arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 955 records · Page 53Linked to original sources

arXiv2023

The dg Leavitt algebra, singular Yoneda category and singularity category

For any finite dimensional algebra $Λ$ given by a quiver with relations, we prove that its dg singularity category is quasi-equivalent to the perfect dg derived category of a dg Leavitt path algebra. The result might be viewed as a deformed version of the known description of the dg singularity category of a radical-square-zero algebra in terms of a Leavitt path algebra with trivial differential. The above result is achieved in two steps. We first introduce the singular Yoneda dg category of $Λ$, which is quasi-equivalent to the dg singularity category of $Λ$. The construction of this new dg category follows from a general operation for dg categories, namely an explicit dg localization inverting a natural transformation from the identity functor to a dg endofunctor. This localization turns out to be quasi-equivalent to a dg quotient category. Secondly, we prove that the endomorphism algebra of the quotient of $Λ$ modulo its Jacobson radical in the singular Yoneda dg category is isomorphic to the dg Leavitt path algebra. The appendix is devoted to an alternative proof of the result using Koszul-Moore duality and derived localizations.

math.RT↗

arXiv2023

MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results

Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completion have relied on stereo vision or structured light techniques, recent advances in deep learning have enabled more accurate and efficient completion of depth maps from RGB images and sparse ToF measurements. To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition. The competition aimed to encourage research in this area by providing a standardized dataset and evaluation metrics to compare the accuracy of different approaches. In this report, we present the results of the competition and analyze the strengths and weaknesses of the top-performing methods. We also discuss the implications of our findings for future research in RGB+sparse ToF depth completion. We hope that this competition and report will help to advance the state-of-the-art in this important area of research. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023.

cs.CV↗

arXiv2023

Two kinds of numerical algorithms for ultra-slow diffusion equations

In this article, two kinds of numerical algorithms are derived for the ultra-slow (or superslow) diffusion equation in one and two space dimensions, where the ultra-slow diffusion is characterized by the Caputo-Hadamard fractional derivative of order $α\in (0,1)$. To describe the spatial interaction, the Riesz fractional derivative and the fractional Laplacian are used in one and two space dimensions, respectively. The Caputo-Hadamard derivative is discretized by two typical approximate formulae, i.e., L2-1$_σ$ and L1-2 methods. The spatial fractional derivatives are discretized by the 2-nd order finite difference methods. When L2-1$_σ$ discretization is used, the derived numerical scheme is unconditionally stable with error estimate $\mathcal{O}(τ^{2}+h^{2})$ for all $α\in (0, 1)$, in which $τ$ and $h$ are temporal and spatial stepsizes, respectively. When L1-2 discretization is used, the derived numerical scheme is stable with error estimate $\mathcal{O}(τ^{3-α}+h^{2})$ for $α\in (0, 0.3738)$. The illustrative examples displayed are in line with the theoretical analysis.

math.NA↗

arXiv2023

Nonzero angular momentum density wave phases in SU($N$) fermions with singlet-bond and triplet-current interactions

We employ the sign-problem-free projector determinant quantum Monte Carlo method to study a microscopic model of SU($N$) fermions with singlet-bond and triplet-current interactions on the square lattice. We find the gapped singlet $p_x$ and gapless triplet $d_{x^2-y^2}$ density wave states in the half-filled $N=4$ model. Specifically, the triplet $d_{x^2-y^2}$ density wave order is observed in the weak triplet-current interaction regime. As the triplet-current interaction strength is further increased, our simulations demonstrate a transition to the singlet $p_x$ density wave state, accompanied by a gapped mixed-ordered area where the two orders coexist. With increasing singlet-bond interaction strength, the triplet $d_{x^2-y^2}$-wave order persists up to a critical point after which the singlet $p_x$ density wave state is stabilized, while the ground state is disordered in between the two ordered phases. The analytical continuation is then performed to derive the single-particle spectrum. In the spectra of triplet $d_{x^2-y^2}$ and singlet $p_x$ density waves, the anisotropic Dirac cone and the parabolic shape around the Dirac point are observed, respectively. As for the mixed-ordered area, a single-particle gap opens and the velocities remain anisotropic at the Dirac point.

cond-mat.str-el↗

arXiv2023

Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios

Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy. However, most of the DL-based AMC methods have a large number of parameters and high computational complexity, and they cannot be directly applied to low-resource scenarios with limited computing power and storage space. In this letter, we propose a fast AMC method with lightweight and low-complexity using ultra lite convolutional neural network (ULCNN) consisting of data augmentation, complex-valued convolution, separable convolution, channel attention, and channel shuffle. Simulation results demonstrate that our proposed ULCNN-based AMC method achieves an average accuracy of 62.47% on RML2016.10a and only 9,751 parameters. Moreover, ULCNN is verified on a typical edge device (Raspberry Pi), where the interference time per sample is about 0.775 ms. The reproducible code can be downloaded from GitHub\footnote{https://github.com/BeechburgPieStar/Ultra-Lite-Convolutional-Neural-Network-for-Automatic-Modulation-Classification}.

