arXiv subjects

Guoqiang Zhang

Publications and source records attributed to Guoqiang Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2022

High-energy-density plasma in femtosecond-laser-irradiated nanowire array targets for nuclear reactions

In this work, the high-energy-density plasmas (HEDP) evolved from joule-class-femtosecond-laser-irradiated nanowire array (NWA) targets are numerically and experimentally studied. The particle-in-cell (PIC) simulations indicate that ions accelerated in the sheath field around the nanowires' surface were eventually confined in NWA plasma, contributing most to the high energy densities. The protons emitted from the front surface of targets provide rich information about the interaction. The electron and ion energy densities in a broad target parameter range are given. Compared to planar targets, the ion energy density is one order of magnitude higher, and the volume of the HEDP is several-fold larger. At optimal target parameters, 8% of the laser energy can be converted to confined protons and results in ion energy densities of up to GJ/cm3 level. Experimental measurements of the emitted ions and neutrons from 2H(d, n)3He fusion from polyethylene and deuterated polyethylene NWA targets confirm the above results.

physics.plasm-ph↗

arXiv2022

Alpha-particle generation from H-11B fusion initiated by laser-accelerated boron ions

Here we report the generation of MeV alpha-particles from H-11B fusion initiated by laser-accelerated boron ions. Boron ions with maximum energy of 6MeV and fluence of 10^9/MeV/sr@5MeV were generated from 60-nm-thick self-supporting boron nanofoils irradiated by 1J femtosecond pulses at an intensity of 10^19W/cm^2. By bombarding secondary hydrogenous targets with the boron ions, 3*10^5/sr alpha-particles from H-11B fusion were registered, which is consistent with the theoretical yield calculated from the measured boron energy spectra. Our results demonstrate an alternative way toward ultrashort MeV alpha-particle sources employing compact femtosecond lasers. The ion acceleration and product measurement scheme are referential for the studies on the ion stopping power and cross-section of the H-11B reaction in solid or plasma.

physics.plasm-ph↗

arXiv2022

A Stacking Ensemble Approach for Supervised Video Summarization

Video summarization methods are usually classified into shot-level or frame-level methods, which are individually used in a general way. This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization. Firstly, we build up a stacking model to predict both the key frame probabilities and the temporal interest segments simultaneously. The two components are then combined via soft decision fusion to obtain the final scores of each frame in the video. A joint loss function is proposed for the model training. The ablation experimental results show that the proposed method outperforms both the two corresponding individual method. Furthermore, extensive experimental results on two benchmark datasets shows its superior performance in comparison with the state-of-the-art methods.

cs.CV↗

arXiv2022

cuHARM : a new GPU accelerated GR-MHD code and its application to ADAF disks

We introduce a new GPU-accelerated general-relativistic magneto-hydrodynamic (GR-MHD) code based on HARM which we call cuHARM. The code is written in CUDA-C and uses OpenMP to parallelize multi-GPU setups. Our code allows us to run high resolution simulations of accretion disks and the formation and structure of jets without the need of multi-node supercomputer infrastructure. A $256^3$ simulation is well within the reach of an Nvidia DGX-V100 server, with the computation being a factor about 10 times faster if only the CPU was used. We use this code to examine several disk structures all in the SANE state. We find that: (i) increasing the magnetic field, while in the SANE state does not affect the mass accretion rate; (ii) Simultaneous increase of the disk size and the magnetic field, while keeping the ratio of energies fixed, lead to the destruction of the jet once the magnetic flux through the horizon decrease below a certain limit. This demonstrates that the existence of the jet is not a linear function of the initial magnetic field strength; (iii) the structure of the jet is a weak function of the adiabatic index of the gas, with relativistic gas tend to have a wider jet.

astro-ph.HE↗

arXiv2022

New Opportunities in Femto-to-Nanometer Scale with High-Intensity Lasers

On the scale of nanometer to femtometer, there have several puzzles, including neutron lifetime, proton charge radius, and deep Dirac level, etc. With the development of high-intensity laser technologies, lasers today can induce extremely strong electromagnetic (EM) fields. Electrons in deep shells of atoms, as well as the atomic nucleus themself, can be affected by the laser EM fields. This may provide a new experimental platform for studies the physical processes on the femto-to-nanometer scale, where atomic physics and nuclear physics coexist. In this paper, we review possible new opportunities for studying the puzzles on femto-to-nanometer scale with high-intensity lasers.

