Upper limit ofTc for the copper oxides

The value of the critical temperature of the cuprates correlates with the doping level and is affected by the interplay of two competing factors: (1) the increase in carrier concentration, and (2) the pair-breaking effect of magnetic impurities. An analysis of the temperature dependence of the critical field leads to the conclusion that magnetic impurities are present even in a sample with the maximum observed value ofT _c.A new parameter, intrinsicT _c (T _cintr), which is its value in the absence of magnetic impurities, is introduced. The maximum value ofT _cintr, which corresponds to the maximum doping level, appears to be similar for different cuprates and to be equal to 160–170 K. This is the upper limit ofT _c in the cuprates.     

Parametric algorithms for 2-cyclic robot scheduling with interval processing times

Consider an m-machine production line for processing identical parts served by a mobile robot. The problem is to find the minimum cycle time for 2-cyclic schedules, that is, schedules in which exactly two parts enter and two parts leave the production line during each cycle. This work treats a special case of the 2-cyclic robot scheduling problem when the robot route is given and operation durations are chosen from prescribed intervals. A strongly polynomial algorithm of time complexity O(m ⁸log m) is proposed.     

Sequence classification via large margin hidden Markov models

We address the sequence classification problem using a probabilistic model based on hidden Markov models (HMMs). In contrast to commonly-used likelihood-based learning methods such as the joint/conditional maximum likelihood estimator, we introduce a discriminative learning algorithm that focuses on class margin maximization. Our approach has two main advantages: (i) As an extension of support vector machines (SVMs) to sequential, non-Euclidean data, the approach inherits benefits of margin-based classifiers, such as the provable generalization error bounds. (ii) Unlike many algorithms based on non-parametric estimation of similarity measures that enforce weak constraints on the data domain, our approach utilizes the HMM’s latent Markov structure to regularize the model in the high-dimensional sequence space. We demonstrate significant improvements in classification performance of the proposed method in an extensive set of evaluations on time-series sequence data that frequently appear in data mining and computer vision domains.     

Stability conditions for integral delay systems

In this paper we consider a special class of integral delay systems arising in several stability problems of time‐delay systems. For these integral systems we derive stability and robust stability conditions in terms of Lyapunov–Krasovskii functionals. More explicitly, after providing the stability conditions we compute quadratic functionals and apply them to derive exponential estimates for solutions, and robust stability conditions for perturbed integral delay systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Single‐cell‐gap transflective liquid‐crystal display and the use of photoalignment technology

Abstract— In this paper, transflective liquid‐crystal‐display (LCD) technology will be reviewed, and several new single‐cell‐gap transflective LCD configurations are proposed. Photoalignment technology is studied especially for transflective‐LCD applications. In order to realize the optimal performance of the display as well as a matched transmittance/reflectance voltage curve (TVC/RVC) for the transflective configurations, two different single‐cell‐gap transflective‐LCD approaches will be discussed. The first one is the dual‐mode single‐cell‐gap approach, in which different liquid‐crystal modes are applied to the transmissive and reflective subpixels of the transflective LCD. The other approach is the single‐mode s ingle‐cell‐gap approach, in which an in‐cell retardation film is applied to adjust the performance and TVC/RVC matching of a transflective LCD. Photoalignment technology is used to fabricate the dual‐mode liquid‐crystal cell in the first approach and also the in‐cell retardation film in the second approach. Prototypes of the proposed configurations have been fabricated, which show good performance and a matched TVC/RVC.     

On calibration error of randomized forecasting algorithms

It has been recently shown that calibration with an error less than Δ>0$\Delta >0$ is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are obtained by random rounding the deterministic forecasts up to Δ$\Delta$. We show that this error cannot be improved for a vast majority of sequences: we prove that, using a probabilistic algorithm, we can effectively generate with probability close to one a sequence “resistant” to any randomized rounding forecasting with an error much smaller than Δ$\Delta$. We also reformulate this result by means of a probabilistic game.     

Robust resource allocation in a cluster based imaging system

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.     

Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach

Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty.     

VerifyThis 2015

VerifyThis 2015 was a one-day program verification competition which took place on April 12th, 2015 in London, UK, as part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2015). It was the fourth instalment in the VerifyThis competition series. This article provides an overview of the VerifyThis 2015 event, the challenges that were posed during the competition, and a high-level overview of the solutions to these challenges. It concludes with the results of the competition and some ideas and thoughts for future instalments of VerifyThis.     

Knowledge transfer in SVM and neural networks

The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.