return a list of no more than ten technical approaches (hardware and software) that will enable the development of a systemmore » that achieves the Department's goals for exascale computing. Numerous reports over the past few years have documented the technical challenges and the non¬-viability of simply scaling existing computer designs to reach exascale. The technical challenges revolve around energy consumption, memory performance, resilience, extreme concurrency, and big data. Drawing from these reports and more recent experience, this ASCAC subcommittee has identified the top ten computing technology advancements that are critical to making a capable, economically viable, exascale system.« less

Critical phenomena in communication/computation networks with various topologies and suboptimal to optimal resource allocation

NASA Astrophysics Data System (ADS)

Cogoni, Marco; Busonera, Giovanni; Anedda, Paolo; Zanetti, Gianluigi

2015-01-01

We generalize previous studies on critical phenomena in communication networks [1,2] by adding computational capabilities to the nodes. In our model, a set of tasks with random origin, destination and computational structure is distributed on a computational network, modeled as a graph. By varying the temperature of a Metropolis Montecarlo, we explore the global latency for an optimal to suboptimal resource assignment at a given time instant. By computing the two-point correlation function for the local overload, we study the behavior of the correlation distance (both for links and nodes) while approaching the congested phase: a transition from peaked to spread g(r) is seen above a critical (Montecarlo) temperature Tc. The average latency trend of the system is predicted by averaging over several network traffic realizations while maintaining a spatially detailed information for each node: a sharp decrease of performance is found over Tc independently of the workload. The globally optimized computational resource allocation and network routing defines a baseline for a future comparison of the transition behavior with respect to existing routing strategies [3,4] for different network topologies.

Estimation of cardiac conductivities in ventricular tissue by a variational approach

NASA Astrophysics Data System (ADS)

Yang, Huanhuan; Veneziani, Alessandro

2015-11-01

The bidomain model is the current standard model to simulate cardiac potential propagation. The numerical solution of this system of partial differential equations strongly depends on the model parameters and in particular on the cardiac conductivities. Unfortunately, it is quite problematic to measure these parameters in vivo and even more so in clinical practice, resulting in no common agreement in the literature. In this paper we consider a variational data assimilation approach to estimating those parameters. We consider the parameters as control variables to minimize the mismatch between the computed and the measured potentials under the constraint of the bidomain system. The existence of a minimizer of the misfit function is proved with the phenomenological Rogers-McCulloch ionic model, that completes the bidomain system. We significantly improve the numerical approaches in the literature by resorting to a derivative-based optimization method with settlement of some challenges due to discontinuity. The improvement in computational efficiency is confirmed by a 2D test as a direct comparison with approaches in the literature. The core of our numerical results is in 3D, on both idealized and real geometries, with the minimal ionic model. We demonstrate the reliability and the stability of the conductivity estimation approach in the presence of noise and with an imperfect knowledge of other model parameters.

An analytical approach for the simulation of flow in a heterogeneous confined aquifer with a parameter zonation structure

NASA Astrophysics Data System (ADS)

Huang, Ching-Sheng; Yeh, Hund-Der

2016-11-01

This study introduces an analytical approach to estimate drawdown induced by well extraction in a heterogeneous confined aquifer with an irregular outer boundary. The aquifer domain is divided into a number of zones according to the zonation method for representing the spatial distribution of a hydraulic parameter field. The lateral boundary of the aquifer can be considered under the Dirichlet, Neumann or Robin condition at different parts of the boundary. Flow across the interface between two zones satisfies the continuities of drawdown and flux. Source points, each of which has an unknown volumetric rate representing the boundary effect on the drawdown, are allocated around the boundary of each zone. The solution of drawdown in each zone is expressed as a series in terms of the Theis equation with unknown volumetric rates from the source points. The rates are then determined based on the aquifer boundary conditions and the continuity requirements. The estimated aquifer drawdown by the present approach agrees well with a finite element solution developed based on the Mathematica function NDSolve. As compared with the existing numerical approaches, the present approach has a merit of directly computing the drawdown at any given location and time and therefore takes much less computing time to obtain the required results in engineering applications.

The impact of computer-based versus "traditional" textbook science instruction on selected student learning outcomes

NASA Astrophysics Data System (ADS)

Rothman, Alan H.

This study reports the results of research designed to examine the impact of computer-based science instruction on elementary school level students' science content achievement, their attitude about science learning, their level of critical thinking-inquiry skills, and their level of cognitive and English language development. The study compared these learning outcomes resulting from a computer-based approach compared to the learning outcomes from a traditional, textbook-based approach to science instruction. The computer-based approach was inherent in a curriculum titled The Voyage of the Mimi , published by The Bank Street College Project in Science and Mathematics (1984). The study sample included 209 fifth-grade students enrolled in three schools in a suburban school district. This sample was divided into three groups, each receiving one of the following instructional treatments: (a) Mixed-instruction primarily based on the use of a hardcopy textbook in conjunction with computer-based instructional materials as one component of the science course; (b) Non-Traditional, Technology-Based -instruction fully utilizing computer-based material; and (c) Traditional, Textbook-Based-instruction utilizing only the textbook as the basis for instruction. Pre-test, or pre-treatment, data related to each of the student learning outcomes was collected at the beginning of the school year and post-test data was collected at the end of the school year. Statistical analyses of pre-test data were used as a covariate to account for possible pre-existing differences with regard to the variables examined among the three student groups. This study concluded that non-traditional, computer-based instruction in science significantly improved students' attitudes toward science learning and their level of English language development. Non-significant, positive trends were found for the following student learning outcomes: overall science achievement and development of critical thinking-inquiry skills. These conclusions support the value of a non-traditional, computer-based approach to instruction, such as exemplified by The Voyage of the Mimi curriculum, and a recommendation for reform in science teaching that has recommended the use of computer technology to enhance learning outcomes from science instruction to assist in reversing the trend toward what has been perceived to be relatively poor science performance by American students, as documented by the 1996 Third International Mathematics and Science Study (TIMSS).

An optimization approach for observation association with systemic uncertainty applied to electro-optical systems

NASA Astrophysics Data System (ADS)

Worthy, Johnny L.; Holzinger, Marcus J.; Scheeres, Daniel J.

2018-06-01

The observation to observation measurement association problem for dynamical systems can be addressed by determining if the uncertain admissible regions produced from each observation have one or more points of intersection in state space. An observation association method is developed which uses an optimization based approach to identify local Mahalanobis distance minima in state space between two uncertain admissible regions. A binary hypothesis test with a selected false alarm rate is used to assess the probability that an intersection exists at the point(s) of minimum distance. The systemic uncertainties, such as measurement uncertainties, timing errors, and other parameter errors, define a distribution about a state estimate located at the local Mahalanobis distance minima. If local minima do not exist, then the observations are not associated. The proposed method utilizes an optimization approach defined on a reduced dimension state space to reduce the computational load of the algorithm. The efficacy and efficiency of the proposed method is demonstrated on observation data collected from the Georgia Tech Space Object Research Telescope.

A review of combined experimental and computational procedures for assessing biopolymer structure–process–property relationships

PubMed Central

Gronau, Greta; Krishnaji, Sreevidhya T.; Kinahan, Michelle E.; Giesa, Tristan; Wong, Joyce Y.; Kaplan, David L.; Buehler, Markus J.

