arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 721 records · Page 40Linked to original sources

arXiv2021

Beam smoothing based on prism pair for multistep pulse compressor in PW lasers

Ultra-short ultra-intense laser provides unprecedented experimental tools and extreme physical conditions to explore frontier secrets of nature. Recently, multistep pulse compressor (MPC) was proposed to break through the limitation of the size and damage threshold of the grating in the compressor during the realization of higher peak power laser. In the MPC methods, beam smoothing in the pre-compressor is a very important process. Here, beam smoothing based on prism pair were studied technically, in which both the spatial profiles and the spectral dispersive properties were analyzed in detail. The simulation results show clearly that the prism pair can effectively smooth the laser beam. Furthermore, the beam smoothing is much more efficiency with shorter separated distance if two prism pairs are arranged to induce spatial dispersion at one direction or two directions. The results of beam smoothing here will help the optimized optical designs in all PW laser systems to improve their output and running safety.

physics.optics↗

arXiv2021

NAS-FCOS: Efficient Search for Object Detection Architectures

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the best of our knowledge, most of the recent NAS studies on object detection tasks fail to satisfactorily strike a balance between performance and efficiency of the resulting models, let alone the excessive amount of computational resources cost by those algorithms. Here we propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely, FCOS [36], using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs. The discovered architectures surpass state-of-the-art object detection models (such as Faster R-CNN, Retina-Net and, FCOS) by 1.0% to 5.4% points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS method for object detection. Code is available at https://github.com/Lausannen/NAS-FCOS.

cs.CV↗

arXiv2021

On the Willmore problem for surfaces with symmetry

The Willmore Problem seeks the surface in $\mathbb S^3\subset\mathbb R^4$ of a given topological type minimizing the squared-mean-curvature energy $W = \int |\mathbf{H}_{\mathbb{R}^4}|^2 = \operatorname{area} + \int H_{\mathbb{S}^3}^2$. The longstanding Willmore Conjecture that the Clifford torus minimizes $W$ among genus-$1$ surfaces is now a theorem of Marques and Neves [19], but the general conjecture [10] that Lawson's [16] minimal surface $ξ_{g,1}\subset\mathbb S^3$ minimizes $W$ among surfaces of genus $g>1$ remains open. Here we prove this conjecture under the additional assumption that the competitor surfaces $M\subset\mathbb S^3$ share the ambient symmetries of $ξ_{g,1}$. Specifcally, we show each Lawson surface $ξ_{m,k}$ satisfies the analogous $W$-minimizing property under a somewhat smaller symmetry group ${G}_{m,k}<SO(4)$, using a local computation of the orbifold Euler number $χ_o(M/{G}_{m,k})$ to exclude certain intersection patterns of $M$ with the great circles fixed by generators of ${G}_{m,k}$. We also describe a genus 2 example where the Willmore Problem may not be solvable among surfaces with its symmetry.

math.DG↗

arXiv2021

StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

We propose StyleNeRF, a 3D-aware generative model for photo-realistic high-resolution image synthesis with high multi-view consistency, which can be trained on unstructured 2D images. Existing approaches either cannot synthesize high-resolution images with fine details or yield noticeable 3D-inconsistent artifacts. In addition, many of them lack control over style attributes and explicit 3D camera poses. StyleNeRF integrates the neural radiance field (NeRF) into a style-based generator to tackle the aforementioned challenges, i.e., improving rendering efficiency and 3D consistency for high-resolution image generation. We perform volume rendering only to produce a low-resolution feature map and progressively apply upsampling in 2D to address the first issue. To mitigate the inconsistencies caused by 2D upsampling, we propose multiple designs, including a better upsampler and a new regularization loss. With these designs, StyleNeRF can synthesize high-resolution images at interactive rates while preserving 3D consistency at high quality. StyleNeRF also enables control of camera poses and different levels of styles, which can generalize to unseen views. It also supports challenging tasks, including zoom-in and-out, style mixing, inversion, and semantic editing.

