When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998). This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators. To compare our estimators with the optimal we obtained from martingale estimating functions, see Sorensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out.

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The increase in societal awareness towards environmental issues has accrued the responsibility of goods producers, which at present came to encompass the entire product life cycle. Recently, the efficient design and operation of supply chains with return flows have, in particular, become a major challenge for many companies, given the high number of factors involved and their intricate interactions. In this paper, a multi-period and multi-product network model for the simultaneous design and planning of supply chains with reverse flows is proposed. A graph approach based on the conventional concepts of nodes and arcs is employed to model the network, where it is assumed that any network node is a transformation point of inbound into outbound flows, which in the limit may not differ, and that related arcs describe products flows along the chain. In here the formulation of time adopts a management perspective, i.e., the strategic design of the supply chain is dealt simultaneously with the tactical planning of its operation, which covers supply, production, storage and distribution. An example based on a Portuguese industry case is studied in order to validate both the applicability and adequacy of the model to real world problems.

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In 1968, Israel Gohberg and Naum Krupnik discovered that local spectra of singular integral operators with piecewise continuous coefficients on Lebesgue spaces \(L^p(\Gamma)\) over Lyapunov curves have the shape of circular arcs. About 25 years later, Albrecht Böttcher and Yuri Karlovich realized that these circular arcs metamorphose to so-called logarithmic leaves with a median separating point when Lyapunov curves metamorphose to arbitrary Carleson curves. We show that this result remains valid in a more general setting of variable Lebesgue spaces \(L^{p(\cdot)}(\Gamma)\) where \(p:\Gamma\to(1,\infty)\) satisfies the Dini-Lipschitz condition. One of the main ingredients of the proof is a new condition for the boundedness of the Cauchy singular integral operator on variable Lebesgue spaces with weights related to oscillations of Carleson curves.

We prove a Fredholm criterion for operators in the Banach algebra of singular integral operators with matrix piecewise continuous coefficients acting on a variable Lebesgue space with a radial oscillating weight over a logarithmic Carleson curve. The local spectra of these operators are massive and have a shape of spiralic horns depending on the value of the variable exponent, the spirality indices of the curve, and the Matuszewska-Orlicz indices of the weight at each point. These results extend (partially) the results of A. Böttcher, Yu. Karlovich, and V. Rabinovich for standard Lebesgue spaces to the case of variable Lebesgue spaces.

SmART (Smart Application Reconfiguration Tool) is a framework for the automatic configuration of systems and applications. The tool implements an application configuration workflow that resorts to the similarities between configuration files (i.e., patterns such as parameters, comments and blocks) to allow a syntax independent manipulation and transformation of system and application configuration files.Without compromising its generality, SmART targets virtualized IT infrastructures, configuring virtual appliances and its applications. SmART reduces the time required to (re)configure a set of applications by automating time-consuming steps of the process, independently of the nature of the application to be configured. Industrial experimentation and utilization of SmART show that the framework is able to correctly transform a large amount of configuration files into a generic syntax and back to their original syntax. They also show that the elapsed time in that process is adequate to what would be expected of an interactive tool. SmART is currently being integrated into the VIRTU bundle, whose trial version is available for download from the projects web page.

Some performance issues of transactional memory are caused by unnecessary abort situations where non serializable and yet non conflicting transactions are scheduled to execute concurrently. Smartly relaxing the isolation properties of transactions may overcome these issues and attain considerable performance improvements. However, it is known that relaxing isolation restrictions may lead to runtime anomalies. In some situations, like database management systems, developers may choose that compromise, hence avoiding anomalies explicitly. Memory transactions protect the state of the program, therefore execution anomalies may have more severe consequences in the semantics of programs. So, the compromise between a relaxed isolation strategy and enforcing the necessary program correctness is harder to setup. The solution we devise is to statically analyse programs to detect the kind of anomalies that emerge under snapshot isolation. Our approach allows a compiler to either warn the developer about the possible snapshot isolation anomalies in a given program, or possibly inform automatic correctness strategies to ensure Serializability.

Programs containing concurrency anomalies will most probably exhibit harmful erroneous and unpredictable behaviors. To ensure program correctness, the sources of those anomalies must be located and corrected. Concurrency anomalies in Transactional Memory (TM) programs should also be diagnosed and fixed. In this paper we propose a framework to deal with two different categories of concurrency anomalies in TM. First, we will address low-level TM anomalies, also called dataraces, which arise from executing programs in weak isolation. Secondly, we will address high-level TM anomalies, also called high-level dataraces, bringing the programmers attention to pairs of transactions that the programmer has misspecified, and should have been combined into a single transaction. Our framework was validated against a set of programs with well known anomalies and demonstrated high accuracy and effectiveness, thus contributing for improving the correctness of TM programs

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