Refinement Algorithm Research Articles

A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics.

AimThis study is aimed at developing a novel admixture-adjusted pharmacogenomic approach to individually refine warfarin dosing in Caribbean Hispanic patients.Patients & MethodsA multiple linear regression analysis of effective warfarin doses versus relevant genotypes, admixture, clinical and demographic factors was performed in 255 patients and further validated externally in another cohort of 55 individuals.ResultsThe admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. For patients at the highest risk of adverse events, 45.5% of the dose predictions using the developed pharmacogenetic model resulted in ideal dose as compared with only 29% when using the clinical non-genetic algorithm (p<0.001). The admixture-driven pharmacogenetic algorithm predicted 58% of warfarin dose variance when externally validated in 55 individuals from an independent validation cohort (MAE = 0.89 mg/day, 24% mean bias).ConclusionsResults supported our rationale to incorporate individual’s genotypes and unique admixture metrics into pharmacogenetic refinement models in order to increase predictability when expanding them to admixed populations like Caribbean Hispanics.Trial RegistrationClinicalTrials.gov NCT01318057

Globally Structured Three-Dimensional Analysis-Suitable T-Splines: Definition, Linear Independence and $m$-graded local refinement

This paper addresses the linear independence of T-splines that correspond to refinements of three-dimensional tensor-product meshes. We give an abstract definition of analysis-suitability, and prove that it is equivalent to dual-compatibility, wich guarantees linear independence of the T-spline blending functions. In addition, we present a local refinement algorithm that generates analysis-suitable meshes and has linear computational complexity in terms of the number of marked and generated mesh elements.

Dynamics of a liquid drop in porous medium saturated by another liquid under gravity

The work deals with numerical simulations of settling or ascension process of a liquid drop in porous media saturated by another liquid. The calculations were carried out using the Darcy model by Level set method with adaptive mesh refinement algorithm that dynamically refines computational mesh near interface. It is shown that the drop is unstable and the finger instability develops at the forefront of moving drop for any ratio of the viscosities of liquids. Under modulated pressure gradient small-scale perturbations of interface are suppressed and in the case of modulation with large enough intensity drop becomes stable.

A Proposed Network Balance Index for Heterogeneous Networks

Load balancing and fairness are used in the heterogeneous network literature to describe how the users or user rates are distributed across the network. While quantitative metrics of fairness exist, there is no formal metric for quantifying load balance. In this letter, we demonstrate that a “fair” network may not be a balanced one, since fairness is from a user perspective, while load balance is from a network perspective. We propose a new network balance index (NBI) metric to measure the load balance across the network, which accounts for transmit powers, bias values for cell range expansion, and multiplicatively weighted Voronoi coverage areas of heterogeneous base stations. We show an application of the proposed metric by implementing a user association refinement algorithm, which aims to improve the NBI metric. Our mathematical analysis and simulations establish that when users are heavily clustered, increasing the NBI metric using the proposed algorithm also increases the sum rate despite decreasing fairness. This highlights the usefulness of the proposed NBI metric in network planning.

Conforming Restricted Delaunay Mesh Generation for Piecewise Smooth Complexes

A Frontal-Delaunay refinement algorithm for mesh generation in piecewise smooth domains is described. Built using a restricted Delaunay framework, this new algorithm combines a number of novel features, including: (i) an unweighted, conforming restricted Delaunay representation for domains specified as a (non-manifold) collection of piecewise smooth surface patches and curve segments, (ii) a protection strategy for domains containing curve segments that subtend sharply acute angles, and (iii) a new class of off-centre refinement rules designed to achieve high-quality point-placement along embedded curve features. Experimental comparisons show that the new Frontal-Delaunay algorithm outperforms a classical (statically weighted) restricted Delaunay- refinement technique for a number of three-dimensional benchmark problems.

Automated checkerboard detection and indexing using circular boundaries

This paper presents a new algorithm for automated checkerboard detection and indexing. Automated checkerboard detection is essential for reducing user inputs in any camera calibration process. We adopt an iterative refinement algorithm to extract corner candidates. In order to utilize the characteristics of checkerboard corners, we extract a circular boundary from each candidate and find its sign-changing indices. We initialize an arbitrary point and its neighboring two points as seeds and assign world coordinates to the other points. The largest set of world-coordinate-assigned points is selected as the detected checkerboard. The performance of the proposed algorithm is evaluated using images with various sizes and particular conditions.

A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules.

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macromolecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate alignments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orientations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimensional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large (60S/50S) and the small (40S/30S) subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolution single particle analysis of macromolecular complexes with dynamic conformations.

