2,680 research outputs found
Speed and Accuracy of Static Image Discrimination by Rats
When discriminating dynamic noisy sensory signals, human and primate subjects
achieve higher accuracy when they take more time to decide, an effect
attributed to accumulation of evidence over time to overcome neural noise. We
measured the speed and accuracy of twelve freely behaving rats discriminating
static, high contrast photographs of real-world objects for water reward in a
self-paced task. Response latency was longer in correct trials compared to
error trials. Discrimination accuracy increased with response latency over the
range of 500-1200ms. We used morphs between previously learned images to vary
the image similarity parametrically, and thereby modulate task difficulty from
ceiling to chance. Over this range we find that rats take more time before
responding in trials with more similar stimuli. We conclude that rats'
perceptual decisions improve with time even in the absence of temporal
information in the stimulus, and that rats modulate speed in response to
discrimination difficulty to balance speed and accuracy
Creativity and Autonomy in Swarm Intelligence Systems
This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor
The Importance of Outcrossing of Highbush Blueberry Cultivars Blueray and Bluecrop
Some flowering plants require cross pollination, while others rely on selfing. Many, like highbush blueberries, use both, but one method may still be more effective. Cross pollination has been studied in blueberry cultivars which have shown a variety of results. I looked at if outcrossing improved berry traits in cultivars Blueray and Bluecrop. I pollinated Blueray and Bluecrop flowers with within-bush, within-cultivar, and between-cultivar pollen. The proportion of flowers that set fruit, berry mass, viable seed count, proportion of viable seeds, large seed count, proportion of large seed count, and sugar content of berries were compared across cultivars and pollination treatment. Fruit set, viable seed count, proportion of viable seeds, and sugar content showed no significant difference. However, for both cultivars, within-cultivar crossing resulted in smaller berries than selfing or between-cultivar crossing. Additionally, large seed count and proportion were significantly greater in cultivar-crossed berries. Upon this finding, I looked at the correlation between large seeds and mass and found significance. Although bush-crossing negatively impacted the blueberries, cultivar-crossing improved them, so outcrossing between Blueray and Bluecrop produces mixed results. Overall, outcrossing did not have a net positive effect on Blueray and Bluecrop blueberries and potentially should be avoided by farmers
NAG: Network for Adversary Generation
Adversarial perturbations can pose a serious threat for deploying machine
learning systems. Recent works have shown existence of image-agnostic
perturbations that can fool classifiers over most natural images. Existing
methods present optimization approaches that solve for a fooling objective with
an imperceptibility constraint to craft the perturbations. However, for a given
classifier, they generate one perturbation at a time, which is a single
instance from the manifold of adversarial perturbations. Also, in order to
build robust models, it is essential to explore the manifold of adversarial
perturbations. In this paper, we propose for the first time, a generative
approach to model the distribution of adversarial perturbations. The
architecture of the proposed model is inspired from that of GANs and is trained
using fooling and diversity objectives. Our trained generator network attempts
to capture the distribution of adversarial perturbations for a given classifier
and readily generates a wide variety of such perturbations. Our experimental
evaluation demonstrates that perturbations crafted by our model (i) achieve
state-of-the-art fooling rates, (ii) exhibit wide variety and (iii) deliver
excellent cross model generalizability. Our work can be deemed as an important
step in the process of inferring about the complex manifolds of adversarial
perturbations.Comment: CVPR 201
Tracking object poses in the context of robust body pose estimates
This work focuses on tracking objects being used by humans. These objects are often small, fast moving and heavily occluded by the user. Attempting to recover their 3D position and orientation over time is a challenging research problem. To make progress we appeal to the fact that these objects are often used in a consistent way. The body poses of different people using the same object tend to have similarities, and, when considered relative to those body poses, so do the respective object poses. Our intuition is that, in the context of recent advances in body-pose tracking from RGB-D data, robust object-pose tracking during human-object interactions should also be possible. We propose a combined generative and discriminative tracking framework able to follow gradual changes in object-pose over time but also able to re-initialise object-pose upon recognising distinctive body-poses. The framework is able to predict object-pose relative to a set of independent coordinate systems, each one centred upon a different part of the body. We conduct a quantitative investigation into which body parts serve as the best predictors of object-pose over the course of different interactions. We find that while object-translation should be predicted from nearby body parts, object-rotation can be more robustly predicted by using a much wider range of body parts. Our main contribution is to provide the first object-tracking system able to estimate 3D translation and orientation from RGB-D observations of human-object interactions. By tracking precise changes in object-pose, our method opens up the possibility of more detailed computational reasoning about human-object interactions and their outcomes. For example, in assistive living systems that go beyond just recognising the actions and objects involved in everyday tasks such as sweeping or drinking, to reasoning that a person has missed sweeping under the chair or not drunk enough water today. © 2014 Elsevier B.V. All rights reserved
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