Improving the Computational Efficiency in Symmetrical Numeric Constraint Satisfaction Problems

Models are used in science and engineering for experimentation, analysis, diagnosis or design. In some cases, they can be considered as numeric constraint satisfaction problems (NCSP). Many models are symmetrical NCSP. The consideration of symmetries ensures that NCSP-solver will find solutions if they exist on a smaller search space. Our work proposes a strategy to perform it. We transform the symmetrical NCSP into a newNCSP by means of addition of symmetry-breaking constraints before the search begins. The specification of a library of possible symmetries for numeric constraints allows an easy choice of these new constraints. The summarized results of the studied cases show the suitability of the symmetry-breaking constraints to improve the solving process of certain types of symmetrical NCSP. Their possible speedup facilitates the application of modelling and solving larger and more realistic problems.Ministerio de Ciencia y Tecnología DIP2003-0666-02-

How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation

A framework for semiqualitative reasoning in engineering applications

In most cases the models for experimentation, analysis, or design in engineering applications take into account only quantitative knowledge. Sometimes there is a qualitative knowledge that is convenient to consider in order to obtain better conclusions. These qualitative concepts can be labels such as ``high,’ ’ ``very negative,’ ’ ``little acid,’ ’ ``monotonically increasing’ ’ or symbols such as ¾; º, etc. . . Engineers have already used this type of knowledge implicitly in many activities. The framework that we present here lets us express explicitly this knowledge. This work makes the following contributions. First, we identify the most important classes of qualitative concepts in engineering activities. Second, we present a novel methodology to integrate both qualitative and quantitative knowledge. Third, we obtain signi® cant conclusions automatically. It is named semiqualitative reasoning. Qualitative concepts are represented by means of closed real intervals. This approximation is accepted in the area of Arti® cial Intelligence. A modeling language is speci® ed to represent qualitative and quantitative knowledge of the model. A numeric constraint satisfaction problem is obtained by means of corresponding rules of transformation of the semantics of this language. In order to obtain conclusions, we have developed algorithms that treat the problem in a symbolic and numeric way. The interval conclusions obtained are transformed into qualitative labels through a linguistic interpretation. Finally, the capabilities of this methodology are illustrated on different problems

Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights (cf. Courville & Thompson, 2001; Nimon, Roberts, & Gavrilova, 2010; Zientek, Capraro, & Capraro, 2008), often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what and how independent variables contribute to a regression equation. Thus, this paper presents a guidebook of variable importance measures that inform MR results, linking measures to a theoretical framework that demonstrates the complementary roles they play when interpreting regression findings. We also provide a data-driven example of how to publish MR results that demonstrates how to present a more complete picture of the contributions variables make to a regression equation. We end with several recommendations for practice regarding how to integrate multiple variable importance measures into MR analyses

B-> D* zero-recoil formfactor and the heavy quark expansion in QCD: a systematic study

We present a QCD analysis of heavy quark mesons focussing on the B -> D* formfactor at zero recoil, F_D*(1). An advanced treatment of the perturbative corrections in the Wilsonian approach is presented. We estimate the higher-order power corrections to the OPE sum rule and describe a refined analysis of the nonresonant continuum contribution. In the framework of a model-independent approach, we show that the inelastic contribution in the phenomenological part of the OPE is related to the mQ-dependence of the hyperfine splitting and conclude that the former is large, lowering the prediction for F_D*(1) down to about 0.86. This likewise implies an enhanced yield of radial and D-wave charm excitations in semileptonic B decays and alleviates the problem with the inclusive yield of the wide excited states. We also apply the approach to the expectation values of dimension 7 and 8 local operators and to a few other issues in the heavy quark expansion.Comment: 70 pages, 13 figure

APPLICATION OF QUALITATIVE REASONING IN ENGINEERING

Qualitative reasoning is an alternative problem-solving technique useful for the conceptual design of structures. Qualitative reasoning represents the relationships between parameters in a model, and a search computation assigns values represented by intervals and relevant points in the behavior. The traditional difference between analysis and design or input and output parameters in a procedural computation is not existent in qualitative reasoning, since all the parameters in a model are equally represented. Qualitative reasoning derives values for parameters even with incomplete and imprecise knowledge about the model. This work presents a qualitative structural analysis framework, suitable for the evaluation of conceptual designs as well as for tutoring systems. The framework has been implemented in a computer program called Agrippa using the computer language Prolog. Based on a representation of fundamental principles of equilibrium, compatibility, and force-deformation and an incomplete knowledge of geometry and topology, Agrippa derives the signs and relative magnitude of forces and displacements for three-dimensional models of structures

Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights (cf. Courville & Thompson, 2001; Nimon, Roberts, & Gavrilova, 2010; Zientek, Capraro, & Capraro, 2008), often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what and how independent variables contribute to a regression equation. Thus, this paper presents a guidebook of variable importance measures that inform MR results, linking measures to a theoretical framework that demonstrates the complementary roles they play when interpreting regression findings. We also provide a data-driven example of how to publish MR results that demonstrates how to present a more complete picture of the contributions variables make to a regression equation. We end with several recommendations for practice regarding how to integrate multiple variable importance measures into MR analyses. Accessed 103,722 times on https://pareonline.net from April 29, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right