arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 181 records · Page 10Linked to original sources

arXiv2025

Non-thermal production of Higgsino dark matter by late-decaying scalar fields

We study the non-thermal production of the Higgsino dark matter (DM). Assuming that the lightest neutral Higgsino is the lightest supersymmetric particle (LSP) in minimal supersymmetric standard model, we calculate the relic abundance of the Higgsino LSP produced by the decay of late-decaying scalar field. In the calculation of the relic abundance, we have properly included the effects of coannihilation as well as the non-perturbative effect (known as the Sommerfeld effect). Contrary to the case of the thermal-relic scenario, in which the observed DM abundance is realized with the Higgsino mass of ~ 1.2 TeV, Higgsino DM is possible with lighter Higgsino mass as the reheating temperature becomes lower than the Higgsino mass. The reheating temperature relevant for realizing the correct DM density is presented as a funciton of the Higgsino mass.

hep-ph↗

arXiv2025

Test of LGAD as Potential Next-Generation μSR Spectrometer Detectors

Muon Spin Rotation/Relaxation/Resonance ($μ$SR) is a versatile and powerful non-destructive technology for investigating the magnetic properties of materials at the microscopic level. The $μ$SR technique typically utilizes fully spin polarized beams of positive muons generated at particle accelerator facilities and measures the evolution of the muon spin polarization inside a sample to extract information about the local magnetic environment in materials. With the development of accelerator technologies, intensities of muon beams are being continuously improved, which will cause a pile-up problem to the $μ$SR spectrometer. The first muon source in China, named MELODY, is currently under construction and will be a pulsed source of muons operated at a repetition frequency of only 1 Hz due to limitations of the accelerator system at CSNS. Consequently, there is a strong motivation to operate MELODY at significantly higher muon intensities. This necessitates an upgrade of the detector system inside the spectrometer, which should be smaller and faster to accommodate the increased intensity per pulse of muons. The Low Gain Avalanche Diode (LGAD), characterized by a typical pulse width of 2 ns and a segmentation size in the centimeters range, has the potential to significantly improve the counting rates of $μ$SR spectrometers that utilize a high intensity pulsed muon source. Thus, it is expected that the LGAD detector is a promising candidate to enhance the performance of $μ$SR spectrometers at the new MELODY muon source.To validate this, tests on the LGAD were conducted at the ISIS pulsed muon source at the Rutherford Appleton Laboratory, UK. This paper will describe the setup of the candidate LGAD devices and the subsequent analysis of the experiment data.

physics.ins-det↗

arXiv2025

Revealing Fano Resonance in Dirac Materials ZrTe5 through Raman Scattering

We explore the Fano resonance in ZrTe5, using Raman scattering measurements. We identified two closely spaced B2g phonon modes, B2g I and B2g II, around 9 meV and 10 meV, respectively. Interestingly, only B2g I exhibited the Fano resonance, an outcome of quantum interference between discrete phonon modes and continuous electronic excitations. This is consistent with the much stronger electron-phonon coupling of B2g I mode demonstrated by first-principles calculations. Additionally, temperature-dependent measurements highlight an enhanced Fano asymmetry at elevated temperatures, contributed by the thermal renormalization of the band structure and electron-phonon coupling. This study offers insights into the complex interrelation of electron-phonon coupling, thermal effects, and Fano resonances in ZrTe5.

cond-mat.mtrl-sci↗

arXiv2025

PixelThink: Towards Efficient Chain-of-Pixel Reasoning

Existing reasoning segmentation approaches typically fine-tune multimodal large language models (MLLMs) using image-text pairs and corresponding mask labels. However, they exhibit limited generalization to out-of-distribution scenarios without an explicit reasoning process. Although recent efforts leverage reinforcement learning through group-relative policy optimization (GRPO) to enhance reasoning ability, they often suffer from overthinking - producing uniformly verbose reasoning chains irrespective of task complexity. This results in elevated computational costs and limited control over reasoning quality. To address this problem, we propose PixelThink, a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty to regulate reasoning generation within a reinforcement learning paradigm. The model learns to compress reasoning length in accordance with scene complexity and predictive confidence. To support comprehensive evaluation, we introduce ReasonSeg-Diff, an extended benchmark with annotated reasoning references and difficulty scores, along with a suite of metrics designed to assess segmentation accuracy, reasoning quality, and efficiency jointly. Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance. Our work contributes novel perspectives towards efficient and interpretable multimodal understanding. The code and model will be publicly available.

