arXiv subjects

Guanghui Li

Publications and source records attributed to Guanghui Li.

At least 19 recordsLinked to original sources

arXiv2026

Probing Gluon Linear Polarization with Dihadron Fragmentation in $\chi_b$ Decays

The dihadron fragmentation function (DiFF) of a linearly polarized gluon has not yet been accessed experimentally, leaving an important aspect of spin-dependent gluon hadronization unexplored. We show that, at leading order, the color-singlet decay channel of the $P$-wave bottomonium state $\chi_{b0}$ produces two energetic gluons with correlated linear polarizations. Within collinear factorization, their fragmentation into separate dihadron pairs generates an Artru--Collins-type angular correlation that provides the first direct probe of the linearly polarized gluon DiFF, while the corresponding semi-inclusive decay rate constrains the unpolarized gluon DiFF. A spectator-model benchmark indicates percent-level asymmetries, potentially within reach of existing Belle data. A dedicated Belle~II data set would substantially improve the statistical precision, enabling more stringent constraints on the kinematic dependence of the linearly polarized gluon DiFF.

hep-ph↗

arXiv2026

Quantum Information of Photon Pairs at Lepton Colliders

Photon pairs have provided an ideal laboratory for exploring entanglement and Bell inequality violation in low-energy experiments. Extending such studies to high-energy colliders is of great interest but has yet to be explored. Exploiting the photon conversion process for nearly on-shell photons, we formulate a factorization framework and an effective two-qubit description, which enable access to quantum information encoded in photon pairs. Using the existing Belle data set, we estimate that a $7.4\sigma$ violation of the Bell inequality could be achieved. The same framework can also probe quantum discord and nonstabilizerness, which could be measured with precisions of 5.6\% and 1.6\%, respectively. All the reconstructed results from photon conversion in the two-qubit framework are found to be consistent with the kinematic approach of real photons, and the formalism can apply to other spin-1 systems in an appropriate two-qubit limit.

hep-ph↗

arXiv2026

Cycle Based Computational Pipeline for Extracting Instantaneous Whisking Frequency

Whisking is a rhythmic and adaptive behavior that rodents use to probe and interact with their environment, and the frequency of movement reflects both sensorimotor processing and internal brain states. A robust and traditional method of whisker frequency estimation uses power spectral analysis of whisker position spanning several cycles. To improve the temporal resolution of whisker movement, we here estimate the period for each cycle, hence indirectly extracting an instantaneous frequency. We do this using markerless estimation of whisker position and identifying the peak and trough for each cycle. The cycle period is extracted, and artifacts are rejected with a ripple exclusion validator based on peak prominence and sequential amplitude filtering. The method is compared with power spectral estimation, using the Fourier transform of a temporal window of 0.5 seconds. We find that frequency estimation using a fixed window does not capture transient variability, while the cycle by cycle method recovers higher, time-resolved frequencies. The cycle by cycle approach also reveals the expected cycle-level variability. Artifact rejection through subsequence filtering removed spurious frequencies above 30 Hz, aligning refined frequencies with established physiological bounds (4 to 28 Hz). This pipeline provides an alternative solution for real time compatible frequency estimation, which better captures temporal variation at the expense of precision in frequency estimation.

q-bio.QM↗

arXiv2026

East Asian VLBI Network astrometry toward the star-forming region G040.96+02.48 in the Extreme Outer Galaxy

Accurate astrometric measurements for star-forming regions located on the far side of the Milky Way remain scarce. In this work, we present the astrometric results for a 22\,GHz water maser associated with star-forming region G040.96+02.48 located on the far side of the Milky Way, using the East Asian VLBI Network. The target water maser's proper motion was determined to be ($\mu_{\alpha}\cos\delta, \mu_{\delta}$) = ($-2.06_{-0.51}^{+0.53}$, $-2.95_{-0.44}^{+0.45}$)~mas~yr$^{-1}$. The derived three-dimensional kinematic distance to the star-forming region is 20.2$\pm$3.2\,kpc, placing it slightly outside the Outer Scutum$-$Centaurus Arm. The corresponding vertical height of 872$\pm$139\,pc indicates a significant warp of the outer Galactic disk, which is in good agreement with the latest precessing warp model. Moreover, the resulting peculiar motions reveal a complex kinematic pattern, characterized by a large outward radial velocity of $-32\pm$18\,km~s$^{-1}$. Our observations substantially expand the valuable sample of star-forming regions with accurate astrometric measurements in the Extreme Outer Galaxy.

