arXiv subjects

Zhen Chen

Publications and source records attributed to Zhen Chen.

At least 199 records · Page 11Linked to original sources

arXiv2022

Disentangling magnetic and grain contrast in polycrystalline FeGe thin films using four-dimensional Lorentz scanning transmission electron microscopy

The study of nanoscale chiral magnetic order in polycrystalline materials with a strong Dzyaloshinkii-Moriya interaction (DMI) is interesting for the observation of magnetic phenomena at grain boundaries and interfaces. One such material is sputter-deposited B20 FeGe on Si, which has been actively investigated as the basis for low-power, high-density magnetic memory technology in a scalable material platform. Although conventional Lorentz electron microscopy provides the requisite spatial resolution to probe chiral magnetic textures in single-crystal FeGe, probing the magnetism of sputtered B20 FeGe is more challenging because the sub-micron crystal grains add confounding contrast. We address the challenge of disentangling magnetic and grain contrast by applying 4-dimensional Lorentz scanning transmission electron microscopy using an electron microscope pixel array detector. Supported by analytical and numerical models, we find that the most important parameter for imaging magnetic materials with polycrystalline grains is the ability for the detector to sustain large electron doses, where having a high-dynamic range detector becomes extremely important. Despite the small grain size in sputtered B20 FeGe on Si, using this approach we are still able to observe helicity switching of skyrmions and magnetic helices across two adjacent grains as they thread through neighboring grains. We reproduce this effect using micromagnetic simulations by assuming that the grains have distinct orientation and magnetic chirality and find that magnetic helicity couples to crystal chirality. Our methodology for imaging magnetic textures is applicable to other thin-film magnets used for spintronics and memory applications, where an understanding of how magnetic order is accommodated in polycrystalline materials is important.

cond-mat.mtrl-sci↗

arXiv2022

Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning

Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long time prediction of the evolution of the unknown system. Training a DNN with low generalization error is a particularly important task in this case as error is accumulated over time. Because of the inherent randomness in DNN training, chiefly in stochastic optimization, there is uncertainty in the resulting prediction, and therefore in the generalization error. Hence, the generalization error can be viewed as a random variable with some probability distribution. Well-trained DNNs, particularly those with many hyperparameters, typically result in probability distributions for generalization error with low bias but high variance. High variance causes variability and unpredictably in the results of a trained DNN. This paper presents a computational technique which decreases the variance of the generalization error, thereby improving the reliability of the DNN model to generalize consistently. In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step. A mathematical foundation for the method is presented, including results regarding the distribution of the local truncation error. In addition, three time-dependent differential equation problems are considered as numerical examples, demonstrating the effectiveness of the method to decrease variance of DNN predictions generally.

cs.LG↗

arXiv2021

Optomechanical Anti-lasing with Infinite Group Delay at a Phase Singularity

Singularities that symbolize abrupt changes and exhibit extraordinary behavior are of broad interest. We experimentally study optomechanically induced singularities in a compound system consisting of a three-dimensional aluminum superconducting cavity and a metalized high-coherence silicon nitride membrane resonator. Mechanically-induced coherent perfect absorption and anti-lasing occur simultaneously under a critical optomechanical coupling strength. Meanwhile, the phase around the cavity resonance undergoes an abrupt $π$-phase transition, which further flips the phase slope in the frequency dependence. The observed infinite-discontinuity in the phase slope defines a singularity, at which the group velocity is dramatically changed. Around the singularity, an abrupt transition from an infinite group advance to delay is demonstrated by measuring a Gaussian-shaped waveform propagating. Our experiment may broaden the scope of realizing extremely long group delays by taking advantage of singularities.

quant-ph↗

arXiv2021

Very-High Dynamic Range, 10,000 frames/second Pixel Array Detector for Electron Microscopy

Precision and accuracy of quantitative scanning transmission electron microscopy (STEM) methods such as ptychography, and the mapping of electric, magnetic and strain fields depend on the dose. Reasonable acquisition time requires high beam current and the ability to quantitatively detect both large and minute changes in signal. A new hybrid pixel array detector (PAD), the second-generation Electron Microscope Pixel Array Detector (EMPAD-G2), addresses this challenge by advancing the technology of a previous generation PAD, the EMPAD. The EMPAD-G2 images continuously at a frame-rates up to 10 kHz with a dynamic range that spans from low-noise detection of single electrons to electron beam currents exceeding 180 pA per pixel, even at electron energies of 300 keV. The EMPAD-G2 enables rapid collection of high-quality STEM data that simultaneously contain full diffraction information from unsaturated bright field disks to usable Kikuchi bands and higher-order Laue zones. Test results from 80 to 300 keV are presented, as are first experimental results demonstrating ptychographic reconstructions, strain and polarization maps. We introduce a new information metric, the Maximum Usable Imaging Speed (MUIS), to identify when a detector becomes electron-starved, saturated or its pixel count is mismatched with the beam current.

