4,128 research outputs found
Kervolutional Neural Networks
Convolutional neural networks (CNNs) have enabled the state-of-the-art
performance in many computer vision tasks. However, little effort has been
devoted to establishing convolution in non-linear space. Existing works mainly
leverage on the activation layers, which can only provide point-wise
non-linearity. To solve this problem, a new operation, kervolution (kernel
convolution), is introduced to approximate complex behaviors of human
perception systems leveraging on the kernel trick. It generalizes convolution,
enhances the model capacity, and captures higher order interactions of
features, via patch-wise kernel functions, but without introducing additional
parameters. Extensive experiments show that kervolutional neural networks (KNN)
achieve higher accuracy and faster convergence than baseline CNN.Comment: oral paper in CVPR 201
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
This paper addresses a multi-pursuer single-evader pursuit-evasion game where
the free-moving evader moves faster than the pursuers. Most of the existing
works impose constraints on the faster evader such as limited moving area and
moving direction. When the faster evader is allowed to move freely without any
constraint, the main issues are how to form an encirclement to trap the evader
into the capture domain, how to balance between forming an encirclement and
approaching the faster evader, and what conditions make the capture possible.
In this paper, a distributed pursuit algorithm is proposed to enable pursuers
to form an encirclement and approach the faster evader. An algorithm that
balances between forming an encirclement and approaching the faster evader is
proposed. Moreover, sufficient capture conditions are derived based on the
initial spatial distribution and the speed ratios of the pursuers and the
evader. Simulation and experimental results on ground robots validate the
effectiveness and practicability of the proposed method
Measuring the universal synchronization properties of coupled oscillators across the Hopf instability
When a driven oscillator loses phase-locking to a master oscillator via a
Hopf bifurcation, it enters a bounded-phase regime in which its average
frequency is still equal to the master frequency, but its phase displays
temporal oscillations. Here we characterize these two synchronization regimes
in a laser experiment, by measuring the spectrum of the phase fluctuations
across the bifurcation. We find experimentally, and confirm numerically, that
the low frequency phase noise of the driven oscillator is strongly suppressed
in both regimes in the same way. Thus the long-term phase stability of the
master oscillator is transferred to the driven one, even in the absence of
phase-locking. The numerical study of a generic, minimal model suggests that
such behavior is universal for any periodically driven oscillator near a Hopf
bifurcation point.Comment: 5 pages, 5 figure
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