1,320 research outputs found
Deep Cross-Modal Audio-Visual Generation
Cross-modal audio-visual perception has been a long-lasting topic in
psychology and neurology, and various studies have discovered strong
correlations in human perception of auditory and visual stimuli. Despite works
in computational multimodal modeling, the problem of cross-modal audio-visual
generation has not been systematically studied in the literature. In this
paper, we make the first attempt to solve this cross-modal generation problem
leveraging the power of deep generative adversarial training. Specifically, we
use conditional generative adversarial networks to achieve cross-modal
audio-visual generation of musical performances. We explore different encoding
methods for audio and visual signals, and work on two scenarios:
instrument-oriented generation and pose-oriented generation. Being the first to
explore this new problem, we compose two new datasets with pairs of images and
sounds of musical performances of different instruments. Our experiments using
both classification and human evaluations demonstrate that our model has the
ability to generate one modality, i.e., audio/visual, from the other modality,
i.e., visual/audio, to a good extent. Our experiments on various design choices
along with the datasets will facilitate future research in this new problem
space
Asteroid lightcurves from the Palomar Transient Factory survey: Rotation periods and phase functions from sparse photometry
We fit 54,296 sparsely-sampled asteroid lightcurves in the Palomar Transient
Factory to a combined rotation plus phase-function model. Each lightcurve
consists of 20+ observations acquired in a single opposition. Using 805
asteroids in our sample that have reference periods in the literature, we find
the reliability of our fitted periods is a complicated function of the period,
amplitude, apparent magnitude and other attributes. Using the 805-asteroid
ground-truth sample, we train an automated classifier to estimate (along with
manual inspection) the validity of the remaining 53,000 fitted periods. By this
method we find 9,033 of our lightcurves (of 8,300 unique asteroids) have
reliable periods. Subsequent consideration of asteroids with multiple
lightcurve fits indicate 4% contamination in these reliable periods. For 3,902
lightcurves with sufficient phase-angle coverage and either a reliably-fit
period or low amplitude, we examine the distribution of several phase-function
parameters, none of which are bimodal though all correlate with the bond albedo
and with visible-band colors. Comparing the theoretical maximal spin rate of a
fluid body with our amplitude versus spin-rate distribution suggests that, if
held together only by self-gravity, most asteroids are in general less dense
than 2 g/cm, while C types have a lower limit of between 1 and 2 g/cm,
in agreement with previous density estimates. For 5-20km diameters, S types
rotate faster and have lower amplitudes than C types. If both populations share
the same angular momentum, this may indicate the two types' differing ability
to deform under rotational stress. Lastly, we compare our absolute magnitudes
and apparent-magnitude residuals to those of the Minor Planet Center's nominal
, rotation-neglecting model; our phase-function plus Fourier-series
fitting reduces asteroid photometric RMS scatter by a factor of 3.Comment: 35 pages, 29 figures. Accepted 15-Apr-2015 to The Astronomical
Journal (AJ). Supplementary material including ASCII data tables will be
available through the publishing journal's websit
Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model
Background: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery. Results: We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation. Conclusions: Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery
Effect of Predictor Dependence on Variable Selection for Linear and Log-Linear Regression
We propose a Bayesian approach to the Dirichlet-Multinomial (DM) regression model, which uses horseshoe, Laplace, and horseshoe plus priors for shrinkage and selection. The Dirichlet-Multinomial model can be used to find the significant association between a set of available covariates and taxa for a microbiome sample. We incorporate the covariates in a log-linear regression framework. We design a simulation study to make a comparison among the performance of the three shrinkage priors in terms of estimation accuracy and the ability to detect true signals. Our results have clearly separated the performance of the three priors and indicated that the horseshoe plus prior outperforms both horseshoe and Laplace priors under low dependence for the compositional data model in the Dirichlet-Multinomial regression framework. We have also seen that heavy dependence among the covariates reduces the rate of variable selection and deteriorates the estimation errors compared to low dependence
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Bifurcation analysis in an associative memory model
We previously reported the chaos induced by the frustration of interaction in
a non-monotonic sequential associative memory model, and showed the chaotic
behaviors at absolute zero. We have now analyzed bifurcation in a stochastic
system, namely a finite-temperature model of the non-monotonic sequential
associative memory model. We derived order-parameter equations from the
stochastic microscopic equations. Two-parameter bifurcation diagrams obtained
from those equations show the coexistence of attractors, which do not appear at
absolute zero, and the disappearance of chaos due to the temperature effect.Comment: 19 page
- …