arXiv subjects

Xin Qiao

Publications and source records attributed to Xin Qiao.

14 recordsLinked to original sources

arXiv2026

SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture

Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moisture time-series analysis either rely on threshold-based rules or data-hungry machine learning or deep learning models that are limited in adaptability and interpretability. In this study, we propose SPADE (Soil moisture Pattern and Anomaly DEtection), which, to the best of our knowledge, is the first LLM-based framework specifically developed for soil moisture time-series analysis. Using GPT-4.1 and domain-informed prompts, SPADE performs zero-shot joint identification of wetting events and anomalies without task-specific annotation, training, or fine-tuning. By converting time-series observations into a textual representation, SPADE identifies wetting-event timing, estimates sensor-level moisture responses, detects and classifies multiple predefined anomaly types, and generates structured, human-readable reports. SPADE was evaluated using real-world soil moisture data collected from commercial and research farms encompassing four crop types across the United States. Compared with the evaluated training-free baselines, SPADE achieved higher anomaly recall and F1-score, strong precision and recall for wetting-event detection, and high accuracy in classifying the observed anomaly types. Its structured reports summarize event timing, anomaly type, concise explanations, and sensor-level moisture responses, supporting practical interpretation of soil moisture patterns. These outputs may support soil moisture review, anomaly screening, and relative comparison of wetting responses rather than direct irrigation prescription.

cs.AI↗

arXiv2026

WeedExpert-R1: Incentivizing Botanical Reasoning in MLLMs with Reinforcement Learning for Precision Weed Grounding

Precision weed control requires species-level identification and instance-level localization. However, conventional object detectors use a closed vocabulary, limiting their deployment across regions, and cannot explain their predictions in complex agricultural scenes. Multimodal large language models (MLLMs) offer visual grounding and reasoning capabilities, but insufficient botanical knowledge can cause hallucinations in fine-grained weed identification. This study introduces WeedExpert-R1, a multimodal model that learns visually grounded botanical reasoning through verifiable rewards. A domain-specific Chain-of-Thought synthesis pipeline combines a human-curated botanical trait dictionary with an Auditor-Synthesizer LLM workflow to generate reasoning data for supervised fine-tuning. Group Relative Policy Optimization is then applied with rewards for format, accuracy, instance count, and response length. Across 37 weed species from six datasets, WeedExpert-R1-4B achieved 75.82 percent exact-set precision at an IoU threshold of 0.5, 89.30 percent precision, and 87.81 percent recall. It outperformed proprietary models, including GPT-5.4 and Gemini-3.1-Pro, and larger open-source models, including Qwen3-VL-30B-Instruct and Gemma-4-31B-it. Results on unseen species further demonstrate its open-vocabulary capability and potential for deployment across diverse regions and crops without retraining.

cs.CV↗

arXiv2026

GSM-GS: Geometry-Constrained Single and Multi-view Gaussian Splatting for Surface Reconstruction

Recently, 3D Gaussian Splatting has emerged as a prominent research direction owing to its ultrarapid training speed and high-fidelity rendering capabilities. However, the unstructured and irregular nature of Gaussian point clouds poses challenges to reconstruction accuracy. This limitation frequently causes high-frequency detail loss in complex surface microstructures when relying solely on routine strategies. To address this limitation, we propose GSM-GS: a synergistic optimization framework integrating single-view adaptive sub-region weighting constraints and multi-view spatial structure refinement. For single-view optimization, we leverage image gradient features to partition scenes into texture-rich and texture-less sub-regions. The reconstruction quality is enhanced through adaptive filtering mechanisms guided by depth discrepancy features. This preserves high-weight regions while implementing a dual-branch constraint strategy tailored to regional texture variations, thereby improving geometric detail characterization. For multi-view optimization, we introduce a geometry-guided cross-view point cloud association method combined with a dynamic weight sampling strategy. This constructs 3D structural normal constraints across adjacent point cloud frames, effectively reinforcing multi-view consistency and reconstruction fidelity. Extensive experiments on public datasets demonstrate that our method achieves both competitive rendering quality and geometric reconstruction. See our interactive project page

cs.CV↗

arXiv2026

FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation

Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In the second stage, imputed features are refined using class-aware propagation, which incorporates pseudo-labels and neighborhood entropy to promote consistency. We evaluated FSD-CAP on multiple datasets. With $99.5\%$ of features missing across five benchmark datasets, FSD-CAP achieves average accuracies of $80.06\%$ (structural) and $81.01\%$ (uniform) in node classification, close to the $81.31\%$ achieved by a standard GCN with full features. For link prediction under the same setting, it reaches AUC scores of $91.65\%$ (structural) and $92.41\%$ (uniform), compared to $95.06\%$ for the fully observed case. Furthermore, FSD-CAP demonstrates superior performance on both large-scale and heterophily datasets when compared to other models.

