Abstract
The execution model on which most tabling engines are based allocates a choice point whenever a new tabled subgoal is called. This happens even when the call is deterministic. however, sortie of the information from the choice point; is never used when evaluating deterministic tabled calls with batched scheduling. Moreover, when a deterministic answer is found for a. deterministic tabled call, the call can be completed early and the corresponding choice point can be removed. Thus, if applying batched scheduling to a long deterministic computation the system may end up consuming memory and evaluating calls unnecessarily. In this paper, we propose a solution that, tries to reduce this memory and execution overhead to a minimum. Our experimental results show that;, for deterministic tabled calls and tabled answers with batched scheduling, it; is possible not. only to reduce the memory usage overhead, but also the running time of tire execution.

Abstract
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. There are no golden standards for assessing performance in non-stationary environments. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of Predictive Sequential methods for error estimate - the prequential error. The prequential error allows us to monitor the evolution of the performance of models that evolve over time. Nevertheless, it is known to be a pessimistic estimator in comparison to holdout estimates. To obtain more reliable estimators we need some forgetting mechanism. Two viable alternatives are: sliding windows and fading factors. We observe that the prequential error converges to an holdout estimator when estimated over a sliding window or using fading factors. We present illustrative examples of the use of prequential error estimators, using fading factors, for the tasks of: i) assessing performance of a learning algorithm; ii) comparing learning algorithms; iii) hypothesis testing using McNemar test; and iv) change detection using Page-Hinkley test. In these tasks, the prequential error estimated using fading factors provide reliable estimators. In comparison to sliding windows, fading factors are faster and memory-less, a requirement for streaming applications. This paper is a contribution to a discussion in the good-practices on performance assessment when learning dynamic models that evolve over time.

Abstract

Abstract
An interferometric setup for measuring Polarization Mode Dispersion (PMD) setup was tested. Using a low-coherence technique with a Michelson interferometer, it was possible to measure values of PMD from detected Differential Group Delay (DGD) values in two reels. The low-coherence source bounds the minimum value of DGD detected to 0.13 ps, leading to a minimum value detected around 0.14 ps. The mean value of PMD measured over a period of several days for one reel was 0.0405 +/- 0.0007 ps/km(1/2), and 0.0463 +/- 0.0044 ps/km(1/2) for the other. Stochastic and random behavior of PMD was observed.

Abstract
A critical component in the implementation of an efficient tabling system is the design of the data structures and algorithms to access and manipulate tabled data. Arguably, the most successful data structure for tabling is tries. However, when used in applications that pose many queries and/or have a large number of answers, tabling call build arbitrarily many and/or very large tables, quickly filling Lip memory. In this paper, we propose a new design for the table space organization where all terms in tabled subgoal calls and tabled answers are represented only once in a common global trie instead of being spread over several different trie data structures. Our initial experiments using the YapTab tabling system show significant reductions oil memory usage without compromising running time.

Abstract
The integration of large shares of wind generation in power systems requires the development of new algorithms and forecasting tools for making decisions in the operational domain taking into account wind generation forecast uncertainties. One of these decisions regards operating reserve requirements to meet load and wind variations. The aim of this paper is therefore to address this issue from a risk evaluation perspective, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision-making. The new reserve management tool described in this paper is intended to support the Transmission System Operator (TSO) in defining on-line the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. A case-study based on the Portuguese power system demonstrates the usefulness and efficiency of the tool.