arXiv subjects

Heow Pueh Lee

Publications and source records attributed to Heow Pueh Lee.

10 recordsLinked to original sources

arXiv2026

ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains

Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into Large Language Models (LLMs). However, false, inaccurate, and misleading information in news and social media poses a serious challenge to real-world RAG systems, especially in multi-hop QA, where complex multi-step reasoning can be misled by even a single deceptive misinformation segment in the retrieved documents. Existing approaches mainly rely on implicit alignment or explicit regulation, but their limited ability to assess fine-grained information reliability makes them vulnerable to deceptive misinformation that is semantically relevant to the question yet factually incorrect, leading to erroneous answers. To address this limitation, we propose ReliableRAG, which, to the best of our knowledge, is the first reliability-driven framework that mitigates deceptive misinformation in multi-hop QA through fine-grained evaluation of individual triples. ReliableRAG first extracts information segments from source documents and represents them as structured triples. It then quantifies triple reliability by combining query-triple semantic relevance with triple credibility, retaining only the top-$K$ reliable and non-redundant triples. Based on these refined triples, ReliableRAG autoregressively constructs robust reasoning chains to consolidate trustworthy evidence and filter deceptive misinformation, producing accurate answers faithful to reliable information. Experiments on three multi-hop QA datasets show that ReliableRAG outperforms existing methods, substantially improving the factual reliability and robustness of RAG systems under deceptive misinformation injection.

cs.CL↗

arXiv2026

LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation

Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning. Traditional symbolic regression (SR) methods often struggle to identify accurate equations within vast combinatorial search spaces, largely due to their inability to incorporate essential domain-specific prior knowledge. Furthermore, reliance on pointwise evaluations and discrete finite differences inherently amplifies high-frequency noise, creating deceptive fitness landscapes that derail the optimization process. To resolve these bottlenecks, we propose LLM-PDESR, a framework that integrates the structural hypothesis generation of Large Language Models (LLMs) with a mathematically rigorous evaluation environment. By employing C^4-continuous quintic splines for robust differentiation and subdomain weighted residuals as natural low-pass filters, our approach effectively mitigates the fitness landscape distortion that plagues existing methods. A Pareto-driven feedback loop then enables the LLM to iteratively refine candidate equations, balancing predictive accuracy with structural parsimony. We evaluate LLM-PDESR on 23 canonical PDEs and five structurally novel equations (including a multivariate system) specifically designed to preclude dataset memorization and test true discovery capabilities. Demonstrating real-world applicability, the framework successfully extracts a consistent structural skeleton for an interpretable 1D dynamical surrogate (1D-CACE) directly from noisy ERA5 reanalysis data. Extensive experiments and out-of-distribution testing confirm that LLM-PDESR significantly outperforms state-of-the-art methodologies in structural recovery, noise resilience, and the avoidance of spurious complexity and equation bloat.

cs.LG↗

arXiv2026

RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement

Low-light images often suffer from severe noise, detail loss, and color distortion, which hinder downstream multimedia analysis and retrieval tasks. The degradation in low-light images is complex: luminance and chrominance are coupled, while within the chrominance, noise and details are deeply entangled, preventing existing methods from simultaneously correcting color distortion, suppressing noise, and preserving fine details. To tackle the above challenges, we propose a novel hierarchical decoupling framework (RHVI-FDD). At the macro level, we introduce the RHVI transform, which mitigates the estimation bias caused by input noise and enables robust luminance-chrominance decoupling. At the micro level, we design a Frequency-Domain Decoupling (FDD) module with three branches for further feature separation. Using the Discrete Cosine Transform, we decompose chrominance features into low, mid, and high-frequency bands that predominantly represent global tone, local details, and noise components, which are then processed by tailored expert networks in a divide-and-conquer manner and fused via an adaptive gating module for content-aware fusion. Extensive experiments on multiple low-light datasets demonstrate that our method consistently outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality.

cs.CV↗

arXiv2025

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution

To address the weight coupling problem, certain studies introduced few-shot Neural Architecture Search (NAS) methods, which partition the supernet into multiple sub-supernets. However, these methods often suffer from computational inefficiency and tend to provide suboptimal partitioning schemes. To address this problem more effectively, we analyze the weight coupling problem from a novel perspective, which primarily stems from distinct modules in succeeding layers imposing conflicting gradient directions on the preceding layer modules. Based on this perspective, we propose the Gradient Contribution (GC) method that efficiently computes the cosine similarity of gradient directions among modules by decomposing the Vector-Jacobian Product during supernet backpropagation. Subsequently, the modules with conflicting gradient directions are allocated to distinct sub-supernets while similar ones are grouped together. To assess the advantages of GC and address the limitations of existing Graph Neural Architecture Search methods, which are limited to searching a single type of Graph Neural Networks (Message Passing Neural Networks (MPNNs) or Graph Transformers (GTs)), we propose the Unified Graph Neural Architecture Search (UGAS) framework, which explores optimal combinations of MPNNs and GTs. The experimental results demonstrate that GC achieves state-of-the-art (SOTA) performance in supernet partitioning quality and time efficiency. In addition, the architectures searched by UGAS+GC outperform both the manually designed GNNs and those obtained by existing NAS methods. Finally, ablation studies further demonstrate the effectiveness of all proposed methods.

