arXiv subjects

Ran Cheng

Publications and source records attributed to Ran Cheng.

At least 145 records · Page 8Linked to original sources

arXiv2020

Multi-objective Neural Architecture Search with Almost No Training

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results, most existing NAS algorithms are computationally prohibitive to execute due to the costly iterations of stochastic gradient descent (SGD) training. In this work, we propose an effective alternative, dubbed Random-Weight Evaluation (RWE), to rapidly estimate the performance of network architectures. By just training the last linear classification layer, RWE reduces the computational cost of evaluating an architecture from hours to seconds. When integrated within an evolutionary multi-objective algorithm, RWE obtains a set of efficient architectures with state-of-the-art performance on CIFAR-10 with less than two hours' searching on a single GPU card. Ablation studies on rank-order correlations and transfer learning experiments to ImageNet have further validated the effectiveness of RWE.

cs.CV↗

arXiv2020

Spin fluctuations in quantized transport of magnetic topological insulators

In magnetic topological insulators, quantized electronic transport is interwined with spontaneous magnetic ordering, as magnetization controls band gaps, hence band topology, through the exchange interaction. We show that considering the exchange gaps at the mean-field level is inadequate to predict phase transitions between electronic states of distinct topology. Thermal spin fluctuations disturbing the magnetization can act as frozen disorders that strongly scatter electrons, reducing the onset temperature of quantized transport appreciably even in the absence of structural impurities. This effect, which has hitherto been overlooked, provides an alternative explanation of recent experiments on intrinsic magnetic topological insulators.

cond-mat.mes-hall↗

arXiv2020

Magnonic Su-Schrieffer-Heeger Model in Honeycomb Ferromagnets

Topological electronics has extended its richness to non-electronic systems where phonons and magnons can play the role of electrons. In particular, topological phases of magnons can be enabled by the Dzyaloshinskii-Moriya interaction (DMI) which acts as an effective spin-orbit coupling. We show that besides DMI, an alternating arrangement of Heisenberg exchange interactions critically determines the magnon band topology, realizing a magnonic analog of the Su-Schrieffer-Heeger model. On a honeycomb ferromagnet with perpendicular anisotropy, we calculate the topological phase diagram, the chiral edge states, and the associated magnon Hall effect by allowing the relative strength of exchange interactions on different links to be tunable. Including weak phonon-magnon hybridization does not change the result. Candidate materials are discussed.

cond-mat.mes-hall↗

arXiv2020

Moiré magnons in twisted bilayer magnets with collinear order

We explore the moiré magnon bands in twisted bilayer magnets with next-nearest neighboring Dzyaloshinskii-Moriya interactions, assuming that the out-of-plane collinear magnetic order is preserved under weak interlayer coupling. By calculating the magnonic band structures and the topological Chern numbers for four representative cases, we find that (i) the valley moiré bands are extremely flat over a wide range of continuous twist angles; (ii) the topological Chern numbers of the lowest few flat bands vary significantly with the twist angle; and (iii) the lowest few topological flat bands in bilayer antiferromagnets entail nontrivial thermal spin transport in the transverse direction; These properties make twisted bilayer magnets an ideal platform to study the magnonic counterparts of moiré electrons, where the statistical distinction between magnons and electrons leads to fundamentally new physical behavior.

cond-mat.mes-hall↗

arXiv2020

Current-induced CrI3 surface spin-flop transition probed by proximity magnetoresistance in Pt

By exploiting proximity coupling, we probe the spin state of the surface layers of CrI3, a van der Waals magnetic semiconductor, by measuring the induced magnetoresistance (MR) of Pt in Pt/CrI3 nano-devices. We fabricate the devices with clean and stable interfaces by placing freshly exfoliated CrI3 flake atop pre-patterned thin Pt strip and encapsulating the Pt/CrI3 heterostructure with hexagonal boron nitride (hBN) in a protected environment. In devices consisting of a wide range of CrI3 thicknesses (30 to 150 nm), we observe that an abrupt upward jump in Pt MR emerge at a 2 T magnetic field applied perpendicularly to the layers when the current density exceeds 2.5x10^10 A/m2, followed by a gradual decrease over a range of 5 T. These distinct MR features suggest a spin-flop transition which reveals strong antiferromagnetic interlayer coupling in the surface layers of CrI3. We study the current dependence by holding the Pt/CrI3 sample at approximately the same temperature to exclude the joule heating effect, and find that the MR jump increases with the current density, indicating a spin current origin. This spin current effect provides a new route to control spin configurations in insulating antiferromagnets, which is potentially useful for spintronic applications.

