arXiv subjects

You Zhou

Publications and source records attributed to You Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2026

Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Cameras

Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras--ultra-high-speed, high-dynamic-range vision sensors--in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network's performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

cs.CV↗

arXiv2025

Nearly query-optimal classical shadow estimation of unitary channels

Classical shadow estimation (CSE) is a powerful tool for learning the properties of quantum states and quantum processes. Here we consider the CSE task for quantum unitary channels. By querying an unknown unitary channel $\mathcal{U}$ multiple times in quantum experiments, the goal is to learn a classical description from which one can accurately predict many different linear properties of the channel, i.e., the expectation values of arbitrary observables measured on the output of $\mathcal{U}$ upon arbitrary input states. Based on collective measurements on multiple systems, we propose a query efficient protocol for this task, whose query complexity has a quadratic advantage over the previous best approach for this problem, and almost saturates the information-theoretic lower bound. To further enhance practicality, we also present a variant protocol using only single-copy measurements, which still offers much better query performance than previous protocols that do not use quantum memory, and can serve as a key subroutine for learning an arbitrary unknown Hamiltonian from dynamics. In addition to linear properties of unitary channels, our protocol can also be applied to simultaneously predict many non-linear properties, such as out-of-time-ordered correlators.

quant-ph↗

arXiv2025

Shaping the Mantle: The Role of Superheated Core After Giant Impacts

The Moon-forming giant impact significantly influenced the initial thermal state of Earth's mantle by generating a global magma ocean, marking the onset of mantle evolution. Recent Smoothed Particle Hydrodynamics (SPH) simulations indicate that such a collision would produce a superheated core, whose cooling would strongly influence subsequent mantle dynamics. Here, we present systematic SPH simulations of diverse giant-impact scenarios and show that the superheated core formed after the impact can trigger secondary mantle melting, thereby governing the final state of the magma ocean. To further quantify this effect, we employ a parameterized mantle-melting model to evaluate the influence of secondary melting on the lower mantle. Our results suggest three possible outcomes: complete mantle melting, the formation of a basal melt layer, or the initiation of an early superplume. Combined with recent two-phase magma-ocean solidification models, we infer that all three scenarios would result in basal melt layers of varying thickness, partially retaining the thermal energy of the superheated core. In the canonical Moon-forming scenario, the superheated core would rapidly transfer heat to Earth's lower mantle, causing secondary mantle melting within approximately 277-5983 years and generating either a basal melt layer or a fully molten mantle. Both outcomes would effectively erase primordial heterogeneities in the lower mantle and impose distinct pathways for its subsequent thermal evolution.

astro-ph.EP↗

arXiv2025

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios

This paper proposes a momentum-constrained hybrid heuristic trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios, integrating trajectory sampling generation, optimization and evaluation with residual-enhanced deep reinforcement learning (DRL). In the first stage, heuristic trajectory sampling cluster (HTSC) is generated in the Frenet coordinate system using third-order interpolation with fifth-order polynomials and momentum-constrained trajectory optimization (MTO) constraints to ensure smoothness and feasibility. After first stage cost evaluation, the second stage leverages a residual-enhanced actor-critic network with LSTM-based temporal feature modeling to adaptively refine trajectory selection in the Cartesian coordinate system. A dual-stage cost modeling mechanism (DCMM) with weight transfer aligns semantic priorities across stages, supporting human-centered optimization. Experimental results demonstrate that the proposed LSTM-ResB-PPO achieves significantly faster convergence, attaining stable policy performance in approximately half the training iterations required by the PPO baseline, while simultaneously enhancing both reward outcomes and training stability. Compared to baseline method, the selected model reduces average cost and cost variance by 30.3% and 53.3%, and lowers ego and obstacle risks by over 77%. These findings validate the framework's effectiveness in enhancing robustness, safety, and real-time feasibility in complex assistive planning tasks.

