37,754 research outputs found
Foam-like compression behavior of fibrin networks
The rheological properties of fibrin networks have been of long-standing
interest. As such there is a wealth of studies of their shear and tensile
responses, but their compressive behavior remains unexplored. Here, by
characterization of the network structure with synchronous measurement of the
fibrin storage and loss moduli at increasing degrees of compression, we show
that the compressive behavior of fibrin networks is similar to that of cellular
solids. A non-linear stress-strain response of fibrin consists of three
regimes: 1) an initial linear regime, in which most fibers are straight, 2) a
plateau regime, in which more and more fibers buckle and collapse, and 3) a
markedly non-linear regime, in which network densification occurs {{by bending
of buckled fibers}} and inter-fiber contacts. Importantly, the spatially
non-uniform network deformation included formation of a moving "compression
front" along the axis of strain, which segregated the fibrin network into
compartments with different fiber densities and structure. The Young's modulus
of the linear phase depends quadratically on the fibrin volume fraction while
that in the densified phase depends cubically on it. The viscoelastic plateau
regime corresponds to a mixture of these two phases in which the fractions of
the two phases change during compression. We model this regime using a
continuum theory of phase transitions and analytically predict the storage and
loss moduli which are in good agreement with the experimental data. Our work
shows that fibrin networks are a member of a broad class of natural cellular
materials which includes cancellous bone, wood and cork
Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics
Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system
A constitutive law for cross-linked actin networks by homogenization techniques
Inspired by experiments on the actin driven propulsion of micrometer sized
beads we develop and study a minimal mechanical model of a two-dimensional
network of stiff elastic filaments grown from the surface of a cylinder.
Starting out from a discrete model of the network structure and of its
microscopic mechanical behavior we derive a macroscopic constitutive law by
homogenization techniques. We calculate the axisymmetric equilibrium state and
study its linear stability depending on the microscopic mechanical properties.
We find that thin networks are linearly stable, whereas thick networks are
unstable. The critical thickness for the change in stability depends on the
ratio of the microscopic elastic constants. The instability is induced by the
increase in the compressive load on the inner network layers as the thickness
of the network increases. The here employed homogenization approach combined
with more elaborate microscopic models can serve as a basis to study the
evolution of polymerizing actin networks and the mechanism of actin driven
motion.Comment: 19 pages, 7 figure
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
With the aim of developing a fast yet accurate algorithm for compressive
sensing (CS) reconstruction of natural images, we combine in this paper the
merits of two existing categories of CS methods: the structure insights of
traditional optimization-based methods and the speed of recent network-based
ones. Specifically, we propose a novel structured deep network, dubbed
ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm
(ISTA) for optimizing a general norm CS reconstruction model. To cast
ISTA into deep network form, we develop an effective strategy to solve the
proximal mapping associated with the sparsity-inducing regularizer using
nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms,
shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than
being hand-crafted. Moreover, considering that the residuals of natural images
are more compressible, an enhanced version of ISTA-Net in the residual domain,
dubbed {ISTA-Net}, is derived to further improve CS reconstruction.
Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform
existing state-of-the-art optimization-based and network-based CS methods by
large margins, while maintaining fast computational speed. Our source codes are
available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.Comment: 10 pages, 6 figures, 4 Tables. To appear in CVPR 201
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
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