arXiv subjects

Rui Peng

Publications and source records attributed to Rui Peng.

At least 37 records · Page 2Linked to original sources

arXiv2025

Simplification of chiral nuclear forces near the unitarity limit

Modern theory approaches for describing atomic nuclei often make use of on an effective theory that constructs the interaction between nucleons systematically based on Quantum Chromodynamics (QCD), exploiting constraints arising from the approximate chiral symmetry of QCD. The tensor nuclear force produced by one-pion exchange is an important feature that arises naturally in this framework. In this work we show that, however, the tensor force is suppressed by the large nucleon-nucleon scattering lengths in combination with the smallness of the pion mass. Based on this observation, we propose a new scheme for a chiral nuclear force that is able to describe $NN$ phase shifts up to the center-of-mass momenta $k \simeq 300$ MeV while treating pion exchange as a perturbation. Our much simplified leading-order force provides a microscopic explanation for the recent success of various short-range nuclear forces from the perspective of chiral effective field theory, and it shares with those approaches an approximate Wigner SU(4) symmetry, as well as the closeness to the unitarity limit (infinite nucleon-nucleon scattering lengths), as guiding principles. Compared to previous approaches to perturbative-pion interactions, our force also adjusts the ordering of short-range contact interactions, by means of which we overcome convergence problems of the expansion that were previously assumed to severely limit its usefulness. We demonstrate the performance of our approach with numerical calculations of $NN$ scattering up to fourth order, in addition to studies of $3N$ and $4N$ bound-state properties.

nucl-th↗

arXiv2025

MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics

Large Language Models (LLMs) have demonstrated remarkable capabilities on general text; however, their proficiency in specialized scientific domains that require deep, interconnected knowledge remains largely uncharacterized. Metabolomics presents unique challenges with its complex biochemical pathways, heterogeneous identifier systems, and fragmented databases. To systematically evaluate LLM capabilities in this domain, we introduce MetaBench, the first benchmark for metabolomics assessment. Curated from authoritative public resources, MetaBench evaluates five capabilities essential for metabolomics research: knowledge, understanding, grounding, reasoning, and research. Our evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks: while models perform well on text generation tasks, cross-database identifier grounding remains challenging even with retrieval augmentation. Model performance also decreases on long-tail metabolites with sparse annotations. With MetaBench, we provide essential infrastructure for developing and evaluating metabolomics AI systems, enabling systematic progress toward reliable computational tools for metabolomics research.

cs.CL↗

arXiv2025

Unconventional tunnel magnetoresistance scaling with altermagnets

In conventional magnetic tunnel junctions (MTJs), the tunnel magnetoresistance (TMR) typically increases with barrier thickness as electron transmission in the antiparallel configuration decays faster than that of the parallel configuration. In this work, we reveal an anomalous scaling effect in altermagnetic tunnel junctions (AMTJs), where the TMR decreases anomalously with an increasing barrier thickness. The anomalous scaling originates from the overlapping spin-split branches forming a transmission path that cannot be suppressed in the antiparallel state. Such phenomenon is explained by a double-barrier model and is further demonstrated using ab initio quantum transport simulations in 2D V2Te2O/Cr2Se2O/V2Te2O and V2Te2O/ZnSe/V2Te2O AMTJs. Our work identifies a peculiar unexpected transport characteristic of AMTJ, providing a fundamental limit on AMTJ device design and illustrating the potential optimal design of AMTJ at the ultrascaled monolayer limit.

