arXiv subjects

Xiao Li

Publications and source records attributed to Xiao Li.

At least 217 records · Page 12Linked to original sources

arXiv2025

The development of vibration modes propagation method to perform wave-optics simulation of beamline vibration

The evolution from 3rd to 4th generation synchrotron radiation (SR) sources provide promising potential improvements in X-ray techniques, particularly in spatial resolution for imaging, temporal resolution for dynamic studies, and beam size control for nanoprobes. Achieving these enhancements demands effective vibration suppression in beamline systems. This challenge drives the need for optical designs that ensure efficient photon transport while maintaining vibration within acceptable thresholds. To address the advanced coherence requirements of fourth-generation SR sources, wave-optics simulations must be incorporated into optical design processes. We therefore propose a vibration mode propagation method using wave-optics techniques for beamline vibration simulation. Our approach achieves an almost 40-fold computational acceleration in actual beamline models compared to conventional methods, enabling direct analysis of propagating wavefront vibrations. This framework allows systematic evaluation of intensity distribution variations, coherence changes, and beam positioning errors caused by mechanical vibrations.

physics.optics↗

arXiv2025

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos

Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples, a process that is both time-consuming and prone to errors. Consequently, 3D reconstruction training has been confined to either synthetic 3D datasets or small-scale datasets with annotated poses. In this study, we investigate the feasibility of 3D reconstruction using unposed video data of various objects. We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose. UVRM uses a transformer network to implicitly aggregate video frames into a pose-invariant latent feature space, which is then decoded into a tri-plane 3D representation. To obviate the need for ground-truth pose annotations during training, UVRM employs a combination of the score distillation sampling (SDS) method and an analysis-by-synthesis approach, progressively synthesizing pseudo novel-views using a pre-trained diffusion model. We qualitatively and quantitatively evaluate UVRM's performance on the G-Objaverse and CO3D datasets without relying on pose information. Extensive experiments show that UVRM is capable of effectively and efficiently reconstructing a wide range of 3D objects from unposed videos.

cs.CV↗

arXiv2025

Coexistence of distinct mobility edges in a 1D quasiperiodic mosaic model

We introduce a one-dimensional quasiperiodic mosaic model with analytically solvable mobility edges that exhibit different phase transitions depending on the system parameters. Specifically, by combining mosaic quasiperiodic next-nearest-neighbor hoppings and quasiperiodic on-site potentials, we rigorously demonstrate the existence of two distinct types of mobility edges: those separating extended and critical states, and those separating extended and localized states. Using Avila's global theory, we derive exact analytical expressions for these mobility edges and determine the parameter regimes where each type dominates. Our numerical calculations confirm these analytical results through fractal dimension analysis. Furthermore, we propose an experimentally feasible scheme to realize this model using Bose-Einstein condensates in optical lattices with engineered momentum-state transitions. We also investigate the effects of many-body interactions under mean-field approximation. Our work provides a fertile ground for studying the coexistence of different types of mobility edges in quasiperiodic systems and suggests a feasible experimental platform to observe and control these transitions.

cond-mat.dis-nn↗

arXiv2025

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model's order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with $0<α\leq 1$, exhibits superior positional accuracy and robustness against additive noises for TVQP applications. Extensive empirical evaluations, including simulations with two types of robotic manipulators and experiments with a Flexiv Rizon robot, have validated the SPTC-AN-FOZNN's effectiveness in precise tracking and computational efficiency, establishing its utility for robust kinematic control.

eess.SY↗

arXiv2025

HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks

Understanding and reasoning over diagrams is a fundamental aspect of human intelligence. While Large Multimodal Models (LMMs) have demonstrated impressive capabilities across various tasks, existing benchmarks lack comprehensive evaluation of their diagram interpretation and reasoning abilities, particularly in coding contexts. We present HumanEval-V, a rigorous benchmark of human-annotated coding tasks that spans six task types and evaluates diverse visual reasoning capabilities. Each task features carefully crafted diagrams paired with function signatures and test cases, employing novel code generation tasks to thoroughly assess models' diagram comprehension. Through extensive experiments with 22 LMMs, we find that even top-performing models achieve modest success rates, with Claude 3.5 Sonnet reaching only 36.8% pass@1, highlighting substantial room for improvement. Our analysis reveals that current LMMs struggle with spatial transformations, topological relationships, and dynamic patterns that humans find intuitive. These findings provide valuable insights for advancing LMMs' visual reasoning abilities. We have open-sourced our code and benchmark at https://github.com/HumanEval-V/HumanEval-V-Benchmark.

