Refinement Procedure Research Articles

Coalgebras for Bisimulation of Weighted Automata over Semirings

Weighted automata are a generalization of nondeterministic automata that associate a weight drawn from a semiring $K$ with every transition and every state. Their behaviours can be formalized either as weighted language equivalence or weighted bisimulation. In this paper we explore the properties of weighted automata in the framework of coalgebras over (i) the category $\mathsf{SMod}$ of semimodules over a semiring $K$ and $K$-linear maps, and (ii) the category $\mathsf{Set}$ of sets and maps. We show that the behavioural equivalences defined by the corresponding final coalgebras in these two cases characterize weighted language equivalence and weighted bisimulation, respectively. These results extend earlier work by Bonchi et al. using the category $\mathsf{Vect}$ of vector spaces and linear maps as the underlying model for weighted automata with weights drawn from a field $K$. The key step in our work is generalizing the notions of linear relation and linear bisimulation of Boreale from vector spaces to semimodules using the concept of the kernel of a $K$-linear map in the sense of universal algebra. We also provide an abstract procedure for forward partition refinement for computing weighted language equivalence. Since for weighted automata defined over semirings the problem is undecidable in general, it is guaranteed to halt only in special cases. We provide sufficient conditions for the termination of our procedure. Although the results are similar to those of Bonchi et al., many of our proofs are new, especially those about the coalgebra in $\mathsf{SMod}$ characterizing weighted language equivalence.

An abstraction-refinement framework for verifying strategic properties in multi-agent systems with imperfect information

We investigate the verification of Multi-Agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Furthermore, to deal with cases in which the value of a specification is undefined, we develop a novel automata-theoretic technique for the linear-time logic (LTL), then apply it to finding “failure” states. The latter can then be fed into a refinement procedure, thus providing a sound, albeit incomplete, verification method. We illustrate the overall procedure in a variant of the Train Gate Controller scenario and a simple voting protocol under imperfect information and perfect recall. We also present an implementation of our procedure and provide preliminary experimental results.

Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.

Predicate Abstraction Refinement in Thread-Modular Analysis

Thread-modular approach over predicate abstraction is an efficient technique for software verification of complicated real-world source code. One of the main problems in the technique is a predicate abstraction refinement in a multithreaded case. A default predicate refiner considers only a path related to one thread, and does not refine the thread-modular environment. For instance, if we have applied an effect from the second thread to the current one, then the path in the second thread to the applied effect is not refined. Our goal was to develop a more precise refinement procedure, reusing a default predicate refiner to refine both: a path in a current thread and a path to an effect in the environment. The idea is to construct a joined boolean formula from these two paths. Since some variables may be common, a key challenge is to correctly rename and equate variables in two parts of the formula to accurately represent the way threads interact. It is essential to get reliable predicates that can potentially prove spuriousness of the path. The proposed approach is implemented on top of CPAchecker framework. It is evaluated on standard SV-COMP benchmark set, and the results show some benefit. Evaluation on the real-world software does not demonstrate significant accuracy increase, as the described flaw of predicate refinement is not the only reason of false positive results. While the proposed approach can successfully prove some specific paths to be spurious, it is not enough to fully prove correctness of some programs. However, the approach has further potential for improvements.

Deep Learning Aided State Estimation for Guarded Semi-Markov Switching Systems With Soft Constraints

This paper investigates the problem of state estimation for guarded semi-Markov switching systems with soft constraints. We first construct the switching system in a multiple model manner and derive the recursive Bayesian filter compatible with the sojourn time and base state dependent mode transitions. To solve the intractable conditioned mode transition probabilities, we develop deep learning based classifiers with long short-term memory networks capturing the temporal dependencies. Various network structures are designed to handle different situations where knowledge of the transition probability matrix is available or deficient. Furthermore, we incorporate sequential Monte Carlo techniques into the multiple model framework to resolve the local filtering task. A novel particle refinement procedure exploiting the constraint information is proposed to improve the efficiency of particle propagation and prevent the exponential increase of particle number simultaneously. Simulations on a robotic manipulator and an autonomous vehicle tracking task validate the effectiveness of the proposed state estimation method.