Localized and Precise Boundary Detection in 3-D Wireless Sensor Networks

This research focuses on distributed and localized algorithms for precise boundary detection in 3-D wireless networks. Our objectives are twofold. First, we aim to identify the nodes on the boundaries of a 3-D network, which serve as a key attribute that characterizes the network, especially in such geographic exploration tasks as terrain and underwater reconnaissance. Second, we construct locally planarized 2-manifold surfaces for inner and outer boundaries in order to enable available graph theory tools to be applied on 3-D surfaces, such as embedding, localization, partition, and greedy routing among many others. To achieve the first objective, we propose a Unit Ball Fitting (UBF) algorithm that discovers a majority of boundary nodes, followed by a refinement algorithm, named Isolated Fragment Filtering (IFF), to remove isolated nodes that are misinterpreted as boundary nodes. Based on the identified boundary nodes, we develop an algorithm that constructs a locally planarized triangular mesh surface for each 3-D boundary. Our proposed scheme is localized, requiring information within 1-hop neighborhood only. We further extend the schemes for online boundary detection in mobile sensor networks aiming to achieve low overhead. Our simulation and experimental results demonstrate that the proposed algorithms can effectively identify boundary nodes and surfaces, even under high measurement errors.

Closing the gap between electron and X-ray crystallography.

The development of a proper refinement algorithm that takes into account dynamical scattering guarantees, for electron crystallography, results approaching X-rays in terms of precision, accuracy and reliability. The combination of such dynamical refinement and electron diffraction tomography establishes a complete pathway for the structure characterization of single sub-micrometric crystals.

A distributed range-free correction vector based localization refinement algorithm

Localization problem is an important and challenging topic in today's wireless sensor networks. In this paper, a novel localization refinement algorithm for LAEP, which is a range-free localization algorithm by using expected hop progress, has been put forward. The proposed localization refinement algorithm, called as CVLR, is based on position correction vectors and can resolve the LAEP's hop-distance ambiguity problem, which can lead to adjacent unknown nodes localized at the same or very close positions. CVLR can make full use of the relative position relationship of 1-hop neighboring nodes (called as CVLR1), or 1-hop and 2-hop neighboring nodes (called as CVLR2), to iteratively refine their localization positions. Furthermore, from localization accuracy and energy dissipation perspective, we optimize the communication process of CVLR2 and propose an energy-efficient improved CVLR. Simulation results show that the localization accuracy of CVLR1, CVLR2, and the improved CVLR are obviously higher than that of LAEP and DV-RND.

A local anisotropic adaptive algorithm for the solution of low-Mach transient combustion problems

A novel numerical algorithm for the simulation of transient combustion problems at low Mach and moderately high Reynolds numbers is presented. These problems are often characterized by the existence of a large disparity of length and time scales, resulting in the development of directional flow features, such as slender jets, boundary layers, mixing layers, or flame fronts. This makes local anisotropic adaptive techniques quite advantageous computationally. In this work we propose a local anisotropic refinement algorithm using, for the spatial discretization, unstructured triangular elements in a finite element framework. For the time integration, the problem is formulated in the context of semi-Lagrangian schemes, introducing the semi-Lagrange-Galerkin (SLG) technique as a better alternative to the classical semi-Lagrangian (SL) interpolation. The good performance of the numerical algorithm is illustrated by solving a canonical laminar combustion problem: the flame/vortex interaction. First, a premixed methane-air flame/vortex interaction with simplified transport and chemistry description (Test I) is considered. Results are found to be in excellent agreement with those in the literature, proving the superior performance of the SLG scheme when compared with the classical SL technique, and the advantage of using anisotropic adaptation instead of uniform meshes or isotropic mesh refinement. As a more realistic example, we then conduct simulations of non-premixed hydrogen-air flame/vortex interactions (Test II) using a more complex combustion model which involves state-of-the-art transport and chemical kinetics. In addition to the analysis of the numerical features, this second example allows us to perform a satisfactory comparison with experimental visualizations taken from the literature.

Two-tier image annotation model based on a multi-label classifier and fuzzy-knowledge representation scheme

