Price-Earnings Ratio and Influence Factors: Evidence From China

This paper studies relations between P/E ratios and influence factors. It employs data of average P/E ratios in Shanghai and Shenzhen stock markets, as well as the companies’ P/E ratios from Hushen 300 Index on empirical research. It aims to reveal correlations between P/E ratios and influence factors, the impact of influence factors on P/E ratios and to build regression models for estimating and forecasting P/E ratios. The purpose of the study is to provide theoretical model foundations for estimating and forecasting of P/E ratios for investors when judging investment values according to P/E ratios and corresponding indices. It also gives an instruction for the IPO pricing. The empirical researches are divided into two parts, one on the market average P/E ratios and the other on the companies’ individual P/E ratios. Descriptive analysis, correlation analysis and regression process are used to examine the correlations. Finally regression models are derived to supply theoretical model reference for estimation and prediction on P/E ratios. The empirical results demonstrate that macroeconomics indices have limited effect on market average P/E ratios for the market’s weak reflection of national economy. Industrial and financial indices should be taken into account when estimating the companies’ individual P/E ratios. Moreover, the research effect will be better with more factors employed.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

New type of solutions for the critical Lane-Emden system

In this paper, we consider the critical Lane-Emden system \begin{align*} \begin{cases} -\Delta u=K_1(y)v^p,\quad y\in \mathbb{R}^N,&\\ -\Delta v=K_2(y)u^q,\quad y\in \mathbb{R}^N,&\\ u,v>0, \end{cases} \end{align*} where $N\geq 5$, $p,q\in (1,\infty)$ with $\frac{1}{p+1}+\frac{1}{q+1}=\frac{N-2}{N}$, $K_1(y)$ and $K_2(y)$ are positive radial potentials. Under suitable conditions on $K_1(y)$ and $K_2(y)$, we construct a new family of solutions to this system, which are centred at points lying on the top and the bottom circles of a cylinder

PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing

Diffusion models have showcased their remarkable capability to synthesize diverse and high-quality images, sparking interest in their application for real image editing. However, existing diffusion-based approaches for local image editing often suffer from undesired artifacts due to the pixel-level blending of the noised target images and diffusion latent variables, which lack the necessary semantics for maintaining image consistency. To address these issues, we propose PFB-Diff, a Progressive Feature Blending method for Diffusion-based image editing. Unlike previous methods, PFB-Diff seamlessly integrates text-guided generated content into the target image through multi-level feature blending. The rich semantics encoded in deep features and the progressive blending scheme from high to low levels ensure semantic coherence and high quality in edited images. Additionally, we introduce an attention masking mechanism in the cross-attention layers to confine the impact of specific words to desired regions, further improving the performance of background editing. PFB-Diff can effectively address various editing tasks, including object/background replacement and object attribute editing. Our method demonstrates its superior performance in terms of image fidelity, editing accuracy, efficiency, and faithfulness to the original image, without the need for fine-tuning or training.Comment: 18 pages, 15 figure

Three-way interactions with latent variables: a maximum likelihood approach

Two-way interaction in latent variables has been a topic of considerable theoretical and practical interest among psychological methodologists. Since the seminal work of Kenny and Judd (1984), much research has focused on the use of product indicators for the estimation of latent moderation effects. These methods are usually difficult to use, and many popular approaches lack solid statistical justification. In recent years, the development of full-information maximum likelihood for nonlinear latent variables models provided a new approach to the estimation of latent variable interaction effects. However, a particular kind of three-way interaction, i.e., two-way latent variable interactions over an observed grouping variable, has received little attention. In this thesis, existing literature is reviewed and studied to arrive at a derivation of the full-information maximum likelihood estimator for three-way interactions in latent variables. It is also shown that this new method of estimation and testing can be implemented in Mplus (Muthén & Muthén, 1998–2007) using mixture modelling. To study the properties of this new estimation method, a simulation study is conducted, and the new method is shown to have superior performance than an existing method proposed by Marsh, Wen, and Hau (2004)

Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/Comment: This paper has been accepted by ACM Multimedia 202

Real-time Dispatchable Region of Active Distribution Networks Based on a Tight Convex Relaxation Model

The uncertainty in distributed renewable generation poses security threats to the real-time operation of distribution systems. The real-time dispatchable region (RTDR) can be used to assess the ability of power systems to accommodate renewable generation at a given base point. DC and linearized AC power flow models are typically used for bulk power systems, but they are not suitable for low-voltage distribution networks with large r/x ratios. To balance accuracy and computational efficiency, this paper proposes an RTDR model of AC distribution networks using tight convex relaxation. Convex hull relaxation is adopted to reformulate the AC power flow equations, and the convex hull is approximated by a polyhedron without much loss of accuracy. Furthermore, an efficient adaptive constraint generation algorithm is employed to construct an approximate RTDR to meet the requirements of real-time dispatch. Case studies on the modified IEEE 33-bus distribution system validate the computational efficiency and accuracy of the proposed method