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Yong Wang

Publications and source records attributed to Yong Wang.

At least 379 records · Page 21Linked to original sources
arXiv2023

NFTDisk: Visual Detection of Wash Trading in NFT Markets

With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with a disk metaphor to overview NFT transactions and a flow-based visualization module to reveal detailed NFT flows at multiple levels. We conduct two case studies and an in-depth user interview with 14 NFT investors to evaluate NFTDisk. The results demonstrate its effectiveness in exploring wash trading activities in NFT markets.

cs.HC↗
arXiv2022

Riemannian geometry of noncommutative super surfaces

In this paper, a Riemannian geometry of noncommutative super surfaces is developed which generalizes [4] to the super case. The notions of metric and connections on such noncommutative super surfaces are introduced and it is shown that the connections are metric-compatible and have zero torsion when the super metric is symmetric, giving rise to the corresponding super Riemann curvature. The latter also satisfies the noncommutative super analogue of the first and second Bianchi identities. We also give some examples and study them in details.

math.DG↗
arXiv2022

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL

cs.LG↗
arXiv2022

Geometric structures of Morris-Thorne wormhole metric with the semi-symmetric non-metric connections

Spacetime is a 4-dimensional connected Lorentzian manifold. In this paper, we extend the Levi-Civita connection in the definition of spacetime to the semi-symmetric non-metric connection and conclude geometric structures admitted by the metric (1.1) with the semi-symmetric non-metric connections. Obviously, Morris-Thorne spacetime is Ricci generalized pseudosymmetry, Ricci generalized projectively pseudosymmetry, and has conformal 2-forms that are recurrent, etc. It also is an Einstein manifold of level 2 and 3-quasi-Einstein manifold.

math.DG↗
arXiv2022

Large Bilinear Magnetoresistance from Rashba Spin-Splitting on the Surface of a Topological Insulator

In addition to the topologically protected linear dispersion, a band-bending-confined two-dimensional electron gas with tunable Rashba spin-splitting (RSS) was found to coexist with the topological surface states on the surface of topological insulators (TIs). Here, we report the observation of large bilinear magnetoresistance (BMR) in Bi2Se3 films decorated with transition metal atoms. The magnitude of the BMR sensitively depends on the type and amount of atoms deposited, with a maximum achieved value close to those of strong Rashba semiconductors. Our first-principles calculations reproduce the quantum well states and reveal sizable RSS in all Bi2Se3 heterostructures with broken inversion symmetry. Our results show that charge-spin interconversion through RSS states in TIs can be fine-tuned through surface atom deposition and easily detected via BMR for potential spintronic applications.

cond-mat.mes-hall↗
arXiv2022

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.

cs.LG↗
arXiv2022

Dynamic generation of spin spirals of moiré trapped carriers via exciton mediated spin interactions

Stacking transition metal dichalcogenides (TMDs) to form moiré superlattices has provided exciting opportunities to explore many-body correlation phenomena of the moiré trapped carriers. TMDs bilayers, on the other hand, host long-lived interlayer exciton (IX), an elementary excitation of long spin-valley lifetime that can be optically or electrically injected. Here we find that, through the Coulomb exchange between mobile IXs and carriers, the IX bath can mediate both Heisenberg and Dzyaloshinskii-Moriya type spin interactions between moiré trapped carriers, controllable by exciton density and exciton spin current respectively. We show the strong Heisenberg interaction, and the extraordinarily long-ranged Dzyaloshinskii-Moriya interaction here can jointly establish robust spin spiral magnetic orders in Mott-Wigner crystal states at various filling factors, with spiral direction controlled by exciton current.

cond-mat.mes-hall↗
arXiv2022

HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar

Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recognition using mmWave radar. In order to explore more dynamic information, we propose point flow as a novel point clouds descriptor. We also devise a dynamic frame sampling module to promote the efficiency of computation without deteriorating performance noticeably. To prove the superiority of our methods, we perform extensive experiments on two public mmWave radar-based gait recognition datasets, and the results demonstrate that our model is superior to existing state-of-the-art methods.

cs.CV↗
arXiv2022

Algebraic Schouten Solitons of Three-Dimensional Lorentzian Lie Groups

In [10], Wears defined and studied algebraic T-solitons. In this paper, we give the definition of algebraic Schouten solitons as a special T-soliton and classify algebraic Schouten solitons associated to Levi-Civita connections, canonical connections and Kobayashi-Nomizu connections on three-dimensional Lorentzian Lie groups with some product structure.

math.DG↗
arXiv2022

Toward Intention Discovery for Early Malice Detection in Bitcoin

Bitcoin has been subject to illicit activities more often than probably any other financial assets, due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all the three properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without satisfying interpretability and are only available for retrospective analysis of a specific illicit type. First, we present asset transfer paths, which aim to describe addresses' early characteristics. Next, with a decision tree based strategy for feature selection and segmentation, we split the entire observation period into different segments and encode each as a segment vector. After clustering all these segment vectors, we get the global status vectors, essentially the basic unit to describe the whole intention. Finally, a hierarchical self-attention predictor predicts the label for the given address in real time. A survival module tells the predictor when to stop and proposes the status sequence, namely intention. % With the type-dependent selection strategy and global status vectors, our model can be applied to detect various illicit activities with strong interpretability. The well-designed predictor and particular loss functions strengthen the model's prediction speed and interpretability one step further. Extensive experiments on three real-world datasets show that our proposed algorithm outperforms state-of-the-art methods. Besides, additional case studies justify our model can not only explain existing illicit patterns but can also find new suspicious characters.

