arXiv subjects

Yuan Ren

Publications and source records attributed to Yuan Ren.

At least 55 records · Page 3Linked to original sources

arXiv2023

Spectral conditions for forbidden subgraphs in bipartite graphs

A graph $G$ is $H$-free, if it contains no $H$ as a subgraph. A graph is said to be \emph{$H$-minor free}, if it does not contain $H$ as a minor. In recent years, Nikiforov asked that what is the maximum spectral radius of an $H$-free graph of order $n$? In this paper, we consider about some Brualdi-Solheid-Turán type problems on bipartite graphs. In 2015, Zhai, Lin and Gong proved that if $G$ is a bipartite graph with order $n \geq 2k+2$ and $ρ(G)\geq ρ(K_{k,n-k})$, then $G$ contains a $C_{2k+2}$ unless $G \cong K_{k,n-k}$ [Linear Algebra Appl. 471 (2015)]. Firstly, we give a new and more simple proof for the above theorem. Secondly, we prove that if $G$ is a bipartite graph with order $n \geq 2k+2$ and $ρ(G)\geq ρ(K_{k,n-k})$, then $G$ contains all $T_{2k+3}$ unless $G \cong K_{k,n-k}$. Finally, we prove that among all outerplanar bipartite graphs on $n>344569$ vertices, $K_{1,n-1}$ attains the maximum spectral radius.

math.CO↗

arXiv2023

AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation

LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Perception Network (AOP-Net), a LiDAR-based multi-task framework that combines 3D object detection and panoptic segmentation. In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud. Also, a new 2D backbone that intertwines Multi-Layer Perceptron (MLP) and convolution layers is designed to further improve the detection task performance. Finally, a novel module is proposed to guide the detection head by recovering useful features discarded during down-sampling operations in the 3D backbone. This module leverages estimated instance segmentation masks to recover detailed information from each candidate object. The AOP-Net achieves state-of-the-art performance for published works on the nuScenes benchmark for both 3D object detection and panoptic segmentation tasks. Also, experiments show that our method easily adapts to and significantly improves the performance of any BEV-based 3D object detection method.

cs.CV↗

arXiv2022

PCGen: Point Cloud Generator for LiDAR Simulation

Data is a fundamental building block for LiDAR perception systems. Unfortunately, real-world data collection and annotation is extremely costly & laborious. Recently, real data based LiDAR simulators have shown tremendous potential to complement real data, due to their scalability and high-fidelity compared to graphics engine based methods. Before simulation can be deployed in the real-world, two shortcomings need to be addressed. First, existing methods usually generate data which are more noisy and complete than the real point clouds, due to 3D reconstruction error and pure geometry-based raycasting method. Second, prior works on simulation for object detection focus solely on rigid objects, like cars, but VRUs, like pedestrians, are important road participants. To tackle the first challenge, we propose FPA raycasting and surrogate model raydrop. FPA enables the simulation of both point cloud coordinates and sensor features, while taking into account reconstruction noise. The ray-wise surrogate raydrop model mimics the physical properties of LiDAR's laser receiver to determine whether a simulated point would be recorded by a real LiDAR. With minimal training data, the surrogate model can generalize to different geographies and scenes, closing the domain gap between raycasted and real point clouds. To tackle the simulation of deformable VRU simulation, we employ SMPL dataset to provide a pedestrian simulation baseline and compare the domain gap between CAD and reconstructed objects. Applying our pipeline to perform novel sensor synthesis, results show that object detection models trained by simulation data can achieve similar result as the real data trained model.

