arXiv subjects

Kang Shen

Publications and source records attributed to Kang Shen.

6 recordsLinked to original sources

arXiv2026

Dissipative Time Quasicrystals from Multilevel Interference

Boundary time crystals exhibit spontaneous breaking of continuous time-translation symmetry through persistent periodic oscillations in driven-dissipative many-body systems. Here, we show that multilevel interference provides a natural route beyond periodic order, enabling dissipative time quasicrystals without externally imposed quasiperiodic driving. We consider a collectively driven-dissipative four-level ensemble with two degenerate excited states and two degenerate ground states. In the thermodynamic limit, the exact mean-field dynamics reduces to an irrational flow on a two-dimensional torus, yielding quasiperiodic order parameters with discrete spectra generated by two incommensurate fundamental frequencies. Vanishing maximal Lyapunov exponents demonstrate that the nonlinear self-consistent dynamics remains nonchaotic. Our results establish a minimal interference-induced mechanism for time-quasiperiodic order and open a route toward higher-dimensional quasiperiodic dynamics in multilevel systems.

quant-ph↗

arXiv2025

Multiensemble Superradiance for Distributed Quantum Sensing

Multiensemble superradiance extends Dicke superradiance to multiple ensembles and supports dark states whose properties depend on the initial state. In the large-\(N\) limit, we derive analytical covariance matrices for these dark states, revealing inter-ensemble entanglement that enhances quantum metrology. The minimum eigenvalue, determined by the curvature of the superradiance potential, corresponds to the optimal multiparameter spin-squeezing coefficient, which is given by the \emph{Rayleigh quotient} of the spin-squeezing matrix, linking metrological sensitivity to the geometric structure of the underlying dynamics. The multiparameter squeezing coefficient provides a variational framework for optimizing metrological performance. These results enable optimal estimation of arbitrary linear combinations of multiple parameters, offering a concrete protocol for distributed quantum sensing and a promising route toward multimode quantum interferometry.

quant-ph↗

arXiv2024

Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge

Compound Expression Recognition (CER) is vital for effective interpersonal interactions. Human emotional expressions are inherently complex due to the presence of compound expressions, requiring the consideration of both local and global facial cues for accurate judgment. In this paper, we propose an ensemble learning-based solution to address this complexity. Our approach involves training three distinct expression classification models using convolutional networks, Vision Transformers, and multiscale local attention networks. By employing late fusion for model ensemble, we combine the outputs of these models to predict the final results. Our method demonstrates high accuracy on the RAF-DB datasets and is capable of recognizing expressions in certain portions of the C-EXPR-DB through zero-shot learning.

cs.CV↗

arXiv2024

Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge

In this paper, we present our approach to addressing the challenges of the 7th ABAW competition. The competition comprises three sub-challenges: Valence Arousal (VA) estimation, Expression (Expr) classification, and Action Unit (AU) detection. To tackle these challenges, we employ state-of-the-art models to extract powerful visual features. Subsequently, a Transformer Encoder is utilized to integrate these features for the VA, Expr, and AU sub-challenges. To mitigate the impact of varying feature dimensions, we introduce an affine module to align the features to a common dimension. Overall, our results significantly outperform the baselines.

cs.CV↗

arXiv2024

Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification

The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention due to the potential advantages associated with integrating quantum principles to enhance various facets of machine learning algorithms and computations. However, the current investigated serial structure of HQCNN, wherein information sequentially passes from one network to another, often imposes limitations on the trainability and expressivity of the network. In this study, we introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN). The dataset information is simultaneously fed into both the spiking neural network and the variational quantum circuits, with the outputs amalgamated in proportion to their individual contributions. We systematically assess the impact of diverse PPF-QSNN parameters on network performance for image classification, aiming to identify the optimal configuration. Numerical results on the MNIST dataset unequivocally illustrate that our proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study introduces a novel and effective amalgamation approach for HQCNN, thereby laying the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.

quant-ph↗

arXiv2021

Numerical Energy Analysis of In-wheel Motor Driven Autonomous Electric Vehicles

Autonomous electric vehicles are being widely studied nowadays as the future technology of ground transportation, while the autonomous electric vehicles based on conventional powertrain system limit their energy and power transmission efficiencies and may hinder their broad applications in future. Here we report a study on the energy consumption and efficiency improvement of a mid-size autonomous electric vehicle driven by in-wheel motors, through the development of a numerical energy model, validated with the actual driving data and implemented in a case study. The energy analysis was conducted under three driving conditions: flat road, upslope, and downslope driving to examine the energy consumption, with the energy-saving potential of the in-wheel-motor driven powertrain system systematically explored and discussed. Considering the energy recovery from the regenerative braking, energy consumption and regenerated energy were calculated in specific driving cycles based on vehicle dynamics and autonomous driving patterns. A case study was conducted using the baseline electric vehicle driving data in West Los Angeles. It was found that an in-wheel motor driven autonomous electric vehicle can save up to 17.5% of energy compared with a conventional electric vehicle during the slope driving. Using the efficiency maps of a commercial in-wheel motor, the numerical energy model and validated results obtained from this study are in line with actual situations, and can be used to support sustainable development of more energy-efficient autonomous electric vehicles in the future.

cs.RO↗