arXiv subjects

Xiang Liu

Publications and source records attributed to Xiang Liu.

At least 181 records · Page 10Linked to original sources

arXiv2025

Reevaluating the $ψ(4160)$ Resonance Parameter Using $B^+\to K^+μ^+μ^-$ Data in the Context of Unquenched Charmonium Spectroscopy

A puzzling phenomenon, where the measured mass of the $ψ(4160)$ is pushed higher, presents a challenge to current theoretical models of hadron spectroscopy. This study suggests that the issue arises from analyses based on the outdated quenched charmonium spectrum. In the past two decades, the discovery of new hadronic states has emphasized the importance of the unquenched effect. Under the unquenched picture, six vector charmonium states-$ψ(4040)$, $ψ(4160)$, $ψ(4220)$, $ψ(4380)$, $ψ(4415)$, and $ψ(4500)$-are identified in the $4 \sim 4.5$ GeV range, contrasting with the three states predicted in the quenched model. We reevaluate the resonance parameters of the $ψ(4160)$ using the di-muon invariant mass spectrum of $B^+ \to K^+ μ^+ μ^-$ and unquenched charmonium spectroscopy. Our analysis finds the $ψ(4160)$ mass at $4145.76 \pm 4.48$ MeV, indicating previous overestimations. This conclusion is supported by analyzing $e^+e^- \to D_s \bar{D}_s^*$. Our findings have significant implications for both hadron spectroscopy and search for new physics signals by $R_K$.

hep-ph↗

arXiv2025

Manifold Topological Deep Learning for Biomedical Data

Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging as a promising paradigm in data science. However, TDL has not been developed for data on differentiable manifolds, including images, due to the challenges posed by differential topology. We address this challenge by introducing manifold topological deep learning (MTDL) for the first time. To highlight the power of Hodge theory rooted in differential topology, we consider a simple convolutional neural network (CNN) in MTDL. In this novel framework, original images are represented as smooth manifolds with vector fields that are decomposed into three orthogonal components based on Hodge theory. These components are then concatenated to form an input image for the CNN architecture. The performance of MTDL is evaluated using the MedMNIST v2 benchmark database, which comprises 717,287 biomedical images from eleven 2D and six 3D datasets. MTDL significantly outperforms other competing methods, extending TDL to a wide range of data on smooth manifolds.

eess.IV↗

arXiv2025

Unveiling the Composition of the Single-Charm Molecular Pentaquarks: Insights from Radiative Decay and Magnetic Moment

In order to unravel the composition of the isoscalar $DN$, $D^*N$, $D_1N$, and $D_2^*N$ molecular pentaquarks, we carry out a systematic investigation of their M1 and E1 radiative decays and magnetic moment properties. Using the constituent quark model and taking into account the $S$-$D$ wave mixing effect and the coupled channel effect, our analysis yields numerical results indicating that the M1 and E1 radiative decays and the magnetic moment properties of these molecular pentaquarks provide some insights into their inner structures. These results can provide crucial clues for distinguishing their spin-parity quantum numbers and configurations in experimental studies. In addition, we highlight the importance of the electromagnetic properties as key observables for elucidating the inner structures of the observed $Λ_c^{+} (2940)$ and $Λ_c^{+} (2910)$. We hope that these findings can inspire our experimental colleagues to further explore the family of the single-charm molecular pentaquarks and to investigate the inner structures of the $Λ_c^{+} (2940)$ and $Λ_c^{+} (2910)$ in future studies.

hep-ph↗

arXiv2025

The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?

Motivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention from researchers. However, current methods predominantly emphasize maintaining the performance of compressed LLMs, as measured by perplexity or simple accuracy on tasks of common sense knowledge QA and basic arithmetic reasoning. In this blog, we present a brief review of recent advancements in LLMs related to retrieval-augmented generation, multi-step reasoning, external tools, and computational expressivity, all of which substantially enhance LLM performance. Then, we propose a lottery LLM hypothesis suggesting that for a given LLM and task, there exists a smaller lottery LLM capable of producing the same performance as the original LLM with the assistance of multi-step reasoning and external tools. Based on the review of current progress in LLMs, we discuss and summarize the essential capabilities that the lottery LLM and KV cache compression must possess, which are currently overlooked in existing methods.

cs.LG↗

arXiv2025

Revisiting heavy-flavor conserving weak decays of charmed baryons within the nonrelativistic quark model

In this work, we investigate the heavy-flavor conserving weak decays of charmed baryons, specifically the processes $Ξ_{c} \to Λ_{c} π$ and $Ω_{c} \to Ξ_{c} π$, where the pole model is employed to account for the nonfactorizable contributions. Additionally, the nonperturbative parameters involved are determined within the framework of the nonrelativistic quark model, utilizing exact baryon wave functions obtained by solving the Schrödinger equation with a nonrelativistic potential. By considering the mixing angle $θ\in (24.4^\circ, 32.4^\circ)$ for $Ξ_{c}-Ξ_{c}^{\prime}$ mixing, we find that the experimental data can be effectively reproduced. Furthermore, we estimate the branching ratios: $\mathcal{B}(Ξ_{c}^{+} \to Λ_{c}^{+} π^{0}) = (8.69 \sim 9.79) \times 10^{-3}$, $\mathcal{B}(Ω_{c}^{0} \to Ξ_{c}^{+} π^{-}) = (11.3 \sim 12.1) \times 10^{-3}$, and $\mathcal{B}(Ω_{c}^{0} \to Ξ_{c}^{+} π^{0}) = (4.67 \sim 5.23) \times 10^{-3}$. These predictions can be tested in ongoing experiments at LHCb, Belle II, and BESIII.

hep-ph↗

arXiv2025

Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder

Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex environment may lead to motion-blur or occlusion problems, which raise the tough challenge to real-time recognition with high accuracy and robust. In this article, we propose IECES-network which with improved encoders and Siamese net. The three-stage approach of our method includes Efficient-CNN based encoders, Siamese backbone and the fully-connected layers. We firstly use convolutional encoders to extract and encode the traffic sign features of augmented training samples and standard images. Then, we design the Siamese neural network with Efficient-CNN based encoder and contrastive loss function, which can be trained to improve the robustness of TSR problem when facing the samples of motion-blur and occlusion by computing the distance between inputs and templates. Additionally, the template branch of the proposed network can be stopped when executing the recognition tasks after training to raise the process speed of our real-time model, and alleviate the computational resource and parameter scale. Finally, we recombined the feature code and a fully-connected layer with SoftMax function to classify the codes of samples and recognize the category of traffic signs. The results of experiments on the Tsinghua-Tencent 100K dataset and the German Traffic Sign Recognition Benchmark dataset demonstrate the performance of the proposed IECESnetwork. Compared with other state-of-the-art methods, in the case of motion-blur and occluded environment, the proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale, respectively. Moreover, processing time of our model is 0.1s per frame, of which the speed is increased by 1.5 times compared with existing methods.

cs.CV↗

arXiv2025

Finding Interaction Mechanism between Exotic Molecule and Conventional Hadron

An intriguing and important question arises: how do hadronic molecules interact with conventional hadrons? In this work, we propose a novel mechanism to address this issue, focusing on the interactions between hidden-charm molecular pentaquarks ($P_c$) and nucleons ($N$). By applying this mechanism, we derive the corresponding interaction potentials, enabling the prediction of the bound state properties. Our results indicate the possible existence of $P_cN$ bound states-an entirely new form of matter, ripe for exploration in the era of high-precision hadron spectroscopy. Notably, this mechanism extends beyond the $P_cN$ systems and can be applied to any hadronic molecules interacting with conventional hadrons, offering the potential for discovering a variety of novel bound states.