cond-mat.mes-hall↗

arXiv2025

Correlation-driven 3d Heavy Fermion behavior in LiV2O4

LiV2O4 is a spinel-structured compound that stands out as the first known 3d-electron system exhibiting typical heavy fermion behavior. A central question is how such strong mass renormalization emerges in the absence of f-electrons. In this work, we investigate the three-dimensional electronic structure of LiV2O4 thin films using angle-resolved photoemission spectroscopy (ARPES). We identify that an electron-like flat band is derived from a1g orbitals, along with a highly dispersive e'g band strongly coupled with phonons. The overall agreement with dynamical mean-field theory (DMFT) calculations highlights the essential role of inter-orbital Hund's coupling in reducing the a1g bandwidth to 25 meV, approaching a Mott state. Notably, we find that heavy-fermion behavior arises from additional renormalization at the a1g band near the Fermi level, likely driven by many-body interactions at energy scales down to a few meV and potentially linked to geometric frustration inherent to the spinel lattice. These results provide crucial insights into the origin of the heavy fermion behavior in 3d-electron systems.

cond-mat.str-el↗

arXiv2025

Thermoelectricity evidence for quantum criticality in clean infinite-layer nickelate films

We investigate the Seebeck coefficient ($S$) in infinite-layer nickelate films with different disorder levels. The disordered NdNiO$_{2}$ film exhibits a flat $S/T$ curve, whereas cleaner samples display a logarithmic divergence with decreasing temperature, followed by a pronounced ``hump'' near 25 K. These distinct behaviors reveal a disorder-driven transition from band-structure-dominated transport to quantum-critical-dominated transport. Below the ``hump'' temperature, four-fold symmetry breaking is observed in the in-plane angular magnetoresistance, indicating the presence of short-range antiferromagnetic order in parent infinite-layer nickelate films. Furthermore, the logarithmic divergence in $S/T$ is also observed in a clean superconducting Sm$_{0.73}$Ca$_{0.05}$Eu$_{0.22}$NiO$_{2}$ film, where it coexists with linear-in-temperature resistivity over the same temperature range. These findings demonstrate the existence of quantum criticality over a wide doping range in clean infinite-layer nickelate films, similar to cuprates, which highlights the central role of antiferromagnetic spin correlations in their superconducting pairing mechanisms.

cond-mat.supr-con↗

arXiv2025

Ferroelastic Altermagnetism

Synergizing altermagnetism and other ferroic orders, such as ferroelectric switchable altermagnetism [Phys. Rev. Lett. 134, 106801 (2025) and ibid. 106802 (2025)], offers an effective route to achieve nonvolatile switching of altermagnetic spin splitting. In this work, by synergizing altermagnetism and ferroelasticity, we propose the concept of ferroelastic altermagnets in which the ferroelastic crystal reorientation can drive multistate nonvolatile switching of the altermagnetic spin splitting via altermagnetoelastic effect. Using monolayers RuF4 and CuF2 as material candidates, we demonstrate 2-state and 3-state altermagnetic spin splitting switching as driven by ferroelastic strain states. Transport calculation shows that multistate spin conductivities can be ferroelastically encoded in an ferroelastic altermagnet, thus suggesting the potential of ferroelastic altermagnetic as nonvolatile nanomechanical spin switches. The proposed concept of ferroelastic altermagnetism enriches the emerging landscape of multiferroic altermagnetism, paving a way towards altermagnetic-based straintronic device applications.

cond-mat.mtrl-sci↗

arXiv2025

Observation of Electride-like $s$ States Coexisting with Correlated $d$ Electrons in NdNiO$_2$

Despite exhibiting a similar $d_{x^2-y^2}$ band character to cuprates, infinite-layer nickelates host additional electron pockets that distinguish them from single-band cuprates. The elusive orbital origin of these electron pockets has led to competing theoretical scenarios. Here, using polarization-dependent and resonant angle-resolved photoemission spectroscopy (ARPES), we determine the orbital character of the Fermi surfaces in NdNiO$_2$. Our data reveal that the electron-like pocket arises predominantly from interstitial $s$ states, with negligible contributions from rare-earth 5$d$ and 4$f$ orbitals near the Fermi level. The observation of well-defined quantum well states indicates a uniform distribution of these interstitial electrons throughout the film thickness. By comparing with electronic structure of LaNiO$_2$, we find that the rare-earth element modulates the Ni-derived bands and hopping integrals through a chemical pressure effect. These findings clarify the role of rare-earth elements in shaping the low-energy electronic structure and establish the presence of electride-like interstitial $s$ states in a correlated oxide system, where electrons occupy lattice voids rather than atomic orbitals. The electride-like character offer new insight into the self-doping and superconductivity in infinite-layer nickelates.

