arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 163 records · Page 9Linked to original sources

arXiv2025

The bound and resonant states of $D^{()}D^{()}$ and $D^{()}\bar{D}^{()}$ with the complex scaling method

We perform a systematic study of the possible molecular states composed of a pair of heavy mesons such as $D^{(*)}D^{(*)}$, $D^{(*)}\bar{D}^{(*)}$ in the framework of the one-boson-exchange model. The exchanged bosons include the pseudoscalar, scalar and vector mesons($π$, $σ$, $ρ$, $ω$). We use the Bonn approximation to get the interaction potential of one-boson-exchange model, then apply the complex scaling method to calculate the bound and resonant states. The results indicate that the $D^{(*)}D^{(*)}$ and $D^{(*)}\bar{D}^{(*)}$ system can not only form several bound states, but also a P-wave resonant state. The hadron molecular state model can explain the structure of $T_{cc}^+$ as a bound state $DD^{*}$ with quantum number $I(J^P) = 0(1^+)$. In addition, we also discovered other bound and resonant states, which have the potential to be observed experimentally.

hep-ph↗

arXiv2025

SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection

Detection of face forgery videos remains a formidable challenge in the field of digital forensics, especially the generalization to unseen datasets and common perturbations. In this paper, we tackle this issue by leveraging the synergy between audio and visual speech elements, embarking on a novel approach through audio-visual speech representation learning. Our work is motivated by the finding that audio signals, enriched with speech content, can provide precise information effectively reflecting facial movements. To this end, we first learn precise audio-visual speech representations on real videos via a self-supervised masked prediction task, which encodes both local and global semantic information simultaneously. Then, the derived model is directly transferred to the forgery detection task. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of cross-dataset generalization and robustness, without the participation of any fake video in model training. Code is available at https://github.com/Eleven4AI/SpeechForensics.

cs.CV↗

arXiv2025

Hybrid Long and Short Range Flows for Point Cloud Filtering

Point cloud capture processes are error-prone and introduce noisy artifacts that necessitate filtering/denoising. Recent filtering methods often suffer from point clustering or noise retaining issues. In this paper, we propose Hybrid Point Cloud Filtering ($\textbf{HybridPF}$) that considers both short-range and long-range filtering trajectories when removing noise. It is well established that short range scores, given by $\nabla_{x}\log p(x_t)$, may provide the necessary displacements to move noisy points to the underlying clean surface. By contrast, long range velocity flows approximate constant displacements directed from a high noise variant patch $x_0$ towards the corresponding clean surface $x_1$. Here, noisy patches $x_t$ are viewed as intermediate states between the high noise variant and the clean patches. Our intuition is that long range information from velocity flow models can guide the short range scores to align more closely with the clean points. In turn, score models generally provide a quicker convergence to the clean surface. Specifically, we devise two parallel modules, the ShortModule and LongModule, each consisting of an Encoder-Decoder pair to respectively account for short-range scores and long-range flows. We find that short-range scores, guided by long-range features, yield filtered point clouds with good point distributions and convergence near the clean surface. We design a joint loss function to simultaneously train the ShortModule and LongModule, in an end-to-end manner. Finally, we identify a key weakness in current displacement based methods, limitations on the decoder architecture, and propose a dynamic graph convolutional decoder to improve the inference process. Comprehensive experiments demonstrate that our HybridPF achieves state-of-the-art results while enabling faster inference speed.

cs.GR↗

arXiv2025

Emergent dynamical Kondo coherence and competing magnetic order in a correlated kagome flat-band metal CsCr6Sb6

Correlated kagome metals host unique electronic states that enable exotic quantum phenomena. In the recently emerged CsCr6Sb6, these manifest through Kondo behavior from localized Cr-3d electrons and unprecedented band flattening near the Fermi level. Yet the intricate interplay among Kondo screening, magnetic frustration, and electronic correlations remains poorly understood-a fundamental gap we address through multifaceted experimental and theoretical approaches. Our angle-resolved photoemission spectroscopy measurements reveal electronic correlation-renormalized flat bands and muon spin relaxation study detect short-range magnetic order at TN ~ 80 K. Complementing these findings, density-functional theory and dynamical mean-field theory calculations identify a coherent-incoherent crossover at TN, with a remarkable restoration of coherence accompanying local moment suppression-an anomalous hallmark of Kondo behavior. Intriguingly, despite strong interlayer antiferromagnetic coupling, the system evades long-range magnetic order due to competing magnetic configurations separated by sub-meV energy differences. These insights establish CsCr6Sb6 as a prototypical platform for investigating dynamical Kondo screening in correlated flat-band systems, opening new avenues to study flat band physics and frustrated magnetism in correlated kagome lattices.

