Multisymplectic unified formalism for Einstein-Hilbert gravity

NASA Astrophysics Data System (ADS)

Gaset, Jordi; Román-Roy, Narciso

2018-03-01

We present a covariant multisymplectic formulation for the Einstein-Hilbert model of general relativity. As it is described by a second-order singular Lagrangian, this is a gauge field theory with constraints. The use of the unified Lagrangian-Hamiltonian formalism is particularly interesting when it is applied to these kinds of theories, since it simplifies the treatment of them, in particular, the implementation of the constraint algorithm, the retrieval of the Lagrangian description, and the construction of the covariant Hamiltonian formalism. In order to apply this algorithm to the covariant field equations, they must be written in a suitable geometrical way, which consists of using integrable distributions, represented by multivector fields of a certain type. We apply all these tools to the Einstein-Hilbert model without and with energy-matter sources. We obtain and explain the geometrical and physical meaning of the Lagrangian constraints and we construct the multimomentum (covariant) Hamiltonian formalisms in both cases. As a consequence of the gauge freedom and the constraint algorithm, we see how this model is equivalent to a first-order regular theory, without gauge freedom. In the case of the presence of energy-matter sources, we show how some relevant geometrical and physical characteristics of the theory depend on the type of source. In all the cases, we obtain explicitly multivector fields which are solutions to the gravitational field equations. Finally, a brief study of symmetries and conservation laws is done in this context.

Optimization Techniques for Analysis of Biological and Social Networks

DTIC Science & Technology

2012-03-28

analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms , test and fine...alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms , and heuristics. Originally, clusters...systematic fashion under a unifying theoretical and algorithmic framework. Optimization, Complex Networks, Social Network Analysis, Computational

Unified selective sorting approach to analyse multi-electrode extracellular data

NASA Astrophysics Data System (ADS)

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-06-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Unified selective sorting approach to analyse multi-electrode extracellular data

PubMed Central

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-01-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. PMID:27339770

Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks

PubMed Central

Talbot, Thomas R; Schaffner, William; Bloch, Karen C; Daniels, Titus L; Miller, Randolph A

2011-01-01

Objective The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners. Design Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. Measurements The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool. Results Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. Limitations Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance. Conclusion Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening. PMID:21606134

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma--Analysis Using Diagnostic Nodes.

PubMed

Rothermundt, Christian; Bailey, Alexandra; Cerbone, Linda; Eisen, Tim; Escudier, Bernard; Gillessen, Silke; Grünwald, Viktor; Larkin, James; McDermott, David; Oldenburg, Jan; Porta, Camillo; Rini, Brian; Schmidinger, Manuela; Sternberg, Cora; Putora, Paul M

2015-09-01

With the advent of targeted therapies, many treatment options in the first-line setting of metastatic clear cell renal cell carcinoma (mccRCC) have emerged. Guidelines and randomized trial reports usually do not elucidate the decision criteria for the different treatment options. In order to extract the decision criteria for the optimal therapy for patients, we performed an analysis of treatment algorithms from experts in the field. Treatment algorithms for the treatment of mccRCC from experts of 11 institutions were obtained, and decision trees were deduced. Treatment options were identified and a list of unified decision criteria determined. The final decision trees were analyzed with a methodology based on diagnostic nodes, which allows for an automated cross-comparison of decision trees. The most common treatment recommendations were determined, and areas of discordance were identified. The analysis revealed heterogeneity in most clinical scenarios. The recommendations selected for first-line treatment of mccRCC included sunitinib, pazopanib, temsirolimus, interferon-α combined with bevacizumab, high-dose interleukin-2, sorafenib, axitinib, everolimus, and best supportive care. The criteria relevant for treatment decisions were performance status, Memorial Sloan Kettering Cancer Center risk group, only or mainly lung metastases, cardiac insufficiency, hepatic insufficiency, age, and "zugzwang" (composite of multiple, related criteria). In the present study, we used diagnostic nodes to compare treatment algorithms in the first-line treatment of mccRCC. The results illustrate the heterogeneity of the decision criteria and treatment strategies for mccRCC and how available data are interpreted and implemented differently among experts. The data provided in the present report should not be considered to serve as treatment recommendations for the management of treatment-naïve patients with multiple metastases from metastatic clear cell renal cell carcinoma outside a clinical trial; however, the data highlight the different treatment options and the criteria used to select them. The diversity in decision making and how results from phase III trials can be interpreted and implemented differently in daily practice are demonstrated. ©AlphaMed Press.

FORUM: The Algorithmic Way of Life is Best and Responses.

ERIC Educational Resources Information Center

Maurer, Stephen B.; And Others

1985-01-01

The forum is focused on thinking about and with algorithms as a way of unifying all one's mathematical endeavors. The lead article by Maurer presents examples and discussion of this point. Responses, often disagreeing with his views, are by Douglas, Korte, Hilton, Renz, Smorynski, Hammersley, and Halmos. (MNS)

A novel VLSI processor architecture for supercomputing arrays

NASA Technical Reports Server (NTRS)

Venkateswaran, N.; Pattabiraman, S.; Devanathan, R.; Ahmed, Ashaf; Venkataraman, S.; Ganesh, N.

1993-01-01

Design of the processor element for general purpose massively parallel supercomputing arrays is highly complex and cost ineffective. To overcome this, the architecture and organization of the functional units of the processor element should be such as to suit the diverse computational structures and simplify mapping of complex communication structures of different classes of algorithms. This demands that the computation and communication structures of different class of algorithms be unified. While unifying the different communication structures is a difficult process, analysis of a wide class of algorithms reveals that their computation structures can be expressed in terms of basic IP,IP,OP,CM,R,SM, and MAA operations. The execution of these operations is unified on the PAcube macro-cell array. Based on this PAcube macro-cell array, we present a novel processor element called the GIPOP processor, which has dedicated functional units to perform the above operations. The architecture and organization of these functional units are such to satisfy the two important criteria mentioned above. The structure of the macro-cell and the unification process has led to a very regular and simpler design of the GIPOP processor. The production cost of the GIPOP processor is drastically reduced as it is designed on high performance mask programmable PAcube arrays.

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

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

Machnes, S.; Institute for Theoretical Physics, University of Ulm, D-89069 Ulm; Sander, U.

2011-08-15

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions aremore » pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.« less

The development of algorithms for the deployment of new version of GEM-detector-based acquisition system

NASA Astrophysics Data System (ADS)

Krawczyk, Rafał D.; Czarski, Tomasz; Kolasiński, Piotr; Linczuk, Paweł; Poźniak, Krzysztof T.; Chernyshova, Maryna; Kasprowicz, Grzegorz; Wojeński, Andrzej; Zabolotny, Wojciech; Zienkiewicz, Paweł

2016-09-01

This article is an overview of what has been implemented in the process of development and testing the GEM detector based acquisition system in terms of post-processing algorithms. Information is given on mex functions for extended statistics collection, unified hex topology and optimized S-DAQ algorithm for splitting overlapped signals. Additional discussion on bottlenecks and major factors concerning optimization is presented.

Subspace algorithms for identifying separable-in-denominator 2D systems with deterministic-stochastic inputs

NASA Astrophysics Data System (ADS)

Ramos, José A.; Mercère, Guillaume

2016-12-01

In this paper, we present an algorithm for identifying two-dimensional (2D) causal, recursive and separable-in-denominator (CRSD) state-space models in the Roesser form with deterministic-stochastic inputs. The algorithm implements the N4SID, PO-MOESP and CCA methods, which are well known in the literature on 1D system identification, but here we do so for the 2D CRSD Roesser model. The algorithm solves the 2D system identification problem by maintaining the constraint structure imposed by the problem (i.e. Toeplitz and Hankel) and computes the horizontal and vertical system orders, system parameter matrices and covariance matrices of a 2D CRSD Roesser model. From a computational point of view, the algorithm has been presented in a unified framework, where the user can select which of the three methods to use. Furthermore, the identification task is divided into three main parts: (1) computing the deterministic horizontal model parameters, (2) computing the deterministic vertical model parameters and (3) computing the stochastic components. Specific attention has been paid to the computation of a stabilised Kalman gain matrix and a positive real solution when required. The efficiency and robustness of the unified algorithm have been demonstrated via a thorough simulation example.

Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis.

PubMed

Kremer, Lukas P M; Leufken, Johannes; Oyunchimeg, Purevdulam; Schulze, Stefan; Fufezan, Christian

2016-03-04

Proteomics data integration has become a broad field with a variety of programs offering innovative algorithms to analyze increasing amounts of data. Unfortunately, this software diversity leads to many problems as soon as the data is analyzed using more than one algorithm for the same task. Although it was shown that the combination of multiple peptide identification algorithms yields more robust results, it is only recently that unified approaches are emerging; however, workflows that, for example, aim to optimize search parameters or that employ cascaded style searches can only be made accessible if data analysis becomes not only unified but also and most importantly scriptable. Here we introduce Ursgal, a Python interface to many commonly used bottom-up proteomics tools and to additional auxiliary programs. Complex workflows can thus be composed using the Python scripting language using a few lines of code. Ursgal is easily extensible, and we have made several database search engines (X!Tandem, OMSSA, MS-GF+, Myrimatch, MS Amanda), statistical postprocessing algorithms (qvality, Percolator), and one algorithm that combines statistically postprocessed outputs from multiple search engines ("combined FDR") accessible as an interface in Python. Furthermore, we have implemented a new algorithm ("combined PEP") that combines multiple search engines employing elements of "combined FDR", PeptideShaker, and Bayes' theorem.

Upper cervical injuries: Clinical results using a new treatment algorithm

PubMed Central

Joaquim, Andrei F.; Ghizoni, Enrico; Tedeschi, Helder; Yacoub, Alexandre R. D.; Brodke, Darrel S.; Vaccaro, Alexander R.; Patel, Alpesh A.

2015-01-01

Introduction: Upper cervical injuries (UCI) have a wide range of radiological and clinical presentation due to the unique complex bony, ligamentous and vascular anatomy. We recently proposed a rational approach in an attempt to unify prior classification system and guide treatment. In this paper, we evaluate the clinical results of our algorithm for UCI treatment. Materials and Methods: A prospective cohort series of patients with UCI was performed. The primary outcome was the AIS. Surgical treatment was proposed based on our protocol: Ligamentous injuries (abnormal misalignment, facet perched or locked, increase atlanto-dens interval) were treated surgically. Bone fractures without ligamentous injuries were treated with a rigid cervical orthosis, with exception of fractures in the dens base with risk factors for non-union. Results: Twenty-three patients treated initially conservatively had some follow-up (mean of 171 days, range from 60 to 436 days). All of them were neurologically intact. None of the patients developed a new neurological deficit. Fifteen patients were initially surgically treated (mean of 140 days of follow-up, ranging from 60 to 270 days). In the surgical group, preoperatively, 11 (73.3%) patients were AIS E, 2 (13.3%) AIS C and 2 (13.3%) AIS D. At the final follow-up, the American Spine Injury Association (ASIA) score was: 13 (86.6%) AIS E and 2 (13.3%) AIS D. None of the patients had neurological worsening during the follow-up. Conclusions: This prospective cohort suggested that our UCI treatment algorithm can be safely used. Further prospective studies with longer follow-up are necessary to further establish its clinical validity and safety. PMID:25788816

Anomaly-Related Pathologic Atlantoaxial Displacement in Pediatric Patients.

PubMed

Pavlova, Olga M; Ryabykh, Sergey O; Burcev, Alexander V; Gubin, Alexander V

2018-06-01

To analyze clinical and radiologic features of pathologic atlantoaxial displacement (PAAD) in pediatric patients and to compose a treatment algorithm for anomaly-related PAAD. Criteria of different types of PAAD and treatment algorithms have been widely reported in the literature but are difficult to apply to patients with odontoid abnormalities, C2-C3 block, spina bifida C1, and children. We evaluated results of treatment of 29 pediatric patients with PAAD caused by congenital anomalies of the craniovertebral junction (CVJ), treated in Ilizarov Center in 2009-2017, including 20 patients with atlantoaxial displacement (AAD) and 9 patients with atlantoaxial rotatory fixation. There were 14 males (48.3%) and 15 females (51.7%). We singled out 3 groups of patients: nonsyndromic (6 patients, 20.7%), Klippel-Feil syndrome (13 patients, 44.8%), and syndromic (10 patients, 34.5%). Odontoid abnormalities and C1 dysplasia were widely represented in the syndromic group. Local symptoms predominated in the nonsyndromic and KFS groups. In the syndromic group, all patients had AAD and myelopathy. A pronounced decrease of space available for chord C1 and increase of anterior atlantodental interval were noted compared with other groups. We present a unified treatment algorithm of pediatric anomaly-related PAAD. Syndromic AAD are often accompanied by anterior and central dislocation and myelopathy and atlantooccipital dissociation. These patients require early aggressive surgical treatment. Nonsyndromic and Klippel-Feil syndrome AAD, atlantoaxial subluxation, and atlantoaxial fixation often manifest by local symptoms and need to eliminate CVJ instability. Existing classifications of symptomatic atlantoaxial displacement are not always suitable for patients with CVJ abnormalities. Copyright © 2018 Elsevier Inc. All rights reserved.

Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text

PubMed Central

Bejan, Cosmin Adrian; Wei, Wei-Qi; Denny, Joshua C

2015-01-01

Objective To evaluate the contribution of the MEDication Indication (MEDI) resource and SemRep for identifying treatment relations in clinical text. Materials and methods We first processed clinical documents with SemRep to extract the Unified Medical Language System (UMLS) concepts and the treatment relations between them. Then, we incorporated MEDI into a simple algorithm that identifies treatment relations between two concepts if they match a medication-indication pair in this resource. For a better coverage, we expanded MEDI using ontology relationships from RxNorm and UMLS Metathesaurus. We also developed two ensemble methods, which combined the predictions of SemRep and the MEDI algorithm. We evaluated our selected methods on two datasets, a Vanderbilt corpus of 6864 discharge summaries and the 2010 Informatics for Integrating Biology and the Bedside (i2b2)/Veteran's Affairs (VA) challenge dataset. Results The Vanderbilt dataset included 958 manually annotated treatment relations. A double annotation was performed on 25% of relations with high agreement (Cohen's κ = 0.86). The evaluation consisted of comparing the manual annotated relations with the relations identified by SemRep, the MEDI algorithm, and the two ensemble methods. On the first dataset, the best F1-measure results achieved by the MEDI algorithm and the union of the two resources (78.7 and 80, respectively) were significantly higher than the SemRep results (72.3). On the second dataset, the MEDI algorithm achieved better precision and significantly lower recall values than the best system in the i2b2 challenge. The two systems obtained comparable F1-measure values on the subset of i2b2 relations with both arguments in MEDI. Conclusions Both SemRep and MEDI can be used to extract treatment relations from clinical text. Knowledge-based extraction with MEDI outperformed use of SemRep alone, but superior performance was achieved by integrating both systems. The integration of knowledge-based resources such as MEDI into information extraction systems such as SemRep and the i2b2 relation extractors may improve treatment relation extraction from clinical text. PMID:25336593

Implicit gas-kinetic unified algorithm based on multi-block docking grid for multi-body reentry flows covering all flow regimes

NASA Astrophysics Data System (ADS)

Peng, Ao-Ping; Li, Zhi-Hui; Wu, Jun-Lin; Jiang, Xin-Yu

2016-12-01

Based on the previous researches of the Gas-Kinetic Unified Algorithm (GKUA) for flows from highly rarefied free-molecule transition to continuum, a new implicit scheme of cell-centered finite volume method is presented for directly solving the unified Boltzmann model equation covering various flow regimes. In view of the difficulty in generating the single-block grid system with high quality for complex irregular bodies, a multi-block docking grid generation method is designed on the basis of data transmission between blocks, and the data structure is constructed for processing arbitrary connection relations between blocks with high efficiency and reliability. As a result, the gas-kinetic unified algorithm with the implicit scheme and multi-block docking grid has been firstly established and used to solve the reentry flow problems around the multi-bodies covering all flow regimes with the whole range of Knudsen numbers from 10 to 3.7E-6. The implicit and explicit schemes are applied to computing and analyzing the supersonic flows in near-continuum and continuum regimes around a circular cylinder with careful comparison each other. It is shown that the present algorithm and modelling possess much higher computational efficiency and faster converging properties. The flow problems including two and three side-by-side cylinders are simulated from highly rarefied to near-continuum flow regimes, and the present computed results are found in good agreement with the related DSMC simulation and theoretical analysis solutions, which verify the good accuracy and reliability of the present method. It is observed that the spacing of the multi-body is smaller, the cylindrical throat obstruction is greater with the flow field of single-body asymmetrical more obviously and the normal force coefficient bigger. While in the near-continuum transitional flow regime of near-space flying surroundings, the spacing of the multi-body increases to six times of the diameter of the single-body, the interference effects of the multi-bodies tend to be negligible. The computing practice has confirmed that it is feasible for the present method to compute the aerodynamics and reveal flow mechanism around complex multi-body vehicles covering all flow regimes from the gas-kinetic point of view of solving the unified Boltzmann model velocity distribution function equation.

An LMI approach to design H(infinity) controllers for discrete-time nonlinear systems based on unified models.

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

Toward a unifying framework for evolutionary processes.

PubMed

Paixão, Tiago; Badkobeh, Golnaz; Barton, Nick; Çörüş, Doğan; Dang, Duc-Cuong; Friedrich, Tobias; Lehre, Per Kristian; Sudholt, Dirk; Sutton, Andrew M; Trubenová, Barbora

2015-10-21

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Unified Mathematical Approach to Image Analysis.

DTIC Science & Technology

1987-08-31

describes four instances of the paradigm in detail. Directions for ongoing and future research are also indicated. Keywords: Image processing; Algorithms; Segmentation; Boundary detection; tomography; Global image analysis .

Color transfer algorithm in medical images

NASA Astrophysics Data System (ADS)

Wang, Weihong; Xu, Yangfa

2007-12-01

In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.

Unified algorithm of cone optics to compute solar flux on central receiver

NASA Astrophysics Data System (ADS)

Grigoriev, Victor; Corsi, Clotilde

2017-06-01

Analytical algorithms to compute flux distribution on central receiver are considered as a faster alternative to ray tracing. They have quite too many modifications, with HFLCAL and UNIZAR being the most recognized and verified. In this work, a generalized algorithm is presented which is valid for arbitrary sun shape of radial symmetry. Heliostat mirrors can have a nonrectangular profile, and the effects of shading and blocking, strong defocusing and astigmatism can be taken into account. The algorithm is suitable for parallel computing and can benefit from hardware acceleration of polygon texturing.

Canonical Duality Theory and Algorithms for Solving Some Challenging Problems in Global Optimization and Decision Science

DTIC Science & Technology

2015-09-24

algorithms for solving real- world problems. Within the past five years, 2 books, 5 journal special issues, and about 60 papers have been published...Four international conferences have been organized, including the 3rd World Congress of Global Optimization. A unified methodology and algorithm have...been developed with real- world applications. This grant has been used to support and co-support three post-doctors, three PhD students, one part

A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging.

PubMed

Koay, Cheng Guan; Chang, Lin-Ching; Carew, John D; Pierpaoli, Carlo; Basser, Peter J

2006-09-01

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these objective functions, i.e., Hessian matrices. These theoretical connections provide new insights in designing efficient algorithms for NLS and constrained NLS (CNLS) estimation. Here, we propose novel algorithms of full Newton-type for the NLS and CNLS estimations, which are evaluated with Monte Carlo simulations and compared with the commonly used Levenberg-Marquardt method. The proposed methods have a lower percent of relative error in estimating the trace and lower reduced chi2 value than those of the Levenberg-Marquardt method. These results also demonstrate that the accuracy of an estimate, particularly in a nonlinear estimation problem, is greatly affected by the Hessian matrix. In other words, the accuracy of a nonlinear estimation is algorithm-dependent. Further, this study shows that the noise variance in diffusion weighted signals is orientation dependent when signal-to-noise ratio (SNR) is low (

Analysis of miRNA expression profile based on SVM algorithm

NASA Astrophysics Data System (ADS)

Ting-ting, Dai; Chang-ji, Shan; Yan-shou, Dong; Yi-duo, Bian

2018-05-01

Based on mirna expression spectrum data set, a new data mining algorithm - tSVM - KNN (t statistic with support vector machine - k nearest neighbor) is proposed. the idea of the algorithm is: firstly, the feature selection of the data set is carried out by the unified measurement method; Secondly, SVM - KNN algorithm, which combines support vector machine (SVM) and k - nearest neighbor (k - nearest neighbor) is used as classifier. Simulation results show that SVM - KNN algorithm has better classification ability than SVM and KNN alone. Tsvm - KNN algorithm only needs 5 mirnas to obtain 96.08 % classification accuracy in terms of the number of mirna " tags" and recognition accuracy. compared with similar algorithms, tsvm - KNN algorithm has obvious advantages.

A single-index threshold Cox proportional hazard model for identifying a treatment-sensitive subset based on multiple biomarkers.

PubMed

He, Ye; Lin, Huazhen; Tu, Dongsheng

2018-06-04

In this paper, we introduce a single-index threshold Cox proportional hazard model to select and combine biomarkers to identify patients who may be sensitive to a specific treatment. A penalized smoothed partial likelihood is proposed to estimate the parameters in the model. A simple, efficient, and unified algorithm is presented to maximize this likelihood function. The estimators based on this likelihood function are shown to be consistent and asymptotically normal. Under mild conditions, the proposed estimators also achieve the oracle property. The proposed approach is evaluated through simulation analyses and application to the analysis of data from two clinical trials, one involving patients with locally advanced or metastatic pancreatic cancer and one involving patients with resectable lung cancer. Copyright © 2018 John Wiley & Sons, Ltd.

Generic-distributed framework for cloud services marketplace based on unified ontology.

PubMed

Hasan, Samer; Valli Kumari, V

2017-11-01

Cloud computing is a pattern for delivering ubiquitous and on demand computing resources based on pay-as-you-use financial model. Typically, cloud providers advertise cloud service descriptions in various formats on the Internet. On the other hand, cloud consumers use available search engines (Google and Yahoo) to explore cloud service descriptions and find the adequate service. Unfortunately, general purpose search engines are not designed to provide a small and complete set of results, which makes the process a big challenge. This paper presents a generic-distrusted framework for cloud services marketplace to automate cloud services discovery and selection process, and remove the barriers between service providers and consumers. Additionally, this work implements two instances of generic framework by adopting two different matching algorithms; namely dominant and recessive attributes algorithm borrowed from gene science and semantic similarity algorithm based on unified cloud service ontology. Finally, this paper presents unified cloud services ontology and models the real-life cloud services according to the proposed ontology. To the best of the authors' knowledge, this is the first attempt to build a cloud services marketplace where cloud providers and cloud consumers can trend cloud services as utilities. In comparison with existing work, semantic approach reduced the execution time by 20% and maintained the same values for all other parameters. On the other hand, dominant and recessive attributes approach reduced the execution time by 57% but showed lower value for recall.

Update of the Mexican College of Rheumatology guidelines for the pharmacologic treatment of rheumatoid arthritis.

PubMed

Cardiel, Mario H; Díaz-Borjón, Alejandro; Vázquez del Mercado Espinosa, Mónica; Gámez-Nava, Jorge Iván; Barile Fabris, Leonor A; Pacheco Tena, César; Silveira Torre, Luis H; Pascual Ramos, Virginia; Goycochea Robles, María Victoria; Aguilar Arreola, Jorge Enrique; González Díaz, Verónica; Alvarez Nemegyei, José; González-López, Laura del Carmen; Salazar Páramo, Mario; Portela Hernández, Margarita; Castro Colín, Zully; Xibillé Friedman, Daniel Xavier; Alvarez Hernández, Everardo; Casasola Vargas, Julio; Cortés Hernández, Miguel; Flores-Alvarado, Diana E; Martínez Martínez, Laura A; Vega-Morales, David; Flores-Suárez, Luis Felipe; Medrano Ramírez, Gabriel; Barrera Cruz, Antonio; García González, Adolfo; López López, Susana Marisela; Rosete Reyes, Alejandra; Espinosa Morales, Rolando

2014-01-01

The pharmacologic management of rheumatoid arthritis has progressed substantially over the past years. It is therefore desirable that existing information be periodically updated. There are several published international guidelines for the treatment of rheumatoid arthritis that hardly adapt to the Mexican health system because of its limited healthcare resources. Hence, it is imperative to unify the existing recommendations and to incorporate them to a set of clinical, updated recommendations; the Mexican College of Rheumatology developed these recommendations in order to offer an integral management approach of rheumatoid arthritis according to the resources of the Mexican health system. To review, update and improve the available evidence within clinical practice guidelines on the pharmacological management of rheumatoid arthritis and produce a set of recommendations adapted to the Mexican health system, according to evidence available through December 2012. The working group was composed of 30 trained and experienced rheumatologists with a high quality of clinical knowledge and judgment. Recommendations were based on the highest quality evidence from the previously established treatment guidelines, meta-analysis and controlled clinical trials for the adult population with rheumatoid arthritis. During the conformation of this document, each working group settled the existing evidence from the different topics according to their experience. Finally, all the evidence and decisions were unified into a single document, treatment algorithm and drug standardization tables. This update of the Mexican Guidelines for the Pharmacologic Treatment of Rheumatoid Arthritis provides the highest quality information available at the time the working group undertook this review and contextualizes its use for the complex Mexican health system. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Deep linear autoencoder and patch clustering-based unified one-dimensional coding of image and video

NASA Astrophysics Data System (ADS)

Li, Honggui

2017-09-01

This paper proposes a unified one-dimensional (1-D) coding framework of image and video, which depends on deep learning neural network and image patch clustering. First, an improved K-means clustering algorithm for image patches is employed to obtain the compact inputs of deep artificial neural network. Second, for the purpose of best reconstructing original image patches, deep linear autoencoder (DLA), a linear version of the classical deep nonlinear autoencoder, is introduced to achieve the 1-D representation of image blocks. Under the circumstances of 1-D representation, DLA is capable of attaining zero reconstruction error, which is impossible for the classical nonlinear dimensionality reduction methods. Third, a unified 1-D coding infrastructure for image, intraframe, interframe, multiview video, three-dimensional (3-D) video, and multiview 3-D video is built by incorporating different categories of videos into the inputs of patch clustering algorithm. Finally, it is shown in the results of simulation experiments that the proposed methods can simultaneously gain higher compression ratio and peak signal-to-noise ratio than those of the state-of-the-art methods in the situation of low bitrate transmission.

Regularization and computational methods for precise solution of perturbed orbit transfer problems

NASA Astrophysics Data System (ADS)

Woollands, Robyn Michele

The author has developed a suite of algorithms for solving the perturbed Lambert's problem in celestial mechanics. These algorithms have been implemented as a parallel computation tool that has broad applicability. This tool is composed of four component algorithms and each provides unique benefits for solving a particular type of orbit transfer problem. The first one utilizes a Keplerian solver (a-iteration) for solving the unperturbed Lambert's problem. This algorithm not only provides a "warm start" for solving the perturbed problem but is also used to identify which of several perturbed solvers is best suited for the job. The second algorithm solves the perturbed Lambert's problem using a variant of the modified Chebyshev-Picard iteration initial value solver that solves two-point boundary value problems. This method converges over about one third of an orbit and does not require a Newton-type shooting method and thus no state transition matrix needs to be computed. The third algorithm makes use of regularization of the differential equations through the Kustaanheimo-Stiefel transformation and extends the domain of convergence over which the modified Chebyshev-Picard iteration two-point boundary value solver will converge, from about one third of an orbit to almost a full orbit. This algorithm also does not require a Newton-type shooting method. The fourth algorithm uses the method of particular solutions and the modified Chebyshev-Picard iteration initial value solver to solve the perturbed two-impulse Lambert problem over multiple revolutions. The method of particular solutions is a shooting method but differs from the Newton-type shooting methods in that it does not require integration of the state transition matrix. The mathematical developments that underlie these four algorithms are derived in the chapters of this dissertation. For each of the algorithms, some orbit transfer test cases are included to provide insight on accuracy and efficiency of these individual algorithms. Following this discussion, the combined parallel algorithm, known as the unified Lambert tool, is presented and an explanation is given as to how it automatically selects which of the three perturbed solvers to compute the perturbed solution for a particular orbit transfer. The unified Lambert tool may be used to determine a single orbit transfer or for generating of an extremal field map. A case study is presented for a mission that is required to rendezvous with two pieces of orbit debris (spent rocket boosters). The unified Lambert tool software developed in this dissertation is already being utilized by several industrial partners and we are confident that it will play a significant role in practical applications, including solution of Lambert problems that arise in the current applications focused on enhanced space situational awareness.

Unified Method for Delay Analysis of Random Multiple Access Algorithms.

DTIC Science & Technology

1985-08-01

packets in the first cell of the stack. The rules of the algorithm yield the following relation for the wi’s: n-1 n w 0= ; W =1; i i 9Q h I+ + zwI .+N...for computer communica- tions", in Proc. 1970 Fall Joint Computer Conf., AFIPS Press, vol. 37, 1970, pp. 281 -285. (15] N. D. Vvedenskaya and B. S

Two stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test study.

PubMed

Samanipour, Saer; Baz-Lomba, Jose A; Alygizakis, Nikiforos A; Reid, Malcolm J; Thomaidis, Nikolaos S; Thomas, Kevin V

2017-06-09

LC-HR-QTOF-MS recently has become a commonly used approach for the analysis of complex samples. However, identification of small organic molecules in complex samples with the highest level of confidence is a challenging task. Here we report on the implementation of a two stage algorithm for LC-HR-QTOF-MS datasets. We compared the performances of the two stage algorithm, implemented via NIVA_MZ_Analyzer™, with two commonly used approaches (i.e. feature detection and XIC peak picking, implemented via UNIFI by Waters and TASQ by Bruker, respectively) for the suspect analysis of four influent wastewater samples. We first evaluated the cross platform compatibility of LC-HR-QTOF-MS datasets generated via instruments from two different manufacturers (i.e. Waters and Bruker). Our data showed that with an appropriate spectral weighting function the spectra recorded by the two tested instruments are comparable for our analytes. As a consequence, we were able to perform full spectral comparison between the data generated via the two studied instruments. Four extracts of wastewater influent were analyzed for 89 analytes, thus 356 detection cases. The analytes were divided into 158 detection cases of artificial suspect analytes (i.e. verified by target analysis) and 198 true suspects. The two stage algorithm resulted in a zero rate of false positive detection, based on the artificial suspect analytes while producing a rate of false negative detection of 0.12. For the conventional approaches, the rates of false positive detection varied between 0.06 for UNIFI and 0.15 for TASQ. The rates of false negative detection for these methods ranged between 0.07 for TASQ and 0.09 for UNIFI. The effect of background signal complexity on the two stage algorithm was evaluated through the generation of a synthetic signal. We further discuss the boundaries of applicability of the two stage algorithm. The importance of background knowledge and experience in evaluating the reliability of results during the suspect screening was evaluated. Copyright © 2017 Elsevier B.V. All rights reserved.

A unifying framework for rigid multibody dynamics and serial and parallel computational issues

NASA Technical Reports Server (NTRS)

Fijany, Amir; Jain, Abhinandan

1989-01-01

A unifying framework for various formulations of the dynamics of open-chain rigid multibody systems is discussed. Their suitability for serial and parallel processing is assessed. The framework is based on the derivation of intrinsic, i.e., coordinate-free, equations of the algorithms which provides a suitable abstraction and permits a distinction to be made between the computational redundancy in the intrinsic and extrinsic equations. A set of spatial notation is used which allows the derivation of the various algorithms in a common setting and thus clarifies the relationships among them. The three classes of algorithms viz., O(n), O(n exp 2) and O(n exp 3) or the solution of the dynamics problem are investigated. Researchers begin with the derivation of O(n exp 3) algorithms based on the explicit computation of the mass matrix and it provides insight into the underlying basis of the O(n) algorithms. From a computational perspective, the optimal choice of a coordinate frame for the projection of the intrinsic equations is discussed and the serial computational complexity of the different algorithms is evaluated. The three classes of algorithms are also analyzed for suitability for parallel processing. It is shown that the problem belongs to the class of N C and the time and processor bounds are of O(log2/2(n)) and O(n exp 4), respectively. However, the algorithm that achieves the above bounds is not stable. Researchers show that the fastest stable parallel algorithm achieves a computational complexity of O(n) with O(n exp 4), respectively. However, the algorithm that achieves the above bounds is not stable. Researchers show that the fastest stable parallel algorithm achieves a computational complexity of O(n) with O(n exp 2) processors, and results from the parallelization of the O(n exp 3) serial algorithm.

Implementation and evaluation of various demons deformable image registration algorithms on a GPU.

PubMed

Gu, Xuejun; Pan, Hubert; Liang, Yun; Castillo, Richard; Yang, Deshan; Choi, Dongju; Castillo, Edward; Majumdar, Amitava; Guerrero, Thomas; Jiang, Steve B

2010-01-07

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A gray-scale-based DIR algorithm called demons and five of its variants were implemented on GPUs using the compute unified device architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4D CT images with an average size of 256 x 256 x 100 and more than 1100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 s to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency and ease of implementation.

Automated Conflict Resolution, Arrival Management and Weather Avoidance for ATM

NASA Technical Reports Server (NTRS)

Erzberger, H.; Lauderdale, Todd A.; Chu, Yung-Cheng

2010-01-01

The paper describes a unified solution to three types of separation assurance problems that occur in en-route airspace: separation conflicts, arrival sequencing, and weather-cell avoidance. Algorithms for solving these problems play a key role in the design of future air traffic management systems such as NextGen. Because these problems can arise simultaneously in any combination, it is necessary to develop integrated algorithms for solving them. A unified and comprehensive solution to these problems provides the foundation for a future air traffic management system that requires a high level of automation in separation assurance. The paper describes the three algorithms developed for solving each problem and then shows how they are used sequentially to solve any combination of these problems. The first algorithm resolves loss-of-separation conflicts and is an evolution of an algorithm described in an earlier paper. The new version generates multiple resolutions for each conflict and then selects the one giving the least delay. Two new algorithms, one for sequencing and merging of arrival traffic, referred to as the Arrival Manager, and the other for weather-cell avoidance are the major focus of the paper. Because these three problems constitute a substantial fraction of the workload of en-route controllers, integrated algorithms to solve them is a basic requirement for automated separation assurance. The paper also reviews the Advanced Airspace Concept, a proposed design for a ground-based system that postulates redundant systems for separation assurance in order to achieve both high levels of safety and airspace capacity. It is proposed that automated separation assurance be introduced operationally in several steps, each step reducing controller workload further while increasing airspace capacity. A fast time simulation was used to determine performance statistics of the algorithm at up to 3 times current traffic levels.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

PubMed Central

Vianney Kinani, Jean Marie; Gallegos Funes, Francisco; Mújica Vargas, Dante; Ramos Díaz, Eduardo; Arellano, Alfonso

2017-01-01

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time. PMID:29158887

A Unified, Transdiagnostic Treatment for Adolescents With Chronic Pain and Comorbid Anxiety and Depression

PubMed Central

Allen, Laura B.; Tsao, Jennie C.I.; Seidman, Laura C.; Ehrenreich-May, Jill; Zeltzer, Lonnie K.

2017-01-01

Chronic pain disorders represent a significant public health concern, particularly for children and adolescents. High rates of comorbid anxiety and unipolar mood disorders often complicate psychological interventions for chronic pain. Unified treatment approaches, based on emotion regulation skills, are applicable to a broad range of emotional disorders and suggest the possibility of extending these interventions to chronic pain and pain-related dysfunction. This case report describes the use of a unified protocol for treatment of an adolescent boy with chronic daily headache and social anxiety and an adolescent girl with whole body pain and depression. Following weekly, 50-minute individual treatment sessions, the boy demonstrated notable improvement in emotional symptoms, emotion regulation skills, somatization, and functional disability. The girl showed some improvement on measures of anxiety and depression, although there appeared to be a worsening of pain symptoms and somatization. However, both patients demonstrated improvement over follow-up. This case study illustrates the potential utility of a unified treatment approach targeting pain and emotional symptoms from an emotion regulation perspective in an adolescent population. PMID:28824271

Gas-kinetic unified algorithm for hypersonic flows covering various flow regimes solving Boltzmann model equation in nonequilibrium effect

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

Li, Zhihui; Ma, Qiang; Wu, Junlin

2014-12-09

Based on the Gas-Kinetic Unified Algorithm (GKUA) directly solving the Boltzmann model equation, the effect of rotational non-equilibrium is investigated recurring to the kinetic Rykov model with relaxation property of rotational degrees of freedom. The spin movement of diatomic molecule is described by moment of inertia, and the conservation of total angle momentum is taken as a new Boltzmann collision invariant. The molecular velocity distribution function is integrated by the weight factor on the internal energy, and the closed system of two kinetic controlling equations is obtained with inelastic and elastic collisions. The optimization selection technique of discrete velocity ordinatemore » points and numerical quadrature rules for macroscopic flow variables with dynamic updating evolvement are developed to simulate hypersonic flows, and the gas-kinetic numerical scheme is constructed to capture the time evolution of the discretized velocity distribution functions. The gas-kinetic boundary conditions in thermodynamic non-equilibrium and numerical procedures are studied and implemented by directly acting on the velocity distribution function, and then the unified algorithm of Boltzmann model equation involving non-equilibrium effect is presented for the whole range of flow regimes. The hypersonic flows involving non-equilibrium effect are numerically simulated including the inner flows of shock wave structures in nitrogen with different Mach numbers of 1.5-Ma-25, the planar ramp flow with the whole range of Knudsen numbers of 0.0009-Kn-10 and the three-dimensional re-entering flows around tine double-cone body.« less

Some implementational issues of convection schemes for finite volume formulations

NASA Technical Reports Server (NTRS)

Thakur, Siddharth; Shyy, Wei

1993-01-01

Two higher-order upwind schemes - second-order upwind and QUICK - are examined in terms of their interpretation, implementation as well as performance for a recirculating flow in a lid-driven cavity, in the context of a control volume formulation using the SIMPLE algorithm. The present formulation of these schemes is based on a unified framework wherein the first-order upwind scheme is chosen as the basis, with the remaining terms being assigned to the source term. The performance of these schemes is contrasted with the first-order upwind and second-order central difference schemes. Also addressed in this study is the issue of boundary treatment associated with these higher-order upwind schemes. Two different boundary treatments - one that uses a two-point scheme consistently within a given control volume at the boundary, and the other that maintains consistency of flux across the interior face between the adjacent control volumes - are formulated and evaluated.

Some implementational issues of convection schemes for finite-volume formulations

NASA Technical Reports Server (NTRS)

Thakur, Siddharth; Shyy, Wei

1993-01-01

Two higher-order upwind schemes - second-order upwind and QUICK - are examined in terms of their interpretation, implementations, as well as performance for a recirculating flow in a lid-driven cavity, in the context of a control-volume formulation using the SIMPLE algorithm. The present formulation of these schemes is based on a unified framework wherein the first-order upwind scheme is chosen as the basis, with the remaining terms being assigned to the source term. The performance of these schemes is contrasted with the first-order upwind and second-order central difference schemes. Also addressed in this study is the issue of boundary treatment associated with these higher-order upwind schemes. Two different boundary treatments - one that uses a two-point scheme consistently within a given control volume at the boundary, and the other that maintains consistency of flux across the interior face between the adjacent control volumes - are formulated and evaluated.

The utilization of unified protocols in behavioral cognitive therapy in transdiagnostic group subjects: A clinical trial.

PubMed

de Ornelas Maia, Ana Claudia Corrêa; Nardi, Antonio Egidio; Cardoso, Adriana

2015-02-01

The practicing of protocols based on behavioral cognitive therapy (CBT) have been frequently used in the last decades and adapted to better manage the necessities of patients and providers. The goal is to build a treatment that is evidence-based - for that reason the unified protocol for multiple emotional disorders (transdiagnostics) have been utilized to simplify treatment - without losing scientific traits. The main goal of this study is to evaluate the unified protocol in groups of patients with depression and anxiety disorders. In a pool of 48 subjects, divided in two groups, one was submitted to 12 intervention sessions of the unified protocol while the other was solely given medication. MINI, BAI and BDI were the instruments used at the beginning and at the end of treatment. The results were highly significant (p<0.001) in as much as with the improvement of anxiety and depressive disorders as it was in the group which was treated with the unified protocol compared with the group which was only given medication Limitations of this study were the number of sample participants and the non-randomization of subjects in both groups. Group therapy has not been largely implemented though it is deemed very useful for treatments when the unified protocol is used in transdiagnostic patients. Not only does it allow for emotional stabilizing and socialization but it also enables subjects with an altruistic feeling amongst themselves. Copyright © 2014 Elsevier B.V. All rights reserved.

Unified Protocol for the Transdiagnostic Treatment of Emotional Disorders: Protocol Development and Initial Outcome Data

ERIC Educational Resources Information Center

Ellard, Kristen K.; Fairholme, Christopher P.; Boisseau, Christina L.; Farchione, Todd J.; Barlow, David H.

2010-01-01

The Unified Protocol (UP) is a transdiagnostic, emotion-focused cognitive-behavioral treatment developed to be applicable across the emotional disorders. The UP consists of 4 core modules: increasing emotional awareness, facilitating flexibility in appraisals, identifying and preventing behavioral and emotional avoidance, and situational and…

GREIT: a unified approach to 2D linear EIT reconstruction of lung images.

PubMed

Adler, Andy; Arnold, John H; Bayford, Richard; Borsic, Andrea; Brown, Brian; Dixon, Paul; Faes, Theo J C; Frerichs, Inéz; Gagnon, Hervé; Gärber, Yvo; Grychtol, Bartłomiej; Hahn, Günter; Lionheart, William R B; Malik, Anjum; Patterson, Robert P; Stocks, Janet; Tizzard, Andrew; Weiler, Norbert; Wolf, Gerhard K

2009-06-01

Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.

Evolution of abdominal wall reconstruction: development of a unified algorithm with improved outcomes.

PubMed

Koltz, Peter F; Frey, Jordan D; Bell, Derek E; Girotto, John A; Christiano, Jose G; Langstein, Howard N

2013-11-01

Ventral hernia repair (VHR) continues to evolve and now frequently includes some form of component separation (CS) for large defects. To determine the optimal technique for VHR, we evaluated our outcomes before and after we refined and simplified our algorithm for repair. One hundred five consecutive patients undergoing VHR for large midline hernias over 9 years were examined. Patients were divided into those operated on after (group 1) and before (group 2) the institution of our simplified algorithm. Our algorithm emphasizes careful patient selection and a stepwise approach including, but not limited to, bilateral CS if appropriate, preservation of large perforators, retrorectus mesh placement as appropriate, linea alba or midline fascial closure, and vertical panniculectomy. Primary outcomes evaluated included wound infection, dehiscence, and hernia recurrence. Seventy-eight (74.3%) patients underwent repair using our algorithm (group 1), whereas 27 (25.7%) underwent repair before utilization of this algorithm (group 2). Ninety-eight (93.3%) underwent CS, whereas 7 (6.7%) underwent another form of VHR. There was no significant difference in patient age or defect size. The mean follow-up period in days for patients in group 1 and group 2 were 184.02 and 526.06, respectively (P < 0.001). Hernia recurrence in group 1 was 2.6% versus 29.6% in group 2 (P < 0.001). The incidence of wound infection in group 1 was 10.3%, whereas that in group 2 was 33.3% (P < 0.001). The rate of wound dehiscence in group 1 was 17.9% versus 25.9% in group 2 (P < 0.001). Simplifying and unifying our algorithm for VHR, notably with utilization of CS, has yielded improved results. Recurrence and wound healing complications using this approach are favorable compared with published outcomes.

Combining Multiple Rupture Models in Real-Time for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Minson, S. E.; Wu, S.; Beck, J. L.; Heaton, T. H.

2015-12-01

The ShakeAlert earthquake early warning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user's location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user's distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.

Rarefied gas flow simulations using high-order gas-kinetic unified algorithms for Boltzmann model equations

NASA Astrophysics Data System (ADS)

Li, Zhi-Hui; Peng, Ao-Ping; Zhang, Han-Xin; Yang, Jaw-Yen

2015-04-01

This article reviews rarefied gas flow computations based on nonlinear model Boltzmann equations using deterministic high-order gas-kinetic unified algorithms (GKUA) in phase space. The nonlinear Boltzmann model equations considered include the BGK model, the Shakhov model, the Ellipsoidal Statistical model and the Morse model. Several high-order gas-kinetic unified algorithms, which combine the discrete velocity ordinate method in velocity space and the compact high-order finite-difference schemes in physical space, are developed. The parallel strategies implemented with the accompanying algorithms are of equal importance. Accurate computations of rarefied gas flow problems using various kinetic models over wide ranges of Mach numbers 1.2-20 and Knudsen numbers 0.0001-5 are reported. The effects of different high resolution schemes on the flow resolution under the same discrete velocity ordinate method are studied. A conservative discrete velocity ordinate method to ensure the kinetic compatibility condition is also implemented. The present algorithms are tested for the one-dimensional unsteady shock-tube problems with various Knudsen numbers, the steady normal shock wave structures for different Mach numbers, the two-dimensional flows past a circular cylinder and a NACA 0012 airfoil to verify the present methodology and to simulate gas transport phenomena covering various flow regimes. Illustrations of large scale parallel computations of three-dimensional hypersonic rarefied flows over the reusable sphere-cone satellite and the re-entry spacecraft using almost the largest computer systems available in China are also reported. The present computed results are compared with the theoretical prediction from gas dynamics, related DSMC results, slip N-S solutions and experimental data, and good agreement can be found. The numerical experience indicates that although the direct model Boltzmann equation solver in phase space can be computationally expensive, nevertheless, the present GKUAs for kinetic model Boltzmann equations in conjunction with current available high-performance parallel computer power can provide a vital engineering tool for analyzing rarefied gas flows covering the whole range of flow regimes in aerospace engineering applications.

