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Researcher profile

Liang Xu

Liang Xu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningComputer Visioneess.SYSystems and Controlphysics.opticsArtificial Intelligenceastro-ph.GAComputation and Languagemath.OCphysics.plasm-phquant-phGraphicsInformation Retrievalmath.DS

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Published work

24 published item(s)

preprint2026arXiv

PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation

Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Notably, body inter-penetration is a pervasive issue from both data acquisition to the generated results, which significantly undermines the realism and usability. Previous generative models either ignored this issue or introduced computationally expensive mesh-level loss functions to alleviate inter-body collisions. In this paper, we propose a general-purpose and computationally efficient optimization strategy named PhysiGen to explicitly integrate collision-aware physical constraints for human-human interaction generation. Specifically, we simplify the high-resolution human body mesh into geometric primitives to greatly reduce the cost of inter-person collision detection. Moreover, we identify the collision regions as the guidance of the optimization directions. PhysiGen is plug-and-play and can be readily integrated into existing human interaction generation models. Extensive cross-dataset and cross-model experiments show that our method can effectively reduce interpenetration and significantly improve visual coherence and physical plausibility compared to the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Evaluating Modules in Graph Contrastive Learning

The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semantically similar and dissimilar sample pairs to encode the semantics into node or graph embeddings. However, most existing works only performed \textbf{model-level} evaluation, and did not explore the combination space of modules for more comprehensive and systematic studies. For effective \textbf{module-level} evaluation, we propose a framework that decomposes GCL models into four modules: (1) a \textbf{sampler} to generate anchor, positive and negative data samples (nodes or graphs); (2) an \textbf{encoder} and a \textbf{readout} function to get sample embeddings; (3) a \textbf{discriminator} to score each sample pair (anchor-positive and anchor-negative); and (4) an \textbf{estimator} to define the loss function. Based on this framework, we conduct controlled experiments over a wide range of architectural designs and hyperparameter settings on node and graph classification tasks. Specifically, we manage to quantify the impact of a single module, investigate the interaction between modules, and compare the overall performance with current model architectures. Our key findings include a set of module-level guidelines for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an MLP encoder associated with Sum readout could achieve competitive performance on graph classification. Finally, we release our implementations and results as OpenGCL, a modularized toolkit that allows convenient reproduction, standard model and module evaluation, and easy extension. OpenGCL is available at \url{https://github.com/thunlp/OpenGCL}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Finding Quasars behind the Galactic Plane. II. Spectroscopic Identifications of 204 Quasars at $|b|< 20°$

Quasars behind the Galactic plane (GPQs) are important astrometric references and valuable probes of Galactic gas, yet the search for GPQs is difficult due to severe extinction and source crowding in the Galactic plane. In this paper, we present a sample of 204 spectroscopically confirmed GPQs at |b|<20°, 191 of which are new discoveries. This GPQ sample covers a wide redshift range from 0.069 to 4.487. For the subset of 230 observed GPQ candidates, the lower limit of the purity of quasars is 85.2%, and the lower limit of the fraction of stellar contaminants is 6.1%. Using a multicomponent spectral fitting, we measure the emission line and continuum flux of the GPQs, and estimate their single-epoch virial black hole masses. Due to selection effects raised from Galactic extinction and target magnitude, these GPQs have higher black hole masses and continuum luminosities in comparison to the SDSS DR7 quasar sample. The spectral-fitting results and black hole mass estimates are compiled into a main spectral catalog, and an extended spectral catalog of GPQs. The successful identifications prove the reliability of both our GPQ selection methods and the GPQ candidate catalog, shedding light on the astrometric and astrophysical programs that make use of a large sample of GPQs in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems

In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role. Usually, it is assumed that system matrices are known, from which the spectral radius can be directly computed. Instead, we consider the setting where the system is affected by process noise, and one has only finitely many samples of system input and state measurements. We provide two methods for estimating the spectral radius and derive error bounds that hold with high probability. Moreover, we show how to use the derived results to test stabilizability for networked control systems (NCSs) with lossy channels when only finitely many samples of the system input, state, and packet drop sequence are available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Formation mechanism of the rotating spoke in partially magnetized plasmas

Rotating spokes commonly occur in partially magnetized plasmas devices. In this paper, the driving mechanism behind the formation of an m=1 rotating spoke mode in a magnetically enhanced hollow cathode arc discharge is investigated by means of 2D radial-azimuthal particle-in-cell/Monte Carlo collision simulations with a uniform axial magnetic field. We find that the formation of the spoke potential hump region can be explained as a result of the positive anode sheath collapse due to the lower hybrid type instability evolving into the long wavelength regime. It is shown that an initial short-wavelength instability in the non-neutral anode sheath undergoes a sequence of transitions into the large scale modes. The sheath non-neutrality effect on the instability is considered and incorporated in the two-fluid linear theory of the lower hybrid instability. The unstable modes predicted by the theory in the linear phase and nonlinear evolution are in good agreement with the fluctuation modes developed in the particle simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fractal superconducting nanowires detect infrared single photons with 84% system detection efficiency, 1.02 polarization sensitivity, and 20.8 ps timing resolution