physics.ins-det↗

arXiv2021

Exploring Gradient Flow Based Saliency for DNN Model Compression

Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead. Traditional model pruning methods such as l-1 pruning that evaluates the channel significance for DNN pay too much attention to the local analysis of each channel and make use of the magnitude of the entire feature while ignoring its relevance to the batch normalization (BN) and ReLU layer after each convolutional operation. To overcome these problems, we propose a new model pruning method from a new perspective of gradient flow in this paper. Specifically, we first theoretically analyze the channel's influence based on Taylor expansion by integrating the effects of BN layer and ReLU activation function. Then, the incorporation of the first-order Talyor polynomial of the scaling parameter and the shifting parameter in the BN layer is suggested to effectively indicate the significance of a channel in a DNN. Comprehensive experiments on both image classification and image denoising tasks demonstrate the superiority of the proposed novel theory and scheme. Code is available at https://github.com/CityU-AIM-Group/GFBS.

cs.CV↗

arXiv2021

A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization

Matrix multiplication is the bedrock in Deep Learning inference application. When it comes to hardware acceleration on edge computing devices, matrix multiplication often takes up a great majority of the time. To achieve better performance in edge computing, we introduce a low-power Multi-layer Perceptron (MLP) accelerator based on a pipelined matrix multiplication scheme and a nonuniform quantization methodology. The implementation is running on Field-programmable Gate Array (FPGA) devices and tested its performance on handwritten digit classification and Q-learning tasks. Results show that our method can achieve better performance with fewer power consumption.

cs.LG↗

arXiv2021

Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client. Specifically, we devise a Progressive Fourier Aggregation (PFA) at the server to achieve more stable and effective global knowledge gathering by integrating client models from low-frequency to high-frequency gradually. Moreover, with an introduced deputy model to receive the aggregated server model, we design a Deputy-Enhanced Transfer (DET) strategy at the client and conduct three steps of Recover-Exchange-Sublimate to ameliorate the personalized local model by transferring the global knowledge smoothly. Extensive experiments on real-world dermoscopic FL dataset prove that our personalized retrogress-resilient framework outperforms state-of-the-art FL methods, as well as the generalization on an out-of-distribution cohort. The code and dataset are available at https://github.com/CityU-AIM-Group/PRR-FL.

cs.CV↗

arXiv2021

Parity-symmetry-breaking quantum phase transition via parametric drive in a cavity magnonic system

We study the parity-symmetry-breaking quantum phase transition (QPT) in a cavity magnonic system driven by a parametric field, where the magnons in a ferrimagnetic yttrium-iron-garnet sphere strongly couple to a microwave cavity. With appropriate parameters, this cavity magnonic system can exhibit a rich phase diagram, including the parity-symmetric phase, parity-symmetry-broken phase, and bistable phase. When increasing the drive strength beyond a critical threshold, the cavity magnonic system undergoes either a first- or second-order nonequilibrium QPT from the parity-symmetric phase with microscopic excitations to the parity-symmetry-broken phase with macroscopic excitations, depending on the parameters of the system. Our work provides an alternate way to engineer the QPT in a hybrid quantum system containing the spin ensemble in a ferri- or ferromagnetic material with strong exchange interactions.

quant-ph↗

arXiv2021

Normalized solutions for nonlinear Schrödinger systems with special mass-mixed terms: The linear couple case

In this paper, we prove the existence of positive solutions $(λ_1,λ_2, u,v)\in \R^2\times H^1(\R^N, \R^2)$ to the following coupled Schrödinger system $$\begin{cases} -Δu + λ_1 u= μ_1|u|^{p-2}u+βv \quad &\hbox{in}\;\RN, \\ -Δv + λ_2 v= μ_2|v|^{q-2}v+βu \quad &\hbox{in}\;\RN, \end{cases}$$ satisfying the normalization constraints $\displaystyle\int_{\RN}u^2 =a, ~ \int_{\RN}v^2 =b$. The parameters $μ_1,μ_2,β>0$ are prescribed and the masses $a,b>0$. Here $2+\frac{4}{N} 0$ and $β>0$, provided $2\leqslant N\leqslant 4$. For the Sobolev critical case with $N=3,4$, it can be viewed as a counterpart of the Brezis-Nirenberg critical semilinear elliptic problem for the system case in the context of normalized solutions. Under some suitable assumptions, we obtain the existence or non-existence of positive normalized ground state solution.

