arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 55 records · Page 3Linked to original sources

arXiv2026

Measurement of $Ξ^-/\barΞ^{+}$ production in jets from $Z$ boson decays with the DELPHI open data

The production rates of $Ξ^{-}/\barΞ^{+}$ baryons in energy-ranked jets produced in $Z\to\text{hadrons}$ decays are measured using $3.2$ million hadronic $Z$ events recorded by the DELPHI experiment. Jets are reconstructed using the Durham algorithm with $y_{\text{cut}}=0.005$. Quark- and gluon-enriched jet samples are obtained by ranking the jet energies in three-jet events. The softest jet are found to produce fewer $Ξ^{-}/\barΞ^{+}$ and less energetic baryons than the other jets. The ratio of $Ξ^{-}/\barΞ^{+}$ production rates in gluon and quark jets, each normalized to the corresponding mean charged-particle multiplicity, is measured to be $1.21 \pm 0.18~\mathrm{(stat.)} \pm 0.26~\mathrm{(syst.)}$. The result is consistent with the JETSET expectation and the OPAL measurements of $K_S^0$ and $Λ$ productions in $Z$ decays. This study presents the first measurement of the gluon-to-quark production ratio for baryons containing two $s$ quarks, providing new insights into strange-quark production and hadronization. Future $e^{+}e^{-}$ colliders such as CEPC and FCCee will provide much larger $Z$-boson samples and will allow far more precise studies of the subject.

hep-ex↗

arXiv2026

A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome

Motivated by a growing body of research emphasizing the importance of modeling within-subject (WS) variability in longitudinal biomarkers and its association with health outcomes, this paper proposes a semiparametric joint model for both the mean and WS variability of a longitudinal biomarker, jointly with competing-risk time-to-event outcomes. We derive an expectation-maximization algorithm for parameter estimation and a profile-likelihood method for standard error estimation and inference, which allows time-dependent covariates and general forms of the latent association structure. Furthermore, we optimize the implementation of our joint model when the survival submodel includes only time-independent baseline covariates and shared random effects, allowing it to scale effectively to biobank-scale data involving tens of thousands of subjects. Our method demonstrates satisfactory performance in simulations, whereas classical joint models that assume homogeneous WS variability may suffer from substantial estimation bias, invalid inference, and inferior prediction when confronted with heterogeneous WS variability. We illustrate the utility of our method using the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Our analysis demonstrates that associations between WS blood pressure variability and cardiovascular outcomes, previously observed in clinical trials involving relatively homogeneous populations, extend to a more ethnically diverse and generally healthier cohort, and that explicitly modeling heterogeneous WS variability substantially enhances risk discrimination. A user-friendly R package, \textbf{JMH}, has been developed for the proposed shared random effects model with efficient implementation and is publicly available on the Comprehensive R Archive Network https://CRAN.R-project.org/package=JMH.

stat.ME↗

arXiv2026

A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference

Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment. Existing accelerators, such as Dadu-Corki, improve efficiency but treat VLA models as full-precision workloads, leaving substantial redundancy in both memory and computation underexploited. In this paper, we propose VQVLA, an algorithm-hardware co-design framework that accelerates VLA inference by exploiting weight similarity and execution dynamics. We first introduce MotionVQ, a motion-aware vector quantization scheme that dynamically adjusts quantization precision based on the robot's execution state, reducing memory access while preserving task success rate. We then propose a merged-centroid vectorized GEMM paradigm that operates on the codebook-index representation, eliminating redundant multiplications through spatial aggregation and temporal reuse of centroids. To realize these optimizations, we design an accelerator that efficiently supports dynamic precision selection and centroid-reuse computation. Experimental results show that VQVLA achieves 6.5x, 2.8x, 1.9x, 3.3x, and 4.3x speedup over the A100 GPU, Dadu-Corki, LUT-DLA, CodeGEMM, and ShiftAddLLM, respectively, with negligible accuracy degradation.

