arXiv subjects

Peng Peng

Publications and source records attributed to Peng Peng.

At least 19 recordsLinked to original sources

arXiv2026

Comparison Theorems for the Profile Curve Equation of Rotationally Symmetric Self-Shrinkers

Mean curvature flow is a fundamental geometric evolution equation in which a submanifold moves in the normal direction with velocity equal to its mean curvature vector. Self-shrinkers arise naturally as self-similar solutions to the mean curvature flow and play an important role as models for finite-time singularities. Among nontrivial examples of compact embedded self-shrinkers, the rotationally symmetric self-shrinking torus constructed by Angenent is one of the most important. However, the uniqueness of the Angenent torus remains a major open problem. In this paper, we study rotationally symmetric self-shrinkers of type $\mathbb{S}^{1}\times \mathbb{S}^{n-1}$ from the point of view of ordinary differential equations. We analyze the profile curves of rotationally symmetric self-shrinkers, focusing on the behavior of their vertical points and the curves traced out by these points as the initial height varies. We give a new proof of the existence of the Angenent torus by showing that two families of vertical-point trajectories must intersect. We further derive the linearized equation associated with the rotationally symmetric self-shrinker equation and apply a Sturm-type comparison theorem to obtain sufficient conditions for the monotonicity of horizontal-point trajectories. In particular, we prove a comparison theorem for solutions near the spherical self-shrinker $x^{2}+r^{2}=2n$, and establish partial monotonicity results for the curves of horizontal points. These results provide a possible approach to the uniqueness problem for the Angenent torus.

math.DG↗

arXiv2026

Existence of rotationally symmetric embedded f-minimal tori

We generalize Angenent's shrinking torus \cite{Angenent1992} by proving the existence of an embedded $n$-dimensional $f$-minimal hypersurface diffeomorphic to $S^1\times S^{n-1}$ in $\mathbb{R}^{n+1}$ equipped with the metric $$ds^{2}=e^{-\frac{f(|x|^2)}{2n}}{\sum^{n+1}_{i=1}dx^{2}_{i}},$$ where $f$ is a convex function of $|x|^2$ satisfying $m\leq f'\leq M$ for some positive constants $m$ and $M$.

math.DG↗

arXiv2026

HaS: Accelerating RAG through Homology-Aware Speculative Retrieval

Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) at inference by retrieving external documents as context. However, retrieval becomes increasingly time-consuming as the knowledge databases grow in size. Existing acceleration strategies either compromise accuracy through approximate retrieval, or achieve marginal gains by reusing results of strictly identical queries. We propose HaS, a homology-aware speculative retrieval framework that performs low-latency speculative retrieval over restricted scopes to obtain candidate documents, followed by validating whether they contain the required knowledge. The validation, grounded in the homology relation between queries, is formulated as a homologous query re-identification task: once a previously observed query is identified as a homologous re-encounter of the incoming query, the draft is deemed acceptable, allowing the system to bypass slow full-database retrieval. Benefiting from the prevalence of homologous queries under real-world popularity patterns, HaS achieves substantial efficiency gains. Extensive experiments demonstrate that HaS reduces retrieval latency by 23.74% and 36.99% across datasets with only a 1-2% marginal accuracy drop. As a plug-and-play solution, HaS also significantly accelerates complex multi-hop queries in modern agentic RAG pipelines. Source code is available at: https://github.com/ErrEqualsNil/HaS.

