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Researcher profile

Mingyang Liu

Mingyang Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
quant-phArtificial IntelligenceComputational ComplexityComputer Science and Game TheoryComputer Visionecon.THeess.IVMachine Learningphysics.opticsQuantitative Methods

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Published work

5 published item(s)

preprint2026arXiv

Computing Equilibrium beyond Unilateral Deviation

Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no guarantees against profitable coordinated deviations by coalitions. Although the literature proposes solution concepts that provide stability against multilateral deviations (\emph{e.g.}, strong Nash and coalition-proof equilibrium), these generally fail to exist. In this paper, we study an alternative solution concept that minimizes coalitional deviation incentives, rather than requiring them to vanish, and is therefore guaranteed to exist. Specifically, we focus on minimizing the average gain of a deviating coalition, and extend the framework to weighted-average and maximum-within-coalition gains. In contrast, the minimum-gain analogue is shown to be computationally intractable. For the average-gain and maximum-gain objectives, we prove a lower bound on the complexity of computing such an equilibrium and present an algorithm that matches this bound. Finally, we use our framework to solve the \emph{Exploitability Welfare Frontier} (EWF), the maximum attainable social welfare subject to a given exploitability (the maximum gain over all unilateral deviations).

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and generalizable detection methods from electrocardiogram (ECG) recordings. Although deep learning has advanced automated AF diagnosis, existing approaches often struggle to exploit complementary time-frequency information effectively, limiting both robustness under intra-dataset and generalization across diverse clinical datasets. To address these challenges, we propose a cross-modal deep learning framework comprising two key components: a Bidirectional Gating Module (BGM) and a Cross-modal Supervised Contrastive Learning (CSCL) strategy. The BGM facilitates dynamic, reciprocal refinement between time and frequency domain features, enhancing model robustness to signal variations within a dataset. Meanwhile, CSCL explicitly structures the joint embedding space by pulling together label-consistent samples and pushing apart different ones, thereby improving inter-class separability and enabling strong cross-dataset generalization. We evaluate our method through five-fold cross-validation on the AFDB and the CPSC2021 dataset, as well as bidirectional cross-dataset experiments (training on one and testing on the other). Results show consistent improvements over state-of-the-art methods across multiple metrics, demonstrating that our approach achieves both high intra-dataset robustness and excellent cross-dataset generalization. We further demonstrate that our method achieves high computational efficiency and anti-interference capability, making it suitable for edge deployment.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Scaleable LED-pumped Room-temperature Maser using a Multi-blade Optical Injector

Though the performance of room-temperature masers has improved over the last decade, relatively little attention has been paid to the optics used to pump the maser's gain medium. In this work, we investigate a novel multi-blade optical ``injector'' that permits more effective and more scaleable pumping. The reported work encompasses an interdisciplinary mix of conceptualization, simulation, crystal growth, fabrication, and microwave engineering. Our gain medium is pentacene dissolved as a solid solution with para-terphenyl (Pc:PTP) molecular crystal. We accurately determine this pentacene's molecular absorption cross-section as a function of wavelength. Ray-tracing is then used to assess how different designs of waveguide inject light into the Pc:PTP crystal. A multi-blade injector made of high-refractive-index glass (namely Ohara S-TIH6) is predicted to pump it more completely and uniformly than previous designs. Upon hand-fabricating such an injector and Bridgman-growing a crystal of 0.1% Pc:PTP over it, an experimental maser oscillator using this combined injector-crystal assembly is demonstrated. The performance and scaleability of multiblade injection vis-a-vis alternative strategies is analyzed.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Single-LED-pumped, room-temperature, solid-state maser

Through their ability to achieve cryogenic levels of noise performance while operating at room temperature, optically-pumped, solid-state (OPSS) masers show great promise as quantum sensors, oscillators, and amplifiers. We here demonstrate maser oscillation in a microwave cavity containing a crystal of pentacene-doped para-terphenyl (ptc:ptp) pumped by a single, chip-scale LED. Here, unlike previous work, the size of the pump source does not dominate the size of the maser system as a whole. This miniaturization is achieved through invasive optical pumping in the form of a waveguide, the tip of which is embedded into the maser crystal. Using experimental measurements combined with microwave and optical simulations, we find that our approach offers at least a factor-of-2 enhancement in cooperativity over end-on optical excitation. We use our simulations to define a figure of merit for maser pumping efficiency, and conclude that there remains significant headroom to improve the performance of ptc:ptp masers through improved optical design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation

The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. We further propose an efficient semi-supervised learning framework to address the shortage of labeled data. Extensive experiments showed that our proposed network outperforms most existing networks and the semi-supervised learning framework can outperform the base network by 3.0% and 8.2% in terms of Dice coefficient and sensitivity.

0 citations0 reviewsNo code linkedOpen access