arXiv subjects

Jie Zhao

Publications and source records attributed to Jie Zhao.

At least 217 records · Page 12Linked to original sources

arXiv2019

Complications in the interpretation of the charge asymmetry dependent $π$ flow for the chiral magnetic wave

The charge asymmetry ($A_{\rm ch}$) dependence of the $π^{-}$ and $π^{+}$ elliptic flow difference, $Δv_{2}(A_{\rm ch})$, has been regarded as a sensitive observable for the possible chiral magnetic wave (CMW) in relativistic heavy ion collisions. In this work, we first demonstrate that, due to non-flow backgrounds, the flow measurements by the Q-cumulant method using all charged particles as reference introduce a trivial linear term to $Δv_{2}(A_{\rm ch})$. The trivial slope can be negative in the triangle flow difference $Δv_{3}(A_{\rm ch})$ if the non-flow is dominated by back-to-back pairs. After eliminating the trivial term, we find that the non-flow between like-sign pairs gives rise to an additional positive slope to $Δv_{2}(A_{\rm ch})$ because of the larger dilution effect to $π^{+}$ ($π^{-}$) at positive (negative) $A_{\rm ch}$. We further find that the competition between different $π$ sources can introduce another non-trivial linear-$A_{\rm ch}$ term due to their different multiplicity fluctuations and anisotropic flows. We then study the effect of neutral cluster (resonance) decays as a mechanism for local charge conservation on the slope parameter of $Δv_{2}(A_{\rm ch})$. We find that the slope parameter is sensitive to the kinematics of those neutral clusters. Light resonances give positive slopes while heavy resonances give negative slopes. Local charge conservation from continuum cluster mass distribution can give a positive slope parameter comparable to experimental data. Our studies indicate that many non-CMW physics mechanisms can give rise to a $A_{\rm ch}$-dependent $Δv_{2}(A_{\rm ch})$ and the interpretation of $Δv_{2}(A_{\rm ch})$ in terms of the CMW is delicate.

nucl-th↗

arXiv2019

Object grasping planning for the situation when soft and rigid objects are mixed together

In this paper, we propose a object detection method expressed as rotated bounding box to solve grasping challenge in the scenes where rigid objects and soft objects are mixed together. Compared with traditional detection methods, this method can output the angle information of rotated objects and thus can guarantee that within each rotated bounding box, there is a single instance. This technology is especially useful in the case of pile of objects with different orientations. In our method, when uncategorized objects with specific geometry shapes (rectangle or cylinder) are detected, the program will conclude that some rigid objects are covered by the towels. If no covered objects are detected, the grasp planning is based on 3D point cloud obtained from the mapping between 2D object detection result and its corresponding 3D point cloud. Based on the information provided by the 3D bounding box covering the object, grasping strategy for multiple cluttered rigid objects, collision avoidance strategy are proposed. The proposed method is verified by the experiment in which rigid objects and towels are mixed together.

cs.RO↗

arXiv2019

Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper

In industry assembly lines, parts feeding machines are widely employed as the prologue of the whole procedure. They play the role of sorting the parts randomly placed in bins to the state with specified pose. With the help of the parts feeding machines, the subsequent assembly processes by robot arm can always start from the same condition. Thus it is expected that function of parting feeding machine and the robotic assembly can be integrated with one robot arm. This scheme can provide great flexibility and can also contribute to reduce the cost. The difficulties involved in this scheme lie in the fact that in the part feeding phase, the pose of the part after grasping may be not proper for the subsequent assembly. Sometimes it can not even guarantee a stable grasp. In this paper, we proposed a method to integrate parts feeding and assembly within one robot arm. This proposal utilizes a specially designed gripper tip mounted on the jaws of a two-fingered gripper. With the modified gripper, in-hand manipulation of the grasped object is realized, which can ensure the control of the orientation and offset position of the grasped object. The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly.

cs.RO↗

arXiv2019

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.

cs.RO↗

arXiv2019

Observation of anti-PT symmetry phase transition in the magnon-cavity-magnon coupled system