Automatic image annotation involves automatically assigning useful keywords to an unlabelled image. The major goal is to bridge the so-called semantic gap between the available image features and the keywords that people might use to annotate images. Although different people will most likely use different words to annotate the same image, most people can use object or scene labels when searching for images.We propose a two-tier annotation model where the first tier corresponds to object-level and the second tier to scene-level annotation. In the first tier, images are annotated with labels of objects present in them, using multi-label classification methods on low-level features extracted from images. Scene-level annotation is performed in the second tier, using the originally developed inference-based algorithms for annotation refinement and for scene recognition. These algorithms use a fuzzy knowledge representation scheme based on Fuzzy Petri Net, KRFPNs, that is defined to enable reasoning with concepts useful for image annotation. To define the elements of the KRFPNs scheme, novel data-driven algorithms for acquisition of fuzzy knowledge are proposed.The proposed image annotation model is evaluated separately on the first and on the second tier using a dataset of outdoor images. The results outperform the published results obtained on the same image collection, both on the object-level and on scene-level annotation. Different subsets of features composed of dominant colours, image moments, and GIST descriptors, as well as different classification methods (RAKEL, ML-kNN and Naïve Bayes), were tested in the first tier. The results of scene level annotation in the second tier are also compared with a common classification method (Naïve Bayes) and have shown superior performance. The proposed model enables the expanding of image annotation with new concepts regardless of their level of abstraction.

Fine-Grained Access Control via XACML Policy Optimization in Cloud Computing

One primary challenge of enforcing access control in cloud computing is how to ensure access with high efficiency while preserving data security. This paper proposes a fine-grained access control method for cloud resources. The basic idea is to use XACML as access control language and to optimize policies by data fragmentation and policy refinement algorithms. Through data fragmentation, the accessible resources are divided into disjoint data blocks, and each of them will be combined with a set of policy rules. This helps to refine the policy and to avoid data leakage caused by rule conflicting on the resource intersections. Finally, the disjoint data blocks and the optimized policy are distributed in the three-layered cloud, and the decision to a request is made by rule matching on a specific resource rather than traversing the whole policy rules. Experiments show that our proposal enjoys higher efficiency in cloud-based access control.

Deformable complex network for refining low-resolution X-ray structures.

In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.

Multicenter Clinical Trials Using 18F-FDG PET to Measure Early Response to Oncologic Therapy: Effects of Injection-to-Acquisition Time Variability on Required Sample Size

Uptake time (interval between tracer injection and image acquisition) affects the SUV measured for tumors in (18)F-FDG PET images. With dissimilar uptake times, changes in tumor SUVs will be under- or overestimated. This study examined the influence of uptake time on tumor response assessment using a virtual clinical trials approach. Tumor kinetic parameters were estimated from dynamic (18)F-FDG PET scans of breast cancer patients and used to simulate time-activity curves for 45-120 min after injection. Five-minute uptake time frames followed 4 scenarios: the first was a standardized static uptake time (the SUV from 60 to 65 min was selected for all scans), the second was uptake times sampled from an academic PET facility with strict adherence to standardization protocols, the third was a distribution similar to scenario 2 but with greater deviation from standards, and the fourth was a mixture of hurried scans (45- to 65-min start of image acquisition) and frequent delays (58- to 115-min uptake time). The proportion of out-of-range scans (<50 or >70 min, or >15-min difference between paired scans) was 0%, 20%, 44%, and 64% for scenarios 1, 2, 3, and 4, respectively. A published SUV correction based on local linearity of uptake-time dependence was applied in a separate analysis. Influence of uptake-time variation was assessed as sensitivity for detecting response (probability of observing a change of ≥30% decrease in (18)F-FDG PET SUV given a true decrease of 40%) and specificity (probability of observing an absolute change of <30% given no true change). Sensitivity was 96% for scenario 1, and ranged from 73% for scenario 4 (95% confidence interval, 70%-76%) to 92% (90%-93%) for scenario 2. Specificity for all scenarios was at least 91%. Single-arm phase II trials required an 8%-115% greater sample size for scenarios 2-4 than for scenario 1. If uptake time is known, SUV correction methods may raise sensitivity to 87%-95% and reduce the sample size increase to less than 27%. Uptake-time deviations from standardized protocols occur frequently, potentially decreasing the performance of (18)F-FDG PET response biomarkers. Correcting SUV for uptake time improves sensitivity, but algorithm refinement is needed. Stricter uptake-time control and effective correction algorithms could improve power and decrease costs for clinical trials using (18)F-FDG PET endpoints.