Evolutionary Approach for Relative Gene Expression Algorithms

PubMed Central

Czajkowski, Marcin

2014-01-01

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space. PMID:24790574

Mobility Research for Future Vehicles: A Methodology to Create a Unified Trade-Off Environment for Advanced Aerospace Vehicle

DTIC Science & Technology

2018-01-31

Language for SeBBAS ............................................................... 23 2.4.3 Running SeBBAS Algorithm in MATLAB...Input File Error Checking ................................................................................................... 76 4.4.3 Running ...99 6.2 5- Blade Rotor System Investigation

Applying Advances in GPM Radiometer Intercalibration and Algorithm Development to a Long-Term TRMM/GPM Global Precipitation Dataset

NASA Astrophysics Data System (ADS)

Berg, W. K.

2016-12-01

The Global Precipitation Mission (GPM) Core Observatory, which was launched in February of 2014, provides a number of advances for satellite monitoring of precipitation including a dual-frequency radar, high frequency channels on the GPM Microwave Imager (GMI), and coverage over middle and high latitudes. The GPM concept, however, is about producing unified precipitation retrievals from a constellation of microwave radiometers to provide approximately 3-hourly global sampling. This involves intercalibration of the input brightness temperatures from the constellation radiometers, development of an apriori precipitation database using observations from the state-of-the-art GPM radiometer and radars, and accounting for sensor differences in the retrieval algorithm in a physically-consistent way. Efforts by the GPM inter-satellite calibration working group, or XCAL team, and the radiometer algorithm team to create unified precipitation retrievals from the GPM radiometer constellation were fully implemented into the current version 4 GPM precipitation products. These include precipitation estimates from a total of seven conical-scanning and six cross-track scanning radiometers as well as high spatial and temporal resolution global level 3 gridded products. Work is now underway to extend this unified constellation-based approach to the combined TRMM/GPM data record starting in late 1997. The goal is to create a long-term global precipitation dataset employing these state-of-the-art calibration and retrieval algorithm approaches. This new long-term global precipitation dataset will incorporate the physics provided by the combined GPM GMI and DPR sensors into the apriori database, extend prior TRMM constellation observations to high latitudes, and expand the available TRMM precipitation data to the full constellation of available conical and cross-track scanning radiometers. This combined TRMM/GPM precipitation data record will thus provide a high-quality high-temporal resolution global dataset for use in a wide variety of weather and climate research applications.

A Unified, Transdiagnostic Treatment for Adolescents with Chronic Pain and Comorbid Anxiety and Depression

ERIC Educational Resources Information Center

Allen, Laura B.; Tsao, Jennie C. I.; Seidman, Laura C.; Ehrenreich-May, Jill; Zeltzer, Lonnie K.

2012-01-01

Chronic pain disorders represent a significant public health concern, particularly for children and adolescents. High rates of comorbid anxiety and unipolar mood disorders often complicate psychological interventions for chronic pain. Unified treatment approaches, based on emotion regulation skills, are applicable to a broad range of emotional…

Optimism in the face of uncertainty supported by a statistically-designed multi-armed bandit algorithm.

PubMed

Kamiura, Moto; Sano, Kohei

2017-10-01

The principle of optimism in the face of uncertainty is known as a heuristic in sequential decision-making problems. Overtaking method based on this principle is an effective algorithm to solve multi-armed bandit problems. It was defined by a set of some heuristic patterns of the formulation in the previous study. The objective of the present paper is to redefine the value functions of Overtaking method and to unify the formulation of them. The unified Overtaking method is associated with upper bounds of confidence intervals of expected rewards on statistics. The unification of the formulation enhances the universality of Overtaking method. Consequently we newly obtain Overtaking method for the exponentially distributed rewards, numerically analyze it, and show that it outperforms UCB algorithm on average. The present study suggests that the principle of optimism in the face of uncertainty should be regarded as the statistics-based consequence of the law of large numbers for the sample mean of rewards and estimation of upper bounds of expected rewards, rather than as a heuristic, in the context of multi-armed bandit problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Action Recommendation for Cyber Resilience

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

Choudhury, Sutanay; Rodriguez, Luke R.; Curtis, Darren S.

2015-09-01

This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems.

A class of least-squares filtering and identification algorithms with systolic array architectures

NASA Technical Reports Server (NTRS)

Kalson, Seth Z.; Yao, Kung

1991-01-01

A unified approach is presented for deriving a large class of new and previously known time- and order-recursive least-squares algorithms with systolic array architectures, suitable for high-throughput-rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Exponentially weighted least-squares criteria are considered for both sliding and growing memory.

Correction for the 17O interference in δ(13C) measurements when analyzing CO2 with stable isotope mass spectrometry

USGS Publications Warehouse

Coplen, Tyler B.; Brand, Willi A.; Assonov, Sergey S.

2010-01-01

Measurements of δ(13C) determined on CO2 with an isotope-ratio mass spectrometer (IRMS) must be corrected for the amount of 17O in the CO2. For data consistency, this must be done using identical methods by different laboratories. This report aims at unifying data treatment for CO2 IRMS by proposing (i) a unified set of numerical values, and (ii) a unified correction algorithm, based on a simple, linear approximation formula. Because the oxygen of natural CO2 is derived mostly from the global water pool, it is recommended that a value of 0.528 be employed for the factor λ, which relates differences in 17O and 18O abundances. With the currently accepted N(13C)/N(12C) of 0.011 180(28) in VPDB (Vienna Peedee belemnite) reevaluation of data yields a value of 0.000 393(1) for the oxygen isotope ratio N(17O)/N(16O) of the evolved CO2. The ratio of these quantities, a ratio of isotope ratios, is essential for the 17O abundance correction: [N(17O)/N(16O)]/[N(13C)/N(12C)] = 0.035 16(8). The equation [δ(13C) ≈ 45δVPDB-CO2 + 2 17R/13R (45δVPDB-CO2 – λ46δVPDB-CO2)] closely approximates δ(13C) values with less than 0.010 ‰ deviation for normal oxygen-bearing materials and no more than 0.026 ‰ in extreme cases. Other materials containing oxygen of non-mass-dependent isotope composition require a more specific data treatment. A similar linear approximation is also suggested for δ(18O). The linear approximations are easy to implement in a data spreadsheet, and also help in generating a simplified uncertainty budget.

Complexity of Kronecker Operations on Sparse Matrices with Applications to the Solution of Markov Models

NASA Technical Reports Server (NTRS)

Buchholz, Peter; Ciardo, Gianfranco; Donatelli, Susanna; Kemper, Peter

1997-01-01

We present a systematic discussion of algorithms to multiply a vector by a matrix expressed as the Kronecker product of sparse matrices, extending previous work in a unified notational framework. Then, we use our results to define new algorithms for the solution of large structured Markov models. In addition to a comprehensive overview of existing approaches, we give new results with respect to: (1) managing certain types of state-dependent behavior without incurring extra cost; (2) supporting both Jacobi-style and Gauss-Seidel-style methods by appropriate multiplication algorithms; (3) speeding up algorithms that consider probability vectors of size equal to the "actual" state space instead of the "potential" state space.

Convergence Results on Iteration Algorithms to Linear Systems

PubMed Central

Wang, Zhuande; Yang, Chuansheng; Yuan, Yubo

2014-01-01

In order to solve the large scale linear systems, backward and Jacobi iteration algorithms are employed. The convergence is the most important issue. In this paper, a unified backward iterative matrix is proposed. It shows that some well-known iterative algorithms can be deduced with it. The most important result is that the convergence results have been proved. Firstly, the spectral radius of the Jacobi iterative matrix is positive and the one of backward iterative matrix is strongly positive (lager than a positive constant). Secondly, the mentioned two iterations have the same convergence results (convergence or divergence simultaneously). Finally, some numerical experiments show that the proposed algorithms are correct and have the merit of backward methods. PMID:24991640

Global Binary Optimization on Graphs for Classification of High Dimensional Data

DTIC Science & Technology

2014-09-01

Buades et al . in [10] introduce a new non-local means algorithm for image denoising and compare it to some of the best methods. In [28], Grady de...scribes a random walk algorithm for image seg- mentation using the solution to a Dirichlet prob- lem. Elmoataz et al . present generalizations of the...graph Laplacian [19] for image denoising and man- ifold smoothing. Couprie et al . in [16] propose a parameterized graph-based energy function that unifies

Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy

PubMed Central

Lu, Chao; Chelikani, Sudhakar; Jaffray, David A.; Milosevic, Michael F.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. PMID:22328178

The Unified Classification System (UCS): improving our understanding of periprosthetic fractures.

PubMed

Duncan, C P; Haddad, F S

2014-06-01

Periprosthetic fractures are an increasingly common complication following joint replacement. The principles which underpin their evaluation and treatment are common across the musculoskeletal system. The Unified Classification System proposes a rational approach to treatment, regardless of the bone that is broken or the joint involved. ©2014 The British Editorial Society of Bone & Joint Surgery.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

PubMed

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

Support Vector Data Descriptions and k-Means Clustering: One Class?

PubMed

Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms.

PubMed

De Sa, Christopher; Zhang, Ce; Olukotun, Kunle; Ré, Christopher

2015-12-01

Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of machine learning problems. Researchers and industry have developed several techniques to optimize SGD's runtime performance, including asynchronous execution and reduced precision. Our main result is a martingale-based analysis that enables us to capture the rich noise models that may arise from such techniques. Specifically, we use our new analysis in three ways: (1) we derive convergence rates for the convex case (Hogwild!) with relaxed assumptions on the sparsity of the problem; (2) we analyze asynchronous SGD algorithms for non-convex matrix problems including matrix completion; and (3) we design and analyze an asynchronous SGD algorithm, called Buckwild!, that uses lower-precision arithmetic. We show experimentally that our algorithms run efficiently for a variety of problems on modern hardware.

The high performance parallel algorithm for Unified Gas-Kinetic Scheme

NASA Astrophysics Data System (ADS)

Li, Shiyi; Li, Qibing; Fu, Song; Xu, Jinxiu

2016-11-01

A high performance parallel algorithm for UGKS is developed to simulate three-dimensional flows internal and external on arbitrary grid system. The physical domain and velocity domain are divided into different blocks and distributed according to the two-dimensional Cartesian topology with intra-communicators in physical domain for data exchange and other intra-communicators in velocity domain for sum reduction to moment integrals. Numerical results of three-dimensional cavity flow and flow past a sphere agree well with the results from the existing studies and validate the applicability of the algorithm. The scalability of the algorithm is tested both on small (1-16) and large (729-5832) scale processors. The tested speed-up ratio is near linear ashind thus the efficiency is around 1, which reveals the good scalability of the present algorithm.

A GPU-paralleled implementation of an enhanced face recognition algorithm

NASA Astrophysics Data System (ADS)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

A unified approach to computer analysis and modeling of spacecraft environmental interactions

NASA Technical Reports Server (NTRS)

Katz, I.; Mandell, M. J.; Cassidy, J. J.

1986-01-01

A new, coordinated, unified approach to the development of spacecraft plasma interaction models is proposed. The objective is to eliminate the unnecessary duplicative work in order to allow researchers to concentrate on the scientific aspects. By streamlining the developmental process, the interchange between theories and experimentalists is enhanced, and the transfer of technology to the spacecraft engineering community is faster. This approach is called the UNIfied Spacecraft Interaction Model (UNISIM). UNISIM is a coordinated system of software, hardware, and specifications. It is a tool for modeling and analyzing spacecraft interactions. It will be used to design experiments, to interpret results of experiments, and to aid in future spacecraft design. It breaks a Spacecraft Ineraction analysis into several modules. Each module will perform an analysis for some physical process, using phenomenology and algorithms which are well documented and have been subject to review. This system and its characteristics are discussed.

Unified constitutive material models for nonlinear finite-element structural analysis. [gas turbine engine blades and vanes

NASA Technical Reports Server (NTRS)

Kaufman, A.; Laflen, J. H.; Lindholm, U. S.

1985-01-01

Unified constitutive material models were developed for structural analyses of aircraft gas turbine engine components with particular application to isotropic materials used for high-pressure stage turbine blades and vanes. Forms or combinations of models independently proposed by Bodner and Walker were considered. These theories combine time-dependent and time-independent aspects of inelasticity into a continuous spectrum of behavior. This is in sharp contrast to previous classical approaches that partition inelastic strain into uncoupled plastic and creep components. Predicted stress-strain responses from these models were evaluated against monotonic and cyclic test results for uniaxial specimens of two cast nickel-base alloys, B1900+Hf and Rene' 80. Previously obtained tension-torsion test results for Hastelloy X alloy were used to evaluate multiaxial stress-strain cycle predictions. The unified models, as well as appropriate algorithms for integrating the constitutive equations, were implemented in finite-element computer codes.

The Unified Database for BM@N experiment data handling

NASA Astrophysics Data System (ADS)

Gertsenberger, Konstantin; Rogachevsky, Oleg

2018-04-01

The article describes the developed Unified Database designed as a comprehensive relational data storage for the BM@N experiment at the Joint Institute for Nuclear Research in Dubna. The BM@N experiment, which is one of the main elements of the first stage of the NICA project, is a fixed target experiment at extracted Nuclotron beams of the Laboratory of High Energy Physics (LHEP JINR). The structure and purposes of the BM@N setup are briefly presented. The article considers the scheme of the Unified Database, its attributes and implemented features in detail. The use of the developed BM@N database provides correct multi-user access to actual information of the experiment for data processing. It stores information on the experiment runs, detectors and their geometries, different configuration, calibration and algorithm parameters used in offline data processing. An important part of any database - user interfaces are presented.

Computational modeling of chemo-electro-mechanical coupling: A novel implicit monolithic finite element approach

PubMed Central

Wong, J.; Göktepe, S.; Kuhl, E.

2014-01-01

Summary Computational modeling of the human heart allows us to predict how chemical, electrical, and mechanical fields interact throughout a cardiac cycle. Pharmacological treatment of cardiac disease has advanced significantly over the past decades, yet it remains unclear how the local biochemistry of an individual heart cell translates into global cardiac function. Here we propose a novel, unified strategy to simulate excitable biological systems across three biological scales. To discretize the governing chemical, electrical, and mechanical equations in space, we propose a monolithic finite element scheme. We apply a highly efficient and inherently modular global-local split, in which the deformation and the transmembrane potential are introduced globally as nodal degrees of freedom, while the chemical state variables are treated locally as internal variables. To ensure unconditional algorithmic stability, we apply an implicit backward Euler finite difference scheme to discretize the resulting system in time. To increase algorithmic robustness and guarantee optimal quadratic convergence, we suggest an incremental iterative Newton-Raphson scheme. The proposed algorithm allows us to simulate the interaction of chemical, electrical, and mechanical fields during a representative cardiac cycle on a patient-specific geometry, robust and stable, with calculation times on the order of four days on a standard desktop computer. PMID:23798328

R3D: Reduction Package for Integral Field Spectroscopy

NASA Astrophysics Data System (ADS)

Sánchez, Sebastián. F.

2011-06-01

R3D was developed to reduce fiber-based integral field spectroscopy (IFS) data. The package comprises a set of command-line routines adapted for each of these steps, suitable for creating pipelines. The routines have been tested against simulations, and against real data from various integral field spectrographs (PMAS, PPAK, GMOS, VIMOS and INTEGRAL). Particular attention is paid to the treatment of cross-talk. R3D unifies the reduction techniques for the different IFS instruments to a single one, in order to allow the general public to reduce different instruments data in an homogeneus, consistent and simple way. Although still in its prototyping phase, it has been proved to be useful to reduce PMAS (both in the Larr and the PPAK modes), VIMOS and INTEGRAL data. The current version has been coded in Perl, using PDL, in order to speed-up the algorithm testing phase. Most of the time critical algorithms have been translated to C[float=][/float], and it is our intention to translate all of them. However, even in this phase R3D is fast enough to produce valuable science frames in reasonable time.

Predominance Diagrams, a Useful Tool for the Correlation of the Precipitation-Solubility Equilibrium with Other Ionic Equilibria

ERIC Educational Resources Information Center

Pereira, Constantino Fernandez; Alcalde, Manuel; Villegas, Rosario; Vale, Jose

2007-01-01

The four types of ionic equilibria--acid-base, redox, precipitation, and complexation--have certain similarities, which has led some authors to develop a unified treatment of them. These authors have highlighted the common aspects and tried to find a systemization of the equilibria that would facilitate learning them. In this unified treatment,…

Unified Framework for Deriving Simultaneous Equation Algorithms for Water Distribution Networks

EPA Science Inventory

The known formulations for steady state hydraulics within looped water distribution networks are re-derived in terms of linear and non-linear transformations of the original set of partly linear and partly non-linear equations that express conservation of mass and energy. All of ...

Five analogies between a King's Speech treatment and contemporary play therapies.

PubMed

Terr, Lenore C

2012-01-01

Psychiatric patients frequently respond positively to play therapy, which may rely on psychoanalytic, Jungian, cognitive-behavioral, familial, school-based, or other theories. I wished to determine if there were unifying principles that tie together these various types of play treatments. The fact-based film, The King's Speech, vividly illustrates play utilized by Lionel Logue in his speech treatment (1926-1939) of the future King of England. In the film I found five analogies to the play therapy I employ in office practice. The play scenes in The King's Speech point to five unifying principles among contemporary play therapies: (1) the crucial nature of the relationship, (2) the centrality of having fun, (3) the occasional reliance on others, (4) the interjection of pithy talk, and (5) the usefulness of a little drama. No matter what theory a play therapist ascribes to, these five unifying principles should be kept in mind during treatment.

A new uniformly valid asymptotic integration algorithm for elasto-plastic creep and unified viscoplastic theories including continuum damage

NASA Technical Reports Server (NTRS)

Chulya, Abhisak; Walker, Kevin P.

1991-01-01

A new scheme to integrate a system of stiff differential equations for both the elasto-plastic creep and the unified viscoplastic theories is presented. The method has high stability, allows large time increments, and is implicit and iterative. It is suitable for use with continuum damage theories. The scheme was incorporated into MARC, a commercial finite element code through a user subroutine called HYPELA. Results from numerical problems under complex loading histories are presented for both small and large scale analysis. To demonstrate the scheme's accuracy and efficiency, comparisons to a self-adaptive forward Euler method are made.

A new uniformly valid asymptotic integration algorithm for elasto-plastic-creep and unified viscoplastic theories including continuum damage

NASA Technical Reports Server (NTRS)

Chulya, A.; Walker, K. P.

1989-01-01

A new scheme to integrate a system of stiff differential equations for both the elasto-plastic creep and the unified viscoplastic theories is presented. The method has high stability, allows large time increments, and is implicit and iterative. It is suitable for use with continuum damage theories. The scheme was incorporated into MARC, a commercial finite element code through a user subroutine called HYPELA. Results from numerical problems under complex loading histories are presented for both small and large scale analysis. To demonstrate the scheme's accuracy and efficiency, comparisons to a self-adaptive forward Euler method are made.

The algorithm stitching for medical imaging

NASA Astrophysics Data System (ADS)

Semenishchev, E.; Marchuk, V.; Voronin, V.; Pismenskova, M.; Tolstova, I.; Svirin, I.

2016-05-01

In this paper we propose a stitching algorithm of medical images into one. The algorithm is designed to stitching the medical x-ray imaging, biological particles in microscopic images, medical microscopic images and other. Such image can improve the diagnosis accuracy and quality for minimally invasive studies (e.g., laparoscopy, ophthalmology and other). The proposed algorithm is based on the following steps: the searching and selection areas with overlap boundaries; the keypoint and feature detection; the preliminary stitching images and transformation to reduce the visible distortion; the search a single unified borders in overlap area; brightness, contrast and white balance converting; the superimposition into a one image. Experimental results demonstrate the effectiveness of the proposed method in the task of image stitching.

Software for Data Analysis with Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Roy, H. Scott

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: problem decomposition and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus.

PubMed

Afzal, Zubair; Pons, Ewoud; Kang, Ning; Sturkenboom, Miriam C J M; Schuemie, Martijn J; Kors, Jan A

2014-11-29

In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists' letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners' entries and a regular expression based temporality module. The ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively. The ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development.

Three-dimensional photoacoustic tomography based on graphics-processing-unit-accelerated finite element method.

PubMed

Peng, Kuan; He, Ling; Zhu, Ziqiang; Tang, Jingtian; Xiao, Jiaying

2013-12-01

Compared with commonly used analytical reconstruction methods, the frequency-domain finite element method (FEM) based approach has proven to be an accurate and flexible algorithm for photoacoustic tomography. However, the FEM-based algorithm is computationally demanding, especially for three-dimensional cases. To enhance the algorithm's efficiency, in this work a parallel computational strategy is implemented in the framework of the FEM-based reconstruction algorithm using a graphic-processing-unit parallel frame named the "compute unified device architecture." A series of simulation experiments is carried out to test the accuracy and accelerating effect of the improved method. The results obtained indicate that the parallel calculation does not change the accuracy of the reconstruction algorithm, while its computational cost is significantly reduced by a factor of 38.9 with a GTX 580 graphics card using the improved method.

Review and Implementation Status of Prior Defense Business Board Recommendations

DTIC Science & Technology

2007-04-01

Resource Management • Support unified models for shared services , and be prepared to adjust forward approaches for a Unified Medical Command...models for shared services – including by and between Veterans Affairs and Defense, electronic information exchange, disease treatment and prevention...www.dod.mil/dbb/pdf/DBB- Report-on-the-Military.pdf. • Continue to support unified models for shared services – including by and between Veterans Affairs

Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis

PubMed Central

2012-01-01

Background Chaos Game Representation (CGR) is an iterated function that bijectively maps discrete sequences into a continuous domain. As a result, discrete sequences can be object of statistical and topological analyses otherwise reserved to numerical systems. Characteristically, CGR coordinates of substrings sharing an L-long suffix will be located within 2-L distance of each other. In the two decades since its original proposal, CGR has been generalized beyond its original focus on genomic sequences and has been successfully applied to a wide range of problems in bioinformatics. This report explores the possibility that it can be further extended to approach algorithms that rely on discrete, graph-based representations. Results The exploratory analysis described here consisted of selecting foundational string problems and refactoring them using CGR-based algorithms. We found that CGR can take the role of suffix trees and emulate sophisticated string algorithms, efficiently solving exact and approximate string matching problems such as finding all palindromes and tandem repeats, and matching with mismatches. The common feature of these problems is that they use longest common extension (LCE) queries as subtasks of their procedures, which we show to have a constant time solution with CGR. Additionally, we show that CGR can be used as a rolling hash function within the Rabin-Karp algorithm. Conclusions The analysis of biological sequences relies on algorithmic foundations facing mounting challenges, both logistic (performance) and analytical (lack of unifying mathematical framework). CGR is found to provide the latter and to promise the former: graph-based data structures for sequence analysis operations are entailed by numerical-based data structures produced by CGR maps, providing a unifying analytical framework for a diversity of pattern matching problems. PMID:22551152

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking †

PubMed Central

Kiku, Daisuke; Okutomi, Masatoshi

2017-01-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking. PMID:29194407

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking.

PubMed

Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi

2017-12-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

GPU accelerated fuzzy connected image segmentation by using CUDA.

PubMed

Zhuge, Ying; Cao, Yong; Miller, Robert W

2009-01-01

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

Direct simulation Monte Carlo method for the Uehling-Uhlenbeck-Boltzmann equation.

PubMed

Garcia, Alejandro L; Wagner, Wolfgang

2003-11-01

In this paper we describe a direct simulation Monte Carlo algorithm for the Uehling-Uhlenbeck-Boltzmann equation in terms of Markov processes. This provides a unifying framework for both the classical Boltzmann case as well as the Fermi-Dirac and Bose-Einstein cases. We establish the foundation of the algorithm by demonstrating its link to the kinetic equation. By numerical experiments we study its sensitivity to the number of simulation particles and to the discretization of the velocity space, when approximating the steady-state distribution.

An integrated algorithm for hypersonic fluid-thermal-structural numerical simulation

NASA Astrophysics Data System (ADS)

Li, Jia-Wei; Wang, Jiang-Feng

2018-05-01

In this paper, a fluid-structural-thermal integrated method is presented based on finite volume method. A unified integral equations system is developed as the control equations for physical process of aero-heating and structural heat transfer. The whole physical field is discretized by using an up-wind finite volume method. To demonstrate its capability, the numerical simulation of Mach 6.47 flow over stainless steel cylinder shows a good agreement with measured values, and this method dynamically simulates the objective physical processes. Thus, the integrated algorithm proves to be efficient and reliable.

A unified and efficient framework for court-net sports video analysis using 3D camera modeling

NASA Astrophysics Data System (ADS)

Han, Jungong; de With, Peter H. N.

2007-01-01

The extensive amount of video data stored on available media (hard and optical disks) necessitates video content analysis, which is a cornerstone for different user-friendly applications, such as, smart video retrieval and intelligent video summarization. This paper aims at finding a unified and efficient framework for court-net sports video analysis. We concentrate on techniques that are generally applicable for more than one sports type to come to a unified approach. To this end, our framework employs the concept of multi-level analysis, where a novel 3-D camera modeling is utilized to bridge the gap between the object-level and the scene-level analysis. The new 3-D camera modeling is based on collecting features points from two planes, which are perpendicular to each other, so that a true 3-D reference is obtained. Another important contribution is a new tracking algorithm for the objects (i.e. players). The algorithm can track up to four players simultaneously. The complete system contributes to summarization by various forms of information, of which the most important are the moving trajectory and real-speed of each player, as well as 3-D height information of objects and the semantic event segments in a game. We illustrate the performance of the proposed system by evaluating it for a variety of court-net sports videos containing badminton, tennis and volleyball, and we show that the feature detection performance is above 92% and events detection about 90%.

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

NASA Astrophysics Data System (ADS)

Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

Sparse Bayesian Learning for Nonstationary Data Sources

NASA Astrophysics Data System (ADS)

Fujimaki, Ryohei; Yairi, Takehisa; Machida, Kazuo

This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.

Pattern-oriented modeling of agent-based complex systems: Lessons from ecology

USGS Publications Warehouse

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-01-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

NASA Astrophysics Data System (ADS)

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-11-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Edge Pushing is Equivalent to Vertex Elimination for Computing Hessians

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

Wang, Mu; Pothen, Alex; Hovland, Paul

We prove the equivalence of two different Hessian evaluation algorithms in AD. The first is the Edge Pushing algorithm of Gower and Mello, which may be viewed as a second order Reverse mode algorithm for computing the Hessian. In earlier work, we have derived the Edge Pushing algorithm by exploiting a Reverse mode invariant based on the concept of live variables in compiler theory. The second algorithm is based on eliminating vertices in a computational graph of the gradient, in which intermediate variables are successively eliminated from the graph, and the weights of the edges are updated suitably. We provemore » that if the vertices are eliminated in a reverse topological order while preserving symmetry in the computational graph of the gradient, then the Vertex Elimination algorithm and the Edge Pushing algorithm perform identical computations. In this sense, the two algorithms are equivalent. This insight that unifies two seemingly disparate approaches to Hessian computations could lead to improved algorithms and implementations for computing Hessians. Read More: http://epubs.siam.org/doi/10.1137/1.9781611974690.ch11« less

SENSITIVITY OF OZONE AND AEROSOL PREDICTIONS TO THE TRANSPORT ALGORITHMS IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

EPA's Models-3 CMAQ system is intended to provide a community modeling paradigm that allows continuous improvement of the one-atmosphere modeling capability in a unified fashion. CMAQ's modular design promotes incorporation of several sets of science process modules representing ...

DEMO: Action Recommendation for Cyber Resilience

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

Rodriguez, Luke R.; Curtis, Darren S.; Choudhury, Sutanay

In this demonstration we show the usefulness of our unifying graph-based model for the representation of infrastructure, behavior, and missions of cyber enterprise in both a software simulation and on an Amazon Web Services (AWS) instance. We show the effectiveness of our recommendation algorithm for preserving various system health metrics in both cases.

Sediment transport modeling in deposited bed sewers: unified form of May's equations using the particle swarm optimization algorithm.

PubMed

Safari, Mir Jafar Sadegh; Shirzad, Akbar; Mohammadi, Mirali

2017-08-01

May proposed two dimensionless parameters of transport (η) and mobility (F s ) for self-cleansing design of sewers with deposited bed condition. The relationships between those two parameters were introduced in conditional form for specific ranges of F s , which makes it difficult to use as a practical tool for sewer design. In this study, using the same experimental data used by May and employing the particle swarm optimization algorithm, a unified equation is recommended based on η and F s . The developed model is compared with original May relationships as well as corresponding models available in the literature. A large amount of data taken from the literature is used for the models' evaluation. The results demonstrate that the developed model in this study is superior to May and other existing models in the literature. Due to the fact that in May's dimensionless parameters more effective variables in the sediment transport process in sewers with deposited bed condition are considered, it is concluded that the revised May equation proposed in this study is a reliable model for sewer design.

Unifying time evolution and optimization with matrix product states

NASA Astrophysics Data System (ADS)

Haegeman, Jutho; Lubich, Christian; Oseledets, Ivan; Vandereycken, Bart; Verstraete, Frank

2016-10-01

We show that the time-dependent variational principle provides a unifying framework for time-evolution methods and optimization methods in the context of matrix product states. In particular, we introduce a new integration scheme for studying time evolution, which can cope with arbitrary Hamiltonians, including those with long-range interactions. Rather than a Suzuki-Trotter splitting of the Hamiltonian, which is the idea behind the adaptive time-dependent density matrix renormalization group method or time-evolving block decimation, our method is based on splitting the projector onto the matrix product state tangent space as it appears in the Dirac-Frenkel time-dependent variational principle. We discuss how the resulting algorithm resembles the density matrix renormalization group (DMRG) algorithm for finding ground states so closely that it can be implemented by changing just a few lines of code and it inherits the same stability and efficiency. In particular, our method is compatible with any Hamiltonian for which ground-state DMRG can be implemented efficiently. In fact, DMRG is obtained as a special case of our scheme for imaginary time evolution with infinite time step.

Unified framework for automated iris segmentation using distantly acquired face images.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2012-09-01

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

Image-algebraic design of multispectral target recognition algorithms

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.; Ritter, Gerhard X.

1994-06-01

In this paper, we discuss methods for multispectral ATR (Automated Target Recognition) of small targets that are sensed under suboptimal conditions, such as haze, smoke, and low light levels. In particular, we discuss our ongoing development of algorithms and software that effect intelligent object recognition by selecting ATR filter parameters according to ambient conditions. Our algorithms are expressed in terms of IA (image algebra), a concise, rigorous notation that unifies linear and nonlinear mathematics in the image processing domain. IA has been implemented on a variety of parallel computers, with preprocessors available for the Ada and FORTRAN languages. An image algebra C++ class library has recently been made available. Thus, our algorithms are both feasible implementationally and portable to numerous machines. Analyses emphasize the aspects of image algebra that aid the design of multispectral vision algorithms, such as parameterized templates that facilitate the flexible specification of ATR filters.

Tertiary care centre adherence to unified guidelines for management of periprosthetic joint infections: a gap analysis

PubMed Central

Armstrong, Mitchel D.; Carli, Alberto V.; Abdelbary, Hesham; Poitras, Stephane; Lapner, Peter; Beaulé, Paule E.

2018-01-01

Background The success rate of surgical treatment for periprosthetic joint infection (PJI) remains inconsistent in the literature. Variability in PJI clinical guidelines and surgeon adherence to guidelines could affect treatment success. The objectives of this study were to appraise current recommendations for PJI management and develop a unified clinical standard of care, to perform a gap analysis of PJI cases in a tertiary institution to determine the rate of guideline adherence, and to determine if adherence to unified PJI guidelines affected 2-year treatment outcomes. Methods We appraised the PJI guidelines from 3 academic medical societies, and consistent statements were aggregated. We retrospectively reviewed all PJI cases in a tertiary care institution. We defined PJI based on Musculoskeletal Infection Society PJI criteria. Surgeon adherence to preoperative, intraoperative, surgical and medical management guidelines was calculated, and we evaluated the association between guideline adherence and 2-year treatment outcomes. Results The institutional rate of PJI was 1.13% (38 of 3368). Treatment success was 57.8% at 2 years. Unified guideline adherence percentages varied substantially: 92% of patients had preoperative erythrocyte sedimentation rate and C-reactive protein, 97% had intraoperative tissue cultures, 42% had appropriate preoperative arthrocentesis, and 74% underwent guideline-appropriate surgery. Performing appropriate preoperative arthrocentesis significantly correlated with positive treatment outcomes at 2 years (p = 0.028). Conclusion Adherence to PJI guidelines varies considerably, indicating that clinicians are either unaware of them or do not recognize their value for PJI treatment. This study shows the need for institution-based PJI treatment pathways that are consistent with published guidelines and the need to monitor adherence. PMID:29368675

From square-well to Janus: Improved algorithm for integral equation theory and comparison with thermodynamic perturbation theory within the Kern-Frenkel model

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

Giacometti, Achille, E-mail: achille.giacometti@unive.it; Gögelein, Christoph, E-mail: christoph.goegelein@ds.mpg.de; Lado, Fred, E-mail: lado@ncsu.edu

2014-03-07

Building upon past work on the phase diagram of Janus fluids [F. Sciortino, A. Giacometti, and G. Pastore, Phys. Rev. Lett. 103, 237801 (2009)], we perform a detailed study of integral equation theory of the Kern-Frenkel potential with coverage that is tuned from the isotropic square-well fluid to the Janus limit. An improved algorithm for the reference hypernetted-chain (RHNC) equation for this problem is implemented that significantly extends the range of applicability of RHNC. Results for both structure and thermodynamics are presented and compared with numerical simulations. Unlike previous attempts, this algorithm is shown to be stable down to themore » Janus limit, thus paving the way for analyzing the frustration mechanism characteristic of the gas-liquid transition in the Janus system. The results are also compared with Barker-Henderson thermodynamic perturbation theory on the same model. We then discuss the pros and cons of both approaches within a unified treatment. On balance, RHNC integral equation theory, even with an isotropic hard-sphere reference system, is found to be a good compromise between accuracy of the results, computational effort, and uniform quality to tackle self-assembly processes in patchy colloids of complex nature. Further improvement in RHNC however clearly requires an anisotropic reference bridge function.« less

An impatient evolutionary algorithm with probabilistic tabu search for unified solution of some NP-hard problems in graph and set theory via clique finding.

PubMed

Guturu, Parthasarathy; Dantu, Ram

2008-06-01

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

Real-time individualization of the unified model of performance.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Balkin, Thomas J; Reifman, Jaques

2017-12-01

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform. © 2017 European Sleep Research Society.

Spray Combustion Modeling with VOF and Finite-Rate Chemistry

NASA Technical Reports Server (NTRS)

Chen, Yen-Sen; Shang, Huan-Min; Liaw, Paul; Wang, Ten-See

1996-01-01

A spray atomization and combustion model is developed based on the volume-of-fluid (VOF) transport equation with finite-rate chemistry model. The gas-liquid interface mass, momentum and energy conservation laws are modeled by continuum surface force mechanisms. A new solution method is developed such that the present VOF model can be applied for all-speed range flows. The objectives of the present study are: (1) to develop and verify the fractional volume-of-fluid (VOF) cell partitioning approach into a predictor-corrector algorithm to deal with multiphase (gas-liquid) free surface flow problems; (2) to implement the developed unified algorithm in a general purpose computational fluid dynamics (CFD) code, Finite Difference Navier-Stokes (FDNS), with droplet dynamics and finite-rate chemistry models; and (3) to demonstrate the effectiveness of the present approach by simulating benchmark problems of jet breakup/spray atomization and combustion. Modeling multiphase fluid flows poses a significant challenge because a required boundary must be applied to a transient, irregular surface that is discontinuous, and the flow regimes considered can range from incompressible to highspeed compressible flows. The flow-process modeling is further complicated by surface tension, interfacial heat and mass transfer, spray formation and turbulence, and their interactions. The major contribution of the present method is to combine the novel feature of the Volume of Fluid (VOF) method and the Eulerian/Lagrangian method into a unified algorithm for efficient noniterative, time-accurate calculations of multiphase free surface flows valid at all speeds. The proposed method reformulated the VOF equation to strongly couple two distinct phases (liquid and gas), and tracks droplets on a Lagrangian frame when spray model is required, using a unified predictor-corrector technique to account for the non-linear linkages through the convective contributions of VOF. The discontinuities within the sharp interface will be modeled as a volume force to avoid stiffness. Formations of droplets, tracking of droplet dynamics and modeling of the droplet breakup/evaporation, are handled through the same unified predictor-corrector procedure. Thus the new algorithm is non-iterative and is flexible for general geometries with arbitrarily complex topology in free surfaces. The FDNS finite-difference Navier-Stokes code is employed as the baseline of the current development. Benchmark test cases of shear coaxial LOX/H2 liquid jet with atomization/combustion and impinging jet test cases are investigated in the present work. Preliminary data comparisons show good qualitative agreement between data and the present analysis. It is indicative from these results that the present method has great potential to become a general engineering design analysis and diagnostics tool for problems involving spray combustion.

Anti-dynamic-crosstalk method for single photon LIDAR detection

NASA Astrophysics Data System (ADS)

Zhang, Fan; Liu, Qiang; Gong, Mali; Fu, Xing

2017-11-01

With increasing number of vehicles equipped with light detection and ranging (LIDAR), crosstalk is identified as a critical and urgent issue in the range detection for active collision avoidance. Chaotic pulse position modulation (CPPM) applied in the transmitting pulse train has been shown to prevent crosstalk as well as range ambiguity. However, static and unified strategy on discrimination threshold and the number of accumulated pulse is not valid against crosstalk with varying number of sources and varying intensity of each source. This paper presents an adaptive algorithm to distinguish the target echo from crosstalk with dynamic and unknown level of intensity in the context of intelligent vehicles. New strategy is given based on receiver operating characteristics (ROC) curves that consider the detection requirements of the probability of detection and false alarm for the scenario with varying crosstalk. In the adaptive algorithm, the detected results are compared by the new strategy with both the number of accumulated pulses and the threshold being raised step by step, so that the target echo can be exactly identified from crosstalk with the dynamic and unknown level of intensity. The validity of the algorithm has been verified through the experiments with a single photon detector and the time correlated single photo counting (TCSPC) technique, demonstrating a marked drop in required shots for identifying the target compared with static and unified strategy

The Development of the Unified Protocol for the Transdiagnostic Treatment of Emotional Disorders: A Case Study

PubMed Central

Boisseau, Christina L.; Farchione, Todd J.; Fairholme, Christopher P.; Ellard, Kristen K.; Barlow, David H.

2013-01-01

A detailed description of treatment utilizing the Unified Protocol (UP), a transdiagnostic emotion-focused cognitive-behavioral treatment, is presented using a clinical case example treated during the most current phase of an ongoing randomized controlled trial of the UP. The implementation of the UP in its current, modular version is illustrated. A working case conceptualization is presented from the perspective of the UP drawing from theory and research that underlies current transdiagnostic approaches to treatment and consistent with recent dimensional classification proposals (Brown & Barlow, in press). Treatment is illustrated module-by-module describing how the principles of the UP were applied in the presented case. PMID:23997572

Cognitive-behavior therapy for problem gambling: a critique of current treatments and proposed new unified approach.

PubMed

Tolchard, Barry

2017-06-01

There is evidence supporting the use of cognitive-behavioral therapy (CBT) in the treatment of problem gambling. Despite this, little is known about how CBT works and which particular approach is most effective. This paper aims to synthesize the evidence for current CBT and propose a more unified approach to treatment. A literature review and narrative synthesis of the current research evidence of CBT for the treatment of problem gambling was conducted, focusing on the underlying mechanisms within the treatment approach. Several CBT approaches were critiqued. These can be divided into forms of exposure therapy (including aversion techniques, systematic desensitization and other behavioral experiments) those focusing on cognitive restructuring techniques (such as reinforcement of nongambling activity, use of diaries, motivational enhancement and audio-playback techniques and third wave techniques including mindfulness. Findings, in relation to the treatment actions, from this synthesis are reported. The debate surrounding the treatment of problem gambling has been conducted as an either/or rather than a both/and discourse. This paper proposes a new, unified approach to the treatment of problem gambling that incorporates the best elements of both exposure and cognitive restructuring techniques, alongside the use of techniques borrowed from mindfulness and other CBT approaches.

The decoding of Reed-Solomon codes

NASA Technical Reports Server (NTRS)

Mceliece, R. J.

1988-01-01

Reed-Solomon (RS) codes form an important part of the high-rate downlink telemetry system for the Magellan mission, and the RS decoding function for this project will be done by DSN. Although the basic idea behind all Reed-Solomon decoding algorithms was developed by Berlekamp in 1968, there are dozens of variants of Berlekamp's algorithm in current use. An attempt to restore order is made by presenting a mathematical theory which explains the working of almost all known RS decoding algorithms. The key innovation that makes this possible is the unified approach to the solution of the key equation, which simultaneously describes the Berlekamp, Berlekamp-Massey, Euclid, and continued fractions approaches. Additionally, a detailed analysis is made of what can happen to a generic RS decoding algorithm when the number of errors and erasures exceeds the code's designed correction capability, and it is shown that while most published algorithms do not detect as many of these error-erasure patterns as possible, by making a small change in the algorithms, this problem can be overcome.

The Integrated Care Team Approach of the Department of Veterans Affairs (VA): Geriatric Primary Care.

PubMed

Rasin-Waters, Donna; Abel, Valerie; Kearney, Lisa K; Zeiss, Antonette

2018-05-01

Historically, integrated mental and behavioral healthcare in the Department of Veterans Affairs (VA) commenced with initiatives in geriatrics. Innovation and system-wide expansion has occurred over decades and culminated in a unified vision for training and practice in the VA medical home model: Patient Aligned Care Team or PACT approach. In one VA hospital, the integration of neuropsychological services in geriatric primary care is pivotal and increases access for patients, as well as contributing to timely and effective care on an interprofessional team. The development and innovative use of an algorithm to identify problems with cognition, health literacy, and mental and behavioral health has been pragmatic and provides useful information for collaborative treatment planning in GeriPACT, VA geriatric primary care. Use of the algorithm also assists with decision-making regarding brief versus comprehensive neuropsychological assessment in the primary care setting. The model presented here was developed by supervising neuropsychologists as part of a postdoctoral residency program in geropsychology. However, postdoctoral residency programs in neuropsychology, as well as neuropsychological clinics, can also use this model to integrate neuropsychological assessment and interventions in geriatric primary care settings.

Integrated Approach To Design And Analysis Of Systems

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Iverson, David L.

1993-01-01

Object-oriented fault-tree representation unifies evaluation of reliability and diagnosis of faults. Programming/fault tree described more fully in "Object-Oriented Algorithm For Evaluation Of Fault Trees" (ARC-12731). Augmented fault tree object contains more information than fault tree object used in quantitative analysis of reliability. Additional information needed to diagnose faults in system represented by fault tree.

Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams

NASA Astrophysics Data System (ADS)

Besold, Tarek R.; Kühnberger, Kai-Uwe; Plaza, Enric

2017-10-01

Concept blending - a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements, and enables reasoning and inference over the combination - is taken as a key element of creative thought and combinatorial creativity. In this article, we summarise our work towards the development of a computational-level and algorithmic-level account of concept blending, combining approaches from computational analogy-making and case-based reasoning (CBR). We present the theoretical background, as well as an algorithmic proposal integrating higher-order anti-unification matching and generalisation from analogy with amalgams from CBR. The feasibility of the approach is then exemplified in two case studies.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

An accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems using gradient-based diffusion and tau-leaping.

