arXiv subjects

Yadong Wang

Publications and source records attributed to Yadong Wang.

34 records · Page 2Linked to original sources

arXiv2021

Topologically Protected Ferroelectric Domain Wall Memory with Large Readout Current

The discovery and precise manipulation of atomic-size conductive ferroelectric domain defects, such as geometrically confined walls, offer new opportunities for a wide range of prospective electronic devices, and the so-called walltronics is emerging consequently. Here we demonstrate the highly stable and fatigue-resistant nonvolatile ferroelectric memory device based on deterministic creation and erasure of conductive domain wall geometrically confined inside a topological domain structure. By introducing a pair of delicately designed co-axial electrodes onto the epitaxial BiFeO3 film, one can easily create quadrant center topological polar domain structure. More importantly, a reversible switching of such center topological domain structure between the convergent state with highly conductive confined wall and the divergent state with insulating confined wall can be realized, hence resulting in an apparent resistance change with a large On/Off ratio > 104 and a technically preferred readout current (up to 40 nA). Owing to the topological robustness of the center domain structure, the device exhibits the excellent restoration repeatability over 106 cycles and a long retention over 12 days (> 106 s). This work demonstrates a good example for implementing the exotic polar topologies in high-performance nanoscale devices, and would spur more interest in exploring the rich emerging applications of these exotic topological states.

physics.app-ph↗

arXiv2020

Electric-field-driven Non-volatile Multi-state Switching of Individual Skyrmions in a Multiferroic Heterostructure

Electrical manipulation of skyrmions attracts considerable attention for its rich physics and promising applications. To date, such a manipulation is realized mainly via spin-polarized current based on spin-transfer torque or spin-orbital torque effect. However, this scheme is energy-consuming and may produce massive Joule heating. To reduce energy dissipation and risk of heightened temperatures of skyrmion-based devices, an effective solution is to use electric field instead of current as stimulus. Here, we realize an electric-field manipulation of skyrmions in a nanostructured ferromagnetic/ferroelectrical heterostructure at room temperature via an inverse magneto-mechanical effect. Intriguingly, such a manipulation is non-volatile and exhibits a multi-state feature. Numerical simulations indicate that the electric-field manipulation of skyrmions originates from strain-mediated modification of effective magnetic anisotropy and Dzyaloshinskii-Moriya interaction. Our results open a direction for constructing low-energy-dissipation, non-volatile, and multi-state skyrmion-based spintronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Quasi-one-dimensional metallic conduction channels in exotic ferroelectric topological defects

Ferroelectric topological objects (e.g. vortices, skyrmions) provide a fertile ground for exploring emerging physical properties that could potentially be utilized in future configurable nanoelectronic devices. Here, we demonstrate quasi-one-dimensional metallic high conduction channels along two types of exotic topological defects, i.e. the topological cores of (i) a quadrant vortex domain structure and (ii) a center domain (monopole-like) structure confined in high quality BiFeO3 nanoisland array, abbreviated as the vortex core and the center core. We unveil via phase-field simulations that the superfine (< 3 nm) metallic conduction channels along center cores arise from the screening charge carriers confined at the core whereas the high conductance of vortex cores results from a field-induced twisted state. These conducting channels can be repeatedly and reversibly created and deleted by manipulating the two topological states via an electric field, leading to an apparent electroresistance effect with an on/off ratio higher than 103. These results open up the possibility of utilizing these functional one-dimensional topological objects in high-density nanoelectronic devices such as ultrahigh density nonvolatile memory.

cond-mat.mtrl-sci↗

arXiv2018

DeepMutation: Mutation Testing of Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.

cs.SE↗

arXiv2018

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.

cs.SE↗

arXiv2018

Combinatorial Testing for Deep Learning Systems

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.

cs.SE↗

arXiv2018

Spontaneous symmetry breaking and trapping of temporal Kerr cavity solitons by pulsed or amplitude modulated driving fields

We report on a systematic study of temporal Kerr cavity soliton dynamics in the presence of pulsed or amplitude modulated driving fields. In stark contrast to the more extensively studied case of phase modulations, we find that Kerr cavity solitons are not always attracted to maxima or minima of driving field amplitude inhomogeneities. Instead, we find that the solitons are attracted to temporal positions associated with specific driving field values that depend only on the cavity detuning. We describe our findings in light of a spontaneous symmetry breaking instability that physically ensues from a competition between coherent driving and nonlinear propagation effects. In addition to identifying a new type of Kerr cavity soliton behaviour, our results provide valuable insights to practical cavity configurations employing pulsed or amplitude modulated driving fields.

