arXiv subjects

Hung Son Nguyen

Publications and source records attributed to Hung Son Nguyen.

5 recordsLinked to original sources

arXiv2026

Unified Weak-to-Strong Coupling Transitions and Radiation Interference Induced by Vertical-Symmetry Breaking in Photonic Crystal Slabs

Vertical-symmetry breaking provides a versatile means of coupling leaky photonic-crystal resonances that are otherwise protected by opposite out-of-plane parity. Here, we develop a unified two-mode framework for the weak-to-strong coupling transition induced by vertical-symmetry breaking in photonic crystal slabs. The symmetry-breaking perturbation simultaneously generates a near-field coherent coupling and modifies the overlap of the radiation channels of the two parent resonances. Their interplay drives the transition from frequency crossings to avoided crossings through an exceptional point (EP), while also governing radiative linewidth exchange through Friedrich-Wintgen interference. Using a radiation-vector description resolved into the upper and lower half-spaces, we show that total radiation cancellation and one-sided radiation cancellation correspond, respectively, to quasi-bound states in the continuum (quasi-BIC) and quasi-unidirectional guided resonances (quasi-UGR). This framework distinguishes the spectral condition for EP formation from the far-field conditions controlling linewidth suppression and directional emission. We validate this picture numerically and experimentally in square-lattice photonic crystal slabs. Tuning the superstrate index drives a weak-to-strong coupling transition through an EP, while partial etching yields a broad off-$Γ$ quasi-BIC regime with strongly asymmetric top and bottom radiation. We further apply the same radiation-vector framework to a laterally shifted bilayer grating, where quasi-BICs and quasi-UGR emerge along a continuous symmetry-breaking pathway. These results establish vertical-symmetry breaking as a general route for controlling hybridization, radiation interference, and directional leakage in photonic crystal slabs.

physics.optics↗

arXiv2025

Generalized Non-Hermitian Hamiltonian for Guided Resonances in Photonic Crystal Slabs

We develop a generalized non-Hermitian Hamiltonian formalism for guided resonances in photonic crystal slabs, derived directly from Maxwell's equations through a systematic guided-mode expansion. By expanding the electromagnetic fields over the complete mode basis of an unpatterned slab and systematically integrating out radiative Fabry--Pérot channels, we obtain the analytical operator structure of the Hamiltonian, which treats guided-mode coupling and radiation losses on equal footing. The resulting Hamiltonian provides explicit expressions for both dispersive and radiative coupling terms in terms of modal overlap integrals and Fourier components of the permittivity modulation. For specific geometries, the Hamiltonian coefficients can be extracted from full-wave simulations enabling accurate modeling without phenomenological assumptions. As a case study, we investigate hexagonal lattices with both preserved and broken $C_6$ symmetry, demonstrating predictive agreement for complex band structures, near-field distributions, and far-field polarization patterns. In particular, the formalism reproduces symmetry-protected bound states in the continuum (BICs) at the $Γ$ point, accidental off-$Γ$ BICs near the $Γ$ point, and the emergence of chiral exceptional points (EPs). It also captures the tunable behavior of eigenmodes near the $K$ point, including Dirac-point shifts and the emergence of quasi-BICs or bandgap openings, depending on the nature of $C_6$ symmetry breaking. We further demonstrate in the Appendix that the same formalism extends naturally to other symmetry classes, including $C_2$ (1D grating) and $C_4$ (square lattice) photonic crystal slabs. This approach enables predictive and efficient modeling of complex photonic resonances, revealing their topological and symmetry-protected characteristics in non-Hermitian systems.

physics.optics↗

arXiv2022

Speeding Up Recommender Systems Using Association Rules

Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy. In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.

cs.LG↗

arXiv2022

Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.

cs.AI↗

arXiv2021

A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviors autonomously. Deep Interactive Reinforcement Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choosing actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use that causes a duplicate process at the same state for a revisit. In this paper, we present Broad-persistent Advising (BPA), a broad-persistent advising approach that retains and reuses the processed information. It not only helps trainers to give more general advice relevant to similar states instead of only the current state but also allows the agent to speed up the learning process. We test the proposed approach in two continuous robotic scenarios, namely, a cart pole balancing task and a simulated robot navigation task. The obtained results show that the performance of the agent using BPA improves while keeping the number of interactions required for the trainer in comparison to the DeepIRL approach.

cs.RO↗