hep-ph↗

arXiv2025

One-shot Federated Learning Methods: A Practical Guide

One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication overhead issues associated with multiple rounds of data exchange in traditional Federated Learning (FL). OFL demonstrates the practical potential for integration with future approaches that require collaborative training models, such as large language models (LLMs). However, current OFL methods face two major challenges: data heterogeneity and model heterogeneity, which result in subpar performance compared to conventional FL methods. Worse still, despite numerous studies addressing these limitations, a comprehensive summary is still lacking. To address these gaps, this paper presents a systematic analysis of the challenges faced by OFL and thoroughly reviews the current methods. We also offer an innovative categorization method and analyze the trade-offs of various techniques. Additionally, we discuss the most promising future directions and the technologies that should be integrated into the OFL field. This work aims to provide guidance and insights for future research.

cs.LG↗

arXiv2025

Understanding of the BESIII measurement of (anti)hyperon-nucleon scattering

Focusing on the recent measurements of the scattering processes $\barΛ p \rightarrow \barΛ p$ and $Λp \rightarrow Λp$ reported by BESIII, we offer a dynamical interpretation of the differences in the measured total and differential cross sections for both processes. This discrepancy arises because the $u$-channel contribution is forbidden for $\barΛ p \rightarrow \barΛ p$ but allowed for $Λp \rightarrow Λp$. Additionally, we examine the enhancement effect in the forward angle region of the differential cross section for $\barΛ p$ elastic scattering, attributed to the $t$-channel contribution. This work serves as a test to the scattering mechanism involved in these reactions.

hep-ph↗

arXiv2025

HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interactions, further validating HODDI's effectiveness. Our findings highlight the inherent value of higher-order information in drug-side effect prediction and position HODDI as a benchmark dataset for advancing research in pharmacovigilance, drug safety, and personalized medicine. The dataset and codes are available at https://github.com/TIML-Group/HODDI.

cs.LG↗

arXiv2025

Intrinsic Pulse Widths of FRB 20121102A and Calculation of Broadening from Propagation and Instrumental Effects

The pulse widths of fast radio bursts are always broadened due to the scattering of the plasma medium through which the electromagnetic wave passes. The recorded pulse width will be further affected by the radio telescopes since the sampling time and the bandwidth cannot be infinitely small. In this study, we focus on the pulse widths of at least 3287 bursts detected from FRB 20121102A as of October 2023. Various effects such as the scattering broadening, the redshift-induced broadening and the instrumental broadening are examined. At a redshift of 0.193, the pulse width is broadened by a factor of approximately 0.16 due to cosmological time dilation. It is found that the instrumental broadening only contributes a fraction of $10^{-3}$ -- $10^{-1}$ to the observed pulse width. The scattering broadening is even smaller, which constitutes a tiny fraction of $10^{-5}$ -- $10^{-2}$ in the observed pulse width. After correcting for these broadenings, the intrinsic pulse width is derived for each burst. The maximum and minimum pulse widths at different frequencies are highlighted. The intrinsic widths of most bursts are in a narrow range of 1 -- 10 ms, which leads to a quasi-linear correlation between the fluence and the peak flux. Besides, the mean value and the dispersion range of intrinsic pulse width are found to scale with the central frequency as $ν^{-1.2 \pm 0.2}$ and $ν^{-1.7 \pm 0.6}$, respectively.

astro-ph.HE↗

arXiv2024

Learning Radiance Fields from a Single Snapshot Compressive Image

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene structure from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, reducing storage and transmission requirements and offering potential privacy protection. Inspired by this, we take one step further to recover the encoded 3D scene information leveraging powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we propose SCINeRF, in which we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. In addition, we further integrate the popular 3D Gaussian Splatting (3DGS) framework and propose SCISplat to improve 3D scene reconstruction quality and training/rendering speed by explicitly optimizing point clouds into 3D Gaussian representations. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view synthesis. Moreover, our method also exhibits the ability to render high frame-rate multi-view consistent images in real time by leveraging SCI and the rendering capabilities of 3DGS. Codes will be available at: https://github.com/WU- CVGL/SCISplat.

