373 research outputs found
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Vision research has been shaped by the seminal insight that we can understand
the higher-tier visual cortex from the perspective of multiple functional
pathways with different goals. In this paper, we try to give a computational
account of the functional organization of this system by reasoning from the
perspective of multi-task deep neural networks. Machine learning has shown that
tasks become easier to solve when they are decomposed into subtasks with their
own cost function. We hypothesize that the visual system optimizes multiple
cost functions of unrelated tasks and this causes the emergence of a ventral
pathway dedicated to vision for perception, and a dorsal pathway dedicated to
vision for action. To evaluate the functional organization in multi-task deep
neural networks, we propose a method that measures the contribution of a unit
towards each task, applying it to two networks that have been trained on either
two related or two unrelated tasks, using an identical stimulus set. Results
show that the network trained on the unrelated tasks shows a decreasing degree
of feature representation sharing towards higher-tier layers while the network
trained on related tasks uniformly shows high degree of sharing. We conjecture
that the method we propose can be used to analyze the anatomical and functional
organization of the visual system and beyond. We predict that the degree to
which tasks are related is a good descriptor of the degree to which they share
downstream cortical-units.Comment: 16 pages, 5 figure
Analyzing the Dependency of {ConvNets} on Spatial Information
Intuitively, image classification should profit from using spatial information. Recent work, however, suggests that this might be overrated in standard CNNs. In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information. We propose spatial shuffling and GAP+FC to destroy spatial information during both training and testing phases. Interestingly, we observe that spatial information can be deleted from later layers with small performance drops, which indicates spatial information at later layers is not necessary for good performance. For example, test accuracy of VGG-16 only drops by 0.03% and 2.66% with spatial information completely removed from the last 30% and 53% layers on CIFAR100, respectively. Evaluation on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152) shows an overall consistent pattern
Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm
Leads and pressure ridges are dominant features of the Arctic sea
ice cover. Not only do they affect heat loss and surface drag, but they also
provide insight into the underlying physics of sea ice deformation. Due to
their elongated shape they are referred to as linear kinematic features (LKFs).
This paper introduces two methods that detect and track LKFs in sea ice
deformation data and establish an LKF data set for the entire observing
period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms
are available as open-source code and applicable to any gridded sea ice drift
and deformation data. The LKF detection algorithm classifies pixels with
higher deformation rates compared to the immediate environment as LKF pixels,
divides the binary LKF map into small segments, and reconnects multiple
segments into individual LKFs based on their distance and orientation
relative to each other. The tracking algorithm uses sea ice drift information
to estimate a first guess of LKF distribution and identifies tracked features
by the degree of overlap between detected features and the first guess. An
optimization of the parameters of both algorithms, as well as an
extensive evaluation of both algorithms against handpicked features in a
reference data set, is presented. A LKF data set is derived from RGPS deformation data for
the years from 1996 to 2008 that enables a comprehensive description of LKFs.
LKF densities and LKF intersection angles derived from this data set agree
well with previous estimates. Further, a stretched exponential distribution
of LKF length, an exponential tail in the distribution of LKF lifetimes, and
a strong link to atmospheric drivers, here Arctic cyclones, are derived from
the data set. Both algorithms are applied to output of a numerical sea ice
model to compare the LKF intersection angles in a high-resolution Arctic
sea ice simulation with the LKF data set.</p
Modification of spintronic terahertz emitter performance through defect engineering
Spintronic ferromagnetic/non-magnetic heterostructures are novel sources for
the generation of THz radiation based on spin-to-charge conversion in the
layers. The key technological and scientific challenge of THz spintronic
emitters is to increase their intensity and frequency bandwidth. Our work
reveals the factors to engineer spintronic Terahertz generation by introducing
the scattering lifetime and the interface transmission for spin polarized,
non-equilibrium electrons. We clarify the influence of the electron-defect
scattering lifetime on the spectral shape and the interface transmission on the
THz amplitude, and how this is linked to structural defects of bilayer
emitters. The results of our study define a roadmap of the properties of
emitted as well as detected THz-pulse shapes and spectra that is essential for
future applications of metallic spintronic THz emitters.Comment: 33 pages, 13 figure
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