arXiv subjects

Jie Lu

Publications and source records attributed to Jie Lu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Systematic analysis of the transition form factors of $B_c$ to $D$-wave charmonia and corresponding semileptonic decays

In this article, the vector, axial vector and tensor form factors of $B_c$ to $D$-wave charmonia $ψ_J$ ($J=1,2,3$) and $η_{c2}$ are analyzed within the framework of three-point QCD sum rules. With the estimated vector and axial vector form factors, we study the decay widths and branching ratios of semileptonic decays $B_c\toψ_Jl\bar{ν_l}$ and $B_c\toη_{c2}l\bar{ν_l}$. Our results indicate that the semileptonic decay branching ratios of $B_c$ to $D$-wave charmonia decreases as the total angler momentum of the final state $D$-wave charmonia increases. For decay processes $B_c\to ψ_1l\barν_l$, $B_c\to ψ_2[η_{c2}]l\barν_l$ and $B_c\to ψ_3l\barν_l$, the branching ratios can reach the order $10^{-3}$, $10^{-4}$ and $10^{-5}$, respectively, which can provide a valuable reference for future experimental measurements for $B_c$ meson by LHCb collaboration. Furthermore, these results also can provide useful information to study the properties of $B_c$ meson and $D$-wave charmonia.

hep-ph↗

arXiv2025

Analysis of the semileptonic decays $Λ_b\toΛ_cl\barν_l$ and $Ξ_b\toΞ_cl\barν_l$ in QCD sum rules

In this article, the electroweak transition form factors of $Λ_b\toΛ_c$ and $Ξ_b\toΞ_c$ are analyzed within the framework of three-point QCD sum rules. In phenomenological side, all possible couplings of interpolating current to hadronic states are considered. In QCD side, the perturbative part and the contributions of vacuum condensates up to dimension 8 are also included. With the estimated form factors, we study the decay widths and branching ratios of semileptonic decays $Λ_b\toΛ_cl\barν_l$ and $Ξ_b\toΞ_cl\barν_l$ ($l=e,μ$ and $τ$). Our results for the branching ratios of $Λ_b\toΛ_cl\barν_l$ are comparable with experimental data and the results from other collaborations. In addition, our prediction for the branching ratio of $Ξ_b\toΞ_cl\barν_l$ can provide a valuable reference for future experimental measurements.

hep-ph↗

arXiv2025

Resilient Contrastive Pre-training under Non-Stationary Drift

The remarkable success of large-scale contrastive pre-training has been largely driven by by vast yet static datasets. However, as the scaling paradigm evolves, this paradigm encounters a fundamental challenge when applied to dynamic data streams characterized by concept drift - unpredictable changes in the underlying data distribution. This paper aims to advance robust pre-training under such non-stationary environments. We begin by revealing that conventional contrastive pre-training methods are highly susceptible to concept drift, resulting in significant substantial bias and instability within the learned feature representations. To systematically analyze these effects, we develop a structural causal model that elucidates how drift acts as a confounder, distorting the learned representations. Based on these causal insights, we propose Resilient Contrastive Pre-training (RCP), a novel method that incorporates causal intervention. RCP formulates a causally-informed objective to mitigate drift-induced biases through targeted interventions. The method is designed for simple and scalable implementation and exhibits notable adaptability, promoting robust and autonomous pre-training on non-stationary data. Comprehensive experiments across various downstream tasks consistently demonstrate that RCP effectively alleviates the detrimental impact of concept drift, yielding more resilient and generalizable representations.

cs.LG↗

arXiv2025

On the Convex Interpolation for Linear Operators

The worst-case performance of an optimization method on a problem class can be analyzed using a finite description of the problem class, known as interpolation conditions. In this work, we study interpolation conditions for linear operators given scalar products between discrete inputs and outputs. First, we show that if only convex constraints on the scalar products of inputs and outputs are allowed,it is only possible to characterize classes of linear operators or symmetric linear operator whose all singular values or eigenvalues belong to some subset of R. Then, we propose new interpolation conditions for linear operators with minimal and maximal singular values and linear operators whose eigenvalues or singular values belong to unions of subsets. Finally, we illustrate the new interpolation conditions through the analysis of the Gradient and Chambolle-Pock methods. It allows to obtain new numerical worst-case guarantees on these methods.

