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Researcher profile

Jing Liang

Jing Liang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Roboticsgr-qcComputer Visioncond-mat.mes-hallcond-mat.supr-coneess.SPhep-thInformation TheoryMachine Learningmath.IT

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Published work

15 published item(s)

preprint2026arXiv

Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement

We introduce Paired-CSLiDAR (CSLiDAR), a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement: refining a ground-scan pose within a 50 m-radius aerial crop. The benchmark contains 12,683 ground-aerial pairs across 6 evaluation sites and per-scan reference 6-DoF alignments for sub-meter root-mean-square error (RMSE) evaluation. Because aerial scans capture rooftops and canopy while ground scans capture facades and under-canopy, the two modalities share only a fraction of their geometry, primarily the terrain surface, causing standard registration methods and learned correspondence models to converge to metrically incorrect local minima. We propose Residual-Guided Stratified Registration (RGSR), a training-free, geometry-only refinement pipeline that exploits the shared ground plane through height-stratified ICP, reversed registration directions, and confidence-gated accept-if-better selection. RGSR achieves 86.0% S@0.75 m and 99.8% S@1.0 m on the primary benchmark of 9,012 scans, outperforming both the confidence-gated cascade at 83.7% and GeoTransformer at 76.3%. We validate RMSE-based pose selection with independent survey control and trajectory consistency, and show that added Fourier-Mellin BEV proposals can reduce RMSE while increasing actual pose error under extreme partial overlap. The dataset and code are being prepared for public release.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta

Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-the-art works have yielded impressive results, but the diversity is still limited. In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork. The reconstruction subnetwork captures intra-category transformation, i.e., "delta", between same-category pairs. The generation subnetwork generates sample-specific "delta" for an input image, which is combined with this input image to generate a new image within the same category. Besides, an adversarial delta matching loss is designed to link the above two subnetworks together. Extensive experiments on five few-shot image datasets demonstrate the effectiveness of our proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-Resolution Image Harmonization via Collaborative Dual Transformations

Given a composite image, image harmonization aims to adjust the foreground to make it compatible with the background. High-resolution image harmonization is in high demand, but still remains unexplored. Conventional image harmonization methods learn global RGB-to-RGB transformation which could effortlessly scale to high resolution, but ignore diverse local context. Recent deep learning methods learn the dense pixel-to-pixel transformation which could generate harmonious outputs, but are highly constrained in low resolution. In this work, we propose a high-resolution image harmonization network with Collaborative Dual Transformation (CDTNet) to combine pixel-to-pixel transformation and RGB-to-RGB transformation coherently in an end-to-end network. Our CDTNet consists of a low-resolution generator for pixel-to-pixel transformation, a color mapping module for RGB-to-RGB transformation, and a refinement module to take advantage of both. Extensive experiments on high-resolution benchmark dataset and our created high-resolution real composite images demonstrate that our CDTNet strikes a good balance between efficiency and effectiveness. Our used datasets can be found in https://github.com/bcmi/CDTNet-High-Resolution-Image-Harmonization.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Image-Goal Navigation in Complex Environments via Modular Learning

We present a novel approach for image-goal navigation, where an agent navigates with a goal image rather than accurate target information, which is more challenging. Our goal is to decouple the learning of navigation goal planning, collision avoidance, and navigation ending prediction, which enables more concentrated learning of each part. This is realized by four different modules. The first module maintains an obstacle map during robot navigation. The second predicts a long-term goal on the real-time map periodically, which can thus convert an image-goal navigation task to several point-goal navigation tasks. To achieve these point-goal navigation tasks, the third module plans collision-free command sets for navigating to these long-term goals. The final module stops the robot properly near the goal image. The four modules are designed or maintained separately, which helps cut down the search time during navigation and improves the generalization to previously unseen real scenes. We evaluate the method in both a simulator and in the real world with a mobile robot. The results in real complex environments show that our method attains at least a $17\%$ increase in navigation success rate and a $23\%$ decrease in navigation collision rate over some state-of-the-art models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joule-Thomson expansion of d-dimensional charged AdS black holes with cloud of strings and quintessence