2013-01-01

Tailored biomaterials with tunable functional properties are desirable for many applications ranging from drug delivery to regenerative medicine. To improve the predictability of biopolymer materials functionality, multiple design parameters need to be considered, along with appropriate models. In this article we review the state of the art of synthesis and processing related to the design of biopolymers, with an emphasis on the integration of bottom-up computational modeling in the design process. We consider three prominent examples of well-studied biopolymer materials – elastin, silk, and collagen – and assess their hierarchical structure, intriguing functional properties and categorize existing approaches to study these materials. We find that an integrated design approach in which both experiments and computational modeling are used has rarely been applied for these materials due to difficulties in relating insights gained on different length- and time-scales. In this context, multiscale engineering offers a powerful means to accelerate the biomaterials design process for the development of tailored materials that suit the needs posed by the various applications. The combined use of experimental and computational tools has a very broad applicability not only in the field of biopolymers, but can be exploited to tailor the properties of other polymers and composite materials in general. PMID:22938765

A review of combined experimental and computational procedures for assessing biopolymer structure-process-property relationships.

PubMed

Gronau, Greta; Krishnaji, Sreevidhya T; Kinahan, Michelle E; Giesa, Tristan; Wong, Joyce Y; Kaplan, David L; Buehler, Markus J

2012-11-01

Tailored biomaterials with tunable functional properties are desirable for many applications ranging from drug delivery to regenerative medicine. To improve the predictability of biopolymer materials functionality, multiple design parameters need to be considered, along with appropriate models. In this article we review the state of the art of synthesis and processing related to the design of biopolymers, with an emphasis on the integration of bottom-up computational modeling in the design process. We consider three prominent examples of well-studied biopolymer materials - elastin, silk, and collagen - and assess their hierarchical structure, intriguing functional properties and categorize existing approaches to study these materials. We find that an integrated design approach in which both experiments and computational modeling are used has rarely been applied for these materials due to difficulties in relating insights gained on different length- and time-scales. In this context, multiscale engineering offers a powerful means to accelerate the biomaterials design process for the development of tailored materials that suit the needs posed by the various applications. The combined use of experimental and computational tools has a very broad applicability not only in the field of biopolymers, but can be exploited to tailor the properties of other polymers and composite materials in general. Copyright © 2012 Elsevier Ltd. All rights reserved.

A depth-first search algorithm to compute elementary flux modes by linear programming.

PubMed

Quek, Lake-Ee; Nielsen, Lars K

2014-07-30

The decomposition of complex metabolic networks into elementary flux modes (EFMs) provides a useful framework for exploring reaction interactions systematically. Generating a complete set of EFMs for large-scale models, however, is near impossible. Even for moderately-sized models (<400 reactions), existing approaches based on the Double Description method must iterate through a large number of combinatorial candidates, thus imposing an immense processor and memory demand. Based on an alternative elementarity test, we developed a depth-first search algorithm using linear programming (LP) to enumerate EFMs in an exhaustive fashion. Constraints can be introduced to directly generate a subset of EFMs satisfying the set of constraints. The depth-first search algorithm has a constant memory overhead. Using flux constraints, a large LP problem can be massively divided and parallelized into independent sub-jobs for deployment into computing clusters. Since the sub-jobs do not overlap, the approach scales to utilize all available computing nodes with minimal coordination overhead or memory limitations. The speed of the algorithm was comparable to efmtool, a mainstream Double Description method, when enumerating all EFMs; the attrition power gained from performing flux feasibility tests offsets the increased computational demand of running an LP solver. Unlike the Double Description method, the algorithm enables accelerated enumeration of all EFMs satisfying a set of constraints.

In silico polypharmacology of natural products.

PubMed

Fang, Jiansong; Liu, Chuang; Wang, Qi; Lin, Ping; Cheng, Feixiong

2017-04-27

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: Challenges, opportunities, and research needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burgess-Herbert, Sarah L., E-mail: sarah.burgess@alum.mit.edu; Euling, Susan Y.

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionarymore » biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.« less

An Multivariate Distance-Based Analytic Framework for Connectome-Wide Association Studies

PubMed Central

Shehzad, Zarrar; Kelly, Clare; Reiss, Philip T.; Craddock, R. Cameron; Emerson, John W.; McMahon, Katie; Copland, David A.; Castellanos, F. Xavier; Milham, Michael P.

2014-01-01

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome. PMID:24583255

Space Network Control Conference on Resource Allocation Concepts and Approaches

NASA Technical Reports Server (NTRS)

Moe, Karen L. (Editor)

1991-01-01

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Hierarchical detection of red lesions in retinal images by multiscale correlation filtering

NASA Astrophysics Data System (ADS)

Zhang, Bob; Wu, Xiangqian; You, Jane; Li, Qin; Karray, Fakhri

2009-02-01

This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge (ROC) competition website and we conclude our method to be effective and efficient.

On the heteroclinic connection problem for multi-well gradient systems

NASA Astrophysics Data System (ADS)

Zuniga, Andres; Sternberg, Peter

2016-10-01

We revisit the existence problem of heteroclinic connections in RN associated with Hamiltonian systems involving potentials W :RN → R having several global minima. Under very mild assumptions on W we present a simple variational approach to first find geodesics minimizing length of curves joining any two of the potential wells, where length is computed with respect to a degenerate metric having conformal factor √{ W}. Then we show that when such a minimizing geodesic avoids passing through other wells of the potential at intermediate times, it gives rise to a heteroclinic connection between the two wells. This work improves upon the approach of [22] and represents a more geometric alternative to the approaches of e.g. [5,10,14,17] for finding such connections.

Safe Onboard Guidance and Control Under Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Blackmore, Lars James

2011-01-01

An algorithm was developed that determines the fuel-optimal spacecraft guidance trajectory that takes into account uncertainty, in order to guarantee that mission safety constraints are satisfied with the required probability. The algorithm uses convex optimization to solve for the optimal trajectory. Convex optimization is amenable to onboard solution due to its excellent convergence properties. The algorithm is novel because, unlike prior approaches, it does not require time-consuming evaluation of multivariate probability densities. Instead, it uses a new mathematical bounding approach to ensure that probability constraints are satisfied, and it is shown that the resulting optimization is convex. Empirical results show that the approach is many orders of magnitude less conservative than existing set conversion techniques, for a small penalty in computation time.

Quantum Chemical Approach to Estimating the Thermodynamics of Metabolic Reactions

PubMed Central

Jinich, Adrian; Rappoport, Dmitrij; Dunn, Ian; Sanchez-Lengeling, Benjamin; Olivares-Amaya, Roberto; Noor, Elad; Even, Arren Bar; Aspuru-Guzik, Alán

2014-01-01

Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism. PMID:25387603

View synthesis using parallax invariance

NASA Astrophysics Data System (ADS)

Dornaika, Fadi

2001-06-01

View synthesis becomes a focus of attention of both the computer vision and computer graphics communities. It consists of creating novel images of a scene as it would appear from novel viewpoints. View synthesis can be used in a wide variety of applications such as video compression, graphics generation, virtual reality and entertainment. This paper addresses the following problem. Given a dense disparity map between two reference images, we would like to synthesize a novel view of the same scene associated with a novel viewpoint. Most of the existing work is relying on building a set of 3D meshes which are then projected onto the new image (the rendering process is performed using texture mapping). The advantages of our view synthesis approach are as follows. First, the novel view is specified by a rotation and a translation which are the most natural way to express the virtual location of the camera. Second, the approach is able to synthesize highly realistic images whose viewing position is significantly far away from the reference viewpoints. Third, the approach is able to handle the visibility problem during the synthesis process. Our developed framework has two main steps. The first step (analysis step) consists of computing the homography at infinity, the epipoles, and thus the parallax field associated with the reference images. The second step (synthesis step) consists of warping the reference image into a new one, which is based on the invariance of the computed parallax field. The analysis step is working directly on the reference views, and only need to be performed once. Examples of synthesizing novel views using either feature correspondences or dense disparity map have demonstrated the feasibility of the proposed approach.

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography.

