5,961 research outputs found
Stationary Points of O'Hara's Knot Energies
In this article we study the regularity of stationary points of the knot
energies introduced by O'Hara in the range . In a
first step we prove that is on the set of all regular embedded
closed curves belonging to and calculate its derivative.
After that we use the structure of the Euler-Lagrange equation to study the
regularity of stationary points of plus a positive multiple of the
length. We show that stationary points of finite energy are of class
- so especially all local minimizers of among curves with fixed
length are smooth.Comment: Corrected typo
Ms Pac-Man versus Ghost Team CEC 2011 competition
Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE
Assembly Bias and Splashback in Galaxy Clusters
We use publicly available data for the Millennium Simulation to explore the
implications of the recent detection of assembly bias and splashback signatures
in a large sample of galaxy clusters. These were identified in the SDSS/DR8
photometric data by the redMaPPer algorithm and split into high- and
low-concentration subsamples based on the projected positions of cluster
members. We use simplified versions of these procedures to build cluster
samples of similar size from the simulation data. These match the observed
samples quite well and show similar assembly bias and splashback signals.
Previous theoretical work has found the logarithmic slope of halo density
profiles to have a well-defined minimum whose depth decreases and whose radius
increases with halo concentration. Projected profiles for the observed and
simulated cluster samples show trends with concentration which are opposite to
these predictions. In addition, for high-concentration clusters the minimum
slope occurs at significantly smaller radius than predicted. We show that these
discrepancies all reflect confusion between splashback features and features
imposed on the profiles by the cluster identification and concentration
estimation procedures. The strong apparent assembly bias is not reflected in
the three-dimensional distribution of matter around clusters. Rather it is a
consequence of the preferential contamination of low-concentration clusters by
foreground or background groups.Comment: 17 pages, 16 figures, 3 tables, accepted versio
Inelastic Confinement-Induced Resonances in Low-Dimensional Quantum Systems
A theoretical model is presented describing the confinement-induced
resonances observed in the recent loss experiment of Haller et al. [Phys. Rev.
Lett. 104, 153203 (2010)]. These resonances originate from possible molecule
formation due to the coupling of center-of-mass and relative motion. A
corresponding model is verified by ab initio calculations and predicts the
resonance positions in 1D as well as in 2D confinement in agreement with the
experiment. This resolves the contradiction of the experimental observations to
previous theoretical predictions.Comment: 5 pages, 4 figure
Ensemble Kalman filter for neural network based one-shot inversion
We study the use of novel techniques arising in machine learning for inverse
problems. Our approach replaces the complex forward model by a neural network,
which is trained simultaneously in a one-shot sense when estimating the unknown
parameters from data, i.e. the neural network is trained only for the unknown
parameter. By establishing a link to the Bayesian approach to inverse problems,
an algorithmic framework is developed which ensures the feasibility of the
parameter estimate w.r. to the forward model. We propose an efficient,
derivative-free optimization method based on variants of the ensemble Kalman
inversion. Numerical experiments show that the ensemble Kalman filter for
neural network based one-shot inversion is a promising direction combining
optimization and machine learning techniques for inverse problems
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