arXiv subjects

Yanchao Zhang

Publications and source records attributed to Yanchao Zhang.

At least 19 recordsLinked to original sources

arXiv2026

NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation

Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience. However, the sparse and elongated morphology of neurons poses significant challenges to existing segmentation methods. These methods struggle to preserve both local details and global topology, leading to fragmented results. To address this, we propose NeuroRefiner, a multi-agent system that formalizes the human expert workflow involving iterative global observation and local editing. Specifically, NeuroRefiner comprises three collaborative agents dedicated to diagnosing topological errors, generating correction instructions, and validating refinement quality. To facilitate agent instruction-guided segmentation refinement, we propose TopoRefineNet, a dedicated 3D U-Net-based tool that leverages cross-modality feature fusion to generate refined masks. Through multi-round agent reasoning and voxel-level editing, NeuroRefiner produces topologically more accurate segmentations with enhanced interpretability. Experiments on the BigNeuron, CWMBS, and ZBFWB datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably achieving a 3.02% improvement in F1 score on the challenging ZBFWB dataset.

cs.CV↗

arXiv2026

Securing Contrastive mmWave-based Human Activity Recognition against Adversarial Label Flipping

Wireless Human Activity Recognition (HAR), leveraging their non-intrusive nature, has the potential to revolutionize various sectors, including healthcare, virtual reality, and surveillance. The advent of millimeter wave (mmWave) technology has significantly enhanced the capabilities of wireless HAR systems. This paper presents the first systematic study on the vulnerabilities of mmWave-based HAR to label flipping poisoning attacks in the context of supervised contrastive learning. We identify three label poisoning attacks on the contrastive mmWave-based HAR and propose corresponding countermeasures. The efficacy of the attacks and also our countermeasures are experimentally validated on a prototype system. The attacks and countermeasures can be easily extended to other wireless HAR systems, thereby promoting security considerations in system design and deployment.

cs.CR↗

arXiv2026

Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification

Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of brain function. However, when processing terabyte- or petabyte-scale electron microscopy data, over-segmentation inherent in automated reconstruction algorithms remains a critical bottleneck, requiring extensive manual proofreading spanning person-years. To alleviate the heavy reliance on annotated data and the limited flexibility of conventional tracing methods, we propose a training-free, targeted neuron tracing framework. Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies through a probing-verification cycle. To achieve robust zero-shot semantic verification, we further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2. This strategy resolves intra-slice splits via Planar Ensemble Consensus and inter-slice splits via Axial Spatio-Temporal Propagation. Notably, we integrate the proposed workflow into the Neuroglancer visualization platform, enabling an interactive human-in-the-loop proofreading system. Experimental results demonstrate that the proposed method outperforms supervised baselines and reduces manual proofreading time by 33.4%. The source code is publicly available at https://github.com/HeadLiuYun/Probe-EM.

cs.CV↗

arXiv2026

SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy

Volume microscopy, including electron and light microscopy, suffers from severe anisotropic resolution due to physical axial sectioning. Existing self-supervised axial super-resolution (ASR) methods face a trilemma bounded by overly smoothed regression textures, structural hallucinations of pure diffusion models, and prohibitive inference latency. In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level: a frozen topological network and a diffusion refiner are optimized by disjoint objectives, separating low-frequency topology formulation from high-frequency detail enhancement. Building on this deterministic skeleton, we exploit a unified cycle-consistent mechanism on input sparse slices to simultaneously extract a real-domain residual prior and bidirectionally align the diffusion refiner, washing away cross-plane artifacts without synthetic bias. By truncating the reverse diffusion process with this physical prior, SkelEM achieves high-fidelity detail restoration in merely $\le 5$ steps. To rigorously assess cross-instrument generalization, we further introduce BRAVE-ASR, a new benchmark of co-aligned anisotropic and isotropic volumes acquired on a Plasma-FIB instrument. Across public benchmarks, SkelEM achieves the most favorable balance across the fidelity-perception trade-off among self-supervised methods, with state-of-the-art downstream membrane segmentation performance and robust zero-shot generalization across distinct modalities.