physics.optics↗

arXiv2018

Writing and Erasing of Temporal Kerr Cavity Solitons via Intensity Modulation of the Cavity Driving Field

We experimentally and numerically study the use of intensity modulation for the controlled addressing of temporal Kerr cavity solitons. Using a coherently driven fiber ring resonator, we demonstrate that a single temporally broad intensity modulation pulse applied on the cavity driving field permits systematic and efficient writing and erasing of ultrashort cavity solitons. We use numerical simulations based on the mean-field Lugiato-Lefever model to investigate the addressing dynamics, and present a simple physical description of the underlying physics.

physics.optics↗

arXiv2017

Stimulated Raman Scattering Imposes Fundamental Limits to the Duration and Bandwidth of Temporal Cavity Solitons

Temporal cavity solitons (CS) are optical pulses that can persist in passive resonators, and they play a key role in the generation of coherent microresonator frequency combs. In resonators made of amorphous materials, such as fused silica, they can exhibit a spectral red-shift due to stimulated Raman scattering. Here we show that this Raman-induced self-frequency-shift imposes a fundamental limit on the duration and bandwidth of temporal CSs. Specifically, we theoretically predict that stimulated Raman scattering introduces a previously unidentified Hopf bifurcation that leads to destabilization of CSs at large pump-cavity detunings, limiting the range of detunings over which they can exist. We have confirmed our theoretical predictions by performing extensive experiments in several different synchronously-driven fiber ring resonators, obtaining results in excellent agreement with numerical simulations. Our results could have significant implications for the future design of Kerr frequency comb systems based on amorphous microresonators.

physics.optics↗

arXiv2017

Comparison among dimensionality reduction techniques based on Random Projection for cancer classification

Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.

cs.LG↗

arXiv2017

Universal mechanism for the binding of temporal cavity solitons

We present theoretical and experimental evidence of a universal mechanism through which temporal cavity solitons of externally-driven, passive, Kerr resonators can form robust long-range bound states. These bound states, sometime also referred to as multi-soliton states or soliton crystals in microresonators, require perturbations to the strict Lugiato-Lefever mean field description of temporal cavity solitons. Binding occurs when the perturbation excites a narrowband resonance in the soliton spectrum, which gives long oscillatory tails to the solitons. Those tails can then interlock for a discrete set of temporal separations between the solitons. The universality of this mechanism is demonstrated in fiber ring cavities by providing experimental observations of long-range bound states ensuing from three different perturbations: third-order dispersion (dispersive wave generation), the periodic nature of the cavity (Kelly sidebands), and the random birefringence of the resonator. Sub-picosecond resolution of bound state separations and their dynamics are obtained by using the dispersive Fourier transform technique. Good agreement with theoretical models, including a new vector mean-field model, is also reported.

physics.optics↗

arXiv2017

Experimental and numerical investigations of switching wave dynamics in a normally dispersive fiber ring resonator

Optical frequency combs generated in normally dispersive Kerr microresonators have been observed to correspond to dark temporal structures, and theoretically explained as interlocked switching waves (also known as domain walls or fronts). The time-domain dynamics that underpin the formation of this type of frequency combs has however so far eluded direct experimental observation. Here we use a closely related system - a synchronously driven optical fibre ring resonator - to experimentally study the dynamics of deterministically excited switching waves. We measure the switching wave velocities across broad parameter regions, and observe clear signatures of interlocking behaviour leading to the formation of persisting dark pulses. Our experimental findings are in good agreement with simulations of the mean-field Lugiato-Lefever equation, and strongly support the nature of normal-dispersion microresonator frequency combs suggested in the literature.

physics.optics↗

arXiv2017

Super cavity solitons and the coexistence of multiple nonlinear states in a tristable passive Kerr resonator