cs.RO↗

arXiv2025

Characterizing the Neutron Skin of $^{48}$Ca Through Collective Flow at the CERN Large Hadron Collider

The recently developed ``imaging-by-smashing" technique has emerged as a powerful approach to connect final-state collective flow phenomena in ultra-relativistic nuclear collisions with the intrinsic structure of the colliding nuclei. While most efforts have focused on constraining nuclear shape properties such as deformation and triaxiality, less attention has been given to the neutron skin, primarily in heavy nuclei such as $^{208}$Pb. In this work, a novel study of the neutron-skin thickness in $^{48}$Ca is presented, based on comparative analyses of $^{48}$Ca+$^{48}$Ca and $^{40}$Ca+$^{40}$Ca collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV. Simulations within the AMPT framework are employed to investigate the impact of varying neutron-skin thicknesses $Δr_{np}$ for $^{48}$Ca on collective flow observables, including anisotropic flow coefficients and mean transverse momentum ($[p_{\rm T}]$) fluctuations. The triangular flow $v_3$, quadrangular flow $v_4$, as well as the variance and skewness of $[p_{\rm T}]$ fluctuations, display notable sensitivity to $Δr_{np}$. These findings indicate that calcium-isotope runs at the LHC could provide an independent and complementary approach to constraining $Δr_{np}$, with the potential to help resolve the current tension between PREX and CREX measurements.

nucl-th↗

arXiv2025

Shaping the Mantle: The Role of the Superheated Core After Giant Impacts

The Moon-forming giant impact significantly influenced the initial thermal state of Earth's mantle by generating a global magma ocean, marking the onset of mantle evolution. Recent Smoothed Particle Hydrodynamics (SPH) simulations indicate that such a collision would produce a superheated core, whose cooling would strongly influence subsequent mantle dynamics. Here, we present systematic SPH simulations of diverse giant-impact scenarios and show that the superheated core formed after the impact can trigger secondary mantle melting, thereby governing the final state of the magma ocean. To further quantify this effect, we employ a parameterized mantle-melting model to evaluate the influence of secondary melting on the lower mantle. Our results suggest three possible outcomes: complete mantle melting, the formation of a basal melt layer, or the initiation of an early superplume. Combined with recent two-phase magma-ocean solidification models, we infer that all three scenarios would result in basal melt layers of varying thickness, partially retaining the thermal energy of the superheated core. In the canonical Moon-forming scenario, the superheated core would rapidly transfer heat to Earth's lower mantle, causing secondary mantle melting within approximately 277--5983 years and generating either a basal melt layer or a fully molten mantle. Both outcomes would effectively erase primordial heterogeneities in the lower mantle and impose distinct pathways for its subsequent thermal evolution.

astro-ph.EP↗

arXiv2025

Near-Field Topology-Optimized Superchiral Metasurfaces for Enhanced Chiral Sensing

The detection and discrimination of molecular chirality are essential for advancing pharmaceutical and biological applications. While nanophotonic platforms offer a route to enhance chiral light-matter interactions, existing device concepts for chiral sensing remain heuristic, resulting in limited chiral enhancement and control over chiral hotspot placement within nanostructures. Here, we introduce an inverse-design framework that directly optimizes superchiral near fields in photonic nanostructures and demonstrate its powerful opportunities for enantioselective analysis. We first show that freeform achiral metasurfaces can be optimized to achieve an 800-fold chiral density enhancement, with fully customizable chiral hotspot placement for direct molecular interaction. We further demonstrate ultrasensitive detection of chiral analytes and achieve a 116-fold increase in detection sensitivity over the native enantiomeric response. Lastly, we extend our platform to determine chiral-molecule concentration and resolve enantiomeric excess in chiral mixtures. Our framework offers a generic route to enabling nanophotonic platforms for detecting chiral compounds and can be integrated with a broad range of spin-based photonic materials for applications in valleytronics, chiral emission control, and topological photonics.