cs.LG↗

arXiv2025

CometNet: Contextual Motif-guided Long-term Time Series Forecasting

Long-term Time Series Forecasting is crucial across numerous critical domains, yet its accuracy remains fundamentally constrained by the receptive field bottleneck in existing models. Mainstream Transformer- and Multi-layer Perceptron (MLP)-based methods mainly rely on finite look-back windows, limiting their ability to model long-term dependencies and hurting forecasting performance. Naively extending the look-back window proves ineffective, as it not only introduces prohibitive computational complexity, but also drowns vital long-term dependencies in historical noise. To address these challenges, we propose CometNet, a novel Contextual Motif-guided Long-term Time Series Forecasting framework. CometNet first introduces a Contextual Motif Extraction module that identifies recurrent, dominant contextual motifs from complex historical sequences, providing extensive temporal dependencies far exceeding limited look-back windows; Subsequently, a Motif-guided Forecasting module is proposed, which integrates the extracted dominant motifs into forecasting. By dynamically mapping the look-back window to its relevant motifs, CometNet effectively harnesses their contextual information to strengthen long-term forecasting capability. Extensive experimental results on eight real-world datasets have demonstrated that CometNet significantly outperforms current state-of-the-art (SOTA) methods, particularly on extended forecast horizons.

cs.CE↗

arXiv2025

Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond

Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degradations-such as signal attenuation, multi-path interference (MPI), and temporal noise-that significantly compromise depth quality. To alleviate this drawback, we propose Learnable Fractional Reaction-Diffusion Dynamics (LFRD2), a hybrid framework that combines the expressive power of neural networks with the interpretability of physical modeling. Specifically, we implement a time-fractional reaction-diffusion module that enables iterative depth refinement with dynamically generated differential orders, capturing long-term dependencies. In addition, we introduce an efficient continuous convolution operator via coefficient prediction and repeated differentiation to further improve restoration quality. Experiments on four benchmark datasets demonstrate the effectiveness of our approach. The code is publicly available at https://github.com/wudiqx106/LFRD2.

cs.CV↗

arXiv2025

Quantum walk with coherent multiple translations induces fast quantum gate operations

Quantum walks with one-dimensional translational symmetry are important for quantum algorithms, where the speed-up of the diffusion speed can be reached if long-range couplings are added. Our work studies a scheme of a ring under the strong resonant modulation that can support discrete-time quantum walk including coherent multiple long-range translations in a natural way along synthetic frequency dimension. These multiple translation paths are added in a coherent way, which makes the walker evolve under the topological band. Therein, not only the fast diffusion speed is expected, but more importantly, we find that single quantum gate operations can be performed in the quasi-momentum space. In particular, we show the arbitrary single-qubit state preparation and an example of CNOT two-qubit gate with only one time step, dramarically increasing quantum algorithms. Our study uses a single ring to provide fast quantum gate operations based on coherent multiple path quantum walk, which may provide unique designs for efficient quantum operations on photonic chips.

quant-ph↗

arXiv2025

A comprehensive review on developments of synthetic dimensions

The concept of synthetic dimensions has emerged as a powerful framework in photonics and atomic physics, enabling the exploration of high-dimensional physics beyond conventional spatial constraints. Originally developed for quantum simulations in high dimensions, synthetic dimensions have since demonstrated advantages in designing novel Hamiltonians and manipulating quantum or optical states for exploring topological physics, and for applications in computing and information processing. Here we provide a comprehensive overview of progress in synthetic dimensions across photonic, atomic, and other physical platforms over the past decade. We showcase different approaches used to construct synthetic dimensions and highlight key physical phenomena enabled by the advantage of such a framework. By offering a unified perspective on developments in this field, we aim to provide insights into how synthetic dimensions can bridge fundamental physics and applied technologies, fostering interdisciplinary engagement in quantum simulation, atomic and photonic engineering, and information processing.

