arXiv subjects

Jun Zhou

Publications and source records attributed to Jun Zhou.

At least 487 records · Page 27Linked to original sources

arXiv2020

Thermal relaxation of magnons and phonons near resonance points in magnetic insulators

We theoretically investigate the energy relaxation rate of magnons and phonons near the resonance points to clarify the underlying mechanism of heat transport in ferromagnetic materials. We find that the simple two-temperature model is valid for the one-phonon/one-magnon process, as the rate of energy exchange between magnons and phonons is proportional to the temperature difference between them, and it is independent of temperature in the high temperature limit. We found that the magnon-phonon relaxation time due to the one-phonon/one-magnon interaction could be reduced to 1.48 $μs$ at the resonance point by applying an external magnetic field. It means that the resonance effect plays a significant role in enhancing the total magnon-phonon energy exchange rate, apart from the higher order interaction processes.

cond-mat.mes-hall↗

arXiv2020

Tailoring Magnetic Anisotropy in Cr$_2$Ge$_2$Te$_6$ by Electrostatic Gating

Electrical control of magnetism of a ferromagnetic semiconductor offers exciting prospects for future spintronic devices for processing and storing information. Here, we report observation of electrically modulated magnetic phase transition and magnetic anisotropy in thin crystal of Cr$_2$Ge$_2$Te$_6$ (CGT), a layered ferromagnetic semiconductor. We show that heavily electron-doped ($\sim$ $10^{14}$ cm$^{-2}$) CGT in an electric double-layer transistor device is found to exhibit hysteresis in magnetoresistance (MR), a clear signature of ferromagnetism, at temperatures up to above 200 K, which is significantly higher than the known Curie temperature of 61 K for an undoped material. Additionally, angle-dependent MR measurements reveal that the magnetic easy axis of this new ground state lies within the layer plane in stark contrast to the case of undoped CGT, whose easy axis points in the out-of-plane direction. We propose that significant doping promotes double-exchange mechanism mediated by free carriers, prevailing over the superexchange mechanism in the insulating state. Our findings highlight that electrostatic gating of this class of materials allows not only charge flow switching but also magnetic phase switching, evidencing their potential for spintronics applications.

cond-mat.mes-hall↗

arXiv2019

Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip

The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidate. Doing away with the need for multipliers significantly reduces energy. For on-device applications, besides computation, communication also incurs a significant amount of energy and time. In this paper, we propose Shenjing, a configurable SNN architecture which fully exposes all on-chip communications to software, enabling software mapping of SNN models with high accuracy at low power. Unlike prior SNN architectures like TrueNorth, Shenjing does not require any model modification and retraining for the mapping. We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency. For the MNIST inference problem using a multilayer perceptron, we were able to achieve an accuracy of 96% while consuming just 1.26mW using 10 Shenjing cores.

cs.ET↗

arXiv2019

Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

In this paper, we propose a normal estimation method for unstructured 3D point clouds. In this method, a feature constraint mechanism called Local Plane Features Constraint (LPFC) is used and then a multi-scale selection strategy is introduced. The LPEC can be used in a single-scale point network architecture for a more stable normal estimation of the unstructured 3D point clouds. In particular, it can partly overcome the influence of noise on a large sampling scale compared to the other methods which only use regression loss for normal estimation. For more details, a subnetwork is built after point-wise features extracted layers of the network and it gives more constraints to each point of the local patch via a binary classifier in the end. Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously.Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization. Specifically, we employed a subnetwork called Scale Estimation Network to extract scale weight information from multi-scale features. More analysis is given about the relations between noise levels, local boundary, and scales in the experiment. These relationships can be a better guide to choosing particular scales for a particular model. Besides, the experimental result shows that our network can distinguish the points on the fitting plane accurately and this can be used to guide the normal estimation and our multi-scale method can improve the results well. Compared to some state-of-the-art surface normal estimators, our method is robust to noise and can achieve competitive results.

cs.GR↗

arXiv2019

Phonon renormalization induced by electric field in ferroelectric P(VDF-TrFE) nanofibers

We report phonon renormalization induced by an external electric field E in ferroelectric poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] nanofibers through measuring the E-dependent thermal conductivity. Our experimental results are in excellent agreement with the theoretical ones derived from the lattice dynamics. The renormalization is attributed to the anharmonicity that modifies the phonon spectrum when the atoms are pulled away from their equilibrium positions by the electric field. Our finding provides an efficient way to manipulate the thermal conductivity by tuning external fields in ferroelectric materials.

