327,267 research outputs found
Slowly evolving geometry in recurrent neural networks I: extreme dilution regime
We study extremely diluted spin models of neural networks in which the
connectivity evolves in time, although adiabatically slowly compared to the
neurons, according to stochastic equations which on average aim to reduce
frustration. The (fast) neurons and (slow) connectivity variables equilibrate
separately, but at different temperatures. Our model is exactly solvable in
equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e.
recall of one pattern). These show that, as the connectivity temperature is
lowered, the volume of the retrieval phase diverges and the fraction of
mis-aligned spins is reduced. Still one always retains a region in the
retrieval phase where recall states other than the one corresponding to the
`condensed' pattern are locally stable, so the associative memory character of
our model is preserved.Comment: 18 pages, 6 figure
Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps
The thermal Sunyaev-Zel'dovich (tSZ) and the kinematic Sunyaev-Zel'dovich
(kSZ) effects trace the distribution of electron pressure and momentum in the
hot Universe. These observables depend on rich multi-scale physics, thus,
simulated maps should ideally be based on calculations that capture baryonic
feedback effects such as cooling, star formation, and other complex processes.
In this paper, we train deep convolutional neural networks with a U-Net
architecture to map from the three-dimensional distribution of dark matter to
electron density, momentum and pressure at ~ 100 kpc resolution. These networks
are trained on a combination of the TNG300 volume and a set of cluster zoom-in
simulations from the IllustrisTNG project. The neural nets are able to
reproduce the power spectrum, one-point probability distribution function,
bispectrum, and cross-correlation coefficients of the simulations more
accurately than the state-of-the-art semi-analytical models. Our approach
offers a route to capture the richness of a full cosmological hydrodynamical
simulation of galaxy formation with the speed of an analytical calculation.Comment: 21 pages, 18 figure
An Improved Stock Price Prediction using Hybrid Market Indicators
In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained
with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction
Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners.
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies
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