arXiv subjects

Peng Chen

Publications and source records attributed to Peng Chen.

At least 289 records · Page 16Linked to original sources

arXiv2022

Continuous Electrical Manipulation of Magnetic Anisotropy and Spin Flopping in van der Waals Ferromagnetic Devices

Controlling the magnetic anisotropy of ferromagnetic materials plays a key role in magnetic switching devices and spintronic applications. Examples of spin-orbit torque devices with different magnetic anisotropy geometries (in-plane or out-of-plane directions) have been demonstrated with novel magnetization switching mechanisms for extended device functionalities. Normally, the intrinsic magnetic anisotropy in ferromagnetic materials is unchanged within a fixed direction, and thus, it is difficult to realize multifunctionality devices. Therefore, continuous modulation of magnetic anisotropy in ferromagnetic materials is highly desired but remains challenging. Here, we demonstrate a gate-tunable magnetic anisotropy transition from out-of-plane to canted and finally to in-plane in layered Fe$_5$GeTe$_2$ by combining the measurements of the angle-dependent anomalous Hall effect and magneto-optical Kerr effect with quantitative Stoner-Wohlfarth analysis. The magnetic easy axis continuously rotates in a spin-flop pathway by gating or temperature modulation. Such observations offer a new avenue for exploring magnetization switching mechanisms and realizing new spintronic functionalities.

cond-mat.mtrl-sci↗

arXiv2022

Low-Cost Beamforming and DOA Estimation Based on One-Bit Reconfigurable Intelligent Surface

In this work, we consider the Direction-of-Arrival (DOA) estimation problem in a low-cost architecture where only one antenna as the receiver is aided by a reconfigurable intelligent surface (RIS). We introduce the one-bit RIS as a signal reflector to enhance signal transmission in non-line-of-sight (NLOS) situations and substantially simplify the physical hardware for DOA estimation. We optimize the beamforming scheme called measurement matrix to focus the echo power on the receiver with the coarse localization information of the targets as the prior. A beamforming scheme based on the modified genetic algorithm is proposed to optimize the measurement matrix, guaranteeing restricted isometry property (RIP) and meeting single beamforming requirements. The DOA results are finely estimated by solving an atomic-norm based sparse reconstruction problem. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance.

eess.SP↗

arXiv2022

A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face class biases due to class variants and background confusion. In this paper, we propose a joint framework that combines more valuable class-aware and class-agnostic alignment guidance to facilitate the segmentation. Specifically, we design a hybrid alignment module which establishes multi-scale query-support correspondences to mine the most relevant class-aware information for each query image from the corresponding support features. In addition, we explore utilizing base-classes knowledge to generate class-agnostic prior mask which makes a distinction between real background and foreground by highlighting all object regions, especially those of unseen classes. By jointly aggregating class-aware and class-agnostic alignment guidance, better segmentation performances are obtained on query images. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that our proposed joint framework performs better, especially on the 1-shot setting.

cs.CV↗

arXiv2022

Large-scale Bayesian optimal experimental design with derivative-informed projected neural network

We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fields. The OED problem seeks to find sensor locations that maximize the expected information gain (EIG) in the solution of the underlying Bayesian inverse problem. Computation of the EIG is usually prohibitive for PDE-based OED problems. To make the evaluation of the EIG tractable, we approximate the (PDE-based) parameter-to-observable map with a derivative-informed projected neural network (DIPNet) surrogate, which exploits the geometry, smoothness, and intrinsic low-dimensionality of the map using a small and dimension-independent number of PDE solves. The surrogate is then deployed within a greedy algorithm-based solution of the OED problem such that no further PDE solves are required. We analyze the EIG approximation error in terms of the generalization error of the DIPNet and show they are of the same order. Finally, the efficiency and accuracy of the method are demonstrated via numerical experiments on OED problems governed by inverse scattering and inverse reactive transport with up to 16,641 uncertain parameters and 100 experimental design variables, where we observe up to three orders of magnitude speedup relative to a reference double loop Monte Carlo method.