physics.optics↗

arXiv2025

Invested and Potential Magic Resources in Measurement-Based Quantum Computation

Magic states and magic gates are crucial for achieving universal quantum computation, but important questions about how magic resources should be implemented to attain maximal quantum advantage have remained unexplored, especially in the context of measurement-based quantum computation (MQC). This work bridges the gap between MQC and the resource theory of magic by introducing the key concepts of "invested" and "potential" magic resources. The former quantifies the magic cost associated with MQC, serving as both a resource witness and a feasible upper bound for the practical realization, and is gate-order independent; The latter represents the maximal achievable magic resource in a given graph structure defining MQC. We utilize both concepts to analyze the quantum Fourier transform (QFT) and provide a fresh perspective on the universality of MQC, highlighting the crucial role of non-Pauli measurements in injecting magic. In particular, we theoretically prove that high-dimensional graphs can generate an exponential advantage of MQC compared to classical computing. We demonstrate experimentally our theoretical findings in a high-fidelity four-photon setup, surpassing conventional magic state injection (MSI) methods in both qubit efficiency and resource utilization. Our findings pave the way for future research exploring magic resource optimization and novel distillation schemes within the MQC framework, advancing fault-tolerant universal quantum computation.

quant-ph↗

arXiv2025

AGO: Adaptive Grounding for Open World 3D Occupancy Prediction

Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vocabulary knowledge from vision-language models (VLMs) offers a promising direction but remains challenging. However, methods based on VLM-derived 2D pseudo-labels with traditional supervision are limited by a predefined label space and lack general prediction capabilities. Direct alignment with pretrained image embeddings, on the other hand, often fails to achieve reliable performance because of inconsistent image and text representations in VLMs. To address these challenges, we propose AGO, a novel 3D occupancy prediction framework with adaptive grounding to handle diverse open-world scenarios. AGO first encodes surrounding images and class prompts into 3D and text embeddings, respectively, leveraging similarity-based grounding training with 3D pseudo-labels. Additionally, a modality adapter maps 3D embeddings into a space aligned with VLM-derived image embeddings, reducing modality gaps. Experiments on Occ3D-nuScenes show that AGO improves unknown object prediction in zero-shot and few-shot transfer while achieving state-of-the-art closed-world self-supervised performance, surpassing prior methods by 4.09 mIoU. Code is available at: https://github.com/EdwardLeeLPZ/AGO.

cs.CV↗

arXiv2025

Auxiliary-Free Replica Shadows: Efficient Estimation of Multiple Nonlinear Quantum Properties

Efficient estimation of nonlinear properties is a significant yet challenging task from quantum information processing to many-body physics. Current methodologies often suffer from an exponential sampling cost or require auxiliary qubits and deep quantum circuits. To address these limitations, we propose an efficient auxiliary-free replica shadow (AFRS) framework, which leverages the power of the joint entangling operation on a few input replicas while integrating the mindset of shadow estimation. We rigorously prove that AFRS can offer exponential improvements in estimation accuracy compared with the conventional shadow method, and facilitate the simultaneous estimation of various nonlinear properties, unlike the destructive swap test. Additionally, we introduce an advanced local-AFRS variant tailored to estimating local observables with constant-depth quantum circuits, significantly simplifying the experimental implementation. Our work paves the way for efficient and practical estimation of nonlinear properties on near-term quantum devices.