math.OC↗

arXiv2025

Globally-Constrained Decentralized Optimization with Variable Coupling

Many realistic decision-making problems in networked scenarios, such as formation control and collaborative task offloading, often involve complicatedly entangled local decisions, which, however, have not been sufficiently investigated yet. Motivated by this, we study a class of decentralized optimization problems with a variable coupling structure that is new to the literature. Specifically, we consider a network of nodes collaborating to minimize a global objective subject to a collection of global inequality and equality constraints, which are formed by the local objective and constraint functions of the nodes. On top of that, we allow such local functions of each node to depend on not only its own decision variable but the decisions of its neighbors as well. To address this problem, we propose a decentralized projected primal-dual algorithm. It first incorporates a virtual-queue technique with a primal-dual-primal scheme, and then linearizes the non-separable objective and constraint functions to enable decentralized implementation. Under mild conditions, we derive $O(1/k)$ convergence rates for both objective error and constraint violations. Finally, two numerical experiments corroborate our theoretical results and illustrate the competitive performance of the proposed algorithm.

math.OC↗

arXiv2025

Autonomous Concept Drift Threshold Determination

Existing drift detection methods focus on designing sensitive test statistics. They treat the detection threshold as a fixed hyperparameter, set once to balance false alarms and late detections, and applied uniformly across all datasets and over time. However, maintaining model performance is the key objective from the perspective of machine learning, and we observe that model performance is highly sensitive to this threshold. This observation inspires us to investigate whether a dynamic threshold could be provably better. In this paper, we prove that a threshold that adapts over time can outperform any single fixed threshold. The main idea of the proof is that a dynamic strategy, constructed by combining the best threshold from each individual data segment, is guaranteed to outperform any single threshold that apply to all segments. Based on the theorem, we propose a Dynamic Threshold Determination algorithm. It enhances existing drift detection frameworks with a novel comparison phase to inform how the threshold should be adjusted. Extensive experiments on a wide range of synthetic and real-world datasets, including both image and tabular data, validate that our approach substantially enhances the performance of state-of-the-art drift detectors.

cs.LG↗

arXiv2025

Distributed Nonconvex Optimization with Double Privacy Protection and Exact Convergence

Motivated by the pervasive lack of privacy protection in existing distributed nonconvex optimization methods, this paper proposes a decentralized proximal primal-dual algorithm enabling double protection of privacy ($\text{DPP}^2$) for minimizing nonconvex sum-utility functions over multi-agent networks, which ensures zero leakage of critical local information during inter-agent communications. We develop a two-tier privacy protection mechanism that first merges the primal and dual variables by means of a variable transformation, followed by embedding an additional random perturbation to further obfuscate the transmitted information. We theoretically establish that $\text{DPP}^2$ ensures differential privacy for local objectives while achieving exact convergence under nonconvex settings. Specifically, $\text{DPP}^2$ converges sublinearly to a stationary point and attains a linear convergence rate under the additional Polyak-Łojasiewicz (P-Ł) condition. Finally, a numerical example demonstrates the superiority of $\text{DPP}^2$ over a number of state-of-the-art algorithms, showcasing the faster, exact convergence achieved by $\text{DPP}^2$ under the same level of differential privacy.

math.OC↗

arXiv2025

Analysis of the strong vertices of hadronic molecules $DK$, $D^K$, $DK^$ and their bottom analogs