cs.RO↗

arXiv2022

Caching Scalable Videos in the Edge of Wireless Cellular Networks

By pre-fetching popular videos into the local caches of edge nodes, wireless edge caching provides an effective means of reducing repeated content deliveries. To meet the various viewing quality requirements of multimedia users, scalable video coding (SVC) is integrated with edge caching, where the constituent layers of scalable videos are flexibly cached and transmitted to users. In this article, we discuss the challenges arising from the different content popularity and various viewing requirements of scalable videos, and present the diverse types of cached contents as well as the corresponding transmission schemes. We provide an overview of the existing caching schemes, and summarize the criteria of making caching decisions. A case study is then presented, where the transmission delay is quantified and used as the performance metric. Simulation results confirm that giving cognizance to the realistic requirements of end users is capable of significantly reducing the content transmission delay, compared to the existing caching schemes operating without SVC. The results also verify that the transmission delay of the proposed random caching scheme is lower than that of the caching scheme which only provides local caching gain.

cs.NI↗

arXiv2022

A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation

3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view information to address the shortcomings of each projection view. Furthermore, foreground semantic information is incorporated to ease the detection task by highlighting the locations of each object class in the feature maps. Finally, a new center density heatmap generated based on the instance-level information further guides the detection backbone by suggesting possible box center locations for objects. Our method works with any BEV-based 3D object detection method, and as shown by extensive experiments on the nuScenes dataset, it provides significant performance gains. Notably, the proposed method based on a single-stage CenterPoint 3D object detection network achieved state-of-the-art performance on nuScenes 3D Detection Benchmark with 67.3 NDS.

cs.CV↗

arXiv2021

Differential thermopower images as a probe of many-body effects in quantum point contacts

Mesoscopic circuit elements such as quantum dots and quantum point contacts (QPCs) offer a uniquely controllable platform for engineering complex quantum devices, whose tunability makes them ideal for generating and investigating interacting quantum systems. However, the conductance measurements commonly employed in mesoscopics experiments are poorly suited to discerning correlated phenomena from those of single-particle origin. Here, we introduce non-equilibrium thermopower measurements as a novel approach to probing the local density of states (LDOS), offering an energy-resolved readout of many-body effects. We combine differential thermopower measurements with non-equilibrium density functional theory (DFT) to both confirm the presence of a localized state at the saddle point of a QPC and reveal secondary states that emerge wherever the reservoir chemical potential intersects the gate-induced potential barrier. These experiments establish differential thermopower imaging as a robust and general approach to exploring quantum many-body effects in mesoscopic circuits.

cond-mat.mes-hall↗

arXiv2021

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In this paper, we propose a new computationally efficient LiDAR based panoptic segmentation framework, called GP-S3Net. GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information. Our new design consists of a novel instance-level network to process the semantic results by constructing a graph convolutional network to identify objects (foreground), which later on are fused with the background classes. Through the fine-grained clusters of the foreground objects from the semantic segmentation backbone, over-segmentation priors are generated and subsequently processed by 3D sparse convolution to embed each cluster. Each cluster is treated as a node in the graph and its corresponding embedding is used as its node feature. Then a GCNN predicts whether edges exist between each cluster pair. We utilize the instance label to generate ground truth edge labels for each constructed graph in order to supervise the learning. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches, by a significant margin across available datasets such as, nuScenes and SemanticPOSS, ranking first on the competitive public SemanticKITTI leaderboard upon publication.

cs.CV↗

arXiv2021

Directly measuring mode purity of single component in superposed optical vortices

Mode purity is an important reference for the quality of the optical vortex. In this work, we propose a self-interference method to directly measure single component in superposed optical vortices based on phase-shifting technology. This method has excellent flexibility and robustness, which can be applied to a variety of occasions and harsh conditions. Careful alignment and optimized error analysis allow us to generate and measure single component with mode purity as high as 99.997%.

