Non-deterministic exponential time has two-prover interactive protocols

We determine the exact power of two-prover interactive proof systems introduced by Ben-Or, Goldwasser, Kilian, and Wigderson (1988). In this system, two all-powerful noncommunicating provers convince a randomizing polynomial time verifier in polynomial time that the inputx belongs to the languageL. We show that the class of languages having tow-prover interactive proof systems is nondeterministic exponential time.We also show that to prove membership in languages inEXP, the honest provers need the power ofEXP only.The first part of the proof of the main result extends recent techniques of polynomial extrapolation used in the single prover case by Lund, Fortnow, Karloff, Nisan, and Shamir.The second part is averification scheme for multilinearity of a function in several variables held by an oracle and can be viewed as an independent result onprogram verification. Its proof rests on combinatorial techniques employing a simple isoperimetric inequality for certain graphs:     

Non-functional properties in the model-driven development of service-oriented systems

Systems based on the service-oriented architecture (SOA) principles have become an important cornerstone of the development of enterprise-scale software applications. They are characterized by separating functions into distinct software units, called services, which can be published, requested and dynamically combined in the production of business applications. Service-oriented systems (SOSs) promise high flexibility, improved maintainability, and simple re-use of functionality. Achieving these properties requires an understanding not only of the individual artifacts of the system but also their integration. In this context, non-functional aspects play an important role and should be analyzed and modeled as early as possible in the development cycle. In this paper, we discuss modeling of non-functional aspects of service-oriented systems, and the use of these models for analysis and deployment. Our contribution in this paper is threefold. First, we show how services and service compositions may be modeled in UML by using a profile for SOA (UML4SOA) and how non-functional properties of service-oriented systems can be represented using the non-functional extension of UML4SOA (UML4SOA-NFP) and the MARTE profile. This enables modeling of performance, security and reliable messaging. Second, we discuss formal analysis of models which respect this design, in particular we consider performance estimates and reliability analysis using the stochastically timed process algebra PEPA as the underlying analytical engine. Last but not least, our models are the source for the application of deployment mechanisms which comprise model-to-model and model-to-text transformations implemented in the framework VIATRA. All techniques presented in this work are illustrated by a running example from an eUniversity case study.     

General distributed economic framework for dynamic spectrum allocation

The emergence of novel radio techniques enables the application of advantageous revolutionary spectrum policies. An important body of research has appeared about possible frequency management schemes, but none of them proposes solutions that meet every related criteria. In this paper we present our work on dynamic spectrum allocation and pricing that offers a distributed mechanism design, well-suited to practical employment issues. Our model handles interference effects without any restricting assumptions, provides universal scalable and incentive-compatible allocation and pricing mechanisms. We provide both analytical and numerical evaluation of the proposed framework, and in either case we prove this latter to be a suitable approach to efficient and flexible spectrum utilization.     

The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary

The main aim of this paper is to predict NO and NO₂ concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO₂ concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged, in order to build a model for predicting NO and NO₂ concentrations several hours in advance. The prediction of NO and NO₂ concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO₂. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, multi-layer perceptron and support vector regression are used to provide efficient non-linear models for NO and NO₂ time series predictions. Multi-layer perceptron is widely used to predict these time series, but support vector regression has not yet been applied for predicting NO and NO₂ concentrations. Three commonly used linear algorithms were considered as references: 1-day persistence, average of several day persistence and linear regression. Based on the good results of the average of several day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO₂, the improvement of the prediction is considerable, however, it is less notable than for NO.     

Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification

This paper examines the applicability of some learning techniques to the classification of phonemes. The methods tested were artificial neural nets (ANN), support vector machines (SVM) and Gaussian mixture modeling (GMM). We compare these methods with a traditional hidden Markov phoneme model (HMM), working with the linear prediction-based cepstral coefficient features (LPCC). We also tried to combine the learners with linear/nonlinear and unsupervised/supervised feature space transformation methods such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), springy discriminant analysis (SDA) and their nonlinear kernel-based counterparts. We found that the discriminative learners can attain the efficiency of HMM, and that after the transformations they can retain the same performance in spite of the severe dimension reduction. The kernel-based transformations brought only marginal improvements compared to their linear counterparts.     

Influence of inhomogeneous static magnetic field-exposure on patients with erosive gastritis: a randomized,self- and placebo-controlled,double-blind,single centre,pilot study

This pilot study was devoted to the effect of static magnetic field (SMF)-exposure on erosive gastritis. The randomized, self- and placebo-controlled, double-blind, pilot study included 16 patients of the 2nd Department of Internal Medicine, Semmelweis University diagnosed with erosive gastritis. The instrumental analysis followed a qualitative (pre-intervention) assessment of the symptoms by the patient: lower heartburn (in the ventricle), upper heartburn (in the oesophagus), epigastric pain, regurgitation, bloating and dry cough. Medical diagnosis included a double-line upper panendoscopy followed by 30 min local inhomogeneous SMF-exposure intervention at the lower sternal region over the stomach with peak-to-peak magnetic induction of 3 mT and 30 mT m⁻¹ gradient at the target site. A qualitative (post-intervention) assessment of the same symptoms closed the examination. Sham- or SMF-exposure was used in a double-blind manner. The authors succeeded in justifying the clinically and statistically significant beneficial effect of the SMF- over sham-exposure on the symptoms of erosive gastritis, the average effect of inhibition was 56% by p = 0.001, n = 42 + 96. This pilot study was aimed to encourage gastroenterologists to test local, inhomogeneous SMF-exposure on erosive gastritis patients, so this intervention may become an evidence-based alternative or complementary method in the clinical use especially in cases when conventional therapy options are contraindicated.     

Optimal conditions for mycelial growth of Schizosaccharomyces japonicus cells in liquid medium: it enables the molecular investigation of dimorphism

The non‐pathogenic dimorphic fission yeast, Schizosaccharomyces japonicus, could be a suitable model organism for investigation of the genetic background of mycelial growth, as it has a haploid chromosome set and its genome is sequenced. Since earlier results have suggested that its morphological transition required solid substrates, but molecular biological experiments would require hyphae production in a liquid medium, we wanted to find circumstances which would enable hyphae production in liquid media. Several external conditions were investigated, but the strongest inducer was fetal bovine serum (FBS). Its positive effect could be hampered by heat and was dependent on pH, temperature and concentration of the serum. Other protein‐containing compounds, such as peptone and bovine serum albumin or amino acids, proved to be ineffective or weak. Generally, the uninduced and induced mycelial growth of Sz. japonicus could be improved by lower external pH and higher temperature. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.