In this work, we analyze the strong vertices of hadronic molecules $DK$, $D^*K$, $DK^*$ and their bottom analogs within the framework of three-point QCD sum rules. The coupling between interpolating currents and low spin particles is considered in the phenomenological side, and the vacuum condensates $\left\langle \bar qq \right\rangle ,\left\langle g_s^2GG \right\rangle ,\left\langle \bar qg_sσGq \right\rangle ,\left\langle g_s^3GGG \right\rangle ,{\left\langle \bar qq \right\rangle ^2}$ are included in the QCD side. As an application of strong coupling constants, we also obtain the partial decay widths of these states, where $Γ_{T_{DK}\rightarrow D_sπ}=10.3_{-2.9}^{+3.0}\mathrm{KeV}$, $Γ_{T_{D^*K}\rightarrow D_s^*π}=24.8_{-5.4}^{+5.5}\mathrm{KeV}$, $Γ_{T_{BK}\rightarrow BK}=101_{-21}^{+36}\mathrm{MeV}$ and $Γ_{T_{B^*K}\rightarrow B^*K}=142_{-25}^{+52}\mathrm{MeV}$. It is shown that the results of $Γ_{T_{DK}\rightarrow D_sπ}$ and $Γ_{T_{D^*K}\rightarrow D_s^*π}$ are compatible with the experimental data of $D_{s0}^*(2317)$ and $D_{s1}(2460)$.

hep-ph↗

arXiv2025

Interband optical conductivity in two-dimensional semi-Dirac bands tilting along the quadratic dispersion

Two-dimensional (2D) semi-Dirac materials feature a unique anisotropic band structure characterized by quadratic dispersion along one spatial direction and linear dispersion along the other, effectively hybridizing ordinary and Dirac fermions. The anisotropy of energy dispersion can be further modulated through band tilting along either spatial direction of the wave vector. We propose a new definition of tilt parameter to characterize Lifshitz phases in 2D semi-Dirac bands tilting along the quadratically dispersing direction. Using linear response theory, we theoretically investigate the interband optical conductivity of 2D tilted semi-Dirac bands. Our analytical zero-temperature results reveal pronounced distinctions from Dirac and semi-Dirac systems tilting along the linearly dispersing direction. Notably, we find that spectral fixed point emerges in the optical conductivity over a specific range of the tilt parameter, a phenomenon explained by the corresponding behavior of the joint density of states. These findings provide a robust theoretical framework for identifying and characterizing 2D tilted semi-Dirac materials and establish clear spectral fingerprints that distinguish different kinds of 2D semi-Dirac bands and Dirac bands. Our predictions can guide future experimental studies of anisotropic band engineering and tilt-dependent phenomena.

cond-mat.mes-hall↗

arXiv2025

Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different modalities. Diagnosing the vulnerability of atherosclerotic plaques directly from carotid 3D MRI images is relatively challenging for both radiologists and conventional 3D vision networks. In clinical practice, radiologists assess patient conditions using a multimodal approach that incorporates various imaging modalities and domain-specific expertise, paving the way for the creation of multimodal diagnostic networks. In this paper, we have developed an effective strategy to leverage radiologists' domain knowledge to automate the diagnosis of carotid plaque vulnerability through Variation inference and Multimodal knowledge Distillation (VMD). This method excels in harnessing cross-modality prior knowledge from limited image annotations and radiology reports within training data, thereby enhancing the diagnostic network's accuracy for unannotated 3D MRI images. We conducted in-depth experiments on the dataset collected in-house and verified the effectiveness of the VMD strategy we proposed.

cs.CV↗

arXiv2025

Analysis of the strong vertices $Λ_cD^{()}N^(1535)$ and $Λ_bB^{()}N^(1535)$ in QCD sum rules

