arXiv subjects

Ziyan Zhang

Publications and source records attributed to Ziyan Zhang.

34 records · Page 2Linked to original sources

arXiv2024

Power Transformer Fault Prediction Based on Knowledge Graphs

In this paper, we address the challenge of learning with limited fault data for power transformers. Traditional operation and maintenance tools lack effective predictive capabilities for potential faults. The scarcity of extensive fault data makes it difficult to apply machine learning techniques effectively. To solve this problem, we propose a novel approach that leverages the knowledge graph (KG) technology in combination with gradient boosting decision trees (GBDT). This method is designed to efficiently learn from a small set of high-dimensional data, integrating various factors influencing transformer faults and historical operational data. Our approach enables accurate safe state assessments and fault analyses of power transformers despite the limited fault characteristic data. Experimental results demonstrate that this method outperforms other learning approaches in prediction accuracy, such as artificial neural networks (ANN) and logistic regression (LR). Furthermore, it offers significant improvements in progressiveness, practicality, and potential for widespread application.

cs.LG↗

arXiv2024

Unifying Graph Contrastive Learning via Graph Message Augmentation

Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that GDA is an important issue for graph contrastive learning. Various GDAs have been developed recently which mainly involve dropping or perturbing edges, nodes, node attributes and edge attributes. However, to our knowledge, it still lacks a universal and effective augmentor that is suitable for different types of graph data. To address this issue, in this paper, we first introduce the graph message representation of graph data. Based on it, we then propose a novel Graph Message Augmentation (GMA), a universal scheme for reformulating many existing GDAs. The proposed unified GMA not only gives a new perspective to understand many existing GDAs but also provides a universal and more effective graph data augmentation for graph self-supervised learning tasks. Moreover, GMA introduces an easy way to implement the mixup augmentor which is natural for images but usually challengeable for graphs. Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning. Experiments on many graph learning tasks demonstrate the effectiveness and benefits of the proposed GMA and GMCL approaches.

cs.LG↗

arXiv2023

VcT: Visual change Transformer for Remote Sensing Image Change Detection

Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the features of the change regions, however, these works are still limited mainly due to the ignorance of mining the unchanged background context information. It is known that one main challenge for change detection is how to obtain the consistent representations for two images involving different variations, such as spatial variation, sunlight intensity, etc. In this work, we demonstrate that carefully mining the common background information provides an important cue to learn the consistent representations for the two images which thus obviously facilitates the visual change detection problem. Based on this observation, we propose a novel Visual change Transformer (VcT) model for visual change detection problem. To be specific, a shared backbone network is first used to extract the feature maps for the given image pair. Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction. Top-K reliable tokens can be mined from the map and refined by using the clustering algorithm. Then, these reliable tokens are enhanced by first utilizing self/cross-attention schemes and then interacting with original features via an anchor-primary attention learning module. Finally, the prediction head is proposed to get a more accurate change map. Extensive experiments on multiple benchmark datasets validated the effectiveness of our proposed VcT model.

cs.CV↗

arXiv2023

Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator

Energy spectroscopy is a powerful tool with diverse applications across various disciplines. The advent of programmable digital quantum simulators opens new possibilities for conducting spectroscopy on various models using a single device. Variational quantum-classical algorithms have emerged as a promising approach for achieving such tasks on near-term quantum simulators, despite facing significant quantum and classical resource overheads. Here, we experimentally demonstrate multi-level variational spectroscopy for fundamental many-body Hamiltonians using a superconducting programmable digital quantum simulator. By exploiting symmetries, we effectively reduce circuit depth and optimization parameters allowing us to go beyond the ground state. Combined with the subspace search method, we achieve full spectroscopy for a 4-qubit Heisenberg spin chain, yielding an average deviation of 0.13 between experimental and theoretical energies, assuming unity coupling strength. Our method, when extended to 8-qubit Heisenberg and transverse-field Ising Hamiltonians, successfully determines the three lowest energy levels. In achieving the above, we introduce a circuit-agnostic waveform compilation method that enhances the robustness of our simulator against signal crosstalk. Our study highlights symmetry-assisted resource efficiency in variational quantum algorithms and lays the foundation for practical spectroscopy on near-term quantum simulators, with potential applications in quantum chemistry and condensed matter physics.

quant-ph↗

arXiv2023

AGFormer: Efficient Graph Representation with Anchor-Graph Transformer

To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However, existing graph Transformers generally employ regular self-attention module for all node-to-node message passing which needs to learn the affinities/relationships between all node's pairs, leading to high computational cost issue. Also, they are usually sensitive to graph noises. To overcome this issue, we propose a novel graph Transformer architecture, termed Anchor Graph Transformer (AGFormer), by leveraging an anchor graph model. To be specific, AGFormer first obtains some representative anchors and then converts node-to-node message passing into anchor-to-anchor and anchor-to-node message passing process. Thus, AGFormer performs much more efficiently and also robustly than regular node-to-node Transformers. Extensive experiments on several benchmark datasets demonstrate the effectiveness and benefits of proposed AGFormer.

