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Researcher profile

Yan Xing

Yan Xing contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
quant-phComputer Visioncond-mat.mes-halleess.IVMachine Learning

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Published work

5 published item(s)

preprint2026arXiv

Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark

Off-road nighttime autonomous driving suffers from unreliable visible-light perception, making infrared modality crucial for accurate freespace detection. However, progress remains limited due to the scarcity of annotated infrared off-road datasets and the inter-frame inconsistencies inherent to current single-frame methods. To address these gaps, we present the IRON dataset, which, to our knowledge, is the first large-scale infrared dataset for off-road temporal freespace detection under all-day conditions, with strong support for nighttime perception. The dataset comprises 24,314 densely annotated infrared images with synchronized RGB images in diverse scenes and different light conditions. Building upon this dataset, we propose IRONet, a novel flow-free framework for temporal freespace detection that addresses inter-frame inconsistencies by aggregating historical context via a memory-attention mechanism and a carefully designed mask decoder. On our IRON dataset, IRONet achieves state-of-the-art performance, reaching 82.93%(+1.19%) IoU and 90.66%(+0.71%) F1 score at real-time inference. Remarkably, IRONet also exhibits robust generalization to RGB modalities on ORFD and Rellis datasets. Overall, our work establishes a foundation for reliable all-day off-road autonomous driving and future research in infrared temporal perception. The code and IRON dataset are available at https://github.com/wsnbws/IRON.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantum transport in a one-dimensional quasicrystal with mobility edges

Quantum transport in a one-dimensional (1D) quasiperiodic lattice with mobility edges is explored. We first investigate the adiabatic pumping between left and right edge modes by resorting to two edge-bulk-edge channels and demonstrate that the success or failure of the adiabatic pumping depends on whether the corresponding bulk subchannel undergoes a localization-delocalization transition. Compared with the paradigmatic Aubry-André (AA) model, the introduction of mobility edges triggers an opposite outcome for successful pumping in the two channels, showing a discrepancy of critical condition, and facilitates the robustness of the adiabatic pumping against quasidisorder. We also consider the transfer between excitations at both boundaries of the lattice and an anomalous phenomenon characterized by the enhanced quasidisorder contributing to the excitation transfer is found. Furthermore, there exists a parametric regime where a nonreciprocal effect emerges in the presence of mobility edges, which leads to a unidirectional transport for the excitation transfer and enables potential applications in the engineering of quantum diodes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation

To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multiscale deconvolution decoder network and a pixel-level classification layer. In the network structure of the encoder, we use multiscale convolution instead of the traditional single-channel convolution. The multiple max-pooling integration module first integrates the output features of each submodule of the encoder network and reduces the number of parameters by convolution using a kernel size of 1. At the same time, each max-pooling layer (the pooling size of each layer is different) is spliced after each convolution to achieve the translation invariance of the feature maps of each submodule. We use the output feature maps from the multiple max-pooling integration module as the input of the decoder network; the multiscale convolution of each submodule in the decoder network is cross-fused with the feature maps generated by the corresponding multiscale convolution in the encoder network. Using the above feature map processing methods solves the sparsity problem after the max-pooling layer-generating matrix and enhances the robustness of the classification. We compare our proposed model with the well-known Fully Convolutional Networks for Semantic Segmentation (FCNs), DecovNet, PSPNet, U-net, SgeNet and other state-of-the-art segmentation networks such as HyperDenseNet, MS-Dual, Espnetv2, Denseaspp using one binary Kaggle 2018 data science bowl dataset and two multiclass dataset and obtain encouraging experimental results.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Topological and nontopological edge states induced by qubit-assisted coupling potentials

In the usual Su-Schrieffer-Heeger (SSH) chain, the topology of the energy spectrum is divided into two categories in different parameter regions. Here we study the topological and nontopological edge states induced by qubit-assisted coupling potentials in circuit quantum electrodynamics (QED) lattice system modelled as a SSH chain. We find that, when the coupling potential added on only one end of the system raises to a certain extent, the strong coupling potential will induce a new topologically nontrivial phase accompanied with the appearance of a nontopological edge state in the whole parameter region, and the novel phase transition leads to the inversion of odd-even effect in the system directly. Furthermore, we also study the topological properties as well as phase transitions when two unbalanced coupling potentials are injected into both the ends of the circuit QED lattice system, and find that the system exhibits three distinguishing phases in the process of multiple flips of energy bands. These phases are significantly different from the previous phase induced via unilateral coupling potential, which is reflected by the existence of a pair of nontopological edge states under strong coupling potential regime. Our scheme provides a feasible and visible method to induce a variety of different kinds of topological and nontopological edge states through controlling the qubit-assisted coupling potentials in circuit QED lattice system both in experiment and theory.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Topological phase induced by distinguishing parameter regimes in cavity optomechanical system with multiple mechanical resonators

We propose two kinds of distinguishing parameter regimes to induce topological Su-Schrieffer-Heeger (SSH) phase in a one dimensional (1D) multi-resonator cavity optomechanical system via modulating the frequencies of both cavity fields and resonators. The introduction of the frequency modulations allows us to eliminate the Stokes heating process for the mapping of the tight-binding Hamiltonian without usual rotating wave approximation, which is totally different from the traditional mapping of the topological tight-binding model. We find that the tight-binding Hamiltonian can be mapped into a topological SSH phase via modifying the Bessel function originating from the frequency modulations of cavity fields and resonators, and the induced SSH phase is independent on the effective optomechanical coupling strength. On the other hand, the insensitivity of the system to the effective optomechanical coupling provides us another new path to induce the topological SSH phase based on the present 1D cavity optomechanical system. And we show that the system can exhibit a topological SSH phase via varying the effective optomechanical coupling strength in an alternative way, which is much more easier to be achieved in experiment. Furthermore, we also construct an analogous bosonic Kitaev model with the trivial topology by keeping the Stokes heating processes. Our scheme provides a steerable platform to investigate the effects of next-nearest-neighboring interactions on the topology of the system.

0 citations0 reviewsNo code linkedOpen access