cs.CV↗

arXiv2021

A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI

Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patient was scanned with T1WI, T2WI, T1WI venous phase (T2WI-V), T1WI arterial phase (T1WI-A), and DWI MRI sequences. We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing and provides a confidence measure for its diagnoses. We evaluate using five-fold cross validation and compare performance against three radiologists, including a senior hepatology radiologist, a junior hepatology radiologist and an abdominal radiologist. Results: the proposed CAD solution achieves a mean F1 score of 0.62, outperforming the abdominal radiologist (0.47), matching the junior hepatology radiologist (0.61), and underperforming the senior hepatology radiologist (0.68). The CAD system can informatively assess its diagnostic confidence, i.e., when only evaluating on the 70% most confident cases the mean f1 score and sensitivity at 80% specificity for HCC vs. others are boosted from 0.62 to 0.71 and 0.84 to 0.92, respectively. Conclusion: the proposed fully-automatic CAD solution can provide good diagnostic performance with informative confidence assessments in finding and discriminating liver lesions from MRI studies.

eess.IV↗

arXiv2021

Ultrafast photonic rainbow with controllable orbital angular momentum

Increasing any degree of freedom of light beam may open a wide application area of this special light beam. Vortex beam with a dimension of orbital angular momentum (OAM) as a useful light source has been widely applied in many fields. Here, unique multicolor concentric ultrafast vortex beams (MUCU-VBs), which are also named ultrafast photonic rainbow, with controllable orbital angular momentum are firstly generated using cascaded four-wave mixing (CFWM) in an yttrium aluminum garnet (YAG) plate. Up to 9 multicolor concentric annular ultrafast vortex sidebands are generated simultaneously. The topological charges of the sidebands, which are controllable by changing the topological charges of the two input pump beams, are measured and in according with the theoretical analysis very well. The novel MUCU-VBs can be manipulated simultaneously in temporal, spatial, spectral domains and OAM state, which open more than one new degree of freedoms of vortex light beam and will be of wide and special applications, such as multicolor pump-probe experiments, simultaneous microparticle manipulation and exploring, and optical communication. Moreover, the special focusing properties of the multicolor ultrafast sidebands, such as multi-focus of different wavelengths, may further extend their application area.

physics.optics↗

arXiv2021

Quantum Dynamics of Optimization Problems

In this letter, by establishing the Schrödinger equation of the optimization problem, the optimization problem is transformed into a constrained state quantum problem with the objective function as the potential energy. The mathematical relationship between the objective function and the wave function is established, and the quantum interpretation of the optimization problem is realized. Under the black box model, the Schrödinger equation of the optimization problem is used to establish the kinetic equation, i.e., the Fokker-Planck equation of the time evolution of the optimization algorithm, and the basic iterative structure of the optimization algorithm is given according to the interpretation of the Fokker-Planck equation. The establishment of the Fokker-Planck equation allows optimization algorithms to be studied using dynamic methods and is expected to become an important theoretical basis for algorithm dynamics.

quant-ph↗

arXiv2021

Properties of the fast fission and the coincident emissions of light charged particles in $^{40}$Ar + $^{197}$Au reactions at 30 MeV/u

The experiment of Ar+Au reactions at 30 MeV/u have been performed using the Compact Spectrometer for Heavy IoN Experiments (CSHINE) in phase I. The light-charged particles are measured by the silicon stripe telescopes in coincidence with the fission fragments recorded by the parallel plate avalanche counters. The distribution properties of the azimuth difference $Δϕ$ and the time-of-flight difference $ΔTOF$ of the fission fragments are presented varying the folding angles which represents the linear momentum transfer from the projectile to the reaction system. The relative abundance of the light charged particles in the fission events to the inclusive events is compared as a function of the laboratory angle $θ_{\rm lab}$ ranging from $18^\circ$ to $60^\circ$ in various folding angle windows. The angular evolution of the yield ratios of p/d and t/d in coincidence with fission fragments is investigated. In a relative comparison, tritons are more abundantly emitted at small angles, while protons are more abundant at large angles. The angular evolution of the neutron richness of the light-charged particles is consistent with the results obtained in previous inclusive experiments.