PubMed

Cai, C; Rodet, T; Legoupil, S; Mohammad-Djafari, A

2013-11-01

Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images. This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed. The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions. The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.

Role of Computational Fluid Dynamics and Wind Tunnels in Aeronautics R and D

NASA Technical Reports Server (NTRS)

Malik, Murjeeb R.; Bushnell, Dennis M.

2012-01-01

The purpose of this report is to investigate the status and future projections for the question of supplantation of wind tunnels by computation in design and to intuit the potential impact of computation approaches on wind-tunnel utilization all with an eye toward reducing the infrastructure cost at aeronautics R&D centers. Wind tunnels have been closing for myriad reasons, and such closings have reduced infrastructure costs. Further cost reductions are desired, and the work herein attempts to project which wind-tunnel capabilities can be replaced in the future and, if possible, the timing of such. If the possibility exists to project when a facility could be closed, then maintenance and other associated costs could be rescheduled accordingly (i.e., before the fact) to obtain an even greater infrastructure cost reduction.

Approaching mathematical model of the immune network based DNA Strand Displacement system.

PubMed

Mardian, Rizki; Sekiyama, Kosuke; Fukuda, Toshio

2013-12-01

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thanh, Vo Hong, E-mail: vo@cosbi.eu; Priami, Corrado, E-mail: priami@cosbi.eu; Department of Mathematics, University of Trento, Trento

We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reactionmore » rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.« less

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

PubMed

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Panel cutting method: new approach to generate panels on a hull in Rankine source potential approximation

NASA Astrophysics Data System (ADS)

Choi, Hee-Jong; Chun, Ho-Hwan; Park, Il-Ryong; Kim, Jin

2011-12-01

In the present study, a new hull panel generation algorithm, namely panel cutting method, was developed to predict flow phenomena around a ship using the Rankine source potential based panel method, where the iterative method was used to satisfy the nonlinear free surface condition and the trim and sinkage of the ship was taken into account. Numerical computations were performed to investigate the validity of the proposed hull panel generation algorithm for Series 60 (CB=0.60) hull and KRISO container ship (KCS), a container ship designed by Maritime and Ocean Engineering Research Institute (MOERI). The computational results were validated by comparing with the existing experimental data.

Development of a realistic stress analysis for fatigue analysis of notched composite laminates

NASA Technical Reports Server (NTRS)

Humphreys, E. A.; Rosen, B. W.

1979-01-01

A finite element stress analysis which consists of a membrane and interlaminar shear spring analysis was developed. This approach was utilized in order to model physically realistic failure mechanisms while maintaining a high degree of computational economy. The accuracy of the stress analysis predictions is verified through comparisons with other solutions to the composite laminate edge effect problem. The stress analysis model was incorporated into an existing fatigue analysis methodology and the entire procedure computerized. A fatigue analysis is performed upon a square laminated composite plate with a circular central hole. A complete description and users guide for the computer code FLAC (Fatigue of Laminated Composites) is included as an appendix.

Study of elastic and inelastic cross sections by positron impact on inert gases

NASA Astrophysics Data System (ADS)

Singh, Suvam; Naghma, Rahla; Kaur, Jaspreet; Antony, Bobby

2018-04-01

In this article, a modified computational method recently introduced is used for the calculation of total, positronium (Ps) formation and ionization cross sections including direct and total ionization cross sections for positron scattering from noble gases. The incident positron is assumed to have energies over a wide range from 5 eV to 5 keV. The positron-atom interaction potential is developed under an optical potential framework and the computations of cross sections for each process are performed by introducing appropriate absorption thresholds. The calculated results obtained by employing this modified approach are found to be in reasonably good agreement with most of the existing data.

A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities.

PubMed

Chen, Yuh-Shyan; Tsai, Yi-Ting

2018-02-06

Mobility management for supporting the location tracking and location-based service (LBS) is an important issue of smart city by providing the means for the smooth transportation of people and goods. The mobility is useful to contribute the innovation in both public and private transportation infrastructures for smart cities. With the assistance of edge/fog computing, this paper presents a fully new mobility management using the proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cities. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud (FMC) and follow-me edge (FME) (or called cloudlet). A user equipment (UE) receives the data, transmitted from original cloud, into the original edge cloud before the handover operation. After the handover operation, an UE searches for a new cloud, called as a migrated cloud, and a new edge cloud, called as a migrated edge cloud near to UE, where the remaining data is migrated from the original cloud to the migrated cloud and all the remaining data are received in the new edge cloud. Existing FMC results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency, and existing FME results do not keep the property of the service migration between data centers for reducing the transmission latency. Our proposed FMCL approach can simultaneously keep the VM migration between cloudlets and service migration between data centers to significantly reduce the transmission latency. The new proposed mobility management using FMCL approach aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. To illustrate the performance achievement, the mathematical analysis and simulation results are examined in terms of the total transmission time, the throughput, the probability of packet loss, and the number of control messages.

A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities

PubMed Central

Tsai, Yi-Ting

2018-01-01

Mobility management for supporting the location tracking and location-based service (LBS) is an important issue of smart city by providing the means for the smooth transportation of people and goods. The mobility is useful to contribute the innovation in both public and private transportation infrastructures for smart cities. With the assistance of edge/fog computing, this paper presents a fully new mobility management using the proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cities. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud (FMC) and follow-me edge (FME) (or called cloudlet). A user equipment (UE) receives the data, transmitted from original cloud, into the original edge cloud before the handover operation. After the handover operation, an UE searches for a new cloud, called as a migrated cloud, and a new edge cloud, called as a migrated edge cloud near to UE, where the remaining data is migrated from the original cloud to the migrated cloud and all the remaining data are received in the new edge cloud. Existing FMC results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency, and existing FME results do not keep the property of the service migration between data centers for reducing the transmission latency. Our proposed FMCL approach can simultaneously keep the VM migration between cloudlets and service migration between data centers to significantly reduce the transmission latency. The new proposed mobility management using FMCL approach aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. To illustrate the performance achievement, the mathematical analysis and simulation results are examined in terms of the total transmission time, the throughput, the probability of packet loss, and the number of control messages. PMID:29415510

Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach

PubMed Central

Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z.; Zhang, Tao; Babadi, Behtash

2018-01-01

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings. PMID:29765298

Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach.

PubMed

Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z; Zhang, Tao; Babadi, Behtash

2018-01-01

Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ 1 -regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.

Phases and stability of non-uniform black strings

NASA Astrophysics Data System (ADS)

Emparan, Roberto; Luna, Raimon; Martínez, Marina; Suzuki, Ryotaku; Tanabe, Kentaro

2018-05-01

We construct solutions of non-uniform black strings in dimensions from D ≈ 9 all the way up to D = ∞, and investigate their thermodynamics and dynamical stability. Our approach employs the large- D perturbative expansion beyond the leading order, including corrections up to 1 /D 4. Combining both analytical techniques and relatively simple numerical solution of ODEs, we map out the ranges of parameters in which non-uniform black strings exist in each dimension and compute their thermodynamics and quasinormal modes with accuracy. We establish with very good precision the existence of Sorkin's critical dimension and we prove that not only the thermodynamic stability, but also the dynamic stability of the solutions changes at it.

Multitarget mixture reduction algorithm with incorporated target existence recursions

NASA Astrophysics Data System (ADS)

Ristic, Branko; Arulampalam, Sanjeev

2000-07-01

The paper derives a deferred logic data association algorithm based on the mixture reduction approach originally due to Salmond [SPIE vol.1305, 1990]. The novelty of the proposed algorithm provides the recursive formulae for both data association and target existence (confidence) estimation, thus allowing automatic track initiation and termination. T he track initiation performance of the proposed filter is investigated by computer simulations. It is observed that at moderately high levels of clutter density the proposed filter initiates tracks more reliably than its corresponding PDA filter. An extension of the proposed filter to the multi-target case is also presented. In addition, the paper compares the track maintenance performance of the MR algorithm with an MHT implementation.