cs.CV↗

arXiv2026

NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation

Neuron segmentation is the cornerstone of reconstructing comprehensive neuronal connectomes, which is essential for deciphering the functional organization of the brain. The irregular morphology and densely intertwined structures of neurons make this task particularly challenging. Prevailing CNN-based methods often fail to resolve ambiguous boundaries due to the lack of long-range context, whereas Transformer-based methods suffer from boundary imprecision caused by the loss of voxel-level details during patch partitioning. To address these limitations, we propose NeuroMamba, a multi-perspective framework that exploits the linear complexity of Mamba to enable patch-free global modeling and synergizes this with complementary local feature modeling, thereby efficiently capturing long-range dependencies while meticulously preserving fine-grained voxel details. Specifically, we design a channel-gated Boundary Discriminative Feature Extractor (BDFE) to enhance local morphological cues. Complementing this, we introduce the Spatial Continuous Feature Extractor (SCFE), which integrates a resolution-aware scanning mechanism into the Visual Mamba architecture to adaptively model global dependencies across varying data resolutions. Finally, a cross-modulation mechanism synergistically fuses these multi-perspective features. Our method demonstrates state-of-the-art performance across four public EM datasets, validating its exceptional adaptability to both anisotropic and isotropic resolutions. The source code will be made publicly available.

cs.CV↗

arXiv2026

Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation

Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotations. Traditional methods augment the training set through geometric and photometric transformations; however, the generated samples remain highly correlated with the original images and lack structural diversity. To address this limitation, we propose a diffusion-based data augmentation framework capable of generating diverse and structurally plausible image-label pairs for neuron segmentation. Specifically, the framework employs a resolution-aware conditional diffusion model with multi-scale conditioning and EM resolution priors to enable voxel-level image synthesis from 3D masks. It further incorporates a biology-guided mask remodeling module that produces augmented masks with enhanced structural realism. Together, these components effectively enrich the training set and improve segmentation performance. On the AC3 and AC4 datasets under low-annotation regimes, our method improves the ARAND metric by 32.1% and 30.7%, respectively, when combined with two different post-processing methods. Our code is available at https://github.com/HeadLiuYun/NeuroDiff.

cs.CV↗

arXiv2026

Thermodynamic uncertainty relations for multi-terminal systems with broken time-reversal symmetry

We investigate the thermodynamic uncertainty relations (TURs) in steady-state transport for a multi-terminal system consisting of two conducting terminals and N-2 probe terminals, within the linear response regime under broken time-reversal symmetry. We independently derive exact bounds on the TURs for the steady-state particle and heat currents under a strong constraint on the Onsager coefficients. Based on our proposed exact bounds, the analysis reveals that the bounds differ for particle and heat currents and are dependent on the system parameters. Furthermore, we demonstrate that under specific parameter conditions, the TURs of the particle and heat currents have a unified minimum value that depends solely on the number of terminals.

cond-mat.stat-mech↗

arXiv2025

Locally Differentially Private Frequency Estimation via Joint Randomized Response

Local Differential Privacy (LDP) has been widely recognized as a powerful tool for providing a strong theoretical guarantee of data privacy to data contributors against an untrusted data collector. Under a typical LDP scheme, each data contributor independently randomly perturbs their data before submitting them to the data collector, which in turn infers valuable statistics about the original data from received perturbed data. Common to existing LDP mechanisms is an inherent trade-off between the level of privacy protection and data utility in the sense that strong data privacy often comes at the cost of reduced data utility. Frequency estimation based on Randomized Response (RR) is a fundamental building block of many LDP mechanisms. In this paper, we propose a novel Joint Randomized Response (JRR) mechanism based on correlated data perturbations to achieve locally differentially private frequency estimation. JRR divides data contributors into disjoint groups of two members and lets those in the same group jointly perturb their binary data to improve frequency-estimation accuracy and achieve the same level of data privacy by hiding the group membership information in contrast to the classical RR mechanism. Theoretical analysis and detailed simulation studies using both real and synthetic datasets show that JRR achieves the same level of data privacy as the classical RR mechanism while improving the frequency-estimation accuracy in the overwhelming majority of the cases by up to two orders of magnitude.

cs.CR↗

arXiv2024

Hot-carrier photocatalysts with energy-selective contacts based on quantum wells and dots

In this paper, we simulate the function of hot-carrier photocatalysts (HCPCs) with quantum well and quantum dot energy-selective contacts (ESCs) in the water-splitting reaction. The transport equations for these ESCs are derived by using ballistic transport theory. The results indicate that thermalization loss from non-ideal ESCs is a primary factor diminishing the efficiency of HCPCs. The performance of HCPCs can be enhanced by optimizing the position of ESCs and the width of the extraction energy. Notably, HCPCs with quantum dot ESCs demonstrate superior performance compared to those with quantum well ESCs.

