118 research outputs found
Distance Guided Channel Weighting for Semantic Segmentation
Recent works have achieved great success in improving the performance of
multiple computer vision tasks by capturing features with a high channel number
utilizing deep neural networks. However, many channels of extracted features
are not discriminative and contain a lot of redundant information. In this
paper, we address above issue by introducing the Distance Guided Channel
Weighting (DGCW) Module. The DGCW module is constructed in a pixel-wise context
extraction manner, which enhances the discriminativeness of features by
weighting different channels of each pixel's feature vector when modeling its
relationship with other pixels. It can make full use of the high-discriminative
information while ignore the low-discriminative information containing in
feature maps, as well as capture the long-range dependencies. Furthermore, by
incorporating the DGCW module with a baseline segmentation network, we propose
the Distance Guided Channel Weighting Network (DGCWNet). We conduct extensive
experiments to demonstrate the effectiveness of DGCWNet. In particular, it
achieves 81.6% mIoU on Cityscapes with only fine annotated data for training,
and also gains satisfactory performance on another two semantic segmentation
datasets, i.e. Pascal Context and ADE20K. Code will be available soon at
https://github.com/LanyunZhu/DGCWNet
Structure and morphology of X-ray selected AGN hosts at 1<z<3 in CANDELS-COSMOS field
We analyze morphologies of the host galaxies of 35 X-ray selected active
galactic nucleus (AGNs) at in the Cosmic Evolution Survey (COSMOS)
field using Hubble Space Telescope/WFC3 imaging taken from the Cosmic Assembly
Near-infrared Deep Extragalactic Legacy Survey (CANDELS). We build a control
sample of 350 galaxies in total, by selecting ten non-active galaxies drawn
from the same field with the similar stellar mass and redshift for each AGN
host. By performing two dimensional fitting with GALFIT on the surface
brightness profile, we find that the distribution of Srsic index (n) of
AGN hosts does not show a statistical difference from that of the control
sample. We measure the nonparametric morphological parameters (the asymmetry
index A, the Gini coefficient G, the concentration index C and the M20 index)
based on point source subtracted images. All the distributions of these
morphological parameters of AGN hosts are consistent with those of the control
sample. We finally investigate the fraction of distorted morphologies in both
samples by visual classification. Only 15% of the AGN hosts have highly
distorted morphologies, possibly due to a major merger or interaction. We find
there is no significant difference in the distortion fractions between the AGN
host sample and control sample. We conclude that the morphologies of X-ray
selected AGN hosts are similar to those of nonactive galaxies and most AGN
activity is not triggered by major merger.Comment: 5 pages, 3 figures, accepted for publication in The Astrophysical
Journal Letter
Realistic Spin Model for Multiferroic NiI
A realistic first-principle-based spin Hamiltonian is constructed for the
type-II multiferroic NiI, using a symmetry-adapted cluster expansion
method. Besides single ion anisotropy and isotropic Heisenberg terms, this
model further includes the Kitaev interaction and a biquadratic term, and can
well reproduce striking features of the experimental helical ground state, that
are, {\it e.g.}, a proper screw state, canting of rotation plane, propagation
direction and period. Using this model to build a phase diagram, it is
demonstrated that, (i) the in-plane propagation direction of
is determined by the Kitaev interaction, instead of the
long-believed exchange frustrations; and (ii) the canting of rotation plane is
also dominantly determined by Kitaev interaction, rather than interlayer
couplings. Furthermore, additional Monte Carlo simulations reveal three
equivalent domains and different topological defects. Since the
ferroelectricity is induced by spins in type-II multiferroics, our work also
implies that Kitaev interaction is closely related to the multiferroicity of
NiI
A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder
Sora Generates Videos with Stunning Geometrical Consistency
The recently developed Sora model [1] has exhibited remarkable capabilities
in video generation, sparking intense discussions regarding its ability to
simulate real-world phenomena. Despite its growing popularity, there is a lack
of established metrics to evaluate its fidelity to real-world physics
quantitatively. In this paper, we introduce a new benchmark that assesses the
quality of the generated videos based on their adherence to real-world physics
principles. We employ a method that transforms the generated videos into 3D
models, leveraging the premise that the accuracy of 3D reconstruction is
heavily contingent on the video quality. From the perspective of 3D
reconstruction, we use the fidelity of the geometric constraints satisfied by
the constructed 3D models as a proxy to gauge the extent to which the generated
videos conform to real-world physics rules. Project page:
