25,698 research outputs found
Local Testing for Membership in Lattices
Motivated by the structural analogies between point lattices and linear error-correcting codes, and by the mature theory on locally testable codes, we initiate a systematic study of local testing for membership in lattices. Testing membership in lattices is also motivated in practice, by applications to integer programming, error detection in lattice-based communication, and cryptography. Apart from establishing the conceptual foundations of lattice testing, our results include the following: 1. We demonstrate upper and lower bounds on the query complexity of local testing for the well-known family of code formula lattices. Furthermore, we instantiate our results with code formula lattices constructed from Reed-Muller codes, and obtain nearly-tight bounds. 2. We show that in order to achieve low query complexity, it is sufficient to design one-sided non-adaptive canonical tests. This result is akin to, and based on an analogous result for error-correcting codes due to Ben-Sasson et al. (SIAM J. Computing 35(1) pp1-21)
A multi-agent based evolutionary algorithm in non-stationary environments
This article is posted here with permission of IEEE - Copyright @ 2008 IEEEIn this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.This work was suported by the Key Program of National Natural Science Foundation of China under Grant No. 70431003, the Science Fund for Creative Research Group of the National Natural Science Foundation of China under Grant No. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09, and the Engineering and Physical Sciences Research Council of the United Kingdom under Grant No. EP/E060722/1
Adaptation to criticality through organizational invariance in embodied agents
Many biological and cognitive systems do not operate deep within one or other
regime of activity. Instead, they are poised at critical points located at
phase transitions in their parameter space. The pervasiveness of criticality
suggests that there may be general principles inducing this behaviour, yet
there is no well-founded theory for understanding how criticality is generated
at a wide span of levels and contexts. In order to explore how criticality
might emerge from general adaptive mechanisms, we propose a simple learning
rule that maintains an internal organizational structure from a specific family
of systems at criticality. We implement the mechanism in artificial embodied
agents controlled by a neural network maintaining a correlation structure
randomly sampled from an Ising model at critical temperature. Agents are
evaluated in two classical reinforcement learning scenarios: the Mountain Car
and the Acrobot double pendulum. In both cases the neural controller appears to
reach a point of criticality, which coincides with a transition point between
two regimes of the agent's behaviour. These results suggest that adaptation to
criticality could be used as a general adaptive mechanism in some
circumstances, providing an alternative explanation for the pervasive presence
of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525
Developments in the Analysis of Spatial Data
Disregarding spatial dependence can invalidate methods for analyzingcross-sectional and panel data. We discuss ongoing work on developingmethods that allow for, test for, or estimate, spatial dependence. Muchof the stress is on nonparametric and semiparametric methods.
Structure identification methods for atomistic simulations of crystalline materials
We discuss existing and new computational analysis techniques to classify
local atomic arrangements in large-scale atomistic computer simulations of
crystalline solids. This article includes a performance comparison of typical
analysis algorithms such as Common Neighbor Analysis, Centrosymmetry Analysis,
Bond Angle Analysis, Bond Order Analysis, and Voronoi Analysis. In addition we
propose a simple extension to the Common Neighbor Analysis method that makes it
suitable for multi-phase systems. Finally, we introduce a new structure
identification algorithm, the Neighbor Distance Analysis, that is designed to
identify atomic structure units in grain boundaries
Adaptive identification and control of structural dynamics systems using recursive lattice filters
A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center
Video Propagation Networks
We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies
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