A MOOD-like compact high order finite volume scheme with adaptive mesh refinement

In this paper, a novel Finite Volume (FV) scheme for obtaining high order approximations of solutions of multi-dimensional hyperbolic systems of conservation laws within an Adaptive Mesh Refinement framework is proposed. It is based on a point-wise polynomial reconstruction that avoids the recalculation of reconstruction stencils and matrices whenever a mesh is refined or coarsened. It also couples both the limiting of the FV scheme and the refinement procedure, taking advantage of the Multi-dimensional Optimal Order Detection (MOOD) detection criteria. The resulting computational procedure is employed to simulate test cases of increasing difficulty using two models of Partial Differential Equations: the Euler system and the radiative M1 model, thus demonstrating its efficiency.

A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method

As a part of a damage tolerance assessment, the goal of this research is to estimate the two-dimensional crack propagation trajectory and its accompanying stress intensity factors (SIFs) using the adaptive finite element method. The adaptive finite element code was developed using the Visual Fortran language. The advancing-front method is used to construct an adaptive mesh structure, whereas the singularity is represented through construction of quarter-point single elements around the crack tip. To generate an optimal mesh, an adaptive mesh refinement procedure based on the posteriori norm stress error estimator is used. The splitting node strategy is used to model the fracture, and the trajectory follows the successive linear extensions for every crack increment. The stress intensity factors (SIFs) for each crack extension increment are calculated using the displacement extrapolation technique. The direction of crack propagation is determined using the theory of maximum circumferential stress. The present study is carried out for two geometries, namely a rectangular structure with two holes and one central crack, and a cracked plate with four holes. The results demonstrate that, depending on the position of the hole, the crack propagates in the direction of the hole due to the unequal stresses at the crack tip, which are caused by the hole’s influence. The results are consistent with other numerical investigations for predicting crack propagation trajectories and SIFs.

A Multilevel Spectral Framework for Scalable Vectorless Power/Thermal Integrity Verification

Vectorless integrity verification is becoming increasingly critical to the robust design of nanoscale integrated circuits. This article introduces a general vectorless integrity verification framework that allows computing the worst-case voltage drops or temperature (gradient) distributions across the entire chip under a set of local and global workload (power density) constraints. To address the computational challenges introduced by the large power grids and three-dimensional mesh-structured thermal grids, we propose a novel spectral approach for highly scalable vectorless verification of large chip designs by leveraging a hierarchy of almost linear-sized spectral sparsifiers of input grids that can well retain effective resistances between nodes. As a result, the vectorless integrity verification solution obtained on coarse-level problems can effectively help compute the solution of the original problem. Our approach is based on emerging spectral graph theory and graph signal processing techniques, which consists of a graph topology sparsification and graph coarsening phase, an edge weight scaling phase, as well as a solution refinement procedure. Extensive experimental results show that the proposed vectorless verification framework can efficiently and accurately obtain worst-case scenarios in even very large designs.

Phase evolution, microstructure and electrical response of KNNT ceramics with Li+ substitution and doping

Lead-free (K0.5Na0.5)(Nb0.7Ta0.3)O3 (KNNT) ceramics with Li+ substitution (KN0.5-xLixNT) and direct (KNNT-xLi) doping at x = 0, 0.01, 0.02, 0.03 and 0.04 mol% were synthesized by the solid-state combustion route. The phase, microstructure, dielectric and ferroelectric properties of the ceramics were examined. The XRD pattern of the ceramics revealed orthorhombic and tetragonal phases in all specimens. The Rietveld refinement procedure showed that increasing either the Li+ substitution or doping levels enhanced the amount of the tetragonal phase. It was found that Li+ doping, either substitutional or additional, enhanced the Curie temperature (T C) by increasing the tetragonal distortion, while the dielectric constant (ε C) decreased. The good remanent P-E loops of the KN0.5-xLixNT ceramics were found with x = 0.01 (P r∼10.89 µC/cm2 and E C∼13.09 kV/cm), while for KNNT-xLi ceramics, it was obtained with x = 0.02 (P r∼15.65 µC/cm2 and E C∼11.46 kV/cm), which were confirmed by remanent P-E hysteresis measurements.