PubMed

Koh, Wonryull; Blackwell, Kim T

2011-04-21

Stochastic simulation of reaction-diffusion systems enables the investigation of stochastic events arising from the small numbers and heterogeneous distribution of molecular species in biological cells. Stochastic variations in intracellular microdomains and in diffusional gradients play a significant part in the spatiotemporal activity and behavior of cells. Although an exact stochastic simulation that simulates every individual reaction and diffusion event gives a most accurate trajectory of the system's state over time, it can be too slow for many practical applications. We present an accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems designed to improve the speed of simulation by reducing the number of time-steps required to complete a simulation run. This method is unique in that it employs two strategies that have not been incorporated in existing spatial stochastic simulation algorithms. First, diffusive transfers between neighboring subvolumes are based on concentration gradients. This treatment necessitates sampling of only the net or observed diffusion events from higher to lower concentration gradients rather than sampling all diffusion events regardless of local concentration gradients. Second, we extend the non-negative Poisson tau-leaping method that was originally developed for speeding up nonspatial or homogeneous stochastic simulation algorithms. This method calculates each leap time in a unified step for both reaction and diffusion processes while satisfying the leap condition that the propensities do not change appreciably during the leap and ensuring that leaping does not cause molecular populations to become negative. Numerical results are presented that illustrate the improvement in simulation speed achieved by incorporating these two new strategies.

Polar exponential sensor arrays unify iconic and Hough space representation

NASA Technical Reports Server (NTRS)

Weiman, Carl F. R.

1990-01-01

The log-polar coordinate system, inherent in both polar exponential sensor arrays and log-polar remapped video imagery, is identical to the coordinate system of its corresponding Hough transform parameter space. The resulting unification of iconic and Hough domains simplifies computation for line recognition and eliminates the slope quantization problems inherent in the classical Cartesian Hough transform. The geometric organization of the algorithm is more amenable to massively parallel architectures than that of the Cartesian version. The neural architecture of the human visual cortex meets the geometric requirements to execute 'in-place' log-Hough algorithms of the kind described here.

A constitutive material model for nonlinear finite element structural analysis using an iterative matrix approach

NASA Technical Reports Server (NTRS)

Koenig, Herbert A.; Chan, Kwai S.; Cassenti, Brice N.; Weber, Richard

1988-01-01

A unified numerical method for the integration of stiff time dependent constitutive equations is presented. The solution process is directly applied to a constitutive model proposed by Bodner. The theory confronts time dependent inelastic behavior coupled with both isotropic hardening and directional hardening behaviors. Predicted stress-strain responses from this model are compared to experimental data from cyclic tests on uniaxial specimens. An algorithm is developed for the efficient integration of the Bodner flow equation. A comparison is made with the Euler integration method. An analysis of computational time is presented for the three algorithms.

Parallelized seeded region growing using CUDA.

PubMed

Park, Seongjin; Lee, Jeongjin; Lee, Hyunna; Shin, Juneseuk; Seo, Jinwook; Lee, Kyoung Ho; Shin, Yeong-Gil; Kim, Bohyoung

2014-01-01

This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests.

Detecting perceptual groupings in textures by continuity considerations

NASA Technical Reports Server (NTRS)

Greene, Richard J.

1990-01-01

A generalization is presented for the second derivative of a Gaussian D(sup 2)G operator to apply to problems of perceptual organization involving textures. Extensions to other problems of perceptual organization are evident and a new research direction can be established. The technique presented is theoretically pleasing since it has the potential of unifying the entire area of image segmentation under the mathematical notion of continuity and presents a single algorithm to form perceptual groupings where many algorithms existed previously. The eventual impact on both the approach and technique of image processing segmentation operations could be significant.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.

PubMed

Bricq, S; Collet, Ch; Armspach, J P

2008-12-01

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

Workflow of the Grover algorithm simulation incorporating CUDA and GPGPU

NASA Astrophysics Data System (ADS)

Lu, Xiangwen; Yuan, Jiabin; Zhang, Weiwei

2013-09-01

The Grover quantum search algorithm, one of only a few representative quantum algorithms, can speed up many classical algorithms that use search heuristics. No true quantum computer has yet been developed. For the present, simulation is one effective means of verifying the search algorithm. In this work, we focus on the simulation workflow using a compute unified device architecture (CUDA). Two simulation workflow schemes are proposed. These schemes combine the characteristics of the Grover algorithm and the parallelism of general-purpose computing on graphics processing units (GPGPU). We also analyzed the optimization of memory space and memory access from this perspective. We implemented four programs on CUDA to evaluate the performance of schemes and optimization. Through experimentation, we analyzed the organization of threads suited to Grover algorithm simulations, compared the storage costs of the four programs, and validated the effectiveness of optimization. Experimental results also showed that the distinguished program on CUDA outperformed the serial program of libquantum on a CPU with a speedup of up to 23 times (12 times on average), depending on the scale of the simulation.

Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

PubMed Central

Joshi, Alark; Scheinost, Dustin; Okuda, Hirohito; Belhachemi, Dominique; Murphy, Isabella; Staib, Lawrence H.; Papademetris, Xenophon

2011-01-01

Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software—BioImage Suite (bioimagesuite.org). PMID:21249532

Response to Toward Unified Satellite Climatology of Aerosol Properties. 3; MODIS versus MISR versus AERONET

NASA Technical Reports Server (NTRS)

Kahn, Ralph A.; Garay, Michael J.; Nelson, David L.; Levy, Robert C.; Bull, Michael A.; Diner, David J.; Martonchik, John V.; Hansen, Earl G.; Remer, Lorraine A.; Tanre, Didler

2010-01-01

A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD), and gives a much less favorable impression of the utility of the satellite products than that presented by the instrument teams and other groups. We trace the reasons for the differing pictures to whether known and previously documented limitations of the products are taken into account in the assessments. Specifically, the analysis approaches differ primarily in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account. Mishchenko et al. also do not distinguish between observational sampling differences and retrieval algorithm error. We assess the implications of the different analysis approaches, and cite examples demonstrating how the MISR and MODIS aerosol products have been applied successfully to a range of scientific investigations.

A multi-band semi-analytical algorithm for estimating chlorophyll-a concentration in the Yellow River Estuary, China.

PubMed

Chen, Jun; Quan, Wenting; Cui, Tingwei

2015-01-01

In this study, two sample semi-analytical algorithms and one new unified multi-band semi-analytical algorithm (UMSA) for estimating chlorophyll-a (Chla) concentration were constructed by specifying optimal wavelengths. The three sample semi-analytical algorithms, including the three-band semi-analytical algorithm (TSA), four-band semi-analytical algorithm (FSA), and UMSA algorithm, were calibrated and validated by the dataset collected in the Yellow River Estuary between September 1 and 10, 2009. By comparing of the accuracy of assessment of TSA, FSA, and UMSA algorithms, it was found that the UMSA algorithm had a superior performance in comparison with the two other algorithms, TSA and FSA. Using the UMSA algorithm in retrieving Chla concentration in the Yellow River Estuary decreased by 25.54% NRMSE (normalized root mean square error) when compared with the FSA algorithm, and 29.66% NRMSE in comparison with the TSA algorithm. These are very significant improvements upon previous methods. Additionally, the study revealed that the TSA and FSA algorithms are merely more specific forms of the UMSA algorithm. Owing to the special form of the UMSA algorithm, if the same bands were used for both the TSA and UMSA algorithms or FSA and UMSA algorithms, the UMSA algorithm would theoretically produce superior results in comparison with the TSA and FSA algorithms. Thus, good results may also be produced if the UMSA algorithm were to be applied for predicting Chla concentration for datasets of Gitelson et al. (2008) and Le et al. (2009).

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

Optimization-Based Robust Nonlinear Control

DTIC Science & Technology

2006-08-01

ABSTRACT New control algorithms were developed for robust stabilization of nonlinear dynamical systems . Novel, linear matrix inequality-based synthesis...was to further advance optimization-based robust nonlinear control design, for general nonlinear systems (especially in discrete time ), for linear...Teel, IEEE Transactions on Control Systems Technology, vol. 14, no. 3, p. 398-407, May 2006. 3. "A unified framework for input-to-state stability in

Stochastic nonlinear dynamics pattern formation and growth models

PubMed Central

Yaroslavsky, Leonid P

2007-01-01

Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature. PMID:17908341

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

PubMed Central

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G.

2012-01-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids. The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable. In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation. We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards. PMID:22347787

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards.

PubMed

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G

2011-07-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.

Initial Alignment for SINS Based on Pseudo-Earth Frame in Polar Regions.

PubMed

Gao, Yanbin; Liu, Meng; Li, Guangchun; Guang, Xingxing

2017-06-16

An accurate initial alignment must be required for inertial navigation system (INS). The performance of initial alignment directly affects the following navigation accuracy. However, the rapid convergence of meridians and the small horizontalcomponent of rotation of Earth make the traditional alignment methods ineffective in polar regions. In this paper, from the perspective of global inertial navigation, a novel alignment algorithm based on pseudo-Earth frame and backward process is proposed to implement the initial alignment in polar regions. Considering that an accurate coarse alignment of azimuth is difficult to obtain in polar regions, the dynamic error modeling with large azimuth misalignment angle is designed. At the end of alignment phase, the strapdown attitude matrix relative to local geographic frame is obtained without influence of position errors and cumbersome computation. As a result, it would be more convenient to access the following polar navigation system. Then, it is also expected to unify the polar alignment algorithm as much as possible, thereby further unifying the form of external reference information. Finally, semi-physical static simulation and in-motion tests with large azimuth misalignment angle assisted by unscented Kalman filter (UKF) validate the effectiveness of the proposed method.

CoCoNUT: an efficient system for the comparison and analysis of genomes

PubMed Central

2008-01-01

Background Comparative genomics is the analysis and comparison of genomes from different species. This area of research is driven by the large number of sequenced genomes and heavily relies on efficient algorithms and software to perform pairwise and multiple genome comparisons. Results Most of the software tools available are tailored for one specific task. In contrast, we have developed a novel system CoCoNUT (Computational Comparative geNomics Utility Toolkit) that allows solving several different tasks in a unified framework: (1) finding regions of high similarity among multiple genomic sequences and aligning them, (2) comparing two draft or multi-chromosomal genomes, (3) locating large segmental duplications in large genomic sequences, and (4) mapping cDNA/EST to genomic sequences. Conclusion CoCoNUT is competitive with other software tools w.r.t. the quality of the results. The use of state of the art algorithms and data structures allows CoCoNUT to solve comparative genomics tasks more efficiently than previous tools. With the improved user interface (including an interactive visualization component), CoCoNUT provides a unified, versatile, and easy-to-use software tool for large scale studies in comparative genomics. PMID:19014477

Verifying Stability of Dynamic Soft-Computing Systems

NASA Technical Reports Server (NTRS)

Wen, Wu; Napolitano, Marcello; Callahan, John

1997-01-01

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

A unified framework for image retrieval using keyword and visual features.

PubMed

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Unifying Inference of Meso-Scale Structures in Networks.

PubMed

Tunç, Birkan; Verma, Ragini

2015-01-01

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

PubMed

Feng, Haihua; Karl, William Clem; Castañon, David A

2008-05-01

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data.

An Improved Method of Pose Estimation for Lighthouse Base Station Extension.

PubMed

Yang, Yi; Weng, Dongdong; Li, Dong; Xun, Hang

2017-10-22

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal.

An Improved Method of Pose Estimation for Lighthouse Base Station Extension

PubMed Central

Yang, Yi; Weng, Dongdong; Li, Dong; Xun, Hang

2017-01-01

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal. PMID:29065509

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Multichannel blind iterative image restoration.

PubMed

Sroubek, Filip; Flusser, Jan

2003-01-01

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately in a single-channel framework, serious conceptual and numerical problems are often encountered. Very recently, an eigenvector-based method (EVAM) was proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied. We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

A real-time architecture for time-aware agents.

PubMed

Prouskas, Konstantinos-Vassileios; Pitt, Jeremy V

2004-06-01

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.

Gradient calculations for dynamic recurrent neural networks: a survey.

PubMed

Pearlmutter, B A

1995-01-01

Surveys learning algorithms for recurrent neural networks with hidden units and puts the various techniques into a common framework. The authors discuss fixed point learning algorithms, namely recurrent backpropagation and deterministic Boltzmann machines, and nonfixed point algorithms, namely backpropagation through time, Elman's history cutoff, and Jordan's output feedback architecture. Forward propagation, an on-line technique that uses adjoint equations, and variations thereof, are also discussed. In many cases, the unified presentation leads to generalizations of various sorts. The author discusses advantages and disadvantages of temporally continuous neural networks in contrast to clocked ones continues with some "tricks of the trade" for training, using, and simulating continuous time and recurrent neural networks. The author presents some simulations, and at the end, addresses issues of computational complexity and learning speed.

On numerical integration and computer implementation of viscoplastic models

NASA Technical Reports Server (NTRS)

Chang, T. Y.; Chang, J. P.; Thompson, R. L.

1985-01-01

Due to the stringent design requirement for aerospace or nuclear structural components, considerable research interests have been generated on the development of constitutive models for representing the inelastic behavior of metals at elevated temperatures. In particular, a class of unified theories (or viscoplastic constitutive models) have been proposed to simulate material responses such as cyclic plasticity, rate sensitivity, creep deformations, strain hardening or softening, etc. This approach differs from the conventional creep and plasticity theory in that both the creep and plastic deformations are treated as unified time-dependent quantities. Although most of viscoplastic models give better material behavior representation, the associated constitutive differential equations have stiff regimes which present numerical difficulties in time-dependent analysis. In this connection, appropriate solution algorithm must be developed for viscoplastic analysis via finite element method.

Recent Theoretical Studies On Excitation and Recombination

NASA Technical Reports Server (NTRS)

Pradhan, Anil K.

2000-01-01

New advances in the theoretical treatment of atomic processes in plasmas are described. These enable not only an integrated, unified, and self-consistent treatment of important radiative and collisional processes, but also large-scale computation of atomic data with high accuracy. An extension of the R-matrix work, from excitation and photoionization to electron-ion recombination, includes a unified method that subsumes both the radiative and the di-electronic recombination processes in an ab initio manner. The extensive collisional calculations for iron and iron-peak elements under the Iron Project are also discussed.

Stochastic characterization of phase detection algorithms in phase-shifting interferometry

DOE PAGES

Munteanu, Florin

2016-11-01

Phase-shifting interferometry (PSI) is the preferred non-contact method for profiling sub-nanometer surfaces. Based on monochromatic light interference, the method computes the surface profile from a set of interferograms collected at separate stepping positions. Errors in the estimated profile are introduced when these positions are not located correctly. In order to cope with this problem, various algorithms that minimize the effects of certain types of stepping errors (linear, sinusoidal, etc.) have been developed. Despite the relatively large number of algorithms suggested in the literature, there is no unified way of characterizing their performance when additional unaccounted random errors are present. Here,more » we suggest a procedure for quantifying the expected behavior of each algorithm in the presence of independent and identically distributed (i.i.d.) random stepping errors, which can occur in addition to the systematic errors for which the algorithm has been designed. As a result, the usefulness of this method derives from the fact that it can guide the selection of the best algorithm for specific measurement situations.« less

Multirate-based fast parallel algorithms for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2012-07-01

Novel algorithms for the multirate and fast parallel implementation of the 2-D discrete Hartley transform (DHT)-based real-valued discrete Gabor transform (RDGT) and its inverse transform are presented in this paper. A 2-D multirate-based analysis convolver bank is designed for the 2-D RDGT, and a 2-D multirate-based synthesis convolver bank is designed for the 2-D inverse RDGT. The parallel channels in each of the two convolver banks have a unified structure and can apply the 2-D fast DHT algorithm to speed up their computations. The computational complexity of each parallel channel is low and is independent of the Gabor oversampling rate. All the 2-D RDGT coefficients of an image are computed in parallel during the analysis process and can be reconstructed in parallel during the synthesis process. The computational complexity and time of the proposed parallel algorithms are analyzed and compared with those of the existing fastest algorithms for 2-D discrete Gabor transforms. The results indicate that the proposed algorithms are the fastest, which make them attractive for real-time image processing.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, F. Landis

1997-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Cognitive Foundry v. 3.0 (OSS)

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

Basilico, Justin; Dixon, Kevin; McClain, Jonathan

2009-11-18

The Cognitive Foundry is a unified collection of tools designed for research and applications that use cognitive modeling, machine learning, or pattern recognition. The software library contains design patterns, interface definitions, and default implementations of reusable software components and algorithms designed to support a wide variety of research and development needs. The library contains three main software packages: the Common package that contains basic utilities and linear algebraic methods, the Cognitive Framework package that contains tools to assist in implementing and analyzing theories of cognition, and the Machine Learning package that provides general algorithms and methods for populating Cognitive Frameworkmore » components from domain-relevant data.« less

Parallelized Seeded Region Growing Using CUDA

PubMed Central

Park, Seongjin; Lee, Hyunna; Seo, Jinwook; Lee, Kyoung Ho; Shin, Yeong-Gil; Kim, Bohyoung

2014-01-01

This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests. PMID:25309619

Analysis of Streamline Separation at Infinity Using Time-Discrete Markov Chains.

PubMed

Reich, W; Scheuermann, G

2012-12-01

Existing methods for analyzing separation of streamlines are often restricted to a finite time or a local area. In our paper we introduce a new method that complements them by allowing an infinite-time-evaluation of steady planar vector fields. Our algorithm unifies combinatorial and probabilistic methods and introduces the concept of separation in time-discrete Markov-Chains. We compute particle distributions instead of the streamlines of single particles. We encode the flow into a map and then into a transition matrix for each time direction. Finally, we compare the results of our grid-independent algorithm to the popular Finite-Time-Lyapunov-Exponents and discuss the discrepancies.

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.

PubMed

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database in which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. This database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.

Matrix elements of explicitly correlated Gaussian basis functions with arbitrary angular momentum

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

Joyce, Tennesse; Varga, Kálmán

2016-05-14

A new algorithm for calculating the Hamiltonian matrix elements with all-electron explicitly correlated Gaussian functions for quantum-mechanical calculations of atoms with arbitrary angular momentum is presented. The calculations are checked on several excited states of three and four electron systems. The presented formalism can be used as unified framework for high accuracy calculations of properties of small atoms and molecules.

Technical Note: scuda: A software platform for cumulative dose assessment.

PubMed

Park, Seyoun; McNutt, Todd; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2016-10-01

Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (scuda) that can be seamlessly integrated into the clinical workflow. scuda consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our image PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.

Technical Note: SCUDA: A software platform for cumulative dose assessment

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

Park, Seyoun; McNutt, Todd; Quon, Harry

Purpose: Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (SCUDA) that can be seamlessly integrated into the clinical workflow. Methods: SCUDA consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our imagemore » PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. Results: The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. Conclusions: The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.« less

GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.

PubMed

Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi

2018-04-01

Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms. Copyright © 2018 Elsevier Ltd. All rights reserved.

A LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function for the Heihe River Basin

NASA Astrophysics Data System (ADS)

Ma, B.; Li, J.; Fan, W.; Ren, H.; Xu, X.

2017-12-01

Leaf area index (LAI) is one of the important parameters of vegetation canopy structure, which can represent the growth condition of vegetation effectively. The accuracy, availability and timeliness of LAI data can be improved greatly, which is of great importance to vegetation-related research, such as the study of atmospheric, land surface and hydrological processes to obtain LAI by remote sensing method. Heihe River Basin is the inland river basin in northwest China. There are various types of vegetation and all kinds of terrain conditions in the basin, so it is helpful for testing the accuracy of the model under the complex surface and evaluating the correctness of the model to study LAI in this area. On the other hand, located in west arid area of China, the ecological environment of Heihe Basin is fragile, LAI is an important parameter to represent the vegetation growth condition, and can help us understand the status of vegetation in the Heihe River Basin. Different from the previous LAI inversion models, the BRDF (bidirectional reflectance distribution function) unified model can be applied for both continuous vegetation and discrete vegetation, it is appropriate to the complex vegetation distribution. LAI is the key input parameter of the model. We establish the inversion algorithm that can exactly retrieve LAI using remote sensing image based on the unified model. First, we determine the vegetation type through the vegetation classification map to obtain the corresponding G function, leaf and surface reflectivity. Then, we need to determine the leaf area index (LAI), the aggregation index (ζ) and the sky scattered light ratio (β) range and the value of the interval, entering all the parameters into the model to calculate the corresponding reflectivity ρ and establish the lookup table of different vegetation. Finally, we can invert LAI on the basis of the established lookup table. The principle of inversion is least squares method. We have produced 1 km LAI products from 2000 to 2014, once every 8 days. The results show that the algorithm owns good stability and can effectively invert LAI in areas with very complex vegetation and terrain conditions.

Unified connected theory of few-body reaction mechanisms in N-body scattering theory

NASA Technical Reports Server (NTRS)

Polyzou, W. N.; Redish, E. F.

1978-01-01

A unified treatment of different reaction mechanisms in nonrelativistic N-body scattering is presented. The theory is based on connected kernel integral equations that are expected to become compact for reasonable constraints on the potentials. The operators T/sub +-//sup ab/(A) are approximate transition operators that describe the scattering proceeding through an arbitrary reaction mechanism A. These operators are uniquely determined by a connected kernel equation and satisfy an optical theorem consistent with the choice of reaction mechanism. Connected kernel equations relating T/sub +-//sup ab/(A) to the full T/sub +-//sup ab/ allow correction of the approximate solutions for any ignored process to any order. This theory gives a unified treatment of all few-body reaction mechanisms with the same dynamic simplicity of a model calculation, but can include complicated reaction mechanisms involving overlapping configurations where it is difficult to formulate models.

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

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

Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin

2011-03-01

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate themore » effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.« less

SSM/I Rain Retrievals Within a Unified All-Weather Ocean Algorithm

NASA Technical Reports Server (NTRS)

Wentz, Frank J.; Spencer, Roy W.

1996-01-01

A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented and compared to two other rainfall climatologies derived from satellites. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T(sub U) for the upwelling radiation. The performance of the algorithm in the absence of rain is discussed in Wentz, and this paper focuses on the rain component of the algorithm. A particular strength of the unified algorithm is its ability to 'orthogonalize' the retrievals so that there is minimum cross-talk between the retrieved parameters. For example, comparisons of the retrieved water vapor with radiosonde observations show that there is very little correlation between the water vapor retrieval error and rain rate. For rain rates from 1 to 15 mm/h, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 GHz and 19.35 GHz. This ratio decreases by about 40% when heavy and light rain co-exist within the SSM/I footprint as compared to the case of uniform rain. This correction has the effect of increasing the rain rate when the spectral ratio of the absorption coefficients is small. Even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the tropics (approx. 5 km) is used to specify the rain layer thickness. We restore realism by reducing the assumed averaged tropical rain layer thickness to 3 km, thereby accounting for the existence of warm rain processes in which the rain layer does not extend to the freezing level. Global rain rates are produced for the 1991 through 1994 period from observations taken by microwave radiometers (SSM/I) that are aboard two polar-orbiting satellites. We find that approximately 6% of the SSM/I observations detect measurable rain rates (R greater than 0.2 mm/h). Zonal averages of the rain rates show the peak at the intertropical convergence zone (ITCZ) is quite narrow in meridional extent and varies from about 7 mm/day in the winter to a maximum 11 mm/day in the summer. Very low precipitation rates (less than 0.3 mm/day) are observed in those areas of subsidence influenced by the large semipermanent anticyclones. In general, these features are similar to those reported in previously published rain climatologies. However, significant differences do exists between our rain rates and those produced by Spencer. These differences seem to be related to non-precipitating cloud water.

Do Specialty Courts Achieve Better Outcomes for Children in Foster Care than General Courts?

ERIC Educational Resources Information Center

Sloan, Frank A.; Gifford, Elizabeth J.; Eldred, Lindsey M.; Acquah, Kofi F.; Blevins, Claire E.

2013-01-01

Objective: This study assessed the effects of unified family and drug treatment courts (DTCs) on the resolution of cases involving foster care children and the resulting effects on school performance. Method: The first analytic step was to assess the impacts of presence of unified and DTCs in North Carolina counties on time children spent in…

Unifying the field: developing an integrative paradigm for behavior therapy.

PubMed

Eifert, G H; Forsyth, J P; Schauss, S L

1993-06-01

The limitations of early conditioning models and treatments have led many behavior therapists to abandon conditioning principles and replace them with loosely defined cognitive theories and treatments. Systematic theory extensions to human behavior, using new concepts and processes derived from and built upon the basic principles, could have prevented the divisive debates over whether psychological dysfunctions are the results of conditioning or cognition and whether they should be treated with conditioning or cognitive techniques. Behavior therapy could also benefit from recent advances in experimental cognitive psychology that provide objective behavioral methods of studying dysfunctional processes. We suggest a unifying paradigm for explaining abnormal behavior that links and integrates different fields of study and processes that are frequently believed to be incompatible or antithetical such as biological vulnerability variables, learned behavioral repertoires, and that also links historical and current antecedents of the problem. An integrative paradigmatic behavioral approach may serve a unifying function in behavior therapy (a) by promoting an understanding of the dysfunctional processes involved in different disorders and (b) by helping clinicians conduct functional analyses that lead to theory-based, individualized, and effective treatments.

The Process of Parallelizing the Conjunction Prediction Algorithm of ESA's SSA Conjunction Prediction Service Using GPGPU

NASA Astrophysics Data System (ADS)

Fehr, M.; Navarro, V.; Martin, L.; Fletcher, E.

2013-08-01

Space Situational Awareness[8] (SSA) is defined as the comprehensive knowledge, understanding and maintained awareness of the population of space objects, the space environment and existing threats and risks. As ESA's SSA Conjunction Prediction Service (CPS) requires the repetitive application of a processing algorithm against a data set of man-made space objects, it is crucial to exploit the highly parallelizable nature of this problem. Currently the CPS system makes use of OpenMP[7] for parallelization purposes using CPU threads, but only a GPU with its hundreds of cores can fully benefit from such high levels of parallelism. This paper presents the adaptation of several core algorithms[5] of the CPS for general-purpose computing on graphics processing units (GPGPU) using NVIDIAs Compute Unified Device Architecture (CUDA).

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Unified heuristics to solve routing problem of reverse logistics in sustainable supply chain

NASA Astrophysics Data System (ADS)

Anbuudayasankar, S. P.; Ganesh, K.; Lenny Koh, S. C.; Mohandas, K.

2010-03-01

A reverse logistics problem, motivated by many real-life applications, is examined where bottles/cans in which products are delivered from a processing depot to customers in one period are available for return to the depot in the following period. The picked-up bottles/cans need to be adjusted in the place of delivery load. This problem is termed as simultaneous delivery and pick-up problem with constrained capacity (SDPC). We develop three unified heuristics based on extended branch and bound heuristic, genetic algorithm and simulated annealing to solve SDPC. These heuristics are also designed to solve standard travelling salesman problem (TSP) and TSP with simultaneous delivery and pick-up (TSDP). We tested the heuristics on standard, derived and randomly generated datasets of TSP, TSDP and SDPC and obtained satisfying results with high convergence in reasonable time.

An algorithmic approach to crustal deformation analysis

NASA Technical Reports Server (NTRS)

Iz, Huseyin Baki

1987-01-01

In recent years the analysis of crustal deformation measurements has become important as a result of current improvements in geodetic methods and an increasing amount of theoretical and observational data provided by several earth sciences. A first-generation data analysis algorithm which combines a priori information with current geodetic measurements was proposed. Relevant methods which can be used in the algorithm were discussed. Prior information is the unifying feature of this algorithm. Some of the problems which may arise through the use of a priori information in the analysis were indicated and preventive measures were demonstrated. The first step in the algorithm is the optimal design of deformation networks. The second step in the algorithm identifies the descriptive model of the deformation field. The final step in the algorithm is the improved estimation of deformation parameters. Although deformation parameters are estimated in the process of model discrimination, they can further be improved by the use of a priori information about them. According to the proposed algorithm this information must first be tested against the estimates calculated using the sample data only. Null-hypothesis testing procedures were developed for this purpose. Six different estimators which employ a priori information were examined. Emphasis was put on the case when the prior information is wrong and analytical expressions for possible improvements under incompatible prior information were derived.

Separation of irradiance and reflectance from observed color images by logarithmical nonlinear diffusion process

NASA Astrophysics Data System (ADS)

Saito, Takahiro; Takahashi, Hiromi; Komatsu, Takashi

2006-02-01

The Retinex theory was first proposed by Land, and deals with separation of irradiance from reflectance in an observed image. The separation problem is an ill-posed problem. Land and others proposed various Retinex separation algorithms. Recently, Kimmel and others proposed a variational framework that unifies the previous Retinex algorithms such as the Poisson-equation-type Retinex algorithms developed by Horn and others, and presented a Retinex separation algorithm with the time-evolution of a linear diffusion process. However, the Kimmel's separation algorithm cannot achieve physically rational separation, if true irradiance varies among color channels. To cope with this problem, we introduce a nonlinear diffusion process into the time-evolution. Moreover, as to its extension to color images, we present two approaches to treat color channels: the independent approach to treat each color channel separately and the collective approach to treat all color channels collectively. The latter approach outperforms the former. Furthermore, we apply our separation algorithm to a high quality chroma key in which before combining a foreground frame and a background frame into an output image a color of each pixel in the foreground frame are spatially adaptively corrected through transformation of the separated irradiance. Experiments demonstrate superiority of our separation algorithm over the Kimmel's separation algorithm.

A unified framework for penalized statistical muon tomography reconstruction with edge preservation priors of lp norm type

NASA Astrophysics Data System (ADS)

Yu, Baihui; Zhao, Ziran; Wang, Xuewu; Wu, Dufan; Zeng, Zhi; Zeng, Ming; Wang, Yi; Cheng, Jianping

2016-01-01

The Tsinghua University MUon Tomography facilitY (TUMUTY) has been built up and it is utilized to reconstruct the special objects with complex structure. Since fine image is required, the conventional Maximum likelihood Scattering and Displacement (MLSD) algorithm is employed. However, due to the statistical characteristics of muon tomography and the data incompleteness, the reconstruction is always instable and accompanied with severe noise. In this paper, we proposed a Maximum a Posterior (MAP) algorithm for muon tomography regularization, where an edge-preserving prior on the scattering density image is introduced to the object function. The prior takes the lp norm (p>0) of the image gradient magnitude, where p=1 and p=2 are the well-known total-variation (TV) and Gaussian prior respectively. The optimization transfer principle is utilized to minimize the object function in a unified framework. At each iteration the problem is transferred to solving a cubic equation through paraboloidal surrogating. To validate the method, the French Test Object (FTO) is imaged by both numerical simulation and TUMUTY. The proposed algorithm is used for the reconstruction where different norms are detailedly studied, including l2, l1, l0.5, and an l2-0.5 mixture norm. Compared with MLSD method, MAP achieves better image quality in both structure preservation and noise reduction. Furthermore, compared with the previous work where one dimensional image was acquired, we achieve the relatively clear three dimensional images of FTO, where the inner air hole and the tungsten shell is visible.

Unified path integral approach to theories of diffusion-influenced reactions

NASA Astrophysics Data System (ADS)

Prüstel, Thorsten; Meier-Schellersheim, Martin

2017-08-01

Building on mathematical similarities between quantum mechanics and theories of diffusion-influenced reactions, we develop a general approach for computational modeling of diffusion-influenced reactions that is capable of capturing not only the classical Smoluchowski picture but also alternative theories, as is here exemplified by a volume reactivity model. In particular, we prove the path decomposition expansion of various Green's functions describing the irreversible and reversible reaction of an isolated pair of molecules. To this end, we exploit a connection between boundary value and interaction potential problems with δ - and δ'-function perturbation. We employ a known path-integral-based summation of a perturbation series to derive a number of exact identities relating propagators and survival probabilities satisfying different boundary conditions in a unified and systematic manner. Furthermore, we show how the path decomposition expansion represents the propagator as a product of three factors in the Laplace domain that correspond to quantities figuring prominently in stochastic spatially resolved simulation algorithms. This analysis will thus be useful for the interpretation of current and the design of future algorithms. Finally, we discuss the relation between the general approach and the theory of Brownian functionals and calculate the mean residence time for the case of irreversible and reversible reactions.

A Unified Framework for Street-View Panorama Stitching

PubMed Central

Li, Li; Yao, Jian; Xie, Renping; Xia, Menghan; Zhang, Wei

2016-01-01

In this paper, we propose a unified framework to generate a pleasant and high-quality street-view panorama by stitching multiple panoramic images captured from the cameras mounted on the mobile platform. Our proposed framework is comprised of four major steps: image warping, color correction, optimal seam line detection and image blending. Since the input images are captured without a precisely common projection center from the scenes with the depth differences with respect to the cameras to different extents, such images cannot be precisely aligned in geometry. Therefore, an efficient image warping method based on the dense optical flow field is proposed to greatly suppress the influence of large geometric misalignment at first. Then, to lessen the influence of photometric inconsistencies caused by the illumination variations and different exposure settings, we propose an efficient color correction algorithm via matching extreme points of histograms to greatly decrease color differences between warped images. After that, the optimal seam lines between adjacent input images are detected via the graph cut energy minimization framework. At last, the Laplacian pyramid blending algorithm is applied to further eliminate the stitching artifacts along the optimal seam lines. Experimental results on a large set of challenging street-view panoramic images captured form the real world illustrate that the proposed system is capable of creating high-quality panoramas. PMID:28025481

Unified Deep Learning Architecture for Modeling Biology Sequence.

PubMed

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

Fast algorithm for chirp transforms with zooming-in ability and its applications.

PubMed

Deng, X; Bihari, B; Gan, J; Zhao, F; Chen, R T

2000-04-01

A general fast numerical algorithm for chirp transforms is developed by using two fast Fourier transforms and employing an analytical kernel. This new algorithm unifies the calculations of arbitrary real-order fractional Fourier transforms and Fresnel diffraction. Its computational complexity is better than a fast convolution method using Fourier transforms. Furthermore, one can freely choose the sampling resolutions in both x and u space and zoom in on any portion of the data of interest. Computational results are compared with analytical ones. The errors are essentially limited by the accuracy of the fast Fourier transforms and are higher than the order 10(-12) for most cases. As an example of its application to scalar diffraction, this algorithm can be used to calculate near-field patterns directly behind the aperture, 0 < or = z < d2/lambda. It compensates another algorithm for Fresnel diffraction that is limited to z > d2/lambdaN [J. Opt. Soc. Am. A 15, 2111 (1998)]. Experimental results from waveguide-output microcoupler diffraction are in good agreement with the calculations.

Asymptotic integration algorithms for nonhomogeneous, nonlinear, first order, ordinary differential equations

NASA Technical Reports Server (NTRS)

Walker, K. P.; Freed, A. D.

1991-01-01

New methods for integrating systems of stiff, nonlinear, first order, ordinary differential equations are developed by casting the differential equations into integral form. Nonlinear recursive relations are obtained that allow the solution to a system of equations at time t plus delta t to be obtained in terms of the solution at time t in explicit and implicit forms. Examples of accuracy obtained with the new technique are given by considering systems of nonlinear, first order equations which arise in the study of unified models of viscoplastic behaviors, the spread of the AIDS virus, and predator-prey populations. In general, the new implicit algorithm is unconditionally stable, and has a Jacobian of smaller dimension than that which is acquired by current implicit methods, such as the Euler backward difference algorithm; yet, it gives superior accuracy. The asymptotic explicit and implicit algorithms are suitable for solutions that are of the growing and decaying exponential kinds, respectively, whilst the implicit Euler-Maclaurin algorithm is superior when the solution oscillates, i.e., when there are regions in which both growing and decaying exponential solutions exist.

Multi-GPU parallel algorithm design and analysis for improved inversion of probability tomography with gravity gradiometry data

NASA Astrophysics Data System (ADS)

Hou, Zhenlong; Huang, Danian

2017-09-01

In this paper, we make a study on the inversion of probability tomography (IPT) with gravity gradiometry data at first. The space resolution of the results is improved by multi-tensor joint inversion, depth weighting matrix and the other methods. Aiming at solving the problems brought by the big data in the exploration, we present the parallel algorithm and the performance analysis combining Compute Unified Device Architecture (CUDA) with Open Multi-Processing (OpenMP) based on Graphics Processing Unit (GPU) accelerating. In the test of the synthetic model and real data from Vinton Dome, we get the improved results. It is also proved that the improved inversion algorithm is effective and feasible. The performance of parallel algorithm we designed is better than the other ones with CUDA. The maximum speedup could be more than 200. In the performance analysis, multi-GPU speedup and multi-GPU efficiency are applied to analyze the scalability of the multi-GPU programs. The designed parallel algorithm is demonstrated to be able to process larger scale of data and the new analysis method is practical.

Formulating face verification with semidefinite programming.

PubMed

Yan, Shuicheng; Liu, Jianzhuang; Tang, Xiaoou; Huang, Thomas S

2007-11-01

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.

Object recognition and localization from 3D point clouds by maximum-likelihood estimation

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise.

PubMed

Le Montagner, Yoann; Angelini, Elsa D; Olivo-Marin, Jean-Christophe

2014-03-01

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.

Towards a web-based decision support tool for selecting appropriate statistical test in medical and biological sciences.

PubMed

Suner, Aslı; Karakülah, Gökhan; Dicle, Oğuz

2014-01-01

Statistical hypothesis testing is an essential component of biological and medical studies for making inferences and estimations from the collected data in the study; however, the misuse of statistical tests is widely common. In order to prevent possible errors in convenient statistical test selection, it is currently possible to consult available test selection algorithms developed for various purposes. However, the lack of an algorithm presenting the most common statistical tests used in biomedical research in a single flowchart causes several problems such as shifting users among the algorithms, poor decision support in test selection and lack of satisfaction of potential users. Herein, we demonstrated a unified flowchart; covers mostly used statistical tests in biomedical domain, to provide decision aid to non-statistician users while choosing the appropriate statistical test for testing their hypothesis. We also discuss some of the findings while we are integrating the flowcharts into each other to develop a single but more comprehensive decision algorithm.

Compressible, multiphase semi-implicit method with moment of fluid interface representation

DOE PAGES

Jemison, Matthew; Sussman, Mark; Arienti, Marco

2014-09-16

A unified method for simulating multiphase flows using an exactly mass, momentum, and energy conserving Cell-Integrated Semi-Lagrangian advection algorithm is presented. The deforming material boundaries are represented using the moment-of-fluid method. Our new algorithm uses a semi-implicit pressure update scheme that asymptotically preserves the standard incompressible pressure projection method in the limit of infinite sound speed. The asymptotically preserving attribute makes the new method applicable to compressible and incompressible flows including stiff materials; enabling large time steps characteristic of incompressible flow algorithms rather than the small time steps required by explicit methods. Moreover, shocks are captured and material discontinuities aremore » tracked, without the aid of any approximate or exact Riemann solvers. As a result, wimulations of underwater explosions and fluid jetting in one, two, and three dimensions are presented which illustrate the effectiveness of the new algorithm at efficiently computing multiphase flows containing shock waves and material discontinuities with large “impedance mismatch.”« less

GPU-accelerated computing for Lagrangian coherent structures of multi-body gravitational regimes

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-04-01

Based on a well-established theoretical foundation, Lagrangian Coherent Structures (LCSs) have elicited widespread research on the intrinsic structures of dynamical systems in many fields, including the field of astrodynamics. Although the application of LCSs in dynamical problems seems straightforward theoretically, its associated computational cost is prohibitive. We propose a block decomposition algorithm developed on Compute Unified Device Architecture (CUDA) platform for the computation of the LCSs of multi-body gravitational regimes. In order to take advantage of GPU's outstanding computing properties, such as Shared Memory, Constant Memory, and Zero-Copy, the algorithm utilizes a block decomposition strategy to facilitate computation of finite-time Lyapunov exponent (FTLE) fields of arbitrary size and timespan. Simulation results demonstrate that this GPU-based algorithm can satisfy double-precision accuracy requirements and greatly decrease the time needed to calculate final results, increasing speed by approximately 13 times. Additionally, this algorithm can be generalized to various large-scale computing problems, such as particle filters, constellation design, and Monte-Carlo simulation.

ELSI: A unified software interface for Kohn–Sham electronic structure solvers

DOE PAGES

Yu, Victor Wen-zhe; Corsetti, Fabiano; Garcia, Alberto; ...

2017-09-15

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aimsmore » to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. As a result, comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.« less

ELSI: A unified software interface for Kohn-Sham electronic structure solvers

NASA Astrophysics Data System (ADS)

Yu, Victor Wen-zhe; Corsetti, Fabiano; García, Alberto; Huhn, William P.; Jacquelin, Mathias; Jia, Weile; Lange, Björn; Lin, Lin; Lu, Jianfeng; Mi, Wenhui; Seifitokaldani, Ali; Vázquez-Mayagoitia, Álvaro; Yang, Chao; Yang, Haizhao; Blum, Volker

2018-01-01

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aims to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.

ELSI: A unified software interface for Kohn–Sham electronic structure solvers

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

Yu, Victor Wen-zhe; Corsetti, Fabiano; Garcia, Alberto

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aimsmore » to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. As a result, comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.« less

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

PubMed Central

Barron, Martin; Zhang, Siyuan

2018-01-01

Abstract Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data. PMID:29140455

Attitude/attitude-rate estimation from GPS differential phase measurements using integrated-rate parameters

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, Landis

1998-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Ontology Matching with Semantic Verification.

PubMed

Jean-Mary, Yves R; Shironoshita, E Patrick; Kabuka, Mansur R

2009-09-01

ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.

Efficient parallel implementation of active appearance model fitting algorithm on GPU.

PubMed

Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

2014-01-01

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

PubMed Central

Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

2014-01-01

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures. PMID:24723812

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

NASA Astrophysics Data System (ADS)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Marginal Consistency: Upper-Bounding Partition Functions over Commutative Semirings.

PubMed

Werner, Tomás

2015-07-01

Many inference tasks in pattern recognition and artificial intelligence lead to partition functions in which addition and multiplication are abstract binary operations forming a commutative semiring. By generalizing max-sum diffusion (one of convergent message passing algorithms for approximate MAP inference in graphical models), we propose an iterative algorithm to upper bound such partition functions over commutative semirings. The iteration of the algorithm is remarkably simple: change any two factors of the partition function such that their product remains the same and their overlapping marginals become equal. In many commutative semirings, repeating this iteration for different pairs of factors converges to a fixed point when the overlapping marginals of every pair of factors coincide. We call this state marginal consistency. During that, an upper bound on the partition function monotonically decreases. This abstract algorithm unifies several existing algorithms, including max-sum diffusion and basic constraint propagation (or local consistency) algorithms in constraint programming. We further construct a hierarchy of marginal consistencies of increasingly higher levels and show than any such level can be enforced by adding identity factors of higher arity (order). Finally, we discuss instances of the framework for several semirings, including the distributive lattice and the max-sum and sum-product semirings.

Molecular Monte Carlo Simulations Using Graphics Processing Units: To Waste Recycle or Not?

PubMed

Kim, Jihan; Rodgers, Jocelyn M; Athènes, Manuel; Smit, Berend

2011-10-11

In the waste recycling Monte Carlo (WRMC) algorithm, (1) multiple trial states may be simultaneously generated and utilized during Monte Carlo moves to improve the statistical accuracy of the simulations, suggesting that such an algorithm may be well posed for implementation in parallel on graphics processing units (GPUs). In this paper, we implement two waste recycling Monte Carlo algorithms in CUDA (Compute Unified Device Architecture) using uniformly distributed random trial states and trial states based on displacement random-walk steps, and we test the methods on a methane-zeolite MFI framework system to evaluate their utility. We discuss the specific implementation details of the waste recycling GPU algorithm and compare the methods to other parallel algorithms optimized for the framework system. We analyze the relationship between the statistical accuracy of our simulations and the CUDA block size to determine the efficient allocation of the GPU hardware resources. We make comparisons between the GPU and the serial CPU Monte Carlo implementations to assess speedup over conventional microprocessors. Finally, we apply our optimized GPU algorithms to the important problem of determining free energy landscapes, in this case for molecular motion through the zeolite LTA.

A Unified and Coherent Land Surface Emissivity Earth System Data Record

NASA Astrophysics Data System (ADS)

Knuteson, R. O.; Borbas, E. E.; Hulley, G. C.; Hook, S. J.; Anderson, M. C.; Pinker, R. T.; Hain, C.; Guillevic, P. C.

2014-12-01

Land Surface Temperature and Emissivity (LST&E) data are essential for a wide variety of studies from calculating the evapo-transpiration of plant canopies to retrieving atmospheric water vapor. LST&E products are generated from data acquired by sensors in low Earth orbit (LEO) and by sensors in geostationary Earth orbit (GEO). Although these products represent the same measure, they are produced at different spatial, spectral and temporal resolutions using different algorithms. The different approaches used to retrieve the temperatures and emissivities result in discrepancies and inconsistencies between the different products. NASA has identified a major need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. This poster will introduce the land surface emissivity product of the NASA MEASUREs project called A Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR). To develop a unified high spectral resolution emissivity database, the MODIS baseline-fit emissivity database (MODBF) produced at the University of Wisconsin-Madison and the ASTER Global Emissivity Database (ASTER GED) produced at JPL will be merged. The unified Emissivity ESDR will be produced globally at 5km in mean monthly time-steps and for 12 bands from 3.6-14.3 micron and extended to 417 bands using a PC regression approach. The poster will introduce this data product. LST&E is a critical ESDR for a wide variety of studies in particular ecosystem and climate modeling.

Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study.