Herein, we focus on the study of Joule-Thomson expansion corresponding to a d-dimensional charged AdS black hole with cloud of strings and quintessence. Then its relevant solution and some thermodynamic properties are investigated. Specifically, we evaluate its Joule-Thomson expansion from four important aspects, including the Joule-Thomson coefficient, inversion curve, isenthalpic curve, and ratio $\frac{T_{i}^{min}}{T_{c}}$. After analysis, different dimensions with strings of cloud and quintessence parameters have different effects on the Joule-Thomson coefficient (the same situation are found for the inversion curve, isenthalpic curve, and ratio $\frac{T_{i}^{min}}{T_{c}}$).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning Quantization in LDPC Decoders

Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spontaneous Polarization Induced Photovoltaic Effect In Rhombohedrally Stacked MoS$_2$

Stacking order in van der Waals materials determines the coupling between atomic layers and is therefore key to the materials' properties. By exploring different stacking orders, many novel physical phenomena have been realized in artificial vdW stacks. Recently, 2D ferroelectricity has been observed in zero-degree aligned hBN and graphene-hBN heterostructures, holding promise in a range of electronic applications. In those artificial stacks, however, the single domain size is limited by the stacking-angle misalignment to about 0.1 to 1 $μ$m, which is incompatible with most optical or optoelectronic applications. Here we show MoS$_2$ in the rhombohedral phase can host a homogeneous spontaneous polarization throughout few-$μ$m-sized exfoliated flakes, as it is a natural crystal requiring no stacking and is, therefore free of misalignment. Utilizing this homogeneous polarization and its induced depolarization field (DEP), we build a graphene-MoS$_2$ based photovoltaic device with high efficiency. The few-layer MoS$_2$ is thinner than most oxide-based ferroelectric films, which allows us to maximize the DEP and study its impact at the atomically thin limit, while the highly uniform polarization achievable in the commensurate crystal enables a tangible path for up-scaling. The external quantum efficiency of our device is up to 16% at room temperature, over one order larger than the highest efficiency observed in bulk photovoltaic devices, owing to the reduced screening in graphene, the exciton-enhanced light-matter interaction, and the ultrafast interlayer relaxation in MoS$_2$. In view of the wide range of bandgap energy in other TMDs, our findings make rhombohedral TMDs a promising and versatile candidate for applications such as energy-efficient photo-detection with high speed and programmable polarity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TerraPN: Unstructured Terrain Navigation using Online Self-Supervised Learning

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness, deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through self-supervised learning, and uses it for autonomous robot navigation. Our method uses RGB images of terrain surfaces and the robot's velocities as inputs, and the IMU vibrations and odometry errors experienced by the robot as labels for self-supervision. Our method computes a surface cost map that differentiates smooth, high-traction surfaces (low navigation costs) from bumpy, slippery, deformable surfaces (high navigation costs). We compute the cost map by non-uniformly sampling patches from the input RGB image by detecting boundaries between surfaces resulting in low inference times (47.27% lower) compared to uniform sampling and existing segmentation methods. We present a novel navigation algorithm that accounts for a surface's cost, computes cost-based acceleration limits for the robot, and dynamically feasible, collision-free trajectories. TerraPN's surface cost prediction can be trained in ~25 minutes for five different surfaces, compared to several hours for previous learning-based segmentation methods. In terms of navigation, our method outperforms previous works in terms of success rates (up to 35.84% higher), vibration cost of the trajectories (up to 21.52% lower), and slowing the robot on bumpy, deformable surfaces (up to 46.76% slower) in different scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Joule-Thomson expansion of the torus-like black hole

In this paper, we study Joule-Thomson effects for the torus-like black hole. The Joule-Thomson coefficients, the inversion curves and the isenthalpic curves are studied. Furthermore, we investigate similarities and differences between the Van der Waals fluid, the torus-like black hole and the charged AdS black holes for the expansion. The isenthalpic curves in the $T-P$ plane are obtained. Moreover, we determine the cooling-heating regions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Autonomous Social Distancing in Urban Environments using a Quadruped Robot

COVID-19 pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19. Against this backdrop, we propose that the surveillance robots can not only monitor but also promote social distancing. Robots can be flexibly deployed and they can take precautionary actions to remind people of practicing social distancing. In this paper, we introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd-aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to over-crowded pedestrians. We demonstrate and validate that our robot can be operated autonomously by conducting several experiments in various urban scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors

We present DenseCAvoid, a novel navigation algorithm for navigating a robot through dense crowds and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses visual sensors and a pedestrian trajectory prediction algorithm to track pedestrians in a set of input frames and provide bounding boxes that extrapolate the pedestrian positions in a future time. Our hybrid approach combines this trajectory prediction with a Deep Reinforcement Learning-based collision avoidance method to train a policy to generate smoother, safer, and more robust trajectories during run-time. We train our policy in realistic 3-D simulations of static and dynamic scenarios with multiple pedestrians. In practice, our hybrid approach generalizes well to unseen, real-world scenarios and can navigate a robot through dense crowds (~1-2 humans per square meter) in indoor scenarios, including narrow corridors and lobbies. As compared to cases where prediction was not used, we observe that our method reduces the occurrence of the robot freezing in a crowd by up to 48%, and performs comparably with respect to trajectory lengths and mean arrival times to goal.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning

We present a novel learning-based collision avoidance algorithm, CrowdSteer, for mobile robots operating in dense and crowded environments. Our approach is end-to-end and uses multiple perception sensors such as a 2-D lidar along with a depth camera to sense surrounding dynamic agents and compute collision-free velocities. Our training approach is based on the sim-to-real paradigm and uses high fidelity 3-D simulations of pedestrians and the environment to train a policy using Proximal Policy Optimization (PPO). We show that our learned navigation model is directly transferable to previously unseen virtual and dense real-world environments. We have integrated our algorithm with differential drive robots and evaluated its performance in narrow scenarios such as dense crowds, narrow corridors, T-junctions, L-junctions, etc. In practice, our approach can perform real-time collision avoidance and generate smooth trajectories in such complex scenarios. We also compare the performance with prior methods based on metrics such as trajectory length, mean time to goal, success rate, and smoothness and observe considerable improvement.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Remarks on the weak cosmic censorship conjecture of RN-AdS black holes with cloud of strings and quintessence under the scalar field

In this paper, we investigate the thermodynamics and weak cosmic censorship conjecture in a RN-AdS black hole with the cloud of strings and quintessence by the scattering of a scalar field. The variations of the thermodynamic variables are calculated in the normal and extended phase spaces. In the normal phase space, where the cosmological constant is considered as a constant, the first and second laws of thermodynamics are satisfied. In the extended phase space, where the cosmological constant and the parameters related to the cloud of strings and quintessence are treated as variables, the first law of thermodynamics is still satisfied and the second law of thermodynamics is indefinite. Moreover, we find that the weak cosmic censorship conjecture is valid for the extremal and near-extremal black holes in both phase spaces.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Thermodynamics and overcharging problem in the extended phase spaces of charged AdS black holes with cloud of strings and quintessence under charged particle absorption

The thermodynamics and overcharging problem in the RN-AdS black hole with the cloud of strings and quintessence are investigated by absorption of scalar particle and fermion in the extend phase space. The cosmological constant is treated as the pressure with a conjugate volume. Besides, the parameters related to quintessence and cloud of strings are treated as thermodynamic variables. Finally we find the first law of thermodynamics is satisfied and the second law of thermodynamics is indefinite. Furthermore, the near-extremal and extremal black holes can not be overcharged.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Studies on the origin of the interfacial superconductivity of Sb2Te3/Fe1+yTe heterostructures

The recent discovery of the interfacial superconductivity (SC) of the Bi2Te3/Fe1+yTe heterostructure has attracted extensive studies due to its potential as a novel platform for trapping and controlling Majorana fermions. Here we present studies of another topological insulator (TI)/Fe1+yTe heterostructure, Sb2Te3/Fe1+yTe, which also enjoys an interfacial two-dimensional SC. The results of transport measurements support that the reduction of excess Fe concentration of the Fe1+yTe layer not only increases the fluctuation of its antiferromagnetic (AFM) order but also enhances the quality of the SC of this heterostructure system. On the other hand, the interfacial SC of this heterostructure was found to have a wider-ranging TI-layer thickness dependence than that of the Bi2Te3/Fe1+yTe heterostructure, which is believed to be attributed to the much higher bulk conductivity of Sb2Te3 that enhances indirect coupling between its top and bottom topological surface states (TSSs). Our results provide the evidence of the interplay among the AFM order, itinerant carries from the TSSs and the induced interfacial SC of the TI/Fe1+yTe heterostructure system.

0 citations0 reviewsNo code linkedOpen access