Research on Customer Relationship, Market Level and Enterprise Innovation Relationship

Enterprise innovation is an important content of enterprise management and a key element to determine the direction, scale and speed of the development of the company. Enterprise innovation has always been the focus of academic attention. Customer relationship is the contractual relationship established between the enterprise and the customer when purchasing and selling transactions. The research found that customer relationship can affect the innovation investment of enterprises to some extent. Based on a sample of listed companies in China's A stock manufacturing industry from 2008 to 2018, this paper empirically examines the impact of concentration of customer relationship on innovation output and its mechanism. The research found that the higher the concentration of customer relationship, the lower the innovation investment. Further research found that the level of marketization can alleviate this inhibition, with the promotion of market-oriented level, the excessive concentration of customer relations on enterprise innovation inhibition can be weakened. This research explores the factors that affect enterprise innovation from different angles, enriches the research on enterprise innovation, and provides a reference for enterprises to improve their innovation level

On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition

This paper proposes an efficient attempt to noisy speech emotion recognition (NSER). Conventional NSER approaches have proven effective in mitigating the impact of artificial noise sources, such as white Gaussian noise, but are limited to non-stationary noises in real-world environments due to their complexity and uncertainty. To overcome this limitation, we introduce a new method for NSER by adopting the automatic speech recognition (ASR) model as a noise-robust feature extractor to eliminate non-vocal information in noisy speech. We first obtain intermediate layer information from the ASR model as a feature representation for emotional speech and then apply this representation for the downstream NSER task. Our experimental results show that 1) the proposed method achieves better NSER performance compared with the conventional noise reduction method, 2) outperforms self-supervised learning approaches, and 3) even outperforms text-based approaches using ASR transcription or the ground truth transcription of noisy speech.Comment: Submitted to ICASSP 202

Topological exact flat bands in two dimensional materials under periodic strain

We study flat bands and their topology in 2D materials with quadratic band crossing points (QBCPs) under periodic strain. In contrast to Dirac points in graphene, where strain acts as a vector potential, strain for QBCPs serves as a director potential with angular momentum $\ell=2$. We prove that when the strengths of the strain fields hit certain ``magic" values, exact flat bands with $C=\pm 1$ emerge at charge neutrality point in the chiral limit, in strong analogy to magic angle twisted bilayer graphene. These flat bands have ideal quantum geometry for the realization of fractional Chern insulators, and they are always fragile topological. The number of flat bands can be doubled for certain point group, and the interacting Hamiltonian is exactly solvable at integer fillings. We further demonstrate the stability of these flat bands against deviations from the chiral limit, and discuss possible realization in 2D materials

Nearly flat Chern band in periodically strained monolayer and bilayer graphene

The flat band is a key ingredient for the realization of interesting quantum states for novel functionalities. In this work, we investigate the conditions for the flat band in both monolayer and bilayer graphene under periodic strain. We find topological nearly flat bands with homogeneous distribution of Berry curvature in both systems. The quantum metric of the nearly flat band closely resembles that for Landau levels. For monolayer graphene, the strain field can be regarded as an effective gauge field, while for Bernal-stacked (AB-stacked) bilayer graphene, its role is beyond the description of gauge field. We also provide an understanding of the origin of the nearly flat band in monolayer graphene in terms of the Jackiw-Rebbi model for Dirac fermions with sign-changing mass. Our work suggests strained graphene as a promising platform for strongly correlated quantum states

Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach

In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the performance of the proposed framework in system model learning via two case studies: a synthetic study focusing on the joint learning of temperature and CO2 fields, and an application to a real-world dataset for CO2 field learning. For building control, we demonstrate that the proposed framework can optimize the control actions and significantly reduce the energy cost while maintaining a comfort and healthy indoor environment. When compared to existing traditional methods, an optimization-based method with ODE models and reinforcement learning, our approach can significantly reduce the energy consumption while guarantees all the safety-critical air quality and control constraints. Promising future research directions involve validating and improving the proposed PDE models through accurate estimation of airflow fields within indoor environments. Additionally, incorporating uncertainty modeling into the PDE framework for HVAC control presents an opportunity to enhance the efficiency and reliability of building HVAC system management