arXiv subjects

Chong Cao

Publications and source records attributed to Chong Cao.

5 recordsLinked to original sources

arXiv2026

Mirror-Symmetry-Enforced Photonic Altermagnet

Altermagnets host momentum-dependent spin splitting without net magnetization, a symmetry-enforced band phenomenon whose photonic analogues have so far been realized only in square lattices governed by fourfold rotation. Here we introduce a photonic altermagnet on a hexagonal lattice whose helicity splitting is governed by mirror rather than rotational symmetry. Elliptical chiral elements of alternating handedness, placed at the vertices of a regular hexagon, leave the two opposite-chirality sublattices connected only by chirality reversal combined with a mirror reflection. Full-wave simulations reveal mirror-related splitting of the two opposite-helicity branches in the band structure and isofrequency contours, with the channels exchanged when the ellipse orientation is reversed. Using a finite photonic crystal slab, we show that such splitting separates a linearly polarized beam into handedness-resolved channels, thus enabling beam splitting and direction-selective helicity filtering with target-helicity output fractions above 0.85 and output paths continuously tunable through the ellipse rotation angle. These results extend photonic altermagnetism to a previously unexplored lattice-symmetry class and establish mirror-symmetric chiral textures as building blocks for altermagnetism-inspired on-chip chiral photonics.

physics.optics↗

arXiv2026

Nonparametric Variational Bayesian Learning for Channel Estimation with OTFS Modulation

Orthogonal time frequency space (OTFS) modulation has demonstrated significant advantages in high-mobility scenarios in future 6G networks. However, existing channel estimation methods often overlook the structured sparsity and clustering characteristics inherent in realistic clustered delay line (CDL) channels, leading to degraded performance in practical systems. To address this issue, we propose a novel nonparametric Bayesian learning (NPBL) framework for OTFS channel estimation. Specifically, a stick-breaking process is introduced to automatically infer the number of multipath components and assign each path to its corresponding cluster. The channel coefficients within each cluster are modeled by a Gaussian mixture distribution to capture complex fading statistics. Furthermore, an effective pruning criterion is designed to eliminate spurious multipath components, thereby enhancing estimation accuracy and reducing computational complexity. Simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean squared error compared to existing methods.

eess.SP↗

arXiv2025

StyGazeTalk: Learning Stylized Generation of Gaze and Head Dynamics

Gaze and head movements play a central role in expressive 3D media, human-agent interaction, and immersive communication. Existing works often model facial components in isolation and lack mechanisms for generating personalized, style-aware gaze behaviors. We propose StyGazeTalk, a multimodal framework that synthesizes synchronized gaze-head dynamics with controllable styles. To support high-fidelity training, we construct HAGE, a high-precision multimodal dataset containing eye-tracking data, audio, head pose, and 3D facial parameters. Experiments show that our method produces temporally coherent, style-consistent gaze-head motions, enhancing realism in 3D face generation.

cs.GR↗

arXiv2022

Single-pixel imaging based on weight sort of the Hadamard basis

Single-pixel imaging (SPI) is very popular in subsampling applications, but the random measurement matrices it typically uses will lead to measurement blindness as well as difficulties in calculation and storage, and will also limit the further reduction in sampling rate. The deterministic Hadamard basis has become an alternative choice due to its orthogonality and structural characteristics. There is evidence that sorting the Hadamard basis is beneficial to further reduce the sampling rate, thus many orderings have emerged, but their relations remain unclear and lack a unified theory. Given this, here we specially propose a concept named selection history, which can record the Hadamard spatial folding process, and build a model based on it to reveal the formation mechanisms of different orderings and to deduce the mutual conversion relationship among them. Then, a weight ordering of the Hadamard basis is proposed. Both numerical simulation and experimental results have demonstrated that with this weight sort technique, the sampling rate, reconstruction time and matrix memory consumption are greatly reduced in comparison to traditional sorting methods. Therefore, we believe that this method may pave the way for real-time single-pixel imaging.

eess.IV↗

arXiv2021

Machine Learning-based Prediction of Porosity for Concrete Containing Supplementary Cementitious Materials

Porosity has been identified as the key indicator of the durability properties of concrete exposed to aggressive environments. This paper applies ensemble learning to predict porosity of high-performance concrete containing supplementary cementitious materials. The concrete samples utilized in this study are characterized by eight composition features including w/b ratio, binder content, fly ash, GGBS, superplasticizer, coarse/fine aggregate ratio, curing condition and curing days. The assembled database consists of 240 data records, featuring 74 unique concrete mixture designs. The proposed machine learning algorithms are trained on 180 observations (75%) chosen randomly from the data set and then tested on the remaining 60 observations (25%). The numerical experiments suggest that the regression tree ensembles can accurately predict the porosity of concrete from its mixture compositions. Gradient boosting trees generally outperforms random forests in terms of prediction accuracy. For random forests, the out-of-bag error based hyperparameter tuning strategy is found to be much more efficient than k-Fold Cross-Validation.

cs.LG↗