cs.LG↗

arXiv2022

Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion

In order to increase the prediction accuracy of the online vehicle velocity prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused with traffic information was presented for the multiple scenarios. Initially, traffic scenarios were established inside the co-simulation environment. In addition, the algorithm of a general regressive neural network (GRNN) paired with datasets of the ego-vehicle, the front vehicle, and traffic lights was used in traffic scenarios, which increasingly improved the prediction accuracy. To ameliorate the robustness of the algorithm, then the strategy was optimized by particle swarm optimization (PSO) and k-fold cross-validation to find the optimal parameters of the neural network in real-time, which constructed a self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to adapt with different operating situations. The self-adaptive online PSO-GRNN VVP strategy was then deployed to a variety of simulated scenarios to test its efficacy under various operating situations. Finally, the simulation results reveal that in urban and highway scenarios, the prediction accuracy is separately increased by 27.8% and 54.5% when compared to the traditional GRNN VVP strategy with fixed parameters utilizing only the historical ego-vehicle velocity dataset.

eess.SY↗

arXiv2022

Chinese grammatical error correction based on knowledge distillation

In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model. In terms of data, the attack test set is constructed by integrating the disturbance into the standard evaluation data set, and the model robustness is evaluated by the attack test set. The experimental results show that the distilled small model can ensure the performance and improve the training speed under the condition of reducing the number of model parameters, and achieve the optimal effect on the attack test set, and the robustness is significantly improved. Code is available at https://github.com/Richard88888/KD-CGEC.

cs.CL↗

arXiv2022

Surface Enhanced Circular Dichroism by Electric and Magnetic Dipole Resonance of Cross Shaped Nanoholes

The near-field interaction of plasmonic nanostructures and chiral molecules induces circular dichroism in achiral nanostructure. The induced circular dichroism (ICD) strength is several orders greater than the molecular inherent CD (MCD), and it generally appears at visual range (VR) where the CD signal is easily detectable. Therefore, the ICD is considered as a potential method to detect single molecular chirality. In this study, cross shaped nanohole (CSN) is proposed for enhancing the MCD signal. The maximum enhancement of the structure reached 450 folds of molecular inherent MCD. Through analysis of the surface plasmon resonance mode, it is found that overlapping of the electric dipole (ED) and magnetic dipole (MD) surface plasmon resonance (SPR) in CSN is essential for the CD enhancement. Furthermore, we investigated the effects of the thickness of structure, length of longer and shorter arms of nanohole, the displacement of shorter arm of the nanohole and x period of CSN on the circular dichroism enhancement, and found the enhancement factor and location of CD enhancement peak to be tunable by the structural parameters

physics.optics↗

arXiv2022

Surface Enhanced Circular Dichroism by Quadrilateral Arranged Nanoholes

Circular dichroism (CD) is absorption difference of right-hand and left-hand circularly polarized light by components. CD spectroscopy technique is an important tool for detecting molecular chirality, but the molecular circular dichroism (MCD) is weak and always appears in the ultraviolet (UV) region that is more noisy than visual range (VR). Induced circular dichroism (ICD) arisen from near-field interaction of plasmonic nanostructures and chiral molecules enhances the detection limit of molecular chirality and usually appears in VR. In this study, quadrilateral arranged nanoholes (QAN) are proposed for enhancing the MCD. The maximum enhancement achieved by the structure is about 200 folds of the MCD. Furthermore, we explained the mechanism of ICD which is generated by symmetry breaking of electric field QAN by molecular dipole. We investigated the effect of surface plasmon resonance (SPR) mode on ICD through analyzing the electric dipole (ED) and magnetic dipole (MD) resonance modes. ICD was found to get enhanced as a result of overlapping of ED and MD resonance around QAN. Besides this investigation, we probed the effects of thickness of structure, length of holes and length of periods on the MCD enhancement

physics.optics↗

arXiv2022

Unified GCNs: Towards Connecting GCNs with CNNs

Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning. Existing graph convolution layers are mainly designed based on graph signal processing and transform aspect which usually suffer from some limitations, such as over-smoothing, over-squashing and non-robustness, etc. As we all know that Convolution Neural Networks (CNNs) have received great success in many computer vision and machine learning. One main aspect is that CNNs leverage many learnable convolution filters (kernels) to obtain rich feature descriptors and thus can have high capacity to encode complex patterns in visual data analysis. Also, CNNs are flexible in designing their network architecture, such as MobileNet, ResNet, Xception, etc. Therefore, it is natural to arise a question: can we design graph convolutional layer as flexibly as that in CNNs? Innovatively, in this paper, we consider connecting GCNs with CNNs deeply from a general perspective of depthwise separable convolution operation. Specifically, we show that GCN and GAT indeed perform some specific depthwise separable convolution operations. This novel interpretation enables us to better understand the connections between GCNs (GCN, GAT) and CNNs and further inspires us to design more Unified GCNs (UGCNs). As two showcases, we implement two UGCNs, i.e., Separable UGCN (S-UGCN) and General UGCN (G-UGCN) for graph data representation and learning. Promising experiments on several graph representation benchmarks demonstrate the effectiveness and advantages of the proposed UGCNs.

