1,821 research outputs found
Saccades to a remembered location elicit spatially specific activation in human retinotopic visual cortex
The possible impact upon human visual cortex from saccades to remembered target locations was investigated using functional magnetic resonance imaging (fMRI). A specific location in the upper-right or upper-left visual quadrant served as the saccadic target. After a delay of 2,400 msec, an auditory signal indicated whether to execute a saccade to that location (go trial) or to cancel the saccade and remain centrally fixated (no-go). Group fMRI analysis revealed activation specific to the remembered target location for executed saccades, in the contralateral lingual gyrus. No-go trials produced similar, albeit significantly reduced, effects. Individual retinotopic mapping confirmed that on go trials, quadrant-specific activations arose in those parts of ventral V1, V2, and V3 that coded the target location for the saccade, whereas on no-go trials, only the corresponding parts of V2 and V3 were significantly activated. These results indicate that a spatial-motor saccadic task (i.e., making an eye movement to a remembered location) is sufficient to activate retinotopic visual cortex spatially corresponding to the target location, and that this activation is also present (though reduced) when no saccade is executed. We discuss the implications of finding that saccades to remembered locations can affect early visual cortex, not just those structures conventionally associated with eye movements, in relation to recent ideas about attention, spatial working memory, and the notion that recently activated representations can be "refreshed" when needed
Pattern recognition, attention, and information bottlenecks in the primate visual system
In its evolution, the primate visual system has developed impressive capabilities for recognizing complex patterns in natural images. This process involves many stages of analysis and a variety of information processing strategies. This paper concentrates on the importance of 'information bottlenecks,' which restrict the amount of information that can be handled at different stages of analysis. These steps are crucial for reducing the overwhelming computational complexity associated with recognizing countless objects from arbitrary viewing angles, distances, and perspectives. The process of directed visual attention is an especially important information bottleneck because of its flexibility in determining how information is routed to high-level pattern recognition centers
Long Range Magnetic Order and the Darwin Lagrangian
We simulate a finite system of confined electrons with inclusion of the
Darwin magnetic interaction in two- and three-dimensions. The lowest energy
states are located using the steepest descent quenching adapted for velocity
dependent potentials. Below a critical density the ground state is a static
Wigner lattice. For supercritical density the ground state has a non-zero
kinetic energy. The critical density decreases with for exponential
confinement but not for harmonic confinement. The lowest energy state also
depends on the confinement and dimension: an antiferromagnetic cluster forms
for harmonic confinement in two dimensions.Comment: 5 figure
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
With the availability of big medical image data, the selection of an adequate
training set is becoming more important to address the heterogeneity of
different datasets. Simply including all the data does not only incur high
processing costs but can even harm the prediction. We formulate the smart and
efficient selection of a training dataset from big medical image data as a
multi-armed bandit problem, solved by Thompson sampling. Our method assumes
that image features are not available at the time of the selection of the
samples, and therefore relies only on meta information associated with the
images. Our strategy simultaneously exploits data sources with high chances of
yielding useful samples and explores new data regions. For our evaluation, we
focus on the application of estimating the age from a brain MRI. Our results on
7,250 subjects from 10 datasets show that our approach leads to higher accuracy
while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding
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Comparative testing of slurry monitors
The US Department of Energy (DOE) has millions of gallons of radioactive liquid and sludge wastes that must be retrieved from underground storage tanks, transferred to treatment facilities, and processed to a final waste form. The wastes will be removed from the current storage tanks by mobilizing the sludge wastes and mixing them with the liquid wastes to create slurries. Each slurry would then be transferred by pipeline to the desired destination. To reduce the risk of plugging a pipeline, the transport properties (e.g., density, suspended solids concentration, viscosity, particle size range) of the slurry should be determined to be within acceptable limits prior to transfer. These properties should also be monitored and controlled within specified limits while the slurry transfer is in progress. The DOE issued a call for proposals for developing on-line instrumentation to measure the transport properties of slurries. In response to the call for proposals, several researchers submitted proposals and were funded to develop slurry monitoring instruments. These newly developed DOE instruments are currently in the prototype stage. Before the instruments were installed in a radioactive application, the DOE wanted to evaluate them under nonradioactive conditions to determine if they were accurate, reliable, and dependable. The goal of this project was to test the performance of the newly developed DOE instruments along with several commercially available instruments. The baseline method for comparison utilized the results from grab-sample analyses
Semi-parametric Expected Shortfall Forecasting
Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Expected shortfall is a tail risk measure, that is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to indirectly model expected shortfall, is generalised to incorporate information on the intra-day range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to semiparametric estimation and forecasts of expectiles, and subsequently of expected shortfall. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favourably with a large range of competitors in an empirical study forecasting seven financial return series over a ten year period
Generalized Robba rings
We prove that any projective coadmissible module over the locally analytic
distribution algebra of a compact -adic Lie group is finitely generated. In
particular, the category of coadmissible modules does not have enough
projectives. In the Appendix a "generalized Robba ring" for uniform pro-
groups is constructed which naturally contains the locally analytic
distribution algebra as a subring. The construction uses the theory of
generalized microlocalization of quasi-abelian normed algebras that is also
developed there. We equip this generalized Robba ring with a self-dual locally
convex topology extending the topology on the distribution algebra. This is
used to show some results on coadmissible modules.Comment: with an appendix by Peter Schneider; revised; new titl
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