PubMed

Pearce, Christopher M; McLeod, Adam; Patrick, Jon; Boyle, Douglas; Shearer, Marianne; Eustace, Paula; Pearce, Mary Catherine

2016-12-20

Every day, patients are admitted to the hospital with conditions that could have been effectively managed in the primary care sector. These admissions are expensive and in many cases are possible to avoid if early intervention occurs. General practitioners are in the best position to identify those at risk of imminent hospital presentation and admission; however, it is not always possible for all the factors to be considered. A lack of shared information contributes significantly to the challenge of understanding a patient's full medical history. Some health care systems around the world use algorithms to analyze patient data in order to predict events such as emergency presentation; however, those responsible for the design and use of such systems readily admit that the algorithms can only be used to assess the populations used to design the algorithm in the first place. The United Kingdom health care system has contributed data toward algorithm development, which is possible through the unified health care system in place there. The lack of unified patient records in Australia has made building an algorithm for local use a significant challenge. Our objective is to use linked patient records to track patient flow through primary and secondary health care in order to develop a tool that can be applied in real time at the general practice level. This algorithm will allow the generation of reports for general practitioners that indicate the relative risk of patients presenting to an emergency department. A previously designed tool was used to deidentify the general practice and hospital records of approximately 100,000 patients. Records were pooled for patients who had attended emergency departments within the Eastern Health Network of hospitals and general practices within the Eastern Health Network catchment. The next phase will involve development of a model using a predictive analytic machine learning algorithm. The model will be developed iteratively, testing the combination of variables that will provide the best predictive model. Records of approximately 97,000 patients who have attended both a general practice and an emergency department have been identified within the database. These records are currently being used to develop the predictive model. Records from general practice and emergency department visits have been identified and pooled for development of the algorithm. The next phase in the project will see validation and live testing of the algorithm in a practice setting. The algorithm will underpin a clinical decision support tool for general practitioners which will be tested for face validity in this initial study into its efficacy. ©Christopher M Pearce, Adam McLeod, Jon Patrick, Douglas Boyle, Marianne Shearer, Paula Eustace, Mary Catherine Pearce. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 20.12.2016.

A unified model of the hierarchical and stochastic theories of gastric cancer

PubMed Central

Song, Yanjing; Wang, Yao; Tong, Chuan; Xi, Hongqing; Zhao, Xudong; Wang, Yi; Chen, Lin

2017-01-01

Gastric cancer (GC) is a life-threatening disease worldwide. Despite remarkable advances in treatments for GC, it is still fatal to many patients due to cancer progression, recurrence and metastasis. Regarding the development of novel therapeutic techniques, many studies have focused on the biological mechanisms that initiate tumours and cause treatment resistance. Tumours have traditionally been considered to result from somatic mutations, either via clonal evolution or through a stochastic model. However, emerging evidence has characterised tumours using a hierarchical organisational structure, with cancer stem cells (CSCs) at the apex. Both stochastic and hierarchical models are reasonable systems that have been hypothesised to describe tumour heterogeneity. Although each model alone inadequately explains tumour diversity, the two models can be integrated to provide a more comprehensive explanation. In this review, we discuss existing evidence supporting a unified model of gastric CSCs, including the regulatory mechanisms of this unified model in addition to the current status of stemness-related targeted therapy in GC patients. PMID:28301871

Joint learning of labels and distance metric.

PubMed

Liu, Bo; Wang, Meng; Hong, Richang; Zha, Zhengjun; Hua, Xian-Sheng

2010-06-01

Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.

The elimination of colour blocks in remote sensing images in VR

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Li, Guohui; Su, Zhenyu

2018-02-01

Aiming at the characteristics in HSI colour space of remote sensing images at different time in VR, a unified colour algorithm is proposed. First the method converted original image from RGB colour space to HSI colour space. Then, based on the invariance of the hue before and after the colour adjustment in the HSI colour space and the brightness translational features of the image after the colour adjustment, establish the linear model which satisfied these characteristics of the image. And then determine the range of the parameters in the model. Finally, according to the established colour adjustment model, the experimental verification is carried out. The experimental results show the proposed model can effectively recover the clear image, and the algorithm is faster. The experimental results show the proposed algorithm can effectively enhance the image clarity and can solve the pigment block problem well.

Web-accessible cervigram automatic segmentation tool

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

Uterine cervix image analysis is of great importance to the study of uterine cervix cancer, which is among the leading cancers affecting women worldwide. In this paper, we describe our proof-of-concept, Web-accessible system for automated segmentation of significant tissue regions in uterine cervix images, which also demonstrates our research efforts toward promoting collaboration between engineers and physicians for medical image analysis projects. Our design and implementation unifies the merits of two commonly used languages, MATLAB and Java. It circumvents the heavy workload of recoding the sophisticated segmentation algorithms originally developed in MATLAB into Java while allowing remote users who are not experienced programmers and algorithms developers to apply those processing methods to their own cervicographic images and evaluate the algorithms. Several other practical issues of the systems are also discussed, such as the compression of images and the format of the segmentation results.

Customization of biomedical terminologies.

PubMed

Homo, Julien; Dupuch, Laëtitia; Benbrahim, Allel; Grabar, Natalia; Dupuch, Marie

2012-01-01

Within the biomedical area over one hundred terminologies exist and are merged in the Unified Medical Language System Metathesaurus, which gives over 1 million concepts. When such huge terminological resources are available, the users must deal with them and specifically they must deal with irrelevant parts of these terminologies. We propose to exploit seed terms and semantic distance algorithms in order to customize the terminologies and to limit within them a semantically homogeneous space. An evaluation performed by a medical expert indicates that the proposed approach is relevant for the customization of terminologies and that the extracted terms are mostly relevant to the seeds. It also indicates that different algorithms provide with similar or identical results within a given terminology. The difference is due to the terminologies exploited. A special attention must be paid to the definition of optimal association between the semantic similarity algorithms and the thresholds specific to a given terminology.

Fast algorithms for Quadrature by Expansion I: Globally valid expansions

NASA Astrophysics Data System (ADS)

Rachh, Manas; Klöckner, Andreas; O'Neil, Michael

2017-09-01

The use of integral equation methods for the efficient numerical solution of PDE boundary value problems requires two main tools: quadrature rules for the evaluation of layer potential integral operators with singular kernels, and fast algorithms for solving the resulting dense linear systems. Classically, these tools were developed separately. In this work, we present a unified numerical scheme based on coupling Quadrature by Expansion, a recent quadrature method, to a customized Fast Multipole Method (FMM) for the Helmholtz equation in two dimensions. The method allows the evaluation of layer potentials in linear-time complexity, anywhere in space, with a uniform, user-chosen level of accuracy as a black-box computational method. Providing this capability requires geometric and algorithmic considerations beyond the needs of standard FMMs as well as careful consideration of the accuracy of multipole translations. We illustrate the speed and accuracy of our method with various numerical examples.

Control of mechanical systems with rolling constraints: Application to dynamic control of mobile robots

NASA Technical Reports Server (NTRS)

Sarkar, Nilanjan; Yun, Xiaoping; Kumar, Vijay

1994-01-01

There are many examples of mechanical systems that require rolling contacts between two or more rigid bodies. Rolling contacts engender nonholonomic constraints in an otherwise holonomic system. In this article, we develop a unified approach to the control of mechanical systems subject to both holonomic and nonholonomic constraints. We first present a state space realization of a constrained system. We then discuss the input-output linearization and zero dynamics of the system. This approach is applied to the dynamic control of mobile robots. Two types of control algorithms for mobile robots are investigated: trajectory tracking and path following. In each case, a smooth nonlinear feedback is obtained to achieve asymptotic input-output stability and Lagrange stability of the overall system. Simulation results are presented to demonstrate the effectiveness of the control algorithms and to compare the performane of trajectory-tracking and path-following algorithms.

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Quantitative analysis of multiple sclerosis: a feasibility study

NASA Astrophysics Data System (ADS)

Li, Lihong; Li, Xiang; Wei, Xinzhou; Sturm, Deborah; Lu, Hongbing; Liang, Zhengrong

2006-03-01

Multiple Sclerosis (MS) is an inflammatory and demyelinating disorder of the central nervous system with a presumed immune-mediated etiology. For treatment of MS, the measurements of white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) are often used in conjunction with clinical evaluation to provide a more objective measure of MS burden. In this paper, we apply a new unifying automatic mixture-based algorithm for segmentation of brain tissues to quantitatively analyze MS. The method takes into account the following effects that commonly appear in MR imaging: 1) The MR data is modeled as a stochastic process with an inherent inhomogeneity effect of smoothly varying intensity; 2) A new partial volume (PV) model is built in establishing the maximum a posterior (MAP) segmentation scheme; 3) Noise artifacts are minimized by a priori Markov random field (MRF) penalty indicating neighborhood correlation from tissue mixture. The volumes of brain tissues (WM, GM) and CSF are extracted from the mixture-based segmentation. Experimental results of feasibility studies on quantitative analysis of MS are presented.

Image analysis of the blood cells for cytomorphodiagnostics and control of the effectiveness treatment

NASA Astrophysics Data System (ADS)

Zhukotsky, Alexander V.; Kogan, Emmanuil M.; Kopylov, Victor F.; Marchenko, Oleg V.; Lomakin, O. A.

1994-07-01

A new method for morphodensitometric analysis of blood cells was applied for medically screening some ecological influence and infection pathologies. A complex algorithm of computational image processing was created for supra molecular restructurings of interphase chromatin of lymphocytes research. It includes specific methods of staining and unifies different quantitative analysis methods. Our experience with the use of a television image analyzer in cytological and immunological studies made it possible to carry out some research in morphometric analysis of chromatin structure in interphase lymphocyte nuclei in genetic and virus pathologies. In our study to characterize lymphocytes as an image-forming system by a rigorous mathematical description we used an approach involving contaminant evaluation of the topography of chromatin network intact and victims' lymphocytes. It is also possible to digitize data, which revealed significant distinctions between control and experiment. The method allows us to observe the minute structural changes in chromatin, especially eu- and hetero-chromatin that were previously studied by genetics only in chromosomes.

Unified Communications: Simplifying DoD Communication Methods

DTIC Science & Technology

2013-04-18

private key to encrypt the hash. The encrypted hash, together with some other information, such as the hashing algorithm , is known as a digital...virtual private network (VPN). The use of a VPN would allow users to access corporate data while encrypting traffic.35 Another layer of protection would...sign and encrypt emails as well as controlling access to restricted sites. PKI uses a combination of public and private keys for encryption and

Bioinspired Concepts: Unified Theory for Complex Biological and Engineering Systems

DTIC Science & Technology

2006-01-01

i.e., data flows of finite size arrive at the system randomly. For such a system , we propose a modified dual scheduling algorithm that stabilizes ...demon. We compute the efficiency of the controller over finite and infinite time intervals, and since the controller is optimal, this yields hard limits...and highly optimized tolerance. PNAS, 102, 2005. 51. G. N. Nair and R. J. Evans. Stabilizability of stochastic linear systems with finite feedback

A unified wall function for compressible turbulence modelling

NASA Astrophysics Data System (ADS)

Ong, K. C.; Chan, A.

2018-05-01

Turbulence modelling near the wall often requires a high mesh density clustered around the wall and the first cells adjacent to the wall to be placed in the viscous sublayer. As a result, the numerical stability is constrained by the smallest cell size and hence requires high computational overhead. In the present study, a unified wall function is developed which is valid for viscous sublayer, buffer sublayer and inertial sublayer, as well as including effects of compressibility, heat transfer and pressure gradient. The resulting wall function applies to compressible turbulence modelling for both isothermal and adiabatic wall boundary conditions with the non-zero pressure gradient. Two simple wall function algorithms are implemented for practical computation of isothermal and adiabatic wall boundary conditions. The numerical results show that the wall function evaluates the wall shear stress and turbulent quantities of wall adjacent cells at wide range of non-dimensional wall distance and alleviate the number and size of cells required.

Development of a unified web-based national HIV/AIDS information system in China

PubMed Central

Mao, Yurong; Wu, Zunyou; Poundstone, Katharine; Wang, Changhe; Qin, Qianqian; Ma, Ye; Ma, Wei

2010-01-01

Background In the past, many data collection systems were in operation for different HIV/AIDS projects in China. We describe the creation of a unified, web-based national HIV/AIDS information system designed to streamline data collection and facilitate data use. Methods Integration of separate HIV/AIDS data systems was carried out in three phases. Phase 1, from January 2006 to December 2007, involved creating a set of unified data collection forms that took into account existing program needs and the reporting requirements of various international organizations. Phase 2, from January to October 2007, involved creating a web-based platform to host the integrated HIV/AIDS data collection system. Phase 3, from November to December 2007, involved pilot testing the new, integrated system prior to nationwide application. Results Eight web-based data collection subsystems based on one platform began operation on 1 January 2008. These eight subsystems cover: (i) HIV/AIDS case reporting; (ii) HIV testing and counselling; (iii) antiretroviral treatment (ART) for adults; (iv) ART for children; (v) behavioural interventions for high-risk groups; (vi) methadone maintenance treatment; (vii) sentinel and behavioural surveillance; and (viii) local county background information. The system provides real-time data to monitor HIV testing, prevention and treatment programs across the country. Conclusion China’s new unified, web-based HIV/AIDS information system has improved the efficiency of data collection, reporting, analysis and use, as well as data quality and security. It is a powerful tool to support policy making, program evaluation and implementation of the national HIV/AIDS program and, thus, may serve a model for other countries. PMID:21113041

Randomized controlled trial of false safety behavior elimination therapy: a unified cognitive behavioral treatment for anxiety psychopathology.

PubMed

Schmidt, Norman B; Buckner, Julia D; Pusser, Andrea; Woolaway-Bickel, Kelly; Preston, Jennifer L; Norr, Aaron

2012-09-01

We tested the efficacy of a unified cognitive-behavioral therapy protocol for anxiety disorders. This group treatment protocol, termed false safety behavior elimination therapy (F-SET), is a cognitive-behavioral approach designed for use across various anxiety disorders such as panic disorder (PD), social anxiety disorder (SAD), and generalized anxiety disorder (GAD). F-SET simplifies, as well as broadens, key therapeutic elements of empirically validated treatments for anxiety disorders to allow for easier delivery to heterogeneous groups of patients with anxiety psychopathology. Patients with a primary anxiety disorder diagnosis (N=96) were randomly assigned to F-SET or a wait-list control. Data indicate that F-SET shows good efficacy and durability when delivered to mixed groups of patients with anxieties (i.e., PD, SAD, GAD) by relatively inexperienced clinicians. Findings are discussed in the context of balancing treatment efficacy and clinical utility. Copyright © 2012. Published by Elsevier Ltd.

Constrained low-rank matrix estimation: phase transitions, approximate message passing and applications

NASA Astrophysics Data System (ADS)

Lesieur, Thibault; Krzakala, Florent; Zdeborová, Lenka

2017-07-01

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on Information Theory Proc. pp 1635-9 and 2015 53rd Annual Allerton Conf. on Communication, Control and Computing (IEEE) pp 680-7) on low-rank matrix estimation in the presence of constraints on the factors into which the matrix is factorized. Low-rank matrix factorization is one of the basic methods used in data analysis for unsupervised learning of relevant features and other types of dimensionality reduction. We present a framework to study the constrained low-rank matrix estimation for a general prior on the factors, and a general output channel through which the matrix is observed. We draw a parallel with the study of vector-spin glass models—presenting a unifying way to study a number of problems considered previously in separate statistical physics works. We present a number of applications for the problem in data analysis. We derive in detail a general form of the low-rank approximate message passing (Low-RAMP) algorithm, that is known in statistical physics as the TAP equations. We thus unify the derivation of the TAP equations for models as different as the Sherrington-Kirkpatrick model, the restricted Boltzmann machine, the Hopfield model or vector (xy, Heisenberg and other) spin glasses. The state evolution of the Low-RAMP algorithm is also derived, and is equivalent to the replica symmetric solution for the large class of vector-spin glass models. In the section devoted to result we study in detail phase diagrams and phase transitions for the Bayes-optimal inference in low-rank matrix estimation. We present a typology of phase transitions and their relation to performance of algorithms such as the Low-RAMP or commonly used spectral methods.

Conversion of wastelands into state ownership for the needs of high-rise construction

NASA Astrophysics Data System (ADS)

Ganebnykh, Elena

2018-03-01

High-rise construction in big cities faces the problem of land shortage in downtown areas. Audit of economic complexes showed a large volume of wastelands. The conversion of wastelands into state and municipal ownership helps in part to solve the problem of the lack of space for high-rise construction in the urban area in the format of infill construction. The article investigates the problem of the conversion of wastelands into state and municipal ownership. The research revealed no clear algorithm for converting wastelands into state and municipal ownership. To form a unified system for identifying such plots, a universal algorithm was developed to identify and convert ownerless immovable property into state or municipal ownership.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

PubMed Central

Wang, Zhenzhu; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm. PMID:28421125

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

PubMed

Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

Generalized look-ahead number conversion from signed digit to complement representation with optical logic operations

NASA Astrophysics Data System (ADS)

Qian, Feng; Li, Guoqiang

2001-12-01

In this paper a generalized look-ahead logic algorithm for number conversion from signed-digit to its complement representation is developed. By properly encoding the signed digits, all the operations are performed by binary logic, and unified logical expressions can be obtained for conversion from modified-signed-digit (MSD) to 2's complement, trinary signed-digit (TSD) to 3's complement, and quaternary signed-digit (QSD) to 4's complement. For optical implementation, a parallel logical array module using electron-trapping device is employed, which is suitable for realizing complex logic functions in the form of sum-of-product. The proposed algorithm and architecture are compatible with a general-purpose optoelectronic computing system.

A problem of optimal control and observation for distributed homogeneous multi-agent system

NASA Astrophysics Data System (ADS)

Kruglikov, Sergey V.

2017-12-01

The paper considers the implementation of a algorithm for controlling a distributed complex of several mobile multi-robots. The concept of a unified information space of the controlling system is applied. The presented information and mathematical models of participants and obstacles, as real agents, and goals and scenarios, as virtual agents, create the base forming the algorithmic and software background for computer decision support system. The controlling scheme assumes the indirect management of the robotic team on the basis of optimal control and observation problem predicting intellectual behavior in a dynamic, hostile environment. A basic content problem is a compound cargo transportation by a group of participants in the case of a distributed control scheme in the terrain with multiple obstacles.

The exact analysis of contingency tables in medical research.

PubMed

Mehta, C R

1994-01-01

A unified view of exact nonparametric inference, with special emphasis on data in the form of contingency tables, is presented. While the concept of exact tests has been in existence since the early work of RA Fisher, the computational complexity involved in actually executing such tests precluded their use until fairly recently. Modern algorithmic advances, combined with the easy availability of inexpensive computing power, has renewed interest in exact methods of inference, especially because they remain valid in the face of small, sparse, imbalanced, or heavily tied data. After defining exact p-values in terms of the permutation principle, we reference algorithms for computing them. Several data sets are then analysed by both exact and asymptotic methods. We end with a discussion of the available software.

Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

NASA Astrophysics Data System (ADS)

Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

2016-03-01

A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

Discontinuous Galerkin methods for Hamiltonian ODEs and PDEs

NASA Astrophysics Data System (ADS)

Tang, Wensheng; Sun, Yajuan; Cai, Wenjun

2017-02-01

In this article, we present a unified framework of discontinuous Galerkin (DG) discretizations for Hamiltonian ODEs and PDEs. We show that with appropriate numerical fluxes the numerical algorithms deduced from DG discretizations can be combined with the symplectic methods in time to derive the multi-symplectic PRK schemes. The resulting numerical discretizations are applied to the linear and nonlinear Schrödinger equations. Some conservative properties of the numerical schemes are investigated and confirmed in the numerical experiments.

Probabilistic arithmetic automata and their applications.

PubMed

Marschall, Tobias; Herms, Inke; Kaltenbach, Hans-Michael; Rahmann, Sven

2012-01-01

We present a comprehensive review on probabilistic arithmetic automata (PAAs), a general model to describe chains of operations whose operands depend on chance, along with two algorithms to numerically compute the distribution of the results of such probabilistic calculations. PAAs provide a unifying framework to approach many problems arising in computational biology and elsewhere. We present five different applications, namely 1) pattern matching statistics on random texts, including the computation of the distribution of occurrence counts, waiting times, and clump sizes under hidden Markov background models; 2) exact analysis of window-based pattern matching algorithms; 3) sensitivity of filtration seeds used to detect candidate sequence alignments; 4) length and mass statistics of peptide fragments resulting from enzymatic cleavage reactions; and 5) read length statistics of 454 and IonTorrent sequencing reads. The diversity of these applications indicates the flexibility and unifying character of the presented framework. While the construction of a PAA depends on the particular application, we single out a frequently applicable construction method: We introduce deterministic arithmetic automata (DAAs) to model deterministic calculations on sequences, and demonstrate how to construct a PAA from a given DAA and a finite-memory random text model. This procedure is used for all five discussed applications and greatly simplifies the construction of PAAs. Implementations are available as part of the MoSDi package. Its application programming interface facilitates the rapid development of new applications based on the PAA framework.

Design Considerations of a Virtual Laboratory for Advanced X-ray Sources

NASA Astrophysics Data System (ADS)

Luginsland, J. W.; Frese, M. H.; Frese, S. D.; Watrous, J. J.; Heileman, G. L.

2004-11-01

The field of scientific computation has greatly advanced in the last few years, resulting in the ability to perform complex computer simulations that can predict the performance of real-world experiments in a number of fields of study. Among the forces driving this new computational capability is the advent of parallel algorithms, allowing calculations in three-dimensional space with realistic time scales. Electromagnetic radiation sources driven by high-voltage, high-current electron beams offer an area to further push the state-of-the-art in high fidelity, first-principles simulation tools. The physics of these x-ray sources combine kinetic plasma physics (electron beams) with dense fluid-like plasma physics (anode plasmas) and x-ray generation (bremsstrahlung). There are a number of mature techniques and software packages for dealing with the individual aspects of these sources, such as Particle-In-Cell (PIC), Magneto-Hydrodynamics (MHD), and radiation transport codes. The current effort is focused on developing an object-oriented software environment using the Rational© Unified Process and the Unified Modeling Language (UML) to provide a framework where multiple 3D parallel physics packages, such as a PIC code (ICEPIC), a MHD code (MACH), and a x-ray transport code (ITS) can co-exist in a system-of-systems approach to modeling advanced x-ray sources. Initial software design and assessments of the various physics algorithms' fidelity will be presented.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

PubMed

Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

2015-01-01

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

A unified framework for gesture recognition and spatiotemporal gesture segmentation.

PubMed

Alon, Jonathan; Athitsos, Vassilis; Yuan, Quan; Sclaroff, Stan

2009-09-01

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

PubMed Central

Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

2015-01-01

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology. PMID:26441628

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

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

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

DOE PAGES

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

Algorithms for optimizing the treatment of depression: making the right decision at the right time.

PubMed

Adli, M; Rush, A J; Möller, H-J; Bauer, M

2003-11-01

Medication algorithms for the treatment of depression are designed to optimize both treatment implementation and the appropriateness of treatment strategies. Thus, they are essential tools for treating and avoiding refractory depression. Treatment algorithms are explicit treatment protocols that provide specific therapeutic pathways and decision-making tools at critical decision points throughout the treatment process. The present article provides an overview of major projects of algorithm research in the field of antidepressant therapy. The Berlin Algorithm Project and the Texas Medication Algorithm Project (TMAP) compare algorithm-guided treatments with treatment as usual. The Sequenced Treatment Alternatives to Relieve Depression Project (STAR*D) compares different treatment strategies in treatment-resistant patients.

Integrated pipeline for inferring the evolutionary history of a gene family embedded in the species tree: a case study on the STIMATE gene family.

PubMed

Song, Jia; Zheng, Sisi; Nguyen, Nhung; Wang, Youjun; Zhou, Yubin; Lin, Kui

2017-10-03

Because phylogenetic inference is an important basis for answering many evolutionary problems, a large number of algorithms have been developed. Some of these algorithms have been improved by integrating gene evolution models with the expectation of accommodating the hierarchy of evolutionary processes. To the best of our knowledge, however, there still is no single unifying model or algorithm that can take all evolutionary processes into account through a stepwise or simultaneous method. On the basis of three existing phylogenetic inference algorithms, we built an integrated pipeline for inferring the evolutionary history of a given gene family; this pipeline can model gene sequence evolution, gene duplication-loss, gene transfer and multispecies coalescent processes. As a case study, we applied this pipeline to the STIMATE (TMEM110) gene family, which has recently been reported to play an important role in store-operated Ca 2+ entry (SOCE) mediated by ORAI and STIM proteins. We inferred their phylogenetic trees in 69 sequenced chordate genomes. By integrating three tree reconstruction algorithms with diverse evolutionary models, a pipeline for inferring the evolutionary history of a gene family was developed, and its application was demonstrated.

Semi-supervised and unsupervised extreme learning machines.

PubMed

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations.

PubMed

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-03-03

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2-3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations

PubMed Central

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-01-01

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2–3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling. PMID:28273814

Joint estimation of motion and illumination change in a sequence of images

NASA Astrophysics Data System (ADS)

Koo, Ja-Keoung; Kim, Hyo-Hun; Hong, Byung-Woo

2015-09-01

We present an algorithm that simultaneously computes optical flow and estimates illumination change from an image sequence in a unified framework. We propose an energy functional consisting of conventional optical flow energy based on Horn-Schunck method and an additional constraint that is designed to compensate for illumination changes. Any undesirable illumination change that occurs in the imaging procedure in a sequence while the optical flow is being computed is considered a nuisance factor. In contrast to the conventional optical flow algorithm based on Horn-Schunck functional, which assumes the brightness constancy constraint, our algorithm is shown to be robust with respect to temporal illumination changes in the computation of optical flows. An efficient conjugate gradient descent technique is used in the optimization procedure as a numerical scheme. The experimental results obtained from the Middlebury benchmark dataset demonstrate the robustness and the effectiveness of our algorithm. In addition, comparative analysis of our algorithm and Horn-Schunck algorithm is performed on the additional test dataset that is constructed by applying a variety of synthetic bias fields to the original image sequences in the Middlebury benchmark dataset in order to demonstrate that our algorithm outperforms the Horn-Schunck algorithm. The superior performance of the proposed method is observed in terms of both qualitative visualizations and quantitative accuracy errors when compared to Horn-Schunck optical flow algorithm that easily yields poor results in the presence of small illumination changes leading to violation of the brightness constancy constraint.

An O([Formula: see text]) algorithm for sorting signed genomes by reversals, transpositions, transreversals and block-interchanges.

PubMed

Yu, Shuzhi; Hao, Fanchang; Leong, Hon Wai

2016-02-01

We consider the problem of sorting signed permutations by reversals, transpositions, transreversals, and block-interchanges. The problem arises in the study of species evolution via large-scale genome rearrangement operations. Recently, Hao et al. gave a 2-approximation scheme called genome sorting by bridges (GSB) for solving this problem. Their result extended and unified the results of (i) He and Chen - a 2-approximation algorithm allowing reversals, transpositions, and block-interchanges (by also allowing transversals) and (ii) Hartman and Sharan - a 1.5-approximation algorithm allowing reversals, transpositions, and transversals (by also allowing block-interchanges). The GSB result is based on introduction of three bridge structures in the breakpoint graph, the L-bridge, T-bridge, and X-bridge that models goodreversal, transposition/transreversal, and block-interchange, respectively. However, the paper by Hao et al. focused on proving the 2-approximation GSB scheme and only mention a straightforward [Formula: see text] algorithm. In this paper, we give an [Formula: see text] algorithm for implementing the GSB scheme. The key idea behind our faster GSB algorithm is to represent cycles in the breakpoint graph by their canonical sequences, which greatly simplifies the search for these bridge structures. We also give some comparison results (running time and computed distances) against the original GSB implementation.

Towards global optimization with adaptive simulated annealing

NASA Astrophysics Data System (ADS)

Forbes, Gregory W.; Jones, Andrew E.

1991-01-01

The structure of the simulated annealing algorithm is presented and its rationale is discussed. A unifying heuristic is then introduced which serves as a guide in the design of all of the sub-components of the algorithm. Simply put this heuristic principle states that at every cycle in the algorithm the occupation density should be kept as close as possible to the equilibrium distribution. This heuristic has been used as a guide to develop novel step generation and temperature control methods intended to improve the efficiency of the simulated annealing algorithm. The resulting algorithm has been used in attempts to locate good solutions for one of the lens design problems associated with this conference viz. the " monochromatic quartet" and a sample of the results is presented. 1 Global optimization in the context oflens design Whatever the context optimization algorithms relate to problems that take the following form: Given some configuration space with coordinates r (x1 . . x) and a merit function written asffr) find the point r whereftr) takes it lowest value. That is find the global minimum. In many cases there is also a set of auxiliary constraints that must be met so the problem statement becomes: Find the global minimum of the merit function within the region defined by E. (r) 0 j 1 2 . . . p and 0 j 1 2 . . . q.

Automatic Debugging Support for UML Designs

NASA Technical Reports Server (NTRS)

Schumann, Johann; Swanson, Keith (Technical Monitor)

2001-01-01

Design of large software systems requires rigorous application of software engineering methods covering all phases of the software process. Debugging during the early design phases is extremely important, because late bug-fixes are expensive. In this paper, we describe an approach which facilitates debugging of UML requirements and designs. The Unified Modeling Language (UML) is a set of notations for object-orient design of a software system. We have developed an algorithm which translates requirement specifications in the form of annotated sequence diagrams into structured statecharts. This algorithm detects conflicts between sequence diagrams and inconsistencies in the domain knowledge. After synthesizing statecharts from sequence diagrams, these statecharts usually are subject to manual modification and refinement. By using the "backward" direction of our synthesis algorithm. we are able to map modifications made to the statechart back into the requirements (sequence diagrams) and check for conflicts there. Fed back to the user conflicts detected by our algorithm are the basis for deductive-based debugging of requirements and domain theory in very early development stages. Our approach allows to generate explanations oil why there is a conflict and which parts of the specifications are affected.

Overlapping communities detection based on spectral analysis of line graphs

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Hu, Rongjing; Huang, Guoming; Wei, Jiaxuan

2018-05-01

Community in networks are often overlapping where one vertex belongs to several clusters. Meanwhile, many networks show hierarchical structure such that community is recursively grouped into hierarchical organization. In order to obtain overlapping communities from a global hierarchy of vertices, a new algorithm (named SAoLG) is proposed to build the hierarchical organization along with detecting the overlap of community structure. SAoLG applies the spectral analysis into line graphs to unify the overlap and hierarchical structure of the communities. In order to avoid the limitation of absolute distance such as Euclidean distance, SAoLG employs Angular distance to compute the similarity between vertices. Furthermore, we make a micro-improvement partition density to evaluate the quality of community structure and use it to obtain the more reasonable and sensible community numbers. The proposed SAoLG algorithm achieves a balance between overlap and hierarchy by applying spectral analysis to edge community detection. The experimental results on one standard network and six real-world networks show that the SAoLG algorithm achieves higher modularity and reasonable community number values than those generated by Ahn's algorithm, the classical CPM and GN ones.

Speeding-up Bioinformatics Algorithms with Heterogeneous Architectures: Highly Heterogeneous Smith-Waterman (HHeterSW).

PubMed

Gálvez, Sergio; Ferusic, Adis; Esteban, Francisco J; Hernández, Pilar; Caballero, Juan A; Dorado, Gabriel

2016-10-01

The Smith-Waterman algorithm has a great sensitivity when used for biological sequence-database searches, but at the expense of high computing-power requirements. To overcome this problem, there are implementations in literature that exploit the different hardware-architectures available in a standard PC, such as GPU, CPU, and coprocessors. We introduce an application that splits the original database-search problem into smaller parts, resolves each of them by executing the most efficient implementations of the Smith-Waterman algorithms in different hardware architectures, and finally unifies the generated results. Using non-overlapping hardware allows simultaneous execution, and up to 2.58-fold performance gain, when compared with any other algorithm to search sequence databases. Even the performance of the popular BLAST heuristic is exceeded in 78% of the tests. The application has been tested with standard hardware: Intel i7-4820K CPU, Intel Xeon Phi 31S1P coprocessors, and nVidia GeForce GTX 960 graphics cards. An important increase in performance has been obtained in a wide range of situations, effectively exploiting the available hardware.

A unified flight control methodology for a compound rotorcraft in fundamental and aerobatic maneuvering flight

NASA Astrophysics Data System (ADS)

Thorsen, Adam

This study investigates a novel approach to flight control for a compound rotorcraft in a variety of maneuvers ranging from fundamental to aerobatic in nature. Fundamental maneuvers are a class of maneuvers with design significance that are useful for testing and tuning flight control systems along with uncovering control law deficiencies. Aerobatic maneuvers are a class of aggressive and complex maneuvers with more operational significance. The process culminating in a unified approach to flight control includes various control allocation studies for redundant controls in trim and maneuvering flight, an efficient methodology to simulate non-piloted maneuvers with varying degrees of complexity, and the setup of an unconventional control inceptor configuration along with the use of a flight simulator to gather pilot feedback in order to improve the unified control architecture. A flight path generation algorithm was developed to calculate control inceptor commands required for a rotorcraft in aerobatic maneuvers. This generalized algorithm was tailored to generate flight paths through optimization methods in order to satisfy target terminal position coordinates or to minimize the total time of a particular maneuver. Six aerobatic maneuvers were developed drawing inspiration from air combat maneuvers of fighter jet aircraft: Pitch-Back Turn (PBT), Combat Ascent Turn (CAT), Combat Descent Turn (CDT), Weaving Pull-up (WPU), Combat Break Turn (CBT), and Zoom and Boom (ZAB). These aerobatic maneuvers were simulated at moderate to high advance ratios while fundamental maneuvers of the compound including level accelerations/decelerations, climbs, descents, and turns were investigated across the entire flight envelope to evaluate controller performance. The unified control system was developed to allow controls to seamlessly transition between manual and automatic allocations while ensuring that the axis of control for a particular inceptor remained constant with flight regime. An energy management system was developed in order to manage performance limits (namely power required) to promote carefree maneuvering and alleviate pilot workload. This system features limits on pilot commands and has additional logic for preserving control margins and limiting maximum speed in a dive. Nonlinear dynamic inversion (NLDI) is the framework of the unified controller, which incorporates primary and redundant controls. The inner loop of the NLDI controller regulates bank angle, pitch attitude, and yaw rate, while the outer loop command structure is varied (three modes). One version uses an outer loop that commands velocities in the longitudinal and vertical axes (velocity mode), another commands longitudinal acceleration and vertical speed (acceleration mode), and the third commands longitudinal acceleration and transitions from velocity to acceleration command in the vertical axis (aerobatic mode). The flight envelope is discretized into low, cruise, and high speed flight regimes. The unified outer loop primary control effectors for the longitudinal and vertical axes (collective pitch, pitch attitude, and propeller pitch) vary depending on flight regime. A weighted pseudoinverse is used to phase either the collective or propeller pitch in/out of a redundant control role. The controllers were evaluated in Penn State's Rotorcraft Flight Simulator retaining the cyclic stick for vertical and lateral axis control along with pedal inceptors for yaw axis control. A throttle inceptor was used in place of the pilot's traditional left hand inceptor for longitudinal axis control. Ultimately, a simple rigid body model of the aircraft was sufficient enough to design a controller with favorable performance and stability characteristics. This unified flight control system promoted a low enough pilot workload so that an untrained pilot (the author) was able to pilot maneuvers of varying complexity with ease. The framework of this unified system is generalized enough to be able to be applied to any rotorcraft with redundant controls. Minimum power propeller thrust shares ranged from 50% - 90% in high speed flight, while lift shares at high speeds tended towards 60% wing and 40% main rotor.

Parametric models to relate spike train and LFP dynamics with neural information processing.

PubMed

Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

2012-01-01

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial-by-trial behavioral performance than existing models of neural information processing. Our results highlight the utility of the unified modeling framework for characterizing spike-LFP recordings obtained during behavioral performance.

SU-F-T-340: Direct Editing of Dose Volume Histograms: Algorithms and a Unified Convex Formulation for Treatment Planning with Dose Constraints

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

Ungun, B; Stanford University School of Medicine, Stanford, CA; Fu, A

2016-06-15

Purpose: To develop a procedure for including dose constraints in convex programming-based approaches to treatment planning, and to support dynamic modification of such constraints during planning. Methods: We present a mathematical approach that allows mean dose, maximum dose, minimum dose and dose volume (i.e., percentile) constraints to be appended to any convex formulation of an inverse planning problem. The first three constraint types are convex and readily incorporated. Dose volume constraints are not convex, however, so we introduce a convex restriction that is related to CVaR-based approaches previously proposed in the literature. To compensate for the conservatism of this restriction,more » we propose a new two-pass algorithm that solves the restricted problem on a first pass and uses this solution to form exact constraints on a second pass. In another variant, we introduce slack variables for each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints. We implement the proposed methods in Python using the convex programming package cvxpy in conjunction with the open source convex solvers SCS and ECOS. Results: We show, for several cases taken from the clinic, that our proposed method meets specified constraints (often with margin) when they are feasible. Constraints are met exactly when we use the two-pass method, and infeasible constraints are replaced with the nearest feasible constraint when slacks are used. Finally, we introduce ConRad, a Python-embedded free software package for convex radiation therapy planning. ConRad implements the methods described above and offers a simple interface for specifying prescriptions and dose constraints. Conclusion: This work demonstrates the feasibility of using modifiable dose constraints in a convex formulation, making it practical to guide the treatment planning process with interactively specified dose constraints. This work was supported by the Stanford BioX Graduate Fellowship and NIH Grant 5R01CA176553.« less

Robust integration schemes for generalized viscoplasticity with internal-state variables. Part 2: Algorithmic developments and implementation

NASA Technical Reports Server (NTRS)

Li, Wei; Saleeb, Atef F.

1995-01-01

This two-part report is concerned with the development of a general framework for the implicit time-stepping integrators for the flow and evolution equations in generalized viscoplastic models. The primary goal is to present a complete theoretical formulation, and to address in detail the algorithmic and numerical analysis aspects involved in its finite element implementation, as well as to critically assess the numerical performance of the developed schemes in a comprehensive set of test cases. On the theoretical side, the general framework is developed on the basis of the unconditionally-stable, backward-Euler difference scheme as a starting point. Its mathematical structure is of sufficient generality to allow a unified treatment of different classes of viscoplastic models with internal variables. In particular, two specific models of this type, which are representative of the present start-of-art in metal viscoplasticity, are considered in applications reported here; i.e., fully associative (GVIPS) and non-associative (NAV) models. The matrix forms developed for both these models are directly applicable for both initially isotropic and anisotropic materials, in general (three-dimensional) situations as well as subspace applications (i.e., plane stress/strain, axisymmetric, generalized plane stress in shells). On the computational side, issues related to efficiency and robustness are emphasized in developing the (local) interative algorithm. In particular, closed-form expressions for residual vectors and (consistent) material tangent stiffness arrays are given explicitly for both GVIPS and NAV models, with their maximum sizes 'optimized' to depend only on the number of independent stress components (but independent of the number of viscoplastic internal state parameters). Significant robustness of the local iterative solution is provided by complementing the basic Newton-Raphson scheme with a line-search strategy for convergence. In the present second part of the report, we focus on the specific details of the numerical schemes, and associated computer algorithms, for the finite-element implementation of GVIPS and NAV models.

A theoretical formulation of wave-vortex interactions

NASA Technical Reports Server (NTRS)

Wu, J. Z.; Wu, J. M.

1989-01-01

A unified theoretical formulation for wave-vortex interaction, designated the '(omega, Pi) framework,' is presented. Based on the orthogonal decomposition of fluid dynamic interactions, the formulation can be used to study a variety of problems, including the interaction of a longitudinal (acoustic) wave and/or transverse (vortical) wave with a main vortex flow. Moreover, the formulation permits a unified treatment of wave-vortex interaction at various approximate levels, where the normal 'piston' process and tangential 'rubbing' process can be approximated dfferently.

Variational nature, integration, and properties of Newton reaction path

NASA Astrophysics Data System (ADS)

Bofill, Josep Maria; Quapp, Wolfgang

2011-02-01

The distinguished coordinate path and the reduced gradient following path or its equivalent formulation, the Newton trajectory, are analyzed and unified using the theory of calculus of variations. It is shown that their minimum character is related to the fact that the curve is located in a valley region. In this case, we say that the Newton trajectory is a reaction path with the category of minimum energy path. In addition to these findings a Runge-Kutta-Fehlberg algorithm to integrate these curves is also proposed.

Variational nature, integration, and properties of Newton reaction path.

PubMed

Bofill, Josep Maria; Quapp, Wolfgang

2011-02-21

The distinguished coordinate path and the reduced gradient following path or its equivalent formulation, the Newton trajectory, are analyzed and unified using the theory of calculus of variations. It is shown that their minimum character is related to the fact that the curve is located in a valley region. In this case, we say that the Newton trajectory is a reaction path with the category of minimum energy path. In addition to these findings a Runge-Kutta-Fehlberg algorithm to integrate these curves is also proposed.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John

2014-01-01

A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Code conversion from signed-digit to complement representation based on look-ahead optical logic operations

NASA Astrophysics Data System (ADS)

Li, Guoqiang; Qian, Feng

2001-11-01

We present, for the first time to our knowledge, a generalized lookahead logic algorithm for number conversion from signed-digit to complement representation. By properly encoding the signed-digits, all the operations are performed by binary logic, and unified logical expressions can be obtained for conversion from modified-signed- digit (MSD) to 2's complement, trinary signed-digit (TSD) to 3's complement, and quarternary signed-digit (QSD) to 4's complement. For optical implementation, a parallel logical array module using an electron-trapping device is employed and experimental results are shown. This optical module is suitable for implementing complex logic functions in the form of the sum of the product. The algorithm and architecture are compatible with a general-purpose optoelectronic computing system.

HUMBLE PROBLEMS

NASA Technical Reports Server (NTRS)

Markley, F. Landis

2006-01-01

Harold Morton introduced a talk by saying that when you wind up an old professor, he tends to talk for a microcentury. I will attempt to keep my comments to that canonical time span. Having failed to find some unifying theme for this talk, I decided to just ramble through my career with a focus on the algorithms, spacecraft, and people I've had the privilege and pleasure to work with. The algorithms, and certainly the spacecraft, are not all mine. The people are some of those whose ideas that have most influenced and inspired my career. The organization of the paper is largely chronological, but I do not hesitate to jump forward or backward in time when the material demands it. The coverage is broad but necessarily shallow; the interested reader can find more detail in the references

Degeneracy in NLP and the development of results motivated by its presence

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

Fiacco, A.; Liu, J.

We study notions of nondegeneracy and several levels of increasing degeneracy from the perspective of the local behavior of a local solution of a nonlinear program when problem parameters are slightly perturbed. This overview may be viewed as a structured survey of sensitivity and stability results: the focus is on progressive levels of degeneracy. We note connections of nondegeneracy with the convergence of algorithms and observe the striking parallel between the effects of nondegeneracy and degeneracy on optimality conditions, stability analysis and algorithmic convergence behavior. Although our orientation here is primarily interpretive and noncritical, we conclude that more effort ismore » needed to unify optimality, stability and convergence theory and more results are needed in all three areas for radically degenerate problems.« less

Effective algorithm for solving complex problems of production control and of material flows control of industrial enterprise

NASA Astrophysics Data System (ADS)

Mezentsev, Yu A.; Baranova, N. V.

2018-05-01

A universal economical and mathematical model designed for determination of optimal strategies for managing subsystems (components of subsystems) of production and logistics of enterprises is considered. Declared universality allows taking into account on the system level both production components, including limitations on the ways of converting raw materials and components into sold goods, as well as resource and logical restrictions on input and output material flows. The presented model and generated control problems are developed within the framework of the unified approach that allows one to implement logical conditions of any complexity and to define corresponding formal optimization tasks. Conceptual meaning of used criteria and limitations are explained. The belonging of the generated tasks of the mixed programming with the class of NP is shown. An approximate polynomial algorithm for solving the posed optimization tasks for mixed programming of real dimension with high computational complexity is proposed. Results of testing the algorithm on the tasks in a wide range of dimensions are presented.

DECONV-TOOL: An IDL based deconvolution software package

NASA Technical Reports Server (NTRS)

Varosi, F.; Landsman, W. B.

1992-01-01

There are a variety of algorithms for deconvolution of blurred images, each having its own criteria or statistic to be optimized in order to estimate the original image data. Using the Interactive Data Language (IDL), we have implemented the Maximum Likelihood, Maximum Entropy, Maximum Residual Likelihood, and sigma-CLEAN algorithms in a unified environment called DeConv_Tool. Most of the algorithms have as their goal the optimization of statistics such as standard deviation and mean of residuals. Shannon entropy, log-likelihood, and chi-square of the residual auto-correlation are computed by DeConv_Tool for the purpose of determining the performance and convergence of any particular method and comparisons between methods. DeConv_Tool allows interactive monitoring of the statistics and the deconvolved image during computation. The final results, and optionally, the intermediate results, are stored in a structure convenient for comparison between methods and review of the deconvolution computation. The routines comprising DeConv_Tool are available via anonymous FTP through the IDL Astronomy User's Library.

