1,217 research outputs found
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
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
Efficiency Enhancement in Polymer Solar Cells With a Polar Small Molecule Both at Interface and in the Bulk Heterojunction Layer
The polar molecules, including ferroelectric materials with large dipole moments, have been applied as interfacial layers to increase the efficiency of organic solar cells by increasing the bounded charge separation, tuning the energy levels, etc. Here, we report a small polar molecule 2-cyano-3- (4-(diphenylamino) phenyl)acrylic acid (TPACA) that can be either blended in the active layer or at the polymer/electrode interface to increase the efficiency of organic solar cell devices after poling. It is found that the built-in potential of the device is increased by 0.2 V after poling under negative bias. Blending TPACA into the active layer has shown to be a universal method to increase the efficiency of polymer solar cells. The efficiency is increased by 30–90% for all the polymer:fullerene systems tested, with the highest efficiency reaching 7.83% for the poly[4,8-bis-(2-ethyl-hexyl-thiophene-5-yl)-benzo[1,2-b:4,5-b’]dithiophene-2,6-diyl]-alt-[2-(2’-ethyl-hexanoyl)-thieno[3,4-b]thiophen-4,6-diyl]: [6,6]-phenyl-C71 -butyric acid methyl ester (PBDTTT-CT:PC70BM) system
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