arXiv subjects

Fengyuan Zhang

Publications and source records attributed to Fengyuan Zhang.

7 recordsLinked to original sources

arXiv2026

The rigity of spaces with cyclic parallel Ricci tensor

The aim of this paper is to classify some special Riemannian manifolds with cyclic parallel Ricci tensor, i.e. \begin{equation} D_{ijk}=\nabla_{i}R_{jk}+\nabla_{j}R_{ki}+\nabla_{k}R_{ij}=0\nonumber \end{equation} These structures include non-compact gradient shrinking Ricci soliton, compact $(m > 1)$-quasi-Einstein manifolds with boundary and critical spaces. We will construct some integral identities and make use of the curvature conditions reasonably to prove that the Ricci tensor is parallel.

math.DG↗

arXiv2026

Where Does AI Innovation Go? Measuring Research Attention Imbalance in AI Music

The rapid growth of artificial intelligence (AI) in music has expanded research from generation and information retrieval to education, health, and governance. Yet this growth does not necessarily imply balanced research attention. Where is research attention directed across diverse music tasks, and how can such imbalance be systematically measured? Existing studies examine AI music from separate technical, application-specific, or bibliometric perspectives, but lack a systematic framework for measuring field-level imbalance. To address this gap, we analyze 6,839 AI music publications from 2015 to April 2026 using a joint taxonomy of 12 application categories and 11 technical method families. We propose the Research Attention Profile, comprising four indicators of technical investment, method allocation, methodological diversity, and frontier-method adoption lag. Results show that technical support is concentrated in scalable, content-oriented tasks, while education, health, and governance remain under-supported. Generation adopts frontier methods after only 0.33 years on average, compared with 4.33 years for education and 5.00 years for health. These findings reveal uneven methodological development and support a more socially responsive AI music research agenda.

cs.CY↗

arXiv2024

Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition

Micro-expressions serve as essential cues for understanding individuals' genuine emotional states. Recognizing micro-expressions attracts increasing research attention due to its various applications in fields such as business negotiation and psychotherapy. However, the intricate and transient nature of micro-expressions poses a significant challenge to their accurate recognition. Most existing works either neglect temporal dependencies or suffer from redundancy issues in clip-level recognition. In this work, we propose a novel framework for micro-expression recognition, named the Adaptive Temporal Motion Guided Graph Convolution Network (ATM-GCN). Our framework excels at capturing temporal dependencies between frames across the entire clip, thereby enhancing micro-expression recognition at the clip level. Specifically, the integration of Adaptive Temporal Motion layers empowers our method to aggregate global and local motion features inherent in micro-expressions. Experimental results demonstrate that ATM-GCN not only surpasses existing state-of-the-art methods, particularly on the Composite dataset, but also achieves superior performance on the latest micro-expression dataset CAS(ME)$^3$.

cs.CV↗

arXiv2023

Indirect measurement of infrared absorption spectrum through thermal emission of meta-cavity array

Controlling thermal emission is essential for various infrared spectroscopy applications. Metasurfaces can be utilized to control multiple degrees of freedom of thermal emission, enabling the compact thermal emission materials and devices. Infrared spectroscopy such as FTIR (Fourier transform infrared spectroscopy), usually requires external infrared radiation source and complex spectroscopic devices for absorption spectrum measurement, which hinders the implementation of integrated compact and portable measurement equipment. Measuring absorption spectrum through the thermal emission of pixelated thermal emitter array can facilitate the integration and miniaturization of measurement setup, which is highly demanded for on-chip spectroscopy applications. Here, we experimentally demonstrate an integrated technology that allows for indirect measurement of the absorption spectrum through the thermal emission of meta-cavity array. This indirect measurement method opens a new avenue for compact infrared spectroscopy analysis.

physics.optics↗

arXiv2023

Multiple symmetry protected BIC lines in two dimensional synthetic parameter space

Bound states in the continuum (BICs) have attracted significant interest in recent years due to their unique optical properties, such as infinite quality factor and wave localization. In order to improve the optical performance of BICs based devices, more degrees of freedom are required to tune BICs in high-dimension parameter space for practical applications. To effectively tune more BICs, we form a 2D synthetic parameter space based on a nanohole metasurface array. Multiple symmetry protected BIC modes with high Q factors can be achieved at high-order symmetry point. Through manipulating asymmetry parameters, BIC lines formed by a series of BIC modes can be found in the 2D synthetic parameter space. Moreover, the electric field distributions are investigated to demonstrate the generation and evolution of BICs. By measuring the absorption spectra, the tuning of multiple BICs with synthet-ic asymmetry parameters is experimentally explored, which agrees well with theoretical results. Therefore, our de-sign can provide new insight for a variety of on-chip applications, such as non-linear devices, integrated nanolasing array and high-resolution sensors for infrared molecular detection.

physics.optics↗

arXiv2022

Multi-Task Learning Framework for Emotion Recognition in-the-wild

This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore the research problems of this challenge from three aspects: 1) For obtaining efficient and robust visual feature representations, we propose MAE-based unsupervised representation learning and IResNet/DenseNet-based supervised representation learning methods; 2) Considering the importance of temporal information in videos, we explore three types of sequential encoders to capture the temporal information, including the encoder based on transformer, the encoder based on LSTM, and the encoder based on GRU; 3) For modeling the correlation between these different tasks (i.e., valence, arousal, expression, and AU) for multi-task affective analysis, we first explore the dependency between these different tasks and propose three multi-task learning frameworks to model the correlations effectively. Our system achieves the performance of $1.7607$ on the validation dataset and $1.4361$ on the test dataset, ranking first in the MTL Challenge. The code is available at https://github.com/AIM3-RUC/ABAW4.

cs.CV↗

arXiv2017

High density array of epitaxial BiFeO3 nanodots with robust and reversibly switchable topological domain states

The exotic topological domains in ferroelectrics and multiferroics have attracted extensive interest in recent years due to their novel functionalities and potential applications in nanoelectronic devices. One of the key challenges for such applications is a realization of robust yet reversibly switchable nanoscale topological domain states with high density, wherein spontaneous topological structures can be individually addressed and controlled. This has been accomplished in our work using high density arrays of epitaxial BiFeO3 (BFO) nanodots with lateral size as small as ~60 nm. We demonstrate various types of spontaneous topological domain structures, including center-convergent domains, center-divergent domains, and double-center domains, which are stable over sufficiently long time yet can be manipulated and reversibly switched by electric field. The formation mechanisms of these topological domain states, assisted by the accumulation of compensating charges on the surface, have also been revealed. These result demonstrated that these reversibly switchable topological domain arrays are promising for applications in high density nanoferroelectric devices such as nonvolatile memories

cond-mat.mtrl-sci↗