physics.optics↗

arXiv2024

Construction of various time-dependent Hamiltonians on a single photonic chip

Integrated photonics provides an important platform for simulating physical models with high-performance chip-scale devices, where the lattice size and the time-dependence of a model are key ingredients for further enriching the functionality of a photonic chip. Here, we propose and demonstrate the construction of various time-dependent Hamiltonian models using a single microresonator on thin-film lithium niobate chip. Such an integrated microresonator holds high quality factor to 10^6, and supports the construction of the synthetic frequency lattice with effective lattice sites up to 152 under the electro-optic modulation. By further applying a bichromatic modulation composed of two radio-frequency signals oppositely detuned from the resonant frequency in the microresonator, we build different time-dependent Hamiltonians with the time-varying nearest-neighbor coupling strength in synthetic frequency lattice. We measure the temporal features from capturing the dynamic band structures of the lattice and demonstrate a variety of time-dependent synthetic lattice models by engineering the driven pattern of the modulation, highlighting great flexibility of the microresonator. Our work shows a photonic chip for simulating versatile time-dependent Hamiltonians, which pushes forward quantum simulations in integrated photonics with great experimental tunability and reconfigurability.

physics.optics↗

arXiv2024

RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods

Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applications, including face anti-spoofing, saliency detection, and trajectory prediction. Depending on the distance of the working range, the implementation schemes of the RGB guided ToF imaging systems are different. Specifically, ToF sensors with a uniform field of illumination, which can output dense depth but have low resolution, are typically used for close-range measurements. In contrast, LiDARs, which emit laser pulses and can only capture sparse depth, are usually employed for long-range detection. In the two cases, depth quality improvement for RGB guided ToF imaging corresponds to two sub-tasks: guided depth super-resolution and guided depth completion. In light of the recent significant boost to the field provided by deep learning, this paper comprehensively reviews the works related to RGB guided ToF imaging, including network structures, learning strategies, evaluation metrics, benchmark datasets, and objective functions. Besides, we present quantitative comparisons of state-of-the-art methods on widely used benchmark datasets. Finally, we discuss future trends and the challenges in real applications for further research.

cs.CV↗

arXiv2023

Depth Super-Resolution from Explicit and Implicit High-Frequency Features

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task.

cs.CV↗

arXiv2023

Observation of non-Hermitian antichiral edge currents

Non-Hermitian topological photonics is of great interest in bridging topological matter with gain/dissipation engineering in optics. A key problem in this direction is the interplay between the effective gauge potential and the non-Hermiticity. Here we tackle this problem in a synthetic non-Hermitian Hall ladder and experimentally observe antichiral edge currents (ACECs) of photons, by tuning the locally uniform effective magnetic flux and the on-site gain/loss. Such ACECs provide a topological method to probe the signatures of the non-Hermitian skin effect (NHSE) from steady-state bulk dynamics. The universality of this method is verified by its generalization to three dimensions. This study paves a way to investigate exotic non-Hermitian topological physics and has potential applications in topological photonics engineering.

physics.optics↗

arXiv2022

Rethinking Blur Synthesis for Deep Real-World Image Deblurring

In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network architecture design. Deblurring models trained on existing synthetic datasets perform poorly on real blurry images due to domain shift. To reduce the domain gap between synthetic and real domains, we propose a novel realistic blur synthesis pipeline to simulate the camera imaging process. As a result of our proposed synthesis method, existing deblurring models could be made more robust to handle real-world blur. Furthermore, we develop an effective deblurring model that captures non-local dependencies and local context in the feature domain simultaneously. Specifically, we introduce the multi-path transformer module to UNet architecture for enriched multi-scale features learning. A comprehensive experiment on three real-world datasets shows that the proposed deblurring model performs better than state-of-the-art methods.

cs.CV↗

arXiv2020

Flexible Image Denoising with Multi-layer Conditional Feature Modulation

For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model. However, in this kind of solution, the noise variance (i.e., noise level) is only deployed to modulate the first layer of convolution feature with channel-wise shifting, which is limited in balancing noise removal and detail preservation. In this paper, we present a novel flexible image enoising network (CFMNet) by equipping an U-Net backbone with multi-layer conditional feature modulation (CFM) modules. In comparison to channel-wise shifting only in the first layer, CFMNet can make better use of noise level information by deploying multiple layers of CFM. Moreover, each CFM module takes onvolutional features from both noisy image and noise level map as input for better trade-off between noise removal and detail preservation. Experimental results show that our CFMNet is effective in exploiting noise level information for flexible non-blind denoising, and performs favorably against the existing deep image denoising methods in terms of both quantitative metrics and visual quality.

eess.IV↗