24,599 research outputs found
Computational Methods for Sparse Solution of Linear Inverse Problems
The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available. Many fundamental questions in electrical engineering, statistics, and applied mathematics can be posed as sparse approximation problems, making these algorithms versatile and relevant to a plethora of applications
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous
quantification of multiple properties of biological tissues. It relies on a
pseudo-random acquisition and the matching of acquired signal evolutions to a
precomputed dictionary. However, the dictionary is not scalable to
higher-parametric spaces, limiting MRF to the simultaneous mapping of only a
small number of parameters (proton density, T1 and T2 in general). Inspired by
diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF
sequence with embedded diffusion-encoding gradients along all three axes to
efficiently encode orientational diffusion and T1 and T2 relaxation. We take
advantage of a convolutional neural network (CNN) to reconstruct multiple
quantitative maps from this single, highly undersampled acquisition. We bypass
expensive dictionary matching by learning the implicit physical relationships
between the spatiotemporal MRF data and the T1, T2 and diffusion tensor
parameters. The predicted parameter maps and the derived scalar diffusion
metrics agree well with state-of-the-art reference protocols. Orientational
diffusion information is captured as seen from the estimated primary diffusion
directions. In addition to this, the joint acquisition and reconstruction
framework proves capable of preserving tissue abnormalities in multiple
sclerosis lesions
DNA nano-mechanics: how proteins deform the double helix
It is a standard exercise in mechanical engineering to infer the external
forces and torques on a body from its static shape and known elastic
properties. Here we apply this kind of analysis to distorted double-helical DNA
in complexes with proteins. We extract the local mean forces and torques acting
on each base-pair of bound DNA from high-resolution complex structures. Our
method relies on known elastic potentials and a careful choice of coordinates
of the well-established rigid base-pair model of DNA. The results are robust
with respect to parameter and conformation uncertainty. They reveal the complex
nano-mechanical patterns of interaction between proteins and DNA. Being
non-trivially and non-locally related to observed DNA conformations, base-pair
forces and torques provide a new view on DNA-protein binding that complements
structural analysis.Comment: accepted for publication in JCP; some minor changes in response to
review 18 pages, 5 figure + supplement: 4 pages, 3 figure
Stochastic Model Predictive Control for Autonomous Mobility on Demand
This paper presents a stochastic, model predictive control (MPC) algorithm
that leverages short-term probabilistic forecasts for dispatching and
rebalancing Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of
self-driving vehicles). We first present the core stochastic optimization
problem in terms of a time-expanded network flow model. Then, to ameliorate its
tractability, we present two key relaxations. First, we replace the original
stochastic problem with a Sample Average Approximation (SAA), and characterize
the performance guarantees. Second, we separate the controller into two
separate parts to address the task of assigning vehicles to the outstanding
customers separate from that of rebalancing. This enables the problem to be
solved as two totally unimodular linear programs, and thus easily scalable to
large problem sizes. Finally, we test the proposed algorithm in two scenarios
based on real data and show that it outperforms prior state-of-the-art
algorithms. In particular, in a simulation using customer data from DiDi
Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in
customer waiting time compared to state of the art non-stochastic algorithms.Comment: Submitting to the IEEE International Conference on Intelligent
Transportation Systems 201
Doping dependent Irreversible Magnetic Properties of Ba(Fe1-xCox)2As2 Single Crystals
We discuss the irreversible magnetic properties of self-flux grown
Ba(Fe1-xCox)2As2 single crystals for a wide range of concentrations covering
the whole phase diagram from the underdoped to the overdoped regime, x=0.038,
0.047, 0.058, 0.071, 0.074, 0.10, 0.106 and 0.118. Samples were characterized
by a magneto-optical method and show excellent spatial uniformity of the
superconducting state. The overall behavior closely follows classical Bean
model of the critical state. The field-dependent magnetization exhibits second
peak at a temperature and doping - dependent magnetic field, Hp. The evolution
of this fishtail feature with doping is discussed. Magnetic relaxation is
time-logarithmic and unusually fast. Similar to cuprates, there is an apparent
crossover from collective elastic to plastic flux creep above Hp. At high
fields, the field dependence of the relaxation rate becomes doping independent.
We discuss our results in the framework of the weak collective pinning and show
that vortex physics in iron-based pnictide crystals is much closer to high-Tc
cuprates than to conventional s-wave (including MgB2) superconductors.Comment: for the special issue of Physica C on iron-based pnictide
superconductor
A correspondence between solution-state dynamics of an individual protein and the sequence and conformational diversity of its family.
Conformational ensembles are increasingly recognized as a useful representation to describe fundamental relationships between protein structure, dynamics and function. Here we present an ensemble of ubiquitin in solution that is created by sampling conformational space without experimental information using "Backrub" motions inspired by alternative conformations observed in sub-Angstrom resolution crystal structures. Backrub-generated structures are then selected to produce an ensemble that optimizes agreement with nuclear magnetic resonance (NMR) Residual Dipolar Couplings (RDCs). Using this ensemble, we probe two proposed relationships between properties of protein ensembles: (i) a link between native-state dynamics and the conformational heterogeneity observed in crystal structures, and (ii) a relation between dynamics of an individual protein and the conformational variability explored by its natural family. We show that the Backrub motional mechanism can simultaneously explore protein native-state dynamics measured by RDCs, encompass the conformational variability present in ubiquitin complex structures and facilitate sampling of conformational and sequence variability matching those occurring in the ubiquitin protein family. Our results thus support an overall relation between protein dynamics and conformational changes enabling sequence changes in evolution. More practically, the presented method can be applied to improve protein design predictions by accounting for intrinsic native-state dynamics
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