arXiv subjects

Qing Zhao

Publications and source records attributed to Qing Zhao.

At least 37 records · Page 2Linked to original sources

arXiv2026

Modeling and Control for UAV with Off-center Slung Load

Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission requirements or mechanical interference. This offset creates coupling in the system's nonlinear dynamics which leads to a complicated motion control problem. In existing research, modeling of the system are performed about the UAV's CoM. In this work we use the point of suspension instead. Based on the new model, a cascade control strategy is developed. In the middle-loop controller, the acceleration of the suspension point is used to regulate the swing angle of the slung load without the need for considering the coupling between the slung load and the UAV. An inner-loop controller is designed to track the UAV's attitude without the need of simplification on the coupling effects. We prove local exponential stability of the closed-loop using Lyapunov approach. Finally, simulations and experiments are conducted to validate the proposed control system.

eess.SY↗

arXiv2025

Watt-level coherent microwave emission from dissipation engineered solid-state quantum batteries

Recently proposed metastability-induced quantum batteries have shown particular promise for coherent microwave generation. However, achieving high-power coherent microwave generation in quantum batteries remains fundamentally challenging due to quantum correlations, aging, and self-discharging processes. For the cavity-quantum-electrodynamics (CQED)-based quantum batteries, a further trade-off arises between strong spin-photon coupling for energy storage and sufficient output coupling for power delivery. To overcome these constraints, we introduce dissipation engineering as a dynamic control strategy that temporally separates energy storage and release. By suppressing emission during charging and rapidly enhancing the output coupling during discharging, we realize nanosecond microwave bursts with watt-level peak power. By optimizing three dissipation schemes, we improve work extraction efficiency of the quantum battery by over two orders of magnitude and achieve high power compression factors outperforming the state-of-the-art techniques, establishing dissipation engineering as a pathway toward room-temperature, high-power coherent microwave sources.

quant-ph↗

arXiv2025

Explicit modeling of density dependence in spatial capture-recapture models

Density dependence occurs at the individual level and thus is greatly influenced by spatial local heterogeneity in habitat conditions. However, density dependence is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) models provide opportunities to study density dependence at the individual level, but such an approach remains to be developed and evaluated. In this study, we developed a SCR model that links habitat use to apparent survival and recruitment through density dependent processes at the individual level. Using simulations, we found that the model can properly inform habitat use, but tends to underestimate the effect of density dependence on apparent survival and recruitment. The reason for such underestimations is likely due to the difficulties of the current model in identifying the locations of unobserved individuals without using environmental covariates to inform these locations. How to accurately estimate the locations of unobserved individuals, and thus density dependence, remains a challenging topic in spatial statistics and statistical ecology.

q-bio.PE↗

arXiv2025

A possible periodic RM evolution in the repeating FRB 20220529

Fast radio bursts (FRBs) are mysterious millisecond-duration radio transients of extragalactic origin. Some of them repeat, while others apparently do not. Investigations of periodic activity in repeating FRB have been conducted to probe their origins. While periodicity in the burst rate has been reported, studies of periodicities in other properties, such as dispersion measure (DM) and rotation measure (RM), are sparse. FRB~20220529 was monitored by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for nearly three years, providing an opportunity to investigate periodicity in its observed properties. Here we report a possible period of $\sim 200$ days in the RM evolution, with a significance of {4.1 $σ$} estimated via the Lomb-Scargle algorithm and {3.1 $σ$} with the phase-folding method. Periodicity in the burst rate was also investigated. It may indicate that the FRB progenitor is in a binary system, which is consistent with the significant RM increase and prompt recovery of this FRB on a week-timescale. Other scenarios, such as a system with an intermediate-mass black hole, are also explored.

astro-ph.HE↗

arXiv2025

Distributed Incast Detection in Data Center Networks

Incast traffic in data centers can lead to severe performance degradation, such as packet loss and increased latency. Effectively addressing incast requires prompt and accurate detection. Existing solutions, including MA-ECN, BurstRadar and Pulser, typically rely on fixed thresholds of switch port egress queue lengths or their gradients to identify microburst caused by incast flows. However, these queue length related methods often suffer from delayed detection and high error rates. In this study, we propose a distributed incast detection method for data center networks at the switch-level, leveraging a probabilistic hypothesis test with an optimal detection threshold. By analyzing the arrival intervals of new flows, our algorithm can immediately determine if a flow is part of an incast traffic from its initial packet. The experimental results demonstrate that our method offers significant improvements over existing approaches in both detection speed and inference accuracy.