A Prediction of Fingertip Force and Joint Kinematics of Individual Fingers using Motoneuron Firing Activities

<h3>Research Objectives</h3> Advanced assistive robotic hands can help individuals with hand impairment to perform daily activities. The detection of intended motor output of individual fingers, however, remains a challenge. The objective of this study was to develop a decoding method to continuously and simultaneously predict fingertip forces and joint kinematics of individual fingers using motoneuron firing frequency. <h3>Design</h3> Blind source separation method was applied to EMG signals to obtain motor unit (MU) firing activities. MU refinement procedure was used to determine finger- and task-specific MU pools. Obtained MU pools were tested on different datasets involving both isometric and dynamic tasks (combined). Populational MU firing frequencies were calculated to estimate the fingertip forces and joint angles concurrently using regression models. Model performance was compared with conventional EMG amplitude-based approach. <h3>Setting</h3> The study was carried out at the NC State/UNC Joint Department of Biomedical Engineering. <h3>Participants</h3> Seven healthy volunteers (Age: 28±7) participated in this study. No participant had any prior neuro-muscular ailments. All participants provided an informed consent. <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measure(s)</h3> Concurrent and continuous prediction of finger kinetics and kinematics via motoneuron activities. <h3>Results</h3> MU approach led to better performance (higher correlation between the predicted and measured output and lower prediction error) in predicting both joint angles and fingertip forces than the EMG amplitude approach. <h3>Conclusion(s)</h3> MU pools can be identified for specific finger-task combinations, which allows concurrent control of fingertip forces and joint angles with assistive robot hands. Separation matrix derived from single finger trials was used applied to combined trials to avoid the computationally intensive nature of HD-EMG decomposition and to determine its usability in real-time. Further development of this method could provide concurrent and continuous control of individual finger movements of highly dexterous robotic hands.

Adaptive mesh refinement in polygonal finite elements using clustering technique

In the adaptive finite element method, the goal is to achieve the desired accuracy with the minimum degrees of freedom. Thus, mesh refinement is required to adjust the mesh size to distribute the error uniformly over the domain. In recent years, research has paid attention to the polygonal elements due to their flexibility for adapting to complex geometries. The most common approach for creating a polygonal mesh is the Voronoi diagram. The main drawback of this approach is the inconsistency of the generated polygonal mesh and the desired mesh density of the adaptive method. This inconsistency is more noticeable in problems where the mesh density changes rapidly in a small region. The adjustment of the size of the elements in polygonal elements is more difficult than triangular or quadrilateral elements because of the greater number of adjacent elements. In the present study, a target error function is defined as the difference of the generated mesh and the desired mesh density in each Gauss point that has been optimized in an iterative algorithm. In the problems where the mesh density changes rapidly, a clustering technique is used to classify different regions of the domain and the Voronoi nuclei of the mesh are located separately. Some additional modification techniques are employed to improve the mesh quality. In problems with cracks, where the Voronoi diagram method has more challenges and some interlocking elements are generated, the problem is modeled without crack initially and then the crack is added to the model and finally the mesh is adapted to the desired mesh density. • An efficient adaptive mesh refinement procedure in polygonal finite elements is presented based on the clustering algorithm. • The difference between the desired and generated mesh sizes over the domain is reduced. • The domain is subdivided into several clusters which have similar mesh densities and seeded separately. • Water wave shifting technique is presented to modify and shift the seeds in the regions with highest size error. • The robustness and accuracy of the new clustering method are proved by comparing with the conventional techniques.

