arXiv subjects

Jun Yin

Publications and source records attributed to Jun Yin.

At least 55 records · Page 3Linked to original sources

arXiv2026

Scaling of Rainfall Intensity and Frequency with Rising Temperatures

Global warming is projected to intensify the hydrological cycle, amplifying risks to ecosystems and society. While extreme rainfall appears to exhibit stronger sensitivity to global warming compared to mean rainfall rates, a unifying physical mechanism capable of explaining this systematic divergence has remained elusive. Here, we integrate theory and data from a global network of nearly 50,000 rain-gauge stations to unravel the rainfall intensity and frequency response to rising temperatures. We show that the distributions of wet-day rainfall depth exhibit self-similar shapes across diverse geographical regions and time periods. Combined with the temperature response of rainfall frequency, this consistently links mean and extreme precipitation at both local and global scales. We find that the most probable change in rainfall intensity follows Clausius-Clapeyron (CC) scaling with variations shaped by a fundamental hydrological constraint. This behavior reflects a dynamic intensification of updrafts in space and time, which produces localized heavy precipitation events enhancing atmospheric moisture depletion and hydrologic losses through runoff and percolation. The resulting reduction in evaporative fluxes slows the replenishment of atmospheric moisture, giving rise to the observed trade-off between rainfall frequency and intensity. These robust scaling laws for rainfall shifts with temperature are essential for climate projection and adaptation planning.

physics.ao-ph↗

arXiv2026

A reconstructed discontinuous approximation for distributed elliptic control problems

In this paper, we present and analyze an interior penalty discontinuous Galerkin method for the distributed elliptic optimal control problems. It is based on a reconstructed discontinuous approximation which admits arbitrarily high-order approximation space with only one unknown per element. Applying this method, we develop a proper discretization scheme that approximates the state and adjoint variables in the approximation space. Our main contributions are twofold: (1) the derivation of both a priori and a posteriori error estimates of the $L^2$-norm and the energy norms, and (2) the implementation of an efficiently solvable discrete system, which is solved via a linearly convergent projected gradient descent method. Numerical experiments are provided to verify the convergence order in a priori error estimate and the efficiency of a posteriori error estimate.

math.NA↗

arXiv2026

Beyond Perfect APIs: A Comprehensive Evaluation of LLM Agents Under Real-World API Complexity

We introduce WildAGTEval, a benchmark designed to evaluate large language model (LLM) agents' function-calling capabilities under realistic API complexity. Unlike prior work that assumes an idealized API system and disregards real-world factors such as noisy API outputs, WildAGTEval accounts for two dimensions of real-world complexity: 1. API specification, which includes detailed documentation and usage constraints, and 2. API execution, which captures runtime challenges. Consequently, WildAGTEval offers (i) an API system encompassing 60 distinct complexity scenarios that can be composed into approximately 32K test configurations, and (ii) user-agent interactions for evaluating LLM agents on these scenarios. Using WildAGTEval, we systematically assess several advanced LLMs and observe that most scenarios are challenging, with irrelevant information complexity posing the greatest difficulty and reducing the performance of strong LLMs by 27.3%. Furthermore, our qualitative analysis reveals that LLMs occasionally distort user intent merely to claim task completion, critically affecting user satisfaction.

cs.CL↗

arXiv2025

Quantum Noise Spectroscopy of Nanoscale Charge Defects in Silicon Carbide at Room Temperature

The nanoscale charge environment critically influences semiconductor physics and device performance. While conventional bulk characterization techniques provide volume-averaged defect properties, they lack the spatial resolution to resolve nanoscale charge heterogeneity and identify microscopic noise sources. Here, we utilize single PL5 centers in 4H-SiC as room-temperature broadband quantum sensors to fill in the gap. We report the first real-time, nanoscale observation of singlecharge tunneling dynamics in a commercial semiconductor at room temperature, by monitoring the random telegraph noise using optically detected magnetic resonance (ODMR). This capability enables an electrical noise imaging technique, showing distinct noise variations across different wafer substrates. By employing dynamical decoupling, we extend noise spectroscopy from near-DC to MHz frequencies, uncovering significant noise spectral density correlations across frequency bands. Finally, we probe MHz-GHz noise and identify its origin via T1 relaxation spectroscopy, obtaining the first nanoscale electron paramagnetic resonance (EPR) spectroscopic fingerprint of charge defects in SiC. These techniques open avenues for characterizing noise environments in semiconductor devices, providing critical insights for optimizing SiC fabrication processes, defect control, and advancing quantum technologies.