physics.optics↗

arXiv2021

S3Net: 3D LiDAR Sparse Semantic Segmentation Network

Semantic Segmentation is a crucial component in the perception systems of many applications, such as robotics and autonomous driving that rely on accurate environmental perception and understanding. In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches. However, they either abandon the valuable 3D topology and geometric relations and suffer from information loss introduced in the projection process or are inefficient. Therefore, there is a need for accurate models capable of processing the 3D driving-scene point cloud in 3D space. In this paper, we propose S3Net, a novel convolutional neural network for LiDAR point cloud semantic segmentation. It adopts an encoder-decoder backbone that consists of Sparse Intra-channel Attention Module (SIntraAM), and Sparse Inter-channel Attention Module (SInterAM) to emphasize the fine details of both within each feature map and among nearby feature maps. To extract the global contexts in deeper layers, we introduce Sparse Residual Tower based upon sparse convolution that suits varying sparsity of LiDAR point cloud. In addition, geo-aware anisotrophic loss is leveraged to emphasize the semantic boundaries and penalize the noise within each predicted regions, leading to a robust prediction. Our experimental results show that the proposed method leads to a large improvement (12\%) compared to its baseline counterpart (MinkNet42 \cite{choy20194d}) on SemanticKITTI \cite{DBLP:conf/iccv/BehleyGMQBSG19} test set and achieves state-of-the-art mIoU accuracy of semantic segmentation approaches.

cs.CV↗

arXiv2021

Measurement and Shaping of Perfect Optical Vortex via Cross Phase

We investigate a method for the measurement and shaping of a perfect optical vortex (POV) by using the cross-phase for the first time. Experimentally, we propose a method to measure the topological charges +3 and +-5 of POVs with the low-order cross-phase (LOCP) at the Fourier plane; we realize shaping intensity distributions of POVs at the Fourier plane with the high-order cross-phase (HOCP) and show the asymmetric modulation ability of the HOCP for an optical field. Furthermore, we discuss the influence of parameters on the optical field in the shaping process. On this basis, we propose a secondary energy control method that combines the characteristics of the POV, which ensures flexible control of the shape while reducing the impact on the orbital angular momentum of the original POV. This work provides an alternative method to measure and shape a POV, which facilitates applications in optical micro-manipulation.

physics.optics↗

arXiv2020

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3D structure and associated semantic labels from "sparse" representations - have been, to a degree, successful in small indoor scenes when provided with dense point clouds or dense depth maps often fused with semantic segmentation maps from RGB images. However, the performance of these systems drop drastically when applied to large outdoor scenes characterized by dynamic and exponentially sparser conditions. Likewise, processing of the entire sparse volume becomes infeasible due to memory limitations and workarounds introduce computational inefficiency as practitioners are forced to divide the overall volume into multiple equal segments and infer on each individually, rendering real-time performance impossible. In this work, we formulate a method that subsumes the sparsity of large-scale environments and present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the SemanticKITTI benchmark. Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion strategy to complement our 3D network, providing robustness to occlusions and extreme sparsity in distant regions. We conduct experiments for the 2D semantic scene completion task and compare the results of our sparse 2D network against several leading LiDAR segmentation models adapted for bird's eye view segmentation on two open-source datasets.

cs.CV↗

arXiv2020

Measurement and Shaping of Circular Airy Vortex via Cross Phase

We apply the CP to CAVs for the first time experimentally. Firstly, we introduce the propagation properties of a CAV with the LOCP in the process of measuring TCs, and measure the TCs of CAVs in the self-focusing areas experimentally. Secondly, we shape the CAVs via the HOCP and discuss the manipulaiton of singularities.

physics.optics↗

arXiv2020

Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching

Aiming to minimize service delay, we propose a new random caching scheme in device-to-device (D2D)-assisted heterogeneous network. To support diversified viewing qualities of multimedia video services, each video file is encoded into a base layer (BL) and multiple enhancement layers (ELs) by scalable video coding (SVC). A super layer, including the BL and several ELs, is transmitted to every user. We define and quantify the service delay of multi-quality videos by deriving successful transmission probabilities when a user is served by a D2D helper, a small-cell base station (SBS) and a macro-cell base station (MBS). We formulate a delay minimization problem subject to the limited cache sizes of D2D helpers and SBSs. The structure of the optimal solutions to the problem is revealed, and then an improved standard gradient projection method is designed to effectively obtain the solutions. Both theoretical analysis and Monte-Carlo simulations validate the successful transmission probabilities. Compared with three benchmark caching policies, the proposed SVC-based random caching scheme is superior in terms of reducing the service delay.