cond-mat.str-el↗

arXiv2025

Bidirectional anisotropic solar energetic particle events observed by Solar Orbiter

Solar Energetic Particle (SEP) events are critical for understanding particle acceleration and transport in the heliosphere. While most SEP events involve outward streaming particles along open magnetic field lines, bidirectional events characterized by simultaneous sunward and anti-sunward particle flows offer unique insights into magnetic field topology and the interplay of multiple acceleration sources. We aim to investigate the origin and transport of energetic particles in two rare bidirectional anisotropic SEP events observed by Solar Orbiter. Both events showed two clear velocity dispersion signatures with opposite particle anisotropies during their onset phase. The sunward streaming protons, characterized by delayed release time, harder spectral index, and higher intensities, may be attributed to coronal mass ejection-driven shock acceleration, while the promptly released anti-sunward streaming protons are likely linked to flare acceleration. Notably, in both cases, small-scale flux ropes were identified in situ during the time intervals corresponding to the bidirectional particle streaming. Path lengths derived for sunward and anti-sunward injections were substantially greater than nominal values of the Parker field lines, further supporting the role of the flux rope in shaping particle trajectories. These observations demonstrate that magnetic flux rope could significantly affect magnetic connectivity to the source region and SEP propagation in the inner heliosphere, while simultaneous velocity dispersion from two distinct particle sources allows for direct constraints on the topology of the flux rope. Our results highlight the value of combining particle anisotropy, release time, source spectra, and magnetic structure diagnostics to unravel SEP transport in complex transient magnetic structures, and also present new challenges for the current SEP transport model.

astro-ph.SR↗

arXiv2025

Light hadronic decays of spin-0 partner of $X(3872)$

The $X(3872)$ is theoretically predicted to have a spin-0 partner state denoted as $X_0$. Assuming the $X_0$ as a $D\bar{D}$ molecular bound state, we calculate the decay widths of $X_0\to VV$ and $X_0 \to PP$ ($V$ and $P$ stand for light vector and pseudoscalar mesons, respectively) via intermediate charmed meson loops. Three different configurations of the $X_0$, i.e., pure neutral components ($θ= 0$), isospin singlet ($θ= π/4$) and pure charged components ($θ= π/2$), are investigated. Within a commonly accepted range of the model parameter $α$, the predicted decay widths of $X_0 \to VV$ are on the order of a few hundred $\mathrm{keV}$, while the decay widths of $X_0 \to PP$ can reach several $\mathrm{MeV}$. The $X_0 \to ρρ$ and $ππ$ have larger decay rates. The relative width ratios between the channels are nearly model-independent. Moreover, among those channels only with isovector or isoscalar mesons, the relevant ratios are also independent of the phase angle. The predicted ratios are helpful for searching the $X_0$ in the future experiments at BESIII and Belle II.

hep-ph↗

arXiv2025

Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization

Decision-making in manufacturing often involves optimizing key process parameters using data collected from simulation experiments. Gaussian processes are widely used to surrogate the underlying system and guide optimization. Uncertainty often inherent in the decisions given by the surrogate model due to limited data and model assumptions. This paper proposes a surrogate model-based framework for estimating the uncertainty of optimal decisions and analyzing its sensitivity with respect to the objective function. The proposed approach is applied to the composite cure process simulation in manufacturing.

stat.AP↗

arXiv2025

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing

3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for resource-constrained platforms. The conventional decoupled preprocessing-rendering dataflow in existing accelerators has two major limitations: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different tile renderings, resulting in substantial computational and data movement overhead. To address these issues, we propose GCC, a novel accelerator designed for fast and energy-efficient 3DGS inference. GCC introduces a novel dataflow featuring: 1) \textit{cross-stage conditional processing}, which interleaves preprocessing and rendering to dynamically skip unnecessary Gaussian preprocessing; and 2) \textit{Gaussian-wise rendering}, ensuring that all rendering operations for a given Gaussian are completed before moving to the next, thereby eliminating duplicated Gaussian loading. We also propose an alpha-based boundary identification method to derive compact and accurate Gaussian regions, thereby reducing rendering costs. We implement our GCC accelerator in 28nm technology. Extensive experiments demonstrate that GCC significantly outperforms the state-of-the-art 3DGS inference accelerator, GSCore, in both performance and energy efficiency.

