arXiv subjects

Qinglan Huang

Publications and source records attributed to Qinglan Huang.

4 recordsLinked to original sources

arXiv2025

Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation

Recommendation systems in AI-based medical diagnostics and treatment constitute a critical component of AI in healthcare. Although some studies have explored this area and made notable progress, healthcare recommendation systems remain in their nascent stage. And these researches mainly target the treatment process such as drug or disease recommendations. In addition to the treatment process, the diagnostic process, particularly determining which medical examinations are necessary to evaluate the condition, also urgently requires intelligent decision support. To bridge this gap, we first formalize the task of medical examination recommendations. Compared to traditional recommendations, the medical examination recommendation involves more complex interactions. This complexity arises from two folds: 1) The historical medical records for examination recommendations are heterogeneous and redundant, which makes the recommendation results susceptible to noise. 2) The correlation between the medical history of patients is often irregular, making it challenging to model spatiotemporal dependencies. Motivated by the above observation, we propose a novel Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation (DST-GKAN) with a two-stage learning paradigm to solve the above challenges. In the first stage, we exploit a task-adaptive diffusion model to distill recommendation-oriented information by reducing the noises in heterogeneous medical data. In the second stage, a spatiotemporal graph KANsformer is proposed to simultaneously model the complex spatial and temporal relationships. Moreover, to facilitate the medical examination recommendation research, we introduce a comprehensive dataset. The experimental results demonstrate the state-of-the-art performance of the proposed method compared to various competitive baselines.

cs.IR↗

arXiv2024

MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?

While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, low-quality output. To effectively address these issues, it is crucial to align these models with desired behaviors based on feedback from a multimodal judge. Despite their significance, current multimodal judges frequently undergo inadequate evaluation of their capabilities and limitations, potentially leading to misalignment and unsafe fine-tuning outcomes. To address this issue, we introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges in providing feedback for image generation models across four key perspectives: alignment, safety, image quality, and bias. Specifically, we evaluate a large variety of multimodal judges including smaller-sized CLIP-based scoring models, open-source VLMs (e.g. LLaVA family), and close-source VLMs (e.g. GPT-4o, Claude 3) on each decomposed subcategory of our preference dataset. Experiments reveal that close-source VLMs generally provide better feedback, with GPT-4o outperforming other judges in average. Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities. Further studies in feedback scale reveal that VLM judges can generally provide more accurate and stable feedback in natural language (Likert-scale) than numerical scales. Notably, human evaluations on end-to-end fine-tuned models using separate feedback from these multimodal judges provide similar conclusions, further confirming the effectiveness of MJ-Bench. All data, code, models are available at https://huggingface.co/MJ-Bench.

cs.CV↗

arXiv2019

Enhanced Plasmonic Photocatalysis through Cooperative Plasmonic--Photonic Hybridization

Plasmonic nanoparticles (NPs) hold tremendous promise for catalyzing light-driven chemical reactions. The conventionally assumed detrimental absorption loss from plasmon damping can now be harvested to drive chemical transformations of the NP adsorbent, through the excitation and transfer of energetic "hot" carriers. The rate and selectivity of plasmonic photocatalysis are dependent on the characteristics of the incident light. By engineering the strength and wavelength of the light harvesting of a NP, it is possible to achieve more efficient and predictive photocatalysts. We report a plasmonic--photonic resonance hybridization strategy to substantially enhance hot electron generation at tunable, narrow-band wavelengths. By coupling the plasmon resonance of silver NPs to the guided mode resonance in a photonic crystal (PC) slab, the reaction rate of a hot-electron-driven reduction conversion is greatly accelerated. The mechanism is broadly compatible with NPs with manifold materials and shapes optimized for the targeted chemistry. The novel enhancement platform sheds light on rational design of high-performance plasmonic photocatalysts.

physics.optics↗

arXiv2018

Nanoantenna-Microcavity Hybrids with Highly Cooperative Plasmonic-Photonic Coupling

Nanoantennas offer the ultimate spatial control over light by concentrating optical energy well below the diffraction limit, whereas their quality factor (Q) is constrained by large radiative and dissipative losses. Dielectric microcavities, on the other hand, are capable of generating a high Q-factor through an extended photon storage time but have a diffraction-limited optical mode volume. Here we bridge the two worlds, by studying an exemplary hybrid system integrating plasmonic gold nanorods acting as nanoantennas with an on-resonance dielectric photonic crystal (PC) slab acting as a low-loss microcavity and, more importantly, by synergistically combining their advantages to produce a much stronger local field enhancement than that of the separate entities. To achieve this synergy between the two polar opposite types of nanophotonic resonant elements, we show that it is crucial to coordinate both the dissipative loss of the nanoantenna and the Q-factor of the low-loss cavity. In comparison to the antenna-cavity coupling approach using a Fabry-Perot resonator, which has proved successful for resonant amplification of the antenna's local field intensity, we theoretically and experimentally show that coupling to a modest-Q PC guided resonance can produce a greater amplification by at least an order of magnitude. The synergistic nanoantenna-microcavity hybrid strategy opens new opportunities for further enhancing nanoscale light-matter interactions to benefit numerous areas such as nonlinear optics, nanolasers, plasmonic hot carrier technology, and surface-enhanced Raman and infrared absorption spectroscopies.

physics.optics↗