arXiv subjects

Xiaoyu Mi

Publications and source records attributed to Xiaoyu Mi.

9 recordsLinked to original sources

arXiv2026

High-Speed and High-Responsivity Asymmetric Waveguide Photodiode with Low Optical Back-Reflection

We numerically demonstrate an asymmetric corner-reflector uni-traveling-carrier waveguide photodiode (UTC-WGPD) that simultaneously suppresses optical back-reflection and breaks the bandwidth-responsivity trade-off. Off-center beam coupling introduces geometric asymmetry to eliminate retroreflection while maintaining efficient light trapping. 3D optoelectronic simulations reveal that at 1550 nm, a 15-μm^2 device exhibits a 275-GHz 3-dB bandwidth, 0.68-A/W responsivity (54.5% quantum efficiency), and -29-dB back-reflection, achieving a 150-GHz bandwidth-efficiency product. This design offers a scalable, low-reflection detection scheme for sub-terahertz transceivers and dense photonic integration.

physics.optics↗

arXiv2023

Ultrafast X-ray Diffraction Probe of Coherent Spin-state Dynamics in Molecules

We propose an approach to probe coherent spin-state dynamics of molecules using circularly polarized hard x-ray pulses. For the dynamically aligned nitric oxide molecules in a coherent superposition spin-orbit coupled electronic state that can be prepared through stimulated Raman scattering, we demonstrate the capability of ultrafast x-ray diffraction to not only reveal the quantum beating of the coherent spin-state wave packet, but also image the spatial spin density of the molecule. With circularly polarized ultrafast x-ray diffraction signal, we show that the electronic density matrix can be retrieved. The spatio-temporal resolving power of ultrafast x-ray diffraction paves the way for tracking transient spatial wave function in molecular dynamics involving spin degree of freedom.

physics.optics↗

arXiv2023

Tracking Berry curvature effect in molecular dynamics by ultrafast magnetic x-ray scattering

The spin-dependent Berry force is a genuine effect of Berry curvature in molecular dynamics, which can dramatically result in spatial spin separation and change of reaction pathways. However, the way to probe the effect of Berry force remains challenging, because the time-reversal (TR) symmetry required for opposite Berry forces conflicts with TR symmetry breaking spin alignment needed to observe the effect, and the net effect could be transient for a molecular wave packet. We demonstrate that in molecular photodissociation, the dissociation rates can be different for molecules with opposite initial spin directions due to Berry force. We showcase that the spatially separated spin density, which is transiently induced by Berry force as the molecular wave packet passes through conical intersection, can be reconstructed from the circular dichroism (CD) of ultrafast non-resonant magnetic x-ray scattering using free electron lasers.

physics.atom-ph↗

arXiv2022

Perspective: Ultrafast Imaging of Molecular Dynamics Using Ultrafast Low-Frequency Lasers, X-ray Free Electron Laser and Electron Pulses

The requirement of high space-time resolution and brightness is a great challenge for imaging atomic motion and making molecular movies. Important breakthroughs in ultrabright tabletop laser, x-ray and electron sources have enabled the direct imaging of evolving molecular structures in chemical processes. And recent experimental advances in preparing ultrafast laser and electron pulses equipped molecular imaging with femtosecond time resolution. This Perspectives present an overview of versatile imaging methods of molecular dynamics. High-order harmonic generation imaging and photoelectron diffraction imaging are based on laser-induced ionization and rescattering processes. Coulomb explosion imaging retrieves molecular structural information by detecting the momentum vectors of fragmented ions. Diffraction imaging encodes molecular structural and electronic information in reciprocal space. We also present various applications of these ultrafast imaging methods in resolving laser-induced nuclear and electronic dynamics.

physics.atom-ph↗

arXiv2021

Multi-modal Affect Analysis using standardized data within subjects in the Wild

Human affective recognition is an important factor in human-computer interaction. However, the method development with in-the-wild data is not yet accurate enough for practical usage. In this paper, we introduce the affective recognition method focusing on facial expression (EXP) and valence-arousal calculation that was submitted to the Affective Behavior Analysis in-the-wild (ABAW) 2021 Contest. When annotating facial expressions from a video, we thought that it would be judged not only from the features common to all people, but also from the relative changes in the time series of individuals. Therefore, after learning the common features for each frame, we constructed a facial expression estimation model and valence-arousal model using time-series data after combining the common features and the standardized features for each video. Furthermore, the above features were learned using multi-modal data such as image features, AU, Head pose, and Gaze. In the validation set, our model achieved a facial expression score of 0.546. These verification results reveal that our proposed framework can improve estimation accuracy and robustness effectively.

cs.CV↗

arXiv2021

Action Units Recognition Using Improved Pairwise Deep Architecture

Facial Action Units (AUs) represent a set of facial muscular activities and various combinations of AUs can represent a wide range of emotions. AU recognition is often used in many applications, including marketing, healthcare, education, and so forth. Although a lot of studies have developed various methods to improve recognition accuracy, it still remains a major challenge for AU recognition. In the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition, we proposed a new automatic Action Units (AUs) recognition method using a pairwise deep architecture to derive the Pseudo-Intensities of each AU and then convert them into predicted intensities. This year, we introduced a new technique to last year's framework to further reduce AU recognition errors due to temporary face occlusion such as hands on face or large face orientation. We obtained a score of 0.65 in the validation data set for this year's competition.

cs.CV↗

arXiv2021

HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning

The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual \textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7\% and 61.8\% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task.

cs.CV↗

arXiv2020

A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wild

Human affective recognition is an important factor in human-computer interaction. However, the method development with in-the-wild data is not yet accurate enough for practical usage. In this paper, we introduce the affective recognition method focusing on valence-arousal (VA) and expression (EXP) that was submitted to the Affective Behavior Analysis in-the-wild (ABAW) 2020 Contest. Since we considered that affective behaviors have many observable features that have their own time frames, we introduced multiple optimized time windows (short-term, middle-term, and long-term) into our analyzing framework for extracting feature parameters from video data. Moreover, multiple modality data are used, including action units, head poses, gaze, posture, and ResNet 50 or Efficient NET features, and are optimized during the extraction of these features. Then, we generated affective recognition models for each time window and ensembled these models together. Also, we fussed the valence, arousal, and expression models together to enable the multi-task learning, considering the fact that the basic psychological states behind facial expressions are closely related to each another. In the validation set, our model achieved a valence-arousal score of 0.498 and a facial expression score of 0.471. These verification results reveal that our proposed framework can improve estimation accuracy and robustness effectively.

cs.CV↗

arXiv2020

Action Units Recognition by Pairwise Deep Architecture

In this paper, we propose a new automatic Action Units (AUs) recognition method used in a competition, Affective Behavior Analysis in-the-wild (ABAW). Our method tackles a problem of AUs label inconsistency among subjects by using pairwise deep architecture. While the baseline score is 0.31, our method achieved 0.67 in validation dataset of the competition.

cs.CV↗