A weighted first-order formulation for solving anisotropic diffusion equations with deep neural networks

In this paper, a new weighted first-order formulation is proposed for solving the anisotropic diffusion equations with deep neural networks. For many numerical schemes, the accurate approximation of anisotropic heat flux is crucial for the overall accuracy. In this work, the heat flux is firstly decomposed into two components along the two eigenvectors of the diffusion tensor, thus the anisotropic heat flux approximation is converted into the approximation of two isotropic components. Moreover, to handle the possible jump of the diffusion tensor across the interface, the weighted first-order formulation is obtained by multiplying this first-order formulation by a weighted function. By the decaying property of the weighted function, the weighted first-order formulation is always well-defined in the pointwise way. Finally, the weighted first-order formulation is solved with deep neural network approximation. Compared to the neural network approximation with the original second-order elliptic formulation, the proposed method can significantly improve the accuracy, especially for the discontinuous anisotropic diffusion problems

STUDY ON THE RELATION BETWEEN SELF CONSISTENCY AND CONGRUENCE AND MENTAL HEALTH OF POSTGRADUATES

Reveal the Relation between Self consistency and congruence and Mental Health of Postgraduates. Adopt self-consistency and congruence questionnaire, mental health questionnaire to carry out questionnaire investigation on 500 postgraduates of four Nanjing colleges and universities. Mental health of postgraduates has extremely negative notable relevance with the disharmony of oneself and experience and it has extremely negative notable relevance with self-flexibility. The degree of self consistency and congruence and self-flexibility of postgraduates with better psychological health situation is higher than that of postgraduates with poor mental health situation. Disharmony of oneself and experience, flexibility function of oneself forecast mentality symptom dissociation amounts to a certain extent. The degree of self-consistency and congruence has a significant influence on mental health

RESEARCH ON MENTAL HEALTH STATUS AND THE RELATIONSHIP BETWEEN SPIRITUAL BELIEF AND SELF – HARMONY

According to the questionnaire survey of 500 graduate students on mental health, spiritual belief and self-harmony, through mathematical statistical analysis, it was found that :(1) overall, the psychological status of graduate students was unhealthy, and there were significant differences in some demographic variables; (2) self-flexibility has a significant positive predictive effect on political belief, nationalism, life pursuit and family pursuit; (3) the rigidity of ego has significant negative and positive predictive effect on nationalism and money pursuit respectively; (4) the disharmony between self and experience has a significant positive predictive effect on religious belief and god worship

Defending against Poisoning Attacks in Aerial Image Semantic Segmentation with Robust Invariant Feature Enhancement

The outstanding performance of deep neural networks (DNNs) in multiple computer vision in recent years has promoted its widespread use in aerial image semantic segmentation. Nonetheless, prior research has demonstrated the high susceptibility of DNNs to adversarial attacks. This poses significant security risks when applying DNNs to safety-critical earth observation missions. As an essential means of attacking DNNs, data poisoning attacks destroy model performance by contaminating model training data, allowing attackers to control prediction results by carefully crafting poisoning samples. Toward building a more robust DNNs-based aerial image semantic segmentation model, in this study, we proposed a robust invariant feature enhancement network (RIFENet) that can resist data poisoning attacks and has superior semantic segmentation performance. The constructed RIFENet improves the resistance to poisoning attacks by extracting and enhancing robust invariant features. Specifically, RIFENet uses a texture feature enhancement module (T-FEM), structural feature enhancement module (S-FEM), global feature enhancement module (G-FEM), and multi-resolution feature fusion module (MR-FFM) to enhance the representation of different robust features in the feature extraction process to suppress the interference of poisoning samples. Experiments on several benchmark aerial image datasets demonstrate that the proposed method is more robust and exhibits better generalization than other state-of-the-art methods

Modification of Collagen Film via Surface Grafting of Taurine Molecular to Promote Corneal Nerve Repair and Epithelization Process

Corneal defects can seriously affect human vision, and keratoplasty is the most widely accepted therapy method for visual rehabilitation. Currently, effective treatment for clinical patients has been restricted due to a serious shortage of donated cornea tissue and high-quality artificial repair materials. As the predominant component of cornea tissue, collagen-based materials have promising applications for corneal repair. However, the corneal nerve repair and epithelization process after corneal transplantation must be improved. This research proposes a new collagen-based scaffold with good biocompatibility and biological functionality enhanced by surface chemical grafting of natural taurine molecular. The chemical composition of collagen-taurine (Col-Tau) material is evaluated by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, and its hydrophilic properties, light transmittance, swelling performance and mechanical tensile properties have been measured. The research results indicate that the Col-Tau sample has high transmittance and good mechanical properties, and exhibits excellent capacity to promote corneal nerve cell growth and the epithelization process of corneal epithelial cells. This novel Col-Tau material, which can be easily prepared at a low cost, should have significant application potential for the treating corneal disease in the future