Validating a Novel Device to Improve Skin Color Matching for Face Transplants.

A prospective study of 100 individuals was conducted. All participants were photographed and scanned with the Nix Color Sensor. Sixty pairwise comparisons were randomly generated. Skin color analysis was performed using photographs and the Nix Color Sensor. Delta E2000 values were compared to mean evaluator ratings using a Spearman correlation analysis. One hundred patients were included. A Spearman's correlation demonstrated a strong inverse correlation between Delta E2000 values and the mean evaluator ratings. The higher the mean evaluator rating for likeness, the lower the delta. A correlation coefficient of -0.850 demonstrates a statistically significant relationship (P < 0.01). When the Delta E2000 rises above 5 there is a significant drop in the mean evaluator ratings. As mean evaluator ratings of 5 and above would be considered adequate for face transplant amongst most plastic surgeons, an E2000 value of 5 or lower should be targeted when matching donors with recipients for face transplant. The Nix Color Sensor positively correlates to the plastic surgeon's perception of skin color and can serve as an adjunct in donor selection for facial VCAs.

Iterative Refinement to Improve Data Quality and Label Consistency by Synergizing Parsimony Model Guidance and Physician Interaction

<h3>Purpose/Objective(s)</h3> Clinical data and labeling are usually noisy and uncertain. Modeling based on such inconsistent data risks overfit and may generate faulty insight on prominent features and regression relationships. We hypothesize inter-sample consistency serves as a rational surrogate for data quality and can be used to guide improvement, in the absence of an absolute ground-truth. To this end, we aim to synergize a deep-learning setting and an iterative interactive refinement procedure for label refinement. <h3>Materials/Methods</h3> We proposed a novel approach by guiding physician-approved label perturbation with parsimonious deep network modeling. The proposed procedure alternates between fitting a parsimonious deep network model to human labels and guiding the human observer to review the labels to identify ill-fitted examples to perform label refinement when appropriate. Convergence is claimed when further iteration stops to produce clinically pronounced improvement, as defined from a statistical equivalence test. To evaluate the efficacy of the refinement, input-output agreement index (IOAI) based on partial ranking consistency is also calculated. We took as a use case in segmenting the lumen and vessel wall from MR vessel wall imaging (VWI) from 80 patients with intracranial atherosclerotic disease, on four locations with high likelihood of plaque presence: the intracranial internal carotid artery, the middle cerebral artery, the intracranial vertebral artery, and the basilar artery. Each segment contained 30 contiguous 2D cross-sectional slices with 0.55 mm slice thickness and 0.10 mm in-plane resolution. A lightweight 2.5D segmentation network was used as the low-dimensional model, and equivalence criterion was defined by one-sided superiority threshold of 0.03 in Dice similarity coefficient (DSC) based on reported performance from existing study. Clinical soundness was further assessed with the variation in normalized wall index as a quantitative imaging index, expected to low if segmented structures were piecewise smooth conforming to clinical insight. <h3>Results</h3> 5-fold cross validation based on the 80*4*30 slice samples showed enhanced modeling performance and better conformality to clinical insight with the final labels. In this use case, DSC improved from 0.893±0.108 to 0.938±0.078 for lumen, from 0.806±0.086 to 0.879±0.072 for vessel wall, the total variation in normalized wall index decreased from 0.757±0.181 to 0.586±0.182, and the input-output agreement index increased from 0.523 to 0.556 by the proposed refinement procedure. <h3>Conclusion</h3> This study demonstrates that inconsistency in clinical labels and physiological limitations can be addressed with an iterative interactive process with parsimonious modeling, taking advantage of flexibility from deep networks. The rationale generalizes to other label settings and tasks.

In-depth investigations of size and occupancies in cobalt ferrite nanoparticles by joint Rietveld refinements of X-ray and neutron powder diffraction data.

