547 research outputs found
Generalizations of the Lax-Milgram theorem
We prove a linear and a nonlinear generalization of the Lax-Milgram theorem.
In particular we give sufficient conditions for a real-valued function defined
on the product of a reflexive Banach space and a normed space to represent all
bounded linear functionals of the latter. We also give two applications to
singular differential equations
Subspaces with a common complement in a Banach space
We study the problem of the existence of a common algebraic complement for a
pair of closed subspaces of a Banach space. We prove the following two
characterizations: (1) The pairs of subspaces of a Banach space with a common
complement coincide with those pairs which are isomorphic to a pair of graphs
of bounded linear operators between two other Banach spaces. (2) The pairs of
subspaces of a Banach space X with a common complement coincide with those
pairs for which there exists an involution S on X exchanging the two subspaces,
such that I+S is bounded from below on their union. Moreover we show that, in a
separable Hilbert space, the only pairs of subspaces with a common complement
are those which are either equivalently positioned or not completely asymptotic
to one another. We also obtain characterizations for the existence of a common
complement for subspaces with closed sum
Entertainment capture through heart rate activity in physical interactive playgrounds
An approach for capturing and modeling individual entertainment (“fun”) preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children’s physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children’s heart rate (HR) signals, and their expressed preferences of how much “fun” particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children’s reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p−value 6 · 10−5). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.peer-reviewe
Enhancing health care via affective computing
Affective computing is a multidisciplinary field that studies the various ways by which computational processes are able to elicit, sense, and detect manifestations of human emotion. While the methods and technology delivered by affective computing have demonstrated very promising results across several domains, their adoption by healthcare is still at its initial stages. With that aim in mind, this commentary paper introduces affective computing to the readership of the journal and praises for the benefits of affect-enabled systems for prognostic, diagnostic and therapeutic purposes.peer-reviewe
A panorama of artificial and computational intelligence in games
This paper attempts to give a high-level overview
of the field of artificial and computational intelligence (AI/CI)
in games, with particular reference to how the different core
research areas within this field inform and interact with each
other, both actually and potentially. We identify ten main
research areas within this field: NPC behavior learning, search
and planning, player modeling, games as AI benchmarks,
procedural content generation, computational narrative, believable
agents, AI-assisted game design, general game artificial
intelligence and AI in commercial games. We view and analyze
the areas from three key perspectives: (1) the dominant AI
method(s) used under each area; (2) the relation of each area
with respect to the end (human) user; and (3) the placement of
each area within a human-computer (player-game) interaction
perspective. In addition, for each of these areas we consider how
it could inform or interact with each of the other areas; in those
cases where we find that meaningful interaction either exists or
is possible, we describe the character of that interaction and
provide references to published studies, if any. We believe that
this paper improves understanding of the current nature of the
game AI/CI research field and the interdependences between
its core areas by providing a unifying overview. We also believe
that the discussion of potential interactions between research
areas provides a pointer to many interesting future research
projects and unexplored subfields.peer-reviewe
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