14,483 research outputs found
Determining the Phase and Amplitude Distortion of a Wavefront using a Plenoptic Sensor
We have designed a plenoptic sensor to retrieve phase and amplitude changes
resulting from a laser beam's propagation through atmospheric turbulence.
Compared with the commonly restricted domain of (-pi, pi) in phase
reconstruction by interferometers, the reconstructed phase obtained by the
plenoptic sensors can be continuous up to a multiple of 2pi. When compared with
conventional Shack-Hartmann sensors, ambiguities caused by interference or low
intensity, such as branch points and branch cuts, are less likely to happen and
can be adaptively avoided by our reconstruction algorithm. In the design of our
plenoptic sensor, we modified the fundamental structure of a light field camera
into a mini Keplerian telescope array by accurately cascading the back focal
plane of its object lens with a microlens array's front focal plane and
matching the numerical aperture of both components. Unlike light field cameras
designed for incoherent imaging purposes, our plenoptic sensor operates on the
complex amplitude of the incident beam and distributes it into a matrix of
images that are simpler and less subject to interference than a global image of
the beam. Then, with the proposed reconstruction algorithms, the plenoptic
sensor is able to reconstruct the wavefront and a phase screen at an
appropriate depth in the field that causes the equivalent distortion on the
beam. The reconstructed results can be used to guide adaptive optics systems in
directing beam propagation through atmospheric turbulence. In this paper we
will show the theoretical analysis and experimental results obtained with the
plenoptic sensor and its reconstruction algorithms.Comment: This article has been accepted by JOSA
The Current State of Normative Agent-Based Systems
Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling
On the Generalization Effects of Linear Transformations in Data Augmentation
Data augmentation is a powerful technique to improve performance in
applications such as image and text classification tasks. Yet, there is little
rigorous understanding of why and how various augmentations work. In this work,
we consider a family of linear transformations and study their effects on the
ridge estimator in an over-parametrized linear regression setting. First, we
show that transformations which preserve the labels of the data can improve
estimation by enlarging the span of the training data. Second, we show that
transformations which mix data can improve estimation by playing a
regularization effect. Finally, we validate our theoretical insights on MNIST.
Based on the insights, we propose an augmentation scheme that searches over the
space of transformations by how uncertain the model is about the transformed
data. We validate our proposed scheme on image and text datasets. For example,
our method outperforms RandAugment by 1.24% on CIFAR-100 using
Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA
Adversarial AutoAugment on CIFAR datasets.Comment: International Conference on Machine learning (ICML) 2020. Added
experimental results on ImageNe
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