cs.NI↗

arXiv2025

The universal size compression effect of nucleon pair in finite nuclei

We systematically investigate the size evolution of the di-neutron (2n), di-proton (2p), and deuteron (d) in 6He, 14Be, 17B, 6Be, 17Ne, and 6Li using microscopic calculations. Remarkably, all nucleon pairs exhibit a universal size compression at the nuclear surface, regardless of their species and binding energies. These features correspond to the BCS- and BEC-like nucleon pairs, which recent experimental techniques can further investigate.

nucl-th↗

arXiv2025

Solvatochromic microcavity ion sensors

A novel copper(II) ion sensor has been developed, achieving both a broad dynamic range and high sensitivity, effectively addressing the limitations of conventional fluorescence-based detection methods. The sensor exploits the hydrolysis reaction between rhodamine B hydrazide (RBH) and copper(II) ions, with optical signal amplification enabled by a whispering-gallery-mode (WGM) microcavity laser. By adjusting the solvent type and utilizing the solvatochromic effect, the detection range of the copper(II) ion and RBH reaction was optimized. The sensor enables rapid, broad dynamic range detection of copper(II) ions, spanning from 5 uM to 3 mM, with a high selectivity, which holds significant potential for future applications in environmental monitoring and biomedical fields.

physics.optics↗

arXiv2025

Metastability-Induced Solid-State Quantum Batteries for Powering Microwave Quantum Electronics

Metastability is ubiquitous in diverse complex systems. In open quantum systems, metastability offers protection against dissipation and decoherence, yet its application in quantum batteries remains unexplored. We propose a solid-state open quantum battery where metastable states enable stable superextensive charging without complicated protocols and energy storage with extended lifetime. Using a realistic organic maser platform, we show the controllable manner of the work extraction from the quantum battery, which can be exploited for on-demand coherent microwave emission at room temperature. These results not only demonstrate the usefulness of metastability for developing the quantum batteries robust against energy losses, but also provide a paradigm of the practical quantum device powered up by quantum batteries.

quant-ph↗

arXiv2025

High-Resolution Imaging of Plant Delayed Luminescence

Delayed luminescence (DL) is a quantized signal that is characteristic of photoexcited molecules entering a relaxed state. Studying DL provides critical insight into photophysical mechanisms through the analysis of specific spatiotemporal dynamics. In this study, we developed a high-sensitivity DL imaging system using a quantitative scientific complementary metal-oxide-semiconductor (qCMOS) camera and a single-photon counting resolution. By optimizing the optical architecture and signal processing algorithms together, we achieved full-field spatiotemporal DL imaging at megapixel resolution (i.e., $2304 \times 4096$ pixels). Key findings include the following: (1) we observed spatial heterogeneity in DL intensity across the leaves of Arabidopsis thaliana, with stronger signals detected in veins and at sites of mechanical injury; (2) species-specific DL responses occur in response to oxidative stress, with Hydrocotyle vulgaris and Ginkgo biloba showing enhanced central DL activity; (3) excitation using white light induced maximum DL intensity, while red and blue light differentially modulated decay kinetics. Finally, we develop a two-level quantum model that links DL dynamics to the populations of excited-state electrons, thereby developing a theoretical framework for future photophysical research. Collectively, this work establishes a theoretical and technological framework for advancing plant phenotyping under stress conditions and optimizing light environments.

physics.optics↗

arXiv2025

Ultrasensitive Label-free Detection of Human CEACAM5 using a WGM Resonator Based on Thin-walled Capillary

Whispering gallery mode (WGM) laser sensors, utilizing the interaction between the in-plane evanescent field and the surface vicinity, provide enhanced sensitivity in label-free sensing for bioanalysis and disease screening. However, the unavoidably excited spiral modes resulting from the weak axial confinement and their sensing potential were overlooked. In this study, a microfluidic biosensor using the localized conical modes of an active resonator based on the thin-walled capillaries was developed, demonstrating ultrasensitive refractive index and biomolecule detection capabilities. This sensor provides nonspecific detection of bovine serum albumin (BSA) and specific detection of Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) with ultra-low detection limits, large sensing range, rapid response, and a cost-effective design, making it a promising candidate for industrial-scale production. The theoretical detection limit for CEACAM5 is as low as 0.38ag/mL (5zM), and the sensitivity in the linear region reaches 0.25nm/(ag/mL). These results are approximately an order of magnitude higher in sensitivity than currently reported active WGM biosensors, demonstrating enormous detection potential for biomarkers.

physics.optics↗

arXiv2025

Towards chemical accuracy for chemi- and physisorption with an efficient density functional

