31,618 research outputs found
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.Comment: Proceedings of ACL 201
Dynamic Structures for Evolving Tactics and Strategies in Team Robotics
The autonomous robot systems of the future will be teams of robots with complementary specialisms. At any instant robot interactions determine relational structures, and sequences of these structures describe the team dynamics as trajectories through space and time. These structures can be represented in algebraic forms that are realizable as dynamic multilevel data structures within individual robots, as the basis of emergent team data structures. Such formalisms are necessary for robots to learn new individual and collective behaviours. The theory is illustrated by the example of robot soccer where robot interactions create structures and trajectories essential to the evolution of new tactics and strategies in a changing environment
-field kinks: stability, exact solutions and new features
We study a class of noncanonical real scalar field models in
-dimensional flat space-time. We first derive the general criterion for
the classical linear stability of an arbitrary static soliton solution of these
models. Then we construct first-order formalisms for some typical models and
derive the corresponding kink solutions. The linear structures of these
solutions are also qualitatively analyzed and compared with the canonical kink
solutions.Comment: 14 pages, 3 figure
About the nature of Kansei information, from abstract to concrete
Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN
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