nucl-ex↗

arXiv2022

Femtosecond pumping of nuclear isomeric states by the Coulomb collision of ions with quivering electrons

Efficient production of nuclear isomers is critical for pioneering applications, like nuclear clocks, nuclear batteries, clean nuclear energy, and nuclear γ-ray lasers. However, due to small production cross sections and quick decays, it is extremely difficult to acquire a significant amount of isomers with short lifetimes via traditional accelerators or reactors because of low beam intensity. Here, for the first time, we experimentally present femtosecond pumping of nuclear isomeric states by the Coulomb excitation of ions with the quivering electrons induced by laser fields. Nuclei populated on the third excited state of 83Kr are generated with a peak efficiency of 2.34*10^15 particles=s from a tabletop hundred-TW laser system. It can be explained by the Coulomb excitation of ions with the quivering electrons during the interaction between laser pulses and clusters at nearly solid densities. This efficient and universal production method can be widely used for pumping isotopes with excited state lifetimes down to picoseconds, and could be a benefit for fields like nuclear transition mechanisms and nuclear γ-ray lasers.

nucl-ex↗

arXiv2021

Higher-order exceptional point in a pseudo-Hermitian cavity optomechanical system

Higher-order exceptional points (EPs), resulting from non-Hermitian degeneracies, have shown greater advantages in sensitive enhancement than second-order EPs (EP2s). Therefore, seeking higher-order EPs in various quantum systems is important for quantum information science. Here we propose a benchmark cavity optomechanical (COM) system consisting of a mechanical resonator (MR) coupled to two cavities via radiation pressure for predicting the third-order exceptional point (EP3). We first give the pseudo-Hermitian condition for the non-Hermitian COM system by taking the bath effects into account. Then we consider the mechanical gain effect and we find that the pseudo-Hermitian COM system without $\mathcal{PT}$ symmetry can host both the EP3 and EP2 for symmetric and asymmetric cavities. In the symmetric case, only the EP3 or EP2 can be predicted in the parameter space, but the EP3 and EP2 can be transformed into each other by tuning the COM coupling strength in the asymmetric case. We further consider the case of one cavity with gain. For this case, the pseudo-Hermitian COM system is $\mathcal{PT}$-symmetric and can also host the EP3 or EP2. The influence of system parameters on them are discussed. Our proposal provides a potential way to realize sensitive detection and study other physical phenomena {around} higher-order EP3 in non-Hermitian COM systems.

quant-ph↗

arXiv2021

Extending AdamW by Leveraging Its Second Moment and Magnitude

Recent work [4] analyses the local convergence of Adam in a neighbourhood of an optimal solution for a twice-differentiable function. It is found that the learning rate has to be sufficiently small to ensure local stability of the optimal solution. The above convergence results also hold for AdamW. In this work, we propose a new adaptive optimisation method by extending AdamW in two aspects with the purpose to relax the requirement on small learning rate for local stability, which we refer to as Aida. Firstly, we consider tracking the 2nd moment r_t of the pth power of the gradient-magnitudes. r_t reduces to v_t of AdamW when p=2. Suppose {m_t} is the first moment of AdamW. It is known that the update direction m_{t+1}/(v_{t+1}+epsilon)^0.5 (or m_{t+1}/(v_{t+1}^0.5+epsilon) of AdamW (or Adam) can be decomposed as the sign vector sign(m_{t+1}) multiplied elementwise by a vector of magnitudes |m_{t+1}|/(v_{t+1}+epsilon)^0.5 (or |m_{t+1}|/(v_{t+1}^0.5+epsilon)). Aida is designed to compute the qth power of the magnitude in the form of |m_{t+1}|^q/(r_{t+1}+epsilon)^(q/p) (or |m_{t+1}|^q/((r_{t+1})^(q/p)+epsilon)), which reduces to that of AdamW when (p,q)=(2,1). Suppose the origin 0 is a local optimal solution of a twice-differentiable function. It is found theoretically that when q>1 and p>1 in Aida, the origin 0 is locally stable only when the weight-decay is non-zero. Experiments are conducted for solving ten toy optimisation problems and training Transformer and Swin-Transformer for two deep learning (DL) tasks. The empirical study demonstrates that in a number of scenarios (including the two DL tasks), Aida with particular setups of (p,q) not equal to (2,1) outperforms the setup (p,q)=(2,1) of AdamW.

