Refinement Algorithm Research Articles

Refining modules to determine functionally significant clusters in molecular networks

BackgroundModule detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks.ResultsThe module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks.ConclusionThe proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

інформаційна технологія Дешифрування антропогенних змін на супутникових зображеннях

В роботі запропоновано інформаційну технологію дешифрування антропогенних змін на різночасових супутникових зображеннях високої просторової здатності. Автоматизована технологія включає методи попередньої обробки знімків (підвищення інформативності, корекції), алгоритм уточнення геопривʼязки перед пошуком змін на різночасових зображеннях високого просторового розрізнення, який ґрунтується на основі методу SIFT. Результати досліджень свідчать про те, що застосування інформаційної технології дозволяє виявляти антропогенні зміни на різночасових зображеннях.

A Robust RGB-D SLAM System With Points and Lines for Low Texture Indoor Environments

High-performance 6D pose estimation and dense 3D mapping with an RGB-D camera has recently attracted substantial research attention since this type of camera can simultaneously capture RGB and depth information. However, there is an unresolved problem in estimating pose and generating highly accurate 3D maps from challenging indoor scenes. This paper presents a real-time simultaneous localization and mapping (SLAM) system based on the RGB-D camera for indoor mobile robots. Our contributions are fourfold. First, we propose a complete high-accuracy SLAM system based on a combination of information from points and lines, which differs from most solutions that rely on only point features. Second, we propose a novel pose solver to handle point and line correspondences, in which a line-based inliers refinement (LBIR) algorithm is proposed to remove outliers. Third, we construct a unified optimization model to concurrently minimize point and line reprojection errors, and extend it to the bundle adjustment (BA) method. Fourth, extensive experiments demonstrate the robustness, accuracy, and real-time performance of the proposed system on public TUM datasets and real world scenes. The empirical results show that the proposed system achieves a comparable or better performance than state-of-the-art methods. Notably, our system can operate in nearly texture-less scenes, while other methods are prone to failure.

Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks

The time-variant fatigue reliability of welded joints subjected to stochastic loading is assessed based on the PHI2 method, which takes the time-variant reliability problem as a two-component parallel system reliability problem. The complex fatigue process that takes place at the weld toe is modeled as a semi-elliptical surface crack growth process. Surrogate models, representing surface crack sizes at a time instant, are applied to solve the reliability problem involving time-consuming fatigue crack growth analyses. Polynomial regression models and Kriging interpolation models are both employed. Their corresponding adaptive procedures are also adopted to improve the reliability results. Some adjustments of the active refinement algorithm based on the Kriging model are proposed to make it more suitable for the present study. The time-variant fatigue reliability assessment of a T-plate welded joint is carried out. Without using the adaptive procedures, the Kriging models can better represent the crack sizes compared with the polynomial models. The outcrossing rate formulated by the PHI2 method is sensitive to the reliability index of the first component of the two-component parallel system. The effectiveness of the adaptive procedures, which improve the reliability results, is demonstrated.

Sequential local mesh refinement solver with separate temporal and spatial adaptivity for non-linear two-phase flow problems

Convergence failure and slow convergence rates are among the biggest challenges with solving the system of non-linear equations numerically. Although mitigated, such issues still linger when using strictly small time steps and unconditionally stable fully implicit schemes. The price that comes with restricting time steps to small scales is the enormous computational load, especially in large-scale models. To address this problem, we introduce a sequential local mesh refinement framework of temporal and spatial adaptivity to optimize convergence rate and prevent convergence failure, while not restricting the whole system to small time steps, thus improving computational efficiency. Two types of error estimators are introduced to estimate the spatial discretization error, the temporal discretization error separately. These estimators provide a global upper bounds on the dual norm of the residual and the non-conformity of the numerical solution for non-linear two phase flow models. The mesh refinement algorithm starts from solving the problem on the coarsest space-time mesh, then the mesh is refined sequentially based on the spatial error estimator and the temporal error estimator. After each refinement, the solution from the previous mesh is used to estimate the initial guess of unknowns on the current mesh for faster convergence. Numerical results are presented to confirm accuracy of our algorithm as compared to the uniformly fine time step and fine spatial discretization solution. We observe around 25 times speedup in the solution time by using our algorithm.