cs.CV↗

arXiv2025

Learning the Frequency Dynamics of the Power System Using Higher-order Dynamic Mode Decomposition

The increasing penetration of renewable energy sources, characterised by low inertia and intermittent disturbances, presents substantial challenges to power system stability. As critical indicators of system stability, frequency dynamics and associated oscillatory phenomena have attracted significant research attention. While existing studies predominantly employ linearized models, our findings demonstrate that linear approximations exhibit considerable errors when predicting frequency oscillation dynamics across multiple time scales, thus necessitating the incorporation of nonlinear characteristics. This paper proposes a data-driven approach based on higher-order dynamical mode decomposition (HODMD) for learning frequency dynamics. The proposed method offers distinct advantages over alternative nonlinear methods, including no prior knowledge required, adaptability to high-dimensional systems, and robust performance. Furthermore, HODMD demonstrates superior capability in capturing system-wide spatio-temporal modes, successfully identifying modal behaviour that remains undetectable through standard Dynamic Mode Decomposition techniques. The efficacy of the proposed methodology is validated through comprehensive case studies on both IEEE 14-bus and WECC systems.

eess.SY↗

arXiv2025

Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression

This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based on recent conditional diffusion (CD) generative models, and it consists of a conditional diffusion model, tensor correction, and error guarantee. Our diffusion model is a mixture of 3D conditioning and 2D denoising U-Net. The approach leverages a 3D block-based compressing module to address spatiotemporal correlations in structured scientific data. Then, the reverse diffusion process for 2D spatial data is conditioned on the ``slices'' of content latent variables produced by the compressing module. After training, the denoising decoder reconstructs the data with zero noise and content latent variables, and thus it is entirely deterministic. The reconstructed outputs of the CD model are further post-processed by our tensor correction and error guarantee steps to control and ensure a maximum error distortion, which is an inevitable requirement in lossy scientific data compression. Our experiments involving two datasets generated by climate and chemical combustion simulations show that our framework outperforms standard convolutional autoencoders and yields competitive compression quality with an existing scientific data compression algorithm.

cs.LG↗

arXiv2025

Repeating fast radio bursts from synchrotron maser radiation in localized plasma blobs: Application to FRB 20121102A

The radiation physics of repeating fast radio bursts (FRBs) remains enigmatic. Motivated by the observed narrow-banded emission spectrum and ambiguous fringe pattern of the spectral peak frequency ($ν_{\rm pk}$) distribution of some repeating FRBs, such as FRB 20121102A, we propose that the bursts from repeating FRBs arise from synchrotron maser radiation in localized blobs within weakly magnetized plasma that relativistically moves toward observers. Assuming the plasma moves toward the observers with a bulk Lorentz factor of $Γ=100$ and the electron distribution in an individual blob is monoenergetic ($γ_{\rm e}\sim300$), our analysis shows that bright and narrow-banded radio bursts with peak flux density $\sim$ 1 ${\rm Jy}$ at peak frequency ($ν_{\rm pk}$) $\sim 3.85$ GHz can be produced by the synchrotron maser emission if the plasma blob has a magnetization factor of $σ\sim10^{-5}$ and a frequency of $ν_{\rm P}\sim 4.5$ MHz. The spectrum of bursts with lower $ν_{\rm pk}$ tends to be narrower. Applying our model to the bursts of FRB 20121102A, the distributions of both the observed $ν_{\rm pk}$ and isotropic energy $E_{\rm iso}$ detected by the Arecibo telescope at the L band and the Green Bank Telescope at the C band are successfully reproduced. We find that the $ν_{\rm P}$ distribution exhibits several peaks, similar to those observed in the $ν_{\rm pk}$ distribution of FRB 20121102A. This implies that the synchrotron maser emission in FRB 20121102A is triggered in different plasma blobs with varying $ν_{\rm P}$, likely due to the inhomogeneity of relativistic electron number density.

astro-ph.HE↗

arXiv2025

Fractional quantum anomalous Hall effect in rhombohedral multilayer graphene with a strong displacement field

