5,106 research outputs found
Lipschitz equivalence of a class of self-similar sets
We consider a class of homogeneous self-similar sets with complete overlaps
and give a sufficient condition for the Lipschitz equivalence between members
in this class.Comment: A remark was added. To appear in Ann. Acad. Sci. Fenn. Mat
Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites
Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR)
based composites is a typical and crucial property in practical applications.
Previous studies show that the abrasion resistance can be calculated by the
multiple linear regression model. In our study, considering this relationship
can also be described into the non-linear conditions, a Multilayer Feed-forward
Neural Networks model with 3 nodes (MLFN-3) was successfully established to
describe the relationship between the abrasion resistance and other properties,
using 23 groups of data, with the RMS error 0.07. Our studies have proved that
Artificial Neural Networks (ANN) model can be used to predict the SSBR-based
composites, which is an accurate and robust process
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.Comment: Accepted by IJCAI 201
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction
Recent works have demonstrated that deep learning (DL) based compressed
sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by
reconstructing MR images from sub-sampled k-space data. However, network
architectures adopted in previous methods are all designed by handcraft. Neural
Architecture Search (NAS) algorithms can automatically build neural network
architectures which have outperformed human designed ones in several vision
tasks. Inspired by this, here we proposed a novel and efficient network for the
MR image reconstruction problem via NAS instead of manual attempts.
Particularly, a specific cell structure, which was integrated into the
model-driven MR reconstruction pipeline, was automatically searched from a
flexible pre-defined operation search space in a differentiable manner.
Experimental results show that our searched network can produce better
reconstruction results compared to previous state-of-the-art methods in terms
of PSNR and SSIM with 4-6 times fewer computation resources. Extensive
experiments were conducted to analyze how hyper-parameters affect
reconstruction performance and the searched structures. The generalizability of
the searched architecture was also evaluated on different organ MR datasets.
Our proposed method can reach a better trade-off between computation cost and
reconstruction performance for MR reconstruction problem with good
generalizability and offer insights to design neural networks for other medical
image applications. The evaluation code will be available at
https://github.com/yjump/NAS-for-CSMRI.Comment: To be appear in Computerized Medical Imaging and Graphic
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