Error indicators and refinement strategies for solving Poisson problems through a RBF partition of unity collocation scheme

In this article adaptive refinement algorithms are presented to solve Poisson problems by a radial basis function partition of unity (RBF-PU) collocation scheme. Since in this context the problem of constructing an adaptive discretization method to be really effective is still open, we propose some error indicators and refinement strategies, so that each of these two essential ingredients takes advantage of the potentiality of the other one. More precisely, the refinement techniques coupled with a local error indicator is an ad-hoc strategy for the RBF-PU method. The resulting scheme turns out to be flexible and the use of efficient searching procedures enables us a fast detection of the regions that adaptively need the addition/removal of points. Several numerical experiments and applications support our study by illustrating the performance of our adaptive algorithms.

Iterative refinement for singular value decomposition based on matrix multiplication

We propose a refinement algorithm for singular value decomposition (SVD) of a real matrix. In the same manner as Newton’s method, the proposed algorithm converges quadratically if a modestly accurate initial guess is given. Since the proposed algorithm is based on matrix multiplication, it can efficiently be implemented. Numerical results demonstrate the excellent performance of the proposed algorithm in terms of the convergence rate and the measured computing time compared to a standard approach using multiple precision arithmetic.

Effect of Temperature Gradient on the Grain Size Homogeneity of SEED Produced Semi-Solid Slurries by Phase-Field Simulation.

The distribution homogeneity of grain size affects the fluidity of the semi-solid slurry, which in turn affects the properties of the casting. One key factor affecting grain size uniformity resides in the nucleation number, which has been studied thoroughly, while the other factor is temperature gradient which has not been investigated yet. In this study, the microstructure evolutions under certain temperature gradients are investigated by experiment and simulation using a two-dimensional quantitative phase-field (PF) model. A parallel and adaptive mesh refinement algorithm is adopted to solve the nonlinear phase-field equations. The results indicate that temperature gradient can affect the size distribution of microstructure in the semi-solid slurry prepared by the SEED process. A higher temperature gradient (in the range of 0.230~0.657 °C/mm) along the radial direction is beneficial to the homogeneity of the grain size in a slurry.

Counterexample Guided Abstraction Refinement for Polyhedral Probabilistic Hybrid Systems

We consider the problem of safety analysis of probabilistic hybrid systems, which capture discrete, continuous and probabilistic behaviors. We present a novel counterexample guided abstraction refinement (CEGAR) algorithm for a subclass of probabilistic hybrid systems, called polyhedral probabilistic hybrid systems (PHS), where the continuous dynamics is specified using a polyhedral set within which the derivatives of the continuous executions lie. Developing a CEGAR algorithm for PHS is complex owing to the branching behavior due to the probabilistic transitions, and the infinite state space due to the real-valued variables. We present a practical algorithm by choosing a succinct representation for counterexamples, an efficient validation algorithm and a constructive method for refinement that ensures progress towards the elimination of a spurious abstract counterexample. The technical details for refinement are non-trivial since there are no clear disjoint sets for separation. We have implemented our algorithm in a Python toolbox called Procegar; our experimental analysis demonstrates the benefits of our method in terms of successful verification results, as well as bug finding.

Phase-field lattice-Boltzmann study on eutectic growth with coupled heat and solute diffusion

The thermosolutal interaction influences eutectic evolution and thus properties of solidified materials. Limited by numerical capability, however, very few studies are performed on the eutectic growth with coupled heat and solute diffusion. Combining the phase-field lattice-Boltzmann approach and a parallel-adaptive mesh refinement algorithm, a novel numerical scheme is developed to efficiently simulate the thermosolutal multiphase eutectic evolution. The contact angles at the triple point agree well with the analytical solution, and the temperature always obtains the local extreme at the solid/liquid interface due to the release of latent heat. The effects of the Lewis number and imposed heat sink on the eutectic evolution are discussed in detail, which includes the change of the lamellar growth velocity and the form of lamellae creation. The dimension is further extended to 3D to investigate the evolution of plate- and rod-like eutectics. How to simulate eutectic evolution with a larger Lewis number (e.g., 106) and how to develop a more general eutectic model are also discussed. As the first attempt to solve the thermosolutal eutectic evolution, our investigation paves a way for further quantitative analysis and direct comparison with experiments.