cs.LG↗

arXiv2021

Coarsening Optimization for Differentiable Programming

This paper presents a novel optimization for differentiable programming named coarsening optimization. It offers a systematic way to synergize symbolic differentiation and algorithmic differentiation (AD). Through it, the granularity of the computations differentiated by each step in AD can become much larger than a single operation, and hence lead to much reduced runtime computations and data allocations in AD. To circumvent the difficulties that control flow creates to symbolic differentiation in coarsening, this work introduces phi-calculus, a novel method to allow symbolic reasoning and differentiation of computations that involve branches and loops. It further avoids "expression swell" in symbolic differentiation and balance reuse and coarsening through the design of reuse-centric segment of interest identification. Experiments on a collection of real-world applications show that coarsening optimization is effective in speeding up AD, producing several times to two orders of magnitude speedups.

cs.PL↗

arXiv2021

GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention

Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0,1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are available at https://github.com/PRIS-CV/GPCA.

cs.LG↗

arXiv2021

Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate overfitting and improve the performance of DNNs. The advanced dropout technique applies a model-free and easily implemented distribution with parametric prior, and adaptively adjusts dropout rate. Specifically, the distribution parameters are optimized by stochastic gradient variational Bayes in order to carry out an end-to-end training. We evaluate the effectiveness of the advanced dropout against nine dropout techniques on seven computer vision datasets (five small-scale datasets and two large-scale datasets) with various base models. The advanced dropout outperforms all the referred techniques on all the datasets.We further compare the effectiveness ratios and find that advanced dropout achieves the highest one on most cases. Next, we conduct a set of analysis of dropout rate characteristics, including convergence of the adaptive dropout rate, the learned distributions of dropout masks, and a comparison with dropout rate generation without an explicit distribution. In addition, the ability of overfitting prevention is evaluated and confirmed. Finally, we extend the application of the advanced dropout to uncertainty inference, network pruning, text classification, and regression. The proposed advanced dropout is also superior to the corresponding referred methods. Codes are available at https://github.com/PRIS-CV/AdvancedDropout.

cs.LG↗

arXiv2021

Parity-dependent shot noise and spin-flip relaxation process in a hybrid superconductor-nanowire quantum dot

We report shot noise measurements for a quantum dot formed in an InAs nanowire suspended between superconducting electrodes. We find a clear alternation for the shot noise value in the Coulomb blockade regime between even and odd electron occupation in the dot, indicating that super-Poissonian (Poissonian) shot noise with the Fano factor reaching around 2 (1) occurs for even (odd) parity. With increasing magnetic field, the parity effect disappears and all the regimes show the Fano factor of around 1. The whole observation in our experiments quantitatively agrees with simulation obtained from full-counting statistics of cotunneling including spin-flip relaxation process, which corresponds to modelling electron motion in a quantum dot with strong spin-orbit interaction.

cond-mat.mes-hall↗

arXiv2021

Revisiting the Primal-Dual Method of Multipliers for Optimisation over Centralised Networks

The primal-dual method of multipliers (PDMM) was originally designed for solving a decomposable optimisation problem over a general network. In this paper, we revisit PDMM for optimisation over a centralized network. We first note that the recently proposed method FedSplit [1] implements PDMM for a centralized network. In [1], Inexact FedSplit (i.e., gradient based FedSplit) was also studied both empirically and theoretically. We identify the cause for the poor reported performance of Inexact FedSplit, which is due to the improper initialisation in the gradient operations at the client side. To fix the issue of Inexact FedSplit, we propose two versions of Inexact PDMM, which are referred to as gradient-based PDMM (GPDMM) and accelerated GPDMM (AGPDMM), respectively. AGPDMM accelerates GPDMM at the cost of transmitting two times the number of parameters from the server to each client per iteration compared to GPDMM. We provide a new convergence bound for GPDMM for a class of convex optimisation problems. Our new bounds are tighter than those derived for Inexact FedSplit. We also investigate the update expressions of AGPDMM and SCAFFOLD to find their similarities. It is found that when the number K of gradient steps at the client side per iteration is K=1, both AGPDMM and SCAFFOLD reduce to vanilla gradient descent with proper parameter setup. Experimental results indicate that AGPDMM converges faster than SCAFFOLD when K>1 while GPDMM converges slightly worse than SCAFFOLD.