astro-ph.GA↗

arXiv2026

Probing the Color-Octet Mechanism via Dihadron Fragmentation in $\chi_b$ Decays

The color-octet (CO) mechanism is a cornerstone of non-relativistic QCD, yet its long-distance matrix elements remain limited, preventing stringent tests of the theory. We demonstrate that the Artru-Collins asymmetry in hadronic decays of the $P$-wave bottomonium state $\chi_{b2}$ provides a direct probe of CO dynamics. The asymmetry arises exclusively from the CO decay channel, whereas the color-singlet (CS) contribution affects only the unpolarized rate, so that a nonzero signal constitutes unambiguous evidence of the CO mechanism. This observable provides a novel way to extract the ratio $\rho_8$ between CO and CS matrix elements. Focusing on $e^+e^-\to\Upsilon(2S)\to\gamma\,\chi_{b2}$ at Belle, we show that the asymmetric beam configuration preserves the asymmetry in the laboratory frame and avoids the strong suppression present in the center-of-mass frame. With the Belle II dataset, $\rho_8$ could be determined with sufficient precision to address the long-standing discrepancy between the lattice calculations and phenomenological determinations.

hep-ph↗

arXiv2026

Automated Place Preference Paradigm for Optogenetic Stimulation of the Pedunculopontine Nucleus Reveals Motor Arrest-Linked Preference Behavior

Understanding how the brain integrates motor suppression with motivational processes remains a fundamental question in neuroscience. The rostral Pedunculopontine nucleus, a brainstem structure involved in motor control, has been shown to induce transient motor arrest upon optogenetic or electrical stimulation. However, our current understanding of its potential role in linking motor suppression with motivational or reinforcement-related processes is still insufficient. To further explore the effects induced by PPN stimulations and infer the potential mechanism underlying its role involved in both motor and emotional regulation, we developed a fully automated, low-cost system combining real-time animal tracking with closed-loop optogenetic stimulation, using the OpenMV Cam H7 Plus and embedded neural network models. The system autonomously detects the rat's position and triggers optical stimulation upon entry into a predefined region of interest, enabling unbiased, unsupervised behavioral assays. Optogenetic activation of CaMKIIa-expressing neurons in the rostral PPN reliably induced transient motor arrest. When motor arrest was spatially paired with a defined region of interest, rats developed a robust place preference after limited training. These results suggest that rostral PPN activation can couple motor inhibition with reinforcement-related behavioral circuitry. Together, our work provides both a technical framework for scalable closed-loop neuroscience experiments and preliminary evidence that the rostral PPN may participate in coordinating motor suppression with motivational processes.

q-bio.NC↗

arXiv2025

LLMBoost: Make Large Language Models Stronger with Boosting

Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or final outputs while overlooking the rich internal representations and interactions across models.In this work, we introduce LLMBoost, a novel ensemble fine-tuning framework that breaks this barrier by explicitly leveraging intermediate states of LLMs. Inspired by the boosting paradigm, LLMBoost incorporates three key innovations. First, a cross-model attention mechanism enables successor models to access and fuse hidden states from predecessors, facilitating hierarchical error correction and knowledge transfer. Second, a chain training paradigm progressively fine-tunes connected models with an error-suppression objective, ensuring that each model rectifies the mispredictions of its predecessor with minimal additional computation. Third, a near-parallel inference paradigm design pipelines hidden states across models layer by layer, achieving inference efficiency approaching single-model decoding. We further establish the theoretical foundations of LLMBoost, proving that sequential integration guarantees monotonic improvements under bounded correction assumptions. Extensive experiments on commonsense reasoning and arithmetic reasoning tasks demonstrate that LLMBoost consistently boosts accuracy while reducing inference latency.

