arXiv subjects

Xian Zhou

Publications and source records attributed to Xian Zhou.

14 recordsLinked to original sources

arXiv2026

Conjugate phase-noise cancellation enables submicrometre dual-comb ranging with free-running megahertz-linewidth lasers

Frequency-domain dual-comb ranging combines rapid acquisition with interferometric sensitivity, but high-performance implementations often rely on mutually coherent or actively stabilised comb sources. Free-running sources can reduce this hardware burden, but their phase noise and drift of the optical frequency offset can blur radio-frequency (RF) comb teeth and weaken probe-reference phase correlation. Previous phase-slope implementations have therefore relied on sufficiently resolved RF teeth, within-coherence-length probe-reference paths or explicit digital tracking of these fluctuations. Here we demonstrate a low-cost frequency-domain dual-comb ranging architecture that combines independent free-running distributed-feedback (DFB) lasers with conjugate phase-noise cancellation (CPNC). By forming a self-conjugate signal before the phases of individual RF-comb teeth are extracted, CPNC cancels the common laser phase factor and drifting optical-frequency-offset term while retaining the distance-dependent phase slope. Using electro-optic combs seeded by DFB lasers with linewidths of 12 MHz and 9 MHz, we achieve an Allan deviation of $219~\mathrm{nm}$ at $246~\mu\mathrm{s}$ and reduce the single-frame distance standard deviation from $558~\mu\mathrm{m}$ to $9.31~\mu\mathrm{m}$ with CPNC. Across the tested megahertz-linewidth configurations, CPNC delivered minimum Allan deviations below $250~\mathrm{nm}$. These results show that CPNC enables submicrometre ranging in a low-cost frequency-domain dual-comb architecture with reduced source-stabilisation and phase-management complexity.

physics.optics↗

arXiv2026

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval

Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.

cs.AI↗

arXiv2026

Accelerated Time-Domain Simulation of Complex Photonic Structures with a Data-Aware Fourier Neural Operator

Efficient and accurate time-domain simulation of electromagnetic fields in complex photonic devices is critical for designing broadband and ultrafast optical components, yet it is often limited by the high computational cost of conventional numerical methods like FDTD. While machine learning approaches show promise in accelerating these simulations, existing models still struggle to simultaneously capture the dynamic field evolution and generalize to complex geometries. In this paper, we introduce a Data-Aware Fourier Neural Operator (DA-FNO) as an innovative neural operator for solving electromagnetic simulations. Applied autoregressively, the model iteratively predicts the time-domain evolution of all field components and automatically terminates upon energy convergence. Our model not only generalizes to complex and randomized geometries but also shows good predictive consistency across the optical C-band (1530-1565nm) when evaluated on the test set. In a representative configuration, it achieves an 11* speedup over conventional methods while maintaining about 95% accuracy across the C-band. This approach provides a new pathway for C-band photonic simulations, potentially facilitating the research, development, and inverse design of novel photonic devices.

physics.optics↗

arXiv2025

Predictive biomarker graphical approach (PRIME) for Precision medicine

Precision medicine is an evolving area in the medical field and rely on biomarkers to make patient enrichment decisions, thereby providing drug development direction. A traditional statistical approach is to find the cut-off that leads to the minimum p-value of the interaction between the biomarker dichotomized at that cut-off and treatment. Such an approach does not incorporate clinical significance and the biomarker is not evaluated on a continuous scale. We are proposing to evaluate the biomarker in a continuous manner from a predicted risk standpoint, based on the model that includes the interaction between the biomarker and treatment. The predicted risk can be graphically displayed to explain the relationship between the outcome and biomarker, whereby suggesting a cut-off for biomarker positive/negative groups. We adapt the TreatmentSelection approach and extend it to account for covariates via G-computation. Other features include biomarker comparisons using net gain summary measures and calibration to assess the model fit. The PRIME (Predictive biomarker graphical approach) approach is flexible in the type of outcome and covariates considered. A R package is available and examples will be demonstrated.