cs.LG↗

arXiv2023

Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.

cs.NE↗

arXiv2021

Investigation of lightweight acoustic curtains for mid-to-high frequency noise insulations

The continuous surge of environmental noise levels has become a vital challenge for humanity. Earlier studies have reported that prolonged exposure to loud noise may cause auditory and non-auditory disorders. Therefore, there is a growing demand for suitable noise barriers. Herein, we have investigated several commercially available curtain fabrics' acoustic performance, potentially used for sound insulation purposes. Thorough experimental investigations have been performed on PVC coated polyester fabrics' acoustical performances and 100 % pure PVC sheets. The PVC-coated polyester fabric exhibited better sound insulation properties, particularly in the mid-to-high frequency range (600-1600 Hz) with a transmission loss of about 11 to 22 dB, while insertion loss of > 10 dB has been achieved. Also, the acoustic performance of multi-layer curtains has been investigated. These multi-layer curtains have shown superior acoustic properties to that of single-layer acoustic curtains.

eess.AS↗

arXiv2020

The perspective of fluid flow behavior of respiratory droplets and aerosols through the facemasks in context of SARS-CoV-2

In the unfortunate event of current ongoing pandemic COVID-19, where vaccination development is still at the initial stage, several preventive control measures such as social distancing, hand-hygiene, and personal protective equipment have been recommended by health professionals and organizations. Among them, the safe wearing of facemasks has played a vital role in reducing the likelihood and severity of infectious respiratory disease transmission. The reported research in facemasks has covered many of their material types, fabrication techniques, mechanism characterization, and application aspects. However, in more recent times, the focus has shifted towards the theoretical investigations of fluid flow mechanisms involved in the virus-laden particles prevention by facemasks. This exciting research domain aims to address the complex fluid transport that led to designing a facemask with a better performance. This review paper discusses the recent updates on fluid flow dynamics through the facemasks. Key design aspects such as thermal comfort and flow resistance are discussed. Furthermore, the recent progress in the investigations on the efficacy of facemasks for prevention of COVID 19 spread and the impact of wearing facemasks are presented. Finally, the potential research directions for analyzing the fluid flow behavior are highlighted.

physics.med-ph↗

arXiv2020

Experimental investigations of acoustic curtains for hospital environment noise mitigations

The continuous increase of hospital noise levels has become a vital challenge for society. The complex soundscapes in the hospital produce unpleasant noise, which may exceed the prescribed noise level for the patients and healthcare professionals. Previous studies have reported that extended exposure to loud noise may cause auditory and nonauditory disorders in healthcare professionals, medical staff, and patients. Therefore, there is an increased interest for the design and fabrication of effective noise barriers for the hospital premises. Herein, we have performed the thorough experimental investigations on the acoustical performances for PVC coated polyester fabrics and 100 % pure PVC sheets. The performances of these potential acoustic curtains have found to be superior to that of existing acoustic curtains for hospitals. Also, the results showed that the sound transmission class rating of PVC curtains are much higher than the existing commercial acoustic curtains.

physics.app-ph↗

arXiv2020

Experimental investigations of psychoacoustic characteristics of household vacuum cleaners

Vacuum cleaners are one of the most widely used household appliances associated with unpleasant noises. Previous studies have indicated the severity of vacuum cleaner noise and its impact on the users nearby. The quantified measurements of the generated noise standalone are not sufficient for the selection or designing of vacuum cleaners. The human perception must also be included for a better assessment of the quality of sound. A hybrid approach known as psychoacoustics, which comprises subjective and objective evaluations of sounds, is widely used in recent times. This paper focuses on the experimental assessment of psychoacoustical matrices for household vacuum cleaners. Three vacuum cleaners with different specifications have been selected as test candidates, and their sound qualities have been analyzed. Besides, the annoyance index has been assessed for these vacuum cleaners.

eess.AS↗

arXiv2017

Theoretical study on the stimulated Brillouin scattering in a sub-wavelength anisotropic waveguide: Acousto-optical coupling coefficients and effects of transverse anisotropies

A theoretical study on the stimulated Brillouin scattering (SBS) in a sub-wavelength anisotropic waveguide is conducted. The optical, photoelastic and mechanical anisotropies of the waveguide materials are all taken into account. First, the integral formulae for calculating the acousto-optical coupling coefficients (AOCCs) due to the photoelastic and moving interface effects in SBS are extended to an optically anisotropic waveguide. Then, with the extended formulae, the SBSs in an elliptical nanowire with strong transverse anisotropies are simulated. In the simulations, the elastic fields are computed with the inclusion of mechanical anisotropy. Observable effects of the strong transverse anisotropies are found in numerical results. Most notably, the SBS gains of some elastic modes are found to be very sensitive to the small misalignment between the waveguide axes and the principal material axes. Detailed physical interpretations of this interesting phenomenon are provided. This interesting phenomenon implies an attractive way for more sensitive tuning of the SBS gain without significantly changing the phononic frequency.

physics.optics↗