A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

Casimir effect of an ideal Bose gas trapped in a generic power-law potential

The Casimir effect of an ideal Bose gas trapped in a generic power-law potential and confined between two slabs with Dirichlet, Neumann, and periodic boundary conditions is investigated systematically, based on the grand potential of the ideal Bose gas, the Casimir potential and force are calculated. The scaling function is obtained and discussed. The special cases of free and harmonic potentials are also discussed. It is found that when T<Tc (where Tc is the critical temperature of Bose-Einstein condensation), the Casimir force is a power-law decay function; when T>Tc, the Casimir force is an exponential decay function; and when T>>Tc, the Casimir force vanishes.Comment: 5 pages, 1 figur

Constructing a Vocational Mathematics Teaching Model Aligned with Industry Needs

With the increasing integration of vocational education and industry, the content and methodology of vocational mathematics courses require systematic restructuring. Based on the context of industry-education integration, this paper analyzes the current issues in vocational mathematics teaching and proposes a reform pathway oriented toward job competencies and rooted in real-world application scenarios. The study advocates for enterprise participation in curriculum design, task-driven instruction, and the cultivation of data analysis capabilities to achieve a seamless connection between mathematical knowledge and vocational skills. Empirical research indicates that this teaching model effectively enhances students’ learning motivation, practical abilities, and job adaptability, providing a valuable reference for the development of vocational mathematics curricula

The proper class generated by weak supplements

We show that, for hereditary rings, the smallest proper classes containing respectively the classes of short exact sequences determined by small submodules, submodules that have supplements and weak supplement submodules coincide. Moreover, we show that this class can be obtained as a natural extension of the class determined by small submodules. We also study injective, projective, coinjective and coprojective objects of this class. We prove that it is coinjectively generated and its global dimension is at most 1. Finally, we describe this class for Dedekind domains in terms of supplement submodules.TUBITAK (107T709

Assessing the risk factors and establishing multivariable prediction models for singleton macrosomia

IntroductionFetal macrosomia is related to adverse neonatal and maternal health outcomes. Therefore, we aimed to evaluate the risk factors for macrosomia and establish multivariable prediction models to enable early identification, prevention, and mitigation of its adverse outcomes.MethodsThis retrospective case-control study included 800 singleton pregnant women who delivered in 2022 at Fujian Maternity and Child Health Hospital and Quanzhou Women and Children's Hospital. They were categorized into the macrosomia [birth weight (BW) ≥ 4,000 g, n = 400] and non-macrosomia (BW = 2,500–3,999 g, n = 400) groups according to the BW of the newborns. Prediction models in singleton fetuses during mid-to-late pregnancy and before delivery were constructed.ResultsMaternal height ≥ 165 cm [odds ratio (OR) = 2.303, 95% confidence interval (CI): 1.232–4.305], pre-pregnancy overweight (OR = 2.166, 95% CI: 1.119–4.195), pre-pregnancy obesity (OR = 3.189, 95% CI: 1.020–9.968), excessive gestational weight gain in the second trimester (OR = 2.083, 95% CI: 1.250–3.470), and at least two abnormal blood glucose values in the oral glucose tolerance test (OR = 5.267, 95% CI: 1.814–15.29) were identified as risk factors for macrosomia. Additionally, maternal abdominal circumference (AC) plus fundal length ≥ 140 cm (OR = 6.283, 95% CI: 3.976–9.927), fetal biparietal diameter ≥ 10 cm (OR = 3.373, 95% CI: 1.103–10.31), fetal head circumference ≥ 35 cm (OR = 3.473, 95% CI: 1.334–9.041), and fetal AC ≥ 36 cm at pre-delivery (OR = 23.46, 95% CI: 14.81–37.16) were risk factors for macrosomia.DiscussionThe construction of the macrosomia prediction model in singleton fetuses during mid-to-late pregnancy and before delivery showed a strong predictive value. This study identified key high-risk factors for macrosomia during the perinatal period. The macrosomia prediction model developed here is expected to enable early identification of macrosomia, allowing for timely interventions aimed at reducing the risk of adverse perinatal outcomes

Dynamic hypergraph convolutional network for no-reference point cloud quality assessment

With the rapid advancement of three-dimensional (3D) sensing technology, point cloud has emerged as one of the most important approaches for representing 3D data. However, quality degradation inevitably occurs during the acquisition, transmission, and process of point clouds. Therefore, point cloud quality assessment (PCQA) with automatic visual quality perception is particularly critical. In the literature, the graph convolutional networks (GCNs) have achieved certain performance in point cloud-related tasks. However, they cannot fully characterize the nonlinear high-order relationship of such complex data. In this paper, we propose a novel no-reference (NR) PCQA method with hypergraph learning. Specifically, a dynamic hypergraph convolutional network (DHCN) composing of a projected image encoder, a point group encoder, a dynamic hypergraph generator, and a perceptual quality predictor, is devised. First, a projected image encoder and a point group encoder are used to extract feature representations from projected images and point groups, respectively. Then, using the feature representations obtained by the two encoders, dynamic hypergraphs are generated during each iteration, aiming to constantly update the interactive information between the vertices of hypergraphs. Finally, we design the perceptual quality predictor to conduct quality reasoning on the generated hypergraphs. By leveraging the interactive information among hypergraph vertices, feature representations are well aggregated, resulting in a notable improvement in the accuracy of quality pediction. Experimental results on several point cloud quality assessment databases demonstrate that our proposed DHCN can achieve state-of-the-art performance. The code will be available at: https://github.com/chenwuwq/DHCN