Technique of Informative Features Selection in Geoacoustic Emission Signals

Studies of geoacoustic emission in a seismically active region in Kamchatka show that geoacoustic signals produce pronounced pulse anomalies during the earthquake preparation and post-seismic relaxation of the local stresses field at the observation point. The qualitative selection of such anomalies is complicated by a strong distortion and weakening of the signal amplitude. A review of existing acoustic emission analysis methods shows that most often researchers turn to the analysis of more accessible to study statistical properties and energy of signals. The distinctive features of the approach proposed by the authors are the extraction of informative features based on the analysis of time and frequency-time structures of geoacoustic signals and the description of various forms of recognizable pulses by a limited pattern set. This study opens up new ideas to develop methods for detecting anomalous behavior of geoacoustic signals, including anomalies before earthquakes.
 The paper describes a technique of information extraction from geoacoustic emission pulse streams of sound frequency range. A geoacoustic pulse mathematical model, reflecting the signal generation process from a variety of elementary sources, is presented. A solution to the problem of detection of geoacoustic signal informative features is presented by the means of description of signal fragments by the matrixes of local extrema amplitude ratios and of interval ratios between them. The result of applying the developed algorithm to describe automatically the structure of the detected pulses and to form a pattern set is shown. The patterns characterize the features of geoacoustic emission signals observed at IKIR FEB RAS field stations. A technique of reduction of the detected pulse set dimensions is presented. It allows us to find patterns similar in structure. A solution to the problem of processing of a large data flow by unifying pulses description and their systematization is proposed. A method to identify a geoacoustic emission pulse model using sparse approximation schemes is suggested. An algorithmic solution of the problem of reducing the computational complexity of the matching pursuit method is described. It is to include an iterative refinement algorithm for the solution at each step in the method. The results of the research allowed the authors to create a tool to investigate the dynamic properties of geoacoustic emission signal in order to develop earthquake prediction detectors.

Autonomous Resource Provisioning and Resource Customization for Mixed Traffics in Virtualized Radio Access Network

Network slicing has been introduced in fifth-generation (5G) systems to satisfy requirements of diverse applications from various service providers operating on a common shared infrastructure. However, heterogeneous characteristics of slices have not been widely explored. In this paper, we investigate dynamic network slicing strategies with mixed traffics in virtualized radio access network (RAN). Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed. Then, a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction. In principle, the DQN algorithm allocates only the necessary resource to slices to satisfy users’ QoS requirements. For fairness in comparison, we reserve all the unused resources back to the slices. In case there is a sudden change in user population in one slice, the algorithm provides isolation. To validate the advantage, system-level simulations are conducted. The results show that the proposed algorithm balances the satisfaction up to about 100% and resource utilization up to 80% against state-of-the-art solutions. The proposed algorithm also improves the performance of slices in mixed traffics against state-of-the-art benchmarks, which fail to balance satisfaction and resource utilization in some slices.

Mesh adaptivity for quasi‐static phase‐field fractures based on a residual‐type a posteriori error estimator

In this work, we consider adaptive mesh refinement for a monolithic phase‐field description for fractures in brittle materials. Our approach is based on an a posteriori error estimator for the phase‐field variational inequality realizing the fracture irreversibility constraint. The key goal is the development of a reliable and efficient residual‐type error estimator for the phase‐field fracture model in each time‐step. Based on this error estimator, error indicators for local mesh adaptivity are extracted. The proposed estimator is based on a technique known for singularly perturbed equations in combination with estimators for variational inequalities. These theoretical developments are used to formulate an adaptive mesh refinement algorithm. For the numerical solution, the fracture irreversibility is imposed using a Lagrange multiplier. The resulting saddle‐point system has three unknowns: displacements, phase‐field, and a Lagrange multiplier for the crack irreversibility. Several numerical experiments demonstrate our theoretical findings with the newly developed estimators and the corresponding refinement strategy.

A multi-level adaptive mesh refinement for an integrated finite element/level set formulation to simulate multiphase flows with surface tension

A multi-level adaptive mesh refinement technique for an integrated finite element/level set formulation was implemented to simulate the incompressible interfacial flows with surface tension by using unstructured P2P1 triangular and tetrahedral meshes. Linear/quadratic elements near the interface were dynamically refined as an interface evolves in order to produce an accurate solution with a lower computational cost. An adaptive mesh refinement algorithm for a linear element has been extended to a quadratic element by using a node-reordering. The proposed algorithm was successfully validated by solving several benchmark problems. Simulation of a static bubble problem was found to give an accurate solution with negligible spurious currents compared with existing results. Simulation results of rising bubble problems in 2D and 3D were also compared against existing numerical and experimental results in terms of terminal rising speed and terminal bubble shape. We have found that the present algorithm provided stable and accurate solutions even for a high density-ratio and that the solution accuracy strongly depended on the mesh resolution near the interface. Further, the present algorithm was shown to be very efficient because the computational overhead by mesh-refinement algorithm was very small compared to the elapsed times consumed for solving the incompressible Navier–Stokes equations and the advection/re-initialization equations of level set method. Lastly, the coalescence scenario of two oblique bubbles was well reproduced by the present 3D simulation.

Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease.

