arXiv subjects

Qian Wu

Publications and source records attributed to Qian Wu.

At least 19 recordsLinked to original sources

arXiv2026

Exploring possible $^3_{\Lambda_c}\text{H}$ bound states through $p\Lambda_c$ femtoscopic correlations

The femtoscopic correlation technique in relativistic heavy-ion collisions provides a unique opportunity to investigate hadron-hadron interactions and possible exotic states. In this work, we study the $p\Lambda_c$ correlation function and its sensitivity to the low-energy $N\Lambda_c$ interaction related to possible $^3_{\Lambda_c}\mathrm{H}$ bound states. Based on the quark delocalization color screening model, three interaction scenarios with different strengths are constructed, and the corresponding spin-averaged $p\Lambda_c$ correlation functions are calculated within the Koonin--Pratt formalism. The results demonstrate that the correlation function is sensitive to the $p\Lambda_c$ interaction strength, with coupled-channel effects and $S$-$D$ wave mixing producing additional enhancements in the correlation signal. These findings suggest that future $p\Lambda_c$ femtoscopic measurements at relativistic heavy-ion collision experiments can provide valuable constraints on the interaction between charmed baryons and nucleons and offer guidance for exploring possible heavy-flavor hypernuclei.

hep-ph↗

arXiv2026

MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education

Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical storylines with states and decision nodes, while a Story Director converts them into dependency-aware multimodal orchestration plans rendered by our released interactive platform. We construct MedGame Bench, a 5,000-case benchmark and evaluation protocol for Medical Narrative Generation and Story Direction. Experiments show that task-specific fine-tuning substantially improves open-source LLMs on MedGame Bench and narrows the gap with commercial models. A pilot student study further shows that learners perceive MedGame as more engaging and useful than text-only alternatives.

cs.CL↗

arXiv2026

A novel approach to proton-boron-11 fusion

Proton-boron-11 (p-$^{11}$B) fusion is a highly attractive aneutronic pathway for clean energy production, offering abundant fuel, negligible neutron activation, and the potential for direct energy conversion of charged $\alpha$ particles. However, its practical implementation is severely hindered by the extremely high Coulomb barrier, necessitating ignition temperatures far beyond those of conventional deuterium-tritium reactions. In this work, we propose a novel approach to enhance the low-energy fusion cross-section by introducing a negative muon ($\mu$). Instead of relying on the thermal equilibrium formation of a muonic molecule, we investigate a kinetic scenario in which a muonic hydrogen atom (p$\mu$) is formed first and subsequently bombarded with a $^{11}$B nucleus. We quantitatively characterize the dynamic screening of the proton's Coulomb field by the tightly bound $\mu$ cloud, the resulting modified Coulomb potential substantially lowers the effective barrier at intermediate separations. We also evaluate the penetrability, reaction cross-section, and reactivity of the p$\mu$-$^{11}$B system, the results indicate that the inclusion of $\mu$ enhances the tunneling probability by several orders of magnitude at incident energies below 100~keV, thereby significantly reducing the threshold for the nuclear reaction. This mechanism offers a promising alternative perspective for catalyzing p-$^{11}$B fusion, and also suggests a potential ignition pathway.

nucl-th↗

arXiv2026

SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification

Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined tree-structured label hierarchy under data-scarce conditions. While current approaches utilize structural constraints from the label hierarchy to maintain parent-child prediction consistency, they face a critical bottleneck, the difficulty in distinguishing semantically similar sibling classes due to insufficient domain knowledge. We introduce an innovative method named Sibling Contrastive Learning with Hierarchical Knowledge-aware Prompt Tuning for few-shot HTC tasks (SCHK-HTC). Our work enhances the model's perception of subtle differences between sibling classes at deeper levels, rather than just enforcing hierarchical rules. Specifically, we propose a novel framework featuring two core components: a hierarchical knowledge extraction module and a sibling contrastive learning mechanism. This design guides model to encode discriminative features at each hierarchy level, thus improving the separability of confusable classes. Our approach achieves superior performance across three benchmark datasets, surpassing existing state-of-the-art methods in most cases. Our code is available at https://github.com/happywinder/SCHK-HTC.