The impact of self-perceived burden, caregiver burden, and dyadic coping on negative emotions in colorectal cancer patient-spousal caregiver dyads: a dyadic analysis

ObjectiveTo explore the correlation between dyadic coping, self-perceived burden, caregiver burden, and anxiety/depression in colorectal cancer patient-spousal caregiver dyads.MethodsThis study surveyed 200 colorectal cancer patient-spousal caregiver dyads from August 2022 to December 2022. It evaluated self-perceived burden (only for patients), caregiver burden (only for spousal caregivers), dyadic coping, anxiety, and depression. It analyzed data through Pearson’s correlation and the actor–partner interdependence mediation model.ResultsSelf-perceived burden and caregiver burden were significantly associated with the anxiety/depression of both individuals in colorectal cancer patient-spousal caregiver dyads; patients’ dyadic coping was associated with self-perceived burden and caregiver burden; caregivers’ dyadic coping was only associated with patients’ dyadic coping and depression. There was an actor–partner mediating effect of self-perceived burden between dyadic coping and anxiety/depression, but there was only a partner-mediating effect of caregiver burden between dyadic coping and anxiety/depression.ConclusionThis study confirmed the interrelationship between self-perceived burden, caregiver burden, dyadic coping, anxiety, and depression. Self-perceived burden and caregiver burden mediated the relationship between dyadic coping and anxiety/depression in colorectal cancer patient-spousal caregiver dyads. This suggests dynamic interventions for self-perceived burden and caregiver burden can be implemented to improve anxiety/depression in both partners based on maintaining healthy dyadic coping between colorectal cancer patient-spousal caregiver dyads

SARS-CoV-2: The Monster Causes COVID-19

Coronaviruses are viruses whose particles look like crowns. SARS-CoV-2 is the seventh member of the human coronavirus family to cause COVID-19 which is regarded as a once-in-a-century pandemic worldwide. It holds has the characteristics of a pandemic, which has broy -55ught many serious negative impacts to human beings. It may take time for humans to fight the pandemic. In addition to humans, SARS-CoV-2 also infects animals such as cats. This review introduces the origins, structures, pathogenic mechanisms, characteristics of transmission, detection and diagnosis, evolution and variation of SARS-CoV-2. We summarized the clinical characteristics, the strategies for treatment and prevention of COVID-19, and analyzed the problems and challenges we face

Acupuncture as Treatment for Female Infertility: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: The effects of acupuncture on female infertility remain controversial. Also, the variation in the participant, interventions, outcomes studied, and trial design may relate to the efficacy of adjuvant acupuncture. The aim of the study is to systematically evaluate the efficacy and safety of acupuncture for female with infertility and hopefully provide reliable guidance for clinicians and patients. Methods: We searched digital databases for relevant studies, including EMBASE, PubMed, Cochrane Library, and Web of Science, and the Cochrane Library up to April 2021, for randomized controlled trials (RCTs) evaluating the effects of acupuncture on women undergoing IVF and other treatment. We included studies with intervention groups using acupuncture and control groups consisting of no acupuncture or sham (placebo) acupuncture. Primary outcomes were clinical pregnancy rate (CPR) and live birth rate (LBR). Meta-regression and subgroup analysis were conducted on the basis of ten prespecified covariates to investigate the variances of the effects of adjuvant acupuncture on pregnancy rates and the sources of heterogeneity. Results: Twenty-seven studies with 7676 participants were included. The results showed that the intervention group contributes more in outcomes including live birth rate (RR = 1.34; 95% CI (1.07, 1.67); P < 0.05), clinical pregnancy rate (RR = 1.43; 95% CI (1.21, 1.69); P < 0.05), biochemical pregnancy rate (RR = 1.42; 95% CI (1.05, 1.91); P < 0.05), ongoing pregnancy rate (RR = 1.25; 95% CI (0.88, 1.79); P < 0.05), adverse events (RR = 1.65; 95% CI (1.15, 2.36); P < 0.05), and implantation rate (MD = 1.19; 95% CI (1.07, 1.33); P < 0.05) when compared with the control group, and the difference is statistically significant. In terms of the number of oocytes retrieved, good-quality embryo rate, miscarriages, and ectopic pregnancy rate, the difference between the acupuncture group and the control group was not statistically significant. Conclusions: Our analysis finds a benefit of acupuncture for outcomes in women with infertility, and the number of acupuncture treatments is a potential influential factor. Given the poor reporting and methodological flaws of existing studies, studies with larger scales and better methodologies are needed to verify these findings. More double-blind RCTs equipped with high quality and large samples are expected for the improvement of the level of evidence