quant-ph↗

arXiv2025

A Generative Model for Disentangling Galaxy Photometric Parameters

Ongoing and future photometric surveys will produce unprecedented volumes of galaxy images, necessitating robust, efficient methods for deriving galaxy morphological parameters at scale. Traditional approaches, such as parametric light-profile fitting, offer valuable insights but become computationally prohibitive when applied to billions of sources. In this work, we propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology. Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters (e.g., flux, half-light radius, Sersic index, ellipticity), and observational conditions. By encoding each galaxy image into a low-dimensional latent representation conditioned on key parameters, our model effectively recovers these morphological features in a disentangled manner, while also reconstructing the original image. The results demonstrate that the CAE approach can accurately and efficiently infer complex structural properties, offering a powerful alternative to existing methods.

astro-ph.IM↗

arXiv2025

A Block Reduction Method for Random Band Matrices with General Variance Profiles

We present a novel block reduction method for the study of a general class of random band matrices (RBM) defined on the $d$-dimensional lattice $\mathbb{Z}_{L}^d:=\{1,2,\ldots,L\}^{d}$ for $d\in \{1,2\}$, with band width $W$ and an almost arbitrary variance profile subject to a core condition. We prove the delocalization of bulk eigenvectors for such RBMs under the assumptions $W\ge L^{1/2+\varepsilon}$ in one dimension and $W\geq L^{\varepsilon}$ in two dimensions, where $\varepsilon$ is an arbitrarily small constant. This result extends the findings of arXiv:2501.01718 and arXiv:2503.07606 on block RBMs to models with general variance profiles. Furthermore, we generalize our results to Wegner orbital models with small interaction strength $λ\ll 1$. Under the sharp condition $λ\gg W^{-d/2}$, we establish optimal lower bounds for the localization lengths of bulk eigenvectors, thereby extending the results of arXiv:2503.11382 to settings with nearly arbitrary potential and hopping terms. Our block reduction method provides a powerful and flexible framework that reduces both the dynamical analysis of the loop hierarchy and the derivation of deterministic estimates for general RBMs to the corresponding analysis of block RBMs, as developed in arXiv:2501.01718, arXiv:2503.07606 and arXiv:2503.11382.

math.PR↗

arXiv2025

Delocalization of Non-Mean-Field Random Matrices in Dimensions $d\ge 3$

We study $N \times N$ random band matrices $H = (H_{xy})$ with mean-zero complex Gaussian entries, where $x,y$ lie on the discrete torus $(\mathbb{Z} / \sqrt[d]{N} \mathbb{Z})^d$ in dimensions $d \ge 3$. The variance profile satisfies $\mathbb{E}|H_{xy}|^2 = S_{xy}$, with $S_{xy} = 0$ whenever the distance between $x$ and $y$ exceeds a bandwidth parameter $W$. We prove that if $W \geq N^{\mathfrak{c}}$ for some constant $\mathfrak{c} > 0$, then in the large-$N$ limit, bulk eigenvectors are delocalized, quantum unique ergodicity (QUE) holds, and the local bulk eigenvalue statistics are universal. Our proof is based on the tree approximation of the loop hierarchy (arXiv:2501.01718) and diagrammatic techniques developed in earlier works (arXiv:1807.02447, arXiv:2104.12048, arXiv:2107.05795, arXiv:2412.15207, arXiv:2503.07606). Besides random band matrices, we also study two classical non-mean-field random matrix models: the Wegner orbital and the block Anderson models. Specifically, we consider Hermitian matrices $H = V + g Ψ$ on the same discrete torus $(\mathbb{Z} / \sqrt[d]{N} \mathbb{Z})^d$, where $V$ is a random block potential consisting of i.i.d. complex Gaussian diagonal blocks of size $W^d \times W^d$, and $Ψ$ encodes the interactions between neighboring blocks--random in the Wegner orbital model and deterministic in the block Anderson model. The parameter $g > 0$ represents the coupling strength between blocks. Assuming again that $W \geq N^{\mathfrak{c}}$, we establish delocalization of bulk eigenvectors, QUE, and bulk universality under the condition $W^{-d/2+\varepsilon}\le g \le \varepsilon^{-1}$ for any small constant $\varepsilon>0$. Combined with the localization results of arXiv:1608.02922 for $g \ll W^{-d/2}$, this identifies a localization--delocalization transition at the scale $g=W^{-d/2}$ in dimensions $d \ge 3$.