In this article, we firstly analyze the mass and pole residue of negative parity nucleon $N^*(1535)$ within the two-point QCD sum rules. Basing on these results, we continuously study the strong coupling constants of vertices $Λ_cDN^*$, $Λ_cD^*N^*$, $Λ_bBN^*$ and $Λ_bB^*N^*$ in the framework of three-point QCD sum rules. At hadron side, all possible couplings of interpolating current to hadronic states are considered. At QCD side, the contributions of vacuum condensate terms $\langle\bar{q}q\rangle$, $\langle g_s^2GG\rangle$, $\langle\bar{q} g_sσGq\rangle$, $\langle\bar{q}q\rangle^2$ and $g_s^2\langle\bar{q}q\rangle^2$ are also considered. By setting the four momentum of $D^{(*)}[B^{(*)}]$ mesons off-shell, the strong coupling constants in deep space-like regions ($Q^2=-q^2\ggΛ_{QCD}^2$) are obtained. Then, the momentum dependent coupling constants in space-like regions are fitted into analytical function $G(Q^2)$ and are extrapolated into time-like regions ($Q^2<0$). Finally, the on-shell values of strong coupling constants are obtained by taking $Q^{2}=-m_{D^{(*)}[B^{(*)}]}^2$. The results are $G_{Λ_cDN^*}(Q^2=-m_D^2)=4.06^{+0.96}_{-0.75}$, $f_{Λ_cD^*N^*}(Q^2=-m_{D^*}^2)=3.73^{+0.68}_{-0.16}$, $g_{Λ_cD^*N^*}(Q^2=-m_{D^*}^2)=9.22^{+3.16}_{-0.36}$, $G_{Λ_bBN^*}(Q^2=-m_B^2)=9.11^{+1.54}_{-1.61}$, $f_{Λ_bB^*N^*}(Q^2=-m_{B^*}^2)=8.55^{+2.69}_{-2.21}$ and $g_{Λ_bB^*N^*}(Q^2=-m_{B^*}^2)=-0.25^{+0.16}_{-0.01}$.

hep-ph↗

arXiv2025

Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation

Machine learning models require datasets for effective training, but directly sharing raw data poses significant privacy risk such as membership inference attacks (MIA). To mitigate the risk, privacy-preserving techniques such as data perturbation, generalization, and synthetic data generation are commonly utilized. However, these methods often degrade data accuracy, specificity, and diversity, limiting the performance of downstream tasks and thus reducing data utility. Therefore, striking an optimal balance between privacy preservation and data utility remains a critical challenge. To address this issue, we introduce a novel bilevel optimization framework for the publication of private datasets, where the upper-level task focuses on data utility and the lower-level task focuses on data privacy. In the upper-level task, a discriminator guides the generation process to ensure that perturbed latent variables are mapped to high-quality samples, maintaining fidelity for downstream tasks. In the lower-level task, our framework employs local extrinsic curvature on the data manifold as a quantitative measure of individual vulnerability to MIA, providing a geometric foundation for targeted privacy protection. By perturbing samples toward low-curvature regions, our method effectively suppresses distinctive feature combinations that are vulnerable to MIA. Through alternating optimization of both objectives, we achieve a synergistic balance between privacy and utility. Extensive experimental evaluations demonstrate that our method not only enhances resistance to MIA in downstream tasks but also surpasses existing methods in terms of sample quality and diversity.

cs.LG↗

arXiv2025

LLMs Struggle with NLI for Perfect Aspect: A Cross-Linguistic Study in Chinese and Japanese

Unlike English, which uses distinct forms (e.g., had, has, will have) to mark the perfect aspect across tenses, Chinese and Japanese lack separate grammatical forms for tense within the perfect aspect, which complicates Natural Language Inference (NLI). Focusing on the perfect aspect in these languages, we construct a linguistically motivated, template-based NLI dataset (1,350 pairs per language). Experiments reveal that even advanced LLMs struggle with temporal inference, particularly in detecting subtle tense and reference-time shifts. These findings highlight model limitations and underscore the need for cross-linguistic evaluation in temporal semantics. Our dataset is available at https://github.com/Lujie2001/CrossNLI.

cs.CL↗

arXiv2025

MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detection

Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform well on known generators, their performance often declines when tested with newly emerging or unseen generative models due to overlapping feature embeddings that hinder accurate cross-generator classification. In this paper, we propose Multimodal Discriminative Representation Learning for Generalizable AI-generated Image Detection (MiraGe), a method designed to learn generator-invariant features. Motivated by theoretical insights on intra-class variation minimization and inter-class separation, MiraGe tightly aligns features within the same class while maximizing separation between classes, enhancing feature discriminability. Moreover, we apply multimodal prompt learning to further refine these principles into CLIP, leveraging text embeddings as semantic anchors for effective discriminative representation learning, thereby improving generalizability. Comprehensive experiments across multiple benchmarks show that MiraGe achieves state-of-the-art performance, maintaining robustness even against unseen generators like Sora.