quant-ph↗

arXiv2025

Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey

Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has broad applications across various real-world applications, including remote sensing element interpretation, ecological environment monitoring, and urban/rural planning. However, domain adaptation in remote sensing poses significant challenges due to differences in data, such as variations in ground sampling distance, imaging modes from various sensors, geographical landscapes, and environmental conditions. In recent years, deep learning has emerged as a powerful tool for feature representation and cross-domain knowledge transfer, leading to widespread adoption in remote sensing tasks. In this paper, we present a comprehensive survey of significant advancements in deep learning based domain adaptation for remote sensing. We first introduce the preliminary knowledge to clarify key concepts, mathematical notations, and the taxonomy of methodologies. We then organize existing algorithms from multiple perspectives, including task categorization, input mode, supervision paradigm, and algorithmic granularity, providing readers with a structured understanding of the field. Next, we review widely used datasets and summarize the performance of state-of-the-art methods to provide an overview of current progress. We also identify open challenges and potential directions to guide future research in domain adaptation for remote sensing. Compared to previous surveys, this work addresses a broader range of domain adaptation tasks in remote sensing, rather than concentrating on a few subfields. It also presents a systematic taxonomy, providing a more comprehensive and organized understanding of the field. As a whole, this survey can inspire the research community, foster understanding, and guide future work in the field.

cs.CV↗

arXiv2025

Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentation

Federated learning enables collaborative training of machine learning models among different clients while ensuring data privacy, emerging as the mainstream for breaking data silos in the healthcare domain. However, the imbalance of medical resources, data corruption or improper data preservation may lead to a situation where different clients possess medical images of different modality. This heterogeneity poses a significant challenge for cross-domain medical image segmentation within the federated learning framework. To address this challenge, we propose a new Federated Domain Adaptation (FedDA) segmentation training framework. Specifically, we propose a feature-level adversarial learning among clients by aligning feature maps across clients through embedding an adversarial training mechanism. This design can enhance the model's generalization on multiple domains and alleviate the negative impact from domain-shift. Comprehensive experiments on three medical image datasets demonstrate that our proposed FedDA substantially achieves cross-domain federated aggregation, endowing single modality client with cross-modality processing capabilities, and consistently delivers robust performance compared to state-of-the-art federated aggregation algorithms in objective and subjective assessment. Our code are available at https://github.com/GGbond-study/FedDA.

cs.CV↗

arXiv2025

Vim-F: Visual State Space Model Benefiting from Learning in the Frequency Domain

In recent years, State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences such as language understanding. Therefore, building efficient and general-purpose visual backbones based on SSMs is a promising direction. Compared to traditional convolutional neural networks (CNNs) and Vision Transformers (ViTs), the performance of Vision Mamba (ViM) methods is not yet fully competitive. To enable SSMs to process image data, ViMs typically flatten 2D images into 1D sequences, inevitably ignoring some 2D local dependencies, thereby weakening the model's ability to interpret spatial relationships from a global perspective. We use Fast Fourier Transform (FFT) to obtain the spectrum of the feature map and add it to the original feature map, enabling ViM to model a unified visual representation in both frequency and spatial domains. The introduction of frequency domain information enables ViM to have a global receptive field during scanning. We propose a novel model called Vim-F, which employs pure Mamba encoders and scans in both the frequency and spatial domains. Moreover, we question the necessity of position embedding in ViM and remove it accordingly in Vim-F, which helps to fully utilize the efficient long-sequence modeling capability of ViM. Finally, we redesign a patch embedding for Vim-F, leveraging a convolutional stem to capture more local correlations, further improving the performance of Vim-F. Code is available at: https://github.com/yws-wxs/Vim-F.

cs.CV↗

arXiv2025

Electronic crystals in layered materials

In modern two-dimensional (2D) materials, such as graphene-based systems and atomically-thin transition-metal dichalcogenides, the interplay of strong electronic correlations, tunable moiré superlattices, and nontrivial band topology has given rise to rich phase diagrams and collective phenomena. Among the novel phases that have been realized, electronic crystals -- states of matter in which itinerant electrons spontaneously crystallize -- play a particularly prominent role. In this Review, we summarize the current status of electron crystallization in van der Waals heterostructures, with emphasis on the experimental platforms and measurement techniques that enable their study. We also highlight open questions and outline future directions that may elucidate more of the fascinating properties of electronic crystals.

