10,856 research outputs found
Fast non-negative deconvolution for spike train inference from population calcium imaging
Calcium imaging for observing spiking activity from large populations of
neurons are quickly gaining popularity. While the raw data are fluorescence
movies, the underlying spike trains are of interest. This work presents a fast
non-negative deconvolution filter to infer the approximately most likely spike
train for each neuron, given the fluorescence observations. This algorithm
outperforms optimal linear deconvolution (Wiener filtering) on both simulated
and biological data. The performance gains come from restricting the inferred
spike trains to be positive (using an interior-point method), unlike the Wiener
filter. The algorithm is fast enough that even when imaging over 100 neurons,
inference can be performed on the set of all observed traces faster than
real-time. Performing optimal spatial filtering on the images further refines
the estimates. Importantly, all the parameters required to perform the
inference can be estimated using only the fluorescence data, obviating the need
to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
The First Two Years of Electromagnetic Follow-Up with Advanced LIGO and Virgo
We anticipate the first direct detections of gravitational waves (GWs) with
Advanced LIGO and Virgo later this decade. Though this groundbreaking technical
achievement will be its own reward, a still greater prize could be observations
of compact binary mergers in both gravitational and electromagnetic channels
simultaneously. During Advanced LIGO and Virgo's first two years of operation,
2015 through 2016, we expect the global GW detector array to improve in
sensitivity and livetime and expand from two to three detectors. We model the
detection rate and the sky localization accuracy for binary neutron star (BNS)
mergers across this transition. We have analyzed a large, astrophysically
motivated source population using real-time detection and sky localization
codes and higher-latency parameter estimation codes that have been expressly
built for operation in the Advanced LIGO/Virgo era. We show that for most BNS
events the rapid sky localization, available about a minute after a detection,
is as accurate as the full parameter estimation. We demonstrate that Advanced
Virgo will play an important role in sky localization, even though it is
anticipated to come online with only one-third as much sensitivity as the
Advanced LIGO detectors. We find that the median 90% confidence region shrinks
from ~500 square degrees in 2015 to ~200 square degrees in 2016. A few distinct
scenarios for the first LIGO/Virgo detections emerge from our simulations.Comment: 17 pages, 11 figures, 5 tables. For accompanying data, see
http://www.ligo.org/scientists/first2year
Evaluation of algorithms for estimating wheat acreage from multispectral scanner data
The author has identified the following significant results. Fourteen different classification algorithms were tested for their ability to estimate the proportion of wheat in an area. For some algorithms, accuracy of classification in field centers was observed. The data base consisted of ground truth and LANDSAT data from 55 sections (1 x 1 mile) from five LACIE intensive test sites in Kansas and Texas. Signatures obtained from training fields selected at random from the ground truth were generally representative of the data distribution patterns. LIMMIX, an algorithm that chooses a pure signature when the data point is close enough to a signature mean and otherwise chooses the best mixture of a pair of signatures, reduced the average absolute error to 6.1% and the bias to 1.0%. QRULE run with a null test achieved a similar reduction
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