Haizhen Wang | Searcharxiv

arXiv2026

Forster energy transfer boosts indirect anisotropic interlayer excitons in 2L-MoSe2/perovskite heterostructures

Interlayer excitons (IXs) in two-dimensional (2D) van der Waals heterostructures have attracted considerable attention due to their unique optical and electronic properties. Owing to the spatially indirect nature, the radiative emission efficiency highly sensitive to interlayer twist angles. Further considering that their uniformly oriented out-of-plane dipole moments limit directional emission, strategies to simultaneously improve emission efficiency and induce optical anisotropy warrant in-depth investigation. In this work, we report significant photoluminescence (PL) enhancement and optical anisotropy of IXs in 2L-MoSe2/perovskite heterostructures mediated by energy transfer from ReS2. We attribute this enhancement to Forster resonance energy transfer (FRET), which increases the 2L-MoSe2 emission by approximately eight-fold at room temperature, and nearly doubles the emission intensity of momentum-indirect IXs in 2L-MoSe2/perovskite heterostructures at 78 K. Importantly, the optical anisotropy of ReS2 can be effectively imprinted onto 2L-MoSe2 and associated indirect IXs during the energy transfer process, yielding a linear dichroism of approximately 1.1 for both intralayer excitons and IXs with identical polarization directions. These findings expand the scope of IX study beyond direct bandgap materials with strong intrinsic emission to include systems with indirect bandgaps, offering new avenues for realizing high-performance polarization-sensitive optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2017

Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment

On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.

cs.CE↗