stat.ME↗

arXiv2025

SalM$^{2}$: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention

Driver attention recognition in driving scenarios is a popular direction in traffic scene perception technology. It aims to understand human driver attention to focus on specific targets/objects in the driving scene. However, traffic scenes contain not only a large amount of visual information but also semantic information related to driving tasks. Existing methods lack attention to the actual semantic information present in driving scenes. Additionally, the traffic scene is a complex and dynamic process that requires constant attention to objects related to the current driving task. Existing models, influenced by their foundational frameworks, tend to have large parameter counts and complex structures. Therefore, this paper proposes a real-time saliency Mamba network based on the latest Mamba framework. As shown in Figure 1, our model uses very few parameters (0.08M, only 0.09~11.16% of other models), while maintaining SOTA performance or achieving over 98% of the SOTA model's performance.

cs.CV↗

arXiv2024

One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs

Learning to manipulate dynamic and deformable objects from a single demonstration video holds great promise in terms of scalability. Previous approaches have predominantly focused on either replaying object relationships or actor trajectories. The former often struggles to generalize across diverse tasks, while the latter suffers from data inefficiency. Moreover, both methodologies encounter challenges in capturing invisible physical attributes, such as forces. In this paper, we propose to interpret video demonstrations through Parameterized Symbolic Abstraction Graphs (PSAG), where nodes represent objects and edges denote relationships between objects. We further ground geometric constraints through simulation to estimate non-geometric, visually imperceptible attributes. The augmented PSAG is then applied in real robot experiments. Our approach has been validated across a range of tasks, such as Cutting Avocado, Cutting Vegetable, Pouring Liquid, Rolling Dough, and Slicing Pizza. We demonstrate successful generalization to novel objects with distinct visual and physical properties.

cs.RO↗

arXiv2023

HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding

The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model's representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.

cs.CV↗

arXiv2023

Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification. However, general deep learning methods for CNNs ignore the influence of complex environmental factor which enlarges the intra-class variance and decreases the inter-class variance. This multiplies the difficulty to extract discriminative features. To overcome this problem, this work develops a novel deep intrinsic decomposition with adversarial learning, namely AdverDecom, for hyperspectral image classification to mitigate the negative impact of environmental factors on classification performance. First, we develop a generative network for hyperspectral image (HyperNet) to extract the environmental-related feature and category-related feature from the image. Then, a discriminative network is constructed to distinguish different environmental categories. Finally, a environmental and category joint learning loss is developed for adversarial learning to make the deep model learn discriminative features. Experiments are conducted over three commonly used real-world datasets and the comparison results show the superiority of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu-sw/Adversarial Learning Intrinsic Decomposition for the sake of reproducibility.

cs.CV↗

arXiv2023

MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification

Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general Transformer mainly considers the global spectral information while ignores the multiscale spatial information of the hyperspectral image. In this paper, we propose a multiscale spectral-spatial convolutional Transformer (MultiscaleFormer) for hyperspectral image classification. First, the developed method utilizes multiscale spatial patches as tokens to formulate the spatial Transformer and generates multiscale spatial representation of each band in each pixel. Second, the spatial representation of all the bands in a given pixel are utilized as tokens to formulate the spectral Transformer and generate the multiscale spectral-spatial representation of each pixel. Besides, a modified spectral-spatial CAF module is constructed in the MultiFormer to fuse cross-layer spectral and spatial information. Therefore, the proposed MultiFormer can capture the multiscale spectral-spatial information and provide better performance than most of other architectures for hyperspectral image classification. Experiments are conducted over commonly used real-world datasets and the comparison results show the superiority of the proposed method.

