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 $z\sim2$ 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 S$\`e$rsic 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 $\sim$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 NiI2_2

A realistic first-principle-based spin Hamiltonian is constructed for the type-II multiferroic NiI$_2$, 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 $\langle1\bar10\rangle$ 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$_2$

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

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

Robust mode-locking in a hybrid ultrafast laser based on nonlinear multimodal interference

We experimentally demonstrate the realization of a half-polarization-maintaining (half-PM) fiber laser, in which mode-locking is provided by a reflective multimode-interference saturable absorber (SA). In the specially designed SA, linearly polarized light is coupled into a 15-cm-long graded-index multimode fiber (GIMF) through the PM fiber, and then reflected back to the PM structure through a mirror pigtailed with a single-mode fiber (SMF). The modulation depth and saturation peak power are measured to be 1.5% and 0.6 W, respectively. The proposed SA device is incorporated into a novel half-PM erbium-doped fiber oscillator, which generates soliton pulses with 409 fs temporal duration at a 33.3 MHz repetition rate. The proposed fiber laser is compared with a conventional non-PM fiber laser mode-locked by nonlinear polarization evolution (NPE) in terms of optical properties such as spectral bandwidth, pulse duration, and stability performance. Short- and long-time stability tests and superior noise performance corroborate robust mode-locking in this setup.Comment: to be published in Optics and Laser Technolog