As the counterpart of PT symmetry, abundant phenomena and potential applications of anti-PT symmetry have been predicted or demonstrated theoretically. However, experimental realization of the coupling required in the anti-PT symmetry is difficult. Here, by coupling two YIG spheres to a microwave cavity, the large cavity dissipation rate makes the magnons coupled dissipatively with each other, thereby obeying a two-dimensional anti-PT Hamiltonian. In terms of the magnon-readout method, a new method adopted here, we demonstrate the validity of our method in constructing an anti-PT system and present the counterintuitive level attraction process. Our work provides a new platform to explore the anti-PT symmetry properties and paves the way to study multi-magnoncavity-polariton systems.

quant-ph↗

arXiv2019

Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images

Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthetic images that maintain the background of coronary angiography and preserve the vascular structures of retinal vessels and coronary arteries. We train and evaluate the proposed model on a dataset of 1092 digital subtraction angiography images, and experiments demonstrate the supreme accuracy of the proposed method on coronary arteries segmentation.

eess.IV↗

arXiv2019

ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning

Extracting multi-scale information is key to semantic segmentation. However, the classic convolutional neural networks (CNNs) encounter difficulties in achieving multi-scale information extraction: expanding convolutional kernel incurs the high computational cost and using maximum pooling sacrifices image information. The recently developed dilated convolution solves these problems, but with the limitation that the dilation rates are fixed and therefore the receptive field cannot fit for all objects with different sizes in the image. We propose an adaptivescale convolutional neural network (ASCNet), which introduces a 3-layer convolution structure in the end-to-end training, to adaptively learn an appropriate dilation rate for each pixel in the image. Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale. We compare the segmentation results using the classic CNN, the dilated CNN and the proposed ASCNet on two types of medical images (The Herlev dataset and SCD RBC dataset). The experimental results show that ASCNet achieves the highest accuracy. Moreover, the automatically generated dilation rates are positively correlated to the sizes of the objects, confirming the effectiveness of the proposed method.

cs.CV↗

arXiv2019

Experimental searches for the chiral magnetic effect in heavy-ion collisions

The chiral magnetic effect (CME) in quantum chromodynamics (QCD) refers to a charge separation (an electric current) of chirality imbalanced quarks generated along an external strong magnetic field. The chirality imbalance results from interactions of quarks, under the approximate chiral symmetry restoration, with metastable local domains of gluon fields of non-zero topological charges out of QCD vacuum fluctuations. Those local domains violate the $\mathcal{P}$ and $\mathcal{CP}$ invariance, potentially offering a solution to the strong $\mathcal{CP}$ problem in explaining the magnitude of the matter-antimatter asymmetry in today's universe. Relativistic heavy-ion collisions, with the likely creation of the high energy density quark-gluon plasma and restoration of the approximate chiral symmetry, and the possibly long-lived strong magnetic field, provide a unique opportunity to detect the CME. Early measurements of the CME-induced charge separation in heavy-ion collisions are dominated by physics backgrounds. Major efforts have been devoted to eliminate or reduce those backgrounds. We review those efforts, with a somewhat historical perspective, and focus on the recent innovative experimental undertakings in the search for the CME in heavy-ion collisions.

nucl-ex↗

arXiv2019

Second harmonic microscopy of poled x-cut thin film lithium niobate: Understanding the contrast mechanism

Thin film lithium niobate is of great recent interest and an understanding of periodically poled thin-films is crucial for both fundamental physics and device developments. Second-harmonic (SH) microscopy allows for the non-invasive visualization and analysis of ferroelectric domain structures and walls. While the technique is well understood in bulk lithium niobate, SH microscopy in thin films is largely influenced by interfacial reflections and resonant enhancements, which depend on film thicknesses and the substrate materials. We present a comprehensive analysis of SH microscopy in x-cut lithium niobate thin films, based on a full three dimensional focus calculations, and accounting for interface reflections. We show that the dominant signal in back-reflection originates from a co-propagating phase-matched process observed through reflections, rather than direct detection of the counter-propagating signal as in bulk samples. We can explain the observation of domain structures in the thin film geometry, and in particular, we show that the SH signal from thin poled films allows to unambiguously distinguish areas, which are completely or only partly inverted in depth.

