Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201

Training neural networks to encode symbols enables combinatorial generalization

Combinatorial generalization - the ability to understand and produce novel combinations of already familiar elements - is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research suggests that conventional neural networks can't solve this problem unless they are endowed with mechanisms specifically engineered for the purpose of representing symbols. In this paper we introduce a novel way of representing symbolic structures in connectionist terms - the vectors approach to representing symbols (VARS), which allows training standard neural architectures to encode symbolic knowledge explicitly at their output layers. In two simulations, we show that neural networks not only can learn to produce VARS representations, but in doing so they achieve combinatorial generalization in their symbolic and non-symbolic output. This adds to other recent work that has shown improved combinatorial generalization under specific training conditions, and raises the question of whether specific mechanisms or training routines are needed to support symbolic processing

A relevância da metáfora visual para a memorização de um logótipo

ABSTRACT : We investigate visual metaphor (visual symbolism) in logotypes, its perception and its effect on memory. Henceforth, a visual standard experiment was developed for that effect. This model can be adapted to other logotypes (fig.4 and fig.6). Our research aims to evaluate the value of the perception of visual metaphor within a logo and its mnemonic consequence on the observer. In general metaphor, or symbolism, is an action, person, place, word or object that represents another to give a different meaning. On our study we evaluate visual metaphors, therefore metaphors that are perceived through visual representation, such as is the case in logos, symbols, logo marks, marks and all derivative paraphernalia of nomenclatures associated to any kind of Visual Identity; be it Visual Corporate Identity or Visual non-Corporate Identity such as services, products and persons. Many designers incorporate universality to symbols in the conception of “logos”. For example: Linden Leader (1994) for FedEx incorporates an arrow, symbolizing to move switily and directly. It is the designer’s exertion and experience that will complement symbolism into a new graphic form, until then unknown. We evaluate the condition of adding a universal graphic form to a graphic creation and its communicative reach.A nossa investigação centra-se na metáfora visual que um logótipo pode conter, e a consequência do encontro dessa metáfora visual na memorização de um logótipo. Um teste modelo foi desenvolvido para esse efeito. Este modelo pode ser adaptado a outros logótipos (fig.4 e fig.6) Em termos gerais uma metáfora, ou símbolo, é uma ação, pessoa, lugar, palavra ou objeto que representa outro para lhe atribuir um significado diferente. No nosso estudo, analisamos metáforas visuais, portanto metáforas codificadas através da representação visual, nomeadamente em logótipos, símbolos, logo-marcas, marcas e/ou toda a parafernália de nomenclatura associada a qualquer tipo de identidade visual; seja identidade visual corporativa ou identidade visual não corporativa, como por exemplo em serviços, produtos e pessoas. Muitos designers incorporam símbolos universais na concepção de logótipos. Linden Leader em 1994 para o logótipo da FedEx incorporou uma seta, que simboliza o movimento rápido e direto. É o esforço e a experiência do designer que complementarão este simbolismo numa nova marca gráfica, até então desconhecida. Avaliaremos a condição de adicionar uma metáfora visual a um logótipo e o resultado do seu alcance comunicativo na memorização do mesmo.info:eu-repo/semantics/publishedVersio

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014