301 research outputs found
Nonparametric spectral analysis with applications to seizure characterization using EEG time series
Understanding the seizure initiation process and its propagation pattern(s)
is a critical task in epilepsy research. Characteristics of the pre-seizure
electroencephalograms (EEGs) such as oscillating powers and high-frequency
activities are believed to be indicative of the seizure onset and spread
patterns. In this article, we analyze epileptic EEG time series using
nonparametric spectral estimation methods to extract information on
seizure-specific power and characteristic frequency [or frequency band(s)].
Because the EEGs may become nonstationary before seizure events, we develop
methods for both stationary and local stationary processes. Based on penalized
Whittle likelihood, we propose a direct generalized maximum likelihood (GML)
and generalized approximate cross-validation (GACV) methods to estimate
smoothing parameters in both smoothing spline spectrum estimation of a
stationary process and smoothing spline ANOVA time-varying spectrum estimation
of a locally stationary process. We also propose permutation methods to test if
a locally stationary process is stationary. Extensive simulations indicate that
the proposed direct methods, especially the direct GML, are stable and perform
better than other existing methods. We apply the proposed methods to the
intracranial electroencephalograms (IEEGs) of an epileptic patient to gain
insights into the seizure generation process.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS185 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimal variance estimation without estimating the mean function
We study the least squares estimator in the residual variance estimation
context. We show that the mean squared differences of paired observations are
asymptotically normally distributed. We further establish that, by regressing
the mean squared differences of these paired observations on the squared
distances between paired covariates via a simple least squares procedure, the
resulting variance estimator is not only asymptotically normal and root-
consistent, but also reaches the optimal bound in terms of estimation variance.
We also demonstrate the advantage of the least squares estimator in comparison
with existing methods in terms of the second order asymptotic properties.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ432 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Facial Motion Prior Networks for Facial Expression Recognition
Deep learning based facial expression recognition (FER) has received a lot of
attention in the past few years. Most of the existing deep learning based FER
methods do not consider domain knowledge well, which thereby fail to extract
representative features. In this work, we propose a novel FER framework, named
Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition
branch to generate a facial mask so as to focus on facial muscle moving
regions. To guide the facial mask learning, we propose to incorporate prior
domain knowledge by using the average differences between neutral faces and the
corresponding expressive faces as the training guidance. Extensive experiments
on three facial expression benchmark datasets demonstrate the effectiveness of
the proposed method, compared with the state-of-the-art approaches.Comment: VCIP 2019, Oral. Code is available at
https://github.com/donydchen/FMPN-FE
A Deep Dive into Blockchain Selfish Mining
This paper studies a fundamental problem regarding the security of blockchain
on how the existence of multiple misbehaving pools influences the profitability
of selfish mining. Each selfish miner maintains a private chain and makes it
public opportunistically for the purpose of acquiring more rewards
incommensurate to his Hashrate. We establish a novel Markov chain model to
characterize all the state transitions of public and private chains. The
minimum requirement of Hashrate together with the minimum delay of being
profitable is derived in close-form. The former reduces to 21.48% with the
symmetric selfish miners, while their competition with asymmetric Hashrates
puts forward a higher requirement of the profitable threshold. The profitable
delay increases with the decrease of the Hashrate of selfish miners, making the
mining pools more cautious on performing selfish mining.Comment: 6 pages, 13 figure
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