Generic Polynomials with Few Parameters

We call a polynomial g(t₁, . . . , t_m,X ) over a field K generic for a group G if it has Galois group G as a polynomial inX , and if every Galois field extension N / L withK L and Gal(N / L) ≤ G arises as the splitting field of a suitable specializationg (λ₁, . . . , λ_m, X) withλ_i L. We discuss how the rationality of the invariant field of a faithful linear representation leads to a generic polynomial which is often particularly simple and therefore useful. Then we consider various examples and applications in characteristic 0 and in positive characteristic. These include results on so-called vectorial polynomials and a generalization of an embedding criterion given by Abhyankar. We give recursive formulas for generic polynomials over a field of defining characteristic for the groups of upper unipotent and upper triangular matrices, and explicit formulae for generic polynomials for the groups GU₂(q²) andGO₃ (q).     

The calculus of context relations

We present the theory of context relations. Context relations are a method for incremental semantic analysis in language-specific editors, which is able to handle incomplete program fragments. The algorithm is generated from the definition of a language's static semantics and is based on inference rules and order-sorted unification. The paper presents the underlying mathematical theory, optimal incremental analysis algorithms, handling of user-defined polymorphism and overloading, and implementation issues. It is intended as the concluding report on a by now mature concept, which has successfully been used to generate efficient incremental type inferencers for languages like ADA and Fortran 8x.     

A Bayesian approach to forecasting daily air-pollutant levels

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed into ordinal or binary predictions given a set of thresholds. We develop a Bayesian approach to forecasting PM\(_{10}\) and O\(_3\) levels that efficiently deals with extensive amounts of input parameters, and test whether it outperforms classical models and experts. The new approach is used to fit models for PM\(_{10}\) and O\(_3\) level forecasting that can be used in daily practice. We also introduce a novel approach for comparing models to experts based on estimated cost matrices. The results for diverse air quality monitoring sites across Slovenia show that Bayesian models outperform classical models in both PM\(_{10}\) and O\(_3\) predictions. The proposed models perform better than experts in PM\(_{10}\) and are on par with experts in O\(_3\) predictions—where experts already base their predictions on predictions from a statistical model. A Bayesian approach—especially using Gaussian processes—offers several advantages: superior performance, robustness to overfitting, more information, and the ability to efficiently adapt to different cost matrices.     

The Nonconvergence of h-Refinement in Prolate Elements

Prolate elements are a “plug-compatible” modification of spectral elements in which Legendre polynomials are replaced by prolate spheroidal wave functions of order zero. Prolate functions contain a“bandwidth parameter” $c \ge 0 $ c ≥ 0 whose value is crucial to numerical performance; the prolate functions reduce to Legendre polynomials for $c\,=\,0$ c = 0 . We show that the optimal bandwidth parameter $c$ c not only depends on the number of prolate modes per element $N$ N , but also on the element widths $h$ h . We prove that prolate elements lack $h$ h -convergence for fixed $c$ c in the sense that the error does not go to zero as the element size $h$ h is made smaller and smaller. Furthermore, the theoretical predictions that Chebyshev and Legendre polynomials require $\pi $ π degrees of freedom per wavelength to resolve sinusoidal functions while prolate series need only 2 degrees of freedom per wavelength are asymptotic limits as $N \rightarrow \infty $ N → ∞ ; we investigate the rather different behavior when $N \sim O(4-10)$ N ～ O ( 4 ? 10 ) as appropriate for spectral elements and prolate elements. On the other hand, our investigations show that there are certain combinations of $N,\,h$ N , h and $c>0$ c > 0 where a prolate basis clearly outperforms the Legendre polynomial approximation.     

Approximate Symmetry Detection in Partial 3D Meshes

Symmetry is a common characteristic in natural and man‐made objects. Its ubiquitous nature can be exploited to facilitate the analysis and processing of computational representations of real objects. In particular, in computer graphics, the detection of symmetries in 3D geometry has enabled a number of applications in modeling and reconstruction. However, the problem of symmetry detection in incomplete geometry remains a challenging task. In this paper, we propose a vote‐based approach to detect symmetry in 3D shapes, with special interest in models with large missing parts. Our algorithm generates a set of candidate symmetries by matching local maxima of a surface function based on the heat diffusion in local domains, which guarantee robustness to missing data. In order to deal with local perturbations, we propose a multi‐scale surface function that is useful to select a set of distinctive points over which the approximate symmetries are defined. In addition, we introduce a vote‐based scheme that is aware of the partiality, and therefore reduces the number of false positive votes for the candidate symmetries. We show the effectiveness of our method in a varied set of 3D shapes and different levels of partiality. Furthermore, we show the applicability of our algorithm in the repair and completion of challenging reassembled objects in the context of cultural heritage.     