math.NA↗

arXiv2022

Optimal design of photonic nanojets under uncertainty

Photonic nanojets (PNJs) have promising applications as optical probes in super-resolution optical microscopy, Raman microscopy, as well as fluorescence microscopy. In this work, we consider optimal design of PNJs using a heterogeneous lens refractive index with a fixed lens geometry and uniform plane wave illumination. In particular, we consider the presence of manufacturing error of heterogeneous lens, and propose a computational framework of Optimization Under Uncertainty (OUU) for robust optimal design of PNJ. We formulate a risk-averse stochastic optimization problem with the objective to minimize both the mean and the variance of a target function, which is constrained by the Helmholtz equation that governs the 2D transverse electric (2D TE) electromagnetic field in a neighborhood of the lens. The design variable is taken as a spatially-varying field variable, where we use a finite element method for its discretization, impose a total variation penalty to promote its sparsity, and employ an adjoint-based BFGS method to solve the resulting high-dimensional optimization problem. We demonstrate that our proposed OUU computational framework can achieve more robust optimal design than a deterministic optimization scheme to significantly mitigate the impact of manufacturing uncertainty.

cs.CE↗

arXiv2022

FIRED: a fine-grained robust performance diagnosis framework for cloud applications

To run a cloud application with the required service quality, operators have to continuously monitor the cloud application's run-time status, detect potential performance anomalies, and diagnose the root causes of anomalies. However, existing models of performance anomaly detection often suffer from low re-usability and robustness due to the diversity of system-level metrics being monitored and the lack of high-quality labeled monitoring data for anomalies. Moreover, the current coarse-grained analysis models make it difficult to locate system-level root causes of the application performance anomalies for effective adaptation decisions. We provide a FIne-grained Robust pErformance Diagnosis (FIRED) framework to tackle those challenges. The framework offers an ensemble of several well-selected base models for anomaly detection using a deep neural network, which adopts weakly-supervised learning considering fewer labels exist in reality. The framework also employs a real-time fine-grained analysis model to locate dependent system metrics of the anomaly. Our experiments show that the framework can achieve the best detection accuracy and algorithm robustness, and it can predict anomalies in four minutes with F1 score higher than 0.8. In addition, the framework can accurately localize the first root causes, and with an average accuracy higher than 0.7 of locating first four root causes.

cs.DC↗

arXiv2022

Performance Bounds for PDE-Constrained Optimization under Uncertainty

Computational approaches to PDE-constrained optimization under uncertainty may involve finite-dimensional approximations of control and state spaces, sample average approximations of measures of risk and reliability, smooth approximations of nonsmooth functions, penalty approximations of constraints as well as many other kinds of inaccuracies. In this paper, we analyze the performance of controls obtained by an approximation-based algorithm and in the process develop estimates of optimality gaps for general optimization problems defined on metric spaces. Under mild assumptions, we establish that limiting controls have arbitrarily small optimality gaps provided that the inaccuracies in the various approximations vanish. We carry out the analysis for a broad class of problems with multiple expectation, risk, and reliability functions involving PDE solutions and appearing in objective as well as constraint expressions. In particular, we address problems with buffered failure probability constraints approximated via an augmented Lagrangian. We demonstrate the framework on an elliptic PDE with a random coefficient field and a distributed control function.

math.OC↗

arXiv2022

Mesa: A Memory-saving Training Framework for Transformers

There has been an explosion of interest in designing high-performance Transformers. While Transformers have delivered significant performance improvements, training such networks is extremely memory intensive owing to storing all intermediate activations that are needed for gradient computation during backpropagation, especially for long sequences. To this end, we present Mesa, a memory-saving training framework for Transformers. Specifically, Mesa uses exact activations during forward pass while storing a low-precision version of activations to reduce memory consumption during training. The low-precision activations are then dequantized during back-propagation to compute gradients. Besides, to address the heterogeneous activation distributions in the multi-head self-attention layers, we propose a head-wise activation quantization strategy, which quantizes activations based on the statistics of each head to minimize the approximation error. To further boost training efficiency, we learn quantization parameters by running estimates. More importantly, by re-investing the saved memory in employing a larger batch size or scaling up model size, we may further improve the performance under constrained computational resources. Extensive experiments on ImageNet, CIFAR-100 and ADE20K demonstrate that Mesa can achieve flexible memory-savings (up to 50%) during training while achieving comparable or even better performance. Code is available at https://github.com/ziplab/Mesa.