The near-unity system detection efficiency (SDE) and excellent timing resolution of superconducting nanowire single-photon detectors (SNSPDs), combined with their other merits, have enabled many classical and quantum photonic applications. However, the prevalent design based on meandering nanowires makes SDE dependent on the polarization states of the incident photons; for unpolarized light, the major merit of high SDE would get compromised, which could be detrimental for photon-starved applications. Here, we create SNSPDs with an arced fractal geometry that almost completely eliminates this polarization dependence of the SDE, and we experimentally demonstrate 84$\pm$3$\%$ SDE, 1.02$^{+0.06}_{-0.02}$ polarization sensitivity at the wavelength of 1575 nm, and 20.8 ps timing jitter in a 0.1-W closed-cycle Gifford-McMahon cryocooler, at the base temperature of 2.0 K. This demonstration provides a novel, practical device structure of SNSPDs, allowing for operation in the visible, near-, and mid-infrared spectral ranges, and paves the way for polarization-insensitive single-photon detection with high SDE and high timing resolution

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretization of continuous-time Hamiltonian systems and include several existing DNN architectures based on ordinary differential equations. Our main result is that a broad set of H-DNNs ensures non-vanishing gradients by design for an arbitrary network depth. This is obtained by proving that, using a semi-implicit Euler discretization scheme, the backward sensitivity matrices involved in gradient computations are symplectic. We also provide an upper-bound to the magnitude of sensitivity matrices and show that exploding gradients can be controlled through regularization. Finally, we enable distributed implementations of backward and forward propagation algorithms in H-DNNs by characterizing appropriate sparsity constraints on the weight matrices. The good performance of H-DNNs is demonstrated on benchmark classification problems, including image classification with the MNIST dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural Energy Casimir Control for Port-Hamiltonian Systems

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availability of suitable Casimir and Lyapunov functions, whose computation are generally intractable. In this paper, we propose a neural network-based framework to learn these functions. We show how to achieve equilibrium assignment by adding suitable regularization terms in the training cost. We also propose a parameterization of Casimir functions for reducing the training complexity. Moreover, the distance between the equilibrium of the learned Lyapunov function and the desired equilibrium is analyzed, which indicates that for small suboptimality gaps, the distance decreases linearly with respect to the training loss. Our methods are backed up by simulations on a pendulum system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust Classification using Contractive Hamiltonian Neural ODEs

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this paper, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contractive if all solutions with different initial conditions converge to each other exponentially fast. As a consequence, perturbations in initial conditions become less and less relevant over time. Since in NODEs the input data corresponds to the initial condition of dynamical systems, we show contractivity can mitigate the effect of input perturbations. More precisely, inspired by NODEs with Hamiltonian dynamics, we propose a class of contractive Hamiltonian NODEs (CH-NODEs). By properly tuning a scalar parameter, CH-NODEs ensure contractivity by design and can be trained using standard backpropagation. Moreover, CH-NODEs enjoy built-in guarantees of non-exploding gradients, which ensure a well-posed training process. Finally, we demonstrate the robustness of CH-NODEs on the MNIST image classification problem with noisy test data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition

Skeleton data carries valuable motion information and is widely explored in human action recognition. However, not only the motion information but also the interaction with the environment provides discriminative cues to recognize the action of persons. In this paper, we propose a joint learning framework for mutually assisted &#34;interacted object localization&#34; and &#34;human action recognition&#34; based on skeleton data. The two tasks are serialized together and collaborate to promote each other, where preliminary action type derived from skeleton alone helps improve interacted object localization, which in turn provides valuable cues for the final human action recognition. Besides, we explore the temporal consistency of interacted object as constraint to better localize the interacted object with the absence of ground-truth labels. Extensive experiments on the datasets of SYSU-3D, NTU60 RGB+D, Northwestern-UCLA and UAV-Human show that our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition. Visualization results show that our method can also provide reasonable interacted object localization results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Observability in Unobservable Systems

In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable. For their estimation, we introduce and exemplify a deep filter, which is a neural network specifically designed for the estimation of targeted state variables without computing the trajectory of the entire system. The observability definition is quantitative rather than a yes or no answer so that one can compare the level of observability between different sensor locations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design