Data Analysis with Graphical Models: Software Tools

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1994-01-01

Probabilistic graphical models (directed and undirected Markov fields, and combined in chain graphs) are used widely in expert systems, image processing and other areas as a framework for representing and reasoning with probabilities. They come with corresponding algorithms for performing probabilistic inference. This paper discusses an extension to these models by Spiegelhalter and Gilks, plates, used to graphically model the notion of a sample. This offers a graphical specification language for representing data analysis problems. When combined with general methods for statistical inference, this also offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper outlines the framework and then presents some basic tools for the task: a graphical version of the Pitman-Koopman Theorem for the exponential family, problem decomposition, and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2016-01-01

In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC-like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.

Transdiagnostic Treatment of Bipolar Disorder and Comorbid Anxiety with the Unified Protocol: A Clinical Replication Series

PubMed Central

Ellard, Kristen K.; Deckersbach, Thilo; Sylvia, Louisa G.; Nierenberg, Andrew A.; Barlow, David H.

2013-01-01

Bipolar disorder (BD) is a chronic, debilitating disorder with recurrent manic and depressive episodes. Over 75% of bipolar patients have a current or lifetime diagnosis of a comorbid anxiety disorder. Comorbid anxiety in BD is associated with greater illness severity, greater functional impairment, and poorer illness-related outcomes. Effectively treating comorbid anxiety in individuals with BD has been recognized as one of the biggest unmet needs in the field of bipolar disorder. Recently, the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) was developed to be applicable to the full range of anxiety and mood disorders, based upon converging evidence from genetics, cognitive and affective neuroscience, and behavioral research suggesting common, core emotion-related pathology. Here, we present a preliminary evaluation of the efficacy of the UP for the treatment of BD with comorbid anxiety, in a clinical replication series consisting of three cases. PMID:22822175

Shape design sensitivity analysis and optimization of three dimensional elastic solids using geometric modeling and automatic regridding. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Yao, Tse-Min; Choi, Kyung K.

1987-01-01

An automatic regridding method and a three dimensional shape design parameterization technique were constructed and integrated into a unified theory of shape design sensitivity analysis. An algorithm was developed for general shape design sensitivity analysis of three dimensional eleastic solids. Numerical implementation of this shape design sensitivity analysis method was carried out using the finite element code ANSYS. The unified theory of shape design sensitivity analysis uses the material derivative of continuum mechanics with a design velocity field that represents shape change effects over the structural design. Automatic regridding methods were developed by generating a domain velocity field with boundary displacement method. Shape design parameterization for three dimensional surface design problems was illustrated using a Bezier surface with boundary perturbations that depend linearly on the perturbation of design parameters. A linearization method of optimization, LINRM, was used to obtain optimum shapes. Three examples from different engineering disciplines were investigated to demonstrate the accuracy and versatility of this shape design sensitivity analysis method.

Unified Computational Methods for Regression Analysis of Zero-Inflated and Bound-Inflated Data

PubMed Central

Yang, Yan; Simpson, Douglas

2010-01-01

Bounded data with excess observations at the boundary are common in many areas of application. Various individual cases of inflated mixture models have been studied in the literature for bound-inflated data, yet the computational methods have been developed separately for each type of model. In this article we use a common framework for computing these models, and expand the range of models for both discrete and semi-continuous data with point inflation at the lower boundary. The quasi-Newton and EM algorithms are adapted and compared for estimation of model parameters. The numerical Hessian and generalized Louis method are investigated as means for computing standard errors after optimization. Correlated data are included in this framework via generalized estimating equations. The estimation of parameters and effectiveness of standard errors are demonstrated through simulation and in the analysis of data from an ultrasound bioeffect study. The unified approach enables reliable computation for a wide class of inflated mixture models and comparison of competing models. PMID:20228950

3D Fluid-Structure Interaction Simulation of Aortic Valves Using a Unified Continuum ALE FEM Model.

PubMed

Spühler, Jeannette H; Jansson, Johan; Jansson, Niclas; Hoffman, Johan

2018-01-01

Due to advances in medical imaging, computational fluid dynamics algorithms and high performance computing, computer simulation is developing into an important tool for understanding the relationship between cardiovascular diseases and intraventricular blood flow. The field of cardiac flow simulation is challenging and highly interdisciplinary. We apply a computational framework for automated solutions of partial differential equations using Finite Element Methods where any mathematical description directly can be translated to code. This allows us to develop a cardiac model where specific properties of the heart such as fluid-structure interaction of the aortic valve can be added in a modular way without extensive efforts. In previous work, we simulated the blood flow in the left ventricle of the heart. In this paper, we extend this model by placing prototypes of both a native and a mechanical aortic valve in the outflow region of the left ventricle. Numerical simulation of the blood flow in the vicinity of the valve offers the possibility to improve the treatment of aortic valve diseases as aortic stenosis (narrowing of the valve opening) or regurgitation (leaking) and to optimize the design of prosthetic heart valves in a controlled and specific way. The fluid-structure interaction and contact problem are formulated in a unified continuum model using the conservation laws for mass and momentum and a phase function. The discretization is based on an Arbitrary Lagrangian-Eulerian space-time finite element method with streamline diffusion stabilization, and it is implemented in the open source software Unicorn which shows near optimal scaling up to thousands of cores. Computational results are presented to demonstrate the capability of our framework.

3D Fluid-Structure Interaction Simulation of Aortic Valves Using a Unified Continuum ALE FEM Model

PubMed Central

Spühler, Jeannette H.; Jansson, Johan; Jansson, Niclas; Hoffman, Johan

2018-01-01

Due to advances in medical imaging, computational fluid dynamics algorithms and high performance computing, computer simulation is developing into an important tool for understanding the relationship between cardiovascular diseases and intraventricular blood flow. The field of cardiac flow simulation is challenging and highly interdisciplinary. We apply a computational framework for automated solutions of partial differential equations using Finite Element Methods where any mathematical description directly can be translated to code. This allows us to develop a cardiac model where specific properties of the heart such as fluid-structure interaction of the aortic valve can be added in a modular way without extensive efforts. In previous work, we simulated the blood flow in the left ventricle of the heart. In this paper, we extend this model by placing prototypes of both a native and a mechanical aortic valve in the outflow region of the left ventricle. Numerical simulation of the blood flow in the vicinity of the valve offers the possibility to improve the treatment of aortic valve diseases as aortic stenosis (narrowing of the valve opening) or regurgitation (leaking) and to optimize the design of prosthetic heart valves in a controlled and specific way. The fluid-structure interaction and contact problem are formulated in a unified continuum model using the conservation laws for mass and momentum and a phase function. The discretization is based on an Arbitrary Lagrangian-Eulerian space-time finite element method with streamline diffusion stabilization, and it is implemented in the open source software Unicorn which shows near optimal scaling up to thousands of cores. Computational results are presented to demonstrate the capability of our framework. PMID:29713288

Experiences of Adolescents in Residential Treatment at the Orthogenic School 2013-2015: A Qualitative Study

ERIC Educational Resources Information Center

Scholom, Elizabeth

2017-01-01

The expansion of treatment interventions in child and adolescent residential milieu therapy in recent decades to include a multiplicity of approaches and techniques has resulted in the reformulation of residential treatment as a "tapestry of therapies" as opposed to a unified effort to maintain a uniquely growth-promoting total…

Efficacy of the unified protocol for the treatment of emotional disorders in the Spanish public mental health system using a group format: study protocol for a multicenter, randomized, non-inferiority controlled trial.

PubMed

Osma, Jorge; Suso-Ribera, Carlos; García-Palacios, Azucena; Crespo-Delgado, Elena; Robert-Flor, Cristina; Sánchez-Guerrero, Ana; Ferreres-Galan, Vanesa; Pérez-Ayerra, Luisa; Malea-Fernández, Amparo; Torres-Alfosea, Mª Ángeles

2018-03-12

Emotional disorders, which include both anxiety and depressive disorders, are the most prevalent psychological disorders according to recent epidemiological studies. Consequently, public costs associated with their treatment have become a matter of concern for public health systems, which face long waiting lists. Because of their high prevalence in the population, finding an effective treatment for emotional disorders has become a key goal of today's clinical psychology. The Unified Protocol for the Transdiagnostic Treatment of Emotional Disorders might serve the aforementioned purpose, as it can be applied to a variety of disorders simultaneously and it can be easily performed in a group format. The study is a multicenter, randomized, non-inferiority controlled clinical trial. Participants will be 220 individuals with emotional disorders, who are randomized to either a treatment as usual (individual cognitive behavioral therapy) or to a Unified Protocol condition in group format. Depression, anxiety, and diagnostic criteria are the primary outcome measures. Secondary measures include the assessment of positive and negative affect, anxiety control, personality traits, overall adjustment, and quality of life. An analysis of treatment satisfaction is also conducted. Assessment points include baseline, post-treatment, and three follow-ups at 3, 6, and 12 months. To control for missing data and possible biases, intention-to-treat and per-protocol analyses will be performed. This is the first randomized, controlled clinical trial to test the effectiveness of a transdiagnostic intervention in a group format for the treatment of emotional disorders in public settings in Spain. Results obtained from this study may have important clinical, social, and economic implications for public mental health settings in Spain. Retrospectively registered at https://clinicaltrials.gov/ . Trial NCT03064477 (March 10, 2017). The trial is active and recruitment is ongoing. Recruitment is expected to finish by January 2020.

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Optimization of microphysics in the Unified Model, using the Micro-genetic algorithm.

NASA Astrophysics Data System (ADS)

Jang, J.; Lee, Y.; Lee, H.; Lee, J.; Joo, S.

2016-12-01

This study focuses on parameter optimization of microphysics in the Unified Model (UM) using the Micro-genetic algorithm (Micro-GA). We need the optimization of microphysics in UM. Because, Microphysics in the Numerical Weather Prediction (NWP) model is important to Quantitative Precipitation Forecasting (QPF). The Micro-GA searches for optimal parameters on the basis of fitness function. The five parameters are chosen. The target parameters include x1, x2 related to raindrop size distribution, Cloud-rain correlation coefficient, Surface droplet number and Droplet taper height. The fitness function is based on the skill score that is BIAS and Critical Successive Index (CSI). An interface between UM and Micro-GA is developed and applied to three precipitation cases in Korea. The cases are (ⅰ) heavy rainfall in the Southern area because of typhoon NAKRI, (ⅱ) heavy rainfall in the Youngdong area, and (ⅲ) heavy rainfall in the Seoul metropolitan area. When the optimized result is compared to the control result (using the UM default value, CNTL), the optimized result leads to improvements in precipitation forecast, especially for heavy rainfall of the late forecast time. Also, we analyze the skill score of precipitation forecasts in terms of various thresholds of CNTL, Optimized result, and experiments on each optimized parameter for five parameters. Generally, the improvement is maximized when the five optimized parameters are used simultaneously. Therefore, this study demonstrates the ability to improve Korean precipitation forecasts by optimizing microphysics in UM.

Graph embedding and extensions: a general framework for dimensionality reduction.

PubMed

Yan, Shuicheng; Xu, Dong; Zhang, Benyu; Zhang, Hong-Jiang; Yang, Qiang; Lin, Stephen

2007-01-01

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.

Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

PubMed Central

Du, Jia; Younes, Laurent; Qiu, Anqi

2011-01-01

This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation. PMID:21281722

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

NASA Astrophysics Data System (ADS)

Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

2014-01-01

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

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

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing

PubMed Central

Wang, Guoli; Ebrahimi, Nader

2014-01-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data. PMID:25821345

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

PubMed

Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

2015-04-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H , such that V ∼ W H . It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H . In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

Predicting crystal growth via a unified kinetic three-dimensional partition model

NASA Astrophysics Data System (ADS)

Anderson, Michael W.; Gebbie-Rayet, James T.; Hill, Adam R.; Farida, Nani; Attfield, Martin P.; Cubillas, Pablo; Blatov, Vladislav A.; Proserpio, Davide M.; Akporiaye, Duncan; Arstad, Bjørnar; Gale, Julian D.

2017-04-01

Understanding and predicting crystal growth is fundamental to the control of functionality in modern materials. Despite investigations for more than one hundred years, it is only recently that the molecular intricacies of these processes have been revealed by scanning probe microscopy. To organize and understand this large amount of new information, new rules for crystal growth need to be developed and tested. However, because of the complexity and variety of different crystal systems, attempts to understand crystal growth in detail have so far relied on developing models that are usually applicable to only one system. Such models cannot be used to achieve the wide scope of understanding that is required to create a unified model across crystal types and crystal structures. Here we describe a general approach to understanding and, in theory, predicting the growth of a wide range of crystal types, including the incorporation of defect structures, by simultaneous molecular-scale simulation of crystal habit and surface topology using a unified kinetic three-dimensional partition model. This entails dividing the structure into ‘natural tiles’ or Voronoi polyhedra that are metastable and, consequently, temporally persistent. As such, these units are then suitable for re-construction of the crystal via a Monte Carlo algorithm. We demonstrate our approach by predicting the crystal growth of a diverse set of crystal types, including zeolites, metal-organic frameworks, calcite, urea and L-cystine.

High-speed reacting flow simulation using USA-series codes

NASA Astrophysics Data System (ADS)

Chakravarthy, S. R.; Palaniswamy, S.

In this paper, the finite-rate chemistry (FRC) formulation for the USA-series of codes and three sets of validations are presented. USA-series computational fluid dynamics (CFD) codes are based on Unified Solution Algorithms including explicity and implicit formulations, factorization and relaxation approaches, time marching and space marching methodolgies, etc., in order to be able to solve a very wide class of CDF problems using a single framework. Euler or Navier-Stokes equations are solved using a finite-volume treatment with upwind Total Variation Diminishing discretization for the inviscid terms. Perfect and real gas options are available including equilibrium and nonequilibrium chemistry. This capability has been widely used to study various problems including Space Shuttle exhaust plumes, National Aerospace Plane (NASP) designs, etc. (1) Numerical solutions are presented showing the full range of possible solutions to steady detonation wave problems. (2) Comparison between the solution obtained by the USA code and Generalized Kinetics Analysis Program (GKAP) is shown for supersonic combustion in a duct. (3) Simulation of combustion in a supersonic shear layer is shown to have reasonable agreement with experimental observations.

Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network

NASA Astrophysics Data System (ADS)

Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng

2017-10-01

Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.

Medical data sheet in safe havens - A tri-layer cryptic solution.

PubMed

Praveenkumar, Padmapriya; Amirtharajan, Rengarajan; Thenmozhi, K; Balaguru Rayappan, John Bosco

2015-07-01

Secured sharing of the diagnostic reports and scan images of patients among doctors with complementary expertise for collaborative treatment will help to provide maximum care through faster and decisive decisions. In this context, a tri-layer cryptic solution has been proposed and implemented on Digital Imaging and Communications in Medicine (DICOM) images to establish a secured communication for effective referrals among peers without compromising the privacy of patients. In this approach, a blend of three cryptic schemes, namely Latin square image cipher (LSIC), discrete Gould transform (DGT) and Rubik׳s encryption, has been adopted. Among them, LSIC provides better substitution, confusion and shuffling of the image blocks; DGT incorporates tamper proofing with authentication; and Rubik renders a permutation of DICOM image pixels. The developed algorithm has been successfully implemented and tested in both the software (MATLAB 7) and hardware Universal Software Radio Peripheral (USRP) environments. Specifically, the encrypted data were tested by transmitting them through an additive white Gaussian noise (AWGN) channel model. Furthermore, the sternness of the implemented algorithm was validated by employing standard metrics such as the unified average changing intensity (UACI), number of pixels change rate (NPCR), correlation values and histograms. The estimated metrics have also been compared with the existing methods and dominate in terms of large key space to defy brute force attack, cropping attack, strong key sensitivity and uniform pixel value distribution on encryption. Copyright © 2015 Elsevier Ltd. All rights reserved.

Unified treatment of the luminosity distance in cosmology

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

Yoo, Jaiyul; Scaccabarozzi, Fulvio, E-mail: jyoo@physik.uzh.ch, E-mail: fulvio@physik.uzh.ch

Comparing the luminosity distance measurements to its theoretical predictions is one of the cornerstones in establishing the modern cosmology. However, as shown in Biern and Yoo, its theoretical predictions in literature are often plagued with infrared divergences and gauge-dependences. This trend calls into question the sanity of the methods used to derive the luminosity distance. Here we critically investigate four different methods—the geometric approach, the Sachs approach, the Jacobi mapping approach, and the geodesic light cone (GLC) approach to modeling the luminosity distance, and we present a unified treatment of such methods, facilitating the comparison among the methods and checkingmore » their sanity. All of these four methods, if exercised properly, can be used to reproduce the correct description of the luminosity distance.« less

Multiple-source multiple-harmonic active vibration control of variable section cylindrical structures: A numerical study

NASA Astrophysics Data System (ADS)

Liu, Jinxin; Chen, Xuefeng; Gao, Jiawei; Zhang, Xingwu

2016-12-01

Air vehicles, space vehicles and underwater vehicles, the cabins of which can be viewed as variable section cylindrical structures, have multiple rotational vibration sources (e.g., engines, propellers, compressors and motors), making the spectrum of noise multiple-harmonic. The suppression of such noise has been a focus of interests in the field of active vibration control (AVC). In this paper, a multiple-source multiple-harmonic (MSMH) active vibration suppression algorithm with feed-forward structure is proposed based on reference amplitude rectification and conjugate gradient method (CGM). An AVC simulation scheme called finite element model in-loop simulation (FEMILS) is also proposed for rapid algorithm verification. Numerical studies of AVC are conducted on a variable section cylindrical structure based on the proposed MSMH algorithm and FEMILS scheme. It can be seen from the numerical studies that: (1) the proposed MSMH algorithm can individually suppress each component of the multiple-harmonic noise with an unified and improved convergence rate; (2) the FEMILS scheme is convenient and straightforward for multiple-source simulations with an acceptable loop time. Moreover, the simulations have similar procedure to real-life control and can be easily extended to physical model platform.

Computational methods for reactive transport modeling: A Gibbs energy minimization approach for multiphase equilibrium calculations

NASA Astrophysics Data System (ADS)

Leal, Allan M. M.; Kulik, Dmitrii A.; Kosakowski, Georg

2016-02-01

We present a numerical method for multiphase chemical equilibrium calculations based on a Gibbs energy minimization approach. The method can accurately and efficiently determine the stable phase assemblage at equilibrium independently of the type of phases and species that constitute the chemical system. We have successfully applied our chemical equilibrium algorithm in reactive transport simulations to demonstrate its effective use in computationally intensive applications. We used FEniCS to solve the governing partial differential equations of mass transport in porous media using finite element methods in unstructured meshes. Our equilibrium calculations were benchmarked with GEMS3K, the numerical kernel of the geochemical package GEMS. This allowed us to compare our results with a well-established Gibbs energy minimization algorithm, as well as their performance on every mesh node, at every time step of the transport simulation. The benchmark shows that our novel chemical equilibrium algorithm is accurate, robust, and efficient for reactive transport applications, and it is an improvement over the Gibbs energy minimization algorithm used in GEMS3K. The proposed chemical equilibrium method has been implemented in Reaktoro, a unified framework for modeling chemically reactive systems, which is now used as an alternative numerical kernel of GEMS.

Towards a Unified Framework for Pose, Expression, and Occlusion Tolerant Automatic Facial Alignment.

PubMed

Seshadri, Keshav; Savvides, Marios

2016-10-01

We propose a facial alignment algorithm that is able to jointly deal with the presence of facial pose variation, partial occlusion of the face, and varying illumination and expressions. Our approach proceeds from sparse to dense landmarking steps using a set of specific models trained to best account for the shape and texture variation manifested by facial landmarks and facial shapes across pose and various expressions. We also propose the use of a novel l1-regularized least squares approach that we incorporate into our shape model, which is an improvement over the shape model used by several prior Active Shape Model (ASM) based facial landmark localization algorithms. Our approach is compared against several state-of-the-art methods on many challenging test datasets and exhibits a higher fitting accuracy on all of them.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

2018-05-01

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

Dynamics simulation and controller interfacing for legged robots

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

Reichler, J.A.; Delcomyn, F.

2000-01-01

Dynamics simulation can play a critical role in the engineering of robotic control code, and there exist a variety of strategies both for building physical models and for interacting with these models. This paper presents an approach to dynamics simulation and controller interfacing for legged robots, and contrasts it to existing approaches. The authors describe dynamics algorithms and contact-resolution strategies for multibody articulated mobile robots based on the decoupled tree-structure approach, and present a novel scripting language that provides a unified framework for control-code interfacing, user-interface design, and data analysis. Special emphasis is placed on facilitating the rapid integration ofmore » control algorithms written in a standard object-oriented language (C++), the production of modular, distributed, reusable controllers, and the use of parameterized signal-transmission properties such as delay, sampling rate, and noise.« less

Optimized Laplacian image sharpening algorithm based on graphic processing unit

NASA Astrophysics Data System (ADS)

Ma, Tinghuai; Li, Lu; Ji, Sai; Wang, Xin; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah

2014-12-01

In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.

Scalable large format 3D displays

NASA Astrophysics Data System (ADS)

Chang, Nelson L.; Damera-Venkata, Niranjan

2010-02-01

We present a general framework for the modeling and optimization of scalable large format 3-D displays using multiple projectors. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors (e.g. tiled, superimposed, combinations of the two) without manual adjustment. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution stereoscopic video at real-time interactive frame rates achieved on commodity graphics hardware. Through complementary polarization, the framework creates high quality multi-projector 3-D displays at low hardware and operational cost for a variety of applications including digital cinema, visualization, and command-and-control walls.

The diversity and unit of reactor noise theory

NASA Astrophysics Data System (ADS)

Kuang, Zhifeng

The study of reactor noise theory concerns questions about cause and effect relationships, and utilisation of random noise in nuclear reactor systems. The diversity of reactor noise theory arises from the variety of noise sources, the various mathematical treatments applied and various practical purposes. The neutron noise in zero- energy systems arises from the fluctuations in the number of neutrons per fission, the time between nuclear events, and the type of reactions. It can be used to evaluate system parameters. The mathematical treatment is based on the master equation of stochastic branching processes. The noise in power reactor systems is given rise by random processes of technological origin such as vibration of mechanical parts, boiling of the coolant, fluctuations of temperature and pressure. It can be used to monitor reactor behaviour with the possibility of detecting malfunctions at an early stage. The mathematical treatment is based on the Langevin equation. The unity of reactor noise theory arises from the fact that useful information from noise is embedded in the second moments of random variables, which lends the possibility of building up a unified mathematical description and analysis of the various reactor noise sources. Exploring such possibilities is the main subject among the three major topics reported in this thesis. The first subject is within the zero power noise in steady media, and we reported on the extension of the existing theory to more general cases. In Paper I, by use of the master equation approach, we have derived the most general Feynman- and Rossi-alpha formulae so far by taking the full joint statistics of the prompt and all the six groups of delayed neutron precursors, and a multiple emission source into account. The involved problems are solved with a combination of effective analytical techniques and symbolic algebra codes (Mathematica). Paper II gives a numerical evaluation of these formulae. An assessment of the contribution of the terms that are novel as compared to the traditional formulae has been made. The second subject treats a problem in power reactor noise with the Langevin formalism. With a very few exceptions, in all previous work the diffusion approximation was used. In order to extend the treatment to transport theory, in Paper III, we introduced a novel method, i.e. Padé approximation via Lanczos algorithm to calculate the transfer function of a finite slab reactor described by one-group transport equation. It was found that the local-global decomposition of the neutron noise, formerly only reproduced in at least 2- group theory, can be reconstructed. We have also showed the existence of a boundary layer of the neutron noise close to the boundary. Finally, we have explored the possibility of building up a unified theory to account for the coexistence of zero power and power reactor noise in a system. In Paper IV, a unified description of the neutron noise is given by the use of backward master equations in a model where the cross section fluctuations are given as a simple binary pseudorandom process. The general solution contains both the zero power and power reactor noise concurrently, and they can be extracted individually as limiting cases of the general solution. It justified the separate treatments of zero power and power reactor noise. The result was extended to the case including one group of delayed neutron precursors in Paper V.

Structured output-feedback controller synthesis with design specifications

NASA Astrophysics Data System (ADS)

Hao, Yuqing; Duan, Zhisheng

2017-03-01

This paper considers the problem of structured output-feedback controller synthesis with finite frequency specifications. Based on the orthogonal space information of input matrix, an improved parameter-dependent Lyapunov function method is first proposed. Then, a two-stage construction method is designed, which depends on an initial centralised controller. Corresponding design conditions for three types of output-feedback controllers are presented in terms of unified representations. Moreover, heuristic algorithms are provided to explore the desirable controllers. Finally, the effectiveness of these proposed methods is illustrated via some practical examples.

Learning, epigenetics, and computation: An extension on Fitch's proposal. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W. Tecumseh Fitch

NASA Astrophysics Data System (ADS)

Okanoya, Kazuo

2014-09-01

The comparative computational approach of Fitch [1] attempts to renew the classical David Marr paradigm of computation, algorithm, and implementation, by introducing evolutionary view of the relationship between neural architecture and cognition. This comparative evolutionary view provides constraints useful in narrowing down the problem space for both cognition and neural mechanisms. I will provide two examples from our own studies that reinforce and extend Fitch's proposal.

Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method

NASA Astrophysics Data System (ADS)

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-01-01

We present a method for determining the free-energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevant regions of the order parameter space. The algorithm is tested on the reconstruction of an alanine dipeptide free-energy landscape.

Well-tempered metadynamics: a smoothly converging and tunable free-energy method.

PubMed

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-01-18

We present a method for determining the free-energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevant regions of the order parameter space. The algorithm is tested on the reconstruction of an alanine dipeptide free-energy landscape.

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.

On recursive least-squares filtering algorithms and implementations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hsieh, Shih-Fu

1990-01-01

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Development of a Unified Protocol for the Treatment of Emotional Disorders in Youth

ERIC Educational Resources Information Center

Ehrenreich, Jill T.; Goldstein, Clark R.; Wright, Lauren R.; Barlow, David H.

2009-01-01

This article reviews the development and initial trial of a treatment for adolescents that targets negative emotionality and associated psychological difficulties--particularly anxiety and depressive disorders--as a more singular entity by utilizing an approach rooted in both emotion science and theory. The rationale for such an approach is based…

Performance modeling of automated manufacturing systems

NASA Astrophysics Data System (ADS)

Viswanadham, N.; Narahari, Y.

A unified and systematic treatment is presented of modeling methodologies and analysis techniques for performance evaluation of automated manufacturing systems. The book is the first treatment of the mathematical modeling of manufacturing systems. Automated manufacturing systems are surveyed and three principal analytical modeling paradigms are discussed: Markov chains, queues and queueing networks, and Petri nets.

Treatment of anorexia nervosa in children and adolescents.

PubMed

Weaver, Laurel; Sit, Lydia; Liebman, Ronald

2012-04-01

In this review, we discuss the treatment of anorexia nervosa (AN) in children and adolescents, highlighting inpatient and outpatient psychiatric treatment. AN is an illness that involves medical and psychological issues; hence, treatment often requires the seamless integration of several medical professionals. It is important that the treatment model be unified and consistent as patients transition from inpatient to outpatient treatment. We briefly describe the therapeutic principles involved in treatment of AN and then give examples of how we employ these principles across treatment settings and with multiple medical professionals.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

SPEEDES - A multiple-synchronization environment for parallel discrete-event simulation

NASA Technical Reports Server (NTRS)

Steinman, Jeff S.

1992-01-01

Synchronous Parallel Environment for Emulation and Discrete-Event Simulation (SPEEDES) is a unified parallel simulation environment. It supports multiple-synchronization protocols without requiring users to recompile their code. When a SPEEDES simulation runs on one node, all the extra parallel overhead is removed automatically at run time. When the same executable runs in parallel, the user preselects the synchronization algorithm from a list of options. SPEEDES currently runs on UNIX networks and on the California Institute of Technology/Jet Propulsion Laboratory Mark III Hypercube. SPEEDES also supports interactive simulations. Featured in the SPEEDES environment is a new parallel synchronization approach called Breathing Time Buckets. This algorithm uses some of the conservative techniques found in Time Bucket synchronization, along with the optimism that characterizes the Time Warp approach. A mathematical model derived from first principles predicts the performance of Breathing Time Buckets. Along with the Breathing Time Buckets algorithm, this paper discusses the rules for processing events in SPEEDES, describes the implementation of various other synchronization protocols supported by SPEEDES, describes some new ones for the future, discusses interactive simulations, and then gives some performance results.

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Parallel fuzzy connected image segmentation on GPU

PubMed Central

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

2011-01-01

Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037

Parallel fuzzy connected image segmentation on GPU.

PubMed

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

2011-07-01

Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

The Impact of a Line Probe Assay Based Diagnostic Algorithm on Time to Treatment Initiation and Treatment Outcomes for Multidrug Resistant TB Patients in Arkhangelsk Region, Russia.

PubMed

Eliseev, Platon; Balantcev, Grigory; Nikishova, Elena; Gaida, Anastasia; Bogdanova, Elena; Enarson, Donald; Ornstein, Tara; Detjen, Anne; Dacombe, Russell; Gospodarevskaya, Elena; Phillips, Patrick P J; Mann, Gillian; Squire, Stephen Bertel; Mariandyshev, Andrei

2016-01-01

In the Arkhangelsk region of Northern Russia, multidrug-resistant (MDR) tuberculosis (TB) rates in new cases are amongst the highest in the world. In 2014, MDR-TB rates reached 31.7% among new cases and 56.9% among retreatment cases. The development of new diagnostic tools allows for faster detection of both TB and MDR-TB and should lead to reduced transmission by earlier initiation of anti-TB therapy. The PROVE-IT (Policy Relevant Outcomes from Validating Evidence on Impact) Russia study aimed to assess the impact of the implementation of line probe assay (LPA) as part of an LPA-based diagnostic algorithm for patients with presumptive MDR-TB focusing on time to treatment initiation with time from first-care seeking visit to the initiation of MDR-TB treatment rather than diagnostic accuracy as the primary outcome, and to assess treatment outcomes. We hypothesized that the implementation of LPA would result in faster time to treatment initiation and better treatment outcomes. A culture-based diagnostic algorithm used prior to LPA implementation was compared to an LPA-based algorithm that replaced BacTAlert and Löwenstein Jensen (LJ) for drug sensitivity testing. A total of 295 MDR-TB patients were included in the study, 163 diagnosed with the culture-based algorithm, 132 with the LPA-based algorithm. Among smear positive patients, the implementation of the LPA-based algorithm was associated with a median decrease in time to MDR-TB treatment initiation of 50 and 66 days compared to the culture-based algorithm (BacTAlert and LJ respectively, p<0.001). In smear negative patients, the LPA-based algorithm was associated with a median decrease in time to MDR-TB treatment initiation of 78 days when compared to the culture-based algorithm (LJ, p<0.001). However, several weeks were still needed for treatment initiation in LPA-based algorithm, 24 days in smear positive, and 62 days in smear negative patients. Overall treatment outcomes were better in LPA-based algorithm compared to culture-based algorithm (p = 0.003). Treatment success rates at 20 months of treatment were higher in patients diagnosed with the LPA-based algorithm (65.2%) as compared to those diagnosed with the culture-based algorithm (44.8%). Mortality was also lower in the LPA-based algorithm group (7.6%) compared to the culture-based algorithm group (15.9%). There was no statistically significant difference in smear and culture conversion rates between the two algorithms. The results of the study suggest that the introduction of LPA leads to faster time to MDR diagnosis and earlier treatment initiation as well as better treatment outcomes for patients with MDR-TB. These findings also highlight the need for further improvements within the health system to reduce both patient and diagnostic delays to truly optimize the impact of new, rapid diagnostics.

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

NASA Astrophysics Data System (ADS)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

The Research and Implementation of MUSER CLEAN Algorithm Based on OpenCL

NASA Astrophysics Data System (ADS)

Feng, Y.; Chen, K.; Deng, H.; Wang, F.; Mei, Y.; Wei, S. L.; Dai, W.; Yang, Q. P.; Liu, Y. B.; Wu, J. P.

2017-03-01

It's urgent to carry out high-performance data processing with a single machine in the development of astronomical software. However, due to the different configuration of the machine, traditional programming techniques such as multi-threading, and CUDA (Compute Unified Device Architecture)+GPU (Graphic Processing Unit) have obvious limitations in portability and seamlessness between different operation systems. The OpenCL (Open Computing Language) used in the development of MUSER (MingantU SpEctral Radioheliograph) data processing system is introduced. And the Högbom CLEAN algorithm is re-implemented into parallel CLEAN algorithm by the Python language and PyOpenCL extended package. The experimental results show that the CLEAN algorithm based on OpenCL has approximately equally operating efficiency compared with the former CLEAN algorithm based on CUDA. More important, the data processing in merely CPU (Central Processing Unit) environment of this system can also achieve high performance, which has solved the problem of environmental dependence of CUDA+GPU. Overall, the research improves the adaptability of the system with emphasis on performance of MUSER image clean computing. In the meanwhile, the realization of OpenCL in MUSER proves its availability in scientific data processing. In view of the high-performance computing features of OpenCL in heterogeneous environment, it will probably become the preferred technology in the future high-performance astronomical software development.

Autoreject: Automated artifact rejection for MEG and EEG data.

PubMed

Jas, Mainak; Engemann, Denis A; Bekhti, Yousra; Raimondo, Federico; Gramfort, Alexandre

2017-10-01

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience. Copyright © 2017 Elsevier Inc. All rights reserved.

A comparison of algorithms for inference and learning in probabilistic graphical models.

PubMed

Frey, Brendan J; Jojic, Nebojsa

2005-09-01

Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.

A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

PubMed

Samdin, S Balqis; Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

2017-04-01

This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.

Computation of elementary modes: a unifying framework and the new binary approach

PubMed Central

Gagneur, Julien; Klamt, Steffen

2004-01-01

Background Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods. Results We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date. Conclusions The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks. PMID:15527509

What does the electroencephalogram tell us about the mechanisms of action of ECT in major depressive disorders?

PubMed

Farzan, Faranak; Boutros, Nash N; Blumberger, Daniel M; Daskalakis, Zafiris J

2014-06-01

Electroconvulsive therapy (ECT) remains to be one of the most effective treatment options in treatment-resistant major depressive disorder (MDD). From the early days, researchers have embarked on extracting information from electroencephalography (EEG) recordings before, during, and after ECT to identify neurophysiological targets of ECT and discover EEG predictors of response to ECT in patients with MDD. In this article, we provide an overview of visually detected and quantitative EEG features that could help in furthering our understanding of the mechanisms of action of ECT in MDD. We further discuss the EEG findings in the context of postulated hypotheses of ECT therapeutic pathways. We introduce an alternative and unifying hypothesis suggesting that ECT may exert its therapeutic efficacy through resetting the aberrant functional connectivity and promoting the generation of new and healthy connections in brain regions implicated in MDD pathophysiology, a mechanism that may be in part mediated by the ECT-induced activation of inhibitory and neuroplasticity mechanisms. We further discuss the added value of EEG markers in the larger context of ECT research and as complementary to neuroimaging and genetic markers. We conclude by drawing attention to the need for longitudinal studies in large cohort of patients and the need for standardization and validation of EEG algorithms of functional connectivity across studies to facilitate the translation of EEG correlates of ECT response in routine clinical practice.

First-line treatment: a critical appraisal of cognitive behavioral therapy developments and alternatives.

PubMed

Arch, Joanna J; Craske, Michelle G

2009-09-01

In this article, the authors assess the successes, remaining challenges, and new developments in cognitive behavioral therapy (CBT) for anxiety disorders. They define CBT, examine treatment components, review treatment efficacy, and discuss the challenges of attrition, long-term follow-up, co-occurring/comorbid disorders, limited treatment comparisons, treatment mediators, and broader implementation. In addition, they present recent developments in cognitive behavioral therapy for anxiety disorders, including linking exposure therapy to basic science, mindfulness and acceptance-based treatments, and unified or transdiagnostic treatment protocols.

An Optimal Order Nonnested Mixed Multigrid Method for Generalized Stokes Problems

NASA Technical Reports Server (NTRS)

Deng, Qingping

1996-01-01

A multigrid algorithm is developed and analyzed for generalized Stokes problems discretized by various nonnested mixed finite elements within a unified framework. It is abstractly proved by an element-independent analysis that the multigrid algorithm converges with an optimal order if there exists a 'good' prolongation operator. A technique to construct a 'good' prolongation operator for nonnested multilevel finite element spaces is proposed. Its basic idea is to introduce a sequence of auxiliary nested multilevel finite element spaces and define a prolongation operator as a composite operator of two single grid level operators. This makes not only the construction of a prolongation operator much easier (the final explicit forms of such prolongation operators are fairly simple), but the verification of the approximate properties for prolongation operators is also simplified. Finally, as an application, the framework and technique is applied to seven typical nonnested mixed finite elements.

Mechanisms of face perception

PubMed Central

Tsao, Doris Y.

2009-01-01

Faces are among the most informative stimuli we ever perceive: Even a split-second glimpse of a person's face tells us their identity, sex, mood, age, race, and direction of attention. The specialness of face processing is acknowledged in the artificial vision community, where contests for face recognition algorithms abound. Neurological evidence strongly implicates a dedicated machinery for face processing in the human brain, to explain the double dissociability of face and object recognition deficits. Furthermore, it has recently become clear that macaques too have specialized neural machinery for processing faces. Here we propose a unifying hypothesis, deduced from computational, neurological, fMRI, and single-unit experiments: that what makes face processing special is that it is gated by an obligatory detection process. We will clarify this idea in concrete algorithmic terms, and show how it can explain a variety of phenomena associated with face processing. PMID:18558862

Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

NASA Astrophysics Data System (ADS)

Gao, Shibo; Cheng, Yongmei; Song, Chunhua

2013-09-01

The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.

Implementation of jump-diffusion algorithms for understanding FLIR scenes

NASA Astrophysics Data System (ADS)

Lanterman, Aaron D.; Miller, Michael I.; Snyder, Donald L.

1995-07-01

Our pattern theoretic approach to the automated understanding of forward-looking infrared (FLIR) images brings the traditionally separate endeavors of detection, tracking, and recognition together into a unified jump-diffusion process. New objects are detected and object types are recognized through discrete jump moves. Between jumps, the location and orientation of objects are estimated via continuous diffusions. An hypothesized scene, simulated from the emissive characteristics of the hypothesized scene elements, is compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. The jump-diffusion process empirically generates the posterior distribution. Both the diffusion and jump operations involve the simulation of a scene produced by a hypothesized configuration. Scene simulation is most effectively accomplished by pipelined rendering engines such as silicon graphics. We demonstrate the execution of our algorithm on a silicon graphics onyx/reality engine.

Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications.

PubMed

Fulop, Sean A; Fitz, Kelly

2006-01-01

A modification of the spectrogram (log magnitude of the short-time Fourier transform) to more accurately show the instantaneous frequencies of signal components was first proposed in 1976 [Kodera et al., Phys. Earth Planet. Inter. 12, 142-150 (1976)], and has been considered or reinvented a few times since but never widely adopted. This paper presents a unified theoretical picture of this time-frequency analysis method, the time-corrected instantaneous frequency spectrogram, together with detailed implementable algorithms comparing three published techniques for its computation. The new representation is evaluated against the conventional spectrogram for its superior ability to track signal components. The lack of a uniform framework for either mathematics or implementation details which has characterized the disparate literature on the schemes has been remedied here. Fruitful application of the method is shown in the realms of speech phonation analysis, whale song pitch tracking, and additive sound modeling.

Acoustic coupled fluid-structure interactions using a unified fast multipole boundary element method.

PubMed

Wilkes, Daniel R; Duncan, Alec J

2015-04-01

This paper presents a numerical model for the acoustic coupled fluid-structure interaction (FSI) of a submerged finite elastic body using the fast multipole boundary element method (FMBEM). The Helmholtz and elastodynamic boundary integral equations (BIEs) are, respectively, employed to model the exterior fluid and interior solid domains, and the pressure and displacement unknowns are coupled between conforming meshes at the shared boundary interface to achieve the acoustic FSI. The low frequency FMBEM is applied to both BIEs to reduce the algorithmic complexity of the iterative solution from O(N(2)) to O(N(1.5)) operations per matrix-vector product for N boundary unknowns. Numerical examples are presented to demonstrate the algorithmic and memory complexity of the method, which are shown to be in good agreement with the theoretical estimates, while the solution accuracy is comparable to that achieved by a conventional finite element-boundary element FSI model.

Experimental determination of spin-dependent electron density by joint refinement of X-ray and polarized neutron diffraction data.

PubMed

Deutsch, Maxime; Claiser, Nicolas; Pillet, Sébastien; Chumakov, Yurii; Becker, Pierre; Gillet, Jean Michel; Gillon, Béatrice; Lecomte, Claude; Souhassou, Mohamed

2012-11-01

New crystallographic tools were developed to access a more precise description of the spin-dependent electron density of magnetic crystals. The method combines experimental information coming from high-resolution X-ray diffraction (XRD) and polarized neutron diffraction (PND) in a unified model. A new algorithm that allows for a simultaneous refinement of the charge- and spin-density parameters against XRD and PND data is described. The resulting software MOLLYNX is based on the well known Hansen-Coppens multipolar model, and makes it possible to differentiate the electron spins. This algorithm is validated and demonstrated with a molecular crystal formed by a bimetallic chain, MnCu(pba)(H(2)O)(3)·2H(2)O, for which XRD and PND data are available. The joint refinement provides a more detailed description of the spin density than the refinement from PND data alone.

Retrieval of Aerosol Microphysical Properties from AERONET Photo-Polarimetric Measurements. 2: A New Research Algorithm and Case Demonstration

NASA Technical Reports Server (NTRS)

Xu, Xiaoguang; Wang, Jun; Zeng, Jing; Spurr, Robert; Liu, Xiong; Dubovik, Oleg; Li, Li; Li, Zhengqiang; Mishchenko, Michael I.; Siniuk, Aliaksandr;

Medical Image Encryption: An Application for Improved Padding Based GGH Encryption Algorithm

PubMed Central

Sokouti, Massoud; Zakerolhosseini, Ali; Sokouti, Babak

2016-01-01

Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n2). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes. PMID:27857824

Regularized variational theories of fracture: A unified approach

NASA Astrophysics Data System (ADS)

Freddi, Francesco; Royer-Carfagni, Gianni

2010-08-01

The fracture pattern in stressed bodies is defined through the minimization of a two-field pseudo-spatial-dependent functional, with a structure similar to that proposed by Bourdin-Francfort-Marigo (2000) as a regularized approximation of a parent free-discontinuity problem, but now considered as an autonomous model per se. Here, this formulation is altered by combining it with structured deformation theory, to model that when the material microstructure is loosened and damaged, peculiar inelastic (structured) deformations may occur in the representative volume element at the price of surface energy consumption. This approach unifies various theories of failure because, by simply varying the form of the class for admissible structured deformations, different-in-type responses can be captured, incorporating the idea of cleavage, deviatoric, combined cleavage-deviatoric and masonry-like fractures. Remarkably, this latter formulation rigorously avoid material overlapping in the cracked zones. The model is numerically implemented using a standard finite-element discretization and adopts an alternate minimization algorithm, adding an inequality constraint to impose crack irreversibility ( fixed crack model). Numerical experiments for some paradigmatic examples are presented and compared for various possible versions of the model.

A unified genetic association test robust to latent population structure for a count phenotype.

PubMed

Song, Minsun

2018-06-04

Confounding caused by latent population structure in genome-wide association studies has been a big concern despite the success of genome-wide association studies at identifying genetic variants associated with complex diseases. In particular, because of the growing interest in association mapping using count phenotype data, it would be interesting to develop a testing framework for genetic associations that is immune to population structure when phenotype data consist of count measurements. Here, I propose a solution for testing associations between single nucleotide polymorphisms and a count phenotype in the presence of an arbitrary population structure. I consider a classical range of models for count phenotype data. Under these models, a unified test for genetic associations that protects against confounding was derived. An algorithm was developed to efficiently estimate the parameters that are required to fit the proposed model. I illustrate the proposed approach using simulation studies and an empirical study. Both simulated and real-data examples suggest that the proposed method successfully corrects population structure. Copyright © 2018 John Wiley & Sons, Ltd.

A preliminary investigation of the effects of the unified protocol on temperament.