Powder X-ray diffraction (PXRD) and neutron powder diffraction (NPD) have been used to investigate the crystal structure of CoFe2O4 nanoparticles prepared via different hydro-thermal synthesis routes, with particular attention given to accurately determining the spinel inversion degrees. The study is divided into four parts. In the first part, the investigations focus on the influence of using different diffraction pattern combinations (NPD, Cu-source PXRD and Co-source PXRD) for the structural modelling. It is found that combining PXRD data from a Co source with NPD data offers a robust structural model. The second part of the study evaluates the reproducibility of the employed multipattern Rietveld refinement procedure using different data sets collected on the same sample, as well as on equivalently prepared samples. The refinement procedure gives reproducible results and reveals that the synthesis method is likewise reproducible since only minor differences are noted between the samples. The third part focuses on the structural consequences of (i) the employed heating rate (achieved using three different hydro-thermal reactor types) and (ii) changing the cobalt salt in the precursors [aqueous salt solutions of Co(CH3COOH)2, Co(NO3)2 and CoCl2] in the synthesis. It is found that increasing the heating rate causes a change in the crystal structure (unit cell and crystallite sizes) while the Co/Fe occupancy and magnetic parameters remain similar in all cases. Also, changing the type of cobalt salt does not alter the final crystal/magnetic structure of the CoFe2O4 nanoparticles. The last part of this study is a consideration of the chemicals and parameters used in the synthesis of the different samples. All the presented samples exhibit a similar crystal and magnetic structure, with only minor deviations. It is also evident that the refinement method used played a key role in the description of the sample.

A robust and accurate adaptive approximation method for a diffuse-interface model of binary-fluid flows

We present an adaptive simulation framework for binary-fluid flows, based on the Abels–Garcke–Grün Navier–Stokes–Cahn–Hilliard (AGG NSCH) diffuse-interface model. The adaptive-refinement procedure is guided by a two-level hierarchical a-posteriori error estimate, and it effectively resolves the spatial multiscale behavior of the diffuse-interface model. To improve the robustness of the solution procedure and avoid severe time-step restrictions for small-interface thicknesses, we introduce an ɛ-continuation procedure, in which the diffuse interface thickness (ɛ) are enlarged on coarse meshes, and the mobility is scaled accordingly. To further accelerate the computations and improve robustness, we apply a modified Backward Euler scheme in the initial stages of the adaptive-refinement procedure in each time step, and a Crank–Nicolson scheme in the final stages of the refinement procedure. To enhance the robustness of the nonlinear solution procedure, we introduce a partitioned solution procedure for the linear tangent problems in Newton’s method, based on a decomposition of the NSCH system into its NS and CH subsystems. We conduct a systematic investigation of the conditioning of the monolithic NSCH tangent matrix and of its NS and CH subsystems for a representative 2D model problem. To illustrate the properties of the presented adaptive simulation framework, we present numerical results for a 2D oscillating water droplet suspended in air, and we validate the obtained results versus those of a corresponding sharp-interface model.

Scheduling Disjoint Setups in a Single-Server Permutation Flow Shop Manufacturing Process

In this paper, a manufacturing process for a single-server permutation Flow Shop Scheduling Problem with sequence dependant, disjoint setups and makespan minimization is considered. The full problem is divided into two levels, and the lower level, aimed at finding an optimal order of setups for a given fixed order of jobs, is tackled. The mathematical model of the problem is presented along with a solution representation. Several problem properties pertaining to the problem solution space are formulated. The connection between the number of feasible solutions and the Catalan numbers is demonstrated and a Dynamic Programming-based algorithm for counting feasible solution is proposed. An elimination property is proved, which allows one to disregard up 99.99% of the solution space for instances with 10 jobs and 4 machines. A refinement procedure allowing us to improve the solution in the time required to evaluate it is shown. To illustrate how the properties can be used, two solving methods were proposed: a Mixed-Integer Linear Programming formulation and Tabu Search metaheuristic. The proposed methods were then tested in a computer experiment using a set instance based on Taillard’s benchmark; the results demonstrated their effectiveness even under a short time limit, proving that they could be used to build algorithms for the full problem.

