1,970 research outputs found
Modeling a Sensor to Improve its Efficacy
Robots rely on sensors to provide them with information about their
surroundings. However, high-quality sensors can be extremely expensive and
cost-prohibitive. Thus many robotic systems must make due with lower-quality
sensors. Here we demonstrate via a case study how modeling a sensor can improve
its efficacy when employed within a Bayesian inferential framework. As a test
bed we employ a robotic arm that is designed to autonomously take its own
measurements using an inexpensive LEGO light sensor to estimate the position
and radius of a white circle on a black field. The light sensor integrates the
light arriving from a spatially distributed region within its field of view
weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by
incorporating an accurate model of the light sensor SSF into the likelihood
function of a Bayesian inference engine, an autonomous system can make improved
inferences about its surroundings. The method presented here is data-based,
fairly general, and made with plug-and play in mind so that it could be
implemented in similar problems.Comment: 18 pages, 8 figures, submitted to the special issue of "Sensors for
Robotics
Interactions of satellite-speed helium atoms with satellite surfaces. 3: Drag coefficients from spatial and energy distributions of reflected helium atoms
Spatial and energy distributions of helium atoms scattered from an anodized 1235-0 aluminum surface as well as the tangential and normal momentum accommodation coefficients calculated from these distributions are reported. A procedure for calculating drag coefficients from measured values of spatial and energy distributions is given. The drag coefficient calculated for a 6061 T-6 aluminum sphere is included
Composite-fermionization of bosons in rapidly rotating atomic traps
The non-perturbative effect of interaction can sometimes make interacting
bosons behave as though they were free fermions. The system of neutral bosons
in a rapidly rotating atomic trap is equivalent to charged bosons coupled to a
magnetic field, which has opened up the possibility of fractional quantum Hall
effect for bosons interacting with a short range interaction. Motivated by the
composite fermion theory of the fractional Hall effect of electrons, we test
the idea that the interacting bosons map into non-interacting spinless fermions
carrying one vortex each, by comparing wave functions incorporating this
physics with exact wave functions available for systems containing up to 12
bosons. We study here the analogy between interacting bosons at filling factors
with non-interacting fermions at for the ground state
as well as the low-energy excited states and find that it provides a good
account of the behavior for small , but interactions between fermions become
increasingly important with . At , which is obtained in the limit
, the fermionization appears to overcompensate for the
repulsive interaction between bosons, producing an {\em attractive}
interactions between fermions, as evidenced by a pairing of fermions here.Comment: 8 pages, 3 figures. Submitted to Phys. Rev.
Quantum computers can search arbitrarily large databases by a single query
This paper shows that a quantum mechanical algorithm that can query
information relating to multiple items of the database, can search a database
in a single query (a query is defined as any question to the database to which
the database has to return a (YES/NO) answer). A classical algorithm will be
limited to the information theoretic bound of at least O(log N) queries (which
it would achieve by using a binary search).Comment: Several enhancements to the original pape
Objectively Measured Physical Activity Varies by Task and Accelerometer Location in Younger and Older Adults
Please refer to the pdf version of the abstract located adjacent to the title
Origins of the Combinatorial Basis of Entropy
The combinatorial basis of entropy, given by Boltzmann, can be written , where is the dimensionless entropy, is the
number of entities and is number of ways in which a given
realization of a system can occur (its statistical weight). This can be
broadened to give generalized combinatorial (or probabilistic) definitions of
entropy and cross-entropy: and , where is the probability of a given
realization, is a convenient transformation function, is a
scaling parameter and an arbitrary constant. If or
satisfy the multinomial weight or distribution, then using
and , and asymptotically
converge to the Shannon and Kullback-Leibler functions. In general, however,
or need not be multinomial, nor may they approach an
asymptotic limit. In such cases, the entropy or cross-entropy function can be
{\it defined} so that its extremization ("MaxEnt'' or "MinXEnt"), subject to
the constraints, gives the ``most probable'' (``MaxProb'') realization of the
system. This gives a probabilistic basis for MaxEnt and MinXEnt, independent of
any information-theoretic justification.
This work examines the origins of the governing distribution ....
(truncated)Comment: MaxEnt07 manuscript, version 4 revise
Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines
We are working to develop automated intelligent agents, which can act and
react as learning machines with minimal human intervention. To accomplish this,
an intelligent agent is viewed as a question-asking machine, which is designed
by coupling the processes of inference and inquiry to form a model-based
learning unit. In order to select maximally-informative queries, the
intelligent agent needs to be able to compute the relevance of a question. This
is accomplished by employing the inquiry calculus, which is dual to the
probability calculus, and extends information theory by explicitly requiring
context. Here, we consider the interaction between two question-asking
intelligent agents, and note that there is a potential information redundancy
with respect to the two questions that the agents may choose to pose. We show
that the information redundancy is minimized by maximizing the joint entropy of
the questions, which simultaneously maximizes the relevance of each question
while minimizing the mutual information between them. Maximum joint entropy is
therefore an important principle of information-based collaboration, which
enables intelligent agents to efficiently learn together.Comment: 8 pages, 1 figure, to appear in the proceedings of MaxEnt 2011 held
in Waterloo, Canad
The Spatial Sensitivity Function of a Light Sensor
The Spatial Sensitivity Function (SSF) is used to quantify a detector's
sensitivity to a spatially-distributed input signal. By weighting the incoming
signal with the SSF and integrating, the overall scalar response of the
detector can be estimated. This project focuses on estimating the SSF of a
light intensity sensor consisting of a photodiode. This light sensor has been
used previously in the Knuth Cyberphysics Laboratory on a robotic arm that
performs its own experiments to locate a white circle in a dark field (Knuth et
al., 2007). To use the light sensor to learn about its surroundings, the
robot's inference software must be able to model and predict the light sensor's
response to a hypothesized stimulus. Previous models of the light sensor
treated it as a point sensor and ignored its spatial characteristics. Here we
propose a parametric approach where the SSF is described by a mixture of
Gaussians (MOG). By performing controlled calibration experiments with known
stimulus inputs, we used nested sampling to estimate the SSF of the light
sensor using an MOG model with the number of Gaussians ranging from one to
five. By comparing the evidence computed for each MOG model, we found that one
Gaussian is sufficient to describe the SSF to the accuracy we require. Future
work will involve incorporating this more accurate SSF into the Bayesian
machine learning software for the robotic system and studying how this detailed
information about the properties of the light sensor will improve robot's
ability to learn.Comment: Published in MaxEnt 200
Origin of Complex Quantum Amplitudes and Feynman's Rules
Complex numbers are an intrinsic part of the mathematical formalism of
quantum theory, and are perhaps its most mysterious feature. In this paper, we
show that the complex nature of the quantum formalism can be derived directly
from the assumption that a pair of real numbers is associated with each
sequence of measurement outcomes, with the probability of this sequence being a
real-valued function of this number pair. By making use of elementary symmetry
conditions, and without assuming that these real number pairs have any other
algebraic structure, we show that these pairs must be manipulated according to
the rules of complex arithmetic. We demonstrate that these complex numbers
combine according to Feynman's sum and product rules, with the modulus-squared
yielding the probability of a sequence of outcomes.Comment: v2: Clarifications, and minor corrections and modifications. Results
unchanged. v3: Minor changes to introduction and conclusio
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