PubMed

Carl, Jenna R; Gallagher, Matthew W; Sauer-Zavala, Shannon E; Bentley, Kate H; Barlow, David H

2014-08-01

Previous research has shown that two dimensions of temperament referred to as neuroticism/behavioral inhibition (N/BI) and extraversion/behavioral activation (E/BA) are key risk factors in the development and maintenance of anxiety and mood disorders (Brown & Barlow, 2009). Given such findings, these temperamental dimensions may represent promising treatment targets for individuals with emotional disorders; however, to date, few studies have investigated the effects of psychological treatments on temperamental constructs generally assumed to be "stable, inflexible, and pervasive" (American Psychiatric Association, 2000). The present study addresses this gap in the literature by examining the effects of the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP; Barlow et al., 2011), a cognitive-behavioral therapy designed to target core processes of N/BI and E/BA temperaments, in a sample of adults with principal anxiety disorders and a range of comorbid conditions. Results revealed small effects of the UP on N/BI and E/BA compared with a waitlist control group at post-treatment. Additionally, decreases in N/BI and increases in E/BA during treatment were associated with improvements in symptoms, functioning, and quality of life. Findings provide preliminary support for the notion that the UP treatment facilitates beneficial changes in dimensions of temperament. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling of reverberant room responses for two-dimensional spatial sound field analysis and synthesis.

PubMed

Bai, Mingsian R; Li, Yi; Chiang, Yi-Hao

2017-10-01

A unified framework is proposed for analysis and synthesis of two-dimensional spatial sound field in reverberant environments. In the sound field analysis (SFA) phase, an unbaffled 24-element circular microphone array is utilized to encode the sound field based on the plane-wave decomposition. Depending on the sparsity of the sound sources, the SFA stage can be implemented in two manners. For sparse-source scenarios, a one-stage algorithm based on compressive sensing algorithm is utilized. Alternatively, a two-stage algorithm can be used, where the minimum power distortionless response beamformer is used to localize the sources and Tikhonov regularization algorithm is used to extract the source amplitudes. In the sound field synthesis (SFS), a 32-element rectangular loudspeaker array is employed to decode the target sound field using pressure matching technique. To establish the room response model, as required in the pressure matching step of the SFS phase, an SFA technique for nonsparse-source scenarios is utilized. Choice of regularization parameters is vital to the reproduced sound field. In the SFS phase, three SFS approaches are compared in terms of localization performance and voice reproduction quality. Experimental results obtained in a reverberant room are presented and reveal that an accurate room response model is vital to immersive rendering of the reproduced sound field.

Conservative boundary conditions for 3D gas dynamics problems

NASA Technical Reports Server (NTRS)

Gerasimov, B. P.; Karagichev, A. B.; Semushin, S. A.

1986-01-01

A method is described for 3D-gas dynamics computer simulation in regions of complicated shape by means of nonadjusted rectangular grids providing unified treatment of various problems. Some test problem computation results are given.

Projective formulation of Maggi's method for nonholonomic systems analysis

NASA Astrophysics Data System (ADS)

Blajer, Wojciech

1992-04-01

A projective interpretation of Maggi'a approach to dynamic analysis of nonholonomic systems is presented. Both linear and nonlinear constraint cases are treatment in unified fashion, using the language of vector spaces and tensor algebra analysis.

Unified-planning, graded-administration, and centralized-controlling: a management modality for treating acquired immune deficiency syndrome with Chinese medicine in Henan Province of China.

PubMed

Xu, Li-Ran; Guo, Hui-jun; Liu, Zhi-bin; Li, Qiang; Yang, Ji-ping; He, Ying

2015-04-01

Henan Province in China has a major epidemic of human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). Chinese medicine (CM) has been used throughout the last decade, and a management modality was developed, which can be described by unified-planning, graded-administration, and centralized-controlling (UGC). The UGC modality has one primary concept (patient-centered medicine from CM theory), four basic foundations (classifying administrative region, characteristics of CM on disease treatment, health resource conditions, and distribution of patients living with HIV), six important relationships (the "three uniformities and three combinations," and the six relationships therein guide the treatment of AIDS with CM), and four key sections (management, operation, records, and evaluation). In this article, the authors introduce the UGC modality, which could be beneficial to developing countries or resource-limited areas for the management of chronic infectious disease.

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2 K, and BIAS values of -0.6 and -2.7 K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8 W/m2, and BIAS values of -2.7 and -14.6 W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

RHIC and LHC Phenomena with a Unified Parton Transport

NASA Astrophysics Data System (ADS)

Bouras, Ioannis; El, Andrej; Fochler, Oliver; Reining, Felix; Senzel, Florian; Uphoff, Jan; Wesp, Christian; Xu, Zhe; Greiner, Carsten

We discuss recent applications of the partonic pQCD based cascade model BAMPS with focus on heavy-ion phenomeneology in hard and soft momentum range. The nuclear modification factor as well as elliptic flow are calculated in BAMPS for RHIC end LHC energies. These observables are also discussed within the same framework for charm and bottom quarks. Contributing to the recent jet-quenching investigations we present first preliminary results on application of jet reconstruction algorithms in BAMPS. Finally, collective effects induced by jets are investigated: we demonstrate the development of Mach cones in ideal matter as well in the highly viscous regime.

RHIC and LHC phenomena with an unified parton transport

NASA Astrophysics Data System (ADS)

Bouras, Ioannis; El, Andrej; Fochler, Oliver; Reining, Felix; Senzel, Florian; Uphoff, Jan; Wesp, Christian; Xu, Zhe; Greiner, Carsten

2012-11-01

We discuss recent applications of the partonic pQCD based cascade model BAMPS with focus on heavy-ion phenomeneology in hard and soft momentum range. The nuclear modification factor as well as elliptic flow are calculated in BAMPS for RHIC end LHC energies. These observables are also discussed within the same framework for charm and bottom quarks. Contributing to the recent jet-quenching investigations we present first preliminary results on application of jet reconstruction algorithms in BAMPS. Finally, collective effects induced by jets are investigated: we demonstrate the development of Mach cones in ideal matter as well in the highly viscous regime.

Multivariate Lipschitz optimization: Survey and computational comparison

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

Hansen, P.; Gourdin, E.; Jaumard, B.

1994-12-31

Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.

Well-tempered metadynamics: a smoothly-converging and tunable free-energy method

NASA Astrophysics Data System (ADS)

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-03-01

We present [1] a method for determining the free energy dependence on a selected number of order parameters using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevantregions of the order parameter space. The algorithm is tested on the reconstruction of alanine dipeptide free energy landscape. [1] A. Barducci, G. Bussi and M. Parrinello, Phys. Rev. Lett., accepted (2007).

CUDA-based real time surgery simulation.

PubMed

Liu, Youquan; De, Suvranu

2008-01-01

In this paper we present a general software platform that enables real time surgery simulation on the newly available compute unified device architecture (CUDA)from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is significantly more flexible with a C language interface. We report implementation of both collision detection and consequent deformation computation algorithms. Our test results indicate that the CUDA enables a twenty times speedup for collision detection and about fifteen times speedup for deformation computation on an Intel Core 2 Quad 2.66 GHz machine with GeForce 8800 GTX.

Quantum Computing Architectural Design

NASA Astrophysics Data System (ADS)

West, Jacob; Simms, Geoffrey; Gyure, Mark

2006-03-01

Large scale quantum computers will invariably require scalable architectures in addition to high fidelity gate operations. Quantum computing architectural design (QCAD) addresses the problems of actually implementing fault-tolerant algorithms given physical and architectural constraints beyond those of basic gate-level fidelity. Here we introduce a unified framework for QCAD that enables the scientist to study the impact of varying error correction schemes, architectural parameters including layout and scheduling, and physical operations native to a given architecture. Our software package, aptly named QCAD, provides compilation, manipulation/transformation, multi-paradigm simulation, and visualization tools. We demonstrate various features of the QCAD software package through several examples.

A Family of Algorithms for Computing Consensus about Node State from Network Data

PubMed Central

Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C.

2013-01-01

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions. PMID:23874167

Allergic rhinitis and inflammatory airway disease: interactions within the unified airspace.

PubMed

Marple, Bradley F

2010-01-01

Allergic rhinitis (AR), the most common chronic allergic condition in outpatient medicine, is associated with immense health care costs and socioeconomic consequences. AR's impact may be partly from interacting of respiratory conditions via allergic inflammation. This study was designed to review potential interactive mechanisms of AR and associated conditions and consider the relevance of a bidirectional "unified airway" respiratory inflammation model on diagnosis and treatment of inflammatory airway disease. MEDLINE was searched for pathophysiology and pathophysiological and epidemiologic links between AR and diseases of the sinuses, lungs, middle ear, and nasopharynx. Allergic-related inflammatory responses or neural and systemic processes fostering inflammatory changes distant from initial allergen provocation may link AR and comorbidities. Treating AR may benefit associated respiratory tract comorbidities. Besides improving AR outcomes, treatment inhibiting eosinophil recruitment and migration, normalizing cytokine profiles, and reducing asthma-associated health care use in atopic subjects would likely ameliorate other upper airway diseases such as acute rhinosinusitis, chronic rhinosinusitis (CRS) with nasal polyposis (NP), adenoidal hypertrophy, and otitis media with effusion. Epidemiological concordance of AR with several airway diseases conforms to a bidirectional "unified airway" respiratory inflammation model based on anatomic and histological upper and lower airway connections. Epidemiology and current understanding of inflammatory, humoral, and neural processes make links between AR and disorders including asthma, otitis media, NP, and CRS plausible. Combining AR with associated conditions increases disease burden; worsened associated illness may accompany worsened AR. AR pharmacotherapies include antihistamines, leukotriene antagonists, intranasal corticosteroids, and immunotherapy; treatments attenuating proinflammatory responses may also benefit associated conditions.

Polycystic ovary syndrome, oral contraceptives and metabolic issues: new perspectives and a unifying hypothesis.

PubMed

Nader, S; Diamanti-Kandarakis, E

2007-02-01

In the chronic treatment of polycystic ovary syndrome (PCOS), oral contraceptive pills (OCPs) are commonly used to induce regular menses, protect the endometrium and ameliorate androgenic symptoms. However, the long-term safety of OCP use in PCOS has not been established, and the literature reveals conflicting data concerning the metabolic effects of OCPs in this patient population, with outcomes ranging from improvement of glucose tolerance to the development of frank diabetes. This article presents new perspectives and a unifying hypothesis concerning the effects of OCPs on carbohydrate metabolism in PCOS and attempts to explain the divergent findings in published reports.

A Unified Framework for Estimating Minimum Detectable Effects for Comparative Short Interrupted Time Series Designs

ERIC Educational Resources Information Center

Price, Cristofer; Unlu, Fatih

2014-01-01

The Comparative Short Interrupted Time Series (C-SITS) design is a frequently employed quasi-experimental method, in which the pre- and post-intervention changes observed in the outcome levels of a treatment group is compared with those of a comparison group where the difference between the former and the latter is attributed to the treatment. The…

Staff Group Unanimity in the Care of Juveniles in Institutional Treatment: Routines, Rituals, and Relationships

ERIC Educational Resources Information Center

Ahonen, Lia; Degner, Jurgen

2013-01-01

One prerequisite for effective institutional care is that staff agree on how to deliver treatment and have a unified view of how to achieve change--in other words, to have staff group unanimity (SGU). This study used the Correctional Program Assessment Inventory (CPAI) 2000, interviews with key staff, and observations of daily activities to…

Improvement in quality of life and sexual functioning in a comorbid sample after the unified protocol transdiagnostic group treatment.

PubMed

de Ornelas Maia, Ana Claudia Corrêa; Sanford, Jenny; Boettcher, Hannah; Nardi, Antonio E; Barlow, David

2017-10-01

Patients with multiple mental disorders often experience sexual dysfunction and reduced quality of life. The unified protocol (UP) is a transdiagnostic treatment for emotional disorders that has the potential to improve quality of life and sexual functioning via improved emotion management. The present study evaluates changes in quality of life and sexual functioning in a highly comorbid sample treated with the UP in a group format. Forty-eight patients were randomly assigned to either a UP active-treatment group or a medication-only control group. Treatment was delivered in 14 sessions over the course of 4 months. Symptoms of anxiety and depression were assessed using the Beck Anxiety Inventory and Beck Depression Inventory. Sexual functioning was assessed by the Arizona Sexual Experience Scale (ASEX), and quality of life was assessed by the World Health Organization Quality of Life-BREF scale (WHOQOL-BREF). Quality of life, anxiety and depression all significantly improved among participants treated with the UP. Some improvement in sexual functioning was also noted. The results support the efficacy of the UP in improving quality of life and sexual functioning in comorbid patients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

PubMed

Kim, Han Byul; Lee, Woong Woo; Kim, Aryun; Lee, Hong Ji; Park, Hye Young; Jeon, Hyo Seon; Kim, Sang Kyong; Jeon, Beomseok; Park, Kwang S

2018-04-01

Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module. Neurologists assessed the tremor symptoms on the Unified Parkinson's Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured data were transformed into the frequency domain and used to construct a two-dimensional image for training the network, and the CNN model was trained by convolving tremor signal images with kernels. The proposed CNN architecture was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 0.85, linear weighted kappa = 0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Specialized Computer Systems for Environment Visualization

NASA Astrophysics Data System (ADS)

Al-Oraiqat, Anas M.; Bashkov, Evgeniy A.; Zori, Sergii A.

2018-06-01

The need for real time image generation of landscapes arises in various fields as part of tasks solved by virtual and augmented reality systems, as well as geographic information systems. Such systems provide opportunities for collecting, storing, analyzing and graphically visualizing geographic data. Algorithmic and hardware software tools for increasing the realism and efficiency of the environment visualization in 3D visualization systems are proposed. This paper discusses a modified path tracing algorithm with a two-level hierarchy of bounding volumes and finding intersections with Axis-Aligned Bounding Box. The proposed algorithm eliminates the branching and hence makes the algorithm more suitable to be implemented on the multi-threaded CPU and GPU. A modified ROAM algorithm is used to solve the qualitative visualization of reliefs' problems and landscapes. The algorithm is implemented on parallel systems—cluster and Compute Unified Device Architecture-networks. Results show that the implementation on MPI clusters is more efficient than Graphics Processing Unit/Graphics Processing Clusters and allows real-time synthesis. The organization and algorithms of the parallel GPU system for the 3D pseudo stereo image/video synthesis are proposed. With realizing possibility analysis on a parallel GPU-architecture of each stage, 3D pseudo stereo synthesis is performed. An experimental prototype of a specialized hardware-software system 3D pseudo stereo imaging and video was developed on the CPU/GPU. The experimental results show that the proposed adaptation of 3D pseudo stereo imaging to the architecture of GPU-systems is efficient. Also it accelerates the computational procedures of 3D pseudo-stereo synthesis for the anaglyph and anamorphic formats of the 3D stereo frame without performing optimization procedures. The acceleration is on average 11 and 54 times for test GPUs.

GPU-based relative fuzzy connectedness image segmentation.

PubMed

Zhuge, Ying; Ciesielski, Krzysztof C; Udupa, Jayaram K; Miller, Robert W

2013-01-01

Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

GPU-based relative fuzzy connectedness image segmentation

PubMed Central

Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

2013-01-01

Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094

A parallel algorithm for the initial screening of space debris collisions prediction using the SGP4/SDP4 models and GPU acceleration

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-05-01

Currently, a tremendous amount of space debris in Earth's orbit imperils operational spacecraft. It is essential to undertake risk assessments of collisions and predict dangerous encounters in space. However, collision predictions for an enormous amount of space debris give rise to large-scale computations. In this paper, a parallel algorithm is established on the Compute Unified Device Architecture (CUDA) platform of NVIDIA Corporation for collision prediction. According to the parallel structure of NVIDIA graphics processors, a block decomposition strategy is adopted in the algorithm. Space debris is divided into batches, and the computation and data transfer operations of adjacent batches overlap. As a consequence, the latency to access shared memory during the entire computing process is significantly reduced, and a higher computing speed is reached. Theoretically, a simulation of collision prediction for space debris of any amount and for any time span can be executed. To verify this algorithm, a simulation example including 1382 pieces of debris, whose operational time scales vary from 1 min to 3 days, is conducted on Tesla C2075 of NVIDIA. The simulation results demonstrate that with the same computational accuracy as that of a CPU, the computing speed of the parallel algorithm on a GPU is 30 times that on a CPU. Based on this algorithm, collision prediction of over 150 Chinese spacecraft for a time span of 3 days can be completed in less than 3 h on a single computer, which meets the timeliness requirement of the initial screening task. Furthermore, the algorithm can be adapted for multiple tasks, including particle filtration, constellation design, and Monte-Carlo simulation of an orbital computation.

Distributed resource allocation under communication constraints

NASA Astrophysics Data System (ADS)

Dodin, Pierre; Nimier, Vincent

2001-03-01

This paper deals with a study of the multi-sensor management problem for multi-target tracking. The collaboration between many sensors observing the same target means that they are able to fuse their data during the information process. Then one must take into account this possibility to compute the optimal association sensors-target at each step of time. In order to solve this problem for real large scale system, one must both consider the information aspect and the control aspect of the problem. To unify these problems, one possibility is to use a decentralized filtering algorithm locally driven by an assignment algorithm. The decentralized filtering algorithm we use in our model is the filtering algorithm of Grime, which relaxes the usual full-connected hypothesis. By full-connected, one means that the information in a full-connected system is totally distributed everywhere at the same moment, which is unacceptable for a real large scale system. We modelize the distributed assignment decision with the help of a greedy algorithm. Each sensor performs a global optimization, in order to estimate other information sets. A consequence of the relaxation of the full- connected hypothesis is that the sensors' information set are not the same at each step of time, producing an information dis- symmetry in the system. The assignment algorithm uses a local knowledge of this dis-symmetry. By testing the reactions and the coherence of the local assignment decisions of our system, against maneuvering targets, we show that it is still possible to manage with decentralized assignment control even though the system is not full-connected.

A novel vehicle tracking algorithm based on mean shift and active contour model in complex environment

NASA Astrophysics Data System (ADS)

Cai, Lei; Wang, Lin; Li, Bo; Zhang, Libao; Lv, Wen

2017-06-01

Vehicle tracking technology is currently one of the most active research topics in machine vision. It is an important part of intelligent transportation system. However, in theory and technology, it still faces many challenges including real-time and robustness. In video surveillance, the targets need to be detected in real-time and to be calculated accurate position for judging the motives. The contents of video sequence images and the target motion are complex, so the objects can't be expressed by a unified mathematical model. Object-tracking is defined as locating the interest moving target in each frame of a piece of video. The current tracking technology can achieve reliable results in simple environment over the target with easy identified characteristics. However, in more complex environment, it is easy to lose the target because of the mismatch between the target appearance and its dynamic model. Moreover, the target usually has a complex shape, but the tradition target tracking algorithm usually represents the tracking results by simple geometric such as rectangle or circle, so it cannot provide accurate information for the subsequent upper application. This paper combines a traditional object-tracking technology, Mean-Shift algorithm, with a kind of image segmentation algorithm, Active-Contour model, to get the outlines of objects while the tracking process and automatically handle topology changes. Meanwhile, the outline information is used to aid tracking algorithm to improve it.

TPS-HAMMER: improving HAMMER registration algorithm by soft correspondence matching and thin-plate splines based deformation interpolation.

PubMed

Wu, Guorong; Yap, Pew-Thian; Kim, Minjeong; Shen, Dinggang

2010-02-01

We present an improved MR brain image registration algorithm, called TPS-HAMMER, which is based on the concepts of attribute vectors and hierarchical landmark selection scheme proposed in the highly successful HAMMER registration algorithm. We demonstrate that TPS-HAMMER algorithm yields better registration accuracy, robustness, and speed over HAMMER owing to (1) the employment of soft correspondence matching and (2) the utilization of thin-plate splines (TPS) for sparse-to-dense deformation field generation. These two aspects can be integrated into a unified framework to refine the registration iteratively by alternating between soft correspondence matching and dense deformation field estimation. Compared with HAMMER, TPS-HAMMER affords several advantages: (1) unlike the Gaussian propagation mechanism employed in HAMMER, which can be slow and often leaves unreached blotches in the deformation field, the deformation interpolation in the non-landmark points can be obtained immediately with TPS in our algorithm; (2) the smoothness of deformation field is preserved due to the nice properties of TPS; (3) possible misalignments can be alleviated by allowing the matching of the landmarks with a number of possible candidate points and enforcing more exact matches in the final stages of the registration. Extensive experiments have been conducted, using the original HAMMER as a comparison baseline, to validate the merits of TPS-HAMMER. The results show that TPS-HAMMER yields significant improvement in both accuracy and speed, indicating high applicability for the clinical scenario. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Short paths in expander graphs

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

Kleinberg, J.; Rubinfeld, R.

Graph expansion has proved to be a powerful general tool for analyzing the behavior of routing algorithms and the interconnection networks on which they run. We develop new routing algorithms and structural results for bounded-degree expander graphs. Our results are unified by the fact that they are all based upon, and extend, a body of work asserting that expanders are rich in short, disjoint paths. In particular, our work has consequences for the disjoint paths problem, multicommodify flow, and graph minor containment. We show: (i) A greedy algorithm for approximating the maximum disjoint paths problem achieves a polylogarithmic approximation ratiomore » in bounded-degree expanders. Although our algorithm is both deterministic and on-line, its performance guarantee is an improvement over previous bounds in expanders. (ii) For a multicommodily flow problem with arbitrary demands on a bounded-degree expander, there is a (1 + {epsilon})-optimal solution using only flow paths of polylogarithmic length. It follows that the multicommodity flow algorithm of Awerbuch and Leighton runs in nearly linear time per commodity in expanders. Our analysis is based on establishing the following: given edge weights on an expander G, one can increase some of the weights very slightly so the resulting shortest-path metric is smooth - the min-weight path between any pair of nodes uses a polylogarithmic number of edges. (iii) Every bounded-degree expander on n nodes contains every graph with O(n/log{sup O(1)} n) nodes and edges as a minor.« less

An Algebraic Approach to the Eigenstates of the Calogero Model

NASA Astrophysics Data System (ADS)

Ujino, Hideaki

2002-11-01

An algebraic treatment of the eigenstates of the (AN-1-) Calogero model is presented, which provides an algebraic construction of the nonsymmetric orthogonal eigenvectors, symmetrization, antisymmetrization and calculation of square norms in a unified way.

Self-Efficacy: Toward a Unifying Theory of Behavioral Change

ERIC Educational Resources Information Center

Bandura, Albert

1977-01-01

This research presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of "self-efficacy". (Editor/RK)

A unified Hyperglycemia and Diabetic ketoacidosis (DKA) insulin infusion protocol based on an Excel algorithm and implemented via Electronic Medical Record (EMR) in Intensive Care Units.

PubMed

Gupta, Deepashree; Kirn, Meredith; Jamkhana, Zafar A; Lee, Richard; Albert, Stewart G; Rollins, Kimberly M

To assess the efficacy of a unified hyperglycemia and diabetic ketoacidosis (DKA) insulin infusion protocol (IIP), based on an Excel algorithm and implemented as an electronic order set, in achieving glycemic targets and minimizing hypoglycemia. An IIP was instituted in medical and surgical intensive care units for post-cardiac surgery (PCS) and other stress hyperglycemia (SH), diabetes hyperglycemia (DH), and DKA. The IIP initiated therapeutic insulin rates at elevated blood glucose (BG), and decreased insulin when target range was achieved. A convenience sample (n=62) was studied; 20 PCS, 15 with DH, 9 with SH, 8 with diabetes on vasopressors, 7 with diabetes on glucocorticoids and 3 with DKA were assessed. The protocol maintained BG at 144±24.7mg/dL for PCS and 167±36mg/dL for patients with diabetes mellitus. It maintained acceptable target range (ATR) (100mg/dL-180mg/dL) 89% of the time for PCS and 67% of the time for patients with diabetes mellitus. There were no measurements of BG<70mg/dL. The protocol lowered the BG at a similar rate and time period in those with diabetes, DKA and those with or without vasopressors or glucocorticoids. To determine long-term efficacy, a retrospective review of Point of Care (POC) RALS (Remote Automated Data System) BG data 2 years post implementation demonstrated fewer episodes of hypoglycemia<70mg/dL and hyperglycemia>240mg/dL and more BG values within ATR. This IIP maintained ATR without hypoglycemia for patients in the ICU setting without requiring complex nursing calculations. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.

A unified framework for group independent component analysis for multi-subject fMRI data

PubMed Central

Guo, Ying; Pagnoni, Giuseppe

2008-01-01

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation. PMID:18650105

A unified radiative magnetohydrodynamics code for lightning-like discharge simulations

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

Chen, Qiang, E-mail: cq0405@126.com; Chen, Bin, E-mail: emcchen@163.com; Xiong, Run

2014-03-15

A two-dimensional Eulerian finite difference code is developed for solving the non-ideal magnetohydrodynamic (MHD) equations including the effects of self-consistent magnetic field, thermal conduction, resistivity, gravity, and radiation transfer, which when combined with specified pulse current models and plasma equations of state, can be used as a unified lightning return stroke solver. The differential equations are written in the covariant form in the cylindrical geometry and kept in the conservative form which enables some high-accuracy shock capturing schemes to be equipped in the lightning channel configuration naturally. In this code, the 5-order weighted essentially non-oscillatory scheme combined with Lax-Friedrichs fluxmore » splitting method is introduced for computing the convection terms of the MHD equations. The 3-order total variation diminishing Runge-Kutta integral operator is also equipped to keep the time-space accuracy of consistency. The numerical algorithms for non-ideal terms, e.g., artificial viscosity, resistivity, and thermal conduction, are introduced in the code via operator splitting method. This code assumes the radiation is in local thermodynamic equilibrium with plasma components and the flux limited diffusion algorithm with grey opacities is implemented for computing the radiation transfer. The transport coefficients and equation of state in this code are obtained from detailed particle population distribution calculation, which makes the numerical model is self-consistent. This code is systematically validated via the Sedov blast solutions and then used for lightning return stroke simulations with the peak current being 20 kA, 30 kA, and 40 kA, respectively. The results show that this numerical model consistent with observations and previous numerical results. The population distribution evolution and energy conservation problems are also discussed.« less

Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.

PubMed

Calvin, Olivia L; McDowell, J J

2016-06-01

The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this experiment, neural networks were developed from the theory to extend the unified theory of reinforcement to operant behavior on single-alternative variable-interval schedules. This area of operant research was selected because previously developed neural networks could be applied to it without significant alteration. Previous research with humans and animals indicates that the pattern of their steady-state behavior is hyperbolic when plotted against the obtained rate of reinforcement (Herrnstein, 1970). A genetic algorithm was used in the first part of the experiment to determine parameter values for the neural networks, because values that were used in previous research did not result in a hyperbolic pattern of behavior. After finding these parameters, hyperbolic and other similar functions were fitted to the behavior produced by the neural networks. The form of the neural network's behavior was best described by an exponentiated hyperbola (McDowell, 1986; McLean and White, 1983; Wearden, 1981), which was derived from the generalized matching law (Baum, 1974). In post-hoc analyses the addition of a baseline rate of behavior significantly improved the fit of the exponentiated hyperbola and removed systematic residuals. The form of this function was consistent with human and animal behavior, but the estimated parameter values were not. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating the chance of success in IVF treatment using a ranking algorithm.

PubMed

Güvenir, H Altay; Misirli, Gizem; Dilbaz, Serdar; Ozdegirmenci, Ozlem; Demir, Berfu; Dilbaz, Berna

2015-09-01

In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.

Generalized concurrence in boson sampling.

PubMed

Chin, Seungbeom; Huh, Joonsuk

2018-04-17

A fundamental question in linear optical quantum computing is to understand the origin of the quantum supremacy in the physical system. It is found that the multimode linear optical transition amplitudes are calculated through the permanents of transition operator matrices, which is a hard problem for classical simulations (boson sampling problem). We can understand this problem by considering a quantum measure that directly determines the runtime for computing the transition amplitudes. In this paper, we suggest a quantum measure named "Fock state concurrence sum" C S , which is the summation over all the members of "the generalized Fock state concurrence" (a measure analogous to the generalized concurrences of entanglement and coherence). By introducing generalized algorithms for computing the transition amplitudes of the Fock state boson sampling with an arbitrary number of photons per mode, we show that the minimal classical runtime for all the known algorithms directly depends on C S . Therefore, we can state that the Fock state concurrence sum C S behaves as a collective measure that controls the computational complexity of Fock state BS. We expect that our observation on the role of the Fock state concurrence in the generalized algorithm for permanents would provide a unified viewpoint to interpret the quantum computing power of linear optics.

On-the-fly reduction of open loops

NASA Astrophysics Data System (ADS)

Buccioni, Federico; Pozzorini, Stefano; Zoller, Max

2018-01-01

Building on the open-loop algorithm we introduce a new method for the automated construction of one-loop amplitudes and their reduction to scalar integrals. The key idea is that the factorisation of one-loop integrands in a product of loop segments makes it possible to perform various operations on-the-fly while constructing the integrand. Reducing the integrand on-the-fly, after each segment multiplication, the construction of loop diagrams and their reduction are unified in a single numerical recursion. In this way we entirely avoid objects with high tensor rank, thereby reducing the complexity of the calculations in a drastic way. Thanks to the on-the-fly approach, which is applied also to helicity summation and for the merging of different diagrams, the speed of the original open-loop algorithm can be further augmented in a very significant way. Moreover, addressing spurious singularities of the employed reduction identities by means of simple expansions in rank-two Gram determinants, we achieve a remarkably high level of numerical stability. These features of the new algorithm, which will be made publicly available in a forthcoming release of the OpenLoops program, are particularly attractive for NLO multi-leg and NNLO real-virtual calculations.

Visual based laser speckle pattern recognition method for structural health monitoring

NASA Astrophysics Data System (ADS)

Park, Kyeongtaek; Torbol, Marco

2017-04-01

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

Integrating R with GIS for innovative geocomputing - the examples of RQGIS and RSAGA

NASA Astrophysics Data System (ADS)

Muenchow, Jannes; Schratz, Patrick; Bangs, Donovan; Brenning, Alexander

2017-04-01

While Geographical information systems (GIS) are good at efficiently manipulating and processing large amounts of geospatial data, the programming language R excels in (geo-)statistical analyses. Thus, bringing GIS algorithms to the R console combines the best of the two worlds, and paves the way for innovative geocomputing. To promote this approach, we will contrast the new RQGIS package with the established RSAGA package in terms of architecture, functionality and ease-of-use. Both packages use already existing Application Programming Interfaces (API), namely the QGIS Python API and SAGA API, to access GIS functionality from within R. Overall, RQGIS has the advantage of providing a unified interface to several GIS toolboxes (GRASS, SAGA, GDAL, etc.) bringing more than 1000 geocomputing algorithms to the R console (though only a subset of the full functionality of a specific third-party provider might be available). To further support the unified interface, QGIS automatically converts the input data into the formats supported by the respective third-party module. Moreover, RQGIS is easier to use than RSAGA due to special convenience functions (open_help, get_args_man, run_qgis). Nevertheless, the experienced SAGA user will most likely prefer the RSAGA package since it lets the user access all SAGA algorithms for a wide range of SAGA versions (currently 2.0.4 - 2.2.3). Additionally, RSAGA includes numerous user-friendly wrapper functions with arguments and meaningful default values (e.g., rsaga.slope). What is more, RSAGA provides the user with special geocomputing R functions, i.e. functions which solely run in R without using SAGA in the background (e.g., pick.from.ascii.grid, grid.predict and multi.local.function). To demonstrate the advantages of each package, we will derive terrain attributes from digital elevation models to model species richness and landslide susceptibility using non-linear generalized linear or generalized additive models. In the end, the choice of RQGIS or RSAGA depends on the user's preferences, expertise and tasks at hand. But both packages will benefit anyone working with large spatio-temporal data in R.

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

PubMed Central

2012-01-01

Background The OpenTox Framework, developed by the partners in the OpenTox project (http://www.opentox.org), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing. Results The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in in vivo studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology. OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources. The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists). Availability The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page http://www.opentox.org/dev/ontology; the OpenTox ontology is available as OWL at http://opentox.org/api/1 1/opentox.owl, the ToxML - OWL conversion utility is an open source resource available at http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/ PMID:22541598

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia.

PubMed

Tcheremenskaia, Olga; Benigni, Romualdo; Nikolova, Ivelina; Jeliazkova, Nina; Escher, Sylvia E; Batke, Monika; Baier, Thomas; Poroikov, Vladimir; Lagunin, Alexey; Rautenberg, Micha; Hardy, Barry

2012-04-24

The OpenTox Framework, developed by the partners in the OpenTox project (http://www.opentox.org), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing. The following related ontologies have been developed for OpenTox: a) Toxicological ontology - listing the toxicological endpoints; b) Organs system and Effects ontology - addressing organs, targets/examinations and effects observed in in vivo studies; c) ToxML ontology - representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology- representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink-ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists). The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page http://www.opentox.org/dev/ontology; the OpenTox ontology is available as OWL at http://opentox.org/api/1 1/opentox.owl, the ToxML - OWL conversion utility is an open source resource available at http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

75 FR 79921 - Fall 2010 Unified Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-20

... securitizations that would be affected by recent changes to generally accepted accounting principles. In effect... generally accepted accounting principles in effect prior to November 15, 2009. The transitional safe harbor... conditions for sale accounting treatment under generally accepted accounting principles as [[Page 79923...

Metonymy as Referential Dependency: Psycholinguistic and Neurolinguistic Arguments for a Unified Linguistic Treatment

ERIC Educational Resources Information Center

Piñango, Maria M.; Zhang, Muye; Foster-Hanson, Emily; Negishi, Michiro; Lacadie, Cheryl; Constable, R. Todd

2017-01-01

We examine metonymy at psycho- and neurolinguistic levels, seeking to adjudicate between two possible processing implementations (one- vs. two-mechanism). We compare highly conventionalized "systematic metonymy" (producer-for-product: "All freshmen read 'O'Connell'") to lesser-conventionalized "circumstantial…

Integrated System Health Management (ISHM) and Autonomy

NASA Technical Reports Server (NTRS)

Figueroa, Fernando; Walker, Mark G.

2018-01-01

Systems capabilities on ISHM (Integrated System Health Management) and autonomy have traditionally been addressed separately. This means that ISHM functions, such as anomaly detection, diagnostics, prognostics, and comprehensive system awareness have not been considered traditionally in the context of autonomy functions such as planning, scheduling, and mission execution. One key reason is that although they address systems capabilities, both ISHM and autonomy have traditionally individually been approached as independent strategies and models for analysis. Additionally, to some degree, a unified paradigm for ISHM and autonomy has been difficult to implement due to limitations of hardware and software. This paper explores a unified treatment of ISHM and autonomy in the context of distributed hierarchical autonomous operations.

Cognitive Hypnotherapy as a Transdiagnostic Protocol for Emotional Disorders.

PubMed

Alladin, Assen; Amundson, Jon

2016-01-01

This article describes cognitive hypnotherapy (CH), an integrative treatment that provides an evidence-based framework for synthesizing clinical practice and research. CH combines hypnotherapy with cognitive-behavior therapy in the management of emotional disorders. This blended version of clinical practice meets criteria for an assimilative model of integrative psychotherapy, which incorporates both theory and empirical findings. Issues related to (a) additive effect of hypnosis in treatment, (b) transdiagnostic consideration, and (c) unified treatment protocols in the treatment of emotional disorders are considered in light of cognitive hypnotherapy.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

PubMed Central

Chen, Qingkui; Zhao, Deyu; Wang, Jingjuan

2017-01-01

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. PMID:28777325

Segmentation and learning in the quantitative analysis of microscopy images

NASA Astrophysics Data System (ADS)

Ruggiero, Christy; Ross, Amy; Porter, Reid

2015-02-01

In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

A deconvolution extraction method for 2D multi-object fibre spectroscopy based on the regularized least-squares QR-factorization algorithm

NASA Astrophysics Data System (ADS)

Yu, Jian; Yin, Qian; Guo, Ping; Luo, A.-li

2014-09-01

This paper presents an efficient method for the extraction of astronomical spectra from two-dimensional (2D) multifibre spectrographs based on the regularized least-squares QR-factorization (LSQR) algorithm. We address two issues: we propose a modified Gaussian point spread function (PSF) for modelling the 2D PSF from multi-emission-line gas-discharge lamp images (arc images), and we develop an efficient deconvolution method to extract spectra in real circumstances. The proposed modified 2D Gaussian PSF model can fit various types of 2D PSFs, including different radial distortion angles and ellipticities. We adopt the regularized LSQR algorithm to solve the sparse linear equations constructed from the sparse convolution matrix, which we designate the deconvolution spectrum extraction method. Furthermore, we implement a parallelized LSQR algorithm based on graphics processing unit programming in the Compute Unified Device Architecture to accelerate the computational processing. Experimental results illustrate that the proposed extraction method can greatly reduce the computational cost and memory use of the deconvolution method and, consequently, increase its efficiency and practicability. In addition, the proposed extraction method has a stronger noise tolerance than other methods, such as the boxcar (aperture) extraction and profile extraction methods. Finally, we present an analysis of the sensitivity of the extraction results to the radius and full width at half-maximum of the 2D PSF.

A unified classifier for robust face recognition based on combining multiple subspace algorithms

NASA Astrophysics Data System (ADS)

Ijaz Bajwa, Usama; Ahmad Taj, Imtiaz; Waqas Anwar, Muhammad

2012-10-01

Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

PubMed

Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

2017-08-04

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Development of independent MU/treatment time verification algorithm for non-IMRT treatment planning: A clinical experience

NASA Astrophysics Data System (ADS)

Tatli, Hamza; Yucel, Derya; Yilmaz, Sercan; Fayda, Merdan

2018-02-01

The aim of this study is to develop an algorithm for independent MU/treatment time (TT) verification for non-IMRT treatment plans, as a part of QA program to ensure treatment delivery accuracy. Two radiotherapy delivery units and their treatment planning systems (TPS) were commissioned in Liv Hospital Radiation Medicine Center, Tbilisi, Georgia. Beam data were collected according to vendors' collection guidelines, and AAPM reports recommendations, and processed by Microsoft Excel during in-house algorithm development. The algorithm is designed and optimized for calculating SSD and SAD treatment plans, based on AAPM TG114 dose calculation recommendations, coded and embedded in MS Excel spreadsheet, as a preliminary verification algorithm (VA). Treatment verification plans were created by TPSs based on IAEA TRS 430 recommendations, also calculated by VA, and point measurements were collected by solid water phantom, and compared. Study showed that, in-house VA can be used for non-IMRT plans MU/TT verifications.

Costs of the Smoking Cessation Program in Brazil

PubMed Central

Mendes, Andréa Cristina Rosa; Toscano, Cristiana Maria; Barcellos, Rosilene Marques de Souza; Ribeiro, Alvaro Luis Pereira; Ritzel, Jonas Bohn; Cunha, Valéria de Souza; Duncan, Bruce Bartholow

2016-01-01

ABSTRACT OBJECTIVE To assess the costs of the Smoking Cessation Program in the Brazilian Unified Health System and estimate the cost of its full implementation in a Brazilian municipality. METHODS The intensive behavioral therapy and treatment for smoking cessation includes consultations, cognitive-behavioral group therapy sessions, and use of medicines. The costs of care and management of the program were estimated using micro-costing methods. The full implementation of the program in the municipality of Goiania, Goias was set as its expansion to meet the demand of all smokers motivated to quit in the municipality that would seek care at Brazilian Unified Health System. We considered direct medical and non-medical costs: human resources, medicines, consumables, general expenses, transport, travels, events, and capital costs. We included costs of federal, state, and municipal levels. The perspective of the analysis was that from the Brazilian Unified Health System. Sensitivity analysis was performed by varying parameters concerning the amount of activities and resources used. Data sources included a sample of primary care health units, municipal and state secretariats of health, and the Brazilian Ministry of Health. The costs were estimated in Brazilian Real (R$) for the year of 2010. RESULTS The cost of the program in Goiania was R$429,079, with 78.0% regarding behavioral therapy and treatment of smoking. The cost per patient was R$534, and, per quitter, R$1,435. The full implementation of the program in the municipality of Goiania would generate a cost of R$20.28 million to attend 35,323 smokers. CONCLUSIONS The Smoking Cessation Program has good performance in terms of cost per patient that quit smoking. In view of the burden of smoking in Brazil, the treatment for smoking cessation must be considered as a priority in allocating health resources. PMID:27849293

Unification of two fractal families

NASA Astrophysics Data System (ADS)

Liu, Ying

1995-06-01

Barnsley and Hurd classify the fractal images into two families: iterated function system fractals (IFS fractals) and fractal transform fractals, or local iterated function system fractals (LIFS fractals). We will call IFS fractals, class 2 fractals and LIFS fractals, class 3 fractals. In this paper, we will unify these two approaches plus another family of fractals, the class 5 fractals. The basic idea is given as follows: a dynamical system can be represented by a digraph, the nodes in a digraph can be divided into two parts: transient states and persistent states. For bilevel images, a persistent node is a black pixel. A transient node is a white pixel. For images with more than two gray levels, a stochastic digraph is used. A transient node is a pixel with the intensity of 0. The intensity of a persistent node is determined by a relative frequency. In this way, the two families of fractals can be generated in a similar way. In this paper, we will first present a classification of dynamical systems and introduce the transformation based on digraphs, then we will unify the two approaches for fractal binary images. We will compare the decoding algorithms of the two families. Finally, we will generalize the discussion to continuous-tone images.

Design Of Computer Based Test Using The Unified Modeling Language

NASA Astrophysics Data System (ADS)

Tedyyana, Agus; Danuri; Lidyawati

2017-12-01

The Admission selection of Politeknik Negeri Bengkalis through interest and talent search (PMDK), Joint Selection of admission test for state Polytechnics (SB-UMPN) and Independent (UM-Polbeng) were conducted by using paper-based Test (PBT). Paper Based Test model has some weaknesses. They are wasting too much paper, the leaking of the questios to the public, and data manipulation of the test result. This reasearch was Aimed to create a Computer-based Test (CBT) models by using Unified Modeling Language (UML) the which consists of Use Case diagrams, Activity diagram and sequence diagrams. During the designing process of the application, it is important to pay attention on the process of giving the password for the test questions before they were shown through encryption and description process. RSA cryptography algorithm was used in this process. Then, the questions shown in the questions banks were randomized by using the Fisher-Yates Shuffle method. The network architecture used in Computer Based test application was a client-server network models and Local Area Network (LAN). The result of the design was the Computer Based Test application for admission to the selection of Politeknik Negeri Bengkalis.

A unified approach for debugging is-a structure and mappings in networked taxonomies

PubMed Central

2013-01-01

Background With the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Results We propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency. Conclusions Semantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively. PMID:23548155

S4MPLE--Sampler for Multiple Protein-Ligand Entities: Methodology and Rigid-Site Docking Benchmarking.

PubMed

Hoffer, Laurent; Chira, Camelia; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-05-19

This paper describes the development of the unified conformational sampling and docking tool called Sampler for Multiple Protein-Ligand Entities (S4MPLE). The main novelty in S4MPLE is the unified dealing with intra- and intermolecular degrees of freedom (DoF). While classically programs are either designed for folding or docking, S4MPLE transcends this artificial specialization. It supports folding, docking of a flexible ligand into a flexible site and simultaneous docking of several ligands. The trick behind it is the formal assimilation of inter-molecular to intra-molecular DoF associated to putative inter-molecular contact axes. This is implemented within the genetic operators powering a Lamarckian Genetic Algorithm (GA). Further novelty includes differentiable interaction fingerprints to control population diversity, and fitting a simple continuum solvent model and favorable contact bonus terms to the AMBER/GAFF force field. Novel applications-docking of fragment-like compounds, simultaneous docking of multiple ligands, including free crystallographic waters-were published elsewhere. This paper discusses: (a) methodology, (b) set-up of the force field energy functions and (c) their validation in classical redocking tests. More than 80% success in redocking was achieved (RMSD of top-ranked pose < 2.0 Å).

Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation.

PubMed

Aguilar, I; Misztal, I; Legarra, A; Tsuruta, S

2011-12-01

Genomic evaluations can be calculated using a unified procedure that combines phenotypic, pedigree and genomic information. Implementation of such a procedure requires the inverse of the relationship matrix based on pedigree and genomic relationships. The objective of this study was to investigate efficient computing options to create relationship matrices based on genomic markers and pedigree information as well as their inverses. SNP maker information was simulated for a panel of 40 K SNPs, with the number of genotyped animals up to 30 000. Matrix multiplication in the computation of the genomic relationship was by a simple 'do' loop, by two optimized versions of the loop, and by a specific matrix multiplication subroutine. Inversion was by a generalized inverse algorithm and by a LAPACK subroutine. With the most efficient choices and parallel processing, creation of matrices for 30 000 animals would take a few hours. Matrices required to implement a unified approach can be computed efficiently. Optimizations can be either by modifications of existing code or by the use of efficient automatic optimizations provided by open source or third-party libraries. © 2011 Blackwell Verlag GmbH.