cond-mat.mtrl-sci↗

arXiv2019

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD) is an effective method to enable Bayesian deep learning on large-scale datasets. Previous theoretical studies have shown various appealing properties of SGLD, ranging from the convergence properties to the generalization bounds. In this paper, we study the properties of SGLD from a novel perspective of membership privacy protection (i.e., preventing the membership attack). The membership attack, which aims to determine whether a specific sample is used for training a given DNN model, has emerged as a common threat against deep learning algorithms. To this end, we build a theoretical framework to analyze the information leakage (w.r.t. the training dataset) of a model trained using SGLD. Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent. Moreover, our theoretical analysis can be naturally extended to other types of Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods. Empirical results on different datasets and models verify our theoretical findings and suggest that the SGLD algorithm can not only reduce the information leakage but also improve the generalization ability of the DNN models in real-world applications.

cs.LG↗

arXiv2019

Pruning from Scratch

Network pruning is an important research field aiming at reducing computational costs of neural networks. Conventional approaches follow a fixed paradigm which first trains a large and redundant network, and then determines which units (e.g., channels) are less important and thus can be removed. In this work, we find that pre-training an over-parameterized model is not necessary for obtaining the target pruned structure. In fact, a fully-trained over-parameterized model will reduce the search space for the pruned structure. We empirically show that more diverse pruned structures can be directly pruned from randomly initialized weights, including potential models with better performance. Therefore, we propose a novel network pruning pipeline which allows pruning from scratch. In the experiments for compressing classification models on CIFAR10 and ImageNet datasets, our approach not only greatly reduces the pre-training burden of traditional pruning methods, but also achieves similar or even higher accuracy under the same computation budgets. Our results facilitate the community to rethink the effectiveness of existing techniques used for network pruning.

cs.CV↗

arXiv2019

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded. Moreover, some recent works, such as the Bayesian GAN, can be re-interpreted based on our theoretical insight from privacy protection. Quantitatively, to evaluate the information leakage of well-trained GAN models, we perform various membership attacks on these models. The results show that previous Lipschitz regularization techniques are effective in not only reducing the generalization gap but also alleviating the information leakage of the training dataset.

cs.LG↗

arXiv2019

Information collection for fraud detection in P2P financial market

Fintech companies have been facing challenges from fraudulent behavior for a long time. Fraud rate in Chinese P2P financial market could go as high as 10%. It is crucial to collect sufficient information of the user as input to the anti-fraud process. Data collection framework for Fintech companies are different fro m conventional internet firms. With individual-based crawling request , we need to deal with new challenges negligible elsewhere . In this paper , we give an outline of how we collect data from the web to facilitate our anti-fraud process. We also overview the challenges and solutions to our problems. Our team at HC Financial Service Group is one of the few companies that are capable of developing full-fledged crawlers on our own.

cs.OH↗

arXiv2019

A Thermal Resistance Network Model for Heat Conduction of Amorphous Polymers

Thermal conductivities (TCs) of the vast majority of amorphous polymers are in a very narrow range, 0.1 $\sim$ 0.5 Wm$^{-1}$K$^{-1}$, although single polymer chains possess TC of orders-of-magnitude higher. Entanglement of polymer chains plays an important role in determining the TC of bulk polymers. We propose a thermal resistance network (TRN) model for TC in amorphous polymers taking into account the entanglement of molecular chains. Our model explains well the physical origin of universally low TC observed in amorphous polymers. The empirical formulae of pressure and temperature dependence of TC can be successfully reproduced from our model not only in solid polymers but also in polymer melts. We further quantitatively explain the anisotropic TC in oriented polymers.

cond-mat.soft↗

arXiv2019

Design triple points, nexus points and related topological phases by stacking monolayers

Triple points and nexus points are two interesting topological phases, which have been reported in some three-dimensional (3D) materials. Here, we propose that triple points, nexus points and related phases, such as topological tangle nodal lines, can be obtained by alternatively stacking two types of monolayers. Two conditions for the stacking monolayers are required: the first condition is that they have a three-fold (C3) rotation symmetry and three mirror planes along the C3 axis; the second condition is that one of the monolayers should be insulating while the other one should be metallic (or semiconducting) and has a double degenerate band and a nondegenerate band at Γpoint around the Fermi level. Hexagonal boron nitride (HBN) and α/α^'-boron sheets (α/α^'-BS) are suggested as candidate materials. Even if HBN is a wide-gap insulator, the interactions between layers lead to crossings of the nondegenerate and double degenerate bands along the direction normal to the nanosheets, and thus form triple/nexus points or related phases. A tight-binding model is adopted to explain the phase transition between triple points, nexus points and other related phases.

cond-mat.mtrl-sci↗

arXiv2019

Evidence for metallic 1T phase, 3d1 electronic configuration and charge density wave order in molecular-beam epitaxy grown monolayer VTe2

We present a combined experimental and theoretical study of monolayer VTe2 grown on highly oriented pyrolytic graphite by molecular-beam epitaxy. Using various in-situ microscopic and spectroscopic techniques, including scanning tunneling microscopy/spectroscopy, synchrotron X-ray and angle-resolved photoemission, and X-ray absorption, together with theoretical analysis by density functional theory calculations, we demonstrate direct evidence of the metallic 1T phase and 3d1 electronic configuration in monolayer VTe2 that also features a (4 x 4) charge density wave order at low temperatures. In contrast to previous theoretical predictions, our element-specific characterization by X-ray magnetic circular dichroism rules out a ferromagnetic order intrinsic to the monolayer. Our findings provide essential knowledge necessary for understanding this interesting yet less explored metallic monolayer in the emerging family of van der Waals magnets.