cs.AR↗

arXiv2025

Flavor Physics at the CEPC: a General Perspective

We discuss the landscape of flavor physics at the Circular Electron-Positron Collider (CEPC), based on the nominal luminosity outlined in its Technical Design Report. The CEPC is designed to operate in multiple modes to address a variety of tasks. At the $Z$ pole, the expected production of 4 Tera $Z$ bosons will provide unique and highly precise measurements of $Z$ boson couplings, while the substantial number of boosted heavy-flavored quarks and leptons produced in clean $Z$ decays will facilitate investigations into their flavor physics with unprecedented precision. We investigate the prospects of measuring various physics benchmarks and discuss their implications for particle theories and phenomenological models. Our studies indicate that, with its highlighted advantages and anticipated excellent detector performance, the CEPC can explore beauty and $τ$ physics in ways that are superior to or complementary with the Belle II and Large-Hadron-Collider-beauty experiments, potentially enabling the detection of new physics at energy scales of 10 TeV and above. This potential also extends to the observation of yet-to-be-discovered rare and exotic processes, as well as testing fundamental principles such as lepton flavor universality, lepton and baryon number conservation, etc., making the CEPC a vibrant platform for flavor physics research. The $WW$ threshold scan, Higgs-factory operation and top-pair productions of the CEPC further enhance its merits in this regard, especially for measuring the Cabibbo-Kobayashi-Maskawa matrix elements, and Flavor-Changing-Neutral-Current physics of Higgs boson and top quarks. We outline the requirements for detector performance and considerations for future development to achieve the anticipated scientific goals.

hep-ex↗

arXiv2025

Routine: A Structural Planning Framework for LLM Agent System in Enterprise

The deployment of agent systems in an enterprise environment is often hindered by several challenges: common models lack domain-specific process knowledge, leading to disorganized plans, missing key tools, and poor execution stability. To address this, this paper introduces Routine, a multi-step agent planning framework designed with a clear structure, explicit instructions, and seamless parameter passing to guide the agent's execution module in performing multi-step tool-calling tasks with high stability. In evaluations conducted within a real-world enterprise scenario, Routine significantly increases the execution accuracy in model tool calls, increasing the performance of GPT-4o from 41.1% to 96.3%, and Qwen3-14B from 32.6% to 83.3%. We further constructed a Routine-following training dataset and fine-tuned Qwen3-14B, resulting in an accuracy increase to 88.2% on scenario-specific evaluations, indicating improved adherence to execution plans. In addition, we employed Routine-based distillation to create a scenario-specific, multi-step tool-calling dataset. Fine-tuning on this distilled dataset raised the model's accuracy to 95.5%, approaching GPT-4o's performance. These results highlight Routine's effectiveness in distilling domain-specific tool-usage patterns and enhancing model adaptability to new scenarios. Our experimental results demonstrate that Routine provides a practical and accessible approach to building stable agent workflows, accelerating the deployment and adoption of agent systems in enterprise environments, and advancing the technical vision of AI for Process.

cs.AI↗

arXiv2025

Constrain the $χ_{cJ}\to D^{()}\bar{D}^{()}$ effective couplings via the $X(3872)\to π^0χ_{cJ}$ decays

The hidden-charm decays serve as irreplaceable platforms for probing the structures of charmonium-like states, such as $X(3872)$, $Y(4260)$, $Z_c(3900)$, and their heavy-quark-symmetry partners. In the hadronic molecular scenario, these hidden-charm decays are denominated by intermediate meson loops (IMLs), and the couplings of $χ_{cJ}\to D^{(\ast)}\bar{D}^{(\ast)}$ are building blocks of the amplitudes for the pionic and radiative transitions of the charmonium-like states to the $χ_{cJ}$ and $h_c$ states, e.g., $X(3872)\to π^0χ_{cJ},\,ππχ_{cJ},\,γχ_{cJ}$ and $Y(4260)\to π^0 h_c,\,ηh_c$. These couplings can not be extracted from the partial decay widths of the $χ_{cJ}$ directly and only have estimated values from the vector meson dominance (VMD) model. Utilizing the recent precise determination of the pole position and the isospin breaking properties of the $X(3872)$, we give an estimation on the upper bounds of the absolute values of the $χ_{cJ}\to D^{(\ast)}\bar{D}^{(\ast)}$ couplings. Our results show that the VMD model may over estimate the $χ_{cJ}\to D^{(\ast)}\bar{D}^{(\ast)}$ couplings considering the $X(3872)$ as a $D\bar{D}^{*}$ hadronic molecule with a binding energy about tens of keV. These upper limits can be used and tested in other hidden-charm transitions of the charmonium-like states to the $χ_{cJ}$ and $h_c$.

