A5053502322- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Dark Matter and Cosmic Phenomena
- Particle Detector Development and Performance
- Emotion and Mood Recognition
- Neutrino Physics Research
- Computational Physics and Python Applications
- Particle Accelerators and Free-Electron Lasers
- Atomic and Subatomic Physics Research
- Human Pose and Action Recognition
- Speech and Audio Processing
- Plant Pathogens and Fungal Diseases
- Medical Imaging Techniques and Applications
- Mycorrhizal Fungi and Plant Interactions
- Pesticide Residue Analysis and Safety
- Fungal Biology and Applications
- Machine Learning and Data Classification
- Sustainable Industrial Ecology
- Particle accelerators and beam dynamics
- E-commerce and Technology Innovations
- Image Processing and 3D Reconstruction
- Advanced Neural Network Applications
- Radiation Detection and Scintillator Technologies
- Yeasts and Rust Fungi Studies
Institute of High Energy Physics
2018-2024
University of Chinese Academy of Sciences
2019-2024
China Telecom
2024
China Telecom (China)
2024
Shanghai Ocean University
2023
Shanghai Harbour Engineering Design & Research Institute
2023
Ministry of Agriculture and Rural Affairs
2023
Boğaziçi University
2022
Hunan University
2022
Chinese Academy of Sciences
2018-2020
Abstract Background Data Quality Monitoring system (DQM) was developed to monitor data quality at BESIII experiment in real time. The stable center-of-mass energy ( $$E_\mathrm{cms}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>E</mml:mi> <mml:mi>cms</mml:mi> </mml:msub> </mml:math> ) is essential for the taking. Online monitoring can help find beam shift Purpose purpose DQM Methods measured using Bhabha scattering process system, due its large cross section...
Human emotion recognition plays an important role in human-computer interaction. In this paper, we present our approach to the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) Classification and Action Unit (AU) Detection Challenge of 5th Workshop Competition on Affective Behavior Analysis in-the-wild (ABAW). Specifically, propose a novel multi-modal fusion model that leverages Temporal Convolutional Networks (TCN) Transformer enhance performance continuous recognition. Our aims...
Human emotion recognition holds a pivotal role in facilitating seamless human-computer interaction. This paper delineates our methodology tackling the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) Classification and Action Unit (AU) Detection Challenge within ambit of 6th Workshop Competition on Affective Behavior Analysis in-the-wild (ABAW). Our study advocates novel approach aimed at refining continuous recognition. We achieve this by initially harnessing pre-training with...
This paper proposes improvement methods for the module GhostModule in lightweight neural networks, which has limited feature representation capabilities. We propose an improved GhostPAModule module. The method is to add pointwise convolution and attention mechanism after cheap operation branch of GhostModule, order model dependencies between features calibrate features. validate this on CIFAR-10 dataset, results show that achieves a 0.94% classification accuracy compared original...
Human emotion recognition plays an important role in human-computer interaction. In this paper, we present our approach to the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) Classification and Action Unit (AU) Detection Challenge of 5th Workshop Competition on Affective Behavior Analysis in-the-wild (ABAW). Specifically, propose a novel multi-modal fusion model that leverages Temporal Convolutional Networks (TCN) Transformer enhance performance continuous recognition. Our aims...