cond-mat.mes-hall↗

arXiv2020

Subterahertz spin pumping from an insulating antiferromagnet

Spin-transfer torque and spin Hall effects combined with their reciprocal phenomena, spin-pumping and inverse spin Hall (ISHE) effects, enable the reading and control of magnetic moments in spintronics. The direct observation of these effects remains elusive in antiferromagnetic-based devices. We report sub-terahertz spin-pumping at the interface of a uniaxial insulating antiferromagnet MnF2 and platinum. The measured ISHE voltage arising from spin-charge conversion in the platinum layer depends on the chirality of the dynamical modes of the antiferromagnet, which is selectively excited and modulated by the handedness of the circularly polarized sub-THz irradiation. Our results open the door to the controlled generation of coherent pure spin currents at THz frequencies.

cond-mat.mtrl-sci↗

arXiv2020

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

cs.NE↗

arXiv2020

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models.Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality.To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs).At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs.Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data.The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

cs.NE↗

arXiv2020

Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction aims to extract the semantic relation between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to the underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. More specifically, the Transformer block is firstly used as the sentence encoder to capture syntactic information of sentences, which mainly utilizes multi-head self-attention to extract features from word level. Then, a more concise sentence-level attention mechanism is adopted to constitute the bag representation, aiming to incorporate valid information of each sentence to effectively represent the bag. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset, which verifies the effectiveness of our model for the DSRE task.

cs.CL↗

arXiv2020

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive. To address this issue, this paper proposes a new evolutionary framework for fast ENAS based on directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted to generate offspring individuals and their fitness is directly evaluated without training. To enhance the feature processing capability of the evolved neural networks, we also encode a channel attention mechanism in the search space. We evaluate the proposed algorithm on the widely used datasets, in comparison with 26 state-of-the-art peer algorithms. Our experimental results show the proposed algorithm is not only computationally much more efficiently, but also highly competitive in learning performance.

cs.NE↗

arXiv2020

PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks

Adversarial images are designed to mislead deep neural networks (DNNs), attracting great attention in recent years. Although several defense strategies achieved encouraging robustness against adversarial samples, most of them fail to improve the robustness on common corruptions such as noise, blur, and weather/digital effects (e.g. frost, pixelate). To address this problem, we propose a simple yet effective method, named Progressive Data Augmentation (PDA), which enables general robustness of DNNs by progressively injecting diverse adversarial noises during training. In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods. We also find that PDA is more efficient than prior arts and able to prevent accuracy drop on clean samples without being attacked. Furthermore, we theoretically show that PDA can control the perturbation bound and guarantee better generalization ability than existing work. Extensive experiments on many benchmarks such as CIFAR-10, SVHN, and ImageNet demonstrate that PDA significantly outperforms its counterparts in various experimental setups.

cs.LG↗

arXiv2019

Topological spin Hall effects and tunable skyrmion Hall effects in uniaxial antiferromagnetic insulators

Recent advances in the physics of current-driven antiferromagnetic skyrmions have observed the absence of a Magnus force. We outline the symmetry reasons for this phenomenon, and show that this cancellation will fail in the case of spin polarized currents. Pairing micromagnetic simulations with semiclassical spin wave transport theory, we demonstrate that skyrmions produce a spin-polarized transverse magnon current, and that spin-polarized magnon currents can in turn produce transverse motion of antiferromagnetic skyrmions. We examine qualitative differences in the frequency dependence of the skyrmion Hall angle between ferromagnetic and antiferromagnetic cases, and close by proposing a simple skyrmion-based magnonic device for demultiplexing of spin channels.

cond-mat.mes-hall↗

arXiv2019

Magnetic domain wall Skyrmions

It is well established that the spin-orbit interaction in heavy metal/ferromagnet heterostructures leads to a significant interfacial Dzyaloshinskii-Moriya Interaction (DMI) that modifies the internal structure of magnetic domain walls (DWs) to favor Néel over Bloch type configurations. However, the impact of such a transition on the structure and stability of internal DW defects (e.g., vertical Bloch lines) has not yet been explored. We present a combination of analytical and micromagnetic calculations to describe a new type of topological excitation called a DW Skyrmion characterized by a $360^\circ$ rotation of the internal magnetization in a Dzyaloshinskii DW. We further propose a method to identify DW Skyrmions experimentally using Fresnel mode Lorentz TEM; simulated images of DW Skyrmions using this technique are presented based on the micromagnetic results.

