17,010 research outputs found
Quantification of myeloperoxidase from human granulocytes as an inflammation marker by enzyme.linked immunosorbent assay
Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions
With this paper, we contribute to the understanding of ant colony
optimization (ACO) algorithms by formally analyzing their runtime behavior. We
study simple MAX-MIN ant systems on the class of linear pseudo-Boolean
functions defined on binary strings of length 'n'. Our investigations point out
how the progress according to function values is stored in pheromone. We
provide a general upper bound of O((n^3 \log n)/ \rho) for two ACO variants on
all linear functions, where (\rho) determines the pheromone update strength.
Furthermore, we show improved bounds for two well-known linear pseudo-Boolean
functions called OneMax and BinVal and give additional insights using an
experimental study.Comment: 19 pages, 2 figure
Passage-time distributions from a spin-boson detector model
The passage-time distribution for a spread-out quantum particle to traverse a
specific region is calculated using a detailed quantum model for the detector
involved. That model, developed and investigated in earlier works, is based on
the detected particle's enhancement of the coupling between a collection of
spins (in a metastable state) and their environment. We treat the continuum
limit of the model, under the assumption of the Markov property, and calculate
the particle state immediately after the first detection. An explicit example
with 15 boson modes shows excellent agreement between the discrete model and
the continuum limit. Analytical expressions for the passage-time distribution
as well as numerical examples are presented. The precision of the measurement
scheme is estimated and its optimization discussed. For slow particles, the
precision goes like , which improves previous estimates,
obtained with a quantum clock model.Comment: 11 pages, 6 figures; minor changes, references corrected; accepted
for publication in Phys. Rev.
Optimal control and inverse optimal control by distribution matching
Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at different time steps or energy efficiency. Manually trading-off these objectives is often difficult and requires a high engineering effort. In this paper, we present a new approach to specify optimal behavior. We directly specify the desired behavior by a distribution over future states or features of the states. For example, the experimenter could choose to reach certain mean positions with given accuracy/variance at specified time steps. Our approach also unifies optimal control and inverse optimal control in one framework. Given a desired state distribution, we estimate a cost function such that the optimal controller matches the desired distribution. If the desired distribution is estimated from expert demonstrations, our approach performs inverse optimal control. We evaluate our approach on several optimal and inverse optimal control tasks on non-linear systems using incremental linearizations similar to differential dynamic programming approaches
The kernel Kalman rule: efficient nonparametric inference with recursive least squares
Nonparametric inference techniques provide promising tools
for probabilistic reasoning in high-dimensional nonlinear systems.
Most of these techniques embed distributions into reproducing
kernel Hilbert spaces (RKHS) and rely on the kernel
Bayesâ rule (KBR) to manipulate the embeddings. However,
the computational demands of the KBR scale poorly
with the number of samples and the KBR often suffers from
numerical instabilities. In this paper, we present the kernel
Kalman rule (KKR) as an alternative to the KBR. The derivation
of the KKR is based on recursive least squares, inspired
by the derivation of the Kalman innovation update. We apply
the KKR to filtering tasks where we use RKHS embeddings
to represent the belief state, resulting in the kernel Kalman filter
(KKF). We show on a nonlinear state estimation task with
high dimensional observations that our approach provides a
significantly improved estimation accuracy while the computational
demands are significantly decreased
Acoustic and aerodynamic performance of a 6-foot-diameter fan for turbofan engines. 1 - Design of facility and QF-1 fan
Design of test facility and prototype fan for turbofan acoustic researc
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