Passive Kerr cavities driven by coherent laser fields display a rich landscape of nonlinear physics, including bistability, pattern formation, and localised dissipative structures (solitons). Their conceptual simplicity has for several decades offered an unprecedented window into nonlinear cavity dynamics, providing insights into numerous systems and applications ranging from all-optical memory devices to microresonator frequency combs. Yet despite the decades of study, a recent theoretical study has surprisingly alluded to an entirely new and unexplored paradigm in the regime where nonlinearly tilted cavity resonances overlap with one another [T. Hansson and S. Wabnitz, J. Opt. Soc. Am. B 32, 1259 (2015)]. We have used synchronously driven fiber ring resonators to experimentally access this regime, and observed the rise of new nonlinear dissipative states. Specifically, we have observed, for the first time to the best of our knowledge, the stable coexistence of dissipative (cavity) solitons and extended modulation instability (Turing) patterns, and performed real time measurements that unveil the dynamics of the ensuing nonlinear structures. When operating in the regime of continuous wave tristability, we have further observed the coexistence of two distinct cavity soliton states, one of which can be identified as a "super" cavity soliton as predicted by Hansson and Wabnitz. Our experimental findings are in excellent agreement with theoretical analyses and numerical simulations of the infinite-dimensional Ikeda map that governs the cavity dynamics. The results from our work reveal that experimental systems can support complex combinations of distinct nonlinear states, and they could have practical implications to future microresonator-based frequency comb sources.

physics.optics↗

arXiv2010

Inner and Outer Bounds for the Public Information Embedding Capacity Region Under Multiple Access Attacks

We consider a public multi-user information embedding (watermarking) system in which two messages (watermarks) are independently embedded into two correlated covertexts and are transmitted through a multiple-access attack channel. The tradeoff between the achievable embedding rates and the average distortions for the two embedders is studied. For given distortion levels, inner and outer bounds for the embedding capacity region are obtained in single-letter form. Tighter bounds are also given for independent covertexts.

cs.IT↗

arXiv2006

Concurrent encoding of frequency and amplitude modulation in human auditory cortex: MEG evidence

A natural sound can be described by dynamic changes in envelope (amplitude) and carrier (frequency), corresponding to amplitude modulation (AM) and frequency modulation (FM) respectively. Although the neural responses to both AM and FM sounds are extensively studied in both animals and humans, it is uncertain how they are co-represented when changed simultaneously but independently, as is typical for ecologically natural signals. This study elucidates the neural coding of such sounds in human auditory cortex using magnetoencephalography (MEG). Using stimuli with both sinusoidal modulated envelope (f_{AM}, 37 Hz) and carrier frequency (f_{FM}, 0.3 - 8 Hz), it is demonstrated that AM and FM stimulus dynamics are co-represented in the neural code of human auditory cortex. The stimulus AM dynamics are represented neurally with AM encoding, by the auditory Steady State Response (aSSR) at f_{AM}. For sounds with slowly changing carrier frequency ((f_{FM} < 5 Hz), it is shown that the stimulus FM dynamics are tracked by the phase of the aSSR, demonstrating neural phase modulation (PM) encoding of the stimulus carrier frequency. For sounds with faster carrier frequency change ((f_{FM} >= 5 Hz), it is shown that modulation encoding of stimulus FM dynamics persists, but the neural encoding is no longer purely PM. This result is consistent with the recruitment of additional neural AM encoding over and above the original neural PM encoding, indicating that both the amplitude and phase of the aSSR at f_{AM} track the stimulus FM dynamics. A neural model is suggested to account for these observations.

q-bio.NC↗

arXiv2005

Fully Complex Magnetoencephalography

Complex numbers appear naturally in biology whenever a system can be analyzed in the frequency domain, such as physiological data from magnetoencephalography (MEG). For example, the MEG steady state response to a modulated auditory stimulus generates a complex magnetic field for each MEG channel, equal to the Fourier transform at the stimulus modulation frequency. The complex nature of these data sets, often not taken advantage of, is fully exploited here with new methods. Whole-head, complex magnetic data can be used to estimate complex neural current sources, and standard methods of source estimation naturally generalize for complex sources. We show that a general complex neural vector source is described by its location, magnitude, and direction, but also by a phase and by an additional perpendicular component. We give natural interpretations of all the parameters for the complex equivalent-current dipole by linking them to the underlying neurophysiology. We demonstrate complex magnetic fields, and their equivalent fully complex current sources, with both simulations and experimental data.

q-bio.NC↗