cond-mat.mes-hall↗

arXiv2018

Electric field switching of the uniaxial magnetic anisotropy of an antiferromagnet

Electric field control of magnetic anisotropy in ferromagnets has been intensively pursued in spintronics to achieve efficient memory and computing devices with low energy consumption. Compared with ferromagnets, antiferromagnets hold huge potential in high-density information storage for their ultrafast spin dynamics and vanishingly small stray field. However, the switching of magnetic anisotropy of antiferromagnets via electric field remains elusive. Here we use ferroelastic strain from piezoelectric materials to switch the uniaxial magnetic anisotropy and the Néel order reversibly in antiferromagnetic Mn2Au films with an electric field of only a few kV/cm at room temperature. Owing to the uniaxial magnetic anisotropy, a ratchet-like switching behavior driven by the Néel spin-orbit torque is observed in the Mn2Au, which can be reversed by electric fields.

cond-mat.mtrl-sci↗

arXiv2018

Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions

Superconducting circuits based on quantum phase-slip junctions (QPSJs) can conduct quantized charge pulses, which naturally resemble action potentials generated by biological neurons. A corresponding synaptic circuit, which works as a weighted connection between two neurons, can also be realized by circuits comprised of QPSJs and magnetic Josephson junctions (MJJs) as a means of charge modulation for quantized charge propagation. In this paper, we present basic neuromorphic components such as neuron and synaptic circuits based on superconducting QPSJs and MJJs. Using a SPICE model developed for QPSJs, neuron and synaptic circuits have been simulated in WRSPICE to demonstrate possible operation. We provide estimates for QPSJ energy dissipation and operation speed based on calculations using simple models. The challenges for implementation of this technology are also briefly discussed.

cs.ET↗

arXiv2018

Interlayer Couplings Mediated by Antiferromagnetic Magnons

Collinear antiferromagnets (AFs) support two degenerate magnon excitations carrying opposite spin polarizations, by which magnons can function as electrons in various spin-related phenomena. In an insulating ferromagnet(F)/AF/F trilayer, we explore the magnon-mediated interlayer coupling by calculating the magnon thermal energy in the AF as a function of the orientations of the Fs. The effect manifests as an interlayer exchange interaction and a perpendicular magnetic anisotropy; they both depend on temperature and the AF thickness. In particular, the exchange interaction turns out to be antiferromagnetic at low temperatures and ferromagnetic at high temperatures, whose magnitude can be $10-100$ $μ$eV for nanoscale separations, allowing experimental verification.

cond-mat.mes-hall↗

arXiv2018

Nonabelian magnonics in antiferromagnets

We present a semiclassical formalism for antiferromagnetic (AFM) magnonics which promotes the central ingredient of spin wave chirality, encoded in a quantity called magnonic isospin, to a first-class citizen of the theory. We use this formalism to unify results of interest from the field under a single chirality-centric formulation. Our main result is that the isospin is governed by unitary time evolution, through a Hamiltonian projected down from the full spin wave dynamics. Because isospin is SU(2)-valued, its dynamics on the Bloch sphere are precisely rotations - which, in general, do not commute. Consequently, the induced group of operations on AFM spin waves is nonabelian. This is a paradigmatic departure from ferromagnetic magnonics, which operates purely within the abelian group generated by spin wave phase and amplitude. Our investigation of this nonabelian magnonics in AFM insulators focuses on studying several simple gate operations, and offering in broad strokes a program of study for interesting new logic families in antiferromagnetic spin wave systems

cond-mat.mes-hall↗

arXiv2018

Magnonic Spin-Transfer Torque in Ferromagnet/Antiferromagnet/Ferromagnet Trilayer

In an antiferromagnet (AF) with uniaxial anisotropy, spin-up and spin-down magnons coexist and form an intrinsic degree of freedom resembling electrons. When polarized by an adjacent ferromagnet (F), a magnonic pure spin current can be thermally generated in an AF. We explore thermal magnon transport in an insulating F/AF/F trilayer where propagating magnons inside the AF spacer can transfer angular momenta between the two Fs. We find that a sufficiently large temperature gradient can switch the downstream F via magnonic spin-transfer torque if it is initially antiparallel with the upstream F. A reciprocal switching is achievable by reversing the temperature gradient. Using typical material parameters, we estimate the threshold to be less than 1 K/nm at room temperature, which can be reduced by raising temperature and enhancing the interfacial exchange coupling.

cond-mat.mes-hall↗