A multilevel tabu search algorithm for balanced partitioning of unstructured grids

SummaryThis paper presents a multilevel algorithm for balanced partitioning of unstructured grids. The grid is partitioned such that the number of interface elements is minimized and each partition contains an equal number of grid elements. The partition refinement of the proposed multilevel algorithm is based on iterative tabu search procedure. In iterative partition refinement algorithms, tie‐breaking in selection of maximum gain vertices affects the performance considerably. A new tie‐breaking strategy in the iterative tabu search algorithm is proposed that leads to improved partitioning quality. Numerical experiments are carried out on various unstructured grids in order to evaluate the performance of the proposed algorithm. The partition results are compared with those produced by the well‐known partitioning package Metis and k‐means clustering algorithm and shown to be superior in terms of edge cut, partition balance, and partition connectivity. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Adaptive kinetic-fluid solvers for heterogeneous computing architectures

We show feasibility and benefits of porting an adaptive multi-scale kinetic-fluid code to CPU–GPU systems. Challenges are due to the irregular data access for adaptive Cartesian mesh, vast difference of computational cost between kinetic and fluid cells, and desire to evenly load all CPUs and GPUs during grid adaptation and algorithm refinement. Our Unified Flow Solver (UFS) combines Adaptive Mesh Refinement (AMR) with automatic cell-by-cell selection of kinetic or fluid solvers based on continuum breakdown criteria. Using GPUs enables hybrid simulations of mixed rarefied-continuum flows with a million of Boltzmann cells each having a 24×24×24 velocity mesh. We describe the implementation of CUDA kernels for three modules in UFS: the direct Boltzmann solver using the discrete velocity method (DVM), the Direct Simulation Monte Carlo (DSMC) solver, and a mesoscopic solver based on the Lattice Boltzmann Method (LBM), all using adaptive Cartesian mesh. Double digit speedups on single GPU and good scaling for multi-GPUs have been demonstrated.

An Implementation of the Fundamental Parameters Approach for Analysis of X-ray Powder Diffraction Line Profiles.

This work presents an open implementation of the Fundamental Parameters Approach (FPA) models for analysis of X-ray powder diffraction line profiles. The original literature describing these models was examined and code was developed to allow for their use within a Python based least squares refinement algorithm. The NIST interest in the FPA method is specific to its ability to account for the optical aberrations of the powder diffraction experiment allowing for an accurate assessment of lattice parameter values. Lattice parameters are one of the primary certified measurands of NIST Standard Reference Materials (SRMs) for powder diffraction. Lattice parameter values obtained from analysis of data from SRMs 640e and 660c using both the NIST FPA Python code and the proprietary, commercial code Topas, that constitutes the only other actively supported, complete implementation of FPA models within a least-squares data analysis environment, agreed to within 2 fm. This level of agreement demonstrates that both the NIST code and Topas constitute an accurate implementation of published FPA models.

Adaptive mesh refinement for non-isothermal multiphase flows in heterogeneous porous media comprising different rock types with tensor permeability

ABSTRACTThis paper presents an adaptive mesh refinement (AMR) algorithm to solve the non-isothermal multiphase flows in heterogeneous porous media comprising different rock types. To address the discontinuity coming from different rock types, we found that the ratios of relative permeability and oil-gas capillary pressure are continuous; hence they are chosen as the judging values for the regridding purpose. Moreover, because of the tensorial permeability and the complex topology of the AMR grid, a coupling flux solution based on the multipoint flux approximation was carefully designed. Numerical examples revealed the accuracy and efficiency of the proposed AMR algorithm.

Automated source term and wind parameter estimation for atmospheric transport and dispersion applications

Abstract Accurate simulations of the atmospheric transport and dispersion (AT&D) of hazardous airborne materials rely heavily on the source term parameters necessary to characterize the initial release and meteorological conditions that drive the downwind dispersion. In many cases the source parameters are not known and consequently based on rudimentary assumptions. This is particularly true of accidental releases and the intentional releases associated with terrorist incidents. When available, meteorological observations are often not representative of the conditions at the location of the release and the use of these non-representative meteorological conditions can result in significant errors in the hazard assessments downwind of the sensors, even when the other source parameters are accurately characterized. Here, we describe a computationally efficient methodology to characterize both the release source parameters and the low-level winds (eg. winds near the surface) required to produce a refined downwind hazard. This methodology, known as the Variational Iterative Refinement Source Term Estimation (STE) Algorithm (VIRSA), consists of a combination of modeling systems. These systems include a back-trajectory based source inversion method, a forward Gaussian puff dispersion model, a variational refinement algorithm that uses both a simple forward AT&D model that is a surrogate for the more complex Gaussian puff model and a formal adjoint of this surrogate model. The back-trajectory based method is used to calculate a “first guess” source estimate based on the available observations of the airborne contaminant plume and atmospheric conditions. The variational refinement algorithm is then used to iteratively refine the first guess STE parameters and meteorological variables. The algorithm has been evaluated across a wide range of scenarios of varying complexity. It has been shown to improve the source parameters for location by several hundred percent (normalized by the distance from source to the closest sampler), and improve mass estimates by several orders of magnitude. Furthermore, it also has the ability to operate in scenarios with inconsistencies between the wind and airborne contaminant sensor observations and adjust the wind to provide a better match between the hazard prediction and the observations.