We investigate the fractional quantum anomalous Hall (FQAH) effect in rhombohedral multilayer graphene (RnG) in the presence of a strong applied displacement field. We first introduce the interacting model of RnG, which includes the noninteracting continuum model and the many-body Coulomb interaction. We then discuss the integer quantum anomalous Hall (IQAH) effect in RnG and the role of the Hartree-Fock approach in understanding its appearance. Next, we explore the FQAH effect in RnG for $n=3$--$6$ using a combination of constrained Hartree-Fock and exact diagonalization methods. We characterize the stability of the FQAH phase by the size of the FQAH gap and find that RnG generally has a stable FQAH phase, although the required displacement field varies significantly among different $n$ values. Our work establishes the theoretical universality of both IQAH and FQAH in RnG.

cond-mat.str-el↗

arXiv2025

On the Importance of Backbone to the Adversarial Robustness of Object Detectors

Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and security. Through experiments, first, we found that existing works on improving the adversarial robustness of object detectors give a false sense of security. Second, we found that adversarially pre-trained backbone networks were essential for enhancing the adversarial robustness of object detectors. We then proposed a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones. Without any modifications to the structure of object detectors, our recipe achieved significantly better adversarial robustness than previous works. Finally, we explored the potential of different modern object detector designs for improving adversarial robustness with our recipe and demonstrated interesting findings, which inspired us to design state-of-the-art (SOTA) robust detectors. Our empirical results set a new milestone for adversarially robust object detection. Code and trained checkpoints are available at https://github.com/thu-ml/oddefense.

cs.CV↗

arXiv2025

SparseFocus: Learning-based One-shot Autofocus for Microscopy with Sparse Content

Autofocus is necessary for high-throughput and real-time scanning in microscopic imaging. Traditional methods rely on complex hardware or iterative hill-climbing algorithms. Recent learning-based approaches have demonstrated remarkable efficacy in a one-shot setting, avoiding hardware modifications or iterative mechanical lens adjustments. However, in this paper, we highlight a significant challenge that the richness of image content can significantly affect autofocus performance. When the image content is sparse, previous autofocus methods, whether traditional climbing-hill or learning-based, tend to fail. To tackle this, we propose a content-importance-based solution, named SparseFocus, featuring a novel two-stage pipeline. The first stage measures the importance of regions within the image, while the second stage calculates the defocus distance from selected important regions. To validate our approach and benefit the research community, we collect a large-scale dataset comprising millions of labelled defocused images, encompassing both dense, sparse and extremely sparse scenarios. Experimental results show that SparseFocus surpasses existing methods, effectively handling all levels of content sparsity. Moreover, we integrate SparseFocus into our Whole Slide Imaging (WSI) system that performs well in real-world applications. The code and dataset will be made available upon the publication of this paper.

cs.CV↗

arXiv2025

Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems.

cs.IT↗

arXiv2025

Forecasting the Volatility of Energy Transition Metals

The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will significantly increase demand for metals critical to renewable energy technologies. Energy Transition Metals (ETMs), including copper, lithium, nickel, cobalt, and rare earth elements, are indispensable for renewable energy generation and the electrification of global economies. However, their markets are characterized by high price volatility due to supply concentration, low substitutability, and limited price elasticity. This paper provides a comprehensive analysis of the price volatility of ETMs, a subset of Critical Raw Materials (CRMs). Using a combination of exploratory data analysis, data reduction, and visualization methods, we identify key features for accurate point and density forecasts. We evaluate various volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models, to determine their forecasting performance. Our findings reveal significant heterogeneity in ETM volatility patterns, which challenge standard groupings by data providers and geological classifications. The results contribute to the literature on CRM economics and commodity volatility, offering novel insights into the complex dynamics of ETM markets and the modeling of their returns and volatilities.

econ.GN↗

arXiv2025

Interaction-enhanced many-body localization in a 1D quasiperiodic model with long-range hopping

We study the many-body localization (MBL) transition in an 1D exactly solvable system with long-range hopping and quasiperiodic on-site potential introduced in Phys. Rev. Lett. 131, 186303 (2023). Unlike other disorder or quasiperiodic model, an interaction-enhanced MBL happens in the moderate interaction regime, which is dubbed as the interaction-enhanced MBL. This counterintuitive phenomenon can be understood by noticing the fragility of the critical band lying at the bottom of the spectrum. The fragile band is localized by other localized states once the interaction is turned on. This mechanism can be verified by introducing a mean-field theory description which can derive highly excited states with high accuracy. The effectiveness of this mean-field theory is captured by the quasihole physics, validated by the particle entanglement spectra.

cond-mat.dis-nn↗

arXiv2025

The Restricted Inverse Optimal Value Problem under Weighted Bottle-neck Hamming distance on trees