math.PR↗

arXiv2025

GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework

Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\times$ with $>$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects.

cs.AI↗

arXiv2025

Mock Observations for the CSST Mission: Integral Field Spectrograph--Instrument Simulation

The Chinese Space Station Survey Telescope (CSST) is a next-generation Stage-IV facility renowned for its wide field of view, high image quality, and multi-band observational capabilities. Among the five instruments onboard the CSST, the Integral Field Spectrograph (IFS) offers the unique ability to simultaneously capture spatial and spectral information across a field of view of no less than $6^{''}\times6^{''}$. Key advantages of the IFS include a high spatial resolution of $0.2^{''}$ and a broad spectral coverage from 350 to 1000 nm, making it an ideal instrument for studying physical processes in the vicinity of supermassive black holes within galaxies. To more accurately assess the technical and scientific performance of the CSST-IFS, it is essential to develop a simulation tool that incorporates realistic effects from all optical components. Such a simulation will form an integral part of the CSST-IFS data and pipeline system, enabling the development of the data reduction pipeline well ahead of actual observations. This paper presents an end-to-end simulation workflow for the CSST-IFS, incorporating a wide range of instrumental effects that may influence its spectral and imaging performance. The simulation accounts for optical diffraction effects introduced by all components, such as image slicers and slit array, as well as sub-pixel effects from gratings. It also includes various detector noises, frame-shifting effects, and charge-transfer inefficiency. Real observational conditions--such as target Doppler shift, cosmic rays, and other in-orbit operational effects--are also considered. We describe the technical implementation of the simulation and present results that quantitatively characterize key instrument parameters.

astro-ph.IM↗

arXiv2025

The Velocity Map Asymmetry of Ionized Gas in MaNGA II. Correlation between Velocity Map Morphology, Star Formation, and Metallicity in Regular Disk Galaxies

The morphology of ionized gas velocity maps provides a direct probe of the internal gas kinematics of galaxies. Using integral field spectroscopy from SDSS-IV MaNGA, we analyze a sample of 528 low-inclination, regular disk galaxies to investigate the correlations between velocity map morphology, star formation rate, and gas-phase metallicity. We quantify velocity map morphology using harmonic expansion and adopt two complementary diagnostics: the global kinematic asymmetry, which traces non-axisymmetric perturbations, and the first-order term ratio, which captures axisymmetric radial motions. We find that galaxies with higher kinematic asymmetry are more likely to deviate from the scaling relations, typically lying either above or below the star formation main sequence and systematically below the mass-metallicity relation. In contrast, the first-order term ratio shows only a correlation with gas-phase metallicity in the low-mass range and no significant dependence on star formation rate. Moreover, galaxies below the mass-metallicity relation generally exhibit higher HI gas fractions. These results suggest that external gas accretion is the primary driver of the observed phenomena: inflowing metal-poor gas increases velocity map asymmetry in disk galaxies, dilutes the metallicity, and triggers enhanced star formation. Feedback-driven outflows, bar- and spiral-driven inflows, and galaxy mergers may also contribute, but likely play a secondary role.