math.AP↗

arXiv2021

Flexible Distributed Matrix Multiplication

The distributed matrix multiplication problem with an unknown number of stragglers is considered, where the goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across N servers. There are up to N - R stragglers but the exact number is not known a priori. Motivated by reducing the computation load of each server, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing task for each server is separated into several subtasks, constructed based on Entangled Polynomial codes by Yu et al. The final results can be obtained from either a larger number of servers with a smaller amount of computation completed per server or a smaller number of servers with a larger amount of computation completed per server. The required finite field size of the proposed solution is less than 2N. Moreover, the optimal design parameters such as the partitioning of the input matrices is discussed. Our constructions can also be generalized to other settings such as batch distributed matrix multiplication and secure distributed matrix multiplication.

cs.DC↗

arXiv2021

High conductivity Polarization-induced 2D hole gases in Undoped GaN/AlN Heterojunctions enabled by Impurity Blocking Layers

High-conductivity undoped GaN/AlN 2D hole gases (2DHGs), the p-type dual of the AlGaN/GaN 2D electron gases (2DEGs), have offered valuable insights into hole transport in GaN and enabled the first GaN GHz RF p-channel FETs. They are an important step towards high-speed and high-power complementary electronics with wide-bandgap semiconductors. These technologically and scientifically relevant 2D hole gases are perceived to be not as robust as the 2DEGs because structurally similar heterostructures exhibit wide variations of the hole density over $Δp_s >$ 7 x 10$^{13}$ cm$^{-2}$, and low mobilities. In this work, we uncover that the variations are tied to undesired dopant impurities such as Silicon and Oxygen floating up from the nucleation interface. By introducing impurity blocking layers (IBLs) in the AlN buffer layer, we eliminate the variability in 2D hole gas densities and transport properties, resulting in a much tighter-control over the 2DHG density variations to $Δp_s \leq$ 1 x 10$^{13}$ cm$^{-2}$ across growths, and a 3x boost in the Hall mobilities. These changes result in a 2-3x increase in hole conductivity when compared to GaN/AlN structures without IBLs.

cond-mat.mes-hall↗

arXiv2021

Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space

We present a numerical framework for deep neural network (DNN) modeling of unknown time-dependent partial differential equations (PDE) using their trajectory data. Unlike the recent work of [Wu and Xiu, J. Comput. Phys. 2020], where the learning takes place in modal/Fourier space, the current method conducts the learning and modeling in physical space and uses measurement data as nodal values. We present a DNN structure that has a direct correspondence to the evolution operator of the underlying PDE, thus establishing the existence of the DNN model. The DNN model also does not require any geometric information of the data nodes. Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids. A set of examples, including linear and nonlinear scalar PDE, system of PDEs, in both one dimension and two dimensions, over structured and unstructured grids, are presented to demonstrate the effectiveness of the proposed DNN modeling. Extension to other equations such as differential-integral equations is also discussed.

cs.LG↗

arXiv2021

A room temperature polar ferromagnetic metal

The advent of long-range magnetic order in non-centrosymmetric compounds has stimulated interest in the possibility of exotic spin transport phenomena and topologically protected spin textures for applications in next-generation spintronics. This work reports a novel wurtzite-structure polar magnetic metal, identified as AA'-stacked (Fe0.5Co0.5)5-xGeTe2, which exhibits a Neel-type skyrmion lattice as well as a Rashba-Edelstein effect at room temperature. Atomic resolution imaging of the structure reveals a structural transition as a function of Co-substitution, leading to the polar phase at 50% Co. This discovery reveals an unprecedented layered polar magnetic system for investigating intriguing spin topologies and ushers in a promising new framework for spintronics.