cs.CV↗

arXiv2022

Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm

Directed self-assembly (DSA) of block-copolymers (BCPs) is one of the most promising developments in the cost-effective production of nanoscale devices. The process makes use of the natural tendency for BCP mixtures to form nanoscale structures upon phase separation. The phase separation can be directed through the use of chemically patterned substrates to promote the formation of morphologies that are essential to the production of semiconductor devices. Moreover, the design of substrate pattern can formulated as an optimization problem for which we seek optimal substrate designs that effectively produce given target morphologies. In this paper, we adopt a phase field model given by a nonlocal Cahn--Hilliard partial differential equation (PDE) based on the minimization of the Ohta--Kawasaki free energy, and present an efficient PDE-constrained optimization framework for the optimal design problem. The design variables are the locations of circular- or strip-shaped guiding posts that are used to model the substrate chemical pattern. To solve the ensuing optimization problem, we propose a variant of an inexact Newton conjugate gradient algorithm tailored to this problem. We demonstrate the effectiveness of our computational strategy on numerical examples that span a range of target morphologies. Owing to our second-order optimizer and fast state solver, the numerical results demonstrate five orders of magnitude reduction in computational cost over previous work. The efficiency of our framework and the fast convergence of our optimization algorithm enable us to rapidly solve the optimal design problem in not only two, but also three spatial dimensions.

math.OC↗

arXiv2022

The commutators of Bochner-Riesz Operators for Hermite operator

In this paper, we study the $L^p$-boundedness of the commutator $[b, S_R^δ(H)](f) = bS_R^δ(H) f - S_R^δ(H)(bf)$ of a BMO function $b$ and the Bochner-Riesz means $S_R^δ(H)$ for Hermite operator $H=-Δ+|x|^2$ on $\mathbb{R}^n$, $n\geq2$. We show that if $δ>δ(q)=n(1/q -1/2)- 1/2$, the commutator $[b,S_R^δ(H)]$ is bounded on $L^p(\mathbb{R}^n)$ whenever $q<p<q'$ and $1\leq q\leq 2n/(n+2)$.

math.AP↗

arXiv2022

Wafer-scale epitaxial growth of the thickness-controllable van der Waals ferromagnet CrTe2 for reliable magnetic memory applications

To harness the intriguing properties of two-dimensional van der Waals (vdW) ferromagnets (FMs) for versatile applications, the key challenge lies in the reliable material synthesis for scalable device production. Here, we demonstrate the epitaxial growth of single-crystalline 1T-CrTe2 thin films on 2-inch sapphire substrates. Benefiting from the uniform surface energy of the dangling bond-free Al2O3(0001) surface, the layer-by-layer vdW growth mode is observed right from the initial growth stage, which warrants precise control of the sample thickness and atomically smooth surface morphology across the entire wafer. Moreover, the presence of the Coulomb interaction at the CrTe2/Al2O3 interface serves as an effective tuning parameter to tailor the anomalous Hall response, and the structural optimization of the CrTe2-based spin-orbit torque device leads to a substantial switching power reduction by 54%. Our results may lay out a general framework for the design of energy-efficient spintronics based on configurable vdW FMs.

cond-mat.mtrl-sci↗

arXiv2022

J-PLUS: A catalogue of globular cluster candidates around the M81/M82/NGC3077 triplet of galaxies

Globular clusters (GCs) are proxies of the formation assemblies of their host galaxies. However, few studies exist targeting GC systems of spiral galaxies up to several effective radii. Through 12-band Javalambre Photometric Local Universe Survey (J-PLUS) imaging, we study the point sources around the M81/M82/NGC3077 triplet in search of new GC candidates. We develop a tailored classification scheme to search for GC candidates based on their similarity to known GCs via a principal components analysis (PCA) projection. Our method accounts for missing data and photometric errors. We report 642 new GC candidates in a region of 3.5 deg$^2$ around the triplet, ranked according to their Gaia astrometric proper motions when available. We find tantalising evidence for an overdensity of GC candidate sources forming a bridge connecting M81 and M82. Finally, the spatial distribution of the GC candidates $(g-i)$ colours is consistent with halo/intra-cluster GCs, i.e. it gets bluer as they get further from the closest galaxy in the field. We further employ a regression-tree based model to estimate the metallicity distribution of the GC candidates based on their J-PLUS bands. The metallicity distribution of the sample candidates is broad and displays a bump towards the metal-rich end. Our list increases the population of GC candidates around the triplet by 3-fold, stresses the usefulness of multi-band surveys in finding these objects, and provides a testbed for further studies analysing their spatial distribution around nearby (spirals) galaxies.