eess.SP↗

arXiv2023

Iterative fluctuation ghost imaging

We present a new technique, iterative fluctuation ghost imaging (IFGI) which dramatically enhances the resolution of ghost imaging (GI). It is shown that, by the fluctuation characteristics of the second-order correlation function, the imaging information with the narrower point spread function (PSF) than the original information can be got. The effects arising from the PSF and the iteration times also be discussed.

physics.optics↗

arXiv2023

Conditional Denoising Diffusion for Sequential Recommendation

Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions. However, two prominent generative models, namely Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs), exhibit challenges that impede achieving optimal performance in sequential recommendation tasks. Specifically, GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. The sparse and noisy nature of sequential recommendation further exacerbates these issues. In response to these limitations, we present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser. This approach streamlines the optimization and generation process by dividing it into easier and tractable steps in a conditional autoregressive manner. Furthermore, we introduce a novel optimization schema that incorporates both cross-divergence loss and contrastive loss. This novel training schema enables the model to generate high-quality sequence/item representations and meanwhile precluding collapse. We conducted comprehensive experiments on four benchmark datasets, and the superior performance achieved by our model attests to its efficacy.

cs.LG↗

arXiv2023

CogDL: A Comprehensive Library for Graph Deep Learning

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.

cs.SI↗

arXiv2023

Adsorption tuning of polarity and magnetism in AgCr2S4 monolayer

As a recent successfully exfoliated non van der Waals layered material, AgCrS2 has received a lot of attentions. Motivated by its structure related magnetic and ferroelectric behavior, a theoretical study on its exfoliated monolayer AgCr2S4 has been carried out in the present work. Based on density functional theory, the ground state and magnetic order of monolayer AgCr2S4 have been determined. The centrosymmetry emerges upon two-dimensional confinement and thus eliminates the bulk polarity. Moreover, two-dimensional ferromagnetism appears in the CrS2 layer of AgCr2S4 and can persist up to room temperature. The surface adsorption has also been taken into consideration, which shows a nonmonotonic effect on the ionic conductivity through ion displacement of the interlayer Ag, but has little impact on the layered magnetic structure.

cond-mat.mtrl-sci↗

arXiv2023

Large Violation of the Wiedemann Franz Law in Heusler, Ferromagnetic, Weyl Semimetal Co$_2$MnAl

The Wiedemann-Franz (WF) law relates the electronic component of the thermal conductivity to the electrical conductivity in metals through the Lorenz number. The WF law has proven to be remarkably robust, however violations have been observed in many topological materials. In this work, we report thermoelectric measurements conducted on Heusler, ferromagnetic, Weyl semimetal Co$_2$MnAl which shows a drastic, temperature dependent violation of the WF law below 300 K. We then discuss our result in the context of known physical explanations for WF law violation. Both the magnitude and temperature dependence of the violation in Co2MnAl are extreme, indicating that there may be more than one effect contributing to the violation in this system.

cond-mat.mtrl-sci↗

arXiv2023

Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i.e., every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.

cs.RO↗

arXiv2023

ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster

As robotics technology advances, dense point cloud maps are increasingly in demand. However, dense reconstruction using a single unmanned aerial vehicle (UAV) suffers from limitations in flight speed and battery power, resulting in slow reconstruction and low coverage. Cluster UAV systems offer greater flexibility and wider coverage for map building. Existing methods of cluster UAVs face challenges with accurate relative positioning, scale drift, and high-speed dense point cloud map generation. To address these issues, we propose a cluster framework for large-scale dense reconstruction and real-time collaborative localization. The front-end of the framework is an improved visual odometry which can effectively handle large-scale scenes. Collaborative localization between UAVs is enabled through a two-stage joint optimization algorithm and a relative pose optimization algorithm, effectively achieving accurate relative positioning of UAVs and mitigating scale drift. Estimated poses are used to achieve real-time dense reconstruction and fusion of point cloud maps. To evaluate the performance of our proposed method, we conduct qualitative and quantitative experiments on real-world data. The results demonstrate that our framework can effectively suppress scale drift and generate large-scale dense point cloud maps in real-time, with the reconstruction speed increasing as more UAVs are added to the system.

cs.RO↗

arXiv2023

HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion

Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The entire registration process is a coarse-to-fine procedure. First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process. To achieve fine matching, 2D registration is performed by extracting 2D boundary points from patches, followed by 3D adjustment. Finally, the results of multiple patch pose estimates are clustered and fused to determine the final pose. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments. In order to compare the robustness of cross-source point cloud registration, the proposed method and generalized iterative closest point method are compared. Furthermore, a metric for describing the degree of point cloud filling is proposed. The experimental results demonstrate that our approach achieves state-of-the-art performance in cross-source point cloud registration.

cs.CV↗

arXiv2023

Analysis of the current status of tuberculosis transmission in China based on a heterogeneity model