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

PubMed Central

Meyer, Arne F.; Williamson, Ross S.; Linden, Jennifer F.; Sahani, Maneesh

2017-01-01

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available. PMID:28127278

The management of abdominal wall hernias – in search of consensus

PubMed Central

Bury, Kamil; Śmietański, Maciej

2015-01-01

Introduction Laparoscopic repair is becoming an increasingly popular alternative in the treatment of abdominal wall hernias. In spite of numerous studies evaluating this technique, indications for laparoscopic surgery have not been established. Similarly, implant selection and fixation techniques have not been unified and are the subject of scientific discussion. Aim To assess whether there is a consensus on the management of the most common ventral abdominal wall hernias among recognised experts. Material and methods Fourteen specialists representing the boards of European surgical societies were surveyed to determine their choice of surgical technique for nine typical primary ventral and incisional hernias. The access method, type of operation, mesh prosthesis and fixation method were evaluated. In addition to the laparoscopic procedures, the number of tackers and their arrangement were assessed. Results In none of the cases presented was a consensus of experts obtained. Laparoscopic and open techniques were used equally often. Especially in the group of large hernias, decisions on repair methods were characterised by high variability. The technique of laparoscopic mesh fixation was a subject of great variability in terms of both method selection and the numbers of tackers and sutures used. Conclusions Recognised experts have not reached a consensus on the management of abdominal wall hernias. Our survey results indicate the need for further research and the inclusion of large cohorts of patients in the dedicated registries to evaluate the results of different surgical methods, which would help in the development of treatment algorithms for surgical education in the future. PMID:25960793

Recursive dynamics for flexible multibody systems using spatial operators

NASA Technical Reports Server (NTRS)

Jain, A.; Rodriguez, G.

1990-01-01

Due to their structural flexibility, spacecraft and space manipulators are multibody systems with complex dynamics and possess a large number of degrees of freedom. Here the spatial operator algebra methodology is used to develop a new dynamics formulation and spatially recursive algorithms for such flexible multibody systems. A key feature of the formulation is that the operator description of the flexible system dynamics is identical in form to the corresponding operator description of the dynamics of rigid multibody systems. A significant advantage of this unifying approach is that it allows ideas and techniques for rigid multibody systems to be easily applied to flexible multibody systems. The algorithms use standard finite-element and assumed modes models for the individual body deformation. A Newton-Euler Operator Factorization of the mass matrix of the multibody system is first developed. It forms the basis for recursive algorithms such as for the inverse dynamics, the computation of the mass matrix, and the composite body forward dynamics for the system. Subsequently, an alternative Innovations Operator Factorization of the mass matrix, each of whose factors is invertible, is developed. It leads to an operator expression for the inverse of the mass matrix, and forms the basis for the recursive articulated body forward dynamics algorithm for the flexible multibody system. For simplicity, most of the development here focuses on serial chain multibody systems. However, extensions of the algorithms to general topology flexible multibody systems are described. While the computational cost of the algorithms depends on factors such as the topology and the amount of flexibility in the multibody system, in general, it appears that in contrast to the rigid multibody case, the articulated body forward dynamics algorithm is the more efficient algorithm for flexible multibody systems containing even a small number of flexible bodies. The variety of algorithms described here permits a user to choose the algorithm which is optimal for the multibody system at hand. The availability of a number of algorithms is even more important for real-time applications, where implementation on parallel processors or custom computing hardware is often necessary to maximize speed.

Tropical geometry of statistical models.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation

NASA Astrophysics Data System (ADS)

Chi, Yuejie

2017-08-01

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

A description of a system of programs for mathematically processing on unified series (YeS) computers photographic images of the Earth taken from spacecraft

NASA Technical Reports Server (NTRS)

Zolotukhin, V. G.; Kolosov, B. I.; Usikov, D. A.; Borisenko, V. I.; Mosin, S. T.; Gorokhov, V. N.

1980-01-01

A description of a batch of programs for the YeS-1040 computer combined into an automated system for processing photo (and video) images of the Earth's surface, taken from spacecraft, is presented. Individual programs with the detailed discussion of the algorithmic and programmatic facilities needed by the user are presented. The basic principles for assembling the system, and the control programs are included. The exchange format within whose framework the cataloging of any programs recommended for the system of processing will be activated in the future is displayed.

Computation of free energy profiles with parallel adaptive dynamics

NASA Astrophysics Data System (ADS)

Lelièvre, Tony; Rousset, Mathias; Stoltz, Gabriel

2007-04-01

We propose a formulation of an adaptive computation of free energy differences, in the adaptive biasing force or nonequilibrium metadynamics spirit, using conditional distributions of samples of configurations which evolve in time. This allows us to present a truly unifying framework for these methods, and to prove convergence results for certain classes of algorithms. From a numerical viewpoint, a parallel implementation of these methods is very natural, the replicas interacting through the reconstructed free energy. We demonstrate how to improve this parallel implementation by resorting to some selection mechanism on the replicas. This is illustrated by computations on a model system of conformational changes.

Advanced computer graphic techniques for laser range finder (LRF) simulation

NASA Astrophysics Data System (ADS)

Bedkowski, Janusz; Jankowski, Stanislaw

2008-11-01

This paper show an advanced computer graphic techniques for laser range finder (LRF) simulation. The LRF is the common sensor for unmanned ground vehicle, autonomous mobile robot and security applications. The cost of the measurement system is extremely high, therefore the simulation tool is designed. The simulation gives an opportunity to execute algorithm such as the obstacle avoidance[1], slam for robot localization[2], detection of vegetation and water obstacles in surroundings of the robot chassis[3], LRF measurement in crowd of people[1]. The Axis Aligned Bounding Box (AABB) and alternative technique based on CUDA (NVIDIA Compute Unified Device Architecture) is presented.

Evaluation of focused ultrasound algorithms: Issues for reducing pre-focal heating and treatment time.

PubMed

Yiannakou, Marinos; Trimikliniotis, Michael; Yiallouras, Christos; Damianou, Christakis

2016-02-01

Due to the heating in the pre-focal field the delay between successive movements in high intensity focused ultrasound (HIFU) are sometimes as long as 60s, resulting to treatment time in the order of 2-3h. Because there is generally a requirement to reduce treatment time, we were motivated to explore alternative transducer motion algorithms in order to reduce pre-focal heating and treatment time. A 1 MHz single element transducer with 4 cm diameter and 10 cm focal length was used. A simulation model was developed that estimates the temperature, thermal dose and lesion development in the pre-focal field. The simulated temperature history that was combined with the motion algorithms produced thermal maps in the pre-focal region. Polyacrylimde gel phantom was used to evaluate the induced pre-focal heating for each motion algorithm used, and also was used to assess the accuracy of the simulation model. Three out of the six algorithms having successive steps close to each other, exhibited severe heating in the pre-focal field. Minimal heating was produced with the algorithms having successive steps apart from each other (square, square spiral and random). The last three algorithms were improved further (with small cost in time), thus eliminating completely the pre-focal heating and reducing substantially the treatment time as compared to traditional algorithms. Out of the six algorithms, 3 were successful in eliminating the pre-focal heating completely. Because these 3 algorithms required no delay between successive movements (except in the last part of the motion), the treatment time was reduced by 93%. Therefore, it will be possible in the future, to achieve treatment time of focused ultrasound therapies shorter than 30 min. The rate of ablated volume achieved with one of the proposed algorithms was 71 cm(3)/h. The intention of this pilot study was to demonstrate that the navigation algorithms play the most important role in reducing pre-focal heating. By evaluating in the future, all commercially available geometries, it will be possible to reduce the treatment time, for thermal ablation protocols intended for oncological targets. Copyright © 2015 Elsevier B.V. All rights reserved.

Group sparse multiview patch alignment framework with view consistency for image classification.

PubMed

Gui, Jie; Tao, Dacheng; Sun, Zhenan; Luo, Yong; You, Xinge; Tang, Yuan Yan

2014-07-01

No single feature can satisfactorily characterize the semantic concepts of an image. Multiview learning aims to unify different kinds of features to produce a consensual and efficient representation. This paper redefines part optimization in the patch alignment framework (PAF) and develops a group sparse multiview patch alignment framework (GSM-PAF). The new part optimization considers not only the complementary properties of different views, but also view consistency. In particular, view consistency models the correlations between all possible combinations of any two kinds of view. In contrast to conventional dimensionality reduction algorithms that perform feature extraction and feature selection independently, GSM-PAF enjoys joint feature extraction and feature selection by exploiting l(2,1)-norm on the projection matrix to achieve row sparsity, which leads to the simultaneous selection of relevant features and learning transformation, and thus makes the algorithm more discriminative. Experiments on two real-world image data sets demonstrate the effectiveness of GSM-PAF for image classification.

Variable cycle control model for intersection based on multi-source information

NASA Astrophysics Data System (ADS)

Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan

2018-05-01

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

Principle and Reconstruction Algorithm for Atomic-Resolution Holography

NASA Astrophysics Data System (ADS)

Matsushita, Tomohiro; Muro, Takayuki; Matsui, Fumihiko; Happo, Naohisa; Hosokawa, Shinya; Ohoyama, Kenji; Sato-Tomita, Ayana; Sasaki, Yuji C.; Hayashi, Kouichi

2018-06-01

Atomic-resolution holography makes it possible to obtain the three-dimensional (3D) structure around a target atomic site. Translational symmetry of the atomic arrangement of the sample is not necessary, and the 3D atomic image can be measured when the local structure of the target atomic site is oriented. Therefore, 3D local atomic structures such as dopants and adsorbates are observable. Here, the atomic-resolution holography comprising photoelectron holography, X-ray fluorescence holography, neutron holography, and their inverse modes are treated. Although the measurement methods are different, they can be handled with a unified theory. The algorithm for reconstructing 3D atomic images from holograms plays an important role. Although Fourier transform-based methods have been proposed, they require the multiple-energy holograms. In addition, they cannot be directly applied to photoelectron holography because of the phase shift problem. We have developed methods based on the fitting method for reconstructing from single-energy and photoelectron holograms. The developed methods are applicable to all types of atomic-resolution holography.

Model Checking Degrees of Belief in a System of Agents

NASA Technical Reports Server (NTRS)

Raimondi, Franco; Primero, Giuseppe; Rungta, Neha

2014-01-01

Reasoning about degrees of belief has been investigated in the past by a number of authors and has a number of practical applications in real life. In this paper we present a unified framework to model and verify degrees of belief in a system of agents. In particular, we describe an extension of the temporal-epistemic logic CTLK and we introduce a semantics based on interpreted systems for this extension. In this way, degrees of beliefs do not need to be provided externally, but can be derived automatically from the possible executions of the system, thereby providing a computationally grounded formalism. We leverage the semantics to (a) construct a model checking algorithm, (b) investigate its complexity, (c) provide a Java implementation of the model checking algorithm, and (d) evaluate our approach using the standard benchmark of the dining cryptographers. Finally, we provide a detailed case study: using our framework and our implementation, we assess and verify the situational awareness of the pilot of Air France 447 flying in off-nominal conditions.

Energy-efficient constellations design and fast decoding for space-collaborative MIMO visible light communications

NASA Astrophysics Data System (ADS)

Zhu, Yi-Jun; Liang, Wang-Feng; Wang, Chao; Wang, Wen-Ya

2017-01-01

In this paper, space-collaborative constellations (SCCs) for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems are considered. Compared with traditional VLC MIMO techniques, such as repetition coding (RC), spatial modulation (SM) and spatial multiplexing (SMP), SCC achieves the minimum average optical power for a fixed minimum Euclidean distance. We have presented a unified SCC structure for 2×2 MIMO VLC systems and extended it to larger MIMO VLC systems with more transceivers. Specifically for 2×2 MIMO VLC, a fast decoding algorithm is developed with decoding complexity almost linear in terms of the square root of the cardinality of SCC, and the expressions of symbol error rate of SCC are presented. In addition, bit mappings similar to Gray mapping are proposed for SCC. Computer simulations are performed to verify the fast decoding algorithm and the performance of SCC, and the results demonstrate that the performance of SCC is better than those of RC, SM and SMP for indoor channels in general.

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.

PubMed

Lee, I; Sikora, R; Shaw, M J

1997-01-01

Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

A comparative study of history-based versus vectorized Monte Carlo methods in the GPU/CUDA environment for a simple neutron eigenvalue problem

NASA Astrophysics Data System (ADS)

Liu, Tianyu; Du, Xining; Ji, Wei; Xu, X. George; Brown, Forrest B.

2014-06-01

For nuclear reactor analysis such as the neutron eigenvalue calculations, the time consuming Monte Carlo (MC) simulations can be accelerated by using graphics processing units (GPUs). However, traditional MC methods are often history-based, and their performance on GPUs is affected significantly by the thread divergence problem. In this paper we describe the development of a newly designed event-based vectorized MC algorithm for solving the neutron eigenvalue problem. The code was implemented using NVIDIA's Compute Unified Device Architecture (CUDA), and tested on a NVIDIA Tesla M2090 GPU card. We found that although the vectorized MC algorithm greatly reduces the occurrence of thread divergence thus enhancing the warp execution efficiency, the overall simulation speed is roughly ten times slower than the history-based MC code on GPUs. Profiling results suggest that the slow speed is probably due to the memory access latency caused by the large amount of global memory transactions. Possible solutions to improve the code efficiency are discussed.

Generalized Minimum-Time Follow-up Approaches Applied to Tasking Electro-Optical Sensor Tasking

NASA Astrophysics Data System (ADS)

Murphy, T. S.; Holzinger, M. J.

This work proposes a methodology for tasking of sensors to search an area of state space for a particular object, group of objects, or class of objects. This work creates a general unified mathematical framework for analyzing reacquisition, search, scheduling, and custody operations. In particular, this work looks at searching for unknown space object(s) with prior knowledge in the form of a set, which can be defined via an uncorrelated track, region of state space, or a variety of other methods. The follow-up tasking can occur from a variable location and time, which often requires searching a large region of the sky. This work analyzes the area of a search region over time to inform a time optimal search method. Simulation work looks at analyzing search regions relative to a particular sensor, and testing a tasking algorithm to search through the region. The tasking algorithm is also validated on a reacquisition problem with a telescope system at Georgia Tech.

Midline Dose Verification with Diode In Vivo Dosimetry for External Photon Therapy of Head and Neck and Pelvis Cancers During Initial Large-Field Treatments

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

Tung, Chuan-Jong; Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan; Yu, Pei-Chieh

2010-01-01

During radiotherapy treatments, quality assurance/control is essential, particularly dose delivery to patients. This study was designed to verify midline doses with diode in vivo dosimetry. Dosimetry was studied for 6-MV bilateral fields in head and neck cancer treatments and 10-MV bilateral and anteroposterior/posteroanterior (AP/PA) fields in pelvic cancer treatments. Calibrations with corrections of diodes were performed using plastic water phantoms; 190 and 100 portals were studied for head and neck and pelvis treatments, respectively. Calculations of midline doses were made using the midline transmission, arithmetic mean, and geometric mean algorithms. These midline doses were compared with the treatment planning systemmore » target doses for lateral or AP (PA) portals and paired opposed portals. For head and neck treatments, all 3 algorithms were satisfactory, although the geometric mean algorithm was less accurate and more uncertain. For pelvis treatments, the arithmetic mean algorithm seemed unacceptable, whereas the other algorithms were satisfactory. The random error was reduced by using averaged midline doses of paired opposed portals because the asymmetric effect was averaged out. Considering the simplicity of in vivo dosimetry, the arithmetic mean and geometric mean algorithm should be adopted for head/neck and pelvis treatments, respectively.« less

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

NASA Astrophysics Data System (ADS)

Romano, Joseph D.; Cornish, Neil. J.

2017-04-01

We review detection methods that are currently in use or have been proposed to search for a stochastic background of gravitational radiation. We consider both Bayesian and frequentist searches using ground-based and space-based laser interferometers, spacecraft Doppler tracking, and pulsar timing arrays; and we allow for anisotropy, non-Gaussianity, and non-standard polarization states. Our focus is on relevant data analysis issues, and not on the particular astrophysical or early Universe sources that might give rise to such backgrounds. We provide a unified treatment of these searches at the level of detector response functions, detection sensitivity curves, and, more generally, at the level of the likelihood function, since the choice of signal and noise models and prior probability distributions are actually what define the search. Pedagogical examples are given whenever possible to compare and contrast different approaches. We have tried to make the article as self-contained and comprehensive as possible, targeting graduate students and new researchers looking to enter this field.

Do specialty courts achieve better outcomes for children in foster care than general courts?

PubMed

Sloan, Frank A; Gifford, Elizabeth J; Eldred, Lindsey M; Acquah, Kofi F; Blevins, Claire E

2013-02-01

This study assessed the effects of unified family and drug treatment courts (DTCs) on the resolution of cases involving foster care children and the resulting effects on school performance. The first analytic step was to assess the impacts of presence of unified and DTCs in North Carolina counties on time children spent in foster care and the type of placement at exit from foster care. In the second step, the same data on foster care placements were merged with school records for youth in Grades 3-8 in public schools. The effect of children's time in foster care and placement outcomes on school performance as measured by math and reading tests, grade retention, and attendance was assessed using child fixed-effects regression. Children in counties with unified family courts experienced shorter foster care spells and higher rates of reunification with parents or primary caregivers. Shorter foster care spells translated into improved school performance measured by end-of-grade reading and math test scores. Adult DTCs were associated with lower probability of reunification with parents/primary caregivers. The shortened time in foster care implies an efficiency gain attributable to unified family courts, which translate into savings for the court system through the use of fewer resources. Children also benefit through shortened stays in temporary placements, which are related to some improved educational outcomes.

Anomaly-free cosmological perturbations in effective canonical quantum gravity

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

Barrau, Aurelien; Bojowald, Martin; Kagan, Mikhail

2015-05-01

This article lays out a complete framework for an effective theory of cosmological perturbations with corrections from canonical quantum gravity. Since several examples exist for quantum-gravity effects that change the structure of space-time, the classical perturbative treatment must be rethought carefully. The present discussion provides a unified picture of several previous works, together with new treatments of higher-order perturbations and the specification of initial states.

Algorithmics - Is There Hope for a Unified Theory?

NASA Astrophysics Data System (ADS)

Hromkovič, Juraj

Computer science was born with the formal definition of the notion of an algorithm. This definition provides clear limits of automatization, separating problems into algorithmically solvable problems and algorithmically unsolvable ones. The second big bang of computer science was the development of the concept of computational complexity. People recognized that problems that do not admit efficient algorithms are not solvable in practice. The search for a reasonable, clear and robust definition of the class of practically solvable algorithmic tasks started with the notion of the class {P} and of {NP}-completeness. In spite of the fact that this robust concept is still fundamental for judging the hardness of computational problems, a variety of approaches was developed for solving instances of {NP}-hard problems in many applications. Our 40-years short attempt to fix the fuzzy border between the practically solvable problems and the practically unsolvable ones partially reminds of the never-ending search for the definition of "life" in biology or for the definitions of matter and energy in physics. Can the search for the formal notion of "practical solvability" also become a never-ending story or is there hope for getting a well-accepted, robust definition of it? Hopefully, it is not surprising that we are not able to answer this question in this invited talk. But to deal with this question is of crucial importance, because only due to enormous effort scientists get a better and better feeling of what the fundamental notions of science like life and energy mean. In the flow of numerous technical results, we must not forget the fact that most of the essential revolutionary contributions to science were done by defining new concepts and notions.

Virtual management of radiology examinations in the virtual radiology environment using common object request broker architecture services.

PubMed

Martinez, R; Rozenblit, J; Cook, J F; Chacko, A K; Timboe, H L

1999-05-01

In the Department of Defense (DoD), US Army Medical Command is now embarking on an extremely exciting new project--creating a virtual radiology environment (VRE) for the management of radiology examinations. The business of radiology in the military is therefore being reengineered on several fronts by the VRE Project. In the VRE Project, a set of intelligent agent algorithms determine where examinations are to routed for reading bases on a knowledge base of the entire VRE. The set of algorithms, called the Meta-Manager, is hierarchical and uses object-based communications between medical treatment facilities (MTFs) and medical centers that have digital imaging network picture archiving and communications systems (DIN-PACS) networks. The communications is based on use of common object request broker architecture (CORBA) objects and services to send patient demographics and examination images from DIN-PACS networks in the MTFs to the DIN-PACS networks at the medical centers for diagnosis. The Meta-Manager is also responsible for updating the diagnosis at the originating MTF. CORBA services are used to perform secure message communications between DIN-PACS nodes in the VRE network. The Meta-Manager has a fail-safe architecture that allows the master Meta-Manager function to float to regional Meta-Manager sites in case of server failure. A prototype of the CORBA-based Meta-Manager is being developed by the University of Arizona's Computer Engineering Research Laboratory using the unified modeling language (UML) as a design tool. The prototype will implement the main functions described in the Meta-Manager design specification. The results of this project are expected to reengineer the process of radiology in the military and have extensions to commercial radiology environments.

Model based approach to UXO imaging using the time domain electromagnetic method

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

Lavely, E.M.

1999-04-01

Time domain electromagnetic (TDEM) sensors have emerged as a field-worthy technology for UXO detection in a variety of geological and environmental settings. This success has been achieved with commercial equipment that was not optimized for UXO detection and discrimination. The TDEM response displays a rich spatial and temporal behavior which is not currently utilized. Therefore, in this paper the author describes a research program for enhancing the effectiveness of the TDEM method for UXO detection and imaging. Fundamental research is required in at least three major areas: (a) model based imaging capability i.e. the forward and inverse problem, (b) detectormore » modeling and instrument design, and (c) target recognition and discrimination algorithms. These research problems are coupled and demand a unified treatment. For example: (1) the inverse solution depends on solution of the forward problem and knowledge of the instrument response; (2) instrument design with improved diagnostic power requires forward and inverse modeling capability; and (3) improved target recognition algorithms (such as neural nets) must be trained with data collected from the new instrument and with synthetic data computed using the forward model. Further, the design of the appropriate input and output layers of the net will be informed by the results of the forward and inverse modeling. A more fully developed model of the TDEM response would enable the joint inversion of data collected from multiple sensors (e.g., TDEM sensors and magnetometers). Finally, the author suggests that a complementary approach to joint inversions is the statistical recombination of data using principal component analysis. The decomposition into principal components is useful since the first principal component contains those features that are most strongly correlated from image to image.« less

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811

Social medicalization and alternative and complementary medicine: the pluralization of health services in the Brazilian Unified Health System.

PubMed

Tesser, Charles Dalcanale; Barros, Nelson Filice de

2008-10-01

Social medicalization transforms people's habits, discourages them from finding their own solutions to certain health problems and places an excess demand on the Unified Health System. With regard to healthcare provision, an alternative to social medicalization is the pluralization of treatment provided by health institutions namely through the recognition and provision of alternative and complementary practices and medicines. The objective of the article was to analyze the potentials and difficulties of alternative and complementary practices and medicines based on clinical and institutional experiences and on the specialist literature. The research concludes that the potential of such a strategy to "demedicalize" is limited and should be included in the remit of the Unified Health System. The article highlights that the Biosciences retain a political and epistemiological hegemony over medicine and that the area of healthcare is dominated by market principles, whereby there is a trend towards the transformation of any kind of knowledge or structured practice related to health-illness processes into goods or procedures to be consumed, and this only reinforces heteronomy and medicalization.

A controlled, randomized, delayed-start study of rasagiline in early Parkinson disease.

PubMed

2004-04-01

Treatment with rasagiline mesylate, an irreversible monoamine oxidase type B inhibitor, improves symptoms of early Parkinson disease (PD). Preclinical studies suggest that this compound may also modify the progression of PD. To compare the effects of early and later initiation of rasagiline on progression of disability in patients with PD. Double-blind, parallel-group, randomized, delayed-start clinical trial. Four hundred four subjects with early PD, not requiring dopaminergic therapy, enrolled at 32 sites in the United States and Canada. Subjects were randomized to receive rasagiline, 1 or 2 mg/d, for 1 year or placebo for 6 months followed by rasagiline, 2 mg/d, for 6 months. Change in total Unified Parkinson's Disease Rating Scale score from baseline to 12 months. Three hundred seventy-one subjects were included in the 1-year efficacy analysis. Subjects treated with rasagiline, 2 mg/d, for 1 year had a 2.29-unit smaller increase in mean adjusted total Unified Parkinson's Disease Rating Scale score compared with subjects treated with placebo for 6 months followed by rasagiline, 2 mg/d, for 6 months (P =.01). The mean adjusted difference between the placebo/rasagiline, 2 mg/d, group and those receiving rasagiline, 1 mg/d, for 1 year was -1.82 unit on the Unified Parkinson's Disease Rating Scale score (P =.05). Subjects treated with rasagiline, 2 and 1 mg/d, for 12 months showed less functional decline than subjects whose treatment was delayed for 6 months.

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

PubMed Central

Ryali, S; Glover, GH; Chang, C; Menon, V

2009-01-01

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on Independent Component Analysis (ICA) for reducing gradient artifacts from spiral in-out and echo-planar pulse sequences at 3T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen et al. 2000), principal component analysis (Niazy et al. 2005), Taylor series (Wan et al. 2006) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (< 12.5 Hz) with high signal-to-noise ratio (SNR > 4). Our algorithms outperformed all of these methods on resting EEG in the theta- and alpha-bands (SNR > 4); however, for all methods, signal recovery was modest (SNR ~ 1) in the beta-band and poor (SNR < 0.3) in the gamma-band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (< 0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3T using spiral in-out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework. PMID:19580873

Caffeine dosing strategies to optimize alertness during sleep loss.

PubMed

Vital-Lopez, Francisco G; Ramakrishnan, Sridhar; Doty, Tracy J; Balkin, Thomas J; Reifman, Jaques

2018-05-28

Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleep-loss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition. © 2018 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

Increasing total cross sections and flavoring of Pomeron in QCD

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

Tan, Chung-I.

A unified treatment of both the elastic and inelastic hadronic production is presented from the viewpoint of a topological expansion of nonperturbative QCD. The phenomenon of increasing total cross sections is examined and its relation to the flavoring of Pomeron is clarified. 12 refs.

EVOLUTION OF AN ANALYTICAL METHOD FOR HALOGENATED FURANONES IN DRINKING WATER

EPA Science Inventory

A unified method of detection for seven halogenated furanones present in drinking waters at the ng/L level has been developed. The use of GC/ECD makes this method amenable to manyenvironmental laboratories and water treatment plants in the United States. Detection limits observe...

A Course on the Physics and Chemistry of Pollution

ERIC Educational Resources Information Center

Hodges, Laurent

1971-01-01

Describes a course on environmental pollution which stresses physical and chemical principles. Course presents a unified discussion of air and water pollution and solid waste with special treatment of pesticides, thermal pollution, radioactivity, and electric power generation. Uses historical and current statistics extensively to set pollution…

Trauma-focused CBT for youth with complex trauma

PubMed Central

Mannarino, Anthony P.; Kliethermes, Matthew; Murray, Laura A.

2013-01-01

Objectives Many youth develop complex trauma, which includes regulation problems in the domains of affect, attachment, behavior, biology, cognition, and perception. Therapists often request strategies for using evidence-based treatments (EBTs) for this population. This article describes practical strategies for applying Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) for youth with complex trauma. Methods TF-CBT treatment phases are described and modifications of timing, proportionality and application are described for youth with complex trauma. Practical applications include a) dedicating proportionally more of the model to the TF-CBT coping skills phase; b) implementing the TF-CBT Safety component early and often as needed throughout treatment; c) titrating gradual exposure more slowly as needed by individual youth; d) incorporating unifying trauma themes throughout treatment; and e) when indicated, extending the TF-CBT treatment consolidation and closure phase to include traumatic grief components and to generalize ongoing safety and trust. Results Recent data from youth with complex trauma support the use of the above TF-CBT strategies to successfully treat these youth. Conclusions The above practical strategies can be incorporated into TF-CBT to effectively treat youth with complex trauma. Practice implications Practical strategies include providing a longer coping skills phase which incorporates safety and appropriate gradual exposure; including relevant unifying themes; and allowing for an adequate treatment closure phase to enhance ongoing trust and safety. Through these strategies therapists can successfully apply TF-CBT for youth with complex trauma. PMID:22749612

Algorithms for System Identification and Source Location.

NASA Astrophysics Data System (ADS)

Nehorai, Arye

This thesis deals with several topics in least squares estimation and applications to source location. It begins with a derivation of a mapping between Wiener theory and Kalman filtering for nonstationary autoregressive moving average (ARMO) processes. Applying time domain analysis, connections are found between time-varying state space realizations and input-output impulse response by matrix fraction description (MFD). Using these connections, the whitening filters are derived by the two approaches, and the Kalman gain is expressed in terms of Wiener theory. Next, fast estimation algorithms are derived in a unified way as special cases of the Conjugate Direction Method. The fast algorithms included are the block Levinson, fast recursive least squares, ladder (or lattice) and fast Cholesky algorithms. The results give a novel derivation and interpretation for all these methods, which are efficient alternatives to available recursive system identification algorithms. Multivariable identification algorithms are usually designed only for left MFD models. In this work, recursive multivariable identification algorithms are derived for right MFD models with diagonal denominator matrices. The algorithms are of prediction error and model reference type. Convergence analysis results obtained by the Ordinary Differential Equation (ODE) method are presented along with simulations. Sources of energy can be located by estimating time differences of arrival (TDOA's) of waves between the receivers. A new method for TDOA estimation is proposed for multiple unknown ARMA sources and additive correlated receiver noise. The method is based on a formula that uses only the receiver cross-spectra and the source poles. Two algorithms are suggested that allow tradeoffs between computational complexity and accuracy. A new time delay model is derived and used to show the applicability of the methods for non -integer TDOA's. Results from simulations illustrate the performance of the algorithms. The last chapter analyzes the response of exact least squares predictors for enhancement of sinusoids with additive colored noise. Using the matrix inversion lemma and the Christoffel-Darboux formula, the frequency response and amplitude gain of the sinusoids are expressed as functions of the signal and noise characteristics. The results generalize the available white noise case.

GPU-completeness: theory and implications

NASA Astrophysics Data System (ADS)

Lin, I.-Jong

2011-01-01

This paper formalizes a major insight into a class of algorithms that relate parallelism and performance. The purpose of this paper is to define a class of algorithms that trades off parallelism for quality of result (e.g. visual quality, compression rate), and we propose a similar method for algorithmic classification based on NP-Completeness techniques, applied toward parallel acceleration. We will define this class of algorithm as "GPU-Complete" and will postulate the necessary properties of the algorithms for admission into this class. We will also formally relate his algorithmic space and imaging algorithms space. This concept is based upon our experience in the print production area where GPUs (Graphic Processing Units) have shown a substantial cost/performance advantage within the context of HPdelivered enterprise services and commercial printing infrastructure. While CPUs and GPUs are converging in their underlying hardware and functional blocks, their system behaviors are clearly distinct in many ways: memory system design, programming paradigms, and massively parallel SIMD architecture. There are applications that are clearly suited to each architecture: for CPU: language compilation, word processing, operating systems, and other applications that are highly sequential in nature; for GPU: video rendering, particle simulation, pixel color conversion, and other problems clearly amenable to massive parallelization. While GPUs establishing themselves as a second, distinct computing architecture from CPUs, their end-to-end system cost/performance advantage in certain parts of computation inform the structure of algorithms and their efficient parallel implementations. While GPUs are merely one type of architecture for parallelization, we show that their introduction into the design space of printing systems demonstrate the trade-offs against competing multi-core, FPGA, and ASIC architectures. While each architecture has its own optimal application, we believe that the selection of architecture can be defined in terms of properties of GPU-Completeness. For a welldefined subset of algorithms, GPU-Completeness is intended to connect the parallelism, algorithms and efficient architectures into a unified framework to show that multiple layers of parallel implementation are guided by the same underlying trade-off.

Current Treatment Algorithms for Patients with Metastatic Non-Small Cell, Non-Squamous Lung Cancer

PubMed Central

Melosky, Barbara

2017-01-01

The treatment paradigm for metastatic non-small cell, non-squamous lung cancer is continuously evolving due to new treatment options and our increasing knowledge of molecular signal pathways. As a result of treatments becoming more efficacious and more personalized, survival for selected groups of non-small cell lung cancer (NSCLC) patients is increasing. In this paper, three algorithms will be presented for treating patients with metastatic non-squamous, NSCLC. These include treatment algorithms for NSCLC patients whose tumors have EGFR mutations, ALK rearrangements, or wild-type/wild-type tumors. As the world of immunotherapy continues to evolve quickly, a future algorithm will also be presented. PMID:28373963

High-resolution chromatography/time-of-flight MSE with in silico data mining is an information-rich approach to reactive metabolite screening.

PubMed

Barbara, Joanna E; Castro-Perez, Jose M

2011-10-30

Electrophilic reactive metabolite screening by liquid chromatography/mass spectrometry (LC/MS) is commonly performed during drug discovery and early-stage drug development. Accurate mass spectrometry has excellent utility in this application, but sophisticated data processing strategies are essential to extract useful information. Herein, a unified approach to glutathione (GSH) trapped reactive metabolite screening with high-resolution LC/TOF MS(E) analysis and drug-conjugate-specific in silico data processing was applied to rapid analysis of test compounds without the need for stable- or radio-isotope-labeled trapping agents. Accurate mass defect filtering (MDF) with a C-heteroatom dealkylation algorithm dynamic with mass range was compared to linear MDF and shown to minimize false positive results. MS(E) data-filtering, time-alignment and data mining post-acquisition enabled detection of 53 GSH conjugates overall formed from 5 drugs. Automated comparison of sample and control data in conjunction with the mass defect filter enabled detection of several conjugates that were not evident with mass defect filtering alone. High- and low-energy MS(E) data were time-aligned to generate in silico product ion spectra which were successfully applied to structural elucidation of detected GSH conjugates. Pseudo neutral loss and precursor ion chromatograms derived post-acquisition demonstrated 50.9% potential coverage, at best, of the detected conjugates by any individual precursor or neutral loss scan type. In contrast with commonly applied neutral loss and precursor-based techniques, the unified method has the advantage of applicability across different classes of GSH conjugates. The unified method was also successfully applied to cyanide trapping analysis and has potential for application to alternate trapping agents. Copyright © 2011 John Wiley & Sons, Ltd.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example. PMID:23515190

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

Use of treatment algorithms for depression.

PubMed

Trivedi, Madhukar H; Fava, Maurizio; Marangell, Lauren B; Osser, David N; Shelton, Richard C

2006-01-01

Depression continues to be a treatment challenge for many physicians-psychiatrists and primary care physicians alike-in part because of the nature of the disorder, but also because of the wide variety of medications and other treatments available, each with a distinct efficacy and safety profile. One way of negotiating treatment decisions is to use treatment guidelines and algorithms. This Commentary, which appears in the September 2006 issue of The Journal of Clinical Psychiatry (2006;67:1458-1465), provides the primary care clinician with insight into the pros and cons of using treatment algorithms to guide the treatment of depression. -Larry Culpepper, M.D.

Transducer Workshop (12th) Held at Melbourne, Florida on 7-9 June 1983.

DTIC Science & Technology

1983-06-01

applications since above 200OF (930C) the heat treatment CES among others, history of the Manganin is changes and (*) The Specific products referenced the...linearization scheme is compromised. and manufacturers’ addresses are given Heat treatment history change means ind thebibliographyi add sesparate pa... pulsating (dynamic) flow, even ment with the Unified Approach to the when readings are averaged over a period Engineering of Measuring Systems on of the

Theories and treatment of drug dependency: a neurochemical perspective.

PubMed

Pakdaman, Sheila; Wilcox, Richard E; Miller, Joseph D

2014-01-01

Treatment of chemical dependence ("addiction") requires an understanding of its effects on the brain. To guide research in the area of chemical dependence, several foundational theories have been developed. These include the incentive salience, receptor down-regulation, opponent process, and psychomotor stimulant theories. These have been important both in summarizing and in guiding investigations. However, the extant theories do not provide a single unified framework nor have they yielded all of the guidance necessary for effective chemical dependence treatment. The present paper summarizes and then integrates these theories and suggests some implications for the treatment followed by this integration.

The Theory behind the Theory in DCT and SCDT: A Response to Rigazio-DiGilio.

ERIC Educational Resources Information Center

Terry, Linda L.

1994-01-01

Responds to previous article by Rigazio-DiGilio on Developmental Counseling and Therapy and Systemic Cognitive-Developmental Therapy as two integrative models that unify individual, family, and network treatment within coconstructive-developmental framework. Discusses hidden complexities in cognitive-developmental ecosystemic integration and…

Potential of DCT/SCDT in Addressing Two Elusive Themes of Mental Health Counseling.

ERIC Educational Resources Information Center

Borders, L. DiAnne

1994-01-01

Responds to previous article by Rigazio-DiGilio on Developmental Counseling and Therapy and Systemic Cognitive-Developmental Therapy as two integrative models that unify individual, family, and network treatment within coconstructive-developmental framework. Considers extent to which model breaks impasse in integrating development into counseling…

Generalizability Theory as a Unifying Framework of Measurement Reliability in Adolescent Research

ERIC Educational Resources Information Center

Fan, Xitao; Sun, Shaojing

2014-01-01

In adolescence research, the treatment of measurement reliability is often fragmented, and it is not always clear how different reliability coefficients are related. We show that generalizability theory (G-theory) is a comprehensive framework of measurement reliability, encompassing all other reliability methods (e.g., Pearson "r,"…

Influence of different dose calculation algorithms on the estimate of NTCP for lung complications.

PubMed

Hedin, Emma; Bäck, Anna

2013-09-06

Due to limitations and uncertainties in dose calculation algorithms, different algorithms can predict different dose distributions and dose-volume histograms for the same treatment. This can be a problem when estimating the normal tissue complication probability (NTCP) for patient-specific dose distributions. Published NTCP model parameters are often derived for a different dose calculation algorithm than the one used to calculate the actual dose distribution. The use of algorithm-specific NTCP model parameters can prevent errors caused by differences in dose calculation algorithms. The objective of this work was to determine how to change the NTCP model parameters for lung complications derived for a simple correction-based pencil beam dose calculation algorithm, in order to make them valid for three other common dose calculation algorithms. NTCP was calculated with the relative seriality (RS) and Lyman-Kutcher-Burman (LKB) models. The four dose calculation algorithms used were the pencil beam (PB) and collapsed cone (CC) algorithms employed by Oncentra, and the pencil beam convolution (PBC) and anisotropic analytical algorithm (AAA) employed by Eclipse. Original model parameters for lung complications were taken from four published studies on different grades of pneumonitis, and new algorithm-specific NTCP model parameters were determined. The difference between original and new model parameters was presented in relation to the reported model parameter uncertainties. Three different types of treatments were considered in the study: tangential and locoregional breast cancer treatment and lung cancer treatment. Changing the algorithm without the derivation of new model parameters caused changes in the NTCP value of up to 10 percentage points for the cases studied. Furthermore, the error introduced could be of the same magnitude as the confidence intervals of the calculated NTCP values. The new NTCP model parameters were tabulated as the algorithm was varied from PB to PBC, AAA, or CC. Moving from the PB to the PBC algorithm did not require new model parameters; however, moving from PB to AAA or CC did require a change in the NTCP model parameters, with CC requiring the largest change. It was shown that the new model parameters for a given algorithm are different for the different treatment types.

Design and evaluation of a failure detection and isolation algorithm for restructurable control systems

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.; Hsu, John Y.

1986-01-01

The use of a decentralized approach to failure detection and isolation for use in restructurable control systems is examined. This work has produced: (1) A method for evaluating fundamental limits to FDI performance; (2) Application using flight recorded data; (3) A working control element FDI system with maximal sensitivity to critical control element failures; (4) Extensive testing on realistic simulations; and (5) A detailed design methodology involving parameter optimization (with respect to model uncertainties) and sensitivity analyses. This project has concentrated on detection and isolation of generic control element failures since these failures frequently lead to emergency conditions and since knowledge of remaining control authority is essential for control system redesign. The failures are generic in the sense that no temporal failure signature information was assumed. Thus, various forms of functional failures are treated in a unified fashion. Such a treatment results in a robust FDI system (i.e., one that covers all failure modes) but sacrifices some performance when detailed failure signature information is known, useful, and employed properly. It was assumed throughout that all sensors are validated (i.e., contain only in-spec errors) and that only the first failure of a single control element needs to be detected and isolated. The FDI system which has been developed will handle a class of multiple failures.

A DOUBLE-RING ALGORITHM FOR MODELING SOLAR ACTIVE REGIONS: UNIFYING KINEMATIC DYNAMO MODELS AND SURFACE FLUX-TRANSPORT SIMULATIONS

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

Munoz-Jaramillo, Andres; Martens, Petrus C. H.; Nandy, Dibyendu

The emergence of tilted bipolar active regions (ARs) and the dispersal of their flux, mediated via processes such as diffusion, differential rotation, and meridional circulation, is believed to be responsible for the reversal of the Sun's polar field. This process (commonly known as the Babcock-Leighton mechanism) is usually modeled as a near-surface, spatially distributed {alpha}-effect in kinematic mean-field dynamo models. However, this formulation leads to a relationship between polar field strength and meridional flow speed which is opposite to that suggested by physical insight and predicted by surface flux-transport simulations. With this in mind, we present an improved double-ring algorithmmore » for modeling the Babcock-Leighton mechanism based on AR eruption, within the framework of an axisymmetric dynamo model. Using surface flux-transport simulations, we first show that an axisymmetric formulation-which is usually invoked in kinematic dynamo models-can reasonably approximate the surface flux dynamics. Finally, we demonstrate that our treatment of the Babcock-Leighton mechanism through double-ring eruption leads to an inverse relationship between polar field strength and meridional flow speed as expected, reconciling the discrepancy between surface flux-transport simulations and kinematic dynamo models.« less

Identification and Management of Nonsystematic Purchase-Task Data: Towards Best Practice

PubMed Central

Stein, Jeffrey S.; Koffarnus, Mikhail N.; Snider, Sarah E.; Quisenberry, Amanda J.; Bickel, Warren K.

2015-01-01

Experimental assessments of demand allow the examination of economic phenomena relevant to the etiology, maintenance, and treatment of addiction and other pathologies (e.g., obesity). Although such assessments have historically been resource-intensive, development and use of purchase tasks—in which participants purchase one or more hypothetical or real commodities across a range of prices—have made data collection more practical and increased the rate of scientific discovery. However, extraneous sources of variability occasionally produce nonsystematic demand data, in which price exerts either no, or inconsistent, effects on purchasing of individual participants. Such data increase measurement error, can often not be interpreted in light of research aims, and likely obscure effects of the variable(s) under investigation. Using data from 494 participants, we introduce and evaluate an algorithm (derived from prior methods) for identifying nonsystematic demand data, wherein individual participants’ demand functions are judged against two general, empirically based assumptions: (1) global, price-dependent reduction in consumption, and (2) consistency in purchasing across prices. We also introduce guidelines for handling nonsystematic data, noting some conditions in which excluding such data from primary analyses may be appropriate and others in which doing so may bias conclusions. Adoption of the methods presented here may serve to unify the research literature and facilitate discovery. PMID:26147181

Assessing the Optimal Position for Vedolizumab in the Treatment of Ulcerative Colitis: A Simulation Model.

PubMed

Scott, Frank I; Shah, Yash; Lasch, Karen; Luo, Michelle; Lewis, James D

2018-01-18

Vedolizumab, an α4β7 integrin monoclonal antibody inhibiting gut lymphocyte trafficking, is an effective treatment for ulcerative colitis (UC). We evaluated the optimal position of vedolizumab in the UC treatment paradigm. Using Markov modeling, we assessed multiple algorithms for the treatment of UC. The base case was a 35-year-old male with steroid-dependent moderately to severely active UC without previous immunomodulator or biologic use. The model included 4 different algorithms over 1 year, with vedolizumab use prior to: initiating azathioprine (Algorithm 1), combination therapy with infliximab and azathioprine (Algorithm 2), combination therapy with an alternative anti-tumor necrosis factor (anti-TNF) and azathioprine (Algorithm 3), and colectomy (Algorithm 4). Transition probabilities and quality-adjusted life-year (QALY) estimates were derived from the published literature. Primary analyses included simulating 100 trials of 100,000 individuals, assessing clinical outcomes, and QALYs. Sensitivity analyses employed longer time horizons and ranges for all variables. Algorithm 1 (vedolizumab use prior to all other therapies) was the preferred strategy, resulting in 8981 additional individuals in remission, 18 fewer cases of lymphoma, and 1087 fewer serious infections per 100,000 patients compared with last-line use (A4). Algorithm 1 also resulted in 0.0197 to 0.0205 more QALYs compared with other algorithms. This benefit increased with longer time horizons. Algorithm 1 was preferred in all sensitivity analyses. The model suggests that treatment algorithms positioning vedolizumab prior to other therapies should be considered for individuals with moderately to severely active steroid-dependent UC. Further prospective research is needed to confirm these simulated results. © 2018 Crohn’s & Colitis Foundation of America. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Physically corrected forward operators for induced emission tomography: a simulation study

NASA Astrophysics Data System (ADS)

Viganò, Nicola Roberto; Solé, Vicente Armando

2018-03-01

X-ray emission tomography techniques over non-radioactive materials allow one to investigate different and important aspects of the matter that are usually not addressable with the standard x-ray transmission tomography, such as density, chemical composition and crystallographic information. However, the quantitative reconstruction of these investigated properties is hindered by additional problems, including the self-attenuation of the emitted radiation. Work has been done in the past, especially concerning x-ray fluorescence tomography, but this has always focused on solving very specific problems. The novelty of this work resides in addressing the problem of induced emission tomography from a much wider perspective, introducing a unified discrete representation that can be used to modify existing algorithms to reconstruct the data of the different types of experiments. The direct outcome is a clear and easy mathematical description of the implementation details of such algorithms, despite small differences in geometry and other practical aspects, but also the possibility to express the reconstruction as a minimization problem, allowing the use of variational methods, and a more flexible modeling of the noise involved in the detection process. In addition, we look at the results of a few selected simulated data reconstructions that describe the effect of physical corrections like the self-attenuation, and the response to noise of the adapted reconstruction algorithms.

