Analysis and Reuse of Plots Using Similarity and Analogy

Abstract: A plot is a partially ordered set of events. Plot analysis is a relevant source of knowledge about the agents behavior when accessing data stored in the database. It relies on logical logs which register the actions of individual agents. This paper proposes techniques to analyze and reuse plots based on the concepts of similarity and analogy, borrowed from cognitive science and linguistics. The concept of similarity is applied to organize plots as a library, and to explore the reuse of plots in the same domain. By contrast, the concept of analogy helps reuse plots across different domains. The techniques proposed in this paper find applications in areas such as computer games and emergency response information systems, as well as some traditional business applications

Heritability of geographic range sizes revisited : a reply to Hunt et al.

Hunt et al.(2005) revisit the issue of range size heritability following our recent article on this topic (Webb and Gaston 2003). In that article, we showed that the range sizes of closely related species tend to be highly dissimilar and argued that this provided evidence to counter Jablonski’s (1987) claim that range size was a heritable species-level trait. Hunt et al. do not dispute the fact that the species pairs that we examined have highly asymmetric range sizes; however, they claim that the statistical technique that we used to assess the significance of this asymmetry is flawed. They then return to correlation analyses to support their assertion that range size is indeed heritable. While some points of technical interest are raised, we disagree with their conclusions and feel that the analyses that they present provide little insight into the ultimate questions

Real Beads On Virtual Strings: Charged Particles On Magnetic Field Lines

We discuss a similarity between the drift of a charged particle inside a slowly moving solenoid and the motion of a fluid element in an ideal incompressible fluid. This similarity can serve as a useful instructional example to illustrate the concepts of magnetic field lines and magnetic confinement. (C) 2012 American Association of Physics Teachers. [http://dx.doi.org/10.1119/1.4746068]Institute for Fusion Studie

A similarity-assisted multi-fidelity approach to conceptual design space exploration

In conceptual design studies engineers typically utilize data-based surrogate models to enable rapid evaluation of design objectives that otherwise would be too computationally expensive and time-consuming to simulate. Due to the computationally expensive simulations, the data-based surrogate models are often trained using small sample sizes, resulting in low-fidelity models which can produce results that are not trustworthy. To mitigate this issue, a similarity-assisted design space exploration method is proposed. The similarity is measured between design points that have been evaluated through lower-fidelity data-based surrogate models and design points that have been evaluated using higher-fidelity physics-based simulations. This similarity information can then be used by design engineers to better understand the trustworthiness of the data produced by the low-fidelity surrogate models. Our numerical experiments demonstrate that such a similarity measurement can be used as an indicator of the trustworthiness of the lower-fidelity model predictions. Moreover, a second similarity metric is proposed for measuring the similarity of new designs to legacy designs, thus highlighting the potential to reuse knowledge, analysis models, and data. The proposed method is demonstrated by means of an aero-engine structural component conceptual design study. An open-source software tool developed to assist in data visualization is also presented

A Theme-Rewriting Approach for Generating Algebra Word Problems

Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\it rewriting} that edits existing human-authored narratives to change their theme without changing the underlying story. We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught. Our rewriting method uses a two-stage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence. Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines. We also release a new dataset of human-authored rewrites of math word problems in several themes.Comment: To appear EMNLP 201

Nature-Inspired Learning Models

Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new learning methods has been found in the mechanics of physical fields found in both micro and macro scale. Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over the well-known real and artificial datasets, compared when possible to the traditional methods

Skewed Factor Models Using Selection Mechanisms

Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-t, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset

Convolutional Neural Networks over Tree Structures for Programming Language Processing

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.Comment: Accepted at AAAI-1