cs.IR↗

arXiv2026

MCI-SQL: Text-to-SQL with Metadata-Complete Context and Intermediate Correction

Text-to-SQL aims to translate natural language queries into SQL statements. Existing methods typically follow a pipeline of pre-processing, schema linking, candidate SQL generation, SQL alignment, and target SQL selection. However, these methods face significant challenges. First, they often struggle with column filtering during schema linking due to difficulties in comprehending raw metadata. Also, the candidate SQL generation process often suffers from reasoning errors, which limits accuracy improvements. To address these limitations, we propose a framework, called MCI-SQL, to efficiently and precisely generate SQL queries. Specifically, we assign metadata-complete contexts to each column, which significantly improves the accuracy of column filtering for schema linking. Also, for candidate SQL generation, we propose an intermediate correction mechanism that validates SQL queries and revises errors in a timely way. Moreover, we also propose effective optimizations in subsequent SQL alignment and selection phases, which further enhance the performance. Experiments on the widely-used BIRD benchmark show that MCI-SQL achieves execution accuracy of 74.45% on the development set and 76.41% on the test set, surpassing current published state-of-the-art results. In addition, we manually identify and correct 412 samples in the BIRD dataset, forming a new version named BIRD-clear, which is released together with our code on GitHub. We also evaluate our methods on BIRD-clear and find that MCI-SQL outperforms baselines by 8.47 percentage points in execution accuracy, further demonstrating the effectiveness and reliability of our framework.

cs.DB↗

arXiv2026

FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction

Spectral computed tomography (CT) with photon-counting detectors holds immense potential for material discrimination and tissue characterization. However, under ultra-low-dose conditions, the sharply degraded signal-to-noise ratio (SNR) in energy-specific projections poses a significant challenge, leading to severe artifacts and loss of structural details in reconstructed images. To address this, we propose FSP-Diff, a full-spectrum prior-enhanced dual-domain latent diffusion framework for ultra-low-dose spectral CT reconstruction. Our framework integrates three core strategies: 1) Complementary Feature Construction: We integrate direct image reconstructions with projection-domain denoised results. While the former preserves latent textural nuances amidst heavy noise, the latter provides a stable structural scaffold to balance detail fidelity and noise suppression. 2) Full-Spectrum Prior Integration: By fusing multi-energy projections into a high-SNR full-spectrum image, we establish a unified structural reference that guides the reconstruction across all energy bins. 3) Efficient Latent Diffusion Synthesis: To alleviate the high computational burden of high-dimensional spectral data, multi-path features are embedded into a compact latent space. This allows the diffusion process to facilitate interactive feature fusion in a lower-dimensional manifold, achieving accelerated reconstruction while maintaining fine-grained detail restoration. Extensive experiments on simulated and real-world datasets demonstrate that FSP-Diff significantly outperforms state-of-the-art methods in both image quality and computational efficiency, underscoring its potential for clinically viable ultra-low-dose spectral CT imaging.

cs.CV↗

arXiv2026

Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction

The clinical application of cone-beam computed tomography (CBCT) is constrained by the inherent trade-off between radiation exposure and image quality. Ultra-sparse angular sampling, employed to reduce dose, introduces severe undersampling artifacts and inter-slice inconsistencies, compromising diagnostic reliability. Existing reconstruction methods often struggle to balance angular continuity with spatial detail fidelity. To address these challenges, we propose a Continuity-driven Synergistic Diffusion with Neural priors (CSDN) for ultra-sparse-view CBCT reconstruction. Neural priors are introduced as a structural foundation to encode a continuous threedimensional attenuation representation, enabling the synthesis of physically consistent dense projections from ultra-sparse measurements. Building upon this neural-prior-based initialization, a synergistic diffusion strategy is developed, consisting of two collaborative refinement paths: a Sinogram Refinement Diffusion (Sino-RD) process that restores angular continuity and a Digital Radiography Refinement Diffusion (DR-RD) process that enforces inter-slice consistency from the projection image perspective. The outputs of the two diffusion paths are adaptively fused by the Dual-Projection Reconstruction Fusion (DPRF) module to achieve coherent volumetric reconstruction. Extensive experiments demonstrate that the proposed CSDN effectively suppresses artifacts and recovers fine textures under ultra-sparse-view conditions, outperforming existing state-of-the-art techniques.

cs.CV↗

arXiv2026

Efficient Cloud-edge Collaborative Approaches to SPARQL Queries over Large RDF graphs

