The nonlinear field equation of the three-point correlation function of galaxies

Based on the field theory of density fluctuation under Newtonian gravity, we obtain analytically the nonlinear equation of 3-pt correlation function $\zeta$ of galaxies in a homogeneous, isotropic, static universe. The density fluctuations have been kept up to second order. By the Fry-Peebles ansatz and the Groth-Peebles ansatz, the equation of $\zeta$ becomes closed and differs from the Gaussian approximate equation. Using the boundary condition inferred from the data of SDSS, we obtain the solution $\zeta(r, u, \theta)$ at fixed $u=2$, which exhibits a shallow $U$-shape along the angle $\theta$ and, nevertheless, decreases monotonously along the radial $r$. We show its difference with the Gaussian solution. As a direct criterion of non-Gaussianity, the reduced $Q(r, u, \theta)$ deviates from the Gaussianity plane $Q=1$, exhibits a deeper $U$-shape along $\theta$ and varies weakly along $r$, agreeing with the observed data.Comment: 11 pages, 6 figure

Secure Healthcare Applications Data Storage in Cloud Using Signal Scrambling Method

A body sensor network that consists of wearable and/or implantable biosensors has been an important front-end for collecting personal health records. It is expected that the full integration of outside-hospital personal health information and hospital electronic health records will further promote preventative health services as well as global health. However, the integration and sharing of health information is bound to bring with it security and privacy issues. With extensive development of healthcare applications, security and privacy issues are becoming increasingly important. This paper addresses the potential security risks of healthcare data in Internet based applications, and proposes a method of signal scrambling as an add-on security mechanism in the application layer for a variety of healthcare information, where a piece of tiny data is used to scramble healthcare records. The former is kept locally whereas the latter, along with security protection, is sent for cloud storage. The tiny data can be derived from a random number generator or even a piece of healthcare data, which makes the method more flexible. The computational complexity and security performance in terms of theoretical and experimental analysis has been investigated to demonstrate the efficiency and effectiveness of the proposed method. The proposed method is applicable to all kinds of data that require extra security protection within complex networks

Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition

The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets. However, it poses great challenges in acquiring clean and consistent annotations for facial expression datasets. On the other hand, self-supervised contrastive learning has gained great popularity due to its simple yet effective instance discrimination training strategy, which can potentially circumvent the annotation issue. Nevertheless, there remain inherent disadvantages of instance-level discrimination, which are even more challenging when faced with complicated facial representations. In this paper, we revisit the use of self-supervised contrastive learning and explore three core strategies to enforce expression-specific representations and to minimize the interference from other facial attributes, such as identity and face styling. Experimental results show that our proposed method outperforms the current state-of-the-art self-supervised learning methods, in terms of both categorical and dimensional facial expression recognition tasks.Comment: Accepted to BMVC 202

N-[(R)-(2-Chloro­phen­yl)(cyclo­pent­yl)meth­yl]-N-[(R)-(2-hydr­oxy-5-methyl­phen­yl)(phen­yl)meth­yl]acetamide

In the title compound, C28H30ClNO2, the cyclo­pentane ring adopts an envelope conformation. In the crystal structure, mol­ecules are linked by inter­molecular O—H⋯O hydrogen bonds, forming chains running along the a axis

A Novel Image Segmentation Algorithm Based on Graph Cut Optimization Problem

Image segmentation, a fundamental task in computer vision, has been widely used in recent years in many fields. Dealing with the graph cut optimization problem obtains the image segmentation results. In this study, a novel algorithm with weighted graphs was constructed to solve the image segmentation problem through minimization of an energy function. A binary vector of the segmentation label was defined to describe both the foreground and the background of an image. To demonstrate the effectiveness of our proposed method, four various types of images were used to construct a series of experiments. Experimental results indicate that compared with other methods, the proposed algorithm can effectively promote the quality of image segmentation under three performance evaluation metrics, namely, misclassification error rate, rate of the number of background pixels, and the ratio of the number of wrongly classified foreground pixels

2,4-Dichloro-6-((1R)-1-{[(R)-(2-chloro­phen­yl)(cyclo­pent­yl)meth­yl]amino}eth­yl)phenol

In the title compound, C20H22Cl3NO, the five-membered ring adopts an envelope conformation, and the two benzene rings are oriented at a dihedral angle of 40.44 (9)°. Intra­molecular O—H⋯N and N—H⋯Cl hydrogen bonding is present. In the crystal, the mol­ecules are linked via weak inter­molecular C—H⋯O hydrogen bonds