cond-mat.supr-con↗

arXiv2025

High Temperature Superconductivity Dominated by Inner Underdoped CuO$_2$ Planes in Quadruple-Layer Cuprate (Cu,C)Ba$_2$Ca$_3$Cu$_4$O$_{11+δ}$

The superconducting transition temperature ($T_{\mathrm{c}}$) of trilayer or quadruple-layer cuprates typically surpasses that of single-layer or bilayer systems. This observation is often interpreted within the ``composite picture", where strong proximity effect between inner CuO$_2$ planes (IPs) and outer CuO$_2$ planes (OPs) is crucial. Albeit intriguing, a straightforward scrutinization of this composite picture is still lacking. In this study, using angle-resolved photoemission spectroscopy to investigate (Cu,C)Ba$_2$Ca$_3$Cu$_4$O$_{11+δ}$ (CuC-1234) with a high $T_{\mathrm{c}}$ of 110~K, we found that the OPs are not superconducting at the $T_{\mathrm{c}}$ of the material. Instead, the large pairing strength and phase coherence concurrently emerge at the underdoped IPs, suggesting that the high $T_{\mathrm{c}}$ is primarily driven by these underdoped IPs. Given that the $T_{\mathrm{c}}$ of CuC-1234 is comparable to other trilayer or quadruple-layer cuprates, our findings suggest that the conventional ``composite picture" is not universally required for achieving high $T_{\mathrm{c}}$. More importantly, we demonstrate that CuO$_2$ planes free of apical oxygen can support superconductivity up to 110~K even at a doping level of 0.07 holes per Cu, a level that lies deep in the underdoped regime of single- and bilayer cuprates. These findings provide new insights into the origin of high $T_{\mathrm{c}}$ in multilayer cuprates.

cond-mat.supr-con↗

arXiv2025

LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling

Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on neural radiance field or 3D Gaussian splatting are limited to modeling fine-scale motion, greatly restricting their application. In this paper, we introduce LocalDyGS, which consists of two parts to adapt our method to both large-scale and fine-scale motion scenes: 1) We decompose a complex dynamic scene into streamlined local spaces defined by seeds, enabling global modeling by capturing motion within each local space. 2) We decouple static and dynamic features for local space motion modeling. A static feature shared across time steps captures static information, while a dynamic residual field provides time-specific features. These are combined and decoded to generate Temporal Gaussians, modeling motion within each local space. As a result, we propose a novel dynamic scene reconstruction framework to model highly dynamic real-world scenes more realistically. Our method not only demonstrates competitive performance on various fine-scale datasets compared to state-of-the-art (SOTA) methods, but also represents the first attempt to model larger and more complex highly dynamic scenes. Project page: https://wujh2001.github.io/LocalDyGS/.

cs.CV↗

arXiv2025

HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs

High-speed obstacle avoidance of uncrewed aerial vehicles (UAVs) in cluttered environments is a significant challenge. Existing UAV planning and obstacle avoidance systems can only fly at moderate speeds or at high speeds over empty or sparse fields. In this article, we propose a hyper-efficient perception and planning system for the high-speed obstacle avoidance of UAVs. The system mainly consists of three modules: 1) A novel incremental robocentric mapping method with distance and gradient information, which takes 89.5% less time compared to existing methods. 2) A novel obstacle-aware topological path search method that generates multiple distinct paths. 3) An adaptive gradient-based high-speed trajectory generation method with a novel time pre-allocation algorithm. With these innovations, the system has an excellent real-time performance with only milliseconds latency in each iteration, taking 79.24% less time than existing methods at high speeds (15 m/s in cluttered environments), allowing UAVs to fly swiftly and avoid obstacles in cluttered environments. The planned trajectory of the UAV is close to the global optimum in both temporal and spatial domains. Finally, extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for high-speed navigation in cluttered environments.