Accurately predicting and testing the types of Pulmonary arterial hypertension (PAH) of each patient using cost-effective microarray-based expression data and machine learning algorithms could greatly help either identifying the most targeting medicine or adopting other therapeutic measures that could correct/restore defective genetic signaling at the early stage. Furthermore, the prediction model construction processes can also help identifying highly informative genes controlling PAH, leading to enhanced understanding of the disease etiology and molecular pathways. In this study, we used several different gene filtering methods based on microarray expression data obtained from a high-quality patient PAH dataset. Following that, we proposed a novel feature selection and refinement algorithm in conjunction with well-known machine learning methods to identify a small set of highly informative genes. Results indicated that clusters of small-expression genes could be extremely informative at predicting and differentiating different forms of PAH. Additionally, our proposed novel feature refinement algorithm could lead to significant enhancement in model performance. To summarize, integrated with state-of-the-art machine learning and novel feature refining algorithms, the most accurate models could provide near-perfect classification accuracies using very few (close to ten) low-expression genes.

VOFTools 3.2: Added VOF functionality to initialize the liquid volume fraction in general convex cells

VOFTools 3.2: Added VOF functionality to initialize the liquid volume fraction in general convex cells

Predictive analytics in health care: how can we know it works?

There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on “black box” machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms.

Hierarchical Adaptive Control System of a Manipulator Based on the Synthesis of a Neural Network of Fuzzy Inference and an Iterative Refinement Algorithm

The main aim of this work is to develop an adaptive control system for a kinematically redundant multilink industrial manipulator. Proposed solution allows to construct a unified real-time control system with the ability to control the accuracy of calculations. In order to achieve the required accuracy of the calculations and the performance of the control system, we propose an algorithm that is based on the so-called hybrid method for finding the solution of the inverse kinematics (IK) problem, including the adaptive neural network and fuzzy inference system with subsequent iterative refinement of numerical solution by the Newton --- Raphson method. The influence of the training sample size on the quality of the obtained initial approximation for the neural network part of the algorithm is described in the paper. The results of experimental studies of the developed hybrid algorithm are presented in comparison with the iterative and neural network methods for three-, five- and eight-link manipulator structures. Paper presents the main steps of the control system synthesis for kinematically redundant industrial manipulator, including the description for developed algorithms for finding an IK solution of multilink structures. The structure of a multi-level hierarchical manipulator control system, based on a programmable logic controller and electric stepping motors with the possibility of integration into the production system at various levels, is presented.

Decoding Chinese User Generated Categories for Fine-Grained Knowledge Harvesting

User Generated Categories (UGCs) are short but informative phrases that reflect how people describe and organize entities. UGCs express semantic relations among entities implicitly hence serve as a rich data source for knowledge harvesting. However, most UGC relation extraction methods focus on English and heavily rely on lexical and syntactic patterns. Applying them directly to Chinese UGCs poses significant challenges because Chinese is an analytic language with flexible language expressions. In this paper, we aim at harvesting fine-grained relations from Chinese UGCs automatically. Based on neural networks and negative sampling, we introduce two word embedding projection models to identify is-a relations. The accuracy of prediction results is improved via a collective refinement algorithm and a hypernym expansion method. We further propose a graph clique mining algorithm to harvest non-taxonomic relations from UGCs, together with their textual patterns. Two experiments are conducted to validate our approach based on Chinese Wikipedia. The first experiment verifies the is-a relation extraction approach achieves high accuracy, outperforming state-of-the-art methods. The second experiment shows that the proposed method can harvest non-taxonomic relations of large quantity and high accuracy, with minimal human intervention.

Multi-frequency super-scattering from sub-wavelength graphene-coated nanotubes

A cylindrical multilayered structure with three coupled graphene shells is shown to behave as a dual-band super-scatterer at mid-infrared frequencies. Under the 2D plasmonic nature of graphene material, multiple scattering resonances are attained in the deep subwavelength regime using an isotropic material. In the proposed structure, we efficiently exploit the existing three plasmonic resonances in each scattering channel. These resonances are tailored by dispersion engineering, which is simply displayed through the Bohr model applied to the associated planar structure. For the super-scatterer design, it is essential that the resonances of multiple channels coincide in a single frequency. Here, for the dual-band performance, the second and third dipole resonances coincide with the first and second quadruple resonances, respectively. A refinement algorithm is developed to obtain the final design parameters. Our strategy offers various degrees of freedom to be tuned in order to control the target working frequency. Our proposed structures have potential applications in the field of imaging, cloaking, biomedicine, solar cells, and optical antennas with improved sensitivity and accuracy due to multiband operation.