cs.NI↗

arXiv2020

A new kind of Hermite-Gaussian-like optical vortex generated by cross-phase

We propose a new kind of optical vortex called the Hermite-Gaussian-like optical vortex (HGOV) inspired by the crossphase (CP). Theoretically, we investigate how the CP is decoupled from the phase of a cylindrical lens. We also investigate the propagation characteristics of an HGOV, which has a Hermitian-Gaussian-like intensity distribution but still retains the orbital angular momentum. Furthermore, we derive the Fresnel diffraction integral of an HGOV and study the purity at infinity. Besides, we show a novel function of the self-measurement of the HGOV. Finally, we show that we can change the relative positions of singularities and the direction of an HGOV precisely, which facilitates applications in optical micromanipulation.

physics.optics↗

arXiv2020

Quadratic torsion subgroups of modular Jacobian varieties

Let $D$ be an odd square-free positive integer and $C$ a divisor of $D$. For any quadratic character $χ$ modulo $C$, we prove that the $χ$-part of the group $J_0(DC)_\text{tor}$ of torsion points of $J_0(DC)$ coincides with the $χ$-part of its cuspidal subgroup, away from those primes of bad reduction or where possible congruences between oldforms and newforms occur.

math.NT↗

arXiv2020

Modern cities emerge as 'super-cells' where enclosed industrial systems are hotspots of goods and services

Prevailing hypotheses recognize cities as 'super-organisms' which both provides organizing principles for cities and fills the scalar gap in the hierarchical living system between ecosystems and the entire planet. However, most analogies between the traits of organisms and cities are inappropriate making the super-organism model impractical as a means to acquire new knowledge. Using a cluster analysis of 15 traits of cities and other living systems, we found that modern cities are more similar to eukaryotic cells than to multicellular organisms. Enclosed industrial systems, such as factories and greenhouses, dominate modern cities and are analogous to organelles as hotspots that provide high-flux goods and services. Therefore, we propose a 'super-cell city model' as more appropriate than the super-organism model. In addition to the theoretical significance, our model also recognizes enclosed industrial systems as functional components that improve the vitality and sustainability of cities.

physics.soc-ph↗

arXiv2020

Optical Vortex Shaping & Multiple Singularities Manipulation via High-order Cross-phase

Increasing demand for practical applications is forcing deeper research into optical vortices (OVs): from the generation and measurement to shaping and multiple singularities manipulation of OVs. Herein, we propose a new type of phase structure called the high-order cross phase (HOCP) can be employed to modulate OVs to implement both polygonal shaping and singularities manipulation. Theoretically, we investigate the propagation characteristics of OVs with the HOCP. In experiments, we achieve the shaping and singularities manipulation of OVs by utilizing the HOCP. On this basis, we discuss the interference patterns of superposed OVs after the modulation. This work provides an alternative method to achieve both polygonal shaping and multiple singularities manipulation, which will facilitate applications in optical micro-manipulation, optical communication, and high-dimensional quantum entanglement.

physics.optics↗

arXiv2020

Exciton interaction induced spin splitting in MoS$_2$ monolayer

By pumping nonresonantly a MoS$_2$ monolayer at $13$ K under a circularly polarized cw laser, we observe exciton energy redshifts that break the degeneracy between B excitons with opposite spin. The energy splitting increases monotonically with the laser power reaching as much as $18$ meV, while it diminishes with the temperature. The phenomenon can be explained theoretically by considering simultaneously the bandgap renormalization which gives rise to the redshift and exciton-exciton Coulomb exchange interaction which is responsible for the spin-dependent splitting. Our results offer a simple scheme to control the valley degree of freedom in MoS$_2$ monolayer and provide an accessible method in investigating many-body exciton exciton interaction in such materials.

cond-mat.mes-hall↗