cs.AI↗

arXiv2026

Information-Optimal Formation Geometry Design for Multimodal UAV Cooperative Perception

The efficacy of unmanned aerial vehicle (UAV) swarm cooperative perception fundamentally depends on three-dimensional (3D) formation geometry, which governs target observability and sensor complementarity. In the literature, the exploitation of formation geometry and its impact on UAV sensing have rarely been studied, which can significantly degrade multimodal cooperative perception in scenarios where heterogeneous payloads (vision cameras and LiDAR) should be geometrically arranged to exploit their complementary strengths while managing communication interference and hardware budgets. To bridge this critical gap, targeting the active tracking phase where a prior target estimate is available, we propose an information-optimal optimization framework that optimizes the allocation of UAVs and multimodal sensors, configures formation geometries, and executes flight control. The UAV-sensor allocation is optimized by the Fisher Information Matrix (FIM) determinant maximization. Under this framework, we introduce an equivalent formation transition strategy that enhances field-of-view (FOV) coverage and reduces dynamic communication interference without compromising perception accuracy. Furthermore, we design a novel Lyapunov-stable flight control scheme with logarithmic potential fields to generate energy-efficient trajectories for formation transitions. Extensive simulations demonstrate that our formation-aware design achieves 25.0\% improvement in FOV coverage, 104.2\% enhancement in communication signal strength, and 84.7\% reduction in energy consumption compared to conventional benchmarks. A high-fidelity Gazebo validation further shows up to 20.2\% gain in perception accuracy over hemispherically sampled formations. These results suggest that task-driven geometric allocation is an important rather than incidental component in next-generation UAV swarm perception systems.

eess.SY↗

arXiv2026

Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection

Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training data. Existing approaches render 3D point clouds into 2D images and leverage pre-trained Vision-Language Models (VLMs) for anomaly detection. However, such strategies inevitably discard geometric details and exhibit limited sensitivity to local anomalies. In this paper, we revisit intrinsic 3D representations and explore the potential of pre-trained Point-Language Models (PLMs) for ZS 3D anomaly detection. We propose BTP (Back To Point), a novel framework that effectively aligns 3D point cloud and textual embeddings. Specifically, BTP aligns multi-granularity patch features with textual representations for localized anomaly detection, while incorporating geometric descriptors to enhance sensitivity to structural anomalies. Furthermore, we introduce a joint representation learning strategy that leverages auxiliary point cloud data to improve robustness and enrich anomaly semantics. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that BTP achieves superior performance in ZS 3D anomaly detection. Code will be available at \href{https://github.com/wistful-8029/BTP-3DAD}{https://github.com/wistful-8029/BTP-3DAD}.

cs.CV↗

arXiv2026

Production of hidden-charm molecular candidates in $ψ(4660)$ decays

We investigate the production of several hidden-charm exotic candidates, including $Z_c(3900)$, $Z_c(4020)$, $Z_{cs}(3985)$, and $Z_2(4250)$, in $ψ(4660)$ decays under the assumption that these states are predominantly hadronic molecules. Treating $ψ(4660)$ as a conventional $ψ(5S)$ charmonium state, the production mechanisms are described through intermediate charmed-meson triangle loops, with its couplings to charmed-meson pairs estimated within the quark model. A systematic analysis of the processes $ψ(4660)\to Z_c(3900)π$, $ψ(4660)\to Z_c(4020)π$, $ψ(4660)\to Z_{cs}(3985)K$, and $ψ(4660)\to Z_2(4250)π$ is performed within a unified framework. The predicted branching fractions are found to be of the order of $10^{-2}$, $10^{-4}$, $10^{-3}$, and $10^{-6}$, respectively, exhibiting only a mild dependence on the cutoff parameter. We further find that the contributions from the $SHH$ intermediate loops dominate over those from the $THH$ and $HHH$ loops in most channels. The sizable production rates obtained in this work indicate that $ψ(4660)$ decays provide a promising platform for probing the molecular nature of charged hidden-charm exotic states and testing their underlying production mechanisms.