astro-ph.GA↗

arXiv2025

EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes

Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external information is diluted by irrelevant material, and (2) error accumulation, which arises in multi-hop reasoning when incomplete or misleading information is incorporated. To address these challenges, we introduce EviNote-RAG, a framework that follows a retrieve-note-answer workflow. Instead of reasoning directly over raw external information, the model first produces Supportive-Evidence Notes (SENs), which concisely preserve answer-critical information and explicitly mark key and uncertainty information to improve accuracy. We further design an entailment-based Evidence Quality Reward (EQR) to ensure that SENs are logically sufficient to derive the final answer, thereby enhancing SENs' quality. Experiments on both in-domain and out-of-domain QA benchmarks show that EviNote-RAG achieves state-of-the-art performance, improving answer accuracy, training stability, robustness, and efficiency. In particular, it yields relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), benefiting from improvements in the reasoning process.

cs.CL↗

arXiv2025

Clustering Result Re-guided Incomplete Multi-view Spectral Clustering

Incomplete multi-view spectral clustering generalizes spectral clustering to multi-view data and simultaneously realizes the partition of multi-view data with missing views. For this category of method, K-means algorithm needs to be performed to generate the clustering result after the procedure of feature extraction. More importantly, the connectivity of samples reflected by the clustering result is not utilized effectively. To overcome these defects, we propose Clustering Result re-Guided Incomplete Multi-view Spectral Clustering (CRG_IMSC). CRG_IMSC obtains the clustering result directly by imposing nonnegative constraint to the extracted feature. Furthermore, it constructs the connectivity matrix according to the result of spectral clustering, and minimizes the residual of self-representation based on the connectivity matrix. A novel iterative algorithm using multiplicative update is developed to solve the optimization problem of CRG_IMSC, and its convergence is proved rigorously. On benchmark datasets, for multi-view data, CRG_IMSC performs better than state-of-the-art clustering methods, and the experimental results also demonstrate the convergence of CRG_IMSC algorithm.

cs.LG↗

arXiv2025

Fingerprinting Organic Molecules for the Inverse Design of Two-Dimensional Hybrid Perovskites with Target Energetics

Artificial intelligence (AI)-assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified new organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.

cond-mat.mtrl-sci↗

arXiv2025

Nonlinear chiral light generation from resonant metasurfaces

Chiral nonlinear response has been explored for decades due to its extreme sensitivity to molecular and structural dissymmetry. Conventional approaches often require bulky systems and produce only static nonlinear chirality. Here, we report on a generic mechanism for the generation and control of nonlinear chiral light in resonant optical systems. We reveal that nonlinear resonant generation of circularly polarized light from achiral dielectric metasurfaces is extremely sensitive to the polarization state of the fundamental wave, and a resonant metasurface can produce light with arbitrary degree of nonlinear chirality (DNC). Experimentally, we demonstrate that the chirality of nonlinear radiation from one metasurface can be continuously tuned from DNC = -0.86 to DNC = 0.94 by simply varying the polarization angle of the incident wave. By further exploiting the instantaneous polarization state, nonlinear chirality has been switched in a delay time step of 3.2 fs, which is orders of magnitude more sensitive than the current state-of-the-art polarization modulation. These results promise to enrich our understanding of nonlinear processes in chiral structures and their manipulation with resonant photonic structures.

physics.optics↗

arXiv2025

UrbanSense:A Framework for Quantitative Analysis of Urban Streetscapes leveraging Vision Large Language Models

Urban cultures and architectural styles vary significantly across cities due to geographical, chronological, historical, and socio-political factors. Understanding these differences is essential for anticipating how cities may evolve in the future. As representative cases of historical continuity and modern innovation in China, Beijing and Shenzhen offer valuable perspectives for exploring the transformation of urban streetscapes. However, conventional approaches to urban cultural studies often rely on expert interpretation and historical documentation, which are difficult to standardize across different contexts. To address this, we propose a multimodal research framework based on vision-language models, enabling automated and scalable analysis of urban streetscape style differences. This approach enhances the objectivity and data-driven nature of urban form research. The contributions of this study are as follows: First, we construct UrbanDiffBench, a curated dataset of urban streetscapes containing architectural images from different periods and regions. Second, we develop UrbanSense, the first vision-language-model-based framework for urban streetscape analysis, enabling the quantitative generation and comparison of urban style representations. Third, experimental results show that Over 80% of generated descriptions pass the t-test (p less than 0.05). High Phi scores (0.912 for cities, 0.833 for periods) from subjective evaluations confirm the method's ability to capture subtle stylistic differences. These results highlight the method's potential to quantify and interpret urban style evolution, offering a scientifically grounded lens for future design.