astro-ph.GA↗

arXiv2022

Looper: An end-to-end ML platform for product decisions

Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support finegrain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection. Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals. During the 2021 production deployment Looper simultaneously hosted 440-1,000 ML models that made 4-6 million real-time decisions per second. We sum up experiences of platform adopters and describe their learning curve.

cs.LG↗

arXiv2022

A Lateral AlGaN/GaN Schottky Barrier Diode with 0.36 V Turn-on Voltage and 10 kV Breakdown Voltage by Using Double Barrier Anode Structure

In this letter, we demonstrate a lateral AlGaN/GaN Schottky barrier diode (SBD) on sapphire substrate with low turn-on voltage (Von) and high breakdown voltage (VBK). By using a double barrier anode (DBA) structure formed by the mixture of Platinum (Pt) and Tantalum (Ta), the Von of the SBD can be as low as 0.36 V with a leakage current of 2.5E-6 A/mm. Supported by the high-quality carbon-doped GaN buffer on sapphire, the VBK can reach more than 10 kV with the anode-to-cathode spacing of 85 μm. Combining the VBK and the specific on-resistance (Ron,sp) of 25.1 mΩ.cm^2, the power figure of merit of the SBD can reach 4.0 GW/cm^2, demonstrating a great potential for the application in ultra-high-voltage electronics.

cond-mat.mtrl-sci↗

arXiv2022

A probability approximation framework: Markov process approach

We view the classical Lindeberg principle in a Markov process setting to establish a probability approximation framework by the associated Itô's formula and Markov operator. As applications, we study the error bounds of the following three approximations: approximating a family of online stochastic gradient descents (SGDs) by a stochastic differential equation (SDE) driven by multiplicative Brownian motion, Euler-Maruyama (EM) discretization for multi-dimensional Ornstein-Uhlenbeck stable process, and multivariate normal approximation. All these error bounds are in Wasserstein-1 distance.

math.PR↗

arXiv2022

Structured Binary Neural Networks for Image Recognition

We propose methods to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models that are specifically friendly to mobile devices with limited power capacity and computation resources. Previous works on quantizing CNNs often seek to approximate the floating-point information using a set of discrete values, which we call value approximation, typically assuming the same architecture as the full-precision networks. Here we take a novel "structure approximation" view of quantization -- it is very likely that different architectures designed for low-bit networks may be better for achieving good performance. In particular, we propose a "network decomposition" strategy, termed Group-Net, in which we divide the network into groups. Thus, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. In addition, we learn effective connections among groups to improve the representation capability. Moreover, the proposed Group-Net shows strong generalization to other tasks. For instance, we extend Group-Net for accurate semantic segmentation by embedding rich context into the binary structure. Furthermore, for the first time, we apply binary neural networks to object detection. Experiments on both classification, semantic segmentation and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Our methods outperform the previous best binary neural networks in terms of accuracy and computation efficiency.

cs.CV↗

arXiv2022

Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization

The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite samples requires solving a variational problem. We study the variational problem in the family of two-layer networks with squared-ReLU activations, towards which we derive a semi-definite programming (SDP) relaxation. This SDP can be viewed as an approximation of the Wasserstein gradient in a broader function family including two-layer networks. By solving the convex SDP, we obtain the optimal approximation of the Wasserstein gradient direction in this class of functions. Numerical experiments including PDE-constrained Bayesian inference and parameter estimation in COVID-19 modeling demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2022

Scalable Multi-view Clustering with Graph Filtering

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches.

cs.LG↗