hep-ph↗

arXiv2026

Study of exotic hadron states in the $DD^{*}$ system via the complex momentum representation and Green's function method

In this paper, we propose a novel approach to investigate exotic hadronic states. For the $DD^{*}$ system, we employ the projection operator method to derive the momentum-space interaction potential. Subsequently, the complex momentum representation (CMR) method is adopted to realize a unified description of bound states, resonant states, and the continuum. By combining the Green's function and the CMR, the scattering phase shifts and cross sections are determined. This integrated approach provides a comprehensive framework for analyzing the scattering dynamics of the $DD^{*}$ system. In the hadronic molecular state framework, the $X(3872)$, $T_{cc}^+$, and $Z_c(3900)$ states can be consistently explained as bound states, while the $G(3900)$ can be interpreted as a $P$-wave resonant state. The decomposition of the scattering phase shifts and cross sections facilitates understanding the roles of resonant and continuum spectrum.

hep-ph↗

arXiv2026

Low-latency FPGA-based electronic control system for fast preparation of defect-free atom arrays

The scalability of neutral atom quantum computing demands integrated electronic control systems with low latency, modular architecture, and real-time feedback capability. Here, we present an FPGA-based electronic control system that eliminates the PC from the feedback loop, integrating photon counting, real-time decision-making, and waveform generation within a unified PXIe architecture. The system achieves a total feedback latency of $282\,\mathrm{μs}$ and is validated in practical experiments by assembling defect-free atom arrays from 24 stochastically loaded optical tweezers. A single-round rearrangement achieves a filling fraction of $\sim96\%$, while feedback-controlled iterative rearrangement over five rounds boosts the success probability for generating a 10-atom defect-free array from $65.7\%$ to $95.4\%$. This system establishes the electronic infrastructure necessary for mid-circuit measurement and real-time quantum error correction on neutral-atom platforms.

quant-ph↗

arXiv2026

Uncertainty-Aware Cross-Modal Remote Sensing Image-Text Retrieval via Evidential Learning

In cross-modal remote sensing image-text retrieval (CMRSITR), test-time remote sensing (RS) images and textual descriptions may deviate from well-curated benchmark conditions due to sensor- and atmosphere-related image degradations and text-side RS-vocabulary heterogeneity. Under such non-ideal conditions, existing CMRSITR methods may produce unreliable retrieval results because they perform retrieval with full certainty for each query and do not distinguish the varying uncertainty across queries. To address this issue, we propose an evidential learning-based CMRSITR (ELC) method for uncertainty-aware retrieval. During the training phase of ELC, evidential learning (EDL) is employed to model the inter-modal correspondences between RS images and textual descriptions as Dirichlet distributions, from which the uncertainty of each query can be obtained. Based on the EDL outputs, uncertainty-correctness alignment learning (UCL) is introduced to align the estimated uncertainty with retrieval correctness, encouraging high uncertainty for incorrect retrieval and low uncertainty for correct retrieval. Furthermore, intra-modal relationship learning (RL) distills the intra-modal similarity structure from pretrained mentor encoders for the trainable encoders, thereby making the Dirichlet distributions modeled by EDL more discriminative. In the test phase of ELC, the estimated uncertainty is compared with a threshold determined by a fixed deferral ratio, where low-uncertainty queries are directly returned and high-uncertainty queries are refined by RS-aware test-time augmentation (RS-TTA). Experimental results demonstrate that ELC achieves competitive retrieval performance compared with state-of-the-art CMRSITR methods and provides stronger robustness under the evaluated RS-specific degradations, including sensor- and atmosphere-related image perturbations and RS-vocabulary heterogeneity.

