944 research outputs found
Reduced Power Graphs of
Given a group , let us connect two non-identity elements by an edge if and
only if one is a power of another. This gives a graph structure on minus
identity, called the reduced power graph. In this paper, we shall find the
exact number of connected components and the exact diameter of each component
for the reduced power graphs of for all prime
power
Deep-neural-network solution of the ab initio nuclear structure
Predicting the structure of quantum many-body systems from the first
principles of quantum mechanics is a common challenge in physics, chemistry,
and material science. Deep machine learning has proven to be a powerful tool
for solving condensed matter and chemistry problems, while for atomic nuclei,
it is still quite challenging because of the complicated nucleon-nucleon
interactions, which strongly couples the spatial, spin, and isospin degrees of
freedom. By combining essential physics of the nuclear wave functions and the
strong expressive power of artificial neural networks, we develop FeynmanNet, a
novel deep-learning variational quantum Monte Carlo approach for \emph{ab
initio} nuclear structure. We show that FeynmanNet can provide very accurate
ground-state energies and wave functions for He, Li, and even up to
O as emerging from the leading-order and next-to-leading-order
Hamiltonians of pionless effective field theory. Compared to the conventional
diffusion Monte Carlo approaches, which suffer from the severe inherent
fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational
way and scales polynomially with the number of nucleons. Therefore, it paves
the way to a highly accurate and efficient \emph{ab initio} method for
predicting nuclear properties based on the realistic interactions between
nucleons.Comment: 13 pages, 3 figure
Rotation-Scale Equivariant Steerable Filters
Incorporating either rotation equivariance or scale equivariance into CNNs
has proved to be effective in improving models' generalization performance.
However, jointly integrating rotation and scale equivariance into CNNs has not
been widely explored. Digital histology imaging of biopsy tissue can be
captured at arbitrary orientation and magnification and stored at different
resolutions, resulting in cells appearing in different scales. When
conventional CNNs are applied to histopathology image analysis, the
generalization performance of models is limited because 1) a part of the
parameters of filters are trained to fit rotation transformation, thus
decreasing the capability of learning other discriminative features; 2)
fixed-size filters trained on images at a given scale fail to generalize to
those at different scales. To deal with these issues, we propose the
Rotation-Scale Equivariant Steerable Filter (RSESF), which incorporates
steerable filters and scale-space theory. The RSESF contains copies of filters
that are linear combinations of Gaussian filters, whose direction is controlled
by directional derivatives and whose scale parameters are trainable but
constrained to span disjoint scales in successive layers of the network.
Extensive experiments on two gland segmentation datasets demonstrate that our
method outperforms other approaches, with much fewer trainable parameters and
fewer GPU resources required. The source code is available at:
https://github.com/ynulonger/RSESF.Comment: Accepted by MIDL 202
Finger Vein Recognition Based on (2D)2 PCA and Metric Learning
Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%
Scale-Equivariant UNet for Histopathology Image Segmentation
Digital histopathology slides are scanned and viewed under different
magnifications and stored as images at different resolutions. Convolutional
Neural Networks (CNNs) trained on such images at a given scale fail to
generalise to those at different scales. This inability is often addressed by
augmenting training data with re-scaled images, allowing a model with
sufficient capacity to learn the requisite patterns. Alternatively, designing
CNN filters to be scale-equivariant frees up model capacity to learn
discriminative features. In this paper, we propose the Scale-Equivariant UNet
(SEUNet) for image segmentation by building on scale-space theory. The SEUNet
contains groups of filters that are linear combinations of Gaussian basis
filters, whose scale parameters are trainable but constrained to span disjoint
scales through the layers of the network. Extensive experiments on a nuclei
segmentation dataset and a tissue type segmentation dataset demonstrate that
our method outperforms other approaches, with much fewer trainable parameters.Comment: This paper was accepted by GeoMedIA 202
ADS_UNet: A Nested UNet for Histopathology Image Segmentation
The UNet model consists of fully convolutional network (FCN) layers arranged
as contracting encoder and upsampling decoder maps. Nested arrangements of
these encoder and decoder maps give rise to extensions of the UNet model, such
as UNete and UNet++. Other refinements include constraining the outputs of the
convolutional layers to discriminate between segment labels when trained end to
end, a property called deep supervision. This reduces feature diversity in
these nested UNet models despite their large parameter space. Furthermore, for
texture segmentation, pixel correlations at multiple scales contribute to the
classification task; hence, explicit deep supervision of shallower layers is
likely to enhance performance. In this paper, we propose ADS UNet, a stage-wise
additive training algorithm that incorporates resource-efficient deep
supervision in shallower layers and takes performance-weighted combinations of
the sub-UNets to create the segmentation model. We provide empirical evidence
on three histopathology datasets to support the claim that the proposed ADS
UNet reduces correlations between constituent features and improves performance
while being more resource efficient. We demonstrate that ADS_UNet outperforms
state-of-the-art Transformer-based models by 1.08 and 0.6 points on CRAG and
BCSS datasets, and yet requires only 37% of GPU consumption and 34% of training
time as that required by Transformers.Comment: To be published in Expert Systems With Application
- ā¦