Automated Extraction of Secondary Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne M.; Haimes, Robert

2005-01-01

The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.

BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach

PubMed Central

2014-01-01

Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package. PMID:24517713

Integrated Workforce Modeling System

NASA Technical Reports Server (NTRS)

Moynihan, Gary P.

2000-01-01

There are several computer-based systems, currently in various phases of development at KSC, which encompass some component, aspect, or function of workforce modeling. These systems may offer redundant capabilities and/or incompatible interfaces. A systems approach to workforce modeling is necessary in order to identify and better address user requirements. This research has consisted of two primary tasks. Task 1 provided an assessment of existing and proposed KSC workforce modeling systems for their functionality and applicability to the workforce planning function. Task 2 resulted in the development of a proof-of-concept design for a systems approach to workforce modeling. The model incorporates critical aspects of workforce planning, including hires, attrition, and employee development.

Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks

NASA Technical Reports Server (NTRS)

Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)

2002-01-01

In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.

Low-speed single-element airfoil synthesis

NASA Technical Reports Server (NTRS)

Mcmasters, J. H.; Henderson, M. L.

1979-01-01

The use of recently developed airfoil analysis/design computational tools to clarify, enrich and extend the existing experimental data base on low-speed, single element airfoils is demonstrated. A discussion of the problem of tailoring an airfoil for a specific application at its appropriate Reynolds number is presented. This problem is approached by use of inverse (or synthesis) techniques, wherein a desirable set of boundary layer characteristics, performance objectives, and constraints are specified, which then leads to derivation of a corresponding viscous flow pressure distribution. Examples are presented which demonstrate the synthesis approach, following presentation of some historical information and background data which motivate the basic synthesis process.

APINetworks: A general API for the treatment of complex networks in arbitrary computational environments

NASA Astrophysics Data System (ADS)

Niño, Alfonso; Muñoz-Caro, Camelia; Reyes, Sebastián

2015-11-01

The last decade witnessed a great development of the structural and dynamic study of complex systems described as a network of elements. Therefore, systems can be described as a set of, possibly, heterogeneous entities or agents (the network nodes) interacting in, possibly, different ways (defining the network edges). In this context, it is of practical interest to model and handle not only static and homogeneous networks but also dynamic, heterogeneous ones. Depending on the size and type of the problem, these networks may require different computational approaches involving sequential, parallel or distributed systems with or without the use of disk-based data structures. In this work, we develop an Application Programming Interface (APINetworks) for the modeling and treatment of general networks in arbitrary computational environments. To minimize dependency between components, we decouple the network structure from its function using different packages for grouping sets of related tasks. The structural package, the one in charge of building and handling the network structure, is the core element of the system. In this work, we focus in this API structural component. We apply an object-oriented approach that makes use of inheritance and polymorphism. In this way, we can model static and dynamic networks with heterogeneous elements in the nodes and heterogeneous interactions in the edges. In addition, this approach permits a unified treatment of different computational environments. Tests performed on a C++11 version of the structural package show that, on current standard computers, the system can handle, in main memory, directed and undirected linear networks formed by tens of millions of nodes and edges. Our results compare favorably to those of existing tools.

Numerical Technology for Large-Scale Computational Electromagnetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sharpe, R; Champagne, N; White, D

The key bottleneck of implicit computational electromagnetics tools for large complex geometries is the solution of the resulting linear system of equations. The goal of this effort was to research and develop critical numerical technology that alleviates this bottleneck for large-scale computational electromagnetics (CEM). The mathematical operators and numerical formulations used in this arena of CEM yield linear equations that are complex valued, unstructured, and indefinite. Also, simultaneously applying multiple mathematical modeling formulations to different portions of a complex problem (hybrid formulations) results in a mixed structure linear system, further increasing the computational difficulty. Typically, these hybrid linear systems aremore » solved using a direct solution method, which was acceptable for Cray-class machines but does not scale adequately for ASCI-class machines. Additionally, LLNL's previously existing linear solvers were not well suited for the linear systems that are created by hybrid implicit CEM codes. Hence, a new approach was required to make effective use of ASCI-class computing platforms and to enable the next generation design capabilities. Multiple approaches were investigated, including the latest sparse-direct methods developed by our ASCI collaborators. In addition, approaches that combine domain decomposition (or matrix partitioning) with general-purpose iterative methods and special purpose pre-conditioners were investigated. Special-purpose pre-conditioners that take advantage of the structure of the matrix were adapted and developed based on intimate knowledge of the matrix properties. Finally, new operator formulations were developed that radically improve the conditioning of the resulting linear systems thus greatly reducing solution time. The goal was to enable the solution of CEM problems that are 10 to 100 times larger than our previous capability.« less

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

PubMed

Putra, I Putu Edy Suardiyana; Brusey, James; Gaura, Elena; Vesilo, Rein

2017-12-22

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k -nearest neighbor ( k -NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

A semi-classical approach to the calculation of highly excited rotational energies for asymmetric-top molecules

PubMed Central

Schmiedt, Hanno; Schlemmer, Stephan; Yurchenko, Sergey N.; Yachmenev, Andrey

2017-01-01

We report a new semi-classical method to compute highly excited rotational energy levels of an asymmetric-top molecule. The method forgoes the idea of a full quantum mechanical treatment of the ro-vibrational motion of the molecule. Instead, it employs a semi-classical Green's function approach to describe the rotational motion, while retaining a quantum mechanical description of the vibrations. Similar approaches have existed for some time, but the method proposed here has two novel features. First, inspired by the path integral method, periodic orbits in the phase space and tunneling paths are naturally obtained by means of molecular symmetry analysis. Second, the rigorous variational method is employed for the first time to describe the molecular vibrations. In addition, we present a new robust approach to generating rotational energy surfaces for vibrationally excited states; this is done in a fully quantum-mechanical, variational manner. The semi-classical approach of the present work is applied to calculating the energies of very highly excited rotational states and it reduces dramatically the computing time as well as the storage and memory requirements when compared to the fullly quantum-mechanical variational approach. Test calculations for excited states of SO2 yield semi-classical energies in very good agreement with the available experimental data and the results of fully quantum-mechanical calculations. PMID:28000807

A Framework for Understanding Physics Students' Computational Modeling Practices

NASA Astrophysics Data System (ADS)