cs.IR↗

arXiv2026

Radiative decays of $X(3872)$ within $D{\bar D}^*$ molecular framework

Within the framework of nonrelativistic effective field theory, we calculate the radiative decay width of the process $X(3872) \to \bar{D} D γ$ while taking into account the $D\bar{D}$ final-state interactions. In this work, the $X(3872)$, with the spin-parity quantum numbers $J^{PC}=1^{++}$, is treated as a $D^*\bar{D} +\rm c.c.$ bound state comprising equal proportions of neutral and charged components. Our numerical calculations predict the tree-level partial decay width of approximately $11.0$ keV for the decay process $X(3872) \to \bar{D}^0 D^0γ$, while the partial width for $X(3872) \to D^- D^+γ$ is less than $1.0$ keV. It is found that the $D\bar{D}$ rescattering effect enhances the tree-level width of $X(3872) \to \bar{D}^0 D^0γ$ by $6\%$. In contrast, the rescattering effect makes a suppression to the charged channel $X(3872)\to D^- D^+γ$ by roughly $38\%$. We expect that the present predictions based on the molecular picture of the $X(3872)$ can be tested by future experiments.

hep-ph↗

arXiv2026

Metamagnetism in UTe2: the roles of itinerancy and localization

The metamagnetic transition in UTe$_2$ plays a key role in stabilizing two enigmatic field-induced superconducting phases. One of these phases (SC2) is truncated by the transition, lying directly below it, while the other (SC3) sits predominantly above it and appears to be stabilized because of it. While numerous pulsed field studies have examined this transition, comparatively few steady field experiments have investigated it. Here we report a suite of measurements of metamgnetism in UTe$_2$, at ambient pressure by torque magnetometry and extraction magnetometry techniques, and of the magnetoconductance under pressure. Our steady field measurements resolve a complex sub-structure within the transition, with separate features that possess different temperature evolutions, pointing to distinct contributions from itinerant and localized moments. The itinerant contribution might relate to a possible spin-density wave state. We theoretically model the evolution of Kondo and RKKY interactions and propose that the SC2 state is stabilized under pressure due to the collapse of magnetic anisotropy, leading to an enhancement of longitudinal spin fluctuations along the hard $b$ axis, which are pair-forming in the $p$-wave channel.

cond-mat.str-el↗

arXiv2026

Probing flavor-diagonal couplings of doubly-charged scalar at low and high energies

We investigate the phenomenology of a TeV-scale doubly-charged scalar from the right-handed sector within the framework of left-right symmetric models. Focusing on its flavor-diagonal couplings to right-handed electrons and muons, we assess probes from both high-energy colliders and low-energy precision experiments. High-energy processes include Bhabha scattering at LEP and future circular electron-positron colliders (CEPC/FCC-ee), direct production at the LHC, and dedicated searches and precision measurements at proposed muon colliders and $μ$TRISTAN. Low-energy observables encompass parity-violating Møller scattering, muon anomalous magnetic moment, and muonium-antimuonium oscillations. Our combined analysis indicates that for a doubly-charged scalar in the $1$-$3$ TeV range, the flavor-diagonal Yukawa couplings to electrons and muons as small as $10^{-2}$ are accessible. Observations of such a doubly-charged scalar would potentially point toward the type-I seesaw mechanism of neutrino masses in the left-right symmetric model with $D$-parity breaking.

