Engineering topics
Xue, Feng
Publications and source records attributed to Xue, Feng.
Tunable quantum anomalous Hall effects in ferromagnetic van der Waals heterostructures
ABSTRACT The quantum anomalous Hall effect (QAHE) has unique advantages in topotronic applications, but it is still challenging to realize the QAHE with tunable magnetic and topological properties for building functional devices. Through systematic first-principles calculations, we predict that the in-plane magnetization induced QAHE with Chern numbers C = ±1 and the out-of-plane magnetization induced QAHE with high Chern numbers C = ±3 can be realized in a single material candidate, which is composed of van der Waals (vdW) coupled Bi and MnBi2Te4 monolayers. The switching between different phases of QAHE can be controlled in multiple ways, such as applying strain or (weak) magnetic field or twisting the vdW materials. The prediction of an experimentally available material system hosting robust, highly tunable QAHE will stimulate great research interest in the field. Our work opens a new avenue for the realization of tunable QAHE and provides a practical material platform for the development of topological electronics.
Multivariate Time Series Anomaly Detection with Few Positive Samples
Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances. In practical settings, particularly industrial time series anomaly detection, we often encounter situations where a large amount of normal operation data is available along with a small number of anomaly events collected over time. This practical situation calls for methodologies to leverage these small number of anomaly events to create a better anomaly detector. In this paper, we introduce two methodologies to address the needs of this practical situation and compared them with recently developed state of the art techniques. Our proposed methods anchor on representative learning of normal operation with autoregressive (AR) model along with loss components to encourage representations that separate normal versus few positive examples. We applied the proposed methods to two industrial anomaly detection datasets and demonstrated effective performance in comparison with approaches from literature. Our study also points out additional challenges with adopting such methods in practical applications.
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series
In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.
Multifunctional two-dimensional van der Waals Janus magnet Cr-based dichalcogenide halides
Two-dimensional van der Waals Janus materials and their heterostructures offer fertile platforms for designing fascinating functionalities. Here, by means of systematic first-principles studies on van der Waals Janus monolayer Cr-based dichalcogenide halides CrYX (Y = S, Se, Te; X = Cl, Br, I), we find that CrSX (X = Cl, Br, I) are the very desirable high T C ferromagnetic semiconductors with an out-of-plane magnetization. Excitingly, by the benefit of the large magnetic moments on ligand S 2- anions, the sought-after large-gap quantum anomalous Hall effect and sizable valley splitting can be achieved through the magnetic proximity effect in van der Waals heterostructures CrSBr/Bi 2 Se 3 /CrSBr and MoTe 2 /CrSBr, respectively. Additionally, we show that large Dzyaloshinskii–Moriya interactions give rise to skyrmion states in CrTeX (X = Cl, Br, I) under external magnetic fields. Our work reveals that two-dimensional Janus magnet Cr-based dichalcogenide halides have appealing multifunctionalities in the applications of topological electronic and valleytronic devices.