Lunk, Brandon Robert

With the growing push to include computational modeling in the physics classroom, we are faced with the need to better understand students' computational modeling practices. While existing research on programming comprehension explores how novices and experts generate programming algorithms, little of this discusses how domain content knowledge, and physics knowledge in particular, can influence students' programming practices. In an effort to better understand this issue, I have developed a framework for modeling these practices based on a resource stance towards student knowledge. A resource framework models knowledge as the activation of vast networks of elements called "resources." Much like neurons in the brain, resources that become active can trigger cascading events of activation throughout the broader network. This model emphasizes the connectivity between knowledge elements and provides a description of students' knowledge base. Together with resources resources, the concepts of "epistemic games" and "frames" provide a means for addressing the interaction between content knowledge and practices. Although this framework has generally been limited to describing conceptual and mathematical understanding, it also provides a means for addressing students' programming practices. In this dissertation, I will demonstrate this facet of a resource framework as well as fill in an important missing piece: a set of epistemic games that can describe students' computational modeling strategies. The development of this theoretical framework emerged from the analysis of video data of students generating computational models during the laboratory component of a Matter & Interactions: Modern Mechanics course. Student participants across two semesters were recorded as they worked in groups to fix pre-written computational models that were initially missing key lines of code. Analysis of this video data showed that the students' programming practices were highly influenced by their existing physics content knowledge, particularly their knowledge of analytic procedures. While this existing knowledge was often applied in inappropriate circumstances, the students were still able to display a considerable amount of understanding of the physics content and of analytic solution procedures. These observations could not be adequately accommodated by the existing literature of programming comprehension. In extending the resource framework to the task of computational modeling, I model students' practices in terms of three important elements. First, a knowledge base includes re- sources for understanding physics, math, and programming structures. Second, a mechanism for monitoring and control describes students' expectations as being directed towards numerical, analytic, qualitative or rote solution approaches and which can be influenced by the problem representation. Third, a set of solution approaches---many of which were identified in this study---describe what aspects of the knowledge base students use and how they use that knowledge to enact their expectations. This framework allows us as researchers to track student discussions and pinpoint the source of difficulties. This work opens up many avenues of potential research. First, this framework gives researchers a vocabulary for extending Resource Theory to other domains of instruction, such as modeling how physics students use graphs. Second, this framework can be used as the basis for modeling expert physicists' programming practices. Important instructional implications also follow from this research. Namely, as we broaden the use of computational modeling in the physics classroom, our instructional practices should focus on helping students understand the step-by-step nature of programming in contrast to the already salient analytic procedures.

Tavaxy: integrating Taverna and Galaxy workflows with cloud computing support.

PubMed

Abouelhoda, Mohamed; Issa, Shadi Alaa; Ghanem, Moustafa

2012-05-04

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts. In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure. Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis.The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.

Cognitive foundations for model-based sensor fusion

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Weijers, Bertus; Mutz, Chris W.

2003-08-01

Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of concurrent fusion, tracking, and detection. The presentation will discuss examples of performance, where computational speedups of many orders of magnitude were attained leading to performance improvements of up to 10 dB (and better).

On Undecidability Aspects of Resilient Computations and Implications to Exascale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, Nageswara S

2014-01-01

Future Exascale computing systems with a large number of processors, memory elements and interconnection links, are expected to experience multiple, complex faults, which affect both applications and operating-runtime systems. A variety of algorithms, frameworks and tools are being proposed to realize and/or verify the resilience properties of computations that guarantee correct results on failure-prone computing systems. We analytically show that certain resilient computation problems in presence of general classes of faults are undecidable, that is, no algorithms exist for solving them. We first show that the membership verification in a generic set of resilient computations is undecidable. We describe classesmore » of faults that can create infinite loops or non-halting computations, whose detection in general is undecidable. We then show certain resilient computation problems to be undecidable by using reductions from the loop detection and halting problems under two formulations, namely, an abstract programming language and Turing machines, respectively. These two reductions highlight different failure effects: the former represents program and data corruption, and the latter illustrates incorrect program execution. These results call for broad-based, well-characterized resilience approaches that complement purely computational solutions using methods such as hardware monitors, co-designs, and system- and application-specific diagnosis codes.« less

Automatic measurement; Mesures automatiques (in French)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ringeard, C.

1974-11-28

By its ability to link-up operations sequentially and memorize the data collected, the computer can introduce a statistical approach in the evaluation of a result. To benefit fully from the advantages of automation, a special effort was made to reduce the programming time to a minimum and to simplify link-ups between the existing system and instruments from different sources. The practical solution of the test laboratory of the C.E.A. Centralized Administration Groupe (GEC) is given.

Measurement-Based Linear Optics

NASA Astrophysics Data System (ADS)

Alexander, Rafael N.; Gabay, Natasha C.; Rohde, Peter P.; Menicucci, Nicolas C.

2017-03-01

A major challenge in optical quantum processing is implementing large, stable interferometers. We offer a novel approach: virtual, measurement-based interferometers that are programed on the fly solely by the choice of homodyne measurement angles. The effects of finite squeezing are captured as uniform amplitude damping. We compare our proposal to existing (physical) interferometers and consider its performance for BosonSampling, which could demonstrate postclassical computational power in the near future. We prove its efficiency in time and squeezing (energy) in this setting.

Communication: Analysing kinetic transition networks for rare events.

PubMed

Stevenson, Jacob D; Wales, David J

2014-07-28

The graph transformation approach is a recently proposed method for computing mean first passage times, rates, and committor probabilities for kinetic transition networks. Here we compare the performance to existing linear algebra methods, focusing on large, sparse networks. We show that graph transformation provides a much more robust framework, succeeding when numerical precision issues cause the other methods to fail completely. These are precisely the situations that correspond to rare event dynamics for which the graph transformation was introduced.

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1983-01-01

Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered.

Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data

NASA Astrophysics Data System (ADS)

Gerber, Florian; Mösinger, Kaspar; Furrer, Reinhard

2017-07-01

Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user-interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component is-besides performance-the limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix.

Langley Ground Facilities and Testing in the 21st Century

NASA Technical Reports Server (NTRS)

Ambur, Damodar R.; Kegelman, Jerome T.; Kilgore, William A.

2010-01-01

A strategic approach for retaining and more efficiently operating the essential Langley Ground Testing Facilities in the 21st Century is presented. This effort takes advantage of the previously completed and ongoing studies at the Agency and National levels. This integrated approach takes into consideration the overall decline in test business base within the nation and reduced utilization in each of the Langley facilities with capabilities to test in the subsonic, transonic, supersonic, and hypersonic speed regimes. The strategy accounts for capability needs to meet the Agency programmatic requirements and strategic goals and to execute test activities in the most efficient and flexible facility operating structure. The structure currently being implemented at Langley offers agility to right-size our capability and capacity from a national perspective, to accommodate the dynamic nature of the testing needs, and will address the influence of existing and emerging analytical tools for design. The paradigm for testing in the retained facilities is to efficiently and reliably provide more accurate and high-quality test results at an affordable cost to support design information needs for flight regimes where the computational capability is not adequate and to verify and validate the existing and emerging computational tools. Each of the above goals are planned to be achieved, keeping in mind the increasing small industry customer base engaged in developing unpiloted aerial vehicles and commercial space transportation systems.

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features.

PubMed

Kleftogiannis, Dimitrios; Theofilatos, Konstantinos; Likothanassis, Spiros; Mavroudi, Seferina

2015-01-01

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

The Quantum Human Computer (QHC) Hypothesis

ERIC Educational Resources Information Center

Salmani-Nodoushan, Mohammad Ali

2008-01-01

This article attempts to suggest the existence of a human computer called Quantum Human Computer (QHC) on the basis of an analogy between human beings and computers. To date, there are two types of computers: Binary and Quantum. The former operates on the basis of binary logic where an object is said to exist in either of the two states of 1 and…

Discovery of novel bacterial toxins by genomics and computational biology.

PubMed

Doxey, Andrew C; Mansfield, Michael J; Montecucco, Cesare

2018-06-01

Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. Copyright © 2018. Published by Elsevier Ltd.

Blending Qualitative and Computational Linguistics Methods for Fidelity Assessment: Experience with the Familias Unidas Preventive Intervention.

PubMed

Gallo, Carlos; Pantin, Hilda; Villamar, Juan; Prado, Guillermo; Tapia, Maria; Ogihara, Mitsunori; Cruden, Gracelyn; Brown, C Hendricks

2015-09-01

Careful fidelity monitoring and feedback are critical to implementing effective interventions. A wide range of procedures exist to assess fidelity; most are derived from observational assessments (Schoenwald and Garland, Psycholog Assess 25:146-156, 2013). However, these fidelity measures are resource intensive for research teams in efficacy/effectiveness trials, and are often unattainable or unmanageable for the host organization to rate when the program is implemented on a large scale. We present a first step towards automated processing of linguistic patterns in fidelity monitoring of a behavioral intervention using an innovative mixed methods approach to fidelity assessment that uses rule-based, computational linguistics to overcome major resource burdens. Data come from an effectiveness trial of the Familias Unidas intervention, an evidence-based, family-centered preventive intervention found to be efficacious in reducing conduct problems, substance use and HIV sexual risk behaviors among Hispanic youth. This computational approach focuses on "joining," which measures the quality of the working alliance of the facilitator with the family. Quantitative assessments of reliability are provided. Kappa scores between a human rater and a machine rater for the new method for measuring joining reached 0.83. Early findings suggest that this approach can reduce the high cost of fidelity measurement and the time delay between fidelity assessment and feedback to facilitators; it also has the potential for improving the quality of intervention fidelity ratings.

