8,098 research outputs found
Eigen-Inference for Energy Estimation of Multiple Sources
In this paper, a new method is introduced to blindly estimate the transmit
power of multiple signal sources in multi-antenna fading channels, when the
number of sensing devices and the number of available samples are sufficiently
large compared to the number of sources. Recent advances in the field of large
dimensional random matrix theory are used that result in a simple and
computationally efficient consistent estimator of the power of each source. A
criterion to determine the minimum number of sensors and the minimum number of
samples required to achieve source separation is then introduced. Simulations
are performed that corroborate the theoretical claims and show that the
proposed power estimator largely outperforms alternative power inference
techniques.Comment: to appear in IEEE Trans. on Information Theory, 17 pages, 13 figure
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern
In this article, a general information-plus-noise transmission model is
assumed, the receiver end of which is composed of a large number of sensors and
is unaware of the noise pattern. For this model, and under reasonable
assumptions, a set of results is provided for the receiver to perform
statistical eigen-inference on the information part. In particular, we
introduce new methods for the detection, counting, and the power and subspace
estimation of multiple sources composing the information part of the
transmission. The theoretical performance of some of these techniques is also
discussed. An exemplary application of these methods to array processing is
then studied in greater detail, leading in particular to a novel MUSIC-like
algorithm assuming unknown noise covariance.Comment: 25 pages, 5 figure
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
We consider the problem of depth estimation from a single monocular image in
this work. It is a challenging task as no reliable depth cues are available,
e.g., stereo correspondences, motions, etc. Previous efforts have been focusing
on exploiting geometric priors or additional sources of information, with all
using hand-crafted features. Recently, there is mounting evidence that features
from deep convolutional neural networks (CNN) are setting new records for
various vision applications. On the other hand, considering the continuous
characteristic of the depth values, depth estimations can be naturally
formulated into a continuous conditional random field (CRF) learning problem.
Therefore, we in this paper present a deep convolutional neural field model for
estimating depths from a single image, aiming to jointly explore the capacity
of deep CNN and continuous CRF. Specifically, we propose a deep structured
learning scheme which learns the unary and pairwise potentials of continuous
CRF in a unified deep CNN framework.
The proposed method can be used for depth estimations of general scenes with
no geometric priors nor any extra information injected. In our case, the
integral of the partition function can be analytically calculated, thus we can
exactly solve the log-likelihood optimization. Moreover, solving the MAP
problem for predicting depths of a new image is highly efficient as closed-form
solutions exist. We experimentally demonstrate that the proposed method
outperforms state-of-the-art depth estimation methods on both indoor and
outdoor scene datasets.Comment: fixed some typos. in CVPR15 proceeding
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
The efficient calculation of Hamiltonian spectra, a problem often intractable
on classical machines, can find application in many fields, from physics to
chemistry. Here, we introduce the concept of an "eigenstate witness" and
through it provide a new quantum approach which combines variational methods
and phase estimation to approximate eigenvalues for both ground and excited
states. This protocol is experimentally verified on a programmable silicon
quantum photonic chip, a mass-manufacturable platform, which embeds entangled
state generation, arbitrary controlled-unitary operations, and projective
measurements. Both ground and excited states are experimentally found with
fidelities >99%, and their eigenvalues are estimated with 32-bits of precision.
We also investigate and discuss the scalability of the approach and study its
performance through numerical simulations of more complex Hamiltonians. This
result shows promising progress towards quantum chemistry on quantum computers.Comment: 9 pages, 4 figures, plus Supplementary Material [New version with
minor typos corrected.
Estimating Depth from RGB and Sparse Sensing
We present a deep model that can accurately produce dense depth maps given an
RGB image with known depth at a very sparse set of pixels. The model works
simultaneously for both indoor/outdoor scenes and produces state-of-the-art
dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI
datasets. We surpass the state-of-the-art for monocular depth estimation even
with depth values for only 1 out of every ~10000 image pixels, and we
outperform other sparse-to-dense depth methods at all sparsity levels. With
depth values for 1/256 of the image pixels, we achieve a mean absolute error of
less than 1% of actual depth on indoor scenes, comparable to the performance of
consumer-grade depth sensor hardware. Our experiments demonstrate that it would
indeed be possible to efficiently transform sparse depth measurements obtained
using e.g. lower-power depth sensors or SLAM systems into high-quality dense
depth maps.Comment: European Conference on Computer Vision (ECCV) 2018. Updated to
camera-ready version with additional experiment
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