hep-ph↗

arXiv2025

Time-Dependent Pseudo $\boldsymbol{R^2}$ for Assessing Predictive Performance in Competing Risks Data

Evaluating and validating the performance of prediction models is a fundamental task in statistics, machine learning, and their diverse applications. However, developing robust performance metrics for competing risks time-to-event data poses unique challenges. We first highlight how certain conventional predictive performance metrics, such as the C-index, Brier score, and time-dependent AUC, can yield undesirable results when comparing predictive performance between different prediction models. To address this research gap, we introduce a novel time-dependent pseudo $R^2$ measure to evaluate the predictive performance of a predictive cumulative incidence function over a restricted time domain under right-censored competing risks time-to-event data. Specifically, we first propose a population-level time-dependent pseudo $R^2$ measures for the competing risk event of interest and then define their corresponding sample versions based on right-censored competing risks time-to-event data. We investigate the asymptotic properties of the proposed measure and demonstrate its advantages over conventional metrics through comprehensive simulation studies and real data applications.

stat.ME↗

arXiv2025

Emerging kinetic-exchange for the enhanced metallic ferromagnetism in CrGeTe$_3$ under pressure

The microscopic origin of ferromagnetism in correlated materials remains heavily debated, particularly for the competing mechanisms governing insulating versus metallic phases. In this work, we theoretically study the electronic structure evolution of CrGeTe$_{3}$ under pressure and provide a consistent explanation to three unique features of this system, i.e. the semiconducting ferromagnetism at low pressure, the metallic ferromagnetism at high pressure, and the enhanced Curie temperature in the metallic phase. We propose that it is the reduced electronic correlation and enhanced $d$-$p$ hybridization that universally drive the continuous evolution of CrGeTe$_{3}$ under pressure and glue the three distinct experimental observations. Central to our discovery is the dual role of metallicity -- it simultaneously establishes kinetically driven exchange via $d$-$p$ hybridization and enables Stoner-type magnetic instability, with the contribution also from the residual super-exchange. Our analyses reveal that {\it intraband} excitations dominate the pressure-enhanced $ω_p^2$ and $T_c$ correlation. These findings establish $d$-$p$ hybridization and electronic correlation as the bridge between localized and itinerant magnetism, at least, in CrGeTe$_{3}$.

cond-mat.str-el↗

arXiv2025

Hole distribution and self-doping enhanced electronic correlation in hole-doped infinite-layer nickelates

The minimal model for infinite-layer nickelates remains under debate, particularly regarding the hybridization between itinerant interstitial-$s$ and the correlated Ni-3$d_{x^2-y^2}$ orbitals, as well as the interaction between $d_{x^2-y^2}$ and other $3d$ orbitals. Additionally, how the doped holes in La$_{1-x}$Sr$_x$NiO$_2$ are distributed among different orbitals remain unresolved. Motivated by recent angle resolved photoemission spectroscopy (ARPES) experiments, we theoretically study the electronic structure of infinite-layer La$_{1-x}$Sr$_x$NiO$_2$ at various doping levels. We find that, unlike the expectation from a rigid band shift, holes are equally distributed to Ni-3$d_{x^2-y^2}$ and interstitial-$s$ orbitals. The role of interstitial-$s$ orbital is further confirmed from the renormalization of Ni-3$d_{x^2-y^2}$ band, for which the coupling between interstitial-$s$ and Ni-3$d_{x^2-y^2}$ exerts a non-negligible impact on the orbital-selective renormalization observed in ARPES. We also discuss the implication of our results to the single-band model, where the interstitial-$s$ orbital in the normal state of La$_{1-x}$Sr$_x$NiO$_2$ acts as charge donator enhancing the correlation of Ni-3$d_{x^2-y^2}$ by increasing its concentration close to half-filling.

cond-mat.str-el↗

arXiv2025

Efficient Implementation of a Semiparametric Joint Model for Multivariate Longitudinal Biomarkers and Competing Risks Time-to-Event Data