cs.LG↗
arXiv2022

Observation of nonlinear planar Hall effect in magnetic insulator/topological insulator heterostructures

Interfacing topological insulators (TIs) with magnetic insulators (MIs) has been widely used to study the interaction between topological surface states and magnetism. Previous transport studies typically interpret the suppression of weak antilocalization or appearance of the anomalous Hall effect as signatures of magnetic proximity effect (MPE) imposed to TIs. Here, we report the observation of nonlinear planar Hall effect (NPHE) in Bi2Se3 films grown on MI thulium and yttrium iron garnet (TmIG and YIG) substrates, which is an order of magnitude larger than that in Bi2Se3 grown on nonmagnetic gadolinium gallium garnet (GGG) substrate. The nonlinear Hall resistance in TmIG/Bi2Se3 depends linearly on the external magnetic field, while that in YIG/Bi2Se3 exhibits an extra hysteresis loop around zero field. The magnitude of the NPHE is found to scale inversely with carrier density. We speculate the observed NPHE is related to the MPE-induced exchange gap opening and out-of-plane spin textures in the TI surface states, which may be used as an alternative transport signature of the MPE in MI/TI heterostructures.

cond-mat.mes-hall↗
arXiv2022

KIC 10417986: Spectroscopic confirmation of the nature of the binary system with a δ Scuti component

KIC 10417986 is a short orbital period (0.0737 d) ellipsoidal variable star with a δ Scuti and γ Doradus hybrid pulsations component discovered by Kepler. The ground-based spectroscopic observations were carried out in the winters of 2020 and 2021 to investigate the binary nature of this star. We derive the orbital parameters using the rvfit code with a result of K1 = 29.7 $\pm$ 1.5 km/s, γ = -18.7 $\pm$ 1.7 km/s, and confirm an orbital period of 0.84495 d instead of the result given by Kepler. The atmospheric parameters of the primary are determined by the synthetic spectra fitting technique with the estimated values of Teff = 7411 $\pm$ 187 K, log g = 4.2 $\pm$ 0.3 dex, [M/H] = 0.08 $\pm$ 0.09 dex and vsini = 52 $\pm$ 11 km/s. KIC 10417986 is a circular orbit binary system. From the single-lined nature and mass function of the star, the derived orbital inclination is 26 $\pm$ 6°, and the mass of the secondary is from 0.43 to 0.7 M_sun, which should be a late-K to early-M type star. Fourteen frequencies are extracted from Kepler light curves, of which six independent frequencies in the high-frequency region are identified as the p-mode pulsations of δ Scuti star, and one independent frequency in the low-frequency region (f2 = 1.3033 c/d) is probably the rotational frequency due to the starspots rather than the ellipsoidal effect or g-mode of γ Doradus.

astro-ph.SR↗
arXiv2022

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.

cs.NI↗
arXiv2022

VACSEN: A Visualization Approach for Noise Awareness in Quantum Computing

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite its advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of the execution result of a quantum algorithm, it is crucial to inform users of the noises of the used quantum computer and the compiled physical circuits. However, an intuitive and systematic way to make users aware of the quantum computing noise is still missing. In this paper, we fill the gap by proposing a novel visualization approach to achieve noise-aware quantum computing. It provides a holistic picture of the noise of quantum computing through multiple interactively coordinated views: a Computer Evolution View with a circuit-like design overviews the temporal evolution of the noises of different quantum computers, a Circuit Filtering View facilitates quick filtering of multiple compiled physical circuits for the same quantum algorithm, and a Circuit Comparison View with a coupled bar chart enables detailed comparison of the filtered compiled circuits. We extensively evaluate the performance of VACSEN through two case studies on quantum algorithms of different scales and an in-depth interviews with 12 quantum computing users. The results demonstrate the effectiveness and usability of VACSEN in achieving noise-aware quantum computing.

quant-ph↗
arXiv2022

StyleSwin: Transformer-based GAN for High-resolution Image Generation

Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity. Hence, the proposed generator adopts Swin transformer in a style-based architecture. To achieve a larger receptive field, we propose double attention which simultaneously leverages the context of the local and the shifted windows, leading to improved generation quality. Moreover, we show that offering the knowledge of the absolute position that has been lost in window-based transformers greatly benefits the generation quality. The proposed StyleSwin is scalable to high resolutions, with both the coarse geometry and fine structures benefit from the strong expressivity of transformers. However, blocking artifacts occur during high-resolution synthesis because performing the local attention in a block-wise manner may break the spatial coherency. To solve this, we empirically investigate various solutions, among which we find that employing a wavelet discriminator to examine the spectral discrepancy effectively suppresses the artifacts. Extensive experiments show the superiority over prior transformer-based GANs, especially on high resolutions, e.g., 1024x1024. The StyleSwin, without complex training strategies, excels over StyleGAN on CelebA-HQ 1024, and achieves on-par performance on FFHQ-1024, proving the promise of using transformers for high-resolution image generation. The code and models will be available at https://github.com/microsoft/StyleSwin.

cs.CV↗