cs.CV↗

arXiv2025

The Feasibility Study of the GeV-Energy Muon Source Based on HIAF

Generating a mono-energetic, high-energy muon beam using accelerator facilities can be very attractive for many purposes, for example, improving muon tomography currently limited by the low flux and wide energy spread of cosmic ray muons, and searching for muon related new physics beyond the Standard Model. One potential accelerator facility is the High Intensity Heavy-Ion Accelerator Facility (HIAF), which is currently under construction in Huizhou City, China. Considering the projectile energy and beamline length, a high-intensity and GeV-energy muon flux could be produced and delivered by the High Energy Fragment Separator beamline of the HIAF facility. In this paper, the flux intensity and purity of muon beam based on HIAF are discussed in detail. For the $μ^+$ beam, the highest muon yield reaches $8.2 \times 10^6 ~ μ$/s with the purity of approximately $2\%$ at a momentum of 3.5 GeV/c; meanwhile, for the $μ^-$ beam, the maximum muon yield is 4.2 $\times 10^6 ~ μ$/s with the purity of around $20\%$ at a momentum of 1.5 GeV/c. The results also indicate that, for muon beams with an energy of several GeV, by applying a suitable purification strategy, we can get a muon beam with a purity of 100\% and an intensity of the order of $10^5 ~ μ$/s.

physics.acc-ph↗

arXiv2025

Observation of Half-Quantum Vorticity in an Iron-based Superconductor

Half-quantum vortices -- topological excitations carrying half the superconducting flux quantum -- are predicted to emerge in spin-triplet superconductors, where the spin component of order parameter enables fractional flux quantization. We present direct transport signatures of half-quantum vorticity in single-crystal Fe(Te,Se), an iron-based superconductor with helical Dirac surface states. Using mesoscopic superconducting ring devices, we observe half-integer quantum oscillations arising from the splitting of quantized fluxoid states, further supported by distinct signatures of trapped half-integer fluxoids. The quantum oscillations are modulated by a background symmetry, set by the relative orientation of the DC bias current and perpendicular magnetic field, consistent with strong spin-orbit coupling. These findings demonstrate the long-sought half-quantum vorticity in spin-polarized superconductors and open new directions toward non-Abelian excitations in quantum materials and scalable topological qubits.

cond-mat.supr-con↗

arXiv2025

Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype

Convolutional neural networks (CNNs) are effective for hyperspectral image (HSI) classification, but their 3D convolutional structures introduce high computational costs and limited generalization in few-shot scenarios. Domain shifts caused by sensor differences and environmental variations further hinder cross-dataset adaptability. Metric-based few-shot learning (FSL) prototype networks mitigate this problem, yet their performance is sensitive to prototype quality, especially with limited samples. To overcome these challenges, a dual-branch residual network that integrates spatial and spectral features via parallel branches is proposed in this letter. Additionally, more robust refined prototypes are obtained through a regulation term. Furthermore, a kernel probability matching strategy aligns source and target domain features, alleviating domain shift. Experiments on four publicly available HSI datasets illustrate that the proposal achieves superior performance compared to other methods.

cs.CV↗

arXiv2025

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting

Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction. Approach:We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process. This enables efficient HR image reconstruction by aligning the degraded HR and LR distributions.We evaluated Res-SRDiff on ultra-high-field brain T1 MP2RAGE maps and T2-weighted prostate images, comparing it with Bicubic, Pix2pix, CycleGAN, and a conventional denoising diffusion probabilistic model with vision transformer backbone (TM-DDPM), using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), gradient magnitude similarity deviation (GMSD), and learned perceptual image patch similarity (LPIPS). Main results: Res-SRDiff significantly outperformed all comparative methods in terms of PSNR, SSIM, and GMSD across both datasets, with statistically significant improvements (p-values<<0.05). The model achieved high-fidelity image restoration with only four sampling steps, drastically reducing computational time to under one second per slice, which is substantially faster than conventional TM-DDPM with around 20 seconds per slice. Qualitative analyses further demonstrated that Res-SRDiff effectively preserved fine anatomical details and lesion morphology in both brain and pelvic MRI images. Significance: Our findings show that Res-SRDiff is an efficient and accurate MRI SR method, markedly improving computational efficiency and image quality. Integrating residual error shifting into the diffusion process allows for rapid and robust HR image reconstruction, enhancing clinical MRI workflows and advancing medical imaging research. The source at:https://github.com/mosaf/Res-SRDiff

cs.CV↗

arXiv2025

Automatically Generating Rules of Malicious Software Packages via Large Language Model

Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks. To tackle this limitation, we propose a novel tool, RuleLLM, which leverages large language models (LLMs) to automate rule generation for OSS ecosystems. RuleLLM extracts metadata and code snippets from malware as its input, producing YARA and Semgrep rules that can be directly deployed in software development. Specifically, the rule generation task involves three subtasks: crafting rules, refining rules, and aligning rules. To validate RuleLLM's effectiveness, we implemented a prototype system and conducted experiments on the dataset of 1,633 malicious packages. The results are promising that RuleLLM generated 763 rules (452 YARA and 311 Semgrep) with a precision of 85.2\% and a recall of 91.8\%, outperforming state-of-the-art (SOTA) tools and scored-based approaches. We further analyzed generated rules and proposed a rule taxonomy: 11 categories and 38 subcategories.

