Abstract
In Computer Science stepwise refinement of algebraic specifications is a well-known formal methodology for rigorous program development. This paper illustrates how techniques from Algebraic Logic, in particular that of interpretation, understood as a multifunction that preserves and reflects logical consequence, capture a number of relevant transformations in the context of software design, reuse, and adaptation, difficult to deal with in classical approaches. Examples include data encapsulation and the decomposition of operations into atomic transactions. But if interpretations open such a new research avenue in program refinement, (conceptual) tools are needed to reason about them. In this line, the paper's main contribution is a study of the correspondence between logical interpretations and morphisms of a particular kind of coalgebras. This opens way to the use of coalgebraic constructions, such as simulation and bisimulation, in the study of interpretations between (abstract) logics.

Abstract
We characterize the minimum number of measurements needed to drive to zero the minimum mean squared error (MMSE) of Gaussian mixture model (GMM) input signals in the low-noise regime. The result also hints at almost phasetransition optimal recovery procedures based on a classification and reconstruction approach.

Abstract
Job shop scheduling problem (JSP) with sequence-dependent setup time and re-entrant work flows is considered in this paper. This is an NP-hard problem which needs to be solved using (meta)heuristic methods (e.g. genetic algorithm (GA)), especially for relatively large instances. However, the GA may face premature convergence (i.e. converging to a local optima), especially for rough solution spaces. In this paper, a fuzzy genetic algorithm (FGA) is proposed to overcome this issue. The objective is to minimize makespan of such problem. Research results show that the FGA outperforms the standard GA and offers better solutions in the same number of runs. © 2013 IEEE.

Abstract
The promotion of quality of life is becoming ever more important in a scenario of regional, national and even international competition among cities, triggered by globalization. Public sites, and green spaces in particular, which are available in varying extent in all urban areas, can bring important benefits to urban vitality and, as a consequence, to quality of life. However, cities are intricate entities and measuring their success in converting the potential for public green space usage into increased quality of life is a difficult task. In order to contribute to the objective of assessing the potential for public green space use, and its consequences on urban vitality, we applied the Data Envelopment Analysis technique to assess a total of 174 European cities. The results detect the best performing cities, and for the cities considered inefficient, a set of benchmarks is identified, whose best practices can be copied to support efforts of performance improvement.

Abstract
Phase-sensitive optical time-domain reflectometry (phi OTDR) is a simple and effective tool allowing the distributed monitoring of vibrations along single-mode fibers. We show in this Letter that modulation instability (MI) can induce a position-dependent signal fading in long-range phi OTDR over conventional optical fibers. This fading leads to a complete masking of the interference signal recorded at certain positions and therefore to a sensitivity loss at these positions. We illustrate this effect both theoretically and experimentally. While this effect is detrimental in the context of distributed vibration analysis using phi OTDR, we also believe that the technique provides a clear and insightful way to evidence the Fermi-Pasta-Ulam recurrence associated with the MI process. (C) 2013 Optical Society of America

Abstract
Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important application areas involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by sampling approaches. These approaches change the distribution of the given training data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present a modification of the well-known Smote algorithm that allows its use on these regression tasks. In an extensive set of experiments we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable. © 2013 Springer-Verlag.