Flow-sensitive, context-sensitive, and object-sensitive information flow control based on program dependence graphs

Information flow control (IFC) checks whether a program can leak secret data to public ports, or whether critical computations can be influenced from outside. But many IFC analyses are imprecise, as they are flow-insensitive, context-insensitive, or object-insensitive; resulting in false alarms. We argue that IFC must better exploit modern program analysis technology, and present an approach based on program dependence graphs (PDG). PDGs have been developed over the last 20 years as a standard device to represent information flow in a program, and today can handle realistic programs. In particular, our dependence graph generator for full Java bytecode is used as the basis for an IFC implementation which is more precise and needs less annotations than traditional approaches. We explain PDGs for sequential and multi-threaded programs, and explain precision gains due to flow-, context-, and object-sensitivity. We then augment PDGs with a lattice of security levels and introduce the flow equations for IFC. We describe algorithms for flow computation in detail and prove their correctness. We then extend flow equations to handle declassification, and prove that our algorithm respects monotonicity of release. Finally, examples demonstrate that our implementation can check realistic sequential programs in full Java bytecode.     

A comparative study of stochastic optimization methods in electric motor design

The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs and improving them according to their experience. However, the existence of reliable numerical simulators and powerful stochastic optimization techniques make it possible to automate the design procedure. We present a comparative study of six stochastic optimization algorithms in designing optimal rotor and stator geometries of a universal electric motor where the primary objective is to minimize the motor power losses. We compare three methods from the domain of evolutionary computation, generational evolutionary algorithm, steady-state evolutionary algorithm and differential evolution, two particle-based methods, particle-swarm optimization and electromagnetism-like algorithm, and a recently proposed multilevel ant stigmergy algorithm. By comparing their performance, the most efficient method for solving the problem is identified and an explanation of its success is offered.     

An empirical examination of application frameworks success based on technology acceptance model

Framework-based development is currently regarded as one of the most promising software development approaches when it comes to improvements in lead time, productivity and quality. However, many frameworks and projects based on frameworks still report failures, which indicate that there are problems related to both frameworks technology and frameworks usage. The objective of our research was to examine the major drivers that have an impact on a framework’s acceptance and a framework’s success. We used the technology acceptance model (TAM) and Seddon’s information systems success model as our underlying theory. Data collected from an online survey of 389 active framework users was used to test hypothesized models. Data analysis was performed via structural equation modeling. Our findings support the post-adoption version of TAM and the relationship between continuous use and the successful use of systems, where more use also means more net benefits. We found that the successful use of frameworks is mainly dependent on two factors: continuous framework usage intention and the perceived usefulness of the framework. Several practical and theoretical implications can be foreseen including advances in framework development guidelines and insight into the relationship between the acceptance and success of frameworks.     

Recommendations for tool-handle material choice based on finite element analysis

Huge areas of work are still done manually and require the usages of different powered and non-powered hand tools. In order to increase the user performance, satisfaction, and lower the risk of acute and cumulative trauma disorders, several researchers have investigated the sizes and shapes of tool-handles. However, only a few authors have investigated tool-handles' materials for further optimising them. Therefore, as presented in this paper, we have utilised a finite-element method for simulating human fingertip whilst grasping tool-handles. We modelled and simulated steel and ethylene propylene diene monomer (EPDM) rubber as homogeneous tool-handle materials and two composites consisting of EPDM rubber and EPDM foam, and also EPDM rubber and PU foam. The simulated finger force was set to obtain characteristic contact pressures of 20 kPa, 40 kPa, 80 kPa, and 100 kPa. Numerical tests have shown that EPDM rubber lowers the contact pressure just slightly. On the other hand, both composites showed significant reduction in contact pressure that could lower the risks of acute and cumulative trauma disorders which are pressure-dependent. Based on the results, it is also evident that a composite containing PU foam with a more evident and flat plateau deformed less at lower strain rates and deformed more when the plateau was reached, in comparison to the composite with EPDM foam. It was shown that hyper-elastic foam materials, which take into account the non-linear behaviour of fingertip soft tissue, can lower the contact pressure whilst maintaining low deformation rate of the tool-handle material for maintaining sufficient rate of stability of the hand tool in the hands. Lower contact pressure also lowers the risk of acute and cumulative trauma disorders, and increases comfort whilst maintaining performance.