6,082 research outputs found
Novel nonlinear kinetic terms for gravitons
A set of novel derivative terms for spin-2 fields are proposed. They are the
wedge products of curvature two-forms and vielbeins. In this work, we
investigate the properties of novel two-derivative terms in the context of
bi-gravity. Based on a minisuperspace analysis, we identify a large class of
bi-gravity models where the Boulware-Deser ghost could be absent. We give a new
perspective that Weyl Gravity and New Massive Gravity belong to this class of
bi-gravity models involving novel derivative terms. In addition, we discuss the
UV cut-off scales, dynamical symmetric conditions and novel higher-derivative
terms.Comment: 19 pages, two columns, v3.1; a reference is adde
Predictive Power of Strong Coupling in Theories with Large Distance Modified Gravity
We consider theories that modify gravity at cosmological distances, and show
that any such theory must exhibit a strong coupling phenomenon, or else it is
either inconsistent or is already ruled out by the solar system observations.
We show that all the ghost-free theories that modify dynamics of spin-2
graviton on asymptotically flat backgrounds, automatically have this property.
Due to the strong coupling effect, modification of the gravitational force is
source-dependent, and for lighter sources sets in at shorter distances. This
universal feature makes modified gravity theories predictive and potentially
testable not only by cosmological observations, but also by precision
gravitational measurements at scales much shorter than the current cosmological
horizon. We give a simple parametrization of consistent large distance modified
gravity theories and their predicted deviations from the Einsteinian metric
near the gravitating sources.Comment: 12 pages, Latex, to be published in New Journal of Physic
Semantic indeterminacy in object relative clauses
This article examined whether semantic indeterminacy plays a role in comprehension of complex structures such as object relative clauses. Study 1 used a gated sentence completion task to assess which alternative interpretations are dominant as the relative clause unfolds; Study 2 compared reading times in object relative clauses containing different animacy configurations to unambiguous passive controls; and Study 3 related completion data and reading data. The results showed that comprehension difficulty was modulated by animacy configuration and voice (active vs. passive). These differences were well correlated with the availability of alternative interpretations as the relative clause unfolds, as revealed by the completion data. In contrast to approaches arguing that comprehension difficulty stems from syntactic complexity, these results suggest that semantic indeterminacy is a major source of comprehension difficulty in object relative clauses. Results are consistent with constraint-based approaches to ambiguity resolution and bring new insights into previously identified sources of difficulty. (C) 2007 Elsevier Inc. All rights reserved
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data
Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method
Kolmogorov-Chaitin complexity has long been believed to be impossible to
approximate when it comes to short sequences (e.g. of length 5-50). However,
with the newly developed \emph{coding theorem method} the complexity of strings
of length 2-11 can now be numerically estimated. We present the theoretical
basis of algorithmic complexity for short strings (ACSS) and describe an
R-package providing functions based on ACSS that will cover psychologists'
needs and improve upon previous methods in three ways: (1) ACSS is now
available not only for binary strings, but for strings based on up to 9
different symbols, (2) ACSS no longer requires time-consuming computing, and
(3) a new approach based on ACSS gives access to an estimation of the
complexity of strings of any length. Finally, three illustrative examples show
how these tools can be applied to psychology.Comment: to appear in "Behavioral Research Methods", 14 pages in journal
format, R package at http://cran.r-project.org/web/packages/acss/index.htm
Extension-based Semantics of Abstract Dialectical Frameworks
One of the most prominent tools for abstract argumentation is the Dung's
framework, AF for short. It is accompanied by a variety of semantics including
grounded, complete, preferred and stable. Although powerful, AFs have their
shortcomings, which led to development of numerous enrichments. Among the most
general ones are the abstract dialectical frameworks, also known as the ADFs.
They make use of the so-called acceptance conditions to represent arbitrary
relations. This level of abstraction brings not only new challenges, but also
requires addressing existing problems in the field. One of the most
controversial issues, recognized not only in argumentation, concerns the
support cycles. In this paper we introduce a new method to ensure acyclicity of
the chosen arguments and present a family of extension-based semantics built on
it. We also continue our research on the semantics that permit cycles and fill
in the gaps from the previous works. Moreover, we provide ADF versions of the
properties known from the Dung setting. Finally, we also introduce a
classification of the developed sub-semantics and relate them to the existing
labeling-based approaches.Comment: To appear in the Proceedings of the 15th International Workshop on
Non-Monotonic Reasoning (NMR 2014
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