cs.CV↗

arXiv2024

A Physics-informed Sheaf Model

Normal mode analysis (NMA) provides a mathematical framework for exploring the intrinsic global dynamics of molecules through the definition of an energy function, where normal modes correspond to the eigenvectors of the Hessian matrix derived from the second derivatives of this function. The energy required to 'trigger' each normal mode is proportional to the square of its eigenvalue, with six zero-eigenvalue modes representing universal translation and rotation, common to all molecular systems. In contrast, modes associated with small non-zero eigenvalues are more easily excited by external forces and are thus closely related to molecular functions. Inspired by the anisotropic network model (ANM), this work establishes a novel connection between normal mode analysis and sheaf theory by introducing a cellular sheaf structure, termed the anisotropic sheaf, defined on undirected, simple graphs, and identifying the conventional Hessian matrix as the sheaf Laplacian. By interpreting the global section space of the anisotropic sheaf as the kernel of the Laplacian matrix, we demonstrate a one-to-one correspondence between the zero-eigenvalue-related normal modes and a basis for the global section space. We further analyze the dimension of this global section space, representing the space of harmonic signals, under conditions typically considered in normal mode analysis. Additionally, we propose a systematic method to streamline the Delaunay triangulation-based construction for more efficient graph generation while preserving the ideal number of normal modes with zero eigenvalues in ANM analysis.

physics.chem-ph↗

arXiv2024

Spectroscopic Properties of Double-Strangeness Molecular Tetraquarks

Inspired by recent advances in the study of the $K^{(*)} \bar K^{(*)}$ molecular tetraquarks and the $H$-dibaryon, we focus on the spectroscopic properties of the $\bar K^{(*)} \bar K^{(*)}$ systems, which exhibit exotic flavor quantum number of $ss\bar q \bar q$. A dynamical analysis is performed using the one-boson-exchange model to describe the effective interactions for these systems, accounting for both $S$-$D$ wave mixing and coupled-channel effects. By solving the coupled-channel Schr$\ddot{\rm o}$dinger equation, we identify the $I(J^P)=0(1^+)$ $\bar K \bar K^*$ and $I(J^P)=0(1^+)$ $\bar K^* \bar K^*$ states as the most likely candidates for double-strangeness molecular tetraquarks. Furthermore, we estimate their strong decay behaviors based on the effective Lagrangian approach, with several channels exhibiting considerable decay widths. Meanwhile, we investigate their magnetic moments and M1 radiative decay widths, shedding light on their inner structures, within the constituent quark model. Finally, we encourage experimentalists to focus on these predicted double-strangeness molecular tetraquark candidates, particularly in $B$ meson decays. Such efforts could pave the way for establishing the molecular tetraquark states in the light-quark sector.

hep-ph↗

arXiv2024

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significant roadblock to large-scale training. For instance, a 7B model typically requires at least 60 GB of GPU memory with full parameter training, which presents challenges for researchers without access to high-resource environments. Parameter Efficient Fine-Tuning techniques such as Low-Rank Adaptation (LoRA) have been proposed to alleviate this problem. However, in most large-scale fine-tuning settings, their performance does not reach the level of full parameter training because they confine the parameter search to a low-rank subspace. Attempting to complement this deficiency, we investigate the layerwise properties of LoRA on fine-tuning tasks and observe an unexpected but consistent skewness of weight norms across different layers. Utilizing this key observation, a surprisingly simple training strategy is discovered, which outperforms both LoRA and full parameter training in a wide range of settings with memory costs as low as LoRA. We name it Layerwise Importance Sampled AdamW (LISA), a promising alternative for LoRA, which applies the idea of importance sampling to different layers in LLMs and randomly freezes most middle layers during optimization. Experimental results show that with similar or less GPU memory consumption, LISA surpasses LoRA or even full parameter tuning in downstream fine-tuning tasks, where LISA consistently outperforms LoRA by over 10%-35% in terms of MT-Bench score while achieving on-par or better performance in MMLU, AGIEval and WinoGrande. On large models, specifically LLaMA-2-70B, LISA surpasses LoRA on MT-Bench, GSM8K, and PubMedQA, demonstrating its effectiveness across different domains.