This paper studies finite-horizon robust tracking control for discrete-time linear systems, based on input-output data. We leverage behavioral theory to represent system trajectories through a set of noiseless historical data, instead of using an explicit system model. By assuming that recent output data available to the controller are affected by noise terms verifying a quadratic bound, we formulate an optimization problem with a linear cost and LMI constraints for solving the robust tracking problem without any approximations. Our approach hinges on a parameterization of noise trajectories compatible with the data-dependent system representation and on a reformulation of the tracking cost, which enables the application of the S-lemma. In addition, we propose a method for reducing the computational complexity and demonstrate that the size of the resulting LMIs does not scale with the number of historical data. Finally, we show that the proposed formulation can easily incorporate actuator disturbances as well as constraints on inputs and outputs. The performance of the new controllers is discussed through simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification

Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practical implementation of these models for skin cancer screening in low resource settings is hindered by their limitations in computational cost and training dataset. To overcome these limitations, we propose a low-cost and high-performance data augmentation strategy that includes two consecutive stages of augmentation search and network search. At the augmentation search stage, the augmentation strategy is optimized in the search space of Low-Cost-Augment (LCA) under the criteria of balanced accuracy (BACC) with 5-fold cross validation. At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC. The efficiency of the proposed data augmentation strategy is verified on the HAM10000 dataset using EfficientNets as a baseline. With the proposed strategy, we are able to reduce the search space to 60 and achieve a high BACC of 0.853 by using a single DCNN model without external database, suitable to be implemented in mobile devices for DCNN-based skin lesion detection in low resource settings.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection

In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common but challenging task in many industries, which plays an important role in network monitoring, facility maintenance, information security, and so on. However, it is very difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world, and complicated abnormal patterns. From recent studies, we are inspired by Nouveau VAE (NVAE) and propose our anomaly detection model: Time series to Image VAE (T2IVAE), an unsupervised model based on NVAE for univariate series, transforming 1D time series to 2D image as input, and adopting the reconstruction error to detect anomalies. Besides, we also apply the Generative Adversarial Networks based techniques to T2IVAE training strategy, aiming to reduce the overfitting. We evaluate our model performance on three datasets, and compare it with other several popular models using F1 score. T2IVAE achieves 0.639 on Numenta Anomaly Benchmark, 0.651 on public dataset from NASA, and 0.504 on our dataset collected from real-world scenario, outperforms other comparison models.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Pushing the Envelope of Thin Crack Detection

In this study, we consider the problem of detecting cracks from the image of a concrete surface for automated inspection of infrastructure, such as bridges. Its overall accuracy is determined by how accurately thin cracks with sub-pixel widths can be detected. Our interest is in making it possible to detect cracks close to the limit of thinness if it can be defined. Toward this end, we first propose a method for training a CNN to make it detect cracks more accurately than humans while training them on human-annotated labels. To achieve this seemingly impossible goal, we intentionally lower the spatial resolution of input images while maintaining that of their labels when training a CNN. This makes it possible to annotate cracks that are too thin for humans to detect, which we call super-human labels. We experimentally show that this makes it possible to detect cracks from an image of one-third the resolution of images used for annotation with about the same accuracy. We additionally propose three methods for further improving the detection accuracy of thin cracks: i) P-pooling to maintain small image structures during downsampling operations; ii) Removal of short-segment cracks in a post-processing step utilizing a prior of crack shapes learned using the VAE-GAN framework; iii) Modeling uncertainty of the prediction to better handle hard labels beyond the limit of CNNs&#39; detection ability, which technically work as noisy labels. We experimentally examine the effectiveness of these methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Approaching quantum-limited metrology with imperfect detectors by using weak-value amplification

Weak value amplification (WVA) is a metrological protocol that amplifies ultra-small physical effects. However, the amplified outcomes necessarily occur with highly suppressed probabilities, leading to the extensive debate on whether the overall measurement precision is improved in comparison to that of conventional measurement (CM). Here, we experimentally demonstrate the unambiguous advantages of WVA that overcome practical limitations including noise and saturation of photo-detection and maintain a shot-noise-scaling precision for a large range of input light intensity well beyond the dynamic range of the photodetector. The precision achieved by WVA is six times higher than that of CM in our setup. Our results clear the way for the widespread use of WVA in applications involving the measurement of small signals including precision metrology and commercial sensors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model

In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl. To better understand this corpus, we conduct language understanding experiments on both small and large scale, and results show that the models trained on this corpus can achieve excellent performance on Chinese. We release a new Chinese vocabulary with a size of 8K, which is only one-third of the vocabulary size used in Chinese Bert released by Google. It saves computational cost and memory while works as good as original vocabulary. We also release both large and tiny versions of the pre-trained model on this corpus. The former achieves the state-of-the-art result, and the latter retains most precision while accelerating training and prediction speed for eight times compared to Bert-base. To facilitate future work on self-supervised learning on Chinese, we release our dataset, new vocabulary, codes, and pre-trained models on Github.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese

In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reflect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labeling tasks and report human performance, as well as its analysis. To facilitate future work on fine-grained NER for Chinese, we release our dataset, baselines, and leader-board.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Consensusability of linear interconnected multi-agent systems

Consensusability is an important property for many multi-agent systems (MASs) as it implies the existence of networked controllers driving the states of MAS subsystems to the same value. Consensusability is of interest even when the MAS subsystems are physically coupled, which is the case for real-world systems such as power networks. In this paper, we study necessary and sufficient conditions for the consensusability of linear interconnected MASs. These conditions are given in terms of the parameters of the subsystem matrices, as well as the eigenvalues of the physical and communication graph Laplacians. Our results show that weak coupling between subsystems and fast information diffusion rates in the physical and communication graphs favor consensusability. Technical results are verified through computer simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PaStaNet: Toward Human Activity Knowledge Engine

Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. <hand, hold, something>, which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Self-acceleration and energy channeling in the saturation of the ion-sound instability in a bounded plasma

A novel regime of the saturation of the Pierce-type ion-sound instability in bounded ion-beam-plasma system is revealed in 1D PIC simulations. It is found that the saturation of the instability is mediated by the oscillating virtual anode potential structure. The periodically oscillating potential barrier separates the incoming beam ions into two groups. One component forms a supersonic beam which is accelerated to an energy exceeding the energy of the initial cold ion beam. The other component is organized as a self-consistent phase space structure of trapped ions with a wide energy spread - the ion hole. The effective temperature (energy spread) of the ions trapped in the hole is lower than the initial beam energy. In the final stage the ion hole expands over the whole system length.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Active Galactic Nuclei with Ultra-fast Outflows Monitoring Project: The Broad-line Region of Mrk 79 as a Disk Wind

We developed a spectroscopic monitoring project to investigate the kinematics of the broad-line region (BLR) in active galactic nuclei (AGN) with ultra-fast outflows (UFOs). Mrk~79 is a radio-quiet AGN with UFOs and warm absorbers, had been monitored by three reverberation mapping (RM) campaigns, but its BLR kinematics is not understood yet. In this paper, we report the results from a new RM-campaign of Mrk~79, which was undertaken by Lijiang 2.4-m telescope. Mrk~79 is seeming to come out the faint state, the mean flux approximates a magnitude fainter than historical record. We successfully measured the lags of the broad emission lines including H$β~\lambda4861$, H$γ~\lambda4340$, He II $\lambda4686$ and He I $\lambda5876$ with respect to the varying AGN continuum. Based on the broad H$β~\lambda4861$ line, we measured black hole (BH) mass of $M_{\bullet}=5.13^{+1.57}_{-1.55}\times10^{7}M_{\odot}$, estimated accretion rates of ${\dot{M}_{\bullet}}=(0.05\pm0.02)~L_{\rm Edd}~c^{-2}$, indicating that Mrk~79 is a sub-Eddington accretor. We found that Mrk~79 deviates from the canonical Radius$-$Luminosity relationship. The marginal blueshift of the broad He II $\lambda4686$ line detected from rms spectrum indicates outflow of high-ionization gas. The velocity-resolved lag profiles of the broad H$γ~\lambda4340$, H$β~\lambda4861$, and He I $\lambda5876$ lines show similar signatures that the largest lag occurs in the red wing of the lines then the lag decreases to both sides. These signatures should suggest that the BLR of Keplerian motion probably exists the outflow gas motion. All findings including UFOs, warm absorbers, and the kinematics of high- and low-ionization BLR, may provide an indirect evidence that the BLR of Mrk~79 probably originates from disk wind.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Stimulated emission depletion microscopy with array detection and photon reassignment

We propose a novel stimulated emission depletion (STED) microscopy based on array detection and photon reassignment. By replacing the single-point detector in traditional STED with a detector array and utilizing the photon reassignment method to recombine the images acquired by each detector, the final photon reassignment STED (prSTED) image could be obtained. We analyze the principle and imaging characteristics of prSTED, and the results indicate that, compared with traditional STED, prSTED can improve the signal-to-noise ratio (SNR) of the image by increasing the obtained photon flux while maintaining the original spatial resolution of STED. In addition, the SNR and resolution of prSTED are strongly correlated with the intensity of depletion beam. Corresponding theoretical and experimental analysis about this feature are also conducted. In general, considering the enhanced signal strength, imaging speed and compatibility with some other imaging techniques, we believe prSTED would be a helpful promotion in biomedical imaging.

0 citations0 reviewsNo code linkedOpen access