cs.SE↗

arXiv2025

Nernst power factor and figure of merit in compensated semimetal ScSb

Recently, topological semimetals have emerged as strong candidates for solid state thermomagnetic refrigerators due to their enhanced Nernst effect. This enhancement arises from the combined contributions of the Berry curvature induced anomalous Nernst coefficient associated with topological bands and the normal Nernst effect resulting from synergistic electron-hole compensation. Generally, these two effects are intertwined in topological semimetals, making it challenging to evaluate them independently. Here, we report the observation of high Nernst effect in the electron hole compensated semimetal ScSb with topologically trivial electronic band structures. Remarkably, we find a high maximum Nernst power factor of $PF_N \sim 35 \times 10^{-4}$ W m$^{-1}$ K$^{-2}$ in ScSb. The Nernst thermopower ($S_{xy}$) exhibits a peak of $\sim$ 47 $μ$V/K at 12 K and 14 T, yielding a Nernst figure of merit ($z_N$) of $\sim 28 \times 10^{-4}$ K$^{-1}$. Notably, despite its trivial electronic band structure, both the $PF_N$ and $z_N$ values of ScSb are comparable to those observed in topological semimetals with Dirac band dispersions. The origin of the large Nernst signal in ScSb is explained by well compensated electron and hole carriers, through Hall resistivity measurements, angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) calculations.

cond-mat.mtrl-sci↗

arXiv2025

Bolometric Superconducting Optical Nanoscopy (BOSON)

Superconducting transition-edge sensors are renowned for their extraordinary photon sensitivity and energy resolution, finding applications spanning quantum information, astronomy, and nanophotonics. Here, we report the development of BOlometric Superconducting Optical Nanoscopy (BOSON), a novel platform that integrates bolometric detection at the superconducting transition edges with near-field optical techniques. BOSON enables the mapping of photoinduced changes in superconductivity with unprecedented spatial resolution and photon sensitivity. By incorporating BOSON with low-dimensional materials, we achieved polariton imaging at nanowatt excitation levels--at least four orders of magnitude lower than the power typically required in prior near-field nanoscopy experiments. Our findings highlight the potential for BOSON to advance scanning probe based optical platforms to enable the detection of photons, polaritons, and Cooper pair dynamics at the nanoscale. This paves the way for quantum sensing applications using single-polariton detection and can offer deeper insights into quasiparticle dynamics.

quant-ph↗

arXiv2025

An Analysis of Malicious Packages in Open-Source Software in the Wild

The open-source software (OSS) ecosystem suffers from security threats caused by malware.However, OSS malware research has three limitations: a lack of high-quality datasets, a lack of malware diversity, and a lack of attack campaign contexts. In this paper, we first build the largest dataset of 24,356 malicious packages from online sources, then propose a knowledge graph to represent the OSS malware corpus and conduct malware analysis in the wild.Our main findings include (1) it is essential to collect malicious packages from various online sources because their data overlapping degrees are small;(2) despite the sheer volume of malicious packages, many reuse similar code, leading to a low diversity of malware;(3) only 28 malicious packages were repeatedly hidden via dependency libraries of 1,354 malicious packages, and dependency-hidden malware has a shorter active time;(4) security reports are the only reliable source for disclosing the malware-based context. Index Terms: Malicious Packages, Software Analysis

cs.CR↗

arXiv2025

Quantum Entanglement between gauge boson pairs at a Muon Collider

Quantum entanglement is one of significant physics phenomena that can be examined at a particle collider. A muon collider can provide a stage on which we can study substantial physics phenomenon, starting from the precision measurements of the Standard Model and beyond to the undiscovered area of physics. In this work, we present a through study of quantum entanglement in $μ^+μ^-\to ZZ$ events at a future muon collider. By fixing the spin density matrix, observables quantifying entanglement between $Z$ boson pairs can be measured. After systematic Monte-Carlo simulation and background analysis, we measure the value of entanglement variables and perform hypothesis testing against the non-entangled hypothesis, finally observing the entanglement of the $ZZ$ system up to $2$ significance level.