We consider the Restricted Inverse Optimal Value Problem (RIOVSP) on trees under weighted bottleneck Hamming distance, denoted as (RIOVSPT$_{BH}$). The problem aims to minimize the total cost under weighted bottle-neck Hamming distance such that the length of the shortest root-leaf path of the tree is lower-bounded by a given value by adjusting the length of some edges. Additionally, the specified lower bound must correspond to the length of a particular root-leaf path. Through careful analysis of the problem's structural properties, we develop an algorithm with $O(n\log n)$ time complexity to solve (RIOVSPT$_{BH}$). Furthermore, by removing the path-length constraint, we derive the Minimum Cost Shortest Path Interdiction Problem on Trees (MCSPIT), for which we present an $O(n\log n)$ time algorithm that operates under weighted bottleneck Hamming distance. Extensive computational experiments demonstrate the efficiency and effectiveness of both algorithms.

cs.DS↗

arXiv2024

Nonlinear valley Hall effect in a bilayer transition metal dichalcogenide

Valley-contrasting Hall transport conventionally relies on the inversion symmetry breaking in two-dimensional systems, which greatly limits the selection range of valley materials. In particular, while monolayer transition metal dichalcogenides have been widely utilized as a well-known class of valley materials in valleytronics, the centrosymmetric nature hinders the realization of valley-contrasting properties in the bilayer counterparts. Here, taking MoS$_{2}$ as an example, we discover valley-contrasting transport in bilayer transition metal dichalcogenides by exploring nonlinear transport regime. Using effective models and first-principles calculations, our work demonstrates that nonvanishing nonlinear valley Hall conductivities emerge in a uniaxially strained MoS$_{2}$ bilayer, owing to strain-induced band tilts of Dirac fermions. With the aid of small spin-orbit-coupling induced band splittings, the conduction bands generate much remarkable nonlinear valley Hall conductivity. Moreover, the nonlinear conductivities are highly tunable through modulating the strength and the direction of the strain, chemical potential, and interlayer gap. Our findings not only expands material choices for valleytronic applications, but also provides opportunities for designing advanced electronic devices that leverage nonlinear valley transports.

cond-mat.mes-hall↗

arXiv2024

Foundation Model for Lossy Compression of Spatiotemporal Scientific Data

We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module. The VAE framework uses hyper-priors to model latent space dependencies, enhancing compression efficiency. The SR module refines low-resolution representations into high-resolution outputs, improving reconstruction quality. By alternating between 2D and 3D convolutions, the model efficiently captures spatiotemporal correlations in scientific data while maintaining low computational cost. Experimental results demonstrate that the FM generalizes well to unseen domains and varying data shapes, achieving up to 4 times higher compression ratios than state-of-the-art methods after domain-specific fine-tuning. The SR module improves compression ratio by 30 percent compared to simple upsampling techniques. This approach significantly reduces storage and transmission costs for large-scale scientific simulations while preserving data integrity and fidelity.

cs.LG↗

arXiv2024

Double interdiction problem on trees on the sum of root-leaf distances by upgrading edges

The double interdiction problem on trees (DIT) for the sum of root-leaf distances (SRD) has significant implications in diverse areas such as transportation networks, military strategies, and counter-terrorism efforts. It aims to maximize the SRD by upgrading edge weights subject to two constraints. One gives an upper bound for the cost of upgrades under certain norm and the other specifies a lower bound for the shortest root-leaf distance (StRD). We utilize both weighted $l_\infty$ norm and Hamming distance to measure the upgrade cost and denote the corresponding (DIT) problem by (DIT$_{H\infty}$) and its minimum cost problem by (MCDIT$_{H\infty}$). We establish the $\mathcal{NP}$-hardness of problem (DIT$_{H\infty}$) by building a reduction from the 0-1 knapsack problem. We solve the problem (DIT$_{H\infty}$) by two scenarios based on the number $N$ of upgrade edges. When $N=1$, a greedy algorithm with $O(n)$ complexity is proposed. For the general case, an exact dynamic programming algorithm within a pseudo-polynomial time is proposed, which is established on a structure of left subtrees by maximizing a convex combination of the StRD and SRD. Furthermore, we confirm the $\mathcal{NP}$-hardness of problem (MCDIT$_{H\infty}$) by reducing from the 0-1 knapsack problem. To tackle problem (MCDIT$_{H\infty}$), a binary search algorithm with pseudo-polynomial time complexity is outlined, which iteratively solves problem (DIT$_{H\infty}$). We culminate our study with numerical experiments, showcasing effectiveness of the algorithm.

math.OC↗