Joint modeling has become increasingly popular for characterizing the association between one or more longitudinal biomarkers and competing risks time-to-event outcomes. However, semiparametric multivariate joint modeling for large-scale data encounter substantial statistical and computational challenges, primarily due to the high dimensionality of random effects and the complexity of estimating nonparametric baseline hazards. These challenges often lead to prolonged computation time and excessive memory usage, limiting the utility of joint modeling for biobank-scale datasets. In this article, we introduce an efficient implementation of a semiparametric multivariate joint model, supported by a normal approximation and customized linear scan algorithms within an expectation-maximization (EM) framework. Our method significantly reduces computation time and memory consumption, enabling the analysis of data from thousands of subjects. The scalability and estimation accuracy of our approach are demonstrated through two simulation studies. We also present an application to the Primary Biliary Cirrhosis (PBC) dataset involving five longitudinal biomarkers as an illustrative example. A user-friendly R package, \texttt{FastJM}, has been developed for the shared random effects joint model with efficient implementation. The package is publicly available on the Comprehensive R Archive Network: https://CRAN.R-project.org/package=FastJM.

stat.ME↗

arXiv2025

Maximal Speed of Glucose Change Significantly Distinguishes Prediabetes from Diabetes

Rapid changes in blood glucose levels can have severe and immediate health consequences, leading to the need to develop indices for assessing these rapid changes based on continuous glucose monitoring (CGM) data. We proposed a CGM index, maxSpeed, that represents the maximum of speed of glucose change (SGC) in a subject, respectively, and conducted a clinical study to investigate this index along with SGC mean (meanSpeed) and SGC standard deviation (sdSpeed), coefficient of variation (CV), standard deviation (SD), glycemic variability percentage (GVP), mean amplitude of glycemic excursions (MAG), mean absolute glucose excursion (MAGE), mean of daily differences (MODD) and continuous overlapping net glycemic action (CONGA). Our study revealed that, there exist multiple patterns in distinguishing non-diabetes, prediabetes, type 1 diabetes (T1D) and type 2 diabetes (T2D). First, maxSpeed significantly distinguishes between either of non-diabetes and prediabetes and either of T1D and T2D. Second, meanSpeed, sdSpeed, GVP and MAG significantly distinguish between non-diabetes and either of T1D and T2D. Third, MODD and CONGA of 24 hours significantly distinguish between non-diabetes and either of T1D and T2D, between T1D and either of prediabetes and T2D. Fourth, SD, MAGE and CONGA of 12 hours significantly distinguish between non-diabetes and either of T1D and T2D, between T1D and pre-diabetes. Fifth, CV significantly distinguishes between T1D and either of Non-diabetes and T2D. maxSpeed assesses the rapid change of glucose in a short term, which is important both biologically and clinicially because our human body may not tolerate too rapid change in a short term.

q-bio.QM↗

arXiv2025

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

The dipionic transition of the $X(3872)$ to the $η_c$ was investigated using an effective Lagrangian approach. In this study, the $X(3872)$ was assumed to be a $D\bar{D}^\ast + \text{c.c.}$ bound state with the quantum numbers $J^{PC}=1^{++}$ and to decay via triangle and box loops. It is found that the partial decay widths arising from the box loops are one order of magnitude greater than those from the triangle ones. The decay widths are model-dependent, as characterized by the cutoff parameter introduced in the form factor. Moreover, the charged and neutral $D\bar{D}^\ast$ configurations of the $X(3872)$ and its mass also influence the decay widths. With our model parameters, the decay widths for the $X(3872)\toπ^+π^-η_c$ can reach up to several tens of keV. We hope that the current calculations within the molecular interpretation will be helpful for the future experiments.

hep-ph↗

arXiv2025

Numerical Simulation Informed Rapid Cure Process Optimization of Composite Structures using Constrained Bayesian Optimization

The present study aimed to solve the cure optimization problem of laminated composites through a statistical approach. The approach consisted of using constrained Bayesian Optimization (cBO) along with a Gaussian process model as a surrogate to rapidly solve the cure optimization problem. The approach was implemented to two case studies including the cure of a simpler flat rectangular laminate and a more complex L-shaped laminate. The cure optimization problem with the objective to minimize cure induced distortion was defined for both case studies. The former case study was two-variable that is used two cure cycle parameters as design variables and was constrained to achieve full cure, while the latter was four-variable and had to satisfy constraints of full cure as well as other cure cycle parameters. The performance of cBO for both case studies was compared to the traditional optimization approach based on Genetic Algorithm (GA). The comparison of results from GA and cBO including deformation and final degree of cure showed significant agreement (error < 4%). The computational efficiency of cBO was calculated by comparing the convergence steps for GA (>1000) and cBO (<50). The computational efficiency of cBO for all optimization cases was found to be > 96%. The case studies conclude that cBO is promising in terms of computational time and accuracy for solving the cure optimization problem.

stat.AP↗