Jerk-level synchronous repetitive motion scheme with gradient-type and zeroing-type dynamics algorithms applied to dual-arm redundant robot system control

NASA Astrophysics Data System (ADS)

Chen, Dechao; Zhang, Yunong

2017-10-01

Dual-arm redundant robot systems are usually required to handle primary tasks, repetitively and synchronously in practical applications. In this paper, a jerk-level synchronous repetitive motion scheme is proposed to remedy the joint-angle drift phenomenon and achieve the synchronous control of a dual-arm redundant robot system. The proposed scheme is novelly resolved at jerk level, which makes the joint variables, i.e. joint angles, joint velocities and joint accelerations, smooth and bounded. In addition, two types of dynamics algorithms, i.e. gradient-type (G-type) and zeroing-type (Z-type) dynamics algorithms, for the design of repetitive motion variable vectors, are presented in detail with the corresponding circuit schematics. Subsequently, the proposed scheme is reformulated as two dynamical quadratic programs (DQPs) and further integrated into a unified DQP (UDQP) for the synchronous control of a dual-arm robot system. The optimal solution of the UDQP is found by the piecewise-linear projection equation neural network. Moreover, simulations and comparisons based on a six-degrees-of-freedom planar dual-arm redundant robot system substantiate the operation effectiveness and tracking accuracy of the robot system with the proposed scheme for repetitive motion and synchronous control.

A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography.

PubMed

Rodrigues, É O; Morais, F F C; Morais, N A O S; Conci, L S; Neto, L V; Conci, A

2016-01-01

The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. Much effort was devoted to achieve minimal user intervention. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation. We compare the performance of several classification algorithms on this task, including neural networks, probabilistic models and decision tree algorithms. Experimental results of the proposed methodology have shown that the mean accuracy regarding both epicardial and mediastinal fats is 98.5% (99.5% if the features are normalized), with a mean true positive rate of 98.0%. In average, the Dice similarity index was equal to 97.6%. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System

PubMed Central

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second. PMID:24891848

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System.

PubMed

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second.

Real-time sensor validation and fusion for distributed autonomous sensors

NASA Astrophysics Data System (ADS)

Yuan, Xiaojing; Li, Xiangshang; Buckles, Bill P.

2004-04-01

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Efficient stochastic approaches for sensitivity studies of an Eulerian large-scale air pollution model

NASA Astrophysics Data System (ADS)

Dimov, I.; Georgieva, R.; Todorov, V.; Ostromsky, Tz.

2017-10-01

Reliability of large-scale mathematical models is an important issue when such models are used to support decision makers. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is crucial for improving the reliability of mathematical models. A comprehensive experimental study of Monte Carlo algorithms based on Sobol sequences for multidimensional numerical integration has been done. A comparison with Latin hypercube sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The concentration values have been generated by the Unified Danish Eulerian Model. The sensitivity study has been done for the areas of several European cities with different geographical locations. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing small by value sensitivity indices. It is a crucial element since even small indices may be important to be estimated in order to achieve a more accurate distribution of inputs influence and a more reliable interpretation of the mathematical model results.

The Day-1 GPM Combined Precipitation Algorithm: IMERG

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Sorooshian, S.; Xie, P.

2012-12-01

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) algorithm will provide the at-launch combined-sensor precipitation dataset being produced by the U.S. GPM Science Team. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three current U.S. algorithms: - the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; - the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following storm motion; and - the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds. The goal is to provide a long-term, fine-scale record of global precipitation from the entire constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. The record will begin January 1998 at the start of the Tropical Rainfall Measuring Mission (TRMM) and extend as GPM records additional data. Although homogeneity is considered desirable, the use of diverse and evolving data sources works against the strict long-term homogeneity that characterizes a Climate Data Record (CDR). This talk will briefly review the design requirements for IMERG, including multiple runs at different latencies (most likely around 4 hours, 12 hours, and 2 months after observation time), various intermediate data fields as part of the IMERG data file, and the plans to bring up IMERG with calibration by TRMM initially, transitioning to GPM when its individual-sensor precipitation algorithms are fully functional. Then we will present some early examples of IMERG data products and compare them with existing products to illustrate how the design of IMERG affects the overall performance of the algorithm.

GPU-based relative fuzzy connectedness image segmentation

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

Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.

2013-01-15

Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzymore » connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.« less

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

Comparison of selected dose calculation algorithms in radiotherapy treatment planning for tissues with inhomogeneities

NASA Astrophysics Data System (ADS)

Woon, Y. L.; Heng, S. P.; Wong, J. H. D.; Ung, N. M.

2016-03-01

Inhomogeneity correction is recommended for accurate dose calculation in radiotherapy treatment planning since human body are highly inhomogeneous with the presence of bones and air cavities. However, each dose calculation algorithm has its own limitations. This study is to assess the accuracy of five algorithms that are currently implemented for treatment planning, including pencil beam convolution (PBC), superposition (SP), anisotropic analytical algorithm (AAA), Monte Carlo (MC) and Acuros XB (AXB). The calculated dose was compared with the measured dose using radiochromic film (Gafchromic EBT2) in inhomogeneous phantoms. In addition, the dosimetric impact of different algorithms on intensity modulated radiotherapy (IMRT) was studied for head and neck region. MC had the best agreement with the measured percentage depth dose (PDD) within the inhomogeneous region. This was followed by AXB, AAA, SP and PBC. For IMRT planning, MC algorithm is recommended for treatment planning in preference to PBC and SP. The MC and AXB algorithms were found to have better accuracy in terms of inhomogeneity correction and should be used for tumour volume within the proximity of inhomogeneous structures.

Progressive Precision Surface Design

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

Duchaineau, M; Joy, KJ

2002-01-11

We introduce a novel wavelet decomposition algorithm that makes a number of powerful new surface design operations practical. Wavelets, and hierarchical representations generally, have held promise to facilitate a variety of design tasks in a unified way by approximating results very precisely, thus avoiding a proliferation of undergirding mathematical representations. However, traditional wavelet decomposition is defined from fine to coarse resolution, thus limiting its efficiency for highly precise surface manipulation when attempting to create new non-local editing methods. Our key contribution is the progressive wavelet decomposition algorithm, a general-purpose coarse-to-fine method for hierarchical fitting, based in this paper on anmore » underlying multiresolution representation called dyadic splines. The algorithm requests input via a generic interval query mechanism, allowing a wide variety of non-local operations to be quickly implemented. The algorithm performs work proportionate to the tiny compressed output size, rather than to some arbitrarily high resolution that would otherwise be required, thus increasing performance by several orders of magnitude. We describe several design operations that are made tractable because of the progressive decomposition. Free-form pasting is a generalization of the traditional control-mesh edit, but for which the shape of the change is completely general and where the shape can be placed using a free-form deformation within the surface domain. Smoothing and roughening operations are enhanced so that an arbitrary loop in the domain specifies the area of effect. Finally, the sculpting effect of moving a tool shape along a path is simulated.« less

An acceleration system for Laplacian image fusion based on SoC

NASA Astrophysics Data System (ADS)

Gao, Liwen; Zhao, Hongtu; Qu, Xiujie; Wei, Tianbo; Du, Peng

2018-04-01

Based on the analysis of Laplacian image fusion algorithm, this paper proposes a partial pipelining and modular processing architecture, and a SoC based acceleration system is implemented accordingly. Full pipelining method is used for the design of each module, and modules in series form the partial pipelining with unified data formation, which is easy for management and reuse. Integrated with ARM processor, DMA and embedded bare-mental program, this system achieves 4 layers of Laplacian pyramid on the Zynq-7000 board. Experiments show that, with small resources consumption, a couple of 256×256 images can be fused within 1ms, maintaining a fine fusion effect at the same time.

Bayesian Community Detection in the Space of Group-Level Functional Differences

PubMed Central

Venkataraman, Archana; Yang, Daniel Y.-J.; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder. PMID:26955022

Bayesian Community Detection in the Space of Group-Level Functional Differences.

PubMed

Venkataraman, Archana; Yang, Daniel Y-J; Pelphrey, Kevin A; Duncan, James S

2016-08-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Visualization Techniques in Space and Atmospheric Sciences

NASA Technical Reports Server (NTRS)

Szuszczewicz, E. P. (Editor); Bredekamp, Joseph H. (Editor)

1995-01-01

Unprecedented volumes of data will be generated by research programs that investigate the Earth as a system and the origin of the universe, which will in turn require analysis and interpretation that will lead to meaningful scientific insight. Providing a widely distributed research community with the ability to access, manipulate, analyze, and visualize these complex, multidimensional data sets depends on a wide range of computer science and technology topics. Data storage and compression, data base management, computational methods and algorithms, artificial intelligence, telecommunications, and high-resolution display are just a few of the topics addressed. A unifying theme throughout the papers with regards to advanced data handling and visualization is the need for interactivity, speed, user-friendliness, and extensibility.

A Unified Statistical Rain-Attenuation Model for Communication Link Fade Predictions and Optimal Stochastic Fade Control Design Using a Location-Dependent Rain-Statistic Database

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1990-01-01

A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.

Image Reconstruction from Highly Undersampled (k, t)-Space Data with Joint Partial Separability and Sparsity Constraints

PubMed Central

Zhao, Bo; Haldar, Justin P.; Christodoulou, Anthony G.; Liang, Zhi-Pei

2012-01-01

Partial separability (PS) and sparsity have been previously used to enable reconstruction of dynamic images from undersampled (k, t)-space data. This paper presents a new method to use PS and sparsity constraints jointly for enhanced performance in this context. The proposed method combines the complementary advantages of PS and sparsity constraints using a unified formulation, achieving significantly better reconstruction performance than using either of these constraints individually. A globally convergent computational algorithm is described to efficiently solve the underlying optimization problem. Reconstruction results from simulated and in vivo cardiac MRI data are also shown to illustrate the performance of the proposed method. PMID:22695345

Unusual Domain Structure and Filamentary Superfluidity for 2D Hard-Core Bosons in Insulating Charge-Ordered Phase

NASA Astrophysics Data System (ADS)

Panov, Yu. D.; Moskvin, A. S.; Rybakov, F. N.; Borisov, A. B.

2016-12-01

We made use of a special algorithm for compute unified device architecture for NVIDIA graphics cards, a nonlinear conjugate-gradient method to minimize energy functional, and Monte-Carlo technique to directly observe the forming of the ground state configuration for the 2D hard-core bosons by lowering the temperature and its evolution with deviation away from half-filling. The novel technique allowed us to examine earlier implications and uncover novel features of the phase transitions, in particular, look upon the nucleation of the odd domain structure, emergence of filamentary superfluidity nucleated at the antiphase domain walls of the charge-ordered phase, and nucleation and evolution of different topological structures.

24 CFR 578.11 - Unified Funding Agency.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true Unified Funding Agency. 578.11... of Care § 578.11 Unified Funding Agency. (a) Becoming a Unified Funding Agency. To become designated as the Unified Funding Agency (UFA) for a Continuum, a collaborative applicant must be selected by...

Transdiagnostic treatment of bipolar disorder and comorbid anxiety using the Unified Protocol for Emotional Disorders: A pilot feasibility and acceptability trial.

PubMed

Ellard, Kristen K; Bernstein, Emily E; Hearing, Casey; Baek, Ji Hyun; Sylvia, Louisa G; Nierenberg, Andrew A; Barlow, David H; Deckersbach, Thilo

2017-09-01

Comorbid anxiety in bipolar disorder (BD) is associated with greater illness severity, reduced treatment response, and greater impairment. Treating anxiety in the context of BD is crucial for improving illness course and outcomes. The current study examined the feasibility, acceptability and preliminary efficacy of the Unified Protocol (UP), a transdiagnostic cognitive behavioral therapy, as an adjunctive treatment to pharmacotherapy for BD and comorbid anxiety disorders. Twenty-nine patients with BD and at least one comorbid anxiety disorder were randomized to pharmacotherapy treatment-as-usual (TAU) or TAU with 18 sessions of the UP (UP+TAU). All patients completed assessments every four weeks to track symptoms, functioning, emotion regulation and temperament. Linear mixed-model regressions were conducted to track symptom changes over time and to examine the relationship between emotion-related variables and treatment response. Satisfaction ratings were equivalent for both treatment groups. Patients in the UP+TAU group evidenced significantly greater reductions over time in anxiety and depression symptoms (Cohen's d's>0.80). Baseline levels of neuroticism, perceived affective control, and emotion regulation ability predicted magnitude of symptom change for the UP+TAU group only. Greater change in perceived control of emotions and emotion regulation skills predicted greater change in anxiety related symptoms. This was a pilot feasibility and acceptability trial; results should be interpreted with caution. Treatment with the UP+TAU was rated high in patient satisfaction, and resulted in significantly greater improvement on indices of anxiety and depression relative to TAU. This suggests that the UP may be a feasible treatment approach for BD with comorbid anxiety. Copyright © 2017 Elsevier B.V. All rights reserved.

The role of negative affectivity and negative reactivity to emotions in predicting outcomes in the unified protocol for the transdiagnostic treatment of emotional disorders.

PubMed

Sauer-Zavala, Shannon; Boswell, James F; Gallagher, Matthew W; Bentley, Kate H; Ametaj, Amantia; Barlow, David H

2012-09-01

The present study aimed to understand the contributions of both the trait tendency to experience negative emotions and how one relates to such experience in predicting symptom change during participation in the Unified Protocol (UP), a transdiagnostic treatment for emotional disorders. Data were derived from a randomized controlled trial comparing the UP to a waitlist control/delayed-treatment condition. First, effect sizes of pre- to post-treatment change for frequency of negative emotions and several variables measuring reactivity to emotional experience (emotional awareness and acceptance, fear of emotions, and anxiety sensitivity) were examined. Second, the relative contributions of change in negative emotions and emotional reactivity in predicting symptom (clinician-rated anxiety, depression, and severity of principal diagnosis) reductions were investigated. Results suggested that decreases in the frequency of negative emotions and reactivity to emotions following participation in the UP were both large in magnitude. Further, two emotional reactivity variables (fear of emotions and anxiety sensitivity) remained significantly related to symptom outcomes when controlling for negative emotions, and accounted for significant incremental variance in their prediction. These findings lend support to the notion that psychological health depends less on the frequency of negative emotions and more on how one relates to these emotions when they occur. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.

PubMed

Amoroso, N; Errico, R; Bruno, S; Chincarini, A; Garuccio, E; Sensi, F; Tangaro, S; Tateo, A; Bellotti, R

2015-11-21

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

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.

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

NASA Astrophysics Data System (ADS)

Amoroso, N.; Errico, R.; Bruno, S.; Chincarini, A.; Garuccio, E.; Sensi, F.; Tangaro, S.; Tateo, A.; Bellotti, R.; Alzheimers Disease Neuroimaging Initiative,the

2015-11-01

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer’s Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice{{}\\text{ADNI}} =0.929+/- 0.003 and Dice{{}\\text{OASIS}} =0.869+/- 0.002 ). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

Does videothoracoscopy improve clinical outcomes when implemented as part of a pleural empyema treatment algorithm?

PubMed Central

Terra, Ricardo Mingarini; Waisberg, Daniel Reis; de Almeida, José Luiz Jesus; Devido, Marcela Santana; Pêgo-Fernandes, Paulo Manuel; Jatene, Fabio Biscegli

2012-01-01

OBJECTIVE: We aimed to evaluate whether the inclusion of videothoracoscopy in a pleural empyema treatment algorithm would change the clinical outcome of such patients. METHODS: This study performed quality-improvement research. We conducted a retrospective review of patients who underwent pleural decortication for pleural empyema at our institution from 2002 to 2008. With the old algorithm (January 2002 to September 2005), open decortication was the procedure of choice, and videothoracoscopy was only performed in certain sporadic mid-stage cases. With the new algorithm (October 2005 to December 2008), videothoracoscopy became the first-line treatment option, whereas open decortication was only performed in patients with a thick pleural peel (>2 cm) observed by chest scan. The patients were divided into an old algorithm (n = 93) and new algorithm (n = 113) group and compared. The main outcome variables assessed included treatment failure (pleural space reintervention or death up to 60 days after medical discharge) and the occurrence of complications. RESULTS: Videothoracoscopy and open decortication were performed in 13 and 80 patients from the old algorithm group and in 81 and 32 patients from the new algorithm group, respectively (p<0.01). The patients in the new algorithm group were older (41±1 vs. 46.3±16.7 years, p = 0.014) and had higher Charlson Comorbidity Index scores [0(0-3) vs. 2(0-4), p = 0.032]. The occurrence of treatment failure was similar in both groups (19.35% vs. 24.77%, p = 0.35), although the complication rate was lower in the new algorithm group (48.3% vs. 33.6%, p = 0.04). CONCLUSIONS: The wider use of videothoracoscopy in pleural empyema treatment was associated with fewer complications and unaltered rates of mortality and reoperation even though more severely ill patients were subjected to videothoracoscopic surgery. PMID:22760892

Evidence-based algorithm for heparin dosing before cardiopulmonary bypass. Part 1: Development of the algorithm.

PubMed

McKinney, Mark C; Riley, Jeffrey B

2007-12-01

The incidence of heparin resistance during adult cardiac surgery with cardiopulmonary bypass has been reported at 15%-20%. The consistent use of a clinical decision-making algorithm may increase the consistency of patient care and likely reduce the total required heparin dose and other problems associated with heparin dosing. After a directed survey of practicing perfusionists regarding treatment of heparin resistance and a literature search for high-level evidence regarding the diagnosis and treatment of heparin resistance, an evidence-based decision-making algorithm was constructed. The face validity of the algorithm decisive steps and logic was confirmed by a second survey of practicing perfusionists. The algorithm begins with review of the patient history to identify predictors for heparin resistance. The definition for heparin resistance contained in the algorithm is an activated clotting time < 450 seconds with > 450 IU/kg heparin loading dose. Based on the literature, the treatment for heparin resistance used in the algorithm is anti-thrombin III supplement. The algorithm seems to be valid and is supported by high-level evidence and clinician opinion. The next step is a human randomized clinical trial to test the clinical procedure guideline algorithm vs. current standard clinical practice.

A unified perspective on robot control - The energy Lyapunov function approach

NASA Technical Reports Server (NTRS)

Wen, John T.

1990-01-01

A unified framework for the stability analysis of robot tracking control is presented. By using an energy-motivated Lyapunov function candidate, the closed-loop stability is shown for a large family of control laws sharing a common structure of proportional and derivative feedback and a model-based feedforward. The feedforward can be zero, partial or complete linearized dynamics, partial or complete nonlinear dynamics, or linearized or nonlinear dynamics with parameter adaptation. As result, the dichotomous approaches to the robot control problem based on the open-loop linearization and nonlinear Lyapunov analysis are both included in this treatment. Furthermore, quantitative estimates of the trade-offs between different schemes in terms of the tracking performance, steady state error, domain of convergence, realtime computation load and required a prior model information are derived.

Social Learning Theory: Toward a Unified Approach of Pediatric Procedural Pain

ERIC Educational Resources Information Center

Page, Lynn Olson; Blanchette, Jennifer A.

2009-01-01

Undermanaged procedural pain has been shown to have short and long term effects on children. While significant progress regarding empirically supported treatments has been made, theoretical bases for the development and management of procedural pain are lacking. This paper examines the role of social learning theory in our current understanding of…

A unified view of acoustic-electrostatic solitons in complex plasmas

NASA Astrophysics Data System (ADS)

McKenzie, J. F.; Doyle, T. B.

2003-03-01

A fluid dynamic approach is used in a unified fully nonlinear treatment of the properties of the dust-acoustic, ion-acoustic and Langmuir-acoustic solitons. The analysis, which is carried out in the wave frame of the soliton, is based on total momentum conservation and Bernoulli-like energy equations for each of the particle species in each wave type, and yields the structure equation for the `heavy' species flow speed in each case. The heavy (cold or supersonic) species is always compressed in the soliton, requiring concomitant contraints on the potential and on the flow speed of the electrons and protons in the wave. The treatment clearly elucidates the crucial role played by the heavy species sonic point in limiting the collective species Mach number, which determines the upper limit for the existence of the soliton and its amplitude, and also shows the essentially similar nature of each soliton type. An exact solution, which highlights these characteristic properties, shows that the three acoustic solitons are in fact the same mathematical entity in different physical disguises.

A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations.

PubMed

Persoon, Lucas C G G; Podesta, Mark; van Elmpt, Wouter J C; Nijsten, Sebastiaan M J J G; Verhaegen, Frank

2011-07-01

A widely accepted method to quantify differences in dose distributions is the gamma (gamma) evaluation. Currently, almost all gamma implementations utilize the central processing unit (CPU). Recently, the graphics processing unit (GPU) has become a powerful platform for specific computing tasks. In this study, we describe the implementation of a 3D gamma evaluation using a GPU to improve calculation time. The gamma evaluation algorithm was implemented on an NVIDIA Tesla C2050 GPU using the compute unified device architecture (CUDA). First, several cubic virtual phantoms were simulated. These phantoms were tested with varying dose cube sizes and set-ups, introducing artificial dose differences. Second, to show applicability in clinical practice, five patient cases have been evaluated using the 3D dose distribution from a treatment planning system as the reference and the delivered dose determined during treatment as the comparison. A calculation time comparison between the CPU and GPU was made with varying thread-block sizes including the option of using texture or global memory. A GPU over CPU speed-up of 66 +/- 12 was achieved for the virtual phantoms. For the patient cases, a speed-up of 57 +/- 15 using the GPU was obtained. A thread-block size of 16 x 16 performed best in all cases. The use of texture memory improved the total calculation time, especially when interpolation was applied. Differences between the CPU and GPU gammas were negligible. The GPU and its features, such as texture memory, decreased the calculation time for gamma evaluations considerably without loss of accuracy.

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning.

PubMed

Han, Miaomiao; Guo, Zhirong; Liu, Haifeng; Li, Qinghua

2018-05-01

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices. Copyright © 2018 Elsevier Ltd. All rights reserved.

A multi-site reconstruction algorithm for bottom-up vulnerability assessment of water resource systems to changing streamflow conditions

NASA Astrophysics Data System (ADS)

Nazemi, A.; Zaerpour, M.

2016-12-01

Current paradigm for assessing the vulnerability of water resource systems to changing streamflow conditions often involves a cascade application of climate and hydrological models to project the future states of streamflow regime, entering to a given water resource system. It is widely warned, however, that the overall uncertainty in this "top-down" modeling enterprise can be large due to the limitations in representing natural and anthropogenic processes that affect future streamflow variability and change. To address this, various types of stress-tests are suggested to assess the vulnerability of water resources systems under a wide range of possible changes in streamflow conditions. The scope of such "bottom-up" assessments can go well beyond top-down projections and therefore provide a basis for monitoring different response modes, under which water resource systems become vulnerable. Despite methodological differences, all bottom-up assessments are equipped with a systematic sampling procedure, with which different possibilities for future climate and/or streamflow conditions can be realized. Regardless of recent developments, currently available streamflow sampling algorithms are still limited, particularly in regional contexts, for which accurate representation of spatiotemporal dependencies in streamflow regime are of major importance. In this presentation, we introduce a new development that enables handling temporal and spatial dependencies in regional streamflow regimes through a unified stochastic reconstruction algorithm. We demonstrate the application of this algorithm accross various Canadian regions. By considering a real-world regional water resources system, we show how the new multi-site reconstruction algorithm can extend the practical utility of bottom-up vulnerability assessment and improve quantifying the associated risk in natural and anthropogenic water systems under unknown future conditions.

Continuous leg dyskinesia assessment in Parkinson's disease -clinical validity and ecological effect.

PubMed

Ramsperger, Robert; Meckler, Stefan; Heger, Tanja; van Uem, Janet; Hucker, Svenja; Braatz, Ulrike; Graessner, Holm; Berg, Daniela; Manoli, Yiannos; Serrano, J Artur; Ferreira, Joaquim J; Hobert, Markus A; Maetzler, Walter

2016-05-01

Dyskinesias in Parkinson's disease (PD) patients are a common side effect of long-term dopaminergic therapy and are associated with motor dysfunctions, including gait and balance deficits. Although promising compounds have been developed to treat these symptoms, clinical trials have failed. This failure may, at least partly, be explained by the lack of objective and continuous assessment strategies. This study tested the clinical validity and ecological effect of an algorithm that detects and quantifies dyskinesias of the legs using a single ankle-worn sensor. Twenty-three PD patients (seven with leg dyskinesias) and 13 control subjects were investigated in the lab. Participants performed purposeful daily activity-like tasks while being video-taped. Clinical evaluation was performed using the leg dyskinesia item of the Unified Dyskinesia Rating Scale. The ecological effect of the developed algorithm was investigated in a multi-center, 12-week, home-based sub-study that included three patients with and seven without dyskinesias. In the lab-based sub-study, the sensor-based algorithm exhibited a specificity of 98%, a sensitivity of 85%, and an accuracy of 0.96 for the detection of dyskinesias and a correlation level of 0.61 (p < 0.001) with the clinical severity score. In the home-based sub-study, all patients could be correctly classified regarding the presence or absence of leg dyskinesias, supporting the ecological relevance of the algorithm. This study provides evidence of clinical validity and ecological effect of an algorithm derived from a single sensor on the ankle for detecting leg dyskinesias in PD patients. These results should motivate the investigation of leg dyskinesias in larger studies using wearable sensors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Implementation of an Algorithm for Prosthetic Joint Infection: Deviations and Problems.

PubMed

Mühlhofer, Heinrich M L; Kanz, Karl-Georg; Pohlig, Florian; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; von Eisenhart-Rothe, Ruediger; Schauwecker, Johannes

The outcome of revision surgery in arthroplasty is based on a precise diagnosis. In addition, the treatment varies based on whether the prosthetic failure is caused by aseptic or septic loosening. Algorithms can help to identify periprosthetic joint infections (PJI) and standardize diagnostic steps, however, algorithms tend to oversimplify the treatment of complex cases. We conducted a process analysis during the implementation of a PJI algorithm to determine problems and deviations associated with the implementation of this algorithm. Fifty patients who were treated after implementing a standardized algorithm were monitored retrospectively. Their treatment plans and diagnostic cascades were analyzed for deviations from the implemented algorithm. Each diagnostic procedure was recorded, compared with the algorithm, and evaluated statistically. We detected 52 deviations while treating 50 patients. In 25 cases, no discrepancy was observed. Synovial fluid aspiration was not performed in 31.8% of patients (95% confidence interval [CI], 18.1%-45.6%), while white blood cell counts (WBCs) and neutrophil differentiation were assessed in 54.5% of patients (95% CI, 39.8%-69.3%). We also observed that the prolonged incubation of cultures was not requested in 13.6% of patients (95% CI, 3.5%-23.8%). In seven of 13 cases (63.6%; 95% CI, 35.2%-92.1%), arthroscopic biopsy was performed; 6 arthroscopies were performed in discordance with the algorithm (12%; 95% CI, 3%-21%). Self-critical analysis of diagnostic processes and monitoring of deviations using algorithms are important and could increase the quality of treatment by revealing recurring faults.

Self-stigma among concealable minorities in Hong Kong: conceptualization and unified measurement.

PubMed

Mak, Winnie W S; Cheung, Rebecca Y M

2010-04-01

Self-stigma refers to the internalized stigma that individuals may have toward themselves as a result of their minority status. Not only can self-stigma dampen the mental health of individuals, it can deter them from seeking professional help lest disclosing their minority status lead to being shunned by service providers. No unified instrument has been developed to measure consistently self-stigma that could be applied to different concealable minority groups. The present study presented findings based on 4 studies on the development and validation of the Self-Stigma Scale, conducted in Hong Kong with community samples of mental health consumers, recent immigrants from Mainland China, and sexual minorities. Upon a series of validation procedures, a 9-item Self-Stigma Scale-Short Form was developed. Initial support on its reliability and construct validity (convergent and criterion validities) were found among 3 stigmatized groups. Utility of this unified measure was to establish an empirical basis upon which self-stigma of different concealable minority groups could be assessed under the same dimensions. Health-care professionals could make use of this short scale to assess potential self-stigmatization among concealable minorities, which may hamper their treatment process as well as their overall well-being.

Photoionization and Recombination

NASA Technical Reports Server (NTRS)

Nahar, Sultana N.

2000-01-01

Theoretically self-consistent calculations for photoionization and (e + ion) recombination are described. The same eigenfunction expansion for the ion is employed in coupled channel calculations for both processes, thus ensuring consistency between cross sections and rates. The theoretical treatment of (e + ion) recombination subsumes both the non-resonant recombination ("radiative recombination"), and the resonant recombination ("di-electronic recombination") processes in a unified scheme. In addition to the total, unified recombination rates, level-specific recombination rates and photoionization cross sections are obtained for a large number of atomic levels. Both relativistic Breit-Pauli, and non-relativistic LS coupling, calculations are carried out in the close coupling approximation using the R-matrix method. Although the calculations are computationally intensive, they yield nearly all photoionization and recombination parameters needed for astrophysical photoionization models with higher precision than hitherto possible, estimated at about 10-20% from comparison with experimentally available data (including experimentally derived DR rates). Results are electronically available for over 40 atoms and ions. Photoionization and recombination of He-, and Li-like C and Fe are described for X-ray modeling. The unified method yields total and complete (e+ion) recombination rate coefficients, that can not otherwise be obtained theoretically or experimentally.

SU-E-T-268: Differences in Treatment Plan Quality and Delivery Between Two Commercial Treatment Planning Systems for Volumetric Arc-Based Radiation Therapy

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

Chen, S; Zhang, H; Zhang, B

2015-06-15

Purpose: To clinically evaluate the differences in volumetric modulated arc therapy (VMAT) treatment plan and delivery between two commercial treatment planning systems. Methods: Two commercial VMAT treatment planning systems with different VMAT optimization algorithms and delivery approaches were evaluated. This study included 16 clinical VMAT plans performed with the first system: 2 spine, 4 head and neck (HN), 2 brain, 4 pancreas, and 4 pelvis plans. These 16 plans were then re-optimized with the same number of arcs using the second treatment planning system. Planning goals were invariant between the two systems. Gantry speed, dose rate modulation, MLC modulation, planmore » quality, number of monitor units (MUs), VMAT quality assurance (QA) results, and treatment delivery time were compared between the 2 systems. VMAT QA results were performed using Mapcheck2 and analyzed with gamma analysis (3mm/3% and 2mm/2%). Results: Similar plan quality was achieved with each VMAT optimization algorithm, and the difference in delivery time was minimal. Algorithm 1 achieved planning goals by highly modulating the MLC (total distance traveled by leaves (TL) = 193 cm average over control points per plan), while maintaining a relatively constant dose rate (dose-rate change <100 MU/min). Algorithm 2 involved less MLC modulation (TL = 143 cm per plan), but greater dose-rate modulation (range = 0-600 MU/min). The average number of MUs was 20% less for algorithm 2 (ratio of MUs for algorithms 2 and 1 ranged from 0.5-1). VMAT QA results were similar for all disease sites except HN plans. For HN plans, the average gamma passing rates were 88.5% (2mm/2%) and 96.9% (3mm/3%) for algorithm 1 and 97.9% (2mm/2%) and 99.6% (3mm/3%) for algorithm 2. Conclusion: Both VMAT optimization algorithms achieved comparable plan quality; however, fewer MUs were needed and QA results were more robust for Algorithm 2, which more highly modulated dose rate.« less

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning.

PubMed

Chen, Wei; Craft, David; Madden, Thomas M; Zhang, Kewu; Kooy, Hanne M; Herman, Gabor T

2010-09-01

To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK'S interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

An efficient direct solver for rarefied gas flows with arbitrary statistics

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

Diaz, Manuel A., E-mail: f99543083@ntu.edu.tw; Yang, Jaw-Yen, E-mail: yangjy@iam.ntu.edu.tw; Center of Advanced Study in Theoretical Science, National Taiwan University, Taipei 10167, Taiwan

2016-01-15

A new numerical methodology associated with a unified treatment is presented to solve the Boltzmann–BGK equation of gas dynamics for the classical and quantum gases described by the Bose–Einstein and Fermi–Dirac statistics. Utilizing a class of globally-stiffly-accurate implicit–explicit Runge–Kutta scheme for the temporal evolution, associated with the discrete ordinate method for the quadratures in the momentum space and the weighted essentially non-oscillatory method for the spatial discretization, the proposed scheme is asymptotic-preserving and imposes no non-linear solver or requires the knowledge of fugacity and temperature to capture the flow structures in the hydrodynamic (Euler) limit. The proposed treatment overcomes themore » limitations found in the work by Yang and Muljadi (2011) [33] due to the non-linear nature of quantum relations, and can be applied in studying the dynamics of a gas with internal degrees of freedom with correct values of the ratio of specific heat for the flow regimes for all Knudsen numbers and energy wave lengths. The present methodology is numerically validated with the unified treatment by the one-dimensional shock tube problem and the two-dimensional Riemann problems for gases of arbitrary statistics. Descriptions of ideal quantum gases including rotational degrees of freedom have been successfully achieved under the proposed methodology.« less

Availability of hospital dental care services under sedation or general anesthesia for individuals with special needs in the Unified Health System for the State of Minas Gerais (SUS-MG), Brazil.

PubMed

Santos, Jacqueline Silva; Valle, Déborah Andrade; Palmier, Andréa Clemente; do Amaral, João Henrique Lara; de Abreu, Mauro Henrique Nogueira Guimarães

2015-02-01

This study identified the demographic characteristics of individuals and dental treatment care under sedation/general anesthesia in a hospital environment in the Unified Health System in the State of Minas Gerais (SUS-MG). All Hospitalization Authorizations (AIHs) for Dental Treatment for Patients with Special Needs procedures were evaluated between July 2011 and June 2012. Demographic and health care variables for treatment were also assessed. Hospitalization rates per 10,000 inhabitants, and health care coverage provided in the state of Minas Gerais and in each of the Broader Health Regions were calculated. Descriptive analysis of data was carried out by calculating the central trend and variability frequency and measurements. All 1,063 AIHs paid during the study period were evaluated, which is equivalent to a rate of 0.54 hospitalizations per 10,000 individuals. The majority of the patients were adult, male, diagnosed with mental or behavioral disorders and resident in 27.7% of the municipalities in Minas Gerais. The procedures were performed in 39 municipalities and the care coverage was equal to 1.58%. The study reveals a classic demographic and clinical profile of patient attendance. Difficulties in establishing a network of dental care were identified.

Comparison of the Performance of the Warfarin Pharmacogenetics Algorithms in Patients with Surgery of Heart Valve Replacement and Heart Valvuloplasty.

PubMed

Xu, Hang; Su, Shi; Tang, Wuji; Wei, Meng; Wang, Tao; Wang, Dongjin; Ge, Weihong

2015-09-01

A large number of warfarin pharmacogenetics algorithms have been published. Our research was aimed to evaluate the performance of the selected pharmacogenetic algorithms in patients with surgery of heart valve replacement and heart valvuloplasty during the phase of initial and stable anticoagulation treatment. 10 pharmacogenetic algorithms were selected by searching PubMed. We compared the performance of the selected algorithms in a cohort of 193 patients during the phase of initial and stable anticoagulation therapy. Predicted dose was compared to therapeutic dose by using a predicted dose percentage that falls within 20% threshold of the actual dose (percentage within 20%) and mean absolute error (MAE). The average warfarin dose for patients was 3.05±1.23mg/day for initial treatment and 3.45±1.18mg/day for stable treatment. The percentages of the predicted dose within 20% of the therapeutic dose were 44.0±8.8% and 44.6±9.7% for the initial and stable phases, respectively. The MAEs of the selected algorithms were 0.85±0.18mg/day and 0.93±0.19mg/day, respectively. All algorithms had better performance in the ideal group than in the low dose and high dose groups. The only exception is the Wadelius et al. algorithm, which had better performance in the high dose group. The algorithms had similar performance except for the Wadelius et al. and Miao et al. algorithms, which had poor accuracy in our study cohort. The Gage et al. algorithm had better performance in both phases of initial and stable treatment. Algorithms had relatively higher accuracy in the >50years group of patients on the stable phase. Copyright © 2015 Elsevier Ltd. All rights reserved.

SU-G-JeP2-02: A Unifying Multi-Atlas Approach to Electron Density Mapping Using Multi-Parametric MRI for Radiation Treatment Planning

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

Ren, S; Tianjin University, Tianjin; Hara, W

Purpose: MRI has a number of advantages over CT as a primary modality for radiation treatment planning (RTP). However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. In the work, a reliable method to map electron density is developed by leveraging the differential contrast of multi-parametric MRI. Methods: We propose a probabilistic Bayesian approach for electron density mapping based on T1 and T2-weighted MRI, using multiple patients as atlases. For each voxel, we compute two conditional probabilities: (1) electron density given its image intensity on T1 and T2-weighted MR images, and (2)more » electron density given its geometric location in a reference anatomy. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function using the Bayesian formalism. The mean value of the posterior probability density function provides the estimated electron density. Results: We evaluated the method on 10 head and neck patients and performed leave-one-out cross validation (9 patients as atlases and remaining 1 as test). The proposed method significantly reduced the errors in electron density estimation, with a mean absolute HU error of 138, compared with 193 for the T1-weighted intensity approach and 261 without density correction. For bone detection (HU>200), the proposed method had an accuracy of 84% and a sensitivity of 73% at specificity of 90% (AUC = 87%). In comparison, the AUC for bone detection is 73% and 50% using the intensity approach and without density correction, respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection based on multi-parametric MRI of the head with highly heterogeneous anatomy. This could allow for accurate dose calculation and reference image generation for patient setup in MRI-based radiation treatment planning.« less

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

PubMed Central

Callaert, Dorothée V.; Ribbens, Annemie; Maes, Frederik; Swinnen, Stephan P.; Wenderoth, Nicole

2014-01-01

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods. PMID:25002845

Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

PubMed

Ly, Trang T; Breton, Marc D; Keith-Hynes, Patrick; De Salvo, Daniel; Clinton, Paula; Benassi, Kari; Mize, Benton; Chernavvsky, Daniel; Place, Jéróme; Wilson, Darrell M; Kovatchev, Boris P; Buckingham, Bruce A

2014-08-01

To determine the safety and efficacy of an automated unified safety system (USS) in providing overnight closed-loop (OCL) control in children and adolescents with type 1 diabetes attending diabetes summer camps. The Diabetes Assistant (DIAS) USS used the Dexcom G4 Platinum glucose sensor (Dexcom) and t:slim insulin pump (Tandem Diabetes Care). An initial inpatient study was completed for 12 participants to evaluate safety. For the main camp study, 20 participants with type 1 diabetes were randomized to either OCL or sensor-augmented therapy (control conditions) per night over the course of a 5- to 6-day diabetes camp. Subjects completed 54 OCL nights and 52 control nights. On an intention-to-treat basis, with glucose data analyzed regardless of system status, the median percent time in range, from 70-150 mg/dL, was 62% (29, 87) for OCL nights versus 55% (25, 80) for sensor-augmented pump therapy (P = 0.233). A per-protocol analysis allowed for assessment of algorithm performance. The median percent time in range, from 70-150 mg/dL, was 73% (50, 89) for OCL nights (n = 41) versus 52% (24, 83) for control conditions (n = 39) (P = 0.037). There was less time spent in the hypoglycemic range <50, <60, and <70 mg/dL during OCL compared with the control period (P = 0.019, P = 0.009, and P = 0.023, respectively). The DIAS USS algorithm is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting. © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Robust Hydrological Forecasting for High-resolution Distributed Models Using a Unified Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Reliable real-time hydrological forecasting, to predict important phenomena such as floods, is invaluable to the society. However, modern high-resolution distributed models have faced challenges when dealing with uncertainties that are caused by the large number of parameters and initial state estimations involved. Therefore, to rely on these high-resolution models for critical real-time forecast applications, considerable improvements on the parameter and initial state estimation techniques must be made. In this work we present a unified data assimilation algorithm called Optimized PareTo Inverse Modeling through Inverse STochastic Search (OPTIMISTS) to deal with the challenge of having robust flood forecasting for high-resolution distributed models. This new algorithm combines the advantages of particle filters and variational methods in a unique way to overcome their individual weaknesses. The analysis of candidate particles compares model results with observations in a flexible time frame, and a multi-objective approach is proposed which attempts to simultaneously minimize differences with the observations and departures from the background states by using both Bayesian sampling and non-convex evolutionary optimization. Moreover, the resulting Pareto front is given a probabilistic interpretation through kernel density estimation to create a non-Gaussian distribution of the states. OPTIMISTS was tested on a low-resolution distributed land surface model using VIC (Variable Infiltration Capacity) and on a high-resolution distributed hydrological model using the DHSVM (Distributed Hydrology Soil Vegetation Model). In the tests streamflow observations are assimilated. OPTIMISTS was also compared with a traditional particle filter and a variational method. Results show that our method can reliably produce adequate forecasts and that it is able to outperform those resulting from assimilating the observations using a particle filter or an evolutionary 4D variational method alone. In addition, our method is shown to be efficient in tackling high-resolution applications with robust results.

[Algorithms for treatment of complex hand injuries].

PubMed

Pillukat, T; Prommersberger, K-J

2011-07-01

The primary treatment strongly influences the course and prognosis of hand injuries. Complex injuries which compromise functional recovery are especially challenging. Despite an apparently unlimited number of injury patterns it is possible to develop strategies which facilitate a standardized approach to operative treatment. In this situation algorithms can be important guidelines for a rational approach. The following algorithms have been proven in the treatment of complex injuries of the hand by our own experience. They were modified according to the current literature and refer to prehospital care, emergency room management, basic strategy in general and reconstruction of bone and joints, vessels, nerves, tendons and soft tissue coverage in detail. Algorithms facilitate the treatment of severe hand injuries. Applying simple yes/no decisions complex injury patterns are split into distinct partial problems which can be managed step by step.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

PubMed

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-07-02

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

PubMed Central

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-01-01

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets. PMID:26147731

Efficient threshold for volumetric segmentation

NASA Astrophysics Data System (ADS)

Burdescu, Dumitru D.; Brezovan, Marius; Stanescu, Liana; Stoica Spahiu, Cosmin; Ebanca, Daniel

2015-07-01

Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

Skeletonization and Partitioning of Digital Images Using Discrete Morse Theory.

PubMed

Delgado-Friedrichs, Olaf; Robins, Vanessa; Sheppard, Adrian

2015-03-01

We show how discrete Morse theory provides a rigorous and unifying foundation for defining skeletons and partitions of grayscale digital images. We model a grayscale image as a cubical complex with a real-valued function defined on its vertices (the voxel values). This function is extended to a discrete gradient vector field using the algorithm presented in Robins, Wood, Sheppard TPAMI 33:1646 (2011). In the current paper we define basins (the building blocks of a partition) and segments of the skeleton using the stable and unstable sets associated with critical cells. The natural connection between Morse theory and homology allows us to prove the topological validity of these constructions; for example, that the skeleton is homotopic to the initial object. We simplify the basins and skeletons via Morse-theoretic cancellation of critical cells in the discrete gradient vector field using a strategy informed by persistent homology. Simple working Python code for our algorithms for efficient vector field traversal is included. Example data are taken from micro-CT images of porous materials, an application area where accurate topological models of pore connectivity are vital for fluid-flow modelling.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

PubMed Central

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time. PMID:26270539

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

PubMed

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Real-time blood flow visualization using the graphics processing unit

NASA Astrophysics Data System (ADS)

Yang, Owen; Cuccia, David; Choi, Bernard

2011-01-01

Laser speckle imaging (LSI) is a technique in which coherent light incident on a surface produces a reflected speckle pattern that is related to the underlying movement of optical scatterers, such as red blood cells, indicating blood flow. Image-processing algorithms can be applied to produce speckle flow index (SFI) maps of relative blood flow. We present a novel algorithm that employs the NVIDIA Compute Unified Device Architecture (CUDA) platform to perform laser speckle image processing on the graphics processing unit. Software written in C was integrated with CUDA and integrated into a LabVIEW Virtual Instrument (VI) that is interfaced with a monochrome CCD camera able to acquire high-resolution raw speckle images at nearly 10 fps. With the CUDA code integrated into the LabVIEW VI, the processing and display of SFI images were performed also at ~10 fps. We present three video examples depicting real-time flow imaging during a reactive hyperemia maneuver, with fluid flow through an in vitro phantom, and a demonstration of real-time LSI during laser surgery of a port wine stain birthmark.