cs.DC↗

arXiv2021

Development of Gated Fiber Detectors for Laser-Induced Strong Electromagnetic Pulse Environments

With the development of laser technologies, nuclear reactions can happen in high-temperature plasma environments induced by lasers and have attracted a lot of attention from different physical disciplines. However, studies on nuclear reactions in plasma are still limited by detecting technologies. This is mainly due to the fact that extremely high electromagnetic pulses (EMPs) can also be induced when high-intensity lasers hit targets to induce plasma, and then cause dysfunction of many types of traditional detectors. Therefore, new particle detecting technologies are highly needed. In this paper, we report a recently developed gated fiber detector which can be used in harsh EMP environments. In this prototype detector, scintillating photons are coupled by fiber and then transferred to a gated photomultiplier tube which is located far away from the EMP source and shielded well. With those measures, the EMPs can be avoided, and this device has the capability to identify a single event of nuclear reaction products generated in laser-induced plasma from noise EMP backgrounds. This new type of detector can be widely used as a Time-of-Flight (TOF) detector in high-intensity laser nuclear physics experiments for detecting neutron, photons, and other charged particles.

physics.ins-det↗

arXiv2021

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is one of the major issues that restrict the performance of graph convolutional networks. In this paper, we propose a stochastic regularization method to tackle the oversmoothing problem. In the proposed method, we stochastically scale features and gradients (SSFG) by a factor sampled from a probability distribution in the training procedure. By explicitly applying a scaling factor to break feature convergence, the oversmoothing issue is alleviated. We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance. Our method does not increase the number of trainable parameters. When used together with ReLU, our SSFG can be seen as a stochastic ReLU activation function. We experimentally validate our SSFG regularization method on three commonly used types of graph networks. Extensive experimental results on seven benchmark datasets for four graph-based tasks demonstrate that our SSFG regularization is effective in improving the overall performance of the baseline graph networks.

cs.LG↗

arXiv2020

Higher-order vector Peregrine solitons and asymptotic estimates for the multi-component nonlinear Schrödinger equations

We first report the first- and higher-order vector Peregrine solitons (alias rational rogue waves) for the any multi-component NLS equations based on the loop group theory, an explicit (n + 1)-multiple eigenvalue of a characteristic polynomial of degree (n + 1) related to the condition of Benjamin-Feir instability, and inverse functions. Particularly, these vector rational rogue waves are parity-time symmetric for some parameter constraints. A systematic and effective approach is proposed to study the asymptotic behaviors of these vector rogue waves such that the decompositions of rogue waves are related to the so-called governing polynomials, which pave a powerful way in the study of vector rogue wave structures of the multi-component integrable systems. The vector rogue waves with maximal amplitudes can be determined via the parameter vectors, which is interesting and useful in the multi-component physical systems.

nlin.SI↗

arXiv2020

Parity-time-symmetric vector rational rogue wave solutions in any n-component nonlinear Schrödinger models

The extreme events are investigated for an $n$-component nonlinear Schrödinger ($n$-NLS) system in the focusing Kerr-like nonlinear media, which appears in many physical fields. We report and discuss the novel multi-parametric families of vector rational rogue wave (RW) solutions featuring the parity-time (PT) symmetry, which are characterized by non-identical boundary conditions for the components, and consistent with the degeneracy of $n$ branches of Benjamin-Feir instability. Explicit examples of PT-symmetric vector RWs are presented. Some parameter constraints can make some components generate the RWs with high amplitudes due to many-body resonant interactions.Effect of a non-integrable deformation of the model on the excitation of vector RWs is also discussed. These results will be useful to design the RW experiments in multi-component physical systems.

nlin.SI↗

arXiv2020

High-efficiency water-window x-ray generation from nanowire array targets irradiated with femtosecond laser pulses

We demonstrate the high-efficiency generation of water-window soft x-ray emissions from polyethylene nanowire array targets irradiated by femtosecond laser pulses at the intensity of 4*10^19 W/cm^2. The experimental results indicate more than one order of magnitude enhancement of the water-window x-ray emissions from the nanowire array targets compared to the planar targets. The highest energy conversion efficiency from laser to water-window x-rays is measured as 0.5%/sr, which comes from the targets with the longest nanowires. Supported by particle-in-cell simulations and atomic kinetic codes, the physics that leads to the high conversion efficiency is discussed.

physics.plasm-ph↗