Numerical calculation of thermo-mechanical problems at large strains based on complex step derivative approximation of tangent stiffness matrices

NASA Astrophysics Data System (ADS)

Balzani, Daniel; Gandhi, Ashutosh; Tanaka, Masato; Schröder, Jörg

2015-05-01

In this paper a robust approximation scheme for the numerical calculation of tangent stiffness matrices is presented in the context of nonlinear thermo-mechanical finite element problems and its performance is analyzed. The scheme extends the approach proposed in Kim et al. (Comput Methods Appl Mech Eng 200:403-413, 2011) and Tanaka et al. (Comput Methods Appl Mech Eng 269:454-470, 2014 and bases on applying the complex-step-derivative approximation to the linearizations of the weak forms of the balance of linear momentum and the balance of energy. By incorporating consistent perturbations along the imaginary axis to the displacement as well as thermal degrees of freedom, we demonstrate that numerical tangent stiffness matrices can be obtained with accuracy up to computer precision leading to quadratically converging schemes. The main advantage of this approach is that contrary to the classical forward difference scheme no round-off errors due to floating-point arithmetics exist within the calculation of the tangent stiffness. This enables arbitrarily small perturbation values and therefore leads to robust schemes even when choosing small values. An efficient algorithmic treatment is presented which enables a straightforward implementation of the method in any standard finite-element program. By means of thermo-elastic and thermo-elastoplastic boundary value problems at finite strains the performance of the proposed approach is analyzed.

Blending Qualitative and Computational Linguistics Methods for Fidelity Assessment: Experience with the Familias Unidas Preventive Intervention

PubMed Central

Gallo, Carlos; Pantin, Hilda; Villamar, Juan; Prado, Guillermo; Tapia, Maria; Ogihara, Mitsunori; Cruden, Gracelyn; Brown, C Hendricks

2014-01-01

Careful fidelity monitoring and feedback are critical to implementing effective interventions. A wide range of procedures exist to assess fidelity; most are derived from observational assessments (Schoenwald et al, 2013). However, these fidelity measures are resource intensive for research teams in efficacy/effectiveness trials, and are often unattainable or unmanageable for the host organization to rate when the program is implemented on a large scale. We present a first step towards automated processing of linguistic patterns in fidelity monitoring of a behavioral intervention using an innovative mixed methods approach to fidelity assessment that uses rule-based, computational linguistics to overcome major resource burdens. Data come from an effectiveness trial of the Familias Unidas intervention, an evidence-based, family-centered preventive intervention found to be efficacious in reducing conduct problems, substance use and HIV sexual risk behaviors among Hispanic youth. This computational approach focuses on “joining,” which measures the quality of the working alliance of the facilitator with the family. Quantitative assessments of reliability are provided. Kappa scores between a human rater and a machine rater for the new method for measuring joining reached .83. Early findings suggest that this approach can reduce the high cost of fidelity measurement and the time delay between fidelity assessment and feedback to facilitators; it also has the potential for improving the quality of intervention fidelity ratings. PMID:24500022

An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

Prediction of physical protein protein interactions

NASA Astrophysics Data System (ADS)

Szilágyi, András; Grimm, Vera; Arakaki, Adrián K.; Skolnick, Jeffrey

2005-06-01

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

The demodulated band transform

PubMed Central

Kovach, Christopher K.; Gander, Phillip E.

2016-01-01

Background Windowed Fourier decompositions (WFD) are widely used in measuring stationary and non-stationary spectral phenomena and in describing pairwise relationships among multiple signals. Although a variety of WFDs see frequent application in electrophysiological research, including the short-time Fourier transform, continuous wavelets, band-pass filtering and multitaper-based approaches, each carries certain drawbacks related to computational efficiency and spectral leakage. This work surveys the advantages of a WFD not previously applied in electrophysiological settings. New Methods A computationally efficient form of complex demodulation, the demodulated band transform (DBT), is described. Results DBT is shown to provide an efficient approach to spectral estimation with minimal susceptibility to spectral leakage. In addition, it lends itself well to adaptive filtering of non-stationary narrowband noise. Comparison with existing methods A detailed comparison with alternative WFDs is offered, with an emphasis on the relationship between DBT and Thomson's multitaper. DBT is shown to perform favorably in combining computational efficiency with minimal introduction of spectral leakage. Conclusion DBT is ideally suited to efficient estimation of both stationary and non-stationary spectral and cross-spectral statistics with minimal susceptibility to spectral leakage. These qualities are broadly desirable in many settings. PMID:26711370

Computational domain discretization in numerical analysis of flow within granular materials

NASA Astrophysics Data System (ADS)

Sosnowski, Marcin

2018-06-01

The discretization of computational domain is a crucial step in Computational Fluid Dynamics (CFD) because it influences not only the numerical stability of the analysed model but also the agreement of obtained results and real data. Modelling flow in packed beds of granular materials is a very challenging task in terms of discretization due to the existence of narrow spaces between spherical granules contacting tangentially in a single point. Standard approach to this issue results in a low quality mesh and unreliable results in consequence. Therefore the common method is to reduce the diameter of the modelled granules in order to eliminate the single-point contact between the individual granules. The drawback of such method is the adulteration of flow and contact heat resistance among others. Therefore an innovative method is proposed in the paper: single-point contact is extended to a cylinder-shaped volume contact. Such approach eliminates the low quality mesh elements and simultaneously introduces only slight distortion to the flow as well as contact heat transfer. The performed analysis of numerous test cases prove the great potential of the proposed method of meshing the packed beds of granular materials.

A computational approach to climate science education with CLIMLAB

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2017-12-01

CLIMLAB is a Python-based software toolkit for interactive, process-oriented climate modeling for use in education and research. It is motivated by the need for simpler tools and more reproducible workflows with which to "fill in the gaps" between blackboard-level theory and the results of comprehensive climate models. With CLIMLAB you can interactively mix and match physical model components, or combine simpler process models together into a more comprehensive model. I use CLIMLAB in the classroom to put models in the hands of students (undergraduate and graduate), and emphasize a hierarchical, process-oriented approach to understanding the key emergent properties of the climate system. CLIMLAB is equally a tool for climate research, where the same needs exist for more robust, process-based understanding and reproducible computational results. I will give an overview of CLIMLAB and an update on recent developments, including: a full-featured, well-documented, interactive implementation of a widely-used radiation model (RRTM) packaging with conda-forge for compiler-free (and hassle-free!) installation on Mac, Windows and Linux interfacing with xarray for i/o and graphics with gridded model data a rich and growing collection of examples and self-computing lecture notes in Jupyter notebook format

Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks

NASA Astrophysics Data System (ADS)

Esfandiar, Pooya; Bonchi, Francesco; Gleich, David F.; Greif, Chen; Lakshmanan, Laks V. S.; On, Byung-Won

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and a quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.

Computation of the phase response curve: a direct numerical approach.