cs.LG↗

arXiv2025

Probing Quark Electromagnetic Properties via Entangled Quark Pairs in Fragmentation Hadrons at Lepton Colliders

Electromagnetic dipole interactions of light quarks induce distinct spin correlations in quark pairs produced at lepton colliders, favoring entangled spin-triplet state aligned along the $\hat{z}$ axis or spin-singlet state. These correlations lead to unique $\cos(\phi_1-\phi_2)$ azimuthal asymmetries in inclusive $\pi^+\pi^-$-dihadron pair production and in back-to-back hadron pairs ($\pi\pi,K\pi,KK$), which are absent in the SM. Using published Belle and BaBar measurements together with projected sensitivities based on ratios of azimuthal asymmetries, we demonstrate that these measurements provide significant constraints on light-quark dipole couplings, with a reduced dependence on poorly known nonperturbative fragmentation functions and free from contamination by other new physics effects. This approach offers a clean and novel probe of light-quark dipole interactions in collider experiments.

hep-ph↗

arXiv2025

Auxiliary-qubit-free quantum approximate optimization algorithm for the minimum dominating set problem

Quantum Approximate Optimization Algorithm (QAOA) is a promising framework for solving combinatorial optimization problems on near-term quantum devices. One such problem is the Minimum Dominating Set (MDS), which is known to be NP-hard. Existing QAOA algorithms for this problem typically require numerous auxiliary qubits, which increases circuit overhead and hardware requirements. In this paper, we propose an auxiliary-qubit-free QAOA algorithm based on Hamiltonian evolution (AQFH-QAOA) for the MDS problem. Unlike previous studies that require numerous auxiliary qubits, our algorithm eliminates the need for auxiliary qubits, thus significantly reducing circuit overhead. In addition, we present an auxiliary-qubit-free optimized implementation of the previously proposed Guerrero's QAOA algorithm (AQFG-QAOA) by utilizing gate decomposition techniques. Through a detailed analysis of gate complexity, we evaluate the applicability of these two algorithms. Numerical experiments demonstrate that our proposed algorithm achieves competitive solution quality compared to existing QAOA algorithms, making it a promising candidate for implementation on near-term quantum devices.

quant-ph↗

arXiv2025

From Membership-Privacy Leakage to Quantum Machine Unlearning

Quantum machine learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical machine learning (ML). In classical ML, a significant challenge is membership-privacy leakage, whereby an attacker can infer from model outputs whether specific data were used in training. When specific data are required to be withdrawn, removing their influence from the trained model becomes necessary. Machine unlearning (MU) addresses this issue by enabling the model to forget the withdrawn data, thereby preventing membership-privacy leakage. However, this leakage remains underexplored in QML. This raises two research questions: do QML models leak membership privacy about their training data, and can MU methods efficiently mitigate such leakage in QML models? We investigate these questions using two quantum neural network (QNN) architectures, a basic QNN and a hybrid QNN, evaluated in noiseless simulations and cloud quantum device demonstrations. To answer the first question, we analyze how quantum constraints shape membership-privacy leakage in QML and then formalize a realistic gray-box threat model accordingly. Based on this, we design a membership inference attack (MIA) tailored to QNN outputs, and our results provide clear evidence of membership leakage in both QNNs. To answer the second question, we propose a quantum machine unlearning (QMU) framework, comprising three MU mechanisms. Evaluations on two QNN architectures show that QMU removes the influence of the withdrawn data while preserving accuracy for retained data. A comparative analysis further characterizes the three MU mechanisms with respect to data dependence, computational cost, and robustness.

quant-ph↗

arXiv2025

Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence Matching

Referring video object segmentation (RVOS) aims to segment objects in videos guided by natural language descriptions. We propose FS-RVOS, a Transformer-based model with two key components: a cross-modal affinity module and an instance sequence matching strategy, which extends FS-RVOS to multi-object segmentation (FS-RVMOS). Experiments show FS-RVOS and FS-RVMOS outperform state-of-the-art methods across diverse benchmarks, demonstrating superior robustness and accuracy.

