arXiv subjects

Jingjing Cao

Publications and source records attributed to Jingjing Cao.

5 recordsLinked to original sources

arXiv2025

Light-engineered Multichannel Quantum Anomalous Hall Effect in High-order Topological Plumbene

Floquet engineering severs as a forceful technique for uncovering high Chern numbers of quantum anomalous Hall (QAH) states with feasible tunability in high-order topologically insulating plumbene, which is readily accessible for experimental investigations. Under the irradiation of righthanded circularly polarized light, we predict a three-stage topological phase transition in plumbene, whether it is in a free-standing form or grown on h-BN. Initially, a metallic state evolves into a K(K')-valley-based QAH state with a Chern number of -8, which then decreases to -6 after the valley gap closes. Finally, a band inversion occurs at the $Γ$ point, resulting in a multichannel QAH state with C = -3. The trigonal warping model accounts for both K(K')-valley-based and $Γ$-pointbased QAH states. Additionally, growing plumbene on a non-van-der-Waals substrate eliminates the K(K')-valley-based topology, leaving only the $Γ$-point-based QAH state with C = +3. Our findings propose the tunability of various high Chern numbers derived from high-order topological insulators, aiming to advance the next-generation dissipationless electronic devices.

cond-mat.mes-hall↗

arXiv2023

Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution.

cs.NI↗

arXiv2020

A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition

With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant. While in reality, there are generally multiple residents at home, which brings greater challenge to recognize activities. In addition, many conventional approaches rely on manual time series data segmentation ignoring the inherent characteristics of events and their heuristic hand-crafted feature generation algorithms are difficult to exploit distinctive features to accurately classify different activities. To address these issues, we propose an end-to-end Tree-Structure Convolutional neural network based framework for Multi-Resident Activity Recognition (TSC-MRAR). First, we treat each sample as an event and obtain the current event embedding through the previous sensor readings in the sliding window without splitting the time series data. Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings. The extracted features are fed into the fully connected layer, which can jointly learn the residents labels and the activity labels simultaneously. Finally, experiments on CASAS datasets demonstrate the high performance in multi-resident activity recognition of our model compared to state-of-the-art techniques.

cs.CV↗

arXiv2019

English Out-of-Vocabulary Lexical Evaluation Task

Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and prediction. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.

cs.CL↗

arXiv2018

A Boosting Method to Face Image Super-resolution

Recently sparse representation has gained great success in face image super-resolution. The conventional sparsity-based methods enforce sparse coding on face image patches and the representation fidelity is measured by $\ell_{2}$-norm. Such a sparse coding model regularizes all facial patches equally, which however ignores distinct natures of different facial patches for image reconstruction. In this paper, we propose a new weighted-patch super-resolution method based on AdaBoost. Specifically, in each iteration of the AdaBoost operation, each facial patch is weighted automatically according to the performance of the model on it, so as to highlight those patches that are more critical for improving the reconstruction power in next step. In this way, through the AdaBoost training procedure, we can focus more on the patches (face regions) with richer information. Various experimental results on standard face database show that our proposed method outperforms state-of-the-art methods in terms of both objective metrics and visual quality.

cs.CV↗