Diagnostic performance of edited 2HG MR spectroscopy of central glioma in the clinical environment

Magnetic Resonance Materials in Physics, Biology and Medicine - Oncometabolite D-2-hydroxyglutarate (2HG) is pooled in isocitrate dehydrogenase (IDH)-mutant glioma cells. Detecting 2HG by MR...     

The tetrahedral finite cell method: Higher‐order immersogeometric analysis on adaptive non‐boundary‐fitted meshes

The finite cell method (FCM) is an immersed domain finite element method that combines higher‐order non‐boundary‐fitted meshes, weak enforcement of Dirichlet boundary conditions, and adaptive quadrature based on recursive subdivision. Because of its ability to improve the geometric resolution of intersected elements, it can be characterized as an immersogeometric method. In this paper, we extend the FCM, so far only used with Cartesian hexahedral elements, to higher‐order non‐boundary‐fitted tetrahedral meshes, based on a reformulation of the octree‐based subdivision algorithm for tetrahedral elements. We show that the resulting TetFCM scheme is fully accurate in an immersogeometric sense, that is, the solution fields achieve optimal and exponential rates of convergence for h‐refinement and p‐refinement, if the immersed geometry is resolved with sufficient accuracy. TetFCM can leverage the natural ability of tetrahedral elements for local mesh refinement in three dimensions. Its suitability for problems with sharp gradients and highly localized features is illustrated by the immersogeometric phase‐field fracture analysis of a human femur bone. Copyright © 2016 John Wiley & Sons, Ltd.     

Attribute selection with fuzzy decision reducts

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.     

On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

Crystal: Integrating Structured Queries into a Tactic Language

We present the language CRStL (Control Rule Strategy Language, pronounce “crystal”) to formulate mathematical reasoning techniques as proof strategies in the context of the proof assistant Ωmega. The language is arranged in two levels, a query language to access mathematical knowledge maintained in development graphs, and a strategy language to annotate the results of these queries with further control information. The two-leveled structure of the language allows the specification of proof techniques in a declarative way. We present the syntax and semantics of CRStL and illustrate its use by examples.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.     

An unfitted hp-adaptive finite element method based on hierarchical B-splines for interface problems of complex geometry

Generating finite element discretizations with direct interface parameterizations constitutes a considerable computational expense in case of complex interface geometries. The paper at hand introduces a B-spline finite element method, which circumvents parameterization of interfaces and offers fast and easy meshing irrespective of the geometric complexity involved. Its core idea is the adaptive approximation of discontinuities by hierarchical grid refinement, which adds several levels of local basis functions in the close vicinity of interfaces, but unfitted to their exact location, so that a simple regular grid of knot span elements can be maintained. Numerical experiments show that an hp-refinement strategy, which simultaneously increases the polynomial degree of the B-spline basis and the levels of refinement around interfaces, achieves exponential rates of convergence despite the presence of discontinuities. It is also demonstrated that the hierarchical B-spline FEM can be used to transfer the recently introduced Finite Cell concept to geometrically nonlinear problems. Its computational performance, imposition of unfitted boundary conditions and fast hierarchical grid generation are illustrated for a set of benchmark problems in one, two and three dimensions, and the advantages of the regular grid approach for complex geometries are demonstrated by the geometrically nonlinear simulation of a voxel based foam composite.     

PF-MPC: Particle filter-model predictive control

In this article, a new model predictive control approach to nonlinear stochastic systems will be presented. The new approach is based on particle filters, which are usually used for estimating states or parameters. Here, two particle filters will be combined, the first one giving an estimate for the actual state based on the actual output of the system; the second one gives an estimate of a control input for the system. This is basically done by adopting the basic model predictive control strategies for the second particle filter. Later in this paper, this new approach is applied to a CSTR (continuous stirred-tank reactor) example and to the inverted pendulum. These two examples show that our approach is also real-time-capable.