cs.CV↗

arXiv2025

Lam-Tung relation breaking effects and weak dipole moments at lepton colliders

The breaking of the Lam-Tung relation in the Drell-Yan process at the LHC exhibits a long-standing tension with the Standard Model (SM) prediction at $\mathcal{O}(\alpha_s^3)$ accuracy. This tension could be explained by weak dipole interactions of leptons and quarks, associated with the $Z$-boson within the framework of the Standard Model Effective Field Theory (SMEFT). In this paper, we propose to cross-check these weak dipole interactions by measuring the violation effects of the Lam-Tung relation at future lepton colliders through the processes $e^+e^- \to Z\gamma \to \ell\bar{\ell}\gamma$ and $e^+e^- \to Z\gamma \to q\bar{q}\gamma$. By considering different decay modes of the $Z$-boson, these channels exhibit distinct sensitivities to various dipole operators, providing a way to disentangle their individual effects. Additionally, the high flavor-tagging efficiencies at lepton colliders could provide strong constraints on the dipole interactions of heavy quarks, such as $b$ and $c$ quarks, which are challenging to probe in the Drell-Yan process at the LHC due to the suppression of parton distribution functions.

hep-ph↗

arXiv2025

Topology-Driven Quantum Architecture Search Framework

The limitations of Noisy Intermediate-Scale Quantum (NISQ) devices have motivated the development of Variational Quantum Algorithms (VQAs), which are designed to potentially achieve quantum advantage for specific tasks. Quantum Architecture Search (QAS) algorithms play a critical role in automating the design of high-performance Parameterized Quantum Circuits (PQCs) for VQAs. However, existing QAS approaches struggle with large search spaces, leading to substantial computational overhead when optimizing large-scale quantum circuits. Extensive empirical analysis reveals that circuit topology has a greater impact on quantum circuit performance than gate types. Based on this insight, we propose the Topology-Driven Quantum Architecture Search (TD-QAS) framework, which first identifies optimal circuit topologies and then fine-tunes the gate types. In the fine-tuning phase, the QAS inherits parameters from the topology search phase, eliminating the need for training from scratch. By decoupling the large search space into separate topology and gate-type components, TD-QAS avoids exploring gate configurations within low-performance topologies, thereby significantly reducing computational complexity. Numerical simulations across various tasks, under both noiseless and noisy conditions, validate the effectiveness of the TD-QAS framework. This framework advances standard QAS algorithms by enabling the identification of high-performance quantum circuits while minimizing computational demands. These findings indicate that TD-QAS deepens our understanding of VQAs and offers broad potential for the development of future QAS algorithms.

quant-ph↗

arXiv2024

Pathfinding pulsar observations with the CVN incorporating the FAST

The importance of Very Long Baseline Interferometry (VLBI) for pulsar research is becoming increasingly prominent and receiving more and more attention. In this paper, we present pathfinding pulsar observation results with the Chinese VLBI Network (CVN) incorporating the Five-hundred-meter Aperture Spherical radio Telescope (FAST). On MJD 60045 (April 11th, 2023), PSRs B0919+06 and B1133+16 were observed with the phase-referencing mode in the L-band using four radio telescopes (FAST, TianMa, Haoping and Nanshan) and correlated with the pulsar binning mode of the distributed FX-style software correlator in Shanghai. After further data processing with the NRAO Astronomical Image Processing System (AIPS), we detected these two pulsars and fitted their current positions with accuracy at the milliarcsecond level. By comparison, our results show significantly better agreement with predicted values based on historical VLBI observations than that with previous timing observations, as pulsar astrometry with the VLBI provides a more direct and model-independent method for accurately obtaining related parameters.