With the increasing use of RDF graphs, storing and querying such data using SPARQL remains a critical problem. Current mainstream solutions rely on cloud-based data management architectures, but often suffer from performance bottlenecks in environments with limited bandwidth or high system load. To address this issue, this paper explores for the first time the integration of edge computing to move graph data storage and processing to edge environments, thereby improving query performance. This approach requires offloading query processing to edge servers, which involves addressing two challenges: data localization and network scheduling. First, the data localization challenge lies in computing the subgraphs maintained on edge servers to quickly identify the servers that can handle specific queries. To address this challenge, we introduce a new concept of pattern-induced subgraphs. Second, the network scheduling challenge involves efficiently assigning queries to edge and cloud servers to optimize overall system performance. We tackle this by constructing a overall system model that jointly captures data distribution, query characteristics, network communication, and computational resources. Accordingly, we further propose a joint formulation of query assignment and computational resource allocation, modeling it as a Mixed Integer Nonlinear Programming (MINLP) problem and solve this problem using a modified branch-and-bound algorithm. Experimental results on real datasets under a real cloud platform demonstrate that our proposed method outperforms the state-of-the-art baseline methods in terms of efficiency. The codes are available on GitHub

cs.DB↗

arXiv2025

Atomically-precise synthesis and simultaneous integration of 2D transition metal dichalcogenides enabled by nano-confinement

Two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides (TMDs), and hBN, exhibit intriguing properties that are sensitive to their atomic-scale structures and can be further enriched through van der Waals (vdW) integration. However, the precise synthesis and clean integration of 2D materials remain challenging. Here, using graphene or hBN as a vdW capping layer, we create a nano-confined environment that directs the growth kinetics of 2D TMDs (e.g., NbSe2 and MoS2), enabling precise formation of TMD monolayers with tailored morphologies, from isolated monolayer domains to large-scale continuous films and intrinsically-patterned rings. Moreover, Janus S-Mo-Se monolayers are synthesized with atomic precision via vdW-protected bottom-plane chalcogen substitution. Importantly, our approach simultaneously produces ultraclean vdW interfaces. This in situ encapsulation reliably preserves air-sensitive materials, as evidenced by the enhanced superconductivity of nano-confined NbSe2 monolayers. Altogether, our study establishes a versatile platform for the controlled synthesis and integration of 2D TMDs for advanced applications.

cond-mat.mtrl-sci↗

arXiv2025

PROMISE: Prompt-Attentive Hierarchical Contrastive Learning for Robust Cross-Modal Representation with Missing Modalities

Multimodal models integrating natural language and visual information have substantially improved generalization of representation models. However, their effectiveness significantly declines in real-world situations where certain modalities are missing or unavailable. This degradation primarily stems from inconsistent representation learning between complete multimodal data and incomplete modality scenarios. Existing approaches typically address missing modalities through relatively simplistic generation methods, yet these approaches fail to adequately preserve cross-modal consistency, leading to suboptimal performance. To overcome this limitation, we propose a novel multimodal framework named PROMISE, a PROMpting-Attentive HIerarchical ContraStive LEarning approach designed explicitly for robust cross-modal representation under conditions of missing modalities. Specifically, PROMISE innovatively incorporates multimodal prompt learning into a hierarchical contrastive learning framework, equipped with a specially designed prompt-attention mechanism. This mechanism dynamically generates robust and consistent representations for scenarios where particular modalities are absent, thereby effectively bridging the representational gap between complete and incomplete data. Extensive experiments conducted on benchmark datasets, along with comprehensive ablation studies, clearly demonstrate the superior performance of PROMISE compared to current state-of-the-art multimodal methods.

cs.CV↗

arXiv2025

Electron Orbital Angular Momentum Polarization in Neutral Atoms

We demonstrate the polarization of electron orbital angular momentum (OAM) in neutral atoms by integrating the Zeeman effect with attosecond transient absorption spectroscopy (ATAS). Using density matrix simulations, we show that in a helium atom, the absorption probability asymmetry between mj=-1 and mj = 1 in the 1s2p state can be precisely controlled by adjusting the time delay between infrared (IR) and extreme ultraviolet (XUV) fields, the strength of an applied static magnetic field, as well as the angle between laser polarization and magnetic field direction. This approach has significant implications across various fields, including quantum computing, quantum communication, and spintronics. Moreover, it paves the way for advancements in applications such as manipulating chemical reactions control, tailoring the magnetic properties of matter, and enabling novel laser emissions.

