1,176 research outputs found
Importance and effectiveness of representing the shapes of Cosserat rods and framed curves as paths in the special Euclidean algebra
We discuss how the shape of a special Cosserat rod can be represented as a
path in the special Euclidean algebra. By shape we mean all those geometric
features that are invariant under isometries of the three-dimensional ambient
space. The representation of the shape as a path in the special Euclidean
algebra is intrinsic to the description of the mechanical properties of a rod,
since it is given directly in terms of the strain fields that stimulate the
elastic response of special Cosserat rods. Moreover, such a representation
leads naturally to discretization schemes that avoid the need for the expensive
reconstruction of the strains from the discretized placement and for
interpolation procedures which introduce some arbitrariness in popular
numerical schemes. Given the shape of a rod and the positioning of one of its
cross sections, the full placement in the ambient space can be uniquely
reconstructed and described by means of a base curve endowed with a material
frame. By viewing a geometric curve as a rod with degenerate point-like cross
sections, we highlight the essential difference between rods and framed curves,
and clarify why the family of relatively parallel adapted frames is not
suitable for describing the mechanics of rods but is the appropriate tool for
dealing with the geometry of curves.Comment: Revised version; 25 pages; 7 figure
A molecular theory for two-photon and three-photon fluorescence polarization
In the analysis of molecular structure and local order in heterogeneous samples, multiphoton excitation of fluorescence affords chemically specific information and high-resolution imaging. This report presents the results of an investigation that secures a detailed theoretical representation of the fluorescence polarization produced by one-, two-, and three-photon excitations, with orientational averaging procedures being deployed to deliver the fully disordered limits. The equations determining multiphoton fluorescence response prove to be expressible in a relatively simple, generic form, and graphs exhibit the functional form of the multiphoton fluorescence polarization. Amongst other features, the results lead to the identification of a condition under which the fluorescence produced through the concerted absorption of any number of photons becomes completely unpolarized. It is also shown that the angular variation of fluorescence intensities is reliable indicator of orientational disorder
Permission-Based Separation Logic for Multithreaded Java Programs
This paper presents a program logic for reasoning about multithreaded
Java-like programs with dynamic thread creation, thread joining and reentrant
object monitors. The logic is based on concurrent separation logic. It is the
first detailed adaptation of concurrent separation logic to a multithreaded
Java-like language. The program logic associates a unique static access
permission with each heap location, ensuring exclusive write accesses and
ruling out data races. Concurrent reads are supported through fractional
permissions. Permissions can be transferred between threads upon thread
starting, thread joining, initial monitor entrancies and final monitor exits.
In order to distinguish between initial monitor entrancies and monitor
reentrancies, auxiliary variables keep track of multisets of currently held
monitors. Data abstraction and behavioral subtyping are facilitated through
abstract predicates, which are also used to represent monitor invariants,
preconditions for thread starting and postconditions for thread joining.
Value-parametrized types allow to conveniently capture common strong global
invariants, like static object ownership relations. The program logic is
presented for a model language with Java-like classes and interfaces, the
soundness of the program logic is proven, and a number of illustrative examples
are presented
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.We would like to thank NVIDIA Corporation for supporting us with the GeForce Titan Xp through the GPU Grant Program. (NVIDIA Corporation; GeForce Titan Xp through the GPU Grant Program)First author draf
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in
solving complex inverse problems. The aim of this work is to develop a novel
CNN framework to reconstruct video sequence of dynamic live cells captured
using a computational microscopy technique, Fourier ptychographic microscopy
(FPM). The unique feature of the FPM is its capability to reconstruct images
with both wide field-of-view (FOV) and high resolution, i.e. a large
space-bandwidth-product (SBP), by taking a series of low resolution intensity
images. For live cell imaging, a single FPM frame contains thousands of cell
samples with different morphological features. Our idea is to fully exploit the
statistical information provided by this large spatial ensemble so as to make
predictions in a sequential measurement, without using any additional temporal
dataset. Specifically, we show that it is possible to reconstruct high-SBP
dynamic cell videos by a CNN trained only on the first FPM dataset captured at
the beginning of a time-series experiment. Our CNN approach reconstructs a
12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared
to the model-based FPM algorithm. In addition, the CNN further reduces the
required number of images in each time frame by ~6X. Overall, this
significantly improves the imaging throughput by reducing both the acquisition
and computational times. The proposed CNN is based on the conditional
generative adversarial network (cGAN) framework. Additionally, we also exploit
transfer learning so that our pre-trained CNN can be further optimized to image
other cell types. Our technique demonstrates a promising deep learning approach
to continuously monitor large live-cell populations over an extended time and
gather useful spatial and temporal information with sub-cellular resolution
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