cs.CV↗

arXiv2020

Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping

Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction. One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint? Humans excel at this task. Our ability to imagine and fill in missing information is tightly coupled with perception: we feel as if we see the world in 3 dimensions, while in fact, information from only the front surface of the world hits our retinas. This paper explores the role of view prediction in the development of 3D visual recognition. We propose neural 3D mapping networks, which take as input 2.5D (color and depth) video streams captured by a moving camera, and lift them to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera. The model also projects its 3D feature maps to novel viewpoints, to predict and match against target views. We propose contrastive prediction losses to replace the standard color regression loss, and show that this leads to better performance on complex photorealistic data. We show that the proposed model learns visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating the motion of the inferred 3D feature maps in videos of dynamic scenes. To the best of our knowledge, this is the first work that empirically shows view prediction to be a scalable self-supervised task beneficial to 3D object detection.

cs.CV↗

arXiv2019

Distributed vibration sensing based on forward transmission and coherent detection

A novel ultra-long distributed vibration sensing (DVS) system using forward transmission and coherent detection is proposed and experimentally demonstrated. In the proposed scheme, a pair of multi-span optical fibers are deployed for sensing, and a loop-back configuration is used by connecting the two fibers at the far end. The homodyne coherent detection is used to retrieve the phase and state-of-polarization (SOP) fluctuations caused by a vibration while the localization of the vibration is realized by tracking the phase changes along the two fibers. The proposed scheme has the advantage of high signal-to-noise ratio (SNR) and ultra-long sensing range due to the nature of forward transmission and coherent detection. In addition, using forward rather than backward scattering allows detection of high frequency vibration signal over a long sensing range. More than 50dB sensing SNR can be obtained after long-haul transmission. Meanwhile, localization of 400 Hz, 1 kHz and 10 kHz vibrations has been experimentally demonstrated with a spatial resolution of less than 50 m over a total of 1008 km sensing fiber. The sensing length can be further extended to even trans-oceanic distances using more fiber spans and erbium-doped fiber amplifiers (EDFAs), making it a promising candidate for proactive fault detection and localization in long-haul and ultra-long-haul fiber links.

eess.SP↗

arXiv2019

Revisiting Flow Information for Traffic Prediction

Traffic prediction is a fundamental task in many real applications, which aims to predict the future traffic volume in any region of a city. In essence, traffic volume in a region is the aggregation of traffic flows from/to the region. However, existing traffic prediction methods focus on modeling complex spatiotemporal traffic correlations and seldomly study the influence of the original traffic flows among regions. In this paper, we revisit the traffic flow information and exploit the direct flow correlations among regions towards more accurate traffic prediction. We introduce a novel flow-aware graph convolution to model dynamic flow correlations among regions. We further introduce an integrated Gated Recurrent Unit network to incorporate flow correlations with spatiotemporal modeling. The experimental results on real-world traffic datasets validate the effectiveness of the proposed method, especially on the traffic conditions with a great change on flows.

eess.SP↗

arXiv2018

On Hodges' Superefficiency and Merits of Oracle Property in Model Selection

The oracle property of model selection procedures has attracted a large volume of favorable publications in the literature, but also faced criticisms of being ineffective and misleading in applications. In this paper, we introduce a class of estimators that can easily produce model selection procedures possessing the oracle property and discuss the merits of the oracle property by analyzing the performance of such estimators in finite sample size theoretically. Specifically, we propose a new type of Hodges' estimators capable of reducing the asymptotic variance of any given estimator over a multi-dimensional subspace of the parameter space, which can easily produce model selection procedures with the oracle and some other desired properties. This new type of oracle estimators, however, perform poorly at some values of the parameters for estimation, and there is no convincing reason to declare that oracle estimators are better than the traditional estimators such as the MLE and LSE. Consequently, the merits of the oracle property for model selection as claimed in the literature are probably grossly overstated, and the criticisms of the oracle property are justifiable.

math.ST↗

arXiv2017

Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled with robot joint limits, dramatically changes the connectivity of the configuration space. We propose a regrasping move, termed "IK-switch", which allows efficiently bridging components of the configuration space that are otherwise mutually disconnected. This move, combined with several other developments, such as a method to stabilize the manipulated object using the environment, a new tree structure, and a compliant control scheme, enables us to address complex closed-chain manipulation tasks, such as flipping a chair frame, which is otherwise impossible to realize using existing multi-arm planning methods.

cs.RO↗