https://sora-geometrical-consistency.github.io/Comment: 5 pages, 3 figure
All-polarization-maintaining linear cavity fiber lasers mode-locked by nonlinear polarization evolution in stretched pulse regime
Nonlinear polarization evolution (NPE) is among the most advanced techniques
for obtaining ultrashort pulses with excellent optical performance. However, it
is challenging to design environmentally stable NPE fiber oscillators using
only polarization-maintaining (PM) fibers. Here, we use the same PM fiber and
non-reciprocal phase shifter to design two different devices, which are capable
of acting as effective NPE saturable absorbers (SAs) in two all-PM linear
cavity fiber lasers. These two laser setups differ in the position of the
non-reciprocal phase shifter, the presence of which is crucial for the proposed
fiber lasers to reduce their mode-locking thresholds and achieve high
repetition rates above 100 MHz. The mode-locking principle and pulse evolution
in the laser cavity are investigated theoretically. The first all-PM fiber
oscillator emits sub-200 fs stretched pulses with low peak powers. The second
oscillator, with a simpler architecture, directly delivers stretched pulses
with high peak powers, the spectral bandwidth greater than 30 nm, and the pulse
duration less than 90 fs. To the best of our knowledge, 79 fs achieved in this
design is the shortest pulse duration provided by PM fiber lasers using NPE
mode-lockers.Comment: to be published in J. Lightwave Tec
A novel deep learning model for glioma epilepsy associated with the identification of human cytomegalovirus infection injuries based on head MR
PurposeIn this study, a deep learning model was established based on head MRI to predict a crucial evaluation parameter in the assessment of injuries resulting from human cytomegalovirus infection: the occurrence of glioma-related epilepsy. The relationship between glioma and epilepsy was investigated, which serves as a significant indicator of labor force impairment.MethodsThis study enrolled 142 glioma patients, including 127 from Shengjing Hospital of China Medical University, and 15 from the Second Affiliated Hospital of Dalian Medical University. T1 and T2 sequence images of patients’ head MRIs were utilized to predict the occurrence of glioma-associated epilepsy. To validate the model’s performance, the results of machine learning and deep learning models were compared. The machine learning model employed manually annotated texture features from tumor regions for modeling. On the other hand, the deep learning model utilized fused data consisting of tumor-containing T1 and T2 sequence images for modeling.ResultsThe neural network based on MobileNet_v3 performed the best, achieving an accuracy of 86.96% on the validation set and 75.89% on the test set. The performance of this neural network model significantly surpassed all the machine learning models, both on the validation and test sets.ConclusionIn this study, we have developed a neural network utilizing head MRI, which can predict the likelihood of glioma-associated epilepsy in untreated glioma patients based on T1 and T2 sequence images. This advancement provides forensic support for the assessment of injuries related to human cytomegalovirus infection
ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions
Endometrial cancer is one of the most common tumors in the female
reproductive system and is the third most common gynecological malignancy that
causes death after ovarian and cervical cancer. Early diagnosis can
significantly improve the 5-year survival rate of patients. With the
development of artificial intelligence, computer-assisted diagnosis plays an
increasingly important role in improving the accuracy and objectivity of
diagnosis, as well as reducing the workload of doctors. However, the absence of
publicly available endometrial cancer image datasets restricts the application
of computer-assisted diagnostic techniques.In this paper, a publicly available
Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation
and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically,
the segmentation section includes PET and CT images, with a total of 7159
images in multiple formats. In order to prove the effectiveness of segmentation
methods on ECPC-IDS, five classical deep learning semantic segmentation methods
are selected to test the image segmentation task. The object detection section
also includes PET and CT images, with a total of 3579 images and XML files with
annotation information. Six deep learning methods are selected for experiments
on the detection task.This study conduct extensive experiments using deep
learning-based semantic segmentation and object detection methods to
demonstrate the differences between various methods on ECPC-IDS. As far as we
know, this is the first publicly available dataset of endometrial cancer with a
large number of multiple images, including a large amount of information
required for image and target detection. ECPC-IDS can aid researchers in
exploring new algorithms to enhance computer-assisted technology, benefiting
both clinical doctors and patients greatly.Comment: 14 pages,6 figure
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