cond-mat.mtrl-sci↗

arXiv2019

Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms

Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e.g., background clutter and rapid changes of target appearance. Alternatively, material information of targets contained in a large amount of bands of hyperspectral images (HSI) is more robust to these difficult conditions. In this paper, we conduct a comprehensive study on how material information can be utilized to boost object tracking from three aspects: benchmark dataset, material feature representation and material based tracking. In terms of benchmark, we construct a dataset of fully-annotated videos, which contain both hyperspectral and color sequences of the same scene. Material information is represented by spectral-spatial histogram of multidimensional gradient, which describes the 3D local spectral-spatial structure in an HSI, and fractional abundances of constituted material components which encode the underlying material distribution. These two types of features are embedded into correlation filters, yielding material based tracking. Experimental results on the collected benchmark dataset show the potentials and advantages of material based object tracking.

cs.CV↗

arXiv2019

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.

cs.LG↗

arXiv2019

A One-step Pruning-recovery Framework for Acceleration of Convolutional Neural Networks

Acceleration of convolutional neural network has received increasing attention during the past several years. Among various acceleration techniques, filter pruning has its inherent merit by effectively reducing the number of convolution filters. However, most filter pruning methods resort to tedious and time-consuming layer-by-layer pruning-recovery strategy to avoid a significant drop of accuracy. In this paper, we present an efficient filter pruning framework to solve this problem. Our method accelerates the network in one-step pruning-recovery manner with a novel optimization objective function, which achieves higher accuracy with much less cost compared with existing pruning methods. Furthermore, our method allows network compression with global filter pruning. Given a global pruning rate, it can adaptively determine the pruning rate for each single convolutional layer, while these rates are often set as hyper-parameters in previous approaches. Evaluated on VGG-16 and ResNet-50 using ImageNet, our approach outperforms several state-of-the-art methods with less accuracy drop under the same and even much fewer floating-point operations (FLOPs).

cs.CV↗

arXiv2019

Ultralow lattice thermal conductivity and electronic properties of monolayer 1T phase semimetal SiTe2 and SnTe2

2H phase (trigonal prismatic D3h) of layered two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted a lot of interests due to the superior electronic and optoelectronic properties. However, flexible electronic devices and thermoelectric performances based on 2H phase have been potentially limited by the strain sensitive electronic band gap and high lattice thermal conductivity (kappa_L). Here, we predict and calculate two 1T (octahedral Oh) phase monolayer telluride materials SnTe2 and SiTe2 with soft mechanics, ultralow kappa_L and electronic properties. The calculated in-plane Young's modulus of monolayer SnTe2 is softer than most of 1T-MX2 compounds. Furthermore, monolayer SiTe2 and SnTe2 also have relatively flexible electronic properties under large biaxial strain, indicating potential flexible electrode materials. Meanwhile, monolayer SiTe2 and SnTe2 both exhibit ultralow \k{appa}_L (2.27 W/mK of SiTe2 and 1.62 W/mK of SnTe2) at room temperature. Considering both acoustic and polar optical phonon scattering of the electronic relaxation time, the figure of merit (ZT) can achieve 0.46 at 600 K and 0.71 at 900 K for monolayer SiTe2 and SnTe2 respectively.

physics.comp-ph↗

arXiv2019

Intrinsic Ferromagnetism in Electrenes

We report intrinsic ferromagnetism in monolayer electrides or electrenes, in which excess electrons act as anions. Our first-principles calculations demonstrate that magnetism in such electron-rich two-dimensional (2D) materials originates from the anionic electrons rather than partially filled d orbitals, which is fundamentally different from ferromagnetism found in other 2D intrinsic magnetic materials. Taking the honeycomb LaBr$_2$ (La$^{3+}$Br$^{-}_{2}\cdot e^{-}$) as an example, our calculations reveal that the excess electron is localized at the center of the hexagon, which leads to strong Stoner-instability of the associated states at the Fermi energy, resulting in spontaneous magnetization and formation of a local moment. The overlap of extended tails of the wave functions of these electrons mediates a long-range ferromagnetic interaction, contributing to a Curie temperature ($T_\textrm{c}$) of 235 K and a coercive field ($H_\textrm{c}$) of 0.53 T, which can be further enhanced by hole doping. The dual nature, localization and extension, of the electronic states suggests a unique mechanism in such magnetic-element-free electrenes as intrinsic 2D ferromagnets.

physics.comp-ph↗

arXiv2019

2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches

Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm, and theoretically exfoliate them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/).

cond-mat.mtrl-sci↗