cs.CV↗

arXiv2025

Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning

Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically assume homogeneous streams and employ static architectures with indiscriminate knowledge fusion, limiting generalizability in complex dynamic environments. To tackle this gap, we propose CAMEL, a dynamic \textbf{C}ollaborative \textbf{A}ssistance \textbf{M}ixture of \textbf{E}xperts \textbf{L}earning framework. It addresses heterogeneity by assigning each stream an independent system with a dedicated feature extractor and task-specific head. Meanwhile, a dynamic pool of specialized private experts captures stream-specific idiosyncratic patterns. Crucially, collaboration across these heterogeneous streams is enabled by a dedicated assistance expert. This expert employs a multi-head attention mechanism to distill and integrate relevant context autonomously from all other concurrent streams. It facilitates targeted knowledge transfer while inherently mitigating negative transfer from irrelevant sources. Furthermore, we propose an Autonomous Expert Tuner (AET) strategy, which dynamically manages expert lifecycles in response to drift. It instantiates new experts for emerging concepts (freezing prior ones to prevent catastrophic forgetting) and prunes obsolete ones. This expert-level plasticity provides a robust and efficient mechanism for online model capacity adaptation. Extensive experiments demonstrate CAMEL's superior generalizability across diverse multistreams and exceptional resilience against complex concept drifts.

cs.LG↗

arXiv2025

Analysis of the hadronic molecules $DK$, $D^K$, $DK^$ and their bottom analogs with QCD sum rules

In this work, we construct the color-singlet-color-singlet type currents to study the masses and pole residues of charm-strange tetraquark states and their bottom analogs with $J^P$ = $0^+$ and $1^+$ by using two-point QCD sum rules, where the vacuum condensates are considered up to dimension 12. The predicted masses for $DK$, $D^*K$ and $DK^*$ molecular states are $2.322_{ - 0.072}^{ + 0.066}$ GeV, $2.457_{ - 0.068}^{ + 0.064}$ GeV and $2.538_{ - 0.062}^{ + 0.059}$ GeV. These results are consistent well with the experimental data of $D_{s0}(2317)$, $D_{s1}(2460)$ and ${D}_{s1}(2536)$, respectively. The theoretical results for $BK$ and $B^*K$ molecular states are $5.970_{ - 0.064}^{ + 0.061}$ GeV and $6.050_{ - 0.064}^{ + 0.062}$ GeV which are all higher than their own thresholds. Finally, the mass of hadronic molecule $BK^*$ is predicted to be $6.158_{ - 0.063}^{ + 0.061}$ GeV. This value is lower than the threshold of $BK^*$, which implies that it may be a bound hadronic molecular state.

hep-ph↗

arXiv2025

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective

An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes.

cs.CL↗

arXiv2025

JBBQ: Japanese Bias Benchmark for Analyzing Social Biases in Large Language Models

With the development of large language models (LLMs), social biases in these LLMs have become a pressing issue. Although there are various benchmarks for social biases across languages, the extent to which Japanese LLMs exhibit social biases has not been fully investigated. In this study, we construct the Japanese Bias Benchmark dataset for Question Answering (JBBQ) based on the English bias benchmark BBQ, with analysis of social biases in Japanese LLMs. The results show that while current open Japanese LLMs with more parameters show improved accuracies on JBBQ, their bias scores increase. In addition, prompts with a warning about social biases and chain-of-thought prompting reduce the effect of biases in model outputs, but there is room for improvement in extracting the correct evidence from contexts in Japanese. Our dataset is available at https://github.com/ynklab/JBBQ_data.

cs.CL↗