PubMed

Govaerts, W; Sautois, B

2006-04-01

Neurons are often modeled by dynamical systems--parameterized systems of differential equations. A typical behavioral pattern of neurons is periodic spiking; this corresponds to the presence of stable limit cycles in the dynamical systems model. The phase resetting and phase response curves (PRCs) describe the reaction of the spiking neuron to an input pulse at each point of the cycle. We develop a new method for computing these curves as a by-product of the solution of the boundary value problem for the stable limit cycle. The method is mathematically equivalent to the adjoint method, but our implementation is computationally much faster and more robust than any existing method. In fact, it can compute PRCs even where the limit cycle can hardly be found by time integration, for example, because it is close to another stable limit cycle. In addition, we obtain the discretized phase response curve in a form that is ideally suited for most applications. We present several examples and provide the implementation in a freely available Matlab code.

Seeing the forest for the trees: Networked workstations as a parallel processing computer

NASA Technical Reports Server (NTRS)

Breen, J. O.; Meleedy, D. M.

1992-01-01

Unlike traditional 'serial' processing computers in which one central processing unit performs one instruction at a time, parallel processing computers contain several processing units, thereby, performing several instructions at once. Many of today's fastest supercomputers achieve their speed by employing thousands of processing elements working in parallel. Few institutions can afford these state-of-the-art parallel processors, but many already have the makings of a modest parallel processing system. Workstations on existing high-speed networks can be harnessed as nodes in a parallel processing environment, bringing the benefits of parallel processing to many. While such a system can not rival the industry's latest machines, many common tasks can be accelerated greatly by spreading the processing burden and exploiting idle network resources. We study several aspects of this approach, from algorithms to select nodes to speed gains in specific tasks. With ever-increasing volumes of astronomical data, it becomes all the more necessary to utilize our computing resources fully.

Fast katz and commuters : efficient estimation of social relatedness in large networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

On, Byung-Won; Lakshmanan, Laks V. S.; Greif, Chen

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and amore » quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.« less

Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models

PubMed Central

Walker, Mark A.; Madduri, Ravi; Rodriguez, Alex; Greenstein, Joseph L.; Winslow, Raimond L.

2016-01-01

We describe the ways in which Galaxy, a web-based reproducible research platform, can be used for web-based sharing of complex computational models. Galaxy allows users to seamlessly customize and run simulations on cloud computing resources, a concept we refer to as Models and Simulations as a Service (MaSS). To illustrate this application of Galaxy, we have developed a tool suite for simulating a high spatial-resolution model of the cardiac Ca2+ spark that requires supercomputing resources for execution. We also present tools for simulating models encoded in the SBML and CellML model description languages, thus demonstrating how Galaxy’s reproducible research features can be leveraged by existing technologies. Finally, we demonstrate how the Galaxy workflow editor can be used to compose integrative models from constituent submodules. This work represents an important novel approach, to our knowledge, to making computational simulations more accessible to the broader scientific community. PMID:26958881

Clinical modeling--a critical analysis.

PubMed

Blobel, Bernd; Goossen, William; Brochhausen, Mathias

2014-01-01

Modeling clinical processes (and their informational representation) is a prerequisite for optimally enabling and supporting high quality and safe care through information and communication technology and meaningful use of gathered information. The paper investigates existing approaches to clinical modeling, thereby systematically analyzing the underlying principles, the consistency with and the integration opportunity to other existing or emerging projects, as well as the correctness of representing the reality of health and health services. The analysis is performed using an architectural framework for modeling real-world systems. In addition, fundamental work on the representation of facts, relations, and processes in the clinical domain by ontologies is applied, thereby including the integration of advanced methodologies such as translational and system medicine. The paper demonstrates fundamental weaknesses and different maturity as well as evolutionary potential in the approaches considered. It offers a development process starting with the business domain and its ontologies, continuing with the Reference Model-Open Distributed Processing (RM-ODP) related conceptual models in the ICT ontology space, the information and the computational view, and concluding with the implementation details represented as engineering and technology view, respectively. The existing approaches reflect at different levels the clinical domain, put the main focus on different phases of the development process instead of first establishing the real business process representation and therefore enable quite differently and partially limitedly the domain experts' involvement. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Numerical discretization-based estimation methods for ordinary differential equation models via penalized spline smoothing with applications in biomedical research.

PubMed

Wu, Hulin; Xue, Hongqi; Kumar, Arun

2012-06-01

Differential equations are extensively used for modeling dynamics of physical processes in many scientific fields such as engineering, physics, and biomedical sciences. Parameter estimation of differential equation models is a challenging problem because of high computational cost and high-dimensional parameter space. In this article, we propose a novel class of methods for estimating parameters in ordinary differential equation (ODE) models, which is motivated by HIV dynamics modeling. The new methods exploit the form of numerical discretization algorithms for an ODE solver to formulate estimating equations. First, a penalized-spline approach is employed to estimate the state variables and the estimated state variables are then plugged in a discretization formula of an ODE solver to obtain the ODE parameter estimates via a regression approach. We consider three different order of discretization methods, Euler's method, trapezoidal rule, and Runge-Kutta method. A higher-order numerical algorithm reduces numerical error in the approximation of the derivative, which produces a more accurate estimate, but its computational cost is higher. To balance the computational cost and estimation accuracy, we demonstrate, via simulation studies, that the trapezoidal discretization-based estimate is the best and is recommended for practical use. The asymptotic properties for the proposed numerical discretization-based estimators are established. Comparisons between the proposed methods and existing methods show a clear benefit of the proposed methods in regards to the trade-off between computational cost and estimation accuracy. We apply the proposed methods t an HIV study to further illustrate the usefulness of the proposed approaches. © 2012, The International Biometric Society.

Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling.

PubMed

Powathil, Gibin G; Swat, Maciej; Chaplain, Mark A J

2015-02-01

The multiscale complexity of cancer as a disease necessitates a corresponding multiscale modelling approach to produce truly predictive mathematical models capable of improving existing treatment protocols. To capture all the dynamics of solid tumour growth and its progression, mathematical modellers need to couple biological processes occurring at various spatial and temporal scales (from genes to tissues). Because effectiveness of cancer therapy is considerably affected by intracellular and extracellular heterogeneities as well as by the dynamical changes in the tissue microenvironment, any model attempt to optimise existing protocols must consider these factors ultimately leading to improved multimodal treatment regimes. By improving existing and building new mathematical models of cancer, modellers can play important role in preventing the use of potentially sub-optimal treatment combinations. In this paper, we analyse a multiscale computational mathematical model for cancer growth and spread, incorporating the multiple effects of radiation therapy and chemotherapy in the patient survival probability and implement the model using two different cell based modelling techniques. We show that the insights provided by such multiscale modelling approaches can ultimately help in designing optimal patient-specific multi-modality treatment protocols that may increase patients quality of life. Copyright © 2014 Elsevier Ltd. All rights reserved.

groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

PubMed

Chae, Minho; Danko, Charles G; Kraus, W Lee

2015-07-16

Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

Biological computational approaches: new hopes to improve (re)programming robustness, regenerative medicine and cancer therapeutics.

PubMed

Ebrahimi, Behnam

2016-01-01

Hundreds of transcription factors (TFs) are expressed and work in each cell type, but the identity of the cells is defined and maintained through the activity of a small number of core TFs. Existing reprogramming strategies predominantly focus on the ectopic expression of core TFs of an intended fate in a given cell type regardless of the state of native/somatic gene regulatory networks (GRNs) of the starting cells. Interestingly, an important point is that how much products of the reprogramming, transdifferentiation and differentiation (programming) are identical to their in vivo counterparts. There is evidence that shows that direct fate conversions of somatic cells are not complete, with target cell identity not fully achieved. Manipulation of core TFs provides a powerful tool for engineering cell fate in terms of extinguishment of native GRNs, the establishment of a new GRN, and preventing installation of aberrant GRNs. Conventionally, core TFs are selected to convert one cell type into another mostly based on literature and the experimental identification of genes that are differentially expressed in one cell type compared to the specific cell types. Currently, there is not a universal standard strategy for identifying candidate core TFs. Remarkably, several biological computational platforms are developed, which are capable of evaluating the fidelity of reprogramming methods and refining existing protocols. The current review discusses some deficiencies of reprogramming technologies in the production of a pure population of authentic target cells. Furthermore, it reviews the role of computational approaches (e.g. CellNet, KeyGenes, Mogrify, etc.) in improving (re)programming methods and consequently in regenerative medicine and cancer therapeutics. Copyright © 2016 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.

Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin.

PubMed

Guzzi, Pietro Hiram; Milenkovic, Tijana

2018-05-01

Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.

Visual texture for automated characterisation of geological features in borehole televiewer imagery

NASA Astrophysics Data System (ADS)

Al-Sit, Waleed; Al-Nuaimy, Waleed; Marelli, Matteo; Al-Ataby, Ali

2015-08-01

Detailed characterisation of the structure of subsurface fractures is greatly facilitated by digital borehole logging instruments, the interpretation of which is typically time-consuming and labour-intensive. Despite recent advances towards autonomy and automation, the final interpretation remains heavily dependent on the skill, experience, alertness and consistency of a human operator. Existing computational tools fail to detect layers between rocks that do not exhibit distinct fracture boundaries, and often struggle characterising cross-cutting layers and partial fractures. This paper presents a novel approach to the characterisation of planar rock discontinuities from digital images of borehole logs. Multi-resolution texture segmentation and pattern recognition techniques utilising Gabor filters are combined with an iterative adaptation of the Hough transform to enable non-distinct, partial, distorted and steep fractures and layers to be accurately identified and characterised in a fully automated fashion. This approach has successfully detected fractures and layers with high detection accuracy and at a relatively low computational cost.

The use of multigrid techniques in the solution of the Elrod algorithm for a dynamically loaded journal bearing. M.S. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Woods, Claudia M.

1988-01-01

A numerical solution to a theoretical model of vapor cavitation in a dynamically loaded journal bearing is developed, utilizing a multigrid iterative technique. The code is compared with a presently existing direct solution in terms of computational time and accuracy. The model is based on the Elrod algorithm, a control volume approach to the Reynolds equation which mimics the Jakobssen-Floberg and Olsson cavitation theory. Besides accounting for a moving cavitation boundary and conservation of mass at the boundary, it also conserves mass within the cavitated region via liquid striations. The mixed nature of the equations (elliptic in the full film zone and nonelliptic in the cavitated zone) coupled with the dynamic aspects of the problem create interesting difficulties for the present solution approach. Emphasis is placed on the methods found to eliminate solution instabilities. Excellent results are obtained for both accuracy and reduction of computational time.

Development of analytical methods of predicting the pressure distribution about a nacelle at transonic speeds: Exact solution

NASA Technical Reports Server (NTRS)

Grossman, B.; Moretti, G.

1973-01-01

A computer program to predict the inviscid, transonic flow field about isolated nacelles was developed. The problem was to be formulated to solve Euler's equations without any approximation (such as small disturbances) and hence the terminology exact solution. The flow field was complicated by the presence of imbedded shock waves, an engine-inlet interface, and exhaust plumes. Furthermore, the transonic nacelles of interest had a very slender but blunt cowl lip. This created two distinct length scales, the length of the nacelle and the cowl lip radius that can differ by several orders of magnitude. These aspects of the flow field presented many numerical difficulties. The approach to the problem was to calculate the nacelle flow field using the method of time-dependent computations (TDC). Although at the time of the issuance of this contract, other approaches to transonic flow calculations existed, it was felt that TDC offered the most effective means of meeting the goals of the contract.

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.; ...

2015-06-24

This study proposes and analyzes a hyperspherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hypersurface of an N-dimensional discontinuous quantity of interest, by virtue of a hyperspherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyperspherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the newmore » technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. In addition, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

A systems approach to computer-based training

NASA Technical Reports Server (NTRS)

Drape, Gaylen W.

1994-01-01

This paper describes the hardware and software systems approach used in the Automated Recertification Training System (ARTS), a Phase 2 Small Business Innovation Research (SBIR) project for NASA Kennedy Space Center (KSC). The goal of this project is to optimize recertification training of technicians who process the Space Shuttle before launch by providing computer-based training courseware. The objectives of ARTS are to implement more effective CBT applications identified through a need assessment process and to provide an ehanced courseware production system. The system's capabilities are demonstrated by using five different pilot applications to convert existing classroom courses into interactive courseware. When the system is fully implemented at NASA/KSC, trainee job performance will improve and the cost of courseware development will be lower. Commercialization of the technology developed as part of this SBIR project is planned for Phase 3. Anticipated spin-off products include custom courseware for technical skills training and courseware production software for use by corporate training organizations of aerospace and other industrial companies.

A hyper-spherical adaptive sparse-grid method for high-dimensional discontinuity detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.

This work proposes and analyzes a hyper-spherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces is proposed. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hyper-surface of an N-dimensional dis- continuous quantity of interest, by virtue of a hyper-spherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyper-spherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of themore » hyper-surface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous error estimates and complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

Modelling brain emergent behaviours through coevolution of neural agents.

PubMed

Maniadakis, Michail; Trahanias, Panos

2006-06-01

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suryanarayana, Phanish, E-mail: phanish.suryanarayana@ce.gatech.edu; Phanish, Deepa

We present an Augmented Lagrangian formulation and its real-space implementation for non-periodic Orbital-Free Density Functional Theory (OF-DFT) calculations. In particular, we rewrite the constrained minimization problem of OF-DFT as a sequence of minimization problems without any constraint, thereby making it amenable to powerful unconstrained optimization algorithms. Further, we develop a parallel implementation of this approach for the Thomas–Fermi–von Weizsacker (TFW) kinetic energy functional in the framework of higher-order finite-differences and the conjugate gradient method. With this implementation, we establish that the Augmented Lagrangian approach is highly competitive compared to the penalty and Lagrange multiplier methods. Additionally, we show that higher-ordermore » finite-differences represent a computationally efficient discretization for performing OF-DFT simulations. Overall, we demonstrate that the proposed formulation and implementation are both efficient and robust by studying selected examples, including systems consisting of thousands of atoms. We validate the accuracy of the computed energies and forces by comparing them with those obtained by existing plane-wave methods.« less

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integratesmore » the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.« less

Eulerian-Lagrangian Simulations of Transonic Flutter Instabilities

NASA Technical Reports Server (NTRS)

Bendiksen, Oddvar O.

1994-01-01

This paper presents an overview of recent applications of Eulerian-Lagrangian computational schemes in simulating transonic flutter instabilities. This approach, the fluid-structure system is treated as a single continuum dynamics problem, by switching from an Eulerian to a Lagrangian formulation at the fluid-structure boundary. This computational approach effectively eliminates the phase integration errors associated with previous methods, where the fluid and structure are integrated sequentially using different schemes. The formulation is based on Hamilton's Principle in mixed coordinates, and both finite volume and finite element discretization schemes are considered. Results from numerical simulations of transonic flutter instabilities are presented for isolated wings, thin panels, and turbomachinery blades. The results suggest that the method is capable of reproducing the energy exchange between the fluid and the structure with significantly less error than existing methods. Localized flutter modes and panel flutter modes involving traveling waves can also be simulated effectively with no a priori knowledge of the type of instability involved.

Rapid computational identification of the targets of protein kinase inhibitors.

PubMed

Rockey, William M; Elcock, Adrian H

2005-06-16

We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.