Tuberculosis (TB) is an infectious disease transmitted through the respiratory system. China is one of the countries with a high burden of TB. Since 2004, an average of more than 800,000 cases of active TB have been reported each year in China. Analyzing the case data from 2004-2018, we find significant differences in TB incidence by age group. Therefore, the effect of age heterogeneous structure on TB transmission needs further study. We develop a model of TB to explore the role of age heterogeneity as a factor in TB transmission. The model is fitted numerically using the nonlinear least squares method to obtain the key parameters in the model, and the basic reproduction number Rv 0.8017 is calculated and the sensitivity anal-ysis of Rv to the parameters is given. The simulation results show that reducing the number of new infections in the elderly population and increasing the recovery rate of elderly patients with the disease could significantly reduce the transmission of tuberculosis. Furthermore the feasibility of achieving the goals of the WHO End TB Strategy in China is assessed, and we obtain that with existing TB control measures it will take another 30 years for China to reach the WHO goal to reduce 90% of the number of new cases by year 2049. However, in theoretical it is feasible to reach the WHO strategic goal of ending tuberculosis by 2035 if the group contact rate in the elderly population can be reduced though it is difficulty to reduce the contact rate.

stat.AP↗

arXiv2023

A BERT-based Unsupervised Grammatical Error Correction Framework

Grammatical error correction (GEC) is a challenging task of natural language processing techniques. While more attempts are being made in this approach for universal languages like English or Chinese, relatively little work has been done for low-resource languages for the lack of large annotated corpora. In low-resource languages, the current unsupervised GEC based on language model scoring performs well. However, the pre-trained language model is still to be explored in this context. This study proposes a BERT-based unsupervised GEC framework, where GEC is viewed as multi-class classification task. The framework contains three modules: data flow construction module, sentence perplexity scoring module, and error detecting and correcting module. We propose a novel scoring method for pseudo-perplexity to evaluate a sentence's probable correctness and construct a Tagalog corpus for Tagalog GEC research. It obtains competitive performance on the Tagalog corpus we construct and open-source Indonesian corpus and it demonstrates that our framework is complementary to baseline method for low-resource GEC task.

cs.CL↗

arXiv2023

Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation

Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally, model generalization can be reflected in the flatness of the loss landscape. Since averaging parameters of multiple models can find flatter minima, we are inspired to extend the process to the sampled convex combinations of multi-student models in OKD. Specifically, by linearly weighting students' parameters in each training batch, we construct a Hybrid-Weight Model(HWM) to represent the parameters surrounding involved students. The supervision loss of HWM can estimate the landscape's curvature of the whole region around students to measure the generalization explicitly. Hence we integrate HWM's loss into students' training and propose a novel OKD framework via parameter hybridization(OKDPH) to promote flatter minima and obtain robust solutions. Considering the redundancy of parameters could lead to the collapse of HWM, we further introduce a fusion operation to keep the high similarity of students. Compared to the state-of-the-art(SOTA) OKD methods and SOTA methods of seeking flat minima, our OKDPH achieves higher performance with fewer parameters, benefiting OKD with lightweight and robust characteristics. Our code is publicly available at https://github.com/tianlizhang/OKDPH.

cs.CV↗

arXiv2023

LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception

LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other. This paper presents LidarMultiNet, a LiDAR-based multi-task network that unifies these three major LiDAR perception tasks. Among its many benefits, a multi-task network can reduce the overall cost by sharing weights and computation among multiple tasks. However, it typically underperforms compared to independently combined single-task models. The proposed LidarMultiNet aims to bridge the performance gap between the multi-task network and multiple single-task networks. At the core of LidarMultiNet is a strong 3D voxel-based encoder-decoder architecture with a Global Context Pooling (GCP) module extracting global contextual features from a LiDAR frame. Task-specific heads are added on top of the network to perform the three LiDAR perception tasks. More tasks can be implemented simply by adding new task-specific heads while introducing little additional cost. A second stage is also proposed to refine the first-stage segmentation and generate accurate panoptic segmentation results. LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance. Notably, LidarMultiNet reaches the official 1st place in the Waymo Open Dataset 3D semantic segmentation challenge 2022 with the highest mIoU and the best accuracy for most of the 22 classes on the test set, using only LiDAR points as input. It also sets the new state-of-the-art for a single model on the Waymo 3D object detection benchmark and three nuScenes benchmarks.

cs.CV↗

arXiv2023

Electric-field-driven spin resonance by on-surface exchange coupling to a single-atom magnet

Coherent control of individual atomic and molecular spins on surfaces has recently been demonstrated by using electron spin resonance (ESR) in a scanning tunneling microscope (STM). Here we present a combined experimental and modeling study of the ESR of a single hydrogenated Ti atom that is exchange-coupled to a Fe adatom located in 0.6-0.8 nm away. Continuous wave and pulsed ESR of the Ti spin showed a Rabi rate with two contributions, one from the tip and the other from the Fe, whose spin interactions with Ti were modulated by the radio-frequency electric field. The Fe contribution is comparable to the tip, as revealed from its dominance when the tip was retracted, and tunable using a vector magnetic field. Our new ESR scheme allows on-surface individual spins to be addressed and coherently controlled without the need for magnetic interaction with a tip. This study establishes a feasible implementation of spin-based multi-qubit systems on surfaces.

cond-mat.mes-hall↗