physics.app-ph↗

arXiv2019

Multi-phase transport model predictions of isobaric collisions with nuclear structures from density functional theory

Isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr collisions were performed at the Relativistic Heavy Ion Collider in 2018. Using the "a multi-phase transport" model with nuclear structures calculated by the density functional theory (DFT), we make predictions for the charged hadron multiplicity distributions and elliptic azimuthal anisotropies in these collisions. Emphases are put on the relative differences between the two collision systems that can decisively discriminate DFT nuclear distributions from the commonly used Woods-Saxon densities.

nucl-th↗

arXiv2019

Mechanism of Synergistic Effects of Neutron- and Gamma-Ray-Radiated PNP Bipolar Transistors

The synergistic effects of neutron and gamma ray radiated PNP transistors are systematically investigated as functions of the neutron fluence, gamma ray dose, and dose rate. We find that the damages show a `tick'-like dependence on the gamma ray dose after the samples are radiated by neutrons. Two negative synergistic effects are derived, both of which have similar magnitudes as the ionization damage (ID) itself. The first one depends linearly on the gamma ray dose, whose slope depends quadratically on the initial displacement damage (DD) and can be attributed to the healing of neutron-radiation-induced defects in silicon. The second one has an exponential decay with the gamma ray dose, whose amplitude shows a rather strong enhanced low-dose-rate sensitivity (ELDRS) effect and can be attributed to the passivation of neutron-induced defects near the silica/silicon interface by the gamma-ray-generated protons in silica, which can penetrate the silica/silicon interface to passivate the neutron-induced defects in silicon. The simulated results based on the proposed model match the experimental data very well, but differ from previous model, which does not assume annihilation or passivation of the displacement defects. The unraveled defect annealing mechanism is important because it implies that displacement damages can be repaired by gamma ray radiation or proton diffusion, which can have important device applications in the space or other extreme environments.

physics.app-ph↗

arXiv2019

Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model

Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image. An accurate nuclei segmentation could thus improve the success rate of cervical cancer screening. In this work, a method of automated cervical nuclei segmentation using Deformable Multipath Ensemble Model (D-MEM) is proposed. The approach adopts a U-shaped convolutional network as a backbone network, in which dense blocks are used to transfer feature information more effectively. To increase the flexibility of the model, we then use deformable convolution to deal with different nuclei irregular shapes and sizes. To reduce the predictive bias, we further construct multiple networks with different settings, which form an ensemble model. The proposed segmentation framework has achieved state-of-the-art accuracy on Herlev dataset with Zijdenbos similarity index (ZSI) of 0.933, and has the potential to be extended for solving other medical image segmentation tasks.

cs.CV↗

arXiv2019

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.

cs.CV↗

arXiv2019

Vision Based Picking System for Automatic Express Package Dispatching

This paper presents a vision based robotic system to handle the picking problem involved in automatic express package dispatching. By utilizing two RealSense RGB-D cameras and one UR10 industrial robot, package dispatching task which is usually done by human can be completed automatically. In order to determine grasp point for overlapped deformable objects, we improved the sampling algorithm proposed by the group in Berkeley to directly generate grasp candidate from depth images. For the purpose of package recognition, the deep network framework YOLO is integrated. We also designed a multi-modal robot hand composed of a two-fingered gripper and a vacuum suction cup to deal with different kinds of packages. All the technologies have been integrated in a work cell which simulates the practical conditions of an express package dispatching scenario. The proposed system is verified by experiments conducted for two typical express items.