nucl-ex↗

arXiv2021

Non-Convex Exact Community Recovery in Stochastic Block Model

Community detection in graphs that are generated according to stochastic block models (SBMs) has received much attention lately. In this paper, we focus on the binary symmetric SBM -- in which a graph of $n$ vertices is randomly generated by first partitioning the vertices into two equal-sized communities and then connecting each pair of vertices with probability that depends on their community memberships -- and study the associated exact community recovery problem. Although the maximum-likelihood formulation of the problem is non-convex and discrete, we propose to tackle it using a popular iterative method called projected power iterations. To ensure fast convergence of the method, we initialize it using a point that is generated by another iterative method called orthogonal iterations, which is a classic method for computing invariant subspaces of a symmetric matrix. We show that in the logarithmic sparsity regime of the problem, with high probability the proposed two-stage method can exactly recover the two communities down to the information-theoretic limit in $\mathcal{O}(n\log^2n/\log\log n)$ time, which is competitive with a host of existing state-of-the-art methods that have the same recovery performance. We also conduct numerical experiments on both synthetic and real data sets to demonstrate the efficacy of our proposed method and complement our theoretical development.

math.OC↗

arXiv2021

Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation

Grasping in cluttered scenes has always been a great challenge for robots, due to the requirement of the ability to well understand the scene and object information. Previous works usually assume that the geometry information of the objects is available, or utilize a step-wise, multi-stage strategy to predict the feasible 6-DoF grasp poses. In this work, we propose to formalize the 6-DoF grasp pose estimation as a simultaneous multi-task learning problem. In a unified framework, we jointly predict the feasible 6-DoF grasp poses, instance semantic segmentation, and collision information. The whole framework is jointly optimized and end-to-end differentiable. Our model is evaluated on large-scale benchmarks as well as the real robot system. On the public dataset, our method outperforms prior state-of-the-art methods by a large margin (+4.08 AP). We also demonstrate the implementation of our model on a real robotic platform and show that the robot can accurately grasp target objects in cluttered scenarios with a high success rate. Project link: https://openbyterobotics.github.io/sscl

cs.RO↗

arXiv2021

Social Force in Pedestrian Crowd

This paper provides a new perspective to understand existing controversy on the social force model. These issues include that the social force disobeys Newton 3rd Law, oscillation phenomenon when one agent is approaching another as well as some questions on the faster-is-slower effect. From the perspective of physics these problems seems difficult to explain. This paper provides a new perspective to understand these issues. We introduce a new concept of desired interpersonal distance to explain how the social force is generated from conscious mind of human. Although the social force disobeys Newton 3rd Law, the whole model is exactly within the Newton Laws to characterize pedestrian motion. The oscillation phenomenon may exist in non-physics entity (i.e., desired velocity and desired interpersonal distance) rather than physics entity (i.e., actual velocity and actual distance), and such oscillation is mitigated by treating non-physics entity as variable rather than constant. Very interestingly, the desired velocity represents the motivation level of pedestrian motion, and the faster-is-slower effect is thus explained by Yerkes-Dodson law, explaining how motivation level could improve or impair human performances in a collective sense. This inverted-U effect is further studied with a falling-down model and the numerical testing is exhibited by using FDS+Evac.

physics.soc-ph↗

arXiv2021

Space-and-time-synchronized simultaneous vehicle tracking/formation using cascaded prescribed-time control