cs.CL↗

arXiv2026

SpiralDiff: Spiral Diffusion with LoRA for RGB-to-RAW Conversion Across Cameras

RAW images preserve superior fidelity and rich scene information compared to RGB, making them essential for tasks in challenging imaging conditions. To alleviate the high cost of data collection, recent RGB-to-RAW conversion methods aim to synthesize RAW images from RGB. However, they overlook two key challenges: (i) the reconstruction difficulty varies with pixel intensity, and (ii) multi-camera conversion requires camera-specific adaptation. To address these issues, we propose SpiralDiff, a diffusion-based framework tailored for RGB-to-RAW conversion with a signal-dependent noise weighting strategy that adapts reconstruction fidelity across intensity levels. In addition, we introduce CamLoRA, a camera-aware lightweight adaptation module that enables a unified model to adapt to different camera-specific ISP characteristics. Extensive experiments on four benchmark datasets demonstrate the superiority of SpiralDiff in RGB-to-RAW conversion quality and its downstream benefits in RAW-based object detection. Our code and model are available at https://github.com/Chuancy-TJU/SpiralDiff.

cs.CV↗

arXiv2026

When Visual Privacy Protection Meets Multimodal Large Language Models

The emergence of Multimodal Large Language Models (MLLMs) and the widespread usage of MLLM cloud services such as GPT-4V raised great concerns about privacy leakage in visual data. As these models are typically deployed in cloud services, users are required to submit their images and videos, posing serious privacy risks. However, how to tackle such privacy concerns is an under-explored problem. Thus, in this paper, we aim to conduct a new investigation to protect visual privacy when enjoying the convenience brought by MLLM services. We address the practical case where the MLLM is a "black box", i.e., we only have access to its input and output without knowing its internal model information. To tackle such a challenging yet demanding problem, we propose a novel framework, in which we carefully design the learning objective with Pareto optimality to seek a better trade-off between visual privacy and MLLM's performance, and propose critical-history enhanced optimization to effectively optimize the framework with the black-box MLLM. Our experiments show that our method is effective on different benchmarks.

cs.CV↗

arXiv2026

Probing a Fifth Force in Muonic Atoms through Lamb Shifts and Hyperfine Structure

Motivated by the ATOMKI anomalies in 8Be and 4He transitions, we study X17-induced Lamb shifts and hyperfine splittings in muonic atoms with stable nuclei up to Z <= 15. The bound-state problem is solved within the Gaussian Expansion Method using a unified Hamiltonian that includes the standard electromagnetic baseline together with vector and pseudoscalar X17 exchange. The spin-independent Lamb shift is described by a coherent vector muon-nucleus interaction, while the spin-dependent hyperfine sector is built isotope by isotope from shell-model spin fractions. We find a clear complementarity between mediator hypotheses: the vector Lamb-shift signal grows toward heavier nuclei, the vector hyperfine scenario favors odd-N nuclei, and the pseudoscalar scenario favors odd-Z nuclei. Using a signal-to-precision ratio, we identify muonic deuterium, muonic helium-3 ion, and muonic helium-4 ion as the most promising near-term Lamb-shift probes among systems with existing precision benchmarks. For future spectroscopy, the largest vector Lamb-shift signal is predicted in muonic silicon-29, while the leading 1S hyperfine targets are silicon-29 for the vector scenario and phosphorus-31 for the pseudoscalar scenario. The main theoretical uncertainty comes from the Schmidt-model treatment of nuclear spin content.

physics.atom-ph↗

arXiv2026

Revisiting p-$^{11}$B Fusion: Updated Cross-sections, Reactivity, and Energy Balance

Recent experimental progress has substantially improved the available cross-section data for the p-$^{11}$B fusion reaction, particularly in energy regions that previously lacked direct measurements. In this study, we develop a high-precision analytical parameterization of the p-$^{11}$B reaction cross-section over the 0--10 MeV energy range, incorporating the new experimental data into a continuous and numerically efficient representation. Using this parameterization, we evaluate the thermonuclear reactivity of the p-$^{11}$B reaction and examine the effects of the dominant resonance at 0.6 MeV and a newly observed resonance around 4.7 MeV. Furthermore, we assess the energy balance by analyzing the fusion power density and the electron bremsstrahlung power density. Our results indicate that p-$^{11}$B fusion is not precluded by bremsstrahlung constraints when contemporary cross-section data and self-consistent thermal modeling are employed.

nucl-th↗

arXiv2025

SELECT: Detecting Label Errors in Real-world Scene Text Data

We introduce SELECT (Scene tExt Label Errors deteCTion), a novel approach that leverages multi-modal training to detect label errors in real-world scene text datasets. Utilizing an image-text encoder and a character-level tokenizer, SELECT addresses the issues of variable-length sequence labels, label sequence misalignment, and character-level errors, outperforming existing methods in accuracy and practical utility. In addition, we introduce Similarity-based Sequence Label Corruption (SSLC), a process that intentionally introduces errors into the training labels to mimic real-world error scenarios during training. SSLC not only can cause a change in the sequence length but also takes into account the visual similarity between characters during corruption. Our method is the first to detect label errors in real-world scene text datasets successfully accounting for variable-length labels. Experimental results demonstrate the effectiveness of SELECT in detecting label errors and improving STR accuracy on real-world text datasets, showcasing its practical utility.