physics.app-ph↗

arXiv2024

From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task

Diffusion models have recently emerged as a powerful technique in image generation, especially for image super-resolution tasks. While 2D diffusion models significantly enhance the resolution of individual images, existing diffusion-based methods for 3D volume super-resolution often struggle with structure discontinuities in axial direction and high sampling costs. In this work, we present a novel approach that leverages the 2D diffusion model and lateral continuity within the volume to enhance 3D volume electron microscopy (vEM) super-resolution. We first simulate lateral degradation with slices in the XY plane and train a 2D diffusion model to learn how to restore the degraded slices. The model is then applied slice-by-slice in the lateral direction of low-resolution volume, recovering slices while preserving inherent lateral continuity. Following this, a high-frequency-aware 3D super-resolution network is trained on the recovery lateral slice sequences to learn spatial feature transformation across slices. Finally, the network is applied to infer high-resolution volumes in the axial direction, enabling 3D super-resolution. We validate our approach through comprehensive evaluations, including image similarity assessments, resolution analysis, and performance on downstream tasks. Our results on two publicly available focused ion beam scanning electron microscopy (FIB-SEM) datasets demonstrate the robustness and practical applicability of our framework for 3D volume super-resolution.

cs.CV↗

arXiv2024

The effect of space charge on photon-enhanced thermionic emission in the presence of the bidirectional discharge

The bidirectional space charge effects in photon-enhanced thermionic emission (PETE) devices are investigated systematically. First, we precisely determine the carrier concentrations and cathode temperatures by taking into account the electron recycling effect, energy balance constraints, and space charge effects arising from the concurrent discharge of the cathode and anode. Next, we analyze the impact of critical parameters, including anode properties and operating conditions, on the space charge barrier distribution and the overall performance of the device. The results demonstrate that the impact of reverse discharge on the net current becomes more pronounced when the PETE device operates at high anode temperatures, low anode work functions, and with a moderate solar concentration ratio and gap width. This discovery not only deepens our understanding of the bidirectional space charge effect, but also provides valuable guidance for the future optimization of PETE device performance.

physics.acc-ph↗

arXiv2024

Stable 3D vortex solitons of high topological charge in a Rydberg-dressed Bose-Einstein condensate with spin-orbit coupling

Stable vortex solitons (VSs) are objects of great interest for fundamental studies and various applications, including particle trapping, microscopy, data encoding, and matter-wave gyroscopes. However, three-dimensional (3D) VSs with high topological charges, supported by self-attractive nonlinearities, are unstable against fragmentation, which eventually leads to internal blowup (supercritical collapse) of the fragments. Here, we propose a scheme for realizing stable 3D VSs with topological charges up to $5$ and $6$ in the two components of a binary, Rydberg-dressed Bose-Einstein condensate (BEC) with spin-orbit coupling (SOC). We show that, if the SOC strength exceeds a critical value, the rotational symmetry of the VSs in the transverse plane gets broken, resulting in separation of the two components. Nevertheless, the VSs with the broken symmetry remain stable. The VS stability domains are identified in the system's parameter space. Moreover, application of torque to the stable VSs sets them in the state of robust gyroscopic precession.

cond-mat.quant-gas↗

arXiv2024

Anomalous thermodiffusion, absolute negative mobility and reverse heat transport in a single quantum dot

We investigate the steady-state transport characteristics of a quantum dot system consisting of a single energy level embedded between two reservoirs under the influence of both the temperature gradient and bias voltage. Within tailored parameter regimes, the system can exhibit three counterintuitive transport phenomena of anomalous thermodiffusion, absolute negative mobility and reverse heat transport respectively. These counterintuitive phenomena do not violate the second law of thermodynamics. Moreover, absolute negative mobility and reverse heat transport can be identified by a reversible energy level. These anomalous transports are different from thermoelectric transports and provide different perspectives for a more comprehensive understanding of the transport characteristics of quantum systems.

cond-mat.mes-hall↗

arXiv2022

Universal optimization efficiency and bounds of Carnot-like heat engines and refrigerators under shortcuts to isothermality