Acquired technology helps KSS launch process for lithium extraction and refinement

To serve as the foundation for the launch of a process comprising a series of specialised separation procedures for lithium extraction and refinement, US-based membrane technology company Koch Separation Solutions Inc (KSS) recently acquired LIONEX technology from Chemionex Inc.

A posteriori error estimation and adaptive mesh refinement for parabolic interface problems using non-conforming immersed finite element method

The purpose of this paper is to study a posteriori error analysis for parabolic interface problems using non-conforming immersed finite element method in a two-dimensional convex polygonal domain. The finite element discretization is such that mesh points need not fit the interface. While the piecewise linear finite elements are employed to approximate the spatial variable, the backward Euler method is used for the time discretization. The basic idea of the immersed finite element method is to modify the basis functions which satisfy the natural jump conditions across the interface. Some new error indicators are introduced to control the error due to non-body fitted mesh. For the adaptive mesh refinement procedure, the residual-based a posteriori error estimates are derived using the energy argument. A global upper and a local lower bounds for the error are established. A space–time adaptive algorithm is provided for the proposed method. Numerical results are reported to illustrate the performance of the derived error indicators.

Object aspect classification and 6DoF pose estimation

• We introduce a simple aspect-representation for 3D objects for 6DoF pose estimation. • This representation is used to cluster appearances of an object as aspects. • Proposed model predicts both the aspect of a given object and the semantic-key points. • Multi-tasking to high confidence key-point vectors hence more accurate results. • Proposed method is significantly faster due to small backbone and limited PnP Space. We present a novel approach to the problem of estimating a given object's 6DoF pose from a single RGB image. Recent works focus on a multi-stage approach, which first detects key-points followed by perspective -n-point pose estimation algorithm and a pose refinement procedure. We show that adding a classifier block estimating the predefined aspects of the objects improves the multi-stage process. This is due to the fact that the additional classifier acts as a constraint simplifying the required neural network and at the same time yielding better key-point selection. We reduce the search space for the key-point selection and exclude false-positives by mapping the appearance of an object to an aspect. The simplified neural network allows faster inference and a smaller footprint. Our experiments show that our hypothesis performs similar to the state-of-the-art on three different datasets. We also show that an off-the-shelf refinement process can further improve our results to surpass state-of-the-art on several objects. Another advantage is, the proposed pipeline can run efficiently on real-time due to the smaller neural network backbone used. The code to replicate this research will be publicly available at https://github.com/greymad/6DoFPoseAspects

Modeling fracture transient flow using the Embedded Discrete Fracture Model with nested local grid refinement

Transient flow around hydraulic fractures is an important topic in the study of tight reservoirs. In this work, the Embedded Discrete Fracture Model (EDFM) is combined with nested local grid refinement (LGR) to improve its accuracy for simulating near-fracture transient flow. An automatic grid refinement procedure is developed to iteratively conduct LGR around complex fractures. In each iteration, the cells intersected by or close to fractures are selected and refined. The size of the refinement region and the level of refinement can be adjusted. Through this process, the cell size in the near-fracture region is reduced rapidly, while the total cell count remains at a relatively low level. An analysis of the optimal refinement ratio for regular Cartesian grids is also conducted. Furthermore, the EDFM is implemented in grids with nested LGR, and connections between the matrix and fractures are properly determined to compute the transmissibility factors as input for reservoir simulators.The developed methodology is first verified against a logarithmic LGR model for different fracture orientations, and the results show that different flow regimes can be reliably simulated. Subsequently, the flow regimes for non-orthogonal, non-parallel, and nonplanar hydraulic fractures are studied with this approach to illustrate the unique patterns of transient flow around complex fractures. This work provides a general and convenient method to perform transient analysis for reservoirs with complex fracture geometries.