cs.CV↗

arXiv2025

EpiPlanAgent: Agentic Automated Epidemic Response Planning

Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness.

cs.AI↗

arXiv2025

SoK: Synthesizing Smart Home Privacy Protection Mechanisms Across Academic Proposals and Commercial Documentations

Pervasive data collection by Smart Home Devices (SHDs) demands robust Privacy Protection Mechanisms (PPMs). The effectiveness of many PPMs, particularly user-facing controls, depends on user awareness and adoption, which are shaped by manufacturers' public documentations. However, the landscape of academic proposals and commercial disclosures remains underexplored. To address this gap, we investigate: (1) What PPMs have academics proposed, and how are these PPMs evaluated? (2) What PPMs do manufacturers document and what factors affect these documentation? To address these questions, we conduct a two-phase study, synthesizing a systematic review of 117 academic papers with an empirical analysis of 86 SHDs' publicly disclosed documentations. Our review of academic literature reveals a strong focus on novel system- and algorithm-based PPMs. However, these proposals neglect deployment barriers (e.g., cost, interoperability), and lack real-world field validation and legal analysis. Concurrently, our analysis of commercial SHDs finds that advanced academic proposals are absent from public discourse. Industry postures are fundamentally reactive, prioritizing compliance via post-hoc data management (e.g., deletion options), rather than the preventative controls favored by academia. The documented protections correspondingly converge on a small set of practical mechanisms, such as physical buttons and localized processing. By synthesizing these findings, we advocate for research to analyze challenges, provide deployable frameworks, real-world field validation, and interoperability solutions to advance practical PPMs.

cs.HC↗

arXiv2025

A controllable anti-P-pseudo-Hermitian mechanical system and its application

A novel anti-P-pseudo-Hermitian mechanical system that integrates piezoelectric actuators and sensors with non-reciprocal coupling into mechanical beams is proposed. This configuration enables the system to exhibit programmable exceptional points (EPs), which are critical for enhancing sensitivity in sensing applications. Our theoretical analysis, supported by numerical simulations and experimental validation, demonstrates the system's capability to detect minute mass variations and identify surface cracks with high precision. This advancement not only contributes to the field of non-Hermitian physics but also paves the way for the development of next-generation mechanical sensors leveraging EP physics.

physics.app-ph↗

arXiv2025

Reaction processes of muon-catalyzed fusion in the muonic molecule $dd\mu$ studied with the tractable $T$-matrix model

Muon-catalyzed fusion has recently regained significant attention due to experimental and theoretical developments being performed. The present authors [Phys. Rev. C {\bf 109} 054625 (2024)] proposed the tractable $T$-matrix model based on the Lippmann-Schwinger equation to approximate the elaborate two- and three-body coupled-channel (CC) calculations [Kamimura, Kino, and Yamashita, Phys. Rev. C {\bf 107}, 034607 (2023)] for the nuclear reaction processes in the muonic molecule $dt\mu$, $(dt\mu)_{J=0} \to\!^4{\rm He} + n + \mu + 17.6 \, {\rm MeV}$. % or $(^4{\rm He}\mu)_{nl} + n + 17.6 \,{\rm MeV}$. The $T$-matrix model well reproduced almost all of the results generated by the CC work. In the present paper, we apply this model to the nuclear reaction processes in the $dd\mu$ molecule, $(dd\mu)_{J=1} \to\!^3{\rm He} + n + \mu +3.27 \,$ MeV or $t + p + \mu + 4.03 \,$ MeV, in which the fusion takes place via the $p$-wave $d$-$d$ relative motion. Recently, significantly different $p$-wave astrophysical $S(E)$ factors of the reaction $d + d \to\!^3{\rm He} + n$ or $t + p$ at $E \! \simeq \! 1$ keV to 1 MeV have been reported experimentally and theoretically by five groups. Employing many sets of nuclear interactions that can reproduce those five cases of $p$-wave $S(E)$ factors, we calculate the fusion rate of the $(dd\mu)_{J=1}$ molecule using three kinds of methods where results are consistent with each other. We also derive the $^3{\rm He}$-$\mu$ sticking probability and the absolute values of the energy and momentum spectra of the emitted muon. The violation of charge symmetry in the $p$-wave $d$-$d$ reaction and the $dd\mu$ fusion reaction is discussed. Information on the emitted 2.45-MeV neutrons and \mbox{1 keV-dominant} muons should be useful for the application of $dd\mu$ fusion.