Understanding molecular adsorption on surfaces underpins many problems in chemistry and materials science. Accurately and efficiently describing the adsorption has been a challenging task for first-principles methods as the process can involve both short-range chemical bond formations and long-range physical interactions, e.g., the van der Waals (vdW) interaction. Density functional theory presents an appealing choice for modeling adsorption reactions, though calculations with many exchange correlation density functional approximations struggle to accurately describe both chemical and physical molecular adsorptions. Here, we propose an efficient density functional approximation that is accurate for both chemical and physical adsorption by concurrently optimizing its semilocal component and the long-range vdW correction against the prototypical adsorption CO/Pt(111) and Ar$_2$ binding energy curve. The resulting functional opens the door to accurate and efficient modeling of general molecular adsorption.

cond-mat.mtrl-sci↗

arXiv2025

Differential Privacy in Kernelized Contextual Bandits via Random Projections

We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space. We study this problem under an additional constraint of Differential Privacy, where the agent needs to ensure that the sequence of query points is differentially private with respect to both the sequence of contexts and rewards. We propose a novel algorithm that achieves the state-of-the-art cumulative regret of $\widetilde{\mathcal{O}}(\sqrt{γ_TT}+\frac{γ_T}{\varepsilon_{\mathrm{DP}}})$ and $\widetilde{\mathcal{O}}(\sqrt{γ_TT}+\frac{γ_T\sqrt{T}}{\varepsilon_{\mathrm{DP}}})$ over a time horizon of $T$ in the joint and local models of differential privacy, respectively, where $γ_T$ is the effective dimension of the kernel and $\varepsilon_{\mathrm{DP}} > 0$ is the privacy parameter. The key ingredient of the proposed algorithm is a novel private kernel-ridge regression estimator which is based on a combination of private covariance estimation and private random projections. It offers a significantly reduced sensitivity compared to its classical counterpart while maintaining a high prediction accuracy, allowing our algorithm to achieve the state-of-the-art performance guarantees.

stat.ML↗

arXiv2025

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition

The deep integration of large language models and automatic speech recognition systems has become a promising research direction with high practical value. To address the overfitting issue commonly observed in Low-Rank Adaptation (LoRA) during the supervised fine-tuning (SFT) stage, this work proposes an innovative training paradigm Iterative LoRA Training (ILT) in combination with an Iterative Pseudo Labeling strategy, effectively enhancing the theoretical upper bound of model performance. Based on Whisper-large-v3 and Qwen2-Audio, we conduct systematic experiments using a three-stage training process: Focus Training, Feed Back Training, and Fix Training. Experimental results demonstrate the effectiveness of the proposed method. Furthermore, the MegaAIS research team applied this technique in the Interspeech 2025 Multilingual Conversational Speech Language Modeling Challenge (MLC-SLM), achieving 4th in Track 1 (Multilingual ASR Task) and 1st place in Track 2 (Speech Separation and Recognition Task), showcasing the practical feasibility and strong application potential of our approach.

cs.CL↗

arXiv2025

Enhanced oil recovery in reservoirs via diffusion-driven $\text{CO}_{2}$ flooding: Experimental insights and material balance modeling

$\text{CO}_{2}$ flooding is central to carbon utilization technologies, yet conventional waterflooding models fail to capture the complex interactions between CO$_2$ and formation fluids. In this study, one- and two-dimensional nuclear magnetic resonance experiments reveal that $\text{CO}_{2}$ markedly enhances crude oil mobility during miscible displacement via multiple synergistic mechanisms, yielding a recovery factor of $60.97\%$, which surpasses that of immiscible displacement (maximum $57.53\%$). Guided by these findings, we propose a convection-diffusion model that incorporates the diffusion coefficient ($D$) and porosity ($ϕ$) as key parameters. This model captures the spatiotemporal evolution of the $\text{CO}_{2}$ front and addresses a key limitation of conventional formulations-the omission of diffusion effects. It improves predictions of gas breakthrough time and enables optimized injection design for low-permeability reservoirs. Extending classical material balance theory, we develop an enhanced $\text{CO}_{2}$ flooding equation that integrates critical transport phenomena. This formulation incorporates $\text{CO}_{2}$ diffusion, oil phase expansion, reservoir adsorption, and gas compressibility to describe the dynamic transport and mass compensation of injected $\text{CO}_{2}$. Validation through experimental and numerical data confirms the model's robustness and applicability under low-permeability conditions. The proposed framework overcomes limitations of physical experiments under extreme environments and offers theoretical insight into oil recovery enhancement and $\text{CO}_{2}$ injection strategy optimization.