cond-mat.str-el↗

arXiv2025

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOLO11 and earlier versions and the area-based self-attention mechanism introduced in YOLOv12 are limited to local information aggregation and pairwise correlation modeling, lacking the capability to capture global multi-to-multi high-order correlations, which limits detection performance in complex scenarios. In this paper, we propose YOLOv13, an accurate and lightweight object detector. To address the above-mentioned challenges, we propose a Hypergraph-based Adaptive Correlation Enhancement (HyperACE) mechanism that adaptively exploits latent high-order correlations and overcomes the limitation of previous methods that are restricted to pairwise correlation modeling based on hypergraph computation, achieving efficient global cross-location and cross-scale feature fusion and enhancement. Subsequently, we propose a Full-Pipeline Aggregation-and-Distribution (FullPAD) paradigm based on HyperACE, which effectively achieves fine-grained information flow and representation synergy within the entire network by distributing correlation-enhanced features to the full pipeline. Finally, we propose to leverage depthwise separable convolutions to replace vanilla large-kernel convolutions, and design a series of blocks that significantly reduce parameters and computational complexity without sacrificing performance. We conduct extensive experiments on the widely used MS COCO benchmark, and the experimental results demonstrate that our method achieves state-of-the-art performance with fewer parameters and FLOPs. Specifically, our YOLOv13-N improves mAP by 3.0\% over YOLO11-N and by 1.5\% over YOLOv12-N. The code and models of our YOLOv13 model are available at: https://github.com/iMoonLab/yolov13.

cs.CV↗

arXiv2025

Measuring Less to Learn More: Quadratic Speedup in learning Nonlinear Properties of Quantum Density Matrices

A fundamental task in quantum information science is to measure nonlinear functionals of quantum states, such as $\mathrm{Tr}(ρ^k O)$. Intuitively, one expects that computing a $k$-th order quantity generally requires $O(k)$ copies of the state $ρ$, and we rigorously establish this lower bound under sample access to $ρ$. Surprisingly, this limitation can be overcome when one has purified access via a unitary that prepares a purification of $ρ$, a scenario naturally arising in quantum simulation and computation. In this setting, we find a different lower bound of $Θ(\sqrt{k})$, and present a quantum algorithm that achieves this bound, demonstrating a quadratic advantage over sample-based methods. The key technical innovation lies in a designed quantum algorithm and optimal polynomial approximation theory -- specifically, Chebyshev polynomial approximations tailored to the boundary behavior of power functions. Our results unveil a fundamental distinction between sample and purified access to quantum states, with broad implications for estimating quantum entropies and quantum Fisher information, realizing quantum virtual distillation and cooling, and evaluating other multiple nonlinear quantum observables with classical shadows.

quant-ph↗

arXiv2025

Approximate k-uniform states: definition, construction and applications

$k$-Uniform states are fundamental to quantum information and computing, with applications in multipartite entanglement and quantum error-correcting codes (QECCs). Prior work has primarily focused on constructing exact $k$-uniform states or proving their nonexistence. However, due to inevitable theoretical approximations and experimental imperfections, generating exact $k$-uniform states is neither feasible nor necessary in practice. In this work, we initiate the study of approximate $k$-uniform states, demonstrating that they are locally indistinguishable from their exact counterparts unless massive measurements are performed. We prove that such states can be constructed with high probability from the Haar-random ensemble and, more efficiently, via shallow random quantum circuits. Furthermore, we establish a connection between approximate $k$-uniform states and approximate QECCs, showing that Haar random constructions yield high-performance codes with linear rates, vanishing proximity, and exponentially small failure probability while random circuits can't construct codes with linear code rate in shallow depth. Finally, we investigate the relationship between approximate QECCs and approximate quantum information masking. Our work lays the foundation for the practical application of $k$-uniform states.

quant-ph↗

arXiv2025

Ultra-Low-Latency Edge Inference for Distributed Sensing

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

math.NA↗