cs.RO↗

arXiv2019

Isolating the chiral magnetic effect from backgrounds by pair invariant mass

Topological gluon configurations in quantum chromodynamics induce quark chirality imbalance in local domains, which can result in the chiral magnetic effect (CME)--an electric charge separation along a strong magnetic field. Experimental searches for the CME in relativistic heavy ion collisions via the charge-dependent azimuthal correlator ($Δγ$) suffer from large backgrounds arising from particle correlations (e.g. due to resonance decays) coupled with the elliptic anisotropy. We propose differential measurements of the $Δγ$ as a function of the pair invariant mass ($m_{\rm inv}$), by restricting to high $m_{\rm inv}$ thus relatively background free, and by studying the $m_{\rm inv}$ dependence to separate the possible CME signal from backgrounds. We demonstrate by model studies the feasibility and effectiveness of such measurements for the CME search.

nucl-ex↗

arXiv2019

Time-dependent generator coordinate method study of mass-asymmetric fission of actinides

Low-energy positive and negative parity collective states in the equilibrium minimum, and the dynamics of induced fission of actinide nuclei are investigated in a unified theoretical framework based on the generator coordinate method (GCM) with the Gaussian overlap approximation (GOA). The collective potential and inertia tensor, both at zero and finite temperature, are computed using the self-consistent multidimensionally constrained relativistic mean field (MDC-RMF) model, based on the energy density functional DD-PC1. Pairing correlations are treated in the BCS approximation with a separable pairing force of finite range. A collective quadrupole-octupole Hamiltonian characterized by zero-temperature axially-symmetric deformation energy surface and perturbative cranking inertia tensor, is used to model the low-lying excitation spectrum. The fission fragment charge distributions are obtained by propagating the initial collective states in time with the time-dependent GCM+GOA that uses the same quadrupole-octupole Hamiltonian, but with the collective potential and inertia tensor computed at finite temperature. The illustrative charge yields of $^{228}$Th, $^{234}$U, $^{240}$Pu, $^{244}$Cm, and $^{250}$Cf are in very good agreement with experiment, and the predicted mass asymmetry is consistent with the result of a recent microscopic study that has attributed the distribution (peak) of the heavier-fragment nuclei to shell-stabilized octupole deformations.

nucl-th↗

arXiv2019

Elliptical flow coalescence to identify the $f_{0}$(980) content

We use a simple coalescence model to generate $f_{0}$(980) particles for three configurations: a ${s\bar{s}}$ meson, a ${u\bar{u}s\bar{s}}$ tetraquark, and a ${K^{+}K^{-}}$ molecule. The phase-space information of the coalescing constituents is taken from a multi-phase transport (AMPT) simulation of heavy-ion collisions. It is shown that the number of constituent quarks scaling of the elliptic flow anisotropy can be used to discern ${s\bar{s}}$ from ${u\bar{u}s\bar{s}}$ and ${K^{+}K^{-}}$ configurations.

nucl-ex↗

arXiv2018

Microscopic self-consistent description of induced fission dynamics: finite temperature effects

The dynamics of induced fission of $^{226}$Th is investigated in a theoretical framework based on the finite-temperature time-dependent generator coordinate method (TDGCM) in the Gaussian overlap approximation (GOA). The thermodynamical collective potential and inertia tensor at temperatures in the interval $T=0 - 1.25$ MeV are calculated using the self-consistent multidimensionally constrained relativistic mean field (MDC-RMF) model, based on the energy density functional DD-PC1. Pairing correlations are treated in the BCS approximation with a separable pairing force of finite range. Constrained RMF+BCS calculations are carried out in the collective space of axially symmetric quadrupole and octupole deformations for the asymmetric fissioning nucleus $^{226}$Th. The collective Hamiltonian is determined by the temperature-dependent free energy surface and perturbative cranking inertia tensor, and the TDGCM+GOA is used to propagate the initial collective state in time. The resulting charge and mass fragment distributions are analyzed as functions of the internal excitation energy. The model can qualitatively reproduce the empirical triple-humped structure of the fission charge and mass distributions already at $T=0$, but the precise experimental position of the asymmetric peaks and the symmetric-fission yield can only be accurately reproduced when the potential and inertia tensor of the collective Hamiltonian are determined at finite temperature, in this particular case between $T=0.75$ MeV and $T=1$ MeV.

nucl-th↗