physics.atom-ph↗

arXiv2025

AGN star dynamics under the Influence of Outflow-Ambient Interactions

Stars with outflows interacting with ambient gas experience accelerations arising from the gravitational feedback induced by the interaction structure. In this work, three-dimensional (3D) local shearing box simulations are performed to investigate the dynamical evolution of a star with outflows embedded in the outer regions of an active galactic nucleus (AGN) disk. Two types of stellar wind are considered: isotropic winds and axisymmetric jets, along with variations in the radial pressure gradient profile. The results show that anti-friction enables AGN stars to acquire angular momentum from the ambient gas, resulting in outward migration away from the disk center. The formation and stability of the head-wind structure, which is crucial for maintaining anti-friction, are sensitive to both the strength of the stellar outflow and the radial pressure gradient of the disk gas. Once the head-wind structure is disrupted, the anti-friction effect ceases to operate effectively. A case study is also presented, focusing on a stellar-mass black hole (sBH) in an AGN disk. It is shown that jet material launched along the z-axis is confined to the trailing side of the object's motion by high gas inflow velocities, thereby activating anti-friction and inducing outward migration. If such an sBH migrates inward initially, the interplay between inward and outward migration may trap it at an equilibrium radius, potentially facilitating the formation and merger of black hole binaries.

astro-ph.GA↗

arXiv2025

FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing

Remote sensing (RS) images are usually produced at an unprecedented scale, yet they are geographically and institutionally distributed, making centralized model training challenging due to data-sharing restrictions and privacy concerns. Federated learning (FL) offers a solution by enabling collaborative model training across decentralized RS data sources without exposing raw data. However, there lacks a realistic federated dataset and benchmark in RS. Prior works typically rely on manually partitioned single dataset, which fail to capture the heterogeneity and scale of real-world RS data, and often use inconsistent experimental setups, hindering fair comparison. To address this gap, we propose a realistic federated RS dataset, termed FedRS. FedRS consists of eight datasets that cover various sensors and resolutions and builds 135 clients, which is representative of realistic operational scenarios. Data for each client come from the same source, exhibiting authentic federated properties such as skewed label distributions, imbalanced client data volumes, and domain heterogeneity across clients. These characteristics reflect practical challenges in federated RS and support evaluation of FL methods at scale. Based on FedRS, we implement 10 baseline FL algorithms and evaluation metrics to construct the comprehensive FedRS-Bench. The experimental results demonstrate that FL can consistently improve model performance over training on isolated data silos, while revealing performance trade-offs of different methods under varying client heterogeneity and availability conditions. We hope FedRS-Bench will accelerate research on large-scale, realistic FL in RS by providing a standardized, rich testbed and facilitating fair comparisons across future works. The source codes and dataset are available at https://fedrs-bench.github.io/.

cs.LG↗

arXiv2025

Feasibility study of multiplexing analog signals from SiPMs for a single layer monolithic PET detector design

Semi monolithic detector designs with a series of stacked thin monolithic scintillator plates and side readout are an attractive approach for potentially achieving very high performance in a positron emission tomography (PET) scanner. In this work, a simulation study of a single layer monolithic detector module was performed with side read out of scintillation light using GATEv8.2. In this design, a single layer LSO crystal was used with dimensions 40 mm*40 mm*40 mm, with 0.60 mm thickness of the ESR (enhanced specular reflector) films covering the crystal's top and bottom surfaces. The photons generated in the scintillation process induced by the gamma ray hitting the crystal were detected by four 1*8 SiPM (silicon photomultiplier) arrays placed along the four sides of the crystal. The scintillation light distribution detected by all of the 32 SiPMs surrounding the crystal layer was then used to extract the gamma-crystal interaction location based on machine learning analysis. In this work, the spatial resolution of the detector module was explored when analog signals from each of the 32 SiPMs were summed to 28, 24, 20, 16, 12, 8, and 4 total outputs. This study showed that good spatial resolution can be achieved even when the number of read out channels is decreased by multiplexing, which can reduce the overall detector manufacturing cost.