cond-mat.mtrl-sci↗

arXiv2021

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impression->click->purchase process is usually performed of audiences for e-commerce platforms. However, it is more difficult to acquire customers in financial advertising (e.g., credit card advertising) than in traditional advertising. On the one hand, the audience multi-step conversion path is longer. On the other hand, the positive feedback is sparser (class imbalance) step by step, and it is difficult to obtain the final positive feedback due to the delayed feedback of activation. Multi-task learning is a typical solution in this direction. While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion. In this paper, we propose an Adaptive Information Transfer Multi-task (AITM) framework, which models the sequential dependence among audience multi-step conversions via the Adaptive Information Transfer (AIT) module. The AIT module can adaptively learn what and how much information to transfer for different conversion stages. Besides, by combining the Behavioral Expectation Calibrator in the loss function, the AITM framework can yield more accurate end-to-end conversion identification. The proposed framework is deployed in Meituan app, which utilizes it to real-timely show a banner to the audience with a high end-to-end conversion rate for Meituan Co-Branded Credit Cards. Offline experimental results on both industrial and public real-world datasets clearly demonstrate that the proposed framework achieves significantly better performance compared with state-of-the-art baselines.

cs.AI↗

arXiv2021

Exceptional Point and Cross-Relaxation Effect in a Hybrid Quantum System

Exceptional points (EPs) are exotic degeneracies of non-Hermitian systems, where the eigenvalues and the corresponding eigenvectors simultaneously coalesce in parameter space, and these degeneracies are sensitive to tiny perturbations on the system. Here we report an experimental observation of the EP in a hybrid quantum system consisting of dense nitrogen (P1) centers in diamond coupled to a coplanar-waveguide resonator. These P1 centers can be divided into three subensembles of spins, and cross relaxation occurs among them. As a new method to demonstrate this EP, we pump a given spin subensemble with a drive field to tune the magnon-photon coupling in a wide range. We observe the EP in the middle spin subensemble coupled to the resonator mode, irrespective of which spin subensemble is actually driven. This robustness of the EP against pumping reveals the key role of the cross relaxation in P1 centers. It offers a novel way to convincingly prove the existence of the cross-relaxation effect via the EP.

quant-ph↗

arXiv2021

Back-n White Neutron Source at CSNS and its Applications

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source (CSNS) that emit through the incoming proton channel were exploited to build a white neutron beam facility (the so-called Back-n white neutron source), which was completed in March 2018. The Back-n neutron beam is very intense, at approximately 2*10^7 n/cm^2/s at 55 m from the target, and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices. In addition, the excellent energy spectrum spanning from 0.5 eV to 200 MeV, and a good time resolution related to the time-of-flight measurements make it a typical white neutron source for nuclear data measurements; its overall performance is among that of the best white neutron sources in the world. Equipped with advanced spectrometers, detectors, and application utilities, the Back-n facility can serve wide applications, with a focus on neutron-induced cross-section measurements. This article presents an overview of the neutron beam characteristics, the experimental setups, and the ongoing applications at Back-n.

physics.acc-ph↗

arXiv2021

Electron ptychography achieves atomic-resolution limits set by lattice vibrations

Transmission electron microscopes use electrons with wavelengths of a few picometers, potentially capable of imaging individual atoms in solids at a resolution ultimately set by the intrinsic size of an atom. Unfortunately, due to imperfections in the imaging lenses and multiple scattering of electrons in the sample, the image resolution reached is 3 to 10 times worse. Here, by inversely solving the multiple scattering problem and overcoming the aberrations of the electron probe using electron ptychography to recover a linear phase response in thick samples, we demonstrate an instrumental blurring of under 20 picometers. The widths of atomic columns in the measured electrostatic potential are now no longer limited by the imaging system, but instead by the thermal fluctuations of the atoms. We also demonstrate that electron ptychography can potentially reach a sub-nanometer depth resolution and locate embedded atomic dopants in all three dimensions with only a single projection measurement.

cond-mat.mtrl-sci↗

arXiv2020

A multi-period multi-product stochastic inventory problem with order-based loan

This paper investigates a multi-product stochastic inventory problem in which a cash-constrained online retailer can adopt order-based loan provided by some Chinese e-commerce platforms to speed up its cash recovery for deferred revenue. We first build deterministic models for the problem and then develop the corresponding stochastic programming models to maximize the retailers' expected profit over the planning horizon. The uncertainty of customer demand is represented by scenario trees, and a scenario reduction technique is used to solve the problem when the scenario trees are too large. We conduct numerical tests based on real data crawling from an online store. The results show that the stochastic model outperforms the deterministic model, especially when the retailer is less cash-constrained. Moreover, the retailer tends to choose using order-based loan when its initial available cash is small or facing long receipt delay length.

math.OC↗