hep-ph↗

arXiv2025

Photocurrent Nanoscopy of Quantum Hall Bulk

Understanding nanoscale electronic and thermal transport of two-dimensional (2D) electron systems in the quantum Hall regime, particularly in the bulk insulating state, poses considerable challenges. One of the primary difficulties arises from the presence of chiral edge channels, whose transport behavior obscures the investigation of the insulating bulk. Using near-field (NF) optical and photocurrent (PC) nanoscopy, we probe real-space variations of the optical and thermal dynamics of graphene in the quantum Hall regime without relying on complex sample or electrode geometries. Near the charge neutrality point (CNP), we detect strong optical and photothermal signals from resonant inter-Landau level (LL) magnetoexciton excitations between the 0th and +-1st LLs, which gradually weaken with increasing doping due to Pauli blocking. Interestingly, at higher doping levels and full integer LL fillings, photothermal signals reappear across the entire sample over a ~10-micrometer scale, indicating unexpectedly long cooling lengths and nonlocal photothermal heating through the insulating bulk. This observation suggests thermal conductivity persists for the localized states even as electronic transport is suppressed - a clear violation of the Wiedemann-Franz (WF) law. Our experiments provide novel insights into nanoscale thermal and electronic transport in incompressible 2D gases, highlighting the roles of magnetoexcitons and chiral edge states in the thermo-optoelectric dynamics of Dirac quantum Hall state.

cond-mat.mes-hall↗

arXiv2025

Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs

We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field of LLM has been witnessing unprecedented advances in pushing the scale and capability of LLM in recent years, training such a large-scale model still involves significant optimization and system challenges. To stabilize the training process, we propose depth-scaled sandwich normalization, which effectively eliminates loss spikes during the training process of deep models. We pre-train our model on 13.2 trillion diverse and high-quality tokens and further enhance its reasoning capabilities during post-training. To perform such large-scale training efficiently, we utilize 8,192 Ascend NPUs with a series of system optimizations. Evaluations on multiple diverse benchmarks indicate that Pangu Ultra significantly advances the state-of-the-art capabilities of dense LLMs such as Llama 405B and Mistral Large 2, and even achieves competitive results with DeepSeek-R1, whose sparse model structure contains much more parameters. Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters. Our model and system will be available for our commercial customers.

cs.CL↗

arXiv2025

Novel $|V_{cb}|$ extraction method via boosted $bc$-tagging with in-situ calibration

We present a novel method for measuring $|V_{cb}|$ at the LHC using an advanced boosted-jet tagger to identify "$bc$ signatures". By associating boosted $W \rightarrow bc$ signals with $bc$-matched jets from top-quark decays, we enable an in-situ calibration of the tagger. This approach significantly suppresses backgrounds while reducing uncertainties in flavor tagging efficiencies -- key to improving measurement precision. Our study is enabled by the development of realistic, AI-based large- and small-radius taggers, Sophon and the newly introduced SophonAK4, validated to match ATLAS and CMS's state-of-the-art taggers. The method complements the conventional small radius jet approach and enables a ~30% improvement in $|V_{cb}|$ precision under HL-LHC projections. As a byproduct, it enhances $H^{\pm} \rightarrow bc$ search sensitivity by a factor of 2--5 over the recent ATLAS result based on Run 2 data. Our work offers a new perspective for the precision $|V_{cb}|$ measurement and highlights the potential of using advanced tagging models to probe unexplored boosted regimes at the LHC.

hep-ph↗

arXiv2025

Dynamical Generation of Higher-order Spin-Orbit Couplings, Topology and Persistent Spin Texture in Light-Irradiated Altermagnets

Altermagnets have been identified as the third category of magnetic materials, exhibiting momentum-dependent spin splitting characterized by even powers of momentum. In this study, we show that when subjected to elliptically polarized light, these materials serve as an exemplary framework for the dynamic generation of topological bands featuring higher-order spin-orbit coupling (SOC). Notably, while the generated Zeeman field remains invariant to the particular altermagnetic ordering, the induced higher-order SOCs are related to the magnitude and symmetry of the altermagnetic order. Specifically, we show that an altermagnet exhibiting $k^n$-spin splitting can generate spin-orbit couplings up to $k^{n-1}$. In the limit of circularly polarized light, the only correction is $k^{n-1}$, with all lower-order contributions being nullified. Interestingly, light-induced SOCs significantly impact the low-energy band topology, where their Chern numbers change by $ΔC =\pm 1,2,3$ for $d,g,f$-wave altermagnets. Finally, we find a critical field in which a persistent spin texture is realized, a highly desirable state with predicted infinite spin lifetime. Our work showcases light as a powerful, controllable tool for engineering complex and exciting phenomena in altermagnets.

cond-mat.mes-hall↗

arXiv2025

Probing and Knocking with Muons

We propose here a set of new methods involving probing and knocking with muons (PKMu). There is a wealth of rich physics to explore with GeV muon beams. Examples include but not limited to: muon scattering can occur at large angles, providing evidence of potential muon-philic dark matter or dark mediator candidates; muon-electron scattering can be used to detect new types of bosons associated with charged lepton flavor violation; precise measurements of GeV-scale muon-electron scattering can be employed to probe quantum correlations.

hep-ph↗