astro-ph.HE↗

arXiv2024

DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation

Neural-based multi-task learning (MTL) has been successfully applied to many recommendation applications. However, these MTL models (e.g., MMoE, PLE) did not consider feature interaction during the optimization, which is crucial for capturing complex high-order features and has been widely used in ranking models for real-world recommender systems. Moreover, through feature importance analysis across various tasks in MTL, we have observed an interesting divergence phenomenon that the same feature can have significantly different importance across different tasks in MTL. To address these issues, we propose Deep Multiple Task-specific Feature Interactions Network (DTN) with a novel model structure design. DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup. We applied DTN to our company's real-world E-commerce recommendation dataset, which consisted of over 6.3 billion samples, the results demonstrated that DTN significantly outperformed state-of-the-art MTL models. Moreover, during online evaluation of DTN in a large-scale E-commerce recommender system, we observed a 3.28% in clicks, a 3.10% increase in orders and a 2.70% increase in GMV (Gross Merchandise Value) compared to the state-of-the-art MTL models. Finally, extensive offline experiments conducted on public benchmark datasets demonstrate that DTN can be applied to various scenarios beyond recommendations, enhancing the performance of ranking models.

cs.IR↗

arXiv2024

Reconfigurable, Multifunctional Origami Electronic Membranes for Mechanical and Environmental Sensing

This work introduces a concept of origami electronic membranes that leverages the design and fabrication of flexible electronics and the mechanical behavior of engineering origami to achieve unique multifunctional, shape-reconfigurable, and adaptive membranes for mechanical and environmental sensing in benign and harsh conditions. This paper presents the materials, design, and fabrication methods for realizing six origami electronic membranes capable of reconfiguring planar or three-dimensional shapes based on the modified flasher, Kresling, Miura-ori, circular, letter, and Tachi-Miura origami patterns. These origami-based, thin-film flexible electronics can obtain both expansion and folding of their shapes, as well as transformation between different geometries. The origami electronic membranes can achieve mechanical and environmental sensing functions such as measuring motions, mechanical strains, temperatures, UV light, and humidity. The results reported here demonstrate the promise of combining engineering origami with flexible electronics to advance the state-of-the-art in multifunctional foldable and deployable electronics and systems.

physics.app-ph↗

arXiv2023

A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction

Deep graph convolution networks (GCNs) have recently shown excellent performance in traffic prediction tasks. However, they face some challenges. First, few existing models consider the influence of auxiliary information, i.e., weather and holidays, which may result in a poor grasp of spatial-temporal dynamics of traffic data. Second, both the construction of a dynamic adjacent matrix and regular graph convolution operations have quadratic computation complexity, which restricts the scalability of GCN-based models. To address such challenges, this work proposes a deep encoder-decoder model entitled AIMSAN. It contains an auxiliary information-aware module (AIM) and sparse cross attention-based graph convolution network (SAN). The former learns multi-attribute auxiliary information and obtains its embedded presentation of different time-window sizes. The latter uses a cross-attention mechanism to construct dynamic adjacent matrices by fusing traffic data and embedded auxiliary data. Then, SAN applies diffusion GCN on traffic data to mine rich spatial-temporal dynamics. Furthermore, AIMSAN considers and uses the spatial sparseness of traffic nodes to reduce the quadratic computation complexity. Experimental results on three public traffic datasets demonstrate that the proposed method outperforms other counterparts in terms of various performance indices. Specifically, the proposed method has competitive performance with the state-of-the-art algorithms but saves 35.74% of GPU memory usage, 42.25% of training time, and 45.51% of validation time on average.

cs.AI↗

arXiv2023

Precessing jet nozzle connecting to a spinning black hole in M87

The nearby radio galaxy M87 offers a unique opportunity to explore the connections between the central supermassive black hole and relativistic jets. Previous studies of the inner region of M87 revealed a wide opening angle for the jet originating near the black hole. The Event Horizon Telescope resolved the central radio source and found an asymmetric ring structure consistent with expectations from General Relativity. With a baseline of 17 years of observations, there was a shift in the jet's transverse position, possibly arising from an eight to ten-year quasi-periodicity. However, the origin of this sideways shift remains unclear. Here we report an analysis of radio observations over 22 years that suggests a period of about 11 years in the position angle variation of the jet. We infer that we are seeing a spinning black hole that induces the Lense-Thirring precession of a misaligned accretion disk. Similar jet precession may commonly occur in other active galactic nuclei but has been challenging to detect owing to the small magnitude and long period of the variation.

astro-ph.HE↗