cs.LG↗

arXiv2024

Production potential of hidden-strange molecular pentaquarks through the $π^-p\rightarrow K^{*}Σ$ process

In this study, applying the effective Lagrangian approach, we investigate the reaction $π^-p\rightarrow K^{*}Σ$ to explore the production of the hidden-strange molecular pentaquarks $P_{s\bar{s}}$. Specifically, we analyze two scenarios, where the $J^P$ quantum numbers of $P_{s\bar{s}}$ are $3/2^-$ and $1/2^-$. Our results show that the contribution from $s$-channel $P_{s\bar{s}}$ exchange dominates the total cross section, surpassing those from $t$-channel $K^{(*)}$ exchange and $u$-channel $Σ$ exchange. Additionally, we present predictions for the differential cross section of the $π^-p\rightarrow K^{*}Σ$ process. Finally, we extend the 2-to-2 scattering process to the 2-to-3 Dalitz process and provide theoretical predictions for this scenario. Our findings suggest that the cross section for the $π^-p \rightarrow K^{*}Σ\rightarrow K^+π^-Σ^0$ process can reach several tens of $μb$ near the threshold, making it highly favorable for experimental measurement. However, the current scarcity of experimental data for the $π^-p\rightarrow K^{*}Σ$ reaction at threshold energy limits our ability to determine the properties of the hidden-strange molecular pentaquarks $P_{s\bar{s}}$ and related physical quantities. Therefore, additional experimental measurements of the $π^-p \rightarrow K^{*}Σ$ reaction at threshold are strongly encouraged, and correlation studies could be pursued at facilities such as J-PARC, AMBER, and upcoming HIKE and HIAF meson beam experiments.

hep-ph↗

arXiv2024

WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking

3D Gaussian Splatting (3DGS) has emerged as a pivotal technique for 3D scene representation, providing rapid rendering speeds and high fidelity. As 3DGS gains prominence, safeguarding its intellectual property becomes increasingly crucial since 3DGS could be used to imitate unauthorized scene creations and raise copyright issues. Existing watermarking methods for implicit NeRFs cannot be directly applied to 3DGS due to its explicit representation and real-time rendering process, leaving watermarking for 3DGS largely unexplored. In response, we propose WATER-GS, a novel method designed to protect 3DGS copyrights through a universal watermarking strategy. First, we introduce a pre-trained watermark decoder, treating raw 3DGS generative modules as potential watermark encoders to ensure imperceptibility. Additionally, we implement novel 3D distortion layers to enhance the robustness of the embedded watermark against common real-world distortions of point cloud data. Comprehensive experiments and ablation studies demonstrate that WATER-GS effectively embeds imperceptible and robust watermarks into 3DGS without compromising rendering efficiency and quality. Our experiments indicate that the 3D distortion layers can yield up to a 20% improvement in accuracy rate. Notably, our method is adaptable to different 3DGS variants, including 3DGS compression frameworks and 2D Gaussian splatting.

cs.CR↗

arXiv2024

Construction of Isoscalar $2^{-+}$ Mesonic States Inspired by the $X(2600)$ Discovery

The BESIII observation of the $X(2600)$ suggests that one possible assignment for its spin-parity quantum numbers is $J^{PC}=2^{-+}$. This finding motivates us to systematically revisit isoscalar pseudotensor mesonic states through a comprehensive spectroscopic study. In this work, we employ a potential model to calculate the mass spectrum of isoscalar pseudotensor meson states with varying radial quantum numbers. Additionally, we analyze their strong decay behaviors, as allowed by the Okubo-Zweig-Iizuka (OZI) rule, using the quark pair creation model. Our findings yield valuable information about their resonant parameters and partial decay widths, which can aid in identifying the observed isoscalar pseudotensor states when combined with current experimental data. Importantly, we also predict several radial excitations of isoscalar pseudotensor mesons that remain unobserved experimentally. Furthermore, we exclude the $X(2600)$ from being classified as an isoscalar pseudotensor meson, which is critical for elucidating the properties of the $X(2600)$.

hep-ph↗