In this paper, we present a space-and-time-synchronized control method with application to the simultaneous tracking/formation. In the framework of polar coordinates, through correlating and decoupling the reference/actual kinematics between the self vehicle and target, time and space are separated, controlled independently. As such, the specified state can be achieved at the predetermined terminal time, meanwhile, the relative trajectory in space is independent of time. In addition, for the stabilization before the predesigned time, a cascaded prescribed-time control theorem is provided as the preliminary of vehicle tracking control. The obtained results can be directly extended to the simultaneous tracking/formation of multiple vehicles. Finally, numerical examples are provided to verify the effectiveness and superiority of the proposed scheme.

eess.SY↗

arXiv2021

A Collaborative Visual SLAM Framework for Service Robots

We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. We design an elegant communication pipeline to enable real-time information sharing between robots. With a novel landmark organization and retrieval method on the server, each robot can acquire landmarks predicted to be in its view, to augment its local map. The framework is general enough to support both RGB-D and monocular cameras, as well as robots with multiple cameras, taking the rigid constraints between cameras into consideration. The proposed framework has been fully implemented and verified with public datasets and live experiments.

cs.RO↗

arXiv2021

Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10 % of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won $1^{st}$ and $2^{nd}$ place in the Tunnel and Urban Circuits, respectively.

cs.RO↗

arXiv2021

Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations

Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation. We show for the first time that a single network can represent scene geometry over time continually without catastrophic forgetting, while achieving promising trade-offs between accuracy and efficiency.

cs.CV↗

arXiv2021

Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions

Advanced service robots require superior tactile intelligence to guarantee human-contact safety and to provide essential supplements to visual and auditory information for human-robot interaction, especially when a robot is in physical contact with a human. Tactile intelligence is an essential capability of perception and recognition from tactile information, based on the learning from a large amount of tactile data and the understanding of the physical meaning behind the data. This report introduces a recently collected and organized dataset "TacAct" that encloses real-time pressure distribution when a human subject touches the arms of a nursing-care robot. The dataset consists of information from 50 subjects who performed a total of 24,000 touch actions. Furthermore, the details of the dataset are described, the data are preliminarily analyzed, and the validity of the collected information is tested through a convolutional neural network LeNet-5 classifying different types of touch actions. We believe that the TacAct dataset would be more than beneficial for the community of human interactive robots to understand the tactile profile under various circumstances.

cs.RO↗

arXiv2021

Testing Kerr Black Hole Mimickers with Quasi-Periodic Oscillations from GRO J1655-40

The measurements of quasi-periodic oscillations (QPOs) provide a quite powerful tool to test the nature of astrophysical black hole candidates in the strong gravitational field regime. In this paper, we use QPOs within the relativistic precession model to test a recently proposed family of rotating black hole mimickers, which reduce to the Kerr metric in a limiting case, and can represent traversable wormholes or regular black holes with one or two horizons, depending on the values of the parameters. In particular, assuming that the compact object of GRO J1655-40 is described by a rotating black hole mimicker, we perform a $χ$-square analysis to fit the parameters of the mimicker with two sets of observed QPO frequencies from GRO J1655-40. Our results indicate that although the metric around the compact object of GRO J1655-40 is consistent with the Kerr metric, a regular black hole with one horizon is favored by the observation data of GRO J1655-40.

gr-qc↗

arXiv2021

Thermodynamics and Phase Structure of an Einstein-Maxwell-scalar Model in Extended Phase Space

In this paper, we study thermodynamics and phase structure of asymptotically AdS hairy and Reissner-Nordström-AdS (RNAdS) black holes in the extended phase space, where the cosmological constant is interpreted as a thermal pressure. The RNAdS and hairy black holes are black hole solutions of an Einstein-Maxwell-scalar (EMS) model with a non-minimal coupling between the scalar and electromagnetic fields. The Smarr relation, the first law of thermodynamics and the free energy are derived for black hole solutions in the EMS model. Moreover, the phase structure of the RNAdS and hairy black holes is investigated in canonical and grand canonical ensembles. Interestingly, RNAdS BH/hairy BH/RNAdS BH reentrant phase transitions, consisting of zeroth-order and second-order phase transitions, are found in both ensembles.

gr-qc↗