arXiv subjects

Xiang Liu

Publications and source records attributed to Xiang Liu.

At least 181 records · Page 10Linked to original sources

arXiv2025

Optimizing for the Shortest Path in Denoising Diffusion Model

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior arts. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://github.com/UnicomAI/ShortDF.

cs.CV↗

arXiv2025

Role of $4S$-$3D$ mixing in explaining the $ω$-like $Y(2119)$ observed in $e^+e^-\toρπ$ and $ρ(1450)π$

The BESIII Collaboration has recently reported a new resonance structure in the cross-section analyses of the $e^+e^- \to ρπ$ and $e^+e^- \to ρ(1450)π$ processes, displaying characteristics akin to an $ω$-meson. However, its measured mass, $M = 2119 \pm 11 \pm 15 \, \text{MeV}$, significantly deviates from the predictions of the unquenched relativized potential model for conventional $ω$ vector meson spectroscopy. To address this discrepancy, we propose a $4S$-$3D$ mixing scheme and investigate the corresponding decay properties within this framework. Our analysis demonstrates that the mixed state $ω_{4S\text{-}3D}^\prime$ exhibits excellent agreement with the observed resonance, not only in mass and width but also in the products of its dielectron width and branching ratios, $Γ_{e^+e^-}^{\mathcal{R}}\times\mathcal{B}_{\mathcal{R}\toρπ}$ and $Γ_{e^+e^-}^{\mathcal{R}}\times\mathcal{B}_{\mathcal{R}\toρ(1450)π\toπ^+π^-π^0}$. The determined mixing angle, $θ= -\left(31.1^{+2.1}_{-3.1}\right)\degree$, strongly supports the interpretation of this structure as the $ω_{4S\text{-}3D}^\prime$ state. Furthermore, we predict dominant decay channels for $ω_{4S\text{-}3D}^\prime$ and its partner state $ω_{4S\text{-}3D}^{\prime\prime}$. These predictions, together with the proposed mixing mechanism, provide crucial guidance for future experimental studies aimed at probing this structure and rigorously testing the $4S$-$3D$ mixing hypothesis.

hep-ph↗

arXiv2025

A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis

While conversational generative AI has shown considerable potential in enhancing decision-making for agricultural professionals, its exploration has predominantly been anchored in text-based interactions. The evolution of multimodal conversational AI, leveraging vast amounts of image-text data from diverse sources, marks a significant stride forward. However, the application of such advanced vision-language models in the agricultural domain, particularly for crop disease diagnosis, remains underexplored. In this work, we present the crop disease domain multimodal (CDDM) dataset, a pioneering resource designed to advance the field of agricultural research through the application of multimodal learning techniques. The dataset comprises 137,000 images of various crop diseases, accompanied by 1 million question-answer pairs that span a broad spectrum of agricultural knowledge, from disease identification to management practices. By integrating visual and textual data, CDDM facilitates the development of sophisticated question-answering systems capable of providing precise, useful advice to farmers and agricultural professionals. We demonstrate the utility of the dataset by finetuning state-of-the-art multimodal models, showcasing significant improvements in crop disease diagnosis. Specifically, we employed a novel finetuning strategy that utilizes low-rank adaptation (LoRA) to finetune the visual encoder, adapter and language model simultaneously. Our contributions include not only the dataset but also a finetuning strategy and a benchmark to stimulate further research in agricultural technology, aiming to bridge the gap between advanced AI techniques and practical agricultural applications. The dataset is available at https: //github.com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench.

cs.CV↗

arXiv2025

Discovery potential of charmonium $2P$ states through the $e^+e^- \to γD\bar{D}$ processes

In this work, we investigate the production of charmonium $2P$ states via the $e^+e^-\to γD\bar{D}$ process at $\sqrt{s} = 4.23$ GeV. Using the measured cross-section data for $e^+e^-\to γX(3872)$ as a reference, we calculate the cross sections for $e^+e^-\to γχ_{c0}(2P)$ and $e^+e^-\to γχ_{c2}(2P)$. Since the $χ_{c0}(2P)$ and $χ_{c2}(2P)$ states predominantly decay into $D\bar{D}$ final states, we also predict the corresponding $D\bar{D}$ invariant mass spectrum for the $e^+e^-\to γD\bar{D}$ process. Our results indicate that $e^+e^-\to γD\bar{D}$ is an ideal process for identifying the $χ_{c0}(2P)$ and $χ_{c2}(2P)$ states, analogous to the $γγ\to D\bar{D}$ and $B^+\to D^+D^-K^+$ processes. This study highlights the discovery potential of charmonium $2P$ states at BESIII and Belle II.

hep-ph↗

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↗