Based on a quantum thermodynamic protocol for shortcut to isothermality that smoothly modify the system-reservoir interaction can significantly speed up an isothermal process while keeping the overall dissipation constant [Phys. Rev. X. 10, 031015 (2020)], we extend the study of optimization performance of Carnot-like heat engines and refrigerators in a straightforward and unified way. We derive the universal optimization efficiency of heat engines and the optimization coefficient of performance of refrigerators under two unified optimization criterions, i.e., chi criterion and omega criterion. We also derived the universal lower and upper bounds for heat engines and refrigerators, and found that these bounds can be reached under extremely asymmetric cases.

quant-ph↗

arXiv2022

A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)

Data augmentation greatly increases the amount of data obtained based on labeled data to save on expenses and labor for data collection and labeling. We present a new approach for data augmentation called nine-dot MLS (ND-MLS). This approach is proposed based on the idea of image defor-mation. Images are deformed based on control points, which are calculated by ND-MLS. The method can generate over 2000 images for one exist-ing dataset in a short time. To verify this data augmentation method, extensive tests were performed covering 3 main tasks of computer vision, namely, classification, detection and segmentation. The results show that 1) in classification, 10 images per category were used for training, and VGGNet can obtain 92% top-1 acc on the MNIST dataset of handwritten digits by ND-MLS. In the Omniglot dataset, the few-shot accuracy usu-ally decreases with the increase in character categories. However, the ND-MLS method has stable performance and obtains 96.5 top-1 acc in Res-Net on 100 different handwritten character classification tasks; 2) in segmentation, under the premise of only ten original images, DeepLab obtains 93.5%, 85%, and 73.3% m_IOU(10) on the bottle, horse, and grass test datasets, respectively, while the cat test dataset obtains 86.7% m_IOU(10) with the SegNet model; 3) with only 10 original images from each category in object detection, YOLO v4 obtains 100% and 97.2% bottle and horse detection, respectively, while the cat dataset obtains 93.6% with YOLO v3. In summary, ND-MLS can perform well on classification, object detec-tion, and semantic segmentation tasks by using only a few data.

cs.CV↗

arXiv2021

Inverse currents in Coulomb-coupled quantum dots

The inverse current, i.e., induced current is opposite to applied force, has recentlybeen found in a classical one-dimensional interacting Hamiltonian system [Phys. Rev.Lett. 124, 110607 (2020)]. In this paper, we show that inverse current also exists in quantum system. Based on Coulomb-coupled quantum dots system, we find that inverse current will appear when Coulomb interaction increases. This does not violate the second law of thermodynamics, since entropy reduction caused by inverse current is compensated by entropy increase caused by forward current, which ensure that total entropy increase of the system is always greater than zero.

quant-ph↗

arXiv2020

Suspended photonic crystal membranes in AlGaAs heterostructures for integrated multi-element optomechanics

We present high-reflectivity mechanical resonators fabricated from AlGaAs heterostructures for use in free-space optical cavities operating in the telecom wavelength regime. The mechanical resonators are fabricated in slabs of GaAs and patterned with a photonic crystal to increase their out-of-plane reflectivity. Characterization of the mechanical modes reveals residual tensile stress in the GaAs device layer. This stress results in higher mechanical frequencies than in unstressed GaAs and can be used for strain engineering of mechanical dissipation. Simultaneously, we find that the finite waist of the incident optical beam leads to a dip in the reflectance spectrum. This feature originates from coupling to a guided resonance of the photonic crystal, an effect that must be taken into account when designing slabs of finite size. The single- and sub-$\upmu$m-spaced double-layer slabs demonstrated here can be directly fabricated on top of a distributed Bragg reflector mirror in the same material platform. Such a platform opens a route for realizing integrated multi-element cavity optomechanical devices and optomechanical microcavities on chip.

physics.app-ph↗

arXiv2020

Thermal rectification and negative differential thermal conductivity based on a parallel-coupled double quantum-dot

We investigate the heat flow transport properties of a parallel-coupled double quantum-dot system connected to two reservoirs with a temperature bias in the Coulomb blockade regime. We demonstrate that the effects of thermal rectification and negative differential thermal conductance (NDTC) exist in this system and analyze the influences of energy level difference and Coulomb interaction on the thermal rectification and NDTC. We find that this system can achieve a high thermal rectification ratio and NDTC when the asymmetry factor of the system is enhanced.

cond-mat.mes-hall↗