hep-ph↗

arXiv2026

DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense

Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to hallucinations, poor temporal reasoning, and shallow grounding in system context. We introduce DEFENGRAPH, an LLM-driven assistant designed to support human defenders during cybersecurity incidents. DEFENGRAPH improves contextual reasoning by integrating a dual-layer Static-Dynamic Knowledge Graph (KG) with graph-based path retrieval, LLM-driven contextual filtering, and reasoning-based re-ranking. The framework grounds LLM outputs in both long-term domain knowledge and evolving event context, enabling faithful and temporally aware decision support. We evaluate DEFENGRAPH in a cyber defense setting using knowledge graphs constructed from heterogeneous security artifacts, including SIEM alerts, system topology, attacker behaviors, and prior defensive actions. The evaluation uses data collected during live Red vs. Blue team cyber range exercises simulating attacks on critical infrastructure, which generate realistic and noisy datasets reflecting real-world defender workflows and system dynamics. Evaluations across four prevalent LLMs show that DEFENGRAPH sets a new state-of-the-art: on GPT-4o it boosts reasoning-recall from 61.45\% to 73.49\% and ticket-action recall from 52.17% to 72.46% (precision 24.49\% to 29.24\%), with similar gains on LLaMA-3 (46.99\% to 61.45\%), DeepSeek-R1 (45.78\% to 56.63\%) and QWen-3 (51.81\% to 59.04\%), while surfacing up to 50 correct defense actions versus 36 for the next best baseline and holding fault rates steady.

cs.CR↗

arXiv2026

CKM determination from $W$ decays with jet flavor tagging at CEPC

This study investigates the sensitivity of the CEPC to CKM matrix elements $|V_{ij}|$ in semileptonic $W$-pair decays using simulated $e^+e^- \to W^+W^- \to μν\bar{q}q^\prime$ samples corresponding to $21.6\,\mathrm{ab}^{-1}$ at $\sqrt{s}=240\,\mathrm{GeV}$, with $|V_{ub}|$ excluded because of its negligible contribution. The projected statistical precisions reach $0.59\,\%$ for $|V_{cb}|$ and $0.01\,\%$ for $|V_{cs}|$, indicating that the CEPC can provide direct, high-precision, and largely model-independent determinations of CKM matrix elements from hadronic $W$ decays. The main sources of systematic uncertainty are discussed, and further improvements are expected from both experimental and theoretical developments, including dedicated data-driven calibrations of detector response and jet flavor tagging performance, as well as more precise higher-order calculations of the relevant electroweak and QCD corrections. Such measurements would provide stringent consistency tests of the Standard Model charged-current flavor structure and offer sensitivity to possible new physics effects through deviations from the Standard Model expectations.

hep-ex↗

arXiv2026

Gravity-mode main-sequence pulsators in the open clusters NGC 3532 and NGC 2516: Instability strip, near-core rotation, and internal structure

Context. Studying pulsating stars in clusters opens a new window onto stellar physics. Gravity-mode (g-mode) pulsators in open clusters allow us to measure their near-core rotation rates and, together with cluster age and mass constraints, test angular momentum transport in stars above about 1.5 solar masses. Aims. We aim to detect g-mode pulsations in member stars of the young open cluster NGC 3532, about 300 Myr old, and to measure their near-core rotation rates and internal properties. Methods. We used TESS photometry to extract light curves of NGC 3532 members. The observed g-mode period spacings allow us to measure near-core rotation rates and the asymptotic period spacing, Pi0. We also fitted isochrones to the colour-magnitude diagrams of NGC 3532 and NGC 2516 to refine the cluster ages and obtain stellar masses. Results. We constrain the observed instability region of young gamma Doradus members from a blue edge at about 7760 K to a red edge at about 7070 K, while some hotter g-mode pulsators are also seen. The near-core rotation rates show a rotation-mass relation similar to that in NGC 2516: below 1.6 solar masses, the rotation rate increases with mass, whereas above 1.6 solar masses the NGC 3532 stars reach a plateau at about 2.8 d^-1. This mass may mark a threshold between different rotational spin-down mechanisms. Existing evolutionary models with angular momentum transport imply that these g-mode pulsators were born rotating above 55 percent of the critical value. Our simplified models, assuming spherical symmetry, angular momentum conservation, and rigid internal rotation, suggest that either mass-dependent initial rotation rates are required, or that minor angular momentum loss still operates above 1.6 solar masses. Finally, our Pi0 measurements reveal a discrepancy with theoretical predictions for some pulsators, as also found in the younger cluster NGC 2516.