cs.RO↗

arXiv2025

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining

Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.g., genomics) for pathological diagnosis. Although deep learning has demonstrated strong potential in pathology, several key challenges persist: (1) fusing heterogeneous data types requires sophisticated strategies beyond simple concatenation due to high computational costs; (2) common scenarios of missing modalities necessitate flexible strategies that allow the model to learn robustly in the absence of certain modalities; (3) the downstream tasks in CPath are diverse, ranging from unimodal to multimodal, cnecessitating a unified model capable of handling all modalities. To address these challenges, we propose ALTER, an any-to-any tri-modal pretraining framework that integrates WSIs, genomics, and pathology reports. The term "any" emphasizes ALTER's modality-adaptive design, enabling flexible pretraining with any subset of modalities, and its capacity to learn robust, cross-modal representations beyond WSI-centric approaches. We evaluate ALTER across extensive clinical tasks including survival prediction, cancer subtyping, gene mutation prediction, and report generation, achieving superior or comparable performance to state-of-the-art baselines.

cs.CV↗

arXiv2025

Asymptotics of the principal eigenvalue of a linear elliptic operator with large advection

Consider the eigenvalue problem of a linear second order elliptic operator: \begin{equation} \nonumber -DΔφ-2α\nabla m(x)\cdot \nablaφ+V(x)φ=λφ \ \hbox{ in }Ω, \end{equation} complemented by the Dirichlet boundary condition or the following general Robin boundary condition: $$ \frac{\partialφ}{\partial n}+β(x)φ=0 \ \ \hbox{ on }\partialΩ, $$ where $Ω\subset\mathbb{R}^N (N\geq1)$ is a bounded smooth domain, $n(x)$ is the unit exterior normal to $\partialΩ$ at $x\in\partialΩ$, $D>0$ and $α>0$ are, respectively, the diffusion and advection coefficients, $m\in C^2(\overlineΩ),\,V\in C(\overlineΩ)$, $β\in C(\partialΩ)$ are given functions, and $β$ allows to be positive, sign-changing or negative. In \cite{PZZ2019}, the asymptotic behavior of the principal eigenvalue of the above eigenvalue problem as $D\to0$ or $D\to\infty$ was studied. In this paper, when $N\geq2$, under proper conditions on the advection function $m$, we establish the asymptotic behavior of the principal eigenvalue as $α\to\infty$, and when $N=1$, we obtain a complete characterization for such asymptotic behavior provided $m'$ changes sign at most finitely many times. Our results complement or improve those in \cite{BHN2005,CL2008,PZ2018} and also partially answer some questions raised in \cite{BHN2005}.

math.AP↗

arXiv2025

Long-time dynamics of a parabolic-ODE SIS epidemic model with saturated incidence mechanism

In this paper, we investigate a parabolic-ODE SIS epidemic model with no-flux boundary conditions in a heterogeneous environment. The model incorporates a saturated infection mechanism ${SI}/(m(x) + S + I)$ with $m \geq,\,\not\equiv 0$. This study is motivated by disease control strategies, such as quarantine and lockdown, that limit population movement. We examine two scenarios: one where the movement of the susceptible population is restricted, and another where the movement of the infected population is neglected. We establish the long-term dynamics of the solutions in each scenario. Compared to previous studies that assume the absence of a saturated incidence function (i.e., $m\equiv 0$), our findings highlight the novel and significant interplay between total population size, transmission risk level, and the saturated incidence function in influencing disease persistence, extinction, and spatial distribution. Numerical simulations are performed to validate the theoretical results, and the implications of the results are discussed in the context of disease control and eradication strategies.

math.AP↗

arXiv2025

Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting

Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. Second, we propose a Key-frame-guided Streaming Strategy that refines each key frame, enabling accurate reconstruction of temporally complex scenes while mitigating error accumulation. We conducted extensive in-domain and cross-domain evaluations, demonstrating that our approach can achieve streaming with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.