nucl-th↗

arXiv2025

Prediction of $p\bar{\Omega}$ states and femtoscopic study

Inspired by recent researches on the $p \Omega$ and $p \bar{\Lambda}$ systems, we investigate the $p \bar{\Omega}$ systems within the framework of a quark model. Our results show that the attraction between a nucleon and $\bar{\Omega}$ is slightly stronger than that between a nucleon and $\Omega$, suggesting that the $p \bar{\Omega}$ system is more likely to form bound states. The dynamic calculations indicate that the $p \bar{\Omega}$ systems with both $J^{P}=1^{-}$ and $2^{-}$ can form bound states, with binding energies deeper than those of the $p \Omega$ systems with $J^{P}=2^{+}$. The scattering phase shift and scattering parameter calculations also support the existence of $p \bar{\Omega}$ states. Additionally, we discuss the behavior of the femtoscopic correlation function for the $p \bar{\Omega}$ pairs for the first time. Considering the significant progress in experimental measurements of the correlation function of the $p \Omega$ system, the further study of the $p \bar{\Omega}$ systems using femtoscopic techniques will be a very valuable work.

hep-ph↗

arXiv2025

Parton distribution functions of ground state mesons composed of $c$ or $b$ quarks

The valence quark parton distribution functions (PDFs) of all ground state heavy mesons that composed of $b$ or $c$ quarks, are discussed; namely, the pseudoscalar $\eta_c(1S)$, $\eta_b(1S)$ and $B_c$, together with the corresponding vector ones, $J/\psi$, $\Upsilon(1S)$ and $B_c^\ast$. We use a QCD-inspired constituent quark model, which has been applied with success to conventional heavy mesons, so that one advantage here is that all parameters have already been fixed by previous studies. The wave functions of the heavy mesons in the rest frame are obtained by solving the Schr\"odinger equation, then boosted to its light-front based on Susskind's Lorentz transformation. The PDFs at the hadron scale, are then obtained by integrating out the transverse momenta of the modulus square of the light-front wave function. Our study shows how the valence quark distributions differ between pseudoscalar and vector mesons, as well as among charmonia, bottomonia and bottom-charmed mesons. Comparisons with other theoretical calculations demonstrate that the PDFs obtained herein are in general narrower but align well with the expected patterns. Moreover, each PDF's point-wise behavior is squeezed with respect to the scale-free parton-like PDF.

hep-ph↗

arXiv2025

Temporal refraction and reflection in modulated mechanical metabeams: theory and physical observation

Wave reflection and refraction at a time interface follow different conservation laws compared to conventional scattering at a spatial interface. This study presents the experimental demonstration of refraction and reflection of flexural waves across a temporal boundary in a continuum based mechanical metabeam, and unveils opportunities that emerge by tailoring temporal scattering phenomena for phononic applications. We observe these phenomena in an elastic beam attached to an array of piezoelectric patches that can vary in time the effective elastic properties of the beam. Frequency conversion and phase conjugation are observed upon a single temporal interface. These results are consistent with the temporal Snell law and Fresnel equations for temporal interfaces. Further, we illustrate the manipulation of amplitude and frequency spectra of flexural wave temporal refraction and reflection through multi stepped temporal interfaces. Finally, by implementing a smooth time variation of wave impedance, we numerically and experimentally demonstrate the capabilities of the temporal metabeam to realize waveform morphing and information coding. Our findings lay the foundation for developing time mechanical metamaterials and time phononic crystals, offering new avenues for advanced phonon manipulation in both wave amplitude and frequency

physics.optics↗

arXiv2024

STARVERI: Efficient and Accurate Verification for Risk-Avoidance Routing in LEO Satellite Networks

Emerging satellite Internet constellations such as SpaceX's Starlink will deploy thousands of broadband satellites and construct Low-Earth Orbit(LEO) satellite networks(LSNs) in space, significantly expanding the boundaries of today's terrestrial Internet. However, due to the unique global LEO dynamics, satellite routers will inevitably pass through uncontrolled areas, suffering from security threats. It should be important for satellite network operators(SNOs) to enable verifiable risk-avoidance routing to identify path anomalies. In this paper, we present STARVERI, a novel network path verification framework tailored for emerging LSNs. STARVERI addresses the limitations of existing crypto-based and delay-based verification approaches and accomplishes efficient and accurate path verification by: (i) adopting a dynamic relay selection mechanism deployed in SNO's operation center to judiciously select verifiable relays for each communication pair over LSNs; and (ii) incorporating a lightweight path verification algorithm to dynamically verify each segment path split by distributed relays. We build an LSN simulator based on real constellation information and the results demonstrate that STARVERI can significantly improve the path verification accuracy and achieve lower router overhead compared with existing approaches.

cs.NI↗

arXiv2024

Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https://cl-detection2023.grand-challenge.org/.

cs.CV↗