physics.ins-det↗

arXiv2025

Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation

Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed data. Extracting discriminative features from few-shot fault data is challenging, and adding new fault classes often demands costly model retraining. Moreover, incremental training of existing methods risks catastrophic forgetting, and severe class imbalance can bias the model's decisions toward normal classes. To tackle these issues, we introduce a Supervised Contrastive knowledge distiLlation for class Incremental Fault Diagnosis (SCLIFD) framework proposing supervised contrastive knowledge distillation for improved representation learning capability and less forgetting, a novel prioritized exemplar selection method for sample replay to alleviate catastrophic forgetting, and the Random Forest Classifier to address the class imbalance. Extensive experimentation on simulated and real-world industrial datasets across various imbalance ratios demonstrates the superiority of SCLIFD over existing approaches. Our code can be found at https://github.com/Zhang-Henry/SCLIFD_TII.

cs.LG↗

arXiv2024

The fate of EMRI-IMRI pairs in AGN accretion disks: hydrodynamic and three body simulations

Extreme-mass-ratio inspirals (EMRIs) and intermediate-mass-ratio inspirals (IMRIs) are important gravitational wave (GW) sources for the Laser Interferometer Space Antenna (LISA). It has been recently suggested that EMRIs and IMRIs can both form in the accretion disk of an active galactic nucleus (AGN). Considering the likely encounter between a sBH and an IMBH during the migration in the AGN disk, Paper I showed that a gap-opening IMBH can drive a surrounding sBH to migrate synchronously. In this work, we extend the study in Paper I with a more sophisticated model. We first use 3D hydrodynamical simulations to study the co-evolution of the disk and the migration of a sBH in the vicinity of an IMBH. We find that the gaseous torque, together with the tidal torque exerted by the IMBH, can drive synchronized migration until $\sim 10$ Schwarzschild radii from the central supermassive black hole (SMBH). We further use a relativistic three-body code to study the final fate of the sBH in the GW-dominated regime. We find that the sBH can be either captured or kicked out by the IMBH, which will result in either two subsequent IMRIs or an EMRI followed by an IMRI. These events will bring rich information about the formation and evolution of sBHs and IMBHs in AGNs.

astro-ph.HE↗

arXiv2024

Gravitational Wave Astronomy With TianQin

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them.

astro-ph.GA↗

arXiv2024

Realizing a spatially correlated lattice interferometer

Atom interferometers provide a powerful tool for measuring physical constants and testifying fundamental physics with unprecedented precision. Conventional atom interferometry focuses on the phase difference between two paths and utilizes matter waves with fixed coherence. Here, we report on realizing a Ramsey-Bord\'e interferometer of coherent matter waves dressed by a moving optical lattice in the gravity direction, and explore the resulting interference along multiple paths with tunable coherence. We investigate spatial correlations of atoms both within the lattice and between two arms by interferometry, and observe the emerging multiple interference peaks owing to the long-range coherence nature of the Bose-Einstein condensate. Our findings agree well with theoretical simulations, paving the way for high-precision interferometry with ultracold atoms.

cond-mat.quant-gas↗

arXiv2024

Atomic transport dynamics in crossed optical dipole trap

We study the dynamical evolution of cold atoms in crossed optical dipole trap theoretically and experimentally. The atomic transport process is accompanied by two competitive kinds of physical mechanics, atomic loading and atomic loss. The loading process normally is negligible in the evaporative cooling experiment on the ground, while it is significant in the preparation of ultra-cold atoms in the space station. Normally, the atomic loading process is much weaker than the atomic loss process, and the atomic number in the center region of the trap decreases monotonically, as reported in previous research. However, when the atomic loading process is comparable to the atomic loss process, the atomic number in the center region of the trap will initially increase to a maximum value and then slowly decrease, and we have observed the phenomenon first. The increase of atomic number in the center region of the trap shows the presence of the loading process, and this will be significant especially under microgravity conditions. We build a theoretical model to analyze the competitive relationship, which coincides with the experimental results well. Furthermore, we have also given the predicted evolutionary behaviors under different conditions. This research provides a solid foundation for further understanding of the atomic transport process in traps. The analysis of loading process is of significant importance for the preparation of ultra-cold atoms in a crossed optical dipole trap under microgravity conditions.

quant-ph↗