cs.CV↗

arXiv2025

Swift4D:Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene

Novel view synthesis has long been a practical but challenging task, although the introduction of numerous methods to solve this problem, even combining advanced representations like 3D Gaussian Splatting, they still struggle to recover high-quality results and often consume too much storage memory and training time. In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian Splatting method that can handle static and dynamic primitives separately, achieving a good trade-off between rendering quality and efficiency, motivated by the fact that most of the scene is the static primitive and does not require additional dynamic properties. Concretely, we focus on modeling dynamic transformations only for the dynamic primitives which benefits both efficiency and quality. We first employ a learnable decomposition strategy to separate the primitives, which relies on an additional parameter to classify primitives as static or dynamic. For the dynamic primitives, we employ a compact multi-resolution 4D Hash mapper to transform these primitives from canonical space into deformation space at each timestamp, and then mix the static and dynamic primitives to produce the final output. This divide-and-conquer method facilitates efficient training and reduces storage redundancy. Our method not only achieves state-of-the-art rendering quality while being 20X faster in training than previous SOTA methods with a minimum storage requirement of only 30MB on real-world datasets. Code is available at https://github.com/WuJH2001/swift4d.

cs.CV↗

arXiv2025

CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning

Unsupervised Multi-View Stereo (MVS) methods have achieved promising progress recently. However, previous methods primarily depend on the photometric consistency assumption, which may suffer from two limitations: indistinguishable regions and view-dependent effects, e.g., low-textured areas and reflections. To address these issues, in this paper, we propose a new dual-level contrastive learning approach, named CL-MVSNet. Specifically, our model integrates two contrastive branches into an unsupervised MVS framework to construct additional supervisory signals. On the one hand, we present an image-level contrastive branch to guide the model to acquire more context awareness, thus leading to more complete depth estimation in indistinguishable regions. On the other hand, we exploit a scene-level contrastive branch to boost the representation ability, improving robustness to view-dependent effects. Moreover, to recover more accurate 3D geometry, we introduce an L0.5 photometric consistency loss, which encourages the model to focus more on accurate points while mitigating the gradient penalty of undesirable ones. Extensive experiments on DTU and Tanks&Temples benchmarks demonstrate that our approach achieves state-of-the-art performance among all end-to-end unsupervised MVS frameworks and outperforms its supervised counterpart by a considerable margin without fine-tuning.

cs.CV↗

arXiv2024

Spatial profiles of a reaction-diffusion epidemic model with nonlinear incidence mechanism and constant total population

This paper examines a susceptible-infected-susceptible (SIS) epidemic reaction-diffusion model with no-flux boundary conditions and constant total population. The infection mechanism in the model is described by a nonlinear term of the form $S^qI^p$ with $0 0$. We explore the spatial profiles of the endemic equilibrium with respect to small movement rates of susceptible and/or infected populations. Our results extend and improve existing results in the literature, providing further insights into how transmission mechanisms and population mobility limitations impact the spread of infectious diseases. We reinforce and complement the theoretical results with numerical simulations.

math.AP↗

arXiv2024

Spatial profiles of a reaction-diffusion epidemic model with nonlinear incidence mechanism and varying total population

This paper considers a susceptible-infected-susceptible (SIS) epidemic reaction-diffusion model with no-flux boundary conditions and varying total population. The interaction of the susceptible and infected people is describe by the nonlinear transmission mechanism of the form $S^qI^p$, where $0 0$. In [39], we have studied a model with a constant total population. In the current paper, we extend our analysis to a model with a varying total population, incorporating birth and death rates. We investigate the asymptotic profiles of the endemic equilibrium when the dispersal rates of susceptible and/or infected individuals are small. Our work is motivated by disease control strategies that limit population movement. To illustrate the main findings, we conduct numerical simulations and provide a discussion of the theoretical results from the view of disease control. We will also compare the results for the models with constant or varying total population.

math.AP↗