physics.flu-dyn↗

arXiv2025

Searching for a Hidden Markov Anomaly over Multiple Processes

We address the problem of detecting an anomalous process among a large number of processes. At each time t, normal processes are in state zero (normal state), while the abnormal process may be in either state zero (normal state) or state one (abnormal state), with the states being hidden. The transition between states for the abnormal process is governed by a Markov chain over time. At each time step, observations can be drawn from a selected subset of processes. Each probed process generates an observation depending on its hidden state, either a typical distribution under state zero or an abnormal distribution under state one. The objective is to design a sequential search strategy that minimizes the expected detection time, subject to an error probability constraint. In contrast to prior works that assume i.i.d. observations, we address a new setting where anomalies evolve according to a hidden Markov model. To this end, we propose a novel algorithm, dubbed Anomaly Detection under Hidden Markov model (ADHM), which dynamically adapts the probing strategy based on accumulated statistical evidence and predictive belief updates over hidden states. ADHM effectively leverages temporal correlations to focus sensing resources on the most informative processes. The algorithm is supported by an asymptotic theoretical foundation, grounded in an oracle analysis that characterizes the fundamental limits of detection under the assumption of a known distribution of the hidden states. In addition, the algorithm demonstrates strong empirical performance, consistently outperforming existing methods in extensive simulations.

eess.SP↗

arXiv2025

ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by amyloid-beta plaques and tau neurofibrillary tangles, which serve as key histopathological features. The identification and segmentation of these lesions are crucial for understanding AD progression but remain challenging due to the lack of large-scale annotated datasets and the impact of staining variations on automated image analysis. Deep learning has emerged as a powerful tool for pathology image segmentation; however, model performance is significantly influenced by variations in staining characteristics, necessitating effective stain normalization and enhancement techniques. In this study, we address these challenges by introducing an open-source dataset (ADNP-15) of neuritic plaques (i.e., amyloid deposits combined with a crown of dystrophic tau-positive neurites) in human brain whole slide images. We establish a comprehensive benchmark by evaluating five widely adopted deep learning models across four stain normalization techniques, providing deeper insights into their influence on neuritic plaque segmentation. Additionally, we propose a novel image enhancement method that improves segmentation accuracy, particularly in complex tissue structures, by enhancing structural details and mitigating staining inconsistencies. Our experimental results demonstrate that this enhancement strategy significantly boosts model generalization and segmentation accuracy. All datasets and code are open-source, ensuring transparency and reproducibility while enabling further advancements in the field.

eess.IV↗

arXiv2025

Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection

In the digital era, social media has become a major conduit for information dissemination, yet it also facilitates the rapid spread of misinformation. Traditional misinformation detection methods primarily focus on surface-level features, overlooking the crucial roles of human empathy in the propagation process. To address this gap, we propose the Dual-Aspect Empathy Framework (DAE), which integrates cognitive and emotional empathy to analyze misinformation from both the creator and reader perspectives. By examining creators' cognitive strategies and emotional appeals, as well as simulating readers' cognitive judgments and emotional responses using Large Language Models (LLMs), DAE offers a more comprehensive and human-centric approach to misinformation detection. Moreover, we further introduce an empathy-aware filtering mechanism to enhance response authenticity and diversity. Experimental results on benchmark datasets demonstrate that DAE outperforms existing methods, providing a novel paradigm for multimodal misinformation detection.

cs.CL↗

arXiv2025

Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg

The performance of medical image segmentation models is usually evaluated using metrics like the Dice score and Hausdorff distance, which compare predicted masks to ground truth annotations. However, when applying the model to unseen data, such as in clinical settings, it is often impractical to annotate all the data, making the model's performance uncertain. To address this challenge, we propose the Segmentation Performance Evaluator (SPE), a framework for estimating segmentation models' performance on unlabeled data. This framework is adaptable to various evaluation metrics and model architectures. Experiments on six publicly available datasets across six evaluation metrics including pixel-based metrics such as Dice score and distance-based metrics like HD95, demonstrated the versatility and effectiveness of our approach, achieving a high correlation (0.956$\pm$0.046) and low MAE (0.025$\pm$0.019) compare with real Dice score on the independent test set. These results highlight its ability to reliably estimate model performance without requiring annotations. The SPE framework integrates seamlessly into any model training process without adding training overhead, enabling performance estimation and facilitating the real-world application of medical image segmentation algorithms. The source code is publicly available

eess.IV↗