cs.CV↗

arXiv2021

Robust Graph Data Learning via Latent Graph Convolutional Representation

Graph Convolutional Representation (GCR) has achieved impressive performance for graph data representation. However, existing GCR is generally defined on the input fixed graph which may restrict the representation capacity and also be vulnerable to the structural attacks and noises. To address this issue, we propose a novel Latent Graph Convolutional Representation (LatGCR) for robust graph data representation and learning. Our LatGCR is derived based on reformulating graph convolutional representation from the aspect of graph neighborhood reconstruction. Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\widetilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w.r.t graph structural attacks and noises. Moreover, LatGCR is implemented in a self-supervised manner and thus provides a basic block for both supervised and unsupervised graph learning tasks. Experiments on several datasets demonstrate the effectiveness and robustness of LatGCR.

cs.CV↗

arXiv2021

Incomplete Graph Representation and Learning via Partial Graph Neural Networks

Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form where attributes of graph nodes are partially unknown/missing. Existing GNNs are generally designed on complete graphs which can not deal with attribute-incomplete graph data directly. To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning. Our work is motivated by the observation that the neighborhood aggregation function in standard GNNs can be equivalently viewed as the neighborhood reconstruction formulation. Based on it, we define two novel partial aggregation (reconstruction) functions on incomplete graph and derive PaGNNs for incomplete graph data learning. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed PaGNNs.

cs.LG↗

arXiv2021

Quantitative Study on Circular Dichroism Induction from Chiral Matter-Achiral Nanostructure Near Field Interactions

The circular dichroism (ICD) induced from interaction of chiral molecules with achiral plasmonic metal nanostructures has improved sensitivity of molecular chirality detection. Although experimental results have presented several orders magnitude of ICD enhancement, but the cause of ICD induction has not arise from previous studies. In this paper, a theory presented for explain mechanism of ICD induction from near field interaction of chiral molecule with achiral nanostructure. By introducing dipole approaching of chiral molecule and a near field approximation of molecule-nanostructure interaction, we derived an analytical formula for ICD. This theory explains mechanism of ICD is that the dipole radiation of chiral molecules breaks the enantiomorphic relationship of the electric field strengths in the nanostructure. Furthermore, the derived formula exhibits ICD proportional to molecules chirality parameter and number density, and it exponentially increases with the inverse cube of distance between molecule and nanostructure. Most importantly, the formula shows that ICD is proportional to strengths of electric field in nanostructure and magnetic field in molecular region. To verify the analytical results, we studied the ICD properties of graphene nanohole metasurface (GNM) and chiral molecule composed system. Numerical results were found consistent with analytical results. Our investigation results are helpful for understanding mechanism of ICD induction, pushing the limit of ICD enhancement and realizing supersensitive detection of molecular chirality.

physics.optics↗

arXiv2019

Inference for Synthetic Control Methods with Multiple Treated Units

Although the Synthetic Control Method (SCM) is now widely applied, its most commonly-used inference method, placebo test, is often problematic, especially when the treatment is not uniquely assigned. This paper discuss the problems with the placebo test under multivariate treatment case. And, to improve the power of inferences, I further propose an Andrews-type procedure as it potentially solve some drawbacks of placebo test. Simulations are conducted to show the Andrews' test is often valid and powerful, compared with the placebo test.

stat.ME↗

arXiv2019

Multiple Graph Adversarial Learning

Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures. One main challenge for multi-graph representation is how to exploit both structure information of each individual graph and correlation information across multiple graphs simultaneously. In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning. MGAL aims to learn an optimal structure-invariant and consistent representation for multiple graphs in a common subspace via a novel adversarial learning framework, which thus incorporates both structure information of intra-graph and correlation information of inter-graphs simultaneously. Based on MGAL, we then provide a unified network for semi-supervised learning task. Promising experimental results demonstrate the effectiveness of MGAL model.

cs.CV↗

arXiv2018

Graph Learning-Convolutional Networks

Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may be not optimal for semi-supervised learning tasks. In this paper, we propose a novel Graph Learning-Convolutional Network (GLCN) for graph data representation and semi-supervised learning. The aim of GLCN is to learn an optimal graph structure that best serves graph CNNs for semi-supervised learning by integrating both graph learning and graph convolution together in a unified network architecture. The main advantage is that in GLCN, both given labels and the estimated labels are incorporated and thus can provide useful 'weakly' supervised information to refine (or learn) the graph construction and also to facilitate the graph convolution operation in GLCN for unknown label estimation. Experimental results on seven benchmarks demonstrate that GLCN significantly outperforms state-of-the-art traditional fixed structure based graph CNNs.

cs.CV↗