astro-ph.SR↗

arXiv2026

Personalized 3D Spatiotemporal Trajectory Privacy Protection with Differential and Distortion Geo-Perturbation

The rapid advancement of location-based services (LBSs) in three-dimensional (3D) domains, such as smart cities and intelligent transportation, has raised concerns over 3D spatiotemporal trajectory privacy protection. However, existing research has not fully addressed the risk of attackers exploiting the spatiotemporal correlation of 3D spatiotemporal trajectories and the impact of height information, both of which can potentially lead to significant privacy leakage. To address these issues, this paper proposes a personalized 3D spatiotemporal trajectory privacy protection mechanism, named 3DSTPM. First, we analyze the characteristics of attackers that exploit spatiotemporal correlations between locations in a trajectory and present the attack model. Next, we exploit the complementary characteristics of 3D geo-indistinguishability (3D-GI) and distortion privacy to find a protection location set (PLS) that obscures the real location for all possible locations. To address the issue of privacy accumulation caused by continuous trajectory queries, we propose a Window-based Adaptive Privacy Budget Allocation (W-APBA), which dynamically allocates privacy budgets to all locations in the current PLS based on their predictability and sensitivity. Finally, we perturb the real location using the allocated privacy budget by the PF (Permute-and-Flip) mechanism, effectively balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that the proposed 3DSTPM effectively reduces QoS loss while meeting the user's personalized privacy protection needs.

cs.CR↗

arXiv2026

Supermoiré Chern mosaic in helical trilayer WSe2

Helically twisted multilayers offer access to moiré physics beyond the single-superlattice paradigm, yet their correlated and topological transport properties remain largely unexplored in semiconductor moiré materials. Here we report magnetotransport measurements of helical trilayer WSe2, in which two coupled moiré patterns relax into a supermoiré landscape composed of inequivalent local topological domains with distinct electronic structures and unequal spatial areas. By electrostatic tuning, we identify a trilayer-hybridized regime where interactions and real-space reconstruction combine to generate a plethora of magnetic and topological states absent in the twisted bilayers. At moiré filling factor $ν$ = -1, we observe a ferromagnetic insulating state that is robust against magnetic field and accompanied by a non-quantized anomalous Hall response ~-4 kOhms. This behaviour is consistent with a time-reversal-symmetry-breaking supermoiré Chern mosaic, in which the Hall response arises from the non-cancelling contributions of local domains with opposite Chern character arranged by the relaxed structure. Under strong magnetic fields, a symmetry-broken Chern insulating state (C = 1) emerges near $ν$ = -2/3, displaying a much larger positive Hall response together with strongly enhanced longitudinal resistance, suggestive of field-reconstructed topological minibands and domain-boundary scattering. These results establish relaxed supermoiré semiconductor trilayers as a platform for spatially organized magnetism and topology beyond the bilayer limit.

cond-mat.str-el↗

arXiv2026

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines

Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding. To address these challenges, we introduce a Large-Scale Multi-Modal Industrial Open-Closed benchmark (MMIOC-1M) containing over one million samples across $14$ super-categories, $29$ industrial scenes, and $351$ defect subcategories. To our knowledge, MMIOC-1M is the first unified largest benchmark supporting both open-vocabulary and closed-set industrial detection, providing valuable pre-training data for LVLMs in industrial scenarios. Furthermore, we propose a Refined Text-Visual Prompt Network (RTVPNet) that incorporates three key innovations: (1) an expert-assisted domain projection mechanism that enables rapid adaptation of general vision models to industrial domains, (2) an energy-based sparse sampling strategy that automatically generates refined visual prompts without manual intervention, and (3) a bidirectional text-visual interaction module that enhances cross-modal semantic alignment and understanding. Extensive experiments demonstrate that RTVPNet achieves state-of-the-art performance on MMIOC-1M, LVIS, and COCO benchmarks while maintaining computational efficiency. The dataset and code are available at https://github.com/hellozzk/MMIO.

cs.AI↗