cs.CV↗

arXiv2025

ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models

Architectural cultures across regions are characterized by stylistic diversity, shaped by historical, social, and technological contexts in addition to geograph-ical conditions. Understanding architectural styles requires the ability to describe and analyze the stylistic features of different architects from various regions through visual observations of architectural imagery. However, traditional studies of architectural culture have largely relied on subjective expert interpretations and historical literature reviews, often suffering from regional biases and limited ex-planatory scope. To address these challenges, this study proposes three core contributions: (1) We construct a professional architectural style dataset named ArchDiffBench, which comprises 1,765 high-quality architectural images and their corresponding style annotations, collected from different regions and historical periods. (2) We propose ArchiLense, an analytical framework grounded in Vision-Language Models and constructed using the ArchDiffBench dataset. By integrating ad-vanced computer vision techniques, deep learning, and machine learning algo-rithms, ArchiLense enables automatic recognition, comparison, and precise classi-fication of architectural imagery, producing descriptive language outputs that ar-ticulate stylistic differences. (3) Extensive evaluations show that ArchiLense achieves strong performance in architectural style recognition, with a 92.4% con-sistency rate with expert annotations and 84.5% classification accuracy, effec-tively capturing stylistic distinctions across images. The proposed approach transcends the subjectivity inherent in traditional analyses and offers a more objective and accurate perspective for comparative studies of architectural culture.

cs.CV↗

arXiv2025

FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs

In the architectural design process, floorplan design is often a dynamic and iterative process. Architects progressively draw various parts of the floorplan according to their ideas and requirements, continuously adjusting and refining throughout the design process. Therefore, the ability to predict a complete floorplan from a partial one holds significant value in the design process. Such prediction can help architects quickly generate preliminary designs, improve design efficiency, and reduce the workload associated with repeated modifications. To address this need, we propose FloorplanMAE, a self-supervised learning framework for restoring incomplete floor plans into complete ones. First, we developed a floor plan reconstruction dataset, FloorplanNet, specifically trained on architectural floor plans. Secondly, we propose a floor plan reconstruction method based on Masked Autoencoders (MAE), which reconstructs missing parts by masking sections of the floor plan and training a lightweight Vision Transformer (ViT). We evaluated the reconstruction accuracy of FloorplanMAE and compared it with state-of-the-art benchmarks. Additionally, we validated the model using real sketches from the early stages of architectural design. Experimental results show that the FloorplanMAE model can generate high-quality complete floor plans from incomplete partial plans. This framework provides a scalable solution for floor plan generation, with broad application prospects.

cs.AI↗

arXiv2025

Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance

In the context of the digital development of architecture, the automatic segmentation of walls and windows is a key step in improving the efficiency of building information models and computer-aided design. This study proposes an automatic segmentation model for building facade walls and windows based on multimodal semantic guidance, called Segment Any Architectural Facades (SAAF). First, SAAF has a multimodal semantic collaborative feature extraction mechanism. By combining natural language processing technology, it can fuse the semantic information in text descriptions with image features, enhancing the semantic understanding of building facade components. Second, we developed an end-to-end training framework that enables the model to autonomously learn the mapping relationship from text descriptions to image segmentation, reducing the influence of manual intervention on the segmentation results and improving the automation and robustness of the model. Finally, we conducted extensive experiments on multiple facade datasets. The segmentation results of SAAF outperformed existing methods in the mIoU metric, indicating that the SAAF model can maintain high-precision segmentation ability when faced with diverse datasets. Our model has made certain progress in improving the accuracy and generalization ability of the wall and window segmentation task. It is